From 553a4d47f80ef7728b73356716bb295830e0d63d Mon Sep 17 00:00:00 2001
From: Romain Broucquart <romain.broucquart@synchrotron-soleil.fr>
Date: Tue, 31 Oct 2023 11:25:17 +0100
Subject: [PATCH] Quick fix on attribute type boolean
* This is a quick fix, needs more work.
* Cast was always to float, which fails for boolean.
* Better handle all the type values and ask for a better cast.
---
ArchiveExtractor.py | 16 +++++++++++-----
1 file changed, 11 insertions(+), 5 deletions(-)
diff --git a/ArchiveExtractor.py b/ArchiveExtractor.py
index cf26417..3617a18 100755
--- a/ArchiveExtractor.py
+++ b/ArchiveExtractor.py
@@ -1,5 +1,5 @@
"""
-Python module for extracting attribute from Arhive Extractor Device.
+Python module for extracting attribute from Archive Extractor Device.
"""
import logging
import datetime
@@ -519,13 +519,19 @@ def _extract_attribute(attribute, method, date1, date2, db):
# =============
# For now we handle multi dimension the same way as scalar, which will get only the first element
if (attrtype=="scalar") or (attrtype=="multi"):
- return _extract_scalar(attribute, method, date1, date2, db)
+ if info["data_type"] == '1':
+ # Boolean data type, quick fix
+ dtype=bool
+ else:
+ dtype=float
+
+ return _extract_scalar(attribute, method, date1, date2, db, dtype)
if attrtype=="vector":
return _extract_vector(attribute, method, date1, date2, db)
##---------------------------------------------------------------------------##
-def _extract_scalar(attribute, method, date1, date2, db):
+def _extract_scalar(attribute, method, date1, date2, db, dtype):
# =====================
if method == "nearest":
@@ -572,12 +578,12 @@ def _extract_scalar(attribute, method, date1, date2, db):
# Transform to datetime - value arrays
- _value = np.asarray(_value, dtype=float)
+ _value = np.asarray(_value, dtype=dtype)
if len(_date) > 0:
_date = _ArrayTimeStampToDatetime(_date/1000.0)
# Fabricate return pandas.Series
- data.append(pd.Series(index=_date, data=_value,name=attribute))
+ data.append(pd.Series(index=_date, data=_value, name=attribute))
# Concatenate chunks
return pd.concat(data)
--
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