snowex-hackweek · jomey · Mar 1, 2024 · Mar 1, 2024 · Mar 1, 2024 · Mar 1, 2024
diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml
@@ -32,7 +32,7 @@ jobs:
       with:
         check_filenames: true
         check_hidden: true
-        skip: '*.js,qaqc.yml'
+        skip: '*.js,qaqc.yml,*.css'
         ignore_words_list: slippy,trough,thw,soop,hist,te,ba,mape
 
     # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml

diff --git a/book/intro.md b/book/intro.md
@@ -8,13 +8,3 @@
 👩‍💻 During a Hackweek teams work collaboratively on different projects. Read more about the projects and results on our [projects page](projects/list_of_projects)
 
 💡 Learn more about hackweeks hosted by the [University of Washington eScience Institute](https://uwhackweek.github.io/hackweeks-as-a-service/intro.html), or check out our publication describing the hackweek educational model {cite:p}`Huppenkothen2018`.
-
-```{admonition} Quick links for the event
-:class: seealso
-* WiFi, University of Washington
-    * UW NetID: event0568
-    * Password: w9K6-x9L6-e5M5
-* JupyterHub: {{ jupyterhub_url }}
-* GitHub organization: {{ github_org_url}}
-* Projects Summary Document: {{ project_summary_url }}
-```
diff --git a/book/projects/project_roadmap.md b/book/projects/project_roadmap.md
@@ -1,6 +1,6 @@
 # Project Roadmap
 
-Each project will progress through these stages: 
+Each project will progress through these stages:
 
 ```{image} ../img/project-timeline.png
 :alt: flowchart of typical hackweek project timelines
@@ -36,15 +36,15 @@ We facilitate a project team formation session at the end of the first day of th
 ## **Teamwork**
 
 * kick-off meeting focuses on hearing from each team member:
-    * What are your unique learning goals? 
+    * What are your unique learning goals?
     * How do you work best? What level of group interaction works for you?
     * What skills and background are you most excited to share with the team?
     * When are you available to meet?
-* set up team computing infrastructure on Slack and GitHub ([Project Initiallization](project_initialization)) 
+* set up team computing infrastructure on Slack and GitHub ([Project Initialization](project_initialization))
 * project work begins and teams have regular check-ins to chart progress
 * project outputs are recorded in a GitHub repository
 
-## **Presentations** 
+## **Presentations**
 
 * each team has 15 minutes to share what was learned, what challenges arose, what might be worth trying next
 * individual contributions to the group effort are recognized and celebrated
@@ -54,7 +54,7 @@ We facilitate a project team formation session at the end of the first day of th
 * We aspire to keep projects going after the hackweek! This is an optional (ideal) outcome of hackweek project work.
 * Project leads/helpers will transition to longer-term engagement via mentorship role (optional/with support).
 * Additional people join a growing Community of Practice (for example, centered on a community software library).
-* software, research results are packaged and shared/published. 
+* software, research results are packaged and shared/published.
 
 
 
diff --git a/book/tutorials/core-datasets/01_data_coverage.ipynb b/book/tutorials/core-datasets/01_data_coverage.ipynb
@@ -48,7 +48,7 @@
    "source": [
     "## Data Coverage\n",
     "### Campaign Summary Table\n",
-    "Each year we build upon our efforts to further investigate the identitifed snow remote sensing science gaps. The summary table lists the focus for each campaign by year and type. "
+    "Each year we build upon our efforts to further investigate the identified snow remote sensing science gaps. The summary table lists the focus for each campaign by year and type. "
    ]
   },
   {
@@ -91,7 +91,7 @@
     "\n",
     "<!-- <figure>\n",
     "<img src='./content/01_campaign_venn_diagram.jpg'>\n",
-    "<figcaption algin = \"left\"> <b>Figure 2.</b> Diagram to differentiate the Intensive Observation Period (IOP) and Time Series (TS) campaign types. The center describes what components remain consistent amongst the different campaign types.    \n",
+    "<figcaption align = \"left\"> <b>Figure 2.</b> Diagram to differentiate the Intensive Observation Period (IOP) and Time Series (TS) campaign types. The center describes what components remain consistent amongst the different campaign types.    \n",
     "<figure> -->\n",
     "\n",
     "![](./content/01_campaign_venn_diagram.jpg)\n",
@@ -113,7 +113,7 @@
     "\n",
     "<!-- <figure>\n",
     "<img src='./content/01_snow-classes-sturm.png'>\n",
-    "<figcaption algin = \"left\"> <b>Figure 3.</b> Snow Classes across North America at 300 m (Sturm and Liston, 2021)    \n",
+    "<figcaption align = \"left\"> <b>Figure 3.</b> Snow Classes across North America at 300 m (Sturm and Liston, 2021)    \n",
     "<figure> -->\n",
     "    \n",
     "![](./content/01_snow-classes-sturm.png)\n",
@@ -134,7 +134,7 @@
     "\n",
     "<!-- <figure>\n",
     "<img src='./content/01_snow-class-coverage.png'>\n",
-    "<figcaption algin = \"left\"> <b>Figure 4.</b> Map of the averaged snow pits by site for the 2020 and 2021 Time Series Campaign. Note, some points are overlapping in the eastern Rocky Mountain region around Colorado. The total number of unique snow pit sites are listed in the legend.     \n",
+    "<figcaption align = \"left\"> <b>Figure 4.</b> Map of the averaged snow pits by site for the 2020 and 2021 Time Series Campaign. Note, some points are overlapping in the eastern Rocky Mountain region around Colorado. The total number of unique snow pit sites are listed in the legend.     \n",
     "<figure> -->\n",
     "    \n",
     "![](./content/01_snow-class-coverage.png)\n",

diff --git a/book/tutorials/core-datasets/02_data_descriptions.ipynb b/book/tutorials/core-datasets/02_data_descriptions.ipynb
@@ -26,7 +26,7 @@
    "metadata": {},
    "source": [
     "## Data Descriptions\n",
-    "### What ground-based data sets are central for all field campagins? \n",
+    "### What ground-based data sets are central for all field campaigns? \n",
     "\n",
     "<img src='./content/02_pits_and_depths.png'>\n",
     "\n",
@@ -220,7 +220,7 @@
     "|UT   |Little Cottonwood Canyon  |Alta Collins      |UTLCAC|40.57210|-111.62997|17       |2         |\n",
     "|UT   |Little Cottonwood Canyon  |Atwater           |UTLCAW|40.59125|-111.63759|20       |5         |\n",
     "\n",
-    "Note - This table is a preliminary comprehensive site summary of the 2020 and 2021 Time Series campaigns. If a site was repeated (e.g. Cameron Pass, Michigan River) then the Pit Count column is a sum of the two years). This is preliminary because we are still adjusting and correcting some of the spatial data after confirming locations and sampling designs with site leads. The Snow Class column was extracted from the 300 m Snow Classification data set (Liston, 2021 and can be accessed [here](https://nsidc.org/data/NSIDC-0768/versions/1) at NSIDC). See the previous notebook to characterize the Snow Classes. This table does not include about ~50 snow pits that were part of the 2021 TS campaign from the Fraser Experiemental Forest because of the modified spatial sampling design, but will be included in the published data set, and the table will be updated to reflect those additional snow pits. \n",
+    "Note - This table is a preliminary comprehensive site summary of the 2020 and 2021 Time Series campaigns. If a site was repeated (e.g. Cameron Pass, Michigan River) then the Pit Count column is a sum of the two years). This is preliminary because we are still adjusting and correcting some of the spatial data after confirming locations and sampling designs with site leads. The Snow Class column was extracted from the 300 m Snow Classification data set (Liston, 2021 and can be accessed [here](https://nsidc.org/data/NSIDC-0768/versions/1) at NSIDC). See the previous notebook to characterize the Snow Classes. This table does not include about ~50 snow pits that were part of the 2021 TS campaign from the Fraser Experimental Forest because of the modified spatial sampling design, but will be included in the published data set, and the table will be updated to reflect those additional snow pits. \n",
     "\n",
     "This table can be accessed as a geojson from [Megan's github gists](https://gist.github.com/meganmason/dde13c46a90875e364b1c25a31bff1d8) \n",
     "\n",

diff --git a/book/tutorials/lidar/2_elevation_differencing.ipynb b/book/tutorials/lidar/2_elevation_differencing.ipynb
@@ -321,14 +321,14 @@
     "sns.kdeplot(data = vh_sd_dem, x = 'SD_mean', y = 'ELEV_mean', shade=True, ax=ax6, cmap='Blues')\n",
     "ax6.set_xlabel('Snow Depth (m)')\n",
     "ax6.set_ylabel('Elevation (m)')\n",
-    "#set xlimt and y limt\n",
+    "#set xlimit and y limit\n",
     "#ax6.set_xlim(0, 2.5)\n",
     "ax6.set_ylim(2900, 3300)\n",
     "\n",
     "sns.kdeplot(data = vh_sd_dem, x = 'SD_mean', y = 'VH_mean', shade=True, ax=ax7, cmap='Blues')\n",
     "ax7.set_xlabel('Snow Depth (m)')\n",
     "ax7.set_ylabel('Vegetation Height (m)')\n",
-    "#set xlimt and y limt\n",
+    "#set xlimit and y limit\n",
     "#ax7.set_xlim(0, 2.5)\n",
     "ax7.set_ylim(-3, 15)\n",
     "\n",

diff --git a/book/tutorials/uavsar/1_accessing_imagery.ipynb b/book/tutorials/uavsar/1_accessing_imagery.ipynb
@@ -27,20 +27,20 @@
    "source": [
     "## What is UAVSAR?\n",
     "\n",
-    "[UAVSAR](https://uavsar.jpl.nasa.gov/education/what-is-uavsar.html) is a low frequency plane-based synthetic aperature radar. UAVSAR stands for \"Uninhabited Aerial Vehicle Synthetic Aperature Radar\". It captures imagery using a L-band radar. This low frequency means it can penetrate into and through clouds, vegetation, and snow.\n",
+    "[UAVSAR](https://uavsar.jpl.nasa.gov/education/what-is-uavsar.html) is a low frequency plane-based synthetic aperture radar. UAVSAR stands for \"Uninhabited Aerial Vehicle Synthetic Aperture Radar\". It captures imagery using a L-band radar. This low frequency means it can penetrate into and through clouds, vegetation, and snow.\n",
     "\n",
     "| frequency (cm) | resolution (rng x azi m) | Swath Width (km) | Polarizations | Launch date |\n",
     "| - | - | - | - | - |\n",
     "| L-band 23| 1.8 x 5.5 | 16 | VV, VH, HV, HH | 2007 |\n",
     "\n",
-    "### NASA SnowEx 2020 and 2021 UAVSAR Campaings\n",
+    "### NASA SnowEx 2020 and 2021 UAVSAR Campaigns\n",
     "\n",
     "During the winter of 2020 and 2021, NASA conducted an L-band InSAR timeseries across the Western US with the goal of tracking changes in SWE. Field teams in 13 different locations in 2020, and in 6 locations in 2021, deployed on the date of the flight to perform calibration and validation observations.\n",
     "\n",
     ":::{figure-md} UAVSAR-map\n",
     "<img src=\"../../img/SnowEx_map.jpg\" alt=\"uavsar map\" width=\"800px\">\n",
     "\n",
-    "Map of the UAVSAR flight locations for NASA SnowEx. Note that the Montana site (Central Agricultral Research Center) is not on this map. Source: Chris Hiemstra\n",
+    "Map of the UAVSAR flight locations for NASA SnowEx. Note that the Montana site (Central Agricultural Research Center) is not on this map. Source: Chris Hiemstra\n",
     ":::\n",
     "\n",
     "---\n",

diff --git a/book/tutorials/uavsar/2_types_of_uavsar_images.ipynb b/book/tutorials/uavsar/2_types_of_uavsar_images.ipynb
@@ -106,11 +106,11 @@
    "source": [
     "# clone tifs to tmp directory\n",
     "os.chdir('/tmp/')\n",
-    "if not os.path.exists('/tmp/uavsar-tutorial-data'):\n",
-    "    !git clone --quiet https://github.com/SnowEx/uavsar-tutorial-data.git\n",
+    "if not os.path.exists('/tmp/uavsar-tutorial-data-2022'):\n",
+    "    !git clone --quiet https://github.com/snowex-hackweek/uavsar-tutorial-data-2022\n",
     "\n",
     "# list files downloaded\n",
-    "data_dir = '/tmp/uavsar-tutorial-data/'\n",
+    "data_dir = '/tmp/uavsar-tutorial-data-2022/'\n",
     "os.chdir(data_dir)\n",
     "# ! ls -l"
    ]

diff --git a/book/tutorials/uavsar/3_interferometric_swe_inversion.ipynb b/book/tutorials/uavsar/3_interferometric_swe_inversion.ipynb
@@ -38,11 +38,11 @@
     "import os\n",
     "# clone tifs to tmp directory\n",
     "os.chdir('/tmp/')\n",
-    "if not os.path.exists('/tmp/uavsar-tutorial-data'):\n",
-    "    !git clone --quiet https://github.com/SnowEx/uavsar-tutorial-data.git\n",
+    "if not os.path.exists('/tmp/uavsar-tutorial-data-2022'):\n",
+    "    !git clone --quiet https://github.com/snowex-hackweek/uavsar-tutorial-data-2022.git\n",
     "\n",
     "# list files downloaded\n",
-    "data_dir = '/tmp/uavsar-tutorial-data/'\n",
+    "data_dir = '/tmp/uavsar-tutorial-data-2022/'\n",
     "os.chdir(data_dir)\n",
     "# ! ls -l"
    ]
@@ -101,7 +101,7 @@
    "outputs": [],
    "source": [
     "# Directory of the uavsar tiffs\n",
-    "data_dir = '/tmp/uavsar-tutorial-data/'\n",
+    "data_dir = '/tmp/uavsar-tutorial-data-2022/'\n",
     "\n",
     "# Mesa Lake Snotel Coordinates\n",
     "snotel_coords = (-108.05, 39.05)"
@@ -174,7 +174,8 @@
     "ax.set_ylim(38.75, 39.3)\n",
     "\n",
     "# Add background map\n",
-    "cx.add_basemap(ax, crs=img.rio.crs, source = cx.providers.Stamen.TerrainLabels)\n",
+    "# Unfortunately, the below line does not render as of 20240301\n",
+    "# cx.add_basemap(ax, crs=img.rio.crs, source = cx.providers.Stamen.TerrainLabels)\n",
     "cx.add_basemap(ax, crs=img.rio.crs, alpha = 0.8, source = cx.providers.USGS.USImageryTopo)\n",
     "plt.title('Overview Map')\n",
     "plt.show()"