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Apr 14, 2026
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Make name required; drop labels/location; unexport TimeSeriesTable; add validate_unit #5

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25 changes: 7 additions & 18 deletions README.md
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<br/>

**TimeDataModel** is a metadata-rich container for time series data. It lets you carry your data *and* its context — name, unit, frequency, timezone, location — as a single, self-describing object, fully interoperable with pandas, NumPy, Polars, and PyArrow.
**TimeDataModel** is a metadata-rich container for time series data. It lets you carry your data *and* its context — name, unit, frequency, timezone — as a single, self-describing object, fully interoperable with pandas, NumPy, Polars, and PyArrow.

**⬇️ [Installation](#installation)**
&ensp;|&ensp;
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| Class | Description |
| :---- | :---------- |
| 📈&nbsp;`TimeSeries` | Univariate time series supporting four temporal shapes |
| 📊&nbsp;`TimeSeriesTable` | Multivariate time series — multiple named columns sharing the same `valid_time` index |
| 📋&nbsp;`TimeSeriesDescriptor` | Frozen, data-free metadata descriptor — register a series structure before any data exists |
| 🔷&nbsp;`DataShape` | Enum that selects which timestamp columns are present: `SIMPLE`, `VERSIONED`, `CORRECTED`, or `AUDIT` |
| ⏱️&nbsp;`Frequency` | ISO 8601 duration-based frequencies (`PT1H`, `P1D`, `P1M`, …) |
| 🏷️&nbsp;`DataType` | Hierarchical taxonomy: `ACTUAL` → `OBSERVATION`, `DERIVED`; `CALCULATED` → `FORECAST`, `SIMULATION`, … |
Expand All @@ -54,7 +54,7 @@ data to fully bi-temporal audit trails:

```python
import pandas as pd
from timedatamodel import TimeSeries, TimeSeriesTable, Frequency
from timedatamodel import TimeSeries, Frequency

# --- Univariate series from a pandas DataFrame ---
df = pd.DataFrame({
Expand Down Expand Up @@ -92,21 +92,19 @@ df_pl = ts.to_polars() # pl.DataFrame
cols = ts.to_list() # dict[str, list] — column-oriented
arr = ts.to_numpy() # dict[str, np.ndarray] — column-oriented (requires numpy)
tbl = ts.to_pyarrow() # pa.Table (requires pyarrow)

# --- Multivariate table — see examples/nb_02_timeseriestable.ipynb ---
```

---

## ✨ Key Features

- 🔷 **Four data shapes** — from `SIMPLE` point-in-time to `AUDIT` full bi-temporal history;
- 🏷️ **Rich metadata** — name, unit, frequency, timezone, data type, location, labels, description on every series;
- 📊 **Multivariate tables** — `TimeSeriesTable` groups co-indexed series with per-column metadata;
- 🏷️ **Metadata** — name, unit, frequency, timezone, data type, description on every series;
- 📋 **Descriptor** — `TimeSeriesDescriptor` carries the same metadata without a DataFrame, for catalog/registration use;
- 🔄 **Format conversions** — `to_pandas`, `to_polars`, `to_list`, `to_numpy`, `to_pyarrow` with lazy optional-dependency checks;
- 📊 **Coverage bar** — `coverage_bar()` renders null coverage as a binned SVG in Jupyter or Unicode blocks in terminal;
- 🗺️ **Geospatial** — attach locations, filter by radius or area, find nearest columns;
- 📏 **Units** — optional [pint](https://pint.readthedocs.io/) integration for dimensional unit conversion;
- 🗺️ **Geospatial primitives** — `GeoLocation` and `GeoArea` for use by consumer layers;
- 📏 **Units** — optional [pint](https://pint.readthedocs.io/) integration for dimensional unit conversion and validation;
- ⚡ **Polars-powered** — backed by the Polars compute engine for high-performance in-memory processing;
- 🐍 **Type-safe** — full type hints with PEP 561 support.

Expand Down Expand Up @@ -136,15 +134,6 @@ pip install -e .[dev]

---

## 📓 Examples

| # | Notebook | Topic |
| :--- | :--- | :--- |
| 01 | [TimeSeries](examples/nb_01_timeseries.ipynb) | Creating, inspecting, and operating on univariate time series |
| 02 | [TimeSeriesTable](examples/nb_02_timeseriestable.ipynb) | Multivariate tables with per-column metadata and spatial filtering |

---

## 🤝 Contributing

Contributions are welcome! Here are some ways to contribute to **TimeDataModel**:
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8 changes: 0 additions & 8 deletions docs/api.md
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:show-inheritance:
```

## TimeSeriesTable

```{eval-rst}
.. autoclass:: timedatamodel.TimeSeriesTable
:members:
:show-inheritance:
```

## DataShape

```{eval-rst}
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2 changes: 0 additions & 2 deletions docs/conf.py
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# TimeSeries internals
"_clone",
"_validate_table",
# TimeSeriesTable internals
"_clone_df",
]),
}

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12 changes: 3 additions & 9 deletions docs/examples/index.md
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# Examples

Step-by-step guides that walk you through the features of timedatamodel.
Each example is a Jupyter notebook that you can download and run interactively.

```{toctree}
:maxdepth: 1

timeseries
timeseriestable
```
Notebook examples are being rewritten. In the meantime, see the
[Quick Start](../usage.md) page and the [Overview](../overview.md) for
runnable snippets.
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