Ok, so after my post a little while ago about converting contours to shapefiles, I spent a day adapting the approach for use in a in a dash-plotly app… and in general it works, and took a ~800 MB file down to a ~14 MB shapefile. But the rendering of the shapefile was still very slooooooow, and due to limitations of how data callbacks update a figure’s data in Dash, I couldn’t just render the background map once. So, after some thinking, I realized that if I “simply” calculated my own map projections, I could use the standard x-y plotting routines (which includes contours, as well as the faster scattergl method for plotting a large number of points).
But then I realized that I only vaguely know what map projections are: I knew different projections vary in how they portray the 3D earth on a 2D surface, but when you start using all the mapping libraries out there, you run into a lot of jargon really fast. So here’s a super basic primer on calculation map projections in Python that explains some of that jargon!
Also, this is the first post I decided to write as a Python notebook. The notebook is in my github learning_shapefiles repo, but github has been having some issues displaying notebooks lately, so you can view the full notebook here.
To start off, pyproj (link), is a python library based on the command-line library https://proj.org/ that provides a large number of algorithms related to map projections. So go install that (simple to do with pip or conda).
In order to use pyproj to project a lat/lon point onto a map projection, it’s helpful to know a few acronyms:
- CRS: coordinate reference system
- EPSG: a database of coordinate reference systems and transformations, different projections covering different regional or local (or global) domains are specified by the number following EPSG. For example, EPSG:4326 is the global reference ellipse used by modern day GPS.
- WGS84 : World Geodetic System, latest revision, a.k.a. WGS1984, same as EPSG:4326
- reference ellipse: an ellipse describing the surface of the Earth on which positions (like latitude and longitude) are defined. GPS points use a reference ellipse that approximates the Earth’s geoid (i.e., the gravitational equipotential surface that sea-level would follow if due to gravity alone, see wiki).
So now that’s out of the way…. to project a single lat/lon point with Pyproj, first import pyproj then initialize a projection (Proj) object:
from pyproj import Proj p=Proj(proj='hammer',ellps='WGS84')
When initializing the Proj (projection) object, you give it the reference ellipse that your lat/lon is defined on, ellps=‘WGS84’, and the projection you want to transform to, in this case a hammer projection. Once you’ve initialized Proj, you can use it to move from lat/lon to cartesian x,y:
lon=-120.5 lat=42.4 x,y=p(lon,lat)
This lon/lat point become (x,y)=(-9894366.0792,5203184.81636). These values in meters by default and are the cartesian x-y distance from the map center for your point.
The Proj object is flexible enough to take lists (and numpy arrays!), so you can project many points at once. It can also take any of the parameters defined for the PROJ projections as keyword arguments. For example, the Hammer projection, has an argument ‘+lon_0’ to set the 0-longitude for the view, and so in pyProj you can use ‘lon_0’ as a keyword argument when using the hammer projection:
p = Proj(proj='hammer',ellps='WGS84',lon_0=-50)
The rest of the notebook goes through how you could construct a grid of lat/lon lines and plot them for different projections. And at the end, I take a digital elevation model of the western US (which gives an elevation for lat/lon points), project the lat/lon points onto a hammer projection to get points of (elevation,x,y) and contour it using the standard matplotlib.pyplot.contourf to get the following:
In my particular application, I’ll now be able to project my data to x,y and then use the cartesian contour functions of dash-plotly! The main drawback of this approach is having to write all the routines for handling rotations and plotting lines on maps… but ultimately I think it will work well with Dash, and it will be nice to be able use scattergl with my map point data.