Slice ModelArrays

[timspainNERSC]

Both advection (#99) and the halo regions needed for MPI (#132) require moving around subsets of ModelArray data. A unified slicing class/library to provide a common interface would be a useful addition to the model.

• [x] Create a Slice class
• [x] Create a class to iterate through a Slice
• [x] Implement basic indexing
• [x] Implement 'rest of the array' arguments (what would be written as [::2] in Python).
• [x] Implement reverse steps
• [x] Throw an exception when passed a step value of zero
• [x] Implement negative indices
• [ ] Implement iterator equality
• [ ] Implement the standard start and end iterators
• [ ] Implement the standard rstart and rend iterators
• [x] Create a class that gives access to a slice of a ModelArray
• [x] Assign a scalar to a slice of a ModelArray.
• [x] Assign one slice of a ModelArray to another.
• [x] Assign a slice of one ModelArray to the entirety of another
• [x] Assign the entirety of one ModelArray to a slice of another
• [x] Assign a buffer (standard container?) to a slice
• [x] Assign a slice to a buffer (standard container?)
• [ ] Doxygen comments

[timspainNERSC]

Currently in progress on the branch slicer.

[TomMelt]

maybe 106445222 assertions is a bit overkill.

Could we reduce the size/dimensionality of this test case? https://github.com/nextsimhub/nextsimdg/blob/5216f7b8343a37de1e4924709820f63e0cff4d18/core/test/Slice_test.cpp#L137-L149

[TomMelt]

Thanks for sharing this branch @timspainNERSC. This will be super helpful.

I had a quick look and maybe I missed it. Is it possible to get the elements from a 1D slice back as a array/vector? possibly a std::array would be most sensible for my use case.

[timspainNERSC]

maybe 106445222 assertions is a bit overkill.

Could we reduce the size/dimensionality of this test case?

https://github.com/nextsimhub/nextsimdg/blob/5216f7b8343a37de1e4924709820f63e0cff4d18/core/test/Slice_test.cpp#L137-L149

😒 I do want a test that examines a high dimensionality array, but I can arrange the test so that there are fewer assertions. I GUESS.

[timspainNERSC]

Thanks for sharing this branch @timspainNERSC. This will be super helpful.

I had a quick look and maybe I missed it. Is it possible to get the elements from a 1D slice back as a array/vector? possibly a std::array would be most sensible for my use case.

Next on the TODO list. I should write that i the issue 🤔

[timspainNERSC]

maybe 106445222 assertions is a bit overkill. Could we reduce the size/dimensionality of this test case? https://github.com/nextsimhub/nextsimdg/blob/5216f7b8343a37de1e4924709820f63e0cff4d18/core/test/Slice_test.cpp#L137-L149

😒 I do want a test that examines a high dimensionality array, but I can arrange the test so that there are fewer assertions. I GUESS.

${\textsf{\color{lightblue}[doctest]}}$ doctest version is "2.4.11" $${\textsf{\color{lightblue}[doctest]}}$$ run with "--help" for options $${\textsf{\color{dandelion}===============================================================================}}$$ $${\textsf{\color{lightblue}[doctest]}}$$ test cases: 4 | $${\textsf{\color{green} 4 passed}}$$ | $${\textsf{\color{black}0 failed}}$$ | $${\textsf{\color{black}0 skipped}}$$ $${\textsf{\color{lightblue}[doctest] }}$$ assertions: 423 | $${\textsf{\color{green}423 passed}}$$ | $${\textsf{\color{black}0 failed}}$$ | $${\textsf{\color{lightblue}[doctest]}}$$ Status: $${\textsf{\color{green}SUCCESS!}}$$

but also

             if (!message.empty()) goto end8d;

😎

[timspainNERSC]

How numpy behaves with a range of positive, 0, negative and absent indices. The test array a has 12 elements, where the value at each index is equal to the index.

Step = 1 (or absent)

stop value	start < 0	start = 0	start > 0	(absent)
Large -ve	a[-6:-16]=[]	a[0:-16]=[]	a[6:-16]=[]	a[:-16]=[]
-length	a[-6:-12]=[]	a[0:-12]=[]	a[6:-12]=[]	a[:-12]=[]
-ve > start	a[-6:-8]=[]	a[0:-8]=[0,..,3]	a[6:-8]=[]	a[:-8]=[0,..,3]
-ve < start	a[-6:-4]=[6,7]	N/A	a[6:-4]=[6,7]	N/A
0	a[-6:0]=[]	a[0:0]=[]	a[6:0]=[]	a[:0]=[]
+ve < start	a[-6:4]=[]	N/A	a[6:4]=[]	N/A
+ve > start	a[-6:8]=[6,7]	a[0:8]=[0,..,7]	a[6:8]=[6,7]	a[:8]=[0,..7]
length	a[-6:12]=[6,..,11]	a[0:12]=[0,..,11]	a[6:12]=[6,..,11]	a[:12]=[0,..,11]
large +ve	a[-6:16]=[6,..,11]	a[0:16]=[0,..,11]	a[6:16]=[6,..,11]	a[:16]=[0,..,11]
absent	a[-6:]=[6,..,11]	a[0:]=[0,..,11]	a[6:]=[6,..,11]	a[:]=[0,..,11]

step = 0

Causes an exception to be thrown.

Step = -1

stop value	start < 0	start = 0	start > 0	(absent)
Large -ve	a[-6:-16:-1]=[6,..,0]	a[0:-16:-1]=[0]	a[6:-16:-1]=[6,..,0]	a[:-16:-1]=[11,..,0]
-length	a[-6:-12:-1]=[6,..,1]	a[0:-12:-1]=[]	a[6:-12:-1]=[6,..,1]	a[:-12:-1]=[11,..,1]
-ve > start	a[-6:-8:-1]=[6,5]	a[0:-8:-1]=[]	a[6:-8:-1]=[6,5]	a[:-8:-1]=[11,..,5]
-ve < start	a[-6:-4:-1]=[]	N/A	a[6:-4:-1]=[]	N/A
0	a[-6:0:-1]=[6,..,1]	a[0:0:-1]=[]	a[6:0:-1]=[6,..,1]	a[:0:-1]=[11,..,1]
+ve < start	a[-6:4:-1]=[6,5]	N/A	a[6:4:-1]=[6,5]	N/A
+ve > start	a[-6:8-1]=[]	a[0:8:-1]=[]	a[6:8:-1]=[6,7]	a[:8:-1]=[11,..,9]
length	a[-6:12:-1]=[]	a[0:12:-1]=[]	a[6:12:-1]=[]	a[:12:-1]=[]
large +ve	a[-6:16:-1]=[]	a[0:16:-1]=[]	a[6:16:-1]=[]	a[:16:-1]=[]
absent	a[-6::-1]=[6,..,0]	a[0::-1]=[0]	a[6::-1]=[6,..,0]	a[::-1]=[11,..,0]

|step| > 1

Larger step sizes behave as expected, stepping the defined number of values from the same start index.