def calculate_defult_TiO2():
'''
Do a single vintage calculation
default market share rate = 0.3
'''
# read data now
TiO2_data = np.loadtxt('../data/TiO2_production_real.csv', delimiter=',')
TiO2_to_paints = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
TiO2_data[:, 1] = TiO2_data[:, 1] * TiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
'''
deal with dictionary here
'''
# set up the model
TiO2_market = vintage_model.vintage_market(TiO2_data,
market_data_dict,
weibull=True)
test = TiO2_market.calculate_market_vintage()
'''Change 26/12/2016 by Yuwei'''
table_of_test = np.zeros((35, 21))
row_num_in_table = 0
for index in range(
len(test)): #find each category e.g. Household & Furniture
index_in_test = test[test.keys()[index]] #create a table of test
for num in range(len(index_in_test)): #num is each phase
list_in_index = index_in_test[index_in_test.keys()[num]]
#print(list_in_index) # save number in each phase e.g. manufacturing, in use
#print(row_num_in_table)
for val in range(len(list_in_index)): #val is year
#print(val)
table_of_test[row_num_in_table, val] = list_in_index[val]
row_num_in_table += 1
#print(table_of_test)
#print np.sum(test['Household & Furniture']['In Use'] + test['Household & Furniture']['End of Life'])
#raw_input()
table_of_sum = np.zeros([1, 21])
'''sum up the total release for each year '''
for i in range(21):
#print(i)
'''only sum up manufacturing release, in use and end of life
(row: 5n,5n+1,5n+2) '''
sum = 0
for row in range(7):
sum += table_of_test[5 * row, i] + table_of_test[
5 * row + 1, i] + table_of_test[5 * row + 2, i]
table_of_sum[0, i] = sum
#print(table_of_sum)
return table_of_sum
# df = FeO2_market.to_dataframe(test)
# FeO2_market.plot_market_vintage()
# df.to_csv('../results/dynamic_results/FeOx_vintage_results_1215.csv')
# df.to_csv('../results/static_results/FeOx_vintage_results_static_1215.csv')
''' done'''
def do_release_market():
Fe_data = np.loadtxt('./data/Fe_production_real.csv',delimiter=',')
market_data_dict = csv_to_dict('./data/coating_market_fake.csv')
SiO2_to_coating = 0.1
Fe_data[:,1] = Fe_data[:,1] * SiO2_to_coating
Fe_market = vintage_model.vintage_market(Fe_data,market_data_dict)
test = Fe_market.calculate_market_vintage()
print Fe_market.tot_releases_year()
Fe_market.plot_market_vintage('In Use')
def do_release_market():
TiO2_data = np.loadtxt('./data/SiO2_production_real.csv',delimiter=',')
market_data_dict = csv_to_dict('./data/coating_market_fake.csv')
TiO2_to_coating = 0.3
TiO2_data[:,1] = TiO2_data[:,1] * TiO2_to_coating
TiO2_market = vintage_model.vintage_market(TiO2_data,market_data_dict)
test = TiO2_market.calculate_market_vintage()
print TiO2_market.tot_releases_year()
TiO2_market.plot_market_vintage()
def do_release_market():
SiO2_data = np.loadtxt('./data/SiO2_production_real.csv', delimiter=',')
market_data_dict = csv_to_dict('./data/coating_market_fake.csv')
SiO2_to_coating = 0.1
SiO2_data[:, 1] = SiO2_data[:, 1] * SiO2_to_coating
SiO2_market = vintage_model.vintage_market(SiO2_data, market_data_dict)
test = SiO2_market.calculate_market_vintage()
print SiO2_market.tot_releases_year()
SiO2_market.plot_market_vintage('')
def calculate_defult_TiO2():
'''
Do a single vintage calculation
default market share rate = 0.3
'''
# read data now
TiO2_data = np.loadtxt('../data/TiO2_production_real.csv',delimiter=',')
TiO2_to_paints = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
TiO2_data[:,1] = TiO2_data[:,1] * TiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
'''
deal with dictionary here
'''
# set up the model
TiO2_market = vintage_model.vintage_market(TiO2_data,market_data_dict, weibull=True)
test = TiO2_market.calculate_market_vintage()
'''Change 26/12/2016 by Yuwei'''
table_of_test = np.zeros((35,21))
row_num_in_table = 0
for index in range(len(test)): #find each category e.g. Household & Furniture
index_in_test = test[test.keys()[index]] #create a table of test
for num in range(len(index_in_test)): #num is each phase
list_in_index = index_in_test[index_in_test.keys()[num]]
#print(list_in_index) # save number in each phase e.g. manufacturing, in use
#print(row_num_in_table)
for val in range(len(list_in_index)): #val is year
#print(val)
table_of_test[row_num_in_table,val] = list_in_index[val]
row_num_in_table += 1
#print(table_of_test)
#print np.sum(test['Household & Furniture']['In Use'] + test['Household & Furniture']['End of Life'])
#raw_input()
table_of_sum = np.zeros([1,21])
'''sum up the total release for each year '''
for i in range(21):
#print(i)
'''only sum up manufacturing release, in use and end of life
(row: 5n,5n+1,5n+2) '''
sum = 0
for row in range(7):
sum += table_of_test[5*row,i]+table_of_test[5*row+1,i]+table_of_test[5*row+2,i]
table_of_sum[0,i] = sum
#print(table_of_sum)
return table_of_sum
# df = FeO2_market.to_dataframe(test)
# FeO2_market.plot_market_vintage()
# df.to_csv('../results/dynamic_results/FeOx_vintage_results_1215.csv')
# df.to_csv('../results/static_results/FeOx_vintage_results_static_1215.csv')
''' done'''
def monte_carlo_analysis(self, round = 100):
results = []
for i in range(round):
print 'Working on round', i
this_market_dict = self._shake_lifetime()
this_market_vintage = vintage_model.vintage_market(self.prod_year_data,this_market_dict)
this_vintage_results = this_market_vintage.calculate_market_vintage()
this_vintage_tot_release = this_market_vintage.tot_releases_year()
results.append(this_vintage_tot_release)
return results
def monte_carlo_analysis(self, round=100):
results = []
for i in range(round):
print 'Working on round', i
this_market_dict = self._shake_lifetime()
this_market_vintage = vintage_model.vintage_market(
self.prod_year_data, this_market_dict)
this_vintage_results = this_market_vintage.calculate_market_vintage(
)
this_vintage_tot_release = this_market_vintage.tot_releases_year()
results.append(this_vintage_tot_release)
return results
def get_results_for_one_vintage(year):
'''
calculate the results for one vintage year
'''
# read data now
SiO2_data = np.loadtxt('../data/SiO2_production_real.csv',delimiter=',')
SiO2_to_paints = 0.1 # what portion of SiO2 are used in coating, paints and pigment market
SiO2_data[:,1] = SiO2_data[:,1] * SiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
SiO2_market = vintage_model.vintage_market(SiO2_data,market_data_dict, weibull=True)
this_year_vintage = SiO2_market.calculate_for_one_vintage(year=2016)
df = SiO2_market.to_dataframe(this_year_vintage)
df.to_csv('../results/dynamic_results/year_vintage/SiO2_vintage_2016.csv')
def calculate_defult_Fe():
'''
Do a single vintage calculation
default in use Rate = 0.33
'''
# read data now
Fe_data = np.loadtxt('../data/Fe_production_real.csv',delimiter=',')
Fe_to_paints = 0.33 # what portion of SiO2 are used in coating, paints and pigment market
Fe_data[:,1] = Fe_data[:,1] * Fe_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
FeO2_market = vintage_model.vintage_market(Fe_data,market_data_dict, weibull=True)
test = FeO2_market.calculate_market_vintage()
df = FeO2_market.to_dataframe(test)
FeO2_market.plot_market_vintage()
df.to_csv('../results/dynamic_results/FeOx_vintage_results_1215.csv')
def calculate_defult_SiO2():
'''
Do a single vintage calculation
default market share = 0.1
'''
# read data now
SiO2_data = np.loadtxt('../data/SiO2_production_real.csv',delimiter=',')
SiO2_to_paints = 0.1 # what portion of SiO2 are used in coating, paints and pigment market
SiO2_data[:,1] = SiO2_data[:,1] * SiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
SiO2_market = vintage_model.vintage_market(SiO2_data,market_data_dict, weibull=True)
test = SiO2_market.calculate_market_vintage()
df = SiO2_market.to_dataframe(test)
SiO2_market.plot_market_vintage()
df.to_csv('../results/dynamic_results/SiO2_vintage_results_0226.csv')
def calculate_defult_Fe():
'''
Do a single vintage calculation
default in use Rate = 0.33
'''
# read data now
Fe_data = np.loadtxt('../data/Fe_production_real.csv', delimiter=',')
Fe_to_paints = 0.33 # what portion of SiO2 are used in coating, paints and pigment market
Fe_data[:, 1] = Fe_data[:, 1] * Fe_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
FeO2_market = vintage_model.vintage_market(Fe_data,
market_data_dict,
weibull=True)
test = FeO2_market.calculate_market_vintage()
df = FeO2_market.to_dataframe(test)
FeO2_market.plot_market_vintage()
df.to_csv('../results/dynamic_results/FeOx_vintage_results_0226.csv')
def get_results_for_one_vintage(year):
'''
calculate the results for one vintage year
'''
# read data now
TiO2_data = np.loadtxt('../data/TiO2_production_real.csv', delimiter=',')
TiO2_to_paints = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
TiO2_data[:, 1] = TiO2_data[:, 1] * TiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
TiO2_market = vintage_model.vintage_market(TiO2_data,
market_data_dict,
weibull=True)
this_year_vintage = TiO2_market.calculate_for_one_vintage(year=2016)
df = TiO2_market.to_dataframe(this_year_vintage)
df.to_csv('../results/dynamic_results/year_vintage/TiO2_vintage_2016.csv')
def calculate_defult_SiO2():
'''
Do a single vintage calculation
default market share = 0.1
'''
# read data now
SiO2_data = np.loadtxt('../data/SiO2_production_real.csv', delimiter=',')
SiO2_to_paints = 0.1 # what portion of SiO2 are used in coating, paints and pigment market
SiO2_data[:, 1] = SiO2_data[:, 1] * SiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
SiO2_market = vintage_model.vintage_market(SiO2_data,
market_data_dict,
weibull=True)
test = SiO2_market.calculate_market_vintage()
df = SiO2_market.to_dataframe(test)
SiO2_market.plot_market_vintage()
df.to_csv('../results/dynamic_results/SiO2_vintage_results_0226.csv')
def calculate_defult_TiO2():
'''
Do a single vintage calculation
default market share rate = 0.3
'''
# read data now
TiO2_data = np.loadtxt('../data/TiO2_production_real.csv',delimiter=',')
TiO2_to_paints = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
TiO2_data[:,1] = TiO2_data[:,1] * TiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
'''
deal with dictionary here
'''
# set up the model
TiO2_market = vintage_model.vintage_market(TiO2_data,market_data_dict, weibull=True)
test = TiO2_market.calculate_market_vintage()
# save the results to data frame and plot the market figure
df = TiO2_market.to_dataframe(test)
df.to_csv('../results/dynamic_results/TiO2_vintage_results_1215.csv')
# df.to_csv('../results/static_results/TiO2_vintage_results_static_1215.csv')
TiO2_market.plot_market_vintage()
def calculate_defult_TiO2():
'''
Do a single vintage calculation
default market share rate = 0.3
'''
# read data now
TiO2_data = np.loadtxt('./data/TiO2_production_real.csv',delimiter=',')
TiO2_to_paints = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
TiO2_data[:,1] = TiO2_data[:,1] * TiO2_to_paints
market_data_dict = csv_to_dict('./data/coating_market_fake.csv')
'''
deal with dictionary here
'''
# set up the model
TiO2_market = vintage_model.vintage_market(TiO2_data,market_data_dict)
test = TiO2_market.calculate_market_vintage()
print np.sum(test['Household & Furniture']['In Use'] + test['Household & Furniture']['End of Life'])
# save the results to data frame and plot the market figure
df = TiO2_market.to_dataframe(test)
df.to_csv('./results/TiO2_vintage_results.csv')
TiO2_market.plot_market_vintage()
def calculate_defult_TiO2():
'''
Do a single vintage calculation
default market share rate = 0.3
'''
# read data now
TiO2_data = np.loadtxt('../data/TiO2_production_real.csv', delimiter=',')
TiO2_to_paints = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
TiO2_data[:, 1] = TiO2_data[:, 1] * TiO2_to_paints
market_data_dict = csv_to_dict('../data/coating_market_fake.csv')
'''
deal with dictionary here
'''
# set up the model
TiO2_market = vintage_model.vintage_market(TiO2_data,
market_data_dict,
weibull=True)
test = TiO2_market.calculate_market_vintage()
# save the results to data frame and plot the market figure
df = TiO2_market.to_dataframe(test)
# df.to_csv('../results/dynamic_results/TiO2_vintage_results_0226.csv')
# df.to_csv('../results/static_results/TiO2_vintage_results_static_1215.csv')
TiO2_market.plot_market_vintage()
explode = (0, 0.1, 0, 0, 0, 0, 0) # only explode the second one
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
for each_key, each_val in vintage_results.iteritems():
this_tot_rel_year = each_val['In Use'][40] + each_val['End of Life'][40]
size.append(this_tot_rel_year)
plt.pie(size,
explode=explode,
labels=labels,
autopct='%1.1f%%',
colors=colors,
shadow=True)
plt.show()
if __name__ == '__main__':
''' Read production data and multiply it with the market share data to coating and paints market'''
tiO2_production_data = np.loadtxt('./data/TiO2_production_real.csv',
delimiter=',')
tiO2_to_coating = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
tiO2_production_data[:, 1] = tiO2_production_data[:, 1] * tiO2_to_coating
''' Set up and calcuate vintage'''
tiO2_market_data = './data/coating_market_fake.csv'
tiO2 = vintage_model.vintage_market(tiO2_production_data, tiO2_market_data)
tiO2_vintage_results_market = tiO2.calculate_market_vintage()
total_release = tiO2.tot_releases_year()
ax = plt.subplot(111)
ax.plot(tiO2_production_data[30::, 0], total_release[30::])
plt.show()
'''
plot a pie chart show the release break down (by market) for the specific year
'''
labels = vintage_results.keys()
size = []
explode = (0,0.1,0,0,0,0,0) # only explode the second one
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
for each_key, each_val in vintage_results.iteritems():
this_tot_rel_year = each_val['In Use'][40] + each_val['End of Life'][40]
size.append(this_tot_rel_year)
plt.pie(size,explode=explode,labels=labels,autopct='%1.1f%%',colors=colors,shadow=True)
plt.show()
if __name__ == '__main__':
''' Read production data and multiply it with the market share data to coating and paints market'''
tiO2_production_data = np.loadtxt('./data/TiO2_production_real.csv',delimiter=',')
tiO2_to_coating = 0.3 # what portion of SiO2 are used in coating, paints and pigment market
tiO2_production_data[:,1] = tiO2_production_data[:,1] * tiO2_to_coating
''' Set up and calcuate vintage'''
tiO2_market_data = './data/coating_market_fake.csv'
tiO2 = vintage_model.vintage_market(tiO2_production_data,tiO2_market_data)
tiO2_vintage_results_market = tiO2.calculate_market_vintage()
total_release = tiO2.tot_releases_year()
ax = plt.subplot(111)
ax.plot(tiO2_production_data[30::,0],total_release[30::])
plt.show()
