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Update use of tesswcs to decrease runtime #16

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Merged
jorgemarpa merged 3 commits into from
Jul 9, 2025
Merged

Update use of tesswcs to decrease runtime #16

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d645e57
Update call to get_pixel_locations
altuson Jul 9, 2025
363d64a
Sort time array
altuson Jul 9, 2025
7879a8b
Bump version numer
altuson Jul 9, 2025
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2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
[tool.poetry]
name = "tess-ephem"
version = "0.6.0"
version = "0.6.1"
description = "Where are Solar System objects located in TESS FFI data?"
license = "MIT"
authors = ["Geert Barentsen <hello@geert.io>",
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2 changes: 1 addition & 1 deletion src/tess_ephem/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,5 +6,5 @@

from .ephem import TessEphem, ephem # noqa: E402

__version__ = "0.6.0"
__version__ = "0.6.1"
__all__ = ["ephem", "TessEphem"]
32 changes: 20 additions & 12 deletions src/tess_ephem/ephem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,24 +144,32 @@ def predict(self, time: Time) -> DataFrame:

sky = self.predict_sky(time)
crd = SkyCoord(sky.ra, sky.dec, unit="deg")
log.info("Started matching the ephemeris to TESS observations")

# Get sector, camera, ccd, col, row at each time.
# Determine TESS sector corresponding to each time
sectors = []
for t in time.jd:
sectors.append(
pointings["Sector"].value[
np.logical_and(t > pointings["Start"], t < pointings["End"])
]
)

log.info("Started matching the ephemeris to TESS observations")
# Get sector, camera, ccd, col, row
df = DataFrame()
for i, t in enumerate(time):
try:
result = locate.get_pixel_locations(crd[i], time=t).to_pandas()
result["time"] = t
df = concat(
[df, result[["time", "Sector", "Camera", "CCD", "Column", "Row"]]]
)
# If object is not observed by TESS at time, skip time.
except ValueError:
continue
for s in np.unique(np.hstack(sectors)):
sector_mask = [s in sublist for sublist in sectors]
result = locate.get_pixel_locations(crd[sector_mask], sector=s).to_pandas()
result["time"] = time[sector_mask][result["Target Index"]]
df = concat(
[df, result[["time", "Sector", "Camera", "CCD", "Column", "Row"]]]
)
Comment on lines +164 to +166
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@jorgemarpa jorgemarpa Jul 9, 2025

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I'm not sure which is faster, if doing a concatenation each time in the for loop vs appending to a list and and then concatenating after the loop.

df = []
for ... in ...:
      ...
      ...
      df.append(result[...])
df = pd.concat(df, axis=0)

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@altuson altuson Jul 9, 2025

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I just tested this and the two methods take the same time to run, so I suggest we leave the code as is.


if len(df) == 0:
log.warning("Warning: Target not observed by TESS at defined times.")
else:
# Reorder df in ascending time
df = df.sort_values(by="time", ascending=True)
# Make column names lowercase in df
df.columns = [x.lower() for x in df.columns]
# Make sector, camera, ccd integers
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