This directory contains the code for a CMSIS SVD parser in Python. The parser is able to read in an input SVD and provide Python objects containing the information from the SVD. This frees the developer (you) from having to worry about the SVD XML and each vendor's little quirks.
You can install the latest stable version from pypi:
pip install -U cmsis-svd
To install the latest development version by doing. If this fails, you may need to update the version of pip you are using.
pip install -U -e 'git+https://github.com/posborne/cmsis-svd.git#egg=cmsis-svd&subdirectory=python'
There's a lot of information you can glean from the SVDs for various platforms. Let's say, for instance, that I wanted to see the names and base address of each peripheral on the Freescale K20 (D7). Since the K20 SVD is packaged with the library, I can do the following:
from cmsis_svd.parser import SVDParser
parser = SVDParser.for_packaged_svd('Freescale', 'MK20D7.svd')
for peripheral in parser.get_device().peripherals:
print("%s @ 0x%08x" % (peripheral.name, peripheral.base_address))
This generates the following output:
FTFL_FlashConfig @ 0x00000400
AIPS0 @ 0x40000000
AIPS1 @ 0x40080000
AXBS @ 0x40004000
DMA @ 0x40008000
FB @ 0x4000c000
FMC @ 0x4001f000
FTFL @ 0x40020000
DMAMUX @ 0x40021000
CAN0 @ 0x40024000
SPI0 @ 0x4002c000
SPI1 @ 0x4002d000
...
The data structures representing the SVD data have the ability to convert themselves to a dictionary suitable for serialization as JSON. This works recursively. To generate JSON data and pretty print it you can do something like the following:
from cmsis_svd.parser import SVDParser
parser = SVDParser.for_packaged_svd('Freescale', 'MK20D7.svd')
svd_dict = parser.get_device().to_dict()
print(json.dumps(svd_dict, sort_keys=True,
indent=4, separators=(',', ': ')))
Once you have the code checked out, you can run the following from this directory to install dependencies:
virtualenv env
source env/bin/activate
pip install -r dev-requirements.txt
Then, to run the tests:
nose2 .
By default, tests will run in parallel according to the number of processors available on the system.
Please open issues and submit pull requests on Github.