EfdClient¶

class lsst_efd_client.EfdClient(efd_name, db_name='efd', port='443', creds_service='https://roundtable.lsst.codes/segwarides/', client=None, convert_influx_index=False)¶

Bases: object

Class to handle connections and basic queries

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).
port : str, optional: Port to use when querying the database (‘443’ by default).
creds_service : str, optional: URL to the service to retrieve credentials (https://roundtable.lsst.codes/segwarides/ by default).
client : object, optional: An instance of a class that ducktypes as aioinflux.InfluxDBClient. The intent is to be able to substitute a mocked client for testing.
convert_influx_index : bool, optional: Convert influxDB time index from TAI to UTC? This is for using 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.

Attributes Summary

`deployment`
`influx_client`	The `aioinflux.client.InfluxDBClient` used for queries.
`subclasses`

Methods Summary

`build_time_range_query`(topic_name, fields, …)	Build a query based on a time range.
`from_name`(efd_name, args, *kwargs)	Construct a client for the specific named subclass.
`get_fields`(topic_name)	Query the list of field names for a topic.
`get_schema`(topic)	Givent a topic, get a list of dictionaries describing the fields
`get_topics`()	Query the list of possible topics.
`list_efd_names`([creds_service])	List all valid names for EFD deployments available.
`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

deployment = ''¶

influx_client = None¶

The aioinflux.client.InfluxDBClient used for queries.

This should be used to execute queries not wrapped by this class.

subclasses = {}¶

Methods Documentation

build_time_range_query(topic_name, fields, start, end, is_window=False, index=None)¶

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: For indexed topics set this to the index of the topic to query (default is None).

Returns:

query : str: A string containing the constructed query statement.

from_name(efd_name, *args, **kwargs)¶

Construct a client for the specific named subclass.

Parameters:	efd_name : `str` Name of the EFD instance for which to construct a client. args Extra arguments to pass to the subclass constructor. *kwargs Extra keyword arguments to pass to the subclass constructor.
Raises:	NotImpementedError Raised if there is no subclass corresponding to the name.

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` List of field names in specified topic.

get_schema(topic)¶

Givent 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 : `Pandas.DataFrame` A dataframe with the schema information for the topic. One row per field.

get_topics()¶

Query the list of possible topics.

Returns:	results : `list` List of valid topics in the database.

classmethod list_efd_names(creds_service='https://roundtable.lsst.codes/segwarides/')¶

List all valid names for EFD deployments available.

Parameters:	creds_service : `str`, optional
Returns:	results : `list` A `list` of `str` each specifying the name of a valid deployment.

select_packed_time_series(topic_name, base_fields, start, end, is_window=False, index=None, ref_timestamp_col='cRIO_timestamp')¶

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: For indexed topics set this to the index of the topic to query (default is False).
ref_timestamp_col : str, optional: Name of the field name to use to assign timestamps to unpacked vector fields (default is ‘cRIO_timestamp’).

Returns:

result : pandas.DataFrame: A pandas.DataFrame containing the results of the query.

select_time_series(topic_name, fields, start, end, is_window=False, index=None)¶

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: For indexed topics set this to the index of the topic to query (default is None).

Returns:

result : pandas.DataFrame: A pandas.DataFrame containing the results of the query.

select_top_n(topic_name, fields, num, time_cut=None, index=None)¶

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 For indexed topics set this to the index of the topic to query (default is `None`)
Returns:	result : `pandas.DataFrame` A `pandas.DataFrame` containing teh results of the query.

Navigation

EfdClient¶