streak | Streak tracking with Google Sheets | GCP library

It does appear as if Chrome has trouble applying a clipping path to images. It seems to be a little worse when the image is moving - as in your case.

However there is an easy fix. Use a instead.

Or I expect switching the gleam from an image to an SVG object with a linearGradient would also work just fine.

Example using a mask instead of a clip path

(Note that I've trimmed the SVG down to just show the important bits)

One option could be:

iRon has provided the crucial pointer: Because $queryResult.leaderboard is an array, which itself has a .Rank property, you must explicitly access the element(s) of interest to get their .rank property value (note that property access is case-insensitive in PowerShell, as PowerShell generally is):

I have solved it.

1.add com.google.android.gms:play-services-ads in android/app/build.gradle dependencies { ...

My approach would be to have a map data structure with one entry for each author that records the number of times it is seen in the window. If the map is implemented using a Hash Table with one list per author then the average lookup time will be O(1) complexity (with only 4000 authors the memory overhead for this isn't too problematic for modern computers). Most modern programming languages, like C++, Java or Python, will have a library implementation of a Hash-based map, so you shouldn't need to code it from scratch.

Create variables, "first" and "last", which give the indices of first and last entries in the current run of unique authors. Both are initialised to the first element of the sequence. Increment the author count in the map for the author referenced by "first".

At each step increment "last" and increment the author count for the the indexed author. If it has been incremented to one, then it must be a new unique entry in the run. If it isn't then we have a duplicate in the run somewhere, so start decrementing the author count corresponding to "first" and then increment "first". If the author count is decremented to zero, that isn't the author with a duplicate in the current run, so we have to continue. If it is decremented to one, you have just found the previous entry that has been duplicated, so the rest of the run should now be unique (as looking at the next element of the sequence can only introduce one duplicate).

At each stage, the difference between "last" and "first" gives you the length of the run of unique entries in the sequence that ends at "last".

The computational complexity of this algorithm is linear in the number of elements of the sequence, and constant in the number of authors (because hash-table look-up is O(1)). I think it will be difficult to do much better than linear time!

I hope I have understood the question correctly, but that would be my approach.

Use map in parse_text function so that you get lists separately.

Most sort algorithms have a complexity O(n log n) and that means they need to perform that many comparisons in order to sort the data. So, no, you aren't going to be able to use Sort for what you are doing.

Secondly, some build in Sort methods switch behavior depending on the size of the list so your user interface may feel very different depending on which algorithm they pick. I've never seen someone use Sort to determine UI-behavior before, novel, but unusual.

If you do want to use a sort algorithm maybe go for insertion sort (compare each new item against existing list using binary search to find where it goes) or quicksort (partition the elements into two sets by comparing one against all others).

But ... I don't think either of these is going to be a great user experience, both will feel repetitive. And, given that this is a subjective question, the answer often isn't a purely linear ordering of items. People aren't consistent and they will create cycles A->B->C->A when they do this.

So here's a suggestion for a UI experience that feels less repetitive, can handle subjective anomalies and is easy to implement:

Pick pairs of images maybe at random and ask the user to rank one over the other. Let the user be inconsistent if they wish. Take each pair A->B that they create and put them in a Graph. Find any nodes in the graph that aren't connected yet, or which only have a single connection, and focus asking how they rank against nodes you've already scored.

That way if they've ranked A->B->C and then rank C->D the algorithm isn't going to keep asking how A and B compare to D.

And finally apply a technique called Topological Sort and ignore any cycles you find. An approximate topological sort if you like.

There's a Graph library (that I wrote) which includes this capability. See this test for an example of calling .TopologicalSortApprox().

Once all the items are in the graph you can keep going, using comparisons that try to flatten the graph closer to a straight line. But at any time if the user gets bored and wants to stop (nobody wants to do n log n comparisons!) you at least have an approximate rank you can use.

By getting the row's index inside of the df.apply() call using row.name, you can generate the 'lb_price' data relative to which row you are currently on.

You can groupby the value column and use .size() to get the size/length of each group.