Modified CDIPS output to present flux values and BTJD time

src/lightkurve/io/cdips.py

@@ -13,8 +13,8 @@
 log = logging.getLogger(__name__)
 
 def read_cdips_lightcurve(filename,
-                            flux_column="IRM1",
-                            include_inst_errs=False,
+                            flux_column="IFL1",
+                            include_inst_errs=True,
                             quality_bitmask=None):
     """Returns a TESS CDIPS `~lightkurve.lightcurve.LightCurve`.
 
@@ -25,6 +25,11 @@ def read_cdips_lightcurve(filename,
     is allowed. The `quality_bitmask` parameter is ignored but accepted for
     compatibility with other data format readers.
 
+    There are several kinds of flux and  magnitudes provided. For consistancy
+    we have chosen to display as default 'IFL1', the flux in aperture 1. 
+    This is given in ADU. 
+    The flux_err is also provided as 'IFE1' in ADU
+
     More information: https://archive.stsci.edu/hlsp/cdips
 
     Parameters
@@ -40,9 +45,10 @@ def read_cdips_lightcurve(filename,
     """
     ap = flux_column[-1]
 
-    # Only the instrumental magnitudes are provided, and are not provided for
-    # trend-filtered light curves. User should select whether to include the
-    # instrumental errors or ignore them
+    # A user can chose to dipsplay the magnitudes or any of the other flux values,
+    # They can do this by using the flux_column key
+    # By default the flux_err for the given flux specified is returned
+
     if include_inst_errs:
         # If fluxes are requested, return flux errors
         if flux_column[:-1].lower()=="ifl":
@@ -63,6 +69,11 @@ def read_cdips_lightcurve(filename,
                                  quality_column=quality_column,
                                  time_format='btjd')
 
+    #The time displayed is in BJD not BTJD. We can fix this by subtracting
+    # 2457000
+    lc["time"] = lc["time"]-2457000
+
+
     # Filter out poor-quality data
     # NOTE: Unfortunately Astropy Table masking does not yet work for columns
     # that are Quantity objects, so for now we remove poor-quality data instead
@@ -74,8 +85,19 @@ def read_cdips_lightcurve(filename,
     quality_mask = (lc['quality']=="G") | (lc['quality']=="0")
     lc = lc[quality_mask]
 
+
+    #This makes sure that the TIC ID is obtained and returned as the plot label
+    tic = lc.meta.get('TICID')
+    if tic is not None:
+        # compatibility with SPOC, QLP, etc.
+        lc.meta["TARGETID"] = tic
+        lc.meta["TICID"] = tic
+        lc.meta["OBJECT"] = f"TIC {tic}"
+        # for Lightkurve's plotting methods
+        lc.meta["LABEL"] = f"TIC {tic}"
+
+
     lc.meta["AUTHOR"] = "CDIPS"
-    lc.meta['TARGETID'] = lc.meta.get('TICID')
     lc.meta['QUALITY_BITMASK'] = 36
     lc.meta['QUALITY_MASK'] = quality_mask
 

tests/io/test_cdips.py

@@ -40,7 +40,8 @@ def test_read_cdips():
         assert type(lc).__name__ == "TessLightCurve"
         assert lc.meta["FLUX_ORIGIN"] == ext.lower()
         # Are `time` and `flux` consistent with the FITS file?
-        assert_array_equal(f[1].data['TMID_BJD'][lc.meta['QUALITY_MASK']],
+        # Removed 2457000 days from the TMID_BJD values 
+        assert_array_equal(f[1].data['TMID_BJD'][lc.meta['QUALITY_MASK']]-2457000,
                            lc.time.value)
         assert_array_equal(f[1].data[ext][lc.meta['QUALITY_MASK']],
                            lc.flux.value)