Builds a houseplant watering check calendar from a list of plants with pot size and material, light, drainage and last watering date. Adjusts each interval for pot, light, humidity and season (by hemisphere, stretching into winter), fits checks onto chosen weekdays, says what 'needs water' means and how much to give, and flags risks like pots with no drainage. Includes a stdlib Python planner with .ics export and --self-test.
---
name: houseplant-watering-schedule-planner
description: Builds a houseplant watering check calendar from a list of plants with pot size and material, light, drainage and last watering date. Adjusts each interval for pot, light, humidity and season (by hemisphere, stretching into winter), fits checks onto chosen weekdays, says what 'needs water' means and how much to give, and flags risks like pots with no drainage. Includes a stdlib Python planner with .ics export and --self-test.
---
# Houseplant Watering Schedule Planner
You help people keep houseplants alive without watering by the calendar alone. The plan tells them when to **check** each plant and what to look for; they water only when the plant's rule says so. You adapt to the real conditions (pot, light, heating, season), keep the routine simple (two fixed check days a week if they want), and point out the setups that cause most deaths: no drainage hole, succulents in dark corners, ferns drying out in terracotta.
## Files in this skill
- `scripts/watering_planner.py` - reads a plants CSV, assigns each plant a watering type, computes a check interval per season, builds the calendar (optionally snapped to weekdays), estimates water per plant, flags risky setups, and can export an .ics file (Python 3.8+ standard library only)
- `references/plant-water-profiles.md` - the six watering types, common plants in each, the "needs water" test and signs of over- and under-watering
- `references/adjustment-factors.md` - how pot size, pot material, light, humidity, season and drainage change the interval, with the multipliers the script uses and how to tune them after a few weeks
- `templates/watering-plan.md` - the one-page plan to hand back (weekly routine, per-plant cheat sheet, warnings, review date)
- `examples/plants.csv` - ten plants in a heated flat at the end of October
- `examples/plan-output.md` - the real output of the planner for that list
## Workflow
### 1. Collect the plant list
For each plant: a name, what it is (species or "looks like a succulent"), pot diameter in cm, pot material (plastic, glazed, terracotta, fabric, self-watering), light where it stands (low = far from a window, medium, bright = bright indirect, direct = sun on the leaves for hours), whether the pot has a drainage hole, and when it was last watered. Also ask the hemisphere, whether the heating is on (dry air), and which days suit a quick check. If the plant is unknown, ask for a photo description or treat it as "top_dry" and say so.
### 2. Run the planner
```
python3 scripts/watering_planner.py examples/plants.csv --start 2026-10-30 --weeks 6 --check-days Mon,Thu --humidity low
python3 scripts/watering_planner.py plants.csv --hemisphere south --weeks 8 --ics plants.ics
python3 scripts/watering_planner.py plants.csv --json
python3 scripts/watering_planner.py --self-test
```
Exit 0 = plan written, 1 = plan written but a HIGH warning needs action (e.g. no drainage), 2 = bad input (unknown material or light, pot size in mm, bad date). Without code, use the type table and multipliers in the references by hand.
### 3. Check the result before handing it over
- Each plant has a check interval now and, if the plan crosses a season, the new interval (e.g. "7 d (winter: 9 d)").
- Plants that want to stay moist (ferns, calathea, peace lily, basil) may need more check days than the routine allows; the planner says so. Suggest a third check day, a plastic pot, or self-watering only for these.
- "Overdue" means the last watering plus the interval is already past: check that plant first.
- Read the warnings and turn HIGH ones into the first action of the plan.
### 4. Deliver
Fill `templates/watering-plan.md`: the weekly routine (which plants on which day), a per-plant cheat sheet with the "water when" test and amount, warnings with what to do, and a review date in 2-3 weeks to lengthen or shorten intervals based on what they found at each check.
## Example result
Ten plants, start Friday 2026-10-30, six weeks, checks on Mondays and Thursdays, heating on (low humidity). Ferns, calathea, the peace lily and basil are checked every check day; monstera about weekly (Thursdays); pothos weekly (Mondays); the snake plant every 16 days, stretching to 21 in December; the ZZ plant every 20 days; the cactus every 6 days now, 9 in winter (it is overdue, last watered 15 October). The peace lily sits in a decorative pot with no hole (HIGH: use a nursery pot inside it and empty the cover pot after watering). The fern in terracotta and the basil would like checks every 1-2 days, which twice a week cannot cover. See `examples/plan-output.md`.
## Rules
- Never prescribe watering on a fixed schedule without a check; always pair each check with the plant's "water when" test.
- Water thoroughly until it drains, then empty the saucer after 15 minutes; never leave a pot standing in water (orchids: soak, then drain fully).
- In winter and in low light, plants use less water: lengthen intervals rather than giving smaller sips.
- Treat yellowing plus wet soil as overwatering, not thirst; say so before suggesting more water.
- Do not guess a species from a vague name; ask or state the type you assumed.
- Mention pets if a plant is toxic only when the user asks about safety or mentions pets/children; do not pad the plan.
FILE:references/plant-water-profiles.md
# Watering types
| Type | Typical plants | Water when | Base check interval* | Winter slow-down |
|---|---|---|---|---|
| succulent | cactus, aloe, echeveria, jade, haworthia, string of pearls | soil dry all the way down: a wooden chopstick pushed to the bottom comes out clean and dry; pot feels light | 10 days | x2.5 (many cacti need almost no water Dec-Feb) |
| dry_tolerant | snake plant (sansevieria), ZZ plant, ponytail palm, yucca, hoya, peperomia | at least 3/4 of the soil dry | 12 days | x2.0 |
| top_dry | pothos, monstera, philodendron, rubber plant, fiddle leaf fig, dracaena, spider plant, money tree, aglaonema, alocasia, anthurium, parlor palm, begonia, Christmas cactus | top third dry (finger test to the second knuckle in a 15 cm pot) | 6 days | x1.6 |
| evenly_moist | Boston and maidenhair fern, calathea, maranta (prayer plant), peace lily, fittonia | top 1-2 cm just dry; never fully dry | 3 days | x1.3 |
| orchid | phalaenopsis in bark | roots silvery-grey (green = still wet) and bark dry; soak 10 min, drain fully | 7 days | x1.4 |
| herb | basil, mint, parsley on a windowsill | top 1-2 cm dry | 2 days | x1.2 |
*Base = spring, 15 cm plastic pot with drainage, medium light, normal humidity. Autumn is halfway between spring and winter; summer is x0.85. See `adjustment-factors.md`.
## How to check (30 seconds per plant)
1. **Finger test:** push a finger into the soil to the depth in the rule. Dry and crumbly = dry; cool and sticking = moist.
2. **Weight:** lift small pots right after watering once, so you know "full" vs "empty".
3. **Chopstick:** for deep pots and succulents, leave a wooden skewer in for a minute; dark and damp means wait.
4. **Moisture meters** are rough guides; they read salts as well as water and are often wrong in bark or very sandy mixes.
## Signs
| Overwatering (more common) | Underwatering |
|---|---|
| yellow lower leaves with soil still wet, soft or black stem base, mushy roots, fungus gnats, sour smell | crisp brown edges, drooping that recovers within hours of watering, soil pulling away from the pot edge, very light pot |
| Fix: stop watering, check roots, repot in dry mix if rotten, lengthen the interval | Fix: soak thoroughly (bottom-water 20-30 min for hydrophobic soil), shorten the interval a little |
Drooping with **wet** soil means roots are damaged: do not add water.
## Watering technique
- Water slowly over the whole surface until water runs out of the hole; wait, then empty the saucer after about 15 minutes.
- Room-temperature water. Calathea and some ferns brown at the tips with hard or softened water; rain or filtered water helps.
- Bottom-watering (stand the pot in water for 20-30 min) works for small pots and dry, shrinking soil; top-water now and then to flush salts.
- Self-watering pots: top up the reservoir when empty and let it stay empty for a day or two now and then so roots get air; not for succulents.
FILE:references/adjustment-factors.md
# Adjustment factors
Check interval = base days (type) x pot size x pot material x light x humidity x season, rounded, limited to 1..45 days. These are the multipliers in `scripts/watering_planner.py`.
| Factor | Value | Multiplier | Why |
|---|---|---|---|
| Pot diameter | under 10 cm | 0.6 | little soil, dries in days |
| | 10-13 cm | 0.8 | |
| | 14-19 cm | 1.0 | reference |
| | 20-27 cm | 1.3 | more soil holds water longer |
| | 28 cm and up | 1.6 | big pots stay wet at the bottom long after the top is dry: check deep |
| Material | plastic, glazed, ceramic, metal | 1.0 | walls do not breathe |
| | terracotta / unglazed clay | 0.75 | walls wick and evaporate water |
| | fabric grow bag | 0.7 | dries from all sides |
| | self-watering | 1.5 | reservoir feeds the soil; check the reservoir instead |
| Light | low (2+ m from a window, north room) | 1.4 | little growth, little use |
| | medium | 1.0 | |
| | bright indirect | 0.85 | |
| | direct sun on the leaves | 0.7 | |
| Humidity | low (heating on, below ~35 %) | 0.85 | soil and leaves lose water faster |
| | normal | 1.0 | |
| | high (bathroom, humidifier) | 1.15 | |
| Season | spring | 1.0 | |
| | summer | 0.85 | |
| | autumn | halfway to winter | growth slows as days shorten |
| | winter | type's winter slow-down (x1.2 to x2.5) | |
Seasons: northern hemisphere Dec-Feb winter, Mar-May spring, Jun-Aug summer, Sep-Nov autumn; southern hemisphere shifted by six months. Next to a radiator or in a heated conservatory, use humidity "low" even in winter.
## Drainage
No hole is the single biggest cause of root rot. Best: keep the plant in its plastic nursery pot inside the decorative pot and empty the outer pot 15 minutes after watering. If it must stay in a pot without a hole, give about half the usual amount and tip the pot to check for standing water. The planner flags this as HIGH.
## Amount
About 15-25 % of the pot volume, until water drains out of the hole. The planner estimates pot volume from the diameter (straight-sided pot about 0.9x as tall as wide), so a 15 cm pot gets about 450 ml, a 24 cm pot about 1.75 l. Succulents and dry-tolerant plants get a thorough soak too, just less often.
## Tune after two or three weeks
- Soil still moist at most checks: lengthen that plant's interval by 25-50 % (or move it to "low" light if it is far from the window).
- Soil bone-dry at every check and the plant droops: shorten by 25 %.
- Write the changes into the CSV (e.g. switch the type or light) and re-run, rather than remembering exceptions.
- Re-run the plan when the heating goes on or off, when you repot (bigger pot = longer interval), or when you move a plant.
## Not covered by the interval
- Newly repotted plants: water once thoroughly, then wait until the rule says so.
- Dormant bulbs and cacti in a cold winter rest (5-12 C): almost no water; a monthly check is enough.
- Fertilizing: only in spring and summer, at half strength, on a watering day.
FILE:templates/watering-plan.md
# Plant care plan: <home / room>
**Period:** <2026-10-30 to 2026-12-10> **Check days:** <Mon and Thu, 5-10 minutes>
**Conditions:** <northern hemisphere, heating on (dry air)> **Review on:** <2026-11-16>
## Do first
- <Peace lily: no drainage hole - move it into a plastic nursery pot inside the cover pot; empty the cover pot 15 min after watering>
- <Spike (cactus): overdue - check on Monday>
## Weekly routine
| Day | Check these plants |
|---|---|
| <Mon> | <ferns, calathea, peace lily, basil, orchid, pothos, cactus (every 2nd Mon in December)> |
| <Thu> | <ferns, calathea, peace lily, basil, orchid, monstera; snake plant every 2-3 weeks> |
## Cheat sheet
| Plant | Water when | About | Notes |
|---|---|---|---|
| <Monty (monstera)> | <top third dry> | <~1.75 l until it drains> | <bright east window> |
| <Sansa (snake plant)> | <3/4 dry> | <~450 ml> | <December: every 3 weeks> |
## How to check
- Finger in the soil to the depth in the "water when" column; water only if dry to that depth.
- Water until it drains, empty the saucer after 15 minutes.
- Wet soil + yellow leaves = too much water: skip and tell me.
## Warnings
- <Fernando (Boston fern) in terracotta dries in 2 days with the heating on: add a Saturday check or move it to a plastic pot>
- <Basil wants daily checks on the sunny sill; bottom-water when you pass by>
## At the review
For each plant, note whether the soil was usually still moist (lengthen) or bone dry (shorten) at checks; I will update the plan.
FILE:examples/plants.csv
name,plant,pot_cm,pot_material,light,drainage,last_watered,notes
Monty,monstera,24,plastic,bright,yes,2026-10-28,living room by the east window
Pothos shelf,pothos,14,glazed,medium,yes,2026-10-27,trailing from the bookshelf
Sansa,snake plant,17,terracotta,low,yes,2026-10-20,hallway corner
Zed,zz plant,20,plastic,medium,yes,,new from the shop
Fernando,boston fern,20,terracotta,bright,yes,2026-10-29,bathroom shelf
Cal,calathea,15,plastic,medium,yes,2026-10-28,bedroom
Spike,cactus,10,terracotta,direct,yes,2026-10-15,south windowsill
Peace lily,peace lily,18,glazed,low,no,2026-10-26,decorative pot with no hole
Orchid,phalaenopsis,12,plastic,bright,yes,2026-10-25,kitchen window
Basil,basil,13,terracotta,direct,yes,2026-10-29,kitchen windowsill
FILE:examples/plan-output.md
# Houseplant watering plan: 2026-10-30 to 2026-12-10
Hemisphere: north Season at start: autumn Humidity: low Check days: Mon, Thu Plants: 10
A check is not a watering: feel the soil and water only when the rule says so.
## Plants
| Plant | Type | Pot | Light | Check every | Water when | About | Checks |
|---|---|---|---|---|---|---|---|
| Monty (monstera) | top_dry | 24 cm plastic | bright | 7 d (winter: 9 d) | top third of the soil dry (about 3-5 cm in a 15 cm pot) | ~1750 ml (until it drains) | 6 |
| Pothos shelf (pothos) | top_dry | 14 cm glazed | medium | 7 d (winter: 8 d) | top third of the soil dry (about 3-5 cm in a 15 cm pot) | ~350 ml (until it drains) | 6 |
| Sansa (snake plant) | dry_tolerant | 17 cm terracotta | low | 16 d (winter: 21 d) | soil dry at least 3/4 of the way down | ~450 ml (until it drains) | 3 |
| Zed (zz plant) | dry_tolerant | 20 cm plastic | medium | 20 d | soil dry at least 3/4 of the way down | ~750 ml (until it drains) | 2 |
| Fernando (boston fern) | evenly_moist | 20 cm terracotta | bright | 2 d (winter: 3 d) | top 1-2 cm just dry; never let it dry out fully | ~1000 ml (until it drains) | 12 |
| Cal (calathea) | evenly_moist | 15 cm plastic | medium | 3 d (winter: 3 d) | top 1-2 cm just dry; never let it dry out fully | ~450 ml (until it drains) | 12 |
| Spike (cactus) (overdue: check first day) | succulent | 10 cm terracotta | direct | 6 d (winter: 9 d) | soil dry all the way down (chopstick comes out clean and dry) | ~100 ml (until it drains) | 6 |
| Peace lily (peace lily) | evenly_moist | 18 cm glazed, NO hole | low | 4 d (winter: 5 d) | top 1-2 cm just dry; never let it dry out fully | ~375 ml, no more | 12 |
| Orchid (phalaenopsis) | orchid | 12 cm plastic | bright | 5 d (winter: 6 d) | roots silvery-grey and bark dry; soak 10 min, drain fully | soak 10 min | 11 |
| Basil (basil) | herb | 13 cm terracotta | direct | 1 d (winter: 1 d) | top 1-2 cm dry | ~350 ml (until it drains) | 12 |
## Calendar
| Date | Day | Season | Check |
|---|---|---|---|
| 2026-11-02 | Mon | autumn | Basil, Cal, Fernando, Orchid, Peace lily, Pothos shelf, Spike, Zed |
| 2026-11-05 | Thu | autumn | Basil, Cal, Fernando, Monty, Orchid, Peace lily, Sansa |
| 2026-11-09 | Mon | autumn | Basil, Cal, Fernando, Orchid, Peace lily, Pothos shelf, Spike |
| 2026-11-12 | Thu | autumn | Basil, Cal, Fernando, Monty, Orchid, Peace lily |
| 2026-11-16 | Mon | autumn | Basil, Cal, Fernando, Orchid, Peace lily, Pothos shelf, Spike |
| 2026-11-19 | Thu | autumn | Basil, Cal, Fernando, Monty, Orchid, Peace lily, Sansa |
| 2026-11-23 | Mon | autumn | Basil, Cal, Fernando, Orchid, Peace lily, Pothos shelf, Spike, Zed |
| 2026-11-26 | Thu | autumn | Basil, Cal, Fernando, Monty, Orchid, Peace lily |
| 2026-11-30 | Mon | autumn | Basil, Cal, Fernando, Orchid, Peace lily, Pothos shelf, Spike |
| 2026-12-03 | Thu | winter | Basil, Cal, Fernando, Monty, Orchid, Peace lily, Sansa |
| 2026-12-07 | Mon | winter | Basil, Cal, Fernando, Peace lily, Pothos shelf, Spike |
| 2026-12-10 | Thu | winter | Basil, Cal, Fernando, Monty, Orchid, Peace lily |
## Warnings
| Sev | Plant | Note |
|---|---|---|
| HIGH | Peace lily | pot has no drainage hole: water pools at the bottom and roots rot. Repot, or keep the plant in a nursery pot inside the cover pot and empty the cover pot 15 min after watering |
| LOW | Fernando | terracotta pot dries this plant very fast; a plastic or glazed pot keeps it evenly moist |
| LOW | Fernando | check days push some checks more than a day late (e.g. due 2026-11-07, planned 2026-11-09); add a check day or feel the soil in between |
| LOW | Basil | check days push some checks more than a day late (e.g. due 2026-11-03, planned 2026-11-05); add a check day or feel the soil in between |
Intervals assume the plant is established in a free-draining mix. After 2-3 weeks, lengthen an interval if the soil is still moist at most checks, shorten it if it is always bone dry.
FILE:scripts/watering_planner.py
#!/usr/bin/env python3
"""Houseplant watering schedule planner (Python 3.8+ standard library only).
Reads a CSV of plants (species or watering type, pot diameter, pot material, light,
drainage, optional last watering date) and builds a check calendar: how often to
check each plant in the current season, what "needs water" means for it, roughly how
much to give, and which plants to check on which date. Intervals stretch
automatically as the plan crosses into autumn and winter (or shrink into summer), by
hemisphere. Checks can be snapped to fixed weekdays (e.g. Mon,Thu) so the plan fits a
routine. Optional .ics export for a calendar app.
A check is not a watering: on each check, feel the soil and water only if the plant's
rule says so. The intervals are starting points; adjust them after two or three weeks
of real checks (see references/adjustment-factors.md).
Usage:
python3 watering_planner.py plants.csv [--start 2026-10-30] [--weeks 4]
[--hemisphere north|south] [--humidity low|normal|high]
[--check-days Mon,Thu] [--ics plan.ics] [--json]
python3 watering_planner.py --self-test
plants.csv columns: name, plant (species or type), pot_cm, pot_material
(plastic|glazed|ceramic|terracotta|fabric|self-watering), light (low|medium|bright|direct),
drainage (yes|no), last_watered (YYYY-MM-DD, optional), notes (optional).
Types: succulent, dry_tolerant, top_dry, evenly_moist, orchid, herb.
Exit codes: 0 = plan written, 1 = plan written with HIGH warnings (e.g. no drainage), 2 = bad input.
"""
import argparse, csv, datetime as dt, io, json, math, sys
PROFILES = {
# base days (spring, 15 cm plastic, medium light), winter factor, rule, share of pot volume per watering
"succulent": (10, 2.5, "soil dry all the way down (chopstick comes out clean and dry)", 0.15),
"dry_tolerant": (12, 2.0, "soil dry at least 3/4 of the way down", 0.15),
"top_dry": (6, 1.6, "top third of the soil dry (about 3-5 cm in a 15 cm pot)", 0.20),
"evenly_moist": (3, 1.3, "top 1-2 cm just dry; never let it dry out fully", 0.20),
"orchid": (7, 1.4, "roots silvery-grey and bark dry; soak 10 min, drain fully", 0.0),
"herb": (2, 1.2, "top 1-2 cm dry", 0.25),
}
SPECIES = {
"cactus": "succulent", "aloe": "succulent", "aloe vera": "succulent", "echeveria": "succulent",
"jade": "succulent", "jade plant": "succulent", "haworthia": "succulent", "string of pearls": "succulent",
"christmas cactus": "top_dry", "snake plant": "dry_tolerant", "sansevieria": "dry_tolerant",
"zz plant": "dry_tolerant", "zamioculcas": "dry_tolerant", "ponytail palm": "dry_tolerant",
"hoya": "dry_tolerant", "pothos": "top_dry", "monstera": "top_dry", "philodendron": "top_dry",
"rubber plant": "top_dry", "ficus elastica": "top_dry", "fiddle leaf fig": "top_dry",
"dracaena": "top_dry", "spider plant": "top_dry", "money tree": "top_dry", "pachira": "top_dry",
"chinese evergreen": "top_dry", "aglaonema": "top_dry", "dieffenbachia": "top_dry",
"yucca": "dry_tolerant", "bird of paradise": "top_dry", "alocasia": "top_dry", "anthurium": "top_dry",
"parlor palm": "top_dry", "peperomia": "dry_tolerant", "begonia": "top_dry",
"fern": "evenly_moist", "boston fern": "evenly_moist", "maidenhair fern": "evenly_moist",
"calathea": "evenly_moist", "prayer plant": "evenly_moist", "maranta": "evenly_moist",
"peace lily": "evenly_moist", "spathiphyllum": "evenly_moist", "fittonia": "evenly_moist",
"orchid": "orchid", "phalaenopsis": "orchid", "basil": "herb", "mint": "herb", "parsley": "herb",
}
MATERIAL = {"plastic": 1.0, "glazed": 1.0, "ceramic": 1.0, "metal": 1.0, "terracotta": 0.75,
"clay": 0.75, "unglazed": 0.75, "fabric": 0.7, "self-watering": 1.5}
LIGHT = {"low": 1.4, "medium": 1.0, "bright": 0.85, "direct": 0.7}
HUMIDITY = {"low": 0.85, "normal": 1.0, "high": 1.15}
SEASON_FACTOR = {"spring": 1.0, "summer": 0.85}
DAY_NAMES = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
class InputError(Exception):
pass
def season_of(d, hemisphere):
m = d.month if hemisphere == "north" else (d.month + 5) % 12 + 1
return {12: "winter", 1: "winter", 2: "winter", 3: "spring", 4: "spring", 5: "spring",
6: "summer", 7: "summer", 8: "summer"}.get(m, "autumn")
def pot_factor(cm):
if cm < 10:
return 0.6
if cm < 14:
return 0.8
if cm < 20:
return 1.0
if cm < 28:
return 1.3
return 1.6
def pot_litres(cm):
# straight-sided pot about 0.9x as tall as wide, minus ~10 % for the rim gap
r = cm / 2.0
return math.pi * r * r * (0.9 * cm) * 0.9 / 1000.0
def interval_days(p, season, humidity):
base, winter, _, _ = PROFILES[p["type"]]
# autumn sits halfway between spring and the plant's winter slow-down
sf = {"winter": winter, "autumn": (1 + winter) / 2}.get(season) or SEASON_FACTOR[season]
days = base * pot_factor(p["pot_cm"]) * MATERIAL[p["material"]] * LIGHT[p["light"]] * HUMIDITY[humidity] * sf
return max(1, min(45, int(round(days))))
def parse_date(s, what):
try:
return dt.date.fromisoformat(s.strip())
except ValueError:
raise InputError(f"bad date '{s}' for {what} (use YYYY-MM-DD)")
def load_plants(text):
rows = list(csv.DictReader(io.StringIO(text)))
if not rows:
raise InputError("plants CSV has no rows")
cols = {c.strip().lower(): c for c in rows[0] if c}
if "name" not in cols:
raise InputError("plants CSV needs at least a 'name' column (plus plant, pot_cm, pot_material, light, drainage)")
plants, warnings = [], []
for i, r in enumerate(rows, 2):
g = lambda k, d="": ((r.get(cols[k]) or "").strip() if k in cols else "") or d
name = g("name")
if not name:
raise InputError(f"row {i}: empty name")
kind = g("plant", g("type", "")).lower()
if kind in PROFILES:
ptype = kind
elif kind in SPECIES:
ptype = SPECIES[kind]
else:
ptype = next((v for k, v in SPECIES.items() if k in kind), None) if kind else None
if ptype is None:
ptype = "top_dry"
warnings.append(("MEDIUM", name, f"unknown plant '{kind or '?'}': using type top_dry; set the plant column to one of {', '.join(PROFILES)}"))
try:
pot = float(g("pot_cm", "0") or 0)
except ValueError:
raise InputError(f"row {i} ({name}): pot_cm must be a number of centimetres")
if pot <= 0:
pot = 15.0
warnings.append(("LOW", name, "no pot size given: assumed 15 cm"))
if pot > 80:
raise InputError(f"row {i} ({name}): pot_cm {pot} looks wrong (centimetres, not millimetres?)")
mat = g("pot_material", "plastic").lower()
if mat not in MATERIAL:
raise InputError(f"row {i} ({name}): pot_material '{mat}' (use {', '.join(MATERIAL)})")
light = g("light", "medium").lower().replace("bright indirect", "bright").replace("full sun", "direct")
if light not in LIGHT:
raise InputError(f"row {i} ({name}): light '{light}' (use low, medium, bright, direct)")
drainage = g("drainage", "yes").lower() not in ("no", "n", "false", "0")
last = parse_date(g("last_watered"), f"{name} last_watered") if g("last_watered") else None
p = {"name": name, "plant": kind or ptype, "type": ptype, "pot_cm": pot, "material": mat,
"light": light, "drainage": drainage, "last_watered": last, "notes": g("notes")}
plants.append(p)
if not drainage:
warnings.append(("HIGH", name, "pot has no drainage hole: water pools at the bottom and roots rot. Repot, or keep the plant in a nursery pot inside the cover pot and empty the cover pot 15 min after watering"))
if ptype == "succulent" and light == "low":
warnings.append(("MEDIUM", name, "succulent in low light: it will stretch and rot easily; move it to the brightest window and water even less"))
if ptype == "evenly_moist" and light == "direct":
warnings.append(("MEDIUM", name, "moisture-loving plant in direct sun: leaves scorch and soil dries within a day; move it out of direct sun"))
if ptype == "evenly_moist" and mat in ("terracotta", "clay", "unglazed", "fabric"):
warnings.append(("LOW", name, f"{mat} pot dries this plant very fast; a plastic or glazed pot keeps it evenly moist"))
if ptype == "orchid" and mat not in ("plastic",):
warnings.append(("LOW", name, "orchids are easiest in a clear plastic pot with drainage, so you can see the roots"))
if mat == "self-watering" and ptype in ("succulent", "dry_tolerant", "orchid"):
warnings.append(("MEDIUM", name, "self-watering pots keep the soil wet; not suited to plants that need to dry out"))
return plants, warnings
def amount_text(p):
_, _, _, share = PROFILES[p["type"]]
if p["type"] == "orchid":
return "soak 10 min"
if p["material"] == "self-watering":
return "refill reservoir"
ml = pot_litres(p["pot_cm"]) * 1000 * share
ml = int(round(ml / 50.0) * 50) or 50
return f"~{ml} ml (until it drains)" if p["drainage"] else f"~{ml // 2} ml, no more"
def snap(due, prev, allowed):
"""Allowed weekday nearest to the due date (ties go earlier), always after prev."""
if not allowed:
return due
best = None
for off in (0, -1, 1, -2, 2, -3, 3):
d = due + dt.timedelta(days=off)
if d > prev and d.weekday() in allowed:
best = d
break
if best is None:
best = max(due, prev + dt.timedelta(days=1))
while best.weekday() not in allowed:
best += dt.timedelta(days=1)
return best
def plan(plants, start, weeks, hemisphere="north", humidity="normal", check_days=None):
end = start + dt.timedelta(days=7 * weeks - 1)
allowed = {DAY_NAMES.index(x) for x in check_days} if check_days else set()
cal, per_plant, notes = {}, [], []
for p in plants:
s0 = season_of(start, hemisphere)
iv = interval_days(p, s0, humidity)
if p["last_watered"]:
due = p["last_watered"] + dt.timedelta(days=iv)
overdue = due < start
first = max(due, start)
else:
overdue, first = False, start
d = snap(first, start - dt.timedelta(days=1), allowed) if allowed else first
dates, prev = [], None
while d <= end:
dates.append(d)
cal.setdefault(d, []).append(p["name"])
step = interval_days(p, season_of(d, hemisphere), humidity)
due = d + dt.timedelta(days=step)
nd = snap(due, d, allowed)
if allowed and (nd - due).days > max(1, step // 5):
notes.append((p["name"], due, nd))
prev, d = d, nd
later = {season_of(x, hemisphere) for x in dates} - {s0}
per_plant.append({**p, "interval_now": iv, "season_now": s0,
"intervals_later": {s: interval_days(p, s, humidity) for s in sorted(later)},
"rule": PROFILES[p["type"]][2], "amount": amount_text(p),
"checks": [x.isoformat() for x in dates], "overdue": overdue})
return {"start": start, "end": end, "hemisphere": hemisphere, "humidity": humidity,
"check_days": check_days or [], "plants": per_plant,
"calendar": [(d, sorted(cal[d], key=str.lower)) for d in sorted(cal)], "late_snaps": notes}
def render(res, warnings):
st, en = res["start"], res["end"]
out = [f"# Houseplant watering plan: {st.isoformat()} to {en.isoformat()}", "",
f"Hemisphere: {res['hemisphere']} Season at start: {season_of(st, res['hemisphere'])} "
f"Humidity: {res['humidity']} Check days: {', '.join(res['check_days']) or 'any day'} Plants: {len(res['plants'])}", "",
"A check is not a watering: feel the soil and water only when the rule says so.", "",
"## Plants", "",
"| Plant | Type | Pot | Light | Check every | Water when | About | Checks |",
"|---|---|---|---|---|---|---|---|"]
for p in res["plants"]:
iv = f"{p['interval_now']} d"
if p["intervals_later"]:
iv += " (" + ", ".join(f"{s}: {v} d" for s, v in p["intervals_later"].items()) + ")"
pot = f"{p['pot_cm']:g} cm {p['material']}" + ("" if p["drainage"] else ", NO hole")
flag = " (overdue: check first day)" if p["overdue"] else ""
out.append(f"| {p['name']} ({p['plant']}){flag} | {p['type']} | {pot} | {p['light']} | {iv} | {p['rule']} | {p['amount']} | {len(p['checks'])} |")
out += ["", "## Calendar", "", "| Date | Day | Season | Check |", "|---|---|---|---|"]
for d, names in res["calendar"]:
out.append(f"| {d.isoformat()} | {DAY_NAMES[d.weekday()]} | {season_of(d, res['hemisphere'])} | {', '.join(names)} |")
if warnings or res["late_snaps"]:
out += ["", "## Warnings", "", "| Sev | Plant | Note |", "|---|---|---|"]
order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
for sev, name, msg in sorted(warnings, key=lambda w: (order[w[0]], w[1].lower())):
out.append(f"| {sev} | {name} | {msg} |")
seen = set()
for name, due, nd in res["late_snaps"]:
if name in seen:
continue
seen.add(name)
out.append(f"| LOW | {name} | check days push some checks more than a day late (e.g. due {due.isoformat()}, planned {nd.isoformat()}); add a check day or feel the soil in between |")
out += ["", "Intervals assume the plant is established in a free-draining mix. After 2-3 weeks, lengthen an interval if the soil is still moist at most checks, shorten it if it is always bone dry."]
return "\n".join(out) + "\n"
def to_ics(res, stamp):
lines = ["BEGIN:VCALENDAR", "VERSION:2.0", "PRODID:-//houseplant-watering-schedule-planner//EN", "CALSCALE:GREGORIAN"]
for d, names in res["calendar"]:
nd = d + dt.timedelta(days=1)
lines += ["BEGIN:VEVENT", f"UID:plants-{d.strftime('%Y%m%d')}@watering-planner", f"DTSTAMP:{stamp}",
f"DTSTART;VALUE=DATE:{d.strftime('%Y%m%d')}", f"DTEND;VALUE=DATE:{nd.strftime('%Y%m%d')}",
"SUMMARY:" + ics_escape("Check plants: " + ", ".join(names)), "TRANSP:TRANSPARENT", "END:VEVENT"]
lines.append("END:VCALENDAR")
return "\r\n".join(fold(l) for l in lines) + "\r\n"
def ics_escape(s):
return s.replace("\\", "\\\\").replace(";", "\\;").replace(",", "\\,").replace("\n", "\\n")
def fold(line):
b = line.encode("utf-8")
if len(b) <= 75:
return line
parts, cur = [], b""
for ch in line:
e = ch.encode("utf-8")
if len(cur) + len(e) > (75 if not parts else 74):
parts.append(cur.decode("utf-8")); cur = b""
cur += e
parts.append(cur.decode("utf-8"))
return "\r\n ".join(parts)
def self_test():
H = "name,plant,pot_cm,pot_material,light,drainage,last_watered\n"
def run(rows, **kw):
plants, warns = load_plants(H + rows)
return plan(plants, kw.pop("start", dt.date(2026, 4, 6)), kw.pop("weeks", 4), **kw), warns
cases = []
def case(name, fn):
try:
ok = bool(fn())
err = ""
except Exception as e: # noqa
ok, err = False, repr(e)
cases.append((name, ok, err))
case("succulent checked less often than fern", lambda: run("A,cactus,15,plastic,medium,yes,\nB,fern,15,plastic,medium,yes,\n")[0]["plants"][0]["interval_now"] > run("A,cactus,15,plastic,medium,yes,\nB,fern,15,plastic,medium,yes,\n")[0]["plants"][1]["interval_now"])
case("base interval top_dry 15 cm plastic medium spring = 6", lambda: run("A,pothos,15,plastic,medium,yes,\n")[0]["plants"][0]["interval_now"] == 6)
case("small pot dries faster", lambda: run("A,pothos,9,plastic,medium,yes,\n")[0]["plants"][0]["interval_now"] < 6)
case("big pot lasts longer", lambda: run("A,pothos,30,plastic,medium,yes,\n")[0]["plants"][0]["interval_now"] > 6)
case("terracotta dries faster than plastic", lambda: run("A,pothos,15,terracotta,medium,yes,\n")[0]["plants"][0]["interval_now"] < 6)
case("low light lengthens, direct sun shortens", lambda: run("A,pothos,15,plastic,low,yes,\n")[0]["plants"][0]["interval_now"] > 6 > run("A,pothos,15,plastic,direct,yes,\n")[0]["plants"][0]["interval_now"])
case("winter stretches succulent 2.5x", lambda: run("A,cactus,15,plastic,medium,yes,\n", start=dt.date(2026, 1, 5))[0]["plants"][0]["interval_now"] == 25)
case("summer shortens", lambda: run("A,pothos,15,plastic,medium,yes,\n", start=dt.date(2026, 7, 6))[0]["plants"][0]["interval_now"] == 5)
case("southern hemisphere: July is winter", lambda: season_of(dt.date(2026, 7, 1), "south") == "winter" and season_of(dt.date(2026, 1, 1), "south") == "summer")
case("season boundaries north", lambda: [season_of(dt.date(2026, m, 15), "north") for m in (2, 3, 5, 6, 8, 9, 11, 12)] == ["winter", "spring", "spring", "summer", "summer", "autumn", "autumn", "winter"])
case("interval grows across autumn->winter in plan", lambda: (lambda r: r["plants"][0]["intervals_later"].get("winter", 0) > r["plants"][0]["interval_now"])(run("A,pothos,15,plastic,medium,yes,\n", start=dt.date(2026, 11, 16), weeks=4)[0]))
case("checks spaced by interval", lambda: run("A,pothos,15,plastic,medium,yes,\n")[0]["plants"][0]["checks"][:3] == ["2026-04-06", "2026-04-12", "2026-04-18"])
case("last_watered sets first check", lambda: run("A,pothos,15,plastic,medium,yes,2026-04-04\n")[0]["plants"][0]["checks"][0] == "2026-04-10")
case("overdue goes to start", lambda: (lambda p: p["overdue"] and p["checks"][0] == "2026-04-06")(run("A,pothos,15,plastic,medium,yes,2026-03-20\n")[0]["plants"][0]))
case("snap to check days (Mon,Thu)", lambda: all(dt.date.fromisoformat(x).weekday() in (0, 3) for x in run("A,pothos,15,plastic,medium,yes,\nB,cactus,12,terracotta,bright,yes,\n", check_days=["Mon", "Thu"])[0]["calendar"] and [x for p in run("A,pothos,15,plastic,medium,yes,\nB,cactus,12,terracotta,bright,yes,\n", check_days=["Mon", "Thu"])[0]["plants"] for x in p["checks"]]))
case("snap to nearest check day (Sun -> Mon)", lambda: snap(dt.date(2026, 4, 12), dt.date(2026, 4, 6), {0, 3}) == dt.date(2026, 4, 13))
case("snap tie goes earlier (Tue between Mon and Thu? no: Tue -> Mon)", lambda: snap(dt.date(2026, 4, 14), dt.date(2026, 4, 9), {0, 3}) == dt.date(2026, 4, 13))
case("snap never on or before the previous check", lambda: snap(dt.date(2026, 4, 7), dt.date(2026, 4, 6), {0, 3}) == dt.date(2026, 4, 9))
case("autumn between spring and winter", lambda: 6 < run("A,pothos,15,plastic,medium,yes,\n", start=dt.date(2026, 10, 5))[0]["plants"][0]["interval_now"] < run("A,pothos,15,plastic,medium,yes,\n", start=dt.date(2026, 12, 7))[0]["plants"][0]["interval_now"])
case("late snap noted for fast plant", lambda: len(run("A,basil,9,terracotta,direct,yes,\n", check_days=["Mon"])[0]["late_snaps"]) > 0)
case("no drainage -> HIGH warning and half amount", lambda: (lambda r: any(w[0] == "HIGH" for w in r[1]) and "no more" in r[0]["plants"][0]["amount"])(run("A,pothos,15,glazed,medium,no,\n")))
case("unknown plant -> top_dry with warning", lambda: (lambda r: r[0]["plants"][0]["type"] == "top_dry" and any("unknown plant" in w[2] for w in r[1]))(run("A,triffid,15,plastic,medium,yes,\n")))
case("species substring match", lambda: run("A,variegated monstera,15,plastic,medium,yes,\n")[0]["plants"][0]["type"] == "top_dry")
case("succulent low light warning", lambda: any("stretch" in w[2] for w in run("A,echeveria,10,terracotta,low,yes,\n")[1]))
case("fern in direct sun warning", lambda: any("scorch" in w[2] for w in run("A,boston fern,20,plastic,direct,yes,\n")[1]))
case("self-watering succulent warning", lambda: any("self-watering" in w[2] for w in run("A,cactus,15,self-watering,bright,yes,\n")[1]))
case("orchid amount is soak", lambda: run("A,orchid,12,plastic,bright,yes,\n")[0]["plants"][0]["amount"] == "soak 10 min")
case("amount scales with pot", lambda: int(run("A,pothos,25,plastic,medium,yes,\n")[0]["plants"][0]["amount"][1:].split()[0]) > int(run("A,pothos,12,plastic,medium,yes,\n")[0]["plants"][0]["amount"][1:].split()[0]))
case("low humidity shortens", lambda: run("A,fern,20,plastic,medium,yes,\n", humidity="low")[0]["plants"][0]["interval_now"] <= run("A,fern,20,plastic,medium,yes,\n")[0]["plants"][0]["interval_now"])
case("interval never below 1 day", lambda: run("A,basil,6,fabric,direct,yes,\n", start=dt.date(2026, 7, 6), humidity="low")[0]["plants"][0]["interval_now"] >= 1)
case("missing pot size assumed 15", lambda: (lambda r: r[0]["plants"][0]["pot_cm"] == 15 and any("assumed 15" in w[2] for w in r[1]))(run("A,pothos,,plastic,medium,yes,\n")))
def bad(rows):
try:
load_plants(H + rows); return False
except InputError:
return True
case("bad material rejected", lambda: bad("A,pothos,15,wood,medium,yes,\n"))
case("bad light rejected", lambda: bad("A,pothos,15,plastic,dim,yes,\n"))
case("pot in mm rejected", lambda: bad("A,pothos,150,plastic,medium,yes,\n"))
case("bad date rejected", lambda: bad("A,pothos,15,plastic,medium,yes,30/10/2026\n"))
case("ics has one event per date, CRLF, escaped commas", lambda: (lambda r, t: t.count("BEGIN:VEVENT") == len(r["calendar"]) and "\r\n" in t and "\\," in t)(*(lambda r: (r, to_ics(r, "20261030T000000Z")))(run("A,pothos,15,plastic,medium,yes,\nB,fern,15,plastic,medium,yes,\n")[0])))
case("ics lines folded to 75 octets", lambda: all(len(l.encode()) <= 75 for l in to_ics(run("".join(f"Plant number {i},pothos,15,plastic,medium,yes,\n" for i in range(8)))[0], "20261030T000000Z").split("\r\n")))
passed = 0
for name, ok, err in cases:
passed += ok
print(("PASS " if ok else "FAIL ") + name + ("" if ok else f" {err}"))
print(f"{passed}/{len(cases)} passed")
return 0 if passed == len(cases) else 1
def main(argv=None):
ap = argparse.ArgumentParser(description="Build a houseplant watering check calendar")
ap.add_argument("plants", nargs="?")
ap.add_argument("--start", help="first day of the plan, YYYY-MM-DD (default today)")
ap.add_argument("--weeks", type=int, default=4)
ap.add_argument("--hemisphere", choices=["north", "south"], default="north")
ap.add_argument("--humidity", choices=list(HUMIDITY), default="normal", help="low = heating on / below ~35 %%")
ap.add_argument("--check-days", help="snap checks to these weekdays, e.g. Mon,Thu")
ap.add_argument("--ics", help="also write an .ics calendar file")
ap.add_argument("--json", action="store_true")
ap.add_argument("--self-test", action="store_true")
a = ap.parse_args(argv)
if a.self_test:
return self_test()
if not a.plants:
ap.error("give a plants CSV, or --self-test")
try:
start = parse_date(a.start, "--start") if a.start else dt.date.today()
if not 1 <= a.weeks <= 26:
raise InputError("--weeks must be 1..26")
days = None
if a.check_days:
days = [x.strip()[:3].title() for x in a.check_days.split(",") if x.strip()]
for x in days:
if x not in DAY_NAMES:
raise InputError(f"unknown weekday '{x}' in --check-days")
with open(a.plants, encoding="utf-8") as fh:
plants, warnings = load_plants(fh.read())
except (OSError, InputError, UnicodeDecodeError) as e:
print(f"error: {e}", file=sys.stderr)
return 2
res = plan(plants, start, a.weeks, a.hemisphere, a.humidity, days)
if a.ics:
stamp = dt.datetime.now(dt.timezone.utc).strftime("%Y%m%dT%H%M%SZ")
with open(a.ics, "w", encoding="utf-8", newline="") as fh:
fh.write(to_ics(res, stamp))
if a.json:
out = {**res, "start": res["start"].isoformat(), "end": res["end"].isoformat(),
"calendar": [{"date": d.isoformat(), "plants": n} for d, n in res["calendar"]],
"late_snaps": [{"plant": n, "due": d.isoformat(), "planned": p.isoformat()} for n, d, p in res["late_snaps"]],
"plants": [{**p, "last_watered": p["last_watered"].isoformat() if p["last_watered"] else None} for p in res["plants"]],
"warnings": [{"severity": s, "plant": n, "note": m} for s, n, m in warnings]}
print(json.dumps(out, indent=2))
else:
sys.stdout.write(render(res, warnings))
return 1 if any(w[0] == "HIGH" for w in warnings) else 0
if __name__ == "__main__":
sys.exit(main())Compares commute options - car (fuel or EV), transit, bike, e-bike, walking, day-share mixes and park-and-ride legs - per month and year with cash cost, door-to-door time, time cost at your own hourly value, cost per trip and CO2. Picks pass vs pay-per-ride for the days you ride, separates car running costs from fixed costs, and shows the break-even value of an hour. Includes a stdlib Python calculator with --self-test.
---
name: commute-cost-comparison-calculator
description: Compares commute options - car (fuel or EV), transit, bike, e-bike, walking, day-share mixes and park-and-ride legs - per month and year with cash cost, door-to-door time, time cost at your own hourly value, cost per trip and CO2. Picks pass vs pay-per-ride for the days you ride, separates car running costs from fixed costs, and shows the break-even value of an hour. Includes a stdlib Python calculator with --self-test.
---
# Commute Cost Comparison Calculator
You help someone decide how to get to work by putting every option on the same footing: what it costs in cash, how much of their life it takes, and what that time is worth to them. You count what really changes with the choice (fuel, parking, fares, the bike), keep costs they pay anyway out unless they would actually sell the car, and show the trade-off as a clear number: "driving saves 32 lost hours a year and costs $3,697 more, so it pays only if your hour is worth more than $115".
## Files in this skill
- `scripts/commute_cost.py` - reads a scenario JSON (distance, commute days, value of time, options) and prints a comparison table, headline, break-even values of time and per-option notes; supports metric (km, L/100 km) and imperial (mi, mpg), EVs, transit passes with daily caps and employer subsidies, day-share mixes and multi-leg trips (Python 3.8+ standard library only)
- `references/cost-components.md` - every option field, what to include for each mode, where to find the numbers, and typical ranges
- `references/time-value-and-assumptions.md` - how to choose a value of time, usable time on transit, what the totals leave out (health, safety, weather, flexibility) and how to run sensitivity checks
- `templates/commute-comparison.md` - the decision summary to hand back
- `examples/commute.json` - an 11-mile commute with driving, bus + light rail, an e-bike, an e-bike/transit mix and park-and-ride
- `examples/comparison-output.md` - the real output of the calculator for that scenario
## Workflow
### 1. Collect the facts
One-way distance, commute days per week (hybrid work matters a lot) and weeks worked per year, door-to-door minutes per option (including walking, waiting, parking and transfers: ask for a real trip, not the map estimate), and per mode: car fuel economy, fuel or electricity price, parking and tolls, whether they would keep the car anyway; transit fare, pass price, daily cap, employer subsidy; bike or e-bike price, expected life and upkeep. Ask what an hour of their time is worth, or offer a default (half their hourly pay) and show the sensitivity.
### 2. Run the calculator
```
python3 scripts/commute_cost.py examples/commute.json
python3 scripts/commute_cost.py scenario.json --days-per-week 3
python3 scripts/commute_cost.py scenario.json --value-of-time 0 --json
python3 scripts/commute_cost.py --self-test
```
Exit 0 = comparison printed, 2 = bad input (missing field, mix shares not adding up to 1, unknown mode, negative price). Without code, follow the formulas in `references/cost-components.md`.
### 3. Read the result
- The table is sorted by total (cash + time cost); the headline names the cheapest in cash, the cheapest once time counts and the fastest.
- **Break-even value of time** compares every option with the cheapest in cash: an option that costs more is worth it only if your hour is worth more than the number shown. "Never" means it costs more and does not save usable time.
- Time spent on transit that you can use (reading, email) is discounted with `usable_time_share`; the same field can credit bike time that replaces exercise you would do anyway. Say which you used.
- Check the transit note: on 2-3 commute days a week a monthly pass is often dearer than pay-per-ride; the calculator picks the cheaper and says so.
### 4. Deliver
Fill `templates/commute-comparison.md`: recommendation in one sentence, the table, break-even values, what changes the answer (hybrid days, fuel price, parking, value of time), what is not priced, and a trial suggestion (e.g. two weeks of the mix before buying anything).
## Example result
11 miles each way, 5 days a week, 46 weeks, time valued at $20/hour (`examples/commute.json`). Cheapest in cash: bus + light rail at $49/month after a $50 employer subsidy (the $99 pass beats $105 of single fares). Cheapest once time counts: park and ride ($95 cash, $361/month total) because 80 % of the train leg is usable. Driving downtown is fastest (28 min) but costs $357/month with $12/day parking and pays off only if an hour is worth more than $115. The e-bike costs $67/month all-in and emits nothing, but it does not save usable time at these numbers; it wins if the ride replaces gym time. See `examples/comparison-output.md`.
## Rules
- Count only costs that change with the choice; include car insurance, registration and loan costs only if the person would actually give up the car (`count_fixed`).
- Use real door-to-door times, including walking, waiting, parking and transfers.
- Never present the value of time as a fact: it is the person's own choice; show at least one sensitivity run (e.g. `--value-of-time 0` and their wage).
- Keep health, safety and weather out of the money totals but mention them; do not moralize about cars or bikes.
- State every assumption with its number (fuel price, parking, depreciation per mile) so the person can change it.
- Give currency and units as the person uses them; do not mix miles and kilometres in one scenario.
FILE:references/cost-components.md
# Cost components and option fields
Scenario fields: `currency` (USD, EUR, GBP, TRY, ...), `units` (`metric`: km, L/100 km, price per litre; `imperial`: miles, mpg, price per US gallon), `one_way_distance`, `days_per_week` (default 5), `weeks_per_year` (default 46: holidays, sick days, travel), `value_of_time_per_hour`, `options`.
Every option: `name`, `mode`, `minutes_one_way` (door to door), optional `distance_one_way` (defaults to the scenario distance), optional `usable_time_share` (0..1).
## Car (`mode: car`)
| Field | Meaning | Typical |
|---|---|---|
| `fuel_economy_mpg` / `fuel_l_per_100km` | real-world consumption on this route (city driving is worse than the label) | 25-35 mpg / 6-9 L/100 km |
| `fuel_price` | per gallon / per litre | local pump price |
| `fuel: "diesel"` | uses the diesel CO2 factor | petrol default |
| `kwh_per_100` + `electricity_price` | electric car instead of fuel (kWh per 100 mi or km) | 25-35 kWh/100 mi, 16-22 kWh/100 km |
| `grid_kg_co2_per_kwh` | grid intensity for EV CO2 | 0.4 default; 0.05 hydro/nuclear, 0.7 coal-heavy |
| `maintenance_per_distance` | tyres, servicing, repairs per mile/km | $0.08-0.12/mi, 0.05-0.08/km |
| `depreciation_per_distance` | value lost from extra mileage only (not age) | $0.05-0.15/mi |
| `parking_per_day`, `tolls_per_day` | per commute day | workplace parking can dominate |
| `fixed_per_year` + `count_fixed` | insurance, registration, loan interest, age depreciation; counted only when `count_fixed` is true | true only if commuting is the reason to own the car |
Fuel cost = 2 x distance x days / mpg x price (or x L/100 km / 100 x price). CO2: petrol 2.35 kg/L (8.89 kg per US gallon), diesel 2.68 kg/L (10.18 kg/gal), EV kWh x grid factor.
All-in per-mile rates (tax mileage rates, AAA "Your Driving Costs") include fixed costs; they overstate the marginal cost of a commute when the car is kept anyway.
## Transit (`mode: transit`)
| Field | Meaning |
|---|---|
| `fare_per_ride`, `rides_per_day` (default 2) | single fares; add a ride for each paid transfer that is not free |
| `daily_cap` | fare capping per day, if the system has it |
| `monthly_pass` | price per month; the calculator compares 12 passes with pay-per-ride for the actual days and picks the cheaper |
| `subsidy_per_month` | employer or tax-free benefit, deducted (never below zero) |
| `co2_g_per_passenger_km` | default 80 g; electric rail 20-40, diesel bus 80-120 at average load |
| `usable_time_share` | share of the trip you can read or work (seat, no transfer stress): 0.2-0.8 |
## Bike, e-bike, walk (`mode: bike | ebike | walk`)
| Field | Meaning | Typical |
|---|---|---|
| `purchase_price`, `life_years` | spread over the life (default 5 years); not used for walk | e-bike $1,500-3,500 |
| `maintenance_per_year` | tyres, chain, brake pads, battery fund for e-bikes | $100-300 |
| `gear_per_year` | lights, lock, rain gear, helmet replacement | $50-150 |
| `kwh_per_100`, `electricity_price` | e-bike charging | 1-2 kWh per 100 mi |
| `other_per_month` | bike parking, locker, shower membership | 0-30 |
Ownership costs are counted in full even in a mix: you pay for the bike whether you ride 3 or 5 days.
## Mix (`mode: mix`)
`"shares": {"E-bike": 0.6, "Bus + light rail": 0.4}` splits commute days between other options (shares add up to 1). Each part is costed for its own days, so a transit part on 2 days a week usually goes pay-per-ride. Use it for weather-dependent cycling, hybrid driving, or carpool days.
## Multi-leg (`mode: multi`)
`"legs": [ {car leg}, {transit leg} ]` with `transfer_minutes`. Each leg needs its own `distance_one_way` and `minutes_one_way`; costs and minutes are summed. Use it for park and ride, bike + train, or walk + bus.
## Where to get the numbers
- Fuel economy: the car's trip computer over a week of commuting.
- Parking and fares: the operator's price page; check employer benefits (pre-tax transit, parking cash-out).
- Door-to-door time: time one real trip each way at commute hours; add 5 minutes for parking or waiting variability.
FILE:references/time-value-and-assumptions.md
# Value of time and what the totals leave out
## Choosing a value of time
- There is no correct number: it is what an hour of this person's commute is worth to them.
- Common anchor: about half of hourly take-home pay for personal travel (the US Department of Transportation's travel-time guidance uses 50 % of hourly income for personal local trips; business travel is valued at 100 %).
- Someone on a fixed salary who cannot earn more by commuting less may choose a lower value; a freelancer who bills the saved hours may choose a higher one.
- Always show a sensitivity: `--value-of-time 0` (cash only), the chosen value, and their full hourly wage. If the ranking does not change, the decision is robust.
## Usable time
`usable_time_share` discounts time you can use. Rough guide:
| Situation | Share |
|---|---|
| Driving, cycling in traffic, standing on a crowded bus | 0 |
| Bus with a seat, short hops, transfers | 0.2-0.4 |
| Train with a seat and table, 20+ minutes without changes | 0.6-0.8 |
| Bike or walk that replaces exercise you would otherwise do | 0.3-0.7 (say so explicitly) |
## Not in the money totals (mention them)
- **Health:** regular cycling or walking is associated with better cardiovascular health; some people value it highly, others not at all. Use `usable_time_share` if they want it priced.
- **Safety:** road risk per mile differs by mode and route; a protected bike route changes the picture.
- **Weather and season:** use a mix (e.g. 60 % bike days) rather than assuming every day is a bike day.
- **Reliability:** a 40-minute train with a 10-minute spread may beat a 30-minute drive that ranges from 25 to 60; ask about late penalties (childcare pick-up).
- **Flexibility and option value:** having a car for errands on the way, or for emergencies; car share or taxi credits can replace it for rare days (add them as `other_per_month`).
- **Stress and comfort:** do not price it, but ask.
## Sensitivity runs worth doing
- Hybrid work: `--days-per-week 3` often flips pass vs pay-per-ride and makes owning a second car look worse.
- Parking: a change in parking price (or employer parking cash-out) is often the biggest single lever.
- Fuel or electricity price +-25 %.
- Selling a second car: rerun with `count_fixed: true` on the car to see the full saving.
## Sanity checks before presenting
- Cash per trip for driving should be at least fuel + parking per trip.
- Transit cash per month should not exceed the pass price unless the pass does not cover the route.
- Hours per month = minutes one way x 2 x days per month / 60.
- If the "winner" depends on a value of time far from what the person said, say so plainly.
FILE:templates/commute-comparison.md
# Commute options: <home> to <work>
**Trip:** <11 mi each way, 5 days/week, 46 weeks/year> **Value of time used:** <$20/hour (about half of hourly pay)>
**Recommendation:** <one sentence, e.g. "Park and ride: $95/month cash, 40 min door to door, most of the train ride usable for email">
## Comparison (per month)
| Option | Cash | Door to door | Hours | Time cost | Total | CO2 kg/year |
|---|---|---|---|---|---|---|
| <Park and ride> | <$95> | <40 min> | <25.6> | <$266> | <$361> | <999> |
| <Bus + light rail> | <$49> | <46 min> | <29.4> | <$411> | <$460> | <651> |
## When a dearer option pays off
| Option | Extra per year | Lost hours saved | Worth it above |
|---|---|---|---|
| <Drive downtown> | <$3,697> | <32> | <$115/hour> |
## What would change the answer
- <Hybrid work (3 days): transit pass no longer pays; pay per ride ...>
- <Parking cash-out from the employer would make driving $230/month dearer still>
- <If the e-bike ride replaces the gym, the e-bike moves to ...>
## Not priced
- <health, safety on the route, weather days, reliability, needing the car for school pick-up>
## Assumptions
- <fuel $3.40/gal at 28 mpg; maintenance $0.10/mi; mileage depreciation $0.08/mi; parking $12/day>
- <transit pass $99, employer subsidy $50/month, 30 % of the bus/rail time usable>
- <car kept for weekends, so insurance and registration are not counted>
## Next step
<Try the mix for two weeks before buying the e-bike; time each trip door to door.>
FILE:examples/commute.json
{
"currency": "USD",
"units": "imperial",
"one_way_distance": 11,
"days_per_week": 5,
"weeks_per_year": 46,
"value_of_time_per_hour": 20,
"options": [
{
"name": "Drive downtown",
"mode": "car",
"minutes_one_way": 28,
"fuel_economy_mpg": 28,
"fuel_price": 3.4,
"maintenance_per_distance": 0.1,
"depreciation_per_distance": 0.08,
"parking_per_day": 12,
"tolls_per_day": 0,
"fixed_per_year": 1900,
"count_fixed": false
},
{
"name": "Bus + light rail",
"mode": "transit",
"minutes_one_way": 46,
"fare_per_ride": 2.75,
"monthly_pass": 99,
"subsidy_per_month": 50,
"usable_time_share": 0.3
},
{
"name": "E-bike",
"mode": "ebike",
"minutes_one_way": 42,
"purchase_price": 2200,
"life_years": 5,
"maintenance_per_year": 250,
"gear_per_year": 100,
"kwh_per_100": 1.5,
"electricity_price": 0.17
},
{
"name": "E-bike 3 days, transit 2 days",
"mode": "mix",
"shares": {"E-bike": 0.6, "Bus + light rail": 0.4}
},
{
"name": "Park and ride",
"mode": "multi",
"transfer_minutes": 6,
"legs": [
{"mode": "car", "distance_one_way": 4, "minutes_one_way": 10, "fuel_economy_mpg": 28, "fuel_price": 3.4,
"maintenance_per_distance": 0.1, "depreciation_per_distance": 0.08, "parking_per_day": 0},
{"mode": "transit", "distance_one_way": 7, "minutes_one_way": 24, "fare_per_ride": 2.75, "monthly_pass": 99,
"subsidy_per_month": 50, "usable_time_share": 0.8}
]
}
]
}
FILE:examples/comparison-output.md
# Commute cost comparison
One way: 11 mi Commute days: 19.2/month (230/year) Value of time: $20.00/hour Currency: USD
| Option | Cash / month | Cash / year | Per trip | Door to door (one way) | Hours / month | Time cost / month | Total / month | CO2 kg / year |
|---|---|---|---|---|---|---|---|---|
| Park and ride | $95.22 | $1,143 | $2.48 | 40 min | 25.6 | $266 | **$361** | 999 |
| Bus + light rail | $49.00 | $588 | $1.28 | 46 min | 29.4 | $411 | **$460** | 651 |
| E-bike 3 days, transit 2 days | $66.48 | $798 | $1.73 | 44 min | 27.9 | $487 | **$553** | 261 |
| E-bike | $66.91 | $803 | $1.75 | 42 min | 26.8 | $537 | **$604** | 0 |
| Drive downtown | $357 | $4,285 | $9.32 | 28 min | 17.9 | $358 | **$715** | 1,607 |
Sorted by total (cash + time cost).
## Headline
- Cheapest in cash: **Bus + light rail** ($49.00/month).
- Cheapest once time counts: **Park and ride** ($361/month at $20.00/hour).
- Fastest: **Drive downtown** (28 min one way, 17.9 h/month).
## Break-even value of time (vs Bus + light rail, the cheapest in cash)
| Option | Extra cash / year | Lost hours saved / year | Worth it if your hour is worth more than |
|---|---|---|---|
| Drive downtown | $3,697 | 32 | $115/hour |
| E-bike | $215 | none (75 more lost) | never on cost: it does not save usable time |
| E-bike 3 days, transit 2 days | $210 | none (45 more lost) | never on cost: it does not save usable time |
| Park and ride | $555 | 87 | $6.35/hour |
## Notes per option
- **Drive downtown** (car): per month: fuel/energy $51.20, maintenance + depreciation $75.90, parking + tolls $230; fixed costs (insurance, registration) not counted: you keep the car anyway
- **Bus + light rail** (transit): per month: fares $49.00; monthly pass (cheaper than $105/month pay per ride at 19.2 days/month); employer subsidy $50.00/month deducted
- **E-bike** (ebike): per month: bike + upkeep $65.83, fuel/energy $1.08; ownership costs counted in full (they do not shrink on days you do not ride)
- **E-bike 3 days, transit 2 days** (mix): E-bike 60 % of days (11.5/month): ownership costs counted in full (they do not shrink on days you do not ride); Bus + light rail 40 % of days (7.7/month): pay per ride (pass would cost $99.00/month at 7.7 days/month)
- **Park and ride** (multi): leg 2 (transit): monthly pass (cheaper than $105/month pay per ride at 19.2 days/month); leg 2 (transit): employer subsidy $50.00/month deducted
Time cost = hours x value of time x (1 - share of the trip you can use for reading or work). Not included: health benefits, accident risk, weather days and the option value of having a car; see references/time-value-and-assumptions.md.
FILE:scripts/commute_cost.py
#!/usr/bin/env python3
"""Commute cost comparison calculator (Python 3.8+ standard library only).
Compares commute options - car (fuel or electric), transit, bike, e-bike, walking,
day-share mixes ("bike 3 days, train 2 days") and same-day multi-leg trips ("drive to
the station, then train") - per month and per year: cash cost, time spent, time cost at
your own hourly value, total, cost per trip and CO2. For transit it picks the cheaper of
pay-per-ride (with a daily cap) and a monthly pass for the days you actually ride; for
cars it separates running costs from fixed costs you would keep paying anyway. It also
prints break-even values of time: how much an hour must be worth to you before a faster,
dearer option pays off.
Usage:
python3 commute_cost.py scenario.json [--value-of-time 25] [--days-per-week 3] [--json]
python3 commute_cost.py --self-test
scenario.json: {"currency": "USD", "units": "imperial"|"metric", "one_way_distance": 11,
"days_per_week": 5, "weeks_per_year": 46, "value_of_time_per_hour": 20,
"options": [{"name": "...", "mode": "car|transit|bike|ebike|walk|mix|multi", ...}]}
See references/cost-components.md for every option field.
Exit codes: 0 = comparison printed, 2 = bad input.
"""
import argparse, json, sys
PETROL_CO2 = {"metric": 2.35, "imperial": 8.89} # kg CO2 per litre / per US gallon burned (diesel: 2.68 / 10.18)
DIESEL_CO2 = {"metric": 2.68, "imperial": 10.18}
KM_PER_UNIT = {"metric": 1.0, "imperial": 1.609344}
LABELS = {"energy": "fuel/energy", "running": "maintenance + depreciation", "parking_tolls": "parking + tolls",
"fixed": "fixed costs", "fares": "fares", "ownership": "bike + upkeep", "other": "other"}
MODES = {"car", "transit", "bike", "ebike", "walk", "mix", "multi"}
SYMBOLS = {"USD": "$", "EUR": "€", "GBP": "£", "TRY": "₺", "CAD": "C$", "AUD": "A$", "JPY": "¥", "INR": "₹"}
class InputError(Exception):
pass
def num(o, key, default=None, lo=0.0, hi=None, who=""):
v = o.get(key, default)
if v is None:
raise InputError(f"{who}: '{key}' is required")
if isinstance(v, bool) or not isinstance(v, (int, float)):
raise InputError(f"{who}: '{key}' must be a number")
if v < lo or (hi is not None and v > hi):
raise InputError(f"{who}: '{key}' = {v} is out of range" + (f" ({lo}..{hi})" if hi is not None else f" (>= {lo})"))
return float(v)
def car_cost(o, sc, days, who):
"""Annual cost dict for a car over `days` commute days."""
units = sc["units"]
d = num(o, "distance_one_way", sc["distance"], who=who) * 2 * days # user units per year
if "kwh_per_100" in o:
kwh = d / 100 * num(o, "kwh_per_100", who=who)
energy = kwh * num(o, "electricity_price", who=who)
co2 = kwh * num(o, "grid_kg_co2_per_kwh", 0.4, who=who)
else:
if units == "imperial":
vol = d / num(o, "fuel_economy_mpg", lo=1, who=who)
else:
vol = d / 100 * num(o, "fuel_l_per_100km", lo=0.5, who=who)
energy = vol * num(o, "fuel_price", who=who)
co2 = vol * (DIESEL_CO2 if o.get("fuel") == "diesel" else PETROL_CO2)[units]
running = d * (num(o, "maintenance_per_distance", 0, who=who) + num(o, "depreciation_per_distance", 0, who=who))
daily = days * (num(o, "parking_per_day", 0, who=who) + num(o, "tolls_per_day", 0, who=who))
fixed = num(o, "fixed_per_year", 0, who=who) if o.get("count_fixed", False) else 0.0
return {"energy": energy, "running": running, "parking_tolls": daily, "fixed": fixed,
"co2": co2, "notes": ([] if o.get("count_fixed", False) or not o.get("fixed_per_year") else
["fixed costs (insurance, registration) not counted: you keep the car anyway"])}
def transit_cost(o, sc, days, who):
rides = num(o, "rides_per_day", 2, lo=1, who=who)
fare = num(o, "fare_per_ride", who=who)
cap = o.get("daily_cap")
per_day = fare * rides if cap is None else min(fare * rides, num(o, "daily_cap", who=who))
payg_year = per_day * days
best, note = payg_year, "pay per ride"
if o.get("monthly_pass") is not None:
pass_year = num(o, "monthly_pass", who=who) * 12
months_days = days / 12
if pass_year < payg_year:
best, note = pass_year, f"monthly pass (cheaper than {fmt_plain(payg_year / 12, sc['sym'])}/month pay per ride at {months_days:.1f} days/month)"
else:
note = f"pay per ride (pass would cost {fmt_plain(pass_year / 12, sc['sym'])}/month at {months_days:.1f} days/month)"
subsidy = num(o, "subsidy_per_month", 0, who=who) * 12
best = max(0.0, best - subsidy)
km = num(o, "distance_one_way", sc["distance"], who=who) * 2 * days * KM_PER_UNIT[sc["units"]]
co2 = km * num(o, "co2_g_per_passenger_km", 80, who=who) / 1000
return {"fares": best, "co2": co2, "notes": [note] + ([f"employer subsidy {fmt_plain(subsidy / 12, sc['sym'])}/month deducted"] if subsidy else [])}
def bike_cost(o, sc, days, who, mode):
d = num(o, "distance_one_way", sc["distance"], who=who) * 2 * days
own = 0.0
if mode != "walk":
life = num(o, "life_years", 5, lo=0.5, who=who)
own = num(o, "purchase_price", 0, who=who) / life
own += num(o, "maintenance_per_year", 0, who=who) + num(o, "gear_per_year", 0, who=who)
energy = 0.0
if mode == "ebike":
kwh = d / 100 * num(o, "kwh_per_100", 1.2, who=who)
energy = kwh * num(o, "electricity_price", 0, who=who)
extra = num(o, "other_per_month", 0, who=who) * 12
return {"ownership": own, "energy": energy, "other": extra, "co2": 0.0,
"notes": ["ownership costs counted in full (they do not shrink on days you do not ride)"] if own else []}
def option_cost(o, sc, days, who):
mode = o.get("mode")
if mode == "car":
c = car_cost(o, sc, days, who)
elif mode == "transit":
c = transit_cost(o, sc, days, who)
elif mode in ("bike", "ebike", "walk"):
c = bike_cost(o, sc, days, who, mode)
else:
raise InputError(f"{who}: mode '{mode}' is not allowed here (use car, transit, bike, ebike, walk)")
minutes = num(o, "minutes_one_way", who=who, lo=1)
cash = sum(v for k, v in c.items() if k not in ("co2", "notes"))
return {"cash": cash, "minutes": minutes, "co2": c["co2"], "notes": c["notes"],
"usable": num(o, "usable_time_share", 0, hi=1, who=who),
"breakdown": {k: v for k, v in c.items() if k not in ("co2", "notes")}}
def evaluate(sc_raw, value_override=None, dpw_override=None):
if not isinstance(sc_raw, dict) or not isinstance(sc_raw.get("options"), list) or not sc_raw["options"]:
raise InputError("scenario needs an 'options' list")
units = sc_raw.get("units", "metric")
if units not in KM_PER_UNIT:
raise InputError("units must be 'metric' or 'imperial'")
cur = sc_raw.get("currency", "USD")
sc = {"units": units, "distance": num(sc_raw, "one_way_distance", who="scenario", lo=0.1),
"sym": SYMBOLS.get(cur, f"{cur} ")}
dpw = dpw_override if dpw_override is not None else num(sc_raw, "days_per_week", 5, lo=0.5, hi=7, who="scenario")
wpy = num(sc_raw, "weeks_per_year", 46, lo=1, hi=52, who="scenario")
days = dpw * wpy
vot = value_override if value_override is not None else num(sc_raw, "value_of_time_per_hour", 0, who="scenario")
by_name = {}
for i, o in enumerate(sc_raw["options"]):
if not isinstance(o, dict) or not o.get("name") or o.get("mode") not in MODES:
raise InputError(f"option {i + 1}: needs a 'name' and a 'mode' ({', '.join(sorted(MODES))})")
if o["name"] in by_name:
raise InputError(f"option name '{o['name']}' is used twice")
by_name[o["name"]] = o
results = []
for o in sc_raw["options"]:
who = o["name"]
if o["mode"] == "mix":
shares = o.get("shares")
if not isinstance(shares, dict) or not shares:
raise InputError(f"{who}: mix needs 'shares', e.g. {{\"Bike\": 0.6, \"Transit\": 0.4}}")
tot = sum(shares.values())
if abs(tot - 1) > 0.001:
raise InputError(f"{who}: shares add up to {tot:g}, not 1")
cash = co2 = hours = eff = 0.0
notes, parts = [], []
for nm, sh in shares.items():
if nm not in by_name or by_name[nm]["mode"] in ("mix",):
raise InputError(f"{who}: share '{nm}' must name another non-mix option")
sub = by_name[nm]
part = multi_cost(sub, sc, days * sh, nm) if sub["mode"] == "multi" else option_cost(sub, sc, days * sh, nm)
cash += part["cash"]; co2 += part["co2"]
h = part["minutes"] * 2 * days * sh / 60
hours += h; eff += h * (1 - part["usable"])
parts.append(f"{nm} {sh * 100:.0f} % of days ({days * sh / 12:.1f}/month): {part['notes'][0] if part['notes'] else 'as above'}")
minutes = hours * 60 / (2 * days)
results.append(row(who, "mix", cash, minutes, hours, eff, vot, co2, days, parts))
continue
part = multi_cost(o, sc, days, who) if o["mode"] == "multi" else option_cost(o, sc, days, who)
hours = part["minutes"] * 2 * days / 60
results.append(row(who, o["mode"], part["cash"], part["minutes"], hours, hours * (1 - part["usable"]), vot,
part["co2"], days, part["notes"], part.get("breakdown")))
return {"currency": sc_raw.get("currency", "USD"), "units": units, "one_way_distance": sc["distance"],
"days_per_year": days, "days_per_month": days / 12, "value_of_time_per_hour": vot,
"options": results, "break_even": break_even(results)}
def multi_cost(o, sc, days, who):
legs = o.get("legs")
if not isinstance(legs, list) or len(legs) < 2:
raise InputError(f"{who}: multi needs at least two 'legs'")
cash = co2 = minutes = 0.0
notes, usable_min = [], 0.0
for j, leg in enumerate(legs, 1):
if leg.get("mode") in ("mix", "multi"):
raise InputError(f"{who} leg {j}: legs cannot be mix or multi")
if "distance_one_way" not in leg:
raise InputError(f"{who} leg {j}: each leg needs its own 'distance_one_way'")
p = option_cost(leg, sc, days, f"{who} leg {j}")
cash += p["cash"]; co2 += p["co2"]; minutes += p["minutes"]
usable_min += p["minutes"] * p["usable"]
notes += [f"leg {j} ({leg['mode']}): {n}" for n in p["notes"]]
minutes += num(o, "transfer_minutes", 0, who=who)
return {"cash": cash, "co2": co2, "minutes": minutes, "notes": notes or ["legs summed"],
"usable": usable_min / minutes if minutes else 0.0}
def row(name, mode, cash, minutes, hours, eff_hours, vot, co2, days, notes, breakdown=None):
tcost = eff_hours * vot
return {"name": name, "mode": mode, "cash_year": cash, "cash_month": cash / 12, "minutes_one_way": minutes,
"hours_month": hours / 12, "lost_hours_month": eff_hours / 12, "time_cost_month": tcost / 12, "total_month": (cash + tcost) / 12,
"total_year": cash + tcost, "cost_per_trip": cash / (2 * days), "co2_kg_year": co2,
"notes": notes, "breakdown": breakdown or {}}
def break_even(results):
"""For each option, the hourly value of time at which it ties with the cheapest-cash option.
Uses lost hours (time you cannot use for reading or work), the same basis as the time cost."""
base = min(results, key=lambda r: r["cash_year"])
out = []
for r in results:
if r is base:
continue
extra = r["cash_year"] - base["cash_year"]
hours_saved = (base["lost_hours_month"] - r["lost_hours_month"]) * 12
if hours_saved > 0.01:
out.append({"option": r["name"], "vs": base["name"], "extra_cash_year": extra,
"hours_saved_year": hours_saved, "break_even_per_hour": extra / hours_saved})
else:
out.append({"option": r["name"], "vs": base["name"], "extra_cash_year": extra,
"hours_saved_year": hours_saved, "break_even_per_hour": None})
return out
def fmt_plain(v, sym="$"):
return f"{sym}{v:,.0f}" if abs(v) >= 100 else f"{sym}{v:,.2f}"
def render(res):
sym = SYMBOLS.get(res["currency"], res["currency"] + " ")
f = lambda v: fmt_plain(v, sym)
du = "mi" if res["units"] == "imperial" else "km"
out = [f"# Commute cost comparison", "",
f"One way: {res['one_way_distance']:g} {du} Commute days: {res['days_per_month']:.1f}/month ({res['days_per_year']:.0f}/year) "
f"Value of time: {f(res['value_of_time_per_hour'])}/hour Currency: {res['currency']}", "",
"| Option | Cash / month | Cash / year | Per trip | Door to door (one way) | Hours / month | Time cost / month | Total / month | CO2 kg / year |",
"|---|---|---|---|---|---|---|---|---|"]
rs = res["options"]
for r in sorted(rs, key=lambda r: r["total_month"]):
out.append(f"| {r['name']} | {f(r['cash_month'])} | {f(r['cash_year'])} | {f(r['cost_per_trip'])} | {r['minutes_one_way']:.0f} min | "
f"{r['hours_month']:.1f} | {f(r['time_cost_month'])} | **{f(r['total_month'])}** | {r['co2_kg_year']:,.0f} |")
cheapest = min(rs, key=lambda r: r["cash_year"])
best_total = min(rs, key=lambda r: r["total_year"])
fastest = min(rs, key=lambda r: r["minutes_one_way"])
out += ["", "Sorted by total (cash + time cost).", "",
"## Headline", "",
f"- Cheapest in cash: **{cheapest['name']}** ({f(cheapest['cash_month'])}/month).",
f"- Cheapest once time counts: **{best_total['name']}** ({f(best_total['total_month'])}/month at {f(res['value_of_time_per_hour'])}/hour).",
f"- Fastest: **{fastest['name']}** ({fastest['minutes_one_way']:.0f} min one way, {fastest['hours_month']:.1f} h/month)."]
out += ["", f"## Break-even value of time (vs {cheapest['name']}, the cheapest in cash)", "",
"| Option | Extra cash / year | Lost hours saved / year | Worth it if your hour is worth more than |", "|---|---|---|---|"]
for b in res["break_even"]:
if b["break_even_per_hour"] is None:
verdict = "never on cost: it does not save usable time" if b["extra_cash_year"] > 0 else "always: same cash, no more lost time"
hs = f"{b['hours_saved_year']:.0f}" if b["hours_saved_year"] >= 0 else f"none ({-b['hours_saved_year']:.0f} more lost)"
out.append(f"| {b['option']} | {f(b['extra_cash_year'])} | {hs} | {verdict} |")
else:
out.append(f"| {b['option']} | {f(b['extra_cash_year'])} | {b['hours_saved_year']:.0f} | {f(b['break_even_per_hour'])}/hour |")
out += ["", "## Notes per option", ""]
for r in rs:
bd = ", ".join(f"{LABELS.get(k, k)} {f(v / 12)}" for k, v in r["breakdown"].items() if v) if r["breakdown"] else ""
out.append(f"- **{r['name']}** ({r['mode']}): " + "; ".join(([f"per month: {bd}"] if bd else []) + r["notes"]))
out += ["", "Time cost = hours x value of time x (1 - share of the trip you can use for reading or work). "
"Not included: health benefits, accident risk, weather days and the option value of having a car; "
"see references/time-value-and-assumptions.md."]
return "\n".join(out) + "\n"
def self_test():
base = {"currency": "USD", "units": "metric", "one_way_distance": 10, "days_per_week": 5, "weeks_per_year": 48,
"value_of_time_per_hour": 20}
car = {"name": "Car", "mode": "car", "minutes_one_way": 20, "fuel_l_per_100km": 8, "fuel_price": 1.5,
"maintenance_per_distance": 0.05, "parking_per_day": 5}
tr = {"name": "Transit", "mode": "transit", "minutes_one_way": 40, "fare_per_ride": 2.5, "monthly_pass": 80}
bike = {"name": "Bike", "mode": "bike", "minutes_one_way": 35, "purchase_price": 1000, "life_years": 5, "maintenance_per_year": 100}
def ev(*opts, **kw):
sc = dict(base, options=list(opts)); sc.update(kw.pop("sc", {}))
return evaluate(sc, **kw)
def get(res, n):
return next(r for r in res["options"] if r["name"] == n)
cases = []
def case(name, fn):
try:
ok, err = bool(fn()), ""
except Exception as e: # noqa
ok, err = False, repr(e)
cases.append((name, ok, err))
# car: 240 days * 20 km = 4800 km; fuel 384 L * 1.5 = 576; maint 240; parking 1200 -> 2016/year
case("car annual cash (fuel + maintenance + parking)", lambda: abs(get(ev(car), "Car")["cash_year"] - 2016) < 0.01)
case("car CO2 petrol 2.35 kg/L", lambda: abs(get(ev(car), "Car")["co2_kg_year"] - 384 * 2.35) < 0.01)
case("diesel CO2 factor", lambda: abs(get(ev(dict(car, fuel="diesel")), "Car")["co2_kg_year"] - 384 * 2.68) < 0.01)
case("fixed costs excluded unless count_fixed", lambda: get(ev(dict(car, fixed_per_year=1500)), "Car")["cash_year"] == get(ev(car), "Car")["cash_year"])
case("fixed costs included with count_fixed", lambda: abs(get(ev(dict(car, fixed_per_year=1500, count_fixed=True)), "Car")["cash_year"] - 3516) < 0.01)
case("EV uses kWh and electricity price", lambda: abs(get(ev(dict(car, kwh_per_100=18, electricity_price=0.3, fuel_l_per_100km=None)), "Car")["breakdown"]["energy"] - 4800 / 100 * 18 * 0.3) < 0.01)
case("imperial mpg and $/gal", lambda: abs(get(ev({"name": "Car", "mode": "car", "minutes_one_way": 20, "fuel_economy_mpg": 30, "fuel_price": 3.6}, sc={"units": "imperial"}), "Car")["cash_year"] - 4800 / 30 * 3.6) < 0.01)
case("imperial CO2 8.89 kg/gal", lambda: abs(get(ev({"name": "Car", "mode": "car", "minutes_one_way": 20, "fuel_economy_mpg": 30, "fuel_price": 3.6}, sc={"units": "imperial"}), "Car")["co2_kg_year"] - 160 * 8.89) < 0.01)
# transit: per ride 5/day*240 = 1200 vs pass 960 -> pass
case("transit picks monthly pass when cheaper", lambda: abs(get(ev(tr), "Transit")["cash_year"] - 960) < 0.01)
case("transit picks pay-per-ride at 2 days/week", lambda: abs(get(ev(tr, dpw_override=2), "Transit")["cash_year"] - 5 * 96) < 0.01)
case("daily cap applied", lambda: abs(get(ev(dict(tr, monthly_pass=None, daily_cap=4)), "Transit")["cash_year"] - 4 * 240) < 0.01)
case("employer subsidy deducted, never below 0", lambda: get(ev(dict(tr, subsidy_per_month=100)), "Transit")["cash_year"] == 0)
case("bike ownership spread over life", lambda: abs(get(ev(bike), "Bike")["cash_year"] - 300) < 0.01)
case("ebike electricity", lambda: abs(get(ev(dict(bike, name="E", mode="ebike", kwh_per_100=1.5, electricity_price=0.3)), "E")["breakdown"]["energy"] - 4800 / 100 * 1.5 * 0.3) < 0.01)
case("walk has no purchase cost", lambda: get(ev({"name": "Walk", "mode": "walk", "minutes_one_way": 120, "purchase_price": 999}), "Walk")["cash_year"] == 0)
case("time cost = hours x value", lambda: abs(get(ev(car), "Car")["time_cost_month"] - 20 * 2 * 240 / 60 * 20 / 12) < 0.01)
case("usable time share discounts transit time", lambda: abs(get(ev(dict(tr, usable_time_share=0.5)), "Transit")["time_cost_month"] - 40 * 2 * 240 / 60 * 20 * 0.5 / 12) < 0.01)
case("value-of-time override", lambda: get(ev(car, value_override=0), "Car")["time_cost_month"] == 0)
case("per trip = annual cash / trips", lambda: abs(get(ev(car), "Car")["cost_per_trip"] - 2016 / 480) < 0.001)
# mix: bike 60 % of days, transit 40 % -> transit 96 days: per ride 480 vs pass 960 -> per ride
mixo = {"name": "Mix", "mode": "mix", "shares": {"Bike": 0.6, "Transit": 0.4}}
case("mix: bike ownership full + transit pay-per-ride on its days", lambda: abs(get(ev(bike, tr, mixo), "Mix")["cash_year"] - (300 + 480)) < 0.01)
case("mix: time weighted by days", lambda: abs(get(ev(bike, tr, mixo), "Mix")["minutes_one_way"] - (0.6 * 35 + 0.4 * 40)) < 0.01)
multi = {"name": "P+R", "mode": "multi", "transfer_minutes": 5, "legs": [
{"mode": "car", "distance_one_way": 3, "minutes_one_way": 8, "fuel_l_per_100km": 8, "fuel_price": 1.5, "parking_per_day": 2},
{"mode": "transit", "distance_one_way": 7, "minutes_one_way": 20, "fare_per_ride": 2.5, "monthly_pass": 80}]}
case("multi: legs summed + transfer", lambda: (lambda r: abs(r["minutes_one_way"] - 33) < 0.01 and abs(r["cash_year"] - (1440 * 0.08 * 1.5 + 480 + 960)) < 0.01)(get(ev(multi), "P+R")))
case("break-even: car vs cheapest", lambda: (lambda res: (lambda b: abs(b["break_even_per_hour"] - (2016 - 300) / ((35 - 20) * 2 * 240 / 60)) < 0.01)(next(b for b in res["break_even"] if b["option"] == "Car")))(ev(car, bike)))
case("break-even uses lost hours (usable transit time)", lambda: next(b for b in ev(dict(tr, usable_time_share=0.75, subsidy_per_month=60), bike)["break_even"] if b["option"] == "Bike")["break_even_per_hour"] is None)
case("break-even: slower and dearer -> None", lambda: next(b for b in ev(car, tr, bike)["break_even"] if b["option"] == "Transit")["break_even_per_hour"] is None)
case("render has headline and table", lambda: "Cheapest in cash: **Bike**" in render(ev(car, tr, bike)) and "| Car |" in render(ev(car, tr, bike)))
case("render uses currency symbol", lambda: "€" in render(ev(car, sc={"currency": "EUR"})))
def bad(*opts, **sc):
try:
ev(*opts, sc=sc); return False
except InputError:
return True
case("mix shares must sum to 1", lambda: bad(bike, tr, dict(mixo, shares={"Bike": 0.6, "Transit": 0.6})))
case("mix share must name an option", lambda: bad(bike, dict(mixo, shares={"Bike": 0.5, "Train": 0.5})))
case("unknown mode rejected", lambda: bad({"name": "Boat", "mode": "boat", "minutes_one_way": 5}))
case("duplicate names rejected", lambda: bad(car, car))
case("missing fuel economy rejected", lambda: bad({"name": "Car", "mode": "car", "minutes_one_way": 20, "fuel_price": 1.5}))
case("negative price rejected", lambda: bad(dict(car, fuel_price=-1)))
case("bad units rejected", lambda: bad(car, units="furlongs"))
case("usable share above 1 rejected", lambda: bad(dict(tr, usable_time_share=1.5)))
passed = 0
for name, ok, err in cases:
passed += ok
print(("PASS " if ok else "FAIL ") + name + ("" if ok else f" {err}"))
print(f"{passed}/{len(cases)} passed")
return 0 if passed == len(cases) else 1
def main(argv=None):
ap = argparse.ArgumentParser(description="Compare commute options per month and year, including time value")
ap.add_argument("scenario", nargs="?")
ap.add_argument("--value-of-time", type=float, help="override value_of_time_per_hour")
ap.add_argument("--days-per-week", type=float, help="override days_per_week (e.g. 3 for hybrid work)")
ap.add_argument("--json", action="store_true")
ap.add_argument("--self-test", action="store_true")
a = ap.parse_args(argv)
if a.self_test:
return self_test()
if not a.scenario:
ap.error("give a scenario JSON file, or --self-test")
try:
with open(a.scenario, encoding="utf-8") as fh:
sc = json.load(fh)
if a.days_per_week is not None and not 0.5 <= a.days_per_week <= 7:
raise InputError("--days-per-week must be 0.5..7")
res = evaluate(sc, a.value_of_time, a.days_per_week)
except json.JSONDecodeError as e:
print(f"error: scenario is not valid JSON: {e}", file=sys.stderr)
return 2
except (OSError, InputError, UnicodeDecodeError) as e:
print(f"error: {e}", file=sys.stderr)
return 2
print(json.dumps(res, indent=2) if a.json else render(res), end="" if not a.json else "\n")
return 0
if __name__ == "__main__":
sys.exit(main())