hass | home assistant and appdaemon scripts and configuration file

I have a few lists

I'm struggling with the Linux tool "iptables". Following situation: I have a RaspberryPi running with HASS (Home Assistant) connected via ethernet. Now I want to add my inverter to home assistant, but this needs to be done by using his own WiFi network. So I need to forward requests to IP 11.11.11.1 (only this IP, not all trafic) to the wifi network.

Is that doable with iptables? Meaning defining a rule which says "target is 11.11.11.1, so lets put this to the wifi network".

I'm actually not sure whether iptables can do that or not. I read soming about nginx, but not sure how this would work.

Problem (briefly)

Documented functions are not listed on the related group page, but listed on the file's page. One of the functions listed on the file's page does not have documantation despite it is doumented in the source file.

Details

I'm using Doxygen v1.9.2 to document an embedded application written in C. I also use git as versioning tool. I had documented the code for example in v2.0.0 and everything was ok. Recently I made some improvements and added 2 new files and new functionalities in the application. I documented the newly added files as per instruction of doxygen and some stackoverflow topics, but I did not figure out why the new file is partially documented. Since the project is a little large relatively, I will try to illustrate the documentation structure and add only issue related parts to avoid crowd and focus to the issue.

Necessarily sharing he Doxyfile config; comments, blank lines and some directory paths are omitted for brevity:

I want to use a typed list in dart that can be one of the following types

Let X be a set of distinct 64-bit unsigned integers std::uint64_t, each one being interpreted as a bitset representing a subset of {1,2,...,64}.

I want a function to do the following: given a std::uint64_t A, not necessarily in X, list all B in X, such that B is a subset of A, when A, B are interpreted as subsets of {1,2,...,64}.

(Of course, in C++ this condition is just (A & B) == B).

Since A itself need not be in X, I believe that this is not a duplicate of other questions.

X will grow over time (but nothing will be deleted), although there will be far more queries than additions to X.

I am free to choose the data structure representing the elements of X.

Obviously, we could represent X as a std::set or sorted std::vector of std::uint64_t, and I give one algorithm below. But can we do better?

What are good data structures for X and algorithms to do this efficiently? This should be a standard problem but I couldn't find anything.

EDIT: sorry if this is too vague. Obviously, if X were a std::set we could search through all subsets of A, taking time O(2^m log |X|) with m <= N, or all elements of X in time O(|X| log |X|).

Assume that in most cases, the number of B is quite a bit smaller than both 2^m (the number of subsets of A) and |X|. So, we want some kind of algorithm to run in time much less than |X| or 2^m in such cases, ideally in time O(number of B) but that's surely too optimistic. Obviously, O(|X|) cannot be beaten in the worst case.

Obviously some memory overhead for X is expected, and memory is less of a bottleneck than time for me. Using memory roughly 10 * (the memory of X stored as a std::set) is fine. Much more than this is too much. (Asymptotically, anything more than O(|X|) or O(|X| log |X|) memory is probably too much).

Obviously, the use of C++ is not essential: the algorithms/data structures are the important things here.

In the case that X is fixed, maybe Hasse diagrams could work.

It looks like Hasse diagrams would be quite time-consuming to construct each time X grows. (But still maybe worth a try if nothing else comes up). EDIT: maybe not so slow to update, so better than I thought.

The below is just my idea so far; maybe something better can be found?

FINAL edit: since it's closed, probably fairly - the "duplicate" question is pretty close - I won't bother with any further edits. I will probably do the below, but using a probabilistic skip list structure instead of a std::set, and augmented with skip distances (so you can quickly calculate how many X elements remain in an interval, and thus reduce the number of search intervals, by switching to linear search when the intersection gets small). This is similar to Order Statistic Trees given in this question, but skip lists are a lot easier to reimplement than std::set (especially as I don't need deletions).

Represent X as a std::set or sorted std::vector of 64-bit unsigned integers std::uint64_t, using the ordinary numerical order, and do recursive searches within smaller and smaller intervals.

E.g., my query element is A = 10011010. Subsets of A containing the first bit lie in the inclusive interval [10000000, 10011010].

Subsets of A containing the second bit but not the first lie in the interval [00010000, 00011010].

Those with the third but not the second bit are in [00001000, 00001010].

Those with the fourth but not the third bit are in [00000010, 00000010].

Now, within the first interval [10000000, 10011010] you could make two subintervals to search, based on the second bit: [10000000, 10001010] and [10010000, 10011010].

Thus you can break it down recursively in this manner. The total length of search intervals is getting smaller all the time, so this is surely going to be better asymptotically than a trivial linear search through all of X.

E.g., if X = {00000010, 00001000, 00110111, 10011100} then only the first, third, fourth depth-1 intervals would have nonempty intersection with X. The final returned result would be [00000010, 00001000].

Of course this is unbalanced if the X elements are distributed fairly uniformly. We might want the search intervals to have roughly equal width at each depth, and they don't; above, the sizes of the four depth-1 search intervals are, I think, 27, 11, 3, 1, and for larger N the discrepancies could be much bigger.

If there are k bits in the query set A, then you'll have to construct k initial search intervals at depth 1 (searching on ONE bit), then up to 2k search intervals at depth 2, 4k at depth 3, etc.

If I've got it right, since log |X| = O(N) the number of search intervals is O(k + 2k + 4k + ... + 2^n . k) = O(k^2) = O(N^2), where 2^n = O(k), and each one takes O(N) time to construct (actually a bit less since it's the log of a smaller number, but the log doesn't increase much), so it seems like this is an O(N^3) algorithm to construct the search intervals.

Of course the full algorithm is not O(N^3), because each interval may contain many elements, so listing them all cannot be better than O(2^N) in general, but let's ignore this and assume that there are not enough elements of X to overwhelm the O(N^3) estimate.

Another issue is that std::map cannot tell you how many elements lie within an interval (unlike a sorted std::vector) so you don't know when to break off the partitioning and search through all remaining X elements in the interval. Of course, you have an upper bound on the number of X elements (the size of the full interval) but it may be quite poor.

EDIT: the answer to another question shows how to have a std::set-like structure which also quickly gives you the number of elements in a range, which obviously could be adapted to std::map-like structures. This would work well here for pruning (although annoying that, for C++, you'd have to reimplement most of std::map!)

I have a VueJs component, the data was originally on the page which worked well. It populated 3 drop-down menus. I want to move the data to a separate file hosted locally but I cant seem to get it to populate the drop-down menus. At the moment the first drop-down menu only displays the text 'data' which is the text from the top of the JS file. How do I get it to pull in all the data please? Any help is appreciated!

One of the select menus:

I need some help in getting the table final_price values added to each other where the customer_name/customer_id is the same so the row count becomes 3 so example: the customer named Hass H final_price would be:

417 + 21 = 438 on one row

http://sqlfiddle.com/#!17/29cb5/13

I am trying to make a query to see the total price a customer has to pay when their order is completed, but I am having issues with this error statement. How do I get the correct query?

ERROR: argument of HAVING must be type boolean, not type money

Tables involved and inserting some fake quick data:

Some of the customers:

So I got in WPF an ListBox where I got an Button in my ListBoxItem through an DataTemplate. Im adding that Button Like this to my Item: