mirror of
https://github.com/titanscouting/tra-superscript.git
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132 lines
3.6 KiB
Python
132 lines
3.6 KiB
Python
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# contains deprecated functions, not to be used unless nessasary!
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import json
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sample_json = """
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{
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"persistent":{
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"key":{
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"database":"",
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"tba":"",
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"tra":{
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"CLIENT_ID":"",
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"CLIENT_SECRET":"",
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"url": ""
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}
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},
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"config-preference":"local",
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"synchronize-config":false
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},
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"variable":{
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"max-threads":0.5,
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"team":"",
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"event-delay":false,
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"loop-delay":0,
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"reportable":true,
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"teams":[
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],
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"modules":{
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"match":{
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"tests":{
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"balls-blocked":[
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"basic_stats",
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"historical_analysis",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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],
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"balls-collected":[
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"basic_stats",
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"historical_analysis",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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],
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"balls-lower-teleop":[
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"basic_stats",
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"historical_analysis",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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],
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"balls-lower-auto":[
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"basic_stats",
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"historical_analysis",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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],
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"balls-started":[
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"basic_stats",
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"historical_analyss",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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],
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"balls-upper-teleop":[
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"basic_stats",
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"historical_analysis",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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],
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"balls-upper-auto":[
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"basic_stats",
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"historical_analysis",
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"regression_linear",
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"regression_logarithmic",
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"regression_exponential",
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"regression_polynomial",
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"regression_sigmoidal"
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]
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}
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},
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"metric":{
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"tests":{
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"gl2":{
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"score":1500,
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"rd":250,
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"vol":0.06
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},
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}
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},
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"pit":{
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"tests":{
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"wheel-mechanism":true,
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"low-balls":true,
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"high-balls":true,
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"wheel-success":true,
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"strategic-focus":true,
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"climb-mechanism":true,
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"attitude":true
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}
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}
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}
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}
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}
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"""
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def load_config(path, config_vector):
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try:
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f = open(path, "r")
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config_vector.update(json.load(f))
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f.close()
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return 0
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except:
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f = open(path, "w")
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f.write(sample_json)
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f.close()
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return 1
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