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* weighted for DataArray * remove some commented code * pep8 and faulty import tests * add weighted sum, replace 0s in sum_of_wgt * weighted: overhaul tests * weighted: pep8 * weighted: pep8 lines * weighted update docs * weighted: fix typo * weighted: pep8 * undo changes to avoid merge conflict * add weighted to dataarray again * remove super * overhaul core/weighted.py * add DatasetWeighted class * _maybe_get_all_dims return sorted tuple * work on: test_weighted * black and flake8 * Apply suggestions from code review (docs) * restructure interim * restructure classes * update weighted.py * black * use map; add keep_attrs * implement expected_weighted; update tests * add whats new * undo changes to whats-new * F811: noqa where? * api.rst * add to computation * small updates * add example to gallery * typo * another typo * correct docstring in core/common.py * typos * adjust review * clean tests * add test nonequal coords * comment on use of dot * fix erroneous merge * update tests * move example to notebook * move whats-new entry to 15.1 * some doc updates * dot to own function * simplify some tests * Doc updates * very minor changes. * fix & add references * doc: return 0/NaN on 0 weights * Update xarray/core/common.py Co-authored-by: dcherian <deepak@cherian.net> Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>
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| { | ||
| "cells": [ | ||
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| "cell_type": "markdown", | ||
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| "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", | ||
| "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Compare-weighted-and-unweighted-mean-temperature\" data-toc-modified-id=\"Compare-weighted-and-unweighted-mean-temperature-1\"><span class=\"toc-item-num\">1 </span>Compare weighted and unweighted mean temperature</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Data\" data-toc-modified-id=\"Data-1.0.1\"><span class=\"toc-item-num\">1.0.1 </span>Data</a></span></li><li><span><a href=\"#Creating-weights\" data-toc-modified-id=\"Creating-weights-1.0.2\"><span class=\"toc-item-num\">1.0.2 </span>Creating weights</a></span></li><li><span><a href=\"#Weighted-mean\" data-toc-modified-id=\"Weighted-mean-1.0.3\"><span class=\"toc-item-num\">1.0.3 </span>Weighted mean</a></span></li><li><span><a href=\"#Plot:-comparison-with-unweighted-mean\" data-toc-modified-id=\"Plot:-comparison-with-unweighted-mean-1.0.4\"><span class=\"toc-item-num\">1.0.4 </span>Plot: comparison with unweighted mean</a></span></li></ul></li></ul></li></ul></div>" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Compare weighted and unweighted mean temperature\n", | ||
| "\n", | ||
| "\n", | ||
| "Author: [Mathias Hauser](https://github.com/mathause/)\n", | ||
| "\n", | ||
| "\n", | ||
| "We use the `air_temperature` example dataset to calculate the area-weighted temperature over its domain. This dataset has a regular latitude/ longitude grid, thus the gridcell area decreases towards the pole. For this grid we can use the cosine of the latitude as proxy for the grid cell area.\n" | ||
| ] | ||
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| "end_time": "2020-03-17T14:43:57.222351Z", | ||
| "start_time": "2020-03-17T14:43:56.147541Z" | ||
| } | ||
| }, | ||
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| "source": [ | ||
| "%matplotlib inline\n", | ||
| "\n", | ||
| "import cartopy.crs as ccrs\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "import xarray as xr" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Data\n", | ||
| "\n", | ||
| "Load the data, convert to celsius, and resample to daily values" | ||
| ] | ||
| }, | ||
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| "cell_type": "code", | ||
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| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", | ||
| "\n", | ||
| "# to celsius\n", | ||
| "air = ds.air - 273.15\n", | ||
| "\n", | ||
| "# resample from 6-hourly to daily values\n", | ||
| "air = air.resample(time=\"D\").mean()\n", | ||
| "\n", | ||
| "air" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Plot the first timestep:" | ||
| ] | ||
| }, | ||
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| "end_time": "2020-03-17T14:43:59.887120Z", | ||
| "start_time": "2020-03-17T14:43:59.582894Z" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "projection = ccrs.LambertConformal(central_longitude=-95, central_latitude=45)\n", | ||
| "\n", | ||
| "f, ax = plt.subplots(subplot_kw=dict(projection=projection))\n", | ||
| "\n", | ||
| "air.isel(time=0).plot(transform=ccrs.PlateCarree(), cbar_kwargs=dict(shrink=0.7))\n", | ||
| "ax.coastlines()" | ||
| ] | ||
| }, | ||
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| "### Creating weights\n", | ||
| "\n", | ||
| "For a for a rectangular grid the cosine of the latitude is proportional to the grid cell area." | ||
| ] | ||
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| "outputs": [], | ||
| "source": [ | ||
| "weights = np.cos(np.deg2rad(air.lat))\n", | ||
| "weights.name = \"weights\"\n", | ||
| "weights" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
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| "### Weighted mean" | ||
| ] | ||
| }, | ||
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| "air_weighted = air.weighted(weights)\n", | ||
| "air_weighted" | ||
| ] | ||
| }, | ||
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| "cell_type": "code", | ||
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| "end_time": "2020-03-17T14:44:54.334279Z", | ||
| "start_time": "2020-03-17T14:44:54.280022Z" | ||
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| "outputs": [], | ||
| "source": [ | ||
| "weighted_mean = air_weighted.mean((\"lon\", \"lat\"))\n", | ||
| "weighted_mean" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Plot: comparison with unweighted mean\n", | ||
| "\n", | ||
| "Note how the weighted mean temperature is higher than the unweighted." | ||
| ] | ||
| }, | ||
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| "end_time": "2020-03-17T14:45:08.877307Z", | ||
| "start_time": "2020-03-17T14:45:08.673383Z" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "weighted_mean.plot(label=\"weighted\")\n", | ||
| "air.mean((\"lon\", \"lat\")).plot(label=\"unweighted\")\n", | ||
| "\n", | ||
| "plt.legend()" | ||
| ] | ||
| } | ||
| ], | ||
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| "display_name": "Python 3", | ||
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