updates to tool pages!

_layouts/single.html

@@ -51,9 +51,10 @@ <h1 class="page__title" itemprop="headline">
     {% if page.modified %}<meta itemprop="dateModified" content="{{ page.modified | date: "%B %d, %Y" }}">{% endif %}
 
     <div class="page__inner-wrap">
-      {% unless page.header.overlay_color or page.header.overlay_image %}
 
         <header>
+          {% unless page.header.overlay_color or page.header.overlay_image %}
+
           <!-- BEGIN: insert a welcome to the module block on pages that are part of a module set -->
 
           <!-- END: insert welcome block -->
@@ -87,8 +88,25 @@ <h1> <i class="fa fa-6 fa-book" aria-hidden="true"></i> {{ page.module-title }}
           {% comment %}{% if page.read_time %}
             <p class="page__meta"><i class="fa fa-clock-o" aria-hidden="true"></i> {% include read-time.html %}</p>
           {% endif %}{% endcomment %}
+        {% endunless %}
+
+        <!-- For tools pages add authors and tool title -->
+
+        {% if page.header.overlay_color or page.header.overlay_image %}
+          <h2> {{ page.tool_page_title }} </h2>
+          {% if page.authors %}
+            <div class="page__meta">
+              Developers:
+              {% for anAuthor in page.authors %}
+              {% assign currentItem = site.data.authors | where:"name", anAuthor %}
+              <a href="{{ site.url }}{{ site.baseurl }}/authors/{{ currentItem[0].slug }}">{{ currentItem[0].name }}</a>{% if forloop.last == false %}, {% endif %}
+              {% endfor %}
+            </div>
+          {% endif %}
+        {% endif %}
         </header>
-      {% endunless %}
+
+
 
       <section class="page__content" itemprop="text">
         <!-- would like to include the TOC automatically but this does weird things-->

_tools/GeoMAC.md

@@ -3,7 +3,7 @@ layout: single
 title: "GeoMAC_DL"
 excerpt: "Programmatic access to Geospatial Multi-Agency Coordination (GeoMAC) fire data in Python."
 header:
-  image: airtanker-fire.jpg
+  overlay_color: "#000080"
   teaser: airtanker-fire.jpg
   image_alt: "Air tanker dumping flame retardant on a wild fire"
 published: true

_tools/OpenTopoDL.md

@@ -3,7 +3,7 @@ layout: single
 title: "OpenTopoDL"
 excerpt: "Programmatic access to digital elevation models and lidar point clouds from OpenTopo with Python."
 header:
-  image: dem.JPG
+  overlay_color: "#000080"
   teaser: dem.JPG
   image_alt: "Digital elevation model"
 published: true

_tools/abc-classroom.md

@@ -0,0 +1,36 @@
+---
+layout: single
+title: "ABC Classroom"
+excerpt: "A suite of Python command-line utilities that makes it easier to manage a class using GitHub classroom."
+splash_header: "ABC Classroom"
+tool_page_title: "ABC Classroom an Open Source Python Tool for GitHub Classroom"
+authors: ["Leah Wasser", "Karen Cranston", "Tim Head"]
+header:
+  overlay_color: "#000080"
+  teaser: abc-classroom.png
+  image_alt: ABC Classroom Badge
+published: true
+---
+
+# Why ABC Classroom
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3539582.svg)](https://doi.org/10.5281/zenodo.3539582)
+![PyPI](https://img.shields.io/pypi/v/abc-classroom.svg?color=purple&style=plastic)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/abc-classroom.svg?color=purple&label=pypi%20downloads&style=plastic)
+
+Many of us teaching data science are using GitHub Classroom as a way to teach students
+both git and GitHub skills and also potentially collaboration skills that align
+with open source software development best practices. However there are many steps
+associated with using GitHub classroom to manage a class.
+
+Abc-Classroom contains a suite of command-line utilities that make it easier to
+manage a class of students using GitHub classroom by:
+
+1. Making it easier to create template assignment directories that are directly connected to your classroom organization
+2. Making it easier to update those assignments and
+3. (still under development) making it easier to clone all student assignments for grading.
+
+We are currently using nbgrader in our workflow and are thus building this tool
+out to support the use of nbgrader as well.
+
+[Check out abc-classroom on GitHub.](https://github.com/earthlab/abc-classroom)

_tools/earth-analytics-python-environment.md

@@ -0,0 +1,25 @@
+---
+layout: single
+title: "Earth Analytics Python Conda Environment"
+excerpt: "A python environment for working with earth and environment data."
+authors: ["Leah Wasser", "Tim Head", "Filipe", "Jenny Palomino"]
+splash_header: "Earth Analytics Python Conda Environment"
+header:
+  overlay_color: "#000080"
+  teaser: ea-python-envt.png
+  image_alt: Earth Analytics Python Environment Badge
+published: true
+---
+
+[![Build Status](https://travis-ci.com/earthlab/earth-analytics-python-env.svg?branch=master)](https://travis-ci.com/earthlab/earth-analytics-python-env)
+[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/38a49nccgpl1metv?svg=true)](https://ci.appveyor.com/project/mbjoseph/earth-analytics-python-env)
+[![DOI](https://zenodo.org/badge/132847711.svg)](https://zenodo.org/badge/latestdoi/132847711)
+[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/earthlab/earth-analytics-binder/master)
+[![](https://images.microbadger.com/badges/image/earthlab/earth-analytics-python-env.svg)](https://microbadger.com/images/earthlab/earth-analytics-python-env "EA-Environment Docker Stats")
+![Docker Cloud Build Status](https://img.shields.io/docker/cloud/build/earthlab/earth-analytics-python-env?style=plastic)
+
+This is an earth analytics conda environment that can be installed on your
+computer using a `.yaml` file. The tool also includes a docker image that can be
+used to actually run it in a containerized environment.
+
+[Find the earth analytics python conda environment on GitHub.](https://github.com/earthlab/earth-analytics-python-env)

_tools/earthpy.md

@@ -0,0 +1,56 @@
+---
+layout: single
+title: "EarthPy"
+excerpt: "EarthPy makes it easier to plot and manipulate spatial data in Python."
+splash_header: "EarthPy: A Python package for spatial data."
+tool_page_title: "EarthPy for Open Source GIS in Python"
+authors: ["Leah Wasser", "Max Joseph", "Joe McGlinchy"]
+header:
+  overlay_color: "#000080"
+  overlay_filter: rgba(0, 0, 0, 0.3)
+  cta_label: "EarthPy"
+  teaser: earthpy.png
+  image_alt: Earthpy Badge
+published: true
+---
+
+EarthPy makes it easier to plot and manipulate spatial data in Python.
+
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.01886/status.svg)](https://doi.org/10.21105/joss.01886)
+[![pyOpenSci](https://tinyurl.com/y22nb8up)](https://github.com/pyOpenSci/software-review/issues/12)
+![PyPI](https://img.shields.io/pypi/v/earthpy.svg?color=purple&style=plastic)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/earthpy.svg?color=purple&label=pypi%20downloads&style=plastic)
+![Conda](https://img.shields.io/conda/v/conda-forge/earthpy.svg?color=purple&style=plastic)
+![Conda](https://img.shields.io/conda/dn/conda-forge/earthpy.svg?color=purple&label=conda-forge%20downloads&style=plastic)
+
+## Why EarthPy?
+
+Python is a generic programming language designed to support many different applications. Because of this, many commonly
+performed spatial tasks for science including plotting and working with spatial data take many steps of code. EarthPy
+builds upon the functionality developed for raster data (rasterio) and vector data (geopandas) in Python and simplifies the
+code needed to:
+
+* [Stack and crop raster bands from data such as Landsat into an easy to use numpy array](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_raster_stack_crop.html)
+* [Work with masks to set bad pixels such a those covered by clouds and cloud-shadows to NA (`mask_pixels()`)](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_stack_masks.html#sphx-glr-gallery-vignettes-plot-stack-masks-py)
+* [Plot rgb (color), color infrared and other 3 band combination images (`plot_rgb()`)](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_rgb.html)
+* [Plot bands of a raster quickly using `plot_bands()`](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_bands_functionality.html)
+* [Plot histograms for a set of raster files.](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_hist_functionality.html)
+* [Create discrete (categorical) legends](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_draw_legend_docs.html)
+* [Calculate vegetation indices such as Normalized Difference Vegetation Index (`normalized_diff()`)](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_calculate_classify_ndvi.html)
+* [Create hillshade from a DEM](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_dem_hillshade.html)
+* [Clip point, line, and polygon geometries](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/plot_clip.html)
+
+EarthPy also has an `io` module that allows users to
+
+1. Quickly access pre-created data subsets used in the earth-analytics courses hosted
+on [www.earthdatascience.org](https://www.earthdatascience.org)
+2. Download other datasets that they may want to use in their workflows.
+
+EarthPy's design was inspired by the `raster` and `sp` package functionality available to `R` users.
+
+## View Example EarthPy Applications in Our Documentation Gallery  
+
+Check out our [vignette gallery](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/index.html) for
+applied examples of using EarthPy in common spatial workflows.
+
+[Find EarthPy on GitHub.](https://github.com/earthlab/earthpy)

_tools/jupyterhub-github-launch.md

@@ -0,0 +1,31 @@
+---
+layout: single
+title: "Setup JupyterHub Using Google Cloud Through GitHub"
+excerpt: "Infrastructure and operations for the Earth Lab JupyterHubs."
+splash_header: "Control and Launch Multiple JupyterHubs using Google Cloud Using GitHub and Yaml Configuration Files."
+tool_page_title: "Launch and Manage Multiple JupyterHub Instances From GitHub"
+authors: ["Tim Head", "Leah Wasser"]
+header:
+  overlay_color: "#000080"
+  teaser: jupyter-hub-ops.png
+  image_alt: Jupyter Hub Ops Badge
+published: true
+documentation: https://earthlab-hub-ops.readthedocs.io/en/latest/index.html
+---
+
+
+Sometimes you may need different JupyterHub environments for various
+courses, projects and / or workshop. The hub-ops tool, allows you to
+quickly launch a JupyterHub, using Google Cloud. To launch a new hub, you need to
+1. specify the docker container that you wish to use as your environment.
+2. Update a yaml file on github containing the docker information and specifications for the hub including computer power needed.
+3. Specify any files that you'd like to add to the hub. This uses nbgitpuller to grab files from a specific GitHub repo (of your choice!).
+
+This tool was developed to make is easier for Earth Lab to manage their
+JupyterHub environment in support of the earth analytics education program activities.
+
+[![DOI](https://zenodo.org/badge/136452806.svg)](https://zenodo.org/badge/latestdoi/136452806)
+[![Build Status](https://travis-ci.org/earthlab/hub-ops.svg?branch=master)](https://travis-ci.org/earthlab/hub-ops)
+[![Docs Status](https://readthedocs.org/projects/earthlab-hub-ops/badge/?version=latest)](https://readthedocs.org/projects/earthlab-hub-ops/builds/)
+
+[Access the JupyterHub Ops repo on GitHub.](https://github.com/earthlab/hub-ops)

_tools/matplotcheck.md

@@ -0,0 +1,45 @@
+---
+layout: single
+title: "matplotcheck"
+excerpt: "Matplotcheck is a Python package that makes it easier to test plots. It can be used to support autograding Python homework assignments."
+splash_header: "MatPlotCheck: Test and validate matplotlib plots in Python"
+authors: ["Leah Wasser", "Kristen Curry", "Ryan LaRoque"]
+tool_page_title: "Use MatPlotCheck to Autograde Matplotlib Plots in Python"
+header:
+  overlay_color: "#000080"
+  teaser: matplotcheck.png
+  image_alt: Matplotcheck Badge
+published: true
+---
+
+A package for testing different types of matplotlib plots including:
+
+* basic matplotlib plots
+* geopandas spatial vector plots
+* raster plots
+* time series plots
+* folium plots
+
+and more!
+
+[![DOI](https://zenodo.org/badge/138660604.svg)](https://zenodo.org/badge/latestdoi/138660604)
+![PyPI](https://img.shields.io/pypi/v/matplotcheck.svg?color=purple&style=plastic)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/matplotcheck.svg?color=purple&label=pypi%20downloads&style=plastic)
+![Conda](https://img.shields.io/conda/v/conda-forge/matplotcheck.svg?color=purple&style=plastic)
+![Conda](https://img.shields.io/conda/dn/conda-forge/matplotcheck.svg?color=purple&label=conda-forge%20downloads&style=plastic)
+[![Documentation Status](https://readthedocs.org/projects/matplotcheck/badge/?version=latest)](https://matplotcheck.readthedocs.io/en/latest/?badge=latest)
+
+## Why MatPlotCheck?
+There is often a need to test plots particularly when teaching data science
+courses. The Matplotlib API can be complex to navigate when trying to write
+tests. MatPlotCheck was developed to make it easier to test data, titles, axes
+and other elements of Matplotlib plots in support of both autograding and other
+testing needs.
+
+MatPlotCheck was inspired by [plotChecker][cdeac58a] developed by Jess Hamrick.
+
+  [cdeac58a]: https://github.com/jhamrick/plotchecker "Plot Checker"
+
+We spoke with her about our development and decided to extend plotChecker to suite some of the grading needs in our classes which include plots with spatial data using numpy for images and geopandas for vector data.
+
+[Find MatPlotCheck on GitHub.](https://github.com/earthlab/matplotcheck)

_tools/qtoolkit.md

@@ -0,0 +1,20 @@
+---
+layout: single
+title: "qtoolkit"
+excerpt: "An R package to get process and visualize Qualtrics survey data."
+authors: ["Leah Wasser", "Davey Levin"]
+splash_header: "QToolkit: An R Package To Access and Quickly Process Qualtrics Survey Data"
+tool_page_title: "QToolkit: An R Package To Access and Quickly Process Qualtrics Survey Data"
+header:
+  overlay_color: "#000080"
+  teaser: qtoolkit.png
+  image_alt: Qtoolkit Badge
+published: true
+---
+
+[![DOI](https://zenodo.org/badge/107568212.svg)](https://zenodo.org/badge/latestdoi/107568212)
+
+Qtoolkit is an `R` package that allows you to get, process and visualize Qualtrics survey data. `Qtoolkit` allows you to connect to the
+Qualtrics API in order to download surveys, view survey questions and associated metadata and quickly analyze and plot the data outputs.
+
+[Check out QToolkit on GitHub.](https://github.com/earthlab/qtoolkit)

_tools/smapr.md

@@ -3,7 +3,7 @@ layout: single
 title: "smapr"
 excerpt: "An R package for acquiring and processing soil moisture data from the NASA SMAP mission."
 header:
-  image: smap.png
+  overlay_color: "#000080"
   teaser: smap.png
   image_alt: "Intensity plot of soil moisture across the Earth"
 published: true
@@ -13,6 +13,6 @@ published: true
 
 smapr is an R package for acquisition and processing of [NASA (Soil Moisture Active-Passive) SMAP data](http://smap.jpl.nasa.gov/).
 The NASA SMAP mission collects global soil moisture daily, providing an invaluable resource to the research community.
-The smapr package facilitates data search, download, and processing, with the goal of quickly acquiring georeferenced spatial rasters that can be exported to any desired format. 
+The smapr package facilitates data search, download, and processing, with the goal of quickly acquiring georeferenced spatial rasters that can be exported to any desired format.
 
 [Check it out on GitHub!](https://github.com/ropensci/smapr)