def main_1(filepath_1):
filepath_1 = 'input_1.json'
input_1 = _get_json_(filepath_1)
filepath_2 = input_1['filePath'] #读入csv路径
data = pd.read_csv(filepath_2)
# 必须添加header=None，否则默认把第一行数据处理成列名导致缺失
data_1 = pd.DataFrame(data)
num = len(data_1) # 病人总的数目
morning_time = int(input_1['startTime'].split(':')[0]) * 60 + int(
input_1['startTime'].split(':')[1])
afternoon_time = int(input_1['endTime'].split(':')[0]) * 60 + int(
input_1['endTime'].split(':')[1])
n_x = int(input_1['orNum']) #手术室数目
n_y = int(input_1['recoverNum']) #复苏室数目
t_s = int(input_1['minRecoverTime']) #最小复苏时间
calculte_r = calculte(data, n_x, n_y, t_s, morning_time,
afternoon_time) # 实例化
list_doctID, list_patientID, list_operation, list_sleepy, list_clean, list_start, list_index_or, list_of_all = calculte_r._process_data_(
num)
# print(list_doctID, list_sleepy, list_operation, list_clean, list_patientID, list_start, list_index_or)
Encoding = 'RI' # 编码方式为实数
conordis = 1 # 染色体解码后得到的变量是离散的
NIND = 50 # 种群规模
problem = MyProblem(num, n_x, n_y, NIND, list_of_all, morning_time,
afternoon_time)
# n_o为手术室数量，n_r为复苏室数量, chrom为染色体[1,3,2]表示第一台
# 手术在1号手术室在1号手术室内做，o_time[30,100,60]表示第一台手术时长30分钟，
# c_time表示清洁时长，r_time表示复苏时长（0或自定义最小复苏时长默认为60min）
# 生成问题对象
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges,
problem.borders) # 创建区域描述器
population = ea.Population(Encoding, Field, NIND) # 创建种群对象
x_chuandai = 20
id_trace = (np.zeros(
(x_chuandai, num)) * np.nan) # 定义变量记录器，记录决策变量值，初始值为nan
Algorithm_1 = Algorithm(problem, population, id_trace) # 实例化一个算法模板对象
Algorithm_1.MAXGEN = x_chuandai # 最大遗传代数
[population, obj_trace, var_trace,
id_trace] = Algorithm_1.run() # 执行算法模板 #如何返回最优解
best_gen = np.argmin(obj_trace[:, 1]) # 记录最优种群是在哪一代
best_ObjV = obj_trace[best_gen, 1] # 目标函数值最优
#mean_ObjV = obj_trace[best_gen, 0] # 均值
best_paixu = var_trace[best_gen, :] # 最优解
ARRAY = id_trace[best_gen, :]
print('最优的目标函数值为：%s' % (best_ObjV))
print('有效进化代数：%s' % (obj_trace.shape[0]))
print('最优的一代是第 %s 代' % (best_gen + 1))
print('时间已过 %s 秒' % (Algorithm_1.passTime))
# id_trace = (np.zeros((self.MAXGEN, NVAR)) * np.nan) # 定义变量记录器，记录决策变量值，初始值为nan
#返回一个ARRAY将list调整并传给best_result函数
sel_data = list_of_all[:, list(ARRAY.astype(np.int))]
#list_doctID, list_patientID, list_operation, list_sleepy, list_clean, list_start, list_index_or
list_doctID_2 = sel_data[0]
list_patientID_2 = sel_data[1]
list_operation_2 = sel_data[2]
list_sleepy_2 = sel_data[3]
list_clean_2 = sel_data[4]
list_start_2 = sel_data[5]
list_index_or_2 = sel_data[6]
ARRAY_1 = ARRAY.astype(np.int)
best_paixu_1 = best_paixu.astype(np.int)
print('最优解的index:', ARRAY_1)
print('最优解的手术室号:', best_paixu_1)
o_total_time, o_total_r_time, o_total_empty_time, overtime_work, result = calculte_r._best_result_(
best_paixu_1, num, list_sleepy_2, list_operation_2, list_clean_2)
# o_total_time是手术室工作的总时长// o_total_r_time是手术室总复苏时长// o_total_empty_time是手术室总空闲时长// overtime_work是手术室总超时工作时长//
index_or_1, list_clean_1, list_sleepy_1, list_start_1 = calculte_r._get_list_(
num, result, ARRAY_1, list_clean, list_operation)
#用于返回数据
data['复苏时间'] = list_sleepy_1 # 手术室内的复苏时间
data['清洁时间'] = list_clean_1 # 手术室内的清洁时间
data['手术开始时间'] = list_start_1 # 手术室每一台手术的开始时间
data['手术室编码'] = index_or_1 # 手术室内的手术的编码
#存入csv
data.to_csv('output.csv', sep=',', header=True)
#json输出内容
orRatio = str((o_total_time - o_total_r_time - list_clean_1.sum()) /
(o_total_time + o_total_empty_time))
cleanRatio = str(list_clean_1.sum() / (o_total_time + o_total_empty_time))
recoverRoomratio = str(o_total_r_time /
(o_total_time + o_total_empty_time))
emptyRatio = str(o_total_empty_time / (o_total_time + o_total_empty_time))
extraHours = overtime_work
extraHoursRatio = (extraHours / o_total_time)
overtimeRatio = str(overtime_work.sum() / o_total_time)
dict_2 = {}
dict_2["filePath"] = "output.csv"
dict_2["orRatio"] = orRatio # 手术室利用率
dict_2["recoverRatio"] = recoverRoomratio # 复苏室利用率
dict_2["cleanRatio"] = cleanRatio # 用于清洁的时间
dict_2["emptyRatio"] = emptyRatio # 闲置时间比例
dict_2["extraHours"] = extraHours.tolist() # 加班总时间(分钟)
dict_2["extraHoursRatio"] = extraHoursRatio.tolist() # 每一个元素
dict_2["overtimeRatio"] = overtimeRatio # 额外加班时间
# 用于饼图展示的比例：
return dict_2
