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Further minor fixes
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CONTRIBUTING.md

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The `zappend` code compliant to [PEP-8](https://pep8.org/) except for a line
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length of 88 characters as recommended by [black](https://black.readthedocs.io/).
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Since back is un-opinionated regarding the order of imports,
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Since black is un-opinionated regarding the order of imports,
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we use the following three import blocks separated by an empty
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line:
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LICENSE

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MIT License
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Copyright (c) 2023 Brockmann Consult Development
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Copyright (c) 2023–2024 Brockmann Consult Development
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

docs/guide.md

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in section [_Slice Sources_](#slice-sources) below.
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To run the `zappend` tool with [configuration](config.md) you can pass one or more
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configuration files using JSON or YAML format
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configuration files using JSON or YAML format:
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```shell
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zappend -t output/mycube.zarr -c config.yaml inputs/*.nc
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If multiple configuration files are passed, they will be merged into one by
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incrementally updating the first by subsequent ones.
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You can pass configuration settings to the `zappend` Python function by the optional
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`config` keyword argument. Other keyword arguments are interpreted as individual
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configuration settings and will be merged into the one given by `config` argument,
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if any. The `config` keyword argument can be given as local file path or URL
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(type `str`) pointing to a JSON or YAML file. It can also be given as dictionary,
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or as a sequence of the aforementioned types. Configuration sequences are again merged
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into one.
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You can pass configuration settings to the `zappend` Python function with
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the optional `config` keyword argument. Other keyword arguments are
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interpreted as individual configuration settings and will be merged into the
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one given by `config` argument, if any. The `config` keyword argument can be
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given as local file path or URL (type `str`) pointing to a JSON or YAML file.
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It can also be given as dictionary, or as a sequence of the aforementioned
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types. Configuration sequences are again merged into one.
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```python
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import os
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## Dataset Outline
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If no further configuration is supplied, then the target dataset's outline and data
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encoding is fully prescribed by the first slice dataset provided. By default, the
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dimension along subsequent slice datasets are concatenated is `time`. If you use a
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different append dimension, the `append_dim` setting can be used to specify its name:
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encoding is fully prescribed by the first slice dataset provided. By default, the
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dimension along which subsequent slice datasets are concatenated is `time`. If
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you use a different append dimension, the `append_dim` setting can be used to
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specify its name:
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```json
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{
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All other non-variadic dimensions can and should be specified using the
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`fixed_dims` setting which is a mapping from dimension name to the
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fixed dimension sizes, e.g.:
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fixed dimension size, e.g.:
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```json
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{
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By default, without further configuration, all data variables seen in the first
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dataset slice will be included in the target dataset. If only a subset of
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variables shall be used from the slice dataset, they can be specified using the
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`included_variables` setting, which is a list of names of variables that will
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be included:
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variables should be used from the slice dataset, they can be specified using
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the `included_variables` setting, which is a list of names of variables that
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will be included:
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```json
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{
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}
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```
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Often, it is easier to tell which variables should be excluded:
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Often, it is easier to specify which variables should be excluded:
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```json
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{
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}
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```
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If `*` is specified, the effective variable metadata applied is gained by merging a
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given specific metadata, into the common metadata given by `*`, which is eventually
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merged into metadata of the variable in the first dataset slice.
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If `*` is specified, the metadata for a particular variable is generated by
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merging the specific metadata for that variable into the common metadata given
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by `*`, which is eventually merged into metadata of the variable in the first
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dataset slice.
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!!! note
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Only metadata from the first slice dataset is used, metadata of variables from
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subsequent slice datasets is ignored entirely.
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Only metadata from the first slice dataset is used. Metadata of variables
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from subsequent slice datasets is ignored entirely.
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### Dimensions
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docs/start.md

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## Installation
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Installing `zappend` into a Python v3.10+ environment:
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`zappend` requires a Python v3.10+ environment. To install the latest released
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version from PyPI:
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```shell
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pip install zappend
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```
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To install the latest version for development, clone the
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[repository](https://github.com/bcdev/zappend), and with the repository’s root
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directory as the current working directory execute:
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```shell
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pip install --editable .
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```
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## Using the CLI
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Get usage help:
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zappend --help
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```
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Get [configuration](config.md) help:
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Get [configuration](config.md) help in Markdown format (json also available):
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```shell
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zappend --help-config md
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config=config)
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```
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Slice datasets can be passed in a number of ways, please refer to the section
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Slice datasets can be passed in a number of ways; please refer to the section
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[_Slice Sources_](guide.md#slice-sources) in the [User Guide](guide.md).

setup.cfg

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[metadata]
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name = zappend
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version = attr: zappend.version
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version = attr: zappend.__version__
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author = Norman Fomferra
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description = Tool for creating and updating a Zarr datacubes from smaller slices
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long_description = file: README.md

zappend/__init__.py

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# Permissions are hereby granted under the terms of the MIT License:
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# https://opensource.org/licenses/MIT.
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version = "0.2.1.dev0"
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__version__ = "0.2.1.dev0"

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