This post is a quick note on how to convert to and from UTM coordinates using pyproj.

As the end of the year approaches my TODO list gets bigger and bigger and my posts get smaller and smaller... Well, this one is small but I find it useful. I always need to check how to do this every I need to convert to/from UTM coordinates.

`pyproj`

is used under the hood of several python packages like basemap,
cartopy, pyresample. So, if you have any of one those above installed, you
probably already have `pyproj`

as well.

```
from pyproj import Proj
```

Here is am example from real life case that I had to deal recently. Someone gave me the corners of a map in UTM but the sampling site positions as lon, lat collected in WGS84. Here are the data:

```
import numpy as np
from pandas import DataFrame
core = (-44.6360, -23.2278)
print('\nSampling site lon %s, lat %s\n' % (core[0], core[1]))
x = (529025.00, 529114.00, 545227.00, 545582.00)
y = (7422210.00, 7422343.00, 7435702.00, 7435741.00)
df = DataFrame(np.c_[x, y], columns=['Meters East', 'Meters South'])
df
```

All that is needed to solve that problem is a `pyproj`

object for Zone 23K
(near Paraty-Brazil) using `Datum WGS-84`

. Note that we need to specify that
we are in the South Hemisphere.

```
myProj = Proj("+proj=utm +zone=23K, +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
```

The next cell uses the object created (`myProj`

) to convert the UTM positions
to longitude and latitude. That is why we need the *inverse* keyword.

```
lon, lat = myProj(df['Meters East'].values, df['Meters South'].values, inverse=True)
```

To convert back just from comparison:

```
UTMx, UTMy = myProj(lon, lat)
```

And now a quick table to show the results in the notebook,

```
DataFrame(np.c_[UTMx, UTMy, lon, lat], columns=['UTMx', 'UTMy', 'Lon', 'Lat'])
```

Now I can make a map to show the sampling site.

```
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import pyproj
from mpl_toolkits.basemap import Basemap
fig, ax = plt.subplots(figsize=(6, 6))
m = Basemap(projection='merc',
urcrnrlat=lat[1], llcrnrlat=lat[2],
urcrnrlon=lon[1], llcrnrlon=lon[2],
resolution='f',
suppress_ticks=False,
ax=ax)
m.fillcontinents()
m.drawcoastlines()
m.ax = ax
pt = m.plot(core[0], core[1], 'ko', latlon=True)
lons, lats, xs, ys = m.makegrid(200, 200, returnxy=True)
gc = pyproj.Geod(a=m.rmajor, b=m.rminor)
distances = np.zeros(lons.size)
for k, (lo, la) in enumerate(zip(lons.flatten(), lats.flatten())):
_, _, distances[k] = gc.inv(core[0], core[1], lo, la)
distances = distances.reshape(200, 200) # In km.
# Plot perimeters of equal distance.
levels = [1000] # [50, 100, 150]
cs = m.contour(xs, ys, distances, levels, colors='r')
```

```
HTML(html)
```