def summaries(self,
start_time,
end_time,
interval,
summary_types,
calculation_basis=CalculationBasis.TIME_WEIGHTED,
time_type=TimestampCalculation.AUTO):
"""Return one or more summary values for each interval within a time range.
"""
time_range = AF.Time.AFTimeRange(start_time, end_time)
interval = AF.Time.AFTimeSpan.Parse(interval)
summary_types = int(summary_types)
calculation_basis = int(calculation_basis)
time_type = int(time_type)
pivalues = self._summaries(time_range, interval, summary_types,
calculation_basis, time_type)
df = DataFrame()
for summary in pivalues:
key = SummaryType(summary.Key).name
timestamps, values = zip(
*[(PISeries.timestamp_to_index(value.Timestamp.UtcTime),
value.Value) for value in summary.Value])
df = df.join(DataFrame(data={key: values}, index=timestamps),
how='outer')
return df
def filtered_summaries(
self,
start_time,
end_time,
interval,
filter_expression,
summary_types,
calculation_basis=CalculationBasis.TIME_WEIGHTED,
filter_evaluation=ExpressionSampleType.EXPRESSION_RECORDED_VALUES,
filter_interval=None,
time_type=TimestampCalculation.AUTO):
"""Return one or more summary values for each interval within a time range.
"""
time_range = AF.Time.AFTimeRange(start_time, end_time)
interval = AF.Time.AFTimeSpan.Parse(interval)
filter_expression = self._normalize_filter_expression(
filter_expression)
filter_interval = AF.Time.AFTimeSpan.Parse(filter_interval)
pivalues = self._filtered_summaries(time_range, interval,
filter_expression, summary_types,
calculation_basis,
filter_evaluation, filter_interval,
time_type)
df = DataFrame()
for summary in pivalues:
key = SummaryType(summary.Key).name
timestamps, values = zip(
*[(PISeries.timestamp_to_index(value.Timestamp.UtcTime),
value.Value) for value in summary.Value])
df = df.join(DataFrame(data={key: values}, index=timestamps),
how='outer')
return df
def summary(
self,
start_time,
end_time,
summary_types,
calculation_basis=CalculationBasis.TIME_WEIGHTED,
time_type=TimestampCalculation.AUTO,
):
"""Return one or more summary values over a single time range.
"""
time_range = AF.Time.AFTimeRange(start_time, end_time)
summary_types = int(summary_types)
calculation_basis = int(calculation_basis)
time_type = int(time_type)
pivalues = self._summary(time_range, summary_types, calculation_basis,
time_type)
df = DataFrame()
for summary in pivalues:
key = SummaryType(summary.Key).name
value = summary.Value
timestamp = PISeries.timestamp_to_index(value.Timestamp.UtcTime)
value = value.Value
df = df.join(DataFrame(data={key: value}, index=[timestamp]),
how="outer")
return df
def summaries(
self,
start_time,
end_time,
interval,
summary_types,
calculation_basis=CalculationBasis.TIME_WEIGHTED,
time_type=TimestampCalculation.AUTO,
):
"""summaries
Return one or more summary values for each interval within a time range
Args:
start_time (str or datetime): Containing the date, and possibly time,
from which to retrieve the values. This is parsed, together
with `end_time`, using
:afsdk:`AF.Time.AFTimeRange <M_OSIsoft_AF_Time_AFTimeRange__ctor_1.htm>`.
end_time (str or datetime): Containing the date, and possibly time,
until which to retrieve values. This is parsed, together
with `start_time`, using
:afsdk:`AF.Time.AFTimeRange <M_OSIsoft_AF_Time_AFTimeRange__ctor_1.htm>`.
interval (str): String containing the interval at which to extract
data. This is parsed using
:afsdk:`AF.Time.AFTimeSpan.Parse <M_OSIsoft_AF_Time_AFTimeSpan_Parse_1.htm>`.
summary_types (int or PIConsts.SummaryType): Type(s) of summaries
of the data within the requested time range.
calculation_basis (int or PIConsts.CalculationBasis, optional):
Event weighting within an interval. See :ref:`event_weighting`
and :any:`CalculationBasis` for more information. Defaults to
CalculationBasis.TIME_WEIGHTED.
time_type (int or PIConsts.TimestampCalculation, optional):
Timestamp to return for each of the requested summaries. See
:ref:`summary_timestamps` and :any:`TimestampCalculation` for
more information. Defaults to TimestampCalculation.AUTO.
Returns:
pandas.DataFrame: Dataframe with the unique timestamps as row index
and the summary name as column name.
"""
time_range = to_af_time_range(start_time, end_time)
interval = AF.Time.AFTimeSpan.Parse(interval)
summary_types = int(summary_types)
calculation_basis = int(calculation_basis)
time_type = int(time_type)
pivalues = self._summaries(time_range, interval, summary_types,
calculation_basis, time_type)
df = DataFrame()
for summary in pivalues:
key = SummaryType(summary.Key).name
timestamps, values = zip(
*[(timestamp_to_index(value.Timestamp.UtcTime), value.Value)
for value in summary.Value])
df = df.join(DataFrame(data={key: values}, index=timestamps),
how="outer")
return df
def filtered_summaries(
self,
start_time,
end_time,
interval,
filter_expression,
summary_types,
calculation_basis=None,
filter_evaluation=None,
filter_interval=None,
time_type=None,
):
"""filtered_summaries
Return one or more summary values for each interval within a time range
Args:
start_time (str or datetime): String containing the date, and possibly time,
from which to retrieve the values. This is parsed, together
with `end_time`, using
:afsdk:`AF.Time.AFTimeRange <M_OSIsoft_AF_Time_AFTimeRange__ctor_1.htm>`.
end_time (str or datetime): String containing the date, and possibly time,
until which to retrieve values. This is parsed, together
with `start_time`, using
:afsdk:`AF.Time.AFTimeRange <M_OSIsoft_AF_Time_AFTimeRange__ctor_1.htm>`.
interval (str): String containing the interval at which to extract
data. This is parsed using
:afsdk:`AF.Time.AFTimeSpan.Parse <M_OSIsoft_AF_Time_AFTimeSpan_Parse_1.htm>`.
filter_expression (str, optional): Defaults to ''. Query on which
data to include in the results. See :ref:`filtering_values`
for more information on filter queries.
summary_types (int or PIConsts.SummaryType): Type(s) of summaries
of the data within the requested time range.
calculation_basis (int or PIConsts.CalculationBasis, optional):
Event weighting within an interval. See :ref:`event_weighting`
and :any:`CalculationBasis` for more information. Defaults to
CalculationBasis.TIME_WEIGHTED.
filter_evaluation (int or PIConsts,ExpressionSampleType, optional):
Determines whether the filter is applied to the raw events in
the database, of if it is applied to an interpolated series
with a regular interval. Defaults to
ExpressionSampleType.EXPRESSION_RECORDED_VALUES.
filter_interval (str, optional): String containing the interval at
which to extract apply the filter. This is parsed using
:afsdk:`AF.Time.AFTimeSpan.Parse <M_OSIsoft_AF_Time_AFTimeSpan_Parse_1.htm>`.
time_type (int or PIConsts.TimestampCalculation, optional):
Timestamp to return for each of the requested summaries. See
:ref:`summary_timestamps` and :any:`TimestampCalculation` for
more information. Defaults to TimestampCalculation.AUTO.
Returns:
pandas.DataFrame: Dataframe with the unique timestamps as row index
and the summary name as column name.
"""
time_range = to_af_time_range(start_time, end_time)
interval = AF.Time.AFTimeSpan.Parse(interval)
filter_expression = self._normalize_filter_expression(
filter_expression)
calculation_basis = get_enumerated_value(
enumeration=CalculationBasis,
value=calculation_basis,
default=CalculationBasis.TIME_WEIGHTED,
)
filter_evaluation = get_enumerated_value(
enumeration=ExpressionSampleType,
value=filter_evaluation,
default=ExpressionSampleType.EXPRESSION_RECORDED_VALUES,
)
time_type = get_enumerated_value(
enumeration=TimestampCalculation,
value=time_type,
default=TimestampCalculation.AUTO,
)
filter_interval = AF.Time.AFTimeSpan.Parse(filter_interval)
pivalues = self._filtered_summaries(
time_range,
interval,
filter_expression,
summary_types,
calculation_basis,
filter_evaluation,
filter_interval,
time_type,
)
df = DataFrame()
for summary in pivalues:
key = SummaryType(summary.Key).name
timestamps, values = zip(
*[(timestamp_to_index(value.Timestamp.UtcTime), value.Value)
for value in summary.Value])
df = df.join(DataFrame(data={key: values}, index=timestamps),
how="outer")
return df