How Wertek Studio works

BETA feature. Studio is enabled per account; today it's visible only to selected beta accounts. Ask Wertek about availability on your plan.

How it works (concept)

Wertek Studio is an engineering analysis lab inside the web platform: Python notebooks that run in your browser over your organization's data (energy, drives, vibration sensors, incidents). Nothing to install and no server to run — you open a cell, write Python, and it executes locally with scientific libraries already loaded (pandas, numpy, scipy, scikit-learn, matplotlib).

What makes it Wertek is the wertek bridge: an object available in every cell that brings your real data in as a DataFrame and can export a finding back to the platform (an alert rule, an event, a work order). In other words: explore the data and turn the result into something operational, in the same place.

What it is NOT: it doesn't run anything on your equipment, it can't see other organizations' data (each account is isolated), and it doesn't replace the diagnostics module or the CMMS — it's the exploratory layer on top of that data. Each cell has a 30-second execution limit (built for analysis, not heavy jobs).

First 15 minutes

1. Open it: in app.wertek.ai, side menu → Studio (marked BETA). You'll see three tabs: Notebooks, Datasets, and Artifacts.
2. Create a notebook: the + button, blank or from a guided template (Vibration Trend, Energy Anomaly, Correlation, Fault Classifier, RUL Estimation). Templates ship with sample data so you can see the flow; for your real data use the wertek bridge (below).
3. Rename it: click the title at the top, or the pencil icon in the list. Export the whole notebook to .ipynb (opens in Jupyter/VS Code) with the Export button.
4. Bring in your real data in a cell:

   import wertek
   df = await wertek.query("energy_readings", asset="TAB-PTAR-01", days=30)
   df.head()
   

5. Plot what you loaded:

   wertek.plot_trend(df, "power_kw", title="Power — last 30 days")
   

The wertek bridge — what you can call in a cell

Every method that fetches data is async: use await. They return a pandas DataFrame.

Fetch data

• await wertek.query(table, asset=None, days=30, columns=None, limit=10000) — query a source table directly. Ex: await wertek.query("vfd_readings", asset="AST_DRIVE", days=7).
• await wertek.load_dataset(name) — load a saved Dataset (defined in the Datasets tab) as a complete table.

Analyze and plot (no await)

• wertek.plot_trend(df, column, title=None) — time series (auto-detects the created_at column).
• wertek.plot_spectrum(df, column, title=None) — frequency spectrum (FFT).
• wertek.correlation_matrix(df, target=None) — correlation heatmap, or bars against a target column.
• wertek.align_timeseries(df_sensors, df_labels, tolerance="5min") — join two series on nearest timestamp (to cross measurements with events).

Export to production (create a draft artifact)

• await wertek.export_alert(name, asset=None, condition=None, severity="warning") — create an alert rule.
• await wertek.export_iaes_event(asset_id, event_type="asset.health", severity="warning", diagnosis="") — publish an event.
• await wertek.create_wo(asset_id, title, priority="medium", type="predictive") — create a pre-filled work order.

You also have pandas (pd), numpy (np), and matplotlib.pyplot (plt) available directly, no import needed.

Typical flow: from exploration to operation

1. Fetch the data with wertek.query(...) (or save a reusable Dataset in the Datasets tab and load it with load_dataset).
2. Explore: df.describe(), df.head(), plot trend/spectrum, look for correlations.
3. Export the finding with wertek.export_alert(...) → it shows up in the Artifacts tab as a draft.
4. Deploy it: in Artifacts, the Deploy button. A deployed alert rule becomes a real automation rule — a "View in Automations" link appears (Settings → Automations), where it lives and is edited like any other rule. Studio captures the trigger (condition/asset/severity); the actions (notify, create WO) you tune in Automations.
5. Share: export the notebook to .ipynb, or a Dataset to CSV, from their download icons.

Common errors and fixes

• "The cell stopped / the output vanished" — there's a 30-second per-cell limit. Narrow the range (days=) or limit=, or split the work across cells. It's not meant to crunch millions of rows at once.
• "I forgot await and got a weird object (coroutine)" — the methods that fetch or export data (query, load_dataset, export_*, create_wo) are async: always await wertek.query(...).
• "The DataFrame comes back empty" — the asset has no data in that window, or the table/asset name doesn't match. Verify the exact asset tag (Assets page) and try a wider window.
• "I deployed an alert but can't find it in Studio" — that's correct: deploying an alert artifact turns it into an automation rule. Find it in Settings → Automations (the artifact itself shows the link).
• "My rule fires on everything" — if you exported the alert without a condition, the rule becomes any condition. Edit it in Automations to narrow condition, severity, or asset.
• "I don't see Studio in the menu" — it's a BETA feature enabled per account. If it's missing, email support to request beta access.

What's next

• Save reusable queries: define Datasets in the Datasets tab (table + asset + range) and load them with wertek.load_dataset("name") in any notebook.
• From analysis to a live alert: see how rules are managed in CMMS/automations and assisted interpretation in the Copilot.
• Support: for beta access or questions, [email protected] or the platform chat — see general FAQ.