Visualizing data

Plot battery cycling data in Ionworks Studio with stack-cycles, Nyquist EIS, cycle metrics, and AI-powered SQL step filters

Ionworks Studio provides interactive tools for exploring your uploaded battery cycling data. The measurement viewer offers plots with flexible filtering options to help you analyze your experiments in the browser.

For plotting from Python, see reading data — plotting from Python.

Accessing the data viewer

To view measurement data:

1. Navigate to your Cell Specification
2. Select a Cell Instance
3. Click on a Cell Measurement
4. The measurement detail view opens with visualization tools

Visualization tabs

The data viewer has tabs for different analysis perspectives:

Time series tab

The time series view shows high-resolution measurement data over time (or other x-axis variables).

Axis selection

Use the dropdown menus to select which variables to plot on the x-axis and y-axis. Available options depend on the columns present in your measurement data.

Secondary y-axis

Optionally add a secondary y-axis to overlay two variables on the same plot. Select “None” to disable.

The secondary y-axis is disabled when “Stack cycles” mode is active.

Stack cycles mode

Enable Stack cycles to overlay all cycles on top of each other by resetting the x-axis to zero at the start of each cycle. This is useful for:

• Comparing cycle-to-cycle variations
• Identifying degradation patterns
• Visualizing capacity fade

When enabled, cycles are color-coded from blue (early cycles) to yellow (late cycles) using a colorblind-safe palette.

Show step data

Enable Show step data to display detailed step information in hover tooltips. The tooltip shows:

• Cycle and step numbers
• Step metrics (duration, capacity, voltage stats)
• SQL column names for use in filters

Use “Show step data” to discover the column names you need for SQL filtering. The hover tooltip displays the exact column names used in filter queries.

EIS tab

When your measurement contains impedance data (Z_Re [Ohm], Z_Im [Ohm], and Frequency [Hz] columns), an EIS tab appears automatically. This tab displays a Nyquist plot with Z_Re [Ohm] on the x-axis and -Z_Im [Ohm] on the y-axis.

If your data contains multiple steps, each step is plotted as a separate trace with its own color. You can use the step filter to select which steps to display.

Cycles tab

The cycles view shows aggregated metrics per cycle, ideal for tracking degradation and performance trends.

Available cycle metrics

Metric	Description
Discharge capacity [A.h]	Total discharge capacity per cycle
Charge capacity [A.h]	Total charge capacity per cycle
Discharge energy [W.h]	Total discharge energy per cycle
Charge energy [W.h]	Total charge energy per cycle
Coulombic efficiency	Discharge/charge capacity ratio
Energy efficiency	Discharge/charge energy ratio
Capacity throughput [A.h]	Cumulative capacity processed
Energy throughput [W.h]	Cumulative energy processed
Min voltage [V]	Minimum voltage reached in cycle
Max voltage [V]	Maximum voltage reached in cycle
Cycle duration [s]	Total cycle time
Mean temperature [degC]	Average temperature during cycle

Relative / retention mode

For capacity and energy metrics, enable Show relative to display values as a fraction of a reference cycle:

1. Check “Show relative”
2. Enter the reference cycle number (e.g., cycle 1 or cycle 10)
3. Values are shown as value / reference_value

This is useful for visualizing capacity retention curves (e.g., 80% retention after 500 cycles).

Filtering data

The measurement viewer provides multiple filtering methods to focus on specific portions of your data.

Cycle filter

Filter which cycles are displayed using several input methods:

Custom expression

Enter a custom filter expression using Python slice-like syntax:

• Single cycles: 1, 5, 10, 20
• Range: 0:50 (cycles 0 through 50)
• Negative indexing: -10:-1 (last 10 cycles)
• Combined: 0:10, 50, 100:110

First N cycles

Quickly select the first N cycles. Enter the count and the filter automatically generates.

Last N cycles

Select the last N cycles from your dataset.

Range with step

Define a range with optional sampling:

• Min: Starting cycle (default: 0)
• Max: Ending cycle (default: last cycle)
• Step: Sample every Nth cycle (e.g., step=10 shows cycles 0, 10, 20…)

Use stepping for large datasets to reduce plot density while maintaining overall trends.

Step filter (within cycle)

Filter which steps within each cycle are shown. This is useful for isolating specific protocol phases:

• 0 - Show only the first step of each cycle
• 0:2 - Show steps 0, 1, and 2 of each cycle
• 1, 3, 5 - Show specific steps

Step numbers reset to 0 at the start of each cycle in this filter. So step 0 is always the first step of a cycle, regardless of the overall step count. This filter is only available when not using Advanced mode.

Advanced step filter (AI-powered)

The Advanced filter mode lets you filter steps using natural language or SQL. Simply describe what you want to see, and the AI generates the appropriate filter.

Using advanced filter mode

1. Select Advanced mode

   Choose “Advanced” from the filter type dropdown. This is the default when no filter is active.

2. Describe what you want

   Type a natural language description in the text field. For example:

   • “discharge steps longer than 1 hour”
   • “charge steps with capacity > 2 Ah”
   • “rest steps”
3. Click Apply

   The AI generates the SQL filter and applies it immediately. The plot updates to show only matching steps.

Refining your filter

When a filter is active, you can modify it with follow-up prompts:

• “also filter for steps longer than 1 hour” → adds to existing filter
• “change to charge steps instead” → replaces part of the filter
• “remove the duration filter” → removes a specific condition

SQL dialog (advanced editing)

Click the code icon </> to open the SQL dialog for more control:

• AI prompt field: Describe what you want, then click “Generate”
• SQL editor: Edit the SQL WHERE clause directly
• Match mode: Choose how to apply the filter (see below)
• Example queries: Click any example to insert it
• Available columns: Full reference of filterable columns

Match modes

When filtering, choose how matches are applied:

• Matching steps only: Show only the specific steps that match your query
• Full cycles: Show all steps from cycles that contain at least one matching step

Use “Full cycles” mode when you want to filter by a cycle characteristic (like total cycle discharge capacity) but still see the complete cycle data for context.

Available SQL columns

The SQL dialog shows the complete list of available columns. Common columns include:

Step identification:

• step_count - Overall step number
• cycle_count - Cycle number
• step_from_cycler - Original step number from cycler
• cycle_from_cycler - Original cycle number from cycler

Timing:

• duration_s - Step duration in seconds

Capacity (step level):

• charge_capacity_ah - Capacity charged in this step
• discharge_capacity_ah - Capacity discharged in this step

Energy (step level):

• charge_energy_wh - Energy charged in this step
• discharge_energy_wh - Energy discharged in this step

Capacity (cycle level, cumulative):

• cycle_charge_capacity_ah - Total charge capacity for the cycle
• cycle_discharge_capacity_ah - Total discharge capacity for the cycle

Voltage:

• start_voltage_v, end_voltage_v
• min_voltage_v, max_voltage_v, mean_voltage_v

Current:

• min_current_a, max_current_a, mean_current_a

Power:

• min_power_w, max_power_w, mean_power_w

Plot interactions

The visualization plots support standard interactions:

• Zoom: Click and drag to zoom into a region
• Pan: Hold shift and drag to pan
• Reset: Double-click to reset the view
• Hover: Move cursor over data points to see values

Automatic data loading

When you zoom into the time series plot, Ionworks Studio automatically fetches higher-resolution data for the zoomed region. A loading indicator appears while data is being fetched.

For large datasets, the initial view shows downsampled data for performance. Zooming in reveals the full resolution data for that time range.

Best practices

Start with overview, then drill down

Begin with the full dataset view to understand overall trends, then use filters to focus on specific cycles or conditions of interest.

Use stack cycles for degradation analysis

Stack cycles mode makes it easy to spot changes in voltage profiles, capacity, or other characteristics as the cell ages.

Combine filters for complex analysis

Use cycle filtering to select a range of cycles, then add Advanced filtering to isolate specific step types within those cycles.

Use natural language for quick filtering

The Advanced filter understands plain English. Try “discharge steps over 1 hour” or “cycles with high capacity” instead of writing SQL manually.

Check step data for filter column names

Enable “Show step data” and hover over your data to see step metrics and the SQL column names you can reference in filters.

Next steps

Data format

Understand the data structure for better filtering.

Reading data

Retrieve and analyze data via the Python API.