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cffile2023_utils.py
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cffile2023_utils.py
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import sys
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
import pandas as pd
import cryptpandas as crp
class EBITDA:
def __init__(self, pwd, map_loc):
# Read table data
self.read_table(pwd, map_loc)
# Standard input parameters
self.inputs = {}
self.inputs['yr_prod_vol'] = 0 # Tons per year
self.inputs['nb_yr_prod'] = 0 # Nb. of years in production
self.inputs['price_factor'] = 1 # Rescale prices of elements by a factor
self.inputs['conc_factor'] = 1 # Rescale concentrations of elements by a factor
self.inputs['Penalty'] = 0 # [USD/ton] cost for unwanted metals, type in 0 to 100
self.inputs['RefRecovery'] = 0 # typically 0.85
self.inputs['ProsEx'] = 0 # [USD/ton] cost of processing, low 10 USD/ton
self.inputs['Processing'] = 0 # up-concentration before refining, will reduce refinement cost.
self.inputs['RefCost'] = 0 # [USD/Ton](variable, default 70 USD/ton)
self.inputs['CapEx'] = 0 # [USD] (type in, default 1013 mUSD)
self.inputs['ExpEnvEx'] = 0 # [USD] (type in, default 105 mUSD)
self.inputs['OpEx_fix'] = 0 # [USD/yr] (default 200 mUSD)
self.inputs['OpEx_var'] = 0 # [USD/ton/yr] variable with ore production
self.inputs['Include_CO2_Trapping'] = False,
self.inputs['Ultramafic_fraction'] = 0.25 # Fraction of ultramafic in rock tailing
self.inputs['Ultramafic_reactive'] = 0.2 # Fraction reactive to carbonation (CO2 trapping)
self.inputs['CO2_price'] = 100 # Price of stored CO2 [USD/ton]
self.inputs['Magnesite_price'] = 500 # Price of magnesite [USD/ton]
self.inputs['Magnesite_factor'] = 1 # rescale Magnesite production by a factor
self.inputs['selected_location'] = 7 # select number for "Data locations" above
# Select all elements as default
self.selected_elements()
def read_table(self, pwd, map_loc):
# Elements
list_e = ( ['H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne', 'Na', 'Mg', 'Al', 'Si','P',
'S', 'Cl', 'Ar', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn',
'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru',
'Rh', 'Pd', 'Ag', 'Cd', 'In', 'Sn', 'Sb', 'Te', 'I', 'Xe', 'Cs', 'Ba', 'La', 'Ce',
'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf',
'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi', 'Po', 'At', 'Rn',
'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', 'Cf', 'Es', 'Fm',
'Md', 'No', 'Lr', 'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt', 'Ds', 'Rg', 'Cn', 'Uut',
'Fl', 'Uup', 'Lv', 'Uus', 'Uuo'] )
list_n = ( list(range(1, 16)) + list(range(16, 31)) + list(range(31, 45))
+ list(range(45, 59)) + list(range(59, 73)) + list(range(73, 87))
+ list(range(87, 101)) + list(range(101, 114)) + list(range(114, 119)) )
map_n2e = dict(zip(list_n,list_e))
# map on locations
self.map_loc = map_loc
# read file
self.tbl_all = crp.read_encrypted(path='Ffile2023/file', password=pwd)
# change column names
self.tbl_all = self.tbl_all.rename( columns={"a": "element", "b": "concentration [wt]", "c": "value [USD/m2]", "d": "value [USD/Ton]", "e": "location"} )
# Update elements
elems_ = [map_n2e[ie] for ie in self.tbl_all['element'].values]
self.tbl_all['element'] = elems_
# output the different locations
print('Data locations: ')
for i, k in self.map_loc.items():
print( i, ':', k , ' ')
def update(self,inputs_dict):
self.inputs.update(inputs_dict)
if self.inputs['selected_location']:
if 'selected_elements' in inputs_dict.keys():
len_check = sum(self.tbl['element'].isin(self.inputs['selected_elements']))
if len_check<len(self.inputs['selected_elements']):
print('Selected elements not (all) in dataset! Please, select from the list below:')
self.selected_elements()
self.print_elements()
sys.exit()
else:
self.selected_elements()
self.print_elements()
def selected_elements(self):
self.tbl = self.tbl_all[self.tbl_all['location']==self.inputs['selected_location']]
self.inputs['selected_elements'] = list(self.tbl['element'].unique())
def print_elements(self):
all_elements = list(self.tbl['element'].unique())
loc_ = self.inputs['selected_location']
print('Dataset',loc_)
print('All elements in ', self.map_loc[loc_],':\n',all_elements) #self.inputs['selected_elements'])
def profit(self):
self.Profit = ( self.Net_ore_value_sum
- self.Penalty_sum
- self.RefLoss_sum
- self.RefCost_sum
- self.ProsEx_sum
- self.inputs['CapEx']
- self.inputs['ExpEnvEx']
- self.OpEx_sum )
if self.inputs['Include_CO2_Trapping']==True:
self.Profit += self.CO2_value_sum + self.Magnesite_value_sum
def return_on_investment(self):
return_ = ( self.Net_ore_value_sum
- self.Penalty_sum
- self.RefLoss_sum
- self.RefCost_sum
- self.ProsEx_sum
- self.OpEx_sum )
investment_ = self.inputs['CapEx'] + self.inputs['ExpEnvEx']
self.Return_on_Inv = (return_ - investment_)/investment_
def variable_sums(self, nb_yr_prod, ore_val=''):
# value of selected elements
column_name = [icol for icol in self.tbl.columns if '[USD/Ton]' in icol][0]
# sanity check if all the selected elements are present a the location
#self.selected_elements()
len_check = sum(self.tbl['element'].isin(self.inputs['selected_elements']))
if len_check<len(self.inputs['selected_elements']):
print('Selected elements not (all) in dataset!')
self.selected_elements()
self.print_elements()
exit()
else:
self.selected_elements_USD_Ton = np.array([ self.tbl[self.tbl['element']==ie][column_name].values[0] for ie in self.inputs['selected_elements'] ])
# Compute variables
self.total_vol = self.inputs['yr_prod_vol'] * nb_yr_prod
# Element value can be increase/decrease by "price_factor" and "conc_factor"
self.Elements_value = ( self.selected_elements_USD_Ton * self.total_vol
* self.inputs['price_factor'] * self.inputs['conc_factor'] )
if ore_val:
self.Net_ore_value_sum = ore_val * self.total_vol
else:
self.Net_ore_value_sum = np.sum(self.Elements_value)
self.Penalty_sum = (self.inputs['Penalty'] * self.total_vol)
self.RefLoss_sum = (self.Net_ore_value_sum-self.Penalty_sum) * (1-self.inputs['RefRecovery'])
self.RefCost_sum = self.inputs['Processing'] * (self.inputs['RefCost']*self.total_vol)
self.ProsEx_sum = self.inputs['ProsEx'] * self.total_vol
self.OpEx_sum = ( self.inputs['OpEx_fix'] + self.inputs['OpEx_var']*self.inputs['yr_prod_vol'] ) * nb_yr_prod
def CO2_capture(self):
CO2_prod = ( ( self.inputs['yr_prod_vol']*self.inputs['nb_yr_prod'])
*self.inputs['Ultramafic_fraction']
*self.inputs['Ultramafic_reactive'] )
self.CO2_value_sum = CO2_prod * self.inputs['CO2_price'] * self.inputs['price_factor']
weight_CO2_to_Magnesite = 2
reaction_conversion_CO2_to_Magnesite = 1/2
self.Magnesite_value_sum = ( CO2_prod * reaction_conversion_CO2_to_Magnesite * weight_CO2_to_Magnesite
* self.inputs['Magnesite_factor']
* self.inputs['Magnesite_price']
* self.inputs['price_factor'] )
def waterfall(self):
# Call functions
#self.selected_elements()
self.variable_sums(nb_yr_prod = self.inputs['nb_yr_prod'])
if self.inputs['Include_CO2_Trapping']==True:
# Run CO2 capture and magnesite production
self.CO2_capture()
self.profit()
# Make waterfall plotly plot
measure = list(np.repeat('relative', len(self.inputs['selected_elements']) + 9)) + ['total']
x = ( self.inputs['selected_elements']
+ ['Penalty', 'RefLoss', 'RefCost', 'ProsEx', 'CapEx', 'ExpEnvEx', 'OpEx']
+ ['CO2 seq.', 'Magnesite']
+ ['Profit'])
y = ( list(self.Elements_value)
+ list([-self.Penalty_sum, -self.RefLoss_sum, -self.RefCost_sum, -self.ProsEx_sum,
-self.inputs['CapEx'], -self.inputs['ExpEnvEx'], -self.OpEx_sum] )
+ list([self.CO2_value_sum, self.Magnesite_value_sum] )
+ list([self.Profit]) )
else:
self.profit()
# Make waterfall plotly plot
measure = list(np.repeat('relative', len(self.inputs['selected_elements']) + 7)) + ['total']
x = ( self.inputs['selected_elements']
+ ['Penalty', 'RefLoss', 'RefCost', 'ProsEx', 'CapEx', 'ExpEnvEx', 'OpEx', 'Profit'])
y = ( list(self.Elements_value)
+ list([-self.Penalty_sum, -self.RefLoss_sum, -self.RefCost_sum, -self.ProsEx_sum,
-self.inputs['CapEx'], -self.inputs['ExpEnvEx'], -self.OpEx_sum, self.Profit]) )
y = np.array(y)/1e6 # Plot in mUSD
color_profit = "#2E8B57" # green if positive
if self.Profit<0:
color_profit = "#FF2400" # red if negative
fig = go.Figure(go.Waterfall(
measure = measure,
x = x,
textposition = 'auto' , #"outside",
text = np.round(np.array(y)),
y = y,
width=1+0*y,
increasing = {"marker":{"color":"#4682B4"}},
decreasing = {"marker":{"color":"#e67300"}},
totals = {"marker":{"color":color_profit}},
))
fig.update_layout(
title = ( '<b>'+self.map_loc[self.inputs['selected_location']]+'</b>'
+'<br> Production of '+str(self.inputs['yr_prod_vol']/1e6)+' mTon/yr during '+str(self.inputs['nb_yr_prod'])+ ' yrs' ),
yaxis_title="<b>mUSD<b>",
showlegend = False,
plot_bgcolor='white'
)
fig.update_xaxes(
mirror=True,
ticks='outside',
showline=True,
linecolor='black',
gridcolor='lightgrey'
)
fig.update_yaxes(
mirror=True,
ticks='outside',
showline=True,
linecolor='black',
gridcolor='lightgrey',
zerolinecolor='black',
zerolinewidth=2
)
fig.show()
# save fig object to save later
self.fig = fig
def profit_over_time(self, low_yr=1, high_yr=10):
Nb_yr_prod = np.linspace(low_yr, high_yr, 100) # Nb. of years in production
Profits = np.zeros_like(Nb_yr_prod)
for i, nb_yr_prod in enumerate(Nb_yr_prod):
self.variable_sums(nb_yr_prod = nb_yr_prod)
if self.inputs['Include_CO2_Trapping']==True:
self.CO2_capture()
self.profit()
else:
self.profit()
Profits[i] = self.Profit
fig = go.Figure()
fig.add_trace(go.Scatter(x=Nb_yr_prod, y=Profits, name='Profit'))
fig.update_layout(
title = ( '<b>'+self.map_loc[self.inputs['selected_location']]+'</b>'
+'<br> Production of '+str(self.inputs['yr_prod_vol']/1e6)+' mTon/yr during '+str(high_yr)+ ' yrs' ),
xaxis_title="<b>Number of years<b>",
yaxis_title="<b>Profit [USD]<b>",
showlegend = False,
plot_bgcolor='white'
)
fig.update_xaxes(
mirror=True,
ticks='outside',
showline=True,
linecolor='black',
gridcolor='lightgrey'
)
fig.update_yaxes(
mirror=True,
ticks='outside',
showline=True,
linecolor='black',
gridcolor='lightgrey',
zerolinecolor='black',
zerolinewidth=2
)
fig.show()
# save fig object to save later
self.fig = fig
def return_on_investment_over_time(self, low_yr=1, high_yr=10):
Nb_yr_prod = np.linspace(low_yr, high_yr, 100) # Nb. of years in production
RonI = np.zeros_like(Nb_yr_prod)
for i, nb_yr_prod in enumerate(Nb_yr_prod):
self.variable_sums(nb_yr_prod = nb_yr_prod)
self.return_on_investment()
RonI[i] = self.Return_on_Inv
fig = go.Figure()
fig.add_trace(go.Scatter(x=Nb_yr_prod, y=RonI, name='Profit'))
fig.update_layout(
title = ( '<b>'+self.map_loc[self.inputs['selected_location']]+'</b>'
+'<br> Production of '+str(self.inputs['yr_prod_vol']/1e6)+' mTon/yr during '+str(high_yr)+ ' yrs' ),
xaxis_title="<b>Number of years<b>",
yaxis_title="<b>Return on investment<b>",
showlegend = False,
plot_bgcolor='white'
)
fig.update_xaxes(
mirror=True,
ticks='outside',
showline=True,
linecolor='black',
gridcolor='lightgrey'
)
fig.update_yaxes(
mirror=True,
ticks='outside',
showline=True,
linecolor='black',
gridcolor='lightgrey',
zerolinecolor='black',
zerolinewidth=2
)
fig.show()
# save fig object to save later
self.fig = fig
def BarPlot_Value_Systems(self, Selection_elements=[]):
import plotly.express as px
df = pd.DataFrame()
for i in range(1,8):
loc_ = self.map_loc[i].split("-")[0] +'<br>'+ self.map_loc[i].split("-")[1]
tbl_ = self.tbl_all[self.tbl_all['location']==i][['value [USD/Ton]', 'element']]
tbl_ = tbl_[tbl_['element'].isin(Selection_elements[i])]
tbl_['location_name'] = '<b>'+loc_+'<b>'
df = pd.concat([df, tbl_])
fig = go.Figure()
for i, iel in enumerate(df['element'].unique()):
fig.add_trace(go.Bar(x=df[df['element']==iel]['location_name'],
y=df[df['element']==iel]['value [USD/Ton]'],
name=iel))
fig.update_layout(barmode='stack',
yaxis_title="<b>Estimated value [USD/ton<b>]",)
fig.show()
# save fig object to save later
self.fig = fig
def Profit_RonI_xNbYear_yOreVal(self, low_yr=1, high_yr=10, low_ore_val=100, high_ore_val=700, save_pngs=''):
Nb_yr_prod = np.linspace(low_yr, high_yr, 20) # Nb. of years in production
Ore_val = np.linspace(low_ore_val, high_ore_val, 20) # Ore value
RonI = np.zeros( (len(Nb_yr_prod), len(Ore_val)) )
Pro = np.zeros( (len(Nb_yr_prod), len(Ore_val)) )
for i, nb_yr_prod in enumerate(Nb_yr_prod):
for j, ore_val in enumerate(Ore_val):
self.variable_sums(nb_yr_prod = nb_yr_prod, ore_val=ore_val)
self.return_on_investment()
RonI[i,j] = self.Return_on_Inv
if self.inputs['Include_CO2_Trapping']==True:
self.CO2_capture()
self.profit()
else:
self.profit()
Pro[i,j] = self.Profit
fig = go.Figure(data = go.Contour(x=Nb_yr_prod, y=Ore_val, z=Pro.transpose(),
contours=dict(start=0,end=int(Pro.max()),size=round(Pro.max()/1e9/15)*1e9, showlabels = True),
colorbar=dict(title='[USD]'),colorscale='greens'))
#fig.add_contour(x=Nb_yr_prod, y=Ore_val, z=Pro.transpose(), contours_coloring='lines',
# contours=dict(start=int(Pro.min()/1e9)*1e9,end=0,size=1e9, showlabels = True),
# line_width=2,showscale=False)
fig.add_contour(x=Nb_yr_prod, y=Ore_val, z=Pro.transpose(), contours_coloring='lines',
line_width=2,colorscale='Electric',contours=dict(start=0,
end=int(Pro.max())+1,
size=int(Pro.max())+1,
showlabels = True),
showscale=False)
fig.update_layout(title = ( '<b>Profit </b><br>'+'assuming a production of '
+ str(self.inputs['yr_prod_vol']/1e6) + ' mTonOre/yr' ),
xaxis_title="<b>Number of year [yr]<b>",
yaxis_title="<b>Ore Value [USD/ton]<b>",
showlegend = False, plot_bgcolor='white')
fig.update_xaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey' )
fig.update_yaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey',
zerolinecolor='black',zerolinewidth=2)
fig.show()
# save fig object to png
if save_pngs:
self.fig = fig
self.savefig_png(save_pngs+'_Profit_'+str(self.inputs['yr_prod_vol']/1e6) + 'mTonOreYr', width=550, height=500)
fig = go.Figure(data = go.Contour(x=Nb_yr_prod, y=Ore_val, z=RonI.transpose(),
contours=dict(start=0,end=int(RonI.max()),size=int(RonI.max()/15),showlabels = True),
colorbar=dict(title='[factor]'),colorscale='greens'))
#fig.add_contour(x=Nb_yr_prod, y=Ore_val, z=RonI.transpose(), contours_coloring='lines', line=dict(color='red'),
# contours=dict(start=int(RonI.min()),end=0,size=1, showlabels = True),
# line_width=2,showscale=False)
fig.add_contour(x=Nb_yr_prod, y=Ore_val, z=RonI.transpose(), contours_coloring='lines',
line_width=2,colorscale='Electric',contours=dict(start=0,
end=int(RonI.max())+1,
size=int(RonI.max())+1,
showlabels = True),
showscale=False)
fig.update_layout(title = ( '<b>Return on investment </b><br>'+'assuming a production of '
+ str(self.inputs['yr_prod_vol']/1e6)+ ' mTonOre/yr' ),
xaxis_title="<b>Number of year [yr]<b>",
yaxis_title="<b>Ore Value [USD/ton]<b>",
showlegend = False, plot_bgcolor='white')
fig.update_xaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey' )
fig.update_yaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey',
zerolinecolor='black',zerolinewidth=2)
fig.show()
# save fig object to png
if save_pngs:
self.fig = fig
self.savefig_png(save_pngs+'_RonI_'+str(self.inputs['yr_prod_vol']/1e6) + 'mTonOreYr', width=550, height=500)
return Pro, RonI
def Profit_RonI_xTonYr_yOreVal(self, low_t_yr=1, high_t_yr=10, low_ore_val=100, high_ore_val=700, save_pngs=''):
Ton_yr_prod = np.linspace(low_t_yr, high_t_yr, 20) # Nb. of years in production
Ore_val = np.linspace(low_ore_val, high_ore_val, 20) # Ore value
RonI = np.zeros( (len(Ton_yr_prod), len(Ore_val)) )
Pro = np.zeros( (len(Ton_yr_prod), len(Ore_val)) )
for i, ton_yr_prod in enumerate(Ton_yr_prod):
for j, ore_val in enumerate(Ore_val):
self.inputs['yr_prod_vol'] = ton_yr_prod
self.variable_sums(nb_yr_prod = self.inputs['nb_yr_prod'], ore_val=ore_val)
self.return_on_investment()
RonI[i,j] = self.Return_on_Inv
if self.inputs['Include_CO2_Trapping']==True:
self.CO2_capture()
self.profit()
else:
self.profit()
Pro[i,j] = self.Profit
fig = go.Figure(data = go.Contour(x=Ton_yr_prod, y=Ore_val, z=Pro.transpose(),
contours=dict(start=0,end=int(Pro.max()),size=round(Pro.max()/1e9/15)*1e9,showlabels = True),
colorbar=dict(title='[USD]'),colorscale='greens'))
#fig.add_contour(x=Ton_yr_prod, y=Ore_val, z=Pro.transpose(), contours_coloring='lines',
# contours=dict(start=Pro.min(),end=0,size=1e9, showlabels = True),
# line_width=2,showscale=False)
fig.add_contour(x=Ton_yr_prod, y=Ore_val, z=Pro.transpose(), contours_coloring='lines',
line_width=2,colorscale='Electric',contours=dict(start=0,
end=int(Pro.max())+1,
size=int(Pro.max())+1,
showlabels = True),
showscale=False)
fig.update_layout(title = ( '<b>Profit </b><br>'+'after '+str(self.inputs['nb_yr_prod'])+ ' yrs' ),
xaxis_title="<b>Production [TonOre/yr]<b>",
yaxis_title="<b>Ore Value [USD/ton]<b>",
showlegend = False, plot_bgcolor='white')
fig.update_xaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey' )
fig.update_yaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey',
zerolinecolor='black',zerolinewidth=2)
fig.show()
# save fig object to png
if save_pngs:
self.fig = fig
self.savefig_png(save_pngs+'_Profit_'+str(self.inputs['nb_yr_prod'])+'yrs', width=550, height=500)
fig = go.Figure(data = go.Contour(x=Ton_yr_prod, y=Ore_val, z=RonI.transpose(),
contours=dict(start=0,end=int(RonI.max()),size=int(RonI.max()/15),showlabels = True),
colorbar=dict(title='[factor]'),colorscale='greens'))
#fig.add_contour(x=Ton_yr_prod, y=Ore_val, z=RonI.transpose(), contours_coloring='lines',
# contours=dict(start=RonI.min(),end=0,size=1, showlabels = True),
# line_width=2,showscale=False)
fig.add_contour(x=Ton_yr_prod, y=Ore_val, z=RonI.transpose(), contours_coloring='lines',
line_width=2,colorscale='Electric',contours=dict(start=0,
end=int(RonI.max())+1,
size=int(RonI.max())+1,
showlabels = True),
showscale=False)
fig.update_layout(title = ( '<b>Return on investment</b><br>'+'after '+str(self.inputs['nb_yr_prod'])+ ' yrs' ),
xaxis_title="<b>Production [TonOre/yr]<b>",
yaxis_title="<b>Ore Value [USD/ton]<b>",
showlegend = False, plot_bgcolor='white')
fig.update_xaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey' )
fig.update_yaxes(mirror=True,ticks='outside',showline=True,linecolor='black',gridcolor='lightgrey',
zerolinecolor='black',zerolinewidth=2)
fig.show()
# save fig object to png
if save_pngs:
self.fig = fig
self.savefig_png(save_pngs+'_RonI_'+str(self.inputs['nb_yr_prod'])+'yrs', width=550, height=500)
return Pro, RonI
def savefig_html(self, file_name):
self.fig.write_html(file_name+'.html')
def savefig_png(self, file_name, width=900, height=600):
pio.write_image(self.fig, file_name+'.png',scale=4, width=width, height=height)