#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np  # this module handles arrays, but here we need it for its NaN value
import pandas as pd # this module contains a lot of tools for handling tabular data
from matplotlib import pyplot as plt

get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


# define paths to the source files and eventual output file
pathBottle='/ocean/eolson/MEOPAR/obs/NemcekHPLC//All 2015 SoG_bottle.xlsx'
pathPhyto='/ocean/eolson/MEOPAR/obs/NemcekHPLC/2015-2018 Abs phyto groupsCorrected.xlsx'
pathOut='/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2015.csv'


# In[3]:


# load each sheet in the 2015 bottle Excel file and concatenate them together into one table
dfbotlist=list()
for sheet in ('2015-17','2015-20','2015-18','2015-21','2015-19'):
    df0=pd.read_excel(pathBottle,sheet_name=sheet,verbose=True,na_values=(-99,-99.9)) # read each sheet
    df0['Cruise']=sheet  # create and populate Cruise column based on sheet name
    dfbotlist.append(df0) # append the sheet to a list
dfbot=pd.concat(dfbotlist,ignore_index=True,sort=False) # concatenate the list into a single table
# Drop columns with no data in them
l1=set(dfbot.keys())
dfbot.dropna(axis=1,how='all',inplace=True)
print('removed empty columns:',l1-set(dfbot.keys()))


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# list the column names in the resulting table
print(dfbot.keys())


# In[5]:


def subval(idf,colList):
    # first value in colList should be the column you are going to keep
    # follow with other columns that will be used to fill in when that column is NaN
    # in order of precedence
    if len(colList)==2:
        idf[colList[0]]=[r[colList[0]] if not pd.isna(r[colList[0]]) \
                         else  r[colList[1]] for i,r in idf.iterrows()]
    elif len(colList)==3:
        idf[colList[0]]=[r[colList[0]] if not pd.isna(r[colList[0]]) \
                         else  r[colList[1]] if not pd.isna(r[colList[1]]) \
                         else r[colList[2]] for i,r in idf.iterrows()]
    else:
        raise NotImplementedError('Add to code to handle this case')
    idf.drop(columns=list(colList[1:]),inplace=True)
    return idf


# In[6]:


dfbot=subval(dfbot,('Oxygen:Dissolved','Oxygen:Dissolved.1'))
dfbot=subval(dfbot,('Temperature:Primary','Temperature:Secondary'))
dfbot=subval(dfbot,('Salinity:T0:C0','Salinity:T1:C1'))
dfbot=subval(dfbot,('Oxygen:Dissolved:SBE','Oxygen:Dissolved:SBE.1'))
dfbot.rename(columns={'LOC:LATITUDE':'Lat',
                      'LOC:LONGITUDE':'Lon',
                      'Temperature:Primary':'Temperature',
                      'Oxygen:Dissolved:SBE':'Oxygen:Dissolved:CTD',
                      'Salinity:T0:C0':'Salinity',},inplace=True)


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# In[7]:


# define a function that will be applied to the values in the index column;
# this makes it easier to drop non-data rows later
def convertIndex(val):
    try:
        x =int(val)
    except ValueError:
        x=np.nan
    return x


# In[8]:


# load the 2015 phytoplankton data with the following options:
#   sheet_name='2015 CHEMTAX abs results'  -> choose the 2015 sheet
#   usecols='A:I,T:AC'   -> read only columns A:I and T:AC from the Excel sheet
#   skiprows=2     -> start reading at the 3rd row of the sheet, 
#                     which contains the column headings
#   converters={'Index': convertIndex,}   -> apply the function defined above to the Index column
#   verbose = True   -> print extra information/ warnings/ errors
dfPhyto=pd.read_excel(pathPhyto,sheet_name='2015 CHEMTAX abs results',usecols='A:I,T:AC',
                      skiprows=2,converters={'Index': convertIndex,},
                      verbose=True)


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# display rows 48 to 59 of the resulting table
dfPhyto[48:60]


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# now, drop any rows from the table that have NaN values in either of the columns
#  'Index' or 'TchlA (ug/L)'
# This is why we applied a function to the Index column to make sure all 
#  non-numeric Index values would have a consistent NaN entry, making them easy to identify
#  and remove
dfPhyto.dropna(subset=['Index', 'TchlA (ug/L)'],how='any',inplace=True)


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# pandas creates its own index, and after dropping rows I like to reset it -
# this is just for convenience
dfPhyto.reset_index(drop=True,inplace=True)


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# display part of the table, confirming that non-data rows have been removed
dfPhyto[48:60]


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# due to repeated column names in the original spreadsheet, '.1' was appended to the names
# of the phytoplankton columns; 
# these lines correct the column names, removing the '.1':
renameDict=dict()
for colName in dfPhyto.keys():
    if colName.endswith('.1'):
        renameDict[colName]=colName.split('.1')[0]
dfPhyto.rename(columns=renameDict,inplace=True)


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dfPhyto


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# This is the important step- join the two tables ('left' and 'right'), 
#  matching the cruise IDs and sample numbers
#   how='outer'  -> all rows from both the left and the right tables will be included, 
#                   even if they cannot be matched; this makes it easy to check for 
#                   unmatched data later
#   left_on  specifies the name of the column to match in the left table (dfbot) 
#   right_on specifies the name of the column to match in the right table (dfPhyto)
dfout = pd.merge(dfbot, dfPhyto,  how='outer', 
                 left_on=['Cruise','Sample_Number'], right_on = ['Cruise','Sample#'])


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# show the column names in the resulting table
dfout.keys()


# ### Checks

# In[17]:


# Identify cases where phytoplankton data were matched to multiple samples in bottle data:
dftest=pd.merge(dfbot, dfPhyto,how='right', left_on=['Cruise','Sample_Number'],right_on = ['Cruise','Sample#'])


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temp=dftest.groupby(['Cruise','Sample#']).agg({'Cruise':['count']})
temp.columns = ['icount']


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np.unique(temp.icount)


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# check for Phyto samples matched to multiple bottle samples:
temp.loc[temp.icount>1]


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# check for phyto samples not matched to bottle samples:
temp.loc[temp.icount==0]


# In[22]:


temp2=dfout.groupby(['Cruise','Sample_Number']).agg({'Cruise':['count']})
temp2.columns = ['icount']
# this will catch phyto matched to multiple bottle but also bottle with duplicate sample numbers per cruise:
temp2.loc[temp2.icount>1]


# In[23]:


# check for phyto samples not matched to bottle samples:
temp.loc[temp.icount==0]


# In[24]:


# if the output table is longer than either of the input tables, some columns were not matched
len(dfout), len(dfPhyto), len(dfbot)


# In[25]:


# Check that the number of cells with data in the 'Cyanobacteria' column is 
#  the same for the input and output tables:
np.sum(dfPhyto['Cyanobacteria']>=0), np.sum(dfout['Cyanobacteria']>=0)


# In[26]:


# If there were data rows from the phytoplankton table that were not matched to 
#  rows from the bottle table, their indices from the phytoplankton table would be 
#  displayed below (the series [] would not be empty)
print(dfout.loc[dfout['ADM:SCIENTIST'].isna()]['Index'])


# In[27]:


# drop repetetive/unecessary columns:
dfout.drop(labels=['Bin #', 'Index', 'Subgroup', 'Month', 'Station', 'Sample#', 'rep',
                   'depth',],axis=1,inplace=True)


# In[28]:


# truncate phyto group values to 3 decimal places:
for col in ('Cyanobacteria', 'Prasinophytes', 'Cryptophytes', 'Diatoms-1',
       'Diatoms-2', 'Dinoflagellates-1', 'Haptophytes', 'Dictyo', 'Raphido',
       'TchlA (ug/L)'):
    dfout[col]=[np.round(ii,decimals=3) for ii in dfout[col]] # use list comprehension to set values for entire column


# In[29]:


dfout.keys()


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# now write the output table to a .csv file:
dfout.to_csv(pathOut, index=False)  


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