diff --git a/core/ArchiveExtractor.py b/core/ArchiveExtractor.py
index 8e0ff3391afd07e22330298798f1401c7cba81a1..01f6ca243deda0fd03935aa41bd6a016d83b1f4b 100755
--- a/core/ArchiveExtractor.py
+++ b/core/ArchiveExtractor.py
@@ -146,10 +146,12 @@ class ArchiveExtractor:
dateStart : datetime.datetime, string
Start date for extraction. If string, it will be parsed.
+ Example of string format %Y-%m-%d-%H:%M:%S or less precise.
dateStop : datetime.datetime, string, None
Stop date for extraction.
If string, it will be parsed.
+ Example of string format %Y-%m-%d-%H:%M:%S or less precise.
If None, it takes the current date and time.
Default is None (now).
@@ -182,6 +184,22 @@ class ArchiveExtractor:
# Check attribute is in database
self._check_attribute(attribute)
+ # Get info about the attribute
+ info=self.infoattr(attribute)
+ self.logger.debug("Attribute information \n%s"%info)
+
+ # Detect spectrum
+ attrtype="scalar"
+ if int(info["max_dim_x"]) > 1:
+ if int(info["max_dim_y"]) > 0:
+ self.logger.warning("Attribute %s is a (%s; %s) vector. This is poorly handled by this script."%(
+ attribute, info["max_dim_x"], info["max_dim_y"]))
+ attrtype="multi"
+ else:
+ self.logger.info("Attribute %s is a 1D vector, dimension = %s."%(
+ attribute, info["max_dim_x"]))
+ attrtype="vector"
+
# Cut the time horizon in chunks
cdates = self.chunkerize(attribute, dateStart, dateStop)
@@ -192,42 +210,83 @@ class ArchiveExtractor:
# For each date chunk
for i_d in range(len(cdates)-1):
- # Inform on retrieval request
- self.logger.info("Perform ExtractBetweenDates (%s, %s, %s)"%(
- attribute,
- cdates[i_d].strftime(DBDFMT),
- cdates[i_d+1].strftime(DBDFMT))
- )
+ # =============
+ # For now we handle multi dimension the same way as scalar, which will get only the first element
+ if (attrtype=="scalar") or (attrtype=="multi"):
+ # Inform on retrieval request
+ self.logger.info("Perform ExtractBetweenDates (%s, %s, %s)"%(
+ attribute,
+ cdates[i_d].strftime(DBDFMT),
+ cdates[i_d+1].strftime(DBDFMT))
+ )
+
+ cmdreturn = self._cmd_with_retry("ExtractBetweenDates", [
+ attribute,
+ cdates[i_d].strftime(DBDFMT),
+ cdates[i_d+1].strftime(DBDFMT)
+ ])
+
+ # Check command return
+ if cmdreturn is None:
+ logger.error("Could not extract this chunk. Check the device extractor")
+ return None
+
+ # Unpack return
+ _date, _value = cmdreturn
+
+ # Transform to datetime - value arrays
+ # NOTE: it is faster than using pandas.to_datetime()
+ _value = np.asarray(_value, dtype=float)
+ if len(_date) > 0:
+ _date = ArrayTimeStampToDatetime(_date/1000.0)
+
+ value.append(_value)
+ date.append(_date)
+
+ # =============
+ if attrtype=="vector":
+ self.logger.info("Perform GetAttDataBetweenDates (%s, %s, %s)"%(
+ attribute,
+ cdates[i_d].strftime(DBDFMT),
+ cdates[i_d+1].strftime(DBDFMT)
+ ))
+
+ [N,], [name,] = self.extractor.GetAttDataBetweenDates([
+ attribute,
+ cdates[i_d].strftime(DBDFMT),
+ cdates[i_d+1].strftime(DBDFMT)
+ ])
+ N=int(N)
- cmdreturn = self._cmd_with_retry("ExtractBetweenDates", [
- attribute,
- cdates[i_d].strftime(DBDFMT),
- cdates[i_d+1].strftime(DBDFMT)
- ])
+ # Read the history
+ self.logger.debug("Retrieve history of %d values. Dynamic attribute named %s."%(N, name))
+ attrHist = self.extractor.attribute_history(name, N)
- # Check command return
- if cmdreturn is None:
- logger.error("Could not extract this chunk. Check the device extractor")
- return None
+ # Transform to datetime - value arrays
+ _value = np.empty((N, int(info["max_dim_x"])), dtype=float)
+ _value[:] = np.nan
+ _date = np.empty(N, dtype=object)
+ for i_h in range(N):
+ _value[i_h,:attrHist[i_h].dim_x]=attrHist[i_h].value
+ _date[i_h]=attrHist[i_h].time.todatetime()
- # Unpack return
- _date, _value = cmdreturn
+ # Remove dynamic attribute
+ self.logger.debug("Remove dynamic attribute %s."%name)
+ self.extractor.RemoveDynamicAttribute(name)
- # Transform to datetime - value arrays
- # NOTE: it is faster than using pandas.to_datetime()
- _value = np.asarray(_value, dtype=float)
- if len(_date) > 0:
- _date = ArrayTimeStampToDatetime(_date/1000.0)
- value.append(_value)
- date.append(_date)
+ 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."%attribute)
- return pd.Series(index=date, data=value)
+ if attrtype=="vector":
+ return pd.DataFrame(index=date, data=value).dropna(axis=1, how='all')
+ else:
+ return pd.Series(index=date, data=value)
##---------------------------------------------------------------------------##
@@ -249,9 +308,11 @@ class ArchiveExtractor:
dateStart : datetime.datetime, string
Start date for extraction. If string, it will be parsed.
+ Example of string format %Y-%m-%d-%H:%M:%S or less precise.
dateStop : datetime.datetime, string
Stop date for extraction. If string, it will be parsed.
+ Example of string format %Y-%m-%d-%H:%M:%S or less precise.
Default is now (datetime.datetime.now())
timeInterval: datetime.timedelta, string
@@ -299,6 +360,16 @@ class ArchiveExtractor:
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 info about the attribute
+ info=self.infoattr(attribute)
+ self.logger.debug("Attribute information \n%s"%info)
+
+ # Detect spectrum
+ attrtype="scalar"
+ if int(info["max_dim_x"]) > 1:
+ self.logger.error("Attribute is not a scalar. Cannot perform this kind of operation.")
+ return None
+
# Cut data range in time chunks
cdates = [dateStart]
while cdates[-1] < dateStop:
@@ -430,3 +501,32 @@ class ArchiveExtractor:
cdates=[dateStart, dateStop]
return cdates
+
+ def infoattr(self, attribute):
+ """
+ Get informations for an attribute and pack it into a python dict.
+
+ Parameters
+ ----------
+ attribute : String
+ Name of the attribute. Full Tango name i.e. "test/dg/panda/current".
+
+ Returns
+ -------
+ info : dict
+ Dictionnary of propertyname:propertyvalue
+ """
+ info = dict()
+
+ for func in ("GetAttDefinitionData", "GetAttPropertiesData"):
+ R=getattr(self.extractor, func)(attribute)
+ if not R is None:
+ for i in R:
+ _s=i.split("::")
+ info[_s[0]]=_s[1]
+ else:
+ self.logger.warning("Function %s on extractor returned None"%func)
+
+
+ return info
+