Skip to content
Snippets Groups Projects
Commit ccbef0ca authored by BRONES Romain's avatar BRONES Romain
Browse files

First modification towards a module architecture 

* ArchiveExtractor is now a class
* CLI code is moved to a new script
parent 6c3923c2
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
ArchiveExtractor.py
+ 372
− 422
View file @ ccbef0ca
......@@ -13,473 +13,423 @@ 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"
ArrayTimeStampToDatetime = np.vectorize(datetime.datetime.fromtimestamp)
##---------------------------------------------------------------------------##
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
"""
logger.info("Parse date '%s'"%datestr)
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:
logger.debug("Try format '%s'"%f)
try:
date = datetime.datetime.strptime(datestr, f)
except ValueError:
logger.debug("Parsing failed")
if date is None:
logger.error("Could not parse date")
raise ValueError
return date
##---------------------------------------------------------------------------##
def query_ADB_BetweenDates(attr,
dateStart,
dateStop=datetime.datetime.now(),
extractor="archiving/TDBExtractor/4"):
"""
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())
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
"""
# Max number of point per extraction chunks
Nmax = 100000
# 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))
# Get its sampling period in seconds
req=ADB.GetArchivingMode(attr)
logger.debug("GetArchivingMode: "+str(req))
if req[0] == "MODE_P":
samplingPeriod = int(req[1])*10**-3
logger.debug("Attribute is sampled every %g seconds"%samplingPeriod)
elif req[0] == "MODE_EVT":
logger.warning("Attribute is archived on event. Chunks of data are sized with an estimated datarate of 0.1Hz")
samplingPeriod = 10
else:
raise NotImplemented("Archive mode not implemented")
# Evaluate the number of points
est_N = (dateStop-dateStart).total_seconds()/samplingPeriod
logger.debug("Which leads to %d points to extract."%est_N)
# 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
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
logger.debug("Perform ExtractBetweenDates (%s, %s, %s)"%(
attr,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT))
)
_date, _value = ADB.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)
logger.debug("Concatenate chunks")
value = np.concatenate(value)
date = np.concatenate(date)
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)
])
##########################################################################
""" Commodity variables """
# Extractor date format for GetAttDataBetweenDates
DBDFMT = "%Y-%m-%d %H:%M:%S"
##---------------------------------------------------------------------------##
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.
# Extractor date format for GetNearestValue
DBDFMT2 = "%d-%m-%Y %H:%M:%S"
Parameters
----------
attr : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
# Vectorized fromtimestamp function
ArrayTimeStampToDatetime = np.vectorize(datetime.datetime.fromtimestamp)
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)
# Max number of point per extraction chunks
Nmax = 100000
# Loop on dates
for i in range(len(dates)):
# Make retrieval
##########################################################################
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")
answ = ADB.GetNearestValue([attr, dates[i].strftime(DBDFMT2)])
answ = answ.split(";")
return date
realdate[i] = datetime.datetime.fromtimestamp(int(answ[0])/1000)
value[i] = answ[1]
return [realdate, value]
##---------------------------------------------------------------------------##
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.
##########################################################################
""" Command Line Interface """
if __name__ == "__main__":
dateStop : datetime.datetime
Stop date for extraction.
Default is now (datetime.datetime.now())
# Name the logger after the filename
logger = logging.getLogger("ArchiveExtractor")
Exceptions
----------
ValueError
The attribute is not found in the database.
# Default stop date
dateStop = datetime.datetime.now()
NotImplemented
The archive mode returned by the DB is not handled.
# Default stop date
dateStart = datetime.datetime.now()-datetime.timedelta(days=1)
#######################################################
# Install argument parser
import argparse
Return
------
N: int
Number of points on the date range.
parser = argparse.ArgumentParser(description="Extract attributes from the extractor devices.\nVersion %s"%__version__)
"""
# 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))
parser.add_argument("--from", type=dateparse, dest="dateStart",
help="Start date for extraction, format '1990-12-13-22:33:45'. "+
"It is possible to be less precise and drop, seconds, minutes, hours or even day."+
" Default is one day ago",
default=dateStart)
# Get its sampling period in seconds
req=self.extractor.GetArchivingMode(attribute)
self.logger.debug("GetArchivingMode: "+str(req))
parser.add_argument("--to", type=dateparse, dest="dateStop",
help="Stop date for extraction, format '1990-12-13-22:33:45'. It is possible to be less precise and drop, seconds, minutes, hours or even day."+
" Default is now.",
default=dateStop)
if req[0] == "MODE_P":
samplingPeriod = int(req[1])*10**-3
self.logger.debug("Attribute is sampled every %g seconds"%samplingPeriod)
parser.add_argument("--DB", choices=["H", "T", "L"],
default="T", help="Database to extract from. HDB (H) or TDB (T), default: %(default)s")
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
parser.add_argument("--DBN", type=int, default=2,
help="Extractor device number, default: %(default)s")
else:
self.logger.error("Archive mode not implemented in this script")
raise NotImplemented("Archive mode not implemented in this script")
parser.add_argument("--fileout", type=str, default="extracted_%s.npy"%datetime.datetime.now().strftime("%Y%m%d_%H%M%S"),
help="filename of the extraction destination. Default: %(default)s"),
parser.add_argument('--log', type=str, default="INFO",
help="Log level. Default: %(default)s.")
# 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
parser.add_argument('--filemode', action="store_true",
help="Set attribute to filemode."+
" Instead of specifying attributes, put a path to a file containing a list of attributes."+
" The file contains one attribute per line.")
parser.add_argument('attributes', type=str, nargs='+',
help="List of attributes to extract. Full tango path.")
args = parser.parse_args()
##---------------------------------------------------------------------------##
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))
#######################################################
# Configure logger
# 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))
)
# Add a stream handler
s_handler = logging.StreamHandler()
s_handler.setFormatter(logging.Formatter("%(levelname)s\t[%(funcName)s] \t%(message)s"))
ADB.GetAttDataMaxBetweenDates([
attr,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT)
])
# Set level according to command line attribute
s_handler.setLevel(level=getattr(logging, args.log.upper()))
logger.setLevel(level=getattr(logging, args.log.upper()))
logger.addHandler(s_handler)
logger.debug("Parsed arguments: %s"%args)
##---------------------------------------------------------------------------##
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.
logger.info("Archive Extractor %s"%__version__)
Parameters
----------
attr : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
#######################################################
# Filemode or not
if args.filemode:
logger.info("Filemode, openning file %s"%args.attributes[0])
# Read the file. Each line is an attribute
with open(args.attributes[0], "r") as fp:
attributes = fp.readlines()
dates : numpy.ndarray of datetime.datetime
Dates for extraction.
logger.debug("Read lines : %s"%attributes)
extractor : String
Name of the DB Extractor device.
Default is "archiving/TDBExtractor/4"
# Clean end of line
for i_a in range(len(attributes)):
attributes[i_a] = attributes[i_a].rstrip()
Exceptions
----------
ValueError
The attribute is not found in the database.
else:
attributes = args.attributes
Returns
-------
[realdate, value] : array
realdate : numpy.ndarray of datetime.datime objects
Dates of the values
value : numpy.ndarray
Archived values
#######################################################
# Select Extractor
if args.DB == "L":
extractor = "archiving/extractor/%d"%(args.DBN)
else:
extractor = "archiving/%sDBExtractor/%d"%(args.DB, args.DBN)
"""
#######################################################
# Prepare dictionnary for result
results = dict()
# Device Proxy to DB
ADB = tango.DeviceProxy(extractor)
#######################################################
# Extract from database
logger.info("Extract from %s to %s."%(args.dateStart, args.dateStop))
# Give the DB extractor 3 seconds timeout
ADB.set_timeout_millis(3000)
for attr in attributes:
logger.info("Extracting attribute %s..."%attr)
# 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))
for attempt in range(3):
try:
datevalue = query_ADB_BetweenDates(attr, args.dateStart, args.dateStop, extractor)
# Prepare arrays
value = np.empty(len(dates), dtype=float)
realdate = np.empty(len(dates), dtype=object)
# Add to result dictionnary
results[attr] = datevalue
# Loop on dates
for i in range(len(dates)):
# Make retrieval
except ValueError as e:
logger.debug("ErrorMsg: %s"%e)
logger.warning("Failed to extract %s. Skipping..."%attr)
except (tango.CommunicationFailed, tango.DevFailed) as e:
# retry
logger.debug("ErrorMsg: %s"%e)
logger.warning("Failed to extract %s. Retry..."%attr)
break
answ = ADB.GetNearestValue([attr, dates[i].strftime(DBDFMT2)])
answ = answ.split(";")
else:
logger.error("The device %s might have crash.\n"%extractor+
"You should check with Jive and probably restart with Astor.\n")
realdate[i] = datetime.datetime.fromtimestamp(int(answ[0])/1000)
value[i] = answ[1]
# Save all at each step
np.save(args.fileout, results)
return [realdate, value]
logger.info("Extraction done, saved in file %s"%args.fileout)
else:
# Name the logger after the module name
logger = logging.getLogger(__name__)
cli_archiveextractor.py 0 → 100644
+ 135
− 0
View file @ ccbef0ca
##########################################################################
""" Command Line Interface """
if __name__ == "__main__":
# Name the logger after the filename
logger = logging.getLogger("ArchiveExtractor")
# Default stop date
dateStop = datetime.datetime.now()
# Default stop date
dateStart = datetime.datetime.now()-datetime.timedelta(days=1)
#######################################################
# Install argument parser
import argparse
parser = argparse.ArgumentParser(description="Extract attributes from the extractor devices.\nVersion %s"%__version__)
parser.add_argument("--from", type=dateparse, dest="dateStart",
help="Start date for extraction, format '1990-12-13-22:33:45'. "+
"It is possible to be less precise and drop, seconds, minutes, hours or even day."+
" Default is one day ago",
default=dateStart)
parser.add_argument("--to", type=dateparse, dest="dateStop",
help="Stop date for extraction, format '1990-12-13-22:33:45'. It is possible to be less precise and drop, seconds, minutes, hours or even day."+
" Default is now.",
default=dateStop)
parser.add_argument("--DB", choices=["H", "T", "L"],
default="T", help="Database to extract from. HDB (H) or TDB (T), default: %(default)s")
parser.add_argument("--DBN", type=int, default=2,
help="Extractor device number, default: %(default)s")
parser.add_argument("--fileout", type=str, default="extracted_%s.npy"%datetime.datetime.now().strftime("%Y%m%d_%H%M%S"),
help="filename of the extraction destination. Default: %(default)s"),
parser.add_argument('--log', type=str, default="INFO",
help="Log level. Default: %(default)s.")
parser.add_argument('--filemode', action="store_true",
help="Set attribute to filemode."+
" Instead of specifying attributes, put a path to a file containing a list of attributes."+
" The file contains one attribute per line.")
parser.add_argument('attributes', type=str, nargs='+',
help="List of attributes to extract. Full tango path.")
args = parser.parse_args()
#######################################################
# Configure logger
# Add a stream handler
s_handler = logging.StreamHandler()
s_handler.setFormatter(logging.Formatter("%(levelname)s\t[%(funcName)s] \t%(message)s"))
# Set level according to command line attribute
s_handler.setLevel(level=getattr(logging, args.log.upper()))
logger.setLevel(level=getattr(logging, args.log.upper()))
logger.addHandler(s_handler)
logger.debug("Parsed arguments: %s"%args)
logger.info("Archive Extractor %s"%__version__)
#######################################################
# Filemode or not
if args.filemode:
logger.info("Filemode, openning file %s"%args.attributes[0])
# Read the file. Each line is an attribute
with open(args.attributes[0], "r") as fp:
attributes = fp.readlines()
logger.debug("Read lines : %s"%attributes)
# Clean end of line
for i_a in range(len(attributes)):
attributes[i_a] = attributes[i_a].rstrip()
else:
attributes = args.attributes
#######################################################
# Select Extractor
if args.DB == "L":
extractor = "archiving/extractor/%d"%(args.DBN)
else:
extractor = "archiving/%sDBExtractor/%d"%(args.DB, args.DBN)
#######################################################
# Prepare dictionnary for result
results = dict()
#######################################################
# Extract from database
logger.info("Extract from %s to %s."%(args.dateStart, args.dateStop))
for attr in attributes:
logger.info("Extracting attribute %s..."%attr)
for attempt in range(3):
try:
datevalue = query_ADB_BetweenDates(attr, args.dateStart, args.dateStop, extractor)
# Add to result dictionnary
results[attr] = datevalue
except ValueError as e:
logger.debug("ErrorMsg: %s"%e)
logger.warning("Failed to extract %s. Skipping..."%attr)
except (tango.CommunicationFailed, tango.DevFailed) as e:
# retry
logger.debug("ErrorMsg: %s"%e)
logger.warning("Failed to extract %s. Retry..."%attr)
break
else:
logger.error("The device %s might have crash.\n"%extractor+
"You should check with Jive and probably restart with Astor.\n")
# Save all at each step
np.save(args.fileout, results)
logger.info("Extraction done, saved in file %s"%args.fileout)
else:
# Name the logger after the module name
logger = logging.getLogger(__name__)
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment

The place to be for your source code