Using the cartopy shapereader. More advanced mapping with cartopy and matplotlib. Cartopy has exposed an interface to enable easy map creation using matplotlib. Creating a basic map is as simple as telling matplotlib to use a specific map projection, and then adding some coastlines to the axes:.
A list of the available projections to be used with matplotlib can be found on the Cartopy projection list page. The line plt. PlateCarree sets up a GeoAxes instance which exposes a variety of other map related methods, in the case of the previous example, we used the coastlines method to add coastlines to the map. At this point, have a go at picking your own projection and creating a map with an image underlay with coastlines over the top. Once you have the map just the way you want it, data can be added to it in exactly the same way as with normal matplotlib axes.
By default, the coordinate system of any data added to a GeoAxes is the same as the coordinate system of the GeoAxes itself, to control which coordinate system that the given data is in, you can add the transform keyword with an appropriate cartopy.
CRS instance:. Notice how the line in blue between New York and Delhi is not straight on a flat PlateCarree map, this is because the Geodetic coordinate system is a truly spherical coordinate system, where a line between two points is defined as the shortest path between those points on the globe rather than 2d Cartesian space.
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By default, matplotlib automatically sets the limits of your Axes based on the data that you plot. Because cartopy implements a GeoAxes class, this equates to the limits of the resulting map. Sometimes this autoscaling is a desirable feature and other times it is not.
In the next sectionexamples of contouring, block plotting and adding geo-located images are provided for more advanced map based visualisations. Documentation licensed under the Open Government Licence. Quick search. Creating a basic map is as simple as telling matplotlib to use a specific map projection, and then adding some coastlines to the axes: import cartopy. PlateCarree ax. Mollweide ax. CRS instance: import cartopy. Geodeticplt. PlateCarreeplt.
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I'd like to know how to plot the coastline of Ireland in Basemap. I'd like to plot the whole of the UK using Basemap, however I cannot get the coastline of Ireland to plot. I've also tried map. Learn more. Plotting Ireland's coastline using Basemap in matplotlib Ask Question. Asked 4 years, 1 month ago. Active 2 years, 7 months ago. Viewed times. I'm sure Ireland has a coastline, as I've seen it in real life. Thanks in advance. That is strange.
Have you tried adjusting the boundaries of your map e. Obviously Ireland should be shown in your map, but perhaps something strange is happening during clipping.
It simply doesn't work for Active Oldest Votes. I know it's a bit late but you could try this import matplotlib. Bharathvaj Devarajan Bharathvaj Devarajan 29 6 6 bronze badges. As per the comments on the original question, it seems it can be plotted for almost any value for llcrnrlon apart from If I change this in your answer, it still doesn't work.
Sign up or log in Sign up using Google. Sign up using Facebook.This equation will change how you see the world (the logistic map)
Sign up using Email and Password.Requests like that are always troublesome! It is hard to find high resolution datasets for the coastline for South America. They do exist, but are rarely online and, if you find someone that owns such data they usually won't share it. The alternative are the global datasets. This post compare some of the available options. First let's define a plotting function:. Let's start with Google Maps tiles. They are not good for plotting overlays, but they are useful to convey a general idea of the area.
We would like to plot a Bay contour that looks like that, with all the major rivers and islands, but without all the clutter. Here we use Cartopy instead of Basemap because we are loading a custom cut version of the fine resolution database. Here is the commando to do so:. We would be better using this image.
While trying to find the data behind Google tiles we found out that Open Street Maps data are easily available for download. They have the coastlineswater, landand even Antarctic ice sheet polygons. Note that we purposely used white as the facecolor in the coastline example. The OSM coastline data have some open lines, and coloring the polygons would produce a wacky image. The land dataset looks OK though, and it is the most detailed we found so far!
So that data exists somewhere The sad part in this story is that we see high resolution coatline datasets used in several papers published, but unfortunately the data openness movement is still in its infancy in Brazil I already wrote about it here. If you go straight to the Global Administrative Areas data you can do things like this:.
Maps differ from regular figures in the following principle ways:. Mapping is a notoriously hard and complicated problem, mostly due to the complexities of projection.
In this lecture, we will learn about Cartopyone of the most common packages for making maps within python. Another popular and powerful library is Basemap ; however, Basemap is going away and being replaced with Cartopy in the near future. For this reason, new python learners are recommended to learn Cartopy.
Lots of the material in this lesson was adopted from Phil Elson 's excellent Cartopy Tutorial. Phil is the creator of Cartopy and published his tutorial under an open licensemeaning that we can copy, adapt, and redistribute it as long as we give proper attribution. There are many different ways to make a projection, and we will not attempt to explain all of the choices and tradeoffs here.
Instead, you can read Phil's original tutorial for a great overview of this topic. Instead, we will dive into the more practical sides of Caropy usage. Cartopy makes use of the powerful PROJ. Key features of cartopy are its object oriented projection definitions, and its ability to transform points, lines, vectors, polygons and images between those projections. In Cartopy, each projection is a class. Most classes of projection can be configured in projection-specific ways, although Cartopy takes an opinionated stance on sensible defaults.
To do so, we need cartopy's crs module. This is typically imported as ccrs Cartopy Coordinate Reference Systems. Cartopy's projection list tells us that the Plate Carree projection is available with the ccrs. PlateCarree class:.
Note: we need to instantiate the class in order to do anything projection-y with it!
Cartography and Mapping in Python
Cartopy optionally depends upon matplotlib, and each projection knows how to create a matplotlib Axes or AxesSubplot that can represent itself. The Axes that the projection creates is a cartopy. This Axes subclass overrides some of matplotlib's existing methods, and adds a number of extremely useful ones for drawing maps.
That was a little underwhelming, but we can see that the Axes created is indeed one of those GeoAxes[Subplot] instances.
High resolution coastline
One of the most useful methods that this class adds on top of the standard matplotlib Axes class is the coastlines method. With no arguments, it will add the Natural Earthscale coastline data to the map.This tutorial is accompanied by a Jupyter notebook, which can be found here.
The Python package ecosystem is robust, which means that usually there are several packages which all attempt to satisfy the same need. The landscape of mapping packages is no different. The bottom line is that Cartopy provides a very easy, cartographically accurate method for producing maps, and pairs well with other Python tools like geopandas. A central utility of the cartopy package is the ability to define, and transform data among, cartographic projections. The cartopy.
These projections augment the machinery of matplotlib to allow for geospatial plots. It has a convenient set of data loaders for adding context to maps like coastlines, borders, place names, etc. Typically, when creating a new plot in matplotlibwe employ a set of commands like so:. Spatial data is unique because plots containing it i.
For this reason, we employ cartopy to define the mapping between our data and our visualization. Knowing this, we only need to change two lines of our above code as follows:. As with many places, there are local specific projections which are commonly used in a particular area of interest. Check out line 2. Those are variables containing the EPSG codes as defined above. This tells Cartopy to perform its default behavior, which is to plot the entire extent of the projected space.
You can see that each projection is for a small portion of the planet northern and southern Washington, respectively. This illustrates an important point about projections: most of them are defined for a specific, discrete region, and therefore one should choose their projection carefully. However, some projections are better-suited to displaying small slices of the planet rather than the whole thing at once.
These cartographic details will be better covered elsewhere. A few simple modifications to matplotlib code namely the projection keyword can turn any matplotlib plot into a spatially-aware one. Toggle navigation Home. Teaching: 20 min Exercises: min. Questions What is cartopy? How can it help with visualization?We like to show the data, in general, for the whole region and one way of performing, so it to do the geospatial interpolation of the data.
Geospatial interpolation means merely that we obtain the interpolated values of the data at regular grid points, both longitudinally and latitudinally. We wish to plot the data inside the coastline borders of the area, which is our area of study. We can do that by just removing all the grid points outside the perimeter. One way to clip the data outside the coastline path is to manually remove the grid points outside the region, but this method is quite tedious.
We, programmers, love being lazy and that helps us to seek better ways. In this post, we aim to do 1 the interpolation of these data values using the ordinary kriging method and 2 plot the output within the coastline border of Taiwan.
The first step for interpolation is to read the available data. Our data is of the format shown in Figure 2. We first read this data file. Now, we have our required data available in the three variables. Now, we wish to plot the interpolated values. The constraints of the basemap object can be manually defined instead of the minimum and maximum of the latitude and longitude values as used.
This will give us the plot of the interpolated values Figure 3. Here, we do not seek the plot outside the coastline boundary of Taiwan. We wish to mask the data outside the boundary. You must log in to post a comment. Figure 1: Scatter plot of the data. The size and color of the circles represent the data values. Figure 2: Data in the tabular format. Figure 3: Interpolated values without masking the outer region. Figure 4: Interpolated values with the masking of the outer region.
Author Recent Posts. Follow me. Utpal Rai. Graduate Student at Institute of Earth Sciences. I explore the seismic and GPS data analysis methods to understand the crustal, mantle and core structure.
Seismic data gives the capability to inspect the interior of our planet whereas GPS has incredible accuracy for the crustal processes. Latest posts by Utpal Rai see all. Estimation of the degrees of freedom for time series - October 6, Fast and efficient computing in Python using generators - August 31, Exploratory Factor Analysis - August 18, This brief tutorial will look at the Basemap toolkit extension for matplotlib. Basemap allows you to create map plots in python. It extends matplotlib's functionality by adding geographical projections and some datasets for plotting coast lines and political boundaries, among other things.
Or use the Anaconda Navigator to install basemap. Click on the Environments panel, search for the basemap package then select and apply. For our tutorial, we will also be loading some data files that use the netCDF4 format, so you will also need to install netcdf4 if you don't have it already:.
Now let's create a map centered on North America with lines showing the country and state boundaries as well as rivers:. The examples below read in a NOAA climate model for average daily surface temperature in and make map plots of the first day's values:. Toggle navigation Research Computing in Earth Sciences. Schedule Syllabus Assignments Lectures Software. Basemap Tutorial This brief tutorial will look at the Basemap toolkit extension for matplotlib.
Let's see what Basemap has to offer:. Our first example will create a mercator projection plot of the continents. Now let's make a similar plot but with additiona Basemap options that make it look better:. Text 0. Now let's try the same sequence of commands but with a differnt projection:. Here's a how to add a great cirle path and text labels at two locations:.
This example will read in a recent WW3 model and plot up the significant wave height:. The indexing into the data set used by netCDF4 is standard python indexing. So we start with the second timestep Plot the field using Basemap. Dataset 'air.