From 1ca9960a6f5edf102e8870f45bc268cf98f78d9b Mon Sep 17 00:00:00 2001 From: jeffgillan Date: Tue, 5 Sep 2023 09:06:09 -0700 Subject: [PATCH] improvments --- docs/flatgeobuf.md | 4 +- docs/images/zarr.png | Bin 0 -> 89989 bytes docs/index.md | 5 +- docs/notebooks/copc.ipynb | 88 ----------------------------------- docs/notebooks/geojson.ipynb | 88 ----------------------------------- docs/notebooks/stac.ipynb | 88 ----------------------------------- docs/stac.md | 4 -- docs/zarr.md | 22 ++++----- mkdocs.yml | 3 -- 9 files changed, 14 insertions(+), 288 deletions(-) create mode 100644 docs/images/zarr.png delete mode 100644 docs/notebooks/copc.ipynb delete mode 100644 docs/notebooks/geojson.ipynb delete mode 100644 docs/notebooks/stac.ipynb diff --git a/docs/flatgeobuf.md b/docs/flatgeobuf.md index 3aa7a41..28365d7 100644 --- a/docs/flatgeobuf.md +++ b/docs/flatgeobuf.md @@ -28,6 +28,8 @@ Check out this [blog](https://worace.works/2022/02/23/kicking-the-tires-flatgeob ## [GeoParquet](https://geoparquet.org/){target=_blank} -
+GeoParquet is a geospatial data format that is based on the [Apache Parquet format](https://parquet.apache.org/){target=_blank}. Parquet is a columnar storage format that is designed to be efficient for both reading and writing large datasets. GeoParquet adds support for storing geospatial (i.e., vector data with coordinate information). + +
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cloud-native, meani Throughout, we will emphasize Open Science principles such as [FAIR](https://www.go-fair.org/fair-principles/){target=_blank} and [CARE](https://www.gida-global.org/care){target=_blank}, and highlight primarily Open Source tools.
- Helpful skills to have +### Presentation Slides -* a basic understanding of the [Command Line Interface (UNIX)](https://swcarpentry.github.io/shell-novice/){target=_blank} -* a basic understanding of [Python3](https://www.geeksforgeeks.org/introduction-to-python3/#:~:text=Python%20is%20a%20high%2Dlevel,them%20readable%20all%20the%20time.){target=_blank} +This content was delivered at the 2023 Arizona Geographic Information Council meeting in Prescott, AZ in August 2023. Check out the presentation slides [here](https://docs.google.com/presentation/d/1oepKUpMMayO2Qp1APlWHDvRRiiaqcqam5y-KKPhC5V8/edit?usp=sharing){target=_blank}. ## Let's Use the Cloud! diff --git a/docs/notebooks/copc.ipynb b/docs/notebooks/copc.ipynb deleted file mode 100644 index 4320083..0000000 --- a/docs/notebooks/copc.ipynb +++ /dev/null @@ -1,88 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/tyson-swetnam/agic-2022/blob/main/docs/notebooks/copc.ipynb)\n", - "[![image](https://mybinder.org/badge_logo.svg)](https://tyson-swetnam.github.io/agic-2022/notebooks/copc/)\n", - "\n", - "Creating Cloud Optimized Point Clouds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3.10.4 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - 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"module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - }, - "vscode": { - "interpreter": { - "hash": "4f946df053fbf2b937619d3c5458e7af74262f9a954d8797ba0b27400bcafe06" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/notebooks/stac.ipynb b/docs/notebooks/stac.ipynb deleted file mode 100644 index e3a8917..0000000 --- a/docs/notebooks/stac.ipynb +++ /dev/null @@ -1,88 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/tyson-swetnam/agic-2022/blob/main/docs/notebooks/stac.ipynb)\n", - "[![image](https://mybinder.org/badge_logo.svg)](https://tyson-swetnam.github.io/agic-2022/notebooks/stac/)\n", - "\n", - "Creating Spatio-Temporal Asset Catalogs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - 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"varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - }, - "vscode": { - "interpreter": { - "hash": "4f946df053fbf2b937619d3c5458e7af74262f9a954d8797ba0b27400bcafe06" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/stac.md b/docs/stac.md index eed04ba..1fd4577 100644 --- a/docs/stac.md +++ b/docs/stac.md @@ -31,12 +31,8 @@ Use Case 1: A researcher wants to find all the Sentinel-2 imagery that is availa - [STAC Spec on GitHub](https://github.com/radiantearth/stac-spec){target=_blank} -The [Radiant Earth STAC Browser](https://radiantearth.github.io/stac-browser/#/){target=_blank} allows users to search, preview, and access these massive geospatial assets hosted over conventional `https://` endpoints and cloud-base object stores (i.e., `s3://` buckets). - -[STAC Index](https://stacindex.org/) There are four components to making a given STAC run. They can be used independently of one another, but most often they are all used together: diff --git a/docs/zarr.md b/docs/zarr.md index 5b47d65..f7c1e40 100644 --- a/docs/zarr.md +++ b/docs/zarr.md @@ -1,28 +1,24 @@ -[Jump to :material-hand-clap: hands-on lesson :material-school: ](#hands-on) - ## Overview of Zarr -`.zarr` is a data format for chunked, N-dimensional arrays. +[Zarr](https://zarr.readthedocs.io/en/stable/getting_started.html){target=_blank} is a general-purpose, chunked, compressed, N-dimensional array format. It is designed to be efficient for storing and accessing large datasets. Zarr can be used to store a variety of data types, including geospatial raster and vector data. Zarr is also a Python package that provides an implementation of chunked, compressed, N-dimensional arrays. + + +

+ ![Image title](images/zarr.png){ width="600" } +
+
+ +Zarr integrates direclty with [Xarray](https://xarray.dev/){target=_blank}. -Zarr integrates direclty with Xarray. -[Zarr website](https://zarr.dev/){target=_blank} Zarr dependes on [NumPy](https://numpy.org/){target=_blank} and can save data from NumPy arrays. When paired with [Dask](https://www.dask.org/){target=_blank} , Zarr's parallel processing and file transfers increases the read/write speed of your NumPy array data. -The power of Zarr is most easily explained when working with it on the cloud. ## Examples using Zarr [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-zarr-data/){target=_blank} -# Hands On - -Let's drop right into a Jupyter Notebook for a high level overview of Zarr - -[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/tyson-swetnam/agic-2022/blob/main/docs/notebooks/zarr.ipynb) - - \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 69a3d1c..e934a16 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -23,12 +23,9 @@ nav: - SpatioTemporal Asset Catalog: stac.md - Cloud Compute: ide.md - Jupyter Notebook Examples: - - GeoJSON: notebooks/geojson.ipynb - - COPC: notebooks/copc.ipynb - COG: notebooks/cog.ipynb - XArray: notebooks/xarray.ipynb - Zarr: notebooks/zarr.ipynb - - STAC: notebooks/stac.ipynb # Configuration theme: