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