How to show a TESS Observability Map

tesswcs is a tool to provide you with the World Coordinate System (WCS) for all past TESS observations, and predict the WCS of future observations. This notebook shows how to use tesswcs to plot how much of the sky has been observed by TESS by the end of Cycle 7 observations.

import tesswcs
from tesswcs.locate import get_observability_mask
import numpy as np
import matplotlib.pyplot as plt
from astropy.coordinates import SkyCoord
from tqdm import tqdm

Make sure you're using the most recent version, (1.1.7 or higher)

tesswcs.__version__

'1.1.7'

# All the TESS pointings
pointings = tesswcs.pointings[['RA', "Dec", "Roll"]].to_pandas().values

/Users/chedges/miniforge3/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
  from pandas.core import (

To plot observability we will need a grid of RAs and Decs over the sky to check. I've just done a uniform grid in RA and Dec, which is not ideal, you could switch this to a different method.

# Grid of RA and Dec to check
# 2000 points in RA
# 1201 points in Dec

# If you increase the resolution this will take longer to calculate.
RA, Dec = np.mgrid[:360:2000j, -90:90:1201j]

Below we loop through all the pointings, cameras, and CCDs and calculate which points in the RA and Dec grid fall on a camera. This can take a few minutes depending on the resolution of the grid.

# Array to accumulate number of observations
nobs = np.zeros(RA.shape, dtype=float)
# Loop through all the ra, dec and roll of the pointings
for ra, dec, roll in tqdm(pointings, desc='Pointing', leave=True, position=0):
    # Loop through each camera
    for camera in np.arange(1, 5):
        # Loop through each CCD
        for ccd in np.arange(1, 5):
            wcs = tesswcs.WCS.predict(ra, dec, roll, camera, ccd)    
            mask = get_observability_mask(wcs, SkyCoord(RA, Dec, unit='deg')).astype(int)
            nobs += mask

Pointing: 100%|█████████████████████████████████| 96/96 [04:03<00:00,  2.53s/it]

Now we have finished, nobs is the number of times TESS is able to observe that point in the sky. We can plot this map;

fig, ax = plt.subplots(dpi=250)
cmap = plt.get_cmap('viridis')
cmap.set_extremes(under='r')
im = ax.pcolormesh(RA, Dec, nobs, cmap=cmap, vmin=1, vmax=30, shading='nearest')
cbar = plt.colorbar(im, ax=ax)
ax.set(xlabel='RA [deg]', ylabel='Dec [dec]', title=f"TESS Sectors 1-{tesswcs.pointings['Sector'].max()}")
cbar.set_label("Number of Observations")

If we sum up the number of points that are not zero, we get the sky coverage.

print(100*(nobs!=0).sum()/np.prod(nobs.shape), "% of the sky observed by the end of Cycle 7")

98.94787676935887 % of the sky observed by the end of Cycle 7