Analysis¶

There are two classes for analysis: Alternative which provides methods for a Cost Benefit Analysis and Decision which provides methods for a Multiple Criteria Analysis. The Timeline class generates a Gantt Chart.

Alternative¶

[1]:

from datetime import datetime, timedelta
from dpd.analysis import Activity, Alternative, Currency

USD = Currency("USD", 2020, 0.07).discount()

a1 = Alternative(name="Alternative One")
planning = Activity(
    "Planning", datetime(2005, 1, 1), datetime(2009, 12, 31), 100 * USD, 0 * USD
)
construction = Activity(
    "Construction", datetime(2010, 1, 1), datetime(2014, 12, 31), 100 * USD, 0 * USD
)
operations = Activity(
    "Operations",
    construction.End,
    construction.End + timedelta(days=365 * 20 + 4),
    0 * USD,
    400 * USD,
)

for activity in [planning, construction, operations]:
    a1.add_activity(activity)

a1

[1]:

	Start	End	Cost	Benefit
Planning	2005-01-01 00:00:00	2009-12-31 00:00:00	100.0 USD (2020)	0.0 USD (2020)
Construction	2010-01-01 00:00:00	2014-12-31 00:00:00	100.0 USD (2020)	0.0 USD (2020)
Operations	2014-12-31 00:00:00	2034-12-30 00:00:00	0.0 USD (2020)	400.0 USD (2020)

[2]:

a1.benefit_cost_ratio

[2]:

$2 \; \mathrm{}$

[3]:

a1.period_range_pivot().head()

[3]:

	Cost			Benefit
	Planning	Construction	Operations	Planning	Construction	Operations
2005	20.0 USD (2020)	NaN	NaN	0.0 USD (2020)	NaN	NaN
2006	20.0 USD (2020)	NaN	NaN	0.0 USD (2020)	NaN	NaN
2007	20.0 USD (2020)	NaN	NaN	0.0 USD (2020)	NaN	NaN
2008	20.0 USD (2020)	NaN	NaN	0.0 USD (2020)	NaN	NaN
2009	20.0 USD (2020)	NaN	NaN	0.0 USD (2020)	NaN	NaN

[4]:

from matplotlib import pyplot as plt

fig, ax = plt.subplots()

a1.cash_flow_diagram(ax=ax)
ax.get_legend().remove()

/home/docs/checkouts/readthedocs.org/user_builds/dpd/envs/latest/lib/python3.9/site-packages/dpd/analysis/alternative.py:120: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  period_range_pivot["Cost"].applymap(self._plot_na).applymap(lambda x: -x).plot(
/home/docs/checkouts/readthedocs.org/user_builds/dpd/envs/latest/lib/python3.9/site-packages/dpd/analysis/alternative.py:123: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  period_range_pivot["Benefit"].applymap(self._plot_na).plot(

../_images/notebooks_analysis_6_1.png

Timeline¶

[5]:

from dpd.analysis import Timeline

t = Timeline()
for activity in [planning, construction, operations]:
    t.add_activity(activity)

fig, ax = plt.subplots()
t.plot_gantt(ax=ax)
plt.show()

../_images/notebooks_analysis_8_0.png

[6]:

fig, ax = plt.subplots()
a1.timeline.plot_gantt(ax=ax)
plt.show()

../_images/notebooks_analysis_9_0.png

Decision¶

[7]:

from datetime import datetime
from random import randint

from astropy import units

from dpd.analysis import Alternative, Currency, Decision
from dpd.analysis.criteria import AttributeCriterion, CostCriterion, Criterion

USD = Currency("USD", 2020, 0.07).discount()
decision = Decision()

no_build_alternative = Alternative("No Build Alternative")
no_build_alternative.some_cost = 0 * USD
alternative_one = Alternative("Alternative 1")
alternative_one.some_cost = 100 * USD
alternative_two = Alternative("Alternative 2")
alternative_two.some_cost = 200 * USD


@property
def travel_time(self):
    return randint(100, 200) * units.second


setattr(Alternative, "travel_time", travel_time)

decision.add_criterion(AttributeCriterion("Travel Time", weight=1 / units.second))
decision.add_criterion(
    Criterion("Distance", lambda x: 200 * units.meter, weight=1 / units.meter)
)
decision.add_criterion(AttributeCriterion("Some Cost", weight=1 / USD))

for alternative in [no_build_alternative, alternative_one, alternative_two]:
    decision.add_alternative(alternative)

decision

[7]:

	Travel Time	Distance	Some Cost
No Build Alternative	149.0 s	200.0 m	0.0 USD (2020)
Alternative 1	181.0 s	200.0 m	100.0 USD (2020)
Alternative 2	180.0 s	200.0 m	200.0 USD (2020)

[8]:

decision.plot(kind="bar")

[8]:

<Axes: >

../_images/notebooks_analysis_12_1.png

[9]:

decision.multiple_criteria_decision_analysis().plot(kind="bar")

[9]:

<Axes: >

../_images/notebooks_analysis_13_1.png

Values¶

[10]:

from dpd.analysis.values import (
    Value_of_Reduced_Fatalities_and_Injuries,
    Value_of_Travel_Time_Savings,
)

[11]:

Value_of_Reduced_Fatalities_and_Injuries

[11]:

O - No Injury                         3900.0 USD (2020)
C - Possible Injury                  77200.0 USD (2020)
B - Non-incapacitating              151100.0 USD (2020)
A - Incapacitating                  554800.0 USD (2020)
K - Killed                        11600000.0 USD (2020)
U - Injured (Severity Unknown)      210300.0 USD (2020)
Injury Crash                        302600.0 USD (2020)
Fatal Crash                       12837400.0 USD (2020)
Name: Value of Reduced Fatalities and Injuries, dtype: object

[12]:

Value_of_Travel_Time_Savings

[12]:

General Travel Time - Personal                           16.2 USD (2020) / (h person)
General Travel Time - Business                           29.4 USD (2020) / (h person)
General Travel Time - All Purposes                       17.8 USD (2020) / (h person)
Walking Cycling, Waiting, Standing, and Transfer Time    32.4 USD (2020) / (h person)
Commercial Vehicle Operators - Truck Drivers             32.0 USD (2020) / (h person)
Commercial Vehicle Operators - Bus Drivers               33.6 USD (2020) / (h person)
Commercial Vehicle Operators - Transit Rail Operators    50.7 USD (2020) / (h person)
Commercial Vehicle Operators - Locomotive Engineers      52.5 USD (2020) / (h person)
Name: Value of Travel Time Savings, dtype: object

[13]:

from astropy.units import day, minute, year
from dpd.analysis.units import person, usd2020

[14]:

(
    5
    * minute
    * 40000
    * person
    / day
    * Value_of_Travel_Time_Savings[
        "Walking Cycling, Waiting, Standing, and Transfer Time"
    ]
).decompose().to(usd2020 / year)

[14]:

$39447000 \; \mathrm{\frac{USD (2020)}{yr}}$

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