#!/usr/Local/pyroot/PyTangoRoot/bin/python
"""
Python module for extracting attribute from Arhive Extractor Device.
Includes a Command Line Interface.
Can be imported as is to use function in user script.
"""
import logging
import datetime
import numpy as np
import PyTango as tango
__version__ = "1.0.1"
class ArchiveExtractor:
##########################################################################
""" Commodity variables """
# Extractor date format for GetAttDataBetweenDates
DBDFMT = "%Y-%m-%d %H:%M:%S"
# Extractor date format for GetNearestValue
DBDFMT2 = "%d-%m-%Y %H:%M:%S"
# Vectorized fromtimestamp function
ArrayTimeStampToDatetime = np.vectorize(datetime.datetime.fromtimestamp)
# Max number of point per extraction chunks
Nmax = 100000
##########################################################################
def __init__(
self,
ExtractorKind='H', ExtractorNumber=2,
ExtractorPath=None,
logger=logging.getLogger("ArchiveExtractor")
):
"""
Constructor function
Parameters
----------
ExtractorKind: char
Either 'H' or 'T' for HDB or TDB.
ExtractorNumber: int
Number of the archive extractor instance to use.
ExtractorPath: string
Tango path to the extractor.
If this argument is given, it takes precedence over ExtractorKind and ExtractorNumber.
logger: logging.Logger
Logger object to use
Return
------
ArchiveExtractor
"""
# Get logger
self.logger = logger
#######################################################
# Select Extractor
if ExtractorPath is None:
self.extractor = "archiving/%sDBExtractor/%d"%(ExtractKind, ExtractorNumber)
else:
self.extractor = tango.DeviceProxy(ExtractorPath)
self.extractor.set_timeout_millis(3000)
self.logger.debug("Archive Extractor %s used."%self.extractor.name())
##---------------------------------------------------------------------------##
@staticmethod
def dateparse(datestr):
"""
Convenient function to parse date strings.
Global format is %Y-%m-%d-%H:%M:%S and it can be reduced to be less precise.
Parameters
---------
datestr : string
Date as a string, format %Y-%m-%d-%H:%M:%S or less precise.
Exceptions
----------
ValueError
If the parsing failed.
Returns
-------
date : datetime.datetime
Parsed date
"""
# This gives all format that will be tried, in order.
# Stop on first parse success. Raise error if none succeed.
fmt = [
"%Y-%m-%d-%H:%M:%S",
"%Y-%m-%d-%H:%M",
"%Y-%m-%d-%H",
"%Y-%m-%d",
"%Y-%m",
]
date = None
for f in fmt:
try:
date = datetime.datetime.strptime(datestr, f)
except ValueError:
continue
else:
break
else:
raise ValueError("Could not parse argument to a date")
return date
##---------------------------------------------------------------------------##
def evalPoints(
self,
attribute,
dateStart,
dateStop,
):
"""
Evaluate the number of points for the attribute on the date range.
Also checks for its presence.
Parameters
----------
attribute : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
dateStart : datetime.datetime
Start date for extraction.
dateStop : datetime.datetime
Stop date for extraction.
Default is now (datetime.datetime.now())
Exceptions
----------
ValueError
The attribute is not found in the database.
NotImplemented
The archive mode returned by the DB is not handled.
Return
------
N: int
Number of points on the date range.
"""
# Check that the attribute is in the database
self.logger.debug("Check that %s is archived."%attribute)
if not self.extractor.IsArchived(attribute):
self.logger.error("Attribute '%s' is not archived in DB %s"%(attribute, extractor))
raise ValueError("Attribute '%s' is not archived in DB %s"%(attribute, extractor))
# Get its sampling period in seconds
req=self.extractor.GetArchivingMode(attribute)
self.logger.debug("GetArchivingMode: "+str(req))
if req[0] == "MODE_P":
samplingPeriod = int(req[1])*10**-3
self.logger.debug("Attribute is sampled every %g seconds"%samplingPeriod)
elif req[0] == "MODE_EVT":
self.logger.warning("Attribute is archived on event. Chunks of data are sized with an estimated datarate of 0.1Hz")
samplingPeriod = 10
else:
self.logger.error("Archive mode not implemented in this script")
raise NotImplemented("Archive mode not implemented in this script")
# Evaluate the number of points
N = (dateStop-dateStart).total_seconds()/samplingPeriod
self.logger.debug("Which leads to %d points to extract."%est_N)
return N
##---------------------------------------------------------------------------##
def BetweenDates(
self,
attr,
dateStart,
dateStop=datetime.datetime.now(),
):
"""
Query attribute data from an archiver database, get all points between dates.
Use ExtractBetweenDates.
Parameters
----------
attr : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
dateStart : datetime.datetime
Start date for extraction.
dateStop : datetime.datetime
Stop date for extraction.
Default is now (datetime.datetime.now())
Exceptions
----------
ValueError
The attribute is not found in the database.
Returns
-------
[date, value] : array
date : numpy.ndarray of datetime.datime objects
Dates of the values
value : numpy.ndarray
Archived values
"""
# Check and estimate the number of points
est_N = self.evalPoints(attribute, dateStart, dateStop)
# If data chunk is too much, we need to cut it
if est_N > Nmax:
dt = datetime.timedelta(seconds=samplingPeriod)*Nmax
cdates = [dateStart]
while cdates[-1] < dateStop:
cdates.append(cdates[-1]+dt)
cdates[-1] = dateStop
self.logger.debug("Cutting access to %d little chunks of time, %s each."%(len(cdates)-1, dt))
else:
cdates=[dateStart, dateStop]
# Arrays to hold every chunks
value = []
date = []
# For each date chunk
for i_d in range(len(cdates)-1):
# Make retrieval request
self.logger.debug("Perform ExtractBetweenDates (%s, %s, %s)"%(
attr,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT))
)
_date, _value = self.extractor.ExtractBetweenDates([
attr,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT)
])
# Transform to datetime - value arrays
_value = np.asarray(_value, dtype=float)
if len(_date) > 0:
_date = ArrayTimeStampToDatetime(_date/1000.0)
value.append(_value)
date.append(_date)
self.logger.debug("Concatenate chunks")
value = np.concatenate(value)
date = np.concatenate(date)
self.logger.debug("Extraction done for %s."%attr)
return [date, value]
##---------------------------------------------------------------------------##
def query_ADB_BetweenDates_MinMaxMean(
attr,
dateStart,
dateStop=datetime.datetime.now(),
timeinterval=datetime.timedelta(seconds=60),
extractor="archiving/TDBExtractor/4"):
"""
Query attribute data from archiver database.
Divide the time range in time intervals.
Get min, max and mean value on each time interval.
The date stamp is in the middle of the interval.
Parameters
----------
attr : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
dateStart : datetime.datetime
Start date for extraction.
dateStop : datetime.datetime
Stop date for extraction.
Default is now (datetime.datetime.now())
timeinterval : datetime.timedelta
Interval time to divide the time range in chunks.
Default is 1 minute.
extractor : String
Name of the DB Extractor device.
Default is "archiving/TDBExtractor/4"
Exceptions
----------
ValueError
The attribute is not found in the database.
Returns
-------
[date, value] : array
date : numpy.ndarray of datetime.datime objects
Dates of the values
value : numpy.ndarray
Archived values
"""
# TEMP Dev not finished
logger.error("Feature not implemented yet.")
return
# Device Proxy to DB
logger.debug("Instantiate proxy to %s"%extractor)
ADB = tango.DeviceProxy(extractor)
# Give the DB extractor 3 seconds timeout
ADB.set_timeout_millis(3000)
# Check that the attribute is in the database
logger.debug("Check that %s is archived."%attr)
if not ADB.IsArchived(attr):
logger.error("Attribute '%s' is not archived in DB %s"%(attr, extractor))
raise ValueError("Attribute '%s' is not archived in DB %s"%(attr, extractor))
# Cut data range in time chunks
cdates = [dateStart]
while cdates[-1] < dateStop:
cdates.append(cdates[-1]+timeinterval)
cdates[-1] = dateStop
logger.debug("Cutting time range to %d chunks of time, %s each."%(len(cdates)-1, dt))
# Prepare arrays
value_min = np.empty(len(cdates-1))
value_max = np.empty(len(cdates-1))
value_mean = np.empty(len(cdates-1))
# For each time chunk
for i_d in range(len(cdates)-1):
# Make requests
logger.debug("Perform GetAttDataMaxBetweenDates (%s, %s, %s)"%(
attr,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT))
)
ADB.GetAttDataMaxBetweenDates([
attr,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT)
])
##---------------------------------------------------------------------------##
def query_ADB_NearestValue(attr,
dates,
extractor="archiving/TDBExtractor/4"):
"""
Query attribute data from an archiver database, get nearest points from dates.
Use GetNearestValue and perform multiple calls.
For each date in dates, it read the closest sampled value.
Return the real dates of the samples.
Parameters
----------
attr : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
dates : numpy.ndarray of datetime.datetime
Dates for extraction.
extractor : String
Name of the DB Extractor device.
Default is "archiving/TDBExtractor/4"
Exceptions
----------
ValueError
The attribute is not found in the database.
Returns
-------
[realdate, value] : array
realdate : numpy.ndarray of datetime.datime objects
Dates of the values
value : numpy.ndarray
Archived values
"""
# Device Proxy to DB
ADB = tango.DeviceProxy(extractor)
# Give the DB extractor 3 seconds timeout
ADB.set_timeout_millis(3000)
# Check that the attribute is in the database
if not ADB.IsArchived(attr):
raise ValueError("Attribute '%s' is not archived in DB %s"%(attr, extractor))
# Prepare arrays
value = np.empty(len(dates), dtype=float)
realdate = np.empty(len(dates), dtype=object)
# Loop on dates
for i in range(len(dates)):
# Make retrieval
answ = ADB.GetNearestValue([attr, dates[i].strftime(DBDFMT2)])
answ = answ.split(";")
realdate[i] = datetime.datetime.fromtimestamp(int(answ[0])/1000)
value[i] = answ[1]
return [realdate, value]