dump1090-fa/starch/Makefile

all: mypy

mypy:
	MYPYPATH=$(CURDIR)/stubs mypy --show-error-codes starch.py
Move all converters to starch-based implementations (#97) * Switch all conversion routines to use starch. main user-visible changes: * ensure you check out submodules ('git clone --recurse-submodules") * --version shows the CPU features and DSP implementations in use * --wisdom allows overriding of the built-in architecture wisdom * --dcfilter no longer supported * "starch-benchmark" binary will benchmark all options on the current machine and can produce a wisdom file to feed to the --wisdom option If you have a usecase for --dcfilter, please get in touch and let me know - it's an edge case and for now there's no starch/DSP support for it, but support can be written if needed. In almost all cases the new conversion routines are slightly or substantially faster than the old conversion routines. The only case that is slower is SC16/SC16Q11 on a Pi 0, which is around 10% slower due to changing from heavily approximated lookup tables to higher quality results (but SC16 is probably already out of reach of a Pi 0) * No need to build with SC16Q11_TABLE_BITS any more * Add oneoff/uc8_capture_stats (reads a UC8 capture; measures min/max/mean I and Q) * Switch UC8 conversion to 127.4 center, 128 range. Looking at actual UC8 captures from a RTL2832, the mean I and Q are actually at 127.4, so use that as the zero point. This means that the resulting I/Q maximum values could be as large as 127.6. Switch to 128 for simplicity. * Switch to the new UC8 zero offset in benchmarks, fix some bugs * Fix some bugs in SC16/SC16Q11 validation, tighten the max error requirements * Ditch UC8 approximation path, add a NEON VRQSQRTE path. * Tweak the SC16 exact path, add a new impl that uses a mix of u32 & floats. * SC16Q11 impl tweaks: * add a u32->float exact path * ditch the approximation path * add a NEON VRSQRTE path * add a 12-bit table path (using the full signed I/Q value, not absolute value) * Ditch SC16 approximation path, add NEON vrsqrte path * Add oneoff/dsp_error_measurement This runs sample input through the DSP functions that are allowed to be inexact and dumps the results as a TSV suitable for feeding to gnuplot to look at the actual errors. * Update make clean, make wisdom targets * Update wisdom based on benchmarking * Preserve the raw wisdom benchmark data * Update to latest starch * Update .gitignore for new wisdom files * Update starch generated code * Build starch-benchmark as part of the 'all' target * Use wisdom from /etc/dump1090-fa/wisdom.local if present * Package starch-benchmark and a helper script to generate local wisdom data * Remove submodules in preparation for importing them directly * Import cpu_features v0.6.0 from https://github.com/google/cpu_features/releases/tag/v0.6.0 * Import starch at commit a725c8491dc33a321565d451b385131e589d8490 from https://github.com/flightaware/starch 2021-01-21 11:45:00 +00:00			`all: mypy`

			`mypy:`
			`MYPYPATH=$(CURDIR)/stubs mypy --show-error-codes starch.py`