Improve boolean casting

* Now usable for GetNearest

ArchiveExtractor.py

@@ -42,6 +42,13 @@ _DBDFMT2 = "%d-%m-%Y %H:%M:%S"
 # NOTE: it is faster than using pandas.to_datetime()
 _ArrayTimeStampToDatetime = np.vectorize(datetime.datetime.fromtimestamp)
 
+# Vectorized bool map dictionnary
+_ArrayStr2Bool = np.vectorize({
+    "true":True, 't':True,
+    "false":False, 'f':False,
+    }.get)
+
+
 def _check_initialized():
     """
     Check if the module is initialized.
@@ -243,6 +250,48 @@ def _cmd_with_retry(dp, cmd, arg, retry=2):
         break
     return cmdreturn
 
+
+def _cast_bool(value):
+    """
+    Cast a value, or array of values, to boolean.
+    Try to assess the input data type. If string, then try to find true or false word inside.
+
+    Parameters:
+    -----------
+    value: string, integer, or array of such
+        value to convert.
+
+    Return:
+    boolean:
+        value or array of boolean.
+    """
+
+    # Force to array
+    value = np.asarray(value)
+
+    # cast back to single value
+    def castback(v):
+        if v.shape == ():
+            return v.item()
+        return v
+
+    # Simply try to cast to bool first
+    try:
+        value = value.astype("bool")
+        logger.debug("Direct conversion to boolean")
+        return castback(value)
+    except ValueError:
+        # Keep trying to cast
+        pass
+
+    logger.debug("Try to convert to boolean")
+
+    value = np.char.strip(np.char.lower(value))
+    value = _ArrayStr2Bool(value)
+
+    return castback(value)
+
+
 ##########################################################################
 ###                  Module private variables                          ###
 ##########################################################################
@@ -547,8 +596,12 @@ def _extract_scalar(attribute, method, date1, date2, db, dtype):
             logger.error("Could not extract this chunk. Check the device extractor")
             return None
 
+        # Transform by datatype
+        if dtype is bool:
+            _value = _cast_bool(_value)
+
         # Fabricate return pandas.Series
-        d=pd.Series(index=[datetime.datetime.fromtimestamp(int(_date)/1000),], data=[float(_value),], name=attribute)
+        d=pd.Series(index=[datetime.datetime.fromtimestamp(int(_date)/1000),], data=[_value,], name=attribute)
 
         return d
 
@@ -579,7 +632,7 @@ def _extract_scalar(attribute, method, date1, date2, db, dtype):
 
             # Transform to datetime - value arrays
             if dtype is bool:
-                _value = np.asarray([{"true":True, "false":False}[_a.lower()] for _a in _value])
+                _value = _cast_bool(_value)
             else:
                 _value = np.asarray(_value, dtype=dtype)