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QA/QC - Fix a few logged typos
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jomey committed Aug 19, 2024
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4 changes: 2 additions & 2 deletions book/tutorials/snowex-core/01_snowex_data_overview.ipynb
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"![](./content/01_snow-classes-sturm.png)\n",
"**Figure 3.** Snow Classes across North America at 300 m (Sturm and Liston, 2021) -->\n",
"\n",
"As part of the mission statement, SnowEx aims to quantify snow estimation uncertainty across a range of snow classes, terrain and vegetration types. This is important to determine what areas and time periods have high SWE uncertainty across the ensemble of instrument techniques. \n",
"As part of the mission statement, SnowEx aims to quantify snow estimation uncertainty across a range of snow classes, terrain and vegetation types. This is important to determine what areas and time periods have high SWE uncertainty across the ensemble of instrument techniques. \n",
"\n",
" \n",
"![](./content/01_map-n-barchart.png)\n",
"**Figure 2.** Map of the in situ field visits for the duration of SnowEx field campaigns (2017-2023). At this scale, points are overlapping, especially in the eastern Rocky Mountain region around Colorado. The total number of unique visits with recorded SWE are listed in the legend. Upper Right Bar chart of snow classes over the four SnowEx field campagin years. Counts represent in situ field visits. A range of sites in Boreal and Montane Forests occured in open areas such as meadows and clearings. The snow classification colors match those used in [Sturm and Liston, 2021](https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-21-0070.1.xml).\n",
"**Figure 2.** Map of the in situ field visits for the duration of SnowEx field campaigns (2017-2023). At this scale, points are overlapping, especially in the eastern Rocky Mountain region around Colorado. The total number of unique visits with recorded SWE are listed in the legend. Upper Right Bar chart of snow classes over the four SnowEx field campaign years. Counts represent in situ field visits. A range of sites in Boreal and Montane Forests occurred in open areas such as meadows and clearings. The snow classification colors match those used in [Sturm and Liston, 2021](https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-21-0070.1.xml).\n",
"\n",
"<!-- ![](./content/01_snow-classes-barchart.png)\n",
"**Figure 2.** Bar chart of snow classes over the four SnowEx field campagin years. Counts represent in situ field visits. A range of sites in Boreal and Montane Forests occured in open areas such as meadows and clearings. The snow classification colors match those used in [Sturm and Liston, 2021](https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-21-0070.1.xml). -->\n",

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campagin ==> campaign

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occured ==> occurred
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"\n",
"__Tutorial Author Micah_o__: [Micah Johnson](https://github.com/micahjohnson150)\n",
"\n",
"[SnowEx](https://snow.nasa.gov/campaigns/snowex) has introduced a unique opportunity to study SWE in a way thats unprecedented, but with more data comes new challenges. \n",
"[SnowEx](https://snow.nasa.gov/campaigns/snowex) has introduced a unique opportunity to study SWE in a way that's unprecedented, but with more data comes new challenges. \n",
"\n",
"![examples](./images/data_examples.png)\n",
"\n",
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"* AND NOW WITH API!\n",
"\n",
"\n",
"### So whats the catch?\n",
"### So what's the catch?\n",
"New tech can create barriers...\n",
"\n",
"```{figure} ./images/pits_not_bits.jpg\n",
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2 changes: 1 addition & 1 deletion book/tutorials/snowex_database/3_forming_queries.ipynb
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"source": [
"from snowexsql.api import PointMeasurements\n",
"\n",
"# Instatiate the class to use the properties!\n",
"# Instantiate the class to use the properties!\n",
"measurements = PointMeasurements()\n",
"\n",
"# Get the unique data names/types in the table\n",
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4 changes: 2 additions & 2 deletions book/tutorials/snowex_database/4_get_spiral_example.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Excercise: Visualize a Manual Depth Spiral\n",
"# Exercise: Visualize a Manual Depth Spiral\n",
"\n",
"During the SnowEx campaigns a TON of manual snow depths were collected, past surveys for hackweek showed an overhelming interest in the manual \n",
"snow depths dataset. This tutorial shows how easy it is to get at that data in the database while learning how to build queries\n",
"\n",
"**Goal**: Visualize a small subset of snow depth, ideally a full spiral (mostly cause theyre cool!)\n",
"**Goal**: Visualize a small subset of snow depth, ideally a full spiral (mostly cause they are cool!)\n",
"\n",
"**Approach**: \n",
"1. Determine the necessary details for isolating manual depths\n",
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2 changes: 1 addition & 1 deletion book/tutorials/snowex_database/5_plot_raster_example.ipynb
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"metadata": {},
"outputs": [],
"source": [
"# import in the raster measurments class\n",
"# import in the raster measurements class\n",
"from snowexsql.api import RasterMeasurements\n",
"from datetime import datetime \n",
"from rasterio.plot import show\n",
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2 changes: 1 addition & 1 deletion book/tutorials/snowex_database/6_wrap_up.ipynb
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"\n",
"Big thanks to all the dedicated scientists who went out and collected these invaluable datasets. \n",
"\n",
"A huge thanks to HP Marshall who originally gave us the opportunity to build this a couple years ago. And thanks to Joe Meyer who pursued funding to develop it futher. Buy them a beer or better yet volunteer for field work to hang out with them! \n",
"A huge thanks to HP Marshall who originally gave us the opportunity to build this a couple years ago. And thanks to Joe Meyer who pursued funding to develop it further. Buy them a beer or better yet volunteer for field work to hang out with them! \n",
"\n",
"❄️ **Go forth and snow science!** ❄️"
]
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