import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy
import matplotlib
import matplotlib.patheffects as patheffects
from a_model.datascope import Datascope
from a_model.argparsers import SimulationParser
# parse command line arguments
parser = SimulationParser(description=__doc__)
args = parser.parse_args()
# instantiate datascope
datascope = Datascope()
# get past year's worth of cash in bank
historical_cash_in_bank = datascope.balance_sheet.get_historical_cash_in_bank()
# simulate cashflow for the rest of the year
monthly_cash_outcomes = datascope.simulate_monthly_cash(
n_months=args.n_months,
n_universes=args.n_universes,
verbose=args.verbose,
)
# transform the data in a convenient way for plotting
historical_t, historical_cash = zip(*historical_cash_in_bank)
max_cash = max(historical_cash)
monthly_t = [historical_t[-1]]
Also note how this affects the minimum hourly rate that we need to charge if we
are all working half-time.
"""
import argparse
from a_model.datascope import Datascope
from a_model.utils import currency_str
# parse command line arguments
parser = argparse.ArgumentParser(description=__doc__)
args = parser.parse_args()
# instantiate datascope
datascope = Datascope()
# calculate the financials from the information provided in config.ini
print "If we met the happiness goals we have, we would have the "
print "following outcomes..."
print ""
print "%40s%16s" % ("EBIT", '{:.2%}'.format(datascope.ebit()))
print "%40s%15s" % (
"REVENUE PER PERSON",
currency_str(datascope.revenue_per_person()),
)
print "%40s%15s" % (
"MINIMUM HOURLY RATE",
currency_str(datascope.minimum_hourly_rate()),
)
print ""
import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from a_model.datascope import Datascope
from a_model.argparsers import HiringParser
from a_model import utils
from a_model import decorators
# parse command line arguments
parser = HiringParser(description=__doc__)
args = parser.parse_args()
# instantiate datascope
datascope = Datascope()
# simulate finances in our current situation and by adding up to n_n00bs new
# datascopers
@decorators.read_or_run
def get_all_n00b_outcomes():
all_n00b_outcomes = []
for n00b in range(0, args.n_n00bs+1):
if n00b > 0:
datascope.add_person("n00b_%d" % n00b)
monthly_cash_outputs = datascope.simulate_monthly_cash(
n_months=args.n_months,
n_universes=args.n_universes,
verbose=args.verbose,
)
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FuncFormatter
import seaborn as sns
import pandas as pd
from a_model.datascope import Datascope
from a_model.argparsers import SimulationParser
from a_model import utils
# parse command line arguments
parser = SimulationParser(description=__doc__)
args = parser.parse_args()
# instantiate datascope
datascope = Datascope()
# simulate cashflow for the rest of the year
monthly_cash_outcomes = datascope.simulate_monthly_cash(
n_months=args.n_months,
n_universes=args.n_universes,
verbose=args.verbose,
)
# slice the data to get the eoy cash
eoy = datetime.date(datetime.date.today().year, 12, 31)
months_until_eoy = datascope.profit_loss.get_months_from_now(eoy)
cash_buffer = datascope.n_months_buffer * datascope.costs()
person_bonuses = []
for monthly_cash in monthly_cash_outcomes:
eoy_cash = monthly_cash[months_until_eoy]
