Reading data - Ionworks Studio

Once data is uploaded, you can read it back using the ionworks-api Python client. This page covers listing and filtering resources, retrieving full measurement detail, local caching, in-Python plotting, and error handling. For installation and authentication, see the Python API client page. For uploading, see uploading data.

You can find the ID for any cell specification, instance, or measurement from the data visualization pages in Ionworks Studio. The ID is displayed in the URL and in the detail panels.

Listing resources

# List cell specifications (first page)
specs = client.cell_spec.list()
for spec in specs[:5]:
    print(f"  - {spec.name} (form_factor: {spec.form_factor})")

# Get a specific cell spec with full nested data
full_spec = client.cell_spec.get(spec_id)
print(f"Capacity: {full_spec.ratings['capacity']['value']} "
      f"{full_spec.ratings['capacity']['unit']}")

# List instances for a spec and pick the first
instances = client.cell_instance.list(spec_id)
instance = instances[0]

# List measurements for an instance
measurements = client.cell_measurement.list(instance.id)

Filtering and ordering

All list() methods accept keyword-only filter parameters so you can narrow results server-side instead of fetching everything and filtering in Python.

# Search cell specs by name (case-insensitive substring match)
specs = client.cell_spec.list(name="graphite")

# Exact name match
specs = client.cell_spec.list(name_exact="NCM622/Graphite Coin Cell")

# Filter by form factor (cell specs only)
specs = client.cell_spec.list(form_factor="R2032")

# Filter by creator
specs = client.cell_spec.list(created_by_email="jane")

# Date range filters
specs = client.cell_spec.list(
    created_after="2026-01-01T00:00:00Z",
    created_before="2026-04-01T00:00:00Z",
)

# Sort results
specs = client.cell_spec.list(order_by="created_at", order="desc")

Filters work the same way across all three resource types and can be combined with pagination and ordering in a single call:

instances = client.cell_instance.list(
    spec_id,
    limit=50,
    offset=0,
    name="batch-A",
    order_by="updated_at",
    order="desc",
)

measurements = client.cell_measurement.list(
    instance_id,
    measurement_type="time_series",
    created_after="2026-03-01T00:00:00Z",
    order_by="created_at",
    order="asc",
)

Cell measurements support additional date filters for the measurement start time:

measurements = client.cell_measurement.list(
    instance_id,
    started_after="2026-03-01T00:00:00Z",
    started_before="2026-03-31T23:59:59Z",
)

Filter parameters

Parameter	Type	Description	Available on
`name`	`str`	Case-insensitive substring match on name.	All
`name_exact`	`str`	Exact match on name. Takes precedence over `name`.	All
`form_factor`	`str`	Exact match on form factor.	`cell_spec` only
`measurement_type`	`str`	Filter by measurement type (`"time_series"`, `"properties"`, `"file"`).	`cell_measurement` only
`created_by_email`	`str`	Case-insensitive substring match on creator email.	All
`created_after`	`str`	ISO datetime; records created after this time.	All
`created_before`	`str`	ISO datetime; records created before this time.	All
`updated_after`	`str`	ISO datetime; records updated after this time.	All
`updated_before`	`str`	ISO datetime; records updated before this time.	All
`started_after`	`str`	ISO datetime; measurements started after this time.	`cell_measurement` only
`started_before`	`str`	ISO datetime; measurements started before this time.	`cell_measurement` only
`order_by`	`str`	Column to sort by (`"name"`, `"created_at"`, `"updated_at"`, or `"start_time"` for measurements).	All
`order`	`str`	Sort direction: `"asc"` or `"desc"`.	All

Filter parameters can be combined freely with each other and with the limit/offset pagination parameters. The .total property on the returned PaginatedList reflects the total count after filters are applied.

Pagination

All list() calls return a PaginatedList. The limit and offset parameters control which page is fetched.

page = client.cell_spec.list(limit=50, offset=0)
print(f"Showing {page.count} of {page.total} specs")

next_page = client.cell_spec.list(limit=50, offset=50)

Parameter	Type	Default	Description
`limit`	`int`	Server default (1000)	Maximum number of items to return (1 to 1000).
`offset`	`int`	`0`	Number of items to skip before returning results.

The returned PaginatedList behaves like a regular Python list (iterate, index, check length) and also exposes:

Property	Description
`.items`	The list of results for the current page.
`.total`	Total number of matching records across all pages.
`.count`	Number of items in the current page.

To iterate through all results:

all_specs = []
offset = 0
limit = 100
while True:
    page = client.cell_spec.list(limit=limit, offset=offset)
    all_specs.extend(page.items)
    if len(all_specs) >= page.total:
        break
    offset += limit

Measurement detail

client.cell_measurement.detail() retrieves the full measurement and adapts its response based on the measurement type.

measurement_detail = client.cell_measurement.detail(measurement_id)

Time series
Properties
File

Returns time series data, step statistics, and cycle metrics:

Field	Description
`measurement`	Measurement metadata (name, protocol, test setup, notes)
`time_series`	Full time series data as a DataFrame (polars by default)
`steps`	Step-level statistics as a DataFrame
`cycles`	Cycle-level metrics (capacity, efficiency, etc.) as a DataFrame
`specification_id`	ID of the parent cell specification (use `client.cell_spec.get(id)` to fetch)
`instance_id`	ID of the parent cell instance (use `client.cell_instance.get(spec_id, id)` to fetch)

detail = client.cell_measurement.detail(measurement_id)
print(f"Time series shape: {detail.time_series.shape}")
print(detail.cycles.head())

Returns the measurement metadata with properties included. No file data is fetched.

detail = client.cell_measurement.detail(measurement_id)
props = detail.measurement.properties
print(f"Thickness: {props['thickness']['value']} {props['thickness']['unit']}")

Downloads all attached files and returns them as a files dict mapping filename to bytes:

detail = client.cell_measurement.detail(measurement_id)
for filename, content in detail.files.items():
    with open(filename, "wb") as f:
        f.write(content)

Linking to the web app

Use client.urls.measurement() to build a link to a measurement’s detail page in the Ionworks web app. This is useful when you want to surface a clickable link from a notebook, script, or report so collaborators can jump straight to the measurement in Ionworks Studio.

url = client.urls.measurement(measurement_id, project_id)
# https://app.ionworks.com/dashboard/projects/<project_id>/data/measurements/<measurement_id>

Parameter	Type	Description
`measurement_id`	`str`	ID of the measurement to link to.
`project_id`	`str`	ID of the project the measurement belongs to.

The helper runs entirely locally — no network call. The web-app host is derived from the client’s api_url so the link points at the same environment (api.ionworks.com → app.ionworks.com). Set IONWORKS_APP_URL to override, e.g. for local development where the frontend runs on a different port. A common pattern is to render a link next to each result while iterating through measurements:

for m in client.cell_measurement.list(instance_id):
    print(f"{m.name}: {client.urls.measurement(m.id, project_id)}")

Navigator: cached hierarchy walks

Navigator is an opt-in helper that walks the spec → instance → measurement hierarchy and memoises every list and fetch call in memory. Use it when you want to iterate over many specs, instances, or measurements in a single script or notebook and avoid repeating the same API calls. Reach for Navigator when:

You’re writing an analysis script that loops over every measurement on one or more cell specs.
You want deterministic iteration order — listings are returned sorted by name.
You want pagination handled automatically without managing limit and offset yourself.

The underlying sub-clients (client.cell_spec, client.cell_instance, client.cell_measurement) remain the primary API. Navigator is a thin layer on top — use it when you want a single cached view of the hierarchy, and use the sub-clients directly for one-off reads or writes.

from ionworks import Ionworks, Navigator

nav = Navigator(Ionworks())

# Walk every measurement on every instance of every spec
for spec_name in nav.specs():
    for inst in nav.instances(spec_name):
        for m in nav.measurements(inst.id):
            ts = nav.time_series(m.id)
            steps = nav.steps(m.id)
            # ... your analysis ...

Each entity is fetched at most once per Navigator instance. Calling nav.instances("CellA") twice returns the same list without a second API round-trip; the same applies to measurements, steps, and time_series.

Configuration

nav = Navigator(client=Ionworks(), page_size=200)

Parameter	Description
`client`	An existing `Ionworks` client. If omitted, a default client is constructed (which reads `IONWORKS_API_KEY` from the environment).
`page_size`	Items per page when paginating `cell_instance.list` and `cell_measurement.list`. Defaults to `200`.

Looking up a single spec

spec = nav.spec("CellA")

Raises KeyError with the list of available spec names if the name doesn’t match — useful for catching typos.

Invalidating the cache

Battery data is immutable once uploaded, so the only staleness mode is “a new sibling appeared on the platform.” For long-running notebooks where new data may have been uploaded mid-session, you can drop part or all of the cache:

nav.clear()                                   # drop everything
nav.invalidate(spec_name="CellA")             # drop CellA + its instances + measurements
nav.invalidate(instance_id="inst_123")        # drop one instance + its measurements
nav.invalidate(measurement_id="meas_456")     # drop one measurement's steps + time series

Invalidation cascades downward: dropping a spec also drops its instances and their measurements; dropping an instance also drops its measurements.

Navigator caches in memory for the lifetime of the instance. For cross-process or cross-session caching of measurement data on disk, see Local caching below — the two layers compose.

Local caching

The ionworks-api client automatically caches measurement data to disk so repeated reads are fast and avoid unnecessary API calls. Caching is enabled by default and applies to the steps, cycles, steps_and_cycles, and time_series methods on cell_measurement. When you call a method like client.cell_measurement.steps(measurement_id), the client checks a local cache directory before making an API request. If a cached copy exists and hasn’t expired, it’s returned directly. Otherwise, the client fetches from the API, caches the result, and returns it. Cached data is stored as Parquet files in ~/.ionworksdata_cache by default and expires after one hour.

Skipping the cache

Every data-fetching method accepts a use_cache parameter. Set it to False to force a fresh API call without reading from or writing to the local cache:

steps = client.cell_measurement.steps(measurement_id, use_cache=False)
time_series = client.cell_measurement.time_series(measurement_id, use_cache=False)

Configuring the cache

import ionworks

ionworks.set_cache_directory("/path/to/custom/cache")
ionworks.set_cache_ttl(7200)              # 2 hours
ionworks.set_cache_ttl(None)              # never expire
ionworks.set_cache_enabled(False)
ionworks.set_cache_enabled(True)
deleted_count = ionworks.clear_cache()

Function	Description
`set_cache_enabled(bool)`	Enable or disable caching globally.
`get_cache_enabled()`	Return whether caching is currently enabled.
`set_cache_directory(path)`	Set the directory for cache files. Default: `~/.ionworksdata_cache`.
`set_cache_ttl(seconds)`	Set the TTL in seconds. Pass `None` to disable expiration. Default: `3600`.
`get_cache_directory()`	Return the current cache directory path.
`get_cache_ttl()`	Return the current TTL value.
`clear_cache()`	Delete all cached files and return the number deleted.

Cache configuration is global. Changes affect all subsequent API calls in the same Python process.

Plotting from Python

DataLoader includes a plot_data() method for quick matplotlib-based visualization of measurement data. The plot displays voltage and current over time, with an additional temperature subplot when temperature data is available.

from ionworksdata import DataLoader

loader = DataLoader.from_db("measurement-id-here")
fig, ax = loader.plot_data()

The method returns a matplotlib (Figure, Axes) tuple so you can customize the plot further. Pass show=True to display the plot immediately:

fig, ax = loader.plot_data(show=True)

plot_data() automatically loads time series data from the server if it hasn’t been fetched yet.

For the interactive in-browser viewer (with filters, step overlays, SQL), see visualizing data.

Inline time series size limit

When you pass a pandas or polars DataFrame directly in an API call (for example, as part of a pipeline configuration), the client enforces a maximum of 1,000 rows for inline time series data. Larger datasets should be uploaded as measurements first, then referenced by ID.

from ionworks import MeasurementValidationError, IonworksError

try:
    client.pipeline.run(config_with_large_inline_df)
except MeasurementValidationError as e:
    # Specifically handle the size limit violation
    print(e)
    # "Time series has 5000 rows, which exceeds the maximum of 1000 rows
    #  for inline data. Upload the data as a measurement using
    #  client.cell_measurement.create() and reference it with
    #  'db:<measurement_id>' or iwdata.DataLoader.from_db(MEASUREMENT_ID)
    #  instead."
except IonworksError as e:
    print(e)

To work with larger datasets, upload first and reference by ID:

bundle = client.cell_measurement.create(instance_id, measurement_data)

from ionworksdata import DataLoader
loader = DataLoader.from_db(bundle.measurement.id)

Exporting DataLoader configurations

If you have a DataLoader that references a database measurement and you want to export a self-contained configuration (for example, to share with a colleague), use to_local() to embed the data inline:

from ionworksdata import DataLoader

loader = DataLoader.from_db("measurement-id-here")
local_loader = loader.to_local()

# Now to_config() returns the full data instead of a DB reference
config = local_loader.to_config()

to_local() fetches all time series and step data from the server immediately. For very large measurements, this may take a moment.

Error handling

The client raises exceptions for common error cases:

Missing or invalid API credentials
API request errors (raises IonworksError with details)
Inline time series exceeding 1,000 rows (raises MeasurementValidationError, a subclass of IonworksError)

from ionworks import IonworksError

try:
    client.cell_spec.list()
except IonworksError as e:
    print(f"API error: {e}")

See inline time series size limit above for the MeasurementValidationError handling pattern.

API error format

All API errors return a consistent JSON structure:

{
  "error_code": "CONFLICT",
  "message": "Cell specification with this name already exists",
  "detail": {
    "resource_type": "cell_specification",
    "resource_name": "My Cell Spec",
    "existing_id": "abc-123"
  }
}

Field	Type	Description
`error_code`	`string`	Machine-readable error code (e.g. `NOT_FOUND`, `CONFLICT`, `BAD_REQUEST`).
`message`	`string`	Human-readable description of what went wrong.
`detail`	`object \| null`	Optional additional context about the error. Contents vary by error type; may be absent for some errors.

Common HTTP status codes:

Status	Error code	Description
`400`	`BAD_REQUEST`	The request is invalid or missing required fields.
`403`	`FORBIDDEN`	You don’t have permission to access this resource. Also returned for missing or invalid API credentials (the API does not use `401`).
`404`	`NOT_FOUND`	The requested resource does not exist.
`409`	`CONFLICT`	A resource with the same name or identifier already exists. The `detail` field includes the `existing_id` when available.
`429`	`USAGE_LIMIT_REACHED`	Your organization has exceeded its usage quota for this billing cycle.

Full API reference

For the complete Python API reference, see the ionworks-api documentation.

Next steps

Visualize data

Explore uploaded data with the interactive in-browser viewer.

Uploading data

End-to-end upload workflow for specs, instances, and measurements.

Measurements

The three measurement types in detail.

Simulation API

Run simulations and pipelines via the Python API.

​Listing resources

​Filtering and ordering

​Filter parameters

​Pagination

​Measurement detail

​Linking to the web app

​Navigator: cached hierarchy walks

​Configuration

​Looking up a single spec

​Invalidating the cache

​Local caching

​Skipping the cache

​Configuring the cache

​Plotting from Python

​Inline time series size limit

​Exporting DataLoader configurations

​Error handling

​API error format

​Full API reference

​Next steps

Visualize data

Uploading data

Measurements

Simulation API

Listing resources

Filtering and ordering

Filter parameters

Pagination

Measurement detail

Linking to the web app

Navigator: cached hierarchy walks

Configuration

Looking up a single spec

Invalidating the cache

Local caching

Skipping the cache

Configuring the cache

Plotting from Python

Inline time series size limit

Exporting DataLoader configurations

Error handling

API error format

Full API reference

Next steps