EfdClient

class lsst_efd_client.EfdClient(efd_name, db_name='efd', 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).

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.

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, convert_influx_index=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 {CSCName}ID = index’ to the query. (default is None).

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.
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', ref_timestamp_fmt='unix_tai', ref_timestamp_scale='tai', convert_influx_index=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 {CSCName}ID = 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 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.
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, convert_influx_index=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 {CSCName}ID = index’ to the query. (default is None).

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.
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, convert_influx_index=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 {CSCName}ID = index’ to the query. (default is None).

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.
Returns:
result : pandas.DataFrame

A pandas.DataFrame containing teh results of the query.