#!/usr/bin/env python
# coding: utf-8

# # Load Kerchunked dataset with Xarray
# 
# 

# ## Overview
#    
# Within this notebook, we will cover:
# 
# 1. How to load a Kerchunk pre-generated reference file into Xarray as if it were a Zarr store.
# 
# ## Prerequisites
# | Concepts | Importance | Notes |
# | --- | --- | --- |
# | [Kerchunk Basics](../foundations/kerchunk_basics) | Required | Core |
# | [Xarray Tutorial](https://tutorial.xarray.dev/intro.html) | Required | Core |
# 
# - **Time to learn**: 45 minutes
# ---

# ## Opening Reference Dataset with Fsspec and Xarray
# One way of using our reference dataset is opening it with `Xarray`. To do this, we will create an `fsspec` filesystem and pass it to `Xarray`. 

# In[ ]:


# create an fsspec reference filesystem from the Kerchunk output
import fsspec
import xarray as xr
import kerchunk

fs = fsspec.filesystem(
    "reference",
    fo="references/ARG_combined.json",
    remote_protocol="s3",
    skip_instance_cache=True,
)
m = fs.get_mapper("")
ds = xr.open_dataset(m, engine="zarr")


# ## Opening Reference Dataset with Xarray and the `Kerchunk` Engine
# As of writing, the latest version of Kerchunk supports opening an reference dataset with Xarray without specifically creating an fsspec filesystem.  This is the same behavior as the example above, just a few less lines of code. 
# 
# 

# In[ ]:


storage_options = {
    "remote_protocol": "s3",
    "skip_instance_cache": True,
}  # options passed to fsspec
open_dataset_options = {"chunks": {}}  # opens passed to xarray

ds = xr.open_dataset(
    "references/ARG_combined.json",
    engine="kerchunk",
    storage_options=storage_options,
    open_dataset_options=open_dataset_options,
)