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A small team of us (with help from Seeq team members) built a short script to extract signal names from our legacy excel workbooks so that we could push them to Seeq workbench. Perhaps, like us, you are involved in migrating workbooks for monitoring/ reporting over to Seeq and could do with a boost to get started so you can get to real work of setting up the Seeq workbench. The attached script will extract the signal names (assuming you can craft your own regex search filter) from each excel worksheet and then push them to workbench with labeling for organization. Hopefully its a help to some 🙂 signal_transfer_excel2seeq_rev1.ipynb

Users of OSIsoft Asset Framework often want to filter elements and attributes based on the AF Templates they were built on. At this time though, the spy.search command in Seeq Data Lab only filters based on the properties Type, Name, Description, Path, Asset, Datasource Class, Datasource ID, Datasource Name, Data ID, Cache Enabled, and Scoped To. This post discusses a way in which we can still filter elements and/or attributes based on AF Template. Step 1: Retrieve all elements in the AF Database The code below will return all assets in an AF Database that are based on a AF Template whose name contains Location. asset_search = spy.search({"Path":"Example-AF", "Type":"Asset"}, all_properties=True) #Make sure to include all properties since this will also return the AF Template asset_search.dropna(subset=['Template'], inplace=True) # Remove assets not based on a template since we can't filter with NaN values asset_search_location = asset_search[asset_search['Template'].str.contains('Location')] # Apply filter to only consider Location AF Template assets Step 2: Find all relevant attributes This code will retrieve the desired attributes. Note wildcards and regular expression can be used to find multiple attributes. signal_search = spy.search({"Path":"Example-AF", "Type":"Signal", "Name":"Compressor Power"}) #Find desired attributes Step 3: Filter attributes based on if they come from an element from the desired AF template Last step cross references the signals returned with the desired elements. This is done by looking at their paths. # Define a function to recreate paths, items directly beneath the database asset don't have a Path def path_merger(row): row = row.dropna() return ' >> '.join(row) asset_search_location['Full Path'] = asset_search_location[['Path', 'Asset', 'Name']].apply(lambda row: path_merger(row),axis=1) # Create path for the asset that includes its name signal_search['Parent Path'] = signal_search[['Path', 'Asset']].apply(lambda row: path_merger(row),axis=1) # Create path for the parents of the signals signal_search_location = signal_search[signal_search['Parent Path'].isin((asset_search_location['Full Path']))] # Cross reference parent path in signals with full paths in assets to see if these signals are children of the desired elements