From 2fbb8f2865cc65dc8c6b580fb08feeaa0d25e271 Mon Sep 17 00:00:00 2001 From: Joachim Meyer Date: Fri, 1 Mar 2024 14:26:24 -0700 Subject: [PATCH] Tutorials - Fix typos raised by QA/QC --- .github/workflows/qaqc.yaml | 2 +- book/tutorials/core-datasets/01_data_coverage.ipynb | 6 +++--- book/tutorials/core-datasets/02_data_descriptions.ipynb | 2 +- book/tutorials/lidar/2_elevation_differencing.ipynb | 4 ++-- book/tutorials/uavsar/1_accessing_imagery.ipynb | 6 +++--- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index 1a562003..3913a587 100644 --- 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 index 5ff26dff..865d08ba 100644 --- 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", "\n", "\n", "![](./content/01_campaign_venn_diagram.jpg)\n", @@ -113,7 +113,7 @@ "\n", "\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 index dee45674..f5302320 100644 --- 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", "\n", "\n", diff --git a/book/tutorials/lidar/2_elevation_differencing.ipynb b/book/tutorials/lidar/2_elevation_differencing.ipynb index 8ed09ed9..b528cc86 100644 --- 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 index b4ff2c52..06e47b93 100644 --- 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", "\"uavsar\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",