import streamlit as st
import funcs
import pandas as pd
import numpy as np
import plotly.express as px

def adjust_calculation(df2):
  
  colx1, colx2, colx3, colx4 = st.beta_columns((1,1,1,1))
  
  with colx1:
    st.markdown("Block Grade")
    df2.loc[0, 'Resource COG'] = 1000000
    df2.loc[1, 'Resource COG'] = st.slider("Cu %", 0., 2.0, 0.9, 0.1, key="cgsl2")
    df2.loc[2, 'Resource COG'] = st.slider("Au g/t", 0., 2.0, 0.7, 0.1, key="cgsl3")
    cug = df2.loc[1, 'Resource COG']
    aug = df2.loc[2, 'Resource COG']
  with colx2:
    st.markdown("Recoveries")
    df2.loc[4, 'Resource COG'] = st.slider("Cu Recovery", 60., 95., 87., 1.0, key="cgsl4")
    df2.loc[5, 'Resource COG'] = st.slider("Au Recover", 60., 95., 90., 1.0, key="cgsl5")
  with colx3:
    st.markdown("Metal Prices")
    df2.loc[16, 'Resource COG'] = st.slider("Cu Price ($/lbs)", 2.5, 10.0, 3.25, 0.1, key="cgsl6")
    df2.loc[17, 'Resource COG'] = float(st.slider("Au Price ($/oz)", 1000.0, 2000.0, 1500.0, 100.0, key="cgsl7"))
  with colx4:
    st.markdown("Other")
    df2.loc[10, 'Resource COG'] = st.slider("Cu Con Grade", 20.0, 35.0, 25.0, 1.0, key="cgsl8")
    df2.loc[13, 'Comments'] = st.slider("Payabe Cu", 85., 100., 90., 1.0, key="cgsl9")
    df2.loc[14, 'Comments'] = st.slider("Payable Au", 85., 100., 99., 1.0, key="cgsl10")
  
  df2.loc[7, 'Resource COG'] = np.round(df2.loc[0, 'Resource COG']*(df2.loc[1, 'Resource COG']/100.)*(df2.loc[4, 'Resource COG']/100.)*2.20462,0)
  df2.loc[8, 'Resource COG'] = np.round(df2.loc[0, 'Resource COG']*(df2.loc[2, 'Resource COG'])/31.1035*(df2.loc[5, 'Resource COG']/100.), 0)
  df2.loc[9, 'Resource COG'] = np.round(df2.loc[7, 'Resource COG']*1000./2204.62/(df2.loc[10, 'Resource COG']/100.), 0)
  df2.loc[11, 'Resource COG'] = np.round(df2.loc[8, 'Resource COG']*31.1035/df2.loc[9, 'Resource COG'],2)
  
  df2.loc[13, 'Resource COG'] = np.round(df2.loc[7, 'Resource COG']*(df2.loc[13, 'Comments'])/100.,0)
  df2.loc[14, 'Resource COG'] = np.round(df2.loc[8, 'Resource COG']*(df2.loc[14, 'Comments'])/100,0)
  
  df2.loc[18, 'Resource COG'] = np.round(df2.loc[13, 'Resource COG']*df2.loc[16, 'Resource COG'], 0)
  df2.loc[19, 'Resource COG'] = np.round(df2.loc[14, 'Resource COG']*float(df2.loc[17, 'Resource COG']/1000.), 0)
    
  df2.loc[20, 'Resource COG'] = df2.loc[18, 'Resource COG']+df2.loc[19, 'Resource COG']
                                         
  df2.loc[22, 'Resource COG'] = np.round(df2.loc[9, 'Resource COG']*9.0/1000, 0)
  df2.loc[23, 'Resource COG'] = np.round(df2.loc[13, 'Resource COG']*0.09,0)
  df2.loc[24, 'Resource COG'] = np.round(df2.loc[14, 'Resource COG']*5.0/1000, 0)
                                         
  df2.loc[25, 'Resource COG'] = np.round(df2.loc[20, 'Resource COG']-df2.loc[22, 'Resource COG']-df2.loc[23, 'Resource COG']-df2.loc[24, 'Resource COG'], 0)
                                         
  df2.loc[27, 'Resource COG'] = np.round((df2.loc[18, 'Resource COG']-df2.loc[22, 'Resource COG']-0.09*df2.loc[13, 'Resource COG']/1000.)/df2.loc[25, 'Resource COG'], 2)
  df2.loc[28, 'Resource COG'] = np.round((df2.loc[19, 'Resource COG']-df2.loc[23, 'Resource COG']-5.0*df2.loc[14, 'Resource COG']/1000.)/df2.loc[25, 'Resource COG'], 2)
                                         
  df2.loc[30, 'Resource COG'] = np.round((df2.loc[27, 'Resource COG']*df2.loc[25, 'Resource COG']*1000./(df2.loc[0, 'Resource COG']*df2.loc[1, 'Resource COG'])),0)
  df2.loc[31, 'Resource COG'] = np.round((df2.loc[28, 'Resource COG']*df2.loc[25, 'Resource COG']*1000./(df2.loc[0, 'Resource COG']*df2.loc[2, 'Resource COG'])),0)
  
  colz1, colz2, colz3 = st.beta_columns((1,1,1))
  
  metal = ['Cu', 'Au']
  grades = np.array([cug, aug])
  nsr_fact = np.array([df2.loc[30, 'Resource COG'], df2.loc[31, 'Resource COG']])
  blk_rev = grades*nsr_fact
  ddf = pd.DataFrame({'Metal':metal, 'Grade': grades, 'Revenue by Metal Unit': nsr_fact, 'Block Revenue per tonne':blk_rev})
  
  with colz1:
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.table(ddf)    
  with colz2:  
    fig = px.bar(ddf, x='Metal', y='Revenue by Metal Unit', color='Metal')
    fig.update_yaxes(range=(0,200))
    st.plotly_chart(fig,  use_container_width=True)
    
  with colz3:
    fig = px.bar(ddf, x='Metal', y='Block Revenue per tonne', color='Metal')
    fig.update_yaxes(range=(0,200))
    st.plotly_chart(fig,  use_container_width=True)   
           
  # st.table(df2)
  
def cut_off():
  
  df = pd.read_csv("data//Worksheet COGs.csv")
  df = df.fillna("")
  df = df[:32].copy()

  st.title("Cut-off Grade Exercise")
  st.write("")
  st.markdown("## Question 1: Do we send this block to the processing plant?")
  st.write("")
  
  col1, col2 = st.beta_columns([1, 2])
  
  with col1:
    st.image("images//cog1_block.jpg", width=300)
  with col2:
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.write("")
    st.markdown('### Operating costs:')
    st.markdown('* Mining: $50/t')
    st.markdown('* Process: $20/t')
    st.markdown('* G&A: $15/t')
  
  q1_options = ['yes', 'no']
  cog_q1_answer = st.radio("Do we send this block to the processing plant?", options=q1_options, key='cog_q1')
  
  st.write("")
  st.markdown("## Question 2: Complex Cut-off Calculation")
  st.write("")
  st.markdown("### Considering the block grades from Question 1, analyze the NSR calculation and answer the question below.")
  st.write("")
  col3, col4 = st.beta_columns([1, 2])
  with col3:
    text = funcs.get_text_block("cog_q2_intro.txt")
    st.markdown(text)
  with col4:
    st.image("images//nsr_table.png", width=500)
    st.write("*Note that the average grade of the deposit is not known until the cut-off is known. This is normally an early approximation and an iterative process. While it does not impact the calculations, once the average grade is known, a simple cash flow analysis can be performed.")
  q2_options = ['A. I have no idea', 
                'B. The block value is below all cut-off grades', 
                'C. The block value is greater than the marginal but less than the break-even cut-off', 
                'D. The block value exceeds all cut-off grades']
  cog_q2_answer = st.radio("Select the appropriate statement:", options=q2_options, key='cog_q2')
  st.write("")
  st.markdown("## Question 3: Sensitivities")
  st.write("")
  st.markdown("### By Adjusting the various input parameters given below, comment on the following:")
  st.text_area("1. What copper price results in a revenue which is double the break-even cut-off grade", height=5, key='tt1')
  st.text_area("2. What copper grade results in a revenue which is double the break-even cut-off grade", height=5, key='tt2')
  st.write("")
  adjust_calculation(df.copy())