from IPython.core.display import HTML
HTML(open("styles/custom.css", "r").read())

import pandas as pd

req = {
    'A' : [700, 10000],
    'C' : [700, 10000],
    'B1': [700, 10000],
    'B2': [700, 10000]}

display(pd.DataFrame(req.values(), index = req.keys(), columns = ['n_min', 'n_max']))

nutr = {
    'BEF' : {'A': 60, 'C': 20, 'B1': 10, 'B2': 15},
    'CHK' : {'A':  8, 'C':  0, 'B1': 20, 'B2': 20},
    'FSH' : {'A':  8, 'C': 10, 'B1': 15, 'B2': 10},
    'HAM' : {'A': 40, 'C': 40, 'B1': 35, 'B2': 10},
    'MCH' : {'A': 15, 'C': 35, 'B1': 15, 'B2': 15},
    'MTL' : {'A': 70, 'C': 30, 'B1': 15, 'B2': 15},
    'SPG' : {'A': 25, 'C': 50, 'B1': 25, 'B2': 15},
    'TUR' : {'A': 60, 'C': 20, 'B1': 15, 'B2': 10}}

display(pd.DataFrame.from_dict(nutr).transpose())

price ={'BEF' : ['Beef', 3.19],
        'CHK' : ['Chicken', 2.59],
        'FSH' : ['Fish', 2.29],
        'HAM' : ['Ham', 2.89],
        'MCH' : ['Macaroni & Cheese', 1.89],
        'MTL' : ['Meat Loaf', 1.99],
        'SPG' : ['Spaghetti', 1.99],
        'TUR' : ['Turkey', 2.49]}

display(pd.DataFrame(price.values(), index = price.keys(), columns = ['food','price']))

%%script glpsol -m /dev/stdin -o /dev/stdout

var xBEF >= 0; 
var xCHK >= 0; 
var xFSH >= 0;
var xHAM >= 0; 
var xMCH >= 0; 
var xMTL >= 0;
var xSPG >= 0; 
var xTUR >= 0;

minimize Total_Cost:
    3.19*xBEF + 2.59*xCHK + 2.29*xFSH + 2.89*xHAM 
  + 1.89*xMCH + 1.99*xMTL + 1.99*xSPG + 2.49*xTUR;
    
subject to A:
    700 <= 60*xBEF +  8*xCHK +  8*xFSH + 40*xHAM 
         + 15*xMCH + 70*xMTL + 25*xSPG + 60*xTUR <= 10000;
    
subject to C:
    700 <= 20*xBEF +  0*xCHK + 10*xFSH + 40*xHAM
         + 35*xMCH + 30*xMTL + 50*xSPG + 20*xTUR <= 10000;
    
subject to B1:
    700 <= 10*xBEF + 20*xCHK + 15*xFSH + 35*xHAM
         + 15*xMCH + 15*xMTL + 25*xSPG + 15*xTUR <= 10000;
    
subject to B2:
    700 <= 15*xBEF + 20*xCHK + 10*xFSH + 10*xHAM
         + 15*xMCH + 15*xMTL + 15*xSPG + 10*xTUR <= 10000;

end;

%%script glpsol -m /dev/stdin -o /dev/stdout --out output

set FOODS := {'BEF','CHK','FSH','HAM','MCH','MTL','SPG','TUR'};

var x{FOODS} >= 0;

minimize Total_Cost:
    3.19*x['BEF'] + 2.59*x['CHK'] + 2.29*x['FSH'] + 2.89*x['HAM'] 
  + 1.89*x['MCH'] + 1.99*x['MTL'] + 1.99*x['SPG'] + 2.49*x['TUR'];
    
subject to A:
    700 <= 60*x['BEF'] +  8*x['CHK'] +  8*x['FSH'] + 40*x['HAM']
         + 15*x['MCH'] + 70*x['MTL'] + 25*x['SPG'] + 60*x['TUR'] <= 10000;
    
subject to C:
    700 <= 20*x['BEF'] +  0*x['CHK'] + 10*x['FSH'] + 40*x['HAM']
         + 35*x['MCH'] + 30*x['MTL'] + 50*x['SPG'] + 20*x['TUR'] <= 10000;
    
subject to B1:
    700 <= 10*x['BEF'] + 20*x['CHK'] + 15*x['FSH'] + 35*x['HAM']
         + 15*x['MCH'] + 15*x['MTL'] + 25*x['SPG'] + 15*x['TUR'] <= 10000;
    
subject to B2:
    700 <= 15*x['BEF'] + 20*x['CHK'] + 10*x['FSH'] + 10*x['HAM']
         + 15*x['MCH'] + 15*x['MTL'] + 15*x['SPG'] + 10*x['TUR'] <= 10000;

end;

%%script glpsol -m /dev/stdin -o /dev/stdout --out output

set FOODS;
set NUTRS;

param price{FOODS} >= 0;
param n_min{NUTRS} >= 0;
param n_max{NUTRS} >= 0;

var x{FOODS} >= 0;

minimize Total_Cost: sum {f in FOODS} price[f]*x[f];
    
subject to A:
    n_min['A'] <= 60*x['BEF'] +  8*x['CHK'] +  8*x['FSH'] + 40*x['HAM']
                + 15*x['MCH'] + 70*x['MTL'] + 25*x['SPG'] + 60*x['TUR'] <= n_max['A'];
    
subject to C:
    n_min['C'] <= 20*x['BEF'] +  0*x['CHK'] + 10*x['FSH'] + 40*x['HAM']
                + 35*x['MCH'] + 30*x['MTL'] + 50*x['SPG'] + 20*x['TUR'] <= n_max['C'];
    
subject to B1:
    n_min['B1'] <= 10*x['BEF'] + 20*x['CHK'] + 15*x['FSH'] + 35*x['HAM']
                 + 15*x['MCH'] + 15*x['MTL'] + 25*x['SPG'] + 15*x['TUR'] <= n_max['B1'];
    
subject to B2:
    n_min['B2'] <= 15*x['BEF'] + 20*x['CHK'] + 10*x['FSH'] + 10*x['HAM']
                 + 15*x['MCH'] + 15*x['MTL'] + 15*x['SPG'] + 10*x['TUR'] <= n_max['B2'];
        
data;

param : FOODS : price :=
    BEF  3.19
    CHK  2.59
    FSH  2.29
    HAM  2.89
    MCH  1.89
    MTL  1.99
    SPG  1.99
    TUR  2.49;

param : NUTRS : n_min n_max :=
    A    700   10000
    C    700   10000
    B1   700   10000
    B2   700   10000;
    
end;

%%script glpsol -m /dev/stdin -o /dev/stdout --out output

set FOODS;
set NUTRS;

param price{FOODS} >= 0;
param n_min{NUTRS} >= 0;
param n_max{NUTRS} >= 0;
param a{FOODS,NUTRS} >= 0;

var x{FOODS} >= 0;

minimize Total_Cost: sum {f in FOODS} price[f]*x[f];

subject to n_req {n in NUTRS}:
    n_min[n] <= sum {f in FOODS} a[f,n] * x[f] <= n_max[n];

data;

param : FOODS : price :=
    BEF  3.19
    CHK  2.59
    FSH  2.29
    HAM  2.89
    MCH  1.89
    MTL  1.99
    SPG  1.99
    TUR  2.49;

param : NUTRS : n_min n_max :=
    A    700   10000
    C    700   10000
    B1   700   10000
    B2   700   10000;

param a :  A   C  B1  B2 :=
    BEF   60  20  10  15
    CHK    8   0  20  20
    FSH    8  10  15  10
    HAM   40  40  35  10
    MCH   15  35  15  15
    MTL   70  30  15  15
    SPG   25  50  25  15
    TUR   60  20  15  10;
    
end;

%%script glpsol -m /dev/stdin -o output.txt -y display.txt --out output

set FOODS;
set NUTRS;

param price{FOODS} >= 0;
param x_min{FOODS} >= 0;
param x_max{FOODS} >= 0;

param n_min{NUTRS} >= 0;
param n_max{NUTRS} >= 0;

param a{FOODS,NUTRS} >= 0;

var x{f in FOODS} >= 0;

minimize Total_Cost: sum {f in FOODS} price[f]*x[f];

subject to n_req {n in NUTRS}:
    n_min[n] <= sum {f in FOODS} a[f,n] * x[f] <= n_max[n];

solve;

printf "\nMinimum Cost Diet = %6.2f $/week.\n\n", Total_Cost;

table results {f in FOODS} OUT "CSV" "output.csv" "Table":
    f ~ Food,
    x[f] ~ Quantity,
    price[f] ~ Price;
    
data;

param : FOODS : price :=
    BEF  3.19
    CHK  2.59
    FSH  2.29
    HAM  2.89
    MCH  1.89
    MTL  1.99
    SPG  1.99
    TUR  2.49;

param : NUTRS : n_min n_max :=
    A    700   10000
    C    700   10000
    B1   700   10000
    B2   700   10000;

param a :  A   C  B1  B2 :=
    BEF   60  20  10  15
    CHK    8   0  20  20
    FSH    8  10  15  10
    HAM   40  40  35  10
    MCH   15  35  15  15
    MTL   70  30  15  15
    SPG   25  50  25  15
    TUR   60  20  15  10;
    
end;

print output

print(open('output.txt').read())

print(open('display.txt').read())

import pandas
df = pandas.read_csv('output.csv')
display(df)
df['Quantity'].plot(kind='bar').set_xticklabels(df['Food']);

%%script glpsol -m /dev/stdin -o display.txt --out output

set FOODS;
set NUTRS;

param price{FOODS} >= 0;
param x_min{FOODS} >= 0;
param x_max{FOODS} >= 0;

param n_min{NUTRS} >= 0;
param n_max{NUTRS} >= 0;

param a{FOODS,NUTRS} >= 0;

var x{f in FOODS} integer >= x_min[f], <= x_max[f];

minimize Total_Cost: sum {f in FOODS} price[f]*x[f];

subject to n_req {n in NUTRS}:
    n_min[n] <= sum {f in FOODS} a[f,n] * x[f] <= n_max[n];
    
solve;

printf "\nMinimum Cost Diet = %6.2f $/week.\n\n", Total_Cost;

table results {f in FOODS} OUT "CSV" "output.csv" "Table":
    f ~ Food,
    x[f] ~ Quantity,
    price[f] ~ Price;

data;

param : FOODS : price x_min x_max :=
    BEF  3.19  2  30
    CHK  2.59  2  30
    FSH  2.29  2  30
    HAM  2.89  2  30
    MCH  1.89  2  30
    MTL  1.99  2  30
    SPG  1.99  2  30
    TUR  2.49  2  30;

param : NUTRS : n_min n_max :=
    A     700   10000 
    C     700   10000
    B1    700   10000
    B2    700   10000
    NA      0   40000
    CAL 16000   24000;

param a :  A   C  B1  B2    NA  CAL :=
    BEF   60  20  10  15   938  295
    CHK    8   0  20  20  2180  770
    FSH    8  10  15  10   945  440
    HAM   40  40  35  10   278  430
    MCH   15  35  15  15  1182  315
    MTL   70  30  15  15   896  400
    SPG   25  50  25  15  1329  370
    TUR   60  20  15  10  1397  450;
    
end;

print output

print(open('display.txt').read())

import pandas
df = pandas.read_csv('output.csv')
display(df)
df['Quantity'].plot(kind='bar').set_xticklabels(df['Food']);