Luc, could you try the attached version of xtypes.py and see if it makes any difference?
-tk On Sat, Apr 4, 2020 at 5:10 AM Lucas Heijst <[email protected]> wrote: > Tom, Vince, Glenn, > > Glenn, > Yes, I use external mariadb5 databases. > And no, during the creation of the vector plots the memory use of weewx is > not much (2.7 %). > Changing the aggregate_interval has effect, but is not the main cause. > > Tom, Vince, Glenn, > The main cause of the slow vector calculation is the aggregate_type = max > on windgustvec. > Without the max aggregation all 5 vector plots (6h, day, week, month, > year) took together 24 seconds. > > Generated 76 images for report vproReport in 113.71 seconds > > Luc > > ===== > [[[hourwindvec]]] > [[[[windvec]]]] > plot_type = vector > [[[[windgustvec]]]] > plot_type = vector > ### aggregate_type = max > ### aggregate_interval = 900 # == 15 min > > [[[daywindvec]]] > [[[[windvec]]]] > plot_type = vector > [[[[windgustvec]]]] > plot_type = vector > ### aggregate_type = max > ### aggregate_interval = 3600 # == 1 hour > > [[[weekwindvec]]] > [[[[windvec]]]] > plot_type = vector > [[[[windgustvec]]]] > plot_type = vector > ### aggregate_type = max > > [[[monthwindvec]]] > [[[[windvec]]]] > plot_type = vector > [[[[windgustvec]]]] > plot_type = vector > ### aggregate_type = max > ### aggregate_interval = 3600 # == 1 hour > > [[[yearwindvec]]] > [[[[windvec]]]] > plot_type = vector > [[[[windgustvec]]]] > plot_type = vector > ### aggregate_type = max > ===== > > > > > On Friday, 3 April 2020 22:33:50 UTC-3, Glenn McKechnie wrote: >> >> (Sigh, and to the list) >> >> Luc, >> >> I notice a "Launch of report thread aborted: existing report thread >> still running" >> >> Do you use mysql (mariadb)? >> If you do, does its CPU usage (from top) increase? >> >> If so try dropping the aggregate interval = 900 and see if there is a >> difference >> >> On 04/04/2020, Lucas Heijst <[email protected]> wrote: >> > Tom, >> > >> > There was not much info in the syslog, thats why I didnt send it. >> > I included the syslog this time. >> > >> > I was wrong: the reportgenerator didn't hang, only it is VERY slow >> > >> > First I generated all other plots: 71 images in 90 seconds >> > Generated 71 images for report vproReport in 89.58 seconds >> > >> > Then I added a 6-hour vector plot. Extra time for that 6h plot: 70 >> seconds >> > Generated 72 images for report vproReport in 159.32 seconds >> > >> > Then added a 24-hour vector plot. Extra time for that 24h vector plot >> 266 >> > seconds >> > Generated 73 images for report vproReport in 425.79 seconds >> > >> > Estimated time for the other vector plots: >> > week vector plot 31 minutes >> > month vector plot: 137 minutes >> > year vector plot: 27 hours >> > >> > Luc >> > >> > >> > On Friday, 3 April 2020 20:13:16 UTC-3, Tom Keffer wrote: >> >> >> >> Worked fine for me. >> >> >> >> Luc: you know better. We need a log! Perhaps the ReportGenerator is >> not >> >> finishing before the next report is due? Perhaps the program >> segfaulted? >> >> Who knows without a log? >> >> >> >> -tk >> >> [image: image.png] >> >> >> >> On Fri, Apr 3, 2020 at 4:09 PM Lucas Heijst <[email protected] >> >> <javascript:>> wrote: >> >> >> >>> Currently running weewx 4.0.0b18. >> >>> >> >>> The reportgenerator hangs (it never finishes) during calculating of >> the >> >>> section below. >> >>> >> >>> [[[hourwindvec]]] >> >>> [[[[windvec]]]] >> >>> plot_type = vector >> >>> [[[[windgustvec]]]] >> >>> plot_type = vector >> >>> aggregate_type = max >> >>> aggregate_interval = 900 # == 15 min >> >>> >> >>> Luc >> >>> >> >>> -- >> >>> You received this message because you are subscribed to the Google >> Groups >> >>> >> >>> "weewx-development" group. >> >>> To unsubscribe from this group and stop receiving emails from it, >> send an >> >>> >> >>> email to [email protected] <javascript:>. >> >>> To view this discussion on the web visit >> >>> >> https://groups.google.com/d/msgid/weewx-development/601f87df-cbe7-482c-bf49-722a7a5de7bd%40googlegroups.com >> >>> >> >>> < >> https://groups.google.com/d/msgid/weewx-development/601f87df-cbe7-482c-bf49-722a7a5de7bd%40googlegroups.com?utm_medium=email&utm_source=footer> >> >> >>> . >> >>> >> >> >> > >> > -- >> > You received this message because you are subscribed to the Google >> Groups >> > "weewx-development" group. >> > To unsubscribe from this group and stop receiving emails from it, send >> an >> > email to [email protected]. >> > To view this discussion on the web visit >> > >> https://groups.google.com/d/msgid/weewx-development/afee474f-61cb-4c8d-8bfd-5f1c9a18773f%40googlegroups.com. >> >> > >> >> >> -- >> >> >> Cheers >> Glenn >> >> rorpi - read only raspberry pi & various weewx addons >> https://github.com/glennmckechnie >> > -- > You received this message because you are subscribed to the Google Groups > "weewx-development" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To view this discussion on the web visit > https://groups.google.com/d/msgid/weewx-development/a652d38c-0ed1-4171-9510-5ac601336362%40googlegroups.com > <https://groups.google.com/d/msgid/weewx-development/a652d38c-0ed1-4171-9510-5ac601336362%40googlegroups.com?utm_medium=email&utm_source=footer> > . > -- You received this message because you are subscribed to the Google Groups "weewx-development" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/weewx-development/CAPq0zECKNtZNoPb1hTckb3ttdxzhNNwzXc872du%3DmKDDuGOAtQ%40mail.gmail.com.
# # Copyright (c) 2019 Tom Keffer <[email protected]> # # See the file LICENSE.txt for your full rights. # """User-defined extensions to the WeeWX type system""" import math import weedb import weeutil.weeutil import weewx import weewx.units import weewx.wxformulas from weeutil.weeutil import isStartOfDay from weewx.units import ValueTuple # A list holding the type extensions. Each entry should be a subclass of XType, defined below. xtypes = [] class XType(object): """Base class for extensions to the WeeWX type system.""" def get_scalar(self, obs_type, record, db_manager=None): """Calculate a scalar. Specializing versions should raise... - an exception of type `weewx.UnknownType`, if the type `obs_type` is unknown to the function. - an exception of type `weewx.CannotCalculate` if the type is known to the function, but all the information necessary to calculate the type is not there. """ raise weewx.UnknownType def get_series(self, obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None): """Calculate a series, possibly with aggregation. Specializing versions should raise... - an exception of type `weewx.UnknownType`, if the type `obs_type` is unknown to the function. - an exception of type `weewx.CannotCalculate` if the type is known to the function, but all the information necessary to calculate the series is not there. """ raise weewx.UnknownType def get_aggregate(self, obs_type, timespan, aggregate_type, db_manager, **option_dict): """Calculate an aggregation. Specializing versions should raise... - an exception of type `weewx.UnknownType`, if the type `obs_type` is unknown to the function. - an exception of type `weewx.UnknownAggregation` if the aggregation type `aggregate_type` is unknown to the function. - an exception of type `weewx.CannotCalculate` if the type is known to the function, but all the information necessary to calculate the type is not there. """ raise weewx.UnknownAggregation def shut_down(self): """Opportunity to do any clean up.""" pass # ##################### Retrieval functions ########################### def get_scalar(obs_type, record, db_manager=None): """Return a scalar value""" # Search the list, looking for a get_scalar() method that does not raise an exception for xtype in xtypes: try: # Try this function. It will raise an exception if it does not know about the type. return xtype.get_scalar(obs_type, record, db_manager) except weewx.UnknownType: # This function does not know about the type. Move on to the next one. pass # None of the functions worked. raise weewx.UnknownType(obs_type) def get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None): """Return a series (aka vector) of, possibly aggregated, values.""" # Search the list, looking for a get_series() method that does not raise an exception for xtype in xtypes: try: # Try this function. It will raise an exception if it does not know about the type. return xtype.get_series(obs_type, timespan, db_manager, aggregate_type, aggregate_interval) except weewx.UnknownType: # This function does not know about the type. Move on to the next one. pass # None of the functions worked. raise weewx.UnknownType(obs_type) def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict): """Calculate an aggregation over a timespan""" # Search the list, looking for a get_aggregate() method that does not raise an exception for xtype in xtypes: try: # Try this function. It will raise an exception if it doesn't know about the type of # aggregation. return xtype.get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict) except (weewx.UnknownAggregation, weewx.UnknownType): pass raise weewx.UnknownAggregation("%s('%s')" % (aggregate_type, obs_type)) # # ######################## Class ArchiveTable ############################## # class ArchiveTable(XType): """Calculate types and aggregates directly from the archive table""" @staticmethod def get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None): """Get a series, possibly with aggregation, from the main archive database. The general strategy is that if aggregation is asked for, chop the series up into separate chunks, calculating the aggregate for each chunk. Then assemble the results. If no aggregation is called for, just return the data directly out of the database. """ startstamp, stopstamp = timespan start_vec = list() stop_vec = list() data_vec = list() if aggregate_type: # With aggregation unit, unit_group = None, None if aggregate_type == 'cumulative': do_aggregate = 'sum' total = 0 else: do_aggregate = aggregate_type for stamp in weeutil.weeutil.intervalgen(startstamp, stopstamp, aggregate_interval): # Get the aggregate as a ValueTuple agg_vt = get_aggregate(obs_type, stamp, do_aggregate, db_manager) if unit: # It's OK if the unit is unknown (=None). if agg_vt[1] is not None and (unit != agg_vt[1] or unit_group != agg_vt[2]): raise weewx.UnsupportedFeature("Cannot change unit groups " "within an aggregation.") else: unit, unit_group = agg_vt[1], agg_vt[2] start_vec.append(stamp.start) stop_vec.append(stamp.stop) if aggregate_type == 'cumulative': if agg_vt[0] is not None: total += agg_vt[0] data_vec.append(total) else: data_vec.append(agg_vt[0]) else: # Without aggregation. We only know how to get series that are in the database schema: if obs_type not in db_manager.sqlkeys: raise weewx.UnknownType(obs_type) # No aggregation sql_str = "SELECT dateTime, %s, usUnits, `interval` FROM %s " \ "WHERE dateTime >= ? AND dateTime <= ?" % (obs_type, db_manager.table_name) std_unit_system = None for record in db_manager.genSql(sql_str, (startstamp, stopstamp)): # Unpack the record timestamp, value, unit_system, interval = record if std_unit_system: if std_unit_system != unit_system: raise weewx.UnsupportedFeature("Unit type cannot change " "within an aggregation interval.") else: std_unit_system = unit_system start_vec.append(timestamp - interval * 60) stop_vec.append(timestamp) data_vec.append(value) unit, unit_group = weewx.units.getStandardUnitType(std_unit_system, obs_type, aggregate_type) return (ValueTuple(start_vec, 'unix_epoch', 'group_time'), ValueTuple(stop_vec, 'unix_epoch', 'group_time'), ValueTuple(data_vec, unit, unit_group)) # Set of SQL statements to be used for calculating aggregates from the main archive table. agg_sql_dict = { 'diff': "SELECT (b.%(obs_type)s - a.%(obs_type)s) FROM archive a, archive b " "WHERE b.dateTime = (SELECT MAX(dateTime) FROM archive " "WHERE dateTime <= %(stop)s) " "AND a.dateTime = (SELECT MIN(dateTime) FROM archive " "WHERE dateTime >= %(start)s);", 'first': "SELECT %(obs_type)s FROM %(table_name)s " "WHERE dateTime = (SELECT MIN(dateTime) FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL)", 'firsttime': "SELECT MIN(dateTime) FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL", 'last': "SELECT %(obs_type)s FROM %(table_name)s " "WHERE dateTime = (SELECT MAX(dateTime) FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL)", 'lasttime': "SELECT MAX(dateTime) FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL", 'maxtime': "SELECT dateTime FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND " "%(obs_type)s = (SELECT MAX(%(obs_type)s) FROM %(table_name)s " "WHERE dateTime > %(start)s and dateTime <= %(stop)s) " "AND %(obs_type)s IS NOT NULL", 'mintime': "SELECT dateTime FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND " "%(obs_type)s = (SELECT MIN(%(obs_type)s) FROM %(table_name)s " "WHERE dateTime > %(start)s and dateTime <= %(stop)s) " "AND %(obs_type)s IS NOT NULL", 'tderiv': "SELECT (b.%(obs_type)s - a.%(obs_type)s) / (b.dateTime-a.dateTime) " "FROM archive a, archive b " "WHERE b.dateTime = (SELECT MAX(dateTime) FROM archive " "WHERE dateTime <= %(stop)s) " "AND a.dateTime = (SELECT MIN(dateTime) FROM archive " "WHERE dateTime >= %(start)s);", } simple_agg_sql = "SELECT %(aggregate_type)s(%(obs_type)s) FROM %(table_name)s " \ "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " \ "AND %(obs_type)s IS NOT NULL" @staticmethod def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict): """Returns an aggregation of an observation type over a given time period, using the main archive table. obs_type: The type over which aggregation is to be done (e.g., 'barometer', 'outTemp', 'rain', ...) timespan: An instance of weeutil.Timespan with the time period over which aggregation is to be done. aggregate_type: The type of aggregation to be done. db_manager: An instance of weewx.manager.Manager or subclass. option_dict: Not used in this version. returns: A ValueTuple containing the result.""" if aggregate_type not in ['sum', 'count', 'avg', 'max', 'min'] + list( ArchiveTable.agg_sql_dict.keys()): raise weewx.UnknownAggregation(aggregate_type) interpolate_dict = { 'aggregate_type': aggregate_type, 'obs_type': obs_type, 'table_name': db_manager.table_name, 'start': timespan.start, 'stop': timespan.stop } select_stmt = ArchiveTable.agg_sql_dict.get(aggregate_type, ArchiveTable.simple_agg_sql) % interpolate_dict try: row = db_manager.getSql(select_stmt) except weedb.NoColumnError: raise weewx.UnknownType(aggregate_type) value = row[0] if row else None # Look up the unit type and group of this combination of observation type and aggregation: u, g = weewx.units.getStandardUnitType(db_manager.std_unit_system, obs_type, aggregate_type) # Time derivatives have special rules. For example, the time derivative of watt-hours is # watts, scaled by the number of seconds in an hour. The unit group also changes to # group_power. if aggregate_type == 'tderiv': if u == 'watt_second': u = 'watt' elif u == 'watt_hour': u = 'watt' value *= 3600 elif u == 'kilowatt_hour': u = 'kilowatt' value *= 3600 g = 'group_power' # Form the ValueTuple and return it: return weewx.units.ValueTuple(value, u, g) # # ######################## Class DailySummaries ############################## # class DailySummaries(XType): """Calculate from the daily summaries.""" # Set of SQL statements to be used for calculating aggregates from the daily summaries. daily_sql_dict = { 'avg': "SELECT SUM(wsum),SUM(sumtime) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'count': "SELECT SUM(count) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'gustdir': "SELECT max_dir FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND max = (SELECT MAX(max) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s)", 'max': "SELECT MAX(max) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'max_ge': "SELECT SUM(max >= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'max_le': "SELECT SUM(max <= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'maxmin': "SELECT MAX(min) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'maxmintime': "SELECT mintime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND min = (SELECT MAX(min) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime <%(stop)s)", 'maxsum': "SELECT MAX(sum) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'maxsumtime': "SELECT maxtime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND sum = (SELECT MAX(sum) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime <%(stop)s)", 'maxtime': "SELECT maxtime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND max = (SELECT MAX(max) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime <%(stop)s)", 'meanmax': "SELECT AVG(max) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'meanmin': "SELECT AVG(min) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'min': "SELECT MIN(min) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'min_ge': "SELECT SUM(min >= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'min_le': "SELECT SUM(min <= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'minmax': "SELECT MIN(max) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'minmaxtime': "SELECT maxtime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND max = (SELECT MIN(max) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime <%(stop)s)", 'minsum': "SELECT MIN(sum) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'minsumtime': "SELECT mintime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND sum = (SELECT MIN(sum) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime <%(stop)s)", 'mintime': "SELECT mintime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND min = (SELECT MIN(min) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime <%(stop)s)", 'rms': "SELECT SUM(wsquaresum),SUM(sumtime) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'sum': "SELECT SUM(sum) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'sum_ge': "SELECT SUM(sum >= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'sum_le': "SELECT SUM(sum <= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'vecavg': "SELECT SUM(xsum),SUM(ysum),SUM(sumtime) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'vecdir': "SELECT SUM(xsum),SUM(ysum) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", } @staticmethod def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict): """Returns an aggregation of a statistical type for a given time period, by using the daily summaries. obs_type: The type over which aggregation is to be done (e.g., 'barometer', 'outTemp', 'rain', ...) timespan: An instance of weeutil.Timespan with the time period over which aggregation is to be done. aggregate_type: The type of aggregation to be done. db_manager: An instance of weewx.manager.Manager or subclass. option_dict: Not used in this version. returns: A ValueTuple containing the result.""" # Check to see if this is a valid daily summary type: if not hasattr(db_manager, 'daykeys') or obs_type not in db_manager.daykeys: raise weewx.UnknownType(obs_type) aggregate_type = aggregate_type.lower() # Raise exception if we don't know about this type of aggregation if aggregate_type not in DailySummaries.daily_sql_dict: raise weewx.UnknownAggregation(aggregate_type) # We cannot use the day summaries if the starting and ending times of the aggregation # interval are not on midnight boundaries, and are not the first or last records in the # database. if db_manager.first_timestamp is None or db_manager.last_timestamp is None: raise weewx.UnknownAggregation(aggregate_type) if not (isStartOfDay(timespan.start) or timespan.start == db_manager.first_timestamp) \ or not (isStartOfDay(timespan.stop) or timespan.stop == db_manager.last_timestamp): raise weewx.UnknownAggregation(aggregate_type) val = option_dict.get('val') if val is None: target_val = None else: # The following is for backwards compatibility when ValueTuples had # just two members. This hack avoids breaking old skins. if len(val) == 2: if val[1] in ['degree_F', 'degree_C']: val += ("group_temperature",) elif val[1] in ['inch', 'mm', 'cm']: val += ("group_rain",) target_val = weewx.units.convertStd(val, db_manager.std_unit_system)[0] # Form the interpolation dictionary inter_dict = { 'start': weeutil.weeutil.startOfDay(timespan.start), 'stop': timespan.stop, 'obs_key': obs_type, 'aggregate_type': aggregate_type, 'val': target_val, 'table_name': db_manager.table_name } # Run the query against the database: row = db_manager.getSql(DailySummaries.daily_sql_dict[aggregate_type] % inter_dict) # Each aggregation type requires a slightly different calculation. if not row or None in row: # If no row was returned, or if it contains any nulls (meaning that not # all required data was available to calculate the requested aggregate), # then set the resulting value to None. value = None elif aggregate_type in ['min', 'maxmin', 'max', 'minmax', 'meanmin', 'meanmax', 'maxsum', 'minsum', 'sum', 'gustdir']: # These aggregates are passed through 'as is'. value = row[0] elif aggregate_type in ['mintime', 'maxmintime', 'maxtime', 'minmaxtime', 'maxsumtime', 'minsumtime', 'count', 'max_ge', 'max_le', 'min_ge', 'min_le', 'sum_ge', 'sum_le']: # These aggregates are always integers: value = int(row[0]) elif aggregate_type == 'avg': value = row[0] / row[1] if row[1] else None elif aggregate_type == 'rms': value = math.sqrt(row[0] / row[1]) if row[1] else None elif aggregate_type == 'vecavg': value = math.sqrt((row[0] ** 2 + row[1] ** 2) / row[2] ** 2) if row[2] else None elif aggregate_type == 'vecdir': if row == (0.0, 0.0): value = None else: deg = 90.0 - math.degrees(math.atan2(row[1], row[0])) value = deg if deg >= 0 else deg + 360.0 else: # Unknown aggregation. Should not have gotten this far... raise ValueError("Unexpected error. Aggregate type '%s'" % aggregate_type) # Look up the unit type and group of this combination of observation type and aggregation: t, g = weewx.units.getStandardUnitType(db_manager.std_unit_system, obs_type, aggregate_type) # Form the ValueTuple and return it: return weewx.units.ValueTuple(value, t, g) # # ######################## Class AggregateHeatCool ############################## # class AggregateHeatCool(XType): """Calculate heating and cooling degree-days.""" # Default base temperature and unit type for heating and cooling degree days, # as a value tuple default_heatbase = (65.0, "degree_F", "group_temperature") default_coolbase = (65.0, "degree_F", "group_temperature") default_growbase = (50.0, "degree_F", "group_temperature") @staticmethod def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict): """Returns heating and cooling degree days over a time period. obs_type: The type over which aggregation is to be done. Must be one of 'heatdeg', 'cooldeg', or 'growdeg'. timespan: An instance of weeutil.Timespan with the time period over which aggregation is to be done. aggregate_type: The type of aggregation to be done. Must be 'avg' or 'sum'. db_manager: An instance of weewx.manager.Manager or subclass. option_dict: Not used in this version. returns: A ValueTuple containing the result. """ # Check to see whether heating or cooling degree days are being asked for: if obs_type not in ['heatdeg', 'cooldeg', 'growdeg']: raise weewx.UnknownType(obs_type) # Only summation (total) or average heating or cooling degree days is supported: if aggregate_type not in ['sum', 'avg']: raise weewx.UnknownAggregation(aggregate_type) # Get the base for heating and cooling degree-days units_dict = option_dict.get('skin_dict', {}).get('Units', {}) dd_dict = units_dict.get('DegreeDays', {}) heatbase = dd_dict.get('heating_base', AggregateHeatCool.default_heatbase) coolbase = dd_dict.get('cooling_base', AggregateHeatCool.default_coolbase) growbase = dd_dict.get('growing_base', AggregateHeatCool.default_growbase) # Convert to a ValueTuple in the same unit system as the database heatbase_t = weewx.units.convertStd((float(heatbase[0]), heatbase[1], "group_temperature"), db_manager.std_unit_system) coolbase_t = weewx.units.convertStd((float(coolbase[0]), coolbase[1], "group_temperature"), db_manager.std_unit_system) growbase_t = weewx.units.convertStd((float(growbase[0]), growbase[1], "group_temperature"), db_manager.std_unit_system) total = 0.0 count = 0 for daySpan in weeutil.weeutil.genDaySpans(timespan.start, timespan.stop): # Get the average temperature for the day as a value tuple: Tavg_t = DailySummaries.get_aggregate('outTemp', daySpan, 'avg', db_manager) # Make sure it's valid before including it in the aggregation: if Tavg_t is not None and Tavg_t[0] is not None: if obs_type == 'heatdeg': total += weewx.wxformulas.heating_degrees(Tavg_t[0], heatbase_t[0]) elif obs_type == 'cooldeg': total += weewx.wxformulas.cooling_degrees(Tavg_t[0], coolbase_t[0]) else: total += weewx.wxformulas.cooling_degrees(Tavg_t[0], growbase_t[0]) count += 1 if aggregate_type == 'sum': value = total else: value = total / count if count else None # Look up the unit type and group of the result: t, g = weewx.units.getStandardUnitType(db_manager.std_unit_system, obs_type, aggregate_type) # Return as a value tuple return weewx.units.ValueTuple(value, t, g) # ############################# WindVec extensions ######################################### class WindVec(XType): """Extensions for calculating special observation types 'windvec' and 'windgustvec'. It provides functions for calculating series, and for calculating aggregates. """ windvec_types = { 'windvec': ('windSpeed', 'windDir'), 'windgustvec': ('windGust', 'windGustDir') } agg_sql_dict = { 'count': "SELECT COUNT(dateTime), usUnits FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL)", 'first': "SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s " "WHERE dateTime = (SELECT MIN(dateTime) FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL)", 'last': "SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s " "WHERE dateTime = (SELECT MAX(dateTime) FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL)", 'min': "SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL " "ORDER BY %(mag)s ASC LIMIT 1;", # 'min': "SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s " # "WHERE %(mag)s = (SELECT MIN(%(mag)s) FROM %(table_name)s " # "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL)", 'max': "SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL " "ORDER BY %(mag)s DESC LIMIT 1;", # 'max': "SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s " # "WHERE %(mag)s = (SELECT MAX(%(mag)s) FROM %(table_name)s " # "WHERE dateTime > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL)", } # for types 'avg', 'sum' complex_sql_wind = 'SELECT %(mag)s, %(dir)s, usUnits FROM %(table_name)s WHERE dateTime > ? ' \ 'AND dateTime <= ?' @staticmethod def get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None): """Get a series, possibly with aggregation, for special 'wind vector' types. These are typically used for the wind vector plots. """ # Check to see if the requested type is not 'windvec' or 'windgustvec' if obs_type not in WindVec.windvec_types: # The type is not one of the extended wind types. We can't handle it. raise weewx.UnknownType(obs_type) # It is an extended wind type. Prepare the lists that will hold the # final results. start_vec = list() stop_vec = list() data_vec = list() # Is aggregation requested? if aggregate_type: # Yes. Just use the regular series function. When it comes time to do the aggregation, # the specialized function WindVec.get_aggregate() (defined below), will be used. return ArchiveTable.get_series(obs_type, timespan, db_manager, aggregate_type, aggregate_interval) else: # No aggregation desired. However, we have will have to assemble the wind vector from # its flattened types. This SQL select string will select the proper wind types sql_str = 'SELECT dateTime, %s, %s, usUnits, `interval` FROM %s ' \ 'WHERE dateTime >= ? AND dateTime <= ?' \ % (WindVec.windvec_types[obs_type][0], WindVec.windvec_types[obs_type][1], db_manager.table_name) std_unit_system = None for record in db_manager.genSql(sql_str, timespan): ts, magnitude, direction, unit_system, interval = record if std_unit_system: if std_unit_system != unit_system: raise weewx.UnsupportedFeature( "Unit type cannot change within a time interval.") else: std_unit_system = unit_system value = weeutil.weeutil.to_complex(magnitude, direction) start_vec.append(ts - interval * 60) stop_vec.append(ts) data_vec.append(value) unit, unit_group = weewx.units.getStandardUnitType(std_unit_system, obs_type, aggregate_type) return (ValueTuple(start_vec, 'unix_epoch', 'group_time'), ValueTuple(stop_vec, 'unix_epoch', 'group_time'), ValueTuple(data_vec, unit, unit_group)) @staticmethod def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict): """Returns an aggregation of a wind vector type over a timespan by using the main archive table. obs_type: The type over which aggregation is to be done. For this function, it must be 'windvec' or 'windgustvec'. Anything else will cause weewx.UnknownType to be raised. timespan: An instance of weeutil.Timespan with the time period over which aggregation is to be done. aggregate_type: The type of aggregation to be done. For this function, must be 'avg', 'sum', 'count', 'first', 'last', 'min', or 'max'. Anything else will cause weewx.UnknownAggregation to be raised. db_manager: An instance of weewx.manager.Manager or subclass. option_dict: Not used in this version. returns: A ValueTuple containing the result. Note that the value contained in the ValueTuple will be a complex number. """ if obs_type not in WindVec.windvec_types: raise weewx.UnknownType(obs_type) aggregate_type = aggregate_type.lower() # Raise exception if we don't know about this type of aggregation if aggregate_type not in ['avg', 'sum'] + list(WindVec.agg_sql_dict.keys()): raise weewx.UnknownAggregation(aggregate_type) # Form the interpolation dictionary interpolation_dict = { 'dir': WindVec.windvec_types[obs_type][1], 'mag': WindVec.windvec_types[obs_type][0], 'start': weeutil.weeutil.startOfDay(timespan.start), 'stop': timespan.stop, 'table_name': db_manager.table_name } if aggregate_type in WindVec.agg_sql_dict: # For these types (e.g., first, last, etc.), we can do the aggregation in a SELECT # statement. select_stmt = WindVec.agg_sql_dict[aggregate_type] % interpolation_dict row = db_manager.getSql(select_stmt) if row: if aggregate_type == 'count': value, std_unit_system = row else: magnitude, direction, std_unit_system = row value = weeutil.weeutil.to_complex(magnitude, direction) else: std_unit_system = db_manager.std_unit_system value = None else: # The result is more complex, requiring vector arithmetic. We will have to do it # in Python std_unit_system = None xsum = ysum = 0.0 count = 0 select_stmt = WindVec.complex_sql_wind % interpolation_dict for rec in db_manager.genSql(select_stmt, timespan): # Unpack the record mag, direction, unit_system = rec # Ignore rows where magnitude is NULL if mag is None: continue # A good direction is necessary unless the mag is zero: if mag == 0.0 or direction is not None: if std_unit_system: if std_unit_system != unit_system: raise weewx.UnsupportedFeature( "Unit type cannot change within a time interval.") else: std_unit_system = unit_system # An undefined direction is OK (and expected) if the magnitude # is zero. But, in that case, it doesn't contribute to the sums either. if direction is None: # Sanity check if weewx.debug: assert (mag == 0.0) else: xsum += mag * math.cos(math.radians(90.0 - direction)) ysum += mag * math.sin(math.radians(90.0 - direction)) count += 1 # We've gone through the whole interval. Were there any good data? if count: # Form the requested aggregation: if aggregate_type == 'sum': value = complex(xsum, ysum) else: # Must be 'avg' value = complex(xsum, ysum) / count else: value = None # Look up the unit type and group of this combination of observation type and aggregation: t, g = weewx.units.getStandardUnitType(std_unit_system, obs_type, aggregate_type) # Form the ValueTuple and return it: return weewx.units.ValueTuple(value, t, g) class WindVecDaily(XType): """Extension for calculating the average windvec, using the daily summaries.""" @staticmethod def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict): """Optimization for calculating 'avg' aggregations for type 'windvec'. The timespan must be on a daily boundary.""" # We can only do observation type 'windvec' if obs_type != 'windvec': # We can't handle it. raise weewx.UnknownType(obs_type) # We can only do 'avg'' if aggregate_type != 'avg': raise weewx.UnknownAggregation(aggregate_type) # We cannot use the day summaries if the starting and ending times of the aggregation # interval are not on midnight boundaries, and are not the first or last records in the # database. if not (isStartOfDay(timespan.start) or timespan.start == db_manager.first_timestamp) \ or not (isStartOfDay(timespan.stop) or timespan.stop == db_manager.last_timestamp): raise weewx.UnknownAggregation(aggregate_type) sql = 'SELECT SUM(xsum), SUM(ysum), SUM(dirsumtime) ' \ 'FROM %s_day_wind WHERE dateTime>=? AND dateTime<?;' % db_manager.table_name row = db_manager.getSql(sql, timespan) if not row or None in row or not row[2]: # If no row was returned, or if it contains any nulls (meaning that not # all required data was available to calculate the requested aggregate), # then set the resulting value to None. value = None else: value = complex(row[0], row[1]) / row[2] # Look up the unit type and group of the result: t, g = weewx.units.getStandardUnitType(db_manager.std_unit_system, obs_type, aggregate_type) # Return as a value tuple return weewx.units.ValueTuple(value, t, g) # Add instantiated versions to the extension list. Order matters. We want the highly-specialized # versions first, because they might offer optimizations. xtypes.append(WindVecDaily()) xtypes.append(WindVec()) xtypes.append(AggregateHeatCool()) xtypes.append(DailySummaries()) xtypes.append(ArchiveTable())
