diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 5a764c7f..0e1e1880 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -21,7 +21,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-20.04, ubuntu-22.04]
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/poetry.lock b/poetry.lock
index 2b3103ef..9922ec6a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,135 +1,149 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "aiobotocore"
-version = "2.12.3"
+version = "2.15.2"
 description = "Async client for aws services using botocore and aiohttp"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiobotocore-2.12.3-py3-none-any.whl", hash = "sha256:86737685f4625e8f05c4e7a608a07cc97607263279f66cf6b02b640c4eafd324"},
-    {file = "aiobotocore-2.12.3.tar.gz", hash = "sha256:e2a2929207bc5d62eb556106c2224c1fd106d5c65be2eb69f15cc8c34c44c236"},
+    {file = "aiobotocore-2.15.2-py3-none-any.whl", hash = "sha256:d4d3128b4b558e2b4c369bfa963b022d7e87303adb82eec623cec8aa77ae578a"},
+    {file = "aiobotocore-2.15.2.tar.gz", hash = "sha256:9ac1cfcaccccc80602968174aa032bf978abe36bd4e55e6781d6500909af1375"},
 ]
 
 [package.dependencies]
-aiohttp = ">=3.7.4.post0,<4.0.0"
+aiohttp = ">=3.9.2,<4.0.0"
 aioitertools = ">=0.5.1,<1.0.0"
-botocore = ">=1.34.41,<1.34.70"
+botocore = ">=1.35.16,<1.35.37"
 wrapt = ">=1.10.10,<2.0.0"
 
 [package.extras]
-awscli = ["awscli (>=1.32.41,<1.32.70)"]
-boto3 = ["boto3 (>=1.34.41,<1.34.70)"]
+awscli = ["awscli (>=1.34.16,<1.35.3)"]
+boto3 = ["boto3 (>=1.35.16,<1.35.37)"]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+]
 
 [[package]]
 name = "aiohttp"
-version = "3.9.5"
+version = "3.11.8"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aioitertools"
-version = "0.11.0"
+version = "0.12.0"
 description = "itertools and builtins for AsyncIO and mixed iterables"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "aioitertools-0.11.0-py3-none-any.whl", hash = "sha256:04b95e3dab25b449def24d7df809411c10e62aab0cbe31a50ca4e68748c43394"},
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+    {file = "aioitertools-0.12.0-py3-none-any.whl", hash = "sha256:fc1f5fac3d737354de8831cbba3eb04f79dd649d8f3afb4c5b114925e662a796"},
+    {file = "aioitertools-0.12.0.tar.gz", hash = "sha256:c2a9055b4fbb7705f561b9d86053e8af5d10cc845d22c32008c43490b2d8dd6b"},
 ]
 
-[package.dependencies]
-typing_extensions = {version = ">=4.0", markers = "python_version < \"3.10\""}
+[package.extras]
+dev = ["attribution (==1.8.0)", "black (==24.8.0)", "build (>=1.2)", "coverage (==7.6.1)", "flake8 (==7.1.1)", "flit (==3.9.0)", "mypy (==1.11.2)", "ufmt (==2.7.1)", "usort (==1.0.8.post1)"]
+docs = ["sphinx (==8.0.2)", "sphinx-mdinclude (==0.6.2)"]
 
 [[package]]
 name = "aiosignal"
@@ -192,6 +206,23 @@ matplotlib = ">=3.0"
 numpy = ">=1.10"
 scipy = ">=1.7"
 
+[[package]]
+name = "astlib"
+version = "0.12.0"
+description = "A set of python modules for producing simple plots, statistics, common calculations, coordinate conversions, and manipulating FITS images with World Coordinate System (WCS) information"
+optional = false
+python-versions = "*"
+files = [
+    {file = "astLib-0.12.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:ff04b8435be1f114cf251d51683a65aee311a161af7adb7a0dc51b22da2d28c5"},
+    {file = "astLib-0.12.0.tar.gz", hash = "sha256:c189dc08a52408ac7c1266778f702283d1ad33a6e96539293322b1515c9e4d23"},
+]
+
+[package.dependencies]
+astropy = ">=3.2"
+matplotlib = ">=3.0"
+numpy = ">=1.10"
+scipy = ">=1.7"
+
 [[package]]
 name = "astro-kittens"
 version = "1.4.6"
@@ -206,6 +237,24 @@ files = [
 astropy = "*"
 configparser = "*"
 
+[[package]]
+name = "astro-pyxis"
+version = "1.7.6"
+description = "Python Extensions for astronomical Interferometry Scripting"
+optional = false
+python-versions = ">=3.0.0"
+files = [
+    {file = "astro-pyxis-1.7.6.tar.gz", hash = "sha256:93879884b89f95708e9e59adcd3c3ddd695b51d7d29cdce37ea1876fca93b57d"},
+]
+
+[package.dependencies]
+astropy = ">=3.0"
+future = "*"
+matplotlib = "*"
+numpy = ">=1.16"
+python_casacore = "*"
+scipy = "*"
+
 [[package]]
 name = "astro-tigger-lsm"
 version = "1.7.3"
@@ -226,67 +275,89 @@ numpy = "*"
 python-casacore = "*"
 scipy = "*"
 
+[[package]]
+name = "astro-tigger-lsm"
+version = "1.7.4"
+description = "Python libraries and command-line tools for manipulating Tigger LSMs"
+optional = false
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@@ -295,43 +366,43 @@ test = ["hypothesis", "pytest", "pytest-remotedata"]
 
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 ]
 
 [package.dependencies]
@@ -347,134 +418,146 @@ xyzservices = ">=2021.09.1"
 
 [[package]]
 name = "botocore"
-version = "1.34.69"
+version = "1.35.36"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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 python-dateutil = ">=2.1,<3.0.0"
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-    {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""},
-]
+urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.19.19)"]
+crt = ["awscrt (==0.22.0)"]
 
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
 ]
 
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 [[package]]
@@ -509,47 +592,47 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudpickle"
-version = "3.0.0"
+version = "3.1.0"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
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 name = "codex-africanus"
-version = "0.3.6"
+version = "0.4.1"
 description = "Radio Astronomy Building Blocks"
 optional = false
-python-versions = ">=3.9"
+python-versions = "<4.0,>=3.10"
 files = [
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+scipy = {version = ">=1.14.1,<2.0.0", optional = true, markers = "extra == \"scipy\" or extra == \"complete\" or extra == \"complete-cuda\""}
 
 [package.extras]
-astropy = ["astropy (>=4.0)"]
-complete = ["astropy (>=4.0)", "dask[array] (>=2.2.0)", "ducc0 (>=0.9.0)", "flaky", "jax (>=0.2.11)", "jaxlib (>=0.1.65)", "pytest", "python-casacore (>=3.4.0,!=3.5.0)", "scipy (>=1.4.0)"]
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+testing = ["flaky (>=3.8.1,<4.0.0)", "pytest (>=8.0.0,<9.0.0)"]
 
 [[package]]
 name = "colorama"
@@ -579,92 +662,91 @@ wcwidth = "*"
 
 [[package]]
 name = "configparser"
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+version = "7.1.0"
 description = "Updated configparser from stdlib for earlier Pythons."
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 python-versions = ">=3.8"
 files = [
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-
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 [[package]]
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+version = "1.3.1"
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 [package.extras]
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 [[package]]
 name = "cycler"
@@ -683,59 +765,62 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
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-donfig = ">=0.7.0,<0.8.0"
-python-casacore = ">=3.5.1,<4.0.0"
+dask = {version = ">=2023.1.1,<2024.11.0", extras = ["array"]}
+donfig = ">=0.8.0"
+fsspec = ">=2022.7.0"
+numpy = ">=2.0.0"
+python-casacore = ">=3.6.1,<4.0.0"
 s3fs = {version = ">=2023.1.0", optional = true, markers = "extra == \"s3\" or extra == \"complete\""}
-xarray = {version = ">=2023.01.0", optional = true, markers = "extra == \"xarray\" or extra == \"complete\""}
-zarr = {version = ">=2.12.0,<3.0.0", optional = true, markers = "extra == \"zarr\" or extra == \"complete\""}
+xarray = {version = ">=2023.01.0", optional = true, markers = "extra == \"katdal\" or extra == \"xarray\" or extra == \"complete\""}
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 [package.extras]
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+arrow = ["pandas (>=2.1.2,<3.0.0)", "pyarrow (>=14.0.1)"]
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 s3 = ["s3fs (>=2023.1.0)"]
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 xarray = ["xarray (>=2023.01.0)"]
@@ -754,13 +839,13 @@ files = [
 
 [[package]]
 name = "dill"
-version = "0.3.8"
+version = "0.3.9"
 description = "serialize all of Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
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 ]
 
 [package.extras]
@@ -769,30 +854,30 @@ profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
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-version = "2024.4.2"
+version = "2024.10.0"
 description = "Distributed scheduler for Dask"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
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 [package.dependencies]
 click = ">=8.0"
-cloudpickle = ">=1.5.0"
-dask = "2024.4.2"
+cloudpickle = ">=3.0.0"
+dask = "2024.10.0"
 jinja2 = ">=2.10.3"
 locket = ">=1.0.0"
-msgpack = ">=1.0.0"
+msgpack = ">=1.0.2"
 packaging = ">=20.0"
-psutil = ">=5.7.2"
-pyyaml = ">=5.3.1"
+psutil = ">=5.8.0"
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-toolz = ">=0.10.0"
-tornado = ">=6.0.4"
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+toolz = ">=0.11.2"
+tornado = ">=6.2.0"
+urllib3 = ">=1.26.5"
 zict = ">=3.0.0"
 
 [[package]]
@@ -807,26 +892,31 @@ files = [
 
 [[package]]
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 description = "Python package for configuring a python package"
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 ]
 
 [package.dependencies]
 pyyaml = "*"
 
+[package.extras]
+docs = ["cloudpickle", "numpydoc", "pytest", "sphinx (>=4.0.0)"]
+test = ["cloudpickle", "pytest"]
+
 [[package]]
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 [package.extras]
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 ]
 
 [[package]]
 name = "fsspec"
-version = "2024.3.1"
+version = "2024.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1010,7 +1123,8 @@ abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
+dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -1027,6 +1141,9 @@ s3 = ["s3fs"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
 [[package]]
@@ -1042,51 +1159,40 @@ files = [
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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+    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
+    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "importlib-metadata"
-version = "7.1.0"
+version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
-
-[[package]]
-name = "importlib-resources"
-version = "6.4.0"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
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+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "iniconfig"
@@ -1101,13 +1207,13 @@ files = [
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -1127,147 +1233,167 @@ files = [
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 ]
 
+[[package]]
+name = "kittens"
+version = "0.1.2"
+description = "A means of sending friendly images of kittens to a target."
+optional = false
+python-versions = "*"
+files = [
+    {file = "kittens-0.1.2.tar.gz", hash = "sha256:648f3b6a14e3eddf994e9a3f4247646aa8e56831d77395fc5cb81cf2fcc6e0a7"},
+]
+
 [[package]]
 name = "kiwisolver"
-version = "1.4.5"
+version = "1.4.7"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.dependencies]
 contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
-importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21,<2"
+numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[package.extras]
+dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -1452,170 +1593,203 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "meqtrees-cattery"
+version = "1.7.9"
+description = "MeqTrees-based frameworks for simulation and calibration of radio interferometers"
+optional = false
+python-versions = ">=3.0.0"
+files = [
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+]
+
+[package.dependencies]
+astro_kittens = "*"
+astro_pyxis = "*"
+astropy = ">=3.0.0"
+numpy = ">=1.16"
+purr = "*"
+python_casacore = "*"
+scipy = "*"
+six = "*"
+
 [[package]]
 name = "msgpack"
-version = "1.0.8"
+version = "1.1.0"
 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
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+typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "munch"
 version = "2.5.0"
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 ]
 
 [package.dependencies]
 numpy = ">=1.7"
 
 [package.extras]
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 test-extras = ["importlib-metadata"]
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 [[package]]
 name = "numpy"
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 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
 numpy = [
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     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
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 [[package]]
 name = "partd"
-version = "1.4.1"
+version = "1.4.2"
 description = "Appendable key-value storage"
 optional = false
-python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
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 toolz = "*"
 
 [package.extras]
-complete = ["blosc", "numpy (>=1.9.0)", "pandas (>=0.19.0)", "pyzmq"]
+complete = ["blosc", "numpy (>=1.20.0)", "pandas (>=1.3)", "pyzmq"]
 
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 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
@@ -1968,6 +2170,113 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
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 [[package]]
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 description = "Python bindings for ERFA"
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 python-versions = ">=3.9"
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 [package.dependencies]
-numpy = ">=1.19"
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 [package.extras]
 docs = ["sphinx-astropy (>=1.3)"]
@@ -2077,28 +2410,27 @@ test = ["pytest", "pytest-doctestplus (>=0.7)"]
 
 [[package]]
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 description = "Pygments is a syntax highlighting package written in Python."
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 windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyparsing"
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+version = "3.2.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
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 [package.extras]
@@ -2106,13 +2438,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
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 ]
 
 [package.dependencies]
@@ -2120,26 +2452,26 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
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 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "python-casacore"
-version = "3.5.2"
+version = "3.6.1"
 description = "A wrapper around CASACORE, the radio astronomy library"
 optional = false
-python-versions = "*"
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 ]
 
 [package.dependencies]
@@ -2162,84 +2494,86 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2024.1"
+version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "pyyaml"
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 description = "YAML parser and emitter for Python"
 optional = false
-python-versions = ">=3.6"
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 ]
 
 [[package]]
 name = "requests"
-version = "2.31.0"
+version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2254,31 +2588,32 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rich"
-version = "13.7.1"
+version = "13.9.4"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
 markdown-it-py = ">=2.2.0"
 pygments = ">=2.13.0,<3.0.0"
+typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "ruamel-yaml"
-version = "0.17.40"
+version = "0.18.6"
 description = "ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order"
 optional = false
-python-versions = ">=3"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2290,78 +2625,69 @@ jinja2 = ["ruamel.yaml.jinja2 (>=0.2)"]
 
 [[package]]
 name = "ruamel-yaml-clib"
-version = "0.2.8"
+version = "0.2.12"
 description = "C version of reader, parser and emitter for ruamel.yaml derived from libyaml"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
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 aiohttp = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1"
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+fsspec = "==2024.10.0.*"
 
 [package.extras]
 awscli = ["aiobotocore[awscli] (>=2.5.4,<3.0.0)"]
@@ -2369,59 +2695,64 @@ boto3 = ["aiobotocore[boto3] (>=2.5.4,<3.0.0)"]
 
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 ]
 
 [package.dependencies]
-numpy = ">=1.22.4,<2.3"
+numpy = ">=1.23.5,<2.3"
 
 [package.extras]
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+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
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+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "six"
@@ -2447,13 +2778,13 @@ files = [
 
 [[package]]
 name = "stimela"
-version = "2.0rc18"
+version = "2.0"
 description = "Framework for system agnostic pipelines for (not just) radio interferometry"
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2511,13 +2842,43 @@ tomlkit = ">=0.11,<0.12"
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
+version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
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 files = [
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 ]
 
 [[package]]
@@ -2533,74 +2894,73 @@ files = [
 
 [[package]]
 name = "toolz"
-version = "0.12.1"
+version = "1.0.0"
 description = "List processing tools and functional utilities"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.4.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "typeguard"
-version = "4.2.1"
+version = "4.4.1"
 description = "Run-time type checker for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
-typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
+typing-extensions = ">=4.10.0"
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
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 test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "tzdata"
-version = "2024.1"
+version = "2024.2"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
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+    {file = "yarl-1.18.0-py3-none-any.whl", hash = "sha256:dbf53db46f7cf176ee01d8d98c39381440776fcda13779d269a8ba664f69bec0"},
+    {file = "yarl-1.18.0.tar.gz", hash = "sha256:20d95535e7d833889982bfe7cc321b7f63bf8879788fee982c76ae2b24cfb715"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [[package]]
 name = "zarr"
-version = "2.17.2"
+version = "2.18.3"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "zarr-2.17.2-py3-none-any.whl", hash = "sha256:70d7cc07c24280c380ef80644151d136b7503b0d83c9f214e8000ddc0f57f69b"},
-    {file = "zarr-2.17.2.tar.gz", hash = "sha256:2cbaa6cb4e342d45152d4a7a4b2013c337fcd3a8e7bc98253560180de60552ce"},
+    {file = "zarr-2.18.3-py3-none-any.whl", hash = "sha256:b1f7dfd2496f436745cdd4c7bcf8d3b4bc1dceef5fdd0d589c87130d842496dd"},
+    {file = "zarr-2.18.3.tar.gz", hash = "sha256:2580d8cb6dd84621771a10d31c4d777dca8a27706a1a89b29f42d2d37e2df5ce"},
 ]
 
 [package.dependencies]
 asciitree = "*"
 fasteners = {version = "*", markers = "sys_platform != \"emscripten\""}
 numcodecs = ">=0.10.0"
-numpy = ">=1.23"
+numpy = ">=1.24"
 
 [package.extras]
 docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-automodapi", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
@@ -2923,20 +3256,24 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.18.1"
+version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9, <3.12"
-content-hash = "1a2cfe37dde427340bb6fb64b2ef733cbca01f5a62e17ec8290fdfd4bf73439b"
+python-versions = ">=3.10, <3.13"
+content-hash = "01330072cbdca64849b10c233a22ff8f4aafd3ed1ea31d405c6a131030692103"
diff --git a/pyproject.toml b/pyproject.toml
index e3e25604..8d9ba6e5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,21 +24,24 @@ include = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.9, <3.12"
-astro-tigger-lsm = ">=1.7.2, <=1.7.3"
-codex-africanus = {extras = ["dask", "scipy", "astropy", "python-casacore"], version = ">=0.3.6, <=0.3.6"}
+python = ">=3.10, <3.13"
+astro-tigger-lsm = [
+    { version = ">=1.7.2, <=1.7.3", python = "<3.12" },
+    { version = ">=1.7.4, <=1.7.4", python = ">=3.12"}
+]
+codex-africanus = {extras = ["dask", "scipy", "astropy", "python-casacore"], version = ">=0.4.1, <=0.4.1"}
 colorama = ">=0.4.6, <=0.4.6"
 columnar = ">=1.4.1, <=1.4.1"
-dask = {extras = ["diagnostics"], version = ">=2023.5.0, <=2024.4.2"}
-dask-ms = {extras = ["s3", "xarray", "zarr"], version = ">=0.2.20, <=0.2.20"}
-distributed = ">=2023.5.0, <=2024.4.2"
+dask = {extras = ["diagnostics"], version = ">=2023.5.0, <=2024.10.0"}
+dask-ms = {extras = ["s3", "xarray", "zarr"], version = ">=0.2.23, <=0.2.23"}
+distributed = ">=2023.5.0, <=2024.10.0"
 loguru = ">=0.7.0, <=0.7.2"
-matplotlib = ">=3.5.1, <=3.8.2"
+matplotlib = ">=3.5.1, <=3.9.2"
 omegaconf = ">=2.3.0, <=2.3.0"
-pytest = ">=7.3.1, <=7.4.4"
-requests = ">=2.31.0, <=2.31.0"
-"ruamel.yaml" = ">=0.17.26, <=0.17.40"
-stimela = "^2.0rc17"  # Volatile - be less strict.
+pytest = ">=7.3.1, <=8.3.3"
+requests = ">=2.31.0, <=2.32.3"
+"ruamel.yaml" = ">=0.17.26, <=0.18.6"
+stimela = ">=2.0"
 tbump = ">=6.10.0, <=6.11.0"
 
 [tool.poetry.scripts]
diff --git a/quartical/apps/plotter.py b/quartical/apps/plotter.py
index 35f18977..45577001 100644
--- a/quartical/apps/plotter.py
+++ b/quartical/apps/plotter.py
@@ -193,6 +193,9 @@ def to_plot_dict(xdsl, iter_attrs):
 
 
 def _plot(group, xds, args):
+    # get rid of question marks
+    qstrip = lambda x: x.replace('?', 'N/A')
+    group = tuple(map(qstrip, group))
 
     xds = xds.compute(scheduler="single-threaded")
 
diff --git a/quartical/calibration/constructor.py b/quartical/calibration/constructor.py
index ec6b6412..34462fac 100644
--- a/quartical/calibration/constructor.py
+++ b/quartical/calibration/constructor.py
@@ -5,7 +5,7 @@
 
 
 term_spec_tup = namedtuple("term_spec_tup", "name type shape pshape")
-aux_info_fields = ("SCAN_NUMBER", "FIELD_ID", "DATA_DESC_ID")
+log_info_fields = ("SCAN_NUMBER", "FIELD_ID", "DATA_DESC_ID")
 
 
 def construct_solver(
@@ -54,9 +54,9 @@ def construct_solver(
         corr_mode = data_xds.sizes["corr"]
 
         block_id_arr = get_block_id_arr(data_col)
-        aux_block_info = {
-            k: data_xds.attrs.get(k, "?") for k in aux_info_fields
-        }
+        data_xds_meta = data_xds.attrs.copy()
+        for k in log_info_fields:
+            data_xds_meta[k] = data_xds_meta.get(k, "?")
 
         # Grab the number of input chunks - doing this on the data should be
         # safe.
@@ -87,7 +87,7 @@ def construct_solver(
         )
         blocker.add_input("term_spec_list", spec_list, ("row", "chan"))
         blocker.add_input("corr_mode", corr_mode)
-        blocker.add_input("aux_block_info", aux_block_info)
+        blocker.add_input("data_xds_meta", data_xds_meta)
         blocker.add_input("solver_opts", solver_opts)
         blocker.add_input("chain", chain)
 
diff --git a/quartical/calibration/solver.py b/quartical/calibration/solver.py
index 620874c3..ddf3452f 100644
--- a/quartical/calibration/solver.py
+++ b/quartical/calibration/solver.py
@@ -54,7 +54,7 @@ def solver_wrapper(
     solver_opts,
     chain,
     block_id_arr,
-    aux_block_info,
+    data_xds_meta,
     corr_mode,
     **kwargs
 ):
@@ -108,11 +108,11 @@ def solver_wrapper(
         # Perform term specific setup e.g. init gains and params.
         if term.is_parameterized:
             gains, gain_flags, params, param_flags = term.init_term(
-                term_spec, ref_ant, ms_kwargs, term_kwargs
+                term_spec, ref_ant, ms_kwargs, term_kwargs, meta=data_xds_meta
             )
         else:
             gains, gain_flags = term.init_term(
-                term_spec, ref_ant, ms_kwargs, term_kwargs
+                term_spec, ref_ant, ms_kwargs, term_kwargs, meta=data_xds_meta
             )
             # Dummy arrays with standard dtypes - aids compilation.
             params = np.empty(term_pshape, dtype=np.float64)
@@ -190,6 +190,7 @@ def solver_wrapper(
     for ind, (term, iters) in enumerate(zip(cycle(chain), iter_recipe)):
 
         active_term = chain.index(term)
+        active_spec = term_spec_list[term_ind]
 
         ms_fields = term.ms_inputs._fields
         ms_inputs = term.ms_inputs(
@@ -219,13 +220,17 @@ def solver_wrapper(
             term.solve_per
         )
 
-        jhj, conv_iter, conv_perc = term.solver(
-            ms_inputs,
-            mapping_inputs,
-            chain_inputs,
-            meta_inputs,
-            corr_mode
-        )
+        if term.solver:
+            jhj, conv_iter, conv_perc = term.solver(
+                ms_inputs,
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs,
+                corr_mode
+            )
+        else:
+            jhj = np.zeros(getattr(active_spec, "pshape", active_spec.shape))
+            conv_iter, conv_perc = 0, 1
 
         # If reweighting is enabled, do it when the epoch changes, except
         # for the final epoch - we don't reweight if we won't solve again.
@@ -269,7 +274,7 @@ def solver_wrapper(
         corr_mode
     )
 
-    log_chisq(presolve_chisq, postsolve_chisq, aux_block_info, block_id)
+    log_chisq(presolve_chisq, postsolve_chisq, data_xds_meta, block_id)
 
     results_dict["presolve_chisq"] = presolve_chisq
     results_dict["postsolve_chisq"] = postsolve_chisq
diff --git a/quartical/config/argument_schema.yaml b/quartical/config/argument_schema.yaml
index 26ca00c8..af3b369a 100644
--- a/quartical/config/argument_schema.yaml
+++ b/quartical/config/argument_schema.yaml
@@ -1,7 +1,8 @@
 input_ms:
   path:
     required: true
-    dtype: str
+    dtype: URI
+    writable: true
     info:
       Path to input measurement set.
 
@@ -164,7 +165,10 @@ input_model:
 output:
   gain_directory:
     default: gains.qc
-    dtype: str
+    dtype: URI
+    writable: true
+    must_exist: false
+    write_parent_dir: true
     info:
       Name of directory in which QuartiCal gain outputs will be stored.
       Accepts both local and s3 paths. QuartiCal will always produce gain
@@ -172,7 +176,9 @@ output:
 
   log_directory:
     default: logs.qc
-    dtype: str
+    dtype: Directory
+    writable: true
+    must_exist: false
     info:
       Name of directory in which QuartiCal logging outputs will be stored.
       s3 is not currently supported for these outputs.
diff --git a/quartical/config/gain_schema.yaml b/quartical/config/gain_schema.yaml
index 0a462bb8..266c23b7 100644
--- a/quartical/config/gain_schema.yaml
+++ b/quartical/config/gain_schema.yaml
@@ -8,12 +8,15 @@ gain:
       - amplitude
       - delay
       - delay_and_offset
+      - delay_and_tec
       - phase
       - tec_and_offset
       - rotation_measure
       - rotation
       - crosshand_phase
+      - crosshand_phase_null_v
       - leakage
+      - parallactic_angle
     info:
       Type of gain to solve for.
 
diff --git a/quartical/data_handling/angles.py b/quartical/data_handling/angles.py
index 62ca7c34..aa05b1be 100644
--- a/quartical/data_handling/angles.py
+++ b/quartical/data_handling/angles.py
@@ -19,6 +19,46 @@
 _thread_local = threading.local()
 
 
+def assign_parangle_data(ms_path, data_xds_list):
+
+    anttab = xds_from_storage_table(ms_path + "::ANTENNA")[0]
+    feedtab = xds_from_storage_table(ms_path + "::FEED")[0]
+    fieldtab = xds_from_storage_table(ms_path + "::FIELD")[0]
+
+    # We do the following eagerly to reduce graph complexity.
+    feeds = feedtab.POLARIZATION_TYPE.values
+    unique_feeds = np.unique(feeds)
+
+    if np.all([feed in "XxYy" for feed in unique_feeds]):
+        feed_type = "linear"
+    elif np.all([feed in "LlRr" for feed in unique_feeds]):
+        feed_type = "circular"
+    else:
+        raise ValueError("Unsupported feed type/configuration.")
+
+    phase_dirs = fieldtab.PHASE_DIR.values
+
+    updated_data_xds_list = []
+    for xds in data_xds_list:
+        xds = xds.assign(
+            {
+                "RECEPTOR_ANGLE": (
+                    ("ant", "feed"), clone(feedtab.RECEPTOR_ANGLE.data)
+                ),
+                "POSITION": (
+                    ("ant", "xyz"),
+                    clone(anttab.POSITION.data)
+                )
+            }
+        )
+        xds.attrs["FEED_TYPE"] = feed_type
+        xds.attrs["FIELD_CENTRE"] = tuple(phase_dirs[xds.FIELD_ID, 0])
+
+        updated_data_xds_list.append(xds)
+
+    return updated_data_xds_list
+
+
 def make_parangle_xds_list(ms_path, data_xds_list):
     """Create a list of xarray.Datasets containing the parallactic angles."""
 
@@ -266,7 +306,7 @@ def nb_apply_parangle_rot(data_col, parangles, utime_ind, ant1_col, ant2_col,
     v1_imul_v2 = factories.v1_imul_v2_factory(corr_mode)
     v1_imul_v2ct = factories.v1_imul_v2ct_factory(corr_mode)
     valloc = factories.valloc_factory(corr_mode)
-    rotmat = rotation_factory(corr_mode, feed_type)
+    rotmat = factories.rotation_factory(corr_mode, feed_type)
 
     def impl(data_col, parangles, utime_ind, ant1_col, ant2_col,
              corr_mode, feed_type):
@@ -299,43 +339,3 @@ def impl(data_col, parangles, utime_ind, ant1_col, ant2_col,
         return data_col
 
     return impl
-
-
-def rotation_factory(corr_mode, feed_type):
-
-    if feed_type.literal_value == "circular":
-        if corr_mode.literal_value == 4:
-            def impl(rot0, rot1, out):
-                out[0] = np.exp(-1j*rot0)
-                out[1] = 0
-                out[2] = 0
-                out[3] = np.exp(1j*rot1)
-        elif corr_mode.literal_value == 2:  # TODO: Is this sensible?
-            def impl(rot0, rot1, out):
-                out[0] = np.exp(-1j*rot0)
-                out[1] = np.exp(1j*rot1)
-        elif corr_mode.literal_value == 1:  # TODO: Is this sensible?
-            def impl(rot0, rot1, out):
-                out[0] = np.exp(-1j*rot0)
-        else:
-            raise ValueError("Unsupported number of correlations.")
-    elif feed_type.literal_value == "linear":
-        if corr_mode.literal_value == 4:
-            def impl(rot0, rot1, out):
-                out[0] = np.cos(rot0)
-                out[1] = np.sin(rot0)
-                out[2] = -np.sin(rot1)
-                out[3] = np.cos(rot1)
-        elif corr_mode.literal_value == 2:  # TODO: Is this sensible?
-            def impl(rot0, rot1, out):
-                out[0] = np.cos(rot0)
-                out[1] = np.cos(rot1)
-        elif corr_mode.literal_value == 1:  # TODO: Is this sensible?
-            def impl(rot0, rot1, out):
-                out[0] = np.cos(rot0)
-        else:
-            raise ValueError("Unsupported number of correlations.")
-    else:
-        raise ValueError("Unsupported feed type.")
-
-    return factories.qcjit(impl)
diff --git a/quartical/data_handling/ms_handler.py b/quartical/data_handling/ms_handler.py
index 3566deff..13920fcb 100644
--- a/quartical/data_handling/ms_handler.py
+++ b/quartical/data_handling/ms_handler.py
@@ -15,6 +15,13 @@
 from quartical.data_handling.selection import filter_xds_list
 from quartical.data_handling.angles import apply_parangles
 
+DASKMS_ATTRS = {
+    "__daskms_partition_schema__",
+    "SCAN_NUMBER",
+    "FIELD_ID",
+    "DATA_DESC_ID"
+}
+
 
 def read_xds_list(model_columns, ms_opts):
     """Reads a measurement set and generates a list of xarray data sets.
@@ -237,7 +244,8 @@ def write_xds_list(xds_list, ref_xds_list, ms_path, output_opts):
 
         # If the xds has fewer correlations than the measurement set, reindex.
         if xds.sizes["corr"] < ms_n_corr:
-            xds = xds.reindex(corr=corr_types, fill_value=0)
+            # Note that we have to remove chunks from the reindexed axis.
+            xds = xds.reindex(corr=corr_types, fill_value=0).chunk({"corr": -1})
 
             # Do some special handling on the flag column if we reindexed -
             # we need a value dependent fill value.
@@ -292,14 +300,18 @@ def write_xds_list(xds_list, ref_xds_list, ms_path, output_opts):
 
     logger.info("Outputs will be written to {}.", ", ".join(output_cols))
 
+    # Select only the output columns to simplify datasets.
+    xds_list = [xds[list(output_cols)] for xds in xds_list]
+
+    # Remove all coords bar ROWID so that they do not get written.
+    xds_list = [
+        xds.drop_vars(set(xds.coords.keys()) - {"ROWID"}, errors='ignore')
+        for xds in xds_list
+    ]
+
     # Remove attrs added by QuartiCal so that they do not get written.
     for xds in xds_list:
-        xds.attrs.pop("UTIME_CHUNKS", None)
-        xds.attrs.pop("FIELD_NAME", None)
-
-    # Remove coords added by QuartiCal so that they do not get written.
-    xds_list = [xds.drop_vars(["chan", "corr"], errors='ignore')
-                for xds in xds_list]
+        xds.attrs = {k: v for k, v in xds.attrs.items() if k in DASKMS_ATTRS}
 
     with warnings.catch_warnings():  # We anticipate spurious warnings.
         warnings.simplefilter("ignore")
diff --git a/quartical/executor.py b/quartical/executor.py
index b79a9fe8..704ecdaf 100644
--- a/quartical/executor.py
+++ b/quartical/executor.py
@@ -13,7 +13,8 @@
                                                 preprocess_xds_list,
                                                 postprocess_xds_list)
 from quartical.data_handling.model_handler import add_model_graph
-from quartical.data_handling.angles import make_parangle_xds_list
+from quartical.data_handling.angles import (make_parangle_xds_list,
+                                            assign_parangle_data)
 from quartical.calibration.calibrate import add_calibration_graph
 from quartical.statistics.statistics import make_stats_xds_list
 from quartical.statistics.logging import log_summary_stats
@@ -110,6 +111,7 @@ def _execute(exitstack):
 
     # Preprocess the xds_list - initialise some values and fix bad data.
     data_xds_list = preprocess_xds_list(data_xds_list, ms_opts)
+    data_xds_list = assign_parangle_data(ms_opts.path, data_xds_list)
 
     # Make a list of datasets containing the parallactic angles as these
     # can be expensive to compute and may be used several times. NOTE: At
diff --git a/quartical/gains/__init__.py b/quartical/gains/__init__.py
index 84e04cd1..e652419d 100644
--- a/quartical/gains/__init__.py
+++ b/quartical/gains/__init__.py
@@ -6,8 +6,10 @@
 from quartical.gains.tec_and_offset import TecAndOffset
 from quartical.gains.rotation import Rotation
 from quartical.gains.rotation_measure import RotationMeasure
-from quartical.gains.crosshand_phase import CrosshandPhase
+from quartical.gains.crosshand_phase import CrosshandPhase, CrosshandPhaseNullV
 from quartical.gains.leakage import Leakage
+from quartical.gains.delay_and_tec import DelayAndTec
+from quartical.gains.parallactic_angle import ParallacticAngle
 
 
 TERM_TYPES = {
@@ -21,5 +23,8 @@
     "rotation": Rotation,
     "rotation_measure": RotationMeasure,
     "crosshand_phase": CrosshandPhase,
-    "leakage": Leakage
+    "crosshand_phase_null_v": CrosshandPhaseNullV,
+    "leakage": Leakage,
+    "delay_and_tec": DelayAndTec,
+    "parallactic_angle": ParallacticAngle
 }
diff --git a/quartical/gains/amplitude/__init__.py b/quartical/gains/amplitude/__init__.py
index 5e04ba09..a2d2285a 100644
--- a/quartical/gains/amplitude/__init__.py
+++ b/quartical/gains/amplitude/__init__.py
@@ -38,7 +38,7 @@ def make_param_names(cls, correlations):
 
         return [f"amplitude_{c}" for c in param_corr]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/crosshand_phase/__init__.py b/quartical/gains/crosshand_phase/__init__.py
index 80fd8e9c..f9296355 100644
--- a/quartical/gains/crosshand_phase/__init__.py
+++ b/quartical/gains/crosshand_phase/__init__.py
@@ -5,6 +5,9 @@
     crosshand_phase_solver,
     crosshand_params_to_gains
 )
+from quartical.gains.crosshand_phase.null_v_kernel import (
+    null_v_crosshand_phase_solver
+)
 from quartical.gains.general.flagging import (
     apply_gain_flags_to_gains,
     apply_param_flags_to_params
@@ -39,7 +42,7 @@ def make_param_names(cls, correlations):
 
         return [f"crosshand_phase_{c}" for c in param_corr]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
@@ -54,3 +57,8 @@ def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
         apply_gain_flags_to_gains(gain_flags, gains)
 
         return gains, gain_flags, params, param_flags
+
+
+class CrosshandPhaseNullV(CrosshandPhase):
+
+    solver = staticmethod(null_v_crosshand_phase_solver)
\ No newline at end of file
diff --git a/quartical/gains/crosshand_phase/null_v_kernel.py b/quartical/gains/crosshand_phase/null_v_kernel.py
new file mode 100644
index 00000000..33fb4f78
--- /dev/null
+++ b/quartical/gains/crosshand_phase/null_v_kernel.py
@@ -0,0 +1,643 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+from numba import prange, njit
+from numba.typed import List
+from numba.extending import overload
+from quartical.utils.numba import (coerce_literal,
+                                   JIT_OPTIONS,
+                                   PARALLEL_JIT_OPTIONS)
+from quartical.gains.general.generics import (native_intermediaries,
+                                              upsampled_itermediaries,
+                                              per_array_jhj_jhr,
+                                              resample_solints,
+                                              downsample_jhj_jhr,
+                                              invert_gains)
+from quartical.gains.general.flagging import (flag_intermediaries,
+                                              update_gain_flags,
+                                              finalize_gain_flags,
+                                              apply_gain_flags_to_flag_col,
+                                              update_param_flags)
+from quartical.gains.general.convenience import (get_row,
+                                                 get_extents)
+import quartical.gains.general.factories as factories
+from quartical.gains.general.inversion import (invert_factory,
+                                               inversion_buffer_factory)
+
+
+def get_identity_params(corr_mode):
+
+    if corr_mode.literal_value == 4:
+        return np.zeros((1,), dtype=np.float64)
+    else:
+        raise ValueError("Unsupported number of correlations.")
+
+
+@njit(**JIT_OPTIONS)
+def null_v_crosshand_phase_solver(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    corr_mode
+):
+    return null_v_crosshand_phase_solver_impl(
+        ms_inputs,
+        mapping_inputs,
+        chain_inputs,
+        meta_inputs,
+        corr_mode
+    )
+
+
+def null_v_crosshand_phase_solver_impl(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    corr_mode
+):
+    raise NotImplementedError
+
+
+@overload(null_v_crosshand_phase_solver_impl, jit_options=JIT_OPTIONS)
+def nb_null_v_crosshand_phase_solver_impl(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    corr_mode
+):
+
+    coerce_literal(nb_null_v_crosshand_phase_solver_impl, ["corr_mode"])
+
+    identity_params = get_identity_params(corr_mode)
+
+    def impl(
+        ms_inputs,
+        mapping_inputs,
+        chain_inputs,
+        meta_inputs,
+        corr_mode
+    ):
+
+        gains = chain_inputs.gains
+        gain_flags = chain_inputs.gain_flags
+
+        inverse_gains = List()
+        for gain_term in gains:
+            inverse_gains.append(np.empty_like(gain_term))
+        invert_gains(gains, inverse_gains, corr_mode)
+
+        active_term = meta_inputs.active_term
+        max_iter = meta_inputs.iters
+        solve_per = meta_inputs.solve_per
+        dd_term = meta_inputs.dd_term
+        n_thread = meta_inputs.threads
+
+        active_gain = gains[active_term]
+        active_gain_flags = gain_flags[active_term]
+        active_params = chain_inputs.params[active_term]
+
+        # Set up some intemediaries used for flagging.
+        km1_gain = active_gain.copy()
+        km1_abs2_diffs = np.zeros_like(active_gain_flags, dtype=np.float64)
+        abs2_diffs_trend = np.zeros_like(active_gain_flags, dtype=np.float64)
+        flag_imdry = \
+            flag_intermediaries(km1_gain, km1_abs2_diffs, abs2_diffs_trend)
+
+        # Set up some intemediaries used for solving.
+        real_dtype = active_gain.real.dtype
+        param_shape = active_params.shape
+
+        active_t_map_g = mapping_inputs.time_maps[active_term]
+        active_f_map_g = mapping_inputs.freq_maps[active_term]
+
+        # Create more work to do in paralllel when needed, else no-op.
+        resampler = resample_solints(active_t_map_g, param_shape, n_thread)
+
+        # Determine the starts and stops of the rows and channels associated
+        # with each solution interval.
+        extents = get_extents(resampler.upsample_t_map, active_f_map_g)
+
+        upsample_shape = resampler.upsample_shape
+        upsampled_jhj = np.empty(upsample_shape + (upsample_shape[-1],),
+                                 dtype=real_dtype)
+        upsampled_jhr = np.empty(upsample_shape, dtype=real_dtype)
+        jhj = upsampled_jhj[:param_shape[0]]
+        jhr = upsampled_jhr[:param_shape[0]]
+        update = np.zeros(param_shape, dtype=real_dtype)
+
+        upsampled_imdry = upsampled_itermediaries(upsampled_jhj, upsampled_jhr)
+        native_imdry = native_intermediaries(jhj, jhr, update)
+
+        for loop_idx in range(max_iter or 1):
+
+            compute_jhj_jhr(
+                inverse_gains,
+                ms_inputs,
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs,
+                upsampled_imdry,
+                extents,
+                corr_mode
+            )
+
+            if resampler.active:
+                downsample_jhj_jhr(upsampled_imdry, resampler.downsample_t_map)
+
+            if solve_per == "array":
+                per_array_jhj_jhr(native_imdry)
+
+            if not max_iter:  # Non-solvable term, we just want jhj.
+                conv_perc = 0  # Didn't converge.
+                loop_idx = -1  # Did zero iterations.
+                break
+
+            compute_update(native_imdry, corr_mode)
+
+            finalize_update(
+                chain_inputs,
+                meta_inputs,
+                native_imdry,
+                loop_idx,
+                corr_mode
+            )
+
+            # The parameters/gains are correct but we are solving for the
+            # inverse so we update the inverse term here.
+            inverse_gains[active_term][:] = active_gain.conj()
+
+            # Check for gain convergence. Produced as a side effect of
+            # flagging. The converged percentage is based on unflagged
+            # intervals.
+            conv_perc = update_gain_flags(
+                chain_inputs,
+                meta_inputs,
+                flag_imdry,
+                loop_idx,
+                corr_mode,
+                numbness=1e9
+            )
+
+            # Propagate gain flags to parameter flags.
+            update_param_flags(
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs,
+                identity_params
+            )
+
+            if conv_perc >= meta_inputs.stop_frac:
+                break
+
+        # NOTE: Removes soft flags and flags points which have bad trends.
+        finalize_gain_flags(
+            chain_inputs,
+            meta_inputs,
+            flag_imdry,
+            corr_mode
+        )
+
+        # Call this one last time to ensure points flagged by finialize are
+        # propagated (in the DI case).
+        if not dd_term:
+            apply_gain_flags_to_flag_col(
+                ms_inputs,
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs
+            )
+
+        return native_imdry.jhj, loop_idx + 1, conv_perc
+
+    return impl
+
+
+def compute_jhj_jhr(
+    inverse_gains,
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    upsampled_imdry,
+    extents,
+    corr_mode
+):
+    return NotImplementedError
+
+
+@overload(compute_jhj_jhr, jit_options=PARALLEL_JIT_OPTIONS)
+def nb_compute_jhj_jhr(
+    inverse_gains,
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    upsampled_imdry,
+    extents,
+    corr_mode
+):
+
+    coerce_literal(nb_compute_jhj_jhr, ["corr_mode"])
+
+    # We want to dispatch based on this field so we need its type.
+    row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS')
+    row_weights_type = ms_inputs[row_weights_idx]
+
+    imul_rweight = factories.imul_rweight_factory(corr_mode, row_weights_type)
+    v1_imul_v2 = factories.v1_imul_v2_factory(corr_mode)
+    v1_imul_v2ct = factories.v1_imul_v2ct_factory(corr_mode)
+    v1ct_imul_v2 = factories.v1ct_imul_v2_factory(corr_mode)
+    absv1_idiv_absv2 = factories.absv1_idiv_absv2_factory(corr_mode)
+    iunpack = factories.iunpack_factory(corr_mode)
+    iunpackct = factories.iunpackct_factory(corr_mode)
+    imul = factories.imul_factory(corr_mode)
+    iadd = factories.iadd_factory(corr_mode)
+    isub = factories.isub_factory(corr_mode)
+    valloc = factories.valloc_factory(corr_mode)
+    make_loop_vars = factories.loop_var_factory(corr_mode)
+    set_identity = factories.set_identity_factory(corr_mode)
+    compute_jhwj_jhwr_elem = compute_jhwj_jhwr_elem_factory(corr_mode)
+
+    def impl(
+        inverse_gains,
+        ms_inputs,
+        mapping_inputs,
+        chain_inputs,
+        meta_inputs,
+        upsampled_imdry,
+        extents,
+        corr_mode
+    ):
+
+        data = ms_inputs.DATA
+        model = ms_inputs.MODEL_DATA
+        weights = ms_inputs.WEIGHT
+        flags = ms_inputs.FLAG
+        antenna1 = ms_inputs.ANTENNA1
+        antenna2 = ms_inputs.ANTENNA2
+        row_map = ms_inputs.ROW_MAP
+        row_weights = ms_inputs.ROW_WEIGHTS
+
+        # Reverse all the (invernse) gains and mappings.
+        time_maps = mapping_inputs.time_maps[::-1]
+        freq_maps = mapping_inputs.freq_maps[::-1]
+        dir_maps = mapping_inputs.dir_maps[::-1]
+
+        gains = inverse_gains[::-1]
+
+        # Active term in the REVERSED chain.
+        active_term = len(gains) - meta_inputs.active_term - 1
+
+        jhj = upsampled_imdry.jhj
+        jhr = upsampled_imdry.jhr
+
+        _, n_chan, n_dir, n_corr = model.shape
+
+        jhj[:] = 0
+        jhr[:] = 0
+
+        n_tint, n_fint, n_ant, n_gdir, n_param = jhr.shape
+        n_int = n_tint*n_fint
+
+        complex_dtype = gains[active_term].dtype
+        weight_dtype = weights.dtype
+
+        n_gains = len(gains)
+
+        row_starts = extents.row_starts
+        row_stops = extents.row_stops
+        chan_starts = extents.chan_starts
+        chan_stops = extents.chan_stops
+
+        # Determine loop variables based on where we are in the chain.
+        # gt means greater than (n>j) and lt means less than (n<j).
+        all_terms, gt_active, lt_active = make_loop_vars(n_gains, active_term)
+
+        # Parallel over all solution intervals.
+        for i in prange(n_int):
+
+            ti = i//n_fint
+            fi = i - ti*n_fint
+
+            rs = row_starts[ti]
+            re = row_stops[ti]
+            fs = chan_starts[fi]
+            fe = chan_stops[fi]
+
+            rop_pq = valloc(complex_dtype)  # Right-multiply operator for pq.
+            rop_qp = valloc(complex_dtype)  # Right-multiply operator for qp.
+            lop_pq = valloc(complex_dtype)  # Left-multiply operator for pq.
+            lop_qp = valloc(complex_dtype)  # Left-multiply operator for qp.
+
+            w = valloc(weight_dtype)
+            r_pq = valloc(complex_dtype)
+            wr_pq = valloc(complex_dtype)
+            wr_qp = valloc(complex_dtype)
+            v_pqd = valloc(complex_dtype)
+            v_pq = valloc(complex_dtype)
+
+            gains_p = valloc(complex_dtype, leading_dims=(n_gains,))
+            gains_q = valloc(complex_dtype, leading_dims=(n_gains,))
+
+            lop_pq_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+            rop_pq_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+            lop_qp_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+            rop_qp_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+
+            norm_factors = valloc(complex_dtype)
+
+            jhr_tifi = jhr[ti, fi]
+            jhj_tifi = jhj[ti, fi]
+
+            for row_ind in range(rs, re):
+
+                row = get_row(row_ind, row_map)
+                a1_m, a2_m = antenna1[row], antenna2[row]
+
+                for f in range(fs, fe):
+
+                    if flags[row, f]:  # Skip flagged data points.
+                        continue
+
+                    # Apply row weights in the BDA case, otherwise a no-op.
+                    imul_rweight(weights[row, f], w, row_weights, row_ind)
+
+                    lop_pq_arr[:] = 0
+                    rop_pq_arr[:] = 0
+                    lop_qp_arr[:] = 0
+                    rop_qp_arr[:] = 0
+                    v_pq[:] = 0
+                    r_pq[:] = 0
+
+                    for d in range(n_dir):
+
+                        set_identity(lop_pq)
+                        set_identity(lop_qp)
+
+                        # Construct a small contiguous gain array. This makes
+                        # the single term case fractionally slower.
+                        for gi in range(n_gains):
+                            d_m = dir_maps[gi][d]  # Broadcast dir.
+                            t_m = time_maps[gi][row_ind]
+                            f_m = freq_maps[gi][f]
+
+                            gain = gains[gi][t_m, f_m]
+
+                            iunpack(gains_p[gi], gain[a1_m, d_m])
+                            iunpack(gains_q[gi], gain[a2_m, d_m])
+
+                        m = data[row, f]
+                        iunpack(rop_qp, m)
+                        iunpackct(rop_pq, rop_qp)
+
+                        for g in all_terms:
+
+                            g_q = gains_q[g]
+                            v1_imul_v2(g_q, rop_pq, rop_pq)
+
+                            g_p = gains_p[g]
+                            v1_imul_v2(g_p, rop_qp, rop_qp)
+
+                        for g in gt_active:
+
+                            g_p = gains_p[g]
+                            v1_imul_v2ct(rop_pq, g_p, rop_pq)
+
+                            g_q = gains_q[g]
+                            v1_imul_v2ct(rop_qp, g_q, rop_qp)
+
+                        for g in lt_active:
+
+                            g_p = gains_p[g]
+                            v1ct_imul_v2(g_p, lop_pq, lop_pq)
+
+                            g_q = gains_q[g]
+                            v1ct_imul_v2(g_q, lop_qp, lop_qp)
+
+                        out_d = dir_maps[active_term][d]
+
+                        iunpack(lop_pq_arr[out_d], lop_pq)
+                        iadd(rop_pq_arr[out_d], rop_pq)
+
+                        iunpack(lop_qp_arr[out_d], lop_qp)
+                        iadd(rop_qp_arr[out_d], rop_qp)
+
+                        v1ct_imul_v2(lop_pq, gains_p[active_term], v_pqd)
+                        v1_imul_v2ct(v_pqd, rop_pq, v_pqd)
+                        iadd(v_pq, v_pqd)
+
+                    isub(r_pq, v_pq)
+
+                    for d in range(n_gdir):
+
+                        iunpack(wr_pq, r_pq)
+                        iunpackct(wr_qp, wr_pq)
+
+                        lop_pq_d = lop_pq_arr[d]
+                        rop_pq_d = rop_pq_arr[d]
+
+                        compute_jhwj_jhwr_elem(lop_pq_d,
+                                               rop_pq_d,
+                                               w,
+                                               norm_factors,
+                                               gains_p[active_term],
+                                               wr_pq,
+                                               jhr_tifi[a1_m, d],
+                                               jhj_tifi[a1_m, d])
+
+                        lop_qp_d = lop_qp_arr[d]
+                        rop_qp_d = rop_qp_arr[d]
+
+                        compute_jhwj_jhwr_elem(lop_qp_d,
+                                               rop_qp_d,
+                                               w,
+                                               norm_factors,
+                                               gains_q[active_term],
+                                               wr_qp,
+                                               jhr_tifi[a2_m, d],
+                                               jhj_tifi[a2_m, d])
+        return
+    return impl
+
+
+def compute_update(native_imdry, corr_mode):
+    raise NotImplementedError
+
+
+@overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS)
+def nb_compute_update(native_imdry, corr_mode):
+
+    coerce_literal(nb_compute_update, ["corr_mode"])
+
+    # We want to dispatch based on this field so we need its type.
+    jhj = native_imdry[native_imdry.fields.index('jhj')]
+
+    generalised = jhj.ndim == 6
+    inversion_buffer = inversion_buffer_factory(generalised=generalised)
+    invert = invert_factory(corr_mode, generalised=generalised)
+
+    def impl(native_imdry, corr_mode):
+
+        jhj = native_imdry.jhj
+        jhr = native_imdry.jhr
+        update = native_imdry.update
+
+        n_tint, n_fint, n_ant, n_dir, n_param = jhr.shape
+
+        n_int = n_tint * n_fint
+
+        result_dtype = jhr.dtype
+
+        for i in prange(n_int):
+
+            t = i // n_fint
+            f = i - t * n_fint
+
+            buffers = inversion_buffer(n_param, result_dtype)
+
+            for a in range(n_ant):
+                for d in range(n_dir):
+
+                    invert(jhj[t, f, a, d],
+                           jhr[t, f, a, d],
+                           update[t, f, a, d],
+                           buffers)
+
+    return impl
+
+
+def finalize_update(
+    chain_inputs,
+    meta_inputs,
+    native_imdry,
+    loop_idx,
+    corr_mode
+):
+    raise NotImplementedError
+
+
+@overload(finalize_update, jit_options=JIT_OPTIONS)
+def nb_finalize_update(
+    chain_inputs,
+    meta_inputs,
+    native_imdry,
+    loop_idx,
+    corr_mode
+):
+
+    coerce_literal(nb_finalize_update, ["corr_mode"])
+
+    set_identity = factories.set_identity_factory(corr_mode)
+    param_to_gain = param_to_gain_factory(corr_mode)
+
+    def impl(
+        chain_inputs,
+        meta_inputs,
+        native_imdry,
+        loop_idx,
+        corr_mode
+    ):
+
+        dd_term = meta_inputs.dd_term
+        active_term = meta_inputs.active_term
+        pinned_directions = meta_inputs.pinned_directions
+
+        gain = chain_inputs.gains[active_term]
+        gain_flags = chain_inputs.gain_flags[active_term]
+        params = chain_inputs.params[active_term]
+
+        update = native_imdry.update
+
+        n_tint, n_fint, n_ant, n_dir, n_corr = gain.shape
+
+        if dd_term:
+            dir_loop = [d for d in range(n_dir) if d not in pinned_directions]
+        else:
+            dir_loop = [d for d in range(n_dir)]
+
+        for ti in range(n_tint):
+            for fi in range(n_fint):
+                for a in range(n_ant):
+                    for d in dir_loop:
+
+                        p = params[ti, fi, a, d]
+                        g = gain[ti, fi, a, d]
+                        fl = gain_flags[ti, fi, a, d]
+                        upd = update[ti, fi, a, d]
+
+                        if fl == 1:
+                            p[:] = 0
+                            set_identity(g)
+                        else:
+                            # NOTE: Halving the update absolutely required.
+                            p -= 0.5*upd  # Flip sign for non-inverse solution.
+                            param_to_gain(p, g)
+
+    return impl
+
+
+def param_to_gain_factory(corr_mode):
+
+    if corr_mode.literal_value == 4:
+        def impl(params, gain):
+            gain[0] = np.exp(1j*params[0])
+    else:
+        raise ValueError("Crosshand phase can only be solved for with four "
+                         "correlation data.")
+
+    return factories.qcjit(impl)
+
+
+def compute_jhwj_jhwr_elem_factory(corr_mode):
+
+    v1_imul_v2 = factories.v1_imul_v2_factory(corr_mode)
+    unpack = factories.unpack_factory(corr_mode)
+    unpackc = factories.unpackc_factory(corr_mode)
+    iabsdivsq = factories.iabsdivsq_factory(corr_mode)
+    imul = factories.imul_factory(corr_mode)
+
+    if corr_mode.literal_value == 4:
+        def impl(lop, rop, w, normf, gain, res, jhr, jhj):
+
+            _, r_1, r_2, _ = unpack(res)  # NOTE: XX, XY, YX, YY
+            v_res = -0.5j * r_1 + 0.5j * r_2
+
+            res[0] = 0
+            res[1] = 0.5j * v_res
+            res[2] = -0.5j * v_res
+            res[3] = 0
+
+            # Accumulate an element of jhwr.
+            v1_imul_v2(res, rop, res)
+            v1_imul_v2(lop, res, res)
+
+            r_0, _, _, _ = unpack(res)  # NOTE: XX, XY, YX, YY
+
+            gc_0, _, _, _ = unpackc(gain)
+
+            drv_00 = -1j*gc_0
+
+            upd_00 = (drv_00*r_0).real
+
+            jhr[0] += upd_00
+
+            lop_00, lop_01, _, _ = unpack(lop)
+            rop_00, _, rop_01, _ = unpack(rop)  # "Transpose"
+
+            jh_01 = lop_00 * rop_01
+            jh_02 = lop_01 * rop_00
+
+            jh_v = 0.5j * jh_01 - 0.5j * jh_02
+            j_v = jh_v.conjugate()
+
+            jhj_v = jh_v * j_v
+
+            jhj[0, 0] += jhj_v.real
+
+    else:
+        raise ValueError("Crosshand phase can only be solved for with four "
+                         "correlation data.")
+
+    return factories.qcjit(impl)
diff --git a/quartical/gains/delay/__init__.py b/quartical/gains/delay/__init__.py
index 289575d3..2e4a4ff5 100644
--- a/quartical/gains/delay/__init__.py
+++ b/quartical/gains/delay/__init__.py
@@ -52,7 +52,7 @@ def make_param_names(cls, correlations):
 
         return [n.format(c) for c in param_corr for n in template]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/delay_and_offset/__init__.py b/quartical/gains/delay_and_offset/__init__.py
index de88830a..0b74d3a2 100644
--- a/quartical/gains/delay_and_offset/__init__.py
+++ b/quartical/gains/delay_and_offset/__init__.py
@@ -55,7 +55,7 @@ def make_param_names(cls, correlations):
 
         return [n.format(c) for c in param_corr for n in template]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/delay_and_tec/__init__.py b/quartical/gains/delay_and_tec/__init__.py
new file mode 100644
index 00000000..7d58e80a
--- /dev/null
+++ b/quartical/gains/delay_and_tec/__init__.py
@@ -0,0 +1,173 @@
+import numpy as np
+from collections import namedtuple
+from quartical.gains.conversion import no_op, trig_to_angle
+from quartical.gains.parameterized_gain import ParameterizedGain
+from quartical.gains.delay_and_tec.kernel import (
+    delay_and_tec_solver,
+    delay_and_tec_params_to_gains
+)
+from quartical.gains.general.flagging import (
+    apply_gain_flags_to_gains,
+    apply_param_flags_to_params
+)
+
+# Overload the default measurement set inputs to include the frequencies.
+ms_inputs = namedtuple(
+    'ms_inputs', ParameterizedGain.ms_inputs._fields + ('CHAN_FREQ',)
+)
+
+
+class DelayAndTec(ParameterizedGain):
+
+    solver = staticmethod(delay_and_tec_solver)
+    ms_inputs = ms_inputs
+
+    native_to_converted = (
+        (1, (no_op,)),
+        (1, (no_op,))
+    )
+    converted_to_native = (
+        (1, no_op),
+        (1, no_op)
+    )
+    converted_dtype = np.float64
+    native_dtype = np.float64
+
+    def __init__(self, term_name, term_opts):
+
+        super().__init__(term_name, term_opts)
+
+    @classmethod
+    def _make_freq_map(cls, chan_freqs, chan_widths, freq_interval):
+        # Overload gain mapping construction - we evaluate it in every channel.
+        return np.arange(chan_freqs.size, dtype=np.int32)
+
+    @classmethod
+    def make_param_names(cls, correlations):
+
+        # TODO: This is not dasky, unlike the other functions. Delayed?
+        parameterisable = ["XX", "YY", "RR", "LL"]
+
+        param_corr = [c for c in correlations if c in parameterisable]
+
+        template = ("tec_{}", "delay_{}")
+
+        return [n.format(c) for c in param_corr for n in template]
+
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
+        """Initialise the gains (and parameters)."""
+
+        gains, gain_flags, params, param_flags = super().init_term(
+            term_spec, ref_ant, ms_kwargs, term_kwargs
+        )
+
+        # Convert the parameters into gains.
+        delay_and_tec_params_to_gains(
+            params,
+            gains,
+            ms_kwargs["CHAN_FREQ"],
+            term_kwargs[f"{self.name}_param_freq_map"],
+        )
+
+        if self.load_from or not self.initial_estimate:
+
+            apply_param_flags_to_params(param_flags, params, 0)
+            apply_gain_flags_to_gains(gain_flags, gains)
+
+            return gains, gain_flags, params, param_flags
+
+        data = ms_kwargs["DATA"]  # (row, chan, corr)
+        flags = ms_kwargs["FLAG"]  # (row, chan)
+        a1 = ms_kwargs["ANTENNA1"]
+        a2 = ms_kwargs["ANTENNA2"]
+        chan_freq = ms_kwargs["CHAN_FREQ"]
+        t_map = term_kwargs[f"{term_spec.name}_time_map"]
+        f_map = term_kwargs[f"{term_spec.name}_param_freq_map"]
+        _, n_chan, n_ant, n_dir, n_corr = gains.shape
+
+        # We only need the baselines which include the ref_ant.
+        sel = np.where((a1 == ref_ant) | (a2 == ref_ant))
+        a1 = a1[sel]
+        a2 = a2[sel]
+        t_map = t_map[sel]
+        data = data[sel]
+        flags = flags[sel]
+
+        data[flags == 1] = 0  # Ignore UV-cut, otherwise there may be no est.
+
+        utint = np.unique(t_map)
+        ufint = np.unique(f_map)
+
+        for ut in utint:
+            sel = np.where((t_map == ut) & (a1 != a2))
+            ant_map_pq = np.where(a1[sel] == ref_ant, a2[sel], 0)
+            ant_map_qp = np.where(a2[sel] == ref_ant, a1[sel], 0)
+            ant_map = ant_map_pq + ant_map_qp
+
+            ref_data = np.zeros((n_ant, n_chan, n_corr), dtype=np.complex128)
+            counts = np.zeros((n_ant, n_chan), dtype=int)
+            np.add.at(
+                ref_data,
+                ant_map,
+                data[sel]
+            )
+            np.add.at(
+                counts,
+                ant_map,
+                flags[sel] == 0
+            )
+            np.divide(
+                ref_data,
+                counts[:, :, None],
+                where=counts[:, :, None] != 0,
+                out=ref_data
+            )
+
+            for uf in ufint:
+
+                fsel = np.where(f_map == uf)[0]
+                sel_n_chan = fsel.size
+                n = int(np.ceil(2 ** 15 / sel_n_chan)) * sel_n_chan
+
+                fsel_data = ref_data[:, fsel]
+                valid_ant = fsel_data.any(axis=(1, 2))
+
+                fft_data = np.abs(
+                    np.fft.fft(fsel_data, n=n, axis=1)
+                )
+                fft_data = np.fft.fftshift(fft_data, axes=1)
+
+                delta_freq = chan_freq[1] - chan_freq[0]
+                fft_freq = np.fft.fftfreq(n, delta_freq)
+                fft_freq = np.fft.fftshift(fft_freq)
+
+                delay_est_ind_00 = np.argmax(fft_data[..., 0], axis=1)
+                delay_est_00 = fft_freq[delay_est_ind_00]
+                delay_est_00[~valid_ant] = 0
+
+                if n_corr > 1:
+                    delay_est_ind_11 = np.argmax(fft_data[..., -1], axis=1)
+                    delay_est_11 = fft_freq[delay_est_ind_11]
+                    delay_est_11[~valid_ant] = 0
+
+                for t, p, q in zip(t_map[sel], a1[sel], a2[sel]):
+                    if p == ref_ant:
+                        params[t, uf, q, 0, 1] = -delay_est_00[q]
+                        if n_corr > 1:
+                            params[t, uf, q, 0, 3] = -delay_est_11[q]
+                    else:
+                        params[t, uf, p, 0, 1] = delay_est_00[p]
+                        if n_corr > 1:
+                            params[t, uf, p, 0, 3] = delay_est_11[p]
+
+        delay_and_tec_params_to_gains(
+            params,
+            gains,
+            ms_kwargs["CHAN_FREQ"],
+            term_kwargs[f"{self.name}_param_freq_map"],
+        )
+
+        apply_param_flags_to_params(param_flags, params, 0)
+        apply_gain_flags_to_gains(gain_flags, gains)
+
+        return gains, gain_flags, params, param_flags
diff --git a/quartical/gains/delay_and_tec/kernel.py b/quartical/gains/delay_and_tec/kernel.py
new file mode 100644
index 00000000..3b0b9202
--- /dev/null
+++ b/quartical/gains/delay_and_tec/kernel.py
@@ -0,0 +1,948 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+from numba import prange, njit
+from numba.extending import overload
+from quartical.utils.numba import (coerce_literal,
+                                   JIT_OPTIONS,
+                                   PARALLEL_JIT_OPTIONS)
+from quartical.gains.general.generics import (native_intermediaries,
+                                              upsampled_itermediaries,
+                                              per_array_jhj_jhr,
+                                              resample_solints,
+                                              downsample_jhj_jhr)
+from quartical.gains.general.flagging import (flag_intermediaries,
+                                              update_gain_flags,
+                                              finalize_gain_flags,
+                                              apply_gain_flags_to_flag_col,
+                                              update_param_flags,
+                                              apply_gain_flags_to_gains,
+                                              apply_param_flags_to_params)
+from quartical.gains.general.convenience import (get_row,
+                                                 get_extents)
+import quartical.gains.general.factories as factories
+from quartical.gains.general.inversion import (invert_factory,
+                                               inversion_buffer_factory)
+
+
+def get_identity_params(corr_mode):
+
+    if corr_mode.literal_value in (2, 4):
+        return np.zeros((4,), dtype=np.float64)
+    elif corr_mode.literal_value == 1:
+        return np.zeros((2,), dtype=np.float64)
+    else:
+        raise ValueError("Unsupported number of correlations.")
+
+
+@njit(**JIT_OPTIONS)
+def delay_and_tec_solver(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    corr_mode
+):
+    return delay_and_tec_solver_impl(
+        ms_inputs,
+        mapping_inputs,
+        chain_inputs,
+        meta_inputs,
+        corr_mode
+    )
+
+
+def delay_and_tec_solver_impl(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    corr_mode
+):
+    raise NotImplementedError
+
+
+@overload(delay_and_tec_solver_impl, jit_options=JIT_OPTIONS)
+def nb_delay_and_tec_solver_impl(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    corr_mode
+):
+
+    coerce_literal(nb_delay_and_tec_solver_impl, ["corr_mode"])
+
+    identity_params = get_identity_params(corr_mode)
+
+    def impl(
+        ms_inputs,
+        mapping_inputs,
+        chain_inputs,
+        meta_inputs,
+        corr_mode
+    ):
+
+        gains = chain_inputs.gains
+        gain_flags = chain_inputs.gain_flags
+
+        active_term = meta_inputs.active_term
+        max_iter = meta_inputs.iters
+        solve_per = meta_inputs.solve_per
+        dd_term = meta_inputs.dd_term
+        n_thread = meta_inputs.threads
+
+        active_gain = gains[active_term]
+        active_gain_flags = gain_flags[active_term]
+        active_params = chain_inputs.params[active_term]
+
+        # Set up some intemediaries used for flagging.
+        km1_gain = active_gain.copy()
+        km1_abs2_diffs = np.zeros_like(active_gain_flags, dtype=np.float64)
+        abs2_diffs_trend = np.zeros_like(active_gain_flags, dtype=np.float64)
+        flag_imdry = \
+            flag_intermediaries(km1_gain, km1_abs2_diffs, abs2_diffs_trend)
+
+        # Set up some intemediaries used for solving.
+        real_dtype = active_gain.real.dtype
+        param_shape = active_params.shape
+
+        active_t_map_g = mapping_inputs.time_maps[active_term]
+        active_f_map_p = mapping_inputs.param_freq_maps[active_term]
+
+        # Create more work to do in paralllel when needed, else no-op.
+        resampler = resample_solints(active_t_map_g, param_shape, n_thread)
+
+        # Determine the starts and stops of the rows and channels associated
+        # with each solution interval.
+        extents = get_extents(resampler.upsample_t_map, active_f_map_p)
+
+        upsample_shape = resampler.upsample_shape
+        upsampled_jhj = np.empty(upsample_shape + (upsample_shape[-1],),
+                                 dtype=real_dtype)
+        upsampled_jhr = np.empty(upsample_shape, dtype=real_dtype)
+        jhj = upsampled_jhj[:param_shape[0]]
+        jhr = upsampled_jhr[:param_shape[0]]
+        update = np.zeros(param_shape, dtype=real_dtype)
+
+        upsampled_imdry = upsampled_itermediaries(upsampled_jhj, upsampled_jhr)
+        native_imdry = native_intermediaries(jhj, jhr, update)
+
+        # We actually solve for D' = (D(nu_min + nu_max))/2. This helps avoid
+        # numerical issues, but requires some scaling of the parameters.
+        # We actually solve for TEC' = TEC/bandwidth. This helps avoid
+        # numerical issues, but requires some scaling of the parameters.
+        chan_freq = ms_inputs.CHAN_FREQ
+        mid_freq = (chan_freq.min() + chan_freq.max())/2
+        active_params[..., 1::2] *= mid_freq
+        bandwidth = chan_freq.max() - chan_freq.min()
+        active_params[..., 0::2] /= bandwidth
+
+        for loop_idx in range(max_iter or 1):
+
+            compute_jhj_jhr(
+                ms_inputs,
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs,
+                upsampled_imdry,
+                extents,
+                corr_mode
+            )
+
+            if resampler.active:
+                downsample_jhj_jhr(upsampled_imdry, resampler.downsample_t_map)
+
+            if solve_per == "array":
+                per_array_jhj_jhr(native_imdry)
+
+            if not max_iter:  # Non-solvable term, we just want jhj.
+                conv_perc = 0  # Didn't converge.
+                loop_idx = -1  # Did zero iterations.
+                break
+
+            compute_update(native_imdry, corr_mode)
+
+            finalize_update(
+                ms_inputs,
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs,
+                native_imdry,
+                loop_idx,
+                corr_mode
+            )
+
+            # Check for gain convergence. Produced as a side effect of
+            # flagging. The converged percentage is based on unflagged
+            # intervals.
+            conv_perc = update_gain_flags(
+                chain_inputs,
+                meta_inputs,
+                flag_imdry,
+                loop_idx,
+                corr_mode,
+                numbness=1e9
+            )
+
+            # Propagate gain flags to parameter flags.
+            update_param_flags(
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs,
+                identity_params
+            )
+
+            if conv_perc >= meta_inputs.stop_frac:
+                break
+
+        # NOTE: Removes soft flags and flags points which have bad trends.
+        finalize_gain_flags(
+            chain_inputs,
+            meta_inputs,
+            flag_imdry,
+            corr_mode
+        )
+
+        reference_params(
+            ms_inputs,
+            mapping_inputs,
+            chain_inputs,
+            meta_inputs,
+        )
+
+        # Call this one last time to ensure points flagged by finialize are
+        # propagated (in the DI case).
+        if not dd_term:
+            apply_gain_flags_to_flag_col(
+                ms_inputs,
+                mapping_inputs,
+                chain_inputs,
+                meta_inputs
+            )
+
+        # Undo rescaling so that quantities are in native units.
+        active_params[..., 1::2] /= mid_freq
+        native_imdry.jhj[..., 1::2] *= mid_freq ** 2
+        active_params[..., 0::2] *= bandwidth
+        native_imdry.jhj[..., 0::2, 0::2] /= bandwidth ** 2
+
+        return native_imdry.jhj, loop_idx + 1, conv_perc
+
+    return impl
+
+
+def compute_jhj_jhr(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    upsampled_imdry,
+    extents,
+    corr_mode
+):
+    return NotImplementedError
+
+
+@overload(compute_jhj_jhr, jit_options=PARALLEL_JIT_OPTIONS)
+def nb_compute_jhj_jhr(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    upsampled_imdry,
+    extents,
+    corr_mode
+):
+
+    coerce_literal(nb_compute_jhj_jhr, ["corr_mode"])
+
+    # We want to dispatch based on this field so we need its type.
+    row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS')
+    row_weights_type = ms_inputs[row_weights_idx]
+
+    imul_rweight = factories.imul_rweight_factory(corr_mode, row_weights_type)
+    v1_imul_v2 = factories.v1_imul_v2_factory(corr_mode)
+    v1_imul_v2ct = factories.v1_imul_v2ct_factory(corr_mode)
+    v1ct_imul_v2 = factories.v1ct_imul_v2_factory(corr_mode)
+    absv1_idiv_absv2 = factories.absv1_idiv_absv2_factory(corr_mode)
+    iunpack = factories.iunpack_factory(corr_mode)
+    iunpackct = factories.iunpackct_factory(corr_mode)
+    imul = factories.imul_factory(corr_mode)
+    iadd = factories.iadd_factory(corr_mode)
+    isub = factories.isub_factory(corr_mode)
+    valloc = factories.valloc_factory(corr_mode)
+    make_loop_vars = factories.loop_var_factory(corr_mode)
+    set_identity = factories.set_identity_factory(corr_mode)
+    compute_jhwj_jhwr_elem = compute_jhwj_jhwr_elem_factory(corr_mode)
+
+    def impl(
+        ms_inputs,
+        mapping_inputs,
+        chain_inputs,
+        meta_inputs,
+        upsampled_imdry,
+        extents,
+        corr_mode
+    ):
+
+        active_term = meta_inputs.active_term
+
+        data = ms_inputs.DATA
+        model = ms_inputs.MODEL_DATA
+        weights = ms_inputs.WEIGHT
+        flags = ms_inputs.FLAG
+        antenna1 = ms_inputs.ANTENNA1
+        antenna2 = ms_inputs.ANTENNA2
+        row_map = ms_inputs.ROW_MAP
+        row_weights = ms_inputs.ROW_WEIGHTS
+        chan_freq = ms_inputs.CHAN_FREQ
+
+        # NOTE: These are the gain maps.
+        time_maps = mapping_inputs.time_maps
+        freq_maps = mapping_inputs.freq_maps
+        dir_maps = mapping_inputs.dir_maps
+
+        gains = chain_inputs.gains
+
+        jhj = upsampled_imdry.jhj
+        jhr = upsampled_imdry.jhr
+
+        row_starts = extents.row_starts
+        row_stops = extents.row_stops
+        chan_starts = extents.chan_starts
+        chan_stops = extents.chan_stops
+
+        n_row, n_chan, n_dir, n_corr = model.shape
+
+        jhj[:] = 0
+        jhr[:] = 0
+
+        n_tint, n_fint, n_ant, n_gdir, n_param = jhr.shape
+        n_int = n_tint*n_fint
+
+        complex_dtype = gains[active_term].dtype
+        weight_dtype = weights.dtype
+
+        n_gains = len(gains)
+
+        cf_min = chan_freq.min()
+        cf_max = chan_freq.max()
+        cf_mid = (cf_min + cf_max) / 2
+        bandwidth = cf_max - cf_min
+        tec_offset = np.log(cf_min / cf_max)
+
+        # Determine loop variables based on where we are in the chain.
+        # gt means greater than (n>j) and lt means less than (n<j).
+        all_terms, gt_active, lt_active = make_loop_vars(n_gains, active_term)
+
+        # Parallel over all solution intervals.
+        for i in prange(n_int):
+
+            ti = i//n_fint
+            fi = i - ti*n_fint
+
+            rs = row_starts[ti]
+            re = row_stops[ti]
+            fs = chan_starts[fi]
+            fe = chan_stops[fi]
+
+            rop_pq = valloc(complex_dtype)  # Right-multiply operator for pq.
+            rop_qp = valloc(complex_dtype)  # Right-multiply operator for qp.
+            lop_pq = valloc(complex_dtype)  # Left-multiply operator for pq.
+            lop_qp = valloc(complex_dtype)  # Left-multiply operator for qp.
+
+            w = valloc(weight_dtype)
+            r_pq = valloc(complex_dtype)
+            wr_pq = valloc(complex_dtype)
+            wr_qp = valloc(complex_dtype)
+            v_pqd = valloc(complex_dtype)
+            v_pq = valloc(complex_dtype)
+
+            gains_p = valloc(complex_dtype, leading_dims=(n_gains,))
+            gains_q = valloc(complex_dtype, leading_dims=(n_gains,))
+
+            lop_pq_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+            rop_pq_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+            lop_qp_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+            rop_qp_arr = valloc(complex_dtype, leading_dims=(n_gdir,))
+
+            norm_factors = valloc(complex_dtype)
+
+            jhr_tifi = jhr[ti, fi]
+            jhj_tifi = jhj[ti, fi]
+
+            for row_ind in range(rs, re):
+
+                row = get_row(row_ind, row_map)
+                a1_m, a2_m = antenna1[row], antenna2[row]
+
+                for f in range(fs, fe):
+
+                    if flags[row, f]:  # Skip flagged data points.
+                        continue
+
+                    # Apply row weights in the BDA case, otherwise a no-op.
+                    imul_rweight(weights[row, f], w, row_weights, row_ind)
+                    iunpack(r_pq, data[row, f])
+
+                    lop_pq_arr[:] = 0
+                    rop_pq_arr[:] = 0
+                    lop_qp_arr[:] = 0
+                    rop_qp_arr[:] = 0
+                    v_pq[:] = 0
+
+                    for d in range(n_dir):
+
+                        set_identity(lop_pq)
+                        set_identity(lop_qp)
+
+                        # Construct a small contiguous gain array. This makes
+                        # the single term case fractionally slower.
+                        for gi in range(n_gains):
+                            d_m = dir_maps[gi][d]  # Broadcast dir.
+                            t_m = time_maps[gi][row_ind]
+                            f_m = freq_maps[gi][f]
+
+                            gain = gains[gi][t_m, f_m]
+
+                            iunpack(gains_p[gi], gain[a1_m, d_m])
+                            iunpack(gains_q[gi], gain[a2_m, d_m])
+
+                        m = model[row, f, d]
+                        iunpack(rop_qp, m)
+                        iunpackct(rop_pq, rop_qp)
+
+                        for g in all_terms:
+
+                            g_q = gains_q[g]
+                            v1_imul_v2(g_q, rop_pq, rop_pq)
+
+                            g_p = gains_p[g]
+                            v1_imul_v2(g_p, rop_qp, rop_qp)
+
+                        for g in gt_active:
+
+                            g_p = gains_p[g]
+                            v1_imul_v2ct(rop_pq, g_p, rop_pq)
+
+                            g_q = gains_q[g]
+                            v1_imul_v2ct(rop_qp, g_q, rop_qp)
+
+                        for g in lt_active:
+
+                            g_p = gains_p[g]
+                            v1ct_imul_v2(g_p, lop_pq, lop_pq)
+
+                            g_q = gains_q[g]
+                            v1ct_imul_v2(g_q, lop_qp, lop_qp)
+
+                        out_d = dir_maps[active_term][d]
+
+                        iunpack(lop_pq_arr[out_d], lop_pq)
+                        iadd(rop_pq_arr[out_d], rop_pq)
+
+                        iunpack(lop_qp_arr[out_d], lop_qp)
+                        iadd(rop_qp_arr[out_d], rop_qp)
+
+                        v1ct_imul_v2(lop_pq, gains_p[active_term], v_pqd)
+                        v1_imul_v2ct(v_pqd, rop_pq, v_pqd)
+                        iadd(v_pq, v_pqd)
+
+                    absv1_idiv_absv2(v_pq, r_pq, norm_factors)
+                    imul(r_pq, norm_factors)
+                    isub(r_pq, v_pq)
+
+                    # Coefficients introduced by differentiation of exponent.
+                    delay_coeff = \
+                        2 * np.pi * (chan_freq[f]/cf_mid - 1)
+                    tec_coeff = \
+                        2 * np.pi * (bandwidth/chan_freq[f] + tec_offset)
+
+                    for d in range(n_gdir):
+
+                        iunpack(wr_pq, r_pq)
+                        imul(wr_pq, w)
+                        iunpackct(wr_qp, wr_pq)
+
+                        lop_pq_d = lop_pq_arr[d]
+                        rop_pq_d = rop_pq_arr[d]
+
+                        compute_jhwj_jhwr_elem(lop_pq_d,
+                                               rop_pq_d,
+                                               w,
+                                               norm_factors,
+                                               delay_coeff,
+                                               tec_coeff,
+                                               gains_p[active_term],
+                                               wr_pq,
+                                               jhr_tifi[a1_m, d],
+                                               jhj_tifi[a1_m, d])
+
+                        lop_qp_d = lop_qp_arr[d]
+                        rop_qp_d = rop_qp_arr[d]
+
+                        compute_jhwj_jhwr_elem(lop_qp_d,
+                                               rop_qp_d,
+                                               w,
+                                               norm_factors,
+                                               delay_coeff,
+                                               tec_coeff,
+                                               gains_q[active_term],
+                                               wr_qp,
+                                               jhr_tifi[a2_m, d],
+                                               jhj_tifi[a2_m, d])
+        return
+    return impl
+
+
+def compute_update(native_imdry, corr_mode):
+    raise NotImplementedError
+
+
+@overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS)
+def nb_compute_update(native_imdry, corr_mode):
+
+    coerce_literal(nb_compute_update, ["corr_mode"])
+
+    # We want to dispatch based on this field so we need its type.
+    jhj = native_imdry[native_imdry.fields.index('jhj')]
+
+    generalised = jhj.ndim == 6
+    inversion_buffer = inversion_buffer_factory(generalised=generalised)
+    invert = invert_factory(corr_mode, generalised=generalised)
+
+    def impl(native_imdry, corr_mode):
+
+        jhj = native_imdry.jhj
+        jhr = native_imdry.jhr
+        update = native_imdry.update
+
+        n_tint, n_fint, n_ant, n_dir, n_param = jhr.shape
+
+        n_int = n_tint * n_fint
+
+        result_dtype = jhr.dtype
+
+        for i in prange(n_int):
+
+            t = i // n_fint
+            f = i - t * n_fint
+
+            buffers = inversion_buffer(n_param, result_dtype)
+
+            for a in range(n_ant):
+                for d in range(n_dir):
+
+                    invert(jhj[t, f, a, d],
+                           jhr[t, f, a, d],
+                           update[t, f, a, d],
+                           buffers)
+
+    return impl
+
+
+def finalize_update(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    native_imdry,
+    loop_idx,
+    corr_mode
+):
+    raise NotImplementedError
+
+
+@overload(finalize_update, jit_options=JIT_OPTIONS)
+def nb_finalize_update(
+    ms_inputs,
+    mapping_inputs,
+    chain_inputs,
+    meta_inputs,
+    native_imdry,
+    loop_idx,
+    corr_mode
+):
+
+    coerce_literal(nb_finalize_update, ["corr_mode"])
+
+    set_identity = factories.set_identity_factory(corr_mode)
+    param_to_gain = param_to_gain_factory(corr_mode)
+
+    if corr_mode.literal_value in (1, 2, 4):
+        def impl(
+            ms_inputs,
+            mapping_inputs,
+            chain_inputs,
+            meta_inputs,
+            native_imdry,
+            loop_idx,
+            corr_mode
+        ):
+
+            dd_term = meta_inputs.dd_term
+            active_term = meta_inputs.active_term
+            pinned_directions = meta_inputs.pinned_directions
+
+            gain = chain_inputs.gains[active_term]
+            gain_flags = chain_inputs.gain_flags[active_term]
+            params = chain_inputs.params[active_term]
+
+            param_freq_map = mapping_inputs.param_freq_maps[active_term]
+            dir_map = mapping_inputs.dir_maps[active_term]
+
+            update = native_imdry.update
+
+            update /= 2
+            params += update
+
+            n_time, n_freq, n_ant, n_dir, _ = gain.shape
+
+            if dd_term:
+                for pd in pinned_directions:
+                    params[..., pd, :] = 0
+
+            chan_freq = ms_inputs.CHAN_FREQ
+            cf_min = chan_freq.min()
+            cf_max = chan_freq.max()
+            cf_mid = (cf_min + cf_max) / 2
+            bandwidth = cf_max - cf_min
+            tec_offset = np.log(cf_min / cf_max)
+
+            for t in range(n_time):
+                for f in range(n_freq):
+                    f_m = param_freq_map[f]
+                    delay_coeff = \
+                        2 * np.pi * (chan_freq[f]/cf_mid - 1)
+                    tec_coeff = \
+                        2 * np.pi * (bandwidth/chan_freq[f] + tec_offset)
+                    for a in range(n_ant):
+                        for d in range(n_dir):
+
+                            d_m = dir_map[d]
+                            g = gain[t, f, a, d]
+                            fl = gain_flags[t, f, a, d]
+                            p = params[t, f_m, a, d_m]
+
+                            if fl == 1:
+                                set_identity(g)
+                            else:
+                                param_to_gain(p, delay_coeff, tec_coeff, g)
+    else:
+        raise ValueError("Unsupported number of correlations.")
+
+    return impl
+
+
+def param_to_gain_factory(corr_mode):
+
+    if corr_mode.literal_value == 4:
+        def impl(params, delay_coeff, tec_coeff, gain):
+            gain[0] = np.exp(
+                1j * (delay_coeff * params[1] + tec_coeff * params[0])
+            )
+            gain[3] = np.exp(
+                1j * (delay_coeff * params[3] + tec_coeff * params[2])
+            )
+    elif corr_mode.literal_value == 2:
+        def impl(params, delay_coeff, tec_coeff, gain):
+            gain[0] = np.exp(
+                1j * (delay_coeff * params[1] + tec_coeff * params[0])
+            )
+            gain[1] = np.exp(
+                1j * (delay_coeff * params[3] + tec_coeff * params[2])
+            )
+    elif corr_mode.literal_value == 1:
+        def impl(params, delay_coeff, tec_coeff, gain):
+            gain[0] = np.exp(
+                1j * (delay_coeff * params[1] + tec_coeff * params[0])
+            )
+    else:
+        raise ValueError("Unsupported number of correlations.")
+
+    return factories.qcjit(impl)
+
+
+def compute_jhwj_jhwr_elem_factory(corr_mode):
+
+    v1_imul_v2 = factories.v1_imul_v2_factory(corr_mode)
+    unpack = factories.unpack_factory(corr_mode)
+    unpackc = factories.unpackc_factory(corr_mode)
+    iunpack = factories.iunpack_factory(corr_mode)
+    iabsdivsq = factories.iabsdivsq_factory(corr_mode)
+    imul = factories.imul_factory(corr_mode)
+
+    if corr_mode.literal_value == 4:
+        def impl(
+            lop, rop, w, normf, delay_coeff, tec_coeff, gain, res, jhr, jhj
+        ):
+
+            # Effectively apply zero weight to off-diagonal terms.
+            # TODO: Can be tidied but requires moving other weighting code.
+            res[1] = 0
+            res[2] = 0
+
+            # Compute normalization factor.
+            v1_imul_v2(lop, rop, normf)
+            iabsdivsq(normf)
+            imul(res, normf)  # Apply normalization factor to r.
+
+            # Accumulate an element of jhwr.
+            v1_imul_v2(res, rop, res)
+            v1_imul_v2(lop, res, res)
+
+            # Accumulate an element of jhwj.
+
+            r_0, _, _, r_3 = unpack(res)  # NOTE: XX, XY, YX, YY
+
+            _, _, _, g_3 = unpack(gain)
+            gc_0, _, _, gc_3 = unpackc(gain)
+
+            drv_00 = -1j*gc_0
+            drv_23 = -1j*gc_3
+
+            upd_00 = (drv_00*r_0).real
+            upd_11 = (drv_23*r_3).real
+
+            jhr[0] += tec_coeff*upd_00
+            jhr[1] += delay_coeff*upd_00
+            jhr[2] += tec_coeff*upd_11
+            jhr[3] += delay_coeff*upd_11
+
+            w_0, _, _, w_3 = unpack(w)  # NOTE: XX, XY, YX, YY
+            n_0, _, _, n_3 = unpack(normf)
+
+            # Apply normalisation factors by scaling w. # Neglect (set weight
+            # to zero) off diagonal terms.
+            w_0 = n_0 * w_0
+            w_3 = n_3 * w_3
+
+            lop_00, lop_01, lop_10, lop_11 = unpack(lop)
+            rop_00, rop_10, rop_01, rop_11 = unpack(rop)  # "Transpose"
+
+            jh_00 = lop_00 * rop_00
+            jh_03 = lop_01 * rop_01
+
+            j_00 = jh_00.conjugate()
+            j_03 = jh_03.conjugate()
+
+            jh_30 = lop_10 * rop_10
+            jh_33 = lop_11 * rop_11
+
+            j_30 = jh_30.conjugate()
+            j_33 = jh_33.conjugate()
+
+            jhwj_00 = jh_00*w_0*j_00 + jh_03*w_3*j_03
+            jhwj_03 = jh_00*w_0*j_30 + jh_03*w_3*j_33
+            jhwj_33 = jh_30*w_0*j_30 + jh_33*w_3*j_33
+
+            delay_coeffsq = delay_coeff ** 2
+            tec_coeffsq = tec_coeff ** 2
+
+            tmp_0 = jhwj_00.real
+            jhj[0, 0] += tmp_0*tec_coeffsq
+            jhj[0, 1] += tmp_0*tec_coeff*delay_coeff
+            tmp_1 = (jhwj_03*gc_0*g_3).real
+            jhj[0, 2] += tmp_1*tec_coeffsq
+            jhj[0, 3] += tmp_1*tec_coeff*delay_coeff
+
+            jhj[1, 0] = jhj[0, 1]
+            jhj[1, 1] += tmp_0*delay_coeffsq
+            jhj[1, 2] = jhj[0, 3]
+            jhj[1, 3] += tmp_1*delay_coeffsq
+
+            jhj[2, 0] = jhj[0, 2]
+            jhj[2, 1] = jhj[1, 2]
+            tmp_2 = jhwj_33.real
+            jhj[2, 2] += tmp_2*tec_coeffsq
+            jhj[2, 3] += tmp_2*tec_coeff*delay_coeff
+
+            jhj[3, 0] = jhj[0, 3]
+            jhj[3, 1] = jhj[1, 3]
+            jhj[3, 2] = jhj[2, 3]
+            jhj[3, 3] += tmp_2*delay_coeffsq
+
+    elif corr_mode.literal_value == 2:
+        def impl(
+            lop, rop, w, normf, delay_coeff, tec_coeff, gain, res, jhr, jhj
+        ):
+
+            # Compute normalization factor.
+            iunpack(normf, rop)
+            iabsdivsq(normf)
+            imul(res, normf)  # Apply normalization factor to r.
+
+            # Accumulate an element of jhwr.
+            v1_imul_v2(res, rop, res)
+
+            r_0, r_1 = unpack(res)
+            gc_0, gc_1 = unpackc(gain)
+
+            drv_00 = -1j*gc_0
+            drv_23 = -1j*gc_1
+
+            upd_00 = (drv_00*r_0).real
+            upd_11 = (drv_23*r_1).real
+
+            jhr[0] += tec_coeff*upd_00
+            jhr[1] += delay_coeff*upd_00
+            jhr[2] += tec_coeff*upd_11
+            jhr[3] += delay_coeff*upd_11
+
+            # Accumulate an element of jhwj.
+            jh_00, jh_11 = unpack(rop)
+            j_00, j_11 = unpackc(rop)
+            w_00, w_11 = unpack(w)
+            n_00, n_11 = unpack(normf)
+
+            delay_coeffsq = delay_coeff ** 2
+            tec_coeffsq = tec_coeff ** 2
+
+            tmp = (jh_00*n_00*w_00*j_00).real
+            jhj[0, 0] += tmp*tec_coeffsq
+            jhj[0, 1] += tmp*delay_coeff*tec_coeff
+            jhj[1, 0] += tmp*delay_coeff*tec_coeff
+            jhj[1, 1] += tmp*delay_coeffsq
+
+            tmp = (jh_11*n_11*w_11*j_11).real
+            jhj[2, 2] += tmp*tec_coeffsq
+            jhj[2, 3] += tmp*delay_coeff*tec_coeff
+            jhj[3, 2] += tmp*delay_coeff*tec_coeff
+            jhj[3, 3] += tmp*delay_coeffsq
+
+    elif corr_mode.literal_value == 1:
+        def impl(
+            lop, rop, w, normf, delay_coeff, tec_coeff, gain, res, jhr, jhj
+        ):
+
+            # Compute normalization factor.
+            iunpack(normf, rop)
+            iabsdivsq(normf)
+            imul(res, normf)  # Apply normalization factor to r.
+
+            # Accumulate an element of jhwr.
+            v1_imul_v2(res, rop, res)
+
+            r_0 = unpack(res)
+            gc_0 = unpackc(gain)
+
+            drv_00 = -1j*gc_0
+
+            upd_00 = (drv_00*r_0).real
+
+            jhr[0] += tec_coeff*upd_00
+            jhr[1] += delay_coeff*upd_00
+
+            # Accumulate an element of jhwj.
+            jh_00 = unpack(rop)
+            j_00 = unpackc(rop)
+            w_00 = unpack(w)
+            n_00 = unpack(normf)
+
+            delay_coeffsq = delay_coeff ** 2
+            tec_coeffsq = tec_coeff ** 2
+
+            tmp = (jh_00*n_00*w_00*j_00).real
+            jhj[0, 0] += tmp*tec_coeffsq
+            jhj[0, 1] += tmp*delay_coeff*tec_coeff
+            jhj[1, 0] += tmp*delay_coeff*tec_coeff
+            jhj[1, 1] += tmp*delay_coeffsq
+    else:
+        raise ValueError("Unsupported number of correlations.")
+
+    return factories.qcjit(impl)
+
+
+@njit(**JIT_OPTIONS)
+def delay_and_tec_params_to_gains(
+    params,
+    gains,
+    chan_freq,
+    param_freq_map,
+    rescaled=False
+):
+
+    n_time, n_freq, n_ant, n_dir, n_corr = gains.shape
+
+    cf_min = chan_freq.min()
+    cf_max = chan_freq.max()
+    cf_mid = (cf_min + cf_max) / 2
+
+    # DELAY
+    if rescaled:
+        delay_offset = 1.0
+        delay_denom = cf_mid
+    else:
+        delay_offset = cf_mid
+        delay_denom = 1.0
+
+    bandwidth = cf_max - cf_min
+
+    # TEC
+    if rescaled:
+        tec_offset = np.log(cf_min/cf_max)
+        tec_numerator = bandwidth
+    else:
+        tec_offset = np.log(cf_min/cf_max)/bandwidth
+        tec_numerator = 1.0
+
+    for t in range(n_time):
+        for f in range(n_freq):
+            f_m = param_freq_map[f]
+            # Coeff associated with the the delay.
+            delay_coeff = \
+                2 * np.pi * (chan_freq[f] / delay_denom - delay_offset)
+            # Coeff associated with the the tec.
+            tec_coeff = \
+                2 * np.pi * (tec_numerator / chan_freq[f] + tec_offset)
+            for a in range(n_ant):
+                for d in range(n_dir):
+
+                    g = gains[t, f, a, d]
+                    p = params[t, f_m, a, d]
+
+                    g[0] = np.exp(1j*(delay_coeff*p[1] + tec_coeff*p[0]))
+
+                    if n_corr > 1:
+                        g[-1] = np.exp(1j*(delay_coeff*p[3] + tec_coeff*p[2]))
+
+
+@njit(**JIT_OPTIONS)
+def reference_params(ms_inputs, mapping_inputs, chain_inputs, meta_inputs):
+
+    chan_freq = ms_inputs.CHAN_FREQ
+
+    active_term = meta_inputs.active_term
+    ref_ant = meta_inputs.reference_antenna
+
+    gains = chain_inputs.gains[active_term]
+    gain_flags = chain_inputs.gain_flags[active_term]
+    params = chain_inputs.params[active_term]
+    param_flags = chain_inputs.param_flags[active_term]
+
+    param_freq_map = mapping_inputs.param_freq_maps[active_term]
+
+    n_ti, n_fi, n_ant, n_dir, n_corr = params.shape
+
+    ref_params = params[:, :, ref_ant: ref_ant + 1, :, :].copy()
+
+    for t in range(n_ti):
+        for f in range(n_fi):
+            for a in range(n_ant):
+                for d in range(n_dir):
+
+                    p = params[t, f, a, d]
+                    rp = ref_params[t, f, 0, d]
+
+                    if param_flags[t, f, a, d] == 1:
+                        continue
+                    else:
+                        p -= rp
+
+    delay_and_tec_params_to_gains(
+        params, gains, chan_freq, param_freq_map, rescaled=True
+    )
+
+    # Referencing may move flagged gains/params from identity.
+    apply_param_flags_to_params(param_flags, params, 0)
+    apply_gain_flags_to_gains(gain_flags, gains)
diff --git a/quartical/gains/gain.py b/quartical/gains/gain.py
index ca1165fd..0b18d4f2 100644
--- a/quartical/gains/gain.py
+++ b/quartical/gains/gain.py
@@ -344,7 +344,7 @@ def _make_dir_map(cls, n_dir, direction_dependent):
 
         return dir_map
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         (_, _, gain_shape, _) = term_spec
diff --git a/quartical/gains/general/factories.py b/quartical/gains/general/factories.py
index 469854a0..a73bd205 100644
--- a/quartical/gains/general/factories.py
+++ b/quartical/gains/general/factories.py
@@ -798,3 +798,43 @@ def impl(a, b, out):
         out[3, 3] = a11 * b11
 
     return qcjit(impl)
+
+
+def rotation_factory(corr_mode, feed_type):
+
+    if feed_type.literal_value == "circular":
+        if corr_mode.literal_value == 4:
+            def impl(rot0, rot1, out):
+                out[0] = np.exp(-1j*rot0)
+                out[1] = 0
+                out[2] = 0
+                out[3] = np.exp(1j*rot1)
+        elif corr_mode.literal_value == 2:  # TODO: Is this sensible?
+            def impl(rot0, rot1, out):
+                out[0] = np.exp(-1j*rot0)
+                out[1] = np.exp(1j*rot1)
+        elif corr_mode.literal_value == 1:  # TODO: Is this sensible?
+            def impl(rot0, rot1, out):
+                out[0] = np.exp(-1j*rot0)
+        else:
+            raise ValueError("Unsupported number of correlations.")
+    elif feed_type.literal_value == "linear":
+        if corr_mode.literal_value == 4:
+            def impl(rot0, rot1, out):
+                out[0] = np.cos(rot0)
+                out[1] = np.sin(rot0)
+                out[2] = -np.sin(rot1)
+                out[3] = np.cos(rot1)
+        elif corr_mode.literal_value == 2:  # TODO: Is this sensible?
+            def impl(rot0, rot1, out):
+                out[0] = np.cos(rot0)
+                out[1] = np.cos(rot1)
+        elif corr_mode.literal_value == 1:  # TODO: Is this sensible?
+            def impl(rot0, rot1, out):
+                out[0] = np.cos(rot0)
+        else:
+            raise ValueError("Unsupported number of correlations.")
+    else:
+        raise ValueError("Unsupported feed type.")
+
+    return qcjit(impl)
diff --git a/quartical/gains/parallactic_angle/__init__.py b/quartical/gains/parallactic_angle/__init__.py
new file mode 100644
index 00000000..9afc079f
--- /dev/null
+++ b/quartical/gains/parallactic_angle/__init__.py
@@ -0,0 +1,79 @@
+import numpy as np
+from collections import namedtuple
+from quartical.gains.conversion import trig_to_angle
+from quartical.gains.parameterized_gain import ParameterizedGain
+from quartical.gains.parallactic_angle.kernel import (
+    parallactic_angle_params_to_gains
+)
+from quartical.gains.general.flagging import (
+    apply_gain_flags_to_gains,
+    apply_param_flags_to_params
+)
+from quartical.data_handling.angles import _make_parangles
+
+# Overload the default measurement set inputs to include information required
+# to compute the parallactic angles.
+ms_inputs = namedtuple(
+    'ms_inputs', 
+    ParameterizedGain.ms_inputs._fields + \
+        ('RECEPTOR_ANGLE', 'POSITION', 'TIME')
+)
+
+class ParallacticAngle(ParameterizedGain):
+
+    solver = None
+    ms_inputs = ms_inputs
+
+    native_to_converted = (
+        (0, (np.cos,)),
+        (1, (np.sin,))
+    )
+    converted_to_native = (
+        (2, trig_to_angle),
+    )
+    converted_dtype = np.float64
+    native_dtype = np.float64
+
+    def __init__(self, term_name, term_opts):
+
+        super().__init__(term_name, term_opts)
+
+    @classmethod
+    def _make_freq_map(cls, chan_freqs, chan_widths, freq_interval):
+        # Overload gain mapping construction - we evaluate it in every channel.
+        return np.arange(chan_freqs.size, dtype=np.int32)
+
+    @classmethod
+    def make_param_names(cls, correlations):
+        return ["parallactic_angle"]
+
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
+        """Initialise the gains (and parameters)."""
+
+        gains, gain_flags, params, param_flags = super().init_term(
+            term_spec, ref_ant, ms_kwargs, term_kwargs
+        )
+
+        params[...] = _make_parangles(
+            ms_kwargs["TIME"],
+            np.arange(gains.shape[2]),  # TODO: Just for determining n_ant.
+            ms_kwargs["POSITION"],
+            ms_kwargs["RECEPTOR_ANGLE"],
+            meta["FIELD_CENTRE"],
+            "J2000"  # NOTE: Currently hardcoded - will need to be exposed.
+        )[:, None, :, None, :1]  # Assume single value for parallactic angle.
+
+        # Convert the parameters into gains.
+        parallactic_angle_params_to_gains(
+            params,
+            gains,
+            term_kwargs[f"{self.name}_param_freq_map"],
+            feed_type=meta["FEED_TYPE"],
+            corr_mode=gains.shape[-1]
+        )
+
+        # Apply flags to gains and parameters.
+        apply_param_flags_to_params(param_flags, params, 1)
+        apply_gain_flags_to_gains(gain_flags, gains)
+
+        return gains, gain_flags, params, param_flags
diff --git a/quartical/gains/parallactic_angle/kernel.py b/quartical/gains/parallactic_angle/kernel.py
new file mode 100644
index 00000000..177a38ee
--- /dev/null
+++ b/quartical/gains/parallactic_angle/kernel.py
@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+from numba import njit, literally
+from numba.extending import overload
+from quartical.utils.numba import JIT_OPTIONS
+import quartical.gains.general.factories as factories
+
+
+@njit(**JIT_OPTIONS)
+def parallactic_angle_params_to_gains(
+    params,
+    gains,
+    param_freq_map,
+    feed_type,
+    corr_mode
+):
+    return parallactic_angle_params_to_gains_impl(
+        params,
+        gains,
+        param_freq_map,
+        literally(feed_type),
+        literally(corr_mode)
+    )
+
+
+def parallactic_angle_params_to_gains_impl(
+    params,
+    gains,
+    param_freq_map,
+    feed_type,
+    corr_mode
+):
+    return NotImplementedError
+
+
+@overload(
+    parallactic_angle_params_to_gains_impl,
+    jit_options=JIT_OPTIONS,
+    prefer_literal=True
+)
+def nb_parallactic_angle_params_to_gains_impl(
+    params,
+    gains,
+    param_freq_map,
+    feed_type,
+    corr_mode
+):
+
+    rotmat = factories.rotation_factory(corr_mode, feed_type)
+
+    def impl(
+        params,
+        gains,
+        param_freq_map,
+        feed_type,
+        corr_mode
+    ):
+
+        n_time, n_freq, n_ant, n_dir, n_corr = gains.shape
+
+        for t in range(n_time):
+            for f in range(n_freq):
+                f_m = param_freq_map[f]
+                for a in range(n_ant):
+                    for d in range(n_dir):
+
+                        g = gains[t, f, a, d]
+                        p = params[t, f_m, a, d, 0]
+
+                        rotmat(p, p, g)
+
+    return impl
\ No newline at end of file
diff --git a/quartical/gains/parameterized_gain.py b/quartical/gains/parameterized_gain.py
index 4b50f417..ab68361c 100644
--- a/quartical/gains/parameterized_gain.py
+++ b/quartical/gains/parameterized_gain.py
@@ -174,7 +174,7 @@ def _make_param_freq_chunks(cls, param_freq_map):
     def make_param_names(cls, correlations):
         raise NotImplementedError
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         (_, _, gain_shape, param_shape) = term_spec
diff --git a/quartical/gains/phase/__init__.py b/quartical/gains/phase/__init__.py
index 4b2313ae..857f6323 100644
--- a/quartical/gains/phase/__init__.py
+++ b/quartical/gains/phase/__init__.py
@@ -39,7 +39,7 @@ def make_param_names(cls, correlations):
 
         return [f"phase_{c}" for c in param_corr]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/rotation/__init__.py b/quartical/gains/rotation/__init__.py
index 03d03c3d..3ebe5e8e 100644
--- a/quartical/gains/rotation/__init__.py
+++ b/quartical/gains/rotation/__init__.py
@@ -34,7 +34,7 @@ def make_param_names(cls, correlations):
 
         return ["rotation_angle"]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/rotation_measure/__init__.py b/quartical/gains/rotation_measure/__init__.py
index 213d8a6b..37f89bb6 100644
--- a/quartical/gains/rotation_measure/__init__.py
+++ b/quartical/gains/rotation_measure/__init__.py
@@ -71,7 +71,7 @@ def make_f_maps(chan_freqs, chan_widths, f_int):
 
         return f_map_arr
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/tec_and_offset/__init__.py b/quartical/gains/tec_and_offset/__init__.py
index 0360b2c9..6c54b785 100644
--- a/quartical/gains/tec_and_offset/__init__.py
+++ b/quartical/gains/tec_and_offset/__init__.py
@@ -55,7 +55,7 @@ def make_param_names(cls, correlations):
 
         return [n.format(c) for c in param_corr for n in template]
 
-    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs):
+    def init_term(self, term_spec, ref_ant, ms_kwargs, term_kwargs, meta=None):
         """Initialise the gains (and parameters)."""
 
         gains, gain_flags, params, param_flags = super().init_term(
diff --git a/quartical/gains/tec_and_offset/kernel.py b/quartical/gains/tec_and_offset/kernel.py
index dd3d95ac..e41ab75c 100644
--- a/quartical/gains/tec_and_offset/kernel.py
+++ b/quartical/gains/tec_and_offset/kernel.py
@@ -835,6 +835,7 @@ def tec_and_offset_params_to_gains(
     cf_max = chan_freq.max()
     bandwidth = cf_max - cf_min
 
+    # The log term absorbs the leading factor of -1, inverting the fraction.
     if rescaled:
         offset = np.log(cf_min/cf_max)
         numerator = bandwidth
diff --git a/quartical/statistics/logging.py b/quartical/statistics/logging.py
index f0d6021c..8a606593 100644
--- a/quartical/statistics/logging.py
+++ b/quartical/statistics/logging.py
@@ -45,7 +45,9 @@ def log_chisq(pre, post, attrs, block_id=None):
     pre = pre.item()
     post = post.item()
 
-    if np.isfinite(pre) and np.isfinite(post):
+    if pre == 0:  # This may be zero in noise-free tests.
+        fractional_change = 0
+    elif np.isfinite(pre) and np.isfinite(post):
         fractional_change = (post - pre) / pre
     else:
         fractional_change = np.nan
diff --git a/quartical/weights/robust.py b/quartical/weights/robust.py
index 208295d2..3d5e80fa 100644
--- a/quartical/weights/robust.py
+++ b/quartical/weights/robust.py
@@ -18,6 +18,7 @@ def update_icovariance_impl(residuals, flags, etas, icovariance, mode):
 @overload(update_icovariance_impl, jit_options=JIT_OPTIONS)
 def nb_update_icovariance_impl(residuals, flags, etas, icovariance, mode):
 
+    coerce_literal(nb_update_icovariance_impl, ["mode"])
     update_covariance_inner = update_covariance_inner_factory(mode)
 
     def impl(residuals, flags, etas, icovariance, mode):
@@ -91,6 +92,7 @@ def update_etas_impl(residuals, flags, etas, icovariance, dof, mode):
 @overload(update_etas_impl, jit_options=JIT_OPTIONS)
 def nb_update_etas_impl(residuals, flags, etas, icovariance, dof, mode):
 
+    coerce_literal(nb_update_etas_impl, ["mode"])
     update_etas_inner = update_etas_inner_factory(mode)
 
     def impl(residuals, flags, etas, icovariance, dof, mode):
diff --git a/testing/fixtures/data_handling.py b/testing/fixtures/data_handling.py
index 6c6b58a3..0e2ee72e 100644
--- a/testing/fixtures/data_handling.py
+++ b/testing/fixtures/data_handling.py
@@ -2,7 +2,8 @@
 from quartical.data_handling.ms_handler import (read_xds_list,
                                                 preprocess_xds_list)
 from quartical.data_handling.model_handler import add_model_graph
-from quartical.data_handling.angles import make_parangle_xds_list
+from quartical.data_handling.angles import (make_parangle_xds_list,
+                                            assign_parangle_data)
 from quartical.statistics.statistics import make_stats_xds_list
 
 
@@ -23,7 +24,8 @@ def ref_xds_list(read_xds_list_output):
 
 @pytest.fixture(scope="module")
 def preprocessed_xds_list(raw_xds_list, ms_opts):
-    return preprocess_xds_list(raw_xds_list, ms_opts)
+    xdsl = preprocess_xds_list(raw_xds_list, ms_opts)
+    return assign_parangle_data(ms_opts.path, xdsl)
 
 
 @pytest.fixture(scope="module")
diff --git a/testing/tests/gains/test_crosshand_phase_null_v.py b/testing/tests/gains/test_crosshand_phase_null_v.py
new file mode 100644
index 00000000..c92876f4
--- /dev/null
+++ b/testing/tests/gains/test_crosshand_phase_null_v.py
@@ -0,0 +1,172 @@
+from copy import deepcopy
+import pytest
+import numpy as np
+import dask.array as da
+from quartical.calibration.calibrate import add_calibration_graph
+from testing.utils.gains import apply_gains
+
+
+@pytest.fixture(scope="module")
+def opts(base_opts):
+
+    # Don't overwrite base config - instead create a copy and update.
+
+    _opts = deepcopy(base_opts)
+
+    _opts.input_ms.select_corr = [0, 1, 2, 3]
+    _opts.solver.terms = ['G']
+    _opts.solver.iter_recipe = [100]
+    _opts.solver.propagate_flags = False
+    _opts.solver.convergence_criteria = 1e-7
+    _opts.solver.convergence_fraction = 1
+    _opts.solver.threads = 2
+    _opts.G.time_interval = 0
+    _opts.G.type = "crosshand_phase_null_v"
+    _opts.G.solve_per = "array"
+
+    return _opts
+
+
+@pytest.fixture(scope="module")
+def raw_xds_list(read_xds_list_output):
+    # Only use the first xds. This overloads the global fixture.
+    return read_xds_list_output[0][:1]
+
+
+@pytest.fixture(scope="module")
+def true_gain_list(predicted_xds_list):
+
+    gain_list = []
+
+    for xds in predicted_xds_list:
+
+        n_ant = xds.sizes["ant"]
+        utime_chunks = xds.UTIME_CHUNKS
+        n_time = sum(utime_chunks)
+        chan_chunks = xds.chunks["chan"]
+        n_chan = xds.sizes["chan"]
+        n_dir = xds.sizes["dir"]
+        n_corr = xds.sizes["corr"]
+
+        chunking = (len(utime_chunks), chan_chunks, n_ant, n_dir, n_corr)
+
+        bound = np.pi
+
+        da.random.seed(0)
+        amp = da.from_array(np.array([1, 0, 0, 1]))
+        phase = da.random.uniform(size=(1, n_chan, 1, n_dir, n_corr),
+                                  high=bound,
+                                  low=-bound,
+                                  chunks=chunking)
+        phase[..., 1:] = 0  # Only introduce the phase on XX or RR.
+
+        gains = amp[None, None, None, None, :]*da.exp(1j*phase)
+
+        shape = (n_time, n_chan, n_ant, n_dir, n_corr)
+
+        gains = da.broadcast_to(gains[:, :, :1], shape)
+
+        gain_list.append(gains)
+
+    return gain_list
+
+
+@pytest.fixture(scope="module")
+def corrupted_data_xds_list(predicted_xds_list, true_gain_list):
+
+    corrupted_data_xds_list = []
+
+    for xds, gains in zip(predicted_xds_list, true_gain_list):
+
+        n_corr = xds.sizes["corr"]
+
+        ant1 = xds.ANTENNA1.data
+        ant2 = xds.ANTENNA2.data
+        time = xds.TIME.data
+
+        row_inds = \
+            time.map_blocks(lambda x: np.unique(x, return_inverse=True)[1])
+
+        model = da.ones(xds.MODEL_DATA.data.shape, dtype=np.complex128)
+
+        model *= da.from_array([1, 0.1, 0.1, 1])
+
+        data = da.blockwise(apply_gains, ("rfc"),
+                            model, ("rfdc"),
+                            gains, ("rfadc"),
+                            ant1, ("r"),
+                            ant2, ("r"),
+                            row_inds, ("r"),
+                            n_corr, None,
+                            align_arrays=False,
+                            concatenate=True,
+                            dtype=model.dtype)
+
+        corrupted_xds = xds.assign({
+            "DATA": ((xds.DATA.dims), data),
+            "MODEL_DATA": ((xds.MODEL_DATA.dims), model),
+            "FLAG": ((xds.FLAG.dims), da.zeros_like(xds.FLAG.data)),
+            "WEIGHT": ((xds.WEIGHT.dims), da.ones_like(xds.WEIGHT.data))
+            }
+        )
+
+        corrupted_data_xds_list.append(corrupted_xds)
+
+    return corrupted_data_xds_list
+
+
+@pytest.fixture(scope="module")
+def add_calibration_graph_outputs(corrupted_data_xds_list, stats_xds_list,
+                                  solver_opts, chain, output_opts):
+    # Overload this fixture as we need to use the corrupted xdss.
+    return add_calibration_graph(corrupted_data_xds_list, stats_xds_list,
+                                 solver_opts, chain, output_opts)
+
+
+# -----------------------------------------------------------------------------
+
+def test_stokes_v_magnitude(cmp_post_solve_data_xds_list):
+    # Magnitude of stokes V in the corrected data should tend to zero.
+    for xds in cmp_post_solve_data_xds_list:
+        corr_data = xds._CORRECTED_DATA.data
+        res_v = -0.5j*corr_data[...,1] + 0.5j*corr_data[..., 2]
+        np.testing.assert_array_almost_equal(np.abs(res_v), 0)
+
+
+def test_solver_flags(cmp_post_solve_data_xds_list):
+    # The solver should not add addiitonal flags to the test data.
+    for xds in cmp_post_solve_data_xds_list:
+        np.testing.assert_array_equal(xds._FLAG.data, xds.FLAG.data)
+
+
+@pytest.mark.xfail(reason="Ambiguous result - signs may be flipped.")
+def test_gains(cmp_gain_xds_lod, true_gain_list):
+
+    for solved_gain_dict, true_gain in zip(cmp_gain_xds_lod, true_gain_list):
+        solved_gain_xds = solved_gain_dict["G"]
+        solved_gain, solved_flags = da.compute(solved_gain_xds.gains.data,
+                                               solved_gain_xds.gain_flags.data)
+        true_gain = true_gain.compute().copy()  # Make mutable.
+
+        true_gain[np.where(solved_flags)] = 0
+        solved_gain[np.where(solved_flags)] = 0
+
+        # To ensure the missing antenna handling doesn't render this test
+        # useless, check that we have non-zero entries first.
+        assert np.any(solved_gain), "All gains are zero!"
+        np.testing.assert_array_almost_equal(true_gain[:1], solved_gain)
+
+
+def test_gain_flags(cmp_gain_xds_lod):
+
+    for solved_gain_dict in cmp_gain_xds_lod:
+        solved_gain_xds = solved_gain_dict["G"]
+        solved_flags = solved_gain_xds.gain_flags.values
+
+        frows, fchans, fants, fdir = np.where(solved_flags)
+
+        # We know that these antennas are missing in the test data. No other
+        # antennas should have flags.
+        assert set(np.unique(fants)) == {18, 20}
+
+# -----------------------------------------------------------------------------
diff --git a/testing/tests/gains/test_delay_and_tec.py b/testing/tests/gains/test_delay_and_tec.py
new file mode 100644
index 00000000..b3f1e6cb
--- /dev/null
+++ b/testing/tests/gains/test_delay_and_tec.py
@@ -0,0 +1,200 @@
+from copy import deepcopy
+import pytest
+import numpy as np
+import dask.array as da
+from quartical.calibration.calibrate import add_calibration_graph
+from testing.utils.gains import apply_gains, reference_gains
+
+
+@pytest.fixture(scope="module")
+def opts(base_opts, select_corr):
+
+    # Don't overwrite base config - instead create a copy and update.
+
+    _opts = deepcopy(base_opts)
+
+    _opts.input_ms.select_corr = select_corr
+    _opts.solver.terms = ['G']
+    _opts.solver.iter_recipe = [100]
+    _opts.solver.propagate_flags = False
+    _opts.solver.convergence_criteria = 1e-7
+    _opts.solver.convergence_fraction = 1
+    _opts.solver.threads = 2
+    _opts.G.type = "delay_and_tec"
+    _opts.G.freq_interval = 0
+
+    return _opts
+
+
+@pytest.fixture(scope="module")
+def raw_xds_list(read_xds_list_output):
+    # Only use the first xds. This overloads the global fixture.
+    return read_xds_list_output[0][:1]
+
+
+@pytest.fixture(scope="module")
+def true_gain_list(predicted_xds_list):
+
+    gain_list = []
+
+    for xds in predicted_xds_list:
+
+        n_ant = xds.sizes["ant"]
+        utime_chunks = xds.UTIME_CHUNKS
+        n_time = sum(utime_chunks)
+        chan_chunks = xds.chunks["chan"]
+        n_chan = xds.sizes["chan"]
+        n_dir = xds.sizes["dir"]
+        n_corr = xds.sizes["corr"]
+
+        chan_freq = xds.CHAN_FREQ.data
+        chan_width = chan_freq[1] - chan_freq[0]
+        cf_min = chan_freq.min()
+        cf_max = chan_freq.max()
+        band_centre = (cf_min + cf_max) / 2
+
+        max_tec = 1/(1/chan_freq[-1] - 1/chan_freq[-2])
+
+        chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr)
+        tec_chunking = (utime_chunks, 1, n_ant, n_dir, n_corr)
+
+        da.random.seed(0)
+        tec = da.random.uniform(
+            size=(n_time, 1, n_ant, n_dir, n_corr),
+            low=-0.05*max_tec,
+            high=0.05*max_tec,
+            chunks=tec_chunking
+        )
+        tec[:, :, 0, :, :] = 0  # Zero the reference antenna for safety.
+
+        delays = da.random.uniform(
+            size=(n_time, 1, n_ant, n_dir, n_corr),
+            low=-1/(2*chan_width),
+            high=1/(2*chan_width)
+        )
+        delays *= 0.2  # Remove once we have initial enstimates.
+        delays[:, :, 0, :, :] = 0  # Zero the reference antenna for safety.
+
+        amp = da.ones((n_time, n_chan, n_ant, n_dir, n_corr),
+                      chunks=chunking)
+
+        if n_corr == 4:  # This solver only considers the diagonal elements.
+            amp *= da.array([1, 0, 0, 1])
+
+        tec_offset = np.log(cf_min/cf_max)/(cf_max - cf_min)
+        tec_coeff = \
+            2 * np.pi * (1/chan_freq[None, :, None, None, None] + tec_offset)
+        delay_coeff = \
+            2*np.pi*(chan_freq[None, :, None, None, None] - band_centre)
+
+        phase = tec*tec_coeff + delays*delay_coeff 
+        gains = amp*da.exp(1j*phase)
+
+        gain_list.append(gains)
+
+    return gain_list
+
+
+@pytest.fixture(scope="module")
+def corrupted_data_xds_list(predicted_xds_list, true_gain_list):
+
+    corrupted_data_xds_list = []
+
+    for xds, gains in zip(predicted_xds_list, true_gain_list):
+
+        n_corr = xds.sizes["corr"]
+
+        ant1 = xds.ANTENNA1.data
+        ant2 = xds.ANTENNA2.data
+        time = xds.TIME.data
+
+        row_inds = \
+            time.map_blocks(lambda x: np.unique(x, return_inverse=True)[1])
+
+        model = da.ones(xds.MODEL_DATA.data.shape, dtype=np.complex128)
+
+        data = da.blockwise(apply_gains, ("rfc"),
+                            model, ("rfdc"),
+                            gains, ("rfadc"),
+                            ant1, ("r"),
+                            ant2, ("r"),
+                            row_inds, ("r"),
+                            n_corr, None,
+                            align_arrays=False,
+                            concatenate=True,
+                            dtype=model.dtype)
+
+        corrupted_xds = xds.assign({
+            "DATA": ((xds.DATA.dims), data),
+            "MODEL_DATA": ((xds.MODEL_DATA.dims), model),
+            "FLAG": ((xds.FLAG.dims), da.zeros_like(xds.FLAG.data)),
+            "WEIGHT": ((xds.WEIGHT.dims), da.ones_like(xds.WEIGHT.data))
+            }
+        )
+
+        corrupted_data_xds_list.append(corrupted_xds)
+
+    return corrupted_data_xds_list
+
+
+@pytest.fixture(scope="module")
+def add_calibration_graph_outputs(corrupted_data_xds_list, stats_xds_list,
+                                  solver_opts, chain, output_opts):
+    # Overload this fixture as we need to use the corrupted xdss.
+    return add_calibration_graph(corrupted_data_xds_list, stats_xds_list,
+                                 solver_opts, chain, output_opts)
+
+
+# -----------------------------------------------------------------------------
+
+def test_residual_magnitude(cmp_post_solve_data_xds_list):
+    # Magnitude of the residuals should tend to zero.
+    for xds in cmp_post_solve_data_xds_list:
+        residual = xds._RESIDUAL.data
+        if residual.shape[-1] == 4:
+            residual = residual[..., (0, 3)]  # Only check on-diagonal terms.
+        np.testing.assert_array_almost_equal(np.abs(residual), 0)
+
+
+def test_solver_flags(cmp_post_solve_data_xds_list):
+    # The solver should not add addiitonal flags to the test data.
+    for xds in cmp_post_solve_data_xds_list:
+        np.testing.assert_array_equal(xds._FLAG.data, xds.FLAG.data)
+
+
+def test_gains(cmp_gain_xds_lod, true_gain_list):
+
+    for solved_gain_dict, true_gain in zip(cmp_gain_xds_lod, true_gain_list):
+        solved_gain_xds = solved_gain_dict["G"]
+        solved_gain, solved_flags = da.compute(solved_gain_xds.gains.data,
+                                               solved_gain_xds.gain_flags.data)
+        true_gain = true_gain.compute()  # TODO: This could be done elsewhere.
+
+        n_corr = true_gain.shape[-1]
+
+        solved_gain = reference_gains(solved_gain, n_corr)
+        true_gain = reference_gains(true_gain, n_corr)
+
+        true_gain[np.where(solved_flags)] = 0
+        solved_gain[np.where(solved_flags)] = 0
+
+        # To ensure the missing antenna handling doesn't render this test
+        # useless, check that we have non-zero entries first.
+        assert np.any(solved_gain), "All gains are zero!"
+        np.testing.assert_array_almost_equal(true_gain, solved_gain)
+
+
+def test_gain_flags(cmp_gain_xds_lod):
+
+    for solved_gain_dict in cmp_gain_xds_lod:
+        solved_gain_xds = solved_gain_dict["G"]
+        solved_flags = solved_gain_xds.gain_flags.values
+
+        frows, fchans, fants, fdir = np.where(solved_flags)
+
+        # We know that these antennas are missing in the test data. No other
+        # antennas should have flags.
+        assert set(np.unique(fants)) == {18, 20}
+
+
+# -----------------------------------------------------------------------------
diff --git a/testing/tests/gains/test_parallactic_angle.py b/testing/tests/gains/test_parallactic_angle.py
new file mode 100644
index 00000000..945f9552
--- /dev/null
+++ b/testing/tests/gains/test_parallactic_angle.py
@@ -0,0 +1,177 @@
+from copy import deepcopy
+import pytest
+import numpy as np
+import dask.array as da
+from quartical.calibration.calibrate import add_calibration_graph
+from testing.utils.gains import apply_gains
+
+
+@pytest.fixture(scope="module")
+def opts(base_opts):
+
+    # Don't overwrite base config - instead create a copy and update.
+
+    _opts = deepcopy(base_opts)
+
+    _opts.input_ms.select_corr = [0, 1, 2, 3]
+    _opts.input_model.apply_p_jones = False
+    _opts.output.apply_p_jones_inv = False
+    _opts.solver.terms = ['G']
+    _opts.solver.iter_recipe = [0]
+    _opts.solver.propagate_flags = False
+    _opts.solver.convergence_criteria = 1e-7
+    _opts.solver.convergence_fraction = 1
+    _opts.solver.threads = 2
+    _opts.G.freq_interval = 0
+    _opts.G.time_interval = 1
+    _opts.G.type = "parallactic_angle"
+
+    return _opts
+
+
+@pytest.fixture(scope="module")
+def raw_xds_list(read_xds_list_output):
+    # Only use the first xds. This overloads the global fixture.
+    return read_xds_list_output[0][:1]
+
+
+@pytest.fixture(scope="module", params=["linear", "circular"])
+def feed_type(request):
+    return request.param
+
+
+@pytest.fixture(scope="module")
+def true_gain_list(predicted_xds_list, parangle_xds_list, feed_type):
+
+    gain_list = []
+
+    for xds, pxds in zip(predicted_xds_list, parangle_xds_list):
+
+        n_ant = xds.sizes["ant"]
+        utime_chunks = xds.UTIME_CHUNKS
+        n_time = sum(utime_chunks)
+        chan_chunks = xds.chunks["chan"]
+        n_chan = xds.sizes["chan"]
+        n_dir = xds.sizes["dir"]
+        n_corr = xds.sizes["corr"]
+
+        shape = (n_time, n_chan, n_ant, n_dir, n_corr)
+        chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr)
+
+        gains = da.zeros(shape, chunks=chunking, dtype=np.complex128)
+        phase = pxds.PARALLACTIC_ANGLES.data[:, None, :, None, :]
+
+        if feed_type == "circular":
+            gains[..., 0] = da.exp(-1j*phase[..., 0])
+            gains[..., 3] = da.exp(1j*phase[..., 1])
+        elif feed_type == "linear":
+            gains[..., 0] = da.cos(phase[..., 0])
+            gains[..., 1] = da.sin(phase[..., 0])
+            gains[..., 2] = -da.sin(phase[..., 1])
+            gains[..., 3] = da.cos(phase[..., 1])
+        else:
+            raise ValueError(f"Unsupported feed type - {feed_type}.")
+
+        gain_list.append(gains)
+
+    return gain_list
+
+
+@pytest.fixture(scope="module")
+def corrupted_data_xds_list(predicted_xds_list, true_gain_list, feed_type):
+
+    corrupted_data_xds_list = []
+
+    for xds, gains in zip(predicted_xds_list, true_gain_list):
+
+        n_corr = xds.sizes["corr"]
+
+        ant1 = xds.ANTENNA1.data
+        ant2 = xds.ANTENNA2.data
+        time = xds.TIME.data
+
+        row_inds = \
+            time.map_blocks(lambda x: np.unique(x, return_inverse=True)[1])
+
+        model = da.ones(xds.MODEL_DATA.data.shape, dtype=np.complex128)
+
+        model *= da.from_array([1, 0.1, 0.1, 1])
+
+        data = da.blockwise(apply_gains, ("rfc"),
+                            model, ("rfdc"),
+                            gains, ("rfadc"),
+                            ant1, ("r"),
+                            ant2, ("r"),
+                            row_inds, ("r"),
+                            n_corr, None,
+                            align_arrays=False,
+                            concatenate=True,
+                            dtype=model.dtype)
+
+        corrupted_xds = xds.assign({
+            "DATA": ((xds.DATA.dims), data),
+            "MODEL_DATA": ((xds.MODEL_DATA.dims), model),
+            "FLAG": ((xds.FLAG.dims), da.zeros_like(xds.FLAG.data)),
+            "WEIGHT": ((xds.WEIGHT.dims), da.ones_like(xds.WEIGHT.data))
+            }
+        )
+
+        corrupted_xds.attrs["FEED_TYPE"] = feed_type  # Change feed type.
+
+        corrupted_data_xds_list.append(corrupted_xds)
+
+    return corrupted_data_xds_list
+
+
+@pytest.fixture(scope="module")
+def add_calibration_graph_outputs(corrupted_data_xds_list, stats_xds_list,
+                                  solver_opts, chain, output_opts):
+    # Overload this fixture as we need to use the corrupted xdss.
+    return add_calibration_graph(corrupted_data_xds_list, stats_xds_list,
+                                 solver_opts, chain, output_opts)
+
+
+# -----------------------------------------------------------------------------
+
+def test_residual_magnitude(cmp_post_solve_data_xds_list):
+    # Magnitude of the residuals should tend to zero.
+    for xds in cmp_post_solve_data_xds_list:
+        np.testing.assert_array_almost_equal(np.abs(xds._RESIDUAL.data), 0)
+
+
+def test_solver_flags(cmp_post_solve_data_xds_list):
+    # The solver should not add addiitonal flags to the test data.
+    for xds in cmp_post_solve_data_xds_list:
+        np.testing.assert_array_equal(xds._FLAG.data, xds.FLAG.data)
+
+
+def test_gains(cmp_gain_xds_lod, true_gain_list):
+
+    for solved_gain_dict, true_gain in zip(cmp_gain_xds_lod, true_gain_list):
+        solved_gain_xds = solved_gain_dict["G"]
+        solved_gain, solved_flags = da.compute(solved_gain_xds.gains.data,
+                                               solved_gain_xds.gain_flags.data)
+        true_gain = true_gain.compute().copy()  # Make mutable.
+
+        true_gain[np.where(solved_flags)] = 0
+        solved_gain[np.where(solved_flags)] = 0
+
+        # To ensure the missing antenna handling doesn't render this test
+        # useless, check that we have non-zero entries first.
+        assert np.any(solved_gain), "All gains are zero!"
+        np.testing.assert_array_almost_equal(true_gain, solved_gain)
+
+
+def test_gain_flags(cmp_gain_xds_lod):
+
+    for solved_gain_dict in cmp_gain_xds_lod:
+        solved_gain_xds = solved_gain_dict["G"]
+        solved_flags = solved_gain_xds.gain_flags.values
+
+        frows, fchans, fants, fdir = np.where(solved_flags)
+
+        # We know that these antennas are missing in the test data. No other
+        # antennas should have flags.
+        assert set(np.unique(fants)) == {18, 20}
+
+# -----------------------------------------------------------------------------