#!/usr/bin/env python3 from dfatool.loader import RawData, pta_trace_to_aggregate from dfatool.model import PTAModel from dfatool.utils import by_name_to_by_param from dfatool.validation import CrossValidator import os import unittest import pytest import numpy as np class TestSynthetic(unittest.TestCase): def test_model_validation(self): # rng = np.random.default_rng(seed=1312) # requiresy NumPy >= 1.17 np.random.seed(1312) X = np.arange(500) % 50 parameter_names = ["p_mod5", "p_linear"] s1_duration_base = 70 s1_duration_scale = 2 s1_power_base = 50 s1_power_scale = 7 s2_duration_base = 700 s2_duration_scale = 1 s2_power_base = 1500 s2_power_scale = 10 by_name = { "raw_state_1": { "isa": "state", "param": [(x % 5, x) for x in X], "duration": s1_duration_base + np.random.normal(size=X.size, scale=s1_duration_scale), "power": s1_power_base + X + np.random.normal(size=X.size, scale=s1_power_scale), "attributes": ["duration", "power"], }, "raw_state_2": { "isa": "state", "param": [(x % 5, x) for x in X], "duration": s2_duration_base - 2 * X + np.random.normal(size=X.size, scale=s2_duration_scale), "power": s2_power_base + X + np.random.normal(size=X.size, scale=s2_power_scale), "attributes": ["duration", "power"], }, } by_param = by_name_to_by_param(by_name) model = PTAModel(by_name, parameter_names, dict()) static_model = model.get_static() # x ∈ [0, 50] -> mean(X) is 25 self.assertAlmostEqual( static_model("raw_state_1", "duration"), s1_duration_base, places=0 ) self.assertAlmostEqual( static_model("raw_state_1", "power"), s1_power_base + 25, delta=7 ) self.assertAlmostEqual( static_model("raw_state_2", "duration"), s2_duration_base - 2 * 25, delta=2 ) self.assertAlmostEqual( static_model("raw_state_2", "power"), s2_power_base + 25, delta=7 ) param_model, param_info = model.get_fitted() self.assertAlmostEqual( param_model("raw_state_1", "duration", param=[0, 10]), s1_duration_base, places=0, ) self.assertAlmostEqual( param_model("raw_state_1", "duration", param=[0, 50]), s1_duration_base, places=0, ) self.assertAlmostEqual( param_model("raw_state_1", "duration", param=[0, 70]), s1_duration_base, places=0, ) self.assertAlmostEqual( param_model("raw_state_1", "power", param=[0, 10]), s1_power_base + 10, places=0, ) self.assertAlmostEqual( param_model("raw_state_1", "power", param=[0, 50]), s1_power_base + 50, places=0, ) self.assertAlmostEqual( param_model("raw_state_1", "power", param=[0, 70]), s1_power_base + 70, places=0, ) self.assertAlmostEqual( param_model("raw_state_2", "duration", param=[0, 10]), s2_duration_base - 2 * 10, places=0, ) self.assertAlmostEqual( param_model("raw_state_2", "duration", param=[0, 50]), s2_duration_base - 2 * 50, places=0, ) self.assertAlmostEqual( param_model("raw_state_2", "duration", param=[0, 70]), s2_duration_base - 2 * 70, places=0, ) self.assertAlmostEqual( param_model("raw_state_2", "power", param=[0, 10]), s2_power_base + 10, delta=50, ) self.assertAlmostEqual( param_model("raw_state_2", "power", param=[0, 50]), s2_power_base + 50, delta=50, ) self.assertAlmostEqual( param_model("raw_state_2", "power", param=[0, 70]), s2_power_base + 70, delta=50, ) static_quality = model.assess(static_model) param_quality = model.assess(param_model) # static quality reflects normal distribution scale for non-parameterized data # the Root Mean Square Deviation must not be greater the scale (i.e., standard deviation) of the normal distribution # Low Mean Absolute Error (< 2) self.assertTrue(static_quality["by_name"]["raw_state_1"]["duration"]["mae"] < 2) # Low Root Mean Square Deviation (< scale == 2) self.assertTrue( static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"] < 2 ) # Relatively low error percentage (~~ MAE * 100% / s1_duration_base) self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["duration"]["mape"], static_quality["by_name"]["raw_state_1"]["duration"]["mae"] * 100 / s1_duration_base, places=1, ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["duration"]["smape"], static_quality["by_name"]["raw_state_1"]["duration"]["mae"] * 100 / s1_duration_base, places=1, ) # static error is high for parameterized data # MAE == mean(abs(actual value - model value)) # parameter range is [0, 50) -> mean 25, deviation range is [0, 25) -> mean deviation is 12.5 ± gauss scale self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["mae"], 12.5, delta=1 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["rmsd"], 16, delta=2 ) # high percentage error due to low s1_power_base self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["mape"], 19, delta=2 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["smape"], 19, delta=2 ) # parameter range is [0, 100) -> mean deviation is 25 ± gauss scale self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["mae"], 25, delta=2 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["rmsd"], 30, delta=2 ) # low percentage error due to high s2_duration_base (~~ 3.5 %) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["mape"], 25 * 100 / s2_duration_base, delta=1, ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["smape"], 25 * 100 / s2_duration_base, delta=1, ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["mae"], 12.5, delta=2 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["rmsd"], 17, delta=2 ) # low percentage error due to high s2_power_base (~~ 1.7 %) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["mape"], 25 * 100 / s2_power_base, delta=1, ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["smape"], 25 * 100 / s2_power_base, delta=1, ) # raw_state_1/duration does not depend on parameters and delegates to the static model self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["mae"], static_quality["by_name"]["raw_state_1"]["duration"]["mae"], ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["rmsd"], static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"], ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["mape"], static_quality["by_name"]["raw_state_1"]["duration"]["mape"], ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["smape"], static_quality["by_name"]["raw_state_1"]["duration"]["smape"], ) # fitted param-model quality reflects normal distribution scale for all data self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["power"]["mape"], 0.9, places=1 ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1 ) self.assertTrue( param_quality["by_name"]["raw_state_1"]["power"]["mae"] < s1_power_scale ) self.assertTrue( param_quality["by_name"]["raw_state_1"]["power"]["rmsd"] < s1_power_scale ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["power"]["mape"], 7.5, delta=1 ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["power"]["smape"], 7.5, delta=1 ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["mae"], s2_duration_scale, delta=0.2, ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["rmsd"], s2_duration_scale, delta=0.2, ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["mape"], 0.12, delta=0.01, ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["smape"], 0.12, delta=0.01, ) # ... unless the signal-to-noise ratio (parameter range = [0 .. 50] vs. scale = 10) is bad, leading to # increased regression errors self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["mae"] < 15) self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["rmsd"] < 18) # still: low percentage error due to high s2_power_base self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["power"]["mape"], 0.9, places=1 ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1 ) def test_model_crossvalidation_10fold(self): # rng = np.random.default_rng(seed=1312) # requiresy NumPy >= 1.17 np.random.seed(1312) X = np.arange(500) % 50 parameter_names = ["p_mod5", "p_linear"] s1_duration_base = 70 s1_duration_scale = 2 s1_power_base = 50 s1_power_scale = 7 s2_duration_base = 700 s2_duration_scale = 1 s2_power_base = 1500 s2_power_scale = 10 by_name = { "raw_state_1": { "isa": "state", "param": [(x % 5, x) for x in X], "duration": s1_duration_base + np.random.normal(size=X.size, scale=s1_duration_scale), "power": s1_power_base + X + np.random.normal(size=X.size, scale=s1_power_scale), "attributes": ["duration", "power"], }, "raw_state_2": { "isa": "state", "param": [(x % 5, x) for x in X], "duration": s2_duration_base - 2 * X + np.random.normal(size=X.size, scale=s2_duration_scale), "power": s2_power_base + X + np.random.normal(size=X.size, scale=s2_power_scale), "attributes": ["duration", "power"], }, } by_param = by_name_to_by_param(by_name) arg_count = dict() model = PTAModel(by_name, parameter_names, arg_count) validator = CrossValidator(PTAModel, by_name, parameter_names, arg_count) static_quality = validator.kfold(lambda m: m.get_static(), 10) param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10) print(static_quality) # static quality reflects normal distribution scale for non-parameterized data # the Root Mean Square Deviation must not be greater the scale (i.e., standard deviation) of the normal distribution # Low Mean Absolute Error (< 2) self.assertTrue(static_quality["by_name"]["raw_state_1"]["duration"]["mae"] < 2) # Low Root Mean Square Deviation (< scale == 2) self.assertTrue( static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"] < 2 ) # Relatively low error percentage (~~ MAE * 100% / s1_duration_base) self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["duration"]["smape"], static_quality["by_name"]["raw_state_1"]["duration"]["mae"] * 100 / s1_duration_base, places=1, ) # static error is high for parameterized data # MAE == mean(abs(actual value - model value)) # parameter range is [0, 50) -> mean 25, deviation range is [0, 25) -> mean deviation is 12.5 ± gauss scale self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["mae"], 12.5, delta=1 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["rmsd"], 16, delta=2 ) # high percentage error due to low s1_power_base self.assertAlmostEqual( static_quality["by_name"]["raw_state_1"]["power"]["smape"], 19, delta=2 ) # parameter range is [0, 100) -> mean deviation is 25 ± gauss scale self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["mae"], 25, delta=2 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["rmsd"], 30, delta=2 ) # low percentage error due to high s2_duration_base (~~ 3.5 %) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["duration"]["smape"], 25 * 100 / s2_duration_base, delta=1, ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["mae"], 12.5, delta=2 ) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["rmsd"], 17, delta=2 ) # low percentage error due to high s2_power_base (~~ 1.7 %) self.assertAlmostEqual( static_quality["by_name"]["raw_state_2"]["power"]["smape"], 25 * 100 / s2_power_base, delta=1, ) # raw_state_1/duration does not depend on parameters and delegates to the static model self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["mae"], static_quality["by_name"]["raw_state_1"]["duration"]["mae"], ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["rmsd"], static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"], ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["duration"]["smape"], static_quality["by_name"]["raw_state_1"]["duration"]["smape"], ) # fitted param-model quality reflects normal distribution scale for all data self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1 ) self.assertTrue( param_quality["by_name"]["raw_state_1"]["power"]["mae"] < s1_power_scale ) self.assertTrue( param_quality["by_name"]["raw_state_1"]["power"]["rmsd"] < s1_power_scale ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_1"]["power"]["smape"], 7.5, delta=1 ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["mae"], s2_duration_scale, delta=0.2, ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["rmsd"], s2_duration_scale, delta=0.2, ) self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["duration"]["smape"], 0.12, delta=0.01, ) # ... unless the signal-to-noise ratio (parameter range = [0 .. 50] vs. scale = 10) is bad, leading to # increased regression errors self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["mae"] < 15) self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["rmsd"] < 18) # still: low percentage error due to high s2_power_base self.assertAlmostEqual( param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1 ) class TestFromFile(unittest.TestCase): def test_singlefile_rf24(self): raw_data = RawData(["test-data/20170220_164723_RF24_int_A.tar"]) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) model = PTAModel(by_name, parameters, arg_count) self.assertEqual(model.states(), "POWERDOWN RX STANDBY1 TX".split(" ")) self.assertEqual( model.transitions(), "begin epilogue powerDown powerUp setDataRate_num setPALevel_num startListening stopListening write_nb".split( " " ), ) static_model = model.get_static() self.assertAlmostEqual(static_model("POWERDOWN", "power"), 0, places=0) self.assertAlmostEqual(static_model("RX", "power"), 52254, places=0) self.assertAlmostEqual(static_model("STANDBY1", "power"), 7, places=0) self.assertAlmostEqual(static_model("TX", "power"), 18414, places=0) self.assertAlmostEqual(static_model("begin", "energy"), 1652249, places=0) self.assertAlmostEqual(static_model("epilogue", "energy"), 15449, places=0) self.assertAlmostEqual(static_model("powerDown", "energy"), 4547, places=0) self.assertAlmostEqual(static_model("powerUp", "energy"), 1641765, places=0) self.assertAlmostEqual( static_model("setDataRate_num", "energy"), 7749, places=0 ) self.assertAlmostEqual(static_model("setPALevel_num", "energy"), 4700, places=0) self.assertAlmostEqual( static_model("startListening", "energy"), 4309602, places=0 ) self.assertAlmostEqual( static_model("stopListening", "energy"), 193775, places=0 ) self.assertAlmostEqual(static_model("write_nb", "energy"), 218339, places=0) self.assertAlmostEqual( static_model("begin", "rel_energy_prev"), 1649571, places=0 ) self.assertAlmostEqual( static_model("epilogue", "rel_energy_prev"), -744114, places=0 ) self.assertAlmostEqual( static_model("powerDown", "rel_energy_prev"), 3854, places=0 ) self.assertAlmostEqual( static_model("powerUp", "rel_energy_prev"), 1641381, places=0 ) self.assertAlmostEqual( static_model("setDataRate_num", "rel_energy_prev"), 6777, places=0 ) self.assertAlmostEqual( static_model("setPALevel_num", "rel_energy_prev"), 3728, places=0 ) self.assertAlmostEqual( static_model("startListening", "rel_energy_prev"), 4307769, places=0 ) self.assertAlmostEqual( static_model("stopListening", "rel_energy_prev"), -13533693, places=0 ) self.assertAlmostEqual( static_model("write_nb", "rel_energy_prev"), 214618, places=0 ) self.assertAlmostEqual(static_model("begin", "duration"), 19830, places=0) self.assertAlmostEqual(static_model("epilogue", "duration"), 40, places=0) self.assertAlmostEqual(static_model("powerDown", "duration"), 90, places=0) self.assertAlmostEqual(static_model("powerUp", "duration"), 10030, places=0) self.assertAlmostEqual( static_model("setDataRate_num", "duration"), 140, places=0 ) self.assertAlmostEqual(static_model("setPALevel_num", "duration"), 90, places=0) self.assertAlmostEqual( static_model("startListening", "duration"), 260, places=0 ) self.assertAlmostEqual(static_model("stopListening", "duration"), 260, places=0) self.assertAlmostEqual(static_model("write_nb", "duration"), 510, places=0) self.assertAlmostEqual( model.stats.param_dependence_ratio("POWERDOWN", "power", "datarate"), 0, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("POWERDOWN", "power", "txbytes"), 0, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("POWERDOWN", "power", "txpower"), 0, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("RX", "power", "datarate"), 0.99, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("RX", "power", "txbytes"), 0, places=2 ) self.assertAlmostEqual( model.stats.param_dependence_ratio("RX", "power", "txpower"), 0.01, places=2 ) self.assertAlmostEqual( model.stats.param_dependence_ratio("STANDBY1", "power", "datarate"), 0.04, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("STANDBY1", "power", "txbytes"), 0.35, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("STANDBY1", "power", "txpower"), 0.32, places=2, ) self.assertAlmostEqual( model.stats.param_dependence_ratio("TX", "power", "datarate"), 1, places=2 ) self.assertAlmostEqual( model.stats.param_dependence_ratio("TX", "power", "txbytes"), 0.09, places=2 ) self.assertAlmostEqual( model.stats.param_dependence_ratio("TX", "power", "txpower"), 1, places=2 ) param_model, param_info = model.get_fitted() self.assertEqual(param_info("POWERDOWN", "power"), None) self.assertEqual( param_info("RX", "power")["function"].model_function, "0 + regression_arg(0) + regression_arg(1) * np.sqrt(parameter(datarate))", ) self.assertAlmostEqual( param_info("RX", "power")["function"].model_args[0], 48530.7, places=0 ) self.assertAlmostEqual( param_info("RX", "power")["function"].model_args[1], 117, places=0 ) self.assertEqual(param_info("STANDBY1", "power"), None) self.assertEqual( param_info("TX", "power")["function"].model_function, "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * parameter(txpower) + regression_arg(3) * 1/(parameter(datarate)) * parameter(txpower)", ) self.assertEqual( param_info("epilogue", "timeout")["function"].model_function, "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))", ) self.assertEqual( param_info("stopListening", "duration")["function"].model_function, "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))", ) self.assertAlmostEqual( param_model("RX", "power", param=[1, None, None]), 48647, places=-1 ) def test_singlefile_mmparam(self): raw_data = RawData(["test-data/20161221_123347_mmparam.tar"]) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) model = PTAModel(by_name, parameters, arg_count) self.assertEqual(model.states(), "OFF ON".split(" ")) self.assertEqual(model.transitions(), "off setBrightness".split(" ")) static_model = model.get_static() self.assertAlmostEqual(static_model("OFF", "power"), 7124, places=0) self.assertAlmostEqual(static_model("ON", "power"), 17866, places=0) self.assertAlmostEqual(static_model("off", "energy"), 268079197, places=0) self.assertAlmostEqual( static_model("setBrightness", "energy"), 168912773, places=0 ) self.assertAlmostEqual( static_model("off", "rel_energy_prev"), 105040198, places=0 ) self.assertAlmostEqual( static_model("setBrightness", "rel_energy_prev"), 103745586, places=0 ) self.assertAlmostEqual(static_model("off", "duration"), 9130, places=0) self.assertAlmostEqual( static_model("setBrightness", "duration"), 9130, places=0 ) param_lut_model = model.get_param_lut() self.assertAlmostEqual( param_lut_model("OFF", "power", param=[None, None]), 7124, places=0 ) with self.assertRaises(KeyError): param_lut_model("ON", "power", param=[None, None]) param_lut_model("ON", "power", param=["a"]) param_lut_model("ON", "power", param=[0]) self.assertTrue(param_lut_model("ON", "power", param=[0, 0])) param_lut_model = model.get_param_lut(fallback=True) self.assertAlmostEqual( param_lut_model("ON", "power", param=[None, None]), 17866, places=0 ) def test_multifile_lm75x(self): testfiles = [ "test-data/20170116_124500_LM75x.tar", "test-data/20170116_131306_LM75x.tar", ] raw_data = RawData(testfiles) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) model = PTAModel(by_name, parameters, arg_count) self.assertEqual(model.states(), "ACTIVE POWEROFF".split(" ")) self.assertEqual( model.transitions(), "getTemp setHyst setOS shutdown start".split(" ") ) static_model = model.get_static() self.assertAlmostEqual(static_model("ACTIVE", "power"), 332, places=0) self.assertAlmostEqual(static_model("POWEROFF", "power"), 7, places=0) self.assertAlmostEqual(static_model("getTemp", "energy"), 26016748, places=0) self.assertAlmostEqual(static_model("setHyst", "energy"), 22082226, places=0) self.assertAlmostEqual(static_model("setOS", "energy"), 21774238, places=0) self.assertAlmostEqual(static_model("shutdown", "energy"), 11808160, places=0) self.assertAlmostEqual(static_model("start", "energy"), 12445302, places=0) self.assertAlmostEqual( static_model("getTemp", "rel_energy_prev"), 21722720, places=0 ) self.assertAlmostEqual( static_model("setHyst", "rel_energy_prev"), 19001499, places=0 ) self.assertAlmostEqual( static_model("setOS", "rel_energy_prev"), 18693283, places=0 ) self.assertAlmostEqual( static_model("shutdown", "rel_energy_prev"), 11746224, places=0 ) self.assertAlmostEqual( static_model("start", "rel_energy_prev"), 12391462, places=0 ) self.assertAlmostEqual(static_model("getTemp", "duration"), 12740, places=0) self.assertAlmostEqual(static_model("setHyst", "duration"), 9140, places=0) self.assertAlmostEqual(static_model("setOS", "duration"), 9140, places=0) self.assertAlmostEqual(static_model("shutdown", "duration"), 6980, places=0) self.assertAlmostEqual(static_model("start", "duration"), 6980, places=0) def test_multifile_sharp(self): testfiles = [ "test-data/20170116_145420_sharpLS013B4DN.tar", "test-data/20170116_151348_sharpLS013B4DN.tar", ] raw_data = RawData(testfiles) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) model = PTAModel(by_name, parameters, arg_count) self.assertEqual(model.states(), "DISABLED ENABLED".split(" ")) self.assertEqual( model.transitions(), "clear disable enable ioInit sendLine toggleVCOM".split(" "), ) static_model = model.get_static() self.assertAlmostEqual(static_model("DISABLED", "power"), 22, places=0) self.assertAlmostEqual(static_model("ENABLED", "power"), 24, places=0) self.assertAlmostEqual(static_model("clear", "energy"), 14059, places=0) self.assertAlmostEqual(static_model("disable", "energy"), 0, places=0) self.assertAlmostEqual(static_model("enable", "energy"), 0, places=0) self.assertAlmostEqual(static_model("ioInit", "energy"), 0, places=0) self.assertAlmostEqual(static_model("sendLine", "energy"), 37874, places=0) self.assertAlmostEqual(static_model("toggleVCOM", "energy"), 30991, places=0) self.assertAlmostEqual( static_model("clear", "rel_energy_prev"), 13329, places=0 ) self.assertAlmostEqual(static_model("disable", "rel_energy_prev"), 0, places=0) self.assertAlmostEqual(static_model("enable", "rel_energy_prev"), 0, places=0) self.assertAlmostEqual(static_model("ioInit", "rel_energy_prev"), 0, places=0) self.assertAlmostEqual( static_model("sendLine", "rel_energy_prev"), 33447, places=0 ) self.assertAlmostEqual( static_model("toggleVCOM", "rel_energy_prev"), 30242, places=0 ) self.assertAlmostEqual(static_model("clear", "duration"), 30, places=0) self.assertAlmostEqual(static_model("disable", "duration"), 0, places=0) self.assertAlmostEqual(static_model("enable", "duration"), 0, places=0) self.assertAlmostEqual(static_model("ioInit", "duration"), 0, places=0) self.assertAlmostEqual(static_model("sendLine", "duration"), 180, places=0) self.assertAlmostEqual(static_model("toggleVCOM", "duration"), 30, places=0) def test_multifile_mmstatic(self): testfiles = [ "test-data/20170116_143516_mmstatic.tar", "test-data/20170116_142654_mmstatic.tar", ] raw_data = RawData(testfiles) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) model = PTAModel(by_name, parameters, arg_count) self.assertEqual(model.states(), "B G OFF R".split(" ")) self.assertEqual(model.transitions(), "blue green off red".split(" ")) static_model = model.get_static() self.assertAlmostEqual(static_model("B", "power"), 29443, places=0) self.assertAlmostEqual(static_model("G", "power"), 29432, places=0) self.assertAlmostEqual(static_model("OFF", "power"), 7057, places=0) self.assertAlmostEqual(static_model("R", "power"), 49068, places=0) self.assertAlmostEqual(static_model("blue", "energy"), 374440955, places=0) self.assertAlmostEqual(static_model("green", "energy"), 372026027, places=0) self.assertAlmostEqual(static_model("off", "energy"), 372999554, places=0) self.assertAlmostEqual(static_model("red", "energy"), 378936634, places=0) self.assertAlmostEqual( static_model("blue", "rel_energy_prev"), 105535587, places=0 ) self.assertAlmostEqual( static_model("green", "rel_energy_prev"), 102999371, places=0 ) self.assertAlmostEqual( static_model("off", "rel_energy_prev"), 103613698, places=0 ) self.assertAlmostEqual( static_model("red", "rel_energy_prev"), 110474331, places=0 ) self.assertAlmostEqual(static_model("blue", "duration"), 9140, places=0) self.assertAlmostEqual(static_model("green", "duration"), 9140, places=0) self.assertAlmostEqual(static_model("off", "duration"), 9140, places=0) self.assertAlmostEqual(static_model("red", "duration"), 9140, places=0) @pytest.mark.skipif( "TEST_SLOW" not in os.environ, reason="slow test, set TEST_SLOW=1 to run" ) def test_multifile_cc1200(self): testfiles = [ "test-data/20170125_125433_cc1200.tar", "test-data/20170125_142420_cc1200.tar", "test-data/20170125_144957_cc1200.tar", "test-data/20170125_151149_cc1200.tar", "test-data/20170125_151824_cc1200.tar", "test-data/20170125_154019_cc1200.tar", ] raw_data = RawData(testfiles) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) model = PTAModel(by_name, parameters, arg_count) self.assertEqual( model.states(), "IDLE RX SLEEP SLEEP_EWOR SYNTH_ON TX XOFF".split(" ") ) self.assertEqual( model.transitions(), "crystal_off eWOR idle init prepare_xmit receive send setSymbolRate setTxPower sleep txDone".split( " " ), ) static_model = model.get_static() self.assertAlmostEqual(static_model("IDLE", "power"), 9500, places=0) self.assertAlmostEqual(static_model("RX", "power"), 85177, places=0) self.assertAlmostEqual(static_model("SLEEP", "power"), 143, places=0) self.assertAlmostEqual(static_model("SLEEP_EWOR", "power"), 81801, places=0) self.assertAlmostEqual(static_model("SYNTH_ON", "power"), 60036, places=0) self.assertAlmostEqual(static_model("TX", "power"), 92461, places=0) self.assertAlmostEqual(static_model("XOFF", "power"), 780, places=0) self.assertAlmostEqual(static_model("crystal_off", "energy"), 114658, places=0) self.assertAlmostEqual(static_model("eWOR", "energy"), 317556, places=0) self.assertAlmostEqual(static_model("idle", "energy"), 717713, places=0) self.assertAlmostEqual(static_model("init", "energy"), 23028941, places=0) self.assertAlmostEqual(static_model("prepare_xmit", "energy"), 378552, places=0) self.assertAlmostEqual(static_model("receive", "energy"), 380335, places=0) self.assertAlmostEqual(static_model("send", "energy"), 4282597, places=0) self.assertAlmostEqual( static_model("setSymbolRate", "energy"), 962060, places=0 ) self.assertAlmostEqual(static_model("setTxPower", "energy"), 288701, places=0) self.assertAlmostEqual(static_model("sleep", "energy"), 104445, places=0) self.assertEqual(static_model("txDone", "energy"), 0) param_model, param_info = model.get_fitted() self.assertEqual(param_info("IDLE", "power"), None) self.assertEqual( param_info("RX", "power")["function"].model_function, "0 + regression_arg(0) + regression_arg(1) * np.log(parameter(symbolrate) + 1)", ) self.assertEqual(param_info("SLEEP", "power"), None) self.assertEqual(param_info("SLEEP_EWOR", "power"), None) self.assertEqual(param_info("SYNTH_ON", "power"), None) self.assertEqual(param_info("XOFF", "power"), None) self.assertAlmostEqual( param_info("RX", "power")["function"].model_args[0], 84415, places=0 ) self.assertAlmostEqual( param_info("RX", "power")["function"].model_args[1], 206, places=0 ) if __name__ == "__main__": unittest.main()