EfdClient¶

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

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).
internal_scale : str, optional: Time scale to use when converting times to internal formats (‘tai’ by default).
path_to_creds : str, optional: Absolute path to use when reading credentials from disk (‘~/.lsst/notebook_auth.yaml’ 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.

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])
`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[, index])	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/')¶

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, 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. 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¶