import copy from base_optimizer.optimizer_common import * def load_data(filename: str, load_feeder=True, auto_register=True): filename = 'data/' + filename part_content, step_content = False, False part_start_line, step_start_line, part_end_line, step_end_line = -1, -1, -1, -1 line_counter = 0 with open(filename, 'r') as file: line = file.readline() while line: if line == '[Part]\n': part_content = True part_start_line = line_counter elif line == '[Step]\n': step_content = True step_start_line = line_counter elif line == '\n': if part_content: part_content = False part_end_line = line_counter elif step_content: step_content = False step_end_line = line_counter line_counter += 1 line = file.readline() if part_content: part_end_line = line_counter else: step_end_line = line_counter file_data = pd.DataFrame( pd.read_csv(filepath_or_buffer=filename, skiprows=step_start_line + 1, nrows=step_end_line - step_start_line + 1, sep='\t', header=None)) if len(file_data.columns) == 22: data_col = ["machine", "bl", "ref", "x", "y", "z", "r", "part", "desc", "group", "fdr", "nz", "hd", "cs", "cy", "sk", "ar", "fid", "pop", "pl", "lv", "pr"] elif len(file_data.columns) <= 17: data_col = ["ref", "x", "y", "z", "r", "part", "desc", "fdr", "nz", "hd", "cs", "cy", "sk", "bl", "ar", "pl", "lv"] elif len(file_data.columns) <= 18: data_col = ["ref", "x", "y", "z", "r", "part", "desc", "fdr", "nz", "hd", "cs", "cy", "sk", "bl", "ar", "fid", "pl", "lv"] else: data_col = ["ref", "x", "y", "z", "r", "part", "desc", "fdr", "nz", "hd", "cs", "cy", "sk", "bl", "ar", "fid", "", "pl", "lv"] file_data.columns = data_col pcb_data, component_data, feeder_data = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame), defaultdict( pd.DataFrame) # line_data = line_data.dropna(axis=1) step_col = ["ref", "x", "y", "z", "r", "part", "desc", "fdr", "nz", "hd", "cs", "cy", "sk", "bl", "ar", "pl", "lv"] machine_name = defaultdict(int) for _, data in file_data.iterrows(): if "machine" in file_data.columns: if data['machine'] not in machine_name.keys(): machine_name[data['machine']] = len(machine_name) machine_index = machine_name[data['machine']] else: machine_index = 0 pcb_data[machine_index] = pcb_data[machine_index]._append(data[step_col], ignore_index=True) part_col = ["part", "fdr", "nz", 'fdn'] try: if part_start_line != -1: part_data = pd.DataFrame( pd.read_csv(filepath_or_buffer=filename, sep='\t', header=None, skiprows=part_start_line + 1, nrows=part_end_line - part_start_line - 1)) part_data.columns = part_col else: part_data = pd.DataFrame(columns=part_col) except: part_data = pd.DataFrame(columns=part_col) part_data['points'] = 0 part_col = ["part", "fdr", "nz", 'fdn', 'points'] machine_num = len(pcb_data) for machine_index in range(machine_num): component_data[machine_index] = pd.DataFrame(columns=part_col) component_slot = defaultdict(set) for idx, data in pcb_data[machine_index].iterrows(): if pos := data.fdr.find('F') != 0: pcb_data[machine_index].loc[idx, 'fdr'] = data.fdr[pos:pos + 1:1] + data.fdr[pos + 2::] if pos := data.nz.find('F') != -1: pcb_data[machine_index].loc[idx, 'nz'] = data.nz[0:pos:1] + data.nz[pos + 1::] if isinstance(data.hd, str) and (pos := data.hd.find('F') != -1): pcb_data[machine_index].loc[idx, 'hd'] = int(data.hd[pos + 2::]) part, nozzle = data.part, data.nz.split(' ')[1] if part not in component_data[machine_index]['part'].values: if not auto_register: raise Exception("unregistered component: " + component_data[machine_index]['part'].values) else: component_data[machine_index] = pd.concat([component_data[machine_index], pd.DataFrame( [part, 'SM8', nozzle, 0, 0], index=part_col).T], ignore_index=True) # warning_info = 'register component ' + part + ' with default feeder type' # warnings.warn(warning_info, UserWarning) part_index = component_data[machine_index][component_data[machine_index]['part'] == part].index.tolist()[0] component_data[machine_index].loc[part_index, 'points'] += 1 if (fdr := data['fdr'].split(' ')[0]) not in component_slot[part]: component_data[machine_index].loc[part_index, 'fdn'] += 1 component_slot[part].add(fdr) for idx, data in part_data.iterrows(): if data.part in component_slot.keys(): part_data.loc[idx, 'fdn'] = part_data.loc[idx, 'fdn'] - len(component_slot[data.part]) assert part_data.loc[idx, 'fdn'] >= 0 for idx, data in part_data.iterrows(): for machine_index in range(machine_num): if data.part not in component_data[machine_index].part.values: continue part_index = component_data[machine_index][component_data[machine_index].part == data.part].index.tolist()[ 0] if component_data[machine_index].loc[part_index].nz != data.nz: warning_info = 'the nozzle type of component ' + data.part + ' is not consistent with the pcb data' warnings.warn(warning_info, UserWarning) if data.fdn == 0: continue if data.part in component_data[0].part.values: part_index = component_data[0][component_data[0].part == data.part].index.tolist()[0] component_data[0].loc[part_index, 'fdn'] += data.fdn else: component_data[0] = pd.concat([component_data[0], pd.DataFrame(data).T], ignore_index=True) for machine_index in range(machine_num): for idx, data in component_data[machine_index].iterrows(): if data['fdr'][0:3] == 'SME': # 电动供料器和气动供料器参数一致 component_data[machine_index].at[idx, 'fdr'] = data['fdr'][0:2] + data['fdr'][3:] # pcb_data[machine_index].sort_values(by="x", ascending=False, inplace=True) # pcb_data[machine_index].reset_index(inplace=True) # 读取供料器基座数据 feeder_data = defaultdict(pd.DataFrame) if load_feeder: feeder_columns = ['slot', 'part', 'arg'] for machine_index in range(machine_num): feeder_data[machine_index] = pd.DataFrame(columns=feeder_columns) # arg表示是否为预分配，不表示分配数目 for _, data in pcb_data[machine_index].iterrows(): slot, part = data['fdr'].split(' ') if slot[0] != 'F' and slot[0] != 'R': continue slot = int(slot[1:]) if slot[0] == 'F' else int(slot[1:]) + max_slot_index // 2 feeder_data[machine_index] = pd.concat([feeder_data[machine_index], pd.DataFrame([slot, part, 1], index=feeder_columns).T], ignore_index=True) feeder_data[machine_index].drop_duplicates(subset='slot', inplace=True, ignore_index=True) # 随机移除部分已安装的供料器 # drop_index = random.sample(list(range(len(feeder_data))), len(feeder_data) // 2) # feeder_data[machine_index].drop(index=drop_index, inplace=True) feeder_data[machine_index].sort_values(by='slot', ascending=True, inplace=True, ignore_index=True) return pcb_data, component_data, feeder_data def merge_data(partial_pcb_data, partial_component_data): assert len(partial_pcb_data) == len(partial_component_data) machine_num = len(partial_pcb_data) pcb_data, component_data = copy.deepcopy(partial_pcb_data[0]), copy.deepcopy(partial_component_data[0]) for machine_index in range(1, machine_num): pcb_data = pd.concat([pcb_data, partial_pcb_data[machine_index]], ignore_index=True) for _, data in partial_component_data[machine_index].iterrows(): if data.part in component_data.part.values: part_index = component_data[component_data.part == data.part].index.tolist()[0] component_data.loc[part_index, 'points'] += data.points component_data.loc[part_index, 'fdn'] += data.fdn else: component_data = pd.concat([component_data, pd.DataFrame(data).T], ignore_index=True) component_data = component_data[component_data['points'] != 0].reset_index(drop=True) return pcb_data, component_data