> ## Documentation Index > Fetch the complete documentation index at: https://docs.ionworks.com/llms.txt > Use this file to discover all available pages before exploring further. # Analyses > Store features extracted from a cell measurement — like ECM parameters from EIS or LLI/LAM points from RPT data — as parquet-backed analysis records An **analysis** captures features extracted from a single [cell measurement](/data/measurements) — for example, ECM parameters fitted to an EIS sweep, an LLI/LAM degradation point derived from an RPT, or a DCIR value computed from an HPPC pulse. Each analysis is one row tied to one parent measurement, plus a parquet file holding the extracted values. Use analyses when you want to persist the output of a post-processing step alongside the measurement it came from, so downstream tools (fitting, visualization, reporting) can find it later without re-running the extractor. ## When to use an analysis Reach for an analysis whenever you have derived, tabular results that: * Belong to exactly one measurement (the "source of truth" measurement). * You want to keep as structured columns rather than as a raw file attachment on a [file measurement](/data/measurements#file). * May be produced by different extractors over time — analyses are free-form on `analysis_type`, so you can add new kinds without a schema change. Typical examples: | `analysis_type` | Extracted from | Typical columns | | ------------------ | ---------------------------- | ------------------------------------------------ | | `ecm_from_eis` | EIS time-series measurement | `R0`, `R1`, `C1`, ... with units like `Ohm`, `F` | | `lam_lli_from_rpt` | RPT time-series measurement | `LAM_pe`, `LAM_ne`, `LLI` with unit `%` | | `dcir_from_hppc` | HPPC time-series measurement | `dcir_charge`, `dcir_discharge` at various SOCs | `analysis_type` is a free-form string — the backend accepts any non-empty value, so a new extractor never needs a migration or client release. `ecm_from_eis`, `lam_lli_from_rpt`, and `dcir_from_hppc` are the advisory set the Studio UI surfaces by default. ## Anatomy of an analysis Every analysis record carries: | Field | Description | | ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | `measurement_id` | ID of the parent cell measurement this analysis is derived from. | | `name` | User-provided name for the analysis. | | `analysis_type` | Kind of analysis (free-form string, e.g. `"ecm_from_eis"`). | | `columns` | List of `{name, unit, dtype?}` specs describing the parquet columns, for header preview in the UI. | | `metadata` | Loose JSON object — source RPT/cycle identity, extractor parameters, solver settings, etc. | | `notes` | Free-text description of how the analysis was performed. | | `id`, `organization_id`, `created_by`, `created_at`, `updated_at` | Populated by the server. | The tabular values themselves live in a parquet file stored in the `measurement-data` bucket alongside the parent measurement. Signed download URLs are minted on demand. Analyses are managed through the [Python API client](/api-client), which exposes them as `client.analysis`. `create()` accepts a DataFrame directly — the client serialises it to parquet and uploads it for you. ## Creating an analysis ```python theme={null} from ionworks import Ionworks, AnalysisType import polars as pl client = Ionworks() df = pl.DataFrame({"R0": [0.012], "R1": [0.008], "C1": [1.4]}) analysis = client.analysis.create( measurement_id="msmt_abc123", name="ECM fit at 25 °C, 50% SOC", analysis_type=AnalysisType.ECM_FROM_EIS, # or the string "ecm_from_eis" data=df, columns=[ {"name": "R0", "unit": "Ohm"}, {"name": "R1", "unit": "Ohm"}, {"name": "C1", "unit": "F"}, ], metadata={"soc_pct": 50, "temperature_degc": 25, "fit_rms": 0.0021}, notes="Fitted with Randles model, 1e-3 to 1e5 Hz sweep", ) print(analysis.id) ``` `analysis_type` is free-form — pass any string, or one of the `AnalysisType` StrEnum members (`ECM_FROM_EIS`, `LAM_LLI_FROM_RPT`, `DCIR_FROM_HPPC`) for the well-known values. ## Listing and fetching analyses Provide **exactly one** of `measurement_id` or `project_id`. Filter by `measurement_id` to list the analyses derived from a single measurement, or by `project_id` to list every analysis across the measurements in a project. Supplying both, or neither, is an error. ```python theme={null} # All analyses for a single measurement for a in client.analysis.list(measurement_id="msmt_abc123"): print(a.id, a.analysis_type, a.name) # All analyses across a project (limit is capped at 100) page = client.analysis.list(project_id="proj_xyz", limit=100, offset=0) print(page.total) # Single analysis record analysis = client.analysis.get("ana_...") ``` ## Downloading extracted-feature data The parquet file is not returned inline. `get_data()` fetches a signed URL and reads the parquet directly, returning a DataFrame in the [configured backend](/api-client#dataframe-backend): ```python theme={null} df = client.analysis.get_data("ana_...") print(df.head()) ``` Use `get_download_url()` if you'd rather download the raw parquet yourself. The signed URL is valid for 5 minutes: ```python theme={null} url = client.analysis.get_download_url("ana_...") # valid for 5 minutes ``` ## Updating metadata `update()` changes row-level fields only — it does **not** replace the parquet. Any subset of `name`, `analysis_type`, `columns`, `metadata`, and `notes` may be supplied. ```python theme={null} client.analysis.update("ana_...", { "name": "ECM fit at 25 °C, 50% SOC (v2)", "metadata": {"soc_pct": 50, "temperature_degc": 25, "fit_rms": 0.0018}, }) ``` To replace the parquet itself, delete the analysis and re-create it. ## Deleting an analysis Removes the row and its parquet file. ```python theme={null} client.analysis.delete("ana_...") ``` ## Analyses vs. file measurements Both attach data to a measurement, but they serve different purposes: | | Analysis | [File measurement](/data/measurements#file) | | ---------- | ------------------------------------------------------ | -------------------------------------------- | | Parent | One cell measurement | A cell instance directly | | Data shape | Tabular parquet with declared columns/units | Arbitrary files (images, PDFs, numpy arrays) | | Use for | Extracted features, fit parameters, degradation points | SEM photos, X-ray CTs, post-mortem docs | Reach for an analysis when the output is structured tabular data derived from an existing measurement. Reach for a file measurement when the data is a standalone artifact tied to the cell itself. ## Next steps Time series, properties, and file measurement types — the parents of every analysis. List, filter, and retrieve measurements together with their analyses.