5c14780403
This converts all the YAML files to JSON files, as json parsing is built into python instead of being a separate library requiring installation. YAML is a superset of JSON, but putting comments in is not quite as obvious as it is in yaml. The following glue was used to convert yaml to json: python -c 'import sys, yaml, json; json.dump(yaml.load(sys.stdin), sys.stdout, indent=4)' < $1 > $2 Clearly I haven't tested this on every single platform, and this doesn't address the large blobs of yaml in the lpc4300 scripts directory, only the cortex NVIC generation process. I've tested a few IRQ driven example apps, and I've checked the generated output of some known cases like the LM3s that has explicit gaps, and they are all generated correctly.
36 lines
920 B
JSON
36 lines
920 B
JSON
{
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"irqs": {
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"0": "rtc",
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"1": "m4core",
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"2": "dma",
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"4": "flasheepromat",
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"5": "ethernet",
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"6": "sdio",
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"7": "lcd",
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"8": "usb0",
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"9": "usb1",
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"10": "sct",
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"11": "ritimer_or_wwdt",
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"12": "timer0",
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"13": "gint1",
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"14": "pin_int4",
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"15": "timer3",
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"16": "mcpwm",
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"17": "adc0",
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"18": "i2c0_or_irc1",
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"19": "sgpio",
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"20": "spi_or_dac",
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"21": "adc1",
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"22": "ssp0_or_ssp1",
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"23": "eventrouter",
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"24": "usart0",
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"25": "uart1",
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"26": "usart2_or_c_can1",
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"27": "usart3",
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"28": "i2s0_or_i2s1",
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"29": "c_can0"
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},
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"partname_humanreadable": "LPC 43xx series M0 core",
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"partname_doxygen": "LPC43xx (M0)",
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"includeguard": "LIBOPENCM3_LPC43xx_M0_NVIC_H"
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} |