Tutorials - Fix typos raised by QA/QC

snowex-hackweek · Mar 1, 2024 · 2fbb8f2 · 2fbb8f2
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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/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",

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",

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",