💻 Command Line Interface#

The fetchez command line tool allows you to chain modules and hooks together.

fetchez#

Fetch geospatial data with ease.

Fetchez is a streaming ETL pipeline for geospatial data.
It allows you to download data from remote modules (like NOAA or Copernicus),
pipe that data through processing hooks (like clipping or reprojecting),
and save the results to disk. All in a single command or a YAML recipe.

Core Concepts:

Modules  : Data Sources (see fetchez modules)
Hooks    : Processing Steps (see fetchez hooks)
Streams  : Data Streaming (see fetchez streams)
Recipes  : YAML files that define complex pipelines (see fetchez recipes)

Usage

fetchez [OPTIONS] COMMAND [ARGS]...

Options

--version#: Show the version and exit.

--verbose#: Enable verbose debug logging.

--quiet#: Suppress non-error output.

hooks#

Discover, search, and learn about data processors.

Hooks are modular processing steps (filters, transforms, algorithms) that manipulate data streams or files in a pipeline.

How to use Hooks:

1. In YAML Recipes: Defined under hooks (per-module) or global_hooks.

2. In CLI Commands: Appended directly to data sources using the –hook switch or globally with –globl-hook.

* Hooks take a fetchez entry dictionary as input and they return a fetchez dictionary as output.

* Along the way, they may manipulate that entry dictionary in different ways, such as by modifying entry

values, creating artifacts, adding data streams, adding metadata, etc.

* Use fetchez hooks info <hook-name> to get more information about what a hook does.

Hooks run in different stages of the pipeline:

Manifest    : Runs on the initial file manifest before any fetching begins.
File        : Runs of a fetched or local file.
Stream      : Runs on an in-memory data stream of the fetched file.
Collection  : Runs on the final collection of data that has been through the previous stages.

CLI String Syntax (Source + Hooks):

<source> –arg val –hook <hook_name>:arg=val,arg2=val

CLI Examples:

fetchez pipeline –global-hook audit file –path my_data.laz –hook exec:cmd=laszip

Usage

fetchez hooks [OPTIONS] COMMAND [ARGS]...

info#

Show arguments and YAML recipe examples for a specific hook.

Usage

fetchez hooks info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available processing hooks grouped by category.

Usage

fetchez hooks list [OPTIONS]

Options

-s, --search <search>#: Filter hooks by name or keyword.

presets#

Discover, inspect, and copy processing macros.

Presets are YAML macros that chain multiple processing Hooks together
under a single name. If you frequently run the exact same sequence of
filters, you can save them as a Preset and call them with one word.

Usage:

Presets act exactly like Hooks. You can pass them to –hook, –global-hook,

or list them in your Recipe’s hook list.

Usage

fetchez hooks presets [OPTIONS] COMMAND [ARGS]...

copy#

Copy a preset to your local ~/.fetchez/ folder for editing.

Usage

fetchez hooks presets copy [OPTIONS] NAME

Arguments

NAME#: Required argument

dump#

Print the raw YAML definition to the terminal.

Usage

fetchez hooks presets dump [OPTIONS] NAME

Arguments

NAME#: Required argument

info#

Print a clean, readable summary of a preset’s contents.

Usage

fetchez hooks presets info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available built-in and local presets.

Usage

fetchez hooks presets list [OPTIONS]

search#

Search all available processing hooks by keyword.

Usage

fetchez hooks search [OPTIONS] TERM

Arguments

TERM#: Required argument

modules#

Discover, search, and learn about data sources.

Modules are the starting point of any Fetchez pipeline. They are responsible
for discovering and downloading data from remote agencies (like NOAA, USGS,
Copernicus) or loading it from your local file system.

Usage

fetchez modules [OPTIONS] COMMAND [ARGS]...

bundles#

Discover, inspect, and copy module groups.

Bundles are pre-configured YAML lists of Modules. Instead of manually
typing out multiple different data sources and their specific arguments,
you can call a single Bundle that contains them all.

Usage:

Bundles act exactly like Modules. You can pass them directly to fetchez run.

Usage

fetchez modules bundles [OPTIONS] COMMAND [ARGS]...

copy#

Copy a module bundle to your local ~/.fetchez/ folder for editing.

Usage

fetchez modules bundles copy [OPTIONS] NAME

Arguments

NAME#: Required argument

dump#

Print the raw YAML definition to the terminal.

Usage

fetchez modules bundles dump [OPTIONS] NAME

Arguments

NAME#: Required argument

info#

Print a clean, readable summary of a bundle’s contents.

Usage

fetchez modules bundles info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available built-in and local bundles.

Usage

fetchez modules bundles list [OPTIONS]

info#

Get detailed metadata and available CLI arguments for a module.

Usage

fetchez modules info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available modules grouped by category.

Usage

fetchez modules list [OPTIONS]

Options

-s, --search <search>#: Search by name, description, agency, or tag.

search#

Search all available modules by keyword.

Usage

fetchez modules search [OPTIONS] TERM

Arguments

TERM#: Required argument

update-cache#

Forces a clean rescan of all built-in, Globato, and user-defined modules.

Use this if you recently installed a new extension or added a custom Python plugin to your ~/.fetchez/modules/ folder and it isn’t showing up.

Usage

fetchez modules update-cache [OPTIONS]

pipeline#

Fetch/download data and execute processing pipelines.

How CLI Pipelines Work:

The run command allows you to chain multiple Data Modules together

and apply Processing Hooks to them.

* Module Arguments follow the module name (e.g., copernicus –datatype 3).
* Module Hooks (–hook) apply only to the module they follow.
* Global Hooks (–global-hook) apply to all data flowing through the pipeline.

Syntax:

fetchez run -R <W/E/S/N> [–global-hook <name>] <module_1> [–hook <name>] <module_2> …

* Run fetchez modules list to see a full list of supported modules.

Usage

fetchez pipeline [OPTIONS] COMMAND1 [ARGS]... [COMMAND2 [ARGS]...]...

Options

-R, --region <region>#: Bounding box (W/E/S/N)

--global-hook <global_hook>#: Attach a global processing hook

--schema <schema>#: Apply a validation schema (e.g., ‘crm’)

--threads <threads>#: Number of parallel download threads (default: 1).

--export <export>#: Export to YAML instead of executing.

recipes#

Execute, Discover, inspect, and copy complete pipeline workflows.

Recipes are YAML files that define an entire ETL pipeline from start to finish.
They contain the project metadata, the requested Data Modules, and the
Processing Hooks used to filter and stack the data.

Recipes make your data pipelines 100% reproducible. You can version-control

them, share them with colleagues, or run them in batch mode over multiple regions.

Usage

fetchez recipes [OPTIONS] COMMAND [ARGS]...

copy#

Copy a recipe to your local ~/.fetchez/ folder for editing.

Usage

fetchez recipes copy [OPTIONS] NAME

Arguments

NAME#: Required argument

dump#

Print the raw YAML definition to the terminal.

Usage

fetchez recipes dump [OPTIONS] NAME

Arguments

NAME#: Required argument

info#

Print a clean, readable summary of a recipe’s contents.

Usage

fetchez recipes info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available built-in and local recipes.

Usage

fetchez recipes list [OPTIONS]

run#

Execute a YAML recipe by registry name or file path.

Usage

fetchez recipes run [OPTIONS] NAME

Arguments

NAME#: Required argument

schemas#

Discover, search, and learn about recipe schemas.

Schemas are strict validation rulesets that can be applied to Recipes.
They ensure that any data flowing through the pipeline adheres to specific
domain standards (e.g., forcing all output to be in EPSG:4326, or requiring
mandatory metadata tags).

Usage

fetchez recipes schemas [OPTIONS] COMMAND [ARGS]...

info#

Show arguments and YAML recipe examples for a specific hook.

Usage

fetchez recipes schemas info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available processing schemas grouped by category.

Usage

fetchez recipes schemas list [OPTIONS]

Options

-s, --search <search>#: Filter schemas by name or keyword.

validate#

Check a recipe for syntax errors, logical issues, and missing dependencies.

Usage

fetchez recipes validate [OPTIONS] NAME

Arguments

NAME#: Required argument

streams#

Discover, search, and learn about streams.

When Fetchez downloads a file (like a GeoTIFF or NetCDF), it can use Streams
to read the data piece-by-piece in memory. This prevents massive datasets from
crashing your computer’s RAM and gives control over how to process different
datasets.

This command group lets you explore the internal ‘Readers’ that parse the

files, and the ‘Profiles’ that tell those readers exactly how to behave.

Usage

fetchez streams [OPTIONS] COMMAND [ARGS]...

profiles#

Discover, inspect, and copy stream format profiles.

Different datasets are formatted differently (e.g., one NetCDF uses ‘lon’
and ‘lat’, another uses ‘x’ and ‘y’). Profiles are YAML dictionaries that
tell a Reader exactly how to parse a specific dataset’s format.

Usage

fetchez streams profiles [OPTIONS] COMMAND [ARGS]...

copy#

Copy a reader profile to your local ~/.fetchez/ folder for editing.

Usage

fetchez streams profiles copy [OPTIONS] NAME

Arguments

NAME#: Required argument

dump#

Print the raw YAML definition to the terminal.

Usage

fetchez streams profiles dump [OPTIONS] NAME

Arguments

NAME#: Required argument

info#

Print a clean, readable summary of a bundle’s contents.

Usage

fetchez streams profiles info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available built-in and local profiles.

Usage

fetchez streams profiles list [OPTIONS]

Options

-s, --search <search>#: Filter profiles by name or keyword.

readers#

Discover, search, and learn about stream format readers.

Readers are the underlying Python classes (like ‘rasterio-reader’ or
‘netcdf_reader’) that open downloaded files and convert them into standard
streams or chunks for processing.

Usage:

You rarely call Readers directly. They are automatically triggered by the

stream-init hook based on the file extension or the data’s ‘Profile’.

Format reader streams can be initiated with the stream-init hook which

will populate entry[‘stream’] and entry[‘stream-type’] in the pipeline.

Usage

fetchez streams readers [OPTIONS] COMMAND [ARGS]...

info#

Show arguments and YAML recipe examples for a specific reader.

Usage

fetchez streams readers info [OPTIONS] NAME

Arguments

NAME#: Required argument

list#

List all available stream readers grouped by category.

Usage

fetchez streams readers list [OPTIONS]

Options

-s, --search <search>#: Filter readers by name or keyword.