It seems that there is a bug in get_series(). It should pass the option dictionary on to get_aggregate(), but it doesn't.
Try this version of xtypes.py. Also, note that the value will be passed in as a *string*. To get a numeric value, you'll have to do years = int(option_dict['val']) -tk On Sun, Oct 9, 2022 at 8:20 AM Jacques Terrettaz <[email protected]> wrote: > :(. it is not working. In that case, in my get_aggregate function, > option_dict.get('val') is None > > > > Le 9 oct. 2022 à 17:00, Tom Keffer <[email protected]> a écrit : > > Try: > > [[other_images]] > x_label_format = %Y > bottom_label_format = %x %X > time_length = 220752000 # 7 years > aggregate_interval = day > show_daynight = false > [[[trol10]]] > [[[[rol10]]]] > label = "Température : moyenne glissante sur 10 ans" > aggregate_type = rolling_avg_years > val = 10 > data_type = outTemp > > Although, to tell you the truth, I've never tried this! > > -tk > > On Sun, Oct 9, 2022 at 6:48 AM Jacques Terrettaz <[email protected]> > wrote: > >> Thanks Tom, >> >> I have modified my extension, and when the aggregate type is used within >> a tag such as* $day.outTemp.rolling_avg_years(10)* it works. >> >> But it is not working with the ImageGenerator. >> In my extension, I am checking that the aggregate type is >> "rolling_avg_year", and if not it will raise >> a weewx.UnknownAggregationexception >> >> When using the aggregate type without parameter, with this ImageGenerator >> definition : >> >> [[other_images]] >> x_label_format = %Y >> bottom_label_format = %x %X >> time_length = 220752000 # 7 years >> aggregate_interval = day >> show_daynight = false >> [[[trol10]]] >> [[[[rol10]]]] >> label = "Température : moyenne glissante sur 10 ans" >> aggregate_type = rolling_avg_years10 >> data_type = outTemp >> >> The image is generated : >> <trol10_OK.png> >> >> But when using the modified extension accepting a parameter : >> >> [[other_images]] >> x_label_format = %Y >> bottom_label_format = %x %X >> time_length = 220752000 # 7 years >> aggregate_interval = day >> show_daynight = false >> [[[trol10]]] >> [[[[rol10]]]] >> label = "Température : moyenne glissante sur 10 ans" >> aggregate_type = rolling_avg_years(10) >> data_type = outTemp >> >> >> I have the following error : >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: Caught >> unrecoverable exception in generator 'weewx.imagegenerator.ImageGenerator' >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> 'outTemp' or 'rolling_avg_years(10)' >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> Traceback (most recent call last): >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> File "/usr/share/weewx/weewx/reportengine.py", line 197, in run >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> obj.start() >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> File "/usr/share/weewx/weewx/reportengine.py", line 385, in start >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> self.run() >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> File "/usr/share/weewx/weewx/imagegenerator.py", line 42, in run >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> self.gen_images(self.gen_ts) >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> File "/usr/share/weewx/weewx/imagegenerator.py", line 109, in gen_images >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> self.image_dict[timespan][plotname]) >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> File "/usr/share/weewx/weewx/imagegenerator.py", line 249, in gen_plot >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> **option_dict) >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> File "/usr/share/weewx/weewx/xtypes.py", line 116, in get_series >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> raise weewx.UnknownType(msg) >> >> 2022-10-09 15:30:24 weewx[22095] ERROR weewx.reportengine: **** >> weewx.UnknownType: 'outTemp' or 'rolling_avg_years(10)' >> >> >> When called by the ImageGenerator, it appears that the *aggregate type >> *parameter >> of the get_aggregate function is the full value *"rolling_avg_years(10)*" >> and not the value " *"rolling_avg_years*". So in that case it is >> considered as an unknown aggregation because it is not "rolling_avg_years" >> and rejected by my extension. >> >> Is there a special syntax to be used when defining an aggregate type with >> a parameter for the ImageGenerator ? >> >> >> >> >> Le 9 oct. 2022 à 15:07, Tom Keffer <[email protected]> a écrit : >> >> You're on the right track. If you pass in a parameter to an aggregation, >> it appears in "option_dict" in the corresponding get_aggregate() call. If >> you don't name that parameter, it shows up as key "val". So, in your >> example, >> >> $day.outTemp.rolling_avg_years(10) >> >> >> would result in a call to get_aggregate() that looks like: >> >> get_aggregate('outTemp', timespan, 'rolling_avg_years', db_manager, >> val=10) >> >> >> The value 'val' will contain the appropriate number of years to do the >> rolling average. >> >> Hope that helps. >> >> -tk >> >> On Sun, Oct 9, 2022 at 5:54 AM [email protected] <[email protected]> >> wrote: >> >>> Hello, >>> >>> I am writing a Xtype extension providing a new aggregate type that >>> calculates a rolling average of a parameter over years. >>> >>> I started by defining several different aggregate type for different >>> number of years, such as : >>> >>> *rolling_avg_years1* >>> >>> *rolling_avg_years5* >>> >>> *rolling_avg_years10* >>> >>> *rolling_avg_years15* >>> >>> For exemple : *$day.outTemp.rolling_avg_years10* returns the average >>> temperature of the last 3650 days (i.e. 10 years). >>> >>> And it works. >>> >>> But, to allow more flexibility, I would prefer, if possible, to have >>> the number of years as parameter, such as >>> >>> *rolling_avg_years(10)* >>> >>> but I don't know how I could retrieve the parameter in the get_aggregate >>> function of my extension. >>> Any help is appreciated, >>> Thanks >>> >>> -- >>> 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/c17fbd97-4547-4a36-bfc0-35b10aa3b68bn%40googlegroups.com >>> <https://groups.google.com/d/msgid/weewx-development/c17fbd97-4547-4a36-bfc0-35b10aa3b68bn%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> >> >> -- >> You received this message because you are subscribed to a topic in the >> Google Groups "weewx-development" group. >> To unsubscribe from this topic, visit >> https://groups.google.com/d/topic/weewx-development/RyO1T4UFFBk/unsubscribe >> . >> To unsubscribe from this group and all its topics, send an email to >> [email protected]. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/weewx-development/CAPq0zECGbOo6rT7mNdRFvRQAq6TNmWKZO6NZ7evDA6mvUz925g%40mail.gmail.com >> <https://groups.google.com/d/msgid/weewx-development/CAPq0zECGbOo6rT7mNdRFvRQAq6TNmWKZO6NZ7evDA6mvUz925g%40mail.gmail.com?utm_medium=email&utm_source=footer> >> . >> >> >> > -- You received this message because you are subscribed to the Google Groups "weewx-development" group. 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# # Copyright (c) 2019-2021 Tom Keffer <[email protected]> # # See the file LICENSE.txt for your full rights. # """User-defined extensions to the WeeWX type system""" import datetime import time 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, **option_dict): """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, **option_dict): """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, **option_dict): """Return a scalar value""" # Search the list, looking for a get_scalar() method that does not raise an UnknownType # exception for xtype in xtypes: try: # Try this function. Be prepared to catch the TypeError exception if it is a legacy # style XType that does not accept kwargs. try: return xtype.get_scalar(obs_type, record, db_manager, **option_dict) except TypeError: # We likely have a legacy style XType, so try calling it again, but this time # without the kwargs. 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, **option_dict): """Return a series (aka vector) of, possibly aggregated, values.""" # Search the list, looking for a get_series() method that does not raise an UnknownType or # UnknownAggregation exception for xtype in xtypes: try: # Try this function. Be prepared to catch the TypeError exception if it is a legacy # style XType that does not accept kwargs. try: return xtype.get_series(obs_type, timespan, db_manager, aggregate_type, aggregate_interval, **option_dict) except TypeError: # We likely have a legacy style XType, so try calling it again, but this time # without the kwargs. return xtype.get_series(obs_type, timespan, db_manager, aggregate_type, aggregate_interval) except (weewx.UnknownType, weewx.UnknownAggregation): # This function does not know about the type and/or aggregation. # Move on to the next one. pass # None of the functions worked. Raise an exception with a hopefully helpful error message. if aggregate_type: msg = "'%s' or '%s'" % (obs_type, aggregate_type) else: msg = obs_type raise weewx.UnknownType(msg) 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 # UnknownAggregation 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.UnknownType, weewx.UnknownAggregation): 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, **option_dict): """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, **option_dict) if agg_vt[0] is None: continue 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: # 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 # Hit the database. It's possible the type is not in the database, so be prepared # to catch a NoColumnError: try: 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) except weedb.NoColumnError: # The sql type doesn't exist. Convert to an UnknownType error raise weewx.UnknownType(obs_type) 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 > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL ORDER BY dateTime ASC LIMIT 1", '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 > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL ORDER BY dateTime DESC LIMIT 1", '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 IS NOT NULL ORDER BY %(obs_type)s DESC LIMIT 1", 'mintime': "SELECT dateTime FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL ORDER BY %(obs_type)s ASC LIMIT 1", 'not_null': "SELECT 1 FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(obs_type)s IS NOT NULL LIMIT 1", '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. Args: obs_type (str): The type over which aggregation is to be done (e.g., 'barometer', 'outTemp', 'rain', ...) timespan (TimeSpan): An instance of weeutil.Timespan with the time period over which aggregation is to be done. aggregate_type (str): The type of aggregation to be done. db_manager (weewx.manager.Manager): An instance of weewx.manager.Manager or subclass. option_dict (dict): Not used in this version. Returns: ValueTuple: 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) if aggregate_type == 'not_null': value = row is not None else: 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 simple aggregates from the daily summaries. agg_sql_dict = { 'avg': "SELECT SUM(wsum),SUM(sumtime) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s", 'avg_ge': "SELECT SUM((wsum/sumtime) >= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s and sumtime <> 0", 'avg_le': "SELECT SUM((wsum/sumtime) <= %(val)s) FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s and sumtime <> 0", '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 " "ORDER BY max DESC, maxtime ASC LIMIT 1", '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 mintime IS NOT NULL " "ORDER BY min DESC, mintime ASC LIMIT 1", '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 maxtime IS NOT NULL " "ORDER BY sum DESC, maxtime ASC LIMIT 1", 'maxtime': "SELECT maxtime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND maxtime IS NOT NULL " "ORDER BY max DESC, maxtime ASC LIMIT 1", '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 maxtime IS NOT NULL " "ORDER BY max ASC, maxtime ASC ", '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 mintime IS NOT NULL " "ORDER BY sum ASC, mintime ASC LIMIT 1", 'mintime': "SELECT mintime FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s " "AND mintime IS NOT NULL " "ORDER BY min ASC, mintime ASC LIMIT 1", 'not_null': "SELECT count>0 as c FROM %(table_name)s_day_%(obs_key)s " "WHERE dateTime >= %(start)s AND dateTime < %(stop)s ORDER BY c DESC LIMIT 1", '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.""" # We cannot use the daily summaries if there is no aggregation if not aggregate_type: raise weewx.UnknownAggregation(aggregate_type) aggregate_type = aggregate_type.lower() # Raise exception if we don't know about this type of aggregation if aggregate_type not in DailySummaries.agg_sql_dict: raise weewx.UnknownAggregation(aggregate_type) # Check to see whether we can use the daily summaries: DailySummaries._check_eligibility(obs_type, timespan, db_manager, 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.agg_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', 'not_null', 'sum_ge', 'sum_le', 'avg_ge', 'avg_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) # These are SQL statements used for calculating series from the daily summaries. # They include "group_def", which will be replaced with a database-specific GROUP BY clause common = { 'min': "SELECT MIN(dateTime), MAX(dateTime), MIN(min) " "FROM %(day_table)s " "WHERE dateTime>=%(start)s AND dateTime<%(stop)s %(group_def)s", 'max': "SELECT MIN(dateTime), MAX(dateTime), MAX(max) " "FROM %(day_table)s " "WHERE dateTime>=%(start)s AND dateTime<%(stop)s %(group_def)s", 'avg': "SELECT MIN(dateTime), MAX(dateTime), SUM(wsum), SUM(sumtime) " "FROM %(day_table)s " "WHERE dateTime>=%(start)s AND dateTime<%(stop)s %(group_def)s", 'sum': "SELECT MIN(dateTime), MAX(dateTime), SUM(sum) " "FROM %(day_table)s " "WHERE dateTime>=%(start)s AND dateTime<%(stop)s %(group_def)s", 'count': "SELECT MIN(dateTime), MAX(dateTime), SUM(count) " "FROM %(day_table)s " "WHERE dateTime>=%(start)s AND dateTime<%(stop)s %(group_def)s", } # Database- and interval-specific "GROUP BY" clauses. group_defs = { 'sqlite': { 'day': "GROUP BY CAST(" " (julianday(dateTime,'unixepoch','localtime') - 0.5 " " - CAST(julianday(%(sod)s, 'unixepoch','localtime') AS int)) " " / %(agg_days)s " "AS int)", 'month': "GROUP BY strftime('%%Y-%%m',dateTime,'unixepoch','localtime') ", 'year': "GROUP BY strftime('%%Y',dateTime,'unixepoch','localtime') ", }, 'mysql': { 'day': "GROUP BY TRUNCATE((TO_DAYS(FROM_UNIXTIME(dateTime)) " "- TO_DAYS(FROM_UNIXTIME(%(sod)s)))/ %(agg_days)s, 0) ", 'month': "GROUP BY DATE_FORMAT(FROM_UNIXTIME(dateTime), '%%%%Y-%%%%m') ", 'year': "GROUP BY DATE_FORMAT(FROM_UNIXTIME(dateTime), '%%%%Y') ", }, } @staticmethod def get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None, **option_dict): # We cannot use the daily summaries if there is no aggregation if not aggregate_type: raise weewx.UnknownAggregation(aggregate_type) aggregate_type = aggregate_type.lower() # Raise exception if we don't know about this type of aggregation if aggregate_type not in DailySummaries.common: raise weewx.UnknownAggregation(aggregate_type) # Check to see whether we can use the daily summaries: DailySummaries._check_eligibility(obs_type, timespan, db_manager, aggregate_type) # We also have to make sure the aggregation interval is either the length of a nominal # month or year, or some multiple of a calendar day. aggregate_interval = weeutil.weeutil.nominal_spans(aggregate_interval) if aggregate_interval != weeutil.weeutil.nominal_intervals['year'] \ and aggregate_interval != weeutil.weeutil.nominal_intervals['month'] \ and aggregate_interval % 86400: raise weewx.UnknownAggregation(aggregate_interval) # We're good. Proceed. dbtype = db_manager.connection.dbtype interp_dict = { 'agg_days': aggregate_interval / 86400, 'day_table': "%s_day_%s" % (db_manager.table_name, obs_type), 'obs_type': obs_type, 'sod': weeutil.weeutil.startOfDay(timespan.start), 'start': timespan.start, 'stop': timespan.stop, } if aggregate_interval == weeutil.weeutil.nominal_intervals['year']: group_by_group = 'year' elif aggregate_interval == weeutil.weeutil.nominal_intervals['month']: group_by_group = 'month' else: group_by_group = 'day' # Add the database-specific GROUP_BY clause to the interpolation dictionary interp_dict['group_def'] = DailySummaries.group_defs[dbtype][group_by_group] % interp_dict # This is the final SELECT statement. sql_stmt = DailySummaries.common[aggregate_type] % interp_dict start_list = list() stop_list = list() data_list = list() for row in db_manager.genSql(sql_stmt): # Find the start of this aggregation interval. That's easy: it's the minimum value. start_time = row[0] # The stop is a little trickier. It's the maximum dateTime in the interval, plus one # day. The extra day is needed because the timestamp marks the beginning of a day in a # daily summary. stop_date = datetime.date.fromtimestamp(row[1]) + datetime.timedelta(days=1) stop_time = int(time.mktime(stop_date.timetuple())) if aggregate_type in {'min', 'max', 'sum', 'count'}: data = row[2] elif aggregate_type == 'avg': data = row[2] / row[3] if row[3] else None else: # Shouldn't really have made it here. Fail hard raise ValueError("Unknown aggregation type %s" % aggregate_type) start_list.append(start_time) stop_list.append(stop_time) data_list.append(data) # Look up the unit type and group of this combination of observation type and aggregation: unit, unit_group = weewx.units.getStandardUnitType(db_manager.std_unit_system, obs_type, aggregate_type) return (ValueTuple(start_list, 'unix_epoch', 'group_time'), ValueTuple(stop_list, 'unix_epoch', 'group_time'), ValueTuple(data_list, unit, unit_group)) @staticmethod def _check_eligibility(obs_type, timespan, db_manager, aggregate_type): # It has to be a type we know about if not hasattr(db_manager, 'daykeys') or obs_type not in db_manager.daykeys: raise weewx.UnknownType(obs_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) # # ######################## 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' from the main archive table. 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 > %(start)s AND dateTime <= %(stop)s AND %(mag)s IS NOT NULL " "ORDER BY dateTime ASC LIMIT 1", 'last': "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 dateTime DESC LIMIT 1", '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;", '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;", 'not_null' : "SELECT 1, usUnits FROM %(table_name)s " "WHERE dateTime > %(start)s AND dateTime <= %(stop)s " "AND %(mag)s IS NOT NULL LIMIT 1;" } # 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, **option_dict): """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': 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 try: row = db_manager.getSql(select_stmt) except weedb.NoColumnError as e: raise weewx.UnknownType(e) if aggregate_type == 'not_null': value = row is not None std_unit_system = db_manager.std_unit_system else: 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 requested aggregation must be either 'sum' or 'avg', which will require some # arithmetic in Python, so it cannot be done by a simple query. 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' or 'not_null if aggregate_type not in ['avg', 'not_null']: 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) if aggregate_type == 'not_null': # Aggregate type 'not_null' is actually run against 'wind'. return DailySummaries.get_aggregate('wind', timespan, 'not_null', db_manager, **option_dict) 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) class XTypeTable(XType): """Calculate a series for an xtype. An xtype may not necessarily be in the database, so this version calculates it on the fly. Note: this version only works if no aggregation has been requested.""" @staticmethod def get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None, **option_dict): """Get a series of an xtype, by using the main archive table. Works only for no aggregation. """ start_vec = list() stop_vec = list() data_vec = list() if aggregate_type: # This version does not know how to do aggregations, although this could be # added in the future. raise weewx.UnknownAggregation(aggregate_type) else: # No aggregation std_unit_system = None # Hit the database. for record in db_manager.genBatchRecords(*timespan): if std_unit_system: if std_unit_system != record['usUnits']: raise weewx.UnsupportedFeature("Unit system cannot change " "within a series.") else: std_unit_system = record['usUnits'] # Given a record, use the xtypes system to calculate a value: try: value = get_scalar(obs_type, record, db_manager) data_vec.append(value[0]) except weewx.CannotCalculate: data_vec.append(None) start_vec.append(record['dateTime'] - record['interval'] * 60) stop_vec.append(record['dateTime']) unit, unit_group = weewx.units.getStandardUnitType(std_unit_system, obs_type) return (ValueTuple(start_vec, 'unix_epoch', 'group_time'), ValueTuple(stop_vec, 'unix_epoch', 'group_time'), ValueTuple(data_vec, unit, unit_group)) # 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()) xtypes.append(XTypeTable())
