Using Geographic Transforms and Projections

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Geographic Transforms and Projections

Loading the GEOS data

For this analysis we'll be loading some global climate data into yt. A frontend does not exist for this dataset yet, so we'll load it in as a uniform grid with netcdf4.

In [1]:
import os
import re

import netCDF4 as nc4
import numpy as np

import yt
In [2]:
def get_data_path(arg):
    if os.path.exists(arg):
        return arg
        return os.path.join(yt.config.ytcfg.get("yt", "test_data_dir"), arg)
In [3]:
n = nc4.Dataset(get_data_path("geos/GEOS.fp.asm.inst3_3d_aer_Nv.20180822_0900.V01.nc4"))

Using the loaded data we'll fill arrays with the data dimensions and limits. We'll also rename vertical level to altitude to be clearer.

In [4]:
dims = []
sizes = []
bbox = []
ndims = len(n.dimensions)
for dim in n.dimensions.keys():
    size = n.variables[dim].size
    if size > 1:
        bbox.append([n.variables[dim][:].min(), n.variables[dim][:].max()])
dims.reverse()  # Fortran ordering
dims = [f.replace("vertical level", "altitude") for f in dims]
bbox = np.array(bbox)

We'll also load the data into a container dictionary and create a lookup for the short to the long names

In [5]:
w_regex = re.compile(r"([a-zA-Z]+)(.*)")

def regex_parser(s):
        return "**".join(filter(None,
    except AttributeError:
        return s
In [6]:
data = {}
names = {}
for field, d in n.variables.items():
    if d.ndim != ndims:
    units = n.variables[field].units
    units = " * ".join(map(regex_parser, units.split()))
    data[field] = (np.squeeze(d), str(units))
    names[field] = n.variables[field].long_name.replace("_", " ")

Now the data can be loaded with yt's load_uniform_grid function. We also need to say that the geometry is a geographic type. This will ensure that the axes created are matplotlib GeoAxes and that the transform functions are available to use for projections.

In [7]:
ds = yt.load_uniform_grid(data, sizes, 1.0, geometry=("geographic", dims), bbox=bbox)

Default projection with geographic geometry

Now that the data is loaded, we can plot it with a yt SlicePlot along the altitude. This will create a figure with latitude and longitude as the plot axes and the colormap will correspond to the air density. Because no projection type has been set, the geographic geometry type assumes that the data is of the PlateCarree form. The resulting figure will be a Mollweide plot.

In [8]:
p = yt.SlicePlot(ds, "altitude", "AIRDENS")

Note that this doesn't have a lot of contextual information. We can add annotations for the coastlines just as we would with matplotlib. Before the annotations are set, we need to call p._setup_plots to make the axes available for annotation.

In [9]:
p = yt.SlicePlot(ds, "altitude", "AIRDENS")