# Path: lib/fpos/core.py
# def money(value):
# def lcs(a, b):
# 
# Path: lib/fpos/visualise.py
# class PeriodGroup(object):
# class ProgressiveMean(object):
#     def __init__(self, *extractors):
#     def add_all(self, rows):
#     def add(self, row):
#     def groups(self):
# def sum_categories(data):
# def sum_expenses(expenses):
# def income(summed):
# def invert(summed, valid):
# def category_sum_matrix(summed):
# def ignore(summed, *args):
# def surplus(expenses, income):
# def colours(n):
# def name():
# def parse_args(subparser=None):
# def should_graph(name, graph):
# def bar_label(plot, rects, values, oriented="v", offset=130):
# def q1(boxprop):
# def q3(boxprop):
# def mean_error(data, level=0.95):
# def graph_stacked_bar_expenses(months, monthlies, expenses, m_income, remaining):
# def graph_bar_margin(months, m_income, expenses, remaining, save=0):
# def _graph_bar_margin_previous(plot, months, m_margin):
# def _graph_bar_margin_current(plot, months, m_income, expenses, save):
# def _graph_bar_margin_spending(plot, months, m_income, expenses, save, remaining):
# def graph_box_categories(months, categorized):
# def graph_xy_categories(months, categorized, remaining):
# def graph_xy_weekly(weekly):
# def graph_bar_targets(months, monthlies, expenses, m_income, remaining, want_save):
# def days_remaining(today):
#     def __init__(self, month):
#     def month_key(self, date):
#     def maybe_init(self, key):
#     def fill_to(self, date):
#     def update(self, day, value):
#     def head(self):
#     def tail(self):
#     def __str__(self):
# def graph_xy_progressive_mean(months, dailies, m_income, groups, dates):
# def basic_groups(transactions):
# def visualise(table, current_date=False, graph=None, save=0, span=0):
# def main(args=None):
#     R = stats.norm.interval(level, loc=mean, scale=dscale)
# 
# Path: lib/fpos/predict.py
# def group_deltas(group):
#     """ group_deltas(group) -> list(int)
# 
#     Calculate the difference in days between subsequent spend group entries.
#     The length of the returned list is one less than the length of the input
#     list, or zero length if the input list is zero length. The input member
#     list is sorted by ascending date order before the deltas are calculated.
#     """
#     sm = sorted(group, key=lambda x: pd(x[0]))
#     return [ ( pd(e[0]) - pd(sm[p][0]) ).days for p, e in enumerate(sm[1:]) ]
# 
# def group_delta_bins(deltas):
#     """ group_delta_bins(deltas) -> list(int)
# 
#     Takes a value sequence as input and generates a list of length
#     max(deltas) - 1, where the value at each index of the list is a count of
#     the occurrences of the index value in the deltas input. The zeroth index is
#     then discarded as it counts multiple spends for the spend group on a single
#     day; this isn't of interest as the minimum resolution of the calculation is
#     one day.
#     """
#     return np.bincount(deltas)[1:] # Discard the zero bin
# 
# def icmf(bins, threshold=0.75):
#     """Inverse cumulative probability mass function"""
#     s = 0
#     for i, v in enumerate(pmf(bins)):
#         s += v
#         if s > threshold:
#             return i
#     raise ValueError("Whut?")
# 
# Path: lib/fpos/combine.py
# def combine(sources):
#     def _gen():
#         entries = dict((digest_entry(x), x)
#                 for db in sources for x in db if 3 <= len(x))
#         datesort = lambda x: datetime.datetime.strptime(x[0], date_fmt).date()
#         costsort = lambda x: float(x[1])
#         descsort = lambda x: x[2]
#         for v in sorted(sorted(sorted(entries.values(), key=descsort), key=costsort), key=datesort):
#             yield v
#     return _gen()


import csv
import sys
import datetime
import monthdelta
import numpy as np
from .core import date_fmt, month_fmt, money
from collections import namedtuple
from .visualise import PeriodGroup, extract_month, visualise
from .predict import group_deltas, group_delta_bins, icmf
from .combine import combine
from random import random
def calculate_reserved_targets(now, groups):
    current_month = to_month(now)
    reserved_targets = []
    for g in groups:
        try:
            c = generate_commitment(g)
        except ValueError:
            pass
        else:
            if current_month <= c.last <= now:
                rc = generate_reserved_commitment(g)
                rt = generate_reserved_target(now, rc)
                reserved_targets.append(rt)
    return reserved_targets

def get_reserved_target_paid(target):
    return target.commitment.group.transactions[-1].date

def sort_reserved_targets(targets):
    return sorted(targets, key=lambda x: get_reserved_target_paid(x))

def print_reserved(positions):
    columns = [ "Name", "Last", "Paid", "Amount", "Saved", "Net" ]
    print(" | ".join(columns))
    for p in positions:
        print("{} | {} | {} | {} | {} | {}".format(
            p.target.commitment.group.name,
            datetime.datetime.strftime(p.target.commitment.last, date_fmt),
            datetime.datetime.strftime(get_reserved_target_paid(p.target), date_fmt),
            money(p.target.commitment.value),

# Path: lib/fpos/core.py
# def money(value):
# def lcs(a, b):
# 
# Path: lib/fpos/visualise.py
# class PeriodGroup(object):
# class ProgressiveMean(object):
#     def __init__(self, *extractors):
#     def add_all(self, rows):
#     def add(self, row):
#     def groups(self):
# def sum_categories(data):
# def sum_expenses(expenses):
# def income(summed):
# def invert(summed, valid):
# def category_sum_matrix(summed):
# def ignore(summed, *args):
# def surplus(expenses, income):
# def colours(n):
# def name():
# def parse_args(subparser=None):
# def should_graph(name, graph):
# def bar_label(plot, rects, values, oriented="v", offset=130):
# def q1(boxprop):
# def q3(boxprop):
# def mean_error(data, level=0.95):
# def graph_stacked_bar_expenses(months, monthlies, expenses, m_income, remaining):
# def graph_bar_margin(months, m_income, expenses, remaining, save=0):
# def _graph_bar_margin_previous(plot, months, m_margin):
# def _graph_bar_margin_current(plot, months, m_income, expenses, save):
# def _graph_bar_margin_spending(plot, months, m_income, expenses, save, remaining):
# def graph_box_categories(months, categorized):
# def graph_xy_categories(months, categorized, remaining):
# def graph_xy_weekly(weekly):
# def graph_bar_targets(months, monthlies, expenses, m_income, remaining, want_save):
# def days_remaining(today):
#     def __init__(self, month):
#     def month_key(self, date):
#     def maybe_init(self, key):
#     def fill_to(self, date):
#     def update(self, day, value):
#     def head(self):
#     def tail(self):
#     def __str__(self):
# def graph_xy_progressive_mean(months, dailies, m_income, groups, dates):
# def basic_groups(transactions):
# def visualise(table, current_date=False, graph=None, save=0, span=0):
# def main(args=None):
#     R = stats.norm.interval(level, loc=mean, scale=dscale)
# 
# Path: lib/fpos/predict.py
# def group_deltas(group):
#     """ group_deltas(group) -> list(int)
# 
#     Calculate the difference in days between subsequent spend group entries.
#     The length of the returned list is one less than the length of the input
#     list, or zero length if the input list is zero length. The input member
#     list is sorted by ascending date order before the deltas are calculated.
#     """
#     sm = sorted(group, key=lambda x: pd(x[0]))
#     return [ ( pd(e[0]) - pd(sm[p][0]) ).days for p, e in enumerate(sm[1:]) ]
# 
# def group_delta_bins(deltas):
#     """ group_delta_bins(deltas) -> list(int)
# 
#     Takes a value sequence as input and generates a list of length
#     max(deltas) - 1, where the value at each index of the list is a count of
#     the occurrences of the index value in the deltas input. The zeroth index is
#     then discarded as it counts multiple spends for the spend group on a single
#     day; this isn't of interest as the minimum resolution of the calculation is
#     one day.
#     """
#     return np.bincount(deltas)[1:] # Discard the zero bin
# 
# def icmf(bins, threshold=0.75):
#     """Inverse cumulative probability mass function"""
#     s = 0
#     for i, v in enumerate(pmf(bins)):
#         s += v
#         if s > threshold:
#             return i
#     raise ValueError("Whut?")
# 
# Path: lib/fpos/combine.py
# def combine(sources):
#     def _gen():
#         entries = dict((digest_entry(x), x)
#                 for db in sources for x in db if 3 <= len(x))
#         datesort = lambda x: datetime.datetime.strptime(x[0], date_fmt).date()
#         costsort = lambda x: float(x[1])
#         descsort = lambda x: x[2]
#         for v in sorted(sorted(sorted(entries.values(), key=descsort), key=costsort), key=datesort):
#             yield v
#     return _gen()


# Path: lib/fpos/psave.py
import csv
import sys
import datetime
import monthdelta
import numpy as np
from .core import date_fmt, month_fmt, money
from collections import namedtuple
from .visualise import PeriodGroup, extract_month, visualise
from .predict import group_deltas, group_delta_bins, icmf
from .combine import combine
from random import random

def calculate_reserved_targets(now, groups):
    current_month = to_month(now)
    reserved_targets = []
    for g in groups:
        try:
            c = generate_commitment(g)
        except ValueError:
            pass
        else:
            if current_month <= c.last <= now:
                rc = generate_reserved_commitment(g)
                rt = generate_reserved_target(now, rc)
                reserved_targets.append(rt)
    return reserved_targets

def get_reserved_target_paid(target):
    return target.commitment.group.transactions[-1].date

def sort_reserved_targets(targets):
    return sorted(targets, key=lambda x: get_reserved_target_paid(x))

def print_reserved(positions):
    columns = [ "Name", "Last", "Paid", "Amount", "Saved", "Net" ]
    print(" | ".join(columns))
    for p in positions:
        print("{} | {} | {} | {} | {} | {}".format(
            p.target.commitment.group.name,
            datetime.datetime.strftime(p.target.commitment.last, date_fmt),
            datetime.datetime.strftime(get_reserved_target_paid(p.target), date_fmt),
            money(p.target.commitment.value),

            money(p.saved),