Python Timeseries.write_to_db方法代码示例

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def MultiTimeseriesProcessDb(method, timeseries_arg, out_timeseries_id,
                             db, read_tstep_func, transaction=None, 
                             commit=True, options={}):
    out_timeseries = Timeseries(id = out_timeseries_id)
    opts = copy.deepcopy(options)
    if 'append_only' in opts and opts['append_only']:
        bounds = timeseries_bounding_dates_from_db(db, 
                                                   id = out_timeseries_id)
        opts['start_date'] = bounds[1] if bounds else None;
        opts['interval_exclusive'] = True
    tseries_arg={}
    for key in timeseries_arg:
        ts = Timeseries(id=timeseries_arg[key])
        if ('append_only' in opts and opts['append_only']) \
                         and opts['start_date'] is not None:
            ts.read_from_db(db, bottom_only=True)
            if ts.bounding_dates()[0]>opts['start_date']:
                ts.read_from_db(db)
        else:
            ts.read_from_db(db)
        ts.time_step = read_tstep_func(ts.id)
        tseries_arg[key] = ts
    MultiTimeseriesProcess(method, tseries_arg, out_timeseries, opts)
    if 'append_only' in opts and opts['append_only']:
        out_timeseries.append_to_db(db=db, transaction=transaction,
                                    commit=commit)
    else:
        out_timeseries.write_to_db(db=db, transaction=transaction, 
                                   commit=commit)

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def process_dma(dma, bounds):
    """Process DMA timeseries by aggregating all the contained
    households in the DMA"""
    print "Process DMA %s" % (dma,)
    for dma_series in dma.timeseries.all():
        print "Process series %s" % (dma_series,)
        per_capita = dma_series.name.find('capita') > -1
        variable = dma_series.variable.id
        if dma_series.time_step.id == TSTEP_FIFTEEN_MINUTES:
            start = bounds[variable]['fifteen_start']
            end = bounds[variable]['fifteen_end']
            # Fifteen minutes process is DEACTIVATED!
            # We don't process fifteen minutes, it takes too long,
            # maybe we reactivate later after we optimize the
            # algorithm to process only new records
            continue
        elif dma_series.time_step.id == TSTEP_HOURLY:
            start = bounds[variable]['hourly_start']
            end = bounds[variable]['hourly_end']
        elif dma_series.time_step.id == TSTEP_DAILY:
            start = bounds[variable]['daily_start']
            end = bounds[variable]['daily_end']
        elif dma_series.time_step.id == TSTEP_MONTHLY:
            start = bounds[variable]['monthly_start']
            end = bounds[variable]['monthly_end']
        time_step = ReadTimeStep(dma_series.id, dma_series)
        tseries = TSeries(time_step = time_step, id=dma_series.id)
        nhseries = TSeries(time_step = time_step)
        pointer = start
        while pointer<=end:
            tseries[pointer] = 0
            nhseries[pointer] = 0
            pointer = tseries.time_step.next(pointer)
        for household in dma.households.all():
            for h_series_db in household.timeseries.filter(
                    time_step__id=dma_series.time_step.id,
                    variable__id=variable):
                hseries = TSeries(id=h_series_db.id)
                hseries.read_from_db(db.connection)
                pointer = start
                while pointer<=end:
                    try:
                        v = hseries[pointer]
                        if math.isnan(v):
                            pointer = tseries.time_step.next(pointer)
                            continue
                        if per_capita:
                            v = v/float(household.num_of_occupants)
                        tseries[pointer] += v
                        nhseries[pointer] += 1
                    except KeyError:
                        v = 0
                    pointer = tseries.time_step.next(pointer)
        pointer = start
        while pointer<=end:
            if per_capita and nhseries[pointer]>0:
                tseries[pointer] = tseries[pointer] / nhseries[pointer]
            pointer = tseries.time_step.next(pointer)
        tseries.write_to_db(db.connection, commit=True)#False)

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def create_objects(dma, household_identifier, series, force=False):
    """
    When a household is fully parsed then this command is called to create
    database objects thus: user (household owner), household, database time
    series placeholders (for raw data and for processed data), to write actual
    time series data in database and finally to estimate the household
    occupancy.
    """
    print "Processing household %s, user username will be %s as well"%(
            household_identifier, household_identifier)
    # Create user (household owner), household, database series placeholders
    user = create_user(household_identifier)
    household=create_household(household_identifier, user,
            zone=dma.id)
    db_series = create_raw_timeseries(household)
    create_processed_timeseries(household)
    timeseries_data = {}
    # Now we will create timeseries.Timeseries() and we will add
    # parsed values
    for variable in db_series:
        if variable not in ('WaterCold', 'Electricity'):
            continue
        s, e = timeseries_bounding_dates_from_db(db.connection,
                db_series[variable].id)
        if not force and (s or e):
            print 'Raw timeseries id=%s has already data, skipping...'%(
                    db_series[variable].id,)
            continue
        timeseries = TSeries()
        timeseries.id = db_series[variable].id
        total = 0.0
        for timestamp, value in series[variable]:
            if not math.isnan(value):
                total += value
                timeseries[timestamp] = total
            else:
                timeseries[timestamp] = float('NaN')
        timeseries_data[variable] = timeseries
        timeseries.write_to_db(db=db.connection,
                transaction=transaction,
                commit=False)
    if 'WaterCold' in timeseries_data:
        calc_occupancy(timeseries_data['WaterCold'], household)

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def parse_and_save_timeseries(device_id, timeseries_id):
    """
    Reads a RAW timeseries from REST API and saves in our local
    database using the timeseries_id.
    ``device_id`` will be the ``identifier`` used in other functions,
    usualy is the customerID==deviceID
    """
    s, e = timeseries_bounding_dates_from_db(db.connection,
            timeseries_id)
    if s or e:
        print 'Raw timeseries id=%s has already data, skipping...'%(
                timeseries_id,)
        return
    timeseries = TSeries()
    timeseries.id = timeseries_id
    for timestamp, value in ibm_restapi.get_raw_timeseries(device_id):
        timeseries[timestamp] = value
    timeseries.write_to_db(db=db.connection,
            transaction=transaction,
            commit=False)

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def parse_and_save_timeseries(filename, timeseries_id):
    first_line = True
    timeseries = TSeries()
    timeseries.id = timeseries_id
    with open(filename) as fp:
        for line in fp.readlines():
            if first_line:
                first_line = False
                continue
            components = line.split(',')
            date_str = components[1].strip('"')
            value_str = components[2].strip('"')
            value = float(value_str)
            if value<MIN_VALUE or value>=MAX_VALUE:
                value = float('nan')
            tstamp = datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S')
            tstamp = tstamp.replace(second=0)
            timeseries[tstamp] = value
    timeseries.write_to_db(db=db.connection,
            transaction=transaction,
            commit=False)

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def regularize_raw_series(raw_series_db, proc_series_db, rs, re, ps, pe ):
    """
    This function regularize raw_series_db object from database and
    writes a processed proc_series_db in database.
    Raw series is a continuously increasing values time series,
    aggregating the water consumption. Resulting processed timeseries
    contains water consumption for each of its interval. I.e. if the
    timeseries is of 15 minutes time step, then each record contains
    the water consumption for each record period.
    """
    raw_series = TSeries(id=raw_series_db.id)
    raw_series.read_from_db(db.connection)
    # We keep the last value for x-checking reasons, see last print
    # command
    test_value = raw_series[raw_series.bounding_dates()[1]]
    time_step = ReadTimeStep(proc_series_db.id, proc_series_db)
    proc_series = TSeries(id=proc_series_db.id, time_step = time_step)
    # The following code can be used in real conditions to append only
    # new records to db, in a next version
    #if not pe:
    #    start = proc_series.time_step.down(rs)
    #else:
    #    start = proc_series.time_step.up(pe)
    # Instead of the above we use now:
    start = proc_series.time_step.down(rs)
    end = proc_series.time_step.up(re)
    pointer = start
    # Pass 1: Initialize proc_series
    while pointer<=end:
        proc_series[pointer] = float('nan')
        pointer = proc_series.time_step.next(pointer)
    # Pass 2: Transfer cummulative raw series to differences series:
    prev_s = 0
    for i in xrange(len(raw_series)):
        dat, value = raw_series.items(pos=i)
        if not math.isnan(value):
            raw_series[dat] = value-prev_s
            prev_s = value
    # Pass 3: Regularize step: loop over raw series records and distribute
    # floating point values to processed series
    for i in xrange(len(raw_series)):
        dat, value = raw_series.items(pos=i)
        if not math.isnan(value):
            # find previous, next timestamp of the proc time series
            d1 = proc_series.time_step.down(dat)
            d2 = proc_series.time_step.up(dat)
            if math.isnan(proc_series[d1]): proc_series[d1] = 0
            if math.isnan(proc_series[d2]): proc_series[d2] = 0
            if d1==d2: # if dat on proc step then d1=d2
                proc_series[d1] += value
                continue
            dif1 = _dif_in_secs(d1, dat)
            dif2 = _dif_in_secs(dat, d2)
            dif = dif1+dif2
            # Distribute value to d1, d2
            proc_series[d1] += (dif2/dif)*value
            proc_series[d2] += (dif1/dif)*value
    # Uncomment the following line in order to show debug information.
    # Usually the three following sums are consistent by equality. If
    # not equality is satisfied then there is a likelyhood of algorith
    # error
    print raw_series.sum(), proc_series.sum(), test_value
    proc_series.write_to_db(db=db.connection, commit=True) #False)
    #return the full timeseries
    return proc_series

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
def create_objects(data, usernames, force, z_names, z_dict):
    """

    :param data: meter_id -> consumption_type -> [timestamp, volume]
    :param force: True to overwrite
    :return: True for success
    """
    households = []
    # Create user (household owner), household, database series placeholders
    hh_ids = sorted(data.keys())
    found = False
    for hh_id in hh_ids:
        username = usernames[hh_id]
        if username == "PT94993":
            pass
        try:
            zone_name = z_dict[username]
        except KeyError:
            zone_name = z_names[0]
        zone = DMA.objects.get(name=zone_name)
        user, created = create_user(username, hh_id)
        household, found = create_household(hh_id, user, zone.id)
        households.append(household)
        db_series = create_raw_timeseries(household)
        create_processed_timeseries(household)
        timeseries_data = {}
        # Now we will create timeseries.Timeseries() and we will add
        # parsed values
        for variable in db_series:
            if variable not in ('WaterCold', 'Electricity'):
                continue
            exists = False
            s, e = timeseries_bounding_dates_from_db(db.connection,
                                                     db_series[variable].id)
            latest_ts = e
            ts_id = db_series[variable].id
            # checking to see if timeseries records already exist in order
            # to append
            # d = read_timeseries_tail_from_db(db.connection, ts_id)
            total = 0.0
            # if s or e:
            #     exists = True
            #     timeseries = TSeries(ts_id)
            #     timeseries.read_from_db(db.connection)
            # else:
            #     timeseries = TSeries()
            #     timeseries.id = ts_id
            _dict = data[hh_id]
            arr = _dict[variable]
            series = arr
            if not series:
                continue
            earlier = []
            if (not latest_ts) or (latest_ts < series[0][0]):  # append
                timeseries = TSeries()
                timeseries.id = ts_id
                try:
                    tail = read_timeseries_tail_from_db(db.connection, ts_id)
                    total = float(tail[1])  # keep up from last value
                except Exception as e:
                    log.debug(repr(e))
                    total = 0
                for timestamp, value in series:
                    if (not latest_ts) or (timestamp > latest_ts):
                        if not isnan(value):
                            total += value
                            timeseries[timestamp] = total
                        else:
                            timeseries[timestamp] = float('NaN')
                    elif timestamp < latest_ts:
                        earlier.append((timestamp, value))
                timeseries.append_to_db(db=db.connection,
                                        transaction=transaction,
                                        commit=True)
            elif latest_ts >= series[0][0]:
                if not force:  # ignore
                    continue
                else:  # insert
                    for timestamp, value in series:
                        if timestamp < latest_ts:
                            earlier.append((timestamp, value))
            if earlier and ("GR" in username or "GBA" in username):  # insert (only for athens)
                # print "appending %s items for %s" % (len(earlier), username)
                if variable == "WaterCold":
                    ts15 = household \
                        .timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
                                        variable__id=VAR_PERIOD)
                    series15 = TSeries(id=ts15.id)
                elif variable == "Electricity":
                    ts15 = household \
                        .timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
                                        variable__id=VAR_ENERGY_PERIOD)
                    series15 = TSeries(id=ts15.id)
                series15.read_from_db(db.connection)
                for ts, value in earlier:
                    series15[ts] = value
                series15.write_to_db(db=db.connection,
                                     transaction=transaction,
                                     commit=True)

#.........这里部分代码省略.........

# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import write_to_db [as 别名]
Assuming that "dir" is the openmeteo directory, run as follows:
    export PYTHONPATH=dir:dir/enhydris
    export DJANGO_SETTINGS=settings
    ./oldopenmeteo2enhydris.sql

"""

import sys
from datetime import timedelta

from django.db import connection, transaction

from enhydris.hcore import models
from pthelma.timeseries import Timeseries

transaction.enter_transaction_management()
tms = models.Timeseries.objects.filter(time_step__id__in=[4,5])
for tm in tms:
    sys.stderr.write("Doing timeseries %d..." % (tm.id,))
    t = Timeseries(id=tm.id)
    nt = Timeseries(id=tm.id)
    t.read_from_db(connection)
    for (d, value) in t.items():
        d += timedelta(hours=1)
        assert(not d.minute and not d.hour and not d.second and d.day==1,
            "Invalid date "+str(d))
        nt[d] = value
    nt.write_to_db(connection, transaction=transaction, commit=False)
    sys.stderr.write(" Done\n")
transaction.commit()