View All Release Announcements »

Launching Version 12.3 of Wolfram Language & Mathematica

Livecoding & Q&A With Stephen Wolfram

Look What We Made in Five Months!

It’s hard to believe we’ve been doing this for 35 years, building a taller and taller tower of ideas and technology that allow us to reach ever further. In earlier times we used to release the results of efforts only every few years. But in recent times we’ve started doing incremental (“.1”) releases that deliver our latest R&D achievements—both fully fleshed out, and partly as “coming attractions”—much more frequently.

We released Version 12.2 on December 16, 2020. And today, just five months later, we’re releasing Version 12.3. There are some breakthroughs and major new directions in 12.3. But much of what’s in 12.3 is just about making Wolfram Language and Mathematica better, smoother and more convenient to use. Things are faster. More “But what about ___?” cases are handled. Big frameworks are more completely filled out. And there are lots of new conveniences.

There are also the first pieces of what will become large-scale structures in the future. Early functions—already highly useful in their own right—that will in future releases be pieces of major systemwide frameworks.

One way to assess a release is to talk about how many new functions it contains. For Version 12.3 that number is 111 (or about 5 new functions per development-week). It’s a very impressive level of R&D productivity. But particularly for 12.3 it’s just part of the story; there are also 1190 bug fixes (about a quarter for externally reported bugs), and 105 substantially updated and enhanced functions.

Incremental releases are part of our commitment to open development. We’ve also been sharing more kinds of functionality in open-source form (including more than 300 new functions in the Wolfram Function Repository). And we’ve been doing our unique thing of livestreaming our internal design processes. For Version 12.3 it’s once again possible to see just where and how design decisions were made, and the reasoning behind them. And we’ve also had great input from our community (often in real time during livestreams)—that has significantly enhanced the Version 12.3 that we are delivering today.

By the way, when we say “Version 12.3” of Wolfram Language and Mathematica we mean desktop, cloud and engine: all three versions are being released today.

Lots of Little New Conveniences

What should “just work”? What should be made easier? Ever since Version 1.0 we’ve been working hard to figure out what little conveniences we can add to make the Wolfram Language ever more streamlined.

Version 12.3 has our latest batch of conveniences, scattered across many parts of the language. A new dynamic that’s emerged in this version is functions that have essentially been “prototyped” in the Wolfram Function Repository, and then “upgraded” to be built into the system.

Here’s a first example of a new convenience function: SolveValues. The function Solve—originally introduced in Version 1.0—has a very flexible way of representing its results, that allows for different numbers of variables, different numbers of solutions, etc.

&#10005

Solve[x^2 + 3 x + 1 == 0, x]

But often you’re happy to assume a fixed structure for the solution, and you just want to know the values of variables. And that’s what SolveValues gives:

&#10005

SolveValues[x^2 + 3 x + 1 == 0, x]

By the way, there’s also an NSolveValues that gives approximate numerical values:

&#10005

NSolveValues[x^2 + 3 x + 1 == 0, x]

Another example of a new convenience function is NumberDigit. Let’s say you want the 10th digit of π. You can always use RealDigits and then pick out the digit you want:

&#10005

RealDigits[Pi, 10, 20]

But now you can also just use NumberDigit (where now by “10th digit” we’re assuming you mean the coefficient of 10-10):

&#10005

NumberDigit[Pi, -10]

Back in Version 1.0, we just had Sort. In Version 10.3 (2015) we added AlphabeticSort, and then in Version 11.1 (2017) we added NumericalSort. Now in Version 12.3—to round out this family of default types of sorting—we’re adding LexicographicSort. The default sorting sort (as produced by Sort) is:

&#10005

Subsets[{a, b, c, d}]

But here’s true lexicographic order, like you would find in a dictionary:

&#10005

LexicographicSort[Subsets[{a, b, c, d}]]

Another small new function in Version 12.3 is StringTakeDrop:

&#10005

StringTakeDrop["abcdefghijklmn", {2, 5}]

Having this as a single function makes it easier to use in functional programming constructs like this:

&#10005

FoldPairList[StringTakeDrop, "abcdefghijklmn", {2, 3, 4, 5}]

It’s always an important goal to make “standard workflows” as straightforward as possible. For example, in handling graphs we’ve had VertexOutComponent since Version 8.0 (2010). It gives a list of the vertices that can be reached from a given vertex. And for some things that’s exactly what one wants. But sometimes it’s much more convenient to get the subgraph (and in fact in the formalism of our Physics Project that subgraph—that we view as a “geodesic ball”—is a rather central construct). So in Version 12.3 we’ve added VertexOutComponentGraph:

&#10005

VertexOutComponentGraph[CloudGet["http://wolfr.am/VAs5QDwv"], 10, 4]

Another example of a small “workflow improvement” is in HighlightImage. HighlightImage typically takes a list of regions of interest to highlight in the image. But functions like MorphologicalComponents don’t just make lists of regions in an image; instead they produce a “label matrix” that puts numbers to label different regions in an image. So to make the HighlightImage workflow smoother, in Version 12.3 we let it directly use a label matrix, assigning different colors to the differently labeled regions:

&#10005

HighlightImage[
 CloudGet["http://wolfr.am/VAs6lxUj"], MorphologicalComponents]

One thing we work hard to ensure in the Wolfram Language is coherence and interoperability. (And in fact, we have a whole initiative around this that we call “Language Completeness & Consistency”, whose weekly meetings we regularly livestream.) One of the various facets of interoperability is that we want functions to be able to “eat” any reasonable input and turn it into something they can “naturally” handle.

And as a small example of this, something we added in Version 12.3 is automatic conversion between color spaces. Red by default means the RGB color red (RGBColor[1,0,0]). But now

&#10005

Hue[Red]

means turns that red into red in hue space:

&#10005

Hue[Red] // InputForm

Let’s say you’re running a long computation. You often want to get some indication of the progress that’s being made. In Version 6.0 (2007) we added Monitor, and in subsequent versions we’ve added automatic built-in progress reporting for some functions, for example NetTrain. But now we have an initiative underway to systematically add progress reporting for all sorts of functions that can end up doing long computations. ($ProgressReporting = False globally switches it off.)

&#10005

VideoMap[ColorConvert[#Image, "Grayscale"] &, 
 Video["ExampleData/bullfinch.mkv"]]

We work hard in Wolfram Language to make sure that we pick good defaults, for example for how to display things. But sometimes you have to tell the system what kind of “look” you want. And in Version 12.3 we’ve added the option DatasetTheme to specify “themes” for how Dataset objects should be displayed.

Underneath, each theme is just setting specific options, which you could set yourself. But the theme is “bank switching” options in a convenient way. Here’s a basic dataset with default formatting:

&#10005

Dataset[IdentityMatrix[6]]

Here it is looking more “lively” for the web:

&#10005

Dataset[IdentityMatrix[6], DatasetTheme -> "Web"]

You can give various “theme directives” too:

&#10005

Dataset[IdentityMatrix[6], 
 DatasetTheme -> "AlternatingColumnBackgrounds"]

As well as additional hints:

&#10005

Dataset[IdentityMatrix[6], 
 DatasetTheme -> {"AlternatingColumnBackgrounds", LightGreen}]

I’m not sure why we didn’t think of it before, but in Version 11.3 (2018) we introduced a very nice “user interface innovation”: Iconize. And in Version 12.3 we’ve added another piece of polish to iconization. If you select a piece of an expression, then use Iconize in the context (“right-click”) menu, an appropriate subpart of the expression will get iconized, even if the selection you made might have included an extra comma, or been something that can’t be a strict subpart of the expression:

&#10005

Range[20]

Let’s say you generate an object that takes a lot of memory to store:

&#10005

SparseArray[Range[10^7]]

By default, the object is kept in your kernel session, but it’s not stored directly in your notebook—so it won’t persist after you end your current kernel session. In Version 12.3 we’ve added some options for where you can store the data:

&#10005

SparseArray[Range[10^7]]

One important area where we put lots of effort into making things “just work” is in importing and exporting of data. The Wolfram Language now supports about 250 external data formats, with for example new statistical data formats like SAS7BDAT, DTA, POR and SAV being added in Version 12.3.

Lots of Things Got Faster

In addition to all the effort we put into creating new functionality for Wolfram Language, we’re also always trying to make existing functionality better, and faster. And in Version 12.3 there are lots of things that are now faster. One particularly large group of things got faster because of advances in our compiler technology that allow a broader range of Wolfram Language functionality to be compiled directly into optimized machine code. An example of a beneficiary of this is Around.

Here’s a computation with Around:

&#10005

Sin[Around[RandomReal[], 0.001]]

In Version 12.2 doing this 10,000 times takes about 1.3 seconds on my computer:

&#10005

Do[Sin[Around[RandomReal[], 0.001]], 10^4] // Timing

In Version 12.3, it’s roughly 100 times faster:

&#10005

Do[Sin[Around[RandomReal[], 0.001]], 10^4] // Timing

There are lots of different reasons that things got faster in Version 12.3. In the case of Permanent, for example, we were able to use a new and much better algorithm. Here it is in 12.2:

&#10005

Permanent[Table[2.3 i/j, {i, 15}, {j, 15}]] // Timing

And now in 12.3:

&#10005

Permanent[Table[2.3 i/j, {i, 15}, {j, 15}]] // Timing

Another example is date parsing: converting dates from textual to internal form. The main advance here came from realizing that date parsing is often done in bulk, so it makes sense to adaptively cache parts of the operation. And the result, for example in parsing a million dates, is that what used to take many minutes now takes just a few seconds.

One more example is Rasterize, which in Version 12.3 is typically 2 to 4 times faster than in Version 12.2. The reason for this speedup is somewhat subtle. Back when Rasterize was first introduced in Version 6.0 (2007) data transfer speeds between processes were an issue, and so it was a good optimization to compress any data being transferred. But today transfer speeds are much higher, and we have better optimized array data structures—and so compression no longer makes sense, and removing it (together with other codepath optimization) allows Rasterize to be significantly faster.

An important advance in Version 12.1 was the introduction of DataStructure, allowing direct use of optimization data structures (implemented through our new compiler technology). Version 12.3 introduces several new data structures. There’s "ByteTrie" for fast prefix-based lookups (think Autocomplete and GenomeLookup), and there’s "KDTree" for fast geometric lookups (think Nearest). There’s also now "ImmutableVector", which is basically like an ordinary Wolfram Language list, except that it’s optimized for fast appending.

In addition to speed improvements in the computational kernel, Version 12.3 has user interface speed improvements too. Particularly notable is significantly faster rendering on Windows platforms, achieved by using DirectWrite and making use of GPU capabilities.

Pushing the Math Frontier

Version 1 of Mathematica was billed as “A System for Doing Mathematics by Computer”, and—for more than three decades—in every new version of Wolfram Language and Mathematica there’ve been innovations in “doing mathematics by computer”.

For Version 12.3 let’s talk first about symbolic equation solving. Back in Version 3 (1996) we introduced the idea of implicit “Root object” representations for roots of polynomials, allowing us to do exact, symbolic computations even without “explicit formulas” in terms of radicals. Version 7 (2008) then generalized Root to also work for transcendental equations.

What about systems of equations? For polynomials, elimination theory means that systems really aren’t a different story from individual equations; the same Root objects can be used. But for transcendental equations, this isn’t true anymore. But for Version 12.3 we’ve now figured out how to generalize Root objects so they can work with multivariate transcendental roots:

&#10005

Solve[Sin[x y] == x^2 + y && 
  3 x E^y == 2 y E^x + 1 &&
  -3 < x < 3 && -3 < y < 3, {x, y}, Reals]

And because these Root objects are exact, they can for example be evaluated to any precision:

&#10005

N[First[x /. %], 150]

In Version 12.3 there are also some new equations, involving elliptic functions, where exact symbolic results can be given, even without Root objects:

&#10005

Reduce[JacobiSN[x, 2 y] == 1, x]

A major advance in Version 12.3 is being able to solve symbolically any linear system of ODEs (ordinary differential equations) with rational function coefficients.

Sometimes the result involves explicit mathematical functions:

&#10005

DSolve[{Derivative[1][x][t] == -((4 x[t])/t) + (4 y[t])/t, 
  Derivative[1][y][t] == (4/t - t/4) x[t] - (4 y[t])/t}, {x[t], y[t]},
  t]

Sometimes there are integrals—or differential roots—in the results:

&#10005

DSolveValue[{Derivative[1][y][x] + 2 Derivative[1][z][x] == 
    z[x], (-3 + x) x^2 (y[x] + z[x]) + 
     Derivative[1][z][x] == (1 + 3 x^2) z[x]}, {y[x], z[x]}, 
  x] // Simplify

Another ODE advance in Version 12.3 is full coverage of linear ODEs with q-rational function coefficients, in which variables can appear explicitly or implicitly in exponents. The results are exact, though they typically involve differential roots:

&#10005

DSolve[2^x y[x] + ((-1 + 2^x) 
\!\(\*SuperscriptBox[\(y\), 
TagBox[
RowBox[{"(", "4", ")"}],
Derivative],
MultilineFunction->None]\)[x])/(1 + 2^x) == 0, y[x], x]

What about PDEs? For Version 12.2 we introduced a major new framework for modeling with numerical PDEs. And now in Version 12.3 we’ve produced a whole 105-page monograph about symbolic solutions to PDEs:

Monograph

Here’s an equation that in Version 12.2 could be solved numerically:

&#10005

eqns = {Laplacian[u[x, y],{x, y}] == 0, 
   u[x, 0] == Sin[x] && u[0, y] == Sin[y] && u[2, y] == Sin[2 y]};

Now it can be solved exactly and symbolically as well:

&#10005

DSolveValue[eqns, u[x, y], {x, y}]

In addition to linear PDEs, Version 12.3 extends the coverage of special solutions to nonlinear PDEs. Here’s one (with 4 variables) that uses Jacobi’s method:

&#10005

DSolveValue[(\!\(
\*SubscriptBox[\(\[PartialD]\), \(x\)]\(u[x, y, z, t]\)\))^4 == (\!\(
\*SubscriptBox[\(\[PartialD]\), \(y\)]\(u[x, y, z, t]\)\))^2 + (\!\(
\*SubscriptBox[\(\[PartialD]\), \(z\)]\(u[x, y, z, t]\)\))^3 \!\(
\*SubscriptBox[\(\[PartialD]\), \(t\)]\(u[x, y, z, t]\)\), 
 u[x, y, z, t], {x, y, z, t}]

Something added in 12.3 that both supports PDEs and provides new functionality for signal processing is bilateral Laplace transforms (i.e. integrating from –∞ to +∞, like a Fourier transform):

&#10005

BilateralLaplaceTransform[Sin[t] Exp[-t^2], t, s]

Ever since Version 1, we’ve prided ourselves on our coverage of special functions. Over the years we’ve been able to progressively extend that coverage to more and more general special functions. Version 12.3 has several new long-sought classes of special functions. There are the Carlson elliptic integrals. And then there is the Fox H-function.

Back in Version 3 (1996) we introduced MeijerG which dramatically expanded the range of definite integrals that we could do in symbolic form. MeijerG is defined in terms of a Mellin–Barnes integral in the complex plane. It’s a small change in the integrand, but it’s taken 25 years to unravel the necessary mathematics and algorithms to bring us now in Version 12.3 FoxH.

FoxH is a very general function—that encompasses all hypergeometric pFq and Meijer G functions, and much beyond. And now that FoxH is in our language, we’re able to start the process of expanding our integration and other symbolic capabilities to make use of it.

Symbolic Optimization Breakthrough

A major step forward in Version 12.0 was the introduction of industrial-strength convex optimization, routinely handling problems involving millions of variables in the linear case and thousands in the nonlinear case. In Version 12.0 everything had to be numerical (in 12.1 we added integer optimization). In Version 12.3 we’re now adding the possibility for symbolic parameters in large-scale linear and quadratic problems, as in this small example:

&#10005

MinValue[{(x - 1)^2 + (2 y - 1)^2, 
  x + 2 y <= a + b && 2 x - y <= a - b + 1 && 
   x - 2 y <= 2 a - b + 1}, {x, y}]

&#10005

Plot3D[%, {a, -5, 5}, {b, -5, 5}]

In typical convex optimization computations not involving symbolic parameters one aims only for approximate numerical results, and it wasn’t clear whether there was any general method for getting exact numerical results. But for Version 12.3 we’ve found one, and we’re now able to give exact numerical results which you can, for example, evaluate to any precision you want.

Here’s a geometric optimization problem—which can now be solved exactly in terms of transcendental root objects:

&#10005

MinValue[{x^(3/4) + 2 y^(4/5) + 3 z^(5/7), 
  x y z <= 1 && x^E y^\[Pi] z >= 2 && x > 0 && y > 0 && z > 0}, {x, y,
   z}]

Given such an exact solution, it’s now possible to do numerical evaluation to any precision:

&#10005

N[%, 200]

More with Graphs

In case one ever doubted that graphs are important, our Wolfram Physics Project has made it pretty clear over the past year that at the lowest level physics is all about graphs. And in fact our whole Physics Project was basically made possible by the rich graph functionality in the Wolfram Language.

In Version 12.3 we’ve continued to expand that functionality. Here, for example, is a new 3D visualization function for graphs:

&#10005

LayeredGraphPlot3D[KaryTree[255], BoxRatios -> {1, 1, 1}]

And here’s a new 3D graph embedding:

&#10005

Graph3D[GridGraph[{20, 20}], GraphLayout -> "SphericalEmbedding"]

We’ve been able to find spanning trees in graphs since Version 10 (2014). In Version 12.3, however, we’ve generalized FindSpanningTree to directly handle objects—like geo locations—that have some kind of coordinates. Here’s a spanning tree based on the positions of capital cities in Europe:

&#10005

FindSpanningTree[
 EntityClass["Country", "Europe"][
  EntityProperty["Country", "CapitalCity"]]]

And now in Version 12.3 we can use the new GeoGraphPlot to plot this on a map:

&#10005

GeoGraphPlot[%]

By the way, in a “geo graph” there are “geo” ways to route the edges. For example, you can specify that they follow (when possible) driving directions (as provided by TravelDirections):

&#10005

GeoGraphPlot[%%, GraphLayout -> "Driving"]

Euclid Meets Descartes, and More

We’ve been doing a lot with geometry in the past few years, and there’s more to come. In Version 12.0 we introduced “Euclid-style” synthetic geometry. In Version 12.3 we’re connecting to “Descartes-style” analytic geometry, converting geometric descriptions to algebraic formulas.

Given three symbolically specified points, GeometricTest can give the algebraic condition for them to be collinear:

&#10005

GeometricTest[{{a, b}, {c, d}, {e, f}}, "Collinear"]

For the particular case of collinearity, there’s a specific function for doing the test:

&#10005

CollinearPoints[{{a, b}, {c, d}, {e, f}}]

But GeometricTest is much more general in scope—supporting more than 30 kinds of predicates. This gives the condition for a polygon to be convex:

&#10005

GeometricTest[Polygon[{{a, b}, {1, 2}, {3, 3}, {4, 7}}], "Convex"]

And this gives the condition for a polygon to be regular:

&#10005

GeometricTest[Polygon[{{a, b}, {c, d}, {1, 1}, {2, 3}}], "Regular"]

And here’s the condition for three circles to be mutually tangent (and, yes, that ∃ is a little “post Descartes”):

&#10005

GeometricTest[{Circle[{0, 0}, r], Circle[{a, b}, s], 
  Circle[{c, d}, t]}, "Tangent"]

Version 12.3 also has enhancements to core computational geometry. Most notable are RegionDilation and RegionErosion, that essentially convolve regions with each other. RegionDilation effectively finds the whole (“Minkowski sum”) “union region” obtained by translating one region to every point in another region.

Why is this useful? It turns out there are lots of reasons. One example is the “piano mover problem” (AKA the robot motion planning problem). Given, say, a rectangular shape, is there a way to maneuver it (in the simplest case, without rotation) through a house with certain obstacles (like walls)?

Basically what you need to do is take the rectangular shape and “dilate the room” (and the obstacles) with it:

&#10005

RegionDilation[\!\(\*
GraphicsBox[
TagBox[
DynamicModuleBox[{Typeset`mesh = HoldComplete[
BoundaryMeshRegion[{{0., 0.}, {0.11499999999999999`, 0.}, {
        0.11499999999999999`, 0.22999999999999998`}, {0., 
        0.22999999999999998`}}, {
Line[{{1, 2}, {2, 3}, {3, 4}, {4, 1}}]}, 
        Method -> {
         "EliminateUnusedCoordinates" -> True, 
          "DeleteDuplicateCoordinates" -> Automatic, 
          "DeleteDuplicateCells" -> Automatic, "VertexAlias" -> 
          Identity, "CheckOrientation" -> Automatic, 
          "CoplanarityTolerance" -> Automatic, "CheckIntersections" -> 
          Automatic, "BoundaryNesting" -> {{0, 0}}, 
          "SeparateBoundaries" -> False, "TJunction" -> Automatic, 
          "PropagateMarkers" -> True, "ZeroTest" -> Automatic, "Hash" -> 
          740210533488462839}]]}, 
TagBox[GraphicsComplexBox[{{0., 0.}, {0.11499999999999999`, 0.}, {
        0.11499999999999999`, 0.22999999999999998`}, {0., 
        0.22999999999999998`}}, 
{Hue[0.6, 0.3, 0.95], EdgeForm[Hue[0.6, 0.3, 0.75]], 
TagBox[PolygonBox[{{1, 2, 3, 4}}],
Annotation[#, "Geometry"]& ]}],
MouseAppearanceTag["LinkHand"]],
AllowKernelInitialization->False],
"MeshGraphics",
AutoDelete->True,
Editable->False,
Selectable->False],
DefaultBaseStyle->{
     "MeshGraphics", FrontEnd`GraphicsHighlightColor -> 
      Hue[0.1, 1, 0.7]},
ImageSize->{27.866676879084963`, Automatic}]\), 
 CloudGet["http://wolfr.am/VAs8Qsr5"]]

Then if there’s a connected path “left over” from one point to another, then it’s possible to move the piano along that path. (And of course, the same kind of thing can be done for robots in a factory, etc. etc.)

RegionDilation is also useful for “smoothing out” or “offsetting” shapes, for example, for CAD applications:

&#10005

Region[RegionDilation[Triangle[], Disk[]]]

At least in simple cases, one can “go Descartes” with it, and get explicit formulas:

&#10005

RegionDilation[Triangle[], Disk[]]

And, by the way, this all works in any number of dimensions—providing a useful way to generate all sorts of “new shapes” (like a cylinder is the dilation of a disk by a line in 3D).

Yet More Visualization

The Wolfram Language has a huge collection of built-in visualization functions—but there always seem to be more that we figure out can be added. We’ve had ListPlot3D since Version 1.0 (1988); we added ListPointPlot3D in Version 6.0 (2007)—and now in Version 12.3 we’re adding ListLinePlot3D.

Here’s a 3D random walk rendered with ListLinePlot3D:

&#10005

ListLinePlot3D[
 AnglePath3D[RandomReal[{0 \[Degree], 20 \[Degree]}, {1000, 3}]]]

If you give multiple lists of data, ListLinePlot3D plots them each separately:

&#10005

ListLinePlot3D[Table[Array[BitXor[#, n] &, 100], {n, 8}], 
 Filling -> Axis]

We first introduced plotting of vector fields in Version 7.0 (2008), with functions like VectorPlot and StreamPlot—that were substantially enhanced in Versions 12.1 and 12.2. In Version 12.3 we’re now adding StreamPlot3D (as well as ListStreamPlot3D). Here’s a plot of streamlines for a 3D vector field, colored by field strength:

&#10005

StreamPlot3D[{y + x, y - x, z}, {x, -2, 2}, {y, -2, 2}, {z, -3, 3}]

It’s one thing to make a plot; it’s another to give it axes. There’s a remarkable amount of subtlety in specifying how axes—and their ticks and labels—should be drawn. This is going to be a longer story, but in Version 12.3 we have the beginnings of symbolic axis specifications.

Axes work a bit like arrows: first you give the “core structure”, then you say how to annotate it. Here’s an axis that is linearly labeled from 0 to 100 on a line:

&#10005

Graphics[AxisObject[Line[{{-1, -1}, {1, 1}}], {0, 100}]]

And here’s a spiral axis (AKA labeling on a parametric curve):

&#10005

Graphics[
 AxisObject[
  Line[Table[{t Cos[t], t Sin[t]}, {t, 0, 5 Pi, 0.1}]], {0, 100}]]

And, yes, it works in more ornate cases as well:

&#10005

Graphics[AxisObject[HilbertCurve[3], {0, 100}, TickPositions -> 50]]

Among many subtle issues, there’s the question of how tick labels should be oriented with respect to the axis:

&#10005

Graphics[
 AxisObject[
  Line[Table[{t Cos[t], t Sin[t]}, {t, 0, 5 Pi, 0.1}]], {0, 100}, 
  TickPositions -> 50, TickLabelOrientation -> "Parallel"]]

Talking of subtleties in graphics, here’s another one that’s addressed in Version 12.3. Say you’re making a dashed line:

&#10005

Graphics[{Dashing[{.2, .15}], Thickness[.05], 
  Line[{{0, 0}, {1, .1}}]}]

The numbers inside Dashing indicate the length of each dash, and between dashes. In Version 12.3 there’s an additional number you can give: the offset of the start of the first dash:

&#10005

Graphics[{Dashing[{.2, .15}, 0.1], Thickness[.05], 
  Line[{{0, 0}, {1, .1}}]}]

And something else is that you can control the “caps” on each dash, here making them rounded:

&#10005

Graphics[{Dashing[{.2, .15}, 0.1, "Round"], Thickness[.05], 
  Line[{{0, 0}, {1, .1}}]}]

These may all seem like micro-details—but they’re the kinds of things that are important in having Wolfram Language graphics look really good. Like, for example, if you have two dashed axes that cross, you probably want the “dashings” to line up:

&#10005

Graphics[{{Dashing[{0.1`, 0.05`}, 0.0195`], 
   Line[{{-1, 0}, {1, 0}}]}, {Dashing[{0.1`, 0.05`}, 0.0195`], 
   Line[{{0, -1}, {0, 1}}]}}, ImageSize -> 200]

Golden Knots, and Other Material Matters

We’ve talked about it almost since Version 1.0. And now finally in Version 12.3 it’s here! Realistic rendering of surfaces as materials. Here’s a knot, rendered as if it’s made of gold:

&#10005

Graphics3D[{MaterialShading["Gold"], 
  KnotData["SolomonSeal", "ImageData"]}]

Here’s a surface, in velvet:

&#10005

Plot3D[Sin[x + y^2], {x, -7, 7}, {y, -2, 2}, 
 PlotStyle -> MaterialShading["Velvet"]]

In Version 12.3 we’re supporting a bit more than a dozen standard named materials (with more to come). But MaterialShading is also set up to allow you to specify in detail explicit physical and geometrical properties of materials—so you can get effects like this:

&#10005

Graphics3D[{MaterialShading[<|"BaseColor" -> Texture[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJzsvceSJEuSJNi7e9nj/sL+xVz3OEQ7NDPVVfXqocyMDODIMMbYMUaBEryq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"], {{
            0, 256.}, {512., 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
          "Byte", ColorSpace -> "RGB", Interleaving -> False, 
           Magnification -> Automatic],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->32,
ImageSizeRaw->{512., 256.},
PlotRange->{{0, 512.}, {0, 256.}}]\)], 
    "SurfaceNormals" -> Texture[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJzsvdeS3Mi293ck3XyXegW9hW51+UWcGXpvhra9J2fYBSAtgGra9r6bvunN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"], {{0, 256.}, {512., 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
          "Byte", ColorSpace -> "RGB", Interleaving -> False, 
           Magnification -> Automatic],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->32,
ImageSizeRaw->{512., 256.},
PlotRange->{{0, 512.}, {0, 256.}}]\)], "RoughnessCoefficient" -> 0.65,
     "MetallicCoefficient" -> 
     Texture[CloudGet["http://wolfr.am/VAsat6ib"]] |>], Sphere[]}, 
 Lighting -> "ThreePoint"]

Notice, by the way, the new "ThreePoint" setting for Lighting—essentially a simulation of a standard placement of lights in a photography studio.

The story of modeling light interacting with surfaces—and “physically based rendering”—is a complicated one, that Version 12.3 has a whole monograph about:

Monograph

Trees!

Based on the number of new built-in functions the clear winner for the largest new framework in Version 12.3 is the one for trees. We’ve been able to handle trees as a special case of graphs for more than a decade (and of course all symbolic expressions in the Wolfram Language are ultimately represented as trees). But in Version 12.3 we’re introducing trees as first-class objects in the system.

The fundamental object is Tree:

&#10005

Tree[a, {b, Tree[c, {d, e}], f, g}]

Tree takes two arguments: a “payload” (which can be any expression), and a list of subtrees. (And, yes, trees are by default rendered slightly green, in a nod to their botanical analogs.)

There are a variety of “*Tree” functions for constructing trees, and “Tree*” functions for converting trees to other things. RulesTree, for example, constructs a tree from a nested collection of rules:

&#10005

RulesTree[a -> {b, c -> {d, e}, f, g}]

And TreeRules goes the other way, converting a tree to a nested collection of rules:

&#10005

TreeRules[%]

ExpressionTree creates a tree from the structure of an expression:

&#10005

ExpressionTree[Integrate[1/(x^2 - 1), x]]

In a sense, this is a direct representation of a FullForm expression, as shown, for example, in TreeForm. But there are also ways to turn an expression into a tree. This takes the nodes of the tree to contain full subexpressions—so that the expressions on a given level in the tree are essentially what a function like Map would consider to be the expressions at that level (with Heads → True):

&#10005

ExpressionTree[Integrate[1/(x^2 - 1), x], "Subexpressions"]

Here’s another version, now effectively removing the redundancy of nested subexpressions, and treating heads of expressions just like other parts (in “S-expression style”):

&#10005

ExpressionTree[Integrate[1/(x^2 - 1), x], "Atoms"]

Why do we need Tree when we have Graph? The answer is that there are several special features of trees that are important. In a Graph, for example, every node has a name, and the names have to be unique. In a tree, nodes don’t have to be named, but they can have “payloads” that don’t have to be unique. In addition, in a graph, the edges at a given node don’t appear in any particular order; in a tree they do. Finally, a tree has a specific root node; a graph doesn’t necessarily have anything like this.

When we were designing Tree we at first thought we’d have to have separate symbolic representations of whole trees, subtrees and leaf nodes. But it turned out that we were able to make an elegant design with Tree alone. Nodes in a tree typically have the form Tree[payload, {child1child2, ...}] where the childi are subtrees. A node doesn’t necessarily have to have a payload, in which case it can just be given as Tree[{child1child2, ...}]. A leaf node is then Tree[exprNone] or Tree[None].

One very nice feature of this design is that trees can immediately be constructed from subtrees just by nesting expressions:

&#10005

Tree[{\!\(\*
GraphicsBox[
NamespaceBox["Trees",
DynamicModuleBox[{Typeset`tree = HoldComplete[
Tree[$CellContext`a, {
Tree[$CellContext`b, None], 
Tree[$CellContext`c, {
Tree[$CellContext`d, None], 
Tree[$CellContext`e, None]}]}]]}, {
{RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765],
          AbsoluteThickness[1], Opacity[0.7], 
StyleBox[{
           LineBox[{{0.4472135954999579, 1.7888543819998317`}, {0., 
            0.8944271909999159}}], 
           LineBox[{{0.4472135954999579, 1.7888543819998317`}, {
            0.8944271909999159, 0.8944271909999159}}], 
           LineBox[{{0.8944271909999159, 0.8944271909999159}, {
            0.4472135954999579, 0.}}], 
           LineBox[{{0.8944271909999159, 0.8944271909999159}, {
            1.3416407864998738`, 0.}}]},
GraphicsHighlightColor->RGBColor[
           0.403921568627451, 0.8705882352941177, 
            0.7176470588235294]]}, 
{GrayLevel[0], EdgeForm[{GrayLevel[0], Opacity[0.7]}], 
StyleBox[{InsetBox[
FrameBox["a",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->4,
StripOnInput->False], {0.4472135954999579, 1.7888543819998317}], 
           InsetBox[
FrameBox["b",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->0,
StripOnInput->False], {0., 0.8944271909999159}], InsetBox[
FrameBox["c",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->4,
StripOnInput->False], {0.8944271909999159, 0.8944271909999159}], 
           InsetBox[
FrameBox["d",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->0,
StripOnInput->False], {0.4472135954999579, 0.}], InsetBox[
FrameBox["e",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->0,
StripOnInput->False], {1.3416407864998738, 0.}]},
GraphicsHighlightColor->RGBColor[
           0.403921568627451, 0.8705882352941177, 
            0.7176470588235294]]}}]],
BaseStyle->{
      FrontEnd`GraphicsHighlightColor -> 
       RGBColor[
        0.403921568627451, 0.8705882352941177, 0.7176470588235294]},
FormatType->StandardForm,
FrameTicks->None,
ImageSize->{69.1171875, Automatic}]\), \!\(\*
GraphicsBox[
NamespaceBox["Trees",
DynamicModuleBox[{Typeset`tree = HoldComplete[
Tree[$CellContext`a, {
Tree[$CellContext`b, None], 
Tree[$CellContext`c, {
Tree[$CellContext`d, None], 
Tree[$CellContext`e, None]}]}]]}, {
{RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765],
          AbsoluteThickness[1], Opacity[0.7], 
StyleBox[{
           LineBox[{{0.4472135954999579, 1.7888543819998317`}, {0., 
            0.8944271909999159}}], 
           LineBox[{{0.4472135954999579, 1.7888543819998317`}, {
            0.8944271909999159, 0.8944271909999159}}], 
           LineBox[{{0.8944271909999159, 0.8944271909999159}, {
            0.4472135954999579, 0.}}], 
           LineBox[{{0.8944271909999159, 0.8944271909999159}, {
            1.3416407864998738`, 0.}}]},
GraphicsHighlightColor->RGBColor[
           0.403921568627451, 0.8705882352941177, 
            0.7176470588235294]]}, 
{GrayLevel[0], EdgeForm[{GrayLevel[0], Opacity[0.7]}], 
StyleBox[{InsetBox[
FrameBox["a",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->4,
StripOnInput->False], {0.4472135954999579, 1.7888543819998317}], 
           InsetBox[
FrameBox["b",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->0,
StripOnInput->False], {0., 0.8944271909999159}], InsetBox[
FrameBox["c",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->4,
StripOnInput->False], {0.8944271909999159, 0.8944271909999159}], 
           InsetBox[
FrameBox["d",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->0,
StripOnInput->False], {0.4472135954999579, 0.}], InsetBox[
FrameBox["e",
Background->RGBColor[
              0.9607843137254902, 0.9882352941176471, 
               0.9764705882352941],
FrameStyle->Directive[
RGBColor[0.6588235294117647, 0.7294117647058823, 0.7058823529411765], 
               
AbsoluteThickness[1]],
ImageSize->Automatic,
RoundingRadius->0,
StripOnInput->False], {1.3416407864998738, 0.}]},
GraphicsHighlightColor->RGBColor[
           0.403921568627451, 0.8705882352941177, 
            0.7176470588235294]]}}]],
BaseStyle->{
      FrontEnd`GraphicsHighlightColor -> 
       RGBColor[
        0.403921568627451, 0.8705882352941177, 0.7176470588235294]},
FormatType->StandardForm,
FrameTicks->None,
ImageSize->{68.625, Automatic}]\), 
  Tree[{CloudGet["http://wolfr.am/VAsaSro1"]}]}]

By the way, we can turn this into a generic Graph object with TreeGraph:

&#10005

TreeGraph[%]

Notice that since Graph doesn’t pay attention to ordering of nodes, some nodes have effectively been flipped in this rendering. The nodes have also had to be given distinct names in order to preserve the tree structure:

&#10005

Graph[CloudGet["http://wolfr.am/VAsb0AqA"], VertexLabels -> Automatic]

If there’s a generic graph that happens to be a tree, GraphTree converts it to explicit Tree form:

&#10005

GraphTree[KaryTree[20]]

RandomTree produces a random tree of a given size:

&#10005

RandomTree[20]

One can also make trees from nesting functions: NestTree produces a tree by nestedly generating payloads of child nodes from payloads of parent nodes:

&#10005

NestTree[{f[#], g[#]} &, x, 2]

OK, so given a tree, what can we do with it? There are a variety of tree functions that are direct analogs of functions for generic expressions. For example, TreeDepth gives the depth of a tree:

&#10005

TreeDepth[CloudGet["http://wolfr.am/VAsbf4XX"]]

TreeLevel is directly analogous to Level. Here we’re getting subtrees that start at level 2 in the tree:

&#10005

TreeLevel[CloudGet["http://wolfr.am/VAsbnJeT"], {2}]

How do you get a particular subtree of a given tree? Basically it has a position, just as a subexpression would have a position in an ordinary expression:

&#10005

TreeExtract[CloudGet["http://wolfr.am/VAsbnJeT"], {2, 2}]

TreeSelect lets you select subtrees in a given tree:

&#10005

TreeSelect[CloudGet["http://wolfr.am/VAsbnJeT"], TreeDepth[#] > 2 &]

TreeData picks out payloads, by default for the roots of trees (TreeChildren picks out subtrees):

&#10005

TreeData /@ %

There are also TreeCases, TreeCount and TreePosition—which by default search for subtrees whose payloads match a specified pattern. One can do functional programming with trees just like with generic expressions. TreeMap maps a function over (the payloads in) a tree:

&#10005

TreeMap[f, CloudGet["http://wolfr.am/VAsbCysJ"]]

TreeFold does a slightly more complicated operation. Here f is effectively “accumulating data” by scanning the tree, with g being applied to the payload of each leaf (to “initialize the accumulation”):

&#10005

TreeFold[{f, g}, CloudGet["http://wolfr.am/VAsbCysJ"]]

There are lots of things that can be represented by trees. A classic example is family trees. Here’s a case where there’s built-in data we can use:

&#10005

Entity["Person", "QueenElizabethII::f5243"][
 EntityProperty["Person", "Children"]]

This constructs a 2-level family tree:

&#10005

NestTree[#[EntityProperty["Person", "Children"]] &, 
 Entity["Person", "QueenElizabethII::f5243"], 2]

By the way, our Tree system is very scalable, and can happily handle trees with millions of nodes. But in Version 12.3 we’re really just starting out; in subsequent versions there’ll be all sorts of other tree functionality, as well as applications to parse trees, XML trees, etc.

Dates, Times and How Fast Is the Earth Turning?

Dates and times are complicated. Not only does one have to deal with different calendar systems, and different time zones, but there are also different conventions in different languages and regions. Version 12.3 adds support for date and time conventions for more than 700 different “locales”.

Here’s a date with the standard conventions used in Swedish:

&#10005

DateString[Entity["Language", "Swedish::557qk"]]

And this shows the difference between British and American conventions, both for English:

&#10005

{DateString[Entity["LanguageLocale", "en-GB"] ], 
 DateString[Entity["LanguageLocale", "en-US"] ]}

In Version 12.3, there’s a new detailed specification for how date formats should be constructed:

&#10005

DateString[<|"Elements" -> {"Year", "Month", "Day", "DayName"}, 
  "Delimiters" -> "-", 
  "Language" -> Entity["Language", "Armenian::f964n"]|>]

What about going the other way: from a date string to a date object? The new FromDateString does that:

&#10005

FromDateString["2021-05-05-??????????", <|
  "Elements" -> {"Year", "Month", "Day", "DayName"}, 
  "Delimiters" -> "-", 
  "Language" -> Entity["Language", "Armenian::f964n"]|>]

Beyond questions of how to display dates and times, there’s also the question of how exactly times are determined. Since the 1950s there’s been a core standard of “atomic time” (itself complicated by relativistic and gravitational effects). But before then, and still for a variety of applications, one wants to determine time either from the Sun or the stars.

We introduced sidereal (star-based) time in Version 10.0 (2014):

&#10005

SiderealTime[]

And now in Version 12.3 we’re adding solar time, which is based on the position of the Sun in the sky:

&#10005

SolarTime[]

This doesn’t quite align with ordinary time, basically because of Daylight Saving Time and because of the longitude of the observer:

&#10005

TimeObject[]

Things get even more complicated if we want to get precise times in astronomy. And one of the big issues there is knowing the precise orientation of the Earth. In Version 12.3—in preparation for more extensive coverage of astronomy—we’ve added GeoOrientationData.

This tells how much longer than 24 hours the day currently is:

&#10005

GeoOrientationData[Now, "DayDurationExcess"]

In 1800, the day was shorter:

&#10005

GeoOrientationData[
 DateObject[{1800}, "Year", "Gregorian", -4.`], "DayDurationExcess"]

The Leading Edge of Machine Learning & Neural Nets

We first introduced automated machine learning (with Predict and Classify) back in Version 10.0 (2014)—and we’ve been continuing to develop leading-edge machine learning capabilities ever since. Version 12.3 introduces several new much-requested features, particularly aimed at greater analysis and control of machine learning.

Train a predictor to predict “wine quality” from the chemical content of a wine:

&#10005

p = Predict[
  ResourceData["Sample Data: Wine Quality"] -> "WineQuality"]

Use the predictor for a particular wine:

&#10005

p[<|"FixedAcidity" -> 6.`, "VolatileAcidity" -> 0.21`, 
  "CitricAcid" -> 0.38`, "ResidualSugar" -> 0.8`, 
  "Chlorides" -> 0.02`, "FreeSulfurDioxide" -> 22.`, 
  "TotalSulfurDioxide" -> 98.`, "Density" -> 0.98941`, "PH" -> 3.26`, 
  "Sulphates" -> 0.32`, "Alcohol" -> 11.8`|>]

A common question is then: “How did it get that result?”, or, more specifically, “How important were the different features of the wine in getting this result?” In Version 12.3 you can use SHAP values to see the relative importance of different features:

&#10005

p[<|"FixedAcidity" -> 6., "VolatileAcidity" -> 0.21`, 
  "CitricAcid" -> 0.38`, "ResidualSugar" -> 0.8`, 
  "Chlorides" -> 0.02`, "FreeSulfurDioxide" -> 22.`, 
  "TotalSulfurDioxide" -> 98.`, "Density" -> 0.98941`, "PH" -> 3.26`, 
  "Sulphates" -> 0.32`, "Alcohol" -> 11.8`|>, "SHAPValues"]

Here’s a visual version of this “explanation”:

&#10005

BarChart[%, BarOrigin -> Left, 
 ChartLabels -> Placed[Automatic, Before]]

The way SHAP values are computed is basically to see how much results change if different features in the data are dropped. In Version 12.3 we’ve added new options to functions like Predict and Classify to control how in general missing (or dropped) elements in data are handled both for training and evaluation—giving a way to determine, for example, what the uncertainty in a result might be from missing data.

A subtle but important issue in machine learning is calibrating the “confidence” of classifiers. If a classifier says that certain images have 60% probability to be cats, does this mean that 60% of them actually are cats? A raw neural net won’t typically get this right. But one can get closer by recalibrating probabilities using a calibration curve. And in Version 12.3, in addition to automatic recalibration, functions like Classify support the new RecalibrationFunction option that allows you to specify how the recalibration should be done.

An important part of our machine learning framework is in-depth symbolic support for neural nets. And we’ve continued to put the latest neural nets from the research literature into our Neural Net Repository, making them immediately accessible in our framework using NetModel.

In Version 12.3 we’ve added a few extra features to our framework, for example “swish” and “hardswish” activation functions for ElementwiseLayer. “Under the hood” a lot has been going on. We’ve enhanced ONNX import and export, we’ve greatly streamlined the software engineering of our MXNet integration, and we’ve almost finished a native version of our framework for Apple Silicon (in 12.3.0 the framework runs through Rosetta).

We’re always trying to make our machine learning framework as automated as possible. And in achieving this, it’s been very important that we’ve had so many curated net encoders and decoders that you can immediately use on different kinds of data. In Version 12.3 an extension to this is the use of an arbitrary feature extractor as a net encoder, that can be trained as part of your main training process. Why is this important? Well, it gives you a trainable way to feed into a neural net arbitrary collections of data of different kinds, even though there’s no pre-defined way of even knowing how the data can be turned into something like an array of numbers suitable for input to a neural net.

In addition to providing direct access to state-of-the-art machine learning, the Wolfram Language has an increasing number of built-in functions that make powerful internal use of machine learning. One such function is TextCases. And in Version 12.3 TextCases has become significantly stronger, especially in supporting less common text content types, like "Protein" and "BoardGame":

&#10005

TextCases["Candy Land became Milton Bradley's best selling game \
surpassing its previous top seller, Uncle Wiggily.", 
 "BoardGame" -> "Interpretation"]

New in Video

We first introduced video into the Wolfram Language in Version 12.1, and in 12.2 we added many additional video capabilities. In 12.3 we’re adding still more capabilities, with yet more to come.

A major group of new capabilities in 12.3 revolve around programmatic video generation. There are three basic new functions: FrameListVideo, SlideShowVideo and AnimationVideo.

FrameListVideo takes a raw list of images, and assembles a video by treating them as successive raw frames. SlideShowVideo similarly takes a list of images, but now it creates a “slide show video” in which each image is displayed for a specified duration. Here, for example, each image is displayed in the video for 1 second:

&#10005

SlideShowVideo[{\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJzsvQV4XOeZvp+YZI6ZJNuSQbYl2bLFzMzMzMzMzMxMtowyMzuO2bHDDjhx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"], {{0, 113.}, {200., 0}}, {
        0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
       "Byte", ColorSpace -> "RGB", Interleaving -> True, 
        Magnification -> Automatic],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->Automatic,
ImageSizeRaw->{200., 113.},
PlotRange->{{0, 200.}, {0, 113.}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJzkvGWUXUeWNejp+dZMV09Nf9VfVbvKtihTmUpQZoohmZmZlEoxgyWZ7TLb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"], {{0, 113.}, {200., 0}}, {0, 255},
        
ColorFunction->RGBColor],
BoxForm`ImageTag[
       "Byte", ColorSpace -> "RGB", Interleaving -> True, 
        Magnification -> Automatic],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->Automatic,
ImageSizeRaw->{200., 113.},
PlotRange->{{0, 200.}, {0, 113.}}]\), 
   CloudGet["http://wolfr.am/VAscEWfy"]} -> 1]

SlideShowVideo can also do things like take a TimeSeries whose values are images, and turn this into a slide show video.

AnimationVideo doesn’t take existing images; instead it takes an expression and then evaluates it “Manipulate-style” for a range of values of a parameter. (In effect, it’s like a video-making analog of Animate.)

&#10005

AnimationVideo[
 Plot[Sin[a x] + Sin[x], {x, 0, 10}, Sequence[
  ImageSize -> 300, PlotRange -> {-2, 2}, Filling -> Axis, 
   FillingStyle -> LightOrange]], {a, 0, 3}]

What if you want to capture a video, say from a camera? Eventually there’ll be an interactive way to do this in a notebook. But in Version 12.3 we’ve added the underlying programmatic capabilities, and in particular the function VideoRecord. So this records 5 seconds from my default camera:

&#10005

sw = VideoRecord[$DefaultImagingDevice]; Pause[5]; VideoStop[];

And here’s the resulting video:

&#10005

swvid=Video[sw]

But VideoRecord can also use other sources. For example, if you give it a NotebookObject, it will record what’s happening in that notebook. And if you give it a URL (say for a webcam), it’ll record frames that are streaming from that URL:

Icon

A much-requested feature that we’ve added in Version 12.3 is the ability to combine videos, for example compositing one video into another, or assembling each frame as a collage.

So, for example, here’s me green-screen composited with the stream above:

&#10005

Cell[BoxData[
 RowBox[{"Parallelize", "[", 
  RowBox[{"VideoFrameMap", "[", 
   RowBox[{
    RowBox[{
     RowBox[{"ImageCompose", "[", 
      RowBox[{
       RowBox[{"#", "[", 
        RowBox[{"[", "2", "]"}], "]"}], ",", 
       RowBox[{"RemoveBackground", "[", 
        RowBox[{
         RowBox[{"#", "[", 
          RowBox[{"[", "1", "]"}], "]"}], ",", 
         RowBox[{"{", 
          RowBox[{
           InterpretationBox[
            ButtonBox[
             TooltipBox[
              GraphicsBox[{
                {GrayLevel[0], RectangleBox[{0, 0}]}, 
                {GrayLevel[0], RectangleBox[{1, -1}]}, 
                {RGBColor[
                  Rational[89, 255], 
                  Rational[211, 255], 
                  Rational[8, 17]], RectangleBox[{0, -1}, {2, 1}]}},
               AspectRatio->1,
               DefaultBaseStyle->"ColorSwatchGraphics",
               Frame->True,
               
               FrameStyle->RGBColor[
                0.2326797385620915, 0.5516339869281046, 
                 0.3137254901960784],
               FrameTicks->None,
               
               ImageSize->
                Dynamic[{
                 Automatic, 
                  1.35 (CurrentValue["FontCapHeight"]/
                   AbsoluteCurrentValue[Magnification])}],
               PlotRangePadding->None],
              StyleBox[
               RowBox[{"RGBColor", "[", 
                 RowBox[{"{", 
                   RowBox[{
                    FractionBox["89", "255"], ",", 
                    FractionBox["211", "255"], ",", 
                    FractionBox["8", "17"]}], "}"}], "]"}], 
               NumberMarks -> False]],
             Appearance->None,
             BaseStyle->{},
             BaselinePosition->Baseline,
             ButtonFunction:>With[{Typeset`box$ = EvaluationBox[]}, 
               If[
                Not[
                 AbsoluteCurrentValue["Deployed"]], 
                SelectionMove[Typeset`box$, All, Expression]; 
                FrontEnd`Private`$ColorSelectorInitialAlpha = 1; 
                FrontEnd`Private`$ColorSelectorInitialColor = RGBColor[
                   Rational[89, 255], 
                   Rational[211, 255], 
                   Rational[8, 17]]; 
                FrontEnd`Private`$ColorSelectorUseMakeBoxes = True; 
                MathLink`CallFrontEnd[
                  FrontEnd`AttachCell[Typeset`box$, 
                   FrontEndResource["RGBColorValueSelector"], {
                   0, {Left, Bottom}}, {Left, Top}, 
                   "ClosingActions" -> {
                    "SelectionDeparture", "ParentChanged", 
                    "EvaluatorQuit"}]]]],
             DefaultBaseStyle->{},
             Evaluator->Automatic,
             Method->"Preemptive"],
            RGBColor[{
              Rational[89, 255], 
              Rational[211, 255], 
              Rational[8, 17]}],
            Editable->False,
            Selectable->False], ",", "0.1"}], "}"}]}], "]"}]}], "]"}],
      "&"}], ",", 
    RowBox[{"{", 
     RowBox[{"swvid", ",", 
      RowBox[{"Video", "[", "forest", "]"}]}], "}"}]}], "]"}], 
  "]"}]], "Input",
 CellChangeTimes->{{3.83003607610647*^9, 3.830036206745329*^9}, {
  3.8300363050582*^9, 3.830036321953178*^9}, {3.830036639291601*^9, 
  3.8300366458665743`*^9}, {3.8300367180837393`*^9, 
  3.830036752163615*^9}, {3.830279843012519*^9, 
  3.830279844914358*^9}, {3.830279900718885*^9, 
  3.830279908976886*^9}, {3.830280855267188*^9, 
  3.83028085666193*^9}, {3.830460464769618*^9, 3.830460469651174*^9}},
 
 CellLabel->"In[2]:=",
 CellID->1729772881]

Notice that in doing this we’re using Parallelize—which newly works with VideoFrameMap in 12.3.

Version 12.3 also adds some new video-editing capabilities. VideoTimeStretch lets you “warp time” in a video by any specified function. VideoInsert lets you insert a video clip into a video, and VideoReplace lets you replace part of a video with another one.

One of the best things about video in the Wolfram Language is that it can immediately be analyzed using all of the tools in the language. This includes machine learning, and in Version 12.3 we’ve started the process of allowing videos to be encoded for neural net computation. Version 12.3 includes a simple frame-based net encoder for videos, as well as a couple of built-in feature extractors. More will be coming soon, including a variety of video processing and analysis nets in the Wolfram Neural Net Repository.

More in Chemistry

Chemistry is a major new area for Wolfram Language. In Version 12.0 we introduced Molecule as a symbolic representation of a molecule, and we’ve steadily been expanding what can be done with it.

In Version 12.3, for example, there are new properties for Molecule, like "TautomerList" (possible reconfigurations in solution):

&#10005

MoleculePlot /@ Molecule[{"C", 
Atom["C", "HydrogenCount" -> 1], "C", "O", "N", "C", "C", "O", "O", 
    "N"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Single"], 
Bond[{3, 4}, "Double"], 
Bond[{3, 5}, "Single"], 
Bond[{5, 6}, "Single"], 
Bond[{6, 7}, "Single"], 
Bond[{7, 8}, "Double"], 
Bond[{7, 9}, "Single"], 
Bond[{2, 10}, "Single"]}, {StereochemistryElements -> {
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 2, "Direction" -> 
       "Clockwise"]}}]["TautomerList"]

There are also convenience functions like MoleculeName:

&#10005

MoleculeName["CCCCCC(CC(CC)C)CCC"]

And, yes, with MoleculeRecognize you can just clip a structure diagram from a publication and find the name of the molecule:

&#10005

MoleculeName[MoleculeRecognize[CloudGet["http://wolfr.am/VAsdfYsh"]]]

Given a collection of molecules, a question one often wants to ask is “What’s in common between these molecules?” In Version 12.3 we now have the function MoleculeMaximumCommonSubstructure, which is the molecular structure analog of LongestCommonSubsequence:

&#10005

mcs = MoleculeMaximumCommonSubstructure[{Molecule[{
    "N", "C", "N", "C", "N", "C", "C", "N", "C", "N", "C", "O", "C", 
     "C", "O", "C", "O", "C", "O", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Aromatic"], 
Bond[{3, 4}, "Aromatic"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{10, 11}, "Single"], 
Bond[{11, 12}, "Single"], 
Bond[{12, 13}, "Single"], 
Bond[{13, 14}, "Single"], 
Bond[{14, 15}, "Single"], 
Bond[{13, 16}, "Single"], 
Bond[{16, 17}, "Single"], 
Bond[{16, 18}, "Single"], 
Bond[{18, 19}, "Single"], 
Bond[{7, 2}, "Aromatic"], 
Bond[{18, 11}, "Single"], 
Bond[{10, 6}, "Aromatic"], 
Bond[{1, 20}, "Single"], 
Bond[{1, 21}, "Single"], 
Bond[{4, 22}, "Single"], 
Bond[{9, 23}, "Single"], 
Bond[{11, 24}, "Single"], 
Bond[{13, 25}, "Single"], 
Bond[{14, 26}, "Single"], 
Bond[{14, 27}, "Single"], 
Bond[{15, 28}, "Single"], 
Bond[{16, 29}, "Single"], 
Bond[{17, 30}, "Single"], 
Bond[{18, 31}, "Single"], 
Bond[{19, 32}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{32, 3}, {CompressedData["
1:eJwBEQPu/CFib1JlAgAAACAAAAADAAAAzGgnqNJ5IEDneLE7oYUiQOeZck95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"], "Angstroms", {{1}, {
         2}}}]], StereochemistryElements -> {
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 11, 
        "Direction" -> "Counterclockwise"], 
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 13, 
        "Direction" -> "Counterclockwise"], 
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 16, 
        "Direction" -> "Clockwise"], 
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 18, 
        "Direction" -> "Clockwise"]}}], 
   Molecule[{
    "N", "C", "N", "C", "N", "C", "C", "N", "C", "N", "C", "O", "C", 
     "C", "O", "P", "O", "O", "O", "P", "O", "O", "O", "P", "O", "O", 
     "O", "C", "O", "C", "O", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Aromatic"], 
Bond[{3, 4}, "Aromatic"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{10, 11}, "Single"], 
Bond[{11, 12}, "Single"], 
Bond[{12, 13}, "Single"], 
Bond[{13, 14}, "Single"], 
Bond[{14, 15}, "Single"], 
Bond[{15, 16}, "Single"], 
Bond[{16, 17}, "Double"], 
Bond[{16, 18}, "Single"], 
Bond[{16, 19}, "Single"], 
Bond[{19, 20}, "Single"], 
Bond[{20, 21}, "Double"], 
Bond[{20, 22}, "Single"], 
Bond[{20, 23}, "Single"], 
Bond[{23, 24}, "Single"], 
Bond[{24, 25}, "Double"], 
Bond[{24, 26}, "Single"], 
Bond[{24, 27}, "Single"], 
Bond[{13, 28}, "Single"], 
Bond[{28, 29}, "Single"], 
Bond[{28, 30}, "Single"], 
Bond[{30, 31}, "Single"], 
Bond[{7, 2}, "Aromatic"], 
Bond[{30, 11}, "Single"], 
Bond[{10, 6}, "Aromatic"], 
Bond[{1, 32}, "Single"], 
Bond[{1, 33}, "Single"], 
Bond[{4, 34}, "Single"], 
Bond[{9, 35}, "Single"], 
Bond[{11, 36}, "Single"], 
Bond[{13, 37}, "Single"], 
Bond[{14, 38}, "Single"], 
Bond[{14, 39}, "Single"], 
Bond[{18, 40}, "Single"], 
Bond[{22, 41}, "Single"], 
Bond[{26, 42}, "Single"], 
Bond[{27, 43}, "Single"], 
Bond[{28, 44}, "Single"], 
Bond[{29, 45}, "Single"], 
Bond[{30, 46}, "Single"], 
Bond[{31, 47}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{47, 3}, {CompressedData["
1:eJwNz3840wkAx/HFWjYts7FfbeO7H36s0eFyeqh98oRzWLK6cJWrEKrVce2u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"], "Angstroms", {{1}, {2}}}]], 
     StereochemistryElements -> {
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 11, 
        "Direction" -> "Counterclockwise"], 
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 13, 
        "Direction" -> "Counterclockwise"], 
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 28, 
        "Direction" -> "Clockwise"], 
Association[
       "StereoType" -> "Tetrahedral", "ChiralCenter" -> 30, 
        "Direction" -> "Clockwise"]}}]}]

Here’s a diagram of the common part:

&#10005

MoleculePlot[Molecule[{
  "N", "C", "N", "C", "N", "C", "C", "N", "C", "N", "C", "O", "C", 
   "C", "O", "P", "O", "O", "O", "P", "O", "O", "O", "P", "O", "O", 
   "O", "C", "O", "C", "O", "H", "H", "H", "H", "H", "H", "H", "H", 
   "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Aromatic"], 
Bond[{3, 4}, "Aromatic"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{10, 11}, "Single"], 
Bond[{11, 12}, "Single"], 
Bond[{12, 13}, "Single"], 
Bond[{13, 14}, "Single"], 
Bond[{14, 15}, "Single"], 
Bond[{15, 16}, "Single"], 
Bond[{16, 17}, "Double"], 
Bond[{16, 18}, "Single"], 
Bond[{16, 19}, "Single"], 
Bond[{19, 20}, "Single"], 
Bond[{20, 21}, "Double"], 
Bond[{20, 22}, "Single"], 
Bond[{20, 23}, "Single"], 
Bond[{23, 24}, "Single"], 
Bond[{24, 25}, "Double"], 
Bond[{24, 26}, "Single"], 
Bond[{24, 27}, "Single"], 
Bond[{13, 28}, "Single"], 
Bond[{28, 29}, "Single"], 
Bond[{28, 30}, "Single"], 
Bond[{30, 31}, "Single"], 
Bond[{7, 2}, "Aromatic"], 
Bond[{30, 11}, "Single"], 
Bond[{10, 6}, "Aromatic"], 
Bond[{1, 32}, "Single"], 
Bond[{1, 33}, "Single"], 
Bond[{4, 34}, "Single"], 
Bond[{9, 35}, "Single"], 
Bond[{11, 36}, "Single"], 
Bond[{13, 37}, "Single"], 
Bond[{14, 38}, "Single"], 
Bond[{14, 39}, "Single"], 
Bond[{18, 40}, "Single"], 
Bond[{22, 41}, "Single"], 
Bond[{26, 42}, "Single"], 
Bond[{27, 43}, "Single"], 
Bond[{28, 44}, "Single"], 
Bond[{29, 45}, "Single"], 
Bond[{30, 46}, "Single"], 
Bond[{31, 47}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{47, 3}, {CompressedData["
1:eJwNz3840wkAx/HFWjYts7FfbeO7H36s0eFyeqh98oRzWLK6cJWrEKrVce2u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"], "Angstroms", {{1}, {2}}}]], 
   StereochemistryElements -> {
Association[
     "StereoType" -> "Tetrahedral", "ChiralCenter" -> 11, "Direction" -> 
      "Counterclockwise"], 
Association[
     "StereoType" -> "Tetrahedral", "ChiralCenter" -> 13, "Direction" -> 
      "Counterclockwise"], 
Association[
     "StereoType" -> "Tetrahedral", "ChiralCenter" -> 28, "Direction" -> 
      "Clockwise"], 
Association[
     "StereoType" -> "Tetrahedral", "ChiralCenter" -> 30, "Direction" -> 
      "Clockwise"]}}], %]

And now with MoleculeAlign we can see how the molecules actually align in 3D:

&#10005

MoleculePlot3D[
 MoleculeAlign[
  Molecule[{
   "N", "C", "N", "C", "N", "C", "C", "N", "C", "N", "C", "O", "C", 
    "C", "O", "C", "O", "C", "O", "H", "H", "H", "H", "H", "H", "H", 
    "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Aromatic"], 
Bond[{3, 4}, "Aromatic"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{10, 11}, "Single"], 
Bond[{11, 12}, "Single"], 
Bond[{12, 13}, "Single"], 
Bond[{13, 14}, "Single"], 
Bond[{14, 15}, "Single"], 
Bond[{13, 16}, "Single"], 
Bond[{16, 17}, "Single"], 
Bond[{16, 18}, "Single"], 
Bond[{18, 19}, "Single"], 
Bond[{7, 2}, "Aromatic"], 
Bond[{18, 11}, "Single"], 
Bond[{10, 6}, "Aromatic"], 
Bond[{1, 20}, "Single"], 
Bond[{1, 21}, "Single"], 
Bond[{4, 22}, "Single"], 
Bond[{9, 23}, "Single"], 
Bond[{11, 24}, "Single"], 
Bond[{13, 25}, "Single"], 
Bond[{14, 26}, "Single"], 
Bond[{14, 27}, "Single"], 
Bond[{15, 28}, "Single"], 
Bond[{16, 29}, "Single"], 
Bond[{17, 30}, "Single"], 
Bond[{18, 31}, "Single"], 
Bond[{19, 32}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{32, 3}, {CompressedData["
1:eJwBEQPu/CFib1JlAgAAACAAAAADAAAAzGgnqNJ5IEDneLE7oYUiQOeZck95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"], "Angstroms", {{1}, {
        2}}}]], StereochemistryElements -> {
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 11, 
       "Direction" -> "Counterclockwise"], 
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 13, 
       "Direction" -> "Counterclockwise"], 
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 16, 
       "Direction" -> "Clockwise"], 
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 18, 
       "Direction" -> "Clockwise"]}}], 
  Molecule[{
   "N", "C", "N", "C", "N", "C", "C", "N", "C", "N", "C", "O", "C", 
    "C", "O", "P", "O", "O", "O", "P", "O", "O", "O", "P", "O", "O", 
    "O", "C", "O", "C", "O", "H", "H", "H", "H", "H", "H", "H", "H", 
    "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Aromatic"], 
Bond[{3, 4}, "Aromatic"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{10, 11}, "Single"], 
Bond[{11, 12}, "Single"], 
Bond[{12, 13}, "Single"], 
Bond[{13, 14}, "Single"], 
Bond[{14, 15}, "Single"], 
Bond[{15, 16}, "Single"], 
Bond[{16, 17}, "Double"], 
Bond[{16, 18}, "Single"], 
Bond[{16, 19}, "Single"], 
Bond[{19, 20}, "Single"], 
Bond[{20, 21}, "Double"], 
Bond[{20, 22}, "Single"], 
Bond[{20, 23}, "Single"], 
Bond[{23, 24}, "Single"], 
Bond[{24, 25}, "Double"], 
Bond[{24, 26}, "Single"], 
Bond[{24, 27}, "Single"], 
Bond[{13, 28}, "Single"], 
Bond[{28, 29}, "Single"], 
Bond[{28, 30}, "Single"], 
Bond[{30, 31}, "Single"], 
Bond[{7, 2}, "Aromatic"], 
Bond[{30, 11}, "Single"], 
Bond[{10, 6}, "Aromatic"], 
Bond[{1, 32}, "Single"], 
Bond[{1, 33}, "Single"], 
Bond[{4, 34}, "Single"], 
Bond[{9, 35}, "Single"], 
Bond[{11, 36}, "Single"], 
Bond[{13, 37}, "Single"], 
Bond[{14, 38}, "Single"], 
Bond[{14, 39}, "Single"], 
Bond[{18, 40}, "Single"], 
Bond[{22, 41}, "Single"], 
Bond[{26, 42}, "Single"], 
Bond[{27, 43}, "Single"], 
Bond[{28, 44}, "Single"], 
Bond[{29, 45}, "Single"], 
Bond[{30, 46}, "Single"], 
Bond[{31, 47}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{47, 3}, {CompressedData["
1:eJwNz3840wkAx/HFWjYts7FfbeO7H36s0eFyeqh98oRzWLK6cJWrEKrVce2u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"], "Angstroms", {{1}, {2}}}]], 
    StereochemistryElements -> {
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 11, 
       "Direction" -> "Counterclockwise"], 
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 13, 
       "Direction" -> "Counterclockwise"], 
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 28, 
       "Direction" -> "Clockwise"], 
Association[
      "StereoType" -> "Tetrahedral", "ChiralCenter" -> 30, 
       "Direction" -> "Clockwise"]}}], mcs], mcs]

Given our strength in chemistry and in machine learning, we’re now in an interesting position to bring these fields together. And in Version 12.3 we have the beginnings of built-in chemical machine learning. Here are samples of two classes of chemicals:

&#10005

acids = {Molecule[{
    "S", "O", "O", "C", "C", "C", "C", "C", "C", "C", "B", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 5}, "Single"], 
Bond[{1, 10}, "Single"], 
Bond[{2, 11}, "Single"], 
Bond[{2, 19}, "Single"], 
Bond[{3, 11}, "Single"], 
Bond[{3, 20}, "Single"], 
Bond[{4, 6}, "Aromatic"], 
Bond[{4, 7}, "Aromatic"], 
Bond[{4, 11}, "Single"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{5, 8}, "Aromatic"], 
Bond[{6, 12}, "Single"], 
Bond[{7, 9}, "Aromatic"], 
Bond[{7, 13}, "Single"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{8, 14}, "Single"], 
Bond[{9, 15}, "Single"], 
Bond[{10, 16}, "Single"], 
Bond[{10, 17}, "Single"], 
Bond[{10, 18}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{20, 3}, {CompressedData["
1:eJwB8QEO/iFib1JlAgAAABQAAAADAAAAy+WqtmpOAUC0OlJvt4PxP9cVgoWt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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGAQAWIQDQUO3zRi+g99/bHf93NfcEmKkAPX9cUFtlz/7RU3
FGVMfMsG51eZrbYLv83roAlSrnEHLg/R/2E/jL9DrvV14I51cPNgfJh+dPtg
+mHuqRZZ5/6wiuUATD3E/u/2y1946P1fyOQAUXbBHizeJQLnw9QX2oJkGA/A
5N8Eglzwdb8oyFiRb/Yw82H2QdRzHoC4gwVuP8x9UL4DzH8wPgAJz4iI
"]}], 
   Molecule[{
    "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "C", "B", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H"}, {
Bond[{1, 4}, "Single"], 
Bond[{1, 6}, "Single"], 
Bond[{2, 13}, "Single"], 
Bond[{2, 25}, "Single"], 
Bond[{3, 13}, "Single"], 
Bond[{3, 26}, "Single"], 
Bond[{4, 5}, "Single"], 
Bond[{4, 14}, "Single"], 
Bond[{4, 15}, "Single"], 
Bond[{5, 8}, "Single"], 
Bond[{5, 16}, "Single"], 
Bond[{5, 17}, "Single"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{6, 9}, "Aromatic"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 13}, "Single"], 
Bond[{8, 18}, "Single"], 
Bond[{8, 19}, "Single"], 
Bond[{8, 20}, "Single"], 
Bond[{9, 11}, "Aromatic"], 
Bond[{9, 21}, "Single"], 
Bond[{10, 12}, "Aromatic"], 
Bond[{10, 22}, "Single"], 
Bond[{11, 12}, "Aromatic"], 
Bond[{11, 23}, "Single"], 
Bond[{12, 24}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{26, 3}, {CompressedData["
1:eJwBgQJ+/SFib1JlAgAAABoAAAADAAAA2xIKYANw5T9QcdP1A+7LP2Gge91g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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQAmIQ7fu5L7gkRchhh1zr68Ad6/bPfia7/MUJGQeu64sL
bLm+28d5n2C3vS0K51eZrbYLv83r8CYQpOPhfsUNRRkT37LB9cPUw+TzFjPu
YQ2ShMszQABcHqb+m0ZM/6GvP+D2o+uvFlnn/rCK4QBMHqYeJi8KkhZ5Zx+3
y5OHaTWXg5xYFtBrH/brgxS0CsD5djpXZj2T5XI4I3B8147eW/a2II/tZYbz
IfYwObzcvp75ec/H/byFa7pvZ3y3B3v/+uv9kHD4bT+vQe1Q2/Lp+xemb35V
vFUY6t6/+yNPGR3Z+E7OAeLNC3D3FYItYj0Ak4eph/kH6j8HWHjA+AD39scp

"]}], Molecule[{
    "F", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "C", 
     "C", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H"}, {
Bond[{1, 10}, "Single"], 
Bond[{2, 7}, "Single"], 
Bond[{2, 9}, "Single"], 
Bond[{3, 15}, "Single"], 
Bond[{3, 28}, "Single"], 
Bond[{4, 15}, "Single"], 
Bond[{4, 29}, "Single"], 
Bond[{5, 6}, "Single"], 
Bond[{5, 7}, "Single"], 
Bond[{5, 16}, "Single"], 
Bond[{5, 17}, "Single"], 
Bond[{6, 8}, "Single"], 
Bond[{6, 18}, "Single"], 
Bond[{6, 19}, "Single"], 
Bond[{7, 20}, "Single"], 
Bond[{7, 21}, "Single"], 
Bond[{8, 22}, "Single"], 
Bond[{8, 23}, "Single"], 
Bond[{8, 24}, "Single"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{9, 11}, "Aromatic"], 
Bond[{10, 13}, "Aromatic"], 
Bond[{11, 14}, "Aromatic"], 
Bond[{11, 25}, "Single"], 
Bond[{12, 13}, "Aromatic"], 
Bond[{12, 14}, "Aromatic"], 
Bond[{12, 15}, "Single"], 
Bond[{13, 26}, "Single"], 
Bond[{14, 27}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{29, 3}, {CompressedData["
1:eJwByQI2/SFib1JlAgAAAB0AAAADAAAAUQZRdcCD8j86RJqTdcUFQLHjE4X5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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQBWIQDQUOO+RaXwfuWLe/ymy1XfhtXodvGjH9h75+2O/7
uS+4JEXIASQr18rtoLihKGPiWzY4HyZfLbLO/WEVy4E47xPstrdFoXyOAzB5
iHk/9sPkbbmuLy6wFTgAsw9mP8x8TZByjTv2MP0wPkw9WDvXf3uYegj/O1w9
jA9TD3Evu0N6GhA843dYdyO+zH8e8wFVtsapzt2CDnfcmCu4VTgPAAmNOh8J
B4+APxLF4ZwH2i2uHc3dIuYg+ShCfPtF5gMZm18Vb70q4jBhwQ/DZ+t+7Y/6
uvNWl62wQ3pHcuwdt/f7H5tJHYhOEIb7D+ZfiP+EDtwCWisXJgGXB1NdIg5L
CkAiB+yXv/DQ+7+QyQESLX/sYfIwPsx/UP3w+IDxAbOMxbo=
"]}], 
   Molecule[{
    "O", "O", "O", "C", "C", "C", "C", "C", "C", "B", "H", "H", "H", 
     "H", "H", "H", "H"}, {
Bond[{1, 7}, "Single"], 
Bond[{1, 15}, "Single"], 
Bond[{2, 10}, "Single"], 
Bond[{2, 16}, "Single"], 
Bond[{3, 10}, "Single"], 
Bond[{3, 17}, "Single"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{4, 6}, "Aromatic"], 
Bond[{4, 10}, "Single"], 
Bond[{5, 8}, "Aromatic"], 
Bond[{5, 11}, "Single"], 
Bond[{6, 9}, "Aromatic"], 
Bond[{6, 12}, "Single"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{7, 9}, "Aromatic"], 
Bond[{8, 13}, "Single"], 
Bond[{9, 14}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{17, 3}, {CompressedData["
1:eJwBqQFW/iFib1JlAgAAABEAAAADAAAA84DbLJLgC8AJzyREqwbAvwm+SJav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"], "Angstroms", {{1}, {
         2}}}]], 
     AtomDiagramCoordinates -> {{2.866, -2.405}, {3.7321, 2.095}, {2.,
       2.095}, {2.866, 0.595}, {3.7321, 0.095}, {2., 0.095}, {
      2.866, -1.405}, {3.7321, -0.905}, {2., -0.905}, {2.866, 
      1.595}, {4.269, 0.405}, {1.4631, 0.405}, {4.269, -1.215}, {
      1.4631, -1.215}, {2.3291, -2.715}, {3.7321, 2.715}, {2., 
      2.715}}}], 
   Molecule[{
    "O", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "B", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 5}, "Single"], 
Bond[{1, 11}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{2, 12}, "Single"], 
Bond[{3, 13}, "Single"], 
Bond[{3, 23}, "Single"], 
Bond[{4, 13}, "Single"], 
Bond[{4, 24}, "Single"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{5, 8}, "Aromatic"], 
Bond[{6, 9}, "Aromatic"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 13}, "Single"], 
Bond[{8, 14}, "Single"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{9, 15}, "Single"], 
Bond[{10, 16}, "Single"], 
Bond[{11, 17}, "Single"], 
Bond[{11, 18}, "Single"], 
Bond[{11, 19}, "Single"], 
Bond[{12, 20}, "Single"], 
Bond[{12, 21}, "Single"], 
Bond[{12, 22}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{24, 3}, {CompressedData["
1:eJwBUQKu/SFib1JlAgAAABgAAAADAAAA25cgDbc0/T9e3zJoFjD2PzJ8J7OL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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQAGIQrbihKGPiWzaHbxox/Ye+ftnv+7kvuCRFyKFaZJ37
wyqmA3HeJ9htb4s6tL4O3CHXyuhQZbbaLvw2Lwb/DYj7+iVcP7p5oiDjRJ7Z
w9QHgZQHnrCHmQ/TD+PD5BkgAEMe4j62AzDzua4vLrDl+m1/YsbuaRP6OR0g
2h7YS+vfVWFjlIDq/7gfxofJQ/3pwJWhlFNRdXE/b+Ga7tsZ36H2PtgPM/fl
9vXMz3u+7ldw/Jh8Zq6Iw9fb1xuLj3EdgJkH8S/Hgdmh81evlRB3YPRt4fXX
R4Qf1L3w8ILxAbE4o54=
"]}], 
   Molecule[{
    "S", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "B", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 5}, "Aromatic"], 
Bond[{1, 7}, "Aromatic"], 
Bond[{2, 12}, "Single"], 
Bond[{2, 18}, "Single"], 
Bond[{3, 12}, "Single"], 
Bond[{3, 19}, "Single"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{4, 6}, "Aromatic"], 
Bond[{4, 8}, "Aromatic"], 
Bond[{5, 9}, "Aromatic"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{6, 13}, "Single"], 
Bond[{7, 12}, "Single"], 
Bond[{8, 10}, "Aromatic"], 
Bond[{8, 14}, "Single"], 
Bond[{9, 11}, "Aromatic"], 
Bond[{9, 15}, "Single"], 
Bond[{10, 11}, "Aromatic"], 
Bond[{10, 16}, "Single"], 
Bond[{11, 17}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{19, 3}, {CompressedData["
1:eJwB2QEm/iFib1JlAgAAABMAAAADAAAAzlvqhoOz6j+P01Ss6YT3vxo1/03f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"], "Angstroms", {{
         1}, {2}}}]], AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGAQBmIQfaOx2G3KNiEHmdePzKQOvLRX9qpu1ueRdmg+cGqh
67bX+9H49lVmq+3Cb/M6MIDBg/1ofHs086D6RaHyDAyKG4oyJr5lg/I/7Efj
20OVwc1H40PdJwk3rzJihenZamEHEGXt9w1u3pvAHXKtr3+i8+15C9d03874
bg/hv9yPxrefF6d5WqBdFu5/NL49AIFXdGY=
"]}], 
   Molecule[{
    "C", "C", "C", "C", "B", "O", "O", "C", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Double"], 
Bond[{3, 4}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{5, 6}, "Single"], 
Bond[{5, 7}, "Single"], 
Bond[{2, 8}, "Single"], 
Bond[{1, 9}, "Single"], 
Bond[{1, 10}, "Single"], 
Bond[{1, 11}, "Single"], 
Bond[{4, 12}, "Single"], 
Bond[{4, 13}, "Single"], 
Bond[{4, 14}, "Single"], 
Bond[{6, 15}, "Single"], 
Bond[{7, 16}, "Single"], 
Bond[{8, 17}, "Single"], 
Bond[{8, 18}, "Single"], 
Bond[{8, 19}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{19, 3}, {CompressedData["
1:eJwB2QEm/iFib1JlAgAAABMAAAADAAAA+paJWSCX+b+hz7gouhH3PyesOv6B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"], "Angstroms", {{
         1}, {2}}}]], AtomDiagramCoordinates -> CompressedData["
1:eJwBQQG+/iFib1JlAgAAABMAAAACAAAA+wzZ0Kr6AkA4tdB5ch3Bv83AvZ9R
oO0/RgDoaZ354L8w7irIoQ7Cvz7P1Xv2w+A/4+xMf9yKzj/buIOW5Zn/P1aX
2AoBZ/m/HvGHwdZGwD/Aat4JfzwFwOXlD8uk5/I/upLH0zB6/78Y02eADy/1
vwjK3w3yeeE/YNGMDbm0/78RLEtVqYMLQGj+GEJ4AvO/MBUVB63EDECH029+
73vnP8wjylwAsPs/NEkb9kaN8z8s17xhiPb6PyzRHv6C+QJAyET/hg9duT+p
enGyoL8LQBChKshPGvS/zliVP6fGAEDEA2S5imcQwNb4q8oIHek/JGbN0q5P
C8CQvCHmL4j7vzEj1A/2zvk/kNw1gMFJCMDujfEwdKXTvzycHbUiuQnAXqkD
+isl6r9PlZ9NA7H1v8DJopM=
"]}], 
   Molecule[{
    "O", "O", "O", "N", "C", "C", "C", "C", "C", "C", "C", "C", "C", 
     "C", "C", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 16}, "Single"], 
Bond[{1, 17}, "Single"], 
Bond[{2, 16}, "Single"], 
Bond[{2, 18}, "Single"], 
Bond[{3, 17}, "Single"], 
Bond[{3, 18}, "Single"], 
Bond[{4, 11}, "Aromatic"], 
Bond[{4, 12}, "Aromatic"], 
Bond[{5, 13}, "Double"], 
Bond[{5, 16}, "Single"], 
Bond[{5, 19}, "Single"], 
Bond[{6, 14}, "Double"], 
Bond[{6, 17}, "Single"], 
Bond[{6, 20}, "Single"], 
Bond[{7, 15}, "Double"], 
Bond[{7, 18}, "Single"], 
Bond[{7, 21}, "Single"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{8, 10}, "Aromatic"], 
Bond[{8, 22}, "Single"], 
Bond[{9, 11}, "Aromatic"], 
Bond[{9, 23}, "Single"], 
Bond[{10, 12}, "Aromatic"], 
Bond[{10, 24}, "Single"], 
Bond[{11, 25}, "Single"], 
Bond[{12, 26}, "Single"], 
Bond[{13, 27}, "Single"], 
Bond[{13, 28}, "Single"], 
Bond[{14, 29}, "Single"], 
Bond[{14, 30}, "Single"], 
Bond[{15, 31}, "Single"], 
Bond[{15, 32}, "Single"]}, {AtomDiagramCoordinates -> CompressedData["

1:eJxTTMoPSmViYGBQAGIQ/eXvlYqXakwO1SLr3B9WCTiIgigRTodvGjH9h76y
OCyffURhQ9E3exifJYxPd9NcVocdcq2vA3fIOkjr31VhYxSA638uu/yFh95D
exgfZv6bQJCOj/Yw/SDTNO4oOaiyNU517uaB8hUdjnubdzom/LWH8WHyO8H2
ycPlYXz3op/8L8uFoe7lQbNfBO7+64sLbLmuP7ZH9R8Pmv944O6F8Bng/oPw
hRwYoABiPp9D7tF/m6qL2KD+ewn336yZQCCpAhHPhoWPkkOyQITllhNf7GF8
VHk5NHk5uP8gtnJAw1/UoRDom8UF/HD3QOJD1CHilNGRjXof7WF8mP9g6poP
nFrouu21/eGvIA+9tQcAXrPZ1Q==
"]}], 
   Molecule[{
    "O", "O", "O", "C", "C", "C", "C", "C", "C", "B", "H", "H", "H", 
     "H", "H", "H", "H"}, {
Bond[{1, 7}, "Single"], 
Bond[{1, 15}, "Single"], 
Bond[{2, 10}, "Single"], 
Bond[{2, 16}, "Single"], 
Bond[{3, 10}, "Single"], 
Bond[{3, 17}, "Single"], 
Bond[{4, 5}, "Aromatic"], 
Bond[{4, 6}, "Aromatic"], 
Bond[{4, 10}, "Single"], 
Bond[{5, 7}, "Aromatic"], 
Bond[{5, 11}, "Single"], 
Bond[{6, 8}, "Aromatic"], 
Bond[{6, 12}, "Single"], 
Bond[{7, 9}, "Aromatic"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{8, 13}, "Single"], 
Bond[{9, 14}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{17, 3}, {CompressedData["
1:eJwBqQFW/iFib1JlAgAAABEAAAADAAAAerD5FqXzBMDuCn1EKQD6v3Y5KpkZ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"], "Angstroms", {{1}, {
         2}}}]], 
     AtomDiagramCoordinates -> {{2., -1.75}, {4.5981, 1.75}, {2.866, 
      1.75}, {3.7321, 0.25}, {2.866, -0.25}, {4.5981, -0.25}, {
      2.866, -1.25}, {4.5981, -1.25}, {3.7321, -1.75}, {3.7321, 
      1.25}, {2.3291, 0.06}, {5.135, 0.06}, {5.135, -1.56}, {
      3.7321, -2.37}, {2., -2.37}, {4.5981, 2.37}, {2.866, 2.37}}}], 
   Molecule[{
    "O", "O", "O", "N", "C", "C", "C", "C", "C", "C", "B", "H", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 8}, "Single"], 
Bond[{1, 10}, "Single"], 
Bond[{2, 11}, "Single"], 
Bond[{2, 18}, "Single"], 
Bond[{3, 11}, "Single"], 
Bond[{3, 19}, "Single"], 
Bond[{4, 8}, "Aromatic"], 
Bond[{4, 9}, "Aromatic"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{5, 9}, "Aromatic"], 
Bond[{5, 11}, "Single"], 
Bond[{6, 7}, "Aromatic"], 
Bond[{6, 12}, "Single"], 
Bond[{7, 8}, "Aromatic"], 
Bond[{7, 13}, "Single"], 
Bond[{9, 14}, "Single"], 
Bond[{10, 15}, "Single"], 
Bond[{10, 16}, "Single"], 
Bond[{10, 17}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{19, 3}, {CompressedData["
1:eJwB2QEm/iFib1JlAgAAABMAAAADAAAAFzLam0pYBcBRa8uKyk/kPyCIQKbD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"], "Angstroms", {{
         1}, {2}}}]], AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGAQBmIQrbihKGPiWzaHapF17g+rfu2f/Ux2+YsTMg7XFxfY
cl0/vD9vMeMe1iBJh9bXgTvkWr/a+37uCy5JEYKrj/M+wW57WxSuHiYvCpIW
uWZfZbbaLvw2L1wexofo/wTXD+MzQACcD7MfZh7MfCAHKPLO/sSM3dMm9HPC
zLeX1r+rwsYo4VAI5C0u+L4fYg6TAz/31mWVxx/v5y1c030747s9F1jD6/0Q
+rc92B6h3/sjTxkd2fhODm6ee9FP/pfl0jD/2gMApr+NbA==
"]}], 
   Molecule[{
    "F", "F", "F", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", 
     "C", "C", "C", "C", "C", "C", "C", "B", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 20}, "Single"], 
Bond[{2, 20}, "Single"], 
Bond[{3, 20}, "Single"], 
Bond[{4, 8}, "Single"], 
Bond[{4, 9}, "Single"], 
Bond[{5, 21}, "Single"], 
Bond[{5, 32}, "Single"], 
Bond[{6, 21}, "Single"], 
Bond[{6, 33}, "Single"], 
Bond[{7, 8}, "Single"], 
Bond[{7, 12}, "Aromatic"], 
Bond[{7, 13}, "Aromatic"], 
Bond[{8, 22}, "Single"], 
Bond[{8, 23}, "Single"], 
Bond[{9, 11}, "Aromatic"], 
Bond[{9, 15}, "Aromatic"], 
Bond[{10, 11}, "Aromatic"], 
Bond[{10, 18}, "Aromatic"], 
Bond[{10, 20}, "Single"], 
Bond[{11, 24}, "Single"], 
Bond[{12, 16}, "Aromatic"], 
Bond[{12, 25}, "Single"], 
Bond[{13, 17}, "Aromatic"], 
Bond[{13, 26}, "Single"], 
Bond[{14, 16}, "Aromatic"], 
Bond[{14, 17}, "Aromatic"], 
Bond[{14, 21}, "Single"], 
Bond[{15, 19}, "Aromatic"], 
Bond[{15, 27}, "Single"], 
Bond[{16, 28}, "Single"], 
Bond[{17, 29}, "Single"], 
Bond[{18, 19}, "Aromatic"], 
Bond[{18, 30}, "Single"], 
Bond[{19, 31}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{33, 3}, {CompressedData["
1:eJwBKQPW/CFib1JlAgAAACEAAAADAAAAGh8HgFsVGUDshpi01F7vv8rQTEHw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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGBQBGIQbSJoZrPXSNnBjuv64gJbTgcIX9FBvvV14I55Ag4Z
SjkVValycHn3op/8L8ulHUA8ruuH7auOa1pN8mZxOPxVI6b/ENOBy75JAhGW
3FA+3wFp/bsqbIwSDiDj5Fq/7ofpBxvH9Xp/5CmjIxvfyTl8Ayn/+sQeZr8t
WAGjA0y+WmSd+8OqX/Zg4S4RuH6Y+TD7f9Vl7SkRFoTzYebB3AuTh7kHZh5E
PdsBm71B0xTfKcHtg/HR3QcLD1T/sh0ILVGZ/j9AxsEj4I9E8fVv+13BHpZ1
uPo8S/vb9Of7Zz+TXf7ihIxDIdiD/+H+OXsGDPZ//Xul4qWaFNR8Vrj7Iebf
3V9lttou/DavA9AxQIEPcPcDPQP0EfeBlUv8HIQZVeDmw/iiIO+IXLNngAC4
+YobijImvmWDxfcBAAWw57Q=
"]}], 
   Molecule[{
    "F", "F", "F", "O", "C", "C", "C", "B", "H", "H", "H", "H", "H", 
     "H", "H", "H"}, {
Bond[{1, 8}, "Single"], 
Bond[{2, 8}, "Single"], 
Bond[{3, 8}, "Single"], 
Bond[{4, 6}, "Single"], 
Bond[{4, 16}, "Single"], 
Bond[{5, 6}, "Single"], 
Bond[{5, 7}, "Single"], 
Bond[{5, 9}, "Single"], 
Bond[{5, 10}, "Single"], 
Bond[{6, 11}, "Single"], 
Bond[{6, 12}, "Single"], 
Bond[{7, 13}, "Single"], 
Bond[{7, 14}, "Single"], 
Bond[{7, 15}, "Single"]}, {
    AtomDiagramCoordinates -> {{2.702, 1.5}, {0.9699, 1.5}, {1.836, 
      0.}, {0.5369, 4.5369}, {2.269, 4.5369}, {1.4030000000000002`, 
      4.0369}, {3.1350000000000002`, 4.0369}, {1.836, 1.}, {2.6675, 
      5.0119}, {1.8705, 5.0119}, {1.0044, 3.562}, {1.8015, 3.562}, {
      2.825, 3.5}, {3.6719, 3.7269}, {3.4450000000000003`, 4.5739}, {
      0., 4.2269}}}], 
   Molecule[{
    "F", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "C", 
     "C", "C", "C", "C", "B", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H"}, {
Bond[{1, 8}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{2, 7}, "Single"], 
Bond[{3, 18}, "Single"], 
Bond[{3, 29}, "Single"], 
Bond[{4, 18}, "Single"], 
Bond[{4, 30}, "Single"], 
Bond[{5, 6}, "Single"], 
Bond[{5, 8}, "Aromatic"], 
Bond[{5, 9}, "Aromatic"], 
Bond[{6, 19}, "Single"], 
Bond[{6, 20}, "Single"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 12}, "Aromatic"], 
Bond[{8, 15}, "Aromatic"], 
Bond[{9, 16}, "Aromatic"], 
Bond[{9, 21}, "Single"], 
Bond[{10, 11}, "Aromatic"], 
Bond[{10, 22}, "Single"], 
Bond[{11, 13}, "Aromatic"], 
Bond[{11, 18}, "Single"], 
Bond[{12, 14}, "Aromatic"], 
Bond[{12, 23}, "Single"], 
Bond[{13, 14}, "Aromatic"], 
Bond[{13, 24}, "Single"], 
Bond[{14, 25}, "Single"], 
Bond[{15, 17}, "Aromatic"], 
Bond[{15, 26}, "Single"], 
Bond[{16, 17}, "Aromatic"], 
Bond[{16, 27}, "Single"], 
Bond[{17, 28}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{30, 3}, {CompressedData["
1:eJwB4QIe/SFib1JlAgAAAB4AAAADAAAA/ngdzRDyD0DkFLtxMXvRvwoXrLxO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"], "Angstroms", {{1}, {
         2}}}]], AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQA2IQrbihKGPiWzaHN4E75FpfP9zv+7kvuCRFCM6f/Ux2
+YsTMg6tr0EC7A5x3ifYbW+LwvlVZqvtwm/zOlSLrHN/WMVwAKb/m0ZM/6Gv
P/bD1INMC9yxbj/MPpg8qn6OAzD1oiCuyDv7vMWMe1iDJB24ri8usOX6Duej
uw+mHsaH2ccAAXD3weyH8HkOoMpzHICZD/Efs0PU1523umyFHe64MVdwqzAe
yNj8qnjrVRGH6I3738yz+b7/V13WnhJhQah/uA4sTAcpEHaAGPsF7l6g44AW
fNofecroyMZ3cnB5GB/s/HXX9/MWrum+nfHdHiL/Bx5eEP8IHIDJw+yD+Rfq
H3j8wPgAajjYEg==
"]}], 
   Molecule[{
    "F", "F", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "B", 
     "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 8}, "Single"], 
Bond[{2, 9}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{3, 12}, "Single"], 
Bond[{4, 13}, "Single"], 
Bond[{4, 19}, "Single"], 
Bond[{5, 13}, "Single"], 
Bond[{5, 20}, "Single"], 
Bond[{6, 8}, "Aromatic"], 
Bond[{6, 9}, "Aromatic"], 
Bond[{6, 13}, "Single"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 11}, "Aromatic"], 
Bond[{8, 10}, "Aromatic"], 
Bond[{9, 11}, "Aromatic"], 
Bond[{10, 14}, "Single"], 
Bond[{11, 15}, "Single"], 
Bond[{12, 16}, "Single"], 
Bond[{12, 17}, "Single"], 
Bond[{12, 18}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{20, 3}, {CompressedData["
1:eJwB8QEO/iFib1JlAgAAABQAAAADAAAAA9whD+HN/D8TkidwwanuPxbdar+C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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGAQAWIQDQUOouvcH1aJPLOP8z7BbntbFM6vMlttF36b16Fa
BCTAdMD3c19wSYqQQ+vrwB1yrYwOihuKMia+ZYPzYerR9X/TiOk/9PXLfpj6
IKDq14En7GHmwfgw+Tcg416/3A+Th/Fh8hD3sB2Amc91fXGBLddv++UvPPT+
L2SCqv+4HyzeJQLnL5t9RGFDEYsDo28Lr78+0wGYeoj7OA7MAUlncDp8vX29
sfgYF9y/UPsc0Ox3AABVk5HT
"]}], 
   Molecule[{
    "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "B", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H"}, {
Bond[{11, 10}, "Single"], 
Bond[{10, 9}, "Double"], 
Bond[{9, 3}, "Single"], 
Bond[{3, 4}, "Single"], 
Bond[{4, 6}, "Single"], 
Bond[{6, 8}, "Single"], 
Bond[{8, 7}, "Single"], 
Bond[{7, 5}, "Single"], 
Bond[{11, 1}, "Single"], 
Bond[{11, 2}, "Single"], 
Bond[{5, 3}, "Single"], 
Bond[{10, 24}, "Single"], 
Bond[{9, 23}, "Single"], 
Bond[{3, 12}, "Single"], 
Bond[{4, 13}, "Single"], 
Bond[{4, 14}, "Single"], 
Bond[{6, 17}, "Single"], 
Bond[{6, 18}, "Single"], 
Bond[{8, 21}, "Single"], 
Bond[{8, 22}, "Single"], 
Bond[{7, 19}, "Single"], 
Bond[{7, 20}, "Single"], 
Bond[{5, 15}, "Single"], 
Bond[{5, 16}, "Single"], 
Bond[{1, 25}, "Single"], 
Bond[{2, 26}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{26, 3}, {CompressedData["
1:eJwBgQJ+/SFib1JlAgAAABoAAAADAAAAYEc3dGYzEUD5fmkqZ2jvvymVEYQd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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQAmIQ7fu5L7gkRcih9XXgDrlWRgfFDUUZE9+yYfDfgLiv
X+5ngACHbxox/Ye+ftlfZbbaLvw2L5wPk68WWef+sIrpAEwexoeZB+GzwflB
IOMDT9jD1IuCpEWewflc1xcX2HL9tr/smyQQYcntALHmwf447xPstrP/2Kd3
JMfecXu+f/r/CXW/rb7Z9y/4Yfhs3bf91gXnOi7FCTrA+JOtGH1bevkd0NWv
uxFf5j+P8QDMvDtuzBXcKuwHYOphfJh5MPW2IIftZXYQ4N66rNKd64CdzpVZ
z2S54PzlLzz0/i9kgrnX/ldd1p4SYUGof2/Yw8IfGh4OaOHjAADo7bTb
"], 
     StereochemistryElements -> {
Association[
       "StereoType" -> "DoubleBond", "StereoBond" -> {10, 9}, "Value" -> 
        "Opposite", "Ligands" -> {11, 3}]}}], 
   Molecule[{
    "Cl", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "C", 
     "C", "C", "C", "C", "C", "C", "B", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 19}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{2, 11}, "Single"], 
Bond[{3, 20}, "Single"], 
Bond[{3, 35}, "Single"], 
Bond[{4, 20}, "Single"], 
Bond[{4, 36}, "Single"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{5, 7}, "Aromatic"], 
Bond[{6, 9}, "Aromatic"], 
Bond[{6, 13}, "Single"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 14}, "Single"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{8, 10}, "Aromatic"], 
Bond[{8, 20}, "Single"], 
Bond[{9, 21}, "Single"], 
Bond[{10, 22}, "Single"], 
Bond[{11, 12}, "Single"], 
Bond[{11, 23}, "Single"], 
Bond[{11, 24}, "Single"], 
Bond[{12, 15}, "Aromatic"], 
Bond[{12, 16}, "Aromatic"], 
Bond[{13, 25}, "Single"], 
Bond[{13, 26}, "Single"], 
Bond[{13, 27}, "Single"], 
Bond[{14, 28}, "Single"], 
Bond[{14, 29}, "Single"], 
Bond[{14, 30}, "Single"], 
Bond[{15, 17}, "Aromatic"], 
Bond[{15, 31}, "Single"], 
Bond[{16, 18}, "Aromatic"], 
Bond[{16, 32}, "Single"], 
Bond[{17, 19}, "Aromatic"], 
Bond[{17, 33}, "Single"], 
Bond[{18, 19}, "Aromatic"], 
Bond[{18, 34}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{36, 3}, {CompressedData["
1:eJwBcQOO/CFib1JlAgAAACQAAAADAAAAoIENDKmGHEDaEmpKLpTrP/rqh8gN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"], 
         "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGBQAWIQ7fu5L7gkRcjBluv64gJbYYcqs9V24bd5HUA8ruuX
7WHyh79qxPQfEjiguKEoY+JbNjgfpl50nfvDKpFn+2HyYOO4Pu+H6YfxYepb
XwfukGtlhZsH4TMegKlH578BcV+/hLvnG8j6r3/sGSAAbn+c9wl229uicD7M
vmoRkACTA0wexkeV50KT53KA2Qfh8zmgup/3wPIXHnr/FzI5QJzBcgDszy4R
OD/q685bXcBwnabYV1oofdY+Y/Or4q1XRRwUHD8mn4l9aw8Jl9/2/1K/P0lc
+Hk/b+Ga7tsZ3+2DQN4NvLEf5s4dDk2Pjs84vH926PzVayXEHWDqpfXvqrAx
SjjA1IPNnSsCV39ixu5pE/o5HWDhBFMP48PkIeHJA5eH8dHSBzy+YHwAYtH3
eQ==
"]}], 
   Molecule[{
    "F", "F", "F", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", 
     "C", "C", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H"}, {
Bond[{1, 10}, "Single"], 
Bond[{2, 12}, "Single"], 
Bond[{3, 14}, "Single"], 
Bond[{4, 7}, "Single"], 
Bond[{4, 9}, "Single"], 
Bond[{5, 16}, "Single"], 
Bond[{5, 25}, "Single"], 
Bond[{6, 16}, "Single"], 
Bond[{6, 26}, "Single"], 
Bond[{7, 8}, "Single"], 
Bond[{7, 17}, "Single"], 
Bond[{7, 18}, "Single"], 
Bond[{8, 13}, "Single"], 
Bond[{8, 19}, "Single"], 
Bond[{8, 20}, "Single"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{9, 12}, "Aromatic"], 
Bond[{10, 11}, "Aromatic"], 
Bond[{11, 14}, "Aromatic"], 
Bond[{11, 16}, "Single"], 
Bond[{12, 15}, "Aromatic"], 
Bond[{13, 21}, "Single"], 
Bond[{13, 22}, "Single"], 
Bond[{13, 23}, "Single"], 
Bond[{14, 15}, "Aromatic"], 
Bond[{15, 24}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{26, 3}, {CompressedData["
1:eJwBgQJ+/SFib1JlAgAAABoAAAADAAAA1mdIowCw1D/zrSnJWJfuP3IhpNtC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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQAmIQrbihKGPiWzYHruuLC2y5Ptv7fu4LLkkRcvimEdN/
6Ouf/XmLGfewBknC5WHq3wTukGt9/XJ/nPcJdtvbog6tr0ECrA5VZqvtwm/z
wvkw9TDzGCDAoVpknfvDKqYDMPVg469f3g/ji4KkRZ7B3YPuPpj9qOZxHYC5
B6YfxoeZD9MPcR+jQ1FX35NP8zkc7rgxV3CrMB8INdBaKXyB2yF64/4382x+
Q/w3+4+98ZGNenmLf++f/n9C3W+rb/YeAX8kisOZD0Ds+WYPsx/mnkJbkIv5
D0D8zQJ3n7T+XRU2Rgmoex7Dww8qDw8/GB8ASS20FA==
"]}], 
   Molecule[{
    "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", 
     "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H"}, {
Bond[{1, 6}, "Single"], 
Bond[{1, 8}, "Single"], 
Bond[{2, 14}, "Single"], 
Bond[{2, 28}, "Single"], 
Bond[{3, 14}, "Single"], 
Bond[{3, 29}, "Single"], 
Bond[{4, 5}, "Single"], 
Bond[{4, 6}, "Single"], 
Bond[{4, 15}, "Single"], 
Bond[{4, 16}, "Single"], 
Bond[{5, 7}, "Single"], 
Bond[{5, 17}, "Single"], 
Bond[{5, 18}, "Single"], 
Bond[{6, 19}, "Single"], 
Bond[{6, 20}, "Single"], 
Bond[{7, 21}, "Single"], 
Bond[{7, 22}, "Single"], 
Bond[{7, 23}, "Single"], 
Bond[{8, 9}, "Aromatic"], 
Bond[{8, 11}, "Aromatic"], 
Bond[{9, 10}, "Aromatic"], 
Bond[{9, 24}, "Single"], 
Bond[{10, 12}, "Aromatic"], 
Bond[{10, 14}, "Single"], 
Bond[{11, 13}, "Aromatic"], 
Bond[{11, 25}, "Single"], 
Bond[{12, 13}, "Aromatic"], 
Bond[{12, 26}, "Single"], 
Bond[{13, 27}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{29, 3}, {CompressedData["
1:eJwByQI2/SFib1JlAgAAAB0AAAADAAAA7Xa/aSOx5z9bCzIFsdHwv9PY7j7D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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQBWIQXWW22i78Nq/Dm8Adcq2vH+7PW8y4hzVI0qH1NUiA
3cH3c19wSYoQnK+4oShj4ls2h2qRde4PqxgOoPI5DsDM+6YR03/o64/9DBAA
lec5ADMPZFvgjnX7YXxRkLTIO/s47xPstrdFHbiuLy6w5foO56O7D6YexoeZ
B1MPcS+zA8camagUa1YH4yMb9fIWf9//Rn+3Oj83k4NHwB+J4nDGA0VdfU8+
zedwuOPGXMGtwnsg1EBrpfAFbod1N+LL/OexH5hsxejb0ssPlWc8YF1wruNS
nKBD9Mb9b+bZfN8P8ReTw6+3rw9YJgsc4C1c030747s9xP98ByD++G0PNIXX
X5/jQInK9P8T7AQcIMHyBe4/oGeABn3a7170k/9luTRcHsYHe3fddbj/oeEJ
jx8YHwBEyMVt
"]}], 
   Molecule[{
    "Cl", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "B", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 11}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{2, 9}, "Single"], 
Bond[{3, 13}, "Single"], 
Bond[{3, 22}, "Single"], 
Bond[{4, 13}, "Single"], 
Bond[{4, 23}, "Single"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{5, 7}, "Aromatic"], 
Bond[{6, 8}, "Aromatic"], 
Bond[{6, 13}, "Single"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 14}, "Single"], 
Bond[{8, 11}, "Aromatic"], 
Bond[{8, 15}, "Single"], 
Bond[{9, 12}, "Single"], 
Bond[{9, 16}, "Single"], 
Bond[{9, 17}, "Single"], 
Bond[{10, 11}, "Aromatic"], 
Bond[{10, 18}, "Single"], 
Bond[{12, 19}, "Single"], 
Bond[{12, 20}, "Single"], 
Bond[{12, 21}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{23, 3}, {CompressedData["
1:eJwBOQLG/SFib1JlAgAAABcAAAADAAAAYwlKJ+8KDsCb2HSaKbMEwDUhZuPf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"], 
         "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGAQB2IQPfuZ7PIXJ2QcqkXWuT+sYjhQZbbaLvw2r8MOudbX
gTvW7c9bzLiHNUjSgev64gJbru/2vp/7gktShDD4bwJBOh7uj/M+wW57WxSu
Hyb/TSOm/9DXH3DzYOoVNxRlTHzLhqEf5h6Yeph+BgiAmw9TLwpSLvLOvkRl
+v8JdgJQ8/7udy/6yf+yXNoBou3CfluQw/cyO8iJZQGd9mG/nc6VWc9kueB8
mHmFYIWsByDuYHL4Nv3u5PbWe/a8hWu6b2d8t4eaZw8Jh9/2l/Lj2c9JPob7
D+p+B5j/YXwAo2Sk1g==
"]}], 
   Molecule[{
    "O", "O", "O", "O", "C", "C", "C", "C", "C", "C", "C", "C", "C", 
     "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 9}, "Single"], 
Bond[{1, 12}, "Single"], 
Bond[{2, 9}, "Double"], 
Bond[{3, 14}, "Single"], 
Bond[{3, 24}, "Single"], 
Bond[{4, 14}, "Single"], 
Bond[{4, 25}, "Single"], 
Bond[{5, 6}, "Aromatic"], 
Bond[{5, 7}, "Aromatic"], 
Bond[{5, 9}, "Single"], 
Bond[{6, 8}, "Aromatic"], 
Bond[{6, 14}, "Single"], 
Bond[{7, 10}, "Aromatic"], 
Bond[{7, 15}, "Single"], 
Bond[{8, 11}, "Aromatic"], 
Bond[{8, 16}, "Single"], 
Bond[{10, 11}, "Aromatic"], 
Bond[{10, 17}, "Single"], 
Bond[{11, 18}, "Single"], 
Bond[{12, 13}, "Single"], 
Bond[{12, 19}, "Single"], 
Bond[{12, 20}, "Single"], 
Bond[{13, 21}, "Single"], 
Bond[{13, 22}, "Single"], 
Bond[{13, 23}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{25, 3}, {CompressedData["
1:eJwBaQKW/SFib1JlAgAAABkAAAADAAAAWk0+EGbL/D+uVl1Hox/wv2RqY3+J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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGCQBGIQXWW22i78Nq/Dm8Adcq2vH+73/dwXXJIi5CC6zv1h
lcg7+9nPZJe/OCHjwHV9cYEt13f7OO8T7La3RTH4MP15ixn3sAZJOoB4gTvW
7YfJf9OI6T/09cd+mHno9sHUw/RXi4AcwHAAph6mX3FDUcbEt2xw9QwQgGE/
zP0L0ze/Kt4qDJX/uz/ylNGRje/kHCDaLsDVF9qCfMR6ACYPU2+nc2XWM1ku
hzMCx3ft6L1lD1a2lxnOh4TDb/t5DWqH2pZP389buKb7dsZ3e7Dw9df7If5g
cni5fT3z856PcP9D/ecACx8YHwAdLMCt
"]}]};

&#10005

bases = {Molecule[{
Atom["Na", "FormalCharge" -> 1], 
Atom["O", "FormalCharge" -> -1], "H"}, {
Bond[{2, 3}, "Single"]}, {
    AtomDiagramCoordinates -> {{2., 0.25}, {2.866, -0.25}, {3.403, 
      0.06}}}], 
   Molecule[{
    "P", "N", "N", "N", "N", "C", "C", "C", "C", "C", "C", "C", "C", 
     "C", "C", "C", "C", "C", "C", "C", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", 
     "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{1, 3}, "Single"], 
Bond[{1, 4}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{2, 14}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{3, 13}, "Single"], 
Bond[{4, 8}, "Single"], 
Bond[{4, 12}, "Single"], 
Bond[{5, 9}, "Single"], 
Bond[{5, 10}, "Single"], 
Bond[{5, 11}, "Single"], 
Bond[{6, 9}, "Single"], 
Bond[{6, 21}, "Single"], 
Bond[{6, 22}, "Single"], 
Bond[{7, 10}, "Single"], 
Bond[{7, 23}, "Single"], 
Bond[{7, 24}, "Single"], 
Bond[{8, 11}, "Single"], 
Bond[{8, 25}, "Single"], 
Bond[{8, 26}, "Single"], 
Bond[{9, 27}, "Single"], 
Bond[{9, 28}, "Single"], 
Bond[{10, 29}, "Single"], 
Bond[{10, 30}, "Single"], 
Bond[{11, 31}, "Single"], 
Bond[{11, 32}, "Single"], 
Bond[{12, 19}, "Single"], 
Bond[{12, 20}, "Single"], 
Bond[{12, 35}, "Single"], 
Bond[{13, 15}, "Single"], 
Bond[{13, 18}, "Single"], 
Bond[{13, 34}, "Single"], 
Bond[{14, 16}, "Single"], 
Bond[{14, 17}, "Single"], 
Bond[{14, 33}, "Single"], 
Bond[{15, 48}, "Single"], 
Bond[{15, 49}, "Single"], 
Bond[{15, 50}, "Single"], 
Bond[{16, 45}, "Single"], 
Bond[{16, 46}, "Single"], 
Bond[{16, 47}, "Single"], 
Bond[{17, 42}, "Single"], 
Bond[{17, 43}, "Single"], 
Bond[{17, 44}, "Single"], 
Bond[{18, 39}, "Single"], 
Bond[{18, 40}, "Single"], 
Bond[{18, 41}, "Single"], 
Bond[{19, 36}, "Single"], 
Bond[{19, 37}, "Single"], 
Bond[{19, 38}, "Single"], 
Bond[{20, 51}, "Single"], 
Bond[{20, 52}, "Single"], 
Bond[{20, 53}, "Single"]}, {AtomCoordinates -> QuantityArray[
StructuredArray`StructuredData[{53, 3}, {CompressedData["
1:eJw9VAlUVGUUng1hWEYkUSpzySUiUREx9/srR1GO4oIQAmVKFEdBSiQWRRFE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"], "Angstroms", {{1}, {2}}}]], 
     AtomDiagramCoordinates -> CompressedData["
1:eJw1Ugkw1FEcXvZv7a51tK0j7SJJKiEVZla9X9ck6VJUqIgdKqwRTVMRKXTo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"]}], Molecule[{
Atom["C", "FormalCharge" -> -1], "C", 
Atom["Li", "FormalCharge" -> 1], "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{1, 4}, "Single"], 
Bond[{1, 5}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{2, 7}, "Single"], 
Bond[{2, 8}, "Single"]}, {
    AtomDiagramCoordinates -> {{2.866, 0.}, {3.7321, 0.5}, {
      2., -0.5}, {2.556, 0.5369}, {3.176, -0.5369}, {
      4.0421, -0.0369}, {4.269, 0.81}, {3.4221, 1.0369}}}], 
   Molecule[{
Atom["C", "FormalCharge" -> -1], "C", "C", "C", 
Atom["Li", "FormalCharge" -> 1], "H", "H", "H", "H", "H", "H", "H", 
     "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{1, 3}, "Single"], 
Bond[{1, 6}, "Single"], 
Bond[{2, 4}, "Single"], 
Bond[{2, 7}, "Single"], 
Bond[{2, 8}, "Single"], 
Bond[{3, 9}, "Single"], 
Bond[{3, 10}, "Single"], 
Bond[{3, 11}, "Single"], 
Bond[{4, 12}, "Single"], 
Bond[{4, 13}, "Single"], 
Bond[{4, 14}, "Single"]}, {
    AtomDiagramCoordinates -> {{2.866, -0.25}, {2.866, 0.75}, {
      3.7321, -0.75}, {2., 1.25}, {2.866, -1.25}, {3.403, 0.06}, {
      3.4766, 0.6423000000000001}, {3.0781, 1.3326}, {
      3.4221, -1.2869}, {4.269, -1.06}, {
      4.0421, -0.21310000000000004`}, {2.31, 1.7869}, {1.4631, 
      1.56}, {1.69, 0.7131000000000001}}}], Molecule[{
Atom["K", "FormalCharge" -> 1], 
Atom["O", "FormalCharge" -> -1], "H"}, {
Bond[{2, 3}, "Single"]}, {
    AtomDiagramCoordinates -> {{2., 0.25}, {2.866, -0.25}, {3.403, 
      0.06}}}], Molecule[{
Atom["O", "FormalCharge" -> -1], "C", "C", "C", 
Atom["Li", "FormalCharge" -> 1], "H", "H", "H", "H", "H", "H", "H"}, {
    
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Single"], 
Bond[{2, 4}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{3, 8}, "Single"], 
Bond[{3, 9}, "Single"], 
Bond[{4, 10}, "Single"], 
Bond[{4, 11}, "Single"], 
Bond[{4, 12}, "Single"]}, {
    AtomDiagramCoordinates -> {{3.7321, 0.75}, {2.866, 0.25}, {2., 
      0.75}, {2.866, -0.75}, {4.5981, 0.25}, {2.866, 0.87}, {2.31, 
      1.2869}, {1.4631, 1.06}, {1.69, 0.21310000000000004`}, {
      2.246, -0.75}, {2.866, -1.37}, {3.486, -0.75}}}], 
   Molecule[{"Ba", "O", "O", "O", 
Atom["C", "MassNumber" -> 13], "H", "H"}, {
Bond[{2, 5}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{4, 5}, "Double"]}, {
    AtomDiagramCoordinates -> {{1.153, 3.5}, {2.269, 1.5}, {0.5369, 
      1.5}, {1.4030000000000002`, 0.}, {1.4030000000000002`, 1.}, {
      2.8059, 1.19}, {0., 1.19}}}], Molecule[{
Atom["O", "FormalCharge" -> -1], 
Atom["N", "FormalCharge" -> 1], "C", "C", "C", "C", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 19}, "Single"], 
Bond[{2, 3}, "Single"], 
Bond[{2, 4}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{3, 8}, "Single"], 
Bond[{3, 9}, "Single"], 
Bond[{4, 10}, "Single"], 
Bond[{4, 11}, "Single"], 
Bond[{4, 12}, "Single"], 
Bond[{5, 13}, "Single"], 
Bond[{5, 14}, "Single"], 
Bond[{5, 15}, "Single"], 
Bond[{6, 16}, "Single"], 
Bond[{6, 17}, "Single"], 
Bond[{6, 18}, "Single"]}, {AtomDiagramCoordinates -> CompressedData["
1:eJxTTMoPSmViYGAQBmIQ7WPe6Zjw9I399qT6m7aWwg7LZx9R2FD0zR5Gf/l7
peKlGhNU/J/9c9nlLzz0HtrD9MFoXOpyj/7bVF3E5uCXJBBhuYXR4dMlXyDr
v72vaI/XqxZmh3yh5gOnFrI4OCc8vaB0+6v9G/3d6vzcZ+1naUlMvcL53Z4B
CsDSPx/ZX8qPZz8n+dqevXGqc3fOdbg6mDkwdTB7Yepg9qLbA3MXzB6Yu2D2
wPwDCx8ApKyMBQ==
"]}], Molecule[{
Atom["O", "FormalCharge" -> -1], "O", 
Atom["Li", "FormalCharge" -> 1], "H", "H", "H"}, {
Bond[{1, 4}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{2, 6}, "Single"]}, {
    AtomDiagramCoordinates -> {{0.866, 2.5}, {0.7015000000000001, 
      0.}, {0., 3.}, {1.4030000000000002`, 2.81}, {1.2384, 0.31}, {
      0.1645, 0.31}}}], Molecule[{
Atom["Rb", "FormalCharge" -> 1], 
Atom["O", "FormalCharge" -> -1], "O", "H", "H", "H"}, {
Bond[{2, 4}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{3, 6}, "Single"]}, {
    AtomDiagramCoordinates -> {{0., 0.5}, {0.866, 0.}, {
      0.7015000000000001, 2.5}, {1.4030000000000002`, 0.31}, {1.2384, 
      2.81}, {0.1645, 2.81}}}], Molecule[{
Atom["O", "FormalCharge" -> -1], 
Atom["N", "FormalCharge" -> 1], "H", "H", "H", "H", "H"}, {
Bond[{1, 7}, "Single"], 
Bond[{2, 3}, "Single"], 
Bond[{2, 4}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{2, 6}, "Single"]}, {
    AtomDiagramCoordinates -> {{0.0369, 3.0739}, {0.5369, 0.5369}, {
      1.0739, 0.8469}, {0., 0.2269}, {0.2269, 1.0739}, {0.8469, 0.}, {
      1.0369, 3.0739}}}], Molecule[{
Atom["K", "FormalCharge" -> 1], 
Atom["O", "FormalCharge" -> -1], "C", "C", "C", "C", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{2, 4}, "Single"], 
Bond[{3, 4}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{3, 6}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{4, 8}, "Single"], 
Bond[{4, 9}, "Single"], 
Bond[{5, 10}, "Single"], 
Bond[{5, 11}, "Single"], 
Bond[{5, 12}, "Single"], 
Bond[{6, 13}, "Single"], 
Bond[{6, 14}, "Single"], 
Bond[{6, 15}, "Single"]}, {
    AtomDiagramCoordinates -> {{5.4641, 0.75}, {4.5981, 0.25}, {2.866,
       0.25}, {3.7321, 0.75}, {2., 0.75}, {2.866, -0.75}, {2.866, 
      0.87}, {4.1306, 1.225}, {3.3335, 1.225}, {2.31, 1.2869}, {
      1.4631, 1.06}, {1.69, 0.21310000000000004`}, {2.246, -0.75}, {
      2.866, -1.37}, {3.486, -0.75}}}], Molecule[{
Atom["O", "FormalCharge" -> -1], "C", "C", "C", "C", 
Atom["Li", "FormalCharge" -> 1], "H", "H", "H", "H", "H", "H", "H", 
     "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{2, 3}, "Single"], 
Bond[{2, 4}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{3, 8}, "Single"], 
Bond[{3, 9}, "Single"], 
Bond[{4, 10}, "Single"], 
Bond[{4, 11}, "Single"], 
Bond[{4, 12}, "Single"], 
Bond[{5, 13}, "Single"], 
Bond[{5, 14}, "Single"], 
Bond[{5, 15}, "Single"]}, {
    AtomDiagramCoordinates -> {{3.7321, 0.5}, {2.866, 0.}, {
      2., -0.5}, {2.366, 0.866}, {3.366, -0.866}, {4.5981, 0.}, {1.69,
       0.0369}, {1.4631, -0.81}, {2.31, -1.0369}, {2.903, 1.176}, {
      2.056, 1.4030000000000002`}, {1.8291, 0.556}, {
      2.8291, -1.176}, {3.676, -1.4030000000000002`}, {
      3.903, -0.556}}}], Molecule[{
Atom["Cl", "FormalCharge" -> -1], 
Atom["Mg", "FormalCharge" -> 2], 
Atom["C", "FormalCharge" -> -1], "C", "C", "C", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H"}, {
Bond[{3, 4}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{3, 6}, "Single"], 
Bond[{4, 7}, "Single"], 
Bond[{4, 8}, "Single"], 
Bond[{4, 9}, "Single"], 
Bond[{5, 10}, "Single"], 
Bond[{5, 11}, "Single"], 
Bond[{5, 12}, "Single"], 
Bond[{6, 13}, "Single"], 
Bond[{6, 14}, "Single"], 
Bond[{6, 15}, "Single"]}, {
    AtomDiagramCoordinates -> {{4.5981, 0.}, {3.7321, 0.5}, {2.866, 
      0.}, {2., -0.5}, {2.366, 0.866}, {3.366, -0.866}, {1.69, 
      0.0369}, {1.4631, -0.81}, {2.31, -1.0369}, {2.903, 1.176}, {
      2.056, 1.4030000000000002`}, {1.8291, 0.556}, {
      2.8291, -1.176}, {3.676, -1.4030000000000002`}, {
      3.903, -0.556}}}], Molecule[{
Atom["Br", "FormalCharge" -> -1], 
Atom["Mg", "FormalCharge" -> 2], 
Atom["C", "FormalCharge" -> -1], "C", "H", "H", "H", "H", "H"}, {
Bond[{3, 4}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{3, 6}, "Single"], 
Bond[{4, 7}, "Single"], 
Bond[{4, 8}, "Single"], 
Bond[{4, 9}, "Single"]}, {
    AtomDiagramCoordinates -> {{2., 1.25}, {2.866, 0.75}, {
      2.866, -0.25}, {2.866, -1.25}, {3.403, -0.56}, {3.403, 0.06}, {
      2.246, -1.25}, {2.866, -1.87}, {3.486, -1.25}}}], Molecule[{
Atom["N", "FormalCharge" -> -1], "C", "C", 
Atom["Li", "FormalCharge" -> 1], "H", "H", "H", "H", "H", "H"}, {
Bond[{1, 2}, "Single"], 
Bond[{1, 3}, "Single"], 
Bond[{2, 5}, "Single"], 
Bond[{2, 6}, "Single"], 
Bond[{2, 7}, "Single"], 
Bond[{3, 8}, "Single"], 
Bond[{3, 9}, "Single"], 
Bond[{3, 10}, "Single"]}, {
    AtomDiagramCoordinates -> {{2.866, 0.25}, {3.7321, 0.75}, {
      2.866, -0.75}, {2., 0.75}, {4.0421, 0.21310000000000004`}, {
      4.269, 1.06}, {3.4221, 1.2869}, {2.246, -0.75}, {
      2.866, -1.37}, {3.486, -0.75}}}], Molecule[{
Atom["Ca", "FormalCharge" -> 2], 
Atom["O", "FormalCharge" -> -1], 
Atom["O", "FormalCharge" -> -1], "O", "C"}, {
Bond[{2, 5}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{4, 5}, "Double"]}, {
    AtomDiagramCoordinates -> {{0.8536, 2.}, {0.8536, 3.}, {-0.1464, 
      2.}, {-0.8536, 3.7071}, {-0.1464, 3.}}}], 
   Molecule[{"Ni", "Ni", 
Atom["Ni", "FormalCharge" -> 2], "O", "O", "O", "O", 
Atom["O", "FormalCharge" -> -1], 
Atom["O", "FormalCharge" -> -1], "O", "O", "C", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H"}, {
Bond[{4, 13}, "Single"], 
Bond[{4, 14}, "Single"], 
Bond[{5, 15}, "Single"], 
Bond[{5, 16}, "Single"], 
Bond[{6, 17}, "Single"], 
Bond[{6, 18}, "Single"], 
Bond[{7, 19}, "Single"], 
Bond[{7, 20}, "Single"], 
Bond[{8, 12}, "Single"], 
Bond[{9, 12}, "Single"], 
Bond[{10, 12}, "Double"], 
Bond[{11, 21}, "Single"], 
Bond[{11, 22}, "Single"]}, {AtomDiagramCoordinates -> CompressedData["

1:eJxTTMoPSmViYGAQA2IQ7dKd8/z3SkGHxYx7WIWuCDlcX1xgy3X9sX39TdvK
iBU/7S/7JglE7ORz6GP7IOYR8Nu+8FzHpXsKolD1Ig4w/e7MFdwqK3jh+v9v
qv60IeCxve6mue+XH/tuf+DNPBudKwwOMPOkeR/oTljwzP6lmiHHmhhhuPm9
Gm959xmIOTBAgfLtn3VZe97Yy0alWN/vF3KAqYfpNz6yUS/vsbBDwtMLSrd/
ijmsuxFf5h8n5vBcdvkLj3VCDvvmS+nfVRGBu2/i2xp70zg+NPd+hrsXZi+M
b97pCDT6k73VlhNl++RZHSDu+WMvPS9O87TAT/vWmgubI79+swc5z/+ssENF
1VIdZ5nrcD4AUR2Ziw==
"]}], Molecule[{
Atom["Cl", "FormalCharge" -> -1], 
Atom["Mg", "FormalCharge" -> 2], "C", 
Atom["C", "FormalCharge" -> -1], "C", "C", "C", "H", "H", "H", "H", 
     "H", "H", "H", "H", "H", "H", "H"}, {
Bond[{3, 4}, "Single"], 
Bond[{3, 5}, "Single"], 
Bond[{3, 6}, "Single"], 
Bond[{3, 7}, "Single"], 
Bond[{4, 8}, "Single"], 
Bond[{4, 9}, "Single"], 
Bond[{5, 10}, "Single"], 
Bond[{5, 11}, "Single"], 
Bond[{5, 12}, "Single"], 
Bond[{6, 13}, "Single"], 
Bond[{6, 14}, "Single"], 
Bond[{6, 15}, "Single"], 
Bond[{7, 16}, "Single"], 
Bond[{7, 17}, "Single"], 
Bond[{7, 18}, "Single"]}, {
    AtomDiagramCoordinates -> {{2., -0.4145}, {
      2.866, -0.9145000000000001}, {4.5981, 0.0855}, {
      3.7321, -0.4145}, {5.4641, 0.5855}, {
      5.0981, -0.7806000000000001}, {4.0981, 0.9515000000000001}, {
      3.4221, 0.12240000000000001`}, {4.0421, -0.9515000000000001}, {
      5.7741, 0.04850000000000001}, {6.001, 0.8955}, {5.1541, 
      1.1224}, {4.5611, -1.0906}, {
      5.408100000000001, -1.3175000000000001`}, {5.635, -0.4706}, {
      4.635, 1.2615}, {3.7881, 1.4884000000000002`}, {3.5611, 
      0.6415}}}]};

FeatureSpacePlot now has a built-in feature extractor for molecules:

&#10005

FeatureSpacePlot[
 Join[Thread[Style[acids, RGBColor[0.8, 0.2, 0.19215686274509805`]]], 
  Thread[
   Style[bases, RGBColor[
    0.2901960784313726, 0.4392156862745098, 0.8901960784313725]]]]]

Closing the Loop for Control Systems

It’s something we’ve been working towards for more than a decade. How do we connect our capabilities for representing and simulating large-scale engineering and other systems to our capabilities in control theory? Or, in particular, how can we use our control systems capabilities to create practical designs that can be directly deployed in engineering systems? Version 12.3 takes some important steps in answering this, and developing what’s increasingly a fully automated workflow for control systems design.

Let’s start by importing a model that was created in Wolfram System Modeler. In this particular case, it’s a simple model for a submarine:

&#10005

sys = Import["ExampleData/Submarine.mo"]

Given the model (which in this case consists of more than 300 differential-algebraic equations) we can compute the behavior of the system in different situations. Like here’s a plot of how our model submarine responds to an impulse force—basically showing that the depth of the submarine exhibits damped oscillations:

&#10005

SystemModelPlot[sys, {"y"}, 40, <|
  "Inputs" -> {"f" -> (10^7 UnitBox[0.5 - #] &), "deltarho" -> (0 &)}|>]

But now the question is: how can we control the submarine to prevent those oscillations? Basically we want to set up a closed loop in which a controller will take the observed behavior of the submarine and modify its dynamics to make it stable and well damped, say characterized by particular eigenvalues.

So given the underlying system model, how can we design that controller? Well, in Version 12.3 we’ve managed to get it down to just a couple of functions. First we give the model and parameters that are going to be controlled, and specify our design goal by giving the eigenvalues we want:

&#10005

cd = StateFeedbackGains[<|"InputModel" -> sys, 
   "FeedbackInputs" -> {"deltarho"}|>, {-0.75 + 0.2 I, -0.75 - 0.2 I},
   "Data"]

Now we can take this controller and connect it into our system model:

&#10005

csys = ConnectSystemModelController[sys, cd]

As the diagram indicates, this is now a closed-loop system (the original system model has been elided into the gray circle). So now we can look at the behavior of this closed-loop system, given for example the same input as before:

&#10005

SystemModelPlot[csys, {"y"}, 40, <|
  "Inputs" -> {"f" -> (10^7 UnitBox[0.5 - #] &), "deltarho" -> (0 &)}|>,
  PlotRange -> All]

Now there are no oscillations; our controller successfully damped them out and “rejected the disturbance”.

So how did this work? Well, as is typical in this type of control systems design, we first found a linearization of the underlying model, appropriate for the domain in which we were going to be operating:

&#10005

cd["DesignModel"]

We can get out the eigenvalues of this linearized model:

&#10005

TransferFunctionPoles[TransferFunctionModel[cd["DesignModel"]]]

The goal of the controller is to shift these to the desired design location:

&#10005

cd["ClosedLoopPoles"]

So what actually is the controller that was found? Here it is as a nonlinear state space model:

&#10005

cd["ControllerModel"]

And now it’s ready to actually deploy. And for example, we can compile the controller for an Arduino:

&#10005

Needs["MicrocontrollerKit`"];

&#10005

MicrocontrollerEmbedCode[
 ToDiscreteTimeModel[cd["ControllerModel"], 0.1],
 <|"Target" -> "ArduinoUno", "Inputs" -> Table["Serial", 3], 
  "Outputs" -> "Serial"|>, <|"ConnectionPort" -> None|>]

And here’s the actual Arduino C source code:

&#10005

%["SourceCode"]

Needless to say, for a real submarine, one wouldn’t use an Arduino Uno (though that would probably be just fine for a toy submarine). But the point here is that in Version 12.3 we now have a remarkably automated workflow for going from a sophisticated system model to a control system.

It’s Going to Get Easier to Type Code in Notebooks

Ever since Version 3.0 (1996) -> has automatically turned into when you enter in code. And we’ve gradually added additional “input auto replacements”, the most recent being |-> turning into (\[Function]) in Version 12.2. In Version 12.3 we’re generalizing this whole mechanism (using the new AutoOperatorRenderings option), and we’re making <| ... |> automatically turn into <| ... |>, and [[  ... ]] turn into  ... .

So this means for example that instead of your code looking this

&#10005

Range[10][[
 Nest[Flatten[RandomInteger[{1, 10}, {10, 2}][[#]]] &, 1, 5]]]

it’ll immediately turn into this more readable form right when you type it:

&#10005

Range[10][[
 Nest[Flatten[RandomInteger[{1, 10}, {10, 2}][[#]]] &, 1, 5]]]

It might seem odd that it’s taken so many years to go from “automatic ” to “automatic 〚 〛”. But it’s a lot more subtle than you might think, and in fact it’s required a whole new as-you-type approach to code rendering. Back in Version 3.0, the idea was to replace -> with when you type it. So, for example, if you then backspace one character, you’ll delete the whole , rather than simply “removing the >” and reverting to -.

But if you’re dealing with [[ ... ]] you can’t just do this kind of “local replacement” without the potential for confusion with some ]] showing up as while others break apart into ]] as a result of routine editing.

In Version 12.3 what we’re doing is not to make replacements at all, but instead just to render specified sequences of characters (like ]]) in special ways. The result is that we can support very general “ligature-like” behavior, and that backspacing will always exactly reverse characters that were entered.

AutoOperatorRenderings will make code you type look nicer and be easier to read. But there’s a second, more significant change in the way you enter code that’s now available in Version 12.3. It’s still rather experimental, so it hasn’t been turned on by default, but you can explicitly turn it on if you want, just by evaluating

&#10005

CurrentValue[$FrontEnd, DelimiterAutoMatching] = True;

or by checking the checkbox in the Interface tab of the Preferences:

Interface

The basic idea—as the name DelimiterAutoMatching might suggest—is that when you type code, the delimiters you enter are automatically matched, as you type.

So that means that if you type

Input

what you’ll actually see is:

Input

In other words, you’ll automatically get matched delimiters. (And by “delimiters”, we mean any of [ ... ], { ... }, ( ... ), " ... ", [[ ... ]], <| ... |> and (* ... *).)

So what happens to your old typing habits? Well, you can still use them. Because you can enter ] to “type through” the closing ]. And that means you’re typing the exact same characters as before. But the important point is that you don’t need to. The ] is already there for you.

Why is this important? Basically because it means you don’t have to think about matching your delimiters anymore. It’s done automatically for you. Ever since Version 3.0 (1996) we’ve had syntax coloring that indicates when delimiters haven't been closed—and to suggest that you should close them. But now the closing will just happen automatically. And in particular that means that expressions you’re typing will always “look complete”, and won’t have all kinds of structural changes happening as you enter each character.

Needless to say, this is all a lot trickier than it might at first appear. Let’s say you’ve already entered a complicated expression, and now you add an opening delimiter inside it, or, worse, several opening delimiters. Where do the closing delimiters go? How much of the code that’s already there should they enclose? Sometimes it’s fairly obvious, but sometimes it’s not. You can always delete an inappropriately added closing delimiter, but we’re working hard to use the appropriate heuristics to either add the closing delimiter in the right place, or not add it at all.

What’s Wrong with That Code? Code Analysis & Assistance

“Automate everything” is a big theme in what we’re trying to achieve with the Wolfram Language. So what about debugging? Is that something we can automate? We’ve been thinking about this for a long time, and in Version 12.3 we’ve introduced the first steps in our code analysis and assistance system.

We’ve had things like syntax coloring and ^ for missing arguments for decades. And these are extremely useful. But what we want is something more global. Something not so much like spellchecking as like being able to say whether a piece of text means the right thing.

One might think that there’s a kind of philosophical problem with this. In the Wolfram Language any piece of code—so long as it’s syntactically correct—is a symbolic expression which at some level means something. The question is whether that “something” is what you want. And the point is that by knowing the typical structure of “correct code” it’s often possible to make a very good guess. And that’s what our new code analysis system does.

Say you have this simple piece of code:

&#10005

If[x == y, f[e^2 + 4 e + 1], f[e^2 + 4 e + 1]]

In Version 12.3 there’s a new context (“right-click”) menu item Analyze Cell. Here’s what it does:

Analyze Cell

Code Analysis has noticed that the expressions on the two branches of the If are the same (as might have happened if you’d copied and pasted them, intending to change one, but forgetting). It’s not strictly “wrong” to have the branches the same, but it’s almost certainly not what you wanted, and if you had always wanted to give the same expression, then your code would be much less obscure if you just gave the expression.

Here’s a marginally more complicated example:

&#10005

Switch[x,1,a,2,b,True,c]

Now we’ve clicked the description, and got a suggestion about how to fix things—which we can immediately implement just by clicking the suggestion. (The Code Analysis box effectively gives you a preview; click Apply Edits to actually change your original code.)

Does Code Analysis catch real errors? Yes, and we’ve got evidence for that, because we’ve run it on our internal code, as well as on examples in our documentation. For example, in Version 12.2 the documentation for FitRegularization contained the example:

&#10005

Length[basis = Flatten[
   Table[Exp[-((t - \[Tau])/\[Sigma])^2]
     {1, 
      Table[{Cos[\[Omega] t], Sin[\[Omega] t]}, {\[Omega], 
        Drop[Subdivide[0., 150., n\[Omega]], 1]}]},
    {\[Tau], Subdivide[0., 1., n\[Tau]]}]]]

Run Code Analysis and you’ll see

Code Analysis

And, yes, that’s an error: there should be a comma in there.

The Code Analysis in Version 12.3 is just a beginning. We’ll be adding many more cases and suggestions, derived both from symbolic analysis and machine learning. And the interface for the analysis will become more automatic—like spellchecking. But I think Code Analysis is going to be very important for both novice and experienced users of Wolfram Language, injecting automation into yet another domain.

Advances in the Compiler: Portability and Librarying

We have a major long-term project of compiling Wolfram Language directly into efficient machine code. And as the project advances, it’s becoming possible to do more and more of our core development using the compiler. In addition, in things like the Wolfram Function Repository, more and more functions—or fragments of functions—are able to use the compiler.

In Version 12.3 we took an important step to make the workflow for this easier. Let’s say you compile a very simple function:

&#10005

fun = FunctionCompile[Function[Typed[x, "Integer64"], x + 1]]

What is that output? Well, in Version 12.3 it’s a symbolic object that contains raw low-level code:

&#10005

fun[[1]]["CompiledIR"]

But an important point is that everything is right there in this symbolic object. So you can just pick it up and use it:

&#10005

CompiledCodeFunction[
Association[
  "Signature" -> TypeSpecifier[{"Integer64"} -> "Integer64"], "Input" -> 
   Compile`Program[{}, 
Function[
Typed[x, "Integer64"], x + 1]], "ErrorFunction" -> Automatic, 
   "InitializationName" -> 
   "Initialization_18398332_a4ff_43e1_bda5_14c193a2b3d6", 
   "ExpressionName" -> "Main_ExprInvocation", "CName" -> 
   "Main_CInvocation", "FunctionName" -> "Main", "SystemID" -> 
   "MacOSX-x86-64", "VersionData" -> {12.3, 0, 0}, "CompiledIR" -> 
   Association["MacOSX-x86-64" -> ByteArray[CompressedData["
1:eJydVwtwG9W5PqvXSra8Wj8gMpbEyq8qwZjVI7Ic2VTS2omcOEQ2bnHaMNIq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"]]], "orcInstance" -> 
   140467786380800, "orcModuleId" -> 1, "targetMachineId" -> 
   140467786361856], 4886855856, 4886855712, 4886855744, 4886855680, 
  "{\"Integer64\"} -> \"Integer64\""][50]

There’s a slight catch, however. By default, FunctionCompile will generate raw low-level code for the type of computer on which you’re running. But if you take the resulting CompiledCodeFunction to another type of computer, it won’t be able to use the low-level code. (It keeps a copy of the original expression before compilation, so it can still run, but won’t have the efficiency advantage of compiled code.)

In Version 12.3, there’s a new option to FunctionCompile: TargetSystem. And with TargetSystem → All you can tell FunctionCompile to create cross-compiled low-level code for all current systems:

&#10005

fun = FunctionCompile[Function[Typed[x, "Integer64"], x + 1], 
  TargetSystem -> All]

Needless to say, it’s slower to do all that compilation. But the result is a portable object that contains low-level code for all current platforms:

&#10005

fun[[1]]["CompiledIR"]

So if you have a notebook—or a Wolfram Function Repository entry—that contains this kind of CompiledCodeFunction you can send it to anyone, and it will automatically run on their system.

There are a few other subtleties to this. The low-level code created by default in FunctionCompile is actually LLVM IR (intermediate representation) code, not pure machine code. The LLVM IR is optimized for each particular platform, but when the code is loaded on the platform, there’s the small additional step of locally converting to actual machine code. You can use UseEmbeddedLibrary → True to avoid this step, and pre-create a complete library that includes your code.

This will make it slightly faster to load compiled code on your platform, but the catch is that creating a complete library can only be done on a specific platform. We’ve built a prototype of a cloud-based compilation-as-a-service system, but it’s not yet clear if the speed improvement is worth the trouble.

Another new compiler feature for Version 12.3 is that FunctionCompile can now take a list or association of functions that are compiled together, optimizing with all their interdependencies.

The compiler continues to get stronger and broader, with more and more functions and types (like "Integer128") being supported. And to support larger-scale compilation projects, something added in Version 12.3 is CompilerEnvironmentObject. This is a symbolic object that represents a whole collection of compiled resources (for example defined by FunctionDeclaration) that act like a library and can immediately be used to provide an environment for additional compilation that is being done.

Shell, Java, ...: New Built-in External Connections

Over the past several versions we’ve added support for direct interactions with a sequence of external languages, both programmatically through functions like ExternalEvaluate, and as parts of notebooks. Version 12.3 adds support for several additional languages.

First, there’s the shell. Back in Version 1.0, there was the notion of “shell escapes”: type ! at the beginning of a line, and everything after it would be sent to your operating system shell. A third of a century later, it’s a bit more polished and sophisticated, though it’s the same basic idea.

Type > in a notebook, and select Shell, then type your shell command:

&#10005

date

The stdout from the shell will be echoed as it’s generated, and then what comes back will be a symbolic object—from which it’s possible to extract things like exit code, or stdout:

&#10005

%["StandardOutput"]

In earlier versions, we added capabilities for languages like Python, Julia, R, etc., as well as SQL. In this version, we’re also adding support for Octave (yes, the function names are not great):

&#10005

inv(1+eye(4))

But the important point here is that data structures have been connected so that an Octave array comes back as an appropriate expression, in this case a list of lists (containing approximate numbers, because that’s all Octave handles).

By the way, although external language cells in notebooks are nice, you definitely don’t have to use them, and you can use ExternalEvaluate—or ExternalFunction—to do things purely programmatically.

We’ve had tight integration with Java in Wolfram Language through J/Link for more than 20 years. But in Version 12.3 we’ve set things up so that instead of using J/Link’s sophisticated symbolic interface to Java, you can just enter Java code directly in ExternalEvaluate and external language cells:

&#10005

System.getProperty("java.version")

Basic Java data structures are returned as standard Wolfram Language expressions:

&#10005

new int [20]

Java objects are represented symbolically through J/Link:

&#10005

new Object()

Everything interacts seamlessly with J/Link. And for example, you can create Java objects directly using J/Link—that you can subsequently use with Java you enter in an external language cell:

&#10005

Needs["JLink`"]; 
fmt = JavaNew["java.text.DecimalFormat", "#.0000"]

If you define a Java function it gets represented symbolically as an ExternalFunction object:

&#10005

import java.text.DecimalFormat;

String[] formatArray(double[] d, DecimalFormat fmt) {
    String[] result = new String[d.length];
    for (int i = 0; i < d.length; i++)
        result[i] = fmt.format(d[i]);
    return result;
}

This particular function takes a list of numbers, and a Java object—of the kind we created above with J/Link:

&#10005

%[{1, 2, 3}, fmt]

(Yes, this particular operation is extremely easy to do directly in Wolfram Language.)

Blockchain, Storage, Authentication & Cryptography

We first introduced blockchain functionality into Wolfram Language in Version 11.3 (2018), and in each successive version we’re adding more and more blockchain integration. Version 12.3 adds connectivity to the Tezos blockchain:

&#10005

BlockchainBlockData[-1, BlockchainBase -> "Tezos"]

&#10005

<|"BlockHash" -> 
  "BKp9B8Z4zNpMDeSaFe2ZU6tywVUhady46Jji1oizxkRc4WGwpkf", 
 "BlockNumber" -> 1460305, 
 "PreviousBlockHash" -> 
  "BL8qGr1awP9RdeCMRExVvyiVadHJvzo9AJGMTfwnWEZDh8BAZf1", 
 "Protocol" -> "PtEdo2ZkT9oKpimTah6x2embF25oss54njMuPzkJTEi5RqfdZFA", 
 "NextProtocol" -> 
  "PtEdo2ZkT9oKpimTah6x2embF25oss54njMuPzkJTEi5RqfdZFA", 
 "Timestamp" -> 
  DateObject[{2021, 5, 6, 15, 23, 25.`}, "Instant", 
   "Gregorian", -4.`], "ValidationPass" -> 4, 
 "OperationsHash" -> 
  "LLoatzgmfL7B8dz5tdJu6RncRTXmPSBHxNpwbAuKyNc4daJw21m9V", 
 "Fitness" -> {"01", "00000000000c4851"}, 
 "ContextHash" -> 
  "CoVdfkgRAo5QFd5iqhoKX34s4KcaZSTgSee7TsPwmCFaLch7TSnu", 
 "Priority" -> 0, "Nonce" -> "cbbfffdbf95d0300", 
 "Signature" -> DigitalSignature[
Association[
   "Type" -> "EllipticCurve", "CurveName" -> "prime256v1", "R" -> 
    ByteArray[{63, 206, 128, 26, 8, 98, 13, 127, 155, 77, 28, 109, 
      127, 131, 181, 72, 12, 233, 255, 113, 50, 41, 68, 60, 176, 134, 
      219, 28, 96, 233, 234, 145}], "S" -> 
    ByteArray[{203, 169, 245, 106, 175, 234, 118, 156, 176, 232, 249, 
      67, 153, 193, 64, 177, 95, 75, 47, 32, 23, 90, 41, 184, 8, 242, 
      92, 126, 135, 75, 109, 18}], "SignatureType" -> "Deterministic",
     "HashingMethod" -> None]], Sequence[
 "ConsumedGas" -> 791549625, "Baker" -> 
  "tz3RB4aoyjov4KEVRbuhvQ1CKJgBJMWhaeB8", "BlockReward" -> 
  Quantity[40000000, "Mutez"], "BlockFees" -> 
  Quantity[452892, "Mutez"], "TotalTransactions" -> 61, 
  "TransactionList" -> {
   "op6VomseCtH7rizEpty4e1kATAP2wZ95Zc2TnciUHgHDxsjAks4", 
    "opTjVuymKZuSgAvLq7QzfAn6pqgSJzFyYQx2ZYKA8J9AKYjGbUE", 
    "opNNDhSkp2j1s9MNyaH7nW4T8gREpPnDN7zPwnoxS2Q74EzRJYS", 
    "op9Sy24XKKJxwJ1UyBZ4didiyi6TtgDj2rMstCnYmPs9MLyCGra", 
    "oozW8QwRnGvde4yqf1P4xM99Nzb5o1tg4RHRJg3uVEWjYVG1Afw", 
    "op6EFngrBovURGURpKNsbz22r5bcRPaM4XbYCarStWG25X2kuJH", 
    "ooFkZjFM33UjXDLvkhkD1SNUguh8rnnLfmwFefS7YWNR2tCpfVA", 
    "onqdtnVexw5jsxy4P1FTbYRwBcNH1DrppjWgryCv32qqqw1Y7aP", 
    "ooCgPPZi75TbErNqUEpptUvx6mU9T8B1DCsaU4eJYtbrishZqGw", 
    "onudTXp25r5Zf2aZyMMKUAAFzqVQX24jwPNCN2TFPVQEA6hiEMk", 
    "op8eRJ5T3yMLDSzSbH4EA1VpA91VaMECCy6uGASfE1aNVWuu8tb", 
    "onpbU1aussshJatN8xfvqFWn3hNuDUZRD8SSem1XMrkcnnkRPwR", 
    "onwvab7jYybdAX8fmG7ntMXVgQoT1UXi8hHJDiuMu9DmHT7hsDV", 
    "ooPqN4ejaPjbuvn9aoGUzdj7Jb68Sp3CT4WV6ysvrvdEhSjw11u", 
    "oo9YcyDUZiJN5r6nTtr5uU2d6WgFuxby8zygjNbFUAkMbcq2Kkc", 
    "oojLDHmjFXpgcEdLrtu1ri5fkYyMLcxZVw4jxF2TZ3f7ELFkkwu", 
    "oo5znRzP43gSP5TKwaBHD96zkt4xw3QfRqEXFiXAEuUxTn9SDeL", 
    "ooqYjZ6boQBtVxRsBqrHxB9vmJRFkPAYysR2iaXigEQ6NepgDiV", 
    "ooETrujpf9SuB5ErLheg653puC7NEKA1DFLEZHttEBMVa7HaNJa", 
    "opAHbY3xtTUhekjv8Kgzvv6GS1uK3S4Arz5ygJiEVRZu8hSv7Vi", 
    "oodaSNWZfzyUHXa94pJ9vohwZS4FbEmtuZgMZnwf9WJ2mqkDcTW", 
    "oouk2fBfhWPirfcNzdxXKxhDtnyMnvK65hD1MnHPApFuesFuuMt", 
    "opRcK6KBE1Y79GyGzmYsqRPZntxsV1A2gRK4iVZ91x573H1nTRp", 
    "opZVSQx9cVe4rxAv81eurea9GisjyFJc9DdKEiPSpgCB8aUtHG9", 
    "oogML3KCzAZZE1aaagcpXwfoa9R7zqdiiQHC9LT5j6ZcnsfY6P7", 
    "oouk2fBfhWPirfcNzdxXKxhDtnyMnvK65hD1MnHPApFuesFuuMt", 
    "opRcK6KBE1Y79GyGzmYsqRPZntxsV1A2gRK4iVZ91x573H1nTRp", 
    "opZVSQx9cVe4rxAv81eurea9GisjyFJc9DdKEiPSpgCB8aUtHG9", 
    "onvdAawno4HEbLJhS1St7j3RFaCi7rDZSeAzC1DM7Cafs1EadMJ", 
    "onqyJ3Cin5sxuWPvZgcVqXqBeTNFijkthcrz5bS4U8ATwhzodHK", 
    "op2hL6BVZg6y8zzQKQWzk5Bxs8Cr89QCSocFaYoNTVPvAXnQ4Hu", 
    "onijR1dKbGM1zR9dAbasxactqGQC4z6J6b4Uzrccy4vAnCD4Fn7", 
    "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
    "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
    "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
    "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
    "onyUVr21aiaYCL8cQFSZ8S7iMtu3qKRmycWAMwrUELJ6Y8UWkr5", 
    "op2ZjLMAGz9N8uMwDF1eUt4SwPviLHvGEHLukB88vWVTkx4daT7", 
    "onpABZgtFEYCNQNJPDNX56M4UEbyekKY9NibBQw9x11KTqiaCbE", 
    "oogML3KCzAZZE1aaagcpXwfoa9R7zqdiiQHC9LT5j6ZcnsfY6P7", 
    "opPWXEj2p6MHCGfon4nZGVnG3zBXWY2CZtYVRRjVNDBvQPLxnUU", 
    "ooq8gZ1nKE5AqHuJYCjqEMkbYHgK2SDs9T2SZXDkJAZ196RKiBS", 
    "onqJPSCfU3BRzLesTnHHrbQn1FLAGpRCi7TRtxxiHbip9iHMWbU", 
    "onwg53FYDrfnnya3saqbrAEZUbnpTruMPTpqoKVuRazsf1Viwk5", 
    "onhFQfHozg6342FWHmneswFZRdoEuwvtfhmcpQQg1U9FsginVJv", 
    "opTn7nNd5vUBsex4a2bfR3RrZjUmkMeiAWEDttrrSEF3T1dGNZm", 
    "ooWkoM9xWNAd8EqbETpr1k5fEAuiQt598ugmLM54ZVNxjCbtGbp", 
    "oogDaA91FQsyYy3P9kYcJjS3ujmpHb94JGgjA6HpPSkPzgpB15H", 
    "ooaQLZaBXPXTe6aafhe96naZBGcoZi11FbzMhn385FseNEem2xF", 
    "opL6WmqQuWGyN9cNSmCwD8Ma6MV4Ts9So85WgcWp5Ky5tJkmLmh", 
    "ootzhwxQhEwnvrxzTeBWErHdC5m7BRzTfwDa2pSpaSkvRAwzU5K", 
    "oosyhGiANK4fZxc7hZ2j3NVUNiaTXQDPTKPK4GENmL8fNbqUsvB", 
    "ooLh1CUUtYveZTsG3GeA5rHmbsx9nQFTz9ynHGaEgJoC6dJYnti", 
    "oo5qw19WGmppfrnJZLyeRULUsAfjnffaQSLTqUHfLvKpXR3MqYd", 
    "onpj8MabGeVVHM4SkFewBuTzh7ZCzaS5nAHXzrzNCVtSEw2XzuX", 
    "ong2v5BqSaqatFHaxszrGydxBD12d5nURnXHHVwaijpvRow84Af", 
    "opW8UBt6o18DYbtoRX9zJQtzyD8jgU4y3siQpezuHUHCKFwNU7C", 
    "onojr4LVUpN9wCuLsBx3zTvAm7BFiaX6UuQPam6S2GgrQnD4c5Z", 
    "op2QbyAJAMc2XYEksdMKvEUgYgLV2gKUqbXahUjDUeruvixepqT", 
    "opWUQsURSggjD9fcSFvRipqfcCeWgZUt3sSNzNwEs59iu7uGdH3", 
    "onwfFre2mrardqbuM2UE8vJpF9H2hFWZorsPgNgak865KTJEF4k", 
    "opYvNapz6ofynufp1nkr1rRHwM3zS3Zt6ej3byMDPC5HR6hoB4a", 
    "ooRgJQGMHvbVtQkCpC1SoJRz4rCkWxoXFStBxARH5dAVYdnYKFn", 
    "oooFkkTvXw4geySWJbsnyGtv7uWSUCfZikqobG6VcFwXPqV41R9", 
    "oosNr1vxSqq2CJtBx6w89JMwBQykN1eCZjRRMhcVBBYQB1e13iq", 
    "opTmRk498HdVZ9QQxMPRVbrastMCZmYTgZS7A6p8xK2J9Sh8J26", 
    "ooB3dC8hsr9ZXNDVHD6KtAkDKEke2UxS8BfmijiT3eWDYynWiuh", 
    "onpQSBSjVeSx1zRpFhcwCGmhhawy2Za6ur18RoSqTFKcFEFWmpg"}, 
  "TransactionListDetails" -> 
  Association[
   "Endorsement" -> {
     "op6VomseCtH7rizEpty4e1kATAP2wZ95Zc2TnciUHgHDxsjAks4", 
      "opTjVuymKZuSgAvLq7QzfAn6pqgSJzFyYQx2ZYKA8J9AKYjGbUE", 
      "opNNDhSkp2j1s9MNyaH7nW4T8gREpPnDN7zPwnoxS2Q74EzRJYS", 
      "op9Sy24XKKJxwJ1UyBZ4didiyi6TtgDj2rMstCnYmPs9MLyCGra", 
      "oozW8QwRnGvde4yqf1P4xM99Nzb5o1tg4RHRJg3uVEWjYVG1Afw", 
      "op6EFngrBovURGURpKNsbz22r5bcRPaM4XbYCarStWG25X2kuJH", 
      "ooFkZjFM33UjXDLvkhkD1SNUguh8rnnLfmwFefS7YWNR2tCpfVA", 
      "onqdtnVexw5jsxy4P1FTbYRwBcNH1DrppjWgryCv32qqqw1Y7aP", 
      "ooCgPPZi75TbErNqUEpptUvx6mU9T8B1DCsaU4eJYtbrishZqGw", 
      "onudTXp25r5Zf2aZyMMKUAAFzqVQX24jwPNCN2TFPVQEA6hiEMk", 
      "op8eRJ5T3yMLDSzSbH4EA1VpA91VaMECCy6uGASfE1aNVWuu8tb", 
      "onpbU1aussshJatN8xfvqFWn3hNuDUZRD8SSem1XMrkcnnkRPwR", 
      "onwvab7jYybdAX8fmG7ntMXVgQoT1UXi8hHJDiuMu9DmHT7hsDV", 
      "ooPqN4ejaPjbuvn9aoGUzdj7Jb68Sp3CT4WV6ysvrvdEhSjw11u", 
      "oo9YcyDUZiJN5r6nTtr5uU2d6WgFuxby8zygjNbFUAkMbcq2Kkc", 
      "oojLDHmjFXpgcEdLrtu1ri5fkYyMLcxZVw4jxF2TZ3f7ELFkkwu", 
      "oo5znRzP43gSP5TKwaBHD96zkt4xw3QfRqEXFiXAEuUxTn9SDeL", 
      "ooqYjZ6boQBtVxRsBqrHxB9vmJRFkPAYysR2iaXigEQ6NepgDiV", 
      "ooETrujpf9SuB5ErLheg653puC7NEKA1DFLEZHttEBMVa7HaNJa", 
      "opAHbY3xtTUhekjv8Kgzvv6GS1uK3S4Arz5ygJiEVRZu8hSv7Vi", 
      "oodaSNWZfzyUHXa94pJ9vohwZS4FbEmtuZgMZnwf9WJ2mqkDcTW"}, 
    "Reveal" -> {
     "oouk2fBfhWPirfcNzdxXKxhDtnyMnvK65hD1MnHPApFuesFuuMt", 
      "opRcK6KBE1Y79GyGzmYsqRPZntxsV1A2gRK4iVZ91x573H1nTRp", 
      "opZVSQx9cVe4rxAv81eurea9GisjyFJc9DdKEiPSpgCB8aUtHG9", 
      "oogML3KCzAZZE1aaagcpXwfoa9R7zqdiiQHC9LT5j6ZcnsfY6P7"}, 
    "Delegation" -> {
     "oouk2fBfhWPirfcNzdxXKxhDtnyMnvK65hD1MnHPApFuesFuuMt", 
      "opRcK6KBE1Y79GyGzmYsqRPZntxsV1A2gRK4iVZ91x573H1nTRp", 
      "opZVSQx9cVe4rxAv81eurea9GisjyFJc9DdKEiPSpgCB8aUtHG9", 
      "onvdAawno4HEbLJhS1St7j3RFaCi7rDZSeAzC1DM7Cafs1EadMJ"}, 
    "Transaction" -> {
     "onqyJ3Cin5sxuWPvZgcVqXqBeTNFijkthcrz5bS4U8ATwhzodHK", 
      "op2hL6BVZg6y8zzQKQWzk5Bxs8Cr89QCSocFaYoNTVPvAXnQ4Hu", 
      "onijR1dKbGM1zR9dAbasxactqGQC4z6J6b4Uzrccy4vAnCD4Fn7", 
      "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
      "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
      "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
      "opTZK85vLQoU91TwLGvcCjFCKWeWbTxJEus4rYv2SgF97eHRZE5", 
      "onyUVr21aiaYCL8cQFSZ8S7iMtu3qKRmycWAMwrUELJ6Y8UWkr5", 
      "op2ZjLMAGz9N8uMwDF1eUt4SwPviLHvGEHLukB88vWVTkx4daT7", 
      "onpABZgtFEYCNQNJPDNX56M4UEbyekKY9NibBQw9x11KTqiaCbE", 
      "oogML3KCzAZZE1aaagcpXwfoa9R7zqdiiQHC9LT5j6ZcnsfY6P7", 
      "opPWXEj2p6MHCGfon4nZGVnG3zBXWY2CZtYVRRjVNDBvQPLxnUU", 
      "ooq8gZ1nKE5AqHuJYCjqEMkbYHgK2SDs9T2SZXDkJAZ196RKiBS", 
      "onqJPSCfU3BRzLesTnHHrbQn1FLAGpRCi7TRtxxiHbip9iHMWbU", 
      "onwg53FYDrfnnya3saqbrAEZUbnpTruMPTpqoKVuRazsf1Viwk5", 
      "onhFQfHozg6342FWHmneswFZRdoEuwvtfhmcpQQg1U9FsginVJv", 
      "opTn7nNd5vUBsex4a2bfR3RrZjUmkMeiAWEDttrrSEF3T1dGNZm", 
      "ooWkoM9xWNAd8EqbETpr1k5fEAuiQt598ugmLM54ZVNxjCbtGbp", 
      "oogDaA91FQsyYy3P9kYcJjS3ujmpHb94JGgjA6HpPSkPzgpB15H", 
      "ooaQLZaBXPXTe6aafhe96naZBGcoZi11FbzMhn385FseNEem2xF", 
      "opL6WmqQuWGyN9cNSmCwD8Ma6MV4Ts9So85WgcWp5Ky5tJkmLmh", 
      "ootzhwxQhEwnvrxzTeBWErHdC5m7BRzTfwDa2pSpaSkvRAwzU5K", 
      "oosyhGiANK4fZxc7hZ2j3NVUNiaTXQDPTKPK4GENmL8fNbqUsvB", 
      "ooLh1CUUtYveZTsG3GeA5rHmbsx9nQFTz9ynHGaEgJoC6dJYnti", 
      "oo5qw19WGmppfrnJZLyeRULUsAfjnffaQSLTqUHfLvKpXR3MqYd", 
      "onpj8MabGeVVHM4SkFewBuTzh7ZCzaS5nAHXzrzNCVtSEw2XzuX", 
      "ong2v5BqSaqatFHaxszrGydxBD12d5nURnXHHVwaijpvRow84Af", 
      "opW8UBt6o18DYbtoRX9zJQtzyD8jgU4y3siQpezuHUHCKFwNU7C", 
      "onojr4LVUpN9wCuLsBx3zTvAm7BFiaX6UuQPam6S2GgrQnD4c5Z", 
      "op2QbyAJAMc2XYEksdMKvEUgYgLV2gKUqbXahUjDUeruvixepqT", 
      "opWUQsURSggjD9fcSFvRipqfcCeWgZUt3sSNzNwEs59iu7uGdH3", 
      "onwfFre2mrardqbuM2UE8vJpF9H2hFWZorsPgNgak865KTJEF4k", 
      "opYvNapz6ofynufp1nkr1rRHwM3zS3Zt6ej3byMDPC5HR6hoB4a", 
      "ooRgJQGMHvbVtQkCpC1SoJRz4rCkWxoXFStBxARH5dAVYdnYKFn", 
      "oooFkkTvXw4geySWJbsnyGtv7uWSUCfZikqobG6VcFwXPqV41R9", 
      "oosNr1vxSqq2CJtBx6w89JMwBQykN1eCZjRRMhcVBBYQB1e13iq", 
      "opTmRk498HdVZ9QQxMPRVbrastMCZmYTgZS7A6p8xK2J9Sh8J26", 
      "ooB3dC8hsr9ZXNDVHD6KtAkDKEke2UxS8BfmijiT3eWDYynWiuh", 
      "onpQSBSjVeSx1zRpFhcwCGmhhawy2Za6ur18RoSqTFKcFEFWmpg"}]]|>

In addition to doing blockchain transactions and blockchain analytics with Wolfram Language, we’re also doing more and more with computational contracts—for which the full-scale computational language character of the Wolfram Language gives unique opportunities (an example being the creation of “oracles” based on our computational knowledge about the world).

In Version 12.1 we introduced ExternalStorageObject, initially supporting IPFS and Dropbox. In Version 12.3 we’ve added support for Amazon S3 (and, yes, you can store and retrieve a whole bucket of files at a time):

&#10005

ExternalStorageUpload["ExampleData/spikey2.png", "wolfram-bucket", 
 ExternalStorageBase -> "AmazonS3"]

A necessary step in all sorts of external interactions is authentication. And in Version 12.3 we’ve added support for OAuth 2.0 workflows. You create a SecuredAuthenticationKey:

&#10005

SecuredAuthenticationKey[<|"Name" -> "Reddit",
  "OAuthType" -> "ThreeLegged",
  "OAuthVersion" -> "2.0",
  "ConsumerKey" -> "" (*Your key here*), 
  "ConsumerSecret" -> "" (*Your key here*), "ResponseType" -> "code", 
  "Scopes" -> {"read"}, "ScopeDelimiter" -> " ", 
  "VerifierInputFunction" -> "WolframConnectorChannel",
  "AccessTokenURL" -> "https://www.reddit.com/api/v1/access_token",
  "UserAuthorizationURL" -> 
   "https://www.reddit.com/api/v1/authorize",
  "AdditionalParameters" -> <|
    "AuthorizationRequest" -> {"duration" -> "permanent"}|>
  |>]

Then you can make a request using this key:

&#10005

URLRead["https://oauth.reddit.com/api/search_subreddits" , 
 Authentication -> %]

You’ll get a browser window that asks you to log in with your account—and then you’ll be off and running.

For many common external services, we have “pre-packaged” ServiceConnect connections. Often these require authentication. And for OAuth-based APIs (like Reddit or Twitter) we have our WolframConnector app that brokers the external part of the authentication. A new feature of Version 12.3 is that you can also use your own external app to broker that authentication, so you’re not limited by the arrangements made with the external service for the WolframConnector app.

Under the hood for everything we’re talking about here is cryptography. And in Version 12.3 we’ve added some new cryptographic capabilities; in particular we now have support for all elliptic curves in the NIST Digital Signature FIPS 186-4 standard, as well as for Edwards curves that will be part of FIPS 186-5.

We’ve packaged all of this to make it very easy to create blockchain wallets, sign transactions, and encode data for blockchains:

&#10005

BlockchainKeyEncode[PublicKey[
Association[
  "Type" -> "EdwardsCurve", "CurveName" -> "ed25519", 
   "PublicByteArray" -> 
   ByteArray[{129, 57, 198, 230, 91, 48, 63, 133, 232, 63, 173, 17, 
     49, 237, 190, 143, 151, 108, 127, 202, 73, 93, 64, 14, 198, 177, 
     194, 15, 13, 79, 120, 246}], 
   "PublicCurvePoint" -> {
    299335060271590132066951334928298030649197201001541795411746200767\
72068584535, 
     53585483370699320092407628906864478900713122887718404470323110058\
487397628289}]], "Address", BlockchainBase -> "Tezos"]

&#10005

BlockchainAddressData[%, "DelegateData", BlockchainBase -> "Tezos"]

Distributed Computing & Its Management

We first introduced parallel computation in Wolfram Language in the mid-1990s, and in Version 7.0 (2008) we introduced functions like ParallelMap and Parallelize. It’s always been straightforward to set up parallel computation on multiple cores of a single computer. But it’s been more complicated when one also wants to use remote computers.

In Version 12.2 we introduced RemoteKernelObject as a symbolic representation of remote Wolfram Language capabilities. Starting in Version 12.2, this was available for one-shot evaluations with RemoteEvaluate. In Version 12.3 we’ve integrated RemoteKernelObject into parallel computation.

Let’s try this for one of my machines. Here’s a remote kernel object that represents a single kernel on it:

&#10005

RemoteKernelObject["threadripper2"]

Now we can do a computation there, here just asking for the number of processor cores:

&#10005

RemoteEvaluate[%, $ProcessorCount]

Now let’s create a remote kernel object that uses all 64 cores on this machine:

&#10005

RemoteKernelObject["threadripper2", "KernelCount" -> 64]

Now I can launch those kernels (and, yes, it’s much faster and more robust than before):

&#10005

LaunchKernels[%]

Now I can use this to do parallel computations:

&#10005

ParallelEvaluate[$ProcessID, %]

For someone like me who is often involved in doing parallel computations, the streamlining of these capabilities in Version 12.3 will make a big difference.

One feature of functions like ParallelMap is that they’re basically just sending pieces of a computation independently to different processors. Things can get quite complicated when there needs to be communication between processors and everything is happening asynchronously.

The basic science of this is deeply related to the story of multiway graphs and to the origins of quantum mechanics in our Physics Project. But at a practical level of software engineering, it’s about race conditions, thread safety, locking, etc. And in Version 12.3 we’ve added some capabilities around this.

In particular, we’ve added the function WithLock that can lock files (or local objects) during a computation, thereby preventing interference between different processes which attempt to write to the file. WithLock provides a low-level mechanism for ensuring atomicity and thread safety of operations.

There’s a higher-level version of this in LocalSymbol. Say one sets a local symbol to 0:

&#10005

LocalSymbol["/tmp/counter"] = 0

Then launch 40 local parallel kernels (they need to be local so they share files):

&#10005

LaunchKernels[40];

Now, because of locking, the counter will be forced to update sequentially on each kernel:

&#10005

ParallelEvaluate[LocalSymbol["/tmp/counter"]++]

&#10005

LocalSymbol["/tmp/counter"]

For even more, see:

10 comments

  1.  

    When Stephen and Jonathan are doing their live weekly sessions .. are they using the public Mathematica or their own private versions .. thank you advance ..

    Dan Ellwein
    •  

      Hi Dan, usually it’s public versions, but they sometimes use new versions in development.

      Admin
  2.  

    Lots of great improvements. This is the best language for computation.

    Jackreece Ejini
  3.  

    I love the Code Analyzer. It helped clean up a few of my notebooks.

    However, a bug that I reported in October 2020 with 12.2 has not been fixed. Moreover, it seems that calls to SystemModel functions are 30% to 100% slower than those of 12.1.

    I was looking forward to updating but I’m going to stick with 12.1.

    BU
  4.  

    Is version 12.3.1 available now? The cloud appears to be still on 12.3.0.

    Muhammad Ali
    •  

      Version 12.3.1 is primarily a desktop-focused released adding single-sign on (SSO) for our site customers, introducing native support for macOS for Apple Silicon and addressing other bugs and performance improvements. We are rolling out a larger release of Cloud, based on 12.3.1, in the next few months.

      Admin
  5.  

    Looks great.

    What’s the release schedule for various platforms?
    This reads as if everything were released at the same time, but the latest I can find for Raspberry Pi is 12.2

    rcfa
    •  

      The Raspberry Pi release for 12.3 is still in testing. We hope to release it soon.

      Admin
  6.  

    Fantastic- can’t wait! – Why is there such a long time gap between the English/global version of Mathematica and the Japanese language version…. I am still on 12.2 here in Tokyo, when will we get 12.3 in Japan? Thank you so much, Gerhard Fasol

    •  

      There was a small scheduling conflict which caused the Japanese release to be slightly delayed. However, it is expected to be out shortly.

      Admin