fixed tesscut search

src/lksearch/MASTSearch.py

@@ -106,7 +106,11 @@ def __init__(
 
         self.target = target
 
-        if isinstance(table, type(None)):
+        if (
+            isinstance(table, type(None))
+            and isinstance(obs_table, type(None))
+            and isinstance(prod_table, type(None))
+        ):
             self._searchtable_from_target(target)
             self.table = self._fix_table_times(self.table)
 
@@ -320,15 +324,15 @@ def _searchtable_from_table(
         # see if function was passed a joint table
         if isinstance(table, pd.DataFrame):
             self.table = table
-
         # If we don't have a name or a joint table,
         # check to see if tables were passed
+
         elif isinstance(obs_table, pd.DataFrame):
             # If we have an obs table and no name, use it
             self.obs_table = obs_table
             if isinstance(prod_table, type(None)):
                 # get the prod table if we don't have it
-                prod_table = self._search_products(self)
+                prod_table = self._search_prod()
             self.prod_table = prod_table
             self.table = self._join_tables()
         else:

src/lksearch/TESSSearch.py

@@ -8,6 +8,7 @@
 from astropy import units as u
 from astropy.coordinates import SkyCoord
 from astropy.time import Time
+from astroquery.mast import MastClass
 
 # from astropy.utils.decorators import deprecated
 from astroquery.mast import Tesscut
@@ -18,7 +19,7 @@
 
 from .MASTSearch import MASTSearch
 from . import conf, config, log
-from .utils import SearchDeprecationError
+from .utils import SearchDeprecationError, table_keys
 
 PREFER_CLOUD = conf.PREFER_CLOUD
 DOWNLOAD_CLOUD = conf.DOWNLOAD_CLOUD
@@ -88,6 +89,40 @@ def __init__(
             self.mission_search = ["TESS"]
         else:
             self.mission_search = ["TESS", "HLSP"]
+        if pipeline != None:
+            pipeline = np.atleast_1d(pipeline).tolist()
+
+        # Sending 'tesscut' as a pipeline options returns a SearchError to the mast search
+        # If only tesscut is desired, we can just do the search on its own.
+        add_tesscut = True
+        if isinstance(pipeline, list):
+            if "tesscut" in [p.lower() for p in pipeline]:
+                # If only tesscut is requested, we can skip the regular MAST search
+                if [p.lower() for p in pipeline] == ["tesscut"]:
+                    log.debug(f"Only performing TESScut search")
+                    if isinstance(target, SkyCoord):
+                        self.target_search_string = f"{target.ra.deg}, {target.dec.deg}"
+                        self.SkyCoord = target
+                    elif isinstance(target, tuple):
+                        self.target_search_string = f"{target[0]}, {target[1]}"
+                        self.SkyCoord = SkyCoord(*target, frame="icrs", unit="deg")
+                    elif isinstance(target, str):
+                        self.SkyCoord = MastClass().resolve_object(target)
+                        self.target_search_string = (
+                            f"{self.SkyCoord.ra.deg}, {self.SkyCoord.dec.deg}"
+                        )
+                    tcut = self._get_tesscut_info(sector_list=sector)
+                    # Make a df with the same keys as would be returned by the MAST search
+                    mask = [i not in tcut.keys() for i in table_keys]
+                    empty_df = pd.DataFrame(columns=table_keys[mask])
+                    table = pd.concat([empty_df, tcut], axis=1)
+                    add_tesscut = False
+                pipeline = np.delete(
+                    pipeline, [p.lower() for p in pipeline].index("tesscut")
+                )
+            else:
+                # If pipeline is a list without 'tesscut', don't add it.
+                add_tesscut = False
 
         super().__init__(
             target=target,
@@ -101,11 +136,11 @@ def __init__(
             sequence=sector,
         )
 
-        if table is None:
-            if ("TESScut" in np.atleast_1d(pipeline)) or (type(pipeline) is type(None)):
-                self._add_tesscut_products(sector)
-            self._add_TESS_mission_product()
-            self._sort_TESS()
+        if add_tesscut:
+            # Do not append tesscut products if pipeline = 'TESScut' (it's already been done!)
+            self._add_tesscut_products(sector)
+        self._add_TESS_mission_product()
+        self._sort_TESS()
 
     @property
     def HLSPs(self):
@@ -144,7 +179,7 @@ def _target_to_exact_name(self, target):
         return f"{target.group(2)}"
 
     def _add_TESS_mission_product(self):
-        """Determine whick products are HLSPs and which are mission products"""
+        """Determine which products are HLSPs and which are mission products"""
         mission_product = np.zeros(len(self.table), dtype=bool)
         mission_product[self.table["pipeline"] == "SPOC"] = True
         self.table["mission_product"] = mission_product

src/lksearch/utils.py

@@ -1,5 +1,6 @@
 import os
 import sys
+import numpy as np
 from functools import wraps
 
 from . import config
@@ -37,3 +38,70 @@ def wrapper(*args, **kwargs):
                 sys.stdout = old_out
 
     return wrapper
+
+
+# List of keys in a search result table
+table_keys = np.array(
+    [
+        "intentType",
+        "obs_collection_obs",
+        "provenance_name",
+        "instrument_name",
+        "project_obs",
+        "filters_obs",
+        "wavelength_region",
+        "target_name",
+        "target_classification",
+        "obs_id",
+        "s_ra",
+        "s_dec",
+        "dataproduct_type_obs",
+        "proposal_pi",
+        "calib_level_obs",
+        "t_min",
+        "t_max",
+        "exptime",
+        "em_min",
+        "em_max",
+        "obs_title",
+        "t_obs_release",
+        "proposal_id_obs",
+        "proposal_type",
+        "sequence_number",
+        "s_region",
+        "jpegURL",
+        "dataURL",
+        "dataRights_obs",
+        "mtFlag",
+        "srcDen",
+        "obsid",
+        "objID",
+        "objID1",
+        "distance",
+        "obsID",
+        "obs_collection",
+        "dataproduct_type",
+        "description",
+        "type",
+        "dataURI",
+        "productType",
+        "productGroupDescription",
+        "productSubGroupDescription",
+        "productDocumentationURL",
+        "project",
+        "prvversion",
+        "proposal_id",
+        "productFilename",
+        "size",
+        "parent_obsid",
+        "dataRights",
+        "calib_level_prod",
+        "filters_prod",
+        "pipeline",
+        "mission",
+        "year",
+        "start_time",
+        "end_time",
+        "targetid",
+    ]
+)

tests/test_missionsearch.py

@@ -440,9 +440,12 @@ def test_FFI_retrieval():
 def test_tesscut():
     """Can we find and download TESS tesscut tpfs"""
     results = TESSSearch("Kepler 16b", hlsp=False, sector=14)
+    results2 = TESSSearch("Kepler 16b", pipeline="TESScut", sector=14)
     assert len(results) == 12
     assert len(results.cubedata) == 2
-    manifest = results.cubedata[1].download()
+    assert len(results.tesscut) == 1
+    assert len(results.tesscut) == len(results2)
+    manifest = results.tesscut.download()
     assert len(manifest) == 1
 
 
@@ -548,9 +551,9 @@ def test_filter():
 def test_tess_clouduris():
     """regression test - do tesscut/nan's in dataURI column break cloud uri fetching"""
     toi = TESSSearch("TOI 1161", sector=14)
-    # 17 products should be returned
+    # 20 products should be returned
     assert len(toi.cloud_uris) == 20
-    # 5 of them should have cloud uris
+    # 6 of them should have cloud uris
     assert np.sum((toi.cloud_uris.values != None).astype(int)) == 6