Sapphire | Sapphire source code

If you're using Neo4j 4.x or higher, you can UNION the results of the queries in a subquery, and outside of it perform a sum() to get the results into a single row per user:

Move the let randomGemStone line into the findGems function:

From looking at the source of Saphire you will need to use CSS like this which overrides ui-icon etc.

Source: https://www.primefaces.org/sapphire/javax.faces.resource/theme.css.xhtml?ln=primefaces-sapphire-blue

Declare the font face:

Atomicity guarantees on x86 in global observability and persistency are the same. This means that the following operations are persistently atomic:

• A store uop that doesn't cross an 8-byte boundary to a location of any effective memory type, and
• MOVDIR64B.

In addition, the following operations are persistently atomic:

• A cache line flush (CLFLUSH or CLFLUSHOPT),
• A cache line writeback (CLWB), and
• A non-architectural cache line eviction.
• A full write-combining buffer flush on Intel processors. The presence and size of WCBs and the causes of flush are implementation-specific. See: Ordering of Intel non-temporal stores to the same cache line.

There is no architectural persistent atomicity guarantee for everything else, including 64-byte AVX512 vmovntdq / vmovntps full-line stores.

These guarantees apply to Asynchronous DRAM Refresh (ADR) platforms and Enhanced Asynchronous DRAM Refresh (eADR) platforms. (On eADR, the cache hierarchy is in the persistence domain. See: Build Persistent Memory Applications with Reliability Availability and Serviceability.)

This answer is based on my private correspondence with Andy Rudoff (Intel).

Colab is quite similar to jupyter notebook and is free to use. Just send the link to other once you add your code. https://colab.research.google.com/ is the link. A quick guide is: https://colab.research.google.com/github/jckantor/CBE30338/blob/master/docs/01.01-Getting-Started-with-Python-and-Jupyter-Notebooks.ipynb

Idea is create mask for not 0 values and less like 5km, then use DataFrame.dot for matrix multiplication nas last use Series.str.split for new columns joined to original:

I'm pretty sure pandas.DataFrame.groupby is what you need:

You can read in the file using spark.read.json:

As you can see in your URL, the ampersand (&) has two functions:

• As a separator for URL parameters.
• As a character that is part of the value of such a parameter.

To avoid the ambiguity that is presented in your question, you generally urlencode() the URL parameter values.

You would then do something like $ar = urlencode('Merimbula & Sapphire Coast');. If you have no control over how the ar parameter is constructed, then you'll have to remove the ampersand from "Merimbula & Sapphire Coast" and replace it with something else (like 'and').