fixed for new lightkurve units

src/tess_sip/tess_sip.py

@@ -69,14 +69,14 @@ def SIP(tpfs, sigma=5, min_period=10, max_period=100, nperiods=300):
     """
 
     # Get the un-background subtracted data
-    tpfs_uncorr = [(tpf + np.nan_to_num(tpf.flux_bkg))[np.isfinite(tpf.flux_bkg.sum(axis=(1, 2)))] for tpf in tpfs]
+    tpfs_uncorr = [(tpf + np.nan_to_num(tpf.flux_bkg.value))[np.isfinite(tpf.flux_bkg.value.sum(axis=(1, 2)))] for tpf in tpfs]
     apers = [tpf.pipeline_mask for tpf in tpfs_uncorr]
     lc = lk.LightCurveCollection([tpf.to_lightcurve(aperture_mask=aper) for tpf, aper in zip(tpfs_uncorr, apers)]).stitch()
-    lc.flux_err[~np.isfinite(lc.flux_err)] = np.nanmedian(lc.flux_err)
+    lc.flux_err.value[~np.isfinite(lc.flux_err.value)] = np.nanmedian(lc.flux_err.value)
 
     with warnings.catch_warnings():
         warnings.simplefilter('ignore')
-        bkgs = [lk.DesignMatrix(tpf.flux[:, ~aper], name='bkg').pca(3).append_constant().to_sparse() for tpf, aper in zip(tpfs_uncorr, apers)]
+        bkgs = [lk.DesignMatrix(tpf.flux.value[:, ~aper], name='bkg').pca(3).append_constant().to_sparse() for tpf, aper in zip(tpfs_uncorr, apers)]
         for bkg in bkgs:
             bkg.prior_mu[-1] = 1
             bkg.prior_sigma[-1] = 0.1
@@ -85,17 +85,17 @@ def SIP(tpfs, sigma=5, min_period=10, max_period=100, nperiods=300):
             bkg.prior_sigma[:-1] = 0.1
 
     # Split at the datadownlink
-    bkgs = [bkg.split(list((np.where((np.diff(tpf.time) > 0.3))[0] + 1))) for bkg, tpf in zip(bkgs, tpfs_uncorr)]
+    bkgs = [bkg.split(list((np.where(np.diff(tpf.time.jd) > 0.3)[0] + 1))) for bkg, tpf in zip(bkgs, tpfs_uncorr)]
     systematics_dm = vstack(bkgs)
 
-    sigma_f_inv = sparse.csr_matrix(1/lc.flux_err[:, None]**2)
+    sigma_f_inv = sparse.csr_matrix(1/lc.flux_err.value[:, None]**2)
     def fit_model(mask=None, return_model=False):
         if mask is None:
-            mask = np.ones(len(lc.flux), bool)
+            mask = np.ones(len(lc.flux.value), bool)
         sigma_w_inv = dm.X[mask].T.dot(dm.X[mask].multiply(sigma_f_inv[mask])).toarray()
         sigma_w_inv += np.diag(1. / dm.prior_sigma**2)
 
-        B = dm.X[mask].T.dot((lc.flux[mask]/lc.flux_err[mask]**2))
+        B = dm.X[mask].T.dot((lc.flux.value[mask]/lc.flux_err.value[mask]**2))
         B += dm.prior_mu/dm.prior_sigma**2
         w = np.linalg.solve(sigma_w_inv, B)
         werr = ((np.linalg.inv(sigma_w_inv))**0.5).diagonal()
@@ -105,7 +105,7 @@ def fit_model(mask=None, return_model=False):
 
     # Make a dummy design matrix
     period = 27
-    ls_dm = lk.DesignMatrix(lombscargle.implementations.mle.design_matrix(lc.time, frequency=1/period, bias=False, nterms=1), name='LS').to_sparse()
+    ls_dm = lk.DesignMatrix(lombscargle.implementations.mle.design_matrix(lc.time.jd, frequency=1/period, bias=False, nterms=1), name='LS').to_sparse()
     dm = lk.SparseDesignMatrixCollection([systematics_dm, ls_dm]).to_designmatrix(name='design_matrix')
 
     # Do a first pass at 50 days, just to find ridiculous outliers
@@ -118,12 +118,12 @@ def fit_model(mask=None, return_model=False):
     ws_err = np.zeros((len(periods), dm.X.shape[1]))
 
     for idx, period in enumerate(tqdm(periods)):
-        dm.X[:, -ls_dm.shape[1]:] = lombscargle.implementations.mle.design_matrix(lc.time, frequency=1/period, bias=False, nterms=1)
+        dm.X[:, -ls_dm.shape[1]:] = lombscargle.implementations.mle.design_matrix(lc.time.jd, frequency=1/period, bias=False, nterms=1)
         ws[idx], ws_err[idx] = fit_model(mask=mask)
     power = (ws[:, -2]**2 + ws[:, -1]**2)**0.5
 
     am = np.argmax(power)
-    dm.X[:, -ls_dm.shape[1]:] = lombscargle.implementations.mle.design_matrix(lc.time, frequency=1/periods[am], bias=False, nterms=1)
+    dm.X[:, -ls_dm.shape[1]:] = lombscargle.implementations.mle.design_matrix(lc.time.jd, frequency=1/periods[am], bias=False, nterms=1)
     mod = dm.X[:, :-2].dot(ws[am][:-2])