Add more basic examples to the README.md (#23)

* Add more basic examples to the README.md

* Bump flake8

.pre-commit-config.yaml

@@ -4,6 +4,6 @@ repos:
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/flake8.git
-    rev: 4.0.1
+    rev: 6.1.0
     hooks:
       - id: flake8

README.md

@@ -10,25 +10,81 @@ stores from completely independent processes easier.
 
 ## Usage
 
-The primary use-case for this is something like this.  Say you have a lot of
-netCDF files output from a simulation or observational dataset that you would
-like to stitch together into a zarr store.  If you have a way of combining
-those files lazily -- i.e. opening them into dask-backed arrays -- into a
-single dataset, then you can write contiguous "partitions" of that dataset out
-via independent processes.  A "partition" corresponds to a contiguous group of
-dask "chunks." I.e. it can correspond to one or more chunks across any number
-of dimensions.  A key detail is no partition straddles any dask chunks; this
-makes writing from independent processes completely safe.
+The primary use-case is something like this.  Say you have a lot of netCDF files
+output from a simulation or observational dataset that you would like to stitch
+together into a zarr store.  If you have a way of combining those files lazily —
+i.e. opening them into dask-backed arrays — into a single dataset with maybe
+some additional light computations, then you can write contiguous "partitions"
+of that dataset out via independent processes.  A "partition" corresponds to a
+contiguous group of dask "chunks." I.e. it can correspond to one or more chunks
+across any number of dimensions.  A key detail is no partition straddles any
+dask chunks; this makes writing from independent processes completely safe.
 
 `xpartition` provides an accessor called `partition` that implements
-`initialize_store` and `write` methods.  The pattern is to have some function
-that constructs the dataset lazily, then call `initialize_store`, and finally
-in a set of separate processes, call `write`.
+`initialize_store` and `write` methods.  The pattern is to have some code that
+constructs the dataset lazily, then call `initialize_store`, and finally in a
+set of separate processes, call `write`.
 
-The process might look something like this.  Assume through some external
-package we have a function that can construct a dataset lazily.  Let's write a
-couple command-line interfaces that initialize the store and write a
-partition.  We'll start with one called `initialize_store.py`:
+### Simple serial example
+
+Before illustrating a use-case of `xpartition` on a cluster, we can start with a
+simple serial example.  From this example it should be straightforward to
+imagine how to extend this to various distributed computing platforms, whether
+HPC or cloud-based, to do the same thing in parallel.
+
+Assume through some external package we have a function that can construct a
+dataset lazily.  To incrementally write it to zarr using `xpartition` we would
+only need to do the following:
+
+```python
+import xpartition
+
+from external import construct_lazy_dataset
+
+store = "store.zarr"
+partitions = 16
+partition_dims = ["tile", "time"]
+
+ds = construct_lazy_dataset()
+ds.partition.initialize_store(store)
+for partition in range(partitions):
+    ds.partition.write(store, partitions, partition_dims, partition)
+```
+
+The `partition_dims` describe the dimensions over which to partition the
+dataset; if chunks exist along dimensions that are not among the partition
+dimensions, then they will all be grouped together.  If you are not particular
+about this, simply using `ds.dims` will also work out of the box.
+
+### Parallelization using `multiprocessing`
+
+A parallel example can easily be illustrated using the built-in
+`multiprocessing` library; something similar could be done with `dask.bag`:
+
+```python
+import xpartition
+
+from external import construct_lazy_dataset
+
+store = "store.zarr"
+partitions = 16
+partition_dims = ["tile", "time"]
+
+ds = construct_lazy_dataset()
+ds.partition.initialize_store(store)
+with multiprocessing.get_context("spawn").Pool(partitions) as pool:
+    pool.map(
+        ds.partition.mappable_write(store, partitions, partition_dims),
+        range(partitions)
+    )
+```
+
+### Parallelization using a SLURM array job
+
+Finally, the example below describes how one might use `xpartition` on an HPC
+cluster using a SLURM array job.  We first start by writing a couple
+command-line interfaces that initialize the store and write a partition.  We'll
+start with one called `initialize_store.py`:
 
 ```python
 import argparse
@@ -72,7 +128,7 @@ ds = construct_lazy_dataset()
 ds.partition.write(args.store, args.ranks, dims, args.rank)
 ```
 
-Now let's write a couple bash scripts.  The first will be a SLURM array job
+Now we can write a couple bash scripts.  The first will be a SLURM array job
 that writes all the partitions.  The second will be a "main" script that
 controls the whole workflow.
 
@@ -130,8 +186,8 @@ out of memory errors ([this issue](https://github.com/dask/distributed/issues/63
 is perhaps a good summary of the state of things currently in dask). Breaking the 
 problem down in the way that `xpartition` does, allows you to gain the benefits of
 dask's laziness on each independent process, while working in a distributed 
-environment.  *In an ideal world we wouldn't need a package like this -- we would 
-let dask and dask distributed handle everything -- but in practice that does not 
+environment.  *In an ideal world we wouldn't need a package like this — we would
+let dask and dask distributed handle everything — but in practice that does not
 work perfectly yet.*
 
 ## Installation

setup.cfg

@@ -43,12 +43,17 @@ setup_requires =
 	setuptools_scm
 
 [flake8]
-ignore = 
-	E203 # whitespace before ':' - doesn't work well with black
-	E402 # module level import not at top of file
-	E501 # line too long - let black worry about that
-	E731 # do not assign a lambda expression, use a def
-	W503 # line break before binary operator
+ignore =
+	# whitespace before ':' - doesn't work well with black
+	E203
+	# module level import not at top of file
+	E402
+	# line too long - let black worry about that
+	E501
+	# do not assign a lambda expression, use a def
+	E731
+	# line break before binary operator
+	W503
 exclude = 
 	.eggs
 	doc