{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Starting Computations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{admonition} Lesson Content\n",
":class: note, dropdown\n",
"\n",
"- Dataset\n",
"- Some computation\n",
"- Filtering and Masking values\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Context\n",
"\n",
"Yesterday we explored the data structures that `xarray` uses to organize data. Today we are going to use those datastructres to manipulate data!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
"\n",
"The dataset of the day today is NOAA OISST. It is a sea surface temperature dataset that goes back to the 1980s.\n",
"\n",
"- [NOAA NCEI Data listing](https://www.ncei.noaa.gov/products/optimum-interpolation-sst)\n",
"- [THREDDS Catalog](https://www.ncei.noaa.gov/thredds/catalog/OisstBase/NetCDF/V2.1/AVHRR/198210/catalog.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Different from yesterday, where we downloaded a copy of the dataset locally, we will access this data by URL. That means that we won't be downloading it directly. Instead of giving a filepath on our local computer, we are giving and URL from what is called a THREDDS Catalog, and xarray is able to read that."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002
cdm_data_type :
Grid
history :
Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.
date_modified :
2020-05-08T19:05:13Z
date_created :
2020-05-08T19:05:13Z
product_version :
Version v02r01
processing_level :
NOAA Level 4
institution :
NOAA/National Centers for Environmental Information
creator_url :
https://www.ncei.noaa.gov/
creator_email :
oisst-help@noaa.gov
keywords :
Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature
keywords_vocabulary :
Global Change Master Directory (GCMD) Earth Science Keywords
platform :
Ships, buoys, Argo floats, MetOp-A, MetOp-B
platform_vocabulary :
Global Change Master Directory (GCMD) Platform Keywords
Global Change Master Directory (GCMD) Instrument Keywords
standard_name_vocabulary :
CF Standard Name Table (v40, 25 January 2017)
geospatial_lat_min :
-90.0
geospatial_lat_max :
90.0
geospatial_lon_min :
0.0
geospatial_lon_max :
360.0
geospatial_lat_units :
degrees_north
geospatial_lat_resolution :
0.25
geospatial_lon_units :
degrees_east
geospatial_lon_resolution :
0.25
time_coverage_start :
1982-10-07T00:00:00Z
time_coverage_end :
1982-10-07T23:59:59Z
metadata_link :
https://doi.org/10.25921/RE9P-PT57
ncei_template_version :
NCEI_NetCDF_Grid_Template_v2.0
comment :
Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.
sensor :
Thermometer, AVHRR
Conventions :
CF-1.6, ACDD-1.3
references :
Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST and Navy AVHRR SST. Ice data: NCEP Ice and GSFC Ice.
DODS_EXTRA.Unlimited_Dimension :
time
"
],
"text/plain": [
"\n",
"Dimensions: (lat: 720, lon: 1440, time: 1, zlev: 1)\n",
"Coordinates:\n",
" * lat (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
" * lon (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
" * time (time) datetime64[ns] 1982-10-07T12:00:00\n",
" * zlev (zlev) float32 0.0\n",
"Data variables:\n",
" anom (time, zlev, lat, lon) float32 ...\n",
" err (time, zlev, lat, lon) float32 ...\n",
" ice (time, zlev, lat, lon) float32 ...\n",
" sst (time, zlev, lat, lon) float32 ...\n",
"Attributes: (12/38)\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Inter...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pa...\n",
" id: oisst-avhrr-v02r01.19821007.nc\n",
" naming_authority: gov.noaa.ncei\n",
" summary: NOAAs 1/4-degree Daily Optimum Interpola...\n",
" cdm_data_type: Grid\n",
" ... ...\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetC...\n",
" sensor: Thermometer, AVHRR\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" references: Reynolds, et al.(2007) Daily High-Resolu...\n",
" DODS_EXTRA.Unlimited_Dimension: time"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sst = xr.open_dataset(\"https://www.ncei.noaa.gov/thredds/dodsC/OisstBase/NetCDF/V2.1/AVHRR/198210/oisst-avhrr-v02r01.19821007.nc\")\n",
"\n",
"sst"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{admonition} 📝 Check your understanding\n",
":class: tip\n",
"\n",
"What type of data structure is the `sst` object? What are the dimensions, how big is each one, and how many variables are there?\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we go on with our examples for the day, I'm going to do a bit of pre-processing on this data. I'm going to 1) take just the `sst` DataArray (`sst['sst']`), 2) get rid of the vertical depth dimension, `zlev`, since there is just 1 level for sst (the surface)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"sst = sst['sst'].squeeze(dim='zlev', drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some Computation\n",
"\n",
"### Arithmetic"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"sst_kelvin = sst + 273"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{admonition} 🌀 More Info: Broadcasting during arithmetic\n",
":class: note, dropdown\n",
"\n",
"The reason that this works is because numpy (and therefore xarray) uses a technique called **broadcasting**. You can read more about it [here](https://xarray-contrib.github.io/xarray-tutorial/scipy-tutorial/03_computation_with_xarray.html#Broadcasting)\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"### Aggregations\n",
"\n",
"There are a lot (a lot) of built in **methods** that manipulate data. Some common ones are:\n",
"\n",
"| Function | Description |\n",
"| ----------- | ----------- |\n",
"| `.max()` | Maximum |\n",
"| `.min()` | Minimum |\n",
"| `.std()` | Standard deviation |\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
"\n",
"array(-1.8, dtype=float32)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sst.min()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In an earlier lesson we talked about functions/methods as verbs and attributes as adjectives when describing a data object. The methods listed above are examples of these for xarray DataArray objects!\n",
"\n",
"There are a lot of methods for DataArrays. One list (of many on the internet) is [here](https://www.pythonprogramming.in/numpy-aggregate-and-statistical-functions.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{note}\n",
"One way that programming langauges grow is when people build new tools by starting with the tools that someone else already built. This accelerates progress!\n",
"\n",
"You'll notice that the link above lists functions that are part of the `numpy` library. `xarray` builds on top of `numpy`, so we can use resources that others have made for numpy to help us with xarray. While not every numpy function, a lot of the numpy functions are available for xarray.\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{admonition} 📝 Check your understanding\n",
":class: tip\n",
"\n",
"What is the mean value of the sst DataArray?\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reading documentation\n",
"\n",
"To practice looking at documentation, let's look at [the docs page](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.max.html) for the `xarray.DataArray.max()` method.\n",
"\n",
"We notice a few optional arguments - `dim` and `axis`. `dim` takes an integer as an argument and `axis` takes a string. Let's try them out."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
"\n",
"array([[25.96 , 25.99 , 26.029999, ..., 26.17 , 26.099998,\n",
" 26.029999]], dtype=float32)\n",
"Coordinates:\n",
" * lon (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
" * time (time) datetime64[ns] 1982-10-07T12:00:00"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sst.max(dim='lat')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that when we use these arguments instead of smushing the all the data together and taking the maximum value, we are taking the maximum value along a particular axis of data. Whatever axis we specify in the argument disappears after we take the maximum."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{note}\n",
"While we won't cover it here, you can use this paradigm of applying a function on a full dataset or along an axis almost indefinetly in xarray. Even if the function you want to apply isn't a built-in function (maybe it's an algorithm you wrote yourself!), you can apply it using `DataArray.reduce()`.\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{admonition} 📝 Check your understanding\n",
":class: tip\n",
"\n",
"Look at [the documentation page](https://xarray.pydata.org/en/v2022.03.0/generated/xarray.DataArray.std.html) for the `.std()` function in xarray and [the documentation page](https://numpy.org/doc/stable/reference/generated/numpy.std.html) for `.std()` in numpy.\n",
"\n",
"- What does the function do? (Use the numpy page)\n",
"- Name 1 argument to the function and describe what it does.\n",
"- What type of object does the function return?\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering or masking values\n",
"\n",
"Let's start by looking at how we use booleans with data arrays. We saw previously how we could take single values and compare them with comparisons."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# mask the values with ice or err set"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"7 < 10"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = 'hello'\n",
"x == 'hola'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can we make boolean comparisons with xarray data? Turns out we can! We can use the same comparisons (>, <, ==, >=, <=), and it compares every value in the DataArray."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# import numpy as np\n",
"\n",
"# np.printoptions(threshold=20)\n",
"# xr.set_options(display_expand_data=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
"\n",
"array([[[False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False],\n",
" ...,\n",
" [False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False]]])\n",
"Coordinates:\n",
" * lat (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
" * lon (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
" * time (time) datetime64[ns] 1982-10-07T12:00:00"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(sst > 15) & (sst < 20)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(sst > 15).isel(time=0).plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{admonition} 📝 Check your understanding\n",
":class: tip\n",
"\n",
"Create a plot that shows where sst is above 10 degrees.\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Masking values with `xr.where()`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another common kind of data manipulation is to want to give data cells new values based on their old values. For that we will use [`xr.where()`](https://xarray.pydata.org/en/stable/generated/xarray.where.html).\n",
"\n",
"`xr.where()` takes at least three arguments:\n",
"\n",
"> `xr.where(condition, true, false)`\n",
"\n",
"- `condition` should be any type of boolean statement like above that returns a bunch of True/False\n",
"- `true` is what xarray should put into any place that has a True value.\n",
"\n",
"Optionally, you can also add a third argument describing what would happen should happen to the places where the `condition` array is False.\n",
"\n",
"**Note** better to go with the method version of .where ? https://xarray.pydata.org/en/v2022.03.0/generated/xarray.Dataset.where.html \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we see that anywhere that `sst` is greater than 20, xarray will put the word \"warm\". All other places it will put the word \"cold\"."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"