EfdClient#

class lsst_efd_client.EfdClient(efd_name, db_name='efd', creds_service='https://roundtable.lsst.codes/segwarides/', timeout=900, client=None)#

Bases: _EfdClientStatic

Class to handle connections and basic queries asynchronously

Parameters:

efd_name (str) – Name of the EFD instance for which to retrieve credentials.
db_name (str, optional) – Name of the database within influxDB to query (‘efd’ by default).
creds_service (str, optional) – URL to the service to retrieve credentials (https://roundtable.lsst.codes/segwarides/ by default).
timeout (int, optional) – Timeout in seconds for async requests (aiohttp.ClientSession). The default timeout is 900 seconds.
client (object, optional) – An instance of a class that ducktypes as aioinflux.client.InfluxDBClient. The intent is to be able to substitute a mocked client for testing.

Attributes Summary

`mode`
`query_history`	Return query history

Methods Summary

`build_time_range_query`(topic_name, fields, ...)	Build a query based on a time range.
`get_fields`(topic_name)	Query the list of field names for a topic.
`get_schema`(topic)	Given a topic, get a list of dictionaries describing the fields
`get_topics`()	Query the list of possible topics.
`select_packed_time_series`(topic_name, ...[, ...])	Select fields that are time samples and unpack them into a dataframe.
`select_time_series`(topic_name, fields, ...)	Select a time series for a set of topics in a single subsystem
`select_top_n`(topic_name, fields, num[, ...])	Select the most recent N samples from a set of topics in a single subsystem.

Attributes Documentation

mode = 'async'#

query_history#

Return query history

Returns:: results – All queries made with this client instance
Return type:: list

Methods Documentation

build_time_range_query(topic_name, fields, start, end, is_window=False, index=None, convert_influx_index=False, use_old_csc_indexing=False)#

Build a query based on a time range.

Parameters:

topic_name (str) – Name of topic for which to build a query.
fields (str or list) – Name of field(s) to query.
start (astropy.time.Time) – Start time of the time range, if is_window is specified, this will be the midpoint of the range.
end (astropy.time.Time or astropy.time.TimeDelta) – End time of the range either as an absolute time or a time offset from the start time.
is_window (bool, optional) – If set and the end time is specified as a TimeDelta, compute a range centered on the start time (default is False).
index (int, optional) – When index is used, add an ‘AND salIndex = index’ to the query. (default is None).
convert_influx_index (bool, optional) – Convert influxDB time index from TAI to UTC? This is for legacy instances that may still have timestamps stored internally as TAI. Modern instances all store index timestamps as UTC natively. Default is False, don’t translate from TAI to UTC.
use_old_csc_indexing (bool, optional) – When index is used, add an ‘AND {CSCName}ID = index’ to the query which is the old CSC indexing name. (default is False).

Returns:

query – A string containing the constructed query statement.

Return type:

str

async get_fields(topic_name)#

Query the list of field names for a topic.

Parameters:: topic_name (str) – Name of topic to query for field names.
Returns:: results – List of field names in specified topic.
Return type:: list

async get_schema(topic)#

Given a topic, get a list of dictionaries describing the fields

Parameters:: topic (str) – The name of the topic to query. A full list of valid topic names can be obtained using get_schema_topics.
Returns:: result – A dataframe with the schema information for the topic. One row per field.
Return type:: pandas.DataFrame

async get_topics()#

Query the list of possible topics.

Returns:: results – List of valid topics in the database.
Return type:: list

async select_packed_time_series(topic_name, base_fields, start, end, is_window=False, index=None, ref_timestamp_col='cRIO_timestamp', ref_timestamp_fmt='unix_tai', ref_timestamp_scale='tai', convert_influx_index=False, use_old_csc_indexing=False)#

Select fields that are time samples and unpack them into a dataframe.

Parameters:

topic_name (str) – Name of topic to query.
base_fields (str or list) – Base field name(s) that will be expanded to query all vector entries.
start (astropy.time.Time) – Start time of the time range, if is_window is specified, this will be the midpoint of the range.
end (astropy.time.Time or astropy.time.TimeDelta) – End time of the range either as an absolute time or a time offset from the start time.
is_window (bool, optional) – If set and the end time is specified as a TimeDelta, compute a range centered on the start time (default is False).
index (int, optional) – When index is used, add an ‘AND salIndex = index’ to the query. (default is None).
ref_timestamp_col (str, optional) – Name of the field name to use to assign timestamps to unpacked vector fields (default is ‘cRIO_timestamp’).
ref_timestamp_fmt (str, optional) – Format to use to translating ref_timestamp_col values (default is ‘unix_tai’).
ref_timestamp_scale (str, optional) – Time scale to use in translating ref_timestamp_col values (default is ‘tai’).
convert_influx_index (bool, optional) – Convert influxDB time index from TAI to UTC? This is for legacy instances that may still have timestamps stored internally as TAI. Modern instances all store index timestamps as UTC natively. Default is False, don’t translate from TAI to UTC.
use_old_csc_indexing (bool, optional) – When index is used, add an ‘AND {CSCName}ID = index’ to the query which is the old CSC indexing name. (default is False).

Returns:

result – A DataFrame containing the results of the query.

Return type:

pandas.DataFrame

async select_time_series(topic_name, fields, start, end, is_window=False, index=None, convert_influx_index=False, use_old_csc_indexing=False)#

Select a time series for a set of topics in a single subsystem

Parameters:

topic_name (str) – Name of topic to query.
fields (str or list) – Name of field(s) to query.
start (astropy.time.Time) – Start time of the time range, if is_window is specified, this will be the midpoint of the range.
end (astropy.time.Time or astropy.time.TimeDelta) – End time of the range either as an absolute time or a time offset from the start time.
is_window (bool, optional) – If set and the end time is specified as a TimeDelta, compute a range centered on the start time (default is False).
index (int, optional) – When index is used, add an ‘AND salIndex = index’ to the query. (default is None).
convert_influx_index (bool, optional) – Convert influxDB time index from TAI to UTC? This is for legacy instances that may still have timestamps stored internally as TAI. Modern instances all store index timestamps as UTC natively. Default is False, don’t translate from TAI to UTC.
use_old_csc_indexing (bool, optional) – When index is used, add an ‘AND {CSCName}ID = index’ to the query which is the old CSC indexing name. (default is False).

Returns:

result – A DataFrame containing the results of the query.

Return type:

pandas.DataFrame

async select_top_n(topic_name, fields, num, time_cut=None, index=None, convert_influx_index=False, use_old_csc_indexing=False)#

Select the most recent N samples from a set of topics in a single subsystem.

This method does not guarantee sort direction of the returned rows.

Parameters:

topic_name (str) – Name of topic to query.
fields (str or list) – Name of field(s) to query.
num (int) – Number of rows to return.
time_cut (astropy.time.Time, optional) – Use a time cut instead of the most recent entry. (default is None)
index (int, optional) – When index is used, add an ‘AND salIndex = index’ to the query. (default is None).
convert_influx_index (bool, optional) – Convert influxDB time index from TAI to UTC? This is for legacy instances that may still have timestamps stored internally as TAI. Modern instances all store index timestamps as UTC natively. Default is False, don’t translate from TAI to UTC.
use_old_csc_indexing (bool, optional) – When index is used, add an ‘AND {CSCName}ID = index’ to the query which is the old CSC indexing name. (default is False).

Returns:

result – A DataFrame containing the results of the query.

Return type:

pandas.DataFrame