PSRDADA block updates

[telegraphic]

These updates are from https://github.com/Molonglo/bifrost, which is used for UTMOST-2D.

An alternative to this merge is to move PSRDADA functionality into a plugin.

[codecov-commenter]

Codecov Report

Patch coverage: 9.96% and project coverage change: -2.76 :warning:

Comparison is base (e9c34da) 67.04% compared to head (78de6d4) 64.28%.

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Additional details and impacted files

```diff @@ Coverage Diff @@ ## master #194 +/- ## ========================================== - Coverage 67.04% 64.28% -2.76% ========================================== Files 69 68 -1 Lines 7482 6096 -1386 ========================================== - Hits 5016 3919 -1097 + Misses 2466 2177 -289 ``` | [Impacted Files](https://app.codecov.io/gh/ledatelescope/bifrost/pull/194?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=LEDA+Collaboration) | Coverage Δ | | |---|---|---| | [python/bifrost/blocks/dada\_file.py](https://app.codecov.io/gh/ledatelescope/bifrost/pull/194?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=LEDA+Collaboration#diff-cHl0aG9uL2JpZnJvc3QvYmxvY2tzL2RhZGFfZmlsZS5weQ==) | `0.00% <0.00%> (ø)` | | | [python/bifrost/psrdada.py](https://app.codecov.io/gh/ledatelescope/bifrost/pull/194?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=LEDA+Collaboration#diff-cHl0aG9uL2JpZnJvc3QvcHNyZGFkYS5weQ==) | `32.51% <16.00%> (+30.88%)` | :arrow_up: | | [python/bifrost/blocks/psrdada.py](https://app.codecov.io/gh/ledatelescope/bifrost/pull/194?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=LEDA+Collaboration#diff-cHl0aG9uL2JpZnJvc3QvYmxvY2tzL3BzcmRhZGEucHk=) | `19.90% <17.29%> (+15.55%)` | :arrow_up: | ... and [5 files with indirect coverage changes](https://app.codecov.io/gh/ledatelescope/bifrost/pull/194/indirect-changes?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=LEDA+Collaboration)

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[ledatelescope]

Tensor core arithmetic also accelerates 16 bit type data.

On 2/14/23 9:49 AM, jaycedowell wrote:

In python/bifrost/blocks/psrdada.py https://github.com/ledatelescope/bifrost/pull/194#discussion_r1105904988:

• Returns:
• s (str): DADA header string with padding to hdrlen
• """
• s = "HDR_VERSION 1.0\n"
• s+= "HDR_SIZE %i\n" % hdrlen
• keys_to_skip = ('HDR_VERSION', 'HDR_SIZE')

• update parameters from bifrost tensor

• if '_tensor' in hdr_dict.keys():
• print(hdr_dict['_tensor'])
• dtype = hdr_dict['_tensor']['dtype']
• dtype_vals = {
• 'cf32': { 'NBIT': '32', 'NDIM': '2' },
• 'f32': { 'NBIT': '32', 'NDIM': '1' },
• 'ci8': { 'NBIT': '8', 'NDIM': '2' },
• 'i8': { 'NBIT': '8', 'NDIM': '1' } }

Are additional data types expected/allowed?

---

[ledatelescope]

I raised the notion of 16-bit  to accommodate directly time-series output from many-bit ADCs.  i16 would do that.  Quick examination suggests that uint16 is recognized by CUDA7(?) but using it may not result in speed advantage over 4-byte integer math.  Proper research required.

On 2/15/23 1:58 AM, Danny Price wrote:

@.**** commented on this pull request.

---

In python/bifrost/blocks/psrdada.py https://github.com/ledatelescope/bifrost/pull/194#discussion_r1106722410:

• Returns:
• s (str): DADA header string with padding to hdrlen
• """
• s = "HDR_VERSION 1.0\n"
• s+= "HDR_SIZE %i\n" % hdrlen
• keys_to_skip = ('HDR_VERSION', 'HDR_SIZE')

• update parameters from bifrost tensor

• if '_tensor' in hdr_dict.keys():
• print(hdr_dict['_tensor'])
• dtype = hdr_dict['_tensor']['dtype']
• dtype_vals = {
• 'cf32': { 'NBIT': '32', 'NDIM': '2' },
• 'f32': { 'NBIT': '32', 'NDIM': '1' },
• 'ci8': { 'NBIT': '8', 'NDIM': '2' },
• 'i8': { 'NBIT': '8', 'NDIM': '1' } }

There isn't really a strict definition AFAIK. There is no header keyword for float/integer, but I think it's reasonable to add cf64/f64. Would need to decide between i16/ci16 and f16/cf16 though

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