equator | 一个用于比较两个对象的所有属性是否全部相等，并且可以获取所有不相等的属性的比对器

The third augment of glDrawArrays should be number of vertices (not #vertex-components). Calculate the parameter appropriately.

Here is an example with 3 subplots. Each subplot first gets a kdeplot for one region, and then a kdeplot for the other with different colors. Each individual kdeplot uses hue to differentiate between the ages. Also, these age groups are stacked instead of being drawn on top of each other. Further, one age group gets a hatching.

Some manipulations are needed to get a proper legend. set_hach is called both on the legend handles as on the polygon forming the kdeplot. The stacking first creates the last age group, so the hatching needs to reverse the list.

Many variations are possible, also depending on what you want to show and how much the graphs overlap. For example, a new column could be created combining two columns for the hue, and they all could be stacked instead of drawn on top of each other.

Also note that kdeplot has a parameter common_norm that default to True and reduces the graph for each hue-group depending on the group's size.

So I found the problem (posting incase anyone else gets the same issue).

Screen: container: container BoxLayout: id: container orientation: 'vertical' MDToolbar: title: "TRYM" anchor_title: "center"

Use scale_fill_manual() instead of scale_colour_manual().

https://ggplot2.tidyverse.org/reference/scale_manual.html

It's difficult to show this result in 3D, but I'll try my best.

Suppose we have a collection of points in the shape of a cross suspended over the camera, paralell to the ground:

You have a few minor errors in your code that needs to be fixed.

1. Your input statement has to be converted into an int before you use it for computation nauticalmile = onekilo*Kilometers

2. Your division statement has a comma. Python will consider comma as another variable. So remove the comma onekilo = degreesPerMin/10,000 in this statement

3. Your print statement needs to be updated. Don't use input statement in your print statement when you want to just display information. Also, separate out strings and variables with proper quotes. print =input("Kilometers,is,nauticalmile,Nautical miles") needs to be edited to print (Kilometers,"is",nauticalmile,"Nautical miles")

When these changes are made, your program works perfectly. See below updated code.

The solution is rather simple, I was completely misinterpreting how the Orthographic projection works. Projecting and setting the extent both in Orthographic projection will work fine. Here is the solution -

You don't need an eight-byte floating point format for the number of significant figures you expect, and for the limited range of possible values. I would start by converting the data to a sequence of integers of appropriate length that can represent the accuracy and range of your values. It looks like two four-byte integers should suffice. There's a factor-of-two compression right there.

Then replace each point with the difference of that point and the previous one, except for the first point. Now the integers should be smaller, allowing for any general purpose lossless compressor to reduce that further. If the differences are only around 0.1°, you could get another factor of two.

That is a simple example, where the prediction for this point is the last point. If your sequence of points represent a path, you could do something more sophisticated where you model a velocity. In that case you propagate the last point using the last modeled velocity, and subtract that from the current point.

Amendment

I found that the WGS84 reference frame itself is accurate to only 2-5 meters. With three-byte integers, you can get 2.4 meters resolution at the equator. That provides a 62.5% reduction, before differencing and compression. It also gives you a free bit in the latitude, since it has half the range of a longitude. That bit could be used to mark whether this is an absolute coordinate or predicted from the last coordinate.

A query on geohashes returns points within a certain range of geohashes, which are (somewhat) rectangular regions.

A geoquery on a central point and a distance returns points that are in a circle.

Since the two shapes are different, your code uses a client-side check to cut off the points that are outside the circle, but inside the rectangles. This is a normal step when performing geoqueries for a max distance around a point when using geohashes.

Here's an example of this on a map:

The green pins are in a 250km circle around San Francisco, which is what I queried for. The red pins are outside of that circle, but within the set of geohash ranges ([["9q0","9qh"],["9nh","9n~"],["9r0","9rh"],["9ph","9p~"]] here) that needed to be queried to be sure we had all points in range.

As said: this so-called oversampling is inherent to using geohashes to perform point-and-distance queries. In my experience you'll end up reading between 2x and 8x too many documents.

It might be possible to reduce the number of false positives by querying for more-but-smaller ranges, but I don't know of any geo-libraries for Firestore that do that.

I mapped the extra cost as a mental model: finding documents within a certain distance from a point costs me 2x to 8x more than a regular document read.

Moving the operation to Cloud Functions on the server makes no difference on the number of documents that needs to be read, it just changes where they are read. So you can perform the operation on a server to reduce the bandwidth of transferring the documents from the database to the client. But it won't make a difference in the number of documents that need to be read.

As discussed in comments: performing the query on the server does allow you to remove client-side access from the data, so that you can ensure the application code will never see documents that are not within the requested range. So if you're worried about document access, performing the query in a trusted environment (such as a server you control or Cloud Functions) is a good option.

To not pay for the extra document reads, consider finding a service that natively supports geoqueries with a pricing model based on the number of results it returns - instead of the number of results it has to consider. Such a service will (very likely) still consider too many points, but if the pricing model matches what you want that might be worth it.