diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index caf7554..c476df4 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -28,27 +28,12 @@ jobs:
 
       - uses: actions/checkout@v2
 
-      - name: Cache conda pacakges
-        uses: actions/cache@v2
-        env:
-          # Increase this value to reset cache if etc/example-environment.yml has not changed
-          CACHE_NUMBER: 0
-        with:
-          path: ~/conda_pkgs_dir
-          key:
-            conda-${{ steps.detect.outputs.platform }}-${{ env.CACHE_NUMBER }}-${{hashFiles('conda-lock.yml') }}
-
       - name: Set up conda environment
-        uses: conda-incubator/setup-miniconda@v2
+        uses: mamba-org/setup-micromamba@v1
         with:
-          auto-update-conda: false
-          activate-environment: test-env
-          environment-file: conda-${{ steps.detect.outputs.platform }}.lock
-      
-      - name: Install pip deps
-        run: |
-          cat conda-${{ steps.detect.outputs.platform }}.lock | awk '/^# pip/ {print substr($0,7)}' > requirements.txt
-          pip install --no-deps -r requirements.txt
+          environment-file: conda-lock.yml
+          environment-name: ci
+          create-args: --category main dev
 
       - name: Install project
         env:
diff --git a/.gitignore b/.gitignore
index 70009bb..563212d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 *.DS_Store
 *.pyc
 *.egg-info
-data/
 figures/**/*.json.gz
 figures/**/*.pdf
 notebooks/**/*.pdf
diff --git a/README.md b/README.md
index 39007ae..7a06358 100644
--- a/README.md
+++ b/README.md
@@ -18,17 +18,19 @@ sensitivities in different units depending on context.
 ## Installation
 
 The way to install `toise` and its depedencies is
-with `conda`. If you do not already have `miniconda` on your system, obtain the
-installer from https://conda.io/miniconda.html, and install in a location of
-your choice, e.g.:
+with [`micromamba`](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html). If you do not already have `micromamba` on your system,
 
-```sh
-sh ./Miniconda3-latest-Linux-x86_64.sh -p $CONDA_PREFIX
+```console
+"${SHELL}" <(curl -L micro.mamba.pm/install.sh)
 ```
 
-replacing `CONDA_PREFIX` with the prefix you chose.
+or, os macOS with Homebrew:
+
+```console
+brew install micromamba
+```
 
-Then, obtain `toise`:
+Next, obtain `toise`:
 
 ```sh
 git clone git@github.com:icecube/toise.git
@@ -37,21 +39,9 @@ git clone git@github.com:icecube/toise.git
 Then, from the `toise` source directory, create a new environment:
 ```sh
 cd toise
-$CONDA_PREFIX/bin/conda env create -n toise --file environment.yml
-```
-
-This will also download the required data tables.
-
-The above will install the latest available versions of all dependencies. You can also install exactly the versions that were most recently tested with:
-```sh
-cd toise
-
-PLATFORM_LOCKFILE=$(conda info --json | jq -r '"conda-\(.platform).lock"')
-conda create -n toise --file $PLATFORM_LOCKFILE
-cat $PLATFORM_LOCKFILE | awk '/^# pip/ {print substr($0,7)}' > requirements.txt
-conda run -n toise pip install -r requirements.txt -e .
+micromamba create -n toise --file conda-lock.yml
+conda run -n toise pip install -e .
 ```
-This should be much faster, as it does not have to solve for compatible versions of all the dependencies. If you do not have `jq` installed, you can set `PLATFORM_LOCKFILE` by hand to e.g. `conda-osx-64.lock`.
 
 If you have a Jupyter notebook installation from another conda environment, you should now be able to open this notebook in Jupyter and select the "Python [conda env:miniconda3-toise]" kernel.
 
diff --git a/conda-linux-64.lock b/conda-linux-64.lock
deleted file mode 100644
index 88f85f3..0000000
--- a/conda-linux-64.lock
+++ /dev/null
@@ -1,151 +0,0 @@
-# Generated by conda-lock.
-# platform: linux-64
-# input_hash: 13ebdcf8f949e9eb9f28381b24c35bb22c2b973b5e0c104ac3162438a88f4461
-@EXPLICIT
-https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
-https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-1_gnu.tar.bz2#561e277319a41d4f24f5c05a9ef63c04
-https://conda.anaconda.org/conda-forge/linux-64/astropy-5.0-py39hce5d2b2_0.tar.bz2#5a2d9b5961e84cbbac615c2e3fb7e456
-https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.0-h9c3ff4c_0.tar.bz2#5e815e5126c6f7e34ab4496fa1b48dca
-https://conda.anaconda.org/conda-forge/linux-64/boost-1.74.0-py39h5472131_4.tar.bz2#d749fc6bdf3cda293c5714f5f4bb5708
-https://conda.anaconda.org/conda-forge/linux-64/boost-cpp-1.74.0-h359cf19_5.tar.bz2#5778a2e0609975fac9b24ed637180d28
-https://conda.anaconda.org/conda-forge/linux-64/brotli-1.0.9-h7f98852_6.tar.bz2#612385c4a83edb0619fe911d9da317f4
-https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.0.9-h7f98852_6.tar.bz2#9e94bf16f14c78a36561d5019f490d22
-https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54
-https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.18.1-h7f98852_0.tar.bz2#f26ef8098fab1f719c91eb760d63381a
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2021.10.8-ha878542_0.tar.bz2#575611b8a84f45960e87722eeb51fa26
-https://conda.anaconda.org/conda-forge/linux-64/certifi-2021.10.8-py39hf3d152e_1.tar.bz2#67982d98030fde9139a64219dbe4db5e
-https://conda.anaconda.org/conda-forge/linux-64/cfitsio-3.470-hb418390_7.tar.bz2#5bda2e5ca5edb0c207c3ec76f57f8ecc
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-https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h7f98852_6.tar.bz2#b0f44f63f7d771d7670747a1dd5d5ac1
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-https://conda.anaconda.org/conda-forge/noarch/backports-1.0-py_2.tar.bz2#0da16b293affa6ac31812376f8eb79dd
-https://conda.anaconda.org/conda-forge/noarch/backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0.tar.bz2#c5b3edc62d6309088f4970b3eaaa65a6
-https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.4-pyh9f0ad1d_0.tar.bz2#c08b4c1326b880ed44f3ffb04803332f
-https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb
-https://conda.anaconda.org/conda-forge/noarch/decorator-5.1.0-pyhd8ed1ab_0.tar.bz2#71b13ba2e0efcff45646f05d619534b0
-https://conda.anaconda.org/conda-forge/noarch/importlib_resources-5.4.0-pyhd8ed1ab_0.tar.bz2#9fb134dbabe7851a9d71411064b2c30d
-https://conda.anaconda.org/conda-forge/noarch/iniconfig-1.1.1-pyh9f0ad1d_0.tar.bz2#39161f81cc5e5ca45b8226fbb06c6905
-https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2#5071c982548b3a20caf70462f04f5287
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.2.1-pyhd8ed1ab_0.tar.bz2#0a4d10a794fc48016d8d0453365180ad
-https://conda.anaconda.org/conda-forge/noarch/jupyter_client-7.1.0-pyhd8ed1ab_0.tar.bz2#c1460cc25b6a86d1b66ab6c3c471b791
-https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.3-pyhd8ed1ab_0.tar.bz2#be3bfd435802d2c768c6b2439f325f3d
-https://conda.anaconda.org/conda-forge/noarch/more-itertools-8.12.0-pyhd8ed1ab_0.tar.bz2#1338809c9c8e56f1b6f69d4b8e0ed7f7
-https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
-https://conda.anaconda.org/conda-forge/noarch/nbclient-0.5.9-pyhd8ed1ab_0.tar.bz2#21554dd9bbabff27ae87f715fee4d56c
-https://conda.anaconda.org/conda-forge/noarch/nbformat-5.1.3-pyhd8ed1ab_0.tar.bz2#bafa5df6d4f8db69a4d197b4657127e7
-https://conda.anaconda.org/conda-forge/noarch/nbmake-0.10-pyhd8ed1ab_0.tar.bz2#7c480ae8b7cb9474ae9d646e6c19c313
-https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.5.1-pyhd8ed1ab_0.tar.bz2#47a51a5b8f9cc41004c8d7bd88b62447
-https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2#9a66894dfd07c4510beb6b3f9672ccc0
-https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-pyh9f0ad1d_1.tar.bz2#0b2e68acc8c78c8cc392b90983481f58
-https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
-https://conda.anaconda.org/conda-forge/noarch/parso-0.8.2-pyhd8ed1ab_0.tar.bz2#fb40b157bd62b457a1cc82527b63f0b0
-https://conda.anaconda.org/conda-forge/noarch/pexpect-4.8.0-pyh9f0ad1d_2.tar.bz2#5909e7b978141dd80d28dbf9de627827
-https://conda.anaconda.org/conda-forge/noarch/pip-21.3.1-pyhd8ed1ab_0.tar.bz2#e4fe2a9af78ff11f1aced7e62128c6a8
-https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.22-pyha770c72_0.tar.bz2#a749a230f0d950eae1dedf13ff344b15
-https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2#359eeb6536da0e687af562ed265ec263
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
-https://conda.anaconda.org/conda-forge/noarch/pygments-2.10.0-pyhd8ed1ab_0.tar.bz2#32bcce837f1316f1c3208118b6c5e5fc
-https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.0.6-pyhd8ed1ab_0.tar.bz2#3087df8c636c5a00e694605c39ce4982
-https://conda.anaconda.org/conda-forge/noarch/pytest-runner-5.3.1-pyhd8ed1ab_0.tar.bz2#746605234be5c988579ba312035c3959
-https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
-https://conda.anaconda.org/conda-forge/noarch/pytz-2021.3-pyhd8ed1ab_0.tar.bz2#7e4f811bff46a5a6a7e0094921389395
-https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
-https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
-https://conda.anaconda.org/conda-forge/noarch/toolz-0.11.2-pyhd8ed1ab_0.tar.bz2#f348d1590550371edfac5ed3c1d44f7e
-https://conda.anaconda.org/conda-forge/noarch/tqdm-4.62.3-pyhd8ed1ab_0.tar.bz2#24ed42db084efd6e615babec03c2b460
-https://conda.anaconda.org/conda-forge/noarch/traitlets-5.1.1-pyhd8ed1ab_0.tar.bz2#a1bc9765ef9499760e88f568b3a6622c
-https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.0.0-hd8ed1ab_0.tar.bz2#673180987c7cedf380792f046425d37d
-https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.0.0-pyha770c72_0.tar.bz2#867353d58b5967a994dd3cec85166ad8
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2021e-he74cb21_0.tar.bz2#a751ec502589ebdc2eceb183ff602569
-https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.5-pyh9f0ad1d_2.tar.bz2#5266fcd697043c59621fda522b3d78ee
-https://conda.anaconda.org/conda-forge/noarch/wheel-0.37.0-pyhd8ed1ab_1.tar.bz2#3aa2c3e25dd361b453d010388b9cdff1
-https://conda.anaconda.org/conda-forge/noarch/zipp-3.6.0-pyhd8ed1ab_0.tar.bz2#855e2c4622f5eb50a4f6f7167b9ba17a
-# pip easy-cache @ https://files.pythonhosted.org/packages/01/47/1d30783a8ce9df947f4d0c67726b3ecd0dfd22e363684b4eb1a8bf59e6e4/easy_cache-1.1.0-py3-none-any.whl#sha256=d99412159074d39891c83098dd5f0ca63bb56103e46efaa7e8455560680ce282
-# pip pydashi @ https://github.com/IceCube-SPNO/dashi/archive/refs/tags/v2.0.0-alpha.0.tar.gz
-# pip pytest-pinned @ https://files.pythonhosted.org/packages/4a/08/98aad61bab14108d5dc6651d121994f955bd39fdd413bf8a2a2dc4d2ce7f/pytest_pinned-0.4.2-py3-none-any.whl#sha256=ebf8b6cd32772e940c20871f5c09ca35d18155390e649823b8435299a25bd88e
diff --git a/conda-lock.yml b/conda-lock.yml
index 571c538..4d4670f 100644
--- a/conda-lock.yml
+++ b/conda-lock.yml
@@ -1,24 +1,26 @@
-# This lock file was generated by conda-lock (https://github.com/conda-incubator/conda-lock). DO NOT EDIT!
+# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). DO NOT EDIT!
 #
 # A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike
 # e.g. `conda env create`, the resulting environment will not change as new package versions become
 # available, unless you explicitly update the lock file.
 #
 # Install this environment as "YOURENV" with:
-#     conda-lock install -n YOURENV --file conda-lock.yml
+#     conda-lock install -n YOURENV conda-lock.yml
 # This lock contains optional development dependencies. Include them in the installed environment with:
-#     conda-lock install --dev-dependencies -n YOURENV --file conda-lock.yml
+#     conda-lock install --dev-dependencies -n YOURENV conda-lock.yml
 # To update a single package to the latest version compatible with the version constraints in the source:
-#     conda-lock lock --lockfile conda-lock.yml --update PACKAGE
+#     conda-lock lock  --lockfile conda-lock.yml --update PACKAGE
 # To re-solve the entire environment, e.g. after changing a version constraint in the source file:
 #     conda-lock -f environment.yml -f dev-environment.yml --lockfile conda-lock.yml
+version: 1
 metadata:
-  channels:
-  - conda-forge
   content_hash:
-    linux-64: 13ebdcf8f949e9eb9f28381b24c35bb22c2b973b5e0c104ac3162438a88f4461
-    osx-64: 2ddd5b82e77c56c492229b935e16dabe4c4f68bcb2ec2abb298133beac3b6719
-    osx-arm64: f33ec806c688b70fbd1d29cad24483d1ae395a0124fc2fc9c2bca1705893e4ad
+    linux-64: a60006b65182c36afc3664df137bc30d196232a8ff5359d074ca9bd7b3ffea7f
+    osx-64: 7462d7b862d1f4c7d884b73be4631598448d0ee47f490911fee82df83ed3b995
+    osx-arm64: 0b4ba1777785bd8aa779c0dd7dfbbef61a7c3d1e0d89839d452155d857eaf2eb
+  channels:
+  - url: conda-forge
+    used_env_vars: []
   platforms:
   - linux-64
   - osx-64
@@ -27,5101 +29,6725 @@ metadata:
   - environment.yml
   - dev-environment.yml
 package:
-- category: main
-  dependencies: {}
-  hash: md5:d7c89558ba9fa0495403155b64376d81
+- name: _libgcc_mutex
+  version: '0.1'
   manager: conda
-  name: _libgcc_mutex
-  optional: false
   platform: linux-64
+  dependencies: {}
   url: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
-  version: '0.1'
-- category: main
-  dependencies:
-    _libgcc_mutex: 0.1 conda_forge
-    libgomp: '>=7.5.0'
-  hash: md5:561e277319a41d4f24f5c05a9ef63c04
-  manager: conda
-  name: _openmp_mutex
+  hash:
+    md5: d7c89558ba9fa0495403155b64376d81
+    sha256: fe51de6107f9edc7aa4f786a70f4a883943bc9d39b3bb7307c04c41410990726
+  category: main
   optional: false
-  platform: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-1_gnu.tar.bz2
+- name: _openmp_mutex
   version: '4.5'
-- category: main
-  dependencies:
-    python: '>=3.9,<3.10.0a0'
-    python_abi: 3.9.* *_cp39
-  hash: md5:325d6f906987a5ca236cc6544594f894
   manager: conda
-  name: appnope
+  platform: linux-64
+  dependencies:
+    _libgcc_mutex: '0.1'
+    libgomp: '>=7.5.0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
+  hash:
+    md5: 73aaf86a425cc6e73fcf236a5a46396d
+    sha256: fbe2c5e56a653bebb982eda4876a9178aedfc2b545f25d0ce9c4c0b508253d22
+  category: main
   optional: false
+- name: appnope
+  version: 0.1.4
+  manager: conda
   platform: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/appnope-0.1.2-py39h6e9494a_2.tar.bz2
-  version: 0.1.2
-- category: main
   dependencies:
-    python: '>=3.9,<3.10.0a0 *_cpython'
-    python_abi: 3.9.* *_cp39
-  hash: md5:7fb8a37f9e9afaad0210974e1452f84a
-  manager: conda
-  name: appnope
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_0.conda
+  hash:
+    md5: cc4834a9ee7cc49ce8d25177c47b10d8
+    sha256: 45ae2d41f4a4dcf8707633d3d7ae376fc62f0c09b1d063c3049c3f6f8c911670
+  category: main
   optional: false
+- name: appnope
+  version: 0.1.4
+  manager: conda
   platform: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/appnope-0.1.2-py39h2804cbe_2.tar.bz2
-  version: 0.1.2
-- category: main
   dependencies:
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_0.conda
+  hash:
+    md5: cc4834a9ee7cc49ce8d25177c47b10d8
+    sha256: 45ae2d41f4a4dcf8707633d3d7ae376fc62f0c09b1d063c3049c3f6f8c911670
+  category: main
+  optional: false
+- name: astropy
+  version: 6.0.1
+  manager: conda
+  platform: linux-64
+  dependencies:
+    astropy-iers-data: '>=0.2024.2.26.0.28.55'
     importlib-metadata: ''
-    libgcc-ng: '>=9.4.0'
-    numpy: '>=1.19.5,<2.0a0'
+    libgcc-ng: '>=12'
+    numpy: '>=1.26.4,<2.0a0'
     packaging: '>=19.0'
-    pyerfa: '>=2.0'
-    python: '>=3.9,<3.10.0a0'
-    python_abi: 3.9.* *_cp39
+    pyerfa: '>=2.0.1.1'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
     pyyaml: '>=3.13'
-  hash: md5:5a2d9b5961e84cbbac615c2e3fb7e456
-  manager: conda
-  name: astropy
+  url: https://conda.anaconda.org/conda-forge/linux-64/astropy-6.0.1-py312hc7c0aa3_0.conda
+  hash:
+    md5: 7783f5436a1cdf53767ce6710cdd1649
+    sha256: fcf7a99ea0d9259f62eb84fa42c81aaa61316ab6e63feec367eda2948f3ba2f3
+  category: main
   optional: false
-  platform: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/astropy-5.0-py39hce5d2b2_0.tar.bz2
-  version: '5.0'
-- category: main
+- name: astropy
+  version: 6.0.1
+  manager: conda
+  platform: osx-64
   dependencies:
+    astropy-iers-data: '>=0.2024.2.26.0.28.55'
     importlib-metadata: ''
-    numpy: '>=1.19.5,<2.0a0'
+    numpy: '>=1.26.4,<2.0a0'
     packaging: '>=19.0'
-    pyerfa: '>=2.0'
-    python: '>=3.9,<3.10.0a0'
-    python_abi: 3.9.* *_cp39
+    pyerfa: '>=2.0.1.1'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
     pyyaml: '>=3.13'
-  hash: md5:e2273d4681c25a1e1f92c02e3879530d
-  manager: conda
-  name: astropy
+  url: https://conda.anaconda.org/conda-forge/osx-64/astropy-6.0.1-py312h3f2338b_0.conda
+  hash:
+    md5: 057f5d530d337b37ba2cf4c4b43af622
+    sha256: f39386053431883341da28606d779e058c1d31ce123995e749e5bdafb6ef95d2
+  category: main
   optional: false
-  platform: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/astropy-5.0-py39hc89836e_0.tar.bz2
-  version: '5.0'
-- category: main
+- name: astropy
+  version: 6.0.1
+  manager: conda
+  platform: osx-arm64
   dependencies:
+    astropy-iers-data: '>=0.2024.2.26.0.28.55'
     importlib-metadata: ''
-    numpy: '>=1.19.5,<2.0a0'
+    numpy: '>=1.26.4,<2.0a0'
     packaging: '>=19.0'
-    pyerfa: '>=2.0'
-    python: '>=3.9,<3.10.0a0 *_cpython'
-    python_abi: 3.9.* *_cp39
+    pyerfa: '>=2.0.1.1'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
     pyyaml: '>=3.13'
-  hash: md5:7c6f7b89e0488f2e34a9206ddae816a5
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/astropy-6.0.1-py312hf635c46_0.conda
+  hash:
+    md5: 5f46c4840a46c1f6f74ca45d714fcde1
+    sha256: c549da5accf0ed4d2c7ccd5b411fb756fc803dd6f4c295b2f42362b4f4874a3e
+  category: main
+  optional: false
+- name: astropy-iers-data
+  version: 0.2024.5.6.0.29.28
+  manager: conda
+  platform: linux-64
+  dependencies:
+    python: '>=3.8'
+  url: https://conda.anaconda.org/conda-forge/noarch/astropy-iers-data-0.2024.5.6.0.29.28-pyhd8ed1ab_0.conda
+  hash:
+    md5: f346749901ba426e51ac1ef7a8ee6ae6
+    sha256: b67e72b6489753f471233c4ef3cbd215662946d90f49f785c4cca15f347baba0
+  category: main
+  optional: false
+- name: astropy-iers-data
+  version: 0.2024.5.6.0.29.28
   manager: conda
-  name: astropy
+  platform: osx-64
+  dependencies:
+    python: '>=3.8'
+  url: https://conda.anaconda.org/conda-forge/noarch/astropy-iers-data-0.2024.5.6.0.29.28-pyhd8ed1ab_0.conda
+  hash:
+    md5: f346749901ba426e51ac1ef7a8ee6ae6
+    sha256: b67e72b6489753f471233c4ef3cbd215662946d90f49f785c4cca15f347baba0
+  category: main
   optional: false
+- name: astropy-iers-data
+  version: 0.2024.5.6.0.29.28
+  manager: conda
   platform: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/astropy-5.0-py39h20ef057_0.tar.bz2
-  version: '5.0'
-- category: dev
   dependencies:
-    python: '>2.7'
-  hash: md5:d56c596e61b1c4952acf0a9920856c12
+    python: '>=3.8'
+  url: https://conda.anaconda.org/conda-forge/noarch/astropy-iers-data-0.2024.5.6.0.29.28-pyhd8ed1ab_0.conda
+  hash:
+    md5: f346749901ba426e51ac1ef7a8ee6ae6
+    sha256: b67e72b6489753f471233c4ef3cbd215662946d90f49f785c4cca15f347baba0
+  category: main
+  optional: false
+- name: asttokens
+  version: 2.4.1
   manager: conda
-  name: async_generator
-  optional: true
   platform: linux-64
-  url: https://conda.anaconda.org/conda-forge/noarch/async_generator-1.10-py_0.tar.bz2
-  version: '1.10'
-- category: dev
   dependencies:
-    python: '>2.7'
-  hash: md5:d56c596e61b1c4952acf0a9920856c12
+    python: '>=3.5'
+    six: '>=1.12.0'
+  url: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
+  hash:
+    md5: 5f25798dcefd8252ce5f9dc494d5f571
+    sha256: 708168f026df19a0344983754d27d1f7b28bb21afc7b97a82f02c4798a3d2111
+  category: main
+  optional: false
+- name: asttokens
+  version: 2.4.1
   manager: conda
-  name: async_generator
-  optional: true
   platform: osx-64
-  url: https://conda.anaconda.org/conda-forge/noarch/async_generator-1.10-py_0.tar.bz2
-  version: '1.10'
-- category: dev
   dependencies:
-    python: '>2.7'
-  hash: md5:d56c596e61b1c4952acf0a9920856c12
+    python: '>=3.5'
+    six: '>=1.12.0'
+  url: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
+  hash:
+    md5: 5f25798dcefd8252ce5f9dc494d5f571
+    sha256: 708168f026df19a0344983754d27d1f7b28bb21afc7b97a82f02c4798a3d2111
+  category: main
+  optional: false
+- name: asttokens
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diff --git a/conda-osx-arm64.lock b/conda-osx-arm64.lock
deleted file mode 100644
index 6701128..0000000
--- a/conda-osx-arm64.lock
+++ /dev/null
@@ -1,145 +0,0 @@
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-https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.5.1-py39hfb83b0d_0.tar.bz2#0f4bd3b0360c879e0041c148c4b73ff4
-https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.28.2-py39h5161555_0.tar.bz2#99f6c34337f7d1864d6498ec88081d3c
-https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.10.4-h17b34a0_1.tar.bz2#c34e8d42b8dcb72cad14b45a4132e9c7
-https://conda.anaconda.org/conda-forge/osx-arm64/future-0.18.2-py39h2804cbe_4.tar.bz2#153aaad703752be5495a9b3f813a3c6d
-https://conda.anaconda.org/conda-forge/osx-arm64/gmp-6.2.1-h9f76cd9_0.tar.bz2#f8140773b6ca51bf32feec9b4290a8c5
-https://conda.anaconda.org/conda-forge/osx-arm64/hdf5-1.12.1-nompi_h829dc4f_102.tar.bz2#390920929366422ba421c5bfa46b6b0c
-https://conda.anaconda.org/conda-forge/osx-arm64/healpy-1.15.0-py39hf0d0837_2.tar.bz2#72890f8dc11c2d1b3de506a39ee0f612
-https://conda.anaconda.org/conda-forge/osx-arm64/icu-69.1-hbdafb3b_0.tar.bz2#09aa1288507a285c7161732adbf2eb2b
-https://conda.anaconda.org/conda-forge/osx-arm64/importlib-metadata-4.8.2-py39h2804cbe_0.tar.bz2#cc8ba0d83a76b8d1a85e8fad22d4d2dc
-https://conda.anaconda.org/conda-forge/osx-arm64/ipykernel-6.5.0-py39h32adebf_1.tar.bz2#7f94db95eb9e34735dcbf5c1cc1255f3
-https://conda.anaconda.org/conda-forge/osx-arm64/ipython-7.29.0-py39h32adebf_2.tar.bz2#acb04a8f2f19e947078b23831772427c
-https://conda.anaconda.org/conda-forge/osx-arm64/jbig-2.1-h3422bc3_2003.tar.bz2#38e4745944cf5b4598ad7f1cfed5430d
-https://conda.anaconda.org/conda-forge/osx-arm64/jedi-0.18.1-py39h2804cbe_0.tar.bz2#309f836d2f872ef10fb5b00ea1ac38df
-https://conda.anaconda.org/conda-forge/osx-arm64/jpeg-9d-h27ca646_0.tar.bz2#08d2dcb8fc57b70f2cd1ebfb392688ab
-https://conda.anaconda.org/conda-forge/osx-arm64/jupyter_core-4.9.1-py39h2804cbe_1.tar.bz2#d8a602ebb7cbd138329b571030ebac66
-https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.3.2-py39h4d2d688_1.tar.bz2#933758a610c98934d2a151a3bef3db94
-https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.19.2-hfff258c_3.tar.bz2#ee28ad4a96e09bcb309e999178e69e33
-https://conda.anaconda.org/conda-forge/osx-arm64/lazy-object-proxy-1.6.0-py39h5161555_1.tar.bz2#0a751c1a34e06f53703ecd2676e7ecdb
-https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.12-had6a04f_0.tar.bz2#e66c48b683085eaa6627cc61ba76c1e2
-https://conda.anaconda.org/conda-forge/osx-arm64/lerc-3.0-hbdafb3b_0.tar.bz2#8f210e26ca368c3d2b418f213b903aef
-https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-12_osxarm64_openblas.tar.bz2#27435ed0dc1c5d2738b7a4677fa4926b
-https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.0.9-h3422bc3_6.tar.bz2#b3eda06c8994f20d829fbe89ccc4b0b1
-https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.0.9-h3422bc3_6.tar.bz2#5ee99175123dcd24b3857bf467f1044f
-https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlienc-1.0.9-h3422bc3_6.tar.bz2#be28e048477fc17a4b59a66f4f4418a7
-https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-12_osxarm64_openblas.tar.bz2#734c39aa1f5d351a317391ae053cd607
-https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-7.80.0-h8d07098_0.tar.bz2#67a0d5adae197725d7fa375555feac01
-https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-12.0.1-h168391b_0.tar.bz2#d534e631f8fefc2690e11bcd85721f26
-https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.8-h3422bc3_0.tar.bz2#7f395c003efdba93c2acaca381de3bfb
-https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20191231-hc8eb9b7_2.tar.bz2#30e4362988a2623e9eb34337b83e01f9
-https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h642e427_1.tar.bz2#566dbf70fe79eacdb3c3d3d195a27f55
-https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0.dev0-11_0_1_hf114ba7_23.tar.bz2#75f6bd23c894fbb8d353c75edb7041d6
-https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-11.0.1.dev0-hf114ba7_23.tar.bz2#402c05211a9c339c71a1e82ac8b7ad81
-https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-12_osxarm64_openblas.tar.bz2#db2577f4e83769d7e5cd1ec8bd8aecae
-https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.43.0-h62a264d_1.tar.bz2#4d9143890d7def7f935f7869a9a5a898
-https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.18-openmp_h5dd58f0_0.tar.bz2#8860a24cd05b691ea33f8c85f9981d4b
-https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.37-hf7e6567_2.tar.bz2#1c5087c1f23ba00c342cfb725f69a57c
-https://conda.anaconda.org/conda-forge/osx-arm64/libsodium-1.0.18-h27ca646_1.tar.bz2#90859688dbca4735b74c02af14c4c793
-https://conda.anaconda.org/conda-forge/osx-arm64/libssh2-1.10.0-h7a5bd25_2.tar.bz2#b83c0d6ed1be6b4717143e6c74992e35
-https://conda.anaconda.org/conda-forge/osx-arm64/libtiff-4.3.0-h74060c4_2.tar.bz2#80686c6a442d21c98617ebd2eef9d329
-https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.2.1-h3422bc3_0.tar.bz2#5c092854d506da060e1f9ffde1963de1
-https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.2.11-hee7b306_1013.tar.bz2#fe3c74ef0fe456a4011468f860b0c3dc
-https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-12.0.1-hf3c4609_1.tar.bz2#99d8ad7f938fc5b730ec1c610851f1ba
-https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.9.3-hbdafb3b_1.tar.bz2#074b9beb2e563515c0dd46229b18d521
-https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.5.0-py39h5aa4fe7_0.tar.bz2#197c945cce26f59951389f851e9865d9
-https://conda.anaconda.org/conda-forge/osx-arm64/metis-5.1.0-h9f76cd9_1006.tar.bz2#f77d9809ca7c6b8482a66685d4cf55c9
-https://conda.anaconda.org/conda-forge/osx-arm64/mock-4.0.3-py39h2804cbe_2.tar.bz2#d783cfff66b361a0ef3e7cb7bda17605
-https://conda.anaconda.org/conda-forge/osx-arm64/mpfr-4.1.0-h6d7a090_1.tar.bz2#c37f296f76cfb61d4f91613da93789e6
-https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.2-h9aa5885_4.tar.bz2#8a4aa25a5741e65f2338204f8a45d8d8
-https://conda.anaconda.org/conda-forge/osx-arm64/nuflux-2.0.2-py39hcc01ea5_1.tar.bz2#f738dcc325e56d02727e990cfead4e2f
-https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.7.3-py39h7f752ed_2.tar.bz2#96d593e80301a6827dfcbffa25dcfe6a
-https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.21.4-py39h1f3b974_0.tar.bz2#ae8bfff0872f36c053ea4130818d7270
-https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.4.0-h062765e_1.tar.bz2#02ec6e9debe1bf6488ac302592d067fc
-https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.0.0-h3422bc3_2.tar.bz2#a8c39e076c8d531dd9e73b7ff0dea96a
-https://conda.anaconda.org/conda-forge/osx-arm64/pandas-1.3.4-py39h7f752ed_1.tar.bz2#72590755c6995e359cb031dd04e979cb
-https://conda.anaconda.org/conda-forge/osx-arm64/photospline-2.0.7-py39hbfd55a9_0.tar.bz2#0457cf76605ad53e5593af484309f3f1
-https://conda.anaconda.org/conda-forge/osx-arm64/pillow-8.4.0-py39ha74c66e_0.tar.bz2#038dba961ccc2a21630d564ff48afc1d
-https://conda.anaconda.org/conda-forge/osx-arm64/pluggy-1.0.0-py39h2804cbe_2.tar.bz2#636c947ba07c1582065f01453b6e4fef
-https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-1.8.2-py39h5161555_2.tar.bz2#f615f8c81fd4d403d124bc4601d7877b
-https://conda.anaconda.org/conda-forge/osx-arm64/pyerfa-2.0.0.1-py39h20ef057_1.tar.bz2#d7c83deb0e2eda037e2c1a1aaa72af63
-https://conda.anaconda.org/conda-forge/osx-arm64/pyrsistent-0.18.0-py39h5161555_0.tar.bz2#f129a2eb1f8ca70fa5e7e3193d73dc28
-https://conda.anaconda.org/conda-forge/osx-arm64/pytables-3.6.1-py39h086b50d_4.tar.bz2#d73dc114f2d2d296e59bbbc1f5c99685
-https://conda.anaconda.org/conda-forge/osx-arm64/pytest-6.2.5-py39h2804cbe_1.tar.bz2#7f37779bafbefe9d1d32a2f5e17fc4fd
-https://conda.anaconda.org/conda-forge/osx-arm64/python-3.9.7-hc0da0df_3_cpython.tar.bz2#4a9cb0347e7f1f5443ca7720cfd42cfb
-https://conda.anaconda.org/conda-forge/osx-arm64/python_abi-3.9-2_cp39.tar.bz2#ba3082b5441b482d5cfe328d4e7ba0bf
-https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0-py39h5161555_3.tar.bz2#1169532ea0b9456d7ac8171807486614
-https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-22.3.0-py39h02c6a76_1.tar.bz2#8043c857734c4adef5a27ef8ab0ef8e8
-https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.1-hedafd6a_0.tar.bz2#3ab98d5fd433cd392a1b0781391a9e38
-https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.7.2-py39h5060c3b_0.tar.bz2#754073c2b07bbb53f30a1dcc3621f505
-https://conda.anaconda.org/conda-forge/osx-arm64/setuptools-59.2.0-py39h2804cbe_0.tar.bz2#71789b9ebc713ccc0ebae4ce8e07bf71
-https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.36.0-h72a2b83_2.tar.bz2#39b91d8b93b9950c166a6d27ea92f385
-https://conda.anaconda.org/conda-forge/osx-arm64/suitesparse-5.10.1-h7cd81ec_1.tar.bz2#aa2b4cd2c3c870a5da51032c97952e8e
-https://conda.anaconda.org/conda-forge/osx-arm64/tbb-2021.4.0-hc021e02_1.tar.bz2#2abdcb4eb65a719eca9015c6d726cb8a
-https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.11-he1e0b03_1.tar.bz2#dd4c783060a6825a6e73a073c650be16
-https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.1-py39h5161555_2.tar.bz2#fffd4f597cb7b7cf8b7ff38ce3057635
-https://conda.anaconda.org/conda-forge/osx-arm64/xz-5.2.5-h642e427_1.tar.bz2#9ab2316785cb81c464ab9a99512dae71
-https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h642e427_0.tar.bz2#143549bda61a8b9e3a223328cdddc811
-https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.4-hbdafb3b_1.tar.bz2#49132c08da6545dc68351ff8b659ac8e
-https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.2.11-hee7b306_1013.tar.bz2#0b65c3db409dd06257dd879605eddb45
-https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.0-h861e0a7_0.tar.bz2#3a3700f7735317734bf584b11a9475f6
-# pip easy-cache @ https://files.pythonhosted.org/packages/01/47/1d30783a8ce9df947f4d0c67726b3ecd0dfd22e363684b4eb1a8bf59e6e4/easy_cache-1.1.0-py3-none-any.whl#sha256=d99412159074d39891c83098dd5f0ca63bb56103e46efaa7e8455560680ce282
-# pip pydashi @ https://github.com/IceCube-SPNO/dashi/archive/refs/tags/v2.0.0-alpha.0.tar.gz
-# pip pytest-pinned @ https://files.pythonhosted.org/packages/4a/08/98aad61bab14108d5dc6651d121994f955bd39fdd413bf8a2a2dc4d2ce7f/pytest_pinned-0.4.2-py3-none-any.whl#sha256=ebf8b6cd32772e940c20871f5c09ca35d18155390e649823b8435299a25bd88e
diff --git a/environment.yml b/environment.yml
index 7976ac9..6ca1162 100644
--- a/environment.yml
+++ b/environment.yml
@@ -6,7 +6,7 @@ platforms:
   - osx-64
   - osx-arm64
 dependencies:
-  - python >=3.8,<3.10
+  - python >=3.8
   - numpy
   - scipy>=0.15
   - healpy
@@ -22,6 +22,6 @@ dependencies:
   - photospline
   - pip
   - pip:
-    - easy-cache
-    - pydashi @ https://github.com/IceCube-SPNO/dashi/archive/refs/tags/v2.0.0-alpha.0.tar.gz#sha256=b2b8a179f4268d3619a72fc14f8f223f52bc9d51c3ff338863eb8b11c6bc0d5b
+    - easy-cache >=2
+    - pydashi @ https://github.com/IceCube-SPNO/dashi/archive/refs/tags/v2.0.0.tar.gz#sha256=dde4d041e16d64e04e704f3f594c9158e3b0d6a5099fecf634a8b502e292d0bb
     - -e .
diff --git a/figures/detector/Makefile b/figures/detector/Makefile
index b488079..32a251e 100644
--- a/figures/detector/Makefile
+++ b/figures/detector/Makefile
@@ -7,14 +7,12 @@ muon_performance.pdf:
 surface_geometry.pdf:
 	toise-plot detector.surface_geometry -o $@
 
-cascade_aeff.IceCube.json.gz:
-	toise-figure-data detector.cascades.effective_area -d IceCube 0 -o $@
 
-cascade_aeff.Gen2-InIce.json.gz:
-	toise-figure-data detector.cascades.effective_area -d Gen2-InIce 0 -o $@
+gen2_tdr_effective_areas.json.gz:
+	toise-figure-data detector.effective_areas -d IceCube 0 Gen2-InIce 0 Gen2-Radio 0 -o $@
 
-cascade_aeff.pdf : cascade_aeff.IceCube.json.gz cascade_aeff.Gen2-InIce.json.gz
-	toise-plot detector.cascades.effective_area $^ -o $@
+gen2_tdr_effective_areas.pdf: gen2_tdr_effective_areas.json.gz
+	toise-plot detector.effective_areas $^ -o $@
 
 sparse_veto.pdf :
 	toise-plot detector.cascades.sparse_veto -o $@
diff --git a/figures/detector/README.md b/figures/detector/README.md
new file mode 100644
index 0000000..087414b
--- /dev/null
+++ b/figures/detector/README.md
@@ -0,0 +1,11 @@
+# Detector performance figures and backing tables
+
+## gen2_tdr_effective_areas.json.gz
+
+The effective areas are stored in `data`
+
+* data.detector: name of detector configuration
+* data.channel: detection channel (if detector supports more than one)
+* data.energy: edges of neutrino energy bins in GeV
+* data.cos_zenith: edges of cos(zenith angle) bins
+* data.area: neutrino effective area in m^2 with dimensions neutrino type (nue, nuebar, numu, numubar, nutau, nutaubar), neutrino energy, cos(zenith angle)
\ No newline at end of file
diff --git a/toise/data/aeff/Gen2_EHE_NuE_effective_area.npz b/toise/data/aeff/Gen2_EHE_NuE_effective_area.npz
new file mode 100644
index 0000000..8e2cfa6
Binary files /dev/null and b/toise/data/aeff/Gen2_EHE_NuE_effective_area.npz differ
diff --git a/toise/data/aeff/Gen2_EHE_NuMu_effective_area.npz b/toise/data/aeff/Gen2_EHE_NuMu_effective_area.npz
new file mode 100644
index 0000000..f172d93
Binary files /dev/null and b/toise/data/aeff/Gen2_EHE_NuMu_effective_area.npz differ
diff --git a/toise/data/aeff/Gen2_EHE_NuTau_effective_area.npz b/toise/data/aeff/Gen2_EHE_NuTau_effective_area.npz
new file mode 100644
index 0000000..11ce72c
Binary files /dev/null and b/toise/data/aeff/Gen2_EHE_NuTau_effective_area.npz differ
diff --git a/toise/data/aeff/README_Gen2_EHE_effective_areas b/toise/data/aeff/README_Gen2_EHE_effective_areas
new file mode 100644
index 0000000..d1b39c0
--- /dev/null
+++ b/toise/data/aeff/README_Gen2_EHE_effective_areas
@@ -0,0 +1,12 @@
+The files Gen2_EHE_{NuE|NuMu|NuTau}_effective_area.npz hold the effective area
+for a simple EHE analysis port to IceCube Gen2 using simulations with the
+Sunflower 240m detector configuration and pDOMs as detector modules (essentially
+IceCube DOMs with HQE PMTs).
+
+The nu/nubar averaged effective area, given in m2, is binned in three dimensions:
+    - True cos(zenith)
+    - Energy at the detector volume
+    - Neutrino energy at the surface
+
+The binning scheme used is given in the npz file.
+
diff --git a/toise/data/aeff/cosZenDepAeff_z100.txt b/toise/data/aeff/cosZenDepAeff_z100.txt
new file mode 100644
index 0000000..83afeec
--- /dev/null
+++ b/toise/data/aeff/cosZenDepAeff_z100.txt
@@ -0,0 +1,105 @@
+EXPONENT=16.000000
+-0.950000 0.000000
+-0.850000 0.264983
+-0.750000 0.000000
+-0.650000 0.986822
+-0.550000 6.701970
+-0.450000 4.872769
+-0.350000 3.139814
+-0.250000 2.488717
+-0.150000 0.405229
+-0.050000 1.393479
+0.050000 0.356741
+0.150000 0.068571
+0.250000 0.000000
+0.350000 0.014218
+0.450000 0.016063
+0.550000 0.016707
+0.650000 0.007546
+0.750000 0.012484
+0.850000 0.002286
+0.950000 0.000002
+EXPONENT=17.000000
+-0.950000 0.000000
+-0.850000 0.000000
+-0.750000 0.000000
+-0.650000 23.753071
+-0.550000 99.486816
+-0.450000 221.397186
+-0.350000 197.667130
+-0.250000 183.379730
+-0.150000 149.985245
+-0.050000 114.856400
+0.050000 38.194756
+0.150000 4.369019
+0.250000 0.175754
+0.350000 0.040007
+0.450000 0.005473
+0.550000 0.003058
+0.650000 0.000580
+0.750000 0.000231
+0.850000 0.000036
+0.950000 0.000000
+EXPONENT=18.000000
+-0.950000 0.000000
+-0.850000 0.000000
+-0.750000 8.929547
+-0.650000 97.803001
+-0.550000 829.912537
+-0.450000 1634.400024
+-0.350000 1840.284302
+-0.250000 1682.260254
+-0.150000 1287.712280
+-0.050000 1394.238159
+0.050000 350.748871
+0.150000 5.693832
+0.250000 0.045588
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diff --git a/toise/data/aeff/nu_e_Gen2_100m_1.5sigma.json b/toise/data/aeff/nu_e_Gen2_100m_1.5sigma.json
new file mode 100644
index 0000000..6c43b9e
--- /dev/null
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diff --git a/toise/data/aeff/nu_mu_Gen2_100m_1.5sigma.json b/toise/data/aeff/nu_mu_Gen2_100m_1.5sigma.json
new file mode 100644
index 0000000..db0d79a
--- /dev/null
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index 0000000..fd7b868
--- /dev/null
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\ No newline at end of file
diff --git a/toise/data/aeff/review_array_dict_e.json b/toise/data/aeff/review_array_dict_e.json
new file mode 100644
index 0000000..0f845c9
--- /dev/null
+++ b/toise/data/aeff/review_array_dict_e.json
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\ No newline at end of file
diff --git a/toise/data/aeff/review_array_dict_mu.json b/toise/data/aeff/review_array_dict_mu.json
new file mode 100644
index 0000000..447f18c
--- /dev/null
+++ b/toise/data/aeff/review_array_dict_mu.json
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\ No newline at end of file
diff --git a/toise/data/aeff/review_array_dict_tau.json b/toise/data/aeff/review_array_dict_tau.json
new file mode 100644
index 0000000..713d69b
--- /dev/null
+++ b/toise/data/aeff/review_array_dict_tau.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/toise/data/aeff/review_array_shallow_only_dict_e.json b/toise/data/aeff/review_array_shallow_only_dict_e.json
new file mode 100644
index 0000000..acd79f9
--- /dev/null
+++ b/toise/data/aeff/review_array_shallow_only_dict_e.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/toise/data/aeff/review_array_shallow_only_dict_mu.json b/toise/data/aeff/review_array_shallow_only_dict_mu.json
new file mode 100644
index 0000000..c3d2a45
--- /dev/null
+++ b/toise/data/aeff/review_array_shallow_only_dict_mu.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/toise/data/aeff/review_array_shallow_only_dict_tau.json b/toise/data/aeff/review_array_shallow_only_dict_tau.json
new file mode 100644
index 0000000..1ea9cbd
--- /dev/null
+++ b/toise/data/aeff/review_array_shallow_only_dict_tau.json
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+# radio config file for toise framework
+
+#############
+### booleans to apply smearing/efficiencies or not...
+apply_analysis_efficiency: True
+apply_energy_resolution: True
+
+#############
+### detector setup scales effective area with number of stations,
+detector_setup:
+    nstations: 361 # 197 shallow-only / 164 hybrid
+
+#############
+### effective volume of radio review simulations: 144 deep (+surface) + 169 surface-only stations
+# combined to one file
+effective_volume:
+    # e: "param/effective_volume/Veff__baseline_array__config_config_ARZ2020_noise__D01detector_sim__e.json"
+    # mu: "param/effective_volume/Veff__baseline_array__config_config_ARZ2020_noise__D01detector_sim__mu.json"
+    # tau: "param/effective_volume/Veff__baseline_array__config_config_ARZ2020_noise__D01detector_sim__tau.json"
+    e: "param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__e.json"
+    mu: "param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__mu.json"
+    tau: "param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__tau.json"
+    trigger_name: "combined_4channelPA"
+    # number of stations for which the veffs were generated. The veffs will be downscaled by that factor to a per-station veff.
+    # Be sure to set the detector_setup to the same number, unless you want to rescale
+    simulated_stations: 361 # 197 shallow-only / 164 hybrid
+
+#############
+### analysis efficiency is implemented as sigmoid function with
+#  - minval: low energy limit for efficiency (minval values < 0 are allowed, efficiencies are limited to [0,1])
+#  - maxval: high energy limit for efficiency (maxval values > 1 are allowed, efficiencies are limited to [0,1])
+#  - log_turnon_gev: turning point of the sigmoid in log10(E [GeV])
+#  - log_turnon_width: width of the turnon region in log10(E)
+
+
+## for gen2, A, 9deg
+analysis_efficiency:
+    minval: -1000.
+    maxval: 0.85
+    log_turnon_gev: -0.57852369
+    log_turnon_width: 1.13227653
+
+
+
+#############
+### energy resolution for radio, best guess from Christoph for RNO-G sims
+# implemented as Cauchy for larger tails compared to Gaussian
+# does not take into account LPM for nue really
+energy_resolution:
+    loc: 0.01868963
+    scale: 0.14255128
+
+#############
+### angular resolution for radio, best guess from Ilse for deep RNO-G sims
+# implemented as combination of "well-reco" gaus (1) + "bad-reco" components
+# fit to preliminary data for ~70 deg zenith angle, where bulk of events come from
+#angular_resolution:
+#    norm1: 0.69
+#    sigma1: 3.
+#    norm2: 0.31
+#    sigma2: 9.
+#    norm_const: 0.
+angular_resolution:
+     norm1: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9985489110692959, 0.9213451131552166, 0.8529130661463263, 0.7932527700426301, 0.7423642248441232, 0.7002474305508084, 0.6669023871626865, 0.6423290946797551, 0.626527553102016, 0.6194977624294682, 0.6188535517762731, 0.622208750256592, 0.6295633578704247, 0.6409173746177709, 0.6562708004986315, 0.6727311439800853, 0.6874059135292108, 0.700295109146009, 0.7113987308304792, 0.7207167785826225, 0.7290663235596873, 0.7372644369189235, 0.7453111186603315, 0.7532063687839109, 0.760950187289662, 0.7685425741775849, 0.7759835294476788, 0.7832730530999447, 0.7904111451343818, 0.7973978055509909, 0.8042330343497718, 0.8109168315307234, 0.8174491970938467, 0.8238301310391418, 0.8300596333666088, 0.836137704076247, 0.8420643431680572, 0.8478395506420382, 0.8534633264981917, 0.8589356707365159, 0.8642565833570144, 0.8694260643596792, 0.87444411374452, 0.8793107315115307, 0.8840259176607113, 0.8885896721920652, 0.8930019951055925]
+     sigma1: 3.
+     norm2: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0014510889307041142, 0.07865488684478339, 0.14708693385367366, 0.20674722995736994, 0.25763577515587677, 0.2997525694491916, 0.33309761283731354, 0.3576709053202449, 0.373472446897984, 0.3805022375705318, 0.3811464482237269, 0.37779124974340805, 0.3704366421295753, 0.3590826253822291, 0.3437291995013685, 0.32726885601991473, 0.31259408647078923, 0.299704890853991, 0.2886012691695208, 0.2792832214173775, 0.27093367644031274, 0.2627355630810765, 0.2546888813396685, 0.2467936312160891, 0.23904981271033798, 0.23145742582241513, 0.2240164705523212, 0.21672694690005534, 0.20958885486561818, 0.20260219444900907, 0.19576696565022822, 0.18908316846927664, 0.18255080290615333, 0.17616986896085818, 0.16994036663339118, 0.163862295923753, 0.15793565683194277, 0.1521604493579618, 0.14653667350180832, 0.1410643292634841, 0.13574341664298561, 0.13057393564032083, 0.12555588625547998, 0.12068926848846928, 0.11597408233928874, 0.1114103278079348, 0.10699800489440747]
+     sigma2: 9.
+     norm_const: 0.
+
+transfer_matrix:
+  table: "param/transfer_matrices/transfer_matrix_baseline_array.npz" 
+muon_background:
+  table: "param/backgrounds/baseline_GSF_2Dshowerenergy_zenith_SIBYLL23C.pkl"
+  cr_cut: True
+  apply_efficiency: False
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+11900_MUONGUN_Sunflower_240m_recos.hd5_cut.npz
\ No newline at end of file
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+11900_MUONGUN_Sunflower_240m_recos.hd5_cut.npz
\ No newline at end of file
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+11900_MUONGUN_Sunflower_240m_recos.hd5_cut.npz
\ No newline at end of file
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+11900_MUONGUN_Sunflower_240m_recos.hd5_cut.npz
\ No newline at end of file
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+11900_MUONGUN_Sunflower_240m_recos.hd5_cut.npz
\ No newline at end of file
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+11900_MUONGUN_Sunflower_240m_recos.hd5_cut.npz
\ No newline at end of file
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diff --git a/toise/data/gen2-radio/baseline.yaml b/toise/data/gen2-radio/baseline.yaml
new file mode 100644
index 0000000..d2286bd
--- /dev/null
+++ b/toise/data/gen2-radio/baseline.yaml
@@ -0,0 +1,75 @@
+# radio config file for toise framework
+
+#############
+### booleans to apply smearing/efficiencies or not...
+apply_analysis_efficiency: True
+apply_energy_resolution: True
+
+#############
+### detector setup scales effective area with number of stations,
+detector_setup:
+    nstations: 361 # 197 shallow-only / 164 hybrid
+
+#############
+### effective volume of radio review simulations: 144 deep (+surface) + 169 surface-only stations
+# combined to one file
+effective_volume:
+    # e: "param/effective_volume/Veff__baseline_array__config_config_ARZ2020_noise__D01detector_sim__e.json"
+    # mu: "param/effective_volume/Veff__baseline_array__config_config_ARZ2020_noise__D01detector_sim__mu.json"
+    # tau: "param/effective_volume/Veff__baseline_array__config_config_ARZ2020_noise__D01detector_sim__tau.json"
+    e: "param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__e.json"
+    mu: "param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__mu.json"
+    tau: "param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__tau.json"
+    trigger_name: "combined_4channelPA"
+    # number of stations for which the veffs were generated. The veffs will be downscaled by that factor to a per-station veff.
+    # Be sure to set the detector_setup to the same number, unless you want to rescale
+    simulated_stations: 361 # 197 shallow-only / 164 hybrid
+
+#############
+### analysis efficiency is implemented as sigmoid function with
+#  - minval: low energy limit for efficiency (minval values < 0 are allowed, efficiencies are limited to [0,1])
+#  - maxval: high energy limit for efficiency (maxval values > 1 are allowed, efficiencies are limited to [0,1])
+#  - log_turnon_gev: turning point of the sigmoid in log10(E [GeV])
+#  - log_turnon_width: width of the turnon region in log10(E)
+
+
+## for gen2, A, 9deg
+analysis_efficiency:
+    minval: -1000.
+    maxval: 0.85
+    log_turnon_gev: -0.57852369
+    log_turnon_width: 1.13227653
+
+
+
+#############
+### energy resolution for radio, best guess from Christoph for RNO-G sims
+# implemented as Cauchy for larger tails compared to Gaussian
+# does not take into account LPM for nue really
+energy_resolution:
+    loc: 0.01868963
+    scale: 0.14255128
+
+#############
+### angular resolution for radio, best guess from Ilse for deep RNO-G sims
+# implemented as combination of "well-reco" gaus (1) + "bad-reco" components
+# fit to preliminary data for ~70 deg zenith angle, where bulk of events come from
+#angular_resolution:
+#    norm1: 0.69
+#    sigma1: 3.
+#    norm2: 0.31
+#    sigma2: 9.
+#    norm_const: 0.
+angular_resolution:
+     norm1: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9985489110692959, 0.9213451131552166, 0.8529130661463263, 0.7932527700426301, 0.7423642248441232, 0.7002474305508084, 0.6669023871626865, 0.6423290946797551, 0.626527553102016, 0.6194977624294682, 0.6188535517762731, 0.622208750256592, 0.6295633578704247, 0.6409173746177709, 0.6562708004986315, 0.6727311439800853, 0.6874059135292108, 0.700295109146009, 0.7113987308304792, 0.7207167785826225, 0.7290663235596873, 0.7372644369189235, 0.7453111186603315, 0.7532063687839109, 0.760950187289662, 0.7685425741775849, 0.7759835294476788, 0.7832730530999447, 0.7904111451343818, 0.7973978055509909, 0.8042330343497718, 0.8109168315307234, 0.8174491970938467, 0.8238301310391418, 0.8300596333666088, 0.836137704076247, 0.8420643431680572, 0.8478395506420382, 0.8534633264981917, 0.8589356707365159, 0.8642565833570144, 0.8694260643596792, 0.87444411374452, 0.8793107315115307, 0.8840259176607113, 0.8885896721920652, 0.8930019951055925]
+     sigma1: 3.
+     norm2: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0014510889307041142, 0.07865488684478339, 0.14708693385367366, 0.20674722995736994, 0.25763577515587677, 0.2997525694491916, 0.33309761283731354, 0.3576709053202449, 0.373472446897984, 0.3805022375705318, 0.3811464482237269, 0.37779124974340805, 0.3704366421295753, 0.3590826253822291, 0.3437291995013685, 0.32726885601991473, 0.31259408647078923, 0.299704890853991, 0.2886012691695208, 0.2792832214173775, 0.27093367644031274, 0.2627355630810765, 0.2546888813396685, 0.2467936312160891, 0.23904981271033798, 0.23145742582241513, 0.2240164705523212, 0.21672694690005534, 0.20958885486561818, 0.20260219444900907, 0.19576696565022822, 0.18908316846927664, 0.18255080290615333, 0.17616986896085818, 0.16994036663339118, 0.163862295923753, 0.15793565683194277, 0.1521604493579618, 0.14653667350180832, 0.1410643292634841, 0.13574341664298561, 0.13057393564032083, 0.12555588625547998, 0.12068926848846928, 0.11597408233928874, 0.1114103278079348, 0.10699800489440747]
+     sigma2: 9.
+     norm_const: 0.
+
+transfer_matrix:
+  table: "param/transfer_matrices/transfer_matrix_baseline_array.npz" 
+muon_background:
+  table: "param/backgrounds/baseline_GSF_2Dshowerenergy_zenith_SIBYLL23C.pkl"
+  cr_cut: True
+  apply_efficiency: False
diff --git a/toise/data/gen2-radio/param/backgrounds/baseline_GSF_2Dshowerenergy_zenith_SIBYLL23C.pkl b/toise/data/gen2-radio/param/backgrounds/baseline_GSF_2Dshowerenergy_zenith_SIBYLL23C.pkl
new file mode 100644
index 0000000..71c9dad
Binary files /dev/null and b/toise/data/gen2-radio/param/backgrounds/baseline_GSF_2Dshowerenergy_zenith_SIBYLL23C.pkl differ
diff --git a/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__e.json b/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__e.json
new file mode 100644
index 0000000..e9418d3
--- /dev/null
+++ b/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__e.json
@@ -0,0 +1,23762 @@
+[
+    {
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+        "domega": 0.6283185307,
+        "energy": 1e+16,
+        "energy_max": 1e+16,
+        "energy_min": 1e+16,
+        "n_triggered_weighted": {},
+        "thetamax": 1.670963748,
+        "thetamin": 1.5707963268,
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+            "PA_4channel_100Hz": [
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+                32495840.649772245,
+                16.4144784943,
+                100424911.54744254,
+                159880768.56964657
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+            "PA_4channel_1mHz": [
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+                112931534.86809498
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+                28.3849384541,
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+            "combined_4channelPA_highThreshold": [
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+]
\ No newline at end of file
diff --git a/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__mu.json b/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__mu.json
new file mode 100644
index 0000000..1277910
--- /dev/null
+++ b/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__mu.json
@@ -0,0 +1,23615 @@
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\ No newline at end of file
diff --git a/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__tau.json b/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__tau.json
new file mode 100644
index 0000000..705038b
--- /dev/null
+++ b/toise/data/gen2-radio/param/effective_volume_exclusiveTriggers/Veff_withSelectionEfficiencies__baseline_array__config_ARZ2020_noise__D01detector_sim__tau.json
@@ -0,0 +1,23615 @@
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+        "n_triggered_weighted": {},
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+            ]
+        }
+    }
+]
\ No newline at end of file
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+    ],
+    "starting_tracks": [
+      "logpoly",
+      [
+        -12.02248755,
+        29.98400471,
+        -3.72778076,
+        0.13724512
+      ]
+    ],
+    "double_cascades": [
+      "powerlaw",
+      [
+        0.86190289,
+        0.14669257
+      ]
+    ]
+  },
+  "nutau": {
+    "cascades": [
+      "sigmoid",
+      [
+        91.90034143,
+        0.79907467,
+        4.76995429,
+        36.0535759,
+        1.54379368
+      ]
+    ],
+    "starting_tracks": [
+      "logpoly",
+      [
+        -353.501682,
+        251.751001,
+        -64.2850564,
+        7.16802798,
+        -0.292552097
+      ]
+    ],
+    "double_cascades": [
+      "sigmoid",
+      [
+        0.0662404,
+        0.86871992,
+        5.03507898,
+        42.57592219,
+        1.75695386
+      ]
+    ]
+  }
+}
\ No newline at end of file
diff --git a/toise/data/selection_efficiency/sparsecube_doublecascade_efficiency.json b/toise/data/selection_efficiency/sparsecube_doublecascade_efficiency.json
new file mode 100644
index 0000000..7b1eb42
--- /dev/null
+++ b/toise/data/selection_efficiency/sparsecube_doublecascade_efficiency.json
@@ -0,0 +1,86 @@
+{
+  "nue": {
+    "cascades": [
+      "logpoly",
+      [
+        76.58716545,
+        0.28044189
+      ]
+    ],
+    "starting_tracks": [
+      "logpoly",
+      [
+        -86.51210042,
+        75.465016,
+        -19.83143848,
+        2.30308875,
+        -0.1002823
+      ]
+    ],
+    "double_cascades": [
+      "powerlaw",
+      [
+        0.41033528,
+        0.30491248
+      ]
+    ]
+  },
+  "numu": {
+    "cascades": [
+      "logpoly",
+      [
+        104.20865326,
+        -23.01453713,
+        1.85070271
+      ]
+    ],
+    "starting_tracks": [
+      "logpoly",
+      [
+        -21.65258973,
+        32.42965089,
+        -3.75177959,
+        0.12322603
+      ]
+    ],
+    "double_cascades": [
+      "powerlaw",
+      [
+        1.53906934,
+        0.08490755
+      ]
+    ]
+  },
+  "nutau": {
+    "cascades": [
+      "sigmoid",
+      [
+        74.30758109,
+        0.87026468,
+        5.66451424,
+        43.76074189,
+        1.89158367
+      ]
+    ],
+    "starting_tracks": [
+      "sigmoid",
+      [
+        23.83950304,
+        0.79147821,
+        5.33256905,
+        31.43271591,
+        4.08490017
+      ]
+    ],
+    "double_cascades": [
+      "sigmoid",
+      [
+        1.62301934,
+        0.81356809,
+        5.09672689,
+        23.103165,
+        1.92305022
+      ]
+    ]
+  }
+}
\ No newline at end of file
diff --git a/toise/data/selection_efficiency/sunflower_200m_bdt0_efficiency.fits b/toise/data/selection_efficiency/sunflower_200m_bdt0_efficiency.fits
new file mode 100644
index 0000000..c26135e
Binary files /dev/null and b/toise/data/selection_efficiency/sunflower_200m_bdt0_efficiency.fits differ
diff --git a/toise/data/selection_efficiency/sunflower_240_bdt0_efficiency.hdf5 b/toise/data/selection_efficiency/sunflower_240_bdt0_efficiency.hdf5
new file mode 100644
index 0000000..77e27dd
Binary files /dev/null and b/toise/data/selection_efficiency/sunflower_240_bdt0_efficiency.hdf5 differ
diff --git a/toise/data/selection_efficiency/sunflower_240_bdt0_efficiency.pickle b/toise/data/selection_efficiency/sunflower_240_bdt0_efficiency.pickle
new file mode 100644
index 0000000..81f207b
Binary files /dev/null and b/toise/data/selection_efficiency/sunflower_240_bdt0_efficiency.pickle differ
diff --git a/toise/data/selection_efficiency/sunflower_240m_bdt0_efficiency.fits b/toise/data/selection_efficiency/sunflower_240m_bdt0_efficiency.fits
new file mode 100644
index 0000000..7a678ed
Binary files /dev/null and b/toise/data/selection_efficiency/sunflower_240m_bdt0_efficiency.fits differ
diff --git a/toise/data/selection_efficiency/sunflower_300m_bdt0_efficiency.fits b/toise/data/selection_efficiency/sunflower_300m_bdt0_efficiency.fits
new file mode 100644
index 0000000..a9e9c7c
Binary files /dev/null and b/toise/data/selection_efficiency/sunflower_300m_bdt0_efficiency.fits differ
diff --git a/toise/data/veto/udel-veto-passing-fraction.txt b/toise/data/veto/udel-veto-passing-fraction.txt
new file mode 100644
index 0000000..f8ee1e8
--- /dev/null
+++ b/toise/data/veto/udel-veto-passing-fraction.txt
@@ -0,0 +1,21 @@
+# linear expansions in sin^2(zenith) of the parameters a, g1, g2, eng0, curve
+#    def _fitfunc(eng, a, g1, g2, eng0, curve):
+#        """
+#        "When you work in cosmic rays, everything is a broken power law with a smooth roll-off :see_no_evil:"
+#        -- A. Coleman
+#        """
+#        return (
+#            a
+#            * eng**g1
+#            * (1 + (10**eng / 10**eng0) ** curve) ** ((g2 - g1) / curve)
+#        )
+8.018856427742836468e-01
+9.678594064587241197e-02
+-4.883883344585775166e-03
+9.731290023860154903e-02
+-2.323190254120556020e+00
+3.606741142853249482e-02
+1.262219516097010796e+01
+4.577831427506174333e-01
+1.487638305739540590e+00
+2.016882529271495639e-01
diff --git a/toise/figures/cli.py b/toise/figures/cli.py
index f3feb5e..96a777b 100644
--- a/toise/figures/cli.py
+++ b/toise/figures/cli.py
@@ -153,6 +153,9 @@ def _maybe_call_sequence(f: Union[Callable, Sequence[Callable]]):
 
 
 def make_figure_data():
+    import traceback
+    import sys
+    import pdb
     from toise import figures
 
     # find all submodules of toise.figures and import them
@@ -182,6 +185,9 @@ def make_figure_data():
             help="sequence of detector configuration/livetime pairs",
         )
         p.add_argument("-o", "--outputfile", action="append", help="")
+        p.add_argument(
+            "--pdb", action="store_true", help="Drop into debugger on exception"
+        )
         assert "exposures" in spec.parameters, "exposures argument is required"
         _add_options_for_args(p, spec, param_help)
     args = parser.parse_args().__dict__
@@ -195,23 +201,32 @@ def make_figure_data():
                 len(outfiles), len(exposures)
             )
         )
+    do_pdb = args.pop("pdb")
 
     func, setup, teardown = args.pop("command")
-    _maybe_call_sequence(setup)
     try:
-        for exposure, outfile in zip(exposures, outfiles):
-            meta = {
-                "source": func.__module__ + "." + func.__name__,
-                "detectors": exposure,
-                "args": args,
-            }
-            meta["data"] = jsonify(func(tuple(exposure), **args))
-            if not outfile.endswith(".json.gz"):
-                outfile = outfile + ".json.gz"
-            with gzip.open(outfile, "wt") as f:
-                json.dump(meta, f, indent=2)
-    finally:
-        _maybe_call_sequence(teardown)
+        _maybe_call_sequence(setup)
+        try:
+            for exposure, outfile in zip(exposures, outfiles):
+                meta = {
+                    "source": func.__module__ + "." + func.__name__,
+                    "detectors": exposure,
+                    "args": args,
+                }
+                meta["data"] = jsonify(func(tuple(exposure), **args))
+                if not outfile.endswith(".json.gz"):
+                    outfile = outfile + ".json.gz"
+                with gzip.open(outfile, "wt") as f:
+                    json.dump(meta, f, indent=2)
+        finally:
+            _maybe_call_sequence(teardown)
+    except:
+        if do_pdb:
+            extype, value, tb = sys.exc_info()
+            traceback.print_exc()
+            pdb.post_mortem(tb)
+        else:
+            raise
 
 
 def load_gzip(fname):
diff --git a/toise/figures/detector/__init__.py b/toise/figures/detector/__init__.py
index ecd68a8..145f00b 100644
--- a/toise/figures/detector/__init__.py
+++ b/toise/figures/detector/__init__.py
@@ -1,6 +1,5 @@
-from shutil import which
-
-from toise.figures import figure
+from toise.figures import figure, figure_data
+from toise.figures.diffuse.flavor import psi_binning
 
 from toise import surfaces
 import gzip
@@ -562,3 +561,78 @@ def review_2021_array_geometry():
     plt.tight_layout()
 
     return fig
+
+
+@figure_data(setup=psi_binning)
+def effective_areas(exposures):
+    from toise import factory
+
+    pretty_labels = {"Gen2-InIce": "Gen2-Optical"}
+
+    meta = [
+        {
+            "detector": pretty_labels.get(detector, detector),
+            "channel": channel,
+            "energy": neutrino_aeff.get_bin_edges("true_energy").tolist(),
+            "cos_zenith": neutrino_aeff.get_bin_edges("true_zenith_band").tolist(),
+            "area": neutrino_aeff.values.sum(axis=(-2, -1)),
+        }
+        for detector, _ in exposures
+        for channel, (neutrino_aeff, _) in factory.get(detector).items()
+    ]
+    return meta
+
+
+@figure
+def effective_areas(datasets):
+    import matplotlib.pyplot as plt
+    import os
+    from toise import plotting
+
+    nplots = len(datasets[0]["data"])
+    ncol = min(3, nplots)
+    nrow = (nplots - 1) // ncol + 1
+
+    fig, axes = plt.subplots(nrow, ncol, figsize=(3.375 * ncol, 3.375 * nrow))
+    # remove extraneous axes
+    for ax in axes.flat[nplots:]:
+        ax.set_visible(False)
+
+    ylim = (1e-1, 1e7)
+
+    for dataset in datasets:
+        assert dataset["source"] == "toise.figures.detector.effective_areas"
+        for aeff, ax in zip(dataset["data"], axes.flat):
+            energy = np.asarray(aeff["energy"])
+            values = np.asarray(aeff["area"]).mean(axis=0)
+            ct_edges = aeff["cos_zenith"]
+            assert len(ct_edges) == 22
+            idx = (0, 5, 10, 11, 16, 21)
+            for lo, hi in zip(idx[:-1], idx[1:]):
+                avg = values[:, lo:hi].mean(axis=1)
+                line = ax.loglog(
+                    *plotting.stepped_path(energy, avg),
+                    label=f"({-ct_edges[hi]:.2f},{-ct_edges[lo]:.2f}]",
+                )[0]
+                if avg.max() <= ylim[0]:
+                    del ax.lines[-1]
+
+            if "radio" in aeff["detector"].lower():
+                title = aeff["detector"]
+            else:
+                title = f"{aeff['detector']} ({aeff['channel'].replace('_', ' ')})"
+            ax.set_title(title)
+
+            ax.set_xlabel("Neutrino energy (GeV)")
+            ax.set_ylabel("Neutrino effective area (m$^2$)")
+            # ax.set_xlim(1e4, 5e8)
+            ax.set_ylim(*ylim)
+            ax.grid()
+        fig.legend(
+            *ax.get_legend_handles_labels(),
+            frameon=False,
+            title=r"$\sin\delta$",
+            loc="lower right",
+        )
+    plt.tight_layout()
+    return fig
diff --git a/toise/figures/diffuse/flavor.py b/toise/figures/diffuse/flavor.py
index 0dc0d63..97cd13c 100644
--- a/toise/figures/diffuse/flavor.py
+++ b/toise/figures/diffuse/flavor.py
@@ -173,7 +173,7 @@ def make_profile(
     fixed = dict(atmo=1, prompt=1, muon=1)
     if "emu_crit" in nominal:
         fixed["emu_crit"] = nominal["emu_crit"]
-    if gamma_step is None:
+    if gamma_step is None and not suffix:
         fixed["gamma"] = llh.components["gamma"].seed
     bestfit = llh.fit(minimizer_params=minimizer_params, **fixed)
     params = []
@@ -210,7 +210,7 @@ def make_source_profile(
     nominal=dict(),
     suffix="",
     steps=100,
-    minimizer_params=dict(epsilon=1e-2),
+    minimizer_params=dict(tol=None, method="Nelder-Mead"),
     gamma_step=None,
 ):
     if suffix:
diff --git a/toise/multillh.py b/toise/multillh.py
index 2f5bd31..1fe55f0 100644
--- a/toise/multillh.py
+++ b/toise/multillh.py
@@ -312,6 +312,7 @@ def minllh(x):
                     minllh,
                     seeds,
                     bounds=bounds,
+                    method=minimizer_params.get("method"),
                     options=minimizer_params,
                 )
             else:
@@ -331,6 +332,7 @@ def minllh(x):
                         minllh,
                         seeds,
                         bounds=bounds,
+                        method=minimizer_params.get("method"),
                         options=minimizer_params,
                     )
                 else:
diff --git a/toise/pointsource.py b/toise/pointsource.py
index 2e063ae..93bde48 100644
--- a/toise/pointsource.py
+++ b/toise/pointsource.py
@@ -475,7 +475,7 @@ def f(flux_norm):
         ns = total - nb
         logging.getLogger().info(
             "baseline: %.2g actual %.2g ns: %.2g nb: %.2g ts: %.2g"
-            % (baseline, actual, ns, nb, ts(actual))
+            % (baseline, actual[0], ns, nb, ts(actual))
         )
         return actual[0], ns, nb
 
@@ -591,7 +591,7 @@ def f(flux_norm):
     else:
         # actual = optimize.bisect(f, 0, baseline, xtol=baseline*1e-2)
         actual = optimize.fsolve(f, baseline, xtol=tolerance, factor=1, epsfcn=1)
-        logging.getLogger().debug("baseline: %.2g actual %.2g" % (baseline, actual))
+        logging.getLogger().debug("baseline: %.2g actual %.2g" % (baseline, actual[0]))
         allh = asimov_llh(components, ps=actual, **fixed)
         total = nevents(allh, ps=actual, **fixed)
         nb = nevents(allh, ps=0, **fixed)
diff --git a/toise/radio_aeff_generation.py b/toise/radio_aeff_generation.py
index eab8392..39bd795 100644
--- a/toise/radio_aeff_generation.py
+++ b/toise/radio_aeff_generation.py
@@ -508,7 +508,14 @@ def neutrino_interaction_length_ice(flavor, energy_edges):
             else:
                 aeffs_flavor = []
                 for fi, flavor in enumerate(["e", "e", "mu", "mu", "tau", "tau"]):
-                    data = matrix_data[f"transfer_matrix_{flavor}"]
+                    data = interpolate.interp1d(
+                        # interpolate to the nearest neighbor in zenith, subtracting a small amount prefer the value to the right
+                        center(matrix_data["bin_edges"][1]) - 1e-12,
+                        matrix_data[f"transfer_matrix_{flavor}"],
+                        kind="nearest",
+                        axis=1,
+                        bounds_error=False,
+                    )(center(cos_theta))
                     prod_dens = 1.0 / neutrino_interaction_length_ice(
                         fi, neutrino_energy
                     )