shopdb-flask/plugins/printers/services/supply_history.py

"""Read a supply's level history and say what it means.

Two questions come out of the same series of readings:

  * how fast is it draining, and when does it hit empty
  * how many times has it been replaced

Both hang on one observation: a cartridge only ever goes DOWN while it is in
use, so a rise is a replacement. Everything here is built on locating those
rises and treating each stretch between them as one cartridge's life.

The maths is deliberately kept away from Zabbix so it can be tested against a
list of numbers. The service layer fetches; this decides.
"""

from datetime import datetime, timezone

# A reading can wobble by a point without anything happening - SNMP rounding,
# a gauge settling after a power cycle. A rise has to clear this to count as a
# new cartridge rather than noise.
REPLACEMENT_RISE = 10

# A replacement must also LAND high. A fresh cartridge reads near full, so a
# rise that stops mid-range is a gauge bouncing, not a swap. Without this the
# count was any +10 between readings, and the two common noise shapes both
# scored: a supply reading 0 or near-0 while it was out of the machine, then the
# real level again (0 -> 60 counts as +60), and a coarse gauge ticking back up
# after a reseat or a power cycle. That is how one cartridge claimed five
# changes in ninety days.
#
# A site that fits PART-USED cartridges will under-count with this rule. That is
# the right way round: a missed swap widens the run and slows the estimate,
# while a phantom swap resets the run and throws the estimate away entirely.
NEW_CARTRIDGE_LEVEL = 80

# Below this many readings a slope is arithmetic, not evidence. Two points
# through a coarse gauge can "prove" any rate at all.
MIN_POINTS_FOR_ESTIMATE = 4

# Many printers report in 10% steps, so a fortnight can pass on one plateau.
# Without a minimum observed drop the slope reads as zero and the forecast
# says "never", which is worse than saying nothing.
MIN_DROP_FOR_ESTIMATE = 2

# How far the fastest and slowest intervals may differ before a single rate
# stops being a fair summary. A cartridge that ran at 10 percent/day for two
# days and 0.2 percent/day since is not described by any one number, and a
# precise-looking figure invites more trust than it has earned.
RATE_SPREAD_FACTOR = 5

# At or below this, the cartridge is done and the arithmetic stops being the
# useful answer. A supply sitting at 1% that drains a tenth of a point a day
# computes to ten days; a printer at 1% is out of toner as far as anyone
# standing at it is concerned, and it is what should be ordered first. Rate is
# still reported - only the days-left figure is floored.
EMPTY_LEVEL = 5


def _asfloat(value):
    try:
        return float(value)
    except (TypeError, ValueError):
        return None


def normalise(points):
    """[(clock, value)] -> sorted [(datetime, float)], junk dropped.

    Zabbix returns clock as a unix string and value as a string; a history
    table can also carry the odd unparseable row.
    """
    out = []
    for clock, value in points:
        seconds = _asfloat(clock)
        level = _asfloat(value)
        if seconds is None or level is None:
            continue
        out.append((datetime.fromtimestamp(seconds, tz=timezone.utc), level))
    out.sort(key=lambda p: p[0])
    return drop_spikes(out)


def drop_spikes(points, rise=REPLACEMENT_RISE):
    """Remove one-reading dips that RECOVER to where they came from.

    A single reading far below both neighbours, then a recovery, is a big
    upward step that scores as a cartridge change. That is how a cartridge
    claimed five changes in ninety days.

    NOT a Zabbix timeout: an item that does not answer records nothing, it does
    not write a zero. The dip is a value the device really reported - a supply
    pulled out to be shaken and reseated, a door open mid-poll, or a site whose
    preprocessing maps the Printer MIB's "unknown" sentinels (-1/-2/-3, which
    cannot land in the unsigned history table) onto 0.

    The filter keys on SHAPE rather than cause, which is why it holds for all of
    them: a level that comes back to where it was did not get a new cartridge.

    Only a dip that comes BACK to roughly its previous level is removed. A
    genuine near-empty reading before a swap (30, 5, 100) does not recover - it
    jumps to full - so it is kept, and the swap after it still counts.
    """
    if len(points) < 3:
        return points
    out = [points[0]]
    for index in range(1, len(points) - 1):
        previous = points[index - 1][1]
        current = points[index][1]
        following = points[index + 1][1]
        dipped = (previous - current) >= rise and (following - current) >= rise
        recovered = abs(following - previous) <= rise
        if dipped and recovered:
            continue
        out.append(points[index])
    out.append(points[-1])
    return out


def _is_replacement(previous, current, rise=REPLACEMENT_RISE,
                    newlevel=NEW_CARTRIDGE_LEVEL):
    """One definition of "a cartridge was changed", used by every caller.

    The rise must be big enough to clear gauge noise AND land near full, which
    is what a new cartridge reads. Both conditions, because either alone admits
    a shape that is not a swap.
    """
    return (current - previous) >= rise and current >= newlevel


def find_replacements(points, rise=REPLACEMENT_RISE,
                      newlevel=NEW_CARTRIDGE_LEVEL):
    """Timestamps where the level jumped up - one per cartridge change.

    Returns [] for a series that only falls.
    """
    replacements = []
    for (_, previous), (when, current) in zip(points, points[1:]):
        if _is_replacement(previous, current, rise, newlevel):
            replacements.append(when)
    return replacements


def current_run(points, rise=REPLACEMENT_RISE):
    """The readings since the last replacement.

    Fitting across a replacement averages a spent cartridge with a fresh one
    and produces a slope that describes neither.
    """
    if not points:
        return []
    start = 0
    for index in range(1, len(points)):
        if _is_replacement(points[index - 1][1], points[index][1], rise):
            start = index
    return points[start:]


def interval_rates(points):
    """Percent-per-day for each consecutive pair, falling intervals only.

    A rise inside a run is gauge noise (a swap would have ended the run), and
    a flat interval is real information - a cartridge that did not move - so it
    stays in at zero.
    """
    rates = []
    for (whenprev, prev), (when, current) in zip(points, points[1:]):
        days = (when - whenprev).total_seconds() / 86400
        if days <= 0:
            continue
        drop = prev - current
        if drop < 0:
            continue
        rates.append(drop / days)
    return rates


def _median(values):
    ordered = sorted(values)
    count = len(ordered)
    if not count:
        return None
    middle = count // 2
    if count % 2:
        return ordered[middle]
    return (ordered[middle - 1] + ordered[middle]) / 2


def burn_rate(points):
    """Percent consumed per day over these readings, or None.

    THE MEDIAN OF THE PER-INTERVAL RATES, not the slope between the first and
    last reading. Two endpoints cannot tell "steady" from "burst then stopped":
    a cartridge that lost 20 percent in two days and then barely moved for a
    month reads as 0.83 percent/day forever after, so the report keeps promising
    it will run out long after printing slowed. The burst is one interval among
    many to a median, and one of two points to a secant.

    None means "no honest estimate": too few readings, no elapsed time, or a
    drop too small to distinguish from a gauge that has not moved yet.
    """
    if len(points) < MIN_POINTS_FOR_ESTIMATE:
        return None
    total_days = (points[-1][0] - points[0][0]).total_seconds() / 86400
    if total_days <= 0:
        return None
    # The overall drop still gates the estimate: a gauge sitting on one plateau
    # has not proved anything yet, whatever the intervals say.
    if points[0][1] - points[-1][1] < MIN_DROP_FOR_ESTIMATE:
        return None
    rate = _median(interval_rates(points))
    if not rate:
        # Every interval flat or rising, yet the run dropped overall - the
        # movement is all in intervals the median discarded. Fall back to the
        # whole-run slope rather than reporting nothing.
        return (points[0][1] - points[-1][1]) / total_days
    return rate


def rate_is_unstable(points, factor=RATE_SPREAD_FACTOR):
    """True when the intervals disagree enough that one number oversells it."""
    rates = [r for r in interval_rates(points) if r > 0]
    if len(rates) < 2:
        return False
    return max(rates) >= min(rates) * factor


def analyse(points, rise=REPLACEMENT_RISE, currentlevel=None):
    """Everything the report needs about one supply.

    `currentlevel` is the live reading from the printer, when the caller has
    one. It is what the days-left figure is computed against, because it is
    what the report displays: taking the level from the display and the
    countdown from the last stored history point lets the two disagree, and a
    row reading "20% - 4 days left" is read as a broken report, correctly.

    Returns:
      currentlevel     live reading if given, else the latest stored one
      daysleft         at the current rate, or None when there is no estimate
      burnrateperday   percent per day, or None
      reason           why there is no estimate - shown rather than hidden, so
                       a missing forecast is explained instead of looking broken
      replacements     how many times it has been changed in this window
      lastreplaced     when, or None
      basisdays        span of readings the estimate rests on
      points           the run since the last replacement, for the chart
    """
    points = normalise(points)
    result = {
        'currentlevel': currentlevel, 'daysleft': None, 'burnrateperday': None,
        'rateunstable': False,
        'reason': None, 'replacements': 0, 'lastreplaced': None,
        'basisdays': 0, 'points': [],
    }
    if not points:
        # A live level with no history is still worth acting on when it is low.
        if currentlevel is not None and currentlevel <= EMPTY_LEVEL:
            result['daysleft'] = 0
        else:
            result['reason'] = 'no history'
        return result

    replacements = find_replacements(points, rise=rise)
    result['replacements'] = len(replacements)
    result['lastreplaced'] = replacements[-1].isoformat() if replacements else None

    run = current_run(points, rise=rise)
    stored = run[-1][1]
    level = stored if currentlevel is None else currentlevel
    result['currentlevel'] = level
    result['points'] = [(when.isoformat(), level) for when, level in run]
    if len(run) >= 2:
        result['basisdays'] = round(
            (run[-1][0] - run[0][0]).total_seconds() / 86400, 1)

    # A live level well above the stored run means it was swapped since the
    # last stored reading. The run describes the cartridge that came out.
    if currentlevel is not None and currentlevel - stored >= rise:
        result['replacements'] += 1
        result['reason'] = 'replaced recently'
        return result

    rate = burn_rate(run)

    # Empty is empty. Ordering by a rate below this level ranks a dead
    # cartridge behind a healthy one that happens to be draining faster.
    if level <= EMPTY_LEVEL:
        result['daysleft'] = 0
        result['burnrateperday'] = round(rate, 2) if rate is not None else None
        return result

    if rate is None:
        # Say which of the three it is; "no estimate" alone invites a bug report.
        if len(run) < MIN_POINTS_FOR_ESTIMATE:
            result['reason'] = ('replaced recently' if replacements
                                else 'not enough history yet')
        else:
            result['reason'] = 'level has not moved enough to estimate'
        return result

    result['burnrateperday'] = round(rate, 2)
    result['daysleft'] = max(0, int(level / rate))
    # Say so when the intervals disagree wildly. The number is still the best
    # estimate available; the flag stops it reading as a measurement.
    result['rateunstable'] = rate_is_unstable(run)
    return result


# Urgency bands. The report groups by these and the order list is drawn from
# the first two, so they are defined once here rather than in the view - a
# heading that disagrees with what got added to the list is worse than either.
SOON_DAYS = 14
MONTH_DAYS = 30

# What goes on the order list. Two weeks is the horizon that survives a
# delivery: ordering only what is already empty means running empty.
ORDER_HORIZON_DAYS = SOON_DAYS

BANDS = ('empty', 'soon', 'month', 'later')


def band(daysleft):
    """Which urgency band a cartridge belongs in, or None with no estimate."""
    if daysleft is None:
        return None
    if daysleft <= 0:
        return 'empty'
    if daysleft <= SOON_DAYS:
        return 'soon'
    if daysleft <= MONTH_DAYS:
        return 'month'
    return 'later'


def orderlist(cartridges, horizon=ORDER_HORIZON_DAYS):
    """What to buy, grouped by part number: [{partnumber, quantity, ...}].

    The report's whole purpose reduced to a list someone can hand to
    purchasing. Two cartridges of the same part in different printers is a
    quantity of two, which is the number an order needs and the one a
    per-printer table makes you count by hand.

    A cartridge with no part mapped is still listed, under its printer's model
    and colour. Dropping it would quietly shorten the order.
    """
    groups = {}
    for cartridge in cartridges:
        if cartridge.get('daysleft') is None or cartridge['daysleft'] > horizon:
            continue
        parts = cartridge.get('partnumbers') or []
        # Several capacity tiers can match; the first is the standard one and
        # is what the low-supplies report shows first too.
        partnumber = parts[0]['partnumber'] if parts else None
        key = (partnumber, cartridge.get('color'), cartridge.get('model'))
        group = groups.setdefault(key, {
            'partnumber': partnumber,
            'color': cartridge.get('color'),
            'supplytype': cartridge.get('supplytype'),
            'model': cartridge.get('model'),
            'marketingname': parts[0].get('marketingname') if parts else None,
            'alternates': [p['partnumber'] for p in parts[1:]],
            'quantity': 0,
            'printers': [],
        })
        group['quantity'] += 1
        group['printers'].append({
            'printerid': cartridge.get('printerid'),
            'printername': cartridge.get('printername'),
            'daysleft': cartridge.get('daysleft'),
        })

    # Unmapped parts last: they need a decision before they can be ordered.
    return sorted(groups.values(),
                  key=lambda g: (g['partnumber'] is None,
                                 -g['quantity'],
                                 g['partnumber'] or ''))


def soonest(supplies):
    """Days-left of the supply that runs out first, or None if none estimate.

    The report sorts printers by this: a printer is as urgent as its most
    pressing cartridge, and listing it once per supply would scatter it down
    the page.
    """
    days = [s['daysleft'] for s in supplies if s.get('daysleft') is not None]
    return min(days) if days else None