The overall structure of this project and report follows the traditional CRISP-DM format. However, instead of the CRISP-DM’S “4 Modeling” section, we inserted the “6 step modeling process” of Dr. Warren Powell in section 4 of this document. Dr Powell’s unified framework shows great promise for unifying the formalisms of at least a dozen different fields. Using his framework enables easier access to thinking patterns in these other fields that might be beneficial and informative to the sequential decision problem at hand. Traditionally, this kind of problem would be approached from the reinforcement learning perspective. However, using Dr. Powell’s wider and more comprehensive perspective almost certainly provides additional value.
Here is information on Dr. Powell’s perspective on Sequential Decision Analytics.
In order to make a strong mapping between the code in this notebook and the mathematics in the Powell Unified Framework (PUF), we follow the following convention for naming Python identifier names:
X__pi, is read X piS_t, is read ‘S at t’M__Spend_t which is read: “MSpend at t”X__piIS_tI, is read ‘X pi in S at t’I’s are used to imitate the parentheses around the argumentR_tt1, is read ‘R at t+1’R__nt1, is read ‘R at n+1’F for terminal rewardcumF for cumulative rewardC for terminal rewardcumC for cumulative rewardS_t.R_t and S_t.p_t, is read ‘S at t in R at t, and, S at t in p at t’self.State = namedtuple('State', SVarNames) for the ‘class’ of the vectorself.S_t for the ‘instance’ of the vectorx_t.x_t_GB and x_t.x_t_BL, is read ‘x at t in x at t for GB, and, x at t in x at t for BL’self.Decision = namedtuple('Decision', xVarNames) for the ‘class’ of the vectorself.x_t for the ‘instance’ of the vectorManaging the supply of blood is a crucial aspect of healthcare operations at hospitals. The availability of safe and adequate blood plays a vital role in ensuring the successful treatment of patients undergoing surgeries, trauma cases, and various medical conditions. Hospitals employ efficient blood supply management systems to procure, store, distribute, and utilize this life-saving resource effectively.
The management of blood supplies involves careful coordination between multiple stakeholders, including healthcare professionals, blood banks, laboratories, and administrative personnel. By implementing robust procedures and protocols, hospitals aim to optimize the utilization of blood products while maintaining high standards of safety and quality.
One of the primary objectives of blood supply management is to maintain an adequate inventory of blood products. This requires forecasting the demand for different blood types and components based on historical data, patient demographics, and anticipated surgical procedures. Hospitals collaborate closely with blood banks and engage in regular communication to ensure a steady supply of blood that meets the specific needs of their patients.
To ensure the safety and integrity of the blood supply, hospitals adhere to strict regulatory guidelines and quality assurance practices. This includes rigorous screening of blood donors for infectious diseases, compatibility testing, and proper storage and transportation of blood products. Hospitals also maintain comprehensive records and traceability systems to monitor the usage, expiration dates, and disposal of blood units.
Efficient distribution and utilization of blood are essential to prevent wastage and optimize resources. Hospital blood banks work in close coordination with various departments, such as operating rooms, emergency departments, and intensive care units, to monitor blood usage patterns, prioritize cases, and respond to urgent requests promptly. Implementing transfusion guidelines and protocols further ensures the appropriate use of blood products, reducing the risk of unnecessary transfusions and improving patient outcomes.
In conclusion, effective management of blood supplies is paramount in the provision of quality healthcare services at hospitals. By implementing robust systems and protocols, hospitals can ensure the availability of safe and adequate blood products, improve patient care, and contribute to the overall well-being of the communities they serve.
In this project the client had a need to be convinced of the benefits of a formal resource assignment/allocation approach for their need (which was a sensitive military situation). The current use case was chosen as an appropriate example that could be adapted for a solution eventually. Their need was addressed in this series of POCs. The example explored (but also modified in many ways) in this report comes from Dr. Warren Powell (formerly at Princeton). It was chosen as a template to create a POC for the client.
The original code for this example can be found here.
# import pdb
import random
from collections import namedtuple, defaultdict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
import pickle
import matplotlib as mpl
import cvxopt
import os.path
import os
# from certifi.core import where
pd.options.display.float_format = '{:,.4f}'.format
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
! python --versionPython 3.10.12First, we setup all the parameters of the inventory system-under-steer (SUS):
def printParams(params):
print(params)
def loadParams(filename):
parDf = pd.read_excel(filename, sheet_name = 'Parameters')
parDict = parDf.set_index('Index').T.to_dict('list')
params = {key:v for key, value in parDict.items() for v in value}
params['PRINT'] = False
params['PRINT_ALL'] = False
params['OUTPUT_FILENAME'] = 'DetailedOutput.xlsx'
params['SHOW_PLOTS']=False
#Set here bloodtypes and substitutions that are allowed
params['Bloodtypes'] = ['AB+', 'AB-', 'A+', 'A-','B+', 'B-', 'O+', 'O-']
params['NUM_BLD_TYPES'] = len(params['Bloodtypes'])
b = [(x,y) for x in params['Bloodtypes'] for y in params['Bloodtypes']]
f = [False]*(len(params['Bloodtypes'])*len(params['Bloodtypes']))
c = {k:v for k,v in zip(b, f)}
#In case we want to allow subs
c[('AB+', 'AB+')] = True
c[('AB-', 'AB+')] = True
c[('AB-', 'AB-')] = True
c[('A+', 'AB+')] = True
c[('A+', 'A+')] = True
c[('A-', 'AB+')] = True
c[('A-', 'AB-')] = True
c[('A-', 'A+')] = True
c[('A-', 'A-')] = True
c[('B+', 'AB+')] = True
c[('B+', 'B+')] = True
c[('B-', 'AB+')] = True
c[('B-', 'AB-')] = True
c[('B-', 'B+')] = True
c[('B-', 'B-')] = True
c[('O+', 'AB+')] = True
c[('O+', 'A+')] = True
c[('O+', 'B+')] = True
c[('O+', 'O+')] = True
c[('O-', 'AB+')] = True
c[('O-', 'A+')] = True
c[('O-', 'B+')] = True
c[('O-', 'O+')] = True
c[('O-', 'AB-')] = True
c[('O-', 'A-')] = True
c[('O-', 'B-')] = True
c[('O-', 'O-')] = True
params['SubMatrix'] = c #. Table 14.1
# Set here max age of blood
params['MAX_AGE'] = 3
params['Ages'] = list(range(params['MAX_AGE']))
params['NUM_BLD_NODES'] = params['NUM_BLD_TYPES']*params['MAX_AGE']
# Set here blood demand nodes
params['Surgerytypes'] = ['Urgent', 'Elective']
params['Substitution'] = [True]
params['NUM_SUR_TYPES'] = len(params['Surgerytypes'])
params['NUM_DEM_NODES'] = params['NUM_BLD_TYPES']*params['NUM_SUR_TYPES']*len(params['Substitution'])
# Solver params
params['SLOPE_CAPAC_LAST'] = 100000
params['MIN_CONST'] = 0.01
params['EPSILON'] = 0.001
# Set here number of iterations and time periods
params['NUM_TRAINNING_ITER'] = int(params['NUM_TRAINNING_ITER'])
params['NUM_TESTING_ITER'] = int(params['NUM_TESTING_ITER'])
params['NUM_ITER'] = int(params['NUM_TESTING_ITER'] + params['NUM_TRAINNING_ITER']) #Total number of iterations
params['MAX_TIME'] = int(15)
params['Times'] = list(range(params['MAX_TIME']))
# Set here VFA parameters
# - If USE_VFA is set to True we are going to use VFA's when making the decisions -
# - If USE_VFA is set to False, it means that a MYOPIC policy is going to be considered and all the parameters
#related to VFA's (such as DISCOUNT_FACTOR, LOAD_VFA, SAVE_VFA, STEPSIZE_RULE, PROJECTION_ALGO,
#IS_PERTUB,SEED_TRAINING are ignored)
#params['USE_VFA'] = True #If set to True we are going to use VFA's when making the decisions - False means a MYOPIC policy
params['DISCOUNT_FACTOR'] = 0.95
params['LOAD_VFA'] = False #If set to True we are going to initialize the VFA's with VFA's from previous runs - instead of all zeros
params['NAME_LOAD_VFA_PICKLE'] = "Bld_Net10_P_C_Subs.pickle"
params['SAVE_VFA'] = False #If we want to save/update the VFA's to be used in future runs
params['NAME_SAVE_VFA_PICKLE'] = "Bld_Net10_P_C_Subs.pickle"
# Set here the stepsize parameters
params['STEPSIZE_RULE'] = 'C' #Possible values: 'C' for Constant or 'A' for AdaGrad
params['NUM_ITER_STEP_ONE'] = 0 #Number of iterations with stepsize one
# Set here the CONSTANT stepsize parameter (not considered if AdaGrad stepsize is being used)
#params['ALPHA'] = 0.2 #the stepsize for the other iterations
#Set here the AdaGrad stepsize parameters (not considered if Constant stepsize is being used)
params['STEP_EPS'] = 0.00000001
params['ETA'] = 1
# Set here the algorithm that should be use for projection back the slopes that break concavity
# Possible algorithms for projecting back the slopes to enforce concavity are:
# - 'Avg' to average the slopes that break concavity; \
# - 'Copy' to copy the newly updated vbar to the slopes that break concavity
# - 'Up' to update the slopes that break concavity with the current stepsize and vhat
params['PROJECTION_ALGO'] = 'Up'
#Perturb the solution during training iterations for exploration
params['IS_PERTUB'] = False
params['LAMBDA_PERTUB'] = 1
params['PERTUB_GEN'] = np.random.RandomState(13247)
# Set here one step contribution function parameters - BONUSES and PENALTIES
params['AGE_BONUS'] = np.zeros(params['MAX_AGE'])
# params['AGE_BONUS'] = [2]*MAX_AGE
# params['AGE_BONUS'] = list(reversed(list(range(0,MAX_AGE))))
# params['AGE_BONUS'] = list(range(0,MAX_AGE))
# params['AGE_BONUS'] = [0.5, 2] #It has to be the same length as MAX_AGE
params['INFEASIABLE_SUBSTITUTION_PENALTY'] = -50
params['NO_SUBSTITUTION_BONUS'] = 5
params['URGENT_DEMAND_BONUS'] = 30
params['ELECTIVE_DEMAND_BONUS'] = 5
params['DISCARD_BLOOD_PENALTY'] = -10 #applied for the oldest age in the holding/vfa arcs
# Set here Random Seeds
params['SEED_TRAINING'] = 1090377
params['SEED_TESTING'] = 8090373
#Set here the distribution for demand/donation/initial inventory
params['SAMPLING_DIST'] = 'P' #Possible values: 'P' for Poisson or 'U' for uniform
params['POISSON_FACTOR'] = 1
# Set here max demand by blood type (when 'U'niform dist) or mean demand (when 'P'oisson dist)
params['DEFAULT_VALUE_DIST'] = 20
d = [params['DEFAULT_VALUE_DIST']]*params['NUM_BLD_TYPES']
params['MAX_DEM_BY_BLOOD'] = {k:v for k,v in zip(params['Bloodtypes'], d)}
params['MAX_DON_BY_BLOOD'] = {k:v for k,v in zip(params['Bloodtypes'], d)}
# Set here demand by blood type (for blood types that are different than the params['DEFAULT_VALUE_DIST'])
params['MAX_DEM_BY_BLOOD']['AB+'] = 3
params['MAX_DEM_BY_BLOOD']['B+'] = 9
params['MAX_DEM_BY_BLOOD']['O+'] = 18
params['MAX_DEM_BY_BLOOD']['B-'] = 2
params['MAX_DEM_BY_BLOOD']['AB-'] = 3
params['MAX_DEM_BY_BLOOD']['A-'] = 6
params['MAX_DEM_BY_BLOOD']['O-'] = 7
params['MAX_DEM_BY_BLOOD']['A+'] = 14
params['MAX_DEM_BY_BLOOD']['AB+'] = 0
params['MAX_DEM_BY_BLOOD']['B+'] = 0
params['MAX_DEM_BY_BLOOD']['O+'] = 0
params['MAX_DEM_BY_BLOOD']['B-'] = 0
params['MAX_DEM_BY_BLOOD']['AB-'] = 0
params['MAX_DEM_BY_BLOOD']['A-'] = 10
params['MAX_DEM_BY_BLOOD']['O-'] = 10
params['MAX_DEM_BY_BLOOD']['A+'] = 0
params['MAX_DEM_BY_BLOOD']['AB+'] = 3
params['MAX_DEM_BY_BLOOD']['B+'] = 9
params['MAX_DEM_BY_BLOOD']['O+'] = 18
params['MAX_DEM_BY_BLOOD']['B-'] = 2
params['MAX_DEM_BY_BLOOD']['AB-'] = 3
params['MAX_DEM_BY_BLOOD']['A-'] = 6
params['MAX_DEM_BY_BLOOD']['O-'] = 7
params['MAX_DEM_BY_BLOOD']['A+'] = 14
#params['DEFAULT_VALUE_DIST']
# Set here donation by blood type (for blood types that are different than the params['DEFAULT_VALUE_DIST'])
params['MAX_DON_BY_BLOOD']['AB+'] = 0
params['MAX_DON_BY_BLOOD']['B+'] = 0
params['MAX_DON_BY_BLOOD']['O+'] = 0
params['MAX_DON_BY_BLOOD']['B-'] = 0
params['MAX_DON_BY_BLOOD']['AB-'] = 0
params['MAX_DON_BY_BLOOD']['A-'] = 10
params['MAX_DON_BY_BLOOD']['O-'] = 10
params['MAX_DON_BY_BLOOD']['A+'] = 0
params['MAX_DON_BY_BLOOD']['AB+'] = 3
params['MAX_DON_BY_BLOOD']['B+'] = 9
params['MAX_DON_BY_BLOOD']['O+'] = 18
params['MAX_DON_BY_BLOOD']['B-'] = 2
params['MAX_DON_BY_BLOOD']['AB-'] = 3
params['MAX_DON_BY_BLOOD']['A-'] = 6
params['MAX_DON_BY_BLOOD']['O-'] = 7
params['MAX_DON_BY_BLOOD']['A+'] = 14
#The default weights to split the demand of a blood type is equal weights. The only requirement is that each
#weight is positive and they add up to 1.
#Default
params['SURGERYTYPES_PROP'] = {k:1/len(params['Surgerytypes']) for k in params['Surgerytypes']}
params['SUBSTITUTION_PROP'] = {k:1/len(params['Substitution']) for k in params['Substitution']}
# Set here the weights for each surgery type (if different than the default)
params['SURGERYTYPES_PROP']['Urgent'] = 1/2
params['SURGERYTYPES_PROP']['Elective'] = 1 - params['SURGERYTYPES_PROP']['Urgent']
# Set here the weights for each substitution type (if different than the default)
params['SUBSTITUTION_PROP'][True] = 1
#params['SUBSTITUTION_PROP'][False] = 1 - params['SUBSTITUTION_PROP'][True]
#Set here random surge parameters
#params['TIME_PERIODS_SURGE'] = set([4,8,10,12,14])
params['TIME_PERIODS_SURGE'] = set([3,6,10,13])
#SURGE_PROB = 0.7
params['SURGE_FACTOR'] = 6 #The surge demand is always going to be poisson with mean SURGE_FACTOR*params['MAX_DEM_BY_BLOOD'], even if the regular demand distribution is Uniform
#Set here the weights for the utility function - urgent coverage, elective coverage, proportion of blood discarded
params['WEIGHT_URGENT']=10
params['WEIGHT_ELECTIVE']=1
params['WEIGHT_DISCARDED']=3
if (params['SAMPLING_DIST'] == 'P'):
params['MAX_DEM_BY_BLOOD'] = {k: int(v * params['POISSON_FACTOR']) for k, v in params['MAX_DEM_BY_BLOOD'].items()}
params['MAX_DON_BY_BLOOD'] = {k: int(v * params['POISSON_FACTOR']) for k, v in params['MAX_DON_BY_BLOOD'].items()}
params['AVG_TOTAL_DEMAND'] = sum(params['MAX_DEM_BY_BLOOD'].values())
params['AVG_TOTAL_SUPPLY'] = sum(params['MAX_DON_BY_BLOOD'].values())
params['NUM_PARALLEL_LINKS'] = int(params['MAX_AGE']/2 * max(params['MAX_DON_BY_BLOOD'].values()))
#print("Exogenous info dist: Poisson ")
else:
params['AVG_TOTAL_DEMAND'] = sum(params['MAX_DEM_BY_BLOOD'].values())/2
params['AVG_TOTAL_SUPPLY'] = sum(params['MAX_DON_BY_BLOOD'].values())/2
params['NUM_PARALLEL_LINKS'] = int(params['MAX_AGE']/2 * max(params['MAX_DON_BY_BLOOD'].values()))
#print("Exogenous info dist: Uniform")
#Checking if MYOPIC policy
if not params['USE_VFA']:
params['ALPHA'] = 0
params['LOAD_VFA'] = False
params['SAVE_VFA'] = False
params['NUM_TRAINNING_ITER'] = 0
params['NUM_ITER'] = params['NUM_TESTING_ITER']
params['NUM_PARALLEL_LINKS'] = 1
print("Printing params dict\n")
printParams(params)
if (params['SAMPLING_DIST'] == 'P'):
print("Exogenous info dist: Poisson ")
else:
print("Exogenous info dist: Uniform")
print("Demand parameters by blood type ",params['MAX_DEM_BY_BLOOD'])
print("There are ",params['NUM_SUR_TYPES'] * len(params['Substitution'])," demand nodes for each blood type")
print("Weights SURGERYTYPES_PROP ",params['SURGERYTYPES_PROP'])
print("Weights SUBSTITUTION_PROP ",params['SUBSTITUTION_PROP'])
print("Donation parameters by blood type ",params['MAX_DON_BY_BLOOD'])
print("AVG TOTAL DEMAND ",params['AVG_TOTAL_DEMAND'])
print("AVG TOTAL SUPPLY ",params['AVG_TOTAL_SUPPLY'])
print("NUM PARALLEL LINKS ",params['NUM_PARALLEL_LINKS'])
print("Possible surge time periods ", params['TIME_PERIODS_SURGE'])
print("SURGE_PROB ", params['SURGE_PROB'], " and SURGE_FACTOR ", params['SURGE_FACTOR'])
return params
# params = loadParams(f'{base_dir}/Parameters.xlsx')
PARS = loadParams(f'{base_dir}/Parameters.xlsx')Printing params dict
{'BLOOD_FOR_ELECTIVE_PENALTY': -9.0, 'SURGE_PROB': 0.5, 'NUM_TRAINNING_ITER': 0, 'NUM_TESTING_ITER': 20, 'USE_VFA': 0.0, 'ALPHA': 0, 'SAVE_PLOTS': 0.0, 'PRINT': False, 'PRINT_ALL': False, 'OUTPUT_FILENAME': 'DetailedOutput.xlsx', 'SHOW_PLOTS': False, 'Bloodtypes': ['AB+', 'AB-', 'A+', 'A-', 'B+', 'B-', 'O+', 'O-'], 'NUM_BLD_TYPES': 8, 'SubMatrix': {('AB+', 'AB+'): True, ('AB+', 'AB-'): False, ('AB+', 'A+'): False, ('AB+', 'A-'): False, ('AB+', 'B+'): False, ('AB+', 'B-'): False, ('AB+', 'O+'): False, ('AB+', 'O-'): False, ('AB-', 'AB+'): True, ('AB-', 'AB-'): True, ('AB-', 'A+'): False, ('AB-', 'A-'): False, ('AB-', 'B+'): False, ('AB-', 'B-'): False, ('AB-', 'O+'): False, ('AB-', 'O-'): False, ('A+', 'AB+'): True, ('A+', 'AB-'): False, ('A+', 'A+'): True, ('A+', 'A-'): False, ('A+', 'B+'): False, ('A+', 'B-'): False, ('A+', 'O+'): False, ('A+', 'O-'): False, ('A-', 'AB+'): True, ('A-', 'AB-'): True, ('A-', 'A+'): True, ('A-', 'A-'): True, ('A-', 'B+'): False, ('A-', 'B-'): False, ('A-', 'O+'): False, ('A-', 'O-'): False, ('B+', 'AB+'): True, ('B+', 'AB-'): False, ('B+', 'A+'): False, ('B+', 'A-'): False, ('B+', 'B+'): True, ('B+', 'B-'): False, ('B+', 'O+'): False, ('B+', 'O-'): False, ('B-', 'AB+'): True, ('B-', 'AB-'): True, ('B-', 'A+'): False, ('B-', 'A-'): False, ('B-', 'B+'): True, ('B-', 'B-'): True, ('B-', 'O+'): False, ('B-', 'O-'): False, ('O+', 'AB+'): True, ('O+', 'AB-'): False, ('O+', 'A+'): True, ('O+', 'A-'): False, ('O+', 'B+'): True, ('O+', 'B-'): False, ('O+', 'O+'): True, ('O+', 'O-'): False, ('O-', 'AB+'): True, ('O-', 'AB-'): True, ('O-', 'A+'): True, ('O-', 'A-'): True, ('O-', 'B+'): True, ('O-', 'B-'): True, ('O-', 'O+'): True, ('O-', 'O-'): True}, 'MAX_AGE': 3, 'Ages': [0, 1, 2], 'NUM_BLD_NODES': 24, 'Surgerytypes': ['Urgent', 'Elective'], 'Substitution': [True], 'NUM_SUR_TYPES': 2, 'NUM_DEM_NODES': 16, 'SLOPE_CAPAC_LAST': 100000, 'MIN_CONST': 0.01, 'EPSILON': 0.001, 'NUM_ITER': 20, 'MAX_TIME': 15, 'Times': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'DISCOUNT_FACTOR': 0.95, 'LOAD_VFA': False, 'NAME_LOAD_VFA_PICKLE': 'Bld_Net10_P_C_Subs.pickle', 'SAVE_VFA': False, 'NAME_SAVE_VFA_PICKLE': 'Bld_Net10_P_C_Subs.pickle', 'STEPSIZE_RULE': 'C', 'NUM_ITER_STEP_ONE': 0, 'STEP_EPS': 1e-08, 'ETA': 1, 'PROJECTION_ALGO': 'Up', 'IS_PERTUB': False, 'LAMBDA_PERTUB': 1, 'PERTUB_GEN': RandomState(MT19937) at 0x7FF5D1723240, 'AGE_BONUS': array([0., 0., 0.]), 'INFEASIABLE_SUBSTITUTION_PENALTY': -50, 'NO_SUBSTITUTION_BONUS': 5, 'URGENT_DEMAND_BONUS': 30, 'ELECTIVE_DEMAND_BONUS': 5, 'DISCARD_BLOOD_PENALTY': -10, 'SEED_TRAINING': 1090377, 'SEED_TESTING': 8090373, 'SAMPLING_DIST': 'P', 'POISSON_FACTOR': 1, 'DEFAULT_VALUE_DIST': 20, 'MAX_DEM_BY_BLOOD': {'AB+': 3, 'AB-': 3, 'A+': 14, 'A-': 6, 'B+': 9, 'B-': 2, 'O+': 18, 'O-': 7}, 'MAX_DON_BY_BLOOD': {'AB+': 3, 'AB-': 3, 'A+': 14, 'A-': 6, 'B+': 9, 'B-': 2, 'O+': 18, 'O-': 7}, 'SURGERYTYPES_PROP': {'Urgent': 0.5, 'Elective': 0.5}, 'SUBSTITUTION_PROP': {True: 1}, 'TIME_PERIODS_SURGE': {10, 3, 13, 6}, 'SURGE_FACTOR': 6, 'WEIGHT_URGENT': 10, 'WEIGHT_ELECTIVE': 1, 'WEIGHT_DISCARDED': 3, 'AVG_TOTAL_DEMAND': 62, 'AVG_TOTAL_SUPPLY': 62, 'NUM_PARALLEL_LINKS': 1}
Exogenous info dist: Poisson
Demand parameters by blood type {'AB+': 3, 'AB-': 3, 'A+': 14, 'A-': 6, 'B+': 9, 'B-': 2, 'O+': 18, 'O-': 7}
There are 2 demand nodes for each blood type
Weights SURGERYTYPES_PROP {'Urgent': 0.5, 'Elective': 0.5}
Weights SUBSTITUTION_PROP {True: 1}
Donation parameters by blood type {'AB+': 3, 'AB-': 3, 'A+': 14, 'A-': 6, 'B+': 9, 'B-': 2, 'O+': 18, 'O-': 7}
AVG TOTAL DEMAND 62
AVG TOTAL SUPPLY 62
NUM PARALLEL LINKS 1
Possible surge time periods {10, 3, 13, 6}
SURGE_PROB 0.5 and SURGE_FACTOR 6In order to capture the vector attribute values in variable names, we replace all ‘+’ characters for bloodtypes by a ‘p’. Similarly, all ‘-’ characters are replaced by ‘n’.
# modify params['SubMatrix'] to have names with '_'
mySubMatrix = {}
for kv in PARS['SubMatrix'].items():
# print(kv)
mySubMatrix['_'.join(kv[0]).replace('+', 'p').replace('-', 'n')] = kv[1]
# mySubMatrix# modify params['MAX_DON_BY_BLOOD'] to have names with '_'
myMAX_DON_BY_BLOOD = {}
for kv in PARS['MAX_DON_BY_BLOOD'].items():
# print(kv[0].replace('+', 'p').replace('-', 'n'))
myMAX_DON_BY_BLOOD[kv[0].replace('+', 'p').replace('-', 'n')] = kv[1]
myMAX_DON_BY_BLOOD{'ABp': 3, 'ABn': 3, 'Ap': 14, 'An': 6, 'Bp': 9, 'Bn': 2, 'Op': 18, 'On': 7}# modify params['MAX_DEM_BY_BLOOD'] to have names with '_'
myMAX_DEM_BY_BLOOD = {}
for kv in PARS['MAX_DEM_BY_BLOOD'].items():
# print(kv[0].replace('+', 'p').replace('-', 'n'))
myMAX_DEM_BY_BLOOD[kv[0].replace('+', 'p').replace('-', 'n')] = kv[1]
myMAX_DEM_BY_BLOOD{'ABp': 3, 'ABn': 3, 'Ap': 14, 'An': 6, 'Bp': 9, 'Bn': 2, 'Op': 18, 'On': 7}# modify params['SUBSTITUTION_PROP'] to have names with '_'
mySUBSTITUTION_PROP = {}
for kv in PARS['SUBSTITUTION_PROP'].items():
# print(kv[0].replace('+', 'p').replace('-', 'n'))
mySUBSTITUTION_PROP[str(kv[0])] = kv[1]
mySUBSTITUTION_PROP{'True': 1}# modify params['Bloodtypes'] to have names with '_'
myBloodtypes = [el.replace('+','p').replace('-','n') for el in PARS['Bloodtypes']]
myBloodtypes['ABp', 'ABn', 'Ap', 'An', 'Bp', 'Bn', 'Op', 'On']SNAMES = [
'R_t', # BloodInventory
'Rhold_t', # Held BloodInventory
'Dh_t', # Demand
'Rh_t', # Donations
]
xNAMES = ['x_t']
# all possible attribute vectors
aNAMES = []
for i in PARS['Bloodtypes']:
for j in PARS['Ages']:
an = '_'.join([str(i),str(j)]).replace('+', 'p').replace('-', 'n')
aNAMES.append(an)
print(f'{len(aNAMES)=}')
print(aNAMES[:10])
# all possible demand vectors
bNAMES = []
for i in PARS['Bloodtypes']:
for j in PARS['Surgerytypes']:
for k in PARS['Substitution']:
bn = '_'.join([str(i),str(j),str(k)]).replace('+', 'p').replace('-', 'n')
bNAMES.append(bn)
print(f'{len(bNAMES)=}')
print(bNAMES[:10])
abNAMES = []
for a in aNAMES:
for b in bNAMES:
abn = (a + '___' + b)
abNAMES.append(abn)
print(f'{len(abNAMES)=}')
print(abNAMES[:3])
abNAMES[-10:]
#add abNAMES to hold blood
abNAMES_HOLD = []
for a in aNAMES:
a0,a1 = a.split('_')
a_hold = '_'.join([a0, str(int(a1)+1)])
abName_hold = a + '___' + a_hold
abNAMES_HOLD.append(abName_hold)
print(f'{len(abNAMES_HOLD)=}')
print(abNAMES_HOLD)
# abNAMES = abNAMES + abNAMES_HOLD
abNAMES_EXP = abNAMES + abNAMES_HOLD #expanded abNAMES
print(f'{len(abNAMES_EXP)=}')
print(abNAMES_EXP[:3])
abNAMES_EXP[-10:]len(aNAMES)=24
['ABp_0', 'ABp_1', 'ABp_2', 'ABn_0', 'ABn_1', 'ABn_2', 'Ap_0', 'Ap_1', 'Ap_2', 'An_0']
len(bNAMES)=16
['ABp_Urgent_True', 'ABp_Elective_True', 'ABn_Urgent_True', 'ABn_Elective_True', 'Ap_Urgent_True', 'Ap_Elective_True', 'An_Urgent_True', 'An_Elective_True', 'Bp_Urgent_True', 'Bp_Elective_True']
len(abNAMES)=384
['ABp_0___ABp_Urgent_True', 'ABp_0___ABp_Elective_True', 'ABp_0___ABn_Urgent_True']
len(abNAMES_HOLD)=24
['ABp_0___ABp_1', 'ABp_1___ABp_2', 'ABp_2___ABp_3', 'ABn_0___ABn_1', 'ABn_1___ABn_2', 'ABn_2___ABn_3', 'Ap_0___Ap_1', 'Ap_1___Ap_2', 'Ap_2___Ap_3', 'An_0___An_1', 'An_1___An_2', 'An_2___An_3', 'Bp_0___Bp_1', 'Bp_1___Bp_2', 'Bp_2___Bp_3', 'Bn_0___Bn_1', 'Bn_1___Bn_2', 'Bn_2___Bn_3', 'Op_0___Op_1', 'Op_1___Op_2', 'Op_2___Op_3', 'On_0___On_1', 'On_1___On_2', 'On_2___On_3']
len(abNAMES_EXP)=408
['ABp_0___ABp_Urgent_True', 'ABp_0___ABp_Elective_True', 'ABp_0___ABn_Urgent_True']['Bp_2___Bp_3',
'Bn_0___Bn_1',
'Bn_1___Bn_2',
'Bn_2___Bn_3',
'Op_0___Op_1',
'Op_1___Op_2',
'Op_2___Op_3',
'On_0___On_1',
'On_1___On_2',
'On_2___On_3']def contribution(params, aName, bName):
a = aName.split('_')
b = bName.split('_')
if ( #substutition is not allowed
bool(b[2]) == False and a[0] != b[0]) or \
(bool(b[2]) == True and mySubMatrix['_'.join([a[0], b[0]])] == False):
value = params['INFEASIABLE_SUBSTITUTION_PENALTY']
else:
# start giving a bonus depending on the age of the blood
value = 0
# no substitution
if a[0] == b[0]:
value += params['NO_SUBSTITUTION_BONUS']
# filling urgent demand
if b[1] == 'Urgent':
value += params['URGENT_DEMAND_BONUS']
# filling elective demand
else:
value += params['ELECTIVE_DEMAND_BONUS']
if b[1] == 'Elective':
value += params['BLOOD_FOR_ELECTIVE_PENALTY']
return(value)
ContribMatrix = {} #seems to play same role as the demweights of demedges
for an in aNAMES:
demContribs = {}
for bn in bNAMES:
demContribs[bn] = contribution(PARS, an, bn)
ContribMatrix[an] = demContribsL = PARS['NUM_ITER'] #number of sample-paths
T = PARS['MAX_TIME'] #number of transitions/steps in each sample-path
print(f'{L=}, {T=}')
T__sim = 15 #50 #100L=20, T=15The simulation allows for applying a PARS['SURGE_FACTOR'] with probability PARS['SURGE_PROB'] at specified points in a sample path, PARS['TIME_PERIODS_SURGE']
PARS['SURGE_FACTOR'], PARS['SURGE_PROB'], PARS['TIME_PERIODS_SURGE'](6, 0.5, {3, 6, 10, 13})class DemandSimulator():
def __init__(self, T__sim):
self.time = 0
self.T__sim = T__sim
# def simulate_pois(self):
def simulate_pois(self, t):
if self.time > T__sim - 1:
self.time = 0
# if (self.time in PARS['TIME_PERIODS_SURGE'] and \
if (t in PARS['TIME_PERIODS_SURGE'] and \
np.random.uniform(0, 1) < PARS['SURGE_PROB']):
factor = PARS['SURGE_FACTOR']
else:
factor = 1
Dh_tt1 = {
bName:
int(np.random.poisson(
factor * \
myMAX_DEM_BY_BLOOD[bName.split('_')[0]] * \
PARS['SURGERYTYPES_PROP'][bName.split('_')[1]] * \
mySUBSTITUTION_PROP[bName.split('_')[2]]))
for bName in bNAMES
}
self.time += 1
return Dh_tt1
# def simulate_unif(self):
def simulate_unif(self, t):
if self.time > T__sim - 1:
self.time = 0
if (t in PARS['TIME_PERIODS_SURGE'] and \
np.random.uniform(0, 1) < PARS['SURGE_PROB']):
factor = PARS['SURGE_FACTOR']
else:
factor = 0
Dh_tt1 = {
bName:
round(np.random.uniform(
0,
myMAX_DEM_BY_BLOOD[bName.split('_')[0]] * \
PARS['SURGERYTYPES_PROP'][bName.split('_')[1]] * \
mySUBSTITUTION_PROP[bName.split('_')[2]])) + \
factor*int(np.random.poisson(myMAX_DEM_BY_BLOOD[bName.split('_')[0]] * \
PARS['SURGERYTYPES_PROP'][bName.split('_')[1]] * \
mySUBSTITUTION_PROP[bName.split('_')[2]]))
for bName in bNAMES
}
self.time += 1
return Dh_tt1dem_sim = DemandSimulator(T__sim=T__sim)
DemandData = []
for i in range(T__sim):
# d_e = list(dem_sim.simulate_pois().values())
d_e = list(dem_sim.simulate_pois(i).values())
DemandData.append(d_e)
labels = [f'{bn}_demand'for bn in bNAMES]
df = pd.DataFrame.from_records(data=DemandData, columns=labels); df[:10]| ABp_Urgent_True_demand | ABp_Elective_True_demand | ABn_Urgent_True_demand | ABn_Elective_True_demand | Ap_Urgent_True_demand | Ap_Elective_True_demand | An_Urgent_True_demand | An_Elective_True_demand | Bp_Urgent_True_demand | Bp_Elective_True_demand | Bn_Urgent_True_demand | Bn_Elective_True_demand | Op_Urgent_True_demand | Op_Elective_True_demand | On_Urgent_True_demand | On_Elective_True_demand | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | 3 | 1 | 0 | 3 | 5 | 8 | 2 | 3 | 2 | 2 | 1 | 6 | 10 | 3 | 3 |
| 1 | 3 | 2 | 3 | 1 | 10 | 9 | 2 | 4 | 3 | 3 | 0 | 0 | 5 | 5 | 3 | 5 |
| 2 | 1 | 1 | 1 | 1 | 11 | 7 | 2 | 3 | 5 | 2 | 0 | 1 | 8 | 12 | 7 | 5 |
| 3 | 1 | 1 | 1 | 3 | 10 | 6 | 5 | 1 | 9 | 7 | 1 | 0 | 8 | 10 | 2 | 3 |
| 4 | 0 | 0 | 1 | 2 | 10 | 10 | 1 | 4 | 2 | 7 | 0 | 0 | 11 | 9 | 6 | 0 |
| 5 | 0 | 2 | 0 | 1 | 6 | 13 | 4 | 6 | 3 | 11 | 2 | 2 | 10 | 13 | 4 | 5 |
| 6 | 1 | 0 | 2 | 0 | 7 | 5 | 2 | 4 | 3 | 6 | 1 | 0 | 4 | 14 | 3 | 2 |
| 7 | 1 | 0 | 2 | 2 | 8 | 8 | 4 | 0 | 4 | 5 | 0 | 2 | 11 | 8 | 3 | 4 |
| 8 | 5 | 2 | 0 | 2 | 10 | 5 | 2 | 3 | 4 | 5 | 1 | 3 | 9 | 9 | 3 | 7 |
| 9 | 0 | 2 | 1 | 3 | 9 | 9 | 3 | 2 | 3 | 7 | 2 | 1 | 9 | 11 | 2 | 3 |
def plot_output(df1, df2):
n_charts = len(bNAMES)
ylabelsize = 16
mpl.rcParams['lines.linewidth'] = 1.2
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
fig, axs = plt.subplots(n_charts, sharex=True)
fig.set_figwidth(13); fig.set_figheight(12)
fig.suptitle('Demand Simulation\n(Poisson)', fontsize=20)
for i,bn in enumerate(bNAMES):
axs[i].set_ylim(auto=True); axs[i].spines['top'].set_visible(False); axs[i].spines['right'].set_visible(True); axs[i].spines['bottom'].set_visible(False)
leg = axs[i].step(df1[f'{bn}_demand'], random.choice(default_colors))
fig.legend(
[leg],
labels=labels,
title="Demand attributes "+"$b$",
loc='upper right',
fancybox=True,
shadow=True,
ncol=1)
axs[i].set_xlabel('$t\ \mathrm{[weekly\ windows]}$', rotation=0, ha='center', va='center', fontweight='bold', size=ylabelsize)
plot_output(df, None);UserWarning: You have mixed positional and keyword arguments, some input may be discarded.
fig.legend(
We also simulate the blood donations:
class DonationSimulator():
def __init__(self, T__sim):
self.time = 0
self.T__sim = T__sim
def simulate_pois(self):
if self.time > T__sim - 1:
self.time = 0
Rh_tt1 = {
(i.replace('+','p').replace('-','n')):
int(np.random.poisson(PARS['MAX_DON_BY_BLOOD'][i]))
for i in PARS['Bloodtypes']
}
self.time += 1
return Rh_tt1
def simulate_unif(self):
if self.time > T__sim - 1:
self.time = 0
Rh_tt1 = {
(i.replace('+','p').replace('-','n')):
round(np.random.uniform(0, PARS['MAX_DON_BY_BLOOD'][i]))
for i in PARS['Bloodtypes']
}
self.time += 1
return Rh_tt1don_sim = DonationSimulator(T__sim=T__sim)
DonationData = []
for i in range(T__sim):
d_e = list(don_sim.simulate_pois().values())
DonationData.append(d_e)
labels = [f'{i}_donation'for i in myBloodtypes]
df = pd.DataFrame.from_records(data=DonationData, columns=labels); df[:10]| ABp_donation | ABn_donation | Ap_donation | An_donation | Bp_donation | Bn_donation | Op_donation | On_donation | |
|---|---|---|---|---|---|---|---|---|
| 0 | 3 | 4 | 13 | 7 | 9 | 1 | 18 | 4 |
| 1 | 2 | 3 | 17 | 7 | 7 | 3 | 13 | 10 |
| 2 | 3 | 3 | 11 | 7 | 11 | 0 | 21 | 6 |
| 3 | 1 | 2 | 17 | 12 | 7 | 2 | 16 | 5 |
| 4 | 4 | 1 | 19 | 4 | 10 | 1 | 16 | 9 |
| 5 | 4 | 2 | 11 | 6 | 10 | 2 | 27 | 7 |
| 6 | 0 | 2 | 21 | 10 | 12 | 2 | 17 | 5 |
| 7 | 3 | 4 | 14 | 6 | 10 | 1 | 14 | 3 |
| 8 | 2 | 4 | 15 | 4 | 9 | 3 | 19 | 8 |
| 9 | 2 | 5 | 13 | 4 | 12 | 3 | 14 | 10 |
def plot_output(df1, df2):
n_charts = len(myBloodtypes)
ylabelsize = 16
mpl.rcParams['lines.linewidth'] = 1.2
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
fig, axs = plt.subplots(n_charts, sharex=True)
fig.set_figwidth(13); fig.set_figheight(12)
fig.suptitle('Donation Simulation\n(Poisson)', fontsize=20)
for i,bt in enumerate(myBloodtypes):
axs[i].set_ylim(auto=True); axs[i].spines['top'].set_visible(False); axs[i].spines['right'].set_visible(True); axs[i].spines['bottom'].set_visible(False)
leg = axs[i].step(df1[f'{bt}_donation'], random.choice(default_colors))
fig.legend(
[leg],
labels=labels,
title="Donation bloodtype",
loc='upper right',
fancybox=True,
shadow=True,
ncol=1)
axs[i].set_xlabel('$t\ \mathrm{[weekly\ windows]}$', rotation=0, ha='center', va='center', fontweight='bold', size=ylabelsize)
plot_output(df, None);UserWarning: You have mixed positional and keyword arguments, some input may be discarded.
fig.legend(
We will use the data provided by the simulator directly. There is no need to perform additional data preparation.
From the free book by Dr. Powell, Sequential Decision Analytics and Modeling:
The problem of managing blood inventories serves as a particularly elegant illustration of a resource allocation problem. We are going to start by assuming that we are managing inventories at a single ospital, where each week we have to decide which of our blood inventories should be used for the demands that need to be served in the upcoming week.
We have to start with a bit of background about blood. For the purposes of managing blood inventories, we care primarily about blood type and age. Although there is a vast range of differences in the blood of two individuals, for most purposes doctors focus on the eight major blood types: A+ (“A positive”), A- (“A negative”), B+, B-, AB+, AB-, O+, and O-. While the ability to substitute different blood types can depend on the nature of the operation, for most purposes blood can be substituted according to table 14.1:
A second important characteristic of blood is its age. The storage of blood is limited to six weeks, after which it has to be discarded. Hospitals need to anticipate if they think they can use blood before it hits this limit, as it can be transferred to blood centers which monitor inventories at different hospitals within a region. It helps if a hospital can identify blood it will not need as soon as possible so that the blood can be transferred to locations that are running short.
One mechanism for extending the shelflife of blood is to freeze it. Frozen blood can be stored up to 10 years, but it takes at least an hour to thaw, limiting its use in emergency situations or operations where the amount of blood needed is highly uncertain. In addition, once frozen blood is thawed it must be used within 24 hours.
This section attempts to answer three important questions: - What metrics are we going to track? - What decisions do we intend to make? - What are the sources of uncertainty?
For this problem, the only metric we are interested in is the total contribution we make after each decision window. We do not have costs in this problem to assign blood of one type to demand of another type. In other words, we do not, for example, spend money to encourage additional donations. Neither do we include transportation costs when moving inventories from one hospital to another. Instead, we use the contribution function to capture the procedures and preferences of the doctor. For example, we would like to capture the preference that it is better in general not to substitute. Also, to satisfy an urgent demand is more important than to satisfy an elective demand.
We have a separate decision (which will become a decision variable) for each link from a resource vector \(a\) to a demand vector \(b\). Each of these decisions represents an assignment or allocation of an amount of resource away from the inventory for the specific resource with attributes \(a\), to a demand with attributes \(b\).
Two sources of uncertainty are included in this model. Firstly, the demand for blood of a specific bloodtype is uncertain.Then we also do not know the amount of blood units that will be donated for each bloodtype.
A Python class is used to implement the model for the SUM (System Under Management):
class Model():
def __init__(self, S_0_info):
...
...The state variables represent what we need to know. - \(R_t = (R_{ta})_{a \in \cal A}\) where \(\cal{A} = \{(a_1,a_2):a_1 \in \{ \mathrm{AB+,AB-A+,A-,B+,B-,O+,O-}\}, a_2 \in \{ \mathrm{0,1,2} \} \}\) - the inventory on hand at time \(t\) of the resource with attribute \(a\) before we make a new allocation decision, and before we have satisfied any demands arising in time interval \(t\) - measured in inventory units - \(R^{hold}_t = (R^{hold}_{ta})_{a \in \cal A}\) where \(\cal{A} = \{(a_1,a_2):a_1 \in \{ \mathrm{AB+,AB-A+,A-,B+,B-,O+,O-}\}, a_2 \in \{ \mathrm{0,1,2} \} \}\) - the inventory held (not assigned) at time \(t\) of the resource with attribute \(a\) before we make a new allocation decision, and before we have satisfied any demands arising in time interval \(t\) - measured in inventory units
- \(\hat{D}_t = (\hat{D}_{tb})_{b \in \cal B}\) where \(\cal{B} = \{(b_1,b_2,b_2):b_1 \in \{ \mathrm{AB+,AB-,A+,A-,B+,B-,O+,O-}\}, b_2 \in \{ \mathrm{Urgent,Elective}, b_3 \in \{ \mathrm{SubstitutionAllowed?} \} \}\) - the demand at time \(t\) with attribute \(b\) - measured in inventory units - \(\hat{R}_t = (\hat{R}_{ta})_{a \in \cal A}\) where \(\cal{A} = \{(a_1,a_2):a_1 \in \{ \mathrm{AB+,AB-A+,A-,B+,B-,O+,O-}\}, a_2 \in \{ \mathrm{0,1,2} \} \}\) - the donations at time \(t\) of the resource with attribute \(a\) before we make a new allocation decision, and before we have satisfied any demands arising in time interval \(t\) - measured in inventory units
The state is:
The state variables are represented by the following variables in the Model class:
self.State = namedtuple('State', SNAMES) # 'class'
self.S_t = self.build_state(S_0_info) # 'instance'where
SNAMES = [
'R_t', # BloodInventory
'Rhold_t', # Held BloodInventory
'Dh_t', # Demand
'Rh_t', # Donations
]The decision variables represent what we control.
We have a decision variable for each link from a resource with attributes \(a\) to a demand with attributes \(b\). Each of these decisions represents an assignment or allocation of an amount of resource away from the inventory for the specific resource with attributes \(a\), to a demand with attributes \(b\).
\[ \begin{aligned} \sum_{b \in \cal{B}}x_{tab} &= R_{ta} \\ \sum_{a \in \cal{A}}x_{tab} &\le \hat{D}_{tb}, \ \ b \in \cal{B} \\ x_{tab} &\ge 0 \end{aligned} \]
The decision variables are represented by the following variables in the Model class:
self.Decision = namedtuple('Decision', xNAMES) # 'class'where
xNAMES = ['x_t']The exogenous information variables represent what we did not know (when we made a decision). These are the variables that we cannot control directly. The information in these variables become available after we make the decision \(x_{tab}\).
We include random variations in demand as well as resource levels due to blood donations. This is modeled as:
\[ W_{t+1} = (\hat{R}_{t+1}, \hat{D}_{t+1}) = (R_{t+1}, D_{t+1}) \]
The exogenous information is obtained by calls to
DemandSimulator.simulate_pois() DemandSimulator.simulate_unif() DonationSimulator.simulate_pois() DonationSimulator.simulate_unif()
The latest exogenous information can be accessed by calling the W_fn(self, t) method from class Model().
The transition function describes how the state variables evolve over time. Because we currently have two state variables in the state, \(S_t=(R_t,D_t)\), we have the equations:
\[ \begin{aligned} R_{t+1} &= R^x_{t} + \hat{R}_{t+1} \quad (Eq. 1) \\ &= \Delta{R}_{t} + \hat{R}_{t+1} \end{aligned} \]
where \(R^x_t\) is the post-decision resource vector. The matrix \(\Delta R_t\) consists of elements \(\delta_{a'}(a,b)\) where this element is in row \(a'\) and column \((a,b)\). The elements are given by:
\[ \delta_{a'}(a,b) = \begin{cases} 1 & \text{if } a'= a^x_t = a^{M,x}(a_t,b) \\ 0 & \text{otherwise } \end{cases} \]
However, the matrix is for notational convenience. In practise we work with the attribute transition function \(a^{M,x}(a_t,b_t)\)
For demands,
\[ \begin{aligned} D_{t+1} &= D^x_{t} + \hat{D}_{t+1} \quad (Eq. 2) \\ &= D_{t} - \delta {D}_{t}(x) \end{aligned} \]
Collectively, (Eq. 1) and (Eq.2) represent the general transition function:
\[
S_{t+1} = S^M(S_t,X^{\pi}(S_t))
\] The transition function is implemented by the S__M_fn() method in class Model().
The objective function captures the performance metrics of the solution to the problem.
We can write the state-dependant reward (contribution) based on what we will receive between \(t-1\) and \(t\):
\[ C(S_t,x_t) = \sum_{a \in \cal A} \sum_{b \in \cal B}c_{ab}x_{tab} \]
This is a deterministic expression and assumed to be linear.
This leads to the objective function:
\[ \max_{\pi \in \Pi}\mathbb{E}\{\sum_{t=0}^{T}C(S_t,X^{\pi}_t(S_t))|S_0 \} \]
The contribution (reward) function is implemented by the C_fn() method in class Model.
Here is the complete implementation of the Model class:
class Model():
def __init__(self, S_0_info):
self.S_0_info = S_0_info
self.State = namedtuple('State', SNAMES) #. 'class'
self.S_t = self.build_state(S_0_info) #. 'instance'
self.Decision = namedtuple('Decision', xNAMES) #. 'class'
self.cumC = 0.0
self.supersink = ('supersink', np.inf)
self.parallelarr = {}
self.varr = {}
self.sqGrad = {} #store the sum of the squared gradients when using AdaGrad stepsizes
# add parallel edges from hold nodes to supersink
for t in PARS['Times']:
for hld in aNAMES:
parArr = np.zeros(PARS['NUM_PARALLEL_LINKS'])
vArr = np.zeros(PARS['NUM_PARALLEL_LINKS'])
self.parallelarr[(t, hld, self.supersink)] = parArr
self.varr[(t, hld, self.supersink)] = vArr
sqGradArr = np.zeros(PARS['NUM_PARALLEL_LINKS'])
self.sqGrad[(t, hld)] = sqGradArr
def build_state(self, info):
return self.State(*[info[sn] for sn in SNAMES])
def build_decision(self, info):
return self.Decision(*[info[xn] for xn in xNAMES])
# exogenous information = demand from t-1 to t and new donated blood
def W_fn(self, t):
if (PARS['SAMPLING_DIST'] == 'P'):
Dh_tt1 = dem_sim.simulate_pois(t)
Rh_tt1 = don_sim.simulate_pois()
else:
Dh_tt1 = dem_sim.simulate_unif(t)
Rh_tt1 = don_sim.simulate_unif()
self.S_t.Dh_t.update(Dh_tt1)
# update the demand nodes
values = list(self.S_t.Dh_t.values())
for i,itm in enumerate(self.S_t.Dh_t.items()):
self.S_t.Dh_t[itm[0]] = values[i]
# save the donation vector to the model
self.S_t.Rh_t.update(Rh_tt1)
return Dh_tt1, Rh_tt1
def S__M_fn(self, x_t, hld):
# iterate through hold vector
hold = hld
for i,itm in enumerate(self.S_t.Rhold_t.items()):
self.S_t.Rhold_t[itm[0]] = hold[i]
values = list(self.S_t.Rh_t.values())
rev_don = list(reversed(values))
values = list(self.S_t.Rhold_t.values())
myrev_hld = list(reversed(values))
# age the blood at hold node and add in the donations
for i in range(PARS['NUM_BLD_NODES']):
if (i % PARS['MAX_AGE'] == PARS['MAX_AGE']-1):
# add donation
myrev_hld[i] = rev_don[i // PARS['MAX_AGE']]
else:
# age
myrev_hld[i] = myrev_hld[i+1]
myrev_hld = list(reversed(myrev_hld))
# amount at blood node = amount at hold node
for i,itm in enumerate(self.S_t.R_t.items()):
self.S_t.R_t[itm[0]] = myrev_hld[i]
return self.S_t
def C_fn(self, x_t):
x = [x_t.x_t[abn] for abn in abNAMES_EXP]
C = np.dot(x, P.coeff)
return C
def step(self, x_t, hld):
C = self.C_fn(x_t)
self.cumC += C
self.S_t = self.S__M_fn(x_t, hld)
return (self.S_t, self.cumC, x_t) #. for plotting
########################################################################################################
class Exog_Info():
def __init__(self, demand, donation):
# list consisting of blood demand objects
self.demand = demand
# list consisting of blood unit objects donated to the blood inventory
self.donation = donation
def init_R_t_unif():
bldinv_init = {}
for aName in aNAMES:
if aName.split('_')[1]=='0':
bldinv_init[aName] = round(np.random.uniform(0, myMAX_DON_BY_BLOOD[aName.split('_')[0]])*.9)
else:
bldinv_init[aName] = round(np.random.uniform(0, myMAX_DON_BY_BLOOD[aName.split('_')[0]])*(0.1/(PARS['MAX_AGE'] - 1)))
return bldinv_init
def init_R_t_pois():
bldinv_init = {}
for aName in aNAMES:
if aName.split('_')[1]=='0':
bldinv_init[aName] = int(np.random.poisson(myMAX_DON_BY_BLOOD[aName.split('_')[0]])*.9)
else:
bldinv_init[aName] = int(np.random.poisson(myMAX_DON_BY_BLOOD[aName.split('_')[0]])*(0.1/(PARS['MAX_AGE']-1)))
return bldinv_initWe will simulate the demand and blood donations as described in section 4.3.3:
\[ W_{t+1} = (\hat{R}_{t+1}, \hat{D}_{t+1}) = (R_{t+1}, D_{t+1}) \]
There are two main meta-classes of policy design. Each of these has two subclasses: - Policy Search - Policy Function Approximations (PFAs) - Cost Function Approximations (CFAs) - Lookahead - Value Function Approximations (VFAs) - Direct Lookaheads (DLAs)
In this project we will only use one approach: - A technique that exploits the convexity (concavity in our case because we maximize) of the problem in the form of a linear program (LP) (from the VFA class)
The LP policy is implemented by the X__LP() method in class Policy().
The Policy() class implements the policy design. The Python package cvxopt is used to perform the solution of the linear program at each step. Here are some links:
https://www.xiaowenying.com/machine-learning/2019/11/12/transportation-problem.html
https://acme.byu.edu/0000017a-1bb8-db63-a97e-7bfa0bdb0000/vol2lab14-pdf
https://acme.byu.edu/00000179-d4cb-d26e-a37b-fffb576b0000/cvxopt-intro-pdf
import numpy as np
import cvxopt
from collections import (namedtuple, defaultdict)
def initLPMatrices():
#Initializing the matrix for the LP
A = np.zeros((PARS['NUM_BLD_NODES'], PARS['NUM_BLD_NODES']*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])))
for i in range(PARS['NUM_BLD_NODES']):
for j in range(PARS['NUM_BLD_NODES']*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])):
if (j < (i+1)*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])) and (j >= i*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])):
#Checking for feasibility
k=j-i*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])
if (k<PARS['NUM_DEM_NODES']):
an = aNAMES[i]; a = an.split('_')
bn = bNAMES[k]; b = bn.split('_')
if (bool(b[2]) == False and a[0] == b[0]) or (bool(b[2]) == True and mySubMatrix['_'.join((a[0], b[0]))] == True):
A[i,j] = 1
else:
A[i,j] = 1
G = np.zeros((PARS['NUM_DEM_NODES'] + 2*PARS['NUM_BLD_NODES']*PARS['NUM_PARALLEL_LINKS'] + PARS['NUM_DEM_NODES']*PARS['NUM_BLD_NODES'], PARS['NUM_BLD_NODES']*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])))
# ineq constr for sum x_tbd < D_td
for i in range(PARS['NUM_DEM_NODES']):
for j in range(PARS['NUM_BLD_NODES']*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])):
if (j % (PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS']) == i):
G[i,j] = 1.
# ineq constr for parallel links <= SLOPE_CAPAC
for i in range(PARS['NUM_BLD_NODES']):
for j in range(PARS['NUM_PARALLEL_LINKS']):
G[PARS['NUM_DEM_NODES'] + i*PARS['NUM_PARALLEL_LINKS'] + j, (PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])*i + PARS['NUM_DEM_NODES'] + j] = 1
# ineq constr for x_tbd >= 0
for i in range(PARS['NUM_BLD_NODES']):
for j in range(PARS['NUM_DEM_NODES']):
G[PARS['NUM_DEM_NODES'] + PARS['NUM_BLD_NODES']*PARS['NUM_PARALLEL_LINKS'] + i*PARS['NUM_DEM_NODES'] + j, (PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])*i + j] = -1
# ineq constr for x_parallel >= 0
for i in range(PARS['NUM_BLD_NODES']):
for j in range(PARS['NUM_PARALLEL_LINKS']):
G[PARS['NUM_DEM_NODES'] + PARS['NUM_BLD_NODES']*PARS['NUM_PARALLEL_LINKS'] + PARS['NUM_DEM_NODES']*PARS['NUM_BLD_NODES'] + i*PARS['NUM_PARALLEL_LINKS'] + j,(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])*i + PARS['NUM_DEM_NODES'] + j] = -1
h = np.ones(PARS['NUM_DEM_NODES'] + PARS['NUM_BLD_NODES']*PARS['NUM_PARALLEL_LINKS'])
h[PARS['NUM_DEM_NODES']::PARS['NUM_PARALLEL_LINKS']]= PARS['SLOPE_CAPAC_LAST']
h = np.append(h, np.zeros(PARS['NUM_BLD_NODES']*PARS['NUM_DEM_NODES'] + PARS['NUM_BLD_NODES']*PARS['NUM_PARALLEL_LINKS']))
A = cvxopt.matrix(A)
G = cvxopt.matrix(G)
coeff = [\
np.concatenate((
np.array([it[1] for it in ContribMatrix[aName].items()]),
np.zeros(PARS['NUM_PARALLEL_LINKS'])
)) if int(aName.split('_')[1]) < PARS['MAX_AGE']-1 else
np.concatenate((
np.array([it[1] for it in ContribMatrix[aName].items()]),
np.add(np.zeros(PARS['NUM_PARALLEL_LINKS']), PARS['DISCARD_BLOOD_PENALTY'])))
for aName in aNAMES
]
coeff = [ai for a in coeff for ai in a]
coeff = np.array(coeff)
return (coeff, G, h, A)class Policy(): #. Static Stochastic Shortest Path Model policy
def __init__(self):
self.coeff, self.G, self.h, self.A = initLPMatrices()
def X__LP(self, M, t, solDemList, solHoldList):
c_t = [ \
np.concatenate((
np.multiply(np.array([it[1] for it in ContribMatrix[an].items()]), -1),
np.multiply(M.parallelarr[(t, an, M.supersink)], -PARS['DISCOUNT_FACTOR'])
)) if int(an.split('_')[1]) < PARS['MAX_AGE'] - 1 else
np.concatenate((
np.multiply(np.array([it[1] for it in ContribMatrix[an].items()]), -1),
np.add(np.multiply(M.parallelarr[(t, an, M.supersink)], -PARS['DISCOUNT_FACTOR']), -PARS['DISCARD_BLOOD_PENALTY'])
))
for an in aNAMES
]
c = [ai for a in c_t for ai in a]
b = np.array([kv[1] for kv in M.S_t.R_t.items()])
self.h[:PARS['NUM_DEM_NODES']] = [kv[1] for kv in M.S_t.Dh_t.items()]
c = cvxopt.matrix(c)
b = cvxopt.matrix(b, size=(PARS['NUM_BLD_NODES'], 1), tc='d')
h = cvxopt.matrix(self.h)
cvxopt.solvers.options['show_progress'] = False
sol = cvxopt.solvers.lp(
c,
self.G,
h,
self.A,
b,
solver='glpk',
options={'glpk':{'msg_lev':'GLP_MSG_OFF'}})
#sol = cvxopt.solvers.lp(c, self.G, h, self.A, b)
x = sol['x']
x = np.array(x)
x = np.squeeze(x)
d = sol['y'] # dual variables
assert(len(x) == len(abNAMES_EXP))
info = {'x_t': {}}
for i,xn in enumerate(abNAMES_EXP):
info['x_t'][xn] = x[i]
if info['x_t'][xn] > 0.0:
# print(f'%%% {x[i]=}, {info["x_t"][xn]=}, {xn=}')
assert(x[i] == info['x_t'][xn])
dcsn = M.build_decision(info); #print(f'%%% {dcsn=}')
return dcsn, x, d
def X__FillAsNeededFromSingleResource(self, M):
resources = M.S_t.R_t.copy() #don't want to overwrite prematurely
info = {'x_t': {}}
for i,xn in enumerate(abNAMES_EXP):
info['x_t'][xn] = 0
for demand_item in M.S_t.Dh_t.items():
# print(f'{demand_item}')
demand_bloodtype = demand_item[0].split('_')[0]
demand_quantity = demand_item[1]
for resource_item in resources.items():
resource_bloodtype = resource_item[0].split('_')[0]
resource_quantity = resource_item[1]
#resource can be donor AND there is demand AND resource has inventory
if \
(mySubMatrix['_'.join([resource_bloodtype, demand_bloodtype])] == True) and \
(demand_quantity > 0) and \
(resource_item[1] > 0):
# print(f'\t{resource_item}')
if resource_quantity >= demand_quantity:
xn = '___'.join([resource_item[0], demand_item[0]]); #print(f'\t{xn=}')
info['x_t'][xn] = demand_quantity; #print(f'\t{info=}') #fill demand
resources.update({resource_item[0]: (resource_quantity - demand_quantity)}) #update resource
# print(f'\tremaining resource {resource_item[0]} is {resource_quantity - demand_quantity}\n')
break
# for xn in abNAMES_EXP:
# if info['x_t'][xn] > 0.0:
# print(f'%%% {info["x_t"][xn]=}, {xn=}')
dcsn = M.build_decision(info); #print(f'%%% {dcsn=}')
x = np.array(list(dcsn.x_t.values()))
return dcsn, x
def updateVFAs(self, M, l, t, d, slopesList, updateVfaList):
alpha = 0
# set the dual variables to respective parallel arcs
for i in range(PARS['NUM_BLD_NODES']):
# put the value of the dual varible d[i+1] in the parallel arc, associated
# with the amount of resource in the inventory associated with holdnode[i]
# the holdnodes with the oldest age do not get updated
recordSlopes = (
l,
t,
M.Bld_Net.parallelarr[(t, M.Bld_Net.holdnodes[i], M.Bld_Net.supersink)].copy()
)
slopesList.append(recordSlopes)
# index = M.bld_inv[i]
index = M.R_t[i]
if index>=0:
if (t > 0 and M.Bld_Net.holdnodes[i][1] < str(PARS['MAX_AGE']-1)):
vhat=d[i+1]
if index >= PARS['NUM_PARALLEL_LINKS'] - 1:
index = PARS['NUM_PARALLEL_LINKS'] - 1
arr = M.Bld_Net.varr[(t-1,M.Bld_Net.holdnodes[i], M.Bld_Net.supersink)]
sqGradArr = M.Bld_Net.sqGrad[(t-1,M.Bld_Net.holdnodes[i])]
if l < PARS['NUM_ITER_STEP_ONE']:
alpha = 1
else:
if (PARS['STEPSIZE_RULE'] == 'C'):
alpha = PARS['ALPHA']
elif (PARS['STEPSIZE_RULE'] == 'A'):
sqGradArr[index] += np.power(vhat-arr[index],2)
alpha = PARS['ETA']/(np.sqrt(sqGradArr[index]+PARS['STEP_EPS']))
vbar = arr[index]
vnew = alpha*vhat +(1-alpha)*vbar
arr[index] = vnew
recordUpdateVfa = (l,t-1,M.Bld_Net.holdnodes[i][0],M.Bld_Net.holdnodes[i][1],index,vhat,vbar,sqGradArr[index],alpha,vnew)
updateVfaList.append(recordUpdateVfa)
#Projecting back in case the vfa is not concave anymore
if (vnew>vbar): #Look to the left
indSetL=[i for i in list(range(0,index+1)) if arr[i]<=vnew]
if (len(indSetL)>0):
if PARS['PROJECTION_ALGO'] == 'Avg':
avg = np.mean(arr[indSetL])
arr[indSetL]=avg
elif PARS['PROJECTION_ALGO'] == 'Copy':
arr[indSetL]=vnew
else:
if index > 0:
j=index-1
while (j>=0 and arr[j] < arr[j+1]):
arr[j]= alpha*vhat +(1-alpha)*arr[j]
j-=1
else:
arr[index]=vnew
elif (vnew<vbar): #Look to the right
indSetR=[i for i in list(range(index,PARS['NUM_PARALLEL_LINKS'])) if arr[i]>=vnew]
if (len(indSetR)>0):
if PARS['PROJECTION_ALGO'] == 'Avg':
avg = np.mean(arr[indSetR])
arr[indSetR]=avg
elif PARS['PROJECTION_ALGO'] == 'Copy':
arr[indSetR]=vnew
else:
if index < PARS['NUM_PARALLEL_LINKS']-1:
j=index+1
while (j < PARS['NUM_PARALLEL_LINKS'] and arr[j] > arr[j-1]):
arr[j] = alpha*vhat +(1-alpha)*arr[j]
j+=1
else:
arr[index]=vnew
return alpha, slopesList, updateVfaList
def run_policy_sample_paths(
self, T, L, pi, alpha,
demandExoList, donationExoList,
supplyPreList, supplyPostList,
slopesList, solDemList, solHoldList, simuList, updateVfaList, record):
FhatIomega__lI = []
cumC = []
for l in range(L): #for each sample-path
IS_TRAINING = (l < PARS['NUM_TRAINNING_ITER'])
if (l == PARS['NUM_TRAINNING_ITER']):
print("Starting testing iterations! Currently at iteration ", l)
print("Reseting random seed!")
np.random.seed(PARS['SEED_TESTING'])
t_init = time.time()
print('Iteration = ', l)
# Initial inventory
if (PARS['SAMPLING_DIST'] == 'P'):
bldinv_init = init_R_t_pois()
else:
bldinv_init = init_R_t_unif()
# Initial exogenous information
if (PARS['SAMPLING_DIST'] == 'P'):
Dh_tt1 = dem_sim.simulate_pois(0)
Rh_tt1 = don_sim.simulate_pois()
else:
Dh_tt1 = dem_sim.simulate_unif(0)
Rh_tt1 = don_sim.simulate_unif()
#initial state - the donation is irrelevant at time period zero - only the initial invetory counts
S_0_info = {
'R_t': bldinv_init,
'Rhold_t': {an: 0 for an in aNAMES},
'Dh_t': Dh_tt1,
'Rh_t': Rh_tt1
}
M = Model(S_0_info)
#print("Initial demand across {} types and {} urgency states and {} substitution states is {}".format(params['NUM_BLD_TYPES'],params['NUM_SUR_TYPES'],len(params['Substitution']),sum(M.demand)))
cumC.append(0)
record_l = [pi, l]
for t in range(T): #for each transition/step
#Compute the solution for time period t - return
# the solution, the value, the dual and the updated lists
x_t, x, d = getattr(self, pi)(
M,
t,
solDemList,
solHoldList)
xDem = [x[i*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS']):i*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])+PARS['NUM_DEM_NODES']] for i in list(range(PARS['NUM_BLD_NODES']))]
xDemFlat = [xij for xi in xDem for xij in xi]
solDemRec = (l, t, xDem.copy())
solDemList.append(solDemRec)
hld = [np.sum(x[i*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])+PARS['NUM_DEM_NODES']:(i+1)*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])]) for i in list(range(PARS['NUM_BLD_NODES']))]
solHoldRecord = (l, t, hld.copy())
solHoldList.append(solHoldRecord)
hld = np.array(hld)
values = list(M.S_t.R_t.values())
invByBlood = [ \
np.sum(values[i*PARS['MAX_AGE']:(i+1)*PARS['MAX_AGE']])
for i in list(range(len(PARS['Bloodtypes'])))
]
values = list(M.S_t.Dh_t.values())
demByBlood = [ \
np.sum(
values [ \
i*(len(PARS['Surgerytypes'])*len(PARS['Substitution'])): \
(i+1)*(len(PARS['Surgerytypes'])*len(PARS['Substitution']))
]
)
for i in list(range(len(PARS['Bloodtypes'])))
]
xDemFlat = [xij for xi in xDem for xij in xi]
xDemMat = np.array(xDemFlat).reshape(PARS['NUM_BLD_NODES'],PARS['NUM_DEM_NODES'])
xDemMatColSum = xDemMat.sum(axis=0)
covByBlood = [ np.sum(xDemMatColSum[i*(len(PARS['Surgerytypes'])*len(PARS['Substitution'])):(i+1)*(len(PARS['Surgerytypes'])*len(PARS['Substitution']))]) for i in list(range(len(PARS['Bloodtypes']))) ]
covByBlood = np.array(covByBlood).astype(int)
hldByBlood = [int(np.sum(hld[i*PARS['MAX_AGE']:(i+1)*PARS['MAX_AGE']])) for i in list(range(len(PARS['Bloodtypes']))) ]
disByBlood = hld[PARS['MAX_AGE']-1::PARS['MAX_AGE']]
disByBlood = np.array(disByBlood)
disByBlood = disByBlood.astype(int)
if False:
print('Iteration = ', iteration)
print('Time period = ', t)
print('Demand = ', np.sum(M.Bld_Net.demandamount))
print('Supply = ', np.sum(M.Bld_Net.bloodamount))
# print('Blood Used = ', np.sum(M.bld_inv) - np.sum(hld))
print('Blood Used = ', np.sum(M.R_t) - np.sum(hld))
print('Blood Held = ', np.sum(hld))
print('Inventory by BloodType ', invByBlood)
print('Demand By BloodType ', demByBlood)
print('Used By BloodType ', list(covByBlood))
print('Hold By BloodType ', hldByBlood)
print('Discard By BloodType ', list(disByBlood))
print('Contribution = ', val)
print('Donation = ', np.sum(M.donation))
print('\n')
hld = hld.astype(int)
if IS_TRAINING and PARS['IS_PERTUB']:
epsilon = PERTUB_GEN.poisson(LAMBDA_PERTUB, PARS['NUM_BLD_NODES'])
signE = PERTUB_GEN.choice([-1,1], size=PARS['NUM_BLD_NODES'], replace=True, p=None)
hld = hld+epsilon*signE
hld = np.maximum(np.zeros(PARS['NUM_BLD_NODES']),hld)
hld = hld.astype(int)
cumC[l] += np.dot(x, self.coeff)#.
#Grabbing exogenous data to construct data frame
values = list(M.S_t.Dh_t.copy().values())
recordDemandExo = (l, t, values)
demandExoList.append(recordDemandExo)
if (t == 0):
values = list(M.S_t.R_t.values())
recordDonationExo = (l, 0, list(np.array(values)[::PARS['MAX_AGE']]))
donationExoList.append(recordDonationExo)
if (t < PARS['MAX_TIME'] - 1):
values = list(M.S_t.Rh_t.copy().values())
recordDonationExo = (l, t+1, values)#.
donationExoList.append(recordDonationExo)
#Grabbing pre-decision state to construct data frame
values = list(M.S_t.R_t.copy().values())
recordSupplyPre = (l, t, values)#.
supplyPreList.append(recordSupplyPre)
S_t, mycumC, x_t = M.step(x_t, hld)
record_t = [t] + \
[S_t.R_t[an] for an in aNAMES] + \
[S_t.Dh_t[bn] for bn in bNAMES] + \
[mycumC] + \
[x_t.x_t[abn] for abn in abNAMES_EXP]
record.append(record_l + record_t)
if IS_TRAINING:
alpha, slopesList, updateVfaList = P.updateVFAs(
PARS, M, l, t, d, slopesList, updateVfaList)#.
#Grabbing post-decision state to construct data frame
values = list(M.S_t.R_t.copy().values())
recordSupplyPost = (l, t, values)
supplyPostList.append(recordSupplyPost)
M.W_fn(t + 1)
FhatIomega__lI.append(M.cumC)
# copy v to the parallel links
for t in PARS['Times']:
for hld in aNAMES:
parArr = 1 * M.varr[(t, hld, M.supersink)]
M.parallelarr[(t, hld, M.supersink)] = parArr
t_end = time.time()
recordSimu = (
l, # iteration
int(t_end-t_init),
alpha,
cumC[l],
(l < PARS['NUM_TRAINNING_ITER'])
)
simuList.append(recordSimu)
print(f"***Finishing iteration {recordSimu[0]} in {recordSimu[1]:.2f} secs. Total contribution: {recordSimu[3]:.2f}***\n")
return FhatIomega__lI
#End of iterations
# ############## END "run_policy_sample_paths()" #########################
def perform_search_sample_paths(self, T, L, pi):
t_global_init = time.time()
print("********************Started Main*****************\n")
alpha = PARS['ALPHA']
# initializing the random seed for trainning iterations
np.random.seed(PARS['SEED_TRAINING'])
# if (PARS['LOAD_VFA'] and os.path.exists(params['NAME_LOAD_VFA_PICKLE'])):
# pickle_off = open(PARS['NAME_LOAD_VFA_PICKLE'], "rb")
# Other_Bld_Net = pickle.load(pickle_off)
# # Bld_Net.varr = Other_Bld_Net.varr.copy()
# myBLD_NET.varr = Other_Bld_Net.varr.copy()
# # Bld_Net.parallelarr = Other_Bld_Net.parallelarr.copy()
# myBLD_NET.parallelarr = Other_Bld_Net.parallelarr.copy()
# initializing the lists that will store all the info/decisions/states/slopes along the iterations for printing purposes
demandExoList, donationExoList, supplyPreList, supplyPostList, \
slopesList, solDemList, solHoldList, simuList, updateVfaList = [],[],[],[],[],[],[],[],[]
if (PARS['NUM_TRAINNING_ITER'] > 0):
print("\n Starting training iterations\n")
record = []
FhatIomega__lI = self.run_policy_sample_paths(
T, L, pi, alpha,
demandExoList, donationExoList,
supplyPreList, supplyPostList,
# slopesList, solDemList, solHoldList, simuList, updateVfaList)
slopesList, solDemList, solHoldList, simuList, updateVfaList, record)
Fhat_mean = np.array(FhatIomega__lI).mean()
print("Total elapsed time {:.2f} secs".format(time.time()- t_global_init))
return Fhat_mean, demandExoList, donationExoList, supplyPreList, supplyPostList, slopesList, solDemList, solHoldList, simuList, updateVfaList, record
def plot_total_contribution(self, dfSimu):
#Figure 1 - Total Contribution along iterations
ite = np.arange(0, PARS['NUM_ITER'], 1)
ite_TRA = np.arange(0, PARS['NUM_TRAINNING_ITER'], 1)
ite_TES = np.arange(0, PARS['NUM_TESTING_ITER'], 1) + PARS['NUM_TRAINNING_ITER']
fig_ite, ax_ite = plt.subplots(figsize=(16,8))
# ax_ite.plot(ite, dfSimu['ObjVal'],'g-',label='_nolegend_')#.
ax_ite.plot(ite, dfSimu['cumC'],'g-',label='_nolegend_')
ax_ite.plot(ite_TRA, dfSimu['cumC'][ite_TRA],'g-',label="Training",marker='o')
ax_ite.plot(ite_TES, dfSimu['cumC'][ite_TES],'b-',label="Testing",marker='o')
ax_ite.hlines(meanTesting, ite_TES[0], ite_TES[-1], color='b',linestyle='--',label="Avg Testing",linewidth=4)
ax_ite.axvline(ite_TES[0], 0, 1, color='k',linestyle=':')
ax_ite.legend()
ax_ite.set_xlabel('Iterations $l$', fontsize=14)
ax_ite.set_ylabel('$',fontsize=14)
#ax_ite.set_ylim([20000,34000])
ax_ite.set_title(f"Policy {policy}_{surge} - Total contributions \n Avg total contribution during TESTING iterations: ${meanTesting:,}\n Final utility: {modifiedUtil:.0f}", fontsize=18)
ax_ite.set_xticks(ite)
ax_ite.set_xticklabels(list(ite_TRA)+list(np.arange(0, PARS['NUM_TESTING_ITER'], 1)))
for c in ite_TES:
ax_ite.get_xticklabels()[c].set_color("b")
def plot_demand_and_donation(self, dfDemandExo, dfDonationExo):
#Figure 2 - Exogenous processes - Demand and Donation
fig_exo, ax_exo = plt.subplots(2, 1, figsize=(16,8), sharex=True)
dfDemandExoP = \
dfDemandExo[dfDemandExo['l'] >= PARS['NUM_TRAINNING_ITER']].copy()
dfDemandExoP['l'] = dfDemandExoP['l'] - PARS['NUM_TRAINNING_ITER']
dfPrintDemand = \
dfDemandExoP.pivot_table('DemandValue', index='t', columns='l', aggfunc='sum')
leg = dfPrintDemand.plot(
ax=ax_exo[0], title=f"Total Demand "+r"$\hat{D}_t$"+f" - {surge}", legend=False)
ax_exo[0].set_ylabel("units", fontsize=14)
dfDonationExoP = \
dfDonationExo[dfDonationExo['l'] >= PARS['NUM_TRAINNING_ITER']].copy()
dfDonationExoP['l'] = dfDonationExoP['l'] - PARS['NUM_TRAINNING_ITER']
dfPrintDonation = \
dfDonationExoP.pivot_table('DonationValue', index='t', columns='l', aggfunc='sum')
dfPrintDonation.plot(
ax=ax_exo[1], title="Total Donation "+r"$\hat{R}_t$", legend=False)
ax_exo[1].set_xlabel("time $t$", fontsize=14)
ax_exo[1].set_ylabel("units", fontsize=14)
fig_exo.legend(
leg,
labels=list(np.arange(0, PARS['NUM_TESTING_ITER'], 1)),
title="Iteration "+"$l$",
loc="center right",
fancybox=True,
shadow=True)
fig_exo.suptitle(f'Exogenous processes for testing'+'\n'+f'L = {L}, T = {T}', fontsize=18)
def plot_predecision_inventory_by_age(self, dfInv):
#Figure 3 - Pre decision inventory levels by age
fig_inv, ax_inv = plt.subplots(3, 1, figsize=(16,10), sharex=True)
for age in [0,1,2]:
strage=str(age)
dfPrint = dfInv[dfInv.Age == strage].pivot_table('PreInv', index=['t'], columns=['l'], aggfunc='sum')
leg = dfPrint.plot(ax=ax_inv[age], legend=False)
dfPrintAvg = dfInv[dfInv.Age == strage].groupby(['t', 'l'])['PreInv'].sum()
dfPrintAvg = dfPrintAvg.groupby('t').mean()
avg_line = ax_inv[age].plot(dfPrintAvg.index, dfPrintAvg.values, 'k', linestyle=':', marker='s', markersize='12', label='Average')
ax_inv[age].set_title("Age: {} - Avg inventory level: {:.0f}".format(age, dfPrintAvg.values.mean()),fontsize=14)
ax_inv[age].set_ylabel("units",fontsize=14)
ax_inv[2].set_xlabel("time $t$", fontsize=14)
fig_inv.suptitle(f"Policy {policy}_{surge} \n Pre-decision inventory "+r"$R_t$"+f" (all blood types), L = {L}, T = {T}", fontsize=18)
fig_inv.legend(
[leg,avg_line],
labels=list(np.arange(0, PARS['NUM_TESTING_ITER'], 1))+["Avg"],
title="Iteration "+"$l$",
loc='center right',
fancybox=True,
shadow=True,
ncol=1)
def plot_predecision_inventory_by_bloodtype(self, dfInv):
#Figure 4 - Pre decision inventory levels by bloodtype
fig_inv_blood, ax_inv_blood = plt.subplots(4, 2, figsize=(16,10), sharex=True)
row = -1
# for m,b in enumerate(params['Bloodtypes']):
for m,b in enumerate(myBloodtypes):
col= (m)%2
if col == 0:
row+=1
ax_inv_blood[row,col].set_ylabel("units", fontsize=14)
# dfPrint = dfInv[dfInv.BloodType == b].pivot_table('PreInv',index=['Time'],columns=['Iteration'],aggfunc='sum')
dfPrint = dfInv[dfInv.BloodType == b].pivot_table('PreInv', index=['t'], columns=['l'], aggfunc='sum')
leg = dfPrint.plot(ax=ax_inv_blood[row, col], legend=False)
# dfPrintAvg = dfInv[dfInv.BloodType == b].groupby(['Time','Iteration'])['PreInv'].sum()
dfPrintAvg = dfInv[dfInv.BloodType == b].groupby(['t', 'l'])['PreInv'].sum()
# dfPrintAvg = dfPrintAvg.groupby('Time').mean()
dfPrintAvg = dfPrintAvg.groupby('t').mean()
avg_line = ax_inv_blood[row,col].plot(dfPrintAvg.index,dfPrintAvg.values,'k',linestyle=':',marker='s',markersize='10',label='Average')
ax_inv_blood[row,col].set_title("Bloodtype: {} - Avg inventory level: {:.0f}".format(b,dfPrintAvg.values.mean()),fontsize=14)
ax_inv_blood[row,col].set_xlabel("time $t$", fontsize=14)
# fig_inv_blood.suptitle("Policy {}_{} \n Pre-decision inventory level all ages".format(policy,surge))
fig_inv_blood.suptitle(f"Policy {policy}_{surge} \n Pre-decision inventory "+r"$R_t$"+f" (all ages), L = {L}, T = {T}", fontsize=18)
fig_inv_blood.legend(
[leg,avg_line],
labels=list(np.arange(0, PARS['NUM_TESTING_ITER'], 1))+["Avg"],
loc="center right",
title="Iteration "+"$l$")
def plot_predecision_inventory(self, dfInv):
#Figure 5 - Pre decision inventory levels
dfPrintAvg = dfInv.groupby(['t','l'])['PreInv'].sum()
dfPrintAvg = dfPrintAvg.groupby('t').mean()
fig_inv_total, ax_inv_total = plt.subplots(figsize=(16,8))
dfPrint = dfInv.pivot_table('PreInv', index=['t'], columns=['l'], aggfunc='sum')
dfPrint.plot(ax=ax_inv_total, legend=True)
first_legend = ax_inv_total.legend(title="Iteration")
avg_line = ax_inv_total.plot(dfPrintAvg.index,dfPrintAvg.values,'k',linestyle=':',marker='s',markersize='12',label='Average')
ax_inv_total.legend(title="Iteration "+"$l$", fontsize=10)
ax_inv_total.set_xlabel("time $t$", fontsize=14)
ax_inv_total.set_ylabel("units", fontsize=14)
fig_inv_total.suptitle(f"Policy {policy}_{surge} \n Pre-decision inventory "+r"$R_t$"+f" (all blood types and ages), L = {L}, T = {T}\n Average inventory level across time periods: {dfPrintAvg.values.mean():.0f}", fontsize=16)
def plot_discarded_blood_during_testing(self, dfSolHold):
#Figure 6 - Discarded blood during testing iterations
dfDiscarded = dfSolHold.loc[(dfSolHold.Age.astype(int) > PARS['MAX_AGE']-2)&(dfSolHold['l'] >= PARS['NUM_TRAINNING_ITER']),:].copy()
dfDiscarded = dfDiscarded.groupby(['BloodTypeS','t'])['Value'].sum().reset_index()
dfDiscarded['Prop']=100*dfDiscarded['Value']/totalDonation
y_a = dfDiscarded.BloodTypeS.unique()
x_a = dfDiscarded['t'].unique()
discarded_matrix = np.reshape(np.array(dfDiscarded['Value']), (-1, len(x_a)))
fig_dis, ax_dis = plt.subplots(figsize=(16,8))
im = ax_dis.imshow(discarded_matrix, cmap='hot_r', origin='lower', aspect='auto', alpha=.9)
cbar = ax_dis.figure.colorbar(im, ax=ax_dis, label='Units of blood')
ax_dis.set_xticks(np.arange(len(x_a)))
ax_dis.set_yticks(np.arange(len(y_a)))
ax_dis.set_xticklabels(x_a)
ax_dis.set_yticklabels(y_a)
ax_dis.set_xlabel("time $t$", fontsize=14)
ax_dis.set_title(f"Policy {policy}_{surge} \n Total discarded blood during TESTING\n Proportion of blood discarded: {totalDiscarded*100/totalDonation:.2f}%", fontsize=14)
def plot_demand_coverage(self, dfPrintIte):
#Figure 7 - Demand Coverage - Testing iterations
fig_tes, ax_tes = plt.subplots(figsize=(16,8))
ax_tes.plot(dfPrintIte['Urgent'],marker='o')
ax_tes.plot(dfPrintIte['Elective'],marker='o')
ax_tes.set_title(f"Policy {policy}_{surge} \n Average coverage of demand by blood type and urgency level during TESTING iterations\n {coverage} Coverage utility: {utility:.0f}", fontsize=18)
ax_tes.set_xlabel("Bloodtype", fontsize=14)
ax_tes.set_ylabel("Coverage ratio", fontsize=14)
ax_tes.legend(
title="Urgency level",
loc='center left',
bbox_to_anchor=(1, 0.5),
fancybox=True,
shadow=True,
ncol=1)
def plot_demand_coverage_by_bloodtype(self, dfCoverage_agg_ite):
#Figure 8 - Demand Coverage - Along Blood types
iteS = np.arange(0, PARS['NUM_ITER'], 1)
if True:
#selectedIte = sorted(list(set([0,5,10,19]) ))
selectedIte = sorted(list(set([0,5,10,19]) & set(iteS)))
fig_cover, ax_cover = plt.subplots(1,len(selectedIte),figsize=(16,8),sharey=True)
for i,ite in enumerate(selectedIte):
dfPrintIte = dfCoverage_agg_ite[dfCoverage_agg_ite['l']==ite]
dfPrintIte=dfPrintIte.pivot_table('Ratio',index='Bloodtype',columns='Urgency')
typIte='TES'
if ite < PARS['NUM_TRAINNING_ITER']:
typIte='TRA'
if len(selectedIte)==1:
ax_cover.plot(dfPrintIte['Urgent'],marker='o')
ax_cover.plot(dfPrintIte['Elective'],marker='o')
ax_cover.set_title("Iteration {} ({})".format(ite,typIte), fontsize=14)
else:
ax_cover[i].plot(dfPrintIte['Urgent'],marker='o')
ax_cover[i].plot(dfPrintIte['Elective'],marker='o')
ax_cover[i].set_title("Iteration {} ({})".format(ite,typIte), fontsize=14)
fig_cover.suptitle(f"Policy {policy}-{surge} \n Average coverage of demand by blood type and urgency level for different iterations, L = {L}, T = {T}", fontsize=18)
if len(selectedIte)==1:
ax_cover[0].set_xlabel("Bloodtype", fontsize=14)
ax_cover[0].set_ylabel("Coverage ratio", fontsize=14)
ax_cover.legend(title="Urgency level", loc='center left', bbox_to_anchor=(1, 0.5), fancybox=True, shadow=True, ncol=1)
else:
ax_cover[0].set_xlabel("Bloodtype", fontsize=14)
ax_cover[0].set_ylabel("Coverage ratio", fontsize=14)
ax_cover[len(selectedIte)-1].legend(title="Urgency level", loc='center left', bbox_to_anchor=(1, 0.5), fancybox=True, shadow=True, ncol=1)
def plot_demand_coverage_by_timeperiod(self, dfCoverage):
#Figure 9 - Demand Coverage - Along time periods
iteS = np.arange(0, PARS['NUM_ITER'], 1)
if True:
dfCoverage_agg_ite = dfCoverage.groupby(['t', 'Urgency', 'l'])['Ratio'].mean().reset_index()
#selectedIte = sorted(list(set([0,5,10,19]) ))
selectedIte = sorted(list(set([0,5,10,19]) & set(iteS)))
fig_cover_ite, ax_cover_ite = plt.subplots(1,len(selectedIte),figsize=(16,8),sharey=True,sharex=True)
for i,ite in enumerate(selectedIte):
dfPrintIte = dfCoverage_agg_ite[dfCoverage_agg_ite['l']==ite]
dfPrintIte=dfPrintIte.pivot_table('Ratio', index='t', columns='Urgency')
typIte='TES'
if ite < PARS['NUM_TRAINNING_ITER']:
typIte='TRA'
if len(selectedIte)==1:
ax_cover_ite.plot(dfPrintIte['Urgent'],marker='o')
ax_cover_ite.plot(dfPrintIte['Elective'],marker='o')
ax_cover_ite.set_title("Iteration {} ({})".format(ite,typIte), fontsize=14)
ax_cover_ite.set_xticks(list(range(0,PARS['MAX_TIME'],2)))
ax_cover_ite.set_xticklabels(list(range(0,PARS['MAX_TIME'],2)))
ax_cover_ite.set_xlabel("Time Period", fontsize=14)
else:
ax_cover_ite[i].plot(dfPrintIte['Urgent'],marker='o')
ax_cover_ite[i].plot(dfPrintIte['Elective'],marker='o')
ax_cover_ite[i].set_title("Iteration {} ({})".format(ite,typIte), fontsize=14)
ax_cover_ite[i].set_xticks(list(range(0,PARS['MAX_TIME'],2)))
ax_cover_ite[i].set_xticklabels(list(range(0,PARS['MAX_TIME'],2)))
ax_cover_ite[i].set_xlabel("time $t$", fontsize=14)
fig_cover_ite.suptitle(f"Policy {policy}-{surge} \n Average coverage of demand by time period and urgency level for different iterations, L = {L}, T = {T}", fontsize=18)
if len(selectedIte)==1:
ax_cover_ite.set_ylabel("Coverage ratio", fontsize=14)
ax_cover_ite.legend(title="Urgency level",loc='center left', bbox_to_anchor=(1, 0.5),fancybox=True, shadow=True, ncol=1)
else:
ax_cover_ite[0].set_ylabel("Coverage ratio", fontsize=14)
ax_cover_ite[len(selectedIte)-1].legend(title="Urgency level",loc='center left', bbox_to_anchor=(1, 0.5),fancybox=True, shadow=True, ncol=1)
def plot_demand_coverage_histogram(self, dfCoverage):
ite_TRA = np.arange(0, PARS['NUM_TRAINNING_ITER'], 1)
ite_TES = np.arange(0, PARS['NUM_TESTING_ITER'], 1) + PARS['NUM_TRAINNING_ITER']
#Figure 10 - Histogram Demand Coverage - Along time periods
idx = pd.IndexSlice
dfCoverage=dfCoverage[dfCoverage.DemandValue > 0].copy()
uncoverU = dfCoverage.loc[idx[:,['Urgent'],ite_TES,:],['DemandValue']].values.sum() - dfCoverage.loc[idx[:,['Urgent'],ite_TES,:],['Value']].values.sum()
uncoverE = dfCoverage.loc[idx[:,['Elective'],ite_TES,:],['DemandValue']].values.sum() - dfCoverage.loc[idx[:,['Elective'],ite_TES,:],['Value']].values.sum()
uncoverT = dfCoverage.loc[idx[:,:,ite_TES,:],['DemandValue']].values.sum() - dfCoverage.loc[idx[:,:,ite_TES,:],['Value']].values.sum()
demandU = dfCoverage.loc[idx[:,['Urgent'],ite_TES,:],['DemandValue']].values.sum()
demandE = dfCoverage.loc[idx[:,['Elective'],ite_TES,:],['DemandValue']].values.sum()
demandT = dfCoverage.loc[idx[:,:,ite_TES,:],['DemandValue']].values.sum()
fig_hist, ax_hist = plt.subplots(1,3,figsize=(16,8),sharey=True,sharex=True)
ax_hist[0].hist(dfCoverage.loc[idx[:,['Urgent'],ite_TES,:],['Ratio']].values, bins=11,color='tab:blue')
ax_hist[0].set_title("Urgent", fontsize=12)
ax_hist[0].set_ylabel("Count", fontsize=12)
ax_hist[0].set_xlim([0,1])
ax_hist[0].annotate('Uncovered Demand: {:,}\n Total Demand: {:,}'.format(int(uncoverU),demandU),xy=(.8, .975), xycoords='axes fraction',horizontalalignment='right', verticalalignment='top',fontsize=12)
ax_hist[1].hist(dfCoverage.loc[idx[:,['Elective'],ite_TES,:],['Ratio']].values, bins=11,color='tab:orange')
ax_hist[1].set_title("Elective", fontsize=12)
ax_hist[1].set_xlabel("Coverage Ratio", fontsize=12)
ax_hist[1].annotate('Uncovered Demand: {:,}\n Total Demand: {:,}'.format(int(uncoverE),demandE),xy=(.8, .975), xycoords='axes fraction',horizontalalignment='right', verticalalignment='top',fontsize=12)
ax_hist[2].hist(dfCoverage.loc[idx[:,:,ite_TES,:],['Ratio']].values, bins=11,color='tab:gray')
ax_hist[2].set_title("Total", fontsize=14)
ax_hist[2].annotate('Uncovered Demand: {:,}\n Total Demand: {:,}'.format(int(uncoverT),demandT),xy=(.8, .975), xycoords='axes fraction',horizontalalignment='right', verticalalignment='top',fontsize=12)
fig_hist.suptitle(f"Policy {policy}-{surge} \n Histogram of blood coverage - All TESTING iterations and time periods\n {coverage} Coverage utility: {utility:.0f}, L = {L}, T = {T}", fontsize=13)
def plot_train(self, df, policy, comment):
# legendlabels = [r'$\mathrm{opt}$', r'$\mathrm{non}$']
n_a = len(aNAMES) #number of a vectors
n_abNames = 5 #to show
abNAMES_sub = random.sample(abNAMES_EXP, n_abNames)
n_ab = len(abNAMES_sub) #number of ab vectors
n_b = len(bNAMES) #number of b vectors
n_charts = n_ab + n_b + n_a + 1
ylabelsize = 14 #16
mpl.rcParams['lines.linewidth'] = 1.2
mycolors = ['r', 'g', 'b', 'c', 'm', 'y',
'deeppink', 'lightblue', 'teal', 'lightcoral', 'darkorange',
'seagreen', 'turquoise', 'dodgerblue',
'royalblue', 'springgreen', 'indigo', 'darkviolet', 'cornflowerblue', 'purple']
fig, axs = plt.subplots(n_charts, sharex=True)
# fig.set_figwidth(13); fig.set_figheight(9)
fig.set_figwidth(13); fig.set_figheight(20)
fig.suptitle(f'TRAINING OF {policy} POLICY'+'\n'+f'{comment}'+'\n'+ \
f'L = {L}, T = {T}'+ \
'\n'+f'decision variables (random selection of {n_abNames} out of {len(abNAMES_EXP)}), '+ '$x_{tab}$' + \
'\n'+f'demand variables, '+ '$D_{tb}$' + \
'\n'+f'resource variables, '+ '$R_{ta}$',
fontsize=16)
max_y = max(df[[f'x_t_{abn}' for abn in abNAMES_sub]].max(axis=1))
for xi,abn in enumerate(abNAMES_sub):
# axs[xi].set_ylim(auto=True);
axs[xi].set_ylim(0, max_y)
axs[xi].spines['top'].set_visible(False); axs[xi].spines['right'].set_visible(True); axs[xi].spines['bottom'].set_visible(False)
axs[xi].step(df[f'x_t_{abn}'], mycolors[xi%len(mycolors)])
axs[xi].axhline(y=0, color='k', linestyle=':')
ablab = abn.replace('_', '\_')
y1ab = '$x_{t,'+f'{ablab}'+'}$'
axs[xi].set_ylabel(y1ab, rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize)
for j in range(df.shape[0]//T): axs[xi].axvline(x=j*T, color='grey', ls=':')
xi = n_ab
max_y = max(df[[f'D_t_{bn}' for bn in bNAMES]].max(axis=1))
for i,bn in enumerate(bNAMES):
axs[xi+i].set_ylim(0, max_y)
axs[xi+i].spines['top'].set_visible(False); axs[xi+i].spines['right'].set_visible(True); axs[xi+i].spines['bottom'].set_visible(False)
axs[xi+i].step(df[f'D_t_{bn}'], mycolors[i%len(mycolors)])
axs[xi+i].axhline(y=0, color='k', linestyle=':')
blab = bn.replace('_', '\_')
y1ab = '$D_{t,' + f'{blab}' + '}$'
axs[xi+i].set_ylabel(y1ab, rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize)
for j in range(df.shape[0]//T): axs[xi+i].axvline(x=j*T, color='grey', ls=':')
xi = n_ab + n_b
max_y = max(df[[f'R_t_{an}' for an in aNAMES]].max(axis=1))
for i,an in enumerate(aNAMES):
axs[xi+i].set_ylim(0, max_y)
axs[xi+i].spines['top'].set_visible(False); axs[xi+i].spines['right'].set_visible(True); axs[xi+i].spines['bottom'].set_visible(False)
axs[xi+i].step(df[f'R_t_{an}'], mycolors[i%len(mycolors)])
axs[xi+i].axhline(y=0, color='k', linestyle=':')
alab = an.replace('_', '\_')
y1ab = '$R_{t,' + f'{alab}' + '}$'
axs[xi+i].set_ylabel(y1ab, rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize)
for j in range(df.shape[0]//T): axs[xi+i].axvline(x=j*T, color='grey', ls=':')
xi = n_ab + n_b + n_a #cumC
axs[xi].set_ylim(auto=True); axs[xi].spines['top'].set_visible(False); axs[xi].spines['right'].set_visible(True); axs[xi].spines['bottom'].set_visible(False)
axs[xi].step(df['cumC'], 'k')
axs[xi].axhline(y=0, color='k', linestyle=':')
axs[xi].set_ylabel('$\mathrm{cumC}$'+'\n'+'$\mathrm{(Profit)}$'+'\n'+''+'$\mathrm{[\$]}$', rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize);
axs[xi].set_xlabel('$t\ \mathrm{[days]}$', rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize);
for j in range(df.shape[0]//T): axs[xi].axvline(x=j*T, color='grey', ls=':')
# fig.legend(labels=legendlabels, loc='lower left', fontsize=16)
def plot_evalu(self, df_non, df, policy, comment):
# legendlabels = [r'$\mathrm{opt}$', r'$\mathrm{non}$']
n_a = len(aNAMES) #number of a vectors
n_abNames = 10 #5 #to show
abNAMES_sub = random.sample(abNAMES_EXP, n_abNames)
n_ab = len(abNAMES_sub) #number of ab vectors
# n_b = len(bNAMES) #number of b vectors
n_b = 0
n_charts = n_ab + n_b + n_a + 1
ylabelsize = 14 #16
mpl.rcParams['lines.linewidth'] = 1.2
mycolors = ['r', 'g', 'b', 'c', 'm', 'y',
'deeppink', 'lightblue', 'teal', 'lightcoral', 'darkorange',
'seagreen', 'turquoise', 'dodgerblue',
'royalblue', 'springgreen', 'indigo', 'darkviolet', 'cornflowerblue', 'purple']
fig, axs = plt.subplots(n_charts, sharex=True)
# fig.set_figwidth(13); fig.set_figheight(9)
fig.set_figwidth(13); fig.set_figheight(20)
fig.suptitle(f'PERFORMANCE OF OPTIMIZED {policy} POLICY\nOptimal (magenta), Non-optimal (cyan)'+'\n'+f'{comment}'+'\n'+ \
f'L = {L}, T = {T}'+ \
'\n'+f'decision variables (random selection of {n_abNames} out of {len(abNAMES_EXP)}), '+ '$x_{tab}$' + \
'\n'+f'demand variables, '+ '$D_{tb}$' + \
'\n'+f'resource variables, '+ '$R_{ta}$',
fontsize=16)
max_y = max(df[[f'x_t_{abn}' for abn in abNAMES_sub]].max(axis=1))
for xi,abn in enumerate(abNAMES_sub):
# axs[xi].set_ylim(auto=True);
axs[xi].set_ylim(0, max_y)
axs[xi].spines['top'].set_visible(False); axs[xi].spines['right'].set_visible(True); axs[xi].spines['bottom'].set_visible(False)
axs[xi].step(df[f'x_t_{abn}'], 'm')
axs[xi].step(df_non[f'x_t_{abn}'], 'c')
axs[xi].axhline(y=0, color='k', linestyle=':')
ablab = abn.replace('_', '\_')
y1ab = '$x_{t,'+f'{ablab}'+'}$'
axs[xi].set_ylabel(y1ab, rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize)
for j in range(df.shape[0]//T): axs[xi].axvline(x=j*T, color='grey', ls=':')
# xi = n_ab
# max_y = max(df[[f'D_t_{bn}' for bn in bNAMES]].max(axis=1))
# for i,bn in enumerate(bNAMES):
# axs[xi+i].set_ylim(0, max_y)
# axs[xi+i].spines['top'].set_visible(False); axs[xi+i].spines['right'].set_visible(True); axs[xi+i].spines['bottom'].set_visible(False)
# axs[xi+i].step(df[f'D_t_{bn}'], mycolors[i%len(mycolors)]) #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# # axs[xi+i].step(df[f'D_t_{bn}'], mcolors.TABLEAU_COLORS)
# axs[xi+i].axhline(y=0, color='k', linestyle=':')
# blab = bn.replace('_', '\_')
# y1ab = '$D_{t,' + f'{blab}' + '}$'
# axs[xi+i].set_ylabel(y1ab, rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize)
# for j in range(df.shape[0]//T): axs[xi+i].axvline(x=j*T, color='grey', ls=':')
xi = n_ab + n_b
max_y = max(df[[f'R_t_{an}' for an in aNAMES]].max(axis=1))
for i,an in enumerate(aNAMES):
axs[xi+i].set_ylim(0, max_y)
axs[xi+i].spines['top'].set_visible(False); axs[xi+i].spines['right'].set_visible(True); axs[xi+i].spines['bottom'].set_visible(False)
axs[xi+i].step(df[f'R_t_{an}'], 'm')
axs[xi+i].step(df_non[f'R_t_{an}'], 'c')
axs[xi+i].axhline(y=0, color='k', linestyle=':')
alab = an.replace('_', '\_')
y1ab = '$R_{t,' + f'{alab}' + '}$'
axs[xi+i].set_ylabel(y1ab, rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize)
for j in range(df.shape[0]//T): axs[xi+i].axvline(x=j*T, color='grey', ls=':')
xi = n_ab + n_b + n_a #cumC
axs[xi].set_ylim(auto=True); axs[xi].spines['top'].set_visible(False); axs[xi].spines['right'].set_visible(True); axs[xi].spines['bottom'].set_visible(False)
axs[xi].step(df['cumC'], 'm')
axs[xi].step(df_non['cumC'], 'c')
axs[xi].axhline(y=0, color='k', linestyle=':')
axs[xi].set_ylabel('$\mathrm{cumC}$'+'\n'+'$\mathrm{(Profit)}$'+'\n'+''+'$\mathrm{[\$]}$', rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize);
axs[xi].set_xlabel('$t\ \mathrm{[days]}$', rotation=0, ha='right', va='center', fontweight='bold', size=ylabelsize);
for j in range(df.shape[0]//T): axs[xi].axvline(x=j*T, color='grey', ls=':')
# fig.legend(labels=legendlabels, loc='lower left', fontsize=16)P = Policy()L,T(20, 15)%%time
##########################################################################
Fhat__mean_LP, demandExoList, donationExoList, supplyPreList, supplyPostList, slopesList, \
solDemList, solHoldList, simuList, updateVfaList, record_LP = \
P.perform_search_sample_paths(T, L, 'X__LP')
##########################################################################********************Started Main*****************
Starting testing iterations! Currently at iteration 0
Reseting random seed!
Iteration = 0
***Finishing iteration 0 in 1.00 secs. Total contribution: 20757.00***
Iteration = 1
***Finishing iteration 1 in 1.00 secs. Total contribution: 18621.00***
Iteration = 2
***Finishing iteration 2 in 1.00 secs. Total contribution: 21385.00***
Iteration = 3
***Finishing iteration 3 in 0.00 secs. Total contribution: 20102.00***
Iteration = 4
***Finishing iteration 4 in 0.00 secs. Total contribution: 21622.00***
Iteration = 5
***Finishing iteration 5 in 0.00 secs. Total contribution: 24017.00***
Iteration = 6
***Finishing iteration 6 in 0.00 secs. Total contribution: 18841.00***
Iteration = 7
***Finishing iteration 7 in 0.00 secs. Total contribution: 19014.00***
Iteration = 8
***Finishing iteration 8 in 0.00 secs. Total contribution: 20364.00***
Iteration = 9
***Finishing iteration 9 in 0.00 secs. Total contribution: 21357.00***
Iteration = 10
***Finishing iteration 10 in 0.00 secs. Total contribution: 19737.00***
Iteration = 11
***Finishing iteration 11 in 0.00 secs. Total contribution: 21088.00***
Iteration = 12
***Finishing iteration 12 in 0.00 secs. Total contribution: 21818.00***
Iteration = 13
***Finishing iteration 13 in 0.00 secs. Total contribution: 23258.00***
Iteration = 14
***Finishing iteration 14 in 0.00 secs. Total contribution: 19001.00***
Iteration = 15
***Finishing iteration 15 in 1.00 secs. Total contribution: 18192.00***
Iteration = 16
***Finishing iteration 16 in 1.00 secs. Total contribution: 23225.00***
Iteration = 17
***Finishing iteration 17 in 1.00 secs. Total contribution: 14519.00***
Iteration = 18
***Finishing iteration 18 in 0.00 secs. Total contribution: 23284.00***
Iteration = 19
***Finishing iteration 19 in 0.00 secs. Total contribution: 18743.00***
Total elapsed time 15.26 secs
CPU times: user 12.2 s, sys: 8.48 s, total: 20.7 s
Wall time: 15.3 sR_t_labels = ['R_t_'+an for an in aNAMES]
D_t_labels = ['D_t_'+bn for bn in bNAMES]
x_t_labels = ['x_t_'+abn for abn in abNAMES_EXP]
labels = ['piName', 'l'] + \
['t'] + R_t_labels + D_t_labels + ['cumC'] + x_t_labels
# labelsdf_X__LP = pd.DataFrame.from_records(record_LP[:50], columns=labels)
P.plot_train(df_X__LP, 'LP', '(first 50 records)')
df_X__LP.head()| piName | l | t | R_t_ABp_0 | R_t_ABp_1 | R_t_ABp_2 | R_t_ABn_0 | R_t_ABn_1 | R_t_ABn_2 | R_t_Ap_0 | R_t_Ap_1 | R_t_Ap_2 | R_t_An_0 | R_t_An_1 | R_t_An_2 | R_t_Bp_0 | R_t_Bp_1 | R_t_Bp_2 | R_t_Bn_0 | R_t_Bn_1 | R_t_Bn_2 | R_t_Op_0 | R_t_Op_1 | R_t_Op_2 | R_t_On_0 | R_t_On_1 | R_t_On_2 | D_t_ABp_Urgent_True | D_t_ABp_Elective_True | D_t_ABn_Urgent_True | D_t_ABn_Elective_True | D_t_Ap_Urgent_True | D_t_Ap_Elective_True | D_t_An_Urgent_True | D_t_An_Elective_True | D_t_Bp_Urgent_True | D_t_Bp_Elective_True | D_t_Bn_Urgent_True | D_t_Bn_Elective_True | D_t_Op_Urgent_True | D_t_Op_Elective_True | D_t_On_Urgent_True | D_t_On_Elective_True | cumC | x_t_ABp_0___ABp_Urgent_True | x_t_ABp_0___ABp_Elective_True | x_t_ABp_0___ABn_Urgent_True | x_t_ABp_0___ABn_Elective_True | x_t_ABp_0___Ap_Urgent_True | x_t_ABp_0___Ap_Elective_True | x_t_ABp_0___An_Urgent_True | x_t_ABp_0___An_Elective_True | x_t_ABp_0___Bp_Urgent_True | x_t_ABp_0___Bp_Elective_True | x_t_ABp_0___Bn_Urgent_True | x_t_ABp_0___Bn_Elective_True | x_t_ABp_0___Op_Urgent_True | x_t_ABp_0___Op_Elective_True | x_t_ABp_0___On_Urgent_True | x_t_ABp_0___On_Elective_True | x_t_ABp_1___ABp_Urgent_True | x_t_ABp_1___ABp_Elective_True | x_t_ABp_1___ABn_Urgent_True | x_t_ABp_1___ABn_Elective_True | x_t_ABp_1___Ap_Urgent_True | x_t_ABp_1___Ap_Elective_True | x_t_ABp_1___An_Urgent_True | x_t_ABp_1___An_Elective_True | x_t_ABp_1___Bp_Urgent_True | x_t_ABp_1___Bp_Elective_True | x_t_ABp_1___Bn_Urgent_True | x_t_ABp_1___Bn_Elective_True | x_t_ABp_1___Op_Urgent_True | x_t_ABp_1___Op_Elective_True | x_t_ABp_1___On_Urgent_True | x_t_ABp_1___On_Elective_True | x_t_ABp_2___ABp_Urgent_True | x_t_ABp_2___ABp_Elective_True | x_t_ABp_2___ABn_Urgent_True | x_t_ABp_2___ABn_Elective_True | x_t_ABp_2___Ap_Urgent_True | x_t_ABp_2___Ap_Elective_True | x_t_ABp_2___An_Urgent_True | x_t_ABp_2___An_Elective_True | x_t_ABp_2___Bp_Urgent_True | x_t_ABp_2___Bp_Elective_True | x_t_ABp_2___Bn_Urgent_True | x_t_ABp_2___Bn_Elective_True | x_t_ABp_2___Op_Urgent_True | x_t_ABp_2___Op_Elective_True | x_t_ABp_2___On_Urgent_True | x_t_ABp_2___On_Elective_True | x_t_ABn_0___ABp_Urgent_True | x_t_ABn_0___ABp_Elective_True | x_t_ABn_0___ABn_Urgent_True | x_t_ABn_0___ABn_Elective_True | x_t_ABn_0___Ap_Urgent_True | x_t_ABn_0___Ap_Elective_True | x_t_ABn_0___An_Urgent_True | x_t_ABn_0___An_Elective_True | x_t_ABn_0___Bp_Urgent_True | x_t_ABn_0___Bp_Elective_True | x_t_ABn_0___Bn_Urgent_True | x_t_ABn_0___Bn_Elective_True | x_t_ABn_0___Op_Urgent_True | x_t_ABn_0___Op_Elective_True | x_t_ABn_0___On_Urgent_True | x_t_ABn_0___On_Elective_True | x_t_ABn_1___ABp_Urgent_True | x_t_ABn_1___ABp_Elective_True | x_t_ABn_1___ABn_Urgent_True | x_t_ABn_1___ABn_Elective_True | x_t_ABn_1___Ap_Urgent_True | x_t_ABn_1___Ap_Elective_True | x_t_ABn_1___An_Urgent_True | x_t_ABn_1___An_Elective_True | x_t_ABn_1___Bp_Urgent_True | x_t_ABn_1___Bp_Elective_True | x_t_ABn_1___Bn_Urgent_True | x_t_ABn_1___Bn_Elective_True | x_t_ABn_1___Op_Urgent_True | x_t_ABn_1___Op_Elective_True | x_t_ABn_1___On_Urgent_True | x_t_ABn_1___On_Elective_True | x_t_ABn_2___ABp_Urgent_True | x_t_ABn_2___ABp_Elective_True | x_t_ABn_2___ABn_Urgent_True | x_t_ABn_2___ABn_Elective_True | x_t_ABn_2___Ap_Urgent_True | x_t_ABn_2___Ap_Elective_True | x_t_ABn_2___An_Urgent_True | x_t_ABn_2___An_Elective_True | x_t_ABn_2___Bp_Urgent_True | x_t_ABn_2___Bp_Elective_True | x_t_ABn_2___Bn_Urgent_True | x_t_ABn_2___Bn_Elective_True | x_t_ABn_2___Op_Urgent_True | x_t_ABn_2___Op_Elective_True | x_t_ABn_2___On_Urgent_True | x_t_ABn_2___On_Elective_True | x_t_Ap_0___ABp_Urgent_True | x_t_Ap_0___ABp_Elective_True | x_t_Ap_0___ABn_Urgent_True | x_t_Ap_0___ABn_Elective_True | x_t_Ap_0___Ap_Urgent_True | x_t_Ap_0___Ap_Elective_True | x_t_Ap_0___An_Urgent_True | x_t_Ap_0___An_Elective_True | x_t_Ap_0___Bp_Urgent_True | x_t_Ap_0___Bp_Elective_True | x_t_Ap_0___Bn_Urgent_True | x_t_Ap_0___Bn_Elective_True | x_t_Ap_0___Op_Urgent_True | x_t_Ap_0___Op_Elective_True | x_t_Ap_0___On_Urgent_True | x_t_Ap_0___On_Elective_True | x_t_Ap_1___ABp_Urgent_True | x_t_Ap_1___ABp_Elective_True | x_t_Ap_1___ABn_Urgent_True | x_t_Ap_1___ABn_Elective_True | x_t_Ap_1___Ap_Urgent_True | x_t_Ap_1___Ap_Elective_True | x_t_Ap_1___An_Urgent_True | x_t_Ap_1___An_Elective_True | x_t_Ap_1___Bp_Urgent_True | x_t_Ap_1___Bp_Elective_True | x_t_Ap_1___Bn_Urgent_True | x_t_Ap_1___Bn_Elective_True | x_t_Ap_1___Op_Urgent_True | x_t_Ap_1___Op_Elective_True | x_t_Ap_1___On_Urgent_True | x_t_Ap_1___On_Elective_True | x_t_Ap_2___ABp_Urgent_True | x_t_Ap_2___ABp_Elective_True | x_t_Ap_2___ABn_Urgent_True | x_t_Ap_2___ABn_Elective_True | x_t_Ap_2___Ap_Urgent_True | x_t_Ap_2___Ap_Elective_True | x_t_Ap_2___An_Urgent_True | x_t_Ap_2___An_Elective_True | x_t_Ap_2___Bp_Urgent_True | x_t_Ap_2___Bp_Elective_True | x_t_Ap_2___Bn_Urgent_True | x_t_Ap_2___Bn_Elective_True | x_t_Ap_2___Op_Urgent_True | x_t_Ap_2___Op_Elective_True | x_t_Ap_2___On_Urgent_True | x_t_Ap_2___On_Elective_True | x_t_An_0___ABp_Urgent_True | x_t_An_0___ABp_Elective_True | x_t_An_0___ABn_Urgent_True | x_t_An_0___ABn_Elective_True | x_t_An_0___Ap_Urgent_True | x_t_An_0___Ap_Elective_True | x_t_An_0___An_Urgent_True | x_t_An_0___An_Elective_True | x_t_An_0___Bp_Urgent_True | x_t_An_0___Bp_Elective_True | x_t_An_0___Bn_Urgent_True | x_t_An_0___Bn_Elective_True | x_t_An_0___Op_Urgent_True | x_t_An_0___Op_Elective_True | x_t_An_0___On_Urgent_True | x_t_An_0___On_Elective_True | x_t_An_1___ABp_Urgent_True | x_t_An_1___ABp_Elective_True | x_t_An_1___ABn_Urgent_True | x_t_An_1___ABn_Elective_True | x_t_An_1___Ap_Urgent_True | x_t_An_1___Ap_Elective_True | x_t_An_1___An_Urgent_True | x_t_An_1___An_Elective_True | x_t_An_1___Bp_Urgent_True | x_t_An_1___Bp_Elective_True | x_t_An_1___Bn_Urgent_True | x_t_An_1___Bn_Elective_True | x_t_An_1___Op_Urgent_True | x_t_An_1___Op_Elective_True | x_t_An_1___On_Urgent_True | x_t_An_1___On_Elective_True | x_t_An_2___ABp_Urgent_True | x_t_An_2___ABp_Elective_True | x_t_An_2___ABn_Urgent_True | x_t_An_2___ABn_Elective_True | x_t_An_2___Ap_Urgent_True | x_t_An_2___Ap_Elective_True | x_t_An_2___An_Urgent_True | x_t_An_2___An_Elective_True | x_t_An_2___Bp_Urgent_True | x_t_An_2___Bp_Elective_True | x_t_An_2___Bn_Urgent_True | x_t_An_2___Bn_Elective_True | x_t_An_2___Op_Urgent_True | x_t_An_2___Op_Elective_True | x_t_An_2___On_Urgent_True | x_t_An_2___On_Elective_True | x_t_Bp_0___ABp_Urgent_True | x_t_Bp_0___ABp_Elective_True | x_t_Bp_0___ABn_Urgent_True | x_t_Bp_0___ABn_Elective_True | x_t_Bp_0___Ap_Urgent_True | x_t_Bp_0___Ap_Elective_True | x_t_Bp_0___An_Urgent_True | x_t_Bp_0___An_Elective_True | x_t_Bp_0___Bp_Urgent_True | x_t_Bp_0___Bp_Elective_True | x_t_Bp_0___Bn_Urgent_True | x_t_Bp_0___Bn_Elective_True | x_t_Bp_0___Op_Urgent_True | x_t_Bp_0___Op_Elective_True | x_t_Bp_0___On_Urgent_True | x_t_Bp_0___On_Elective_True | x_t_Bp_1___ABp_Urgent_True | x_t_Bp_1___ABp_Elective_True | x_t_Bp_1___ABn_Urgent_True | x_t_Bp_1___ABn_Elective_True | x_t_Bp_1___Ap_Urgent_True | x_t_Bp_1___Ap_Elective_True | x_t_Bp_1___An_Urgent_True | x_t_Bp_1___An_Elective_True | x_t_Bp_1___Bp_Urgent_True | x_t_Bp_1___Bp_Elective_True | x_t_Bp_1___Bn_Urgent_True | x_t_Bp_1___Bn_Elective_True | x_t_Bp_1___Op_Urgent_True | x_t_Bp_1___Op_Elective_True | x_t_Bp_1___On_Urgent_True | x_t_Bp_1___On_Elective_True | x_t_Bp_2___ABp_Urgent_True | x_t_Bp_2___ABp_Elective_True | x_t_Bp_2___ABn_Urgent_True | x_t_Bp_2___ABn_Elective_True | x_t_Bp_2___Ap_Urgent_True | x_t_Bp_2___Ap_Elective_True | x_t_Bp_2___An_Urgent_True | x_t_Bp_2___An_Elective_True | x_t_Bp_2___Bp_Urgent_True | x_t_Bp_2___Bp_Elective_True | x_t_Bp_2___Bn_Urgent_True | x_t_Bp_2___Bn_Elective_True | x_t_Bp_2___Op_Urgent_True | x_t_Bp_2___Op_Elective_True | x_t_Bp_2___On_Urgent_True | x_t_Bp_2___On_Elective_True | x_t_Bn_0___ABp_Urgent_True | x_t_Bn_0___ABp_Elective_True | x_t_Bn_0___ABn_Urgent_True | x_t_Bn_0___ABn_Elective_True | x_t_Bn_0___Ap_Urgent_True | x_t_Bn_0___Ap_Elective_True | x_t_Bn_0___An_Urgent_True | x_t_Bn_0___An_Elective_True | x_t_Bn_0___Bp_Urgent_True | x_t_Bn_0___Bp_Elective_True | x_t_Bn_0___Bn_Urgent_True | x_t_Bn_0___Bn_Elective_True | x_t_Bn_0___Op_Urgent_True | x_t_Bn_0___Op_Elective_True | x_t_Bn_0___On_Urgent_True | x_t_Bn_0___On_Elective_True | x_t_Bn_1___ABp_Urgent_True | x_t_Bn_1___ABp_Elective_True | x_t_Bn_1___ABn_Urgent_True | x_t_Bn_1___ABn_Elective_True | x_t_Bn_1___Ap_Urgent_True | x_t_Bn_1___Ap_Elective_True | x_t_Bn_1___An_Urgent_True | x_t_Bn_1___An_Elective_True | x_t_Bn_1___Bp_Urgent_True | x_t_Bn_1___Bp_Elective_True | x_t_Bn_1___Bn_Urgent_True | x_t_Bn_1___Bn_Elective_True | x_t_Bn_1___Op_Urgent_True | x_t_Bn_1___Op_Elective_True | x_t_Bn_1___On_Urgent_True | x_t_Bn_1___On_Elective_True | x_t_Bn_2___ABp_Urgent_True | x_t_Bn_2___ABp_Elective_True | x_t_Bn_2___ABn_Urgent_True | x_t_Bn_2___ABn_Elective_True | x_t_Bn_2___Ap_Urgent_True | x_t_Bn_2___Ap_Elective_True | x_t_Bn_2___An_Urgent_True | x_t_Bn_2___An_Elective_True | x_t_Bn_2___Bp_Urgent_True | x_t_Bn_2___Bp_Elective_True | x_t_Bn_2___Bn_Urgent_True | x_t_Bn_2___Bn_Elective_True | x_t_Bn_2___Op_Urgent_True | x_t_Bn_2___Op_Elective_True | x_t_Bn_2___On_Urgent_True | x_t_Bn_2___On_Elective_True | x_t_Op_0___ABp_Urgent_True | x_t_Op_0___ABp_Elective_True | x_t_Op_0___ABn_Urgent_True | x_t_Op_0___ABn_Elective_True | x_t_Op_0___Ap_Urgent_True | x_t_Op_0___Ap_Elective_True | x_t_Op_0___An_Urgent_True | x_t_Op_0___An_Elective_True | x_t_Op_0___Bp_Urgent_True | x_t_Op_0___Bp_Elective_True | x_t_Op_0___Bn_Urgent_True | x_t_Op_0___Bn_Elective_True | x_t_Op_0___Op_Urgent_True | x_t_Op_0___Op_Elective_True | x_t_Op_0___On_Urgent_True | x_t_Op_0___On_Elective_True | x_t_Op_1___ABp_Urgent_True | x_t_Op_1___ABp_Elective_True | x_t_Op_1___ABn_Urgent_True | x_t_Op_1___ABn_Elective_True | x_t_Op_1___Ap_Urgent_True | x_t_Op_1___Ap_Elective_True | x_t_Op_1___An_Urgent_True | x_t_Op_1___An_Elective_True | x_t_Op_1___Bp_Urgent_True | x_t_Op_1___Bp_Elective_True | x_t_Op_1___Bn_Urgent_True | x_t_Op_1___Bn_Elective_True | x_t_Op_1___Op_Urgent_True | x_t_Op_1___Op_Elective_True | x_t_Op_1___On_Urgent_True | x_t_Op_1___On_Elective_True | x_t_Op_2___ABp_Urgent_True | x_t_Op_2___ABp_Elective_True | x_t_Op_2___ABn_Urgent_True | x_t_Op_2___ABn_Elective_True | x_t_Op_2___Ap_Urgent_True | x_t_Op_2___Ap_Elective_True | x_t_Op_2___An_Urgent_True | x_t_Op_2___An_Elective_True | x_t_Op_2___Bp_Urgent_True | x_t_Op_2___Bp_Elective_True | x_t_Op_2___Bn_Urgent_True | x_t_Op_2___Bn_Elective_True | x_t_Op_2___Op_Urgent_True | x_t_Op_2___Op_Elective_True | x_t_Op_2___On_Urgent_True | x_t_Op_2___On_Elective_True | x_t_On_0___ABp_Urgent_True | x_t_On_0___ABp_Elective_True | x_t_On_0___ABn_Urgent_True | x_t_On_0___ABn_Elective_True | x_t_On_0___Ap_Urgent_True | x_t_On_0___Ap_Elective_True | x_t_On_0___An_Urgent_True | x_t_On_0___An_Elective_True | x_t_On_0___Bp_Urgent_True | x_t_On_0___Bp_Elective_True | x_t_On_0___Bn_Urgent_True | x_t_On_0___Bn_Elective_True | x_t_On_0___Op_Urgent_True | x_t_On_0___Op_Elective_True | x_t_On_0___On_Urgent_True | x_t_On_0___On_Elective_True | x_t_On_1___ABp_Urgent_True | x_t_On_1___ABp_Elective_True | x_t_On_1___ABn_Urgent_True | x_t_On_1___ABn_Elective_True | x_t_On_1___Ap_Urgent_True | x_t_On_1___Ap_Elective_True | x_t_On_1___An_Urgent_True | x_t_On_1___An_Elective_True | x_t_On_1___Bp_Urgent_True | x_t_On_1___Bp_Elective_True | x_t_On_1___Bn_Urgent_True | x_t_On_1___Bn_Elective_True | x_t_On_1___Op_Urgent_True | x_t_On_1___Op_Elective_True | x_t_On_1___On_Urgent_True | x_t_On_1___On_Elective_True | x_t_On_2___ABp_Urgent_True | x_t_On_2___ABp_Elective_True | x_t_On_2___ABn_Urgent_True | x_t_On_2___ABn_Elective_True | x_t_On_2___Ap_Urgent_True | x_t_On_2___Ap_Elective_True | x_t_On_2___An_Urgent_True | x_t_On_2___An_Elective_True | x_t_On_2___Bp_Urgent_True | x_t_On_2___Bp_Elective_True | x_t_On_2___Bn_Urgent_True | x_t_On_2___Bn_Elective_True | x_t_On_2___Op_Urgent_True | x_t_On_2___Op_Elective_True | x_t_On_2___On_Urgent_True | x_t_On_2___On_Elective_True | x_t_ABp_0___ABp_1 | x_t_ABp_1___ABp_2 | x_t_ABp_2___ABp_3 | x_t_ABn_0___ABn_1 | x_t_ABn_1___ABn_2 | x_t_ABn_2___ABn_3 | x_t_Ap_0___Ap_1 | x_t_Ap_1___Ap_2 | x_t_Ap_2___Ap_3 | x_t_An_0___An_1 | x_t_An_1___An_2 | x_t_An_2___An_3 | x_t_Bp_0___Bp_1 | x_t_Bp_1___Bp_2 | x_t_Bp_2___Bp_3 | x_t_Bn_0___Bn_1 | x_t_Bn_1___Bn_2 | x_t_Bn_2___Bn_3 | x_t_Op_0___Op_1 | x_t_Op_1___Op_2 | x_t_Op_2___Op_3 | x_t_On_0___On_1 | x_t_On_1___On_2 | x_t_On_2___On_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | X__LP | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 19 | 0 | 0 | 10 | 0 | 0 | 8 | 0 | 0 | 1 | 0 | 0 | 14 | 0 | 0 | 6 | 0 | 0 | 1 | 2 | 0 | 1 | 3 | 10 | 0 | 7 | 4 | 10 | 1 | 2 | 12 | 12 | 3 | 3 | 853.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 9.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 12.0000 | 6.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 1 | X__LP | 0 | 1 | 4 | 0 | 0 | 3 | 0 | 0 | 10 | 5 | 0 | 5 | 2 | 0 | 11 | 0 | 0 | 1 | 0 | 0 | 19 | 0 | 0 | 9 | 0 | 0 | 1 | 3 | 2 | 2 | 6 | 8 | 5 | 3 | 6 | 7 | 0 | 1 | 9 | 6 | 3 | 5 | 1,997.0000 | 1.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 6.0000 | 8.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 6.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 9.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2 | X__LP | 0 | 2 | 0 | 2 | 0 | 2 | 0 | 0 | 14 | 0 | 0 | 8 | 0 | 0 | 9 | 0 | 0 | 2 | 0 | 0 | 17 | 0 | 0 | 12 | 3 | 0 | 2 | 0 | 1 | 3 | 9 | 10 | 4 | 5 | 4 | 10 | 1 | 1 | 13 | 16 | 4 | 2 | 3,353.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 9.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 7.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 13.0000 | 6.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 2.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 3 | X__LP | 0 | 3 | 5 | 0 | 2 | 5 | 0 | 0 | 7 | 3 | 0 | 7 | 2 | 0 | 9 | 3 | 0 | 1 | 2 | 0 | 27 | 0 | 0 | 5 | 1 | 3 | 0 | 0 | 4 | 1 | 9 | 2 | 1 | 3 | 4 | 2 | 0 | 0 | 8 | 10 | 4 | 7 | 4,416.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 9.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 8.0000 | 9.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 7.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 4 | X__LP | 0 | 4 | 5 | 4 | 0 | 1 | 1 | 0 | 17 | 0 | 0 | 10 | 2 | 2 | 8 | 0 | 3 | 3 | 0 | 2 | 17 | 18 | 0 | 6 | 3 | 1 | 1 | 2 | 1 | 3 | 5 | 5 | 2 | 3 | 5 | 4 | 1 | 0 | 6 | 3 | 4 | 1 | 5,312.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 6.0000 | 3.0000 | 0.0000 | 0.0000 | 18.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 1.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 |
df_X__LP.size22600df_X__LP = pd.DataFrame.from_records(record_LP[-50:], columns=labels)
P.plot_train(df_X__LP, 'LP', '(last 50 records)')
df_X__LP.head()| piName | l | t | R_t_ABp_0 | R_t_ABp_1 | R_t_ABp_2 | R_t_ABn_0 | R_t_ABn_1 | R_t_ABn_2 | R_t_Ap_0 | R_t_Ap_1 | R_t_Ap_2 | R_t_An_0 | R_t_An_1 | R_t_An_2 | R_t_Bp_0 | R_t_Bp_1 | R_t_Bp_2 | R_t_Bn_0 | R_t_Bn_1 | R_t_Bn_2 | R_t_Op_0 | R_t_Op_1 | R_t_Op_2 | R_t_On_0 | R_t_On_1 | R_t_On_2 | D_t_ABp_Urgent_True | D_t_ABp_Elective_True | D_t_ABn_Urgent_True | D_t_ABn_Elective_True | D_t_Ap_Urgent_True | D_t_Ap_Elective_True | D_t_An_Urgent_True | D_t_An_Elective_True | D_t_Bp_Urgent_True | D_t_Bp_Elective_True | D_t_Bn_Urgent_True | D_t_Bn_Elective_True | D_t_Op_Urgent_True | D_t_Op_Elective_True | D_t_On_Urgent_True | D_t_On_Elective_True | cumC | x_t_ABp_0___ABp_Urgent_True | x_t_ABp_0___ABp_Elective_True | x_t_ABp_0___ABn_Urgent_True | x_t_ABp_0___ABn_Elective_True | x_t_ABp_0___Ap_Urgent_True | x_t_ABp_0___Ap_Elective_True | x_t_ABp_0___An_Urgent_True | x_t_ABp_0___An_Elective_True | x_t_ABp_0___Bp_Urgent_True | x_t_ABp_0___Bp_Elective_True | x_t_ABp_0___Bn_Urgent_True | x_t_ABp_0___Bn_Elective_True | x_t_ABp_0___Op_Urgent_True | x_t_ABp_0___Op_Elective_True | x_t_ABp_0___On_Urgent_True | x_t_ABp_0___On_Elective_True | x_t_ABp_1___ABp_Urgent_True | x_t_ABp_1___ABp_Elective_True | x_t_ABp_1___ABn_Urgent_True | x_t_ABp_1___ABn_Elective_True | x_t_ABp_1___Ap_Urgent_True | x_t_ABp_1___Ap_Elective_True | x_t_ABp_1___An_Urgent_True | x_t_ABp_1___An_Elective_True | x_t_ABp_1___Bp_Urgent_True | x_t_ABp_1___Bp_Elective_True | x_t_ABp_1___Bn_Urgent_True | x_t_ABp_1___Bn_Elective_True | x_t_ABp_1___Op_Urgent_True | x_t_ABp_1___Op_Elective_True | x_t_ABp_1___On_Urgent_True | x_t_ABp_1___On_Elective_True | x_t_ABp_2___ABp_Urgent_True | x_t_ABp_2___ABp_Elective_True | x_t_ABp_2___ABn_Urgent_True | x_t_ABp_2___ABn_Elective_True | x_t_ABp_2___Ap_Urgent_True | x_t_ABp_2___Ap_Elective_True | x_t_ABp_2___An_Urgent_True | x_t_ABp_2___An_Elective_True | x_t_ABp_2___Bp_Urgent_True | x_t_ABp_2___Bp_Elective_True | x_t_ABp_2___Bn_Urgent_True | x_t_ABp_2___Bn_Elective_True | x_t_ABp_2___Op_Urgent_True | x_t_ABp_2___Op_Elective_True | x_t_ABp_2___On_Urgent_True | x_t_ABp_2___On_Elective_True | x_t_ABn_0___ABp_Urgent_True | x_t_ABn_0___ABp_Elective_True | x_t_ABn_0___ABn_Urgent_True | x_t_ABn_0___ABn_Elective_True | x_t_ABn_0___Ap_Urgent_True | x_t_ABn_0___Ap_Elective_True | x_t_ABn_0___An_Urgent_True | x_t_ABn_0___An_Elective_True | x_t_ABn_0___Bp_Urgent_True | x_t_ABn_0___Bp_Elective_True | x_t_ABn_0___Bn_Urgent_True | x_t_ABn_0___Bn_Elective_True | x_t_ABn_0___Op_Urgent_True | x_t_ABn_0___Op_Elective_True | x_t_ABn_0___On_Urgent_True | x_t_ABn_0___On_Elective_True | x_t_ABn_1___ABp_Urgent_True | x_t_ABn_1___ABp_Elective_True | x_t_ABn_1___ABn_Urgent_True | x_t_ABn_1___ABn_Elective_True | x_t_ABn_1___Ap_Urgent_True | x_t_ABn_1___Ap_Elective_True | x_t_ABn_1___An_Urgent_True | x_t_ABn_1___An_Elective_True | x_t_ABn_1___Bp_Urgent_True | x_t_ABn_1___Bp_Elective_True | x_t_ABn_1___Bn_Urgent_True | x_t_ABn_1___Bn_Elective_True | x_t_ABn_1___Op_Urgent_True | x_t_ABn_1___Op_Elective_True | x_t_ABn_1___On_Urgent_True | x_t_ABn_1___On_Elective_True | x_t_ABn_2___ABp_Urgent_True | x_t_ABn_2___ABp_Elective_True | x_t_ABn_2___ABn_Urgent_True | x_t_ABn_2___ABn_Elective_True | x_t_ABn_2___Ap_Urgent_True | x_t_ABn_2___Ap_Elective_True | x_t_ABn_2___An_Urgent_True | x_t_ABn_2___An_Elective_True | x_t_ABn_2___Bp_Urgent_True | x_t_ABn_2___Bp_Elective_True | x_t_ABn_2___Bn_Urgent_True | x_t_ABn_2___Bn_Elective_True | x_t_ABn_2___Op_Urgent_True | x_t_ABn_2___Op_Elective_True | x_t_ABn_2___On_Urgent_True | x_t_ABn_2___On_Elective_True | x_t_Ap_0___ABp_Urgent_True | x_t_Ap_0___ABp_Elective_True | x_t_Ap_0___ABn_Urgent_True | x_t_Ap_0___ABn_Elective_True | x_t_Ap_0___Ap_Urgent_True | x_t_Ap_0___Ap_Elective_True | x_t_Ap_0___An_Urgent_True | x_t_Ap_0___An_Elective_True | x_t_Ap_0___Bp_Urgent_True | x_t_Ap_0___Bp_Elective_True | x_t_Ap_0___Bn_Urgent_True | x_t_Ap_0___Bn_Elective_True | x_t_Ap_0___Op_Urgent_True | x_t_Ap_0___Op_Elective_True | x_t_Ap_0___On_Urgent_True | x_t_Ap_0___On_Elective_True | x_t_Ap_1___ABp_Urgent_True | x_t_Ap_1___ABp_Elective_True | x_t_Ap_1___ABn_Urgent_True | x_t_Ap_1___ABn_Elective_True | x_t_Ap_1___Ap_Urgent_True | x_t_Ap_1___Ap_Elective_True | x_t_Ap_1___An_Urgent_True | x_t_Ap_1___An_Elective_True | x_t_Ap_1___Bp_Urgent_True | x_t_Ap_1___Bp_Elective_True | x_t_Ap_1___Bn_Urgent_True | x_t_Ap_1___Bn_Elective_True | x_t_Ap_1___Op_Urgent_True | x_t_Ap_1___Op_Elective_True | x_t_Ap_1___On_Urgent_True | x_t_Ap_1___On_Elective_True | x_t_Ap_2___ABp_Urgent_True | x_t_Ap_2___ABp_Elective_True | x_t_Ap_2___ABn_Urgent_True | x_t_Ap_2___ABn_Elective_True | x_t_Ap_2___Ap_Urgent_True | x_t_Ap_2___Ap_Elective_True | x_t_Ap_2___An_Urgent_True | x_t_Ap_2___An_Elective_True | x_t_Ap_2___Bp_Urgent_True | x_t_Ap_2___Bp_Elective_True | x_t_Ap_2___Bn_Urgent_True | x_t_Ap_2___Bn_Elective_True | x_t_Ap_2___Op_Urgent_True | x_t_Ap_2___Op_Elective_True | x_t_Ap_2___On_Urgent_True | x_t_Ap_2___On_Elective_True | x_t_An_0___ABp_Urgent_True | x_t_An_0___ABp_Elective_True | x_t_An_0___ABn_Urgent_True | x_t_An_0___ABn_Elective_True | x_t_An_0___Ap_Urgent_True | x_t_An_0___Ap_Elective_True | x_t_An_0___An_Urgent_True | x_t_An_0___An_Elective_True | x_t_An_0___Bp_Urgent_True | x_t_An_0___Bp_Elective_True | x_t_An_0___Bn_Urgent_True | x_t_An_0___Bn_Elective_True | x_t_An_0___Op_Urgent_True | x_t_An_0___Op_Elective_True | x_t_An_0___On_Urgent_True | x_t_An_0___On_Elective_True | x_t_An_1___ABp_Urgent_True | x_t_An_1___ABp_Elective_True | x_t_An_1___ABn_Urgent_True | x_t_An_1___ABn_Elective_True | x_t_An_1___Ap_Urgent_True | x_t_An_1___Ap_Elective_True | x_t_An_1___An_Urgent_True | x_t_An_1___An_Elective_True | x_t_An_1___Bp_Urgent_True | x_t_An_1___Bp_Elective_True | x_t_An_1___Bn_Urgent_True | x_t_An_1___Bn_Elective_True | x_t_An_1___Op_Urgent_True | x_t_An_1___Op_Elective_True | x_t_An_1___On_Urgent_True | x_t_An_1___On_Elective_True | x_t_An_2___ABp_Urgent_True | x_t_An_2___ABp_Elective_True | x_t_An_2___ABn_Urgent_True | x_t_An_2___ABn_Elective_True | x_t_An_2___Ap_Urgent_True | x_t_An_2___Ap_Elective_True | x_t_An_2___An_Urgent_True | x_t_An_2___An_Elective_True | x_t_An_2___Bp_Urgent_True | x_t_An_2___Bp_Elective_True | x_t_An_2___Bn_Urgent_True | x_t_An_2___Bn_Elective_True | x_t_An_2___Op_Urgent_True | x_t_An_2___Op_Elective_True | x_t_An_2___On_Urgent_True | x_t_An_2___On_Elective_True | x_t_Bp_0___ABp_Urgent_True | x_t_Bp_0___ABp_Elective_True | x_t_Bp_0___ABn_Urgent_True | x_t_Bp_0___ABn_Elective_True | x_t_Bp_0___Ap_Urgent_True | x_t_Bp_0___Ap_Elective_True | x_t_Bp_0___An_Urgent_True | x_t_Bp_0___An_Elective_True | x_t_Bp_0___Bp_Urgent_True | x_t_Bp_0___Bp_Elective_True | x_t_Bp_0___Bn_Urgent_True | x_t_Bp_0___Bn_Elective_True | x_t_Bp_0___Op_Urgent_True | x_t_Bp_0___Op_Elective_True | x_t_Bp_0___On_Urgent_True | x_t_Bp_0___On_Elective_True | x_t_Bp_1___ABp_Urgent_True | x_t_Bp_1___ABp_Elective_True | x_t_Bp_1___ABn_Urgent_True | x_t_Bp_1___ABn_Elective_True | x_t_Bp_1___Ap_Urgent_True | x_t_Bp_1___Ap_Elective_True | x_t_Bp_1___An_Urgent_True | x_t_Bp_1___An_Elective_True | x_t_Bp_1___Bp_Urgent_True | x_t_Bp_1___Bp_Elective_True | x_t_Bp_1___Bn_Urgent_True | x_t_Bp_1___Bn_Elective_True | x_t_Bp_1___Op_Urgent_True | x_t_Bp_1___Op_Elective_True | x_t_Bp_1___On_Urgent_True | x_t_Bp_1___On_Elective_True | x_t_Bp_2___ABp_Urgent_True | x_t_Bp_2___ABp_Elective_True | x_t_Bp_2___ABn_Urgent_True | x_t_Bp_2___ABn_Elective_True | x_t_Bp_2___Ap_Urgent_True | x_t_Bp_2___Ap_Elective_True | x_t_Bp_2___An_Urgent_True | x_t_Bp_2___An_Elective_True | x_t_Bp_2___Bp_Urgent_True | x_t_Bp_2___Bp_Elective_True | x_t_Bp_2___Bn_Urgent_True | x_t_Bp_2___Bn_Elective_True | x_t_Bp_2___Op_Urgent_True | x_t_Bp_2___Op_Elective_True | x_t_Bp_2___On_Urgent_True | x_t_Bp_2___On_Elective_True | x_t_Bn_0___ABp_Urgent_True | x_t_Bn_0___ABp_Elective_True | x_t_Bn_0___ABn_Urgent_True | x_t_Bn_0___ABn_Elective_True | x_t_Bn_0___Ap_Urgent_True | x_t_Bn_0___Ap_Elective_True | x_t_Bn_0___An_Urgent_True | x_t_Bn_0___An_Elective_True | x_t_Bn_0___Bp_Urgent_True | x_t_Bn_0___Bp_Elective_True | x_t_Bn_0___Bn_Urgent_True | x_t_Bn_0___Bn_Elective_True | x_t_Bn_0___Op_Urgent_True | x_t_Bn_0___Op_Elective_True | x_t_Bn_0___On_Urgent_True | x_t_Bn_0___On_Elective_True | x_t_Bn_1___ABp_Urgent_True | x_t_Bn_1___ABp_Elective_True | x_t_Bn_1___ABn_Urgent_True | x_t_Bn_1___ABn_Elective_True | x_t_Bn_1___Ap_Urgent_True | x_t_Bn_1___Ap_Elective_True | x_t_Bn_1___An_Urgent_True | x_t_Bn_1___An_Elective_True | x_t_Bn_1___Bp_Urgent_True | x_t_Bn_1___Bp_Elective_True | x_t_Bn_1___Bn_Urgent_True | x_t_Bn_1___Bn_Elective_True | x_t_Bn_1___Op_Urgent_True | x_t_Bn_1___Op_Elective_True | x_t_Bn_1___On_Urgent_True | x_t_Bn_1___On_Elective_True | x_t_Bn_2___ABp_Urgent_True | x_t_Bn_2___ABp_Elective_True | x_t_Bn_2___ABn_Urgent_True | x_t_Bn_2___ABn_Elective_True | x_t_Bn_2___Ap_Urgent_True | x_t_Bn_2___Ap_Elective_True | x_t_Bn_2___An_Urgent_True | x_t_Bn_2___An_Elective_True | x_t_Bn_2___Bp_Urgent_True | x_t_Bn_2___Bp_Elective_True | x_t_Bn_2___Bn_Urgent_True | x_t_Bn_2___Bn_Elective_True | x_t_Bn_2___Op_Urgent_True | x_t_Bn_2___Op_Elective_True | x_t_Bn_2___On_Urgent_True | x_t_Bn_2___On_Elective_True | x_t_Op_0___ABp_Urgent_True | x_t_Op_0___ABp_Elective_True | x_t_Op_0___ABn_Urgent_True | x_t_Op_0___ABn_Elective_True | x_t_Op_0___Ap_Urgent_True | x_t_Op_0___Ap_Elective_True | x_t_Op_0___An_Urgent_True | x_t_Op_0___An_Elective_True | x_t_Op_0___Bp_Urgent_True | x_t_Op_0___Bp_Elective_True | x_t_Op_0___Bn_Urgent_True | x_t_Op_0___Bn_Elective_True | x_t_Op_0___Op_Urgent_True | x_t_Op_0___Op_Elective_True | x_t_Op_0___On_Urgent_True | x_t_Op_0___On_Elective_True | x_t_Op_1___ABp_Urgent_True | x_t_Op_1___ABp_Elective_True | x_t_Op_1___ABn_Urgent_True | x_t_Op_1___ABn_Elective_True | x_t_Op_1___Ap_Urgent_True | x_t_Op_1___Ap_Elective_True | x_t_Op_1___An_Urgent_True | x_t_Op_1___An_Elective_True | x_t_Op_1___Bp_Urgent_True | x_t_Op_1___Bp_Elective_True | x_t_Op_1___Bn_Urgent_True | x_t_Op_1___Bn_Elective_True | x_t_Op_1___Op_Urgent_True | x_t_Op_1___Op_Elective_True | x_t_Op_1___On_Urgent_True | x_t_Op_1___On_Elective_True | x_t_Op_2___ABp_Urgent_True | x_t_Op_2___ABp_Elective_True | x_t_Op_2___ABn_Urgent_True | x_t_Op_2___ABn_Elective_True | x_t_Op_2___Ap_Urgent_True | x_t_Op_2___Ap_Elective_True | x_t_Op_2___An_Urgent_True | x_t_Op_2___An_Elective_True | x_t_Op_2___Bp_Urgent_True | x_t_Op_2___Bp_Elective_True | x_t_Op_2___Bn_Urgent_True | x_t_Op_2___Bn_Elective_True | x_t_Op_2___Op_Urgent_True | x_t_Op_2___Op_Elective_True | x_t_Op_2___On_Urgent_True | x_t_Op_2___On_Elective_True | x_t_On_0___ABp_Urgent_True | x_t_On_0___ABp_Elective_True | x_t_On_0___ABn_Urgent_True | x_t_On_0___ABn_Elective_True | x_t_On_0___Ap_Urgent_True | x_t_On_0___Ap_Elective_True | x_t_On_0___An_Urgent_True | x_t_On_0___An_Elective_True | x_t_On_0___Bp_Urgent_True | x_t_On_0___Bp_Elective_True | x_t_On_0___Bn_Urgent_True | x_t_On_0___Bn_Elective_True | x_t_On_0___Op_Urgent_True | x_t_On_0___Op_Elective_True | x_t_On_0___On_Urgent_True | x_t_On_0___On_Elective_True | x_t_On_1___ABp_Urgent_True | x_t_On_1___ABp_Elective_True | x_t_On_1___ABn_Urgent_True | x_t_On_1___ABn_Elective_True | x_t_On_1___Ap_Urgent_True | x_t_On_1___Ap_Elective_True | x_t_On_1___An_Urgent_True | x_t_On_1___An_Elective_True | x_t_On_1___Bp_Urgent_True | x_t_On_1___Bp_Elective_True | x_t_On_1___Bn_Urgent_True | x_t_On_1___Bn_Elective_True | x_t_On_1___Op_Urgent_True | x_t_On_1___Op_Elective_True | x_t_On_1___On_Urgent_True | x_t_On_1___On_Elective_True | x_t_On_2___ABp_Urgent_True | x_t_On_2___ABp_Elective_True | x_t_On_2___ABn_Urgent_True | x_t_On_2___ABn_Elective_True | x_t_On_2___Ap_Urgent_True | x_t_On_2___Ap_Elective_True | x_t_On_2___An_Urgent_True | x_t_On_2___An_Elective_True | x_t_On_2___Bp_Urgent_True | x_t_On_2___Bp_Elective_True | x_t_On_2___Bn_Urgent_True | x_t_On_2___Bn_Elective_True | x_t_On_2___Op_Urgent_True | x_t_On_2___Op_Elective_True | x_t_On_2___On_Urgent_True | x_t_On_2___On_Elective_True | x_t_ABp_0___ABp_1 | x_t_ABp_1___ABp_2 | x_t_ABp_2___ABp_3 | x_t_ABn_0___ABn_1 | x_t_ABn_1___ABn_2 | x_t_ABn_2___ABn_3 | x_t_Ap_0___Ap_1 | x_t_Ap_1___Ap_2 | x_t_Ap_2___Ap_3 | x_t_An_0___An_1 | x_t_An_1___An_2 | x_t_An_2___An_3 | x_t_Bp_0___Bp_1 | x_t_Bp_1___Bp_2 | x_t_Bp_2___Bp_3 | x_t_Bn_0___Bn_1 | x_t_Bn_1___Bn_2 | x_t_Bn_2___Bn_3 | x_t_Op_0___Op_1 | x_t_Op_1___Op_2 | x_t_Op_2___Op_3 | x_t_On_0___On_1 | x_t_On_1___On_2 | x_t_On_2___On_3 | |
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| 0 | X__LP | 16 | 10 | 1 | 0 | 0 | 2 | 0 | 0 | 11 | 0 | 0 | 10 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 5 | 0 | 0 | 8 | 12 | 10 | 12 | 52 | 39 | 15 | 26 | 31 | 27 | 5 | 4 | 50 | 49 | 27 | 11 | 16,432.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 15.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 9.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 30.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 1 | X__LP | 16 | 11 | 1 | 0 | 0 | 4 | 0 | 0 | 11 | 0 | 0 | 6 | 4 | 0 | 10 | 2 | 0 | 1 | 0 | 0 | 19 | 0 | 0 | 10 | 0 | 0 | 2 | 0 | 3 | 1 | 6 | 10 | 3 | 1 | 3 | 5 | 0 | 2 | 10 | 9 | 2 | 3 | 17,433.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 6.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 7.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2 | X__LP | 16 | 12 | 2 | 0 | 0 | 1 | 4 | 0 | 15 | 0 | 0 | 14 | 3 | 4 | 12 | 4 | 2 | 6 | 0 | 0 | 14 | 0 | 0 | 5 | 3 | 0 | 1 | 0 | 0 | 0 | 10 | 13 | 2 | 1 | 3 | 3 | 2 | 0 | 19 | 7 | 1 | 5 | 18,768.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 10.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 19.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 5.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 3 | X__LP | 16 | 13 | 2 | 0 | 0 | 5 | 0 | 0 | 11 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 0 | 3 | 0 | 0 | 17 | 0 | 0 | 4 | 0 | 0 | 5 | 7 | 10 | 12 | 45 | 30 | 22 | 17 | 27 | 31 | 7 | 6 | 51 | 56 | 23 | 16 | 21,883.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 15.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 14.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 12.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 6.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 14.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 4 | X__LP | 16 | 14 | 1 | 0 | 0 | 1 | 3 | 0 | 15 | 0 | 0 | 7 | 2 | 0 | 13 | 0 | 0 | 2 | 0 | 0 | 18 | 1 | 0 | 9 | 0 | 0 | 1 | 2 | 1 | 1 | 10 | 8 | 4 | 1 | 6 | 2 | 2 | 2 | 10 | 6 | 5 | 3 | 23,225.0000 | 1.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 10.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 6.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 10.0000 | 6.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
# piName_evalu = 'X__LP'
piName_evalu = 'X__FillAsNeededFromSingleResource'
T_evalu = T #20bldinv_init_evalu = init_R_t_pois()
Dh_tt1_evalu = dem_sim.simulate_pois(0)
Rh_tt1_evalu = don_sim.simulate_pois()
S_0_info_evalu = {
'R_t': bldinv_init_evalu,
'Rhold_t': {an: 0 for an in aNAMES},
'Dh_t': Dh_tt1_evalu,
'Rh_t': Rh_tt1_evalu
}
M_evalu = Model(S_0_info_evalu)
P_evalu = Policy()def run_policy_evalu(piName_evalu, T_evalu, model):
record = []
for t in range(T_evalu):
x_t, x = getattr(P_evalu, piName_evalu)(model)
hld = [np.sum(x[i*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])+PARS['NUM_DEM_NODES']:(i+1)*(PARS['NUM_DEM_NODES']+PARS['NUM_PARALLEL_LINKS'])]) for i in list(range(PARS['NUM_BLD_NODES']))]
# solHoldRecord = (l, t, hld.copy())
# solHoldList.append(solHoldRecord)
hld = np.array(hld)
S_t, cumC, x_t = model.step(x_t, hld) # step the model forward one iteration
record_t = [t] + \
[S_t.R_t[an] for an in aNAMES] + \
[S_t.Dh_t[bn] for bn in bNAMES] + \
[cumC] + \
[x_t.x_t[abn] for abn in abNAMES_EXP]
record.append(record_t)
cumC = model.cumC
return cumC, recordpiName_evalu_non = 'X__FillAsNeededFromSingleResource'
cumC, record = run_policy_evalu(piName_evalu_non, T_evalu, M_evalu)
labels = \
['t'] + R_t_labels + D_t_labels + ['cumC'] + x_t_labels
print(f'{int(cumC)=:,}')
df_non = pd.DataFrame.from_records(data=record, columns=labels); df_non[:10]int(cumC)=-11,198| t | R_t_ABp_0 | R_t_ABp_1 | R_t_ABp_2 | R_t_ABn_0 | R_t_ABn_1 | R_t_ABn_2 | R_t_Ap_0 | R_t_Ap_1 | R_t_Ap_2 | R_t_An_0 | R_t_An_1 | R_t_An_2 | R_t_Bp_0 | R_t_Bp_1 | R_t_Bp_2 | R_t_Bn_0 | R_t_Bn_1 | R_t_Bn_2 | R_t_Op_0 | R_t_Op_1 | R_t_Op_2 | R_t_On_0 | R_t_On_1 | R_t_On_2 | D_t_ABp_Urgent_True | D_t_ABp_Elective_True | D_t_ABn_Urgent_True | D_t_ABn_Elective_True | D_t_Ap_Urgent_True | D_t_Ap_Elective_True | D_t_An_Urgent_True | D_t_An_Elective_True | D_t_Bp_Urgent_True | D_t_Bp_Elective_True | D_t_Bn_Urgent_True | D_t_Bn_Elective_True | D_t_Op_Urgent_True | D_t_Op_Elective_True | D_t_On_Urgent_True | D_t_On_Elective_True | cumC | x_t_ABp_0___ABp_Urgent_True | x_t_ABp_0___ABp_Elective_True | x_t_ABp_0___ABn_Urgent_True | x_t_ABp_0___ABn_Elective_True | x_t_ABp_0___Ap_Urgent_True | x_t_ABp_0___Ap_Elective_True | x_t_ABp_0___An_Urgent_True | x_t_ABp_0___An_Elective_True | x_t_ABp_0___Bp_Urgent_True | x_t_ABp_0___Bp_Elective_True | x_t_ABp_0___Bn_Urgent_True | x_t_ABp_0___Bn_Elective_True | x_t_ABp_0___Op_Urgent_True | x_t_ABp_0___Op_Elective_True | x_t_ABp_0___On_Urgent_True | x_t_ABp_0___On_Elective_True | x_t_ABp_1___ABp_Urgent_True | x_t_ABp_1___ABp_Elective_True | x_t_ABp_1___ABn_Urgent_True | x_t_ABp_1___ABn_Elective_True | x_t_ABp_1___Ap_Urgent_True | x_t_ABp_1___Ap_Elective_True | x_t_ABp_1___An_Urgent_True | x_t_ABp_1___An_Elective_True | x_t_ABp_1___Bp_Urgent_True | x_t_ABp_1___Bp_Elective_True | x_t_ABp_1___Bn_Urgent_True | x_t_ABp_1___Bn_Elective_True | x_t_ABp_1___Op_Urgent_True | x_t_ABp_1___Op_Elective_True | x_t_ABp_1___On_Urgent_True | x_t_ABp_1___On_Elective_True | x_t_ABp_2___ABp_Urgent_True | x_t_ABp_2___ABp_Elective_True | x_t_ABp_2___ABn_Urgent_True | x_t_ABp_2___ABn_Elective_True | x_t_ABp_2___Ap_Urgent_True | x_t_ABp_2___Ap_Elective_True | x_t_ABp_2___An_Urgent_True | x_t_ABp_2___An_Elective_True | x_t_ABp_2___Bp_Urgent_True | x_t_ABp_2___Bp_Elective_True | x_t_ABp_2___Bn_Urgent_True | x_t_ABp_2___Bn_Elective_True | x_t_ABp_2___Op_Urgent_True | x_t_ABp_2___Op_Elective_True | x_t_ABp_2___On_Urgent_True | x_t_ABp_2___On_Elective_True | x_t_ABn_0___ABp_Urgent_True | x_t_ABn_0___ABp_Elective_True | x_t_ABn_0___ABn_Urgent_True | x_t_ABn_0___ABn_Elective_True | x_t_ABn_0___Ap_Urgent_True | x_t_ABn_0___Ap_Elective_True | x_t_ABn_0___An_Urgent_True | x_t_ABn_0___An_Elective_True | x_t_ABn_0___Bp_Urgent_True | x_t_ABn_0___Bp_Elective_True | x_t_ABn_0___Bn_Urgent_True | x_t_ABn_0___Bn_Elective_True | x_t_ABn_0___Op_Urgent_True | x_t_ABn_0___Op_Elective_True | x_t_ABn_0___On_Urgent_True | x_t_ABn_0___On_Elective_True | x_t_ABn_1___ABp_Urgent_True | x_t_ABn_1___ABp_Elective_True | x_t_ABn_1___ABn_Urgent_True | x_t_ABn_1___ABn_Elective_True | x_t_ABn_1___Ap_Urgent_True | x_t_ABn_1___Ap_Elective_True | x_t_ABn_1___An_Urgent_True | x_t_ABn_1___An_Elective_True | x_t_ABn_1___Bp_Urgent_True | x_t_ABn_1___Bp_Elective_True | x_t_ABn_1___Bn_Urgent_True | x_t_ABn_1___Bn_Elective_True | x_t_ABn_1___Op_Urgent_True | x_t_ABn_1___Op_Elective_True | x_t_ABn_1___On_Urgent_True | x_t_ABn_1___On_Elective_True | x_t_ABn_2___ABp_Urgent_True | x_t_ABn_2___ABp_Elective_True | x_t_ABn_2___ABn_Urgent_True | x_t_ABn_2___ABn_Elective_True | x_t_ABn_2___Ap_Urgent_True | x_t_ABn_2___Ap_Elective_True | x_t_ABn_2___An_Urgent_True | x_t_ABn_2___An_Elective_True | x_t_ABn_2___Bp_Urgent_True | x_t_ABn_2___Bp_Elective_True | x_t_ABn_2___Bn_Urgent_True | x_t_ABn_2___Bn_Elective_True | x_t_ABn_2___Op_Urgent_True | x_t_ABn_2___Op_Elective_True | x_t_ABn_2___On_Urgent_True | x_t_ABn_2___On_Elective_True | x_t_Ap_0___ABp_Urgent_True | x_t_Ap_0___ABp_Elective_True | x_t_Ap_0___ABn_Urgent_True | x_t_Ap_0___ABn_Elective_True | x_t_Ap_0___Ap_Urgent_True | x_t_Ap_0___Ap_Elective_True | x_t_Ap_0___An_Urgent_True | x_t_Ap_0___An_Elective_True | x_t_Ap_0___Bp_Urgent_True | x_t_Ap_0___Bp_Elective_True | x_t_Ap_0___Bn_Urgent_True | 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| 0 | 0 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -1,020.0000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -1,747.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 2 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -2,474.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 3 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -3,201.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 4 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -3,928.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 5 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -4,655.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 6 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -5,382.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 7 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -6,109.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 8 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -6,836.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 9 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -7,563.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
piName_evalu = 'X__LP'
cumC, record = run_policy_evalu(piName_evalu_non, T_evalu, M_evalu)
labels = \
['t'] + R_t_labels + D_t_labels + ['cumC'] + x_t_labels
print(f'{int(cumC)=:,}')
df = pd.DataFrame.from_records(data=record, columns=labels); df[:10]int(cumC)=-22,103| t | R_t_ABp_0 | R_t_ABp_1 | R_t_ABp_2 | R_t_ABn_0 | R_t_ABn_1 | R_t_ABn_2 | R_t_Ap_0 | R_t_Ap_1 | R_t_Ap_2 | R_t_An_0 | R_t_An_1 | R_t_An_2 | R_t_Bp_0 | R_t_Bp_1 | R_t_Bp_2 | R_t_Bn_0 | R_t_Bn_1 | R_t_Bn_2 | R_t_Op_0 | R_t_Op_1 | R_t_Op_2 | R_t_On_0 | R_t_On_1 | R_t_On_2 | D_t_ABp_Urgent_True | D_t_ABp_Elective_True | D_t_ABn_Urgent_True | D_t_ABn_Elective_True | D_t_Ap_Urgent_True | D_t_Ap_Elective_True | D_t_An_Urgent_True | D_t_An_Elective_True | D_t_Bp_Urgent_True | D_t_Bp_Elective_True | D_t_Bn_Urgent_True | D_t_Bn_Elective_True | D_t_Op_Urgent_True | D_t_Op_Elective_True | D_t_On_Urgent_True | D_t_On_Elective_True | cumC | x_t_ABp_0___ABp_Urgent_True | x_t_ABp_0___ABp_Elective_True | x_t_ABp_0___ABn_Urgent_True | x_t_ABp_0___ABn_Elective_True | x_t_ABp_0___Ap_Urgent_True | x_t_ABp_0___Ap_Elective_True | x_t_ABp_0___An_Urgent_True | x_t_ABp_0___An_Elective_True | x_t_ABp_0___Bp_Urgent_True | x_t_ABp_0___Bp_Elective_True | x_t_ABp_0___Bn_Urgent_True | x_t_ABp_0___Bn_Elective_True | x_t_ABp_0___Op_Urgent_True | x_t_ABp_0___Op_Elective_True | x_t_ABp_0___On_Urgent_True | x_t_ABp_0___On_Elective_True | x_t_ABp_1___ABp_Urgent_True | x_t_ABp_1___ABp_Elective_True | x_t_ABp_1___ABn_Urgent_True | x_t_ABp_1___ABn_Elective_True | x_t_ABp_1___Ap_Urgent_True | x_t_ABp_1___Ap_Elective_True | x_t_ABp_1___An_Urgent_True | x_t_ABp_1___An_Elective_True | x_t_ABp_1___Bp_Urgent_True | x_t_ABp_1___Bp_Elective_True | x_t_ABp_1___Bn_Urgent_True | x_t_ABp_1___Bn_Elective_True | x_t_ABp_1___Op_Urgent_True | x_t_ABp_1___Op_Elective_True | x_t_ABp_1___On_Urgent_True | x_t_ABp_1___On_Elective_True | x_t_ABp_2___ABp_Urgent_True | x_t_ABp_2___ABp_Elective_True | x_t_ABp_2___ABn_Urgent_True | x_t_ABp_2___ABn_Elective_True | x_t_ABp_2___Ap_Urgent_True | x_t_ABp_2___Ap_Elective_True | x_t_ABp_2___An_Urgent_True | x_t_ABp_2___An_Elective_True | x_t_ABp_2___Bp_Urgent_True | x_t_ABp_2___Bp_Elective_True | x_t_ABp_2___Bn_Urgent_True | x_t_ABp_2___Bn_Elective_True | x_t_ABp_2___Op_Urgent_True | x_t_ABp_2___Op_Elective_True | x_t_ABp_2___On_Urgent_True | x_t_ABp_2___On_Elective_True | x_t_ABn_0___ABp_Urgent_True | x_t_ABn_0___ABp_Elective_True | x_t_ABn_0___ABn_Urgent_True | x_t_ABn_0___ABn_Elective_True | x_t_ABn_0___Ap_Urgent_True | x_t_ABn_0___Ap_Elective_True | x_t_ABn_0___An_Urgent_True | x_t_ABn_0___An_Elective_True | x_t_ABn_0___Bp_Urgent_True | x_t_ABn_0___Bp_Elective_True | x_t_ABn_0___Bn_Urgent_True | x_t_ABn_0___Bn_Elective_True | x_t_ABn_0___Op_Urgent_True | x_t_ABn_0___Op_Elective_True | x_t_ABn_0___On_Urgent_True | x_t_ABn_0___On_Elective_True | x_t_ABn_1___ABp_Urgent_True | x_t_ABn_1___ABp_Elective_True | x_t_ABn_1___ABn_Urgent_True | x_t_ABn_1___ABn_Elective_True | x_t_ABn_1___Ap_Urgent_True | x_t_ABn_1___Ap_Elective_True | x_t_ABn_1___An_Urgent_True | x_t_ABn_1___An_Elective_True | x_t_ABn_1___Bp_Urgent_True | x_t_ABn_1___Bp_Elective_True | x_t_ABn_1___Bn_Urgent_True | x_t_ABn_1___Bn_Elective_True | x_t_ABn_1___Op_Urgent_True | x_t_ABn_1___Op_Elective_True | x_t_ABn_1___On_Urgent_True | x_t_ABn_1___On_Elective_True | x_t_ABn_2___ABp_Urgent_True | x_t_ABn_2___ABp_Elective_True | x_t_ABn_2___ABn_Urgent_True | x_t_ABn_2___ABn_Elective_True | x_t_ABn_2___Ap_Urgent_True | x_t_ABn_2___Ap_Elective_True | x_t_ABn_2___An_Urgent_True | x_t_ABn_2___An_Elective_True | x_t_ABn_2___Bp_Urgent_True | x_t_ABn_2___Bp_Elective_True | x_t_ABn_2___Bn_Urgent_True | x_t_ABn_2___Bn_Elective_True | x_t_ABn_2___Op_Urgent_True | x_t_ABn_2___Op_Elective_True | x_t_ABn_2___On_Urgent_True | x_t_ABn_2___On_Elective_True | x_t_Ap_0___ABp_Urgent_True | x_t_Ap_0___ABp_Elective_True | x_t_Ap_0___ABn_Urgent_True | x_t_Ap_0___ABn_Elective_True | x_t_Ap_0___Ap_Urgent_True | x_t_Ap_0___Ap_Elective_True | x_t_Ap_0___An_Urgent_True | x_t_Ap_0___An_Elective_True | x_t_Ap_0___Bp_Urgent_True | x_t_Ap_0___Bp_Elective_True | x_t_Ap_0___Bn_Urgent_True | x_t_Ap_0___Bn_Elective_True | x_t_Ap_0___Op_Urgent_True | x_t_Ap_0___Op_Elective_True | x_t_Ap_0___On_Urgent_True | x_t_Ap_0___On_Elective_True | x_t_Ap_1___ABp_Urgent_True | x_t_Ap_1___ABp_Elective_True | x_t_Ap_1___ABn_Urgent_True | x_t_Ap_1___ABn_Elective_True | x_t_Ap_1___Ap_Urgent_True | x_t_Ap_1___Ap_Elective_True | x_t_Ap_1___An_Urgent_True | x_t_Ap_1___An_Elective_True | x_t_Ap_1___Bp_Urgent_True | x_t_Ap_1___Bp_Elective_True | x_t_Ap_1___Bn_Urgent_True | x_t_Ap_1___Bn_Elective_True | x_t_Ap_1___Op_Urgent_True | x_t_Ap_1___Op_Elective_True | x_t_Ap_1___On_Urgent_True | x_t_Ap_1___On_Elective_True | x_t_Ap_2___ABp_Urgent_True | x_t_Ap_2___ABp_Elective_True | x_t_Ap_2___ABn_Urgent_True | x_t_Ap_2___ABn_Elective_True | x_t_Ap_2___Ap_Urgent_True | x_t_Ap_2___Ap_Elective_True | x_t_Ap_2___An_Urgent_True | x_t_Ap_2___An_Elective_True | x_t_Ap_2___Bp_Urgent_True | x_t_Ap_2___Bp_Elective_True | x_t_Ap_2___Bn_Urgent_True | x_t_Ap_2___Bn_Elective_True | x_t_Ap_2___Op_Urgent_True | x_t_Ap_2___Op_Elective_True | x_t_Ap_2___On_Urgent_True | x_t_Ap_2___On_Elective_True | x_t_An_0___ABp_Urgent_True | x_t_An_0___ABp_Elective_True | x_t_An_0___ABn_Urgent_True | x_t_An_0___ABn_Elective_True | x_t_An_0___Ap_Urgent_True | x_t_An_0___Ap_Elective_True | x_t_An_0___An_Urgent_True | x_t_An_0___An_Elective_True | x_t_An_0___Bp_Urgent_True | x_t_An_0___Bp_Elective_True | x_t_An_0___Bn_Urgent_True | x_t_An_0___Bn_Elective_True | x_t_An_0___Op_Urgent_True | x_t_An_0___Op_Elective_True | x_t_An_0___On_Urgent_True | x_t_An_0___On_Elective_True | x_t_An_1___ABp_Urgent_True | x_t_An_1___ABp_Elective_True | x_t_An_1___ABn_Urgent_True | x_t_An_1___ABn_Elective_True | x_t_An_1___Ap_Urgent_True | x_t_An_1___Ap_Elective_True | x_t_An_1___An_Urgent_True | x_t_An_1___An_Elective_True | x_t_An_1___Bp_Urgent_True | x_t_An_1___Bp_Elective_True | x_t_An_1___Bn_Urgent_True | x_t_An_1___Bn_Elective_True | x_t_An_1___Op_Urgent_True | x_t_An_1___Op_Elective_True | x_t_An_1___On_Urgent_True | x_t_An_1___On_Elective_True | x_t_An_2___ABp_Urgent_True | x_t_An_2___ABp_Elective_True | x_t_An_2___ABn_Urgent_True | x_t_An_2___ABn_Elective_True | x_t_An_2___Ap_Urgent_True | x_t_An_2___Ap_Elective_True | x_t_An_2___An_Urgent_True | x_t_An_2___An_Elective_True | x_t_An_2___Bp_Urgent_True | x_t_An_2___Bp_Elective_True | x_t_An_2___Bn_Urgent_True | x_t_An_2___Bn_Elective_True | x_t_An_2___Op_Urgent_True | x_t_An_2___Op_Elective_True | x_t_An_2___On_Urgent_True | x_t_An_2___On_Elective_True | x_t_Bp_0___ABp_Urgent_True | x_t_Bp_0___ABp_Elective_True | x_t_Bp_0___ABn_Urgent_True | x_t_Bp_0___ABn_Elective_True | x_t_Bp_0___Ap_Urgent_True | x_t_Bp_0___Ap_Elective_True | x_t_Bp_0___An_Urgent_True | x_t_Bp_0___An_Elective_True | x_t_Bp_0___Bp_Urgent_True | x_t_Bp_0___Bp_Elective_True | x_t_Bp_0___Bn_Urgent_True | x_t_Bp_0___Bn_Elective_True | x_t_Bp_0___Op_Urgent_True | x_t_Bp_0___Op_Elective_True | x_t_Bp_0___On_Urgent_True | x_t_Bp_0___On_Elective_True | x_t_Bp_1___ABp_Urgent_True | x_t_Bp_1___ABp_Elective_True | x_t_Bp_1___ABn_Urgent_True | x_t_Bp_1___ABn_Elective_True | x_t_Bp_1___Ap_Urgent_True | x_t_Bp_1___Ap_Elective_True | x_t_Bp_1___An_Urgent_True | x_t_Bp_1___An_Elective_True | x_t_Bp_1___Bp_Urgent_True | x_t_Bp_1___Bp_Elective_True | x_t_Bp_1___Bn_Urgent_True | x_t_Bp_1___Bn_Elective_True | x_t_Bp_1___Op_Urgent_True | x_t_Bp_1___Op_Elective_True | x_t_Bp_1___On_Urgent_True | x_t_Bp_1___On_Elective_True | x_t_Bp_2___ABp_Urgent_True | x_t_Bp_2___ABp_Elective_True | x_t_Bp_2___ABn_Urgent_True | x_t_Bp_2___ABn_Elective_True | x_t_Bp_2___Ap_Urgent_True | x_t_Bp_2___Ap_Elective_True | x_t_Bp_2___An_Urgent_True | x_t_Bp_2___An_Elective_True | x_t_Bp_2___Bp_Urgent_True | x_t_Bp_2___Bp_Elective_True | x_t_Bp_2___Bn_Urgent_True | x_t_Bp_2___Bn_Elective_True | x_t_Bp_2___Op_Urgent_True | x_t_Bp_2___Op_Elective_True | x_t_Bp_2___On_Urgent_True | x_t_Bp_2___On_Elective_True | x_t_Bn_0___ABp_Urgent_True | x_t_Bn_0___ABp_Elective_True | x_t_Bn_0___ABn_Urgent_True | x_t_Bn_0___ABn_Elective_True | 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| 0 | 0 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -11,925.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -12,652.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 2 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -13,379.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 3 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -14,106.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 4 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -14,833.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 5 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -15,560.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 6 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -16,287.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 7 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -17,014.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 8 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -17,741.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 9 | 5 | 0 | 0 | 3 | 0 | 0 | 23 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 3 | 0 | 0 | 20 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 2 | 1 | 5 | 8 | 5 | 1 | 4 | 6 | 0 | 2 | 9 | 12 | 6 | 7 | -18,468.0000 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
# P.plot_evalu(df_non, df_X__LP, 'LP', '(first 50 records)')
P.plot_evalu(df_non, df_X__LP[:T_evalu], 'LP', '')
From the cumC plot we see that the cumulative reward for the optimal policy X__LP keeps on rising. The non-optimal policy X__FillAsNeededFromSingleResource keeps losing money.
# def initOutputListHeaders(params):
def initOutputListHeaders():
labelsDemandExo=['l','t','Bloodtype','Urgency','isSubAllowed','DemandValue']
labelsDonationExo=['l','t','Bloodtype','DonationValue']
labelsSupplyPre=['l','t','BloodType','Age','PreInv']
labelsSupplyPost=['l','t','BloodType','Age','PostInv']
labelsSlopesList=['l','t','BloodType','Age']
vNames = ["v_"+str(r) for r in list(range(PARS['NUM_PARALLEL_LINKS']))]
labelsSlopesList = labelsSlopesList + vNames
headerSolDemList =['l','t','BloodTypeS','Age','BloodTypeD','Urgency','SubsAllowed','isCompatible','Contrib','Value']
headerSolHoldList = ['l','t','BloodTypeS','Age','Value']
# headerSimuList = ['Iteration','ElapsedTime','Stepsize','ObjVal','isTrainning']
headerSimuList = ['l','ElapsedTime','Stepsize','cumC','isTrainning']
headerUpdateVfaList = ['l','t','BloodType','Age','R','vhat','vbarOld','sqGrad','stepsize','vbarNew']
return(labelsDemandExo, labelsDonationExo,labelsSupplyPre,labelsSupplyPost,labelsSlopesList,headerSolDemList,headerSolHoldList,headerSimuList,headerUpdateVfaList)
def convertToDfOutputLists(
# params,
demandExoList, donationExoList,
supplyPreList, supplyPostList,
slopesList, solDemList, solHoldList,
simuList, updateVfaList):
labelsDemandExo, labelsDonationExo, \
labelsSupplyPre, labelsSupplyPost, \
labelsSlopesList, headerSolDemList, headerSolHoldList, \
headerSimuList, headerUpdateVfaList = initOutputListHeaders()
#Flatteting the lists
dfSimu = pd.DataFrame.from_records(simuList, columns=headerSimuList)
demandExoListFlat = [ \
(ite,t,dnode.split('_')[0],dnode.split('_')[1],dnode.split('_')[2],dvalue)
for ite,t,d in demandExoList for dnode,dvalue in zip(bNAMES, d)]
dfDemandExo = pd.DataFrame.from_records(demandExoListFlat, columns=labelsDemandExo)
donationExoListFlat = [ \
(ite,t,dtype,dvalue)
for ite,t,d in donationExoList for dtype,dvalue in zip(myBloodtypes, d)]
dfDonationExo = pd.DataFrame.from_records(donationExoListFlat, columns=labelsDonationExo)
supplyPreListFlat = [ \
(ite,t,bnode.split('_')[0],bnode.split('_')[1],bvalue)
for ite,t,b in supplyPreList for bnode,bvalue in zip(aNAMES, b)]
dfSupplyPre = pd.DataFrame.from_records(supplyPreListFlat, columns=labelsSupplyPre)
supplyPostListFlat = [ \
(ite,t,bnode.split('_')[0],bnode.split('_')[1],bvalue)
for ite,t,b in supplyPostList for bnode,bvalue in zip(aNAMES, b)]
dfSupplyPost = pd.DataFrame.from_records(supplyPostListFlat,columns=labelsSupplyPost)
solDemListFlat = [ \
(ite,
t,
bld.split('_')[0],
bld.split('_')[1],
dem.split('_')[0],
dem.split('_')[1],
dem.split('_')[2],
mySubMatrix['_'.join((bld.split('_')[0], dem.split('_')[0]))],
mySubMatrix['_'.join((bld.split('_')[0], dem.split('_')[0]))], #.seems to play same role as demweights
xbd)
for ite,t,xDem in solDemList
for bld,xb in zip(aNAMES,xDem)
for dem,xbd in zip(bNAMES,xb)]
dfSolDem = pd.DataFrame.from_records(solDemListFlat,columns=headerSolDemList)
solHoldListFlat = [ \
(ite,t,bnode.split('_')[0],bnode.split('_')[1],hvalue)
for ite,t,h in solHoldList
for bnode,hvalue in zip(aNAMES,h)]
dfSolHold = pd.DataFrame.from_records(solHoldListFlat,columns=headerSolHoldList)
slopesListFlat = [ \
(vnode.split('_')[0],vnode.split('_')[1],bnode.split('_')[0],bnode.split('_')[1],*list(vnode.split('_')[2]))
for vnode,bnode in zip(slopesList,aNAMES*PARS['NUM_ITER']*PARS['MAX_TIME'])]
dfSlopes = pd.DataFrame.from_records(slopesListFlat,columns=labelsSlopesList)
dfUpdateVfa = pd.DataFrame.from_records(updateVfaList,columns=headerUpdateVfaList)
return \
dfDemandExo, dfDonationExo, \
dfSupplyPre, dfSupplyPost, \
dfSlopes, dfSolDem, dfSolHold, \
dfSimu, dfUpdateVfa
# def printDfsToOutputFile(params,dfDemandExo, dfDonationExo, dfSupplyPre, dfSupplyPost, dfSlopes, dfSolDem, dfSolHold, dfSimu, dfUpdateVfa):
def printDfsToOutputFile(dfDemandExo, dfDonationExo, dfSupplyPre, dfSupplyPost, dfSlopes, dfSolDem, dfSolHold, dfSimu, dfUpdateVfa):
t_init_print = time.time()
print("Started printing file")
# print to excel file
# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter(PARS['OUTPUT_FILENAME'], engine='xlsxwriter')
# Convert the dataframe to an XlsxWriter Excel object.
dfSimu.to_excel(writer, sheet_name='Simu')
if PARS['PRINT_ALL']:
dfDemandExo.to_excel(writer, sheet_name='DemandExo')
dfDonationExo.to_excel(writer, sheet_name='DonationExo')
dfSupplyPre.to_excel(writer, sheet_name='SupplyPre')
dfSolDem.to_excel(writer, sheet_name='SolDem')
dfSolHold.to_excel(writer, sheet_name='HoldDem')
dfSupplyPost.to_excel(writer, sheet_name='SupplyPost')
dfSlopes.to_excel(writer, sheet_name='SlopesList')
dfUpdateVfa.to_excel(writer, sheet_name='UpdatesVfa')
# Close the Pandas Excel writer and output the Excel file.
writer.save()
print("Finished printing files in {:.2f} secs".format(time.time()-t_init_print))###########################################################################################################################################
if (PARS['SAVE_VFA']):
pickling_on = open(PARS['NAME_SAVE_VFA_PICKLE'],"wb")
pickle.dump(M.Bld_Net, pickling_on)
pickling_on.close()
###########################################################################################################################################
#Computing stats and plots
###########################################################################################################################################
dfDemandExo, dfDonationExo, \
dfSupplyPre, dfSupplyPost, \
dfSlopes, \
dfSolDem, dfSolHold, \
dfSimu, dfUpdateVfa = convertToDfOutputLists(
demandExoList, donationExoList, supplyPreList, supplyPostList,
slopesList, solDemList, solHoldList, simuList, updateVfaList)
policy = PARS['USE_VFA'] and 'VFA-Based' or 'MYOPIC'
surge = PARS['SURGE_PROB']>0 and "SURGE_"+str(PARS['SURGE_PROB']) or "NO_SURGE"
instance = "Policy{}_{}_PEN_{:,}_ALPHA_{:.2f}".format(
policy, surge, PARS['BLOOD_FOR_ELECTIVE_PENALTY'], PARS['ALPHA'])
#Average Contribution
meanTesting = dfSimu.groupby('isTrainning')['cumC'].mean()[False]
#Total Blood discarded
totalDiscarded = dfSolHold.loc[
(dfSolHold.Age.astype(int) > PARS['MAX_AGE']-2) & \
(dfSolHold['l'] >= PARS['NUM_TRAINNING_ITER']), :].copy()['Value'].sum()
#Total Donation
totalDonation = dfDonationExo[
dfDonationExo['l'] >= PARS['NUM_TRAINNING_ITER']].copy()['DonationValue'].sum()
#Coverage
dfCoverage = dfSolDem.groupby(['BloodTypeD', 'Urgency', 'l', 't'])['Value'].sum()
dfCoverage.index = dfCoverage.index.rename("Bloodtype", level=0)
dfCoverage = pd.concat([dfCoverage, dfDemandExo.groupby(['Bloodtype', 'Urgency', 'l', 't'])['DemandValue'].sum()], axis=1)
dfCoverage['Ratio'] = dfCoverage['Value']/dfCoverage['DemandValue']
dfCoverage_agg_ite = dfCoverage.groupby(['Bloodtype', 'Urgency', 'l'])['Ratio'].mean().reset_index()
numTra = PARS['NUM_TRAINNING_ITER']
dfCoverage_agg_test = dfCoverage_agg_ite.query('l >= @numTra')
dfPrintIte = dfCoverage_agg_test.pivot_table('Ratio', index='Bloodtype', columns='Urgency')
finalCoverage=dfCoverage_agg_test.groupby('Urgency')['Ratio'].mean()
coverage = "Average Coverage: - Urgent: {:.2f} Elective: {:.2f} Avg: {:.2f}".format(finalCoverage['Urgent'],finalCoverage['Elective'],dfCoverage_agg_ite['Ratio'].mean())
#dfUtility = dfCoverage.query('Iteration >= @numTra').copy().reset_index()
#dfUtility['Weight']=-1
#dfUtility['Score']=0
#dfUtility.loc[dfUtility.Urgency=="Elective",'Weight']=1
#dfUtility.loc[dfUtility.Urgency=="Urgent",'Weight']=100
#dfUtility.loc[(dfUtility.Urgency=="Urgent") & (dfUtility.Ratio>.9),'Score']=1
#sumRatio=(dfUtility['Ratio']*dfUtility['Weight']).sum()
#sumWeight=(dfUtility['Weight']).sum()
#sumScore=(dfUtility['Score']).sum()
#utility=sumRatio/sumWeight
#Utility function
utility=(PARS['WEIGHT_URGENT']*round(finalCoverage['Urgent'],2)+PARS['WEIGHT_ELECTIVE']*round(finalCoverage['Elective'],2))*100
modifiedUtil=utility-PARS['WEIGHT_DISCARDED']*100*round(totalDiscarded/totalDonation,2)P.plot_total_contribution(dfSimu)
P.plot_demand_and_donation(dfDemandExo, dfDonationExo)UserWarning: You have mixed positional and keyword arguments, some input may be discarded.
fig_exo.legend(
# dfInv = dfSupplyPre[dfSupplyPre.Iteration>=params['NUM_TRAINNING_ITER']].copy()
dfInv = dfSupplyPre[dfSupplyPre['l'] >= PARS['NUM_TRAINNING_ITER']].copy()
# dfInv['Iteration'] = dfInv['Iteration'] - params['NUM_TRAINNING_ITER']
dfInv['Iteration'] = dfInv['l'] - PARS['NUM_TRAINNING_ITER']P.plot_predecision_inventory_by_age(dfInv)UserWarning: You have mixed positional and keyword arguments, some input may be discarded.
fig_inv.legend(
P.plot_predecision_inventory_by_bloodtype(dfInv)UserWarning: You have mixed positional and keyword arguments, some input may be discarded.
fig_inv_blood.legend(
P.plot_predecision_inventory(dfInv)
P.plot_discarded_blood_during_testing(dfSolHold)
P.plot_demand_coverage(dfPrintIte)WARNING:matplotlib.legend:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
P.plot_demand_coverage_by_bloodtype(dfCoverage_agg_ite)WARNING:matplotlib.legend:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
P.plot_demand_coverage_by_timeperiod(dfCoverage)WARNING:matplotlib.legend:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
P.plot_demand_coverage_histogram(dfCoverage)
# if params['SAVE_PLOTS']:
# fig_ite.savefig('{}_Figure1.pdf'.format(instance))
# fig_exo.savefig('{}_Figure2.pdf'.format(instance))
# fig_inv.savefig('{}_Figure3.pdf'.format(instance))
# fig_inv_blood.savefig('{}_Figure4.pdf'.format(instance))
# fig_inv_total.savefig('{}_Figure5.pdf'.format(instance))
# fig_dis.savefig('{}_Figure6.pdf'.format(instance))
# fig_tes.savefig('{}_Figure7.pdf'.format(instance))
# fig_cover.savefig('{}_Figure8.pdf'.format(instance))
# fig_cover_ite.savefig('{}_Figure9.pdf'.format(instance))
# fig_hist.savefig('{}_Figure10.pdf'.format(instance))
# if params['SHOW_PLOTS']:
# plt.show()
###########################################################################################################################################
#Printing the final results
print("\n*******************************************************************************************")
print("Policy {}_{}".format(policy,surge))
print(instance)
print("Average total contribution during TESTING iterations: ${:,}".format(meanTesting))
print(coverage)
print("Proportion of blood discarded: {:.2f}% ".format(totalDiscarded*100/totalDonation))
print("Final utility: {:.0f}".format(modifiedUtil))
print("*********************************************************************************************\n")
with open("OutputAll.txt", "a") as myfile:
print("{}\t{:.2f}\t{:.2f}\t{:.2f}\t{:.2f}\t{:.2f}\t{:.2f}".format(instance,meanTesting,finalCoverage['Urgent'],finalCoverage['Elective'],dfCoverage_agg_ite['Ratio'].mean(),totalDiscarded/totalDonation,modifiedUtil),file=myfile)
###########################################################################################################################################
# print("Total elapsed time {:.2f} secs".format(time.time()- t_global_init)) #.moved to perform_search_sample_paths()
###########################################################################################################################################
#Printing output file
if PARS['PRINT']:
printDfsToOutputFile(PARS,dfDemandExo, dfDonationExo, dfSupplyPre, dfSupplyPost, dfSlopes, dfSolDem, dfSolHold, dfCoverage, dfUpdateVfa)
###########################################################################################################################################
#End Main
###############################################################################################################################################
*******************************************************************************************
Policy MYOPIC_SURGE_0.5
PolicyMYOPIC_SURGE_0.5_PEN_-9.0_ALPHA_0.00
Average total contribution during TESTING iterations: $20,447.25
Average Coverage: - Urgent: 0.92 Elective: 0.64 Avg: 0.78
Proportion of blood discarded: 0.47%
Final utility: 984
*********************************************************************************************