diff options
| -rwxr-xr-x | lib/automata.py | 11 | ||||
| -rw-r--r-- | lib/data_parameters.py | 17 | ||||
| -rw-r--r-- | lib/dfatool.py | 42 | ||||
| -rw-r--r-- | lib/functions.py | 8 | ||||
| -rw-r--r-- | lib/lex.py | 9 | ||||
| -rw-r--r-- | lib/parameters.py | 48 | ||||
| -rwxr-xr-x | lib/protocol_benchmarks.py | 7 | ||||
| -rw-r--r-- | lib/utils.py | 4 |
8 files changed, 81 insertions, 65 deletions
diff --git a/lib/automata.py b/lib/automata.py index b3318e0..69b3969 100755 --- a/lib/automata.py +++ b/lib/automata.py @@ -3,11 +3,14 @@ from .functions import AnalyticFunction, NormalizationFunction from .utils import is_numeric import itertools +import logging import numpy as np import json import queue import yaml +logger = logging.getLogger(__name__) + def _dict_to_list(input_dict: dict) -> list: return [input_dict[x] for x in sorted(input_dict.keys())] @@ -1305,8 +1308,8 @@ class PTA: "power" ] except KeyError: - print( - "[W] skipping model update of state {} due to missing data".format( + logger.warning( + "skipping model update of state {} due to missing data".format( state.name ) ) @@ -1353,8 +1356,8 @@ class PTA: "timeout" ] except KeyError: - print( - "[W] skipping model update of transition {} due to missing data".format( + logger.warning( + "skipping model update of transition {} due to missing data".format( transition.name ) ) diff --git a/lib/data_parameters.py b/lib/data_parameters.py index 1150b71..84eacfd 100644 --- a/lib/data_parameters.py +++ b/lib/data_parameters.py @@ -7,9 +7,12 @@ length of lists, ane more. from .protocol_benchmarks import codegen_for_lib from . import cycles_to_energy, size_to_radio_energy, utils +import logging import numpy as np import ubjson +logger = logging.getLogger(__name__) + def _string_value_length(json): if type(json) == str: @@ -224,7 +227,7 @@ class Protolog: except KeyError: pass except TypeError as e: - print( + logger.error( "TypeError in {} {} {} {}: {} -> {}".format( arch_lib, benchmark, @@ -395,7 +398,7 @@ class Protolog: except KeyError: pass except ValueError: - print( + logger.warning( "cycles_enc is NaN for {} -> {} -> {}".format( arch, lib, key ) @@ -410,7 +413,7 @@ class Protolog: except KeyError: pass except ValueError: - print( + logger.warning( "cycles_ser is NaN for {} -> {} -> {}".format( arch, lib, key ) @@ -425,7 +428,7 @@ class Protolog: except KeyError: pass except ValueError: - print( + logger.warning( "cycles_encser is NaN for {} -> {} -> {}".format( arch, lib, key ) @@ -440,7 +443,7 @@ class Protolog: except KeyError: pass except ValueError: - print( + logger.warning( "cycles_des is NaN for {} -> {} -> {}".format( arch, lib, key ) @@ -455,7 +458,7 @@ class Protolog: except KeyError: pass except ValueError: - print( + logger.warning( "cycles_dec is NaN for {} -> {} -> {}".format( arch, lib, key ) @@ -470,7 +473,7 @@ class Protolog: except KeyError: pass except ValueError: - print( + logger.warning( "cycles_desdec is NaN for {} -> {} -> {}".format( arch, lib, key ) diff --git a/lib/dfatool.py b/lib/dfatool.py index ef3bac7..1e38907 100644 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -75,7 +75,7 @@ def gplearn_to_function(function_str: str): arg_list.append("X{:d}".format(i)) eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str) - print(eval_str) + logger.debug(eval_str) return eval(eval_str, eval_globals) @@ -1371,7 +1371,7 @@ class RawData: for measurement in measurements: if "energy_trace" not in measurement: - logging.warning( + logger.warning( "Skipping {ar:s}/{m:s}: {e:s}".format( ar=self.filenames[measurement["fileno"]], m=measurement["info"].name, @@ -1393,7 +1393,7 @@ class RawData: self._merge_online_and_offline(measurement) num_valid += 1 else: - logging.warning( + logger.warning( "Skipping {ar:s}/{m:s}: {e:s}".format( ar=self.filenames[measurement["fileno"]], m=measurement["info"].name, @@ -1405,14 +1405,14 @@ class RawData: self._merge_online_and_etlog(measurement) num_valid += 1 else: - logging.warning( + logger.warning( "Skipping {ar:s}/{m:s}: {e:s}".format( ar=self.filenames[measurement["fileno"]], m=measurement["info"].name, e=measurement["error"], ), ) - logging.info( + logger.info( "{num_valid:d}/{num_total:d} measurements are valid".format( num_valid=num_valid, num_total=len(measurements) ), @@ -1834,9 +1834,9 @@ class AnalyticModel: try: model[name][key] = model_function(elem[key]) except RuntimeWarning: - logging.warning("Got no data for {} {}".format(name, key)) + logger.warning("Got no data for {} {}".format(name, key)) except FloatingPointError as fpe: - logging.warning("Got no data for {} {}: {}".format(name, key, fpe),) + logger.warning("Got no data for {} {}: {}".format(name, key, fpe),) return model def param_index(self, param_name): @@ -2230,9 +2230,9 @@ class PTAModel: try: model[name][key] = model_function(elem[key]) except RuntimeWarning: - logging.warning("Got no data for {} {}".format(name, key)) + logger.warning("Got no data for {} {}".format(name, key)) except FloatingPointError as fpe: - logging.warning("Got no data for {} {}: {}".format(name, key, fpe),) + logger.warning("Got no data for {} {}: {}".format(name, key, fpe),) return model def get_static(self, use_mean=False): @@ -2730,7 +2730,7 @@ class EnergyTraceLog: self.sample_rate = data_count / (m_duration_us * 1e-6) - logging.debug( + logger.debug( "got {} samples with {} seconds of log data ({} Hz)".format( data_count, m_duration_us * 1e-6, self.sample_rate ), @@ -2837,19 +2837,17 @@ class EnergyTraceLog: for name, duration in expected_transitions: bc, start, stop, end = self.find_barcode(next_barcode) if bc is None: - print('[!!!] did not find transition "{}"'.format(name)) + logger.error('did not find transition "{}"'.format(name)) break next_barcode = end + self.state_duration + duration - logging.debug( + logger.debug( '{} barcode "{}" area: {:0.2f} .. {:0.2f} / {:0.2f} seconds'.format( offline_index, bc, start, stop, end ), ) if bc != name: - logging.debug( - '[!!!] mismatch: expected "{}", got "{}"'.format(name, bc), - ) - logging.debug( + logger.error('mismatch: expected "{}", got "{}"'.format(name, bc),) + logger.debug( "{} estimated transition area: {:0.3f} .. {:0.3f} seconds".format( offline_index, end, end + duration ), @@ -2862,7 +2860,7 @@ class EnergyTraceLog: self.ts_to_index(end + duration + self.state_duration) + 1 ) - logging.debug( + logger.debug( "{} estimated transitionindex: {:0.3f} .. {:0.3f} seconds".format( offline_index, transition_start_index / self.sample_rate, @@ -2962,7 +2960,7 @@ class EnergyTraceLog: + self.led_power / 3 ) - logging.debug( + logger.debug( "looking for barcode starting at {:0.2f} s, threshold is {:0.1f} mW".format( start_ts, sync_threshold_power * 1e3 ), @@ -2996,7 +2994,7 @@ class EnergyTraceLog: barcode_data = self.interval_power[sync_area_start:sync_area_end] - logging.debug( + logger.debug( "barcode search area: {:0.2f} .. {:0.2f} seconds ({} samples)".format( sync_start_ts, sync_end_ts, len(barcode_data) ), @@ -3074,7 +3072,7 @@ class EnergyTraceLog: return content, sym_start, sym_end, padding_bits else: - logging.warning("unable to find barcode") + logger.warning("unable to find barcode") return None, None, None, None @@ -3300,7 +3298,7 @@ class MIMOSA: if cal_r2_mean > cal_0_mean: b_lower = (ua_r2 - 0) / (cal_r2_mean - cal_0_mean) else: - logging.warning("0 uA == %.f uA during calibration" % (ua_r2)) + logger.warning("0 uA == %.f uA during calibration" % (ua_r2)) b_lower = 0 b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean) @@ -3472,7 +3470,7 @@ class MIMOSA: data["substates"] = substates ssum = np.sum(list(map(lambda x: x["duration"], substates["states"]))) if ssum != data["us"]: - logging.warning("duration %d vs %d" % (data["us"], ssum)) + logger.warning("duration %d vs %d" % (data["us"], ssum)) if isa == "transition": # subtract average power of previous state diff --git a/lib/functions.py b/lib/functions.py index 359c8d7..0b849bd 100644 --- a/lib/functions.py +++ b/lib/functions.py @@ -229,7 +229,7 @@ class AnalyticFunction: else: X[i].extend([np.nan] * len(val[model_attribute])) elif key[0] == state_or_tran and len(key[1]) != dimension: - logging.warning( + logger.warning( "Invalid parameter key length while gathering fit data for {}/{}. is {}, want {}.".format( state_or_tran, model_attribute, len(key[1]), dimension ), @@ -263,7 +263,7 @@ class AnalyticFunction: error_function, self._regression_args, args=(X, Y), xtol=2e-15 ) except ValueError as err: - logging.warning( + logger.warning( "Fit failed for {}/{}: {} (function: {})".format( state_or_tran, model_attribute, err, self._model_str ), @@ -273,13 +273,13 @@ class AnalyticFunction: self._regression_args = res.x self.fit_success = True else: - logging.warning( + logger.warning( "Fit failed for {}/{}: {} (function: {})".format( state_or_tran, model_attribute, res.message, self._model_str ), ) else: - logging.warning( + logger.warning( "Insufficient amount of valid parameter keys, cannot fit {}/{}".format( state_or_tran, model_attribute ), @@ -1,4 +1,7 @@ from .sly import Lexer, Parser +import logging + +logger = logging.getLogger(__name__) class TimedWordLexer(Lexer): @@ -38,7 +41,7 @@ class TimedSequenceLexer(Lexer): FUNCTIONSEP = r";" def error(self, t): - print("Illegal character '%s'" % t.value[0]) + logger.error("Illegal character '%s'" % t.value[0]) if t.value[0] == "{" and t.value.find("}"): self.index += 1 + t.value.find("}") else: @@ -153,11 +156,11 @@ class TimedSequenceParser(Parser): def error(self, p): if p: - print("Syntax error at token", p.type) + logger.error("Syntax error at token", p.type) # Just discard the token and tell the parser it's okay. self.errok() else: - print("Syntax error at EOF") + logger.error("Syntax error at EOF") class TimedWord: diff --git a/lib/parameters.py b/lib/parameters.py index bd67cc1..79543a6 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -1,6 +1,7 @@ import itertools import logging import numpy as np +import warnings from collections import OrderedDict from copy import deepcopy from multiprocessing import Pool @@ -163,12 +164,11 @@ def _std_by_param(by_param, all_param_values, state_or_tran, attribute, param_in # vprint(verbose, '[W] parameter value partition for {} is empty'.format(param_value)) if np.all(np.isnan(stddev_matrix)): - print( - "[W] {}/{} parameter #{} has no data partitions -- how did this even happen?".format( - state_or_tran, attribute, param_index + warnings.warn( + "{}/{} parameter #{} has no data partitions. stddev_matrix = {}".format( + state_or_tran, attribute, param_index, stddev_matrix ) ) - print("stddev_matrix = {}".format(stddev_matrix)) return stddev_matrix, 0.0 return ( @@ -203,13 +203,13 @@ def _corr_by_param(by_name, state_or_trans, attribute, param_index): # -> assume no correlation return 0.0 except ValueError: - print( - "[!] Exception in _corr_by_param(by_name, state_or_trans={}, attribute={}, param_index={})".format( + logger.error( + "ValueError in _corr_by_param(by_name, state_or_trans={}, attribute={}, param_index={})".format( state_or_trans, attribute, param_index ) ) - print( - "[!] while executing np.corrcoef(by_name[{}][{}]={}, {}))".format( + logger.error( + "while executing np.corrcoef(by_name[{}][{}]={}, {}))".format( state_or_trans, attribute, by_name[state_or_trans][attribute], @@ -443,8 +443,8 @@ def prune_dependent_parameters(by_name, parameter_names, correlation_threshold=0 correlation != np.nan and np.abs(correlation) > correlation_threshold ): - print( - "[!] Parameters {} <-> {} are correlated with coefficcient {}".format( + logger.debug( + "Parameters {} <-> {} are correlated with coefficcient {}".format( parameter_names[index_1], parameter_names[index_2], correlation, @@ -454,7 +454,7 @@ def prune_dependent_parameters(by_name, parameter_names, correlation_threshold=0 index_to_remove = index_1 else: index_to_remove = index_2 - print( + logger.debug( " Removing parameter {}".format( parameter_names[index_to_remove] ) @@ -581,15 +581,17 @@ class ParamStats: ) > 2 ): - print( - key, - param, - list( - filter( - lambda n: is_numeric(n), - self.distinct_values[key][param], - ) - ), + logger.debug( + "{} can be fitted for param {} on {}".format( + key, + param, + list( + filter( + lambda n: is_numeric(n), + self.distinct_values[key][param], + ) + ), + ) ) return True return False @@ -646,13 +648,15 @@ class ParamStats: depends_on_a_parameter = False for param in self._parameter_names: if self.stats[state_or_tran][attribute]["depends_on_param"][param]: - print("{}/{} depends on {}".format(state_or_tran, attribute, param)) + logger.debug( + "{}/{} depends on {}".format(state_or_tran, attribute, param) + ) depends_on_a_parameter = True if ( len(self.codependent_parameters(state_or_tran, attribute, param)) == 0 ): - print("has no codependent parameters") + logger.debug("... and has no codependent parameters") # Always depends on this parameter, regardless of other parameters' values return False return depends_on_a_parameter diff --git a/lib/protocol_benchmarks.py b/lib/protocol_benchmarks.py index b42e821..d41979f 100755 --- a/lib/protocol_benchmarks.py +++ b/lib/protocol_benchmarks.py @@ -16,8 +16,11 @@ import io import os import re import time +import logging from filelock import FileLock +logger = logging.getLogger(__name__) + class DummyProtocol: def __init__(self): @@ -1838,14 +1841,14 @@ class Benchmark: this_result["data"] = data if value != None: this_result[key] = {"v": value, "ts": int(time.time())} - print( + logger.debug( "{} {} {} ({}) :: {} -> {}".format( libkey, bench_name, bench_index, data, key, value ) ) else: this_result[key] = {"e": error, "ts": int(time.time())} - print( + logger.debug( "{} {} {} ({}) :: {} -> [E] {}".format( libkey, bench_name, bench_index, data, key, error[:500] ) diff --git a/lib/utils.py b/lib/utils.py index 8186ee7..d28ecda 100644 --- a/lib/utils.py +++ b/lib/utils.py @@ -1,7 +1,9 @@ import numpy as np import re +import logging arg_support_enabled = True +logger = logging.getLogger(__name__) def running_mean(x: np.ndarray, N: int) -> np.ndarray: @@ -212,7 +214,7 @@ def filter_aggregate_by_param(aggregate, parameters, parameter_filter): ) ) if len(indices_to_keep) == 0: - print("??? {}->{}".format(parameter_filter, name)) + logger.debug("??? {}->{}".format(parameter_filter, name)) names_to_remove.add(name) else: for attribute in aggregate[name]["attributes"]: |