reading | Papers/Articles | Awesome List library

Ran into the same issue with Node.js 17.0.0. To solve it, I downgraded to version 14.18.1, deleted node_modules and reinstalled.

What is happening:

The function itemToForm() is being called before the this.item is ready.

There are many strategies to avoid this error. A very simple one is to add a catcher at the beginning of the function, like this:

The most basic form of the kind language contains only * (or Type in more modern Haskell; I suspect we'll eventually move away from *) and ->.

But there are more things you can build with that language than you can express by just "counting the number of *s". It's not just the number of * or -> that matter, but how they are nested. For example * -> * -> * is the kind of things that take two type arguments to produce a type, but (* -> *) -> * is the kind of things that take a single argumemt to produce a type where that argument itself must be a thing that takes a type argument to produce a type. data ThreeStars a b = Cons a b makes a type constructor with kind * -> * -> *, while data AlsoThreeStars f = AlsoCons (f Integer) makes a type constructor with kind (* -> *) -> *.

There are several language extensions that add more features to the kind language.

PolyKinds adds kind variables that work exactly the same way type variables work. Now we can have kinds like forall k. (* -> k) -> k.

ConstraintKinds makes constraints (the stuff to the left of the => in type signatures, like Eq a) become ordinary type-level entities in a new kind: Constraint. Rather than the stuff left of the => being special purpose syntax fairly disconnected from the rest of the language, now what is acceptable there is anything with kind Constraint. Classes like Eq become type constructors with kind * -> Constraint; you apply it to a type like Eq Bool to produce a Constraint. The advantage is now we can use all of the language features for manipulating type-level entities to manipulate constraints (including PolyKinds!).

DataKinds adds the ability to create new user-defined kinds containing new type-level things, in exactly the same way that in vanilla Haskell we can create new user-defined types containing new term-level things. (Exactly the same way; the way DataKinds actually works is that it lets you use a data declaration as normal and then you can use the resulting type constructor at either the type or the kind level)

There are also kinds used for unboxed/unlifted types, which must not be ever mixed with "normal" Haskell types because they have a different memory layout; they can't contain thunks to implement lazy evaluation, so the runtime has to know never to try to "enter" them as a code pointer, or look for additional header bits, etc. They need to be kept separate at the kind level so that ordinary type variables of kind * can't be instantiated with these unlifted/unboxed types (which would allow you to pass these types that need special handling to generic code that doesn't know to provide the special handling). I'm vaguely aware of this stuff but have never actually had to use it, so I won't add any more so I don't get anything wrong. (Anyone who knows what they're talking about enough to write a brief summary paragraph here, please feel free to edit the answer)

There are probably some others I'm forgetting. But certainly the kind language is richer than the OP is imagining just with the basic Haskell features, and there is much more to it once you turn on a few (quite widely used) extensions.

Code readability, short-circuiting and it is not guaranteed that Ord will always outperform a || operand. Computer systems are more complicated than expected, even though they are man-made.

There was a case where a for loop with a much more complicated condition ran faster on an IBM. The CPU didn't cool and thus instructions were executed faster, that was a possible reason. What I am trying to say, focus on other areas to improve code than fighting small-cases which will differ depending on the CPU and the boolean evaluation (compiler optimizations).

I made it work by setting --conf to spark.sql.legacy.parquet.int96RebaseModeInRead=CORRECTED --conf spark.sql.legacy.parquet.int96RebaseModeInWrite=CORRECTED --conf spark.sql.legacy.parquet.datetimeRebaseModeInRead=CORRECTED --conf spark.sql.legacy.parquet.datetimeRebaseModeInWrite=CORRECTED.

This is a workaround though and Glue Dev team is working on a fix, although there is no ETA.

Also this is still very buggy. You can not call .show() on a DynamicFrame for example, you need to call it on a DataFrame. Also all my jobs failed where I call data_frame.rdd.isEmpty(), don't ask me why.

Update 24.11.2021: I reached out to the Glue Dev Team and they told me that this is the intended way of fixing it. There is a workaround that can be done inside of the script though:

Today, I could finally install version 30.0.3.

In Android Studio, I could see and install it from here...

After this, and then also having to remove the whitelist plugin (it is not required any more), I could get it to build.

Only hassle now is it outs to a .aab and not an .apk so side load testing now harder. But the main issue, the building is now working (on Windows PC).. Now hopefully can do the same on the Mac.

This is a good question... and I would have hoped there would be a practical guide somewhere. One could question if SO would be a good place to ask this question, but regardless, here's my attempt to summarize the various scale_color_*() and scale_fill_*() functions built into ggplot2. Here, we'll describe the range of functions using scale_color_*(); however, the same general rules will apply for scale_fill_*() functions.

There are 22 functions in all, but happily we can group them intelligently based on practical usage scenarios. There are three key criteria that can be used to define practically how to use each of the scale_color_*() functions:

1. Nature of the mapping data. Is the data mapped to the color aesthetic discrete or continuous? CONTINUOUS data is something that can be explained via real numbers: time, temperature, lengths - these are all continuous because even if your observations are 1 and 2, there can exist something that would have a theoretical value of 1.5. DISCRETE data is just the opposite: you cannot express this data via real numbers. Take, for example, if your observations were: "Model A" and "Model B". There is no obvious way to express something in-between those two. As such, you can only represent these as single colors or numbers.

2. The Colorspace. The color palette used to draw onto the plot. By default, ggplot2 uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.

3. The level of Specification. Generally, once you have defined if the scale function is continuous and in what colorspace, you have variation on the level of control or specification the user will need or can specify. A good example of this is the functions: *_continuous(), *_gradient(), *_gradient2(), and *_gradientn().

We can start off with continuous scales. These functions are all used when applied to observations that are continuous variables (see above). The functions here can further be defined if they are either binned or not binned. "Binning" is just a way of grouping ranges of a continuous variable to all be assigned to a particular color. You'll notice the effect of "binning" is to change the legend keys from a "colorbar" to a "steps" legend.

The continuous example (colorbar legend):

Imo, it's a bug in Paramiko. It does not handle correctly absence of server-sig-algs extension on the server side.

Try disabling rsa-sha2-* on Paramiko side altogether:

If you look closely at the specification of ranges​::​size in [range.prim.size], except when the type of R is the primitive array type, ranges​::​size obtains the size of r by calling the size() member function or passing it into a free function.

And since the parameter type of transform() function is reference, ranges::size(r) cannot be used as a constant expression in the function body, this means we can only get the size of r through the type of R, not the object of R.

However, there are not many standard range types that contain size information, such as primitive arrays, std::array, std::span, and some simple range adaptors. So we can define a function to detect whether R is of these types, and extract the size from its type in a corresponding way.