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How to use psh method in fMBT
Best Python code snippet using fMBT_python
ModelSetUp.py
Source:ModelSetUp.py 
...31 def add_var_e(self, var_name):32 return self.gur_model.addVars(self.e_system.parameter['EName'], ub=float('inf'),lb=-float('inf'), vtype="C", name=var_name)33 def add_var_I(self, var_name):34 return self.gur_model.addVars(self.e_system.parameter['EName'], vtype="B", name=var_name)35 def add_var_psh(self, var_name):36 return self.gur_model.addVars(self.psh_system.parameter['PSHName'], ub=float('inf'),lb=-float('inf'),vtype="C",name=var_name)37 def add_constraint_week_rolling(self):38 print(type(self.e_prev[0]))39 print(type(self.e_system.parameter['EStart']))40 print(type(self.e['Reservoir1']))41 for k in self.e_system.parameter['EName']:42 for i in range(self.one_day_period):43 if i == 0:44 LHS = self.e_prev[i][k] + grb.quicksum(self.psh_gen_prev[i][j] / self.psh_system.parameter['GenEfficiency'] for j in self.psh_system.parameter['PSHName']) \45 - grb.quicksum(self.psh_pump_prev[i][j] * self.psh_system.parameter['PumpEfficiency'] for j in self.psh_system.parameter['PSHName'])46 RHS = self.e_system.parameter['EStart']47 self.gur_model.addConstr(LHS == RHS, name='%s_%s' % ('SOC', i))48 else:49 LHS = self.e_prev[i][k] + grb.quicksum(self.psh_gen_prev[i][j] / self.psh_system.parameter['GenEfficiency'] for j in self.psh_system.parameter['PSHName']) \50 - grb.quicksum(self.psh_pump_prev[i][j] * self.psh_system.parameter['PumpEfficiency'] for j in self.psh_system.parameter['PSHName'])51 RHS = self.e_prev[i-1][k]52 self.gur_model.addConstr(LHS == RHS, name='%s_%s' % ('SOC', i))53 for k in self.e_system.parameter['EName']:54 LHS = self.e[k] + grb.quicksum(self.psh_gen[j] / self.psh_system.parameter['GenEfficiency'] for j in self.psh_system.parameter['PSHName']) \55 - grb.quicksum(self.psh_pump[j] * self.psh_system.parameter['PumpEfficiency'] for j in self.psh_system.parameter['PSHName'])56 RHS = self.e_prev[self.one_day_period-1][k]57 ### if we calculate the first one, we use 'SOC0', and the last we use 'End'; or we choose the SOC0 to "beginning", at the same time the last we use 'SOC'.58 self.gur_model.addConstr(LHS == RHS, name='%s_%s' % ('SOC0', k))59 def add_constraint_week_epsh(self):60 for j in self.psh_system.parameter['PSHName']: # all are lists61 self.gur_model.addConstr(self.psh_gen[j] <= self.psh_system.parameter['GenMax'], name='%s_%s' % ('psh_gen_max0', j))62 self.gur_model.addConstr(self.psh_gen[j] >= self.psh_system.parameter['GenMin'], name='%s_%s' % ('psh_gen_min0', j))63 self.gur_model.addConstr(self.psh_pump[j] <= self.psh_system.parameter['PumpMax'], name='%s_%s' % ('psh_pump_max0', j))64 self.gur_model.addConstr(self.psh_pump[j] >= self.psh_system.parameter['PumpMin'], name='%s_%s' % ('psh_pump_min0', j))65 for i in range(self.one_day_period):66 self.gur_model.addConstr(self.psh_gen_prev[i][j] <= self.psh_system.parameter['GenMax'], name='%s_%s' % ('psh_gen_max'+str(i),j))67 self.gur_model.addConstr(self.psh_gen_prev[i][j] >= self.psh_system.parameter['GenMin'], name='%s_%s' % ('psh_gen_min'+str(i),j))68 self.gur_model.addConstr(self.psh_pump_prev[i][j] <= self.psh_system.parameter['PumpMax'], name='%s_%s' % ('psh_pump_max'+str(i),j))69 self.gur_model.addConstr(self.psh_pump_prev[i][j] >= self.psh_system.parameter['PumpMin'], name='%s_%s' % ('psh_pump_min'+str(i),j))70 for k in self.e_system.parameter['EName']:71 self.gur_model.addConstr(self.e[k] <= self.e_system.parameter['EMax'], name='%s_%s' % ('e_max0', k))72 self.gur_model.addConstr(self.e[k] >= self.e_system.parameter['EMin'], name='%s_%s' % ('e_min0', k))73 for i in range(self.one_day_period):74 self.gur_model.addConstr(self.e_prev[i][k] <= self.e_system.parameter['EMax'], name='%s_%s' % ('e_max'+ str(i), k))75 self.gur_model.addConstr(self.e_prev[i][k] >= self.e_system.parameter['EMin'], name='%s_%s' % ('e_min0'+ str(i), k))76 def add_constraint_curve(self):77 for k in self.e_system.parameter['EName']:78 _temp_sum = 079 for i in range(self.curve.numbers):80 _temp_sum += self.soc[i][k]81 LHS = self.e[k]82 RHS = _temp_sum # RHS=soc[0][k]+soc[1][k]+soc[2][k]+soc[3][k]+soc[4][k]83 self.gur_model.addConstr(LHS == RHS, name='%s_%s' % ('curve', k))84 def add_constraint_soc(self):85 ### how to constraint for d_1I_2 <= soc_1 <=d_1I_1? ############################86 for k in self.e_system.parameter['EName']:87 for i in range(self.curve.numbers):88 name_num = str(i + 1)89 bench_num = i90 if bench_num == 0:91 self.gur_model.addConstr(self.soc[bench_num][k] <= float(self.d[bench_num]) * self.I[bench_num][k],92 name='%s_%s' % ('soc_' + name_num + '_max', k))93 self.gur_model.addConstr(float(self.d[bench_num]) * self.I[bench_num + 1][k] <= self.soc[bench_num][k],94 name='%s_%s' % ('soc_' + name_num + '_min', k))95 elif bench_num == self.curve.numbers - 1:96 self.gur_model.addConstr(self.soc[bench_num][k] <= float(self.d[bench_num]) * self.I[bench_num][k],97 name='%s_%s' % ('soc_' + name_num + '_max', k))98 self.gur_model.addConstr(0 <= self.soc[bench_num][k], name='%s_%s' % ('soc_' + name_num + '_min', k))99 else:100 self.gur_model.addConstr(self.soc[bench_num][k] <= float(self.d[bench_num]) * self.I[bench_num][k],101 name='%s_%s' % ('soc_' + name_num + '_max', k))102 self.gur_model.addConstr(float(self.d[bench_num]) * self.I[bench_num + 1][k] <= self.soc[bench_num][k],103 name='%s_%s' % ('soc_' + name_num + '_min', k))104 def add_constraint_I(self):105 for k in self.e_system.parameter['EName']:106 for i in range(self.curve.numbers - 1):107 name_num = str(i + 1)108 name_num_next = str(i + 2)109 bench_num = i110 self.gur_model.addConstr(self.I[bench_num + 1][k] <= self.I[bench_num][k],111 name='%s_%s' % ('I_' + name_num_next + '_' + name_num, k))112 def add_contraint_terminal(self):113 beta = 0.001 # 0.001114 for k in self.e_system.parameter['EName']:115 curr_day = self.curr_model_para.day_period - self.curr_model_para.curr_day116 LHS_1 = self.e[k] - self.e_system.parameter['EEnd']117 RHS_1 = (curr_day) * self.psh_system.parameter['GenMax'] / (118 self.psh_system.parameter['GenEfficiency'] + beta) # PSHmax_g[0] / PSHefficiency[0]119 self.gur_model.addConstr(LHS_1 <= RHS_1, name='%s_%s' % ('final_upper', k))120 for k in self.e_system.parameter['EName']:121 curr_day = self.curr_model_para.day_period - self.curr_model_para.curr_day122 LHS_2 = self.e[k] - self.e_system.parameter['EEnd']123 RHS_2 = -(curr_day) * self.psh_system.parameter['PumpMax'] * (124 self.psh_system.parameter['PumpEfficiency'] - beta) # PSHmax_p[0] * PSHefficiency[0]125 self.gur_model.addConstr(LHS_2 >= RHS_2, name='%s_%s' % ('final_lower', k))126 def add_week_contraint_terminal(self):127 for k in self.e_system.parameter['EName']:128 LHS = self.e[k] - self.e_system.parameter['EEnd']129 RHS = 0130 self.gur_model.addConstr(LHS == RHS, name='%s_%s' % ('final_up_down', k))131##the following is for set upt elements of optimization problems132 def set_up_week_variable(self):133 # add gen_prev, pump_prev, e_prev134 self.one_day_period = 23135 self.psh_gen_prev = []136 self.psh_pump_prev = []137 self.e_prev = []138 for i in range(self.one_day_period):139 name_num = str(i + 1)140 self.psh_gen_prev.append((self.add_var_psh('first_23_psh_gen_' + name_num)))141 self.psh_pump_prev.append((self.add_var_psh('first_23_psh_pump_' + name_num)))142 self.e_prev.append((self.add_var_e('first_23_e_' + name_num)))143 # add gen/pump144 self.psh_gen = self.add_var_psh('psh_gen_main')145 self.psh_pump = self.add_var_psh('psh_pump_main')146 #add e147 self.e = self.add_var_e('e_main')148 #add soc and I149 #self.len_var = self.curve.numbers #len(self.curve.point_X)-1150 self.soc = []151 self.I = []152 for i in range(self.curve.numbers):153 #for i in range(self.curve.numbers ):154 name_num = str(i + 1)155 self.soc.append(self.add_var_e('soc_' + name_num))156 self.I.append(self.add_var_I('I_' + name_num))157 #add d158 d = []159 for i in range(self.curve.numbers):160 #for i in range(self.curve.numbers ):161 d.append(self.curve.point_X[i+1] - self.curve.point_X[i])162 self.d = d163 self.gur_model.update()164 def set_up_constraint(self):165 # rolling constraint E_start = E_end +pump + gen166 self.add_constraint_week_rolling()167 # upper and lower constraint168 self.add_constraint_week_epsh()169 # curve constraint170 self.add_constraint_curve()171 # constraint for d_1I_2 <= soc_1 <=d_1I_1?##172 self.add_constraint_soc()173 # constraint for I_1<=I_2<=I_3174 self.add_constraint_I()175 # terminal constraint176 if self.curr_model_para.curr_day == 6:####6:177 self.add_week_contraint_terminal()178 self.gur_model.update()179 def set_up_week_object(self):180 self.profit_max = []181 for j in self.psh_system.parameter['PSHName']:182 self.profit_max.append((self.psh_gen[j] - self.psh_pump[j]) * self.lmp.lmp_scenarios)...
PerfectOpt.py
Source:PerfectOpt.py
1import pandas as pd2from gurobipy import *3import gurobipy as grb4import matplotlib.pyplot as plt5import numpy as np6def Perfect_Opt(Total_hour, Input_folder,Output_folder,date, RT_DA ,probabilistic, time_total, scenario):7 print('################################## LAC_PSH_profit_max #', Total_hour, 'probabilistic=',probabilistic,' ##################################')8 ################################### Read Data ####################################9 ##read PSH10 filename = Input_folder + '/PSH.csv'11 Data = pd.read_csv(filename)12 df=pd.DataFrame(Data)13 PSHmin_g = df['GenMin']14 PSHmax_g = df['GenMax']15 PSHmin_p = df['PumpMin']16 PSHmax_p = df['PumpMax']17 PSHefficiency = list(df['Efficiency'])18 PSHname = list(df['Name'])19 ##read E20 filename = Input_folder + '/Reservoir.csv'21 Data = pd.read_csv(filename)22 df=pd.DataFrame(Data)23 Emin = df['Min']24 Emax = df['Max']25 Ename = df['Name']26 Edayend= float(df['End'])27 Estart = df['Start']28 ##read price29 filename = Input_folder + '/DECO_prd_dataframe_Perfect_October 1521 2019.csv'30 Data = pd.read_csv(filename)31 df = pd.DataFrame(Data)32 Column_name = list(Data.columns)33 Column_name = Column_name[scenario]34 lmp_quantiles = [1]35 lmp_scenarios = []36 Nlmp_s = 137 # the scenario you want38 lmp_scenarios.append(df[Column_name])39 lmp_scenarios = list(lmp_scenarios[0])40 e_time_periods = [] # è¿ä¸ªæ¯æ»æ¶é´å°141 for i in range(1, len(lmp_scenarios)):42 #for i in range(len(lmp_scenarios)):43 e_time_periods.append('T' + str(i))44 ################################### Build Model ####################################45 model = Model('DAMarket')46 ### define variables47 psh_max_g={}48 psh_min_g={}49 psh_max_p = {}50 psh_min_p = {}51 e_max={}52 e_min={}53 for i in e_time_periods:54 for k in PSHname:55 for s in range(Nlmp_s):56 psh_max_g[(s, i, k)] = list(PSHmax_g)[list(PSHname).index(k)]57 psh_min_g[(s, i, k)] = list(PSHmin_g)[list(PSHname).index(k)]58 psh_max_p[(s, i, k)] = list(PSHmax_p)[list(PSHname).index(k)]59 psh_min_p[(s, i, k)] = list(PSHmin_p)[list(PSHname).index(k)]60 for i in e_time_periods:61 for f in Ename:62 for s in range(Nlmp_s):63 e_max[(s, i, f)] = list(Emax)[list(Ename).index(f)]64 e_min[(s, i, f)] = list(Emin)[list(Ename).index(f)]65 e_max_inf = {}66 e_min_inf = {}67 psh_max_inf = {}68 psh_min_inf = {}69 for i in e_time_periods:70 for k in PSHname:71 for s in range(Nlmp_s):72 psh_max_inf[(s, i, k)] = float('inf')73 psh_min_inf[(s, i, k)] = -float('inf')74 for i in e_time_periods:75 for f in Ename:76 for s in range(Nlmp_s):77 e_max_inf[(s, i, f)] = float('inf')78 e_min_inf[(s, i, f)] = -float('inf')79 # psh0 and e0 are the first stage deterministic variables, while psh and e are stochastic variables.80 psh0_gen= model.addVars(list(PSHname), ub=float('inf'), lb=-float('inf'), name='PSH0_gen')81 psh0_pump= model.addVars(list(PSHname), ub=float('inf'), lb=-float('inf'), name='PSH0_pump')82 psh_gen = model.addVars(range(Nlmp_s), e_time_periods, list(PSHname), ub = psh_max_inf, lb = psh_min_inf, name = 'PSH_gen')83 psh_pump = model.addVars(range(Nlmp_s), e_time_periods, list(PSHname), ub = psh_max_inf, lb = psh_min_inf, name = 'PSH_pump')84 e0 = model.addVars(list(Ename), ub = float('inf'), lb = -float('inf'), name='E0')85 e = model.addVars(range(Nlmp_s), e_time_periods, list(Ename), ub=e_max_inf, lb=e_min_inf, name='E')86 model.update()87 ### add constraints88 # fix start state of energy stored in reservoir89 for j in Ename:90 print('Estart:', float(Estart))91 LHS = e0[j] + grb.quicksum(psh0_gen[j]/PSHefficiency[PSHname.index(j)] for j in PSHname)\92 - grb.quicksum(psh0_pump[j]*PSHefficiency[PSHname.index(j)] for j in PSHname) - float(Estart)93 RHS = 094 print(LHS, RHS)95 model.addConstr(LHS == RHS, name='%s_%s' % ('SOC0', j))96 # state of charge97 for i in e_time_periods:98 time_id = e_time_periods.index(i)99 if time_id == 0: #ç¸å½äºé¤äºe0ç第ä¸ä¸ªï¼éè¦åe0交æ¢100 for s in range(Nlmp_s):101 for j in Ename:102 print('Estart:',float(Estart))103 LHS = e[s, i, j] + grb.quicksum(psh_gen[s,i,j]/PSHefficiency[PSHname.index(j)] for j in PSHname)\104 - grb.quicksum(psh_pump[s, i, j] * PSHefficiency[PSHname.index(j)] for j in PSHname)- e0[j]105 RHS = 0106 print(LHS,RHS)107 model.addConstr(LHS == RHS, name='%s_%s_%d_%d' % ('SOC', j, time_id, s))108 # if time_id == len(e_time_periods) - 1:109 # for j in Ename:110 # print('Edayend:', Edayend)111 # LHS = Edayend - e[s, i, j]112 # RHS = 0113 # print(LHS,RHS)114 # model.addConstr(LHS == RHS, name='%s_%s_%d_%d' % ('SOC', j, len(e_time_periods),s))115 else:116 for s in range(Nlmp_s):117 time_previous=e_time_periods[time_id-1]118 for j in Ename:119 LHS=e[s,i,j] + grb.quicksum(psh_gen[s,i,j]/PSHefficiency[PSHname.index(j)] for j in PSHname)\120 - grb.quicksum(psh_pump[s,i,j]*PSHefficiency[PSHname.index(j)] for j in PSHname)-e[s,time_previous,j]121 RHS= 0122 print(LHS, RHS)123 model.addConstr(LHS == RHS, name='%s_%s_%d_%d' % ('SOC', j, time_id, s))124 if time_id == len(e_time_periods) - 1:125 for j in Ename:126 print('Edayend:', Edayend)127 LHS = Edayend - e[s, i, j]128 RHS = 0129 print(LHS,RHS)130 model.addConstr(LHS == RHS, name='%s_%s_%d_%d' % ('SOC', j, len(e_time_periods),s))131 # Upper and lower bounds132 for j in PSHname:133 model.addConstr(psh0_gen[j] <= PSHmax_g, name='%s_%s' % ('psh_gen_max0', j))134 model.addConstr(psh0_gen[j] >= PSHmin_g, name='%s_%s' % ('psh_gen_min0', j))135 model.addConstr(psh0_pump[j] <= PSHmax_p, name='%s_%s' % ('psh_pump_max0', j))136 model.addConstr(psh0_pump[j] >= PSHmin_p, name='%s_%s' % ('psh_pump_min0', j))137 for k in Ename:138 model.addConstr(e0[k] <= Emax, name='%s_%s' % ('e_max0', k))139 model.addConstr(e0[k] >= Emin, name='%s_%s' % ('e_min0', k))140 for s in range(Nlmp_s):141 for i in e_time_periods:142 for j in PSHname:143 model.addConstr(psh_gen[s, i, j] <= psh_max_g[s, i, j], name='%s_%s_%s_%s' % ('psh_gen_max',s, i, j))144 model.addConstr(psh_gen[s, i, j] >= psh_min_g[s, i, j], name='%s_%s_%s_%s' % ('psh_gen_min',s, i, j))145 model.addConstr(psh_pump[s, i, j] <= psh_max_p[s, i, j], name='%s_%s_%s_%s' % ('psh_pump_max', s, i, j))146 model.addConstr(psh_pump[s, i, j] >= psh_min_p[s, i, j], name='%s_%s_%s_%s' % ('psh_pump_min', s, i, j))147 for k in Ename:148 model.addConstr(e[s, i, k] <= e_max[s, i, k], name='%s_%s_%s_%s' % ('e_max',s, i, k))149 model.addConstr(e[s, i, k] >= e_min[s, i, k], name='%s_%s_%s_%s' % ('e_min',s, i, k))150 ### Objective function151 psh_max=[]152 for s in range(Nlmp_s):153 p_s = lmp_quantiles[s]154 for j in PSHname:155 psh_max.append((psh0_gen[j] - psh0_pump[j]) * lmp_scenarios[0] * p_s)156 for i in e_time_periods:157 lac_lmp = lmp_scenarios[e_time_periods.index(i)+1] #whyè¿éå ä¸1158 psh_max.append((psh_gen[s, i, j] - psh_pump[s, i, j]) * lac_lmp * p_s)159 obj = quicksum(psh_max)160 ################################### Solve Model ####################################161 model.setObjective(obj, GRB.MAXIMIZE)162 model.optimize()163 ####print the first result164 obj = model.getObjective() # self.calculate_pts(self.optimal_soc_sum)165 # self.optimal_profit = obj.getValue()166 print('the optimal profit is #####', obj.getValue())167 print('#####')168 # print results for variables169 for v in model.getVars():170 print("%s %f" % (v.Varname, v.X))171 ## add psh172 psh0=[]173 PSH0=[]174 for v in [v for v in model.getVars() if 'PSH0_gen' in v.Varname]:175 psh = v.X176 psh0.append(psh)177 for v in [v for v in model.getVars() if 'PSH0_pump' in v.Varname]:178 psh = v.X179 psh0.append(-psh)180 PSH0.append(sum(psh0))181 #if LAC_last_windows:182 psh_last_window_gen=[]183 psh_last_window_pump=[]184 PSH=[PSH0[0]]185 ## add soc186 SOC=[]187 for v in [v for v in model.getVars() if 'PSH_gen' in v.Varname]:188 psh = v.X189 psh_last_window_gen.append(psh)190 for v in [v for v in model.getVars() if 'PSH_pump' in v.Varname]:191 psh = v.X192 psh_last_window_pump.append(psh)193 for i in range(len(psh_last_window_gen)):194 PSH.append(psh_last_window_gen[i]-psh_last_window_pump[i])195 for v in [v for v in model.getVars() if 'E' in v.Varname]:196 soc = v.X197 SOC.append(soc)...
tcp_map.py
Source:tcp_map.py
1# -*- coding: utf-8 -*-2# Copyright 2017, A10 Networks3# Author: Mike Thompson: @mike @t @a10@networks!com4# tcp reference for events, flags, states5_TCP_STATES = [6 'CLOSED',7 'LISTEN',8 'SYN_RCVD',9 'ESTABLISHED',10 'SYN_SENT',11 'FIN_WAIT_1',12 'FIN_WAIT_2',13 'TIME_WAIT',14 'CLOSING',15 'CLOSE_WAIT',16 'LAST_ACK',17]18TCP_EVENTS = [19 'PASSIVE',20 'ACTIVE',21 'SYN',22 'SYNACK',23 'ACK',24 'RDATA',25 'SDATA',26 'FIN',27 'CLOSE',28 'TIMEOUT',29]30_flag_map_hex = {31 0x00: 'NONE',32 0x01: 'FIN',33 0x02: 'SYN',34 0x03: 'FIN-SYN',35 0x04: 'RST',36 0x08: 'PSH',37 0x09: 'FIN-PSH',38 0x0A: 'SYN-PSH',39 0x0B: 'FIN-SYN-PSH',40 0x10: 'ACK',41 0x11: 'FIN-ACK',42 0x12: 'SYN-ACK',43 0x14: 'RST-ACK',44 0x13: 'FIN-SYN-ACK',45 0x18: 'PSH-ACK',46 0x19: 'FIN-PSH-ACK',47 0x1A: 'SYN-PSH-ACK',48 0x1B: 'FIN-SYN-PSH-ACK',49 0x1C: 'RST-PSH-ACK',50 0x20: 'URG',51 0x40: 'ECE',52 0x41: 'FIN-ECE',53 0x42: 'SYN-ECE',54 0x43: 'FIN-SYN-ECE',55 0x48: 'PSH-ECE',56 0x49: 'FIN-PSH-ECE',57 0x4A: 'SYN-PSH-ECE',58 0x4B: 'FIN-SYN-PSH-ECE',59 0x50: 'ACK-ECE',60 0x51: 'FIN-ACK-ECE',61 0x52: 'SYN-ACK-ECE',62 0x53: 'FIN-SYN-ACK-ECE',63 0x58: 'PSH-ACK-ECE',64 0x59: 'FIN-PSH-ACK-ECE',65 0x5A: 'SYN-PSH-ACK-ECE',66 0x5B: 'FIN-SYN-PSH-ACK-ECE',67 0x80: 'CWR',68 0x81: 'FIN-CWR',69 0x82: 'SYN-CWR',70 0x83: 'FIN-SYN-CWR',71 0x88: 'PSH-CWR',72 0x89: 'FIN-PSH-CWR',73 0x8A: 'SYN-PSH-CWR',74 0x8B: 'FIN-SYN-PSH-CWR',75 0x90: 'ACK-CWR',76 0x91: 'FIN-ACK-CWR',77 0x92: 'SYN-ACK-CWR',78 0x93: 'FIN-SYN-ACK-CWR',79 0x98: 'PSH-ACK-CWR',80 0x99: 'FIN-PSH-ACK-CWR',81 0x9A: 'SYN-PSH-ACK-CWR',82 0x9B: 'FIN-SYN-PSH-ACK-CWR',83 0xC0: 'ECE-CWR',84 0xC1: 'FIN-ECE-CWR',85 0xC2: 'SYN-ECE-CWR',86 0xC3: 'FIN-SYN-ECE-CWR',87 0xC8: 'PSH-ECE-CWR',88 0xC9: 'FIN-PSH-ECE-CWR',89 0xCA: 'SYN-PSH-ECE-CWR',90 0xCB: 'FIN-SYN-PSH-ECE-CWR',91 0xD0: 'ACK-ECE-CWR',92 0xD1: 'FIN-ACK-ECE-CWR',93 0xD2: 'SYN-ACK-ECE-CWR',94 0xD3: 'FIN-SYN-ACK-ECE-CWR',95 0xD8: 'PSH-ACK-ECE-CWR',96 0xD9: 'FIN-PSH-ACK-ECE-CWR',97 0xDA: 'SYN-PSH-ACK-ECE-CWR',98 0xDB: 'FIN-SYN-PSH-ACK-ECE-CWR'99}100# tcp_flag_map_dec = {101#102# }103# from collections import OrderedDict104#105# sorted_dictionary = OrderedDict(sorted(_flag_map_hex.items(), key=lambda v: v, reverse=False))106# print '{'107# for k,v in sorted_dictionary.items():108# print str(k) +':"'+v+'"'...
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