Adding AVEVA connection and formatting capabilities.

README.md

@@ -5,7 +5,7 @@ This is a set of tooling to perform useful operations of queryign and moving His
 Currently supports querying data from the following sources, and their requirements
 * VTScada - REST
 * ClearSCADA - Raw Historic Files
-* AVEVA (Wonderware) Historian - InSQL {Coming Soon}
+* AVEVA (Wonderware) Historian - InSQL
 
 The primary function of this tooling is to query a set of tags for a specified date range, compress and process those values as required, and move it into a format which can be easily imported into VTScada.
 
@@ -53,6 +53,13 @@ Each directory contains a Historic XXXXXX directory where XXXXX is the Unique ID
 
 These tools will conver the user start time and end time in a way that will only process the found and required HRD files at a time. This can *greatly* expand the amount of data in the system, it is strongly recommended to have a lot of free space left during queries.
 
+
 ### Setup ClearSCADA Config 
 
 Key Point: In the tags list file, the Source Name field is the unique identifier for the tag name to query. In VTScada this can be something like ```temp\old_value1```. In ClearSCADA, it will be the unique point ID, ex. ```005152```. The leading zeroes can be left out as the script will pad them in front of the integer to determine the correct path.
+
+## AVEVA (Wonderware) Historian - InSQL
+
+Prerequisite: Install the Microsoft ODBC Driver, and assign the correct name to the driver in the *setup.toml* file.
+
+Data can be queried from an AVEVA Historian through an ODBC connection. The Historian provides an interface to SQL, InSQL, which can be used to create data queries.

main.py

@@ -46,33 +46,80 @@ class HistoricalTag:
 #  AVEVA (Wonderware) Historian Functions
 # ----------------------
 
+# aveva_query()
+# ----------------------
+# 
 def aveva_query(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
 
+    current_start_time = start_time
+    current_end_time = start_time
+
+    tag_mappings = []
+    dir_path = output_path + str(start_time.year) + "\\"
+
+    while current_end_time < end_time:
+
+        # Wonderware starts to buckle around 3 month queries, query each timeline 3 months at a time
+        if current_end_time + timedelta(days=92) > end_time:
+            current_end_time = end_time
+        else:
+            current_end_time = current_end_time + timedelta(days=92)
+
+        tag_mappings.append(aveva_query_date(historical_tags, current_start_time, current_end_time))
+        current_start_time = current_start_time + timedelta(days=92)
+
+
+    write_tagmapping_to_file(dir_path + "TagMapping.csv", tag_mappings)
+    
+
+# aveva_query_date()
+# ----------------------
+# 
+def aveva_query_date(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
+
     print("Querying data for: " + str(start_time.year) + " " +
           str(start_time.month) + " " + str(start_time.day))
     dir_path = output_path + str(start_time.year) + "\\"
     create_directory(dir_path)
 
-    ft_start_time = "'" + str(start_time.astimezone(timezone.utc)) + "'"
+    init_string = f'DRIVER={{ODBC Driver 18 for SQL Server}};SERVER={server};' + \
-    ft_end_time = "'" + str(end_time.astimezone(timezone.utc)) + "'"
+        f'DATABASE={database_name};UID={application_user};PWD={application_pass};' + \
-
+        f'ENCRYPT=no'
-    init_string = "driver={SQLOLEDB}; server=" + server + "; database=" + \
-        database_name + "; UID=" + application_user + "; PWD=" + application_pass + ";"
 
     print(init_string)
-    # connection = pyodbc.connect(init_string)
+    connection = pyodbc.connect(init_string)
+
+    tag_mappings = []
 
     for tag in historical_tags:
         if tag.tag_type == "real" or tag.tag_type == "integer":
-            retrieval_mode = "'Average'"
+            retrieval_mode = '"Average"'
         else:
-            retrieval_mode = "'Cyclic'"
+            retrieval_mode = '"Cyclic"'
             
-        query = "SELECT * FROM OpenQuery(INSQL, 'SELECT DateTime, '" + tag.name_source + "' FROM WideHistory WHERE DateTime >= " + \
+        ft_start_time = '"' + str(start_time.astimezone(timezone.utc)) + '"' 
-            ft_start_time + " AND DateTime <= " + ft_end_time + " AND wwRetrievalMode = " + retrieval_mode + \
+        ft_end_time = '"'  + str(end_time.astimezone(timezone.utc)) + '"' 
-                " AND wwResolution = " + str(tag.interval) + "')"
+        ft_tag_str = '['  + str(tag.name_source) + ']'
 
+        query = f"SELECT * FROM OpenQuery(INSQL, 'SELECT DateTime, {ft_tag_str} FROM WideHistory WHERE DateTime >=" + \
+            f"{ft_start_time} AND DateTime <= {ft_end_time} AND wwRetrievalMode = {retrieval_mode}" + \
+            f"AND wwResolution = {str(tag.interval * 1000)}')"
+
+        cursor = connection.cursor()
         print(query)
+        cursor.execute(query)
+        raw_values = cursor.fetchall()
+
+        values = []
+        for row in raw_values:
+            values.append((datetime.timestamp(row[0]), row[1]))
+
+        output_file = prepare_file_for_tag(tag, values, dir_path, end_time)
+
+        if output_file != "":
+            tag_mappings.append((output_file, tag.name_dest))
+
+    return tag_mappings
 
 # ----------------------
 #  ClearSCADA Functions
@@ -332,7 +379,7 @@ def create_directory(path):
 # postprocess_values()
 # ----------------------
 # Process a list of values assumed and clean up timestamps which are within the interval of the last
-# timestamp. Values are assumed to already have been compressed
+# timestamp
 
 
 def postprocess_values(values: List[Union[int, float, None]]):

setup.toml

@@ -8,7 +8,7 @@ output_path = "output\\"
 system_timezone = "Canada/Mountain"
 
 [aveva]
-server_name = "172.16.1.123"
+server_name = "HIST002"
 database_name = "Runtime"
 
 [vtscada]