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# Scope

**Reading** `.npy` and `.npz` files in R.

Writing is out of scope. When working across multiple languages, one should prefer high-performance interoperable formats ([parquet](https://parquet.apache.org/), [Zarr](https://zarr-specs.readthedocs.io/en/latest/v3/core/), etc.).

# Dependencies

Because `{grumpy}` is a intended to be used deep in the dependency graph of other packages, we want to minimize the number of dependencies.
There is currently no dependency, and no new dependency should be added unless it provides a significant performance improvement.

# Function signatures

## Inputs

- The first argument of `read_npy()` and `read_npz()` is named `file` and is a connection or the path to the file to read. This is consistent with base R functions such as `read.csv()`, `readRDS()`, etc.

## Outputs

- The output of `read_npy()` is always an array (i.e., `is.array()` is `TRUE`), even for structured datatypes. This is to keep the output consistent and as conceptually close as possible to the original NumPy array.
- The output of `read_npz()` is always a named list of arrays, even if the `.npz` file contains only one array. A stable output type is important for downstream analysis.

# FAQ

## Why not use `reticulate`?

- When reading `.npy` files with `{reticulate}`, at some point in time, two (one in Python and one in R) or three copies of the data are held in memory. This can be problematic for large files. With `{grumpy}`, one (for types matching R native types; i.e., `int32`, `float64`/`double`) or two copies of the data are held in memory.

  ```{r}
  f <- tempfile(fileext = ".npy")
  np <- reticulate::import("numpy")
  x <- np$random$rand(1000L, 1000L)
  np$save(f, x)

  microbenchmark::microbenchmark(
    reticulate = {
      x <- np$load(f)
    },
    grumpy = {
      x <- grumpy::read_npy(f)
    },
    times = 1e3
  )

  ## On platform that support it, lazy reading offers even better performance for large files
  if (Sys.info()[["sysname"]] %in% c("Linux", "Darwin")) {
    microbenchmark::microbenchmark(
      reticulate = {
        x <- np$load(f)
      },
      grumpy = {
        x <- grumpy::read_npy(f, lazy = TRUE)
      },
      times = 1e3
    )
  }
  ```

- Reading data with `{reticulate}` requires a Python installation and additional python packages, which users in restricted environments may not have access to. `{grumpy}` is a pure R package with no external dependencies. This is especially important as we expect `{grumpy}` to be used deep in the dependency graph of other packages, and we want to minimize the number of dependencies.
- A dedicated R package gives us more flexibility in how edge cases such as 64-bit integers are handled. `{reticulate}` automatically and silently converts 64-bit integers to double, which is a sensible default for many use cases. But we may want to have more control over this behavior, and `{grumpy}` will allow us to do that in the future. Another good example are structured data types (record arrays), which are returned as `data.frame`s, not `array`s, with `{reticulate}`.