tests run, converted to lambda.

docs/tutorials/introduction_to_casatools.md

@@ -198,8 +198,8 @@ ax.set_xlabel(r"$u$ [m]")
 ax.set_ylabel(r"$v$ [m]")
 ```
 
-### Convert baselines to units of kilolambda
-To convert the baselines to units of kilolambda, we need to know the observing frequency (or wavelength). For a spectral window with multiple channels (like this one), the baselines (if expressed in units of kilolambda) will be slightly different for each channel because the observing frequency is different for each channel. Here is one way to go about broadcasting the baselines to all channels and then converting them to units of kilolambda.
+### Convert baselines to units of lambda
+To convert the baselines to units of lambda, we need to know the observing frequency (or wavelength). For a spectral window with multiple channels (like this one), the baselines (if expressed in units of lambda) will be slightly different for each channel because the observing frequency is different for each channel. Here is one way to go about broadcasting the baselines to all channels and then converting them to units of lambda.
 
 ```{code-cell}
 # broadcast to the same shape as the data
@@ -220,11 +220,11 @@ uu = uu / wavelengths  # [lambda]
 vv = vv / wavelengths  # [lambda]
 ```
 
-Let's plot up the baseline coverage for the first channel of the cube (index `0`).
+Let's plot up the baseline coverage for the first channel of the cube (index `0`). For the plot only, we'll convert to units of k$\lambda$ to make it easier to read.
 
 ```{code-cell}
 fig, ax = plt.subplots(nrows=1, figsize=(3.5, 3.5))
-ax.scatter(uu[0], vv[0], s=1.5, rasterized=True, linewidths=0.0, c="k")
+ax.scatter(uu[0] * 1e-3, vv[0] * 1e-3, s=1.5, rasterized=True, linewidths=0.0, c="k")
 ax.set_xlabel(r"$u$ [k$\lambda$]")
 ax.set_ylabel(r"$v$ [k$\lambda$]")
 ```
@@ -422,7 +422,7 @@ mask = ~flag
 ```
 
 ```{code-cell}
-# convert uu and vv to kilolambda
+# convert uu and vv to lambda
 uu, vv, ww = uvw  # unpack into len nvis vectors
 # broadcast to the same shape as the data
 # stub to broadcast uu,vv, and weights to all channels

docs/tutorials/rescale_AS209_weights.md

@@ -558,4 +558,4 @@ for ID in SPECTRAL_WINDOW_ID:
     print(ID, "{:.2f}".format(sigma_rescale_factors[ID]))
 ```
 
-If you wanted to use these visibilities for forward modeling or [Regularized Maximum Likelihood (RML) imaging](https://mpol-dev.github.io/MPoL/), you will want to read and export the correctly flagged and rescaled visibilities and convert baselines to kilolambda.
+If you wanted to use these visibilities for forward modeling or [Regularized Maximum Likelihood (RML) imaging](https://mpol-dev.github.io/MPoL/), you will want to read and export the correctly flagged and rescaled visibilities and convert baselines to lambda.

src/visread/process.py

@@ -4,7 +4,7 @@
 
 def convert_baselines(baselines, freq):
     r"""
-    Convert baselines in meters to kilolambda. Assumes that baselines and freq will broadcast under division.
+    Convert baselines in meters to lambda. Assumes that baselines and freq will broadcast under division.
 
     Args:
         baselines (float or np.array): baselines in [m].

tests/test_process.py

@@ -6,12 +6,12 @@ def test_convert_baselines(data_dict):
     uu = data_dict["uu"]
     freq = data_dict["freq"][0]
 
-    uu_lambda = process.convert_baselines(uu, freq)
+    process.convert_baselines(uu, freq)
 
 
 def test_broadcast_and_convert(data_dict):
     uu = data_dict["uu"]
     vv = data_dict["vv"]
     freq = data_dict["freq"]
 
-    uu_lambda, vv_lambda = process.broadcast_and_convert_baselines(uu, vv, freq)
+    process.broadcast_and_convert_baselines(uu, vv, freq)