import traceback import sys from typing import Optional, Any import numpy as np import pandas as pd from loguru import logger from base.base import BasePointPassportFormer, BaseIdealDataBuilder, PointPassport, GraphicPassport, Settings class PassportFormer(BasePointPassportFormer): def form_passports(self, data: list[pd.DataFrame]) -> list[GraphicPassport]: try: return_data = [self._build_graphic_passport(dataframe) for dataframe in data] except: # TODO: обработка исключений!!! tb = sys.exc_info()[2] # TODO: Нормальные сообщения в лог! tbinfo = traceback.format_tb(tb)[0] pymsg = "Traceback info:\n" + tbinfo + "\nError Info:\n" + str(sys.exc_info()[1]) logger.error(pymsg) return_data = [] finally: self._mediator.notify(self, return_data) def update_settings(self, settings: Settings): self._settings = settings @staticmethod def _find_indexes(signal: str, dataframe: pd.DataFrame) -> tuple[np.ndarray, np.ndarray]: stage_diff = np.diff(dataframe[signal]) start_idx = np.where(stage_diff == 1) finish_idx = np.where(stage_diff == -1) return start_idx[0], finish_idx[0] def _find_events(self, signal: str, times:pd.Series, dataframe: pd.DataFrame) -> tuple[list[float], list[float]]: start_idx, finish_idx = self._find_indexes(signal, dataframe) if len(start_idx) > 0 and len(finish_idx) > 0 and start_idx[0] > finish_idx[0]: start_idx = np.insert(start_idx, 0, 0) start_list = times.iloc[start_idx].tolist() if len(start_idx) > 0 else [] end_list = times.iloc[finish_idx].tolist() if len(finish_idx) > 0 else [] if len(start_list) - len(end_list) == 1: end_list.append(float(times.iloc[-1])) return start_list, end_list def _filter_events(self, times: pd.Series, dataframe: pd.DataFrame) -> tuple[dict[str, list[list[float]]], int]: events = {} point_quantity = 0 if self._clear_stage in self._stages: start_list, end_list = self._find_events(self._clear_stage, times, dataframe) point_quantity = len(start_list) if point_quantity == 0: #TODO: добавить обработку исключения return [] for stage in self._stages: start_list, end_list = self._find_events(stage, times, dataframe) temp = min([len(start_list), len(end_list)]) if temp < point_quantity: print ("cant find enough", stage) start_list += [0]*(point_quantity - temp) end_list += [1]*(point_quantity - temp) events[stage] = [start_list, end_list] return events, point_quantity def _build_ideal_data(self, idealDataBuilder: Optional[BaseIdealDataBuilder] = None, point_settings: Settings = None) -> dict: self.opt_algorithm = idealDataBuilder(point_settings) stage_ideals = { "Closing": self._opt_algorithm.get_closingDF(), "Squeeze": self._opt_algorithm.get_compressionDF(), "Welding": self._opt_algorithm.get_weldingDF(), "Relief": self._opt_algorithm.get_openingDF(), "Oncomming": self._opt_algorithm.get_oncomingDF(), "Ideal cycle": self._opt_algorithm.get_cycle_time(), "Ideal timings": self._opt_algorithm.get_ideal_timings() } return stage_ideals def _generate_cache_key(self, point_settings:Settings) -> tuple[tuple[tuple[str, Any], ...], tuple[tuple[str, Any], ...]]: """ Преобразует point_settings в хешируемый ключ для кэша. """ # Преобразуем словари в отсортированные кортежи пар ключ-значение operator_tuple = frozenset((key, value) for key, value in point_settings.operator.items() if str(key) in self._OptAlgorithm_operator_params) system_tuple = frozenset((key, value) for key, value in point_settings.system.items() if str(key) in self._OptAlgorithm_system_params) return (operator_tuple, system_tuple) def _build_graphic_passport(self, dataframe: pd.DataFrame) -> GraphicPassport: if dataframe is not None: events, point_quantity = self._filter_events(dataframe["time"], dataframe) if point_quantity == 0: return [] else: events = None key = list(self._settings.operator.keys())[0] point_quantity = len(self._settings.operator[key]) graphic_passport = GraphicPassport() graphic_passport.dataframe = dataframe graphic_passport.points_pocket = [] system_settings = {key: value[0] for key, value in self._settings.system.items()} graphic_passport.useful_data = self._form_graphic_useful_data(system_settings) for i in range(point_quantity): point_settings = Settings() point_settings.operator = self._get_operator_settings_part(i) point_settings.system = system_settings point_passport = PointPassport() point_passport.ideal_data = self._form_point_ideal_data(point_settings) point_passport.useful_data = self._form_point_useful_data(point_settings.operator) point_passport.timeframe, point_passport.events = self._form_point_events(events, i) graphic_passport.points_pocket.append(point_passport) return graphic_passport def _form_graphic_useful_data(self, system_settings:dict) -> dict: tesla_time = sum(self._settings.operator.get("Tesla summary time", [])) useful_data = {"tesla_time": tesla_time, "range_ME": system_settings["Range ME, mm"], "k_hardness": system_settings["k_hardness_1"] } return useful_data def _form_point_useful_data(self, operator_settings:dict) -> dict: useful_data = {"thickness": operator_settings["object_thickness"], "L2": operator_settings["distance_l_2"], "force": operator_settings["force_target"]} return useful_data def _form_point_ideal_data(self, point_settings:Settings) -> dict: cache_key = self._generate_cache_key(point_settings) ideal_data = self._ideal_data_cashe.get(cache_key, self._build_ideal_data(idealDataBuilder=IdealDataBuilder, params=point_settings)) self._ideal_data_cashe[cache_key] = ideal_data return ideal_data def _get_operator_settings_part(self, idx:int) -> dict: operator_settings = { key: (value[idx] if idx < len(value) else value[0]) for key, value in self._settings.operator.items() } return operator_settings def _form_point_events(self, events:dict, idx) -> list: timeframe, point_events = None, None if events is not None: idx_shift = idx+1 if events[self._stages[-1]][0][0] == 0 else idx timeframe = [events[self._stages[0]][0][idx], events[self._stages[-1]][1][idx_shift]] point_events = {key: [value[0][idx], value[1][idx]] for key, value in events.items()} return timeframe, point_events class IdealDataBuilder(BaseIdealDataBuilder): def get_closingDF(self) -> pd.DataFrame: return self._get_data(self.Ts['tclose'], self.calcPhaseClose) def get_compressionDF(self) -> pd.DataFrame: return self._get_data(self.Ts['tgrow'], self.calcPhaseGrow) def get_openingDF(self) -> pd.DataFrame: return self._get_data(self.getMarkOpen(), self.calcPhaseOpen) def get_oncomingDF(self) -> pd.DataFrame: return self._get_data(self.Ts['tmovement'], self.calcPhaseMovement) def get_weldingDF(self) -> pd.DataFrame: data = [] X1, X2, V1, V2, F = self.calcPhaseGrow(self.Ts['tgrow']-0.0001) X1, X2, V1, V2 = X1*1000, X2*1000, V1*1000, V2*1000 data.append({ "time":0, "Position FE":X1, "Position ME":X2, "Rotor Speed FE":V1, "Rotor Speed ME":V2, "Force":F }) data.append({ "time":self.welding_time, "Position FE":X1, "Position ME":X2, "Rotor Speed FE":V1, "Rotor Speed ME":V2, "Force":F }) return pd.DataFrame(data) def get_ideal_timings(self) -> list[float]: data = self.Ts ideal_timings = [ data['tclose'], data['tgrow'], self.welding_time, self.getMarkOpen(), data['tmovement'] ] return ideal_timings def _get_data(self, end_timestamp:float, func:function) -> pd.DataFrame: data = [] for i in range (0, int(end_timestamp*self.mul)+1): time = i/self.mul X1, X2, V1, V2, F = func(time) data.append({ "time":time, "Position FE":X1*1000, "Position ME":X2*1000, "Rotor Speed FE":V1*1000, "Rotor Speed ME":V2*1000, "Force":F }) X1, X2, V1, V2, F = func(end_timestamp) data.append({ "time":end_timestamp, "Position FE":X1*1000, "Position ME":X2*1000, "Rotor Speed FE":V1*1000, "Rotor Speed ME":V2*1000, "Force":F }) return pd.DataFrame(data)