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Fix typos in example notebooks #4908

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Feb 15, 2021
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Fix typos in example notebooks #4908

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6c2e0d4
Update monthly-means.ipynb
RichardScottOZ Feb 14, 2021
8cd190c
Update ERA5-GRIB-example.ipynb
RichardScottOZ Feb 14, 2021
f00b58a
Update ROMS_ocean_model.ipynb
RichardScottOZ Feb 14, 2021
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2 changes: 1 addition & 1 deletion doc/examples/ERA5-GRIB-example.ipynb
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Expand Up @@ -11,7 +11,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"GRIB format is commonly used to disemminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
"GRIB format is commonly used to disseminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
]
},
{
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2 changes: 1 addition & 1 deletion doc/examples/ROMS_ocean_model.ipynb
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Expand Up @@ -120,7 +120,7 @@
"source": [
"### A naive vertical slice\n",
"\n",
"Create a slice using the s-coordinate as the vertical dimension is typically not very informative."
"Creating a slice using the s-coordinate as the vertical dimension is typically not very informative."
]
},
{
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6 changes: 3 additions & 3 deletions doc/examples/monthly-means.ipynb
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Expand Up @@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculating Seasonal Averages from Timeseries of Monthly Means \n",
"Calculating Seasonal Averages from Time Series of Monthly Means \n",
"=====\n",
"\n",
"Author: [Joe Hamman](https://github.com/jhamman/)\n",
Expand Down Expand Up @@ -60,10 +60,10 @@
"source": [
"#### Now for the heavy lifting:\n",
"We first have to come up with the weights,\n",
"- calculate the month lengths for each monthly data record\n",
"- calculate the month length for each monthly data record\n",
"- calculate weights using `groupby('time.season')`\n",
"\n",
"Finally, we just need to multiply our weights by the `Dataset` and sum allong the time dimension. Creating a `DataArray` for the month length is as easy as using the `days_in_month` accessor on the time coordinate. The calendar type, in this case `'noleap'`, is automatically considered in this operation."
"Finally, we just need to multiply our weights by the `Dataset` and sum along the time dimension. Creating a `DataArray` for the month length is as easy as using the `days_in_month` accessor on the time coordinate. The calendar type, in this case `'noleap'`, is automatically considered in this operation."
]
},
{
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