Hi @postkulture, you can retrieve facets using the search method, with the facets search parameter to designate which facets you want to retrieve (using an asterisk * will select them all). If you use an empty query, you will get all the values for each facet. You will also get the hits.
The graph has three facets. Where in the_plot can I find it has three facets? Yes, I could get that from the mtcars data frame, or the_plot$data, but I don't want to recreate the data analysis. Rather, I want to inspect the graphical elements of the_plot, so I don't have to duplicate application logic in multiple places. the_plot$facet doesn't show anything I recognize, nor do the other plot variables.
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What values should I put in each box for a smooth surface? Is it 0.5 and 0.1? I really don't mind going lower - I'd rather have a bigger file which may cause niggles sending to others, rather than a model that again comes back with facets. By way of example, the widest curve I have pulled is around 50mm diameter.
You can learn about facets and filtering with the following example. You can copy the following table and paste it using the Clipboard method of starting a project if you would like to try it yourself. Check the "Attempt to parse cell text into numbers" option so that you can use numeric faceting.
When you look back at the text facet display of country names, you should see a smaller list with a reduced count: OpenRefine is now displaying the facets of the 3 matching rows, not the total dataset of 10 rows.
We can combine these facets - say, by narrowing to only the Chinese cities with populations greater than 20 million - simply by clicking in both. You should see 2 matching rows for both these criteria.
You can create a text facet on numeric data, which will treat each entry as a string. This can be useful if you wish, for example, to manually include facets instead of selecting a range, or sort by count, or copy that count.
As mentioned in the overview, facets can be modified or customized by GREL expressions in many ways. For example, to facet by clusters of row numbers with row.index/100 or better visualizing numbers greater than 1000 with max(row.index, 1000).
When you click on your desired square, that two-column comparison will appear in the facets sidebar. From here, you can drag your mouse to draw a rectangle inside the scatterplot, which will narrow down to just the rows matching the points plotted inside that rectangle (as shown by the rectangle inside the square in the image above). This rectangle can be resized by dragging any of the four edges. To draw a new rectangle, simply click and drag your mouse again. To add more scatterplots to the facet sidebar, re-run this process and select a different square.
If you have multiple facets applied, plotted points in your scatterplot displays will be greyed out if they are not part of the current matching data subset. If the rectangle you have drawn within a scatterplot display only includes grey dots, you will see no matching rows.
You may want to explore your textual data with modifications that aren't permanent. Creating custom text facets will load your column into memory, transform the data temporarily, and store those transformations inside the facet.
You may want to explore your numerical data with modifications that aren't permanent. You can also use custom numeric facets to analyze textual data, such as by getting the length of text strings (with value.length()), or by analyzing it as though it were formatted as numbers (with toNumber(value)).
Stars and flags offer you the opportunity to mark specific rows for yourself for later focus. Stars and flags persist through closing and opening your project, and thus can provide a different function than using a permalink to persist your facets. Stars and flags can be used in any way you want, although they are designed to help you flag errors and star rows of particular importance.
You may wish to create a custom subset of your data through a series of separate faceting activities (rather than successively narrowing down with multiple facets applied). For example, you may wish to:
Facets are the most basic abstraction within a schema. They represent a set of attributes that can be associated with an object in the directory and are similar in concept to LDAP object classes. Each directory object may have up to a certain number of facets associated with it. For more information, see Amazon Cloud Directory Limits.
Each facet maintains its own independent set of attributes. Each facet consists of fundamental metadata, such as the facet name, version information, and behaviors. The combination of schema ARNs, facets, and attributes define uniqueness on the object.
Once you have added the necessary facets to your schema, you can apply the schema to your directory and create the applicable objects. For example, you can define a device schema by adding facets such as computers, phones, and tablets. You can then use these facets to create computer objects, phone objects, and tablet objects in the directory to which the schema applies.
Facets provide context to the OpenLineage events. Generally, an OpenLineage event contains the type of the event, who created it, and when the event happened. In addition to the basic information related to the event, it provides facets for more details in four general categories:
I downloaded a model from thingiverse and loaded it into meshmixer to scale and cut it up as it was a bit big to print by itself. After that, I exported it from meshmixer and imported it into prusa slicer. No matter what I export it as (.stl, .3mf, .amf etc) the model in prusaslicer appears faceted instead of the smooth mode model that appears in meshmixer. I realise the mode it made of triangular facets if it an .stl or similar format, however is there a way to get rid of it? Downloading the benchy .stl, it has no noticable faceting when imported to prusaslicer so there is a method out there that works. What am I doing wrong in terms of exporting? It would be nice to not have these ugly faceted models!
Thank you for your reply. I had a look at the video, however they seem to have the opposite issue to me. They have too many facets or triangles and they are reducing them whereas I have too few. I'm going to assume I am dealing with a model that just doesn't have enough 'resolution' and can't be improved (at least without reduction in detail).
Note: You do not need facets to support log processing, livetail search, log explorer search, metric generation from logs, archive forwarding, or rehydration. You also do not need facets for routing logs through to Pipelines and Indexes with filters, or excluding or sampling logs from indexes with exclusion filters.
Note: Although it is not required to create facets to filter on attribute values, defining them on attributes that you often use during investigations can help reduce your time to resolution.
Qualitative facets can have a string or numerical (integer) type. While assigning string type to a dimension works in all case, using integer types on a dimension enables range filtering on top of all aforementioned capabilities. For instance, http.status_code:[200 TO 299] is a valid query to use on a integer-type dimension. See search syntax for reference.
Hidden facets are also hidden from auto-complete in the search bar, and dropdowns (such as measure, group-by) in analytics for the Log Explorer. However, hidden facets are still valid for search queries (in case you copy-paste a log-explorer link for instance).
Use the search box on facets to scope down the whole facet list and navigate more quickly to the one you need to interact with. Facet search uses both facet display name and facet field name to scope results.
As a matter of good practice, always consider using an existing facet rather than creating a new one (see the alias facets section). Using a unique facet for information of a similar nature fosters cross-team collaboration.
Note: Once a facet is created, its content is populated for all new logs. For an optimal usage of the Log Management solution, Datadog recommends using at most 1000 facets.
Faceting requires ordinal or categorical data because there are a discrete number of facets; the associated fx and fy scales are band scales. Quantitative or temporal data can be made ordinal by binning, say using Math.floor. Or, use the interval scale option on the fx or fy scale. Below, we produce a box plot of the weights (in kilograms) of Olympic athletes, faceted by height binned at a 10cm (0.1 meter) interval.
You can mix-and-match faceted and non-faceted marks within the same plot. The non-faceted marks will be repeated across all facets. This is useful for decoration marks, such as a frame, and also for context: below, the entire population of penguins is repeated in each facet as small gray dots, making it easier to see how each facet compares to the whole.
With top-level faceting, any mark that uses the specified facet data will be faceted by default, whereas marks that use different data will be repeated across all facets. Use the mark facet option to change the behavior.
Facets let you create categories on a select group of attributes so that users can refine their search. For example, on an index of books, helpful facets might be author and genre. Algolia also calculates results for each facet. It allows you to display facets and facet counts so that users can filter results. e24fc04721
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