### Build documentation

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/development.rst

Install documentation dependencies and generate the HTML documentation locally.

```shell
cd docs
pip install -r requirements.txt
make html
```

--------------------------------

### Install dependencies and pre-commit hooks

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/development.rst

Install the package in editable mode and initialize pre-commit hooks for quality control.

```shell
pip install -e .
pre-commit install
```

--------------------------------

### Initialize environment and imports

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/grid-types/_thumbnails.ipynb

Setup necessary imports and define the directory for saving thumbnail images.

```python
from pathlib import Path

import cartopy.crs as ccrs
import earthkit.data as ekd
from earthkit.geo import regrid

import earthkit.plots as ekp

THUMBNAILS_DIR = Path("../../../_static/thumbnails/")
```

--------------------------------

### Initialize Data and Environment

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/_thumbnails.ipynb

Sets up the environment and loads sample GRIB data for use in subsequent plotting examples.

```python
from pathlib import Path

import earthkit.data as ekd

import earthkit.plots as ekp

THUMBNAILS_DIR = Path("../../../_static/thumbnails/")

data = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
pressure = data.sel({"parameter.variable": "msl"})
temperature = data.sel({"parameter.variable": "2t"})
```

--------------------------------

### Load sample datasets

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/03-api-layers.ipynb

Initializes sample data from GRIB and NetCDF sources for use in plotting examples.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

# ERA5 monthly averaged 2-metre temperature for December 1993
era5_2t = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")

# An ECMWF HRES forecast containing mean sea-level pressure and wind gusts
joachim = ekd.from_source(
    "url",
    "https://get.ecmwf.int/repository/test-data/metview/gallery/fc_msl_wg_joachim.grib",
).to_fieldlist()

# An ERA5 hourly 2m-temperature time series at a point location
era5_reading_timeseries = ekd.from_source("sample", "era5-reading-timeseries.nc").to_xarray()
```

--------------------------------

### Initialize Data and Environment

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/_thumbnails.ipynb

Setup imports, define the output directory, and load sample ERA5 GRIB data for resampling tasks.

```python
from pathlib import Path

import earthkit.data as ekd
import numpy as np
from earthkit.geo import regrid

from earthkit.plots.resample import Bilinear, NearestNeighbour, Regrid

THUMBNAILS_DIR = Path("../../../_static/thumbnails/")

data = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
pressure = data.sel({"parameter.variable": "msl"})
temperature = data.sel({"parameter.variable": "2t"})
```

--------------------------------

### Create a complete plot

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/01-introduction.ipynb

A full example demonstrating map initialization, data plotting, decorations, and display.

```python
chart = ekp.Map(domain="Europe")
chart.contourf(era5_2t, units="celsius", style="auto")
chart.title("ERA5 monthly averaged {variable_name} over {domain} - {time:%B %Y}")
chart.coastlines()
chart.borders()
chart.gridlines()
chart.legend(label="{variable_name} ({units})")
chart.show()
```

--------------------------------

### Load sample data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/grid-types/_thumbnails.ipynb

Load a sample GRIB2 file to be used for regridding examples.

```python
source = hp = ekd.from_source("sample", "H8_nested_t2.grib2")
```

--------------------------------

### Importing earthkit-plots

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/unstructured-grids.ipynb

Initial setup for the earthkit-plots library.

```python
import numpy as np
import earthkit.plots as ekp
```

--------------------------------

### Importing earthkit libraries

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/02-input-output-formats.ipynb

Initial setup required to access data loading and plotting functionality.

```python
import earthkit.data as ekd

import earthkit.plots as ekp
```

--------------------------------

### Install earthkit-plots via pip

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/README.md

Use this command to install the library from the Python Package Index.

```bash
pip install earthkit-plots
```

--------------------------------

### Mixing Functional and OO APIs

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/03-api-layers.ipynb

Combine functional shortcuts for quick setup with the OO API for detailed refinement.

```python
# Start with the functional API — one line, sensible defaults
chart = ekp.geo.contourf(era5_2t, domain="Europe", units="celsius", title=False, coastlines=False)

# Refine with the OO API — plain statements, full control
chart.coastlines(color="grey", linewidth=0.5)
chart.title("ERA5 temperature — December 1993", fontsize=14)
chart.show()
```

--------------------------------

### Initialize data for plotting

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/07-domains.ipynb

Load sample ERA5 data using earthkit-data to be used in subsequent plotting examples.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")
```

--------------------------------

### Comparing data source formats

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/source-types/source-numpy.ipynb

Demonstrates the consistency of plotting calls across different data sources, noting the extra setup required for numpy arrays.

```python
# numpy (extra setup required)
lats, lons = fl.geography.latlons()
data = nc.to_numpy().squeeze()
metadata = {"units": "K", "long_name": "2 metre temperature", ...}

# GRIB, netCDF, xarray — no extra setup
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")

# In every case, the plot call is the same:
chart = ekp.Map(domain="Europe")
chart.plot(data, units="celsius")  # (pass x=, y=, metadata= for numpy)
```

--------------------------------

### Check earthkit-plots installation path

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/test-unstructured.ipynb

Verifies the location of the installed earthkit-plots package.

```python
earthkit.plots.__file__
```

--------------------------------

### Initialize Data Sources

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/04-layouts.ipynb

Load sample GRIB data for temperature, pressure, and wind fields using earthkit-data.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

# ERA5 monthly averaged 2-metre temperature for December 1993
era5_2t = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")

# An ECMWF HRES forecast containing mean sea-level pressure and wind gusts
joachim = ekd.from_source(
    "url",
    "https://get.ecmwf.int/repository/test-data/metview/gallery/fc_msl_wg_joachim.grib",
).to_fieldlist()

# Storm Ophelia 850 hPa wind U and V components
wind = ekd.from_source("sample", "storm_ophelia_wind_850.grib").to_fieldlist()
wind.ls()
```

--------------------------------

### Initialize Data and Environment

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/contourf/_thumbnails.ipynb

Sets up the environment and loads sample ERA5 data for plotting.

```python
from pathlib import Path

import earthkit.data as ekd

import earthkit.plots as ekp

THUMBNAILS_DIR = Path("../../../_static/thumbnails/")

data = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
pressure = data.sel({"parameter.variable": "msl"})
```

--------------------------------

### Load sample data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/resampling-bilinear.ipynb

Initialize the earthkit data source and inspect the available fields.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

data = ekd.from_source("sample", "test.grib").to_fieldlist()
data.ls()
```

--------------------------------

### Generate Introduction Thumbnail

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/_thumbnails.ipynb

Creates a map visualization with contour and contourf layers, then saves it as a thumbnail.

```python
chart = ekp.Map(domain=["France", "Spain"], figsize=(2, 2))

chart.contourf(temperature, units="celsius", style="auto")
chart.contour(pressure, units="hPa", style="auto", linewidths=[0.4, 0.8])

chart.coastlines()
cbar = chart.legend(label="")
chart.title(fontsize=8, color="grey")

for t in cbar[0].ax.get_xticklabels():
    t.set_fontsize(8)
    t.set_color("grey")

chart.save(THUMBNAILS_DIR / "01-introduction.png", transparent=True)
```

--------------------------------

### Loading a GRIB file

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/source-types/source-grib.ipynb

Fetch a GRIB sample and convert it to a fieldlist for processing.

```python
grib = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib").to_fieldlist()
grib
```

--------------------------------

### Define date range for forecast

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/interactive-bar-plot.ipynb

Sets the start and end dates for the weather data request relative to the current time.

```python
from datetime import datetime, timedelta

today = datetime.now() + timedelta(days=-3)
tomorrow = today + timedelta(days=5)

today_str = today.strftime("%Y-%m-%d")
tomorrow_str = tomorrow.strftime("%Y-%m-%d")
```

--------------------------------

### Load sample data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/string-formatting-units.ipynb

Initialize the earthkit environment and load sample GRIB data for plotting.

```python
import earthkit.plots as ekp
import earthkit.data as ekd

data = ekd.from_source("sample", "storm_ophelia_wind_850.grib")
data.ls()
```

--------------------------------

### Import earthkit libraries

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/time-series/timeseries.ipynb

Initialize the environment by importing the necessary earthkit modules.

```python
import earthkit.plots as ekp
import earthkit.data as ekd
```

--------------------------------

### Apply unit formatting styles

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/string-formatting-units.ipynb

Demonstrate various unit formatting options including default, exponential, fractional, stacked, and raw formats.

```python
plot_with_legend_label()
```

```python
plot_with_legend_label("{variable_name} ({units:~E})")
```

```python
plot_with_legend_label("{variable_name} ({units:~F})")
```

```python
plot_with_legend_label("{variable_name} ({units:~L})")
```

```python
plot_with_legend_label("{variable_name} ({units:L})")
```

```python
plot_with_legend_label("{variable_name} ({units:R})")
```

```python
plot_with_legend_label("{variable_name} ({units:L})", units="km h-1")
```

--------------------------------

### Create a complete map visualization

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/01-introduction.ipynb

Demonstrates the full workflow from data loading to rendering a map with contours, decorations, and legends.

```python
# Import earthkit-data for accessing sample datasets
import earthkit.data as ekd

# Import earthkit-plots to visualise them
import earthkit.plots as ekp

# Download some sample data - ERA5 monthly averaged temperature
era5_2t = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")

# Create a map showing a sub-domain over Europe
chart = ekp.Map(domain="Europe")

# Do a filled contour plot, with units converted to celsius and an automatic style
chart.contourf(era5_2t, units="celsius", style="auto")

# Add a title with metadata formatting
chart.title("ERA5 monthly averaged {variable_name} over {domain} - {time:%B %Y}")

# Add map decorations
chart.coastlines()
chart.borders()
chart.gridlines()

# Add a legend
chart.legend(label="{variable_name} ({units})")

chart.show()
```

--------------------------------

### Importing required libraries

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/time-series/timeseries-climate-stripes.ipynb

Initializes the environment with earthkit packages and configures xarray to preserve CF metadata.

```python
import earthkit.data as ekd
import earthkit.plots as ekp
import earthkit.transforms as ekt
import xarray as xr

xr.set_options(keep_attrs=True)
```

--------------------------------

### Initialize data for contour plotting

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/contour/contour-linestyles.ipynb

Load sample GRIB data and filter for pressure and temperature fields.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

data = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
pressure = data.sel({"parameter.variable": "msl"})
temperature = data.sel({"parameter.variable": "2t"})
```

--------------------------------

### Load sample data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/01-introduction.ipynb

Accessing a remote GRIB file using earthkit-data.

```python
# Download some sample data - ERA5 monthly averaged temperature
era5_2t = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")
```

--------------------------------

### Initialize earthkit environment and styles

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/unstructured-data.ipynb

Imports necessary packages and defines a global style configuration for consistent plotting.

```python
import earthkit.data

import earthkit.plots

LEVELS = [i for i in range(390, 431, 4)]
STYLE = earthkit.plots.styles.Style(
    levels=LEVELS,
    extend="both",
    colors="Spectral_r",
)
```

```python
earthkit.plots.__file__
```

--------------------------------

### Load GRIB data with earthkit-data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/true-color-satellite.ipynb

Initializes the data source and lists the available fields in the GRIB file.

```python
import earthkit.data as ekd

data = ekd.from_source("sample", "rgb-sample.grib").to_fieldlist()

data.ls()
```

--------------------------------

### Initialize map canvas

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/01-introduction.ipynb

Creating a map object with a predefined domain.

```python
# Create a map showing a sub-domain over Europe
chart = ekp.Map(domain="Europe")
```

--------------------------------

### Initialize plotting environment

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/test-unstructured.ipynb

Imports necessary packages and defines a reusable Style object for consistent visualization.

```python
import earthkit.data

import earthkit.plots

LEVELS = [i for i in range(390, 431, 4)]
STYLE = earthkit.plots.styles.Style(
    levels=LEVELS,
    extend="both",
    colors="Spectral_r",
)
```

--------------------------------

### Loading data as a fieldlist

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/Untitled.ipynb

Load a GRIB file as a fieldlist and display its contents.

```python
test = ekd.from_source("sample", "extreme_2t_fc.grib").to_fieldlist()
```

```python
test.ls()
```

--------------------------------

### Load ERA5 temperature data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/05-styles.ipynb

Initialise the environment and load sample GRIB data for plotting.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

temperature, pressure = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
```

--------------------------------

### Create and activate conda environment

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/development.rst

Optional steps for setting up a dedicated conda environment for development.

```shell
conda create -n earthkit-plots python=3.12
conda activate earthkit-plots
```

--------------------------------

### Load sample data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/plotting-and-styling-isolines.ipynb

Imports necessary modules and loads ERA5 mean sea level pressure data.

```python
import earthkit.plots as ekp
import earthkit.data as ekd

temperature, pressure = ekd.from_source("sample", "era5-2t-msl-1985122512.grib")
pressure.ls()
```

--------------------------------

### Load sample forecast data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/string-formatting-time.ipynb

Retrieves sample GRIB data from a URL to demonstrate time-based plotting.

```python
import earthkit.plots as ekp
import earthkit.data as ekd

data = ekd.from_source(
    "url",
    "https://get.ecmwf.int/repository/test-data/metview/gallery/fc_msl_wg_joachim.grib",
)
data.ls()
```

--------------------------------

### Inspecting GRIB contents

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/source-types/source-grib.ipynb

Use the ls() method to display a summary of fields within the GRIB file.

```python
grib.ls()
```

--------------------------------

### Load sample data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/08-unit-conversion.ipynb

Load ERA5 monthly mean temperature data from the sample source.

```python
era5_2t = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")
```

--------------------------------

### Demonstrating format agnosticism

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/source-types/source-grib.ipynb

Compare how different data sources are loaded while maintaining identical plotting syntax.

```python
# GRIB
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")

# netCDF
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc")

# xarray
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc").to_xarray()

# In every case, the plot call is identical:
chart = ekp.Map(domain="Europe")
chart.plot(data, units="celsius")
```

--------------------------------

### Domain Selection and Preview Logic

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/domains-gallery.rst

Functions to populate domain dropdowns, swap map images based on selection, and copy generated Python code to the clipboard.

```javascript
   {name: "Africa", slug: "africa"},
   {name: "Antarctic", slug: "antarctic"},
   {name: "Arctic", slug: "arctic"},
   {name: "Asia", slug: "asia"},
   {name: "Central Europe", slug: "central_europe"},
   {name: "Contiguous United States", slug: "contiguous_united_states"},
   {name: "Europe", slug: "europe"},
   {name: "Global", slug: "global"},
   {name: "Mediterranean", slug: "mediterranean"},
   {name: "North America", slug: "north_america"},
   {name: "North Atlantic", slug: "north_atlantic"},
   {name: "Northeast Europe", slug: "northeast_europe"},
   {name: "Northwest Europe", slug: "northwest_europe"},
   {name: "Oceania", slug: "oceania"},
   {name: "South America", slug: "south_america"},
   {name: "Southeast Europe", slug: "southeast_europe"},
   {name: "Southwest Europe", slug: "southwest_europe"},
   {name: "Svalbard", slug: "svalbard"}
  ]
};
function ekPopulate(category) {
  var sel = document.getElementById('ek-domain-select');
  sel.innerHTML = '';
  EK_LISTS[category].forEach(function(item) {
    var opt = document.createElement('option');
    opt.value = item.name;
    opt.dataset.slug = item.slug;
    opt.textContent = item.name;
    sel.appendChild(opt);
  });
}
function ekDomainSwap(sel) {
  if (!sel || !sel.options.length) return;
  var slug = sel.options[sel.selectedIndex].dataset.slug;
  var name = sel.value;
  var img = document.getElementById('ek-domain-img');
  var err = document.getElementById('ek-domain-error');
  var code = document.getElementById('ek-domain-code');
  img.src = EK_DOMAIN_BASE + slug + '.png';
  img.alt = name;
  err.style.display = 'none';
  img.onerror = function() {
    err.textContent = 'Map preview unavailable for: ' + name;
    err.style.display = '';
  };
  var s = '<span class="ek-str">';
  var e = '</span>';
  var q = '<span class="ek-eq">=</span>';
  var esc = name.replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/"/g,'&quot;');
  code.innerHTML = 'import earthkit.plots as ekp\n\nchart ' + q + ' ekp.Map(domain' + q + s + '"' + esc + '"' + e + ')\nchart.standard_layers()\nchart.title(' + s + '"{domain} | {crs}"' + e + ')\nchart.show()';
}
function ekCopyCode() {
  var text = document.getElementById('ek-domain-code').textContent;
  navigator.clipboard.writeText(text).then(function() {
    var btn = document.getElementById('ek-domain-copy');
    btn.src = '_static/check.svg';
    btn.alt = 'Copied';
    setTimeout(function() { btn.src = '_static/copy.svg'; btn.alt = 'Copy'; }, 1500);
  });
}
function ekCatSwap(catSel) {
  ekPopulate(catSel.value);
  ekDomainSwap(document.getElementById('ek-domain-select'));
}
// Initialise on page load.
(function() {
  ekPopulate('countries');
  ekDomainSwap(document.getElementById('ek-domain-select'));
})();
```

--------------------------------

### Fetching satellite data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/misc/rgb-composite.ipynb

Retrieving a fieldlist from a sample GRIB file.

```python
data = ekd.from_source("sample", "rgb-sample.grib").to_fieldlist()
```

--------------------------------

### Load and Inspect Weather Data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/weather-forecast-steps.ipynb

Initializes the data source from a URL and displays the fieldlist structure.

```python
import earthkit.data as ekd
import earthkit.plots as ekp

import matplotlib.pyplot as plt

data = ekd.from_source(
    "url",
    "https://get.ecmwf.int/repository/test-data/metview/gallery/fc_msl_wg_joachim.grib",
).to_fieldlist()
data.ls()
```

--------------------------------

### Load sample wind data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/plotting-and-styling-vector-data.ipynb

Initializes the earthkit-plots and data modules to load GRIB data for Storm Ophelia.

```python
import earthkit.plots as ekp
import earthkit.data as ekd

data = ekd.from_source("sample", "storm_ophelia_wind_850.grib")
data.ls()
```

```python
data.sel(short_name="v").ls()
```

--------------------------------

### Download Blue Marble Image

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/ancillary/blue-marble.ipynb

Uses wget to fetch the source image file from NASA's Earth Observatory.

```bash
!wget -O blue-marble.jpg https://eoimages.gsfc.nasa.gov/images/imagerecords/74000/74167/world.200410.3x5400x2700.jpg
```

--------------------------------

### Load GRIB dataset

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/03-formats.ipynb

Load a sample ERA5 GRIB file using earthkit-data.

```python
import earthkit.data as ekd

era5_2t_grib = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")
```

--------------------------------

### Loading data as a fieldlist

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/domains/domain-and-crs-from-data.ipynb

Load a sample NetCDF file and display its contents as a fieldlist.

```python
data = ekd.from_source("sample", "efas.nc").to_fieldlist()
data.ls()
```

--------------------------------

### Load GRIB forecast data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/contour/contour-spaghetti.ipynb

Initializes the data sources for high-resolution and ensemble forecasts from the earthkit sample archive.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

ds_fc = ekd.from_source("sample", "fc_storm_st_jude.grib")  # hi-res forecast
ds_en = ekd.from_source("sample", "ens_storm_st_jude.grib")  # ensemble forecast
```

--------------------------------

### Run unit and image tests

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/development.rst

Commands to execute the core test suite, image-based regression tests, or generate new baseline images.

```shell
make unit-tests
```

```shell
make image-tests
```

```shell
make generate-test-images
```

--------------------------------

### Create a basic quiver plot

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/vector-data/basic-quiver.ipynb

Initializes a map domain and overlays wind data from a GRIB file using the quiver method.

```python
import earthkit.data as ekd
import earthkit.plots as ekp

data = ekd.from_source("sample", "storm_ophelia_wind_850.grib")

chart = ekp.Map(domain=[-20, 5, 40, 60])

chart.quiver(data)

chart.coastlines()
chart.land()
chart.gridlines()

chart.show()
```

--------------------------------

### Importing required libraries

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/time-series/timeseries-global-temperature.ipynb

Initial imports for data handling and plotting.

```python
import earthkit.data as ekd
import pandas as pd

import earthkit.plots as ekp
```

--------------------------------

### Load ERA5 data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/grid-types/grid-types-regular-ll.ipynb

Initializes the data source and extracts the temperature field from a GRIB file.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

data = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
temperature = data[0]
```

--------------------------------

### Handling missing metadata for regridding

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/regridding.ipynb

Demonstrates how to manually define grid information for regridding when the dataset metadata is incomplete.

```python
data = ekd.from_source("sample", "healpix-h128-ring-2t.grib")
ds = data.to_xarray()
ds.t.attrs.pop("_earthkit")
ds
```

```python
from earthkit.plots.resample import Regrid

chart = ekp.Map(domain=["France", "Spain"])

chart.contourf(ds, resample=Regrid(in_grid={"grid": "H128", "order": "ring"}))

chart.legend()

chart.coastlines()
chart.borders()

chart.show()
```

--------------------------------

### Render the chart

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/interactive-windrose-plot.ipynb

Displays the final configured chart as a PNG image.

```python
chart.show(renderer="png", width=2000, height=400)
```

--------------------------------

### Plotting xarray dataset with earthkit-plots

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/Untitled.ipynb

Load a GRIB file as an xarray dataset and visualize a specific member using quickplot.

```python
import earthkit.data as ekd
import earthkit.plots as ekp
ds_fc = ekd.from_source("sample", "extreme_2t_fc.grib").to_xarray()
ekp.quickplot(ds_fc["2t"].isel(member=1))
```

--------------------------------

### Plotting temperature and pressure fields

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/temperature-and-pressure.ipynb

Unpacks a GRIB file into temperature and pressure fields and plots them on a map with automatic styling.

```python
import earthkit.data as ekd
import earthkit.plots as ekp

temperature, pressure = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()

chart = ekp.Map(domain="Europe")

chart.plot(temperature, units="celsius")
chart.plot(pressure, units="hPa")

chart.legend(location="right")

chart.coastlines()

chart.title()
chart.gridlines()

chart.show()
```

--------------------------------

### Loading data as an xarray dataset

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/domains/domain-and-crs-from-data.ipynb

Load a sample NetCDF file and convert it into an xarray Dataset.

```python
ds = ekd.from_source("sample", "efas.nc").to_xarray()
ds
```

--------------------------------

### Generate Point Cloud Thumbnail

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/points-and-cells/_thumbnails.ipynb

Renders a point cloud visualization and saves it as a transparent PNG.

```python
chart = ekp.Map(domain="Spain", figsize=(2, 2))
chart.point_cloud(data, s=10, style=style)
chart.ax.set_frame_on(False)

chart.save(THUMBNAILS_DIR / "point-cloud.png", transparent=True)
```

--------------------------------

### Overlaying pressure isobars on filled temperature

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/contourf/contourf-contour-overlay.ipynb

Creates a synoptic chart by layering filled temperature contours with black pressure isobars.

```python
t2m_style = ekp.styles.Style(
    units="celsius",
    colors="RdYlBu_r",
    levels={"step": 4, "reference": 0},
)

msl_style = ekp.styles.Contour(
    units="hPa",
    levels={"step": 4, "reference": 1000},
    colors="black",
    linewidths=[0.6, 0.6, 0.6, 0.6, 1.2],
    labels=True,
)

chart = ekp.Map(domain="Europe")
chart.contourf(temperature, style=t2m_style)
chart.contour(pressure, style=msl_style)
chart.legend()
chart.land()
chart.borders()
chart.coastlines()
chart.title()
chart.show()
```

--------------------------------

### Generate Unstructured Sample Data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/_thumbnails.ipynb

Create random point cloud data for testing resampling visualizations.

```python
import earthkit.plots

# Example data (unstructured points)
points = np.random.rand(60, 2) * 10  # 500 random points
values = np.sin(points[:, 0]) + np.cos(points[:, 1])  # Some sample data

x = points[:, 0]
y = points[:, 1]
```

--------------------------------

### Applying reusable styles and metadata

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/misc/choropleth.ipynb

Control color levels, colormaps, and metadata for units and labels using Style objects and dictionaries.

```python
style = ekp.styles.Style(
    levels=range(-10, 11),
    cmap="RdBu_r",
    units="celsius",
)

ekp.geo.choropleth(
    nuts_mean_2t,
    domain="Europe",
    z="2t",
    labels="{2t:.1f}{units}",
    metadata={"units": "K", "long_name": "2m temperature"},
    style=style,
).show()
```

--------------------------------

### Comparing point_cloud and grid_cells

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/points-and-cells/point-cloud.ipynb

Demonstrates the visual difference between rendering cell centroids as points versus shading the full area of grid cells.

```python
figure = ekp.Figure(rows=1, columns=2, domain=["France", "Spain"])

ax = figure.add_map()
ax.point_cloud(data, style=style, s=10)
ax.title("point_cloud")

ax = figure.add_map()
ax.grid_cells(data, style=style)
ax.title("grid_cells")

figure.coastlines()
figure.borders()
figure.legend(location="right")

figure.show()
```

--------------------------------

### Prepare Numpy Data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/numpy-arrays.ipynb

Initialize random data and coordinate arrays for plotting.

```python
import numpy as np

import earthkit.plots

data = np.random.rand(15, 30)

y = np.linspace(-90, 90, 15)
x = np.linspace(-180, 180, 30)
```

--------------------------------

### Load ORCA grid data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/grid-types/grid-types-orca.ipynb

Initializes the data source for the ORCA grid sea surface temperature field.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

data = ekd.from_source("sample", "orca.grib")
```

--------------------------------

### Visualizing individual channels and composite

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/misc/rgb-composite.ipynb

Creating a multi-panel figure to display individual color channels alongside the final RGB composite.

```python
import cartopy.crs as ccrs

COLORS = {
    "red": (1, 0, 0),
    "green": (0, 1, 0),
    "blue": (0, 0, 1),
}

figure = ekp.Figure(crs=ccrs.NearsidePerspective(), rows=2, columns=2)

for i, color in enumerate(COLORS):
    channel_plot = figure.add_map()
    channel_plot.grid_cells(data[i], colors=["black", COLORS[color]])
    channel_plot.title(f"{color} channel")

composite = figure.add_map()
composite.rgb_composite(data)
composite.title("RGB composite")

figure.title("RGB composite image at {time:%H:%M on %-d %B %Y}")
figure.show()
```

--------------------------------

### Generating sample unstructured data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/unstructured-grids.ipynb

Creates random points and associated values for demonstration.

```python
# Example data (unstructured points)
points = np.random.rand(500, 2) * 10  # 500 random points
values = np.sin(points[:, 0]) + np.cos(points[:, 1])  # Some sample data

x = points[:, 0]
y = points[:, 1]
```

--------------------------------

### Generate Input/Output Formats Thumbnail

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/_thumbnails.ipynb

Creates a multi-panel figure demonstrating support for various data formats.

```python
fig = ekp.Figure(rows=2, columns=2, figsize=(2, 2), domain=["France", "Spain"])

for i, fmt in enumerate(["GRIB", "netCDF", "xarray", "NumPy"]):
    fig.add_map().contourf(temperature, units="celsius", style="auto").title(
        fmt, fontsize=10, fontweight="bold", color="grey"
    )

fig.coastlines()

# fig.show()
fig.save(THUMBNAILS_DIR / "02-input-output-formats.png", transparent=True)
```

--------------------------------

### Compare output resolutions

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/resampling-regrid.ipynb

Shows how the resolution parameter affects the output grid spacing, with finer resolutions providing more detail at the cost of computation time.

```python
style = ekp.styles.Style(
    levels=range(240, 310, 5),
    colors="Spectral_r",
)

figure = ekp.Figure(rows=1, columns=2, domain="Europe")

ax = figure.add_map()
ax.pcolormesh(data, resample=Regrid(resolution=2.0), style=style)
ax.title("resolution=2.0°")

ax = figure.add_map()
ax.pcolormesh(data, resample=Regrid(resolution=0.25), style=style)
ax.title("resolution=0.25°")

figure.coastlines()
figure.legend()

figure.show()
```

--------------------------------

### Loading and plotting GRIB data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/02-input-output-formats.ipynb

Load GRIB data using earthkit-data and visualize it with a map chart.

```python
grib = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")
grib
```

```python
chart = ekp.Map(domain="Europe")
chart.contourf(grib, units="celsius", style="auto")
chart.coastlines()
chart.legend(label="{variable_name} ({units})")
chart.title("{variable_name} – {time:%B %Y}")
chart.show()
```

--------------------------------

### Generate RGB Composite Thumbnail

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/misc/_thumbnails.ipynb

Creates a map visualization using an RGB composite and saves it as a transparent PNG.

```python
chart = ekp.Map(
    crs=ccrs.NearsidePerspective(central_longitude=10, central_latitude=10),
    figsize=(2, 2),
)
chart.rgb_composite(data)
chart.save(THUMBNAILS_DIR / "rgb-composite.png", transparent=True)
```

--------------------------------

### Visualize Gridded Data with earthkit-plots

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/point-data/non-contiguous-gridded-data.ipynb

This snippet initializes a map, adds contour layers for temperature and pressure, and displays the final plot with standard map features.

```python
chart = ekp.Map(domain=countries)
chart.contourf(ds["2t"], units="celsius")
chart.contour(ds["msl"], units="hPa", style="auto")
chart.coastlines()
chart.borders()
chart.gridlines()
chart.legend()
chart.title()
chart.show()
```

--------------------------------

### Generate Domain Thumbnail

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/_thumbnails.ipynb

Configures a map with a NearsidePerspective projection and custom gridlines for a specific domain view.

```python
import cartopy.crs as ccrs

chart = ekp.Map(
    crs=ccrs.NearsidePerspective(central_latitude=52, central_longitude=10),
    figsize=(2, 2),
)

chart.ocean(color="white")
chart.land(transform_first=False)
chart.coastlines(color="lightgrey")
chart.borders(linewidth=0.2, color="lightgrey")

chart.gridlines(draw_labels=False, ylim=70, xstep=10, ystep=10)
chart.gridlines(draw_labels=False, ylim=80, xstep=30, ystep=10)

chart.save(THUMBNAILS_DIR / "06-domains.png", transparent=True)
```

--------------------------------

### Load reduced Gaussian grid data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/grid-types/grid-types-reduced-gg.ipynb

Load an O32 reduced Gaussian grid GRIB file from a remote source.

```python
import earthkit.data as ekd

import earthkit.plots as ekp

data = ekd.from_source(
    "url",
    "https://get.ecmwf.int/repository/test-data/earthkit-regrid/test-data/global_0_360/O32.grib",
)
```

--------------------------------

### Visualize Resampling Methods

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/_thumbnails.ipynb

Generate map visualizations comparing Bilinear and NearestNeighbour resampling for point cloud data.

```python
chart = earthkit.plots.Map(figsize=(2, 2))

chart.point_cloud(x=x, y=y, z=values)
chart.pcolormesh(x=x, y=y, z=values, style=style, resample=Bilinear())
chart.scatter(x=x, marker="X", y=y, edgecolors="black", z=values, c=values, style=style)
chart.ax.set_frame_on(False)
chart.ax.set_extent([2, 8, 2, 8])
chart.save(THUMBNAILS_DIR / "resampling-bilinear.png", transparent=True)
```

```python
chart = earthkit.plots.Map(figsize=(2, 2))

chart.point_cloud(x=x, y=y, z=values)
chart.pcolormesh(x=x, y=y, z=values, style=style, resample=NearestNeighbour())
chart.scatter(x=x, marker="X", y=y, edgecolors="black", z=values, c=values, style=style)
chart.ax.set_frame_on(False)
chart.ax.set_extent([2, 8, 2, 8])
chart.save(THUMBNAILS_DIR / "resampling-nearest-neighbour.png", transparent=True)
```

--------------------------------

### Loading and preparing CSV data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/time-series/timeseries-global-temperature.ipynb

Downloads the global temperature CSV from the C3S portal and loads it into a pandas DataFrame.

```python
C3S_RED = "#941333"

csv_file = "era5_daily_series_2t_global.csv"
data = ekd.from_source(
    "url",
    "https://sites.ecmwf.int/data/climatepulse/data/series/era5_daily_series_2t_global.csv",
)
data.to_target("file", csv_file)
df = pd.read_csv(csv_file, comment="#", index_col="date", parse_dates=True).round(2)
df.head()
```

--------------------------------

### Visualize El Niño data with subplots

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/el-nino.ipynb

Uses earthkit-data to load GRIB files and earthkit-plots to create a multi-row map visualization with custom styles and legends.

```python
import earthkit.data as ekd
import earthkit.plots as ekp

YEARS = {
    1993: "Normal conditions",
    1997: "El Niño",
    1998: "La Niña",
}

DOMAIN = [100, 300, -20, 20]

# data = ekd.from_source(
#     "cds",
#     "reanalysis-era5-single-levels-monthly-means",
#     {
#         "product_type": "monthly_averaged_reanalysis",
#         "variable": "sea_surface_temperature",
#         "year": list(YEARS),
#         "month": "12",
#         "time": "00:00",
#         "area": [20, 100, -20, -60],
#         "grid": [0.25, 0.25],
#     },
# ).to_fieldlist()

data = ekd.from_source("sample", "era5-el-nino.grib").to_fieldlist()

style = ekp.styles.Style(
    colors="Spectral_r",
    levels=range(15, 33),
    units="celsius",
    ticks=range(15, 33),
)

figure = ekp.Figure(rows=3, columns=1, figsize=(8, 7.5))

for i, year in enumerate(YEARS):
    subplot = figure.add_map(domain=DOMAIN)
    subplot.contourf(data[i], style=style)
    subplot.title(f"{YEARS[year]} - ERA5 {short_name!u} monthly mean: {time:%B %Y}")

figure.land(color="#555", zorder=2)
figure.gridlines(xstep=20, ystep=10, sharex=True)

figure.title("{variable_name} in {time:%B} of {time:%Y}", fontsize=14)

figure.legend(label="{variable_name!l} ({units})")

figure.show()
```

--------------------------------

### Create and visualize a union of EU countries

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/domains/domain-union.ipynb

Uses a list of country names to define a union domain and applies it to an earthkit-plots Map object.

```python
import earthkit.plots as ekp
from earthkit.plots.geography import domains

EU_COUNTRIES = [
    "Austria",
    "Belgium",
    "Bulgaria",
    "Croatia",
    "Cyprus",
    "Czech Republic",
    "Denmark",
    "Estonia",
    "Finland",
    "France",
    "Germany",
    "Greece",
    "Hungary",
    "Ireland",
    "Italy",
    "Latvia",
    "Lithuania",
    "Luxembourg",
    "Netherlands",
    "Poland",
    "Portugal",
    "Romania",
    "Slovakia",
    "Slovenia",
    "Spain",
    "Sweden",
]

EU = domains.union(EU_COUNTRIES, name="European Union")

# domain=EU_COUNTRIES is also valid but would lose the name metadata
eu_map = ekp.Map(domain=EU)

eu_map.countries(edgecolor="white")

# We can reuse the same list of EU countries to show a subset of country polygons
eu_map.countries(include=EU_COUNTRIES, facecolor="cornflowerblue", edgecolor="white", labels=True)

eu_map.gridlines()
eu_map.title("{domain}")

# You can pass an earthkit-plots Style directly to the legend method
eu_map.legend(
    ekp.styles.Categorical(
        categories=["Non-EU countries", "EU countries"],
        colors=["lightgrey", "cornflowerblue"],
    )
)

eu_map.show()
```

--------------------------------

### Loading and plotting NetCDF data

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/02-input-output-formats.ipynb

Load NetCDF data and visualize it using the same plotting interface as GRIB.

```python
nc = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc")
nc
```

```python
chart = ekp.Map(domain="Europe")
chart.contourf(nc, units="celsius", style="auto")
chart.coastlines()
chart.legend(label="{variable_name} ({units})")
chart.title("{variable_name} – {time:%B %Y}")
chart.show()
```

--------------------------------

### Configuring categorical style

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/lccs.ipynb

Define levels and colors for the land cover classification categories.

```python
levels, colors = zip(*[
    [0, "#000000"],
    [10, "#ffff64"],
    [20, "#aaf0f0"],
    [30, "#dcf064"],
    [40, "#c8c864"],
    [50, "#006400"],
    [60, "#00a000"],
    [70, "#003c00"],
    [80, "#285000"],
    [90, "#788200"],
    [100, "#8ca000"],
    [110, "#be9600"],
    [120, "#966400"],
    [130, "#ffb432"],
    [140, "#ffdcd2"],
    [150, "#ffebaf"],
    [160, "#00785a"],
    [170, "#009678"],
    [180, "#00dc82"],
    [190, "#c31400"],
    [200, "#fff5d7"],
    [210, "#0046c8"],
    [220, "#ffffff"],
])

style = ekp.styles.Categorical(
    levels=levels,
    colors=colors,
    legend_style=None,
)
```

--------------------------------

### Create Basic Contourf Map

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/contourf/_thumbnails.ipynb

Generates a basic contourf map with custom colors and saves it as a thumbnail.

```python
chart = ekp.Map(domain=[-18, -10.1, 45, 50], figsize=(2, 2))
chart.contourf(pressure, units="hPa", levels={"step": 4}, colors=colors)
chart.ax.set_frame_on(False)

chart.save(THUMBNAILS_DIR / "contourf-introduction.png", transparent=True)
```

--------------------------------

### Subsample Visualization

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/resampling/_thumbnails.ipynb

Visualize subsampled data using pcolormesh and scatter plots.

```python
chart = earthkit.plots.Map(domain=[-18, -10.1, 45, 50], figsize=(2, 2))

chart.pcolormesh(t4, units="celsius", levels=np.arange(8.4, 12.4, 0.2), cmap="Reds")
chart.scatter(
    t2,
    marker="X",
    edgecolors="black",
    cmap="Reds",
    units="celsius",
    levels=np.arange(8.4, 12.4, 0.2),
)
# chart.legend()
chart.ax.set_frame_on(False)
chart.save(THUMBNAILS_DIR / "resampling-subsample.png", transparent=True)
```

--------------------------------

### Chaining map plotting methods

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/01-introduction.ipynb

Demonstrates chaining multiple plotting methods to customize a map with specific domains, units, and overlays.

```python
(
    ekp.geo
    .plot(era5_2t, units="celsius", domain="Australia")
    .administrative_areas(color="brown")
    .gridlines(xstep=10, ystep=10)
    .cities(adjust_labels=True)
)
```

--------------------------------

### Inspecting Functional API Results

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/03-api-layers.ipynb

Demonstrates that functional shortcuts return the same underlying Map and Figure objects as the OO API.

```python
result = ekp.geo.plot(era5_2t, domain="Europe", units="celsius")

print(type(result))  # Map — single panels are unwrapped from the Figure automatically
print(type(result.figure))  # Figure
print(result.layers)  # the Layer objects produced by the contourf call
```

--------------------------------

### Load NetCDF dataset

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/examples/introduction/03-formats.ipynb

Load a sample ERA5 NetCDF file using earthkit-data.

```python
era5_2t_nc = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc")
```

--------------------------------

### HEALPix data download progress

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/grid-types/healpix-point-cloud.ipynb

Displays the download progress output for the sample GRIB file.

```text
healpix-h128-nested-2t.grib:   0%|          | 0.00/385k [00:00<?, ?B/s]
```

--------------------------------

### Plotting vector data with custom styles

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/vector-data/vector-styles.ipynb

Uses earthkit.data to load GRIB files and earthkit.plots to define map domains, vector styles, and annotations.

```python
import earthkit.data as ekd
import earthkit.plots as ekp

data = ekd.from_source("sample", "storm_ophelia_wind_850.grib")

chart = ekp.Map(domain=[-20, 5, 40, 60])

style = ekp.styles.Style(
    colors="plasma_r",
    levels=range(0, 22, 2),
    units="m s-1",
)

chart.quiver(data, style=style)
chart.legend(label="wind speed ({units})", location="right")

chart.land()
chart.coastlines()
chart.gridlines()

chart.title("Storm Ophelia - {level} hPa wind speed and direction\n{time:%H:%M UTC on %-d %B %Y}")

chart.show()
```

--------------------------------

### GRIB file download progress

Source: https://github.com/ecmwf/earthkit-plots/blob/develop/docs/source/examples/gallery/gridded-data/temperature-and-pressure.ipynb

Displays the progress of the GRIB file download.

```text
Result:
era5-2t-msl-1985122512.grib:   0%|          | 0.00/3.96M [00:00<?, ?B/s]
```