diff --git a/ArchiveExtractor.py b/ArchiveExtractor.py
index cd780d47c25fa95f474cd99c89e51ff89cc15bb9..13b6211596cf2938de060b44bae6da4d5c4a1896 100755
--- a/ArchiveExtractor.py
+++ b/ArchiveExtractor.py
@@ -13,20 +13,20 @@ import PyTango as tango
__version__ = "1.0.1"
-class ArchiveExtractor:
+##########################################################################
+""" Commodity variables """
- ##########################################################################
- """ Commodity variables """
+# Extractor date format for GetAttDataBetweenDates
+DBDFMT = "%Y-%m-%d %H:%M:%S"
- # Extractor date format for GetAttDataBetweenDates
- DBDFMT = "%Y-%m-%d %H:%M:%S"
+# Extractor date format for GetNearestValue
+DBDFMT2 = "%d-%m-%Y %H:%M:%S"
- # Extractor date format for GetNearestValue
- DBDFMT2 = "%d-%m-%Y %H:%M:%S"
+# Vectorized fromtimestamp function
+ArrayTimeStampToDatetime = np.vectorize(datetime.datetime.fromtimestamp)
- # Vectorized fromtimestamp function
- ArrayTimeStampToDatetime = np.vectorize(datetime.datetime.fromtimestamp)
+class ArchiveExtractor:
# Max number of point per extraction chunks
Nmax = 100000
@@ -66,7 +66,9 @@ class ArchiveExtractor:
#######################################################
# Select Extractor
if ExtractorPath is None:
- self.extractor = "archiving/%sDBExtractor/%d"%(ExtractKind, ExtractorNumber)
+ self.extractor = tango.DeviceProxy(
+ "archiving/%sDBExtractor/%d"%(ExtractorKind, ExtractorNumber)
+ )
else:
self.extractor = tango.DeviceProxy(ExtractorPath)
@@ -158,41 +160,14 @@ class ArchiveExtractor:
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(
+ def betweenDates(
self,
- attr,
+ attribute,
dateStart,
dateStop=datetime.datetime.now(),
):
@@ -205,11 +180,11 @@ class ArchiveExtractor:
attr : String
Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
- dateStart : datetime.datetime
- Start date for extraction.
+ dateStart : datetime.datetime, string
+ Start date for extraction. If string, it will be parsed.
- dateStop : datetime.datetime
- Stop date for extraction.
+ dateStop : datetime.datetime, string
+ Stop date for extraction. If string, it will be parsed.
Default is now (datetime.datetime.now())
Exceptions
@@ -227,12 +202,47 @@ class ArchiveExtractor:
"""
- # Check and estimate the number of points
- est_N = self.evalPoints(attribute, dateStart, dateStop)
+ # Parse date if it is string
+ if type(dateStart) is str:
+ dateStart = self.dateparse(dateStart)
+ if type(dateStop) is str:
+ dateStop = self.dateparse(dateStop)
+
+ # 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")
+
+
+ # Get the number of points
+ N=self.extractor.GetAttDataBetweenDatesCount([
+ attribute,
+ dateStart.strftime(DBDFMT2),
+ dateStop.strftime(DBDFMT2)
+ ])
+ self.logger.debug("On the period, there is %d entries"%N)
# If data chunk is too much, we need to cut it
- if est_N > Nmax:
- dt = datetime.timedelta(seconds=samplingPeriod)*Nmax
+ if N > self.Nmax:
+ dt = datetime.timedelta(seconds=samplingPeriod)*self.Nmax
+ dt = (dateStop-dateStart)/(N//self.Nmax)
cdates = [dateStart]
while cdates[-1] < dateStop:
cdates.append(cdates[-1]+dt)
@@ -249,13 +259,13 @@ class ArchiveExtractor:
for i_d in range(len(cdates)-1):
# Make retrieval request
self.logger.debug("Perform ExtractBetweenDates (%s, %s, %s)"%(
- attr,
+ attribute,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT))
)
_date, _value = self.extractor.ExtractBetweenDates([
- attr,
+ attribute,
cdates[i_d].strftime(DBDFMT),
cdates[i_d+1].strftime(DBDFMT)
])
@@ -273,7 +283,7 @@ class ArchiveExtractor:
date = np.concatenate(date)
- self.logger.debug("Extraction done for %s."%attr)
+ self.logger.debug("Extraction done for %s."%attribute)
return [date, value]
##---------------------------------------------------------------------------##