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Ncl Climatology Entry (#80)
* climatology ncl entry and raw first take * a few small changes * add functions to index table * Fix index and edit entry * Add receipt and small changes * Updates to entry * add pyproject.toml file * testing codespell * typo and small changes * typo * use .codespellrc for now * fix codespell config * one more try on codespell * fix links in table * eol * fix another link * Update ncl/ncl_index/ncl-index-table.csv Co-authored-by: Andy McKeen <[email protected]> * adjust urls in ncl script * updates to ncl index for consistency * clear notebook outputs * use geocat-datafiles * appease pre-commit * NCL and receipt updates to use new subsets and correct for calendar issues * formatting * tidy up the NCL entries notebook * update to use new datafile * review updates * address comments in ncl index as well * adjust rel_tol * add note about calendar attribute to NCL script * adjust tolerance again and comment * formatting * rel_tol to abs_tol * rel_tol and abs_tol * fix titles and labels * whoops --------- Co-authored-by: Katelyn FitzGerald <[email protected]> Co-authored-by: Cora Schneck <[email protected]>
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‎.codespellrc

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[codespell]
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skip=.git,*.pdf,*.svg
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ignore-regex=^\s*"image/\S+": ".*
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skip = .git,*.pdf,*.svg
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ignore-words-list = ond
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ignore-regex = ^\s*"image/\S+": ".*

‎ncl/ncl_entries/climatology_functions.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Climatology"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Overview\n",
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"The NCL climatology functions listed below can be replicated using `xarray` and/or `geocat.comp`\n",
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"\n",
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"- [calcDayAnomTLL](https://www.ncl.ucar.edu/Document/Functions/Contributed/calcDayAnomTLL.shtml)\n",
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"- [calcMonAnomTLL](https://www.ncl.ucar.edu/Document/Functions/Contributed/calcMonAnomTLL.shtml)\n",
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"- [clmDayTLL](https://www.ncl.ucar.edu/Document/Functions/Contributed/clmDayTLL.shtml)\n",
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"- [clmMonTLL](https://www.ncl.ucar.edu/Document/Functions/Contributed/clmMonTLL.shtml)\n",
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"- [month_to_season](https://www.ncl.ucar.edu/Document/Functions/Contributed/month_to_season.shtml)\n",
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"- [rmMonAnnCycTLL](https://www.ncl.ucar.edu/Document/Functions/Contributed/rmMonAnnCycTLL.shtml)\n",
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"- [stdMonTLL](https://www.ncl.ucar.edu/Document/Functions/Contributed/stdMonTLL.shtml)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## calcDayAnomTLL\n",
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"`calcDayAnomTLL` calculates daily anomalies from a daily data climatology"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"ds = xr.open_dataset(gdf.get(\"applications_files/inputs/CMIP6_sea_ice_daily_subset.nc\"))\n",
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"aice = ds.aice_d\n",
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"DayTLL = aice.groupby(aice.time.dt.dayofyear)\n",
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"clmDayTLL = DayTLL.mean(dim=\"time\")\n",
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"calcDayAnomTLL = DayTLL - clmDayTLL\n",
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"\n",
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"calcDayAnomTLL = calcDayAnomTLL.assign_attrs(long_name=\"sea ice anomaly\")\n",
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"\n",
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"calcDayAnomTLL[0, :, :].plot();"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## calcMonAnomTLL\n",
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"`calcMonAnomTLL` calculates monthly anomalies by subtracting the long-term mean from each point"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"\n",
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"ds = xr.open_dataset(\n",
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" gdf.get(\"applications_files/inputs/CMIP6_sea_ice_monthly_subset.nc\")\n",
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")\n",
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"aice = ds.aice\n",
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"MonTLL = aice.groupby(aice.time.dt.month)\n",
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"clmMonTLL = MonTLL.mean(dim=\"time\")\n",
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"calcMonAnomTLL = MonTLL - clmMonTLL\n",
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"\n",
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"calcMonAnomTLL = calcMonAnomTLL.assign_attrs(long_name=\"sea ice anomaly\")\n",
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"\n",
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"calcMonAnomTLL[0, :, :].plot();"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## clmDayTLL\n",
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"`clmDayTLL` calculates long-term daily means (daily climatology) from daily data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"\n",
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"ds = xr.open_dataset(gdf.get(\"applications_files/inputs/CMIP6_sea_ice_daily_subset.nc\"))\n",
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"aice = ds.aice_d\n",
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"DayTLL = aice.groupby(aice.time.dt.dayofyear)\n",
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"clmDayTLL = DayTLL.mean(dim=\"time\")\n",
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"\n",
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"clmDayTLL[:, 10, 10].plot()\n",
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"plt.title(\"daily climatology\")\n",
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"plt.xlabel(\"day of year\")\n",
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"plt.ylabel(\"sea ice area\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## clmMonTLL\n",
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"`clmMonTLL` calculates long-term monthly means (monthly climatology) from monthly data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"\n",
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"ds = xr.open_dataset(\n",
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" gdf.get(\"applications_files/inputs/CMIP6_sea_ice_monthly_subset.nc\")\n",
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")\n",
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"aice = ds.aice\n",
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"MonTLL = aice.groupby(aice.time.dt.month)\n",
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"clmMonTLL = MonTLL.mean(dim=\"time\")\n",
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"\n",
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"clmMonTLL[:, 10, 10].plot()\n",
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"plt.title(\"monthly climatology\")\n",
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"plt.xlabel(\"month of year\")\n",
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"plt.ylabel(\"sea ice area\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## month_to_season\n",
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"`month_to_season` computes a user-specified three-month seasonal mean (DJF, JFM, FMA, MAM, AMJ, MJJ, JJA, JAS, ASO, SON, OND, NDJ)\n",
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"\n",
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"```{note}\n",
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"You can do something similar with directly with Xarray as shown in [this example in the Xarray documentation](https://docs.xarray.dev/en/stable/examples/monthly-means.html#Calculating-Seasonal-Averages-from-Time-Series-of-Monthly-Means). However, it requires substantially more code and doesn't have as much flexibility with respect to how the seasons are defined.\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"from geocat.comp import month_to_season\n",
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"\n",
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"ds = xr.open_dataset(\n",
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" gdf.get(\"applications_files/inputs/CMIP6_sea_ice_monthly_subset.nc\")\n",
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")\n",
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"aice = ds.aice\n",
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"mon_to_season = month_to_season(aice, \"ASO\")\n",
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"\n",
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"mon_to_season = mon_to_season.assign_attrs(long_name=\"sea ice area\")\n",
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"\n",
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"mon_to_season[0, :, :].plot()\n",
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"\n",
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"plt.title(\"2010 seasonal mean\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## rmMonAnnCycTLL\n",
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"`rmMonAnnCycTLL` removes the annual cycle from monthly data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"\n",
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"ds = xr.open_dataset(\n",
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" gdf.get(\"applications_files/inputs/CMIP6_sea_ice_monthly_subset.nc\")\n",
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")\n",
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"aice = ds.aice\n",
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"MonTLL = aice.groupby(aice.time.dt.month)\n",
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"clmMonTLL = MonTLL.mean(dim=\"time\")\n",
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"rmMonAnnCycTLL = MonTLL - clmMonTLL\n",
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"\n",
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"rmMonAnnCycTLL[:, 10, 10].plot()\n",
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"plt.title(\"annual cycle removed\")\n",
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"plt.ylabel(\"sea ice area\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## stdMonTLL\n",
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"`stdMonTLL` calculates standard deviations of monthly means"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Grab and Go"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import geocat.datafiles as gdf\n",
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"\n",
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"ds = xr.open_dataset(\n",
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" gdf.get(\"applications_files/inputs/CMIP6_sea_ice_monthly_subset.nc\")\n",
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")\n",
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"aice = ds.aice\n",
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"MonTLL = aice.groupby(aice.time.dt.month)\n",
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"stdMonTLL = MonTLL.std(ddof=1)\n",
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"\n",
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"stdMonTLL[0, :, :].plot();"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Python Resources\n",
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"\n",
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"- GeoCAT Applications [climatology page](https://ncar.github.io/geocat-applications/applications/data_analysis/climatology.html)\n",
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"- Climatematch Academy [Xarray Data Analysis and Climatology tutorial](https://comptools.climatematch.io/tutorials/W1D1_ClimateSystemOverview/student/W1D1_Tutorial5.html)\n",
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"- Project Pythia Foundations [Computations and Masks with Xarray tutorial](https://foundations.projectpythia.org/core/xarray/computation-masking.html)\n",
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"- Xarray User Guide [section on time series data](https://docs.xarray.dev/en/stable/user-guide/time-series.html)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎ncl/ncl_entries/ncl_entries.rst

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.. toctree::
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:maxdepth: 1
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climatology_functions.ipynb
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trigonometric_functions.ipynb
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general_applied_math.ipynb
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specx_specxy_anal.ipynb

‎ncl/ncl_index/ncl-index-table.csv

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`cosh <https://www.ncl.ucar.edu/Document/Functions/Built-in/cosh.shtml>`__,"Computes the hyperbolic cosine of numeric types","``math.cosh()`` or ``numpy.cosh()``",`example notebook <../ncl_entries/trigonometric_functions.ipynb#cosh>`__
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`sinh <https://www.ncl.ucar.edu/Document/Functions/Built-in/sinh.shtml>`__,"Computes the hyperbolic sine of numeric types","``math.sinh()`` or ``numpy.sinh()``",`example notebook <../ncl_entries/trigonometric_functions.ipynb#sinh>`__
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`tanh <https://www.ncl.ucar.edu/Document/Functions/Built-in/tanh.shtml>`__,"Computes the hyperbolic tangent of numeric types","``math.tanh()`` or ``numpy.tanh()``",`example notebook <../ncl_entries/trigonometric_functions.ipynb#tanh>`__
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`days_in_month <https://www.ncl.ucar.edu/Document/Functions/Built-in/days_in_month.shtml>`__,"Calculates the number of days in a month given month and year","``cftime.datetime().daysinmonth``",`example notebook <../ncl_entries/days_in_month.ipynb>`__
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`day_of_week <https://www.ncl.ucar.edu/Document/Functions/Built-in/day_of_week.shtml>`__,"Calculates the day of the week given month, day, and year","``cftime.datetime().dayofwk``",`example notebook <../ncl_entries/day_of_week.ipynb>`__
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`days_in_month <https://www.ncl.ucar.edu/Document/Functions/Built-in/days_in_month.shtml>`__,"Calculates the number of days in a month given month and year","``cftime.datetime()``",`example notebook <../ncl_entries/days_in_month.ipynb>`__
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`day_of_week <https://www.ncl.ucar.edu/Document/Functions/Built-in/day_of_week.shtml>`__,"Calculates the day of the week given month, day, and year","``cftime.datetime()``",`example notebook <../ncl_entries/day_of_week.ipynb>`__
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`calcDayAnomTLL <https://www.ncl.ucar.edu/Document/Functions/Contributed/calcDayAnomTLL.shtml>`__,"Calculates daily anomalies from a daily data climatology","``xarray.DataArray.groupby()``",`example notebook <../ncl_entries/climatology_functions.ipynb#calcdayanomtll>`__
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`calcMonAnomTLL <https://www.ncl.ucar.edu/Document/Functions/Contributed/calcMonAnomTLL.shtml>`__,"Calculates monthly anomalies by subtracting the long-term mean from each point","``xarray.DataArray.groupby()``",`example notebook <../ncl_entries/climatology_functions.ipynb#calcmonanomtll>`__
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`clmDayTLL <https://www.ncl.ucar.edu/Document/Functions/Contributed/clmDayTLL.shtml>`__,"Calculates long-term daily means (daily climatology) from daily data","``xarray.DataArray.groupby()``",`example notebook <../ncl_entries/climatology_functions.ipynb#clmdaytll>`__
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`clmMonTLL <https://www.ncl.ucar.edu/Document/Functions/Contributed/clmMonTLL.shtml>`__,"Calculates long-term monthly means (monthly climatology) from monthly data","``xarray.DataArray.groupby()``",`example notebook <../ncl_entries/climatology_functions.ipynb#clmmontll>`__
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`month_to_season <https://www.ncl.ucar.edu/Document/Functions/Contributed/month_to_season.shtml>`__,"Computes a user-specified three-month seasonal mean","``geocat.comp.month_to_season()``",`example notebook <../ncl_entries/climatology_functions.ipynb#month-to-season>`__
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`rmMonAnnCycTLL <https://www.ncl.ucar.edu/Document/Functions/Contributed/rmMonAnnCycTLL.shtml>`__,"Removes the annual cycle from monthly data","``xarray.DataArray.groupby()``",`example notebook <../ncl_entries/climatology_functions.ipynb#rmmonanncyctll>`__
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`stdMonTLL <https://www.ncl.ucar.edu/Document/Functions/Contributed/stdMonTLL.shtml>`__,"Calculates standard deviations of monthly means","``xarray.DataArray.groupby()``",`example notebook <../ncl_entries/climatology_functions.ipynb#stdmontll>`__
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`abs <https://www.ncl.ucar.edu/Document/Functions/Built-in/abs.shtml>`__,"Returns the absolute value of numeric data","``abs()`` or ``numpy.abs()``",`example notebook <../ncl_entries/general_applied_math.ipynb#abs-fabs>`__
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`avg <https://www.ncl.ucar.edu/Document/Functions/Built-in/avg.shtml>`__,"Computes the average of a variable regardless of dimensionality","``numpy.average()`` or ``numpy.mean()``",`example notebook <../ncl_entries/general_applied_math.ipynb#avg>`__
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`cumsum <https://www.ncl.ucar.edu/Document/Functions/Built-in/cumsum.shtml>`__,"Calculates the cumulative sum","``numpy.cumsum()``",`example notebook <../ncl_entries/general_applied_math.ipynb#cumsum>`__

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