---
title: "Zarr V3 Cross-Implementation Interop"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Zarr V3 Cross-Implementation Interop}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%",
  message = FALSE
)
sample_dir <- tools::R_user_dir("pizzarr")
clean <- !dir.exists(sample_dir)
```

This vignette demonstrates
pizzarr reading a Zarr V3 store that was written by
[zarr-python](https://github.com/zarr-developers/zarr-python) 3.x ---
showing that the two implementations are interoperable.

The test store includes arrays with various data types, compression
codecs, chunk layouts, fill values, and nested groups. It was generated
with the script bundled in this project at:
`inst/extdata/fixtures/v3/generate-v3-zarr-python.py`.

## Summary

- **Data types**: bool, uint8, int16, int32, float32, float64
- **Compression**: uncompressed, gzip, zstd, blosc
- **Layouts**: 1D, 2D, 3D; contiguous and ragged chunks
- **Fill values**: default (0) and custom (-9999)
- **Groups**: nested groups with attributes
- **Array metadata**: per-array attributes (units, long_name)

## Open the store

```{r}
library(pizzarr)

store_path <- pizzarr_sample("fixtures/v3/zarr_python_v3.zarr")
root <- zarr_open(store_path)
```

## Root-level attributes

zarr-python embedded these attributes when it created the store:

```{r}
root$get_attrs()$to_list()
```

## Reading different data types

### Integer (int32, gzip compressed)

```{r}
a <- root$get_item("int32_1d")
a$get_shape()
a$as.array()
```

### Float64 (zstd compressed)

```{r}
a <- root$get_item("float64_1d")
a$as.array()
```

### Boolean (uncompressed)

```{r}
a <- root$get_item("bool_1d")
a$as.array()
```

### Unsigned integer (uint8, chunked)

```{r}
a <- root$get_item("uint8_1d")
a$as.array()
```

### Float32 (blosc compressed)

```{r}
a <- root$get_item("float32_1d")
a$as.array()
```

## Multi-dimensional arrays

### 2D int16 (gzip)

```{r}
a <- root$get_item("int16_2d")
a$get_shape()
a$as.array()
```

### 3D float64 (zstd)

```{r}
a <- root$get_item("float64_3d")
a$get_shape()
a$as.array()
```

## Fill values

The `with_fill` array was created with `fill_value = -9999.0` and only
the first chunk was written. The second chunk returns fill values:

```{r}
a <- root$get_item("with_fill")
a$as.array()
```

## Ragged (non-aligned) chunks

When the array shape is not evenly divisible by the chunk shape, edge
chunks are smaller. This 5x7 array uses 3x4 chunks:
```{r}
a <- root$get_item("ragged_2d")
a$get_shape()
a$as.array()
```

## Nested groups and array attributes

The store contains a sub-group with attributes and two arrays that carry
their own metadata:

```{r}
g <- root$get_item("var_group")
g$get_attrs()$to_list()
```

```{r}
temp <- g$get_item("temperature")
temp$get_attrs()$to_list()
temp$as.array()
```

```{r}
pres <- g$get_item("pressure")
pres$get_attrs()$to_list()
pres$as.array()
```

```{r, include=FALSE}
if(clean) unlink(sample_dir, recursive = TRUE)
```
