import re
import matplotlib.pyplot as plt
import matplotlib.ticker as pltticker
import numpy as np
import pandas as pd
from datetime import date, timedelta

def compose(*funcs):
    return lambda x: reduce(lambda acc, f: f(acc), funcs, x)

pd.options.display.max_colwidth = 0
pd.options.display.show_dimensions = False

def line_break(s, n=40):  # line break every n chars
    return re.sub('(.{%d})' % n, '\\1\n', s)                                

def expense_item_hist(row):
    amounts = pd.Series(row['amounts']).map(abs)
    
    # histogram of amounts
    counts, bins, patches = plt.hist(amounts, color=['lightgray'])
    # colour current amount bar red
    for patch, rightside, leftside in zip(patches, bins[1:], bins[:-1]):
        if leftside <= abs(row['Amount']) <= rightside:
            patch.set_facecolor('red')
            
    # add probability density estimate
    amounts.plot(kind="kde", secondary_y=True, style=':')
    plt.axis('off')
    
    # desc on top row
    # date - amount - catg on second row
    plt.title('%s\n%s' % (line_break(row['desc']),
                          row['Entered Date'].date()),
              loc='left')
    plt.title('$%.2f' % abs(row['Amount']), 
              loc='center')
    plt.title(row['catg'], loc='right')
    
def expense_histograms(table, ncols=3, width=16, height=3, hspace=1):
    nrows = ceil(len(table.index) / float(ncols))
    plt.figure(figsize=(width, nrows*height))
    plt.subplots_adjust(hspace=hspace)
    # TODO: axis scale in $10, $100, $1000, $10000 increments
    #loc = pltticker.MaxNLocator()
    for i, (idx, row) in enumerate(table.iterrows()):
        ax = plt.subplot(nrows, ncols, i+1)
        #ax.xaxis.set_major_locator(loc)
        expense_item_hist(row)

statement_csv = "data/eg-statement.csv"
!head {statement_csv}

statement = pd.read_csv(statement_csv, parse_dates=[0, 1], dayfirst=True)
statement['Entered Date'].describe()

#statement.head()
#statement['Transaction Description'].value_counts()

def re_sub(regex, repl): return lambda desc: re.sub(regex, repl, desc)

clean_up = [lambda desc: desc.lower(),
            re_sub('\s+', ' '),
            re_sub('atm#\d+', '<atm>'),
            re_sub('atm owner fee \d+', '<atm owner fee>'),
            re_sub('pos( including cash out)?\s*#\d+', '<pos>'),
            re_sub('receipt no \d+', '<receipt no>'),
            re_sub('crn#\d+', '<crn>'),
            re_sub('\(?ref\.?\d+\)?', '<ref>'),
            re_sub('(au|gb|us)#\d+', '<card>')]

statement['desc'] = statement['Transaction Description'].map(compose(*clean_up))
#statement.head()

def categorise(desc):
    if '<atm>' in desc: return 'atm'
    if '<pos>' in desc: return 'pos'
    if 'paypal' in desc: return 'paypal'
    if 'bpay' in desc: return 'bpay'
    if '<atm owner fee>' in desc: return 'fee'
    if 'transaction fee' in desc: return 'fee'
    if 'visa access card fee' in desc: return 'fee'
    if 'member net transfer' in desc: return 'transfer'
    if 'visa-internode' in desc: return 'bill'
    if 'visa-linode.com' in desc: return 'bill'
    if 'visa-citylink' in desc: return 'bill'
    if 'visa-virgin mobile' in desc: return 'bill'
    return ''

statement['catg'] = statement['desc'].map(categorise)

stats = statement.groupby('desc')['Amount'].agg({#'total' : np.sum, 'average' : np.mean,
                                                 #'min': np.min, 'max': np.max,
                                                 'count': np.count_nonzero,
                                                 'amounts': lambda amounts: list(amounts)})

last_date = statement['Entered Date'].describe()['last']
start_date = last_date - timedelta(days=15)
table = statement.join(stats, on='desc')[statement['Entered Date'].gt(start_date)]
cols = ['Entered Date', 'catg', 'desc', 'Amount']
table.sort(['catg'], inplace=True)

new = table['count'].eq(1)  # only one instance of item description seen
not_new = new.eq(False)
debit = table['Amount'].lt(0)
credit = table['Amount'].gt(0)

# only consider it recurring if we've seen it more than twice,
# otherwise it could be a doubled-up transaction we want to catch
table['recurring'] = [amounts.count(amount) > 1 and len(amounts) > 2
                      for amount, amounts in zip(table['Amount'], table['amounts'])]

recurring = table['recurring'].eq(True)
not_recurring = table['recurring'].eq(False) & not_new

table[cols][debit & new]

expense_histograms(table[cols+['amounts']][debit & not_recurring])

table[cols][debit & recurring]

table[cols][credit & new]

expense_histograms(table[cols+['amounts']][credit & not_recurring])

table[cols][credit & recurring]

left_overs = sorted(statement['desc'].unique())
len(left_overs), left_overs