diff --git a/multidms/data.py b/multidms/data.py
index de697cb..5259026 100644
--- a/multidms/data.py
+++ b/multidms/data.py
@@ -599,7 +599,6 @@ def targets(self) -> dict:
         """The functional scores for each variant in the training data."""
         return self._training_data["y"]
 
-    # TODO, rename mutparser
     @property
     def mutparser(self) -> MutationParser:
         """
@@ -608,7 +607,6 @@ def mutparser(self) -> MutationParser:
         """
         return self._mutparser
 
-    # TODO, rename
     @property
     def parse_mut(self) -> MutationParser:
         """
@@ -618,7 +616,6 @@ def parse_mut(self) -> MutationParser:
         """
         return self.mutparser.parse_mut
 
-    # TODO, document rename issue
     @property
     def parse_muts(self) -> partial:
         """
@@ -628,11 +625,6 @@ def parse_muts(self) -> partial:
         """
         return self._parse_muts
 
-    # TODO should this be cached? how does caching interact with the way in
-    # which we applying this function in parallel?
-    # although, unless the variants are un-collapsed, this cache will be
-    # pretty useless.
-    # although it could be useful for the Model.add_phenotypes_to_df method.
     def convert_subs_wrt_ref_seq(self, condition, aa_subs):
         """
         Covert amino acid substitutions to be with respect to the reference sequence.
diff --git a/multidms/model.py b/multidms/model.py
index 9716bf2..717dfbe 100644
--- a/multidms/model.py
+++ b/multidms/model.py
@@ -209,7 +209,7 @@ def __init__(
         epistatic_model=multidms.biophysical.sigmoidal_global_epistasis,
         output_activation=multidms.biophysical.identity_activation,
         conditional_shifts=True,
-        alpha_d=False,  # TODO raise issue to be squashed in this PR
+        alpha_d=False,
         gamma_corrected=False,
         PRNGKey=0,
         init_beta_naught=0.0,
@@ -805,9 +805,9 @@ def add_phenotypes_to_df(
             if phenotype_as_effect:
                 latent_predictions -= wildtype_df.loc[condition, "predicted_latent"]
             latent_predictions[nan_variant_indices] = onp.nan
-            ret.loc[condition_df.index.values, latent_phenotype_col] = (
-                latent_predictions
-            )
+            ret.loc[
+                condition_df.index.values, latent_phenotype_col
+            ] = latent_predictions
 
             # func_score predictions on binary variants, X
             phenotype_predictions = onp.array(
@@ -819,9 +819,9 @@ def add_phenotypes_to_df(
                     condition, "predicted_func_score"
                 ]
             phenotype_predictions[nan_variant_indices] = onp.nan
-            ret.loc[condition_df.index.values, observed_phenotype_col] = (
-                phenotype_predictions
-            )
+            ret.loc[
+                condition_df.index.values, observed_phenotype_col
+            ] = phenotype_predictions
 
         return ret
 
diff --git a/multidms/model_collection.py b/multidms/model_collection.py
index 62ce102..5f2248d 100644
--- a/multidms/model_collection.py
+++ b/multidms/model_collection.py
@@ -396,10 +396,6 @@ def __init__(self, fit_models):
             )
             all_mutations = set.union(all_mutations, set(fit.data.mutations))
 
-        # add the final training loss to the fit_models dataframe
-        # fit_models["training_loss"] = fit_models.step_loss.apply(lambda x: x[-1])
-        # TODO rename to fit_models_df
-
         # initialize empty columns for conditional loss
         fit_models.assign(
             **{
@@ -447,7 +443,6 @@ def all_mutations(self) -> tuple:
         """The mutations shared by each fitting dataset."""
         return self._all_mutations
 
-    # TODO remove verbose everywhere
     @lru_cache(maxsize=10)
     def split_apply_combine_muts(
         self,
@@ -1009,9 +1004,6 @@ def mut_type(mut):
             return "stop" if mut.endswith("*") else "nonsynonymous"
 
         # apply, drop, and melt
-        # TODO This throws deprecation warning
-        # because of the include_groups argument ...
-        # set to False, and lose the drop call after ...
         sparsity_df = (
             df.drop(columns=to_throw)
             .assign(mut_type=lambda x: x.mutation.apply(mut_type))
diff --git a/notebooks/spike-analysis.ipynb b/notebooks/spike-analysis.ipynb
deleted file mode 100644
index 8e3b3b6..0000000
--- a/notebooks/spike-analysis.ipynb
+++ /dev/null
@@ -1,11386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "594a1d23",
-   "metadata": {},
-   "source": [
-    "# TODO:\n",
-    "\n",
-    "1. Re-create all plots with new models and multidms version\n",
-    "2. re-compute functional scores such that the counts are collapsed first. Could use dms_variants API\n",
-    "3. papermill all parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad1521ca",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "# Spike Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "472a19d2-7d4c-4557-a61c-dfb69e526667",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "This notebook contains the entire empirical analysis on the three spike homologs as seen in our manuscript _Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs_."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e30c6e6a-4f17-4394-a819-1313f178da4e",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Computational platform environment\n",
-    "\n",
-    "This section shows the attributes of the machine which ran this notebook, as well as imports the necessary dependencies."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e68baf77-4b26-4103-832b-784ae38aa147",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Operating system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2f4fa571-9ce4-4f77-8006-0f39d1e0bd22",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NAME=\"Ubuntu\"\n",
-      "VERSION=\"18.04.6 LTS (Bionic Beaver)\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "! grep -E '^(VERSION|NAME)=' /etc/os-release"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "496d5fc4-a7c8-449f-b76c-b6c8bd5df07a",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Hardware (Processors and RAM)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a88613cd-86ec-44ac-b22e-a6a9909b914b",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING: you should run this program as super-user.\n",
-      "                              *-memory\n",
-      "       description: System memory\n",
-      "       physical id: 0\n",
-      "       size: 996GiB\n",
-      "  *-cpu\n",
-      "       product: AMD EPYC 75F3 32-Core Processor\n",
-      "       vendor: Advanced Micro Devices [AMD]\n",
-      "       physical id: 1\n",
-      "       bus info: cpu@0\n",
-      "       size: 1499MHz\n",
-      "       capacity: 2950MHz\n",
-      "       width: 64 bits\n",
-      "       capabilities: fpu fpu_exception wp vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp x86-64 constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca cpufreq\n",
-      "WARNING: output may be incomplete or inaccurate, you should run this program as super-user.\n"
-     ]
-    }
-   ],
-   "source": [
-    "! lshw -class memory -class processor"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef7ba5a7-7605-4375-9af6-4907000cdb57",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "GPU's"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a51e6af0-304a-4e2d-8914-54b9667f901d",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: CUDA_VISIBLE_DEVICES=0\n",
-      "GPU 0: NVIDIA A100 80GB PCIe (UUID: GPU-414cb1bd-372a-4926-b140-b734687c927f)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GPU 1: NVIDIA A100 80GB PCIe (UUID: GPU-e54c2054-5be3-ebd0-e22e-b98441ec664f)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%env CUDA_VISIBLE_DEVICES=0\n",
-    "! nvidia-smi -L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "53fa951c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# built-in libraries\n",
-    "import os\n",
-    "import sys\n",
-    "from itertools import combinations\n",
-    "from collections import defaultdict\n",
-    "import time\n",
-    "import pprint\n",
-    "import copy\n",
-    "import pickle\n",
-    "from functools import reduce\n",
-    "\n",
-    "# external dependencies\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "from scipy.stats import pearsonr\n",
-    "from matplotlib.lines import Line2D\n",
-    "import matplotlib.lines as mlines\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.transforms import (\n",
-    "    Bbox, TransformedBbox, blended_transform_factory)\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import (\n",
-    "    BboxPatch, BboxConnector, BboxConnectorPatch)\n",
-    "import matplotlib.patches as patches\n",
-    "import matplotlib.colors as colors\n",
-    "import numpy as np\n",
-    "import scipy\n",
-    "from tqdm.notebook import tqdm\n",
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import shutil\n",
-    "from Bio.PDB.PDBParser import PDBParser\n",
-    "from Bio.PDB.PDBList import PDBList\n",
-    "from Bio.PDB.DSSP import DSSP\n",
-    "from Bio import SeqIO\n",
-    "import multidms\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "75b7f070-1910-4d3d-a37d-8cc202bc5ed3",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "This was notebook was run with the following `multidms` version."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e1626de0-261d-49be-be47-a920ad38a788",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.3.3'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "multidms.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66cd6595",
-   "metadata": {},
-   "source": [
-    "set papermill parameters which define notebook behavior"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c2938554",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = 'results/spike_analysis'\n",
-    "# define the fitting parameters\n",
-    "scale_coeff_lasso_shift = [0.0, 5.00e-6, 1.00e-05, 2.00e-05, 4.00e-05, 8.00e-05, 1.60e-04, 3.20e-04, 6.40e-04] # the sweep of lasso coefficient params\n",
-    "alpha_d=True\n",
-    "scale_coeff_ridge_alpha_d=1e-3\n",
-    "num_training_steps = 30\n",
-    "iterations_per_step = 1000 # default 20000\n",
-    "# init_beta_naught = 5.0 # We've found that we need to start with a higher beta_naught to get the model to converge correctly,\n",
-    "scale_coeff_ridge_beta = 0.0 # the sweep of ridge coefficient params\n",
-    "train_frac = 0.8 # fraction of data to use for cross validation training.\n",
-    "lasso_choice = 4.00e-05 # the lasso coefficient to use for the final model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7febf8c-7159-4eba-8df1-ba7461b74e98",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Set some global configurations for plotting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1aef9cfe-89e0-4560-9dab-a0cfa2ab5bd4",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(output_dir): os.mkdir(output_dir)\n",
-    "\n",
-    "rc_kwargs = {\n",
-    "    'legend.frameon': False,\n",
-    "    \"font.size\" : 11,\n",
-    "    \"font.weight\" : \"normal\"\n",
-    "}\n",
-    "\n",
-    "plt.rcParams.update(**rc_kwargs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72f26463-32e6-4bb5-a04d-366df3157fc9",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Input Data\n",
-    "Load and organize the funcational score dms data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46f8e5e1",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "We begin with 16 individual sets of barcoded variants and their associated functional scores pre-computed. Each set derives from a single DMS experiment using one of Delta, Omicron BA.1, or Omicron BA.2 as the experimental wildtype. First, we parse the filenames to get experimental attributes tied in with the individual datasets as nested pd.DataFrames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "325abd2b-e238-472d-b49f-8a96ba064bd9",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>library</th>\n",
-       "      <th>replicate</th>\n",
-       "      <th>filename</th>\n",
-       "      <th>condition</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Lib-3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Lib-3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Lib-4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Lib-4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...</td>\n",
-       "      <td>Delta-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...</td>\n",
-       "      <td>Omicron_BA1-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...</td>\n",
-       "      <td>Omicron_BA1-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Omicron_BA1/Lib-2_2022-06-22_thaw-1_VSVG_...</td>\n",
-       "      <td>Omicron_BA1-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Lib-3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Omicron_BA1/Lib-3_2022-06-22_thaw-1_VSVG_...</td>\n",
-       "      <td>Omicron_BA1-3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Omicron_BA2/Lib-1_2022-10-22_thaw-1_VSVG_...</td>\n",
-       "      <td>Omicron_BA2-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>data/Omicron_BA2/Lib-2_2022-10-22_thaw-1_VSVG_...</td>\n",
-       "      <td>Omicron_BA2-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Omicron_BA2/Lib-1_2022-10-22_thaw-2_VSVG_...</td>\n",
-       "      <td>Omicron_BA2-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>data/Omicron_BA2/Lib-2_2022-10-22_thaw-2_VSVG_...</td>\n",
-       "      <td>Omicron_BA2-2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   library  replicate                                           filename  \\\n",
-       "0    Lib-1          1  data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "1    Lib-1          2  data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "2    Lib-3          1  data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "3    Lib-3          2  data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "4    Lib-4          1  data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "5    Lib-4          2  data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "6    Lib-2          1  data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "7    Lib-2          2  data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...   \n",
-       "8    Lib-1          1  data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...   \n",
-       "9    Lib-1          2  data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...   \n",
-       "10   Lib-2          1  data/Omicron_BA1/Lib-2_2022-06-22_thaw-1_VSVG_...   \n",
-       "11   Lib-3          1  data/Omicron_BA1/Lib-3_2022-06-22_thaw-1_VSVG_...   \n",
-       "12   Lib-1          1  data/Omicron_BA2/Lib-1_2022-10-22_thaw-1_VSVG_...   \n",
-       "13   Lib-2          1  data/Omicron_BA2/Lib-2_2022-10-22_thaw-1_VSVG_...   \n",
-       "14   Lib-1          2  data/Omicron_BA2/Lib-1_2022-10-22_thaw-2_VSVG_...   \n",
-       "15   Lib-2          2  data/Omicron_BA2/Lib-2_2022-10-22_thaw-2_VSVG_...   \n",
-       "\n",
-       "        condition  \n",
-       "0         Delta-1  \n",
-       "1         Delta-1  \n",
-       "2         Delta-3  \n",
-       "3         Delta-3  \n",
-       "4         Delta-4  \n",
-       "5         Delta-4  \n",
-       "6         Delta-2  \n",
-       "7         Delta-2  \n",
-       "8   Omicron_BA1-1  \n",
-       "9   Omicron_BA1-1  \n",
-       "10  Omicron_BA1-2  \n",
-       "11  Omicron_BA1-3  \n",
-       "12  Omicron_BA2-1  \n",
-       "13  Omicron_BA2-2  \n",
-       "14  Omicron_BA2-1  \n",
-       "15  Omicron_BA2-2  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "func_score_data = pd.DataFrame()\n",
-    "\n",
-    "for homolog in [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]:\n",
-    "    \n",
-    "    # functional scores\n",
-    "    func_sel = (\n",
-    "        pd.read_csv(f\"data/{homolog}/functional_selections.csv\")\n",
-    "        .assign(\n",
-    "            filename = lambda x: f\"data/{homolog}/\" + \n",
-    "            x.library + \"_\" + \n",
-    "            x.preselection_sample + \n",
-    "            \"_vs_\" + x.postselection_sample + \n",
-    "            \"_func_scores.csv\"\n",
-    "        )\n",
-    "        .assign(\n",
-    "            func_sel_scores_df = lambda x: x.filename.apply(\n",
-    "                lambda f: pd.read_csv(f)\n",
-    "            )   \n",
-    "        )\n",
-    "        .assign(\n",
-    "            len_func_sel_scores_df = lambda x: x.func_sel_scores_df.apply(\n",
-    "                lambda x: len(x)\n",
-    "            )\n",
-    "        )\n",
-    "        .assign(homolog = homolog)\n",
-    "    )\n",
-    "    func_score_data = pd.concat([func_score_data, func_sel]).reset_index(drop=True)\n",
-    "\n",
-    "# Add a column that gives a unique ID to each homolog/DMS experiment\n",
-    "func_score_data['condition'] = func_score_data.apply(\n",
-    "    lambda row: f\"{row['homolog']}-{row['library']}\".replace('-Lib',''),\n",
-    "    axis=1\n",
-    ")\n",
-    "func_score_data[['library', 'replicate', 'filename', 'condition']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f1dcb565-4aba-4ce4-a7a7-c406371fd89f",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Available conditions for fitting are:\n",
-      "- Delta-1\n",
-      "- Delta-3\n",
-      "- Delta-4\n",
-      "- Delta-2\n",
-      "- Omicron_BA1-1\n",
-      "- Omicron_BA1-2\n",
-      "- Omicron_BA1-3\n",
-      "- Omicron_BA2-1\n",
-      "- Omicron_BA2-2\n"
-     ]
-    }
-   ],
-   "source": [
-    "avail_cond_str = '\\n- '.join(list(func_score_data.condition.unique()))\n",
-    "print(f\"Available conditions for fitting are:\\n- {avail_cond_str}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4d4dbd8d-c028-41c2-a891-f001f6a9b237",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Concatentate each of the individual experiments, keeping track of the library and homolog of each. Output noteable features, for a random sample of 10 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "45085862-0225-428a-bdf2-6c806660338b",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a1e66849fba746479806bfd91b433c0d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/16 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>library</th>\n",
-       "      <th>barcode</th>\n",
-       "      <th>aa_substitutions</th>\n",
-       "      <th>func_score</th>\n",
-       "      <th>condition</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>721925</th>\n",
-       "      <td>Lib-3</td>\n",
-       "      <td>CGTAAAGTTCCAACAA</td>\n",
-       "      <td>G769R D950F R1107M N1192S</td>\n",
-       "      <td>-2.1765</td>\n",
-       "      <td>Omicron_BA1-3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>239549</th>\n",
-       "      <td>Lib-4</td>\n",
-       "      <td>AATAATTTTCCTACAC</td>\n",
-       "      <td></td>\n",
-       "      <td>-2.0202</td>\n",
-       "      <td>Delta-4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>816259</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>GATGATACCAAACTAT</td>\n",
-       "      <td>K814T L1024I E1207K</td>\n",
-       "      <td>-2.1526</td>\n",
-       "      <td>Omicron_BA2-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>612980</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>AAATATCCTACAAGAA</td>\n",
-       "      <td>C738Y A890T H1058Y</td>\n",
-       "      <td>-9.0995</td>\n",
-       "      <td>Omicron_BA1-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>368871</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>TAATACCGAATCCCCC</td>\n",
-       "      <td>A893V S939D A1078T</td>\n",
-       "      <td>-4.1550</td>\n",
-       "      <td>Omicron_BA1-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1115330</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>GTATACATGTATGATG</td>\n",
-       "      <td>S71L D1163E S1242N</td>\n",
-       "      <td>0.2762</td>\n",
-       "      <td>Omicron_BA2-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>410949</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>GCATTACTACAAATAA</td>\n",
-       "      <td>N960K</td>\n",
-       "      <td>0.6777</td>\n",
-       "      <td>Omicron_BA1-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>971589</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>CAATATAGCATAGAGG</td>\n",
-       "      <td>R78L</td>\n",
-       "      <td>0.1378</td>\n",
-       "      <td>Omicron_BA2-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>592643</th>\n",
-       "      <td>Lib-2</td>\n",
-       "      <td>ACAAGCTTTGCAACAA</td>\n",
-       "      <td>Y200H</td>\n",
-       "      <td>1.3313</td>\n",
-       "      <td>Omicron_BA1-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381265</th>\n",
-       "      <td>Lib-1</td>\n",
-       "      <td>CTAGTCTCCGACAAAA</td>\n",
-       "      <td>F347S D627G I850L</td>\n",
-       "      <td>-6.8325</td>\n",
-       "      <td>Omicron_BA1-1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        library           barcode           aa_substitutions  func_score  \\\n",
-       "721925    Lib-3  CGTAAAGTTCCAACAA  G769R D950F R1107M N1192S     -2.1765   \n",
-       "239549    Lib-4  AATAATTTTCCTACAC                                -2.0202   \n",
-       "816259    Lib-1  GATGATACCAAACTAT        K814T L1024I E1207K     -2.1526   \n",
-       "612980    Lib-2  AAATATCCTACAAGAA         C738Y A890T H1058Y     -9.0995   \n",
-       "368871    Lib-1  TAATACCGAATCCCCC         A893V S939D A1078T     -4.1550   \n",
-       "1115330   Lib-2  GTATACATGTATGATG         S71L D1163E S1242N      0.2762   \n",
-       "410949    Lib-1  GCATTACTACAAATAA                      N960K      0.6777   \n",
-       "971589    Lib-1  CAATATAGCATAGAGG                       R78L      0.1378   \n",
-       "592643    Lib-2  ACAAGCTTTGCAACAA                      Y200H      1.3313   \n",
-       "381265    Lib-1  CTAGTCTCCGACAAAA          F347S D627G I850L     -6.8325   \n",
-       "\n",
-       "             condition  \n",
-       "721925   Omicron_BA1-3  \n",
-       "239549         Delta-4  \n",
-       "816259   Omicron_BA2-1  \n",
-       "612980   Omicron_BA1-2  \n",
-       "368871   Omicron_BA1-1  \n",
-       "1115330  Omicron_BA2-2  \n",
-       "410949   Omicron_BA1-1  \n",
-       "971589   Omicron_BA2-1  \n",
-       "592643   Omicron_BA1-2  \n",
-       "381265   Omicron_BA1-1  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "func_score_df = pd.DataFrame()\n",
-    "for idx, row in tqdm(func_score_data.iterrows(), total=len(func_score_data)):\n",
-    "    mut_df_replicates = row.func_sel_scores_df.assign(\n",
-    "        homolog=row.homolog,\n",
-    "        library = row.library,\n",
-    "        replicate = row.replicate,\n",
-    "        condition=row.condition\n",
-    "    )\n",
-    "    func_score_df = pd.concat([func_score_df, mut_df_replicates])\n",
-    "\n",
-    "# rename, sort index, and fill na (wildtype values) with empty strings\n",
-    "func_score_df = (func_score_df\n",
-    "    .rename(\n",
-    "        {\"aa_substitutions_reference\":\"aa_substitutions\"}, \n",
-    "        axis=1\n",
-    "    )\n",
-    "    .reset_index(drop=True)\n",
-    "    .fillna(\"\")\n",
-    "    .sort_values(by=\"condition\")\n",
-    ")\n",
-    "func_score_df[[\"library\", \"barcode\", \"aa_substitutions\", \"func_score\", \"condition\"]].sample(10, random_state=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7dabb345-ef8f-4a97-b8eb-cea29a4e4597",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Discard all variants with a pre-selection count of 100."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "35d1b1ae-881a-4031-81d4-89effe50b11c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Of 1135096 variants, 120164 had fewer than the threshold of counts before selection, and were filtered out\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_pre_threshold = len(func_score_df)\n",
-    "func_score_df.query(\"pre_count >= 100\", inplace=True)\n",
-    "print(f\"Of {n_pre_threshold} variants, {n_pre_threshold - len(func_score_df)} had fewer than the threshold of counts before selection, and were filtered out\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a0d44e1-7953-453a-abf1-d69c594d51f4",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "We only require a functional score, aa substitutions, and condition column for instatiating the `multidms.Data` object. drop the rest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "88fb9dc0-0b5d-49e6-8e69-8b38665c3531",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "required_cols = ['func_score', 'aa_substitutions', 'condition']\n",
-    "func_score_df.drop([c for c in func_score_df if c not in required_cols], axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98a6d285-4f71-4c1a-917b-cae96fe3ba0a",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Remove all variants with string-suffixed sites (indels) and stop codon wildtypes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b2640fd3-d17e-49cb-bea5-122ca5a6ba1d",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "736edc307a4a43c69ac5a93020915e47",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1014932 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "stop_wt_vars = []\n",
-    "non_numeric_sites = []\n",
-    "for idx, row in tqdm(func_score_df.iterrows(), total=len(func_score_df)):\n",
-    "    for sub in row[\"aa_substitutions\"].split():\n",
-    "        if sub[0] == \"*\":\n",
-    "            stop_wt_vars.append(idx)\n",
-    "        if not sub[-2].isnumeric():\n",
-    "            non_numeric_sites.append(idx)\n",
-    "\n",
-    "to_drop = set.union(set(stop_wt_vars), set(non_numeric_sites))\n",
-    "func_score_df.drop(to_drop, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b488806a-7942-45a5-8435-295dc487f83f",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "We clip all functional scores at a lower bound of -3.5, and an upper bound of 2.5."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "56d9c8a5-1d05-448a-a2e8-1fb059e00087",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 143177 variants below the clip theshold, and 345 above.\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_below_clip = len(func_score_df.query(f\"func_score < -3.5\"))\n",
-    "n_above_clip = len(func_score_df.query(f\"func_score > 2.5\"))\n",
-    "print(f\"There are {n_below_clip} variants below the clip theshold, and {n_above_clip} above.\")\n",
-    "func_score_df = func_score_df.assign(\n",
-    "    func_score = func_score_df.func_score.clip(-3.5, 2.5)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "016a81aa",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Choose two representative biological replicates for each of the three homologs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "351cebdb-0bdc-49e2-b181-d9f72f5a7a07",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "experiment_conditions = [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]\n",
-    "replicate_1_experiments = [\"Delta-2\", \"Omicron_BA1-2\", \"Omicron_BA2-1\"]\n",
-    "replicate_2_experiments = [\"Delta-4\", \"Omicron_BA1-3\", \"Omicron_BA2-2\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38584590-dceb-4f8b-a8c2-d98eb94bd501",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Organize the two replicates and annotate replicates \"1\" and \"2\". These each represent a distinct training set such that we may train replicate models and compare their results. Output a random sample of 10 variants."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "97cefc92-4ab6-4f50-b23c-4baf7ef017a1",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>func_score</th>\n",
-       "      <th>aa_substitutions</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>replicate</th>\n",
-       "      <th>n_subs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1084142</th>\n",
-       "      <td>-1.8970</td>\n",
-       "      <td>D950L</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>633686</th>\n",
-       "      <td>-3.5000</td>\n",
-       "      <td>N122- D339H A653N F981V</td>\n",
-       "      <td>Omicron_BA1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>729552</th>\n",
-       "      <td>-0.0599</td>\n",
-       "      <td>P26L S1175Q</td>\n",
-       "      <td>Omicron_BA1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1027437</th>\n",
-       "      <td>0.0226</td>\n",
-       "      <td>T827A S1252E</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>907543</th>\n",
-       "      <td>0.2825</td>\n",
-       "      <td>K1157R</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>263261</th>\n",
-       "      <td>0.1846</td>\n",
-       "      <td></td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>711544</th>\n",
-       "      <td>-1.5318</td>\n",
-       "      <td>I1210T S1239G</td>\n",
-       "      <td>Omicron_BA1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>669159</th>\n",
-       "      <td>-0.4898</td>\n",
-       "      <td>I197T D253F L371S R567K A1020T</td>\n",
-       "      <td>Omicron_BA1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1072901</th>\n",
-       "      <td>-3.5000</td>\n",
-       "      <td>S27R S1123F Q1208E</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1028709</th>\n",
-       "      <td>-0.1358</td>\n",
-       "      <td></td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         func_score                aa_substitutions    condition  replicate  \\\n",
-       "1084142     -1.8970                           D950L  Omicron_BA2          2   \n",
-       "633686      -3.5000         N122- D339H A653N F981V  Omicron_BA1          1   \n",
-       "729552      -0.0599                     P26L S1175Q  Omicron_BA1          2   \n",
-       "1027437      0.0226                    T827A S1252E  Omicron_BA2          1   \n",
-       "907543       0.2825                          K1157R  Omicron_BA2          2   \n",
-       "263261       0.1846                                        Delta          1   \n",
-       "711544      -1.5318                   I1210T S1239G  Omicron_BA1          2   \n",
-       "669159      -0.4898  I197T D253F L371S R567K A1020T  Omicron_BA1          2   \n",
-       "1072901     -3.5000              S27R S1123F Q1208E  Omicron_BA2          2   \n",
-       "1028709     -0.1358                                  Omicron_BA2          1   \n",
-       "\n",
-       "         n_subs  \n",
-       "1084142       1  \n",
-       "633686        4  \n",
-       "729552        2  \n",
-       "1027437       2  \n",
-       "907543        1  \n",
-       "263261        0  \n",
-       "711544        2  \n",
-       "669159        5  \n",
-       "1072901       3  \n",
-       "1028709       0  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "func_score_df = pd.concat(\n",
-    "    [\n",
-    "        (\n",
-    "            func_score_df\n",
-    "            .query(\"condition in @replicate_1_experiments\")\n",
-    "            .replace(dict(zip(replicate_1_experiments, experiment_conditions)))\n",
-    "            .assign(replicate=1)\n",
-    "        ),\n",
-    "        (\n",
-    "            func_score_df\n",
-    "            .query(\"condition in @replicate_2_experiments\")\n",
-    "            .replace(dict(zip(replicate_2_experiments, experiment_conditions)))\n",
-    "            .assign(replicate=2)\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "func_score_df = func_score_df.assign(\n",
-    "    n_subs = [\n",
-    "        len(aa_subs.split()) \n",
-    "        for aa_subs in func_score_df.aa_substitutions\n",
-    "    ]\n",
-    ")\n",
-    "func_score_df.sample(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "2a6cfa16-1e96-4acf-8877-9c795bb85233",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "func_score_df.to_csv(f\"{output_dir}/training_functional_scores.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4e9db4d-6b54-4fff-ae53-663b64f7b03e",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Variant barcode and mutation background stats\n",
-    "\n",
-    "In this section we briedly query and visualize charictaristics of the replicate training sets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "753635de-d647-41b8-8cdd-712da222fd32",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Get the expected number substitutions per variant for each condition replicate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "da58d067-e124-402b-9169-6746b5844ee9",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Delta - rep 1 has 2.18671 subs per variant, on average\n",
-      "Delta - rep 2 has 2.29472 subs per variant, on average\n",
-      "Omicron_BA1 - rep 1 has 1.802 subs per variant, on average\n",
-      "Omicron_BA1 - rep 2 has 1.75802 subs per variant, on average\n",
-      "Omicron_BA2 - rep 1 has 2.31117 subs per variant, on average\n",
-      "Omicron_BA2 - rep 2 has 2.32827 subs per variant, on average\n"
-     ]
-    }
-   ],
-   "source": [
-    "for group, group_df in func_score_df.groupby([\"condition\", \"replicate\"]):\n",
-    "    print(f\"{group[0]} - rep {group[1]} has {round(group_df.n_subs.mean(), 5)} subs per variant, on average\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39a80cb6-a561-440a-a71c-8060d572bc12",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Get the number of unique mutations seen in each condition replicate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "72f352bd-ce16-439c-8267-729de5f5b517",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Delta - rep 1 has 28515\n",
-      "Delta - rep 2 has 29158\n",
-      "Omicron_BA1 - rep 1 has 70597\n",
-      "Omicron_BA1 - rep 2 has 62129\n",
-      "Omicron_BA2 - rep 1 has 60397\n",
-      "Omicron_BA2 - rep 2 has 57719\n"
-     ]
-    }
-   ],
-   "source": [
-    "for group, group_df in func_score_df.groupby([\"condition\", \"replicate\"]):\n",
-    "    print(f\"{group[0]} - rep {group[1]} has {len(group_df.aa_substitutions.unique())}\")  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f199e90-91a2-49d0-b6e4-6f2e339340e6",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Visualize the distribution of barcodes per variant, as well as the distribution of unique backgrounds per mutation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "3a795651-b750-4613-b4b0-13577ba7a645",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.simplefilter(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "99d3b95a-a259-4815-9edf-52c5a7fdcb7c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x550 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas = f\"raw_data_summary_barcodes_backgrounds_hist\"\n",
-    "logscale=False\n",
-    "fig, ax = plt.subplots(2,3, sharex=\"row\", sharey=\"row\", figsize=[6.4, 5.5])\n",
-    "\n",
-    "condition_title = {\n",
-    "    \"Delta\":\"Delta\",\n",
-    "    \"Omicron_BA1\" : \"BA.1\",\n",
-    "    \"Omicron_BA2\" : \"BA.2\"\n",
-    "}\n",
-    "\n",
-    "row = 0\n",
-    "for col, (condition, condition_df) in enumerate(func_score_df.groupby(\"condition\")):\n",
-    "    iter_ax = ax[row, col]\n",
-    "    \n",
-    "    mut_df_replicates = condition_df.query(\"aa_substitutions != ''\")\n",
-    "    mut_df_replicates = mut_df_replicates.assign(\n",
-    "        num_muts = [\n",
-    "            len(aa_subs.split())\n",
-    "            for aa_subs in mut_df_replicates.aa_substitutions\n",
-    "        ]\n",
-    "    )\n",
-    "    \n",
-    "    sns.histplot(mut_df_replicates.query(\"num_muts <= 10\"), x=\"num_muts\", ax=iter_ax, hue=\"replicate\", discrete=True)\n",
-    "    for rep, rep_df in mut_df_replicates.groupby(\"replicate\"):\n",
-    "        mean = rep_df['num_muts'].mean()\n",
-    "        iter_ax.axvline(mean, linestyle=(\"-\" if rep == 1 else \"--\"))\n",
-    "    \n",
-    "    if logscale: iter_ax.set_yscale('log')\n",
-    "    if col != 2: \n",
-    "        iter_ax.get_legend().remove()\n",
-    "    n_rep1 = len(mut_df_replicates.query(\"replicate == 1\"))//1000\n",
-    "    n_rep2 = len(mut_df_replicates.query(\"replicate == 2\"))//1000\n",
-    "    iter_ax.text(\n",
-    "        0.1, 1.1, \n",
-    "        f\"$N={n_rep1}K, {n_rep2}K$\", \n",
-    "        ha=\"left\", va=\"top\", \n",
-    "        transform=iter_ax.transAxes\n",
-    "    )\n",
-    "    xscale = \"number of amino-acid substitutions per variant\" if col == 1 else \"\"\n",
-    "    iter_ax.set_xlabel(xscale)\n",
-    "    \n",
-    "    ylabel = f\"variant counts\" if col == 0 else \"\"\n",
-    "    iter_ax.set_ylabel(ylabel)\n",
-    "    iter_ax.set_xticks(\n",
-    "        [i+1 for i in range(10)],\n",
-    "        labels=[i+1 for i in range(10)], \n",
-    "        ha=\"center\",\n",
-    "        size=7,\n",
-    "        rotation=0\n",
-    "    )\n",
-    "    sns.despine(ax=iter_ax)\n",
-    "    iter_ax.set_title(condition_title[condition], y=1.15)\n",
-    "\n",
-    "row = 1\n",
-    "collapsed_bc_df = func_score_df.groupby(\n",
-    "    [\"replicate\", \"condition\", \"aa_substitutions\"]\n",
-    ").aggregate(\"mean\").reset_index()\n",
-    "for col, (condition, condition_df) in enumerate(collapsed_bc_df.groupby(\"condition\")):\n",
-    "    iter_ax = ax[row, col]\n",
-    "    mut_df_replicates = pd.DataFrame()\n",
-    "    for rep, rep_df in condition_df.groupby(\"replicate\"):\n",
-    "        \n",
-    "        times_seen = (\n",
-    "            rep_df[\"aa_substitutions\"].str.split().explode().value_counts()\n",
-    "        )\n",
-    "        if (times_seen == times_seen.astype(int)).all():\n",
-    "            times_seen = times_seen.astype(int)\n",
-    "        times_seen = pd.DataFrame(times_seen)\n",
-    "        times_seen.index.name = f\"mutation\"\n",
-    "        mut_df_replicates = pd.concat([mut_df_replicates, times_seen.assign(replicate=rep).reset_index()])\n",
-    "\n",
-    "    sns.histplot(\n",
-    "        mut_df_replicates.query(\"count <= 50\"), \n",
-    "        x=\"count\", \n",
-    "        ax=iter_ax, \n",
-    "        element='step', \n",
-    "        hue=\"replicate\", \n",
-    "        discrete=True\n",
-    "    )\n",
-    "    \n",
-    "    for rep, rep_df in mut_df_replicates.groupby(\"replicate\"):\n",
-    "        mean = rep_df['count'].mean()\n",
-    "        iter_ax.axvline(mean, linestyle=(\"-\" if rep == 1 else \"--\"))\n",
-    "        \n",
-    "    iter_ax.get_legend().remove()\n",
-    "    n_rep1 = len(mut_df_replicates.query(\"replicate == 1\"))\n",
-    "    n_rep2 = len(mut_df_replicates.query(\"replicate == 2\"))\n",
-    "    iter_ax.text(\n",
-    "        0.1, 1.1, \n",
-    "        f\"$N={n_rep1}, {n_rep2}$\", \n",
-    "        ha=\"left\", va=\"top\", \n",
-    "        transform=iter_ax.transAxes\n",
-    "    )\n",
-    "    \n",
-    "    xscale = \"number of variant backgrounds \\nfor a given amino-acid substitution\" if col == 1 else \"\"\n",
-    "    iter_ax.set_xlabel(xscale)\n",
-    "    \n",
-    "    ylabel = f\"mutation counts\" if col == 0 else \"\"\n",
-    "    iter_ax.set_ylabel(ylabel)\n",
-    "    \n",
-    "    xticks = [i for i in range(0, 51) if i % 5 == 0]\n",
-    "    iter_ax.set_xticks(\n",
-    "        xticks,\n",
-    "        labels=xticks, \n",
-    "        ha=\"center\",\n",
-    "        size=7,\n",
-    "        rotation=0\n",
-    "    )\n",
-    "    \n",
-    "    sns.despine(ax=iter_ax)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "ax[0,0].text(\n",
-    "    -0.1, 1.06, \n",
-    "    f\"A\", \n",
-    "    ha=\"right\", va=\"bottom\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=ax[0,0].transAxes\n",
-    ")\n",
-    "ax[1,0].text(\n",
-    "    -0.1, 1.06, \n",
-    "    f\"B\", \n",
-    "    ha=\"right\", va=\"bottom\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=ax[1,0].transAxes\n",
-    ")\n",
-    "\n",
-    "fig.subplots_adjust(hspace=.6)\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\")\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d53bf8b7-318a-43f2-8852-8c830dc8e538",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Plot the correlation of variant functional scores (averaged across barcodes) between replicates in each condition, as well as the full distribution of functional scores."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "97e5ea85-cccc-43ff-a7c5-f0ca3d60c1ae",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x530 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas = \"replicate_functional_score_correlation_scatter\"\n",
-    "pal = sns.color_palette('tab20')\n",
-    "\n",
-    "fig, ax = plt.subplots(2,3, sharex=\"row\", sharey=\"row\", figsize=[6.4, 5.3])\n",
-    "collapsed_bc_df = func_score_df.groupby(\n",
-    "    [\"replicate\", \"condition\", \"aa_substitutions\"]\n",
-    ").aggregate(\"mean\").reset_index()\n",
-    "collapsed_bc_df = collapsed_bc_df.assign(\n",
-    "    is_stop=[True if \"*\" in aasubs else False for aasubs in collapsed_bc_df.aa_substitutions]\n",
-    ")\n",
-    "\n",
-    "is_stop_alpha_dict = {\n",
-    "    True : 0.5,\n",
-    "    False : 0.2\n",
-    "}\n",
-    "\n",
-    "lim = [-3.8, 2.8]\n",
-    "ticks = np.linspace(-3, 2, 6)\n",
-    "for col, (condition, condition_df) in enumerate(collapsed_bc_df.groupby(\"condition\")):\n",
-    "    \n",
-    "    row = 0\n",
-    "    iter_ax = ax[row, col]\n",
-    "    \n",
-    "    mut_df_replicates = reduce(\n",
-    "        lambda left, right: pd.merge(\n",
-    "            left, right, left_index=True, right_index=True, how=\"inner\"\n",
-    "        ),\n",
-    "        [\n",
-    "            rep_df.rename({\"func_score\":f\"rep_{rep}_func_score\"}, axis=1).set_index(\"aa_substitutions\")\n",
-    "            for rep, rep_df in condition_df.groupby(\"replicate\") \n",
-    "        ],\n",
-    "    )\n",
-    "    \n",
-    "    mut_df_replicates = mut_df_replicates.assign(\n",
-    "        is_stop=[True if \"*\" in aasubs else False for aasubs in mut_df_replicates.index.values]\n",
-    "    )\n",
-    "    mut_df_replicates = mut_df_replicates.assign(\n",
-    "        n_subs=[len(aasubs.split()) for aasubs in mut_df_replicates.index.values]\n",
-    "    )\n",
-    "    \n",
-    "    alpha = [is_stop_alpha_dict[istp] for istp in mut_df_replicates.is_stop]\n",
-    "    sns.scatterplot(\n",
-    "        mut_df_replicates, \n",
-    "        x=\"rep_1_func_score\", \n",
-    "        y=\"rep_2_func_score\", \n",
-    "        ax =iter_ax,\n",
-    "        # alpha=alpha,\n",
-    "        hue=\"is_stop\",\n",
-    "        hue_order=[False, True],\n",
-    "        palette=[\"darkgrey\", \"red\"]\n",
-    "    )\n",
-    "    \n",
-    "    iter_ax.plot([-3.5, 2.5], [-3.5, 2.5], \"--\", lw=2, c=\"royalblue\")\n",
-    "    \n",
-    "    iter_ax.set_ylim(lim)\n",
-    "    iter_ax.set_xlim(lim)\n",
-    "    if col == 0:\n",
-    "        iter_ax.set_yticks(ticks, labels=ticks)\n",
-    "    iter_ax.set_xticks(ticks, labels=ticks, rotation=45)\n",
-    "    \n",
-    "    corr = pearsonr(mut_df_replicates[\"rep_1_func_score\"], mut_df_replicates[\"rep_2_func_score\"])[0]\n",
-    "    iter_ax.annotate(\n",
-    "        f\"$r = {corr:.2f}$\", \n",
-    "        (0.1, 0.9), \n",
-    "        xycoords=\"axes fraction\", \n",
-    "        fontsize=12\n",
-    "    )\n",
-    "    iter_ax.set_title(condition)\n",
-    "    iter_ax.get_legend().remove()\n",
-    "    sns.despine(ax=iter_ax)\n",
-    "    \n",
-    "    row = 1\n",
-    "    iter_ax = ax[row, col]\n",
-    "    sns.violinplot(\n",
-    "        condition_df,\n",
-    "        x=\"is_stop\",\n",
-    "        y=\"func_score\",\n",
-    "        hue=\"replicate\",\n",
-    "        split=True,\n",
-    "        palette=[\"0.5\", \"0.75\"],\n",
-    "        ax=iter_ax\n",
-    "    )\n",
-    "    \n",
-    "    sns.despine(ax=iter_ax)\n",
-    "    if col != 2:\n",
-    "        iter_ax.get_legend().remove()\n",
-    "    else:\n",
-    "        iter_ax.legend(bbox_to_anchor = (1.25, 1.05), title=\"replicate\")\n",
-    "    if col == 0:\n",
-    "        iter_ax.set_yticks(ticks, labels=ticks)\n",
-    "\n",
-    "ax[0,0].set_xlabel(\"\")\n",
-    "ax[0,0].set_ylabel(\"replicate 2 \\n functional score\")\n",
-    "\n",
-    "ax[0,1].set_xlabel(\"replicate 1 functional score\")\n",
-    "ax[0,1].set_title(\"BA.1\")\n",
-    "ax[0,2].set_xlabel(\"\")\n",
-    "ax[0,2].set_title(\"BA.2\")\n",
-    "\n",
-    "ax[1,0].set_xlabel(\"\")\n",
-    "ax[1,0].set_ylabel(\"functional score\")\n",
-    "\n",
-    "ax[1,1].set_xlabel(\"variants contain stop codon mutations\")\n",
-    "ax[1,2].set_xlabel(\"\")\n",
-    "ax[1,2].set_ylabel(\"\")\n",
-    "ax[1,1].set_ylabel(\"\")\n",
-    "\n",
-    "ax[0,0].text(\n",
-    "    -0.1, 1.06, \n",
-    "    f\"A\", \n",
-    "    ha=\"right\", va=\"bottom\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=ax[0,0].transAxes\n",
-    ")\n",
-    "ax[1,0].text(\n",
-    "    -0.1, 1.06, \n",
-    "    f\"B\", \n",
-    "    ha=\"right\", va=\"bottom\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=ax[1,0].transAxes\n",
-    ")\n",
-    "\n",
-    "# fig.suptitle(\"Variant Functional Score \\nReplicate Correlation\")\n",
-    "plt.tight_layout()\n",
-    "fig.subplots_adjust(wspace=0.08, hspace = 0.5)\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\")\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\")\n",
-    "plt.show()"
-   ]
-  },
-  {
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-   "source": [
-    "## Encode data for fitting\n",
-    "\n",
-    "Next, we use the `multidms.Data` class to prep our data for fitting. To see the class docstring describing the required input and keyword arguments, toggle the output for the line below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "167eee35-7890-4c0f-874c-2f0ba974bfa8",
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on class Data in module multidms.data:\n",
-      "\n",
-      "class Data(builtins.object)\n",
-      " |  Data(variants_df: pandas.core.frame.DataFrame, reference: str, alphabet=('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'), collapse_identical_variants=False, condition_colors=('#0072B2', '#CC79A7', '#009E73', '#17BECF', '#BCDB22'), letter_suffixed_sites=False, assert_site_integrity=False, verbose=False, nb_workers=None, name=None)\n",
-      " |  \n",
-      " |  Prep and store one-hot encoding of\n",
-      " |  variant substitutions data.\n",
-      " |  Individual objects of this type can be shared\n",
-      " |  by multiple :py:class:`multidms.Model` Objects\n",
-      " |  for efficiently fitting various models to the same data.\n",
-      " |  \n",
-      " |  Note\n",
-      " |  ----\n",
-      " |  You can initialize a :class:`Data` object with a :class:`pandas.DataFrame`\n",
-      " |  with a row for each variant sampled and annotations\n",
-      " |  provided in the required columns:\n",
-      " |  \n",
-      " |  1. `condition` - Experimental condition from\n",
-      " |      which a sample measurement was obtained.\n",
-      " |  2. `aa_substitutions` - Defines each variant\n",
-      " |      :math:`v` as a string of substitutions (e.g., ``'M3A K5G'``).\n",
-      " |      Note that while conditions may have differing wild types\n",
-      " |      at a given site, the sites between conditions should reference\n",
-      " |      the same site when alignment is performed between\n",
-      " |      condition wild types.\n",
-      " |  3. `func_score` - The functional score computed from experimental\n",
-      " |      measurements.\n",
-      " |  \n",
-      " |  Parameters\n",
-      " |  ----------\n",
-      " |  variants_df : :class:`pandas.DataFrame` or None\n",
-      " |      The variant level information from all experiments you\n",
-      " |      wish to analyze. Should have columns named ``'condition'``,\n",
-      " |      ``'aa_substitutions'``, and ``'func_score'``.\n",
-      " |      See the class note for descriptions of each of the features.\n",
-      " |  reference : str\n",
-      " |      Name of the condition which annotates the reference.\n",
-      " |      variants. Note that for model fitting this class will convert all\n",
-      " |      amino acid substitutions for non-reference condition groups\n",
-      " |      to relative to the reference condition.\n",
-      " |      For example, if the wild type amino acid at site 30 is an\n",
-      " |      A in the reference condition, and a G in a non-reference condition,\n",
-      " |      then a Y30G mutation in the non-reference condition is recorded as an A30G\n",
-      " |      mutation relative to the reference. This way, each condition informs\n",
-      " |      the exact same parameters, even at sites that differ in wild type amino acid.\n",
-      " |      These are encoded in a :class:`binarymap.binarymap.BinaryMap` object for each\n",
-      " |      condition,\n",
-      " |      where all sites that are non-identical to the reference are 1's.\n",
-      " |      For motivation, see the `Model overview` section in :class:`multidms.Model`\n",
-      " |      class notes.\n",
-      " |  alphabet : array-like\n",
-      " |      Allowed characters in mutation strings.\n",
-      " |  collapse_identical_variants : {'mean', 'median', False}\n",
-      " |      If identical variants in ``variants_df`` (same 'aa_substitutions'),\n",
-      " |      exist within individual condition groups,\n",
-      " |      collapse them by taking mean or median of 'func_score', or\n",
-      " |      (if `False`) do not collapse at all. Collapsing will make fitting faster,\n",
-      " |      but *not* a good idea if you are doing bootstrapping.\n",
-      " |  condition_colors : array-like or dict\n",
-      " |      Maps each condition to the color used for plotting. Either a dict keyed\n",
-      " |      by each condition, or an array of colors that are sequentially assigned\n",
-      " |      to the conditions.\n",
-      " |  letter_suffixed_sites: bool\n",
-      " |      True if sites are sequential and integer, False otherwise.\n",
-      " |  assert_site_integrity : bool\n",
-      " |      If True, will assert that all sites in the data frame\n",
-      " |      have the same wild type amino acid, grouped by condition.\n",
-      " |  verbose : bool\n",
-      " |      If True, will print progress bars.\n",
-      " |  nb_workers : int\n",
-      " |      Number of workers to use for parallel operations.\n",
-      " |      If None, will use all available CPUs.\n",
-      " |  name : str or None\n",
-      " |      Name of the data object. If None, will be assigned\n",
-      " |      a unique name based upon the number of data objects\n",
-      " |      instantiated.\n",
-      " |  \n",
-      " |  Example\n",
-      " |  -------\n",
-      " |  Simple example with two conditions (``'a'`` and ``'b'``)\n",
-      " |  \n",
-      " |  >>> import pandas as pd\n",
-      " |  >>> import multidms\n",
-      " |  >>> func_score_data = {\n",
-      " |  ...     'condition' : [\"a\",\"a\",\"a\",\"a\", \"b\",\"b\",\"b\",\"b\",\"b\",\"b\"],\n",
-      " |  ...     'aa_substitutions' : [\n",
-      " |  ...         'M1E', 'G3R', 'G3P', 'M1W', 'M1E',\n",
-      " |  ...         'P3R', 'P3G', 'M1E P3G', 'M1E P3R', 'P2T'\n",
-      " |  ...     ],\n",
-      " |  ...     'func_score' : [2, -7, -0.5, 2.3, 1, -5, 0.4, 2.7, -2.7, 0.3],\n",
-      " |  ... }\n",
-      " |  >>> func_score_df = pd.DataFrame(func_score_data)\n",
-      " |  >>> func_score_df  # doctest: +NORMALIZE_WHITESPACE\n",
-      " |  condition aa_substitutions  func_score\n",
-      " |  0         a              M1E         2.0\n",
-      " |  1         a              G3R        -7.0\n",
-      " |  2         a              G3P        -0.5\n",
-      " |  3         a              M1W         2.3\n",
-      " |  4         b              M1E         1.0\n",
-      " |  5         b              P3R        -5.0\n",
-      " |  6         b              P3G         0.4\n",
-      " |  7         b          M1E P3G         2.7\n",
-      " |  8         b          M1E P3R        -2.7\n",
-      " |  9         b              P2T         0.3\n",
-      " |  \n",
-      " |  Instantiate a ``Data`` Object allowing for stop codon variants\n",
-      " |  and declaring condition `\"a\"` as the reference condition.\n",
-      " |  \n",
-      " |  >>> data = multidms.Data(\n",
-      " |  ...     func_score_df,\n",
-      " |  ...     alphabet = multidms.AAS_WITHSTOP,\n",
-      " |  ...     reference = \"a\",\n",
-      " |  ... )  # doctest: +ELLIPSIS\n",
-      " |  ...\n",
-      " |  \n",
-      " |  Note this may take some time due to the string\n",
-      " |  operations that must be performed when converting\n",
-      " |  amino acid substitutions to be with respect to a\n",
-      " |  reference wild type sequence.\n",
-      " |  \n",
-      " |  After the object has finished being instantiated,\n",
-      " |  we now have access to a few 'static' properties\n",
-      " |  of our data. See individual property docstring\n",
-      " |  for more information.\n",
-      " |  \n",
-      " |  >>> data.reference\n",
-      " |  'a'\n",
-      " |  \n",
-      " |  >>> data.conditions\n",
-      " |  ('a', 'b')\n",
-      " |  \n",
-      " |  >>> data.mutations\n",
-      " |  ('M1E', 'M1W', 'G3P', 'G3R')\n",
-      " |  \n",
-      " |  >>> data.site_map  # doctest: +NORMALIZE_WHITESPACE\n",
-      " |  a  b\n",
-      " |  1  M  M\n",
-      " |  3  G  P\n",
-      " |  \n",
-      " |  >>> data.mutations_df  # doctest: +NORMALIZE_WHITESPACE\n",
-      " |    mutation wts  sites muts  times_seen_a  times_seen_b\n",
-      " |  0      M1E   M      1    E             1           3.0\n",
-      " |  1      M1W   M      1    W             1           0.0\n",
-      " |  2      G3P   G      3    P             1           1.0\n",
-      " |  3      G3R   G      3    R             1           2.0\n",
-      " |  \n",
-      " |  >>> data.variants_df  # doctest: +NORMALIZE_WHITESPACE\n",
-      " |    condition aa_substitutions  func_score var_wrt_ref\n",
-      " |  0         a              M1E         2.0         M1E\n",
-      " |  1         a              G3R        -7.0         G3R\n",
-      " |  2         a              G3P        -0.5         G3P\n",
-      " |  3         a              M1W         2.3         M1W\n",
-      " |  4         b              M1E         1.0     G3P M1E\n",
-      " |  5         b              P3R        -5.0         G3R\n",
-      " |  6         b              P3G         0.4\n",
-      " |  7         b          M1E P3G         2.7         M1E\n",
-      " |  8         b          M1E P3R        -2.7     G3R M1E\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __init__(self, variants_df: pandas.core.frame.DataFrame, reference: str, alphabet=('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'), collapse_identical_variants=False, condition_colors=('#0072B2', '#CC79A7', '#009E73', '#17BECF', '#BCDB22'), letter_suffixed_sites=False, assert_site_integrity=False, verbose=False, nb_workers=None, name=None)\n",
-      " |      See main class docstring.\n",
-      " |  \n",
-      " |  __repr__(self)\n",
-      " |      Returns a string representation of the object.\n",
-      " |  \n",
-      " |  convert_subs_wrt_ref_seq(self, condition, aa_subs)\n",
-      " |      Covert amino acid substitutions to be with respect to the reference sequence.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      condition : str\n",
-      " |          The condition from which aa substitutions are relative to.\n",
-      " |      aa_subs : str\n",
-      " |          A string of amino acid substitutions, relative to the condition sequence,\n",
-      " |          to converted\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      str\n",
-      " |          A string of amino acid substitutions relative to the reference sequence.\n",
-      " |  \n",
-      " |  plot_func_score_boxplot(self, saveas=None, show=True, **kwargs)\n",
-      " |      Plot a boxplot of the functional scores for each condition.\n",
-      " |  \n",
-      " |  plot_times_seen_hist(self, saveas=None, show=True, **kwargs)\n",
-      " |      Plot a histogram of the number of times each mutation was seen.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Readonly properties defined here:\n",
-      " |  \n",
-      " |  binarymaps\n",
-      " |      A dictionary keyed by condition names with values\n",
-      " |      being a ``BinaryMap`` object for each condition.\n",
-      " |  \n",
-      " |  conditions\n",
-      " |      A tuple of all conditions.\n",
-      " |  \n",
-      " |  mutations\n",
-      " |      A tuple of all mutations in the order relative to their index into\n",
-      " |      the binarymap.\n",
-      " |  \n",
-      " |  mutations_df\n",
-      " |      A dataframe summarizing all single mutations\n",
-      " |  \n",
-      " |  mutparser\n",
-      " |      The mutation ``polyclonal.utils.MutationParser`` used\n",
-      " |      to parse mutations.\n",
-      " |  \n",
-      " |  name\n",
-      " |      The name of the data object.\n",
-      " |  \n",
-      " |  non_identical_mutations\n",
-      " |      A dictionary keyed by condition names with values\n",
-      " |      being a string of all mutations that differ from the\n",
-      " |      reference sequence.\n",
-      " |  \n",
-      " |  non_identical_sites\n",
-      " |      A dictionary keyed by condition names with values\n",
-      " |      being a :class:`pandas.DataFrame` indexed by site,\n",
-      " |      with columns for the reference\n",
-      " |      and non-reference amino acid at each site that differs.\n",
-      " |  \n",
-      " |  parse_mut\n",
-      " |      returns a function that splits a single amino acid substitutions\n",
-      " |      into wildtype, site, and mutation\n",
-      " |      using the mutation parser.\n",
-      " |  \n",
-      " |  parse_muts\n",
-      " |      A function that splits amino acid substitutions\n",
-      " |      (a string of more than one) into wildtype, site, and mutation\n",
-      " |      using the mutation parser.\n",
-      " |  \n",
-      " |  reference\n",
-      " |      The name of the reference condition.\n",
-      " |  \n",
-      " |  reference_sequence_conditions\n",
-      " |      A list of conditions that have the same wild type\n",
-      " |      sequence as the reference condition.\n",
-      " |  \n",
-      " |  site_map\n",
-      " |      A dataframe indexed by site, with columns\n",
-      " |      for all conditions giving the wild type amino acid\n",
-      " |      at each site.\n",
-      " |  \n",
-      " |  targets\n",
-      " |      The functional scores for each variant in the training data.\n",
-      " |  \n",
-      " |  training_data\n",
-      " |      A dictionary with keys 'X' and 'y' for the training data.\n",
-      " |  \n",
-      " |  variants_df\n",
-      " |      A dataframe summarizing all variants in the training data.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors defined here:\n",
-      " |  \n",
-      " |  __dict__\n",
-      " |      dictionary for instance variables (if defined)\n",
-      " |  \n",
-      " |  __weakref__\n",
-      " |      list of weak references to the object (if defined)\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data and other attributes defined here:\n",
-      " |  \n",
-      " |  counter = 0\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(multidms.Data)"
-   ]
-  },
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-    "Instantiate an object for each of our two replicate training sets, and append them to a list "
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-     "text": [
-      "inferring site map for Delta\n"
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-      "inferring site map for Omicron_BA1\n"
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-      "inferring site map for Omicron_BA2\n"
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-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
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-     "text": [
-      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
-      "dropping: 10983 variantswhich have mutations at those sites.\n"
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-     "text": [
-      "invalid non-identical-sites: [371], dropping 2041 variants\n",
-      "Converting mutations for Delta\n"
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-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA2\n"
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-      "Finished, time: 36\n",
-      "inferring site map for Delta\n"
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-       "model_id": "6c695ba0cbb549469d86bce8cbe31dfb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/57719 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f064fcf78a4d4cd083bd9d48def2a3ca",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=18626), Label(value='0 / 18626')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
-      "dropping: 10129 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e3ceac43c0dc493c959da9c3d4fd2735",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=17360), Label(value='0 / 17360')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [371], dropping 1873 variants\n",
-      "Converting mutations for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8ca90ecf424945e8b5cc82cf763bd8db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3399), Label(value='0 / 3399'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec9d0f5be4f0443694b87302e7ee88eb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6536), Label(value='0 / 6536'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished, time: 34\n"
-     ]
-    }
-   ],
-   "source": [
-    "datasets = []\n",
-    "for res, fsdf in func_score_df.groupby(\"replicate\"):       \n",
-    "\n",
-    "    start = time.time()\n",
-    "\n",
-    "    # instantiate data object\n",
-    "    data = multidms.Data(\n",
-    "        fsdf,\n",
-    "        collapse_identical_variants=\"mean\",       # take the average variant func score across barcode replicates\n",
-    "        alphabet=multidms.AAS_WITHSTOP_WITHGAP,   # \n",
-    "        reference=\"Omicron_BA1\",\n",
-    "        assert_site_integrity=False,\n",
-    "        verbose=True,\n",
-    "        nb_workers=8,\n",
-    "        name=f\"rep-{res}\" \n",
-    "    )\n",
-    "\n",
-    "    end = time.time()\n",
-    "    prep_time = round(end-start)\n",
-    "    print(f\"Finished, time: {prep_time}\")\n",
-    "\n",
-    "    datasets.append(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c83765e",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Change the datasets colors - the only mutible thing about `Data` Objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "cc25fb48",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# condition colors must be hex-encoded,\n",
-    "palette = [\n",
-    "    '#F97306',\n",
-    "    '#BFBFBF',\n",
-    "    '#9400D3'\n",
-    "]\n",
-    "\n",
-    "conditions = [\n",
-    "    \"Delta\",\n",
-    "    \"Omicron_BA1\",\n",
-    "    \"Omicron_BA2\"\n",
-    "]\n",
-    "\n",
-    "cc = {con:col for con, col in zip(conditions, palette)}\n",
-    "for data in datasets:\n",
-    "    data.condition_colors = cc"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "ecbf7c95-7264-474b-9630-2e13725b304b",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Fit Models (Shrinkage Analysis)\n",
-    "\n",
-    "For each replicate training set, initialize and fit a set models with variable lasso strength coefficients. Here, we use the `multidms.utils.fit_wrapper` function to handle the instatiation, and parameter fitting. Expand the cell output below to view the function docstring."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "56cda88a",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "fit_params = {\n",
-    "    'scale_coeff_lasso_shift': scale_coeff_lasso_shift,\n",
-    "    'alpha_d': [alpha_d],\n",
-    "    'scale_coeff_ridge_alpha_d':[scale_coeff_ridge_alpha_d],\n",
-    "    'scale_coeff_ridge_beta': [scale_coeff_ridge_beta],\n",
-    "    'num_training_steps': [num_training_steps],\n",
-    "    'iterations_per_step':[iterations_per_step],\n",
-    "}\n",
-    "\n",
-    "fit_params[\"dataset\"] = datasets\n",
-    "_, _, models = multidms.fit_models(fit_params, n_threads=-1)\n",
-    "pickle.dump(models, open(f\"{output_dir}/full_models.pkl\", \"wb\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a5d5a99-b809-44a2-ae1f-ef5215f4bb92",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "The cell above saves the models and their relevant hyper-parameters in a `DataFrame` to a `pickle` binary file. Hense, If it's already been run, and you want to execute the code below _without_ re-fitting, the following cell will load that binary file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "cb41b672",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models[\"replicate\"] = models.dataset_name.str.split(\"-\").str[-1].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "151ebbac-2b80-4219-bf08-a3e1d47b1f66",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# set some global variables\n",
-    "# columns name\n",
-    "model_name = \"model\"\n",
-    "\n",
-    "# the number of times a mutation must be seen in each condition to be included in downstream analysis\n",
-    "times_seen_threshold = 1\n",
-    "\n",
-    "# The chosen lasso strength for our final spike model results\n",
-    "chosen_lasso_strength = 4e-5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd8b5620-89bc-48ed-8227-d957189eafc2",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Plot the model loss over training steps, as provided by `fit_wrapper`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "1503f9d3",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x450 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=\"convergence_all_lasso_lines\"\n",
-    "cmap=plt.get_cmap(\"tab20\")\n",
-    "\n",
-    "\n",
-    "fig, ax = plt.subplots(1,figsize=[6.4,4.5])\n",
-    "color_idx = -1\n",
-    "for i, (model, model_row) in enumerate(models.iterrows()):\n",
-    "    if i%2 == 0: color_idx += 1\n",
-    "\n",
-    "    ax.plot(\n",
-    "        [1000 * (s+1) for s in range(len(model_row.step_loss))],\n",
-    "        model_row.step_loss,\n",
-    "        c=cmap.colors[color_idx],\n",
-    "        lw=3,\n",
-    "        linestyle=\"-\" if model_row.replicate == 0 else \"--\",\n",
-    "        label=f\"rep: {model_row.replicate} scale_coeff: {model_row.scale_coeff_lasso_shift}\"\n",
-    "    )\n",
-    "\n",
-    "ticks = range(0, 30001, 5000)\n",
-    "labels = [f\"{t//1000}K\" for t in ticks]\n",
-    "ax.set_xticks(ticks, labels, rotation=0, ha='center')\n",
-    "ax.set_ylabel(\"Model Loss (w/o L1 penalty)\")\n",
-    "ax.set_xlabel(\"Optimization iterations\")\n",
-    "\n",
-    "black_line = mlines.Line2D([], [], color='black', linestyle='-',\n",
-    "                          markersize=5, label='rep 1')\n",
-    "black_dashed = mlines.Line2D([], [], color='black',linestyle='--',\n",
-    "                          markersize=5, label='rep 2')\n",
-    "lasso_color_handles = [\n",
-    "    mlines.Line2D(\n",
-    "        [], [], \n",
-    "        color=color, \n",
-    "        linestyle='-',\n",
-    "        markersize=5,\n",
-    "        linewidth=3,\n",
-    "        label=\"$\\lambda$: \"+str(lasso)\n",
-    "    )\n",
-    "    for lasso, color in zip(models.scale_coeff_lasso_shift.unique(), cmap.colors)\n",
-    "]\n",
-    "\n",
-    "elements = [black_line, black_dashed] + lasso_color_handles\n",
-    "ax.legend(handles=elements, bbox_to_anchor = (1, 1), loc='upper left', frameon=False, fontsize=9)\n",
-    "sns.despine(ax=ax)\n",
-    "ax.set_ylim()\n",
-    "plt.tight_layout()\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddfe76d8",
-   "metadata": {},
-   "source": [
-    "## Model Evaluation and Selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "3dac6610",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_collection = multidms.ModelCollection(models)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "3ec54100",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cache miss - this could take a moment\n",
-      "  dataset_name  scale_coeff_lasso_shift       mut_type    mut_param  sparsity\n",
-      "0        rep-1                 0.000000  nonsynonymous  shift_Delta  0.234082\n",
-      "1        rep-1                 0.000000           stop  shift_Delta  0.247678\n",
-      "2        rep-1                 0.000005  nonsynonymous  shift_Delta  0.312857\n",
-      "3        rep-1                 0.000005           stop  shift_Delta  0.396285\n",
-      "4        rep-1                 0.000010  nonsynonymous  shift_Delta  0.381122\n"
-     ]
-    },
-    {
-     "data": {
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\"encoding\": {\"color\": {\"field\": \"mut_type\", \"title\": \"Mutation type\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"scale_coeff_lasso_shift\", \"type\": \"quantitative\"}, {\"field\": \"sparsity\", \"type\": \"quantitative\"}, {\"field\": \"mut_type\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"axis\": {\"format\": \"%\"}, \"field\": \"sparsity\", \"title\": \"Sparsity\", \"type\": \"quantitative\"}}}], \"height\": 200, \"width\": 200}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.15.1.json\", \"datasets\": {\"data-5084549417905f4d512ecf99f077d1fc\": [{\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.23408163265306123}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.2476780185758514}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.31285714285714283}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.39628482972136225}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.38112244897959185}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.5696594427244582}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.49357142857142855}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.826625386996904}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.6209183673469387}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9752321981424149}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.8726530612244898}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9921428571428571}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9996938775510205}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.2246938775510204}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.16718266253869968}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.3110204081632653}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.3281733746130031}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.3852040816326531}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.48606811145510836}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.4981632653061224}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.7770897832817337}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.6258163265306123}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9628482972136223}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.8138775510204082}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9861224489795918}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9993877551020408}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1483673469387755}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1826625386996904}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.2753061224489796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3622291021671827}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3772448979591837}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.6253869969040248}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.5426530612244898}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.913312693498452}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7054081632653061}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9907120743034056}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9152040816326531}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9889795918367347}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9987755102040816}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1526530612244898}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.17647058823529413}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.27724489795918367}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3993808049535604}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.375}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.631578947368421}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.5333673469387755}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8947368421052632}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.6859183673469388}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9969040247678018}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8577551020408163}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9969040247678018}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9841836734693877}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9986734693877551}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9998979591836735}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.FacetChart(...)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart, sparsity_df = model_collection.shift_sparsity(return_data=True, height_scalar=100) # TODO raise issue to fix height scalar\n",
-    "print(sparsity_df.head())\n",
-    "chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "a1931818",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      datasets mut_param  correlation  scale_coeff_lasso_shift\n",
-      "0  rep-1,rep-2      beta     0.837209                 0.000000\n",
-      "0  rep-1,rep-2      beta     0.839758                 0.000005\n",
-      "0  rep-1,rep-2      beta     0.838824                 0.000010\n",
-      "0  rep-1,rep-2      beta     0.838579                 0.000020\n",
-      "0  rep-1,rep-2      beta     0.837910                 0.000040\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "  #altair-viz-f7dc07190afc466bb366c0da3c9b1a40.vega-embed {\n",
-       "    width: 100%;\n",
-       "    display: flex;\n",
-       "  }\n",
-       "\n",
-       "  #altair-viz-f7dc07190afc466bb366c0da3c9b1a40.vega-embed details,\n",
-       "  #altair-viz-f7dc07190afc466bb366c0da3c9b1a40.vega-embed details summary {\n",
-       "    position: relative;\n",
-       "  }\n",
-       "</style>\n",
-       "<div id=\"altair-viz-f7dc07190afc466bb366c0da3c9b1a40\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-       "  (function(spec, embedOpt){\n",
-       "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-f7dc07190afc466bb366c0da3c9b1a40\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-f7dc07190afc466bb366c0da3c9b1a40\");\n",
-       "    }\n",
-       "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.15.1?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
-       "    };\n",
-       "\n",
-       "    function maybeLoadScript(lib, version) {\n",
-       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-       "      return (VEGA_DEBUG[key] == version) ?\n",
-       "        Promise.resolve(paths[lib]) :\n",
-       "        new Promise(function(resolve, reject) {\n",
-       "          var s = document.createElement('script');\n",
-       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "          s.async = true;\n",
-       "          s.onload = () => {\n",
-       "            VEGA_DEBUG[key] = version;\n",
-       "            return resolve(paths[lib]);\n",
-       "          };\n",
-       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-       "          s.src = paths[lib];\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function showError(err) {\n",
-       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-       "      throw err;\n",
-       "    }\n",
-       "\n",
-       "    function displayChart(vegaEmbed) {\n",
-       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-       "    }\n",
-       "\n",
-       "    if(typeof define === \"function\" && define.amd) {\n",
-       "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-       "    } else {\n",
-       "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.15.1\"))\n",
-       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
-       "        .catch(showError)\n",
-       "        .then(() => displayChart(vegaEmbed));\n",
-       "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-528af9f3e7ff66e88d8f3546ebedc5ad\"}, \"facet\": {\"column\": {\"field\": \"datasets\", \"title\": \"Experiment comparison\", \"type\": \"nominal\"}}, \"spec\": {\"layer\": [{\"mark\": {\"type\": \"point\"}, \"encoding\": {\"color\": {\"field\": \"mut_param\", \"title\": \"Parameter\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"datasets\", \"type\": \"nominal\"}, {\"field\": \"correlation\", \"type\": \"quantitative\"}, {\"field\": \"mut_param\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"field\": \"correlation\", \"title\": \"Correlation\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"line\"}, \"encoding\": {\"color\": {\"field\": \"mut_param\", \"title\": \"Parameter\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"datasets\", \"type\": \"nominal\"}, {\"field\": \"correlation\", \"type\": \"quantitative\"}, {\"field\": \"mut_param\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"field\": \"correlation\", \"title\": \"Correlation\", \"type\": \"quantitative\"}}}], \"height\": 200, \"width\": 200}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.15.1.json\", \"datasets\": {\"data-528af9f3e7ff66e88d8f3546ebedc5ad\": [{\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8372091048402562, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.839758470866261, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8388239747300699, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8385792300278926, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8379096375470336, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8208707002034907, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7974527452084917, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7840767464276763, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.780621374390125, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7111845164132226, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7249261264558755, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7301481445204143, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7334960305283997, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7490186077918847, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.8136853699674704, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.8370136573262008, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.833384467543261, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.8320243726685691, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8401159054391213, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8378323734379057, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8277362205885377, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8100703654501927, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7956989226754073, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8259965308617166, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8370343748715462, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8326461559980456, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8314172424574497, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.821537274254335, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8199699883111343, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8110598559779172, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7938391288532309, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7811071520399709, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8200811231056834, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8359421057151138, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8321069045925462, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8310287515366845, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.5148353166710564, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.5789024998355374, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6212610866995948, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6662643650074891, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6844296489543019, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.7004066139413471, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.59136190596675, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.03520946742125117, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": null, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.5817616167305147, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.5599610107141831, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.568664841463541, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.6434680575655477, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.7210213514327054, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.748040207058886, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.8192028523176726, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.3443723725879912, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": null, \"scale_coeff_lasso_shift\": 0.00064}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.FacetChart(...)"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart, corr_df = model_collection.mut_param_dataset_correlation(width_scalar=200, return_data=True)\n",
-    "print(corr_df.head())\n",
-    "chart"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c3825bc",
-   "metadata": {},
-   "source": [
-    "## Cross Validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "1099a2ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7bfd7b539914294bbcd13084f2ab107",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/27342 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA1\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f5cc7d64e9ae4564b7d4892b41af6196",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/57528 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dac39b1bbc7a46dc909bfd3fcef79af2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 4 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f40c089e51694666876e7f8a5069bbfc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=35445), Label(value='0 / 35445')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
-      "dropping: 9793 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05f9fe50081f4cdfb5ee02e1edd7be7f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=32996), Label(value='0 / 32996')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [371], dropping 1732 variants\n",
-      "Converting mutations for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ea7aefc53ea0426f8e12a7019959ecf8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6380), Label(value='0 / 6380'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b233155471ef4441ad51c861d57039a0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=12861), Label(value='0 / 12861')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a0ba59d8543d47f6aaca459e6ab78968",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/27945 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA1\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "59b20757f0ff444b8bba1c2932e3234b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/50638 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e66bc7040c58488faa6bd9b207b9217a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/54489 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 4 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc499ce331ca4b83aea7ed8031998989",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=33268), Label(value='0 / 33268')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
-      "dropping: 9059 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb6fad497b874d25afcc8f7b24ee94c4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=31004), Label(value='0 / 31004')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [371], dropping 1643 variants\n",
-      "Converting mutations for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c58d437667574bb1ad444a3765a39fec",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6516), Label(value='0 / 6516'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34208439c3f24dd6b16afccc26d2047a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=12347), Label(value='0 / 12347')))…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_frac=0.8\n",
-    "seed=4\n",
-    "train, test = [], {}\n",
-    "for replicate, fs_df in func_score_df.groupby(\"replicate\"):\n",
-    "\n",
-    "    dataset = fs_df.sample(frac=1, random_state=seed)\n",
-    "    train_split, test_split = dataset.iloc[:int(len(dataset) * train_frac)], dataset.iloc[int(len(dataset) * train_frac):]\n",
-    "    name = f\"rep-{replicate}\"\n",
-    "    \n",
-    "    train.append(\n",
-    "        multidms.Data(\n",
-    "            train_split,\n",
-    "            collapse_identical_variants=\"mean\",\n",
-    "            reference=\"Omicron_BA1\", \n",
-    "            alphabet = multidms.AAS_WITHSTOP_WITHGAP, \n",
-    "            verbose=True,\n",
-    "            name = name\n",
-    "        )\n",
-    "    )\n",
-    "\n",
-    "    test[name] = test_split\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "0ce07b8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit_params[\"dataset\"] = train \n",
-    "_, _, models_cv = multidms.model_collection.fit_models(fit_params, n_threads = -1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "e4b14aff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mc = multidms.model_collection.ModelCollection(models_cv)\n",
-    "mc.add_validation_loss(test, overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "56c50e17",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>scale_coeff_lasso_shift</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>loss</th>\n",
-       "      <th>split</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.217511</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.222574</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.00001</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.227321</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.235090</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.00004</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.249183</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  dataset_name scale_coeff_lasso_shift condition      loss     split\n",
-       "0        rep-1                     0.0     Delta  0.217511  training\n",
-       "1        rep-1                0.000005     Delta  0.222574  training\n",
-       "2        rep-1                 0.00001     Delta  0.227321  training\n",
-       "3        rep-1                 0.00002     Delta  0.235090  training\n",
-       "4        rep-1                 0.00004     Delta  0.249183  training"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cross_validation_df = mc.get_conditional_loss_df()\n",
-    "cross_validation_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "99afacd2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x750 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=\"shrinkage_analysis_trace_plots_beta\"\n",
-    "\n",
-    "fig, ax = plt.subplots(3, figsize=[4.5, 7.5], sharex=True)\n",
-    "\n",
-    "# replicate correlation\n",
-    "iter_ax = ax[0]\n",
-    "sns.lineplot(\n",
-    "    data=(\n",
-    "        corr_df\n",
-    "        .query(\"mut_param.str.contains('shift')\")\n",
-    "        .rename({\"mut_param\":\"shift params\"}, axis=1)\n",
-    "        # .replace({\"Data-1\":\"rep-1\", \"Data-2\":\"rep-2\"})\n",
-    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
-    "        .assign(\n",
-    "            scale_coeff_lasso_shift = [\n",
-    "                f\"{l:.1e}\" \n",
-    "                for l in corr_df.query(\"mut_param.str.contains('shift')\").scale_coeff_lasso_shift\n",
-    "            ],\n",
-    "            correlation = lambda x: x.correlation**2\n",
-    "        )\n",
-    "        .reset_index(drop=True)\n",
-    "    ),\n",
-    "    x=\"scale_coeff_lasso_shift\",\n",
-    "    y=\"correlation\",\n",
-    "    style=\"shift params\",\n",
-    "    markers=True,\n",
-    "    ax=iter_ax,\n",
-    "    linewidth=3,\n",
-    "    color=\"black\"\n",
-    ")\n",
-    "iter_ax.set_ylabel(\"rep1 v. rep2\\nshift $(R^2)$\")\n",
-    "# move legend outside of plot\n",
-    "iter_ax.legend(\n",
-    "    bbox_to_anchor = (1, 1), \n",
-    "    loc='upper left', \n",
-    "    frameon=False\n",
-    ")\n",
-    "\n",
-    "\n",
-    "\n",
-    "# plot loss\n",
-    "iter_ax = ax[1]\n",
-    "sns.lineplot(\n",
-    "    data = (\n",
-    "        cross_validation_df.query(\"condition=='total'\")\n",
-    "        .assign(\n",
-    "            # lasso_strength = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift]\n",
-    "            # lasso_strength = lambda x: f\"{x.scale_coeff_lasso_shift:.1e}\"\n",
-    "            lasso_strength = lambda x: x['scale_coeff_lasso_shift'].apply(lambda y: f'{y:.1e}')\n",
-    "        )\n",
-    "    ),\n",
-    "    x=\"lasso_strength\",\n",
-    "    y=\"loss\",\n",
-    "    ax=iter_ax,\n",
-    "    hue=\"split\",\n",
-    "    style=\"dataset_name\",\n",
-    "    palette={\"training\":\"slategrey\", \"validation\":\"#2CA02C\"},\n",
-    "    markers=True,\n",
-    "    linewidth=3\n",
-    ")\n",
-    "# move legend outside of plot\n",
-    "iter_ax.legend(\n",
-    "    bbox_to_anchor = (1, 1), \n",
-    "    loc='upper left', \n",
-    "    frameon=False\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# plot sparsity\n",
-    "iter_ax = ax[2]\n",
-    "sns.lineplot(\n",
-    "    data=(\n",
-    "        sparsity_df\n",
-    "        .rename({\"dataset_name\":\"replicate\"}, axis=1)\n",
-    "        .rename({\"mut_param\":\"shift params\", \"mut_type\":\"mutation type\"}, axis=1)\n",
-    "        # .replace({\"Data-0\":\"rep-1\", \"Data-1\":\"rep-2\"})\n",
-    "        .replace({\"nonsynonymous\":\"nonsynonymous\", \"stop\":\"stop\"})\n",
-    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
-    "        .assign(\n",
-    "            scale_coeff_lasso_shift = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift],\n",
-    "            sparsity_percent = lambda x: x.sparsity * 100,\n",
-    "        )\n",
-    "    ),\n",
-    "    x=\"scale_coeff_lasso_shift\",\n",
-    "    y=\"sparsity_percent\",\n",
-    "    hue=\"mutation type\",\n",
-    "    style=\"replicate\",\n",
-    "    palette={\"nonsynonymous\":\"grey\", \"stop\":\"#E377C2\"},\n",
-    "    markers=True,\n",
-    "    legend=True,\n",
-    "    ax=iter_ax,\n",
-    "    linewidth=3\n",
-    ")\n",
-    "# move legend outside of plot\n",
-    "iter_ax.legend(\n",
-    "    bbox_to_anchor = (1, 1), \n",
-    "    loc='upper left', \n",
-    "    frameon=False\n",
-    ")\n",
-    "# rotate x labels\n",
-    "iter_ax.set_xticklabels(\n",
-    "    iter_ax.get_xticklabels(), \n",
-    "    rotation=90, \n",
-    "    ha='center'\n",
-    ")\n",
-    "iter_ax.set_ylabel(\"sparsity\\n$(\\%\\Delta=0)$\")\n",
-    "iter_ax.set_xlabel(f\"lasso regularization strength ($\\lambda$)\")\n",
-    "\n",
-    "for axes in ax:\n",
-    "    axes.axvline(\n",
-    "        f\"{chosen_lasso_strength:.1e}\", \n",
-    "        color=\"grey\",\n",
-    "        linewidth=10,\n",
-    "        alpha=0.35\n",
-    "    )\n",
-    "\n",
-    "sns.despine(fig)\n",
-    "plt.tight_layout()\n",
-    "# plt.tight_layout()\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "# plt.show()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "298ecafb-3806-4cf9-a4d2-e390e8c5b2d4",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Global epistasis fits\n",
-    "\n",
-    "Here, we take a look at the fit of the sigmoidal global epistasis function (at the chosen lasso coefficient of 5e-5) to the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7ef04f4-6905-4c6f-8150-8fa2149d3ec0",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "For each replicate at the chosen lasso strength, we get the training data predictions using `model.get_variants_df`, and use `model.get_condition_params` paried with `model.model_components` for visualizing the global epistasis function with the current model parameters. See the function docs strings for the relevant details of each."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "641e76a5-0c30-48c9-ab3a-e60fd6532ee0",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chosen_replicate_models = models.query(\"scale_coeff_lasso_shift == @chosen_lasso_strength\")\n",
-    "replicate_data = {}\n",
-    "for row_idx, replicate_row in chosen_replicate_models.iterrows():\n",
-    "    model = replicate_row[model_name]\n",
-    "\n",
-    "    # get training data variants and their respective \n",
-    "    mut_df_replicates = model.get_variants_df(phenotype_as_effect=False)\n",
-    "\n",
-    "    # find the low/high bound of the training data and use those to make\n",
-    "    # global epistasis predictions across the range for plotting\n",
-    "    xlb, xub = [-1, 1] + np.quantile(mut_df_replicates.predicted_latent, [0.05, 1.0])\n",
-    "    additive_model_grid = np.linspace(xlb, xub, num=1000)\n",
-    "\n",
-    "    # make predictions on hypothetical data points between lower, and upper bound\n",
-    "    current_params = model.get_condition_params(model.data.reference)\n",
-    "    latent_preds = model.model_components[\"g\"](current_params[\"theta\"], additive_model_grid)\n",
-    "    shape = (additive_model_grid, latent_preds)   \n",
-    "\n",
-    "    # save and organize the data for plotting\n",
-    "    replicate_data[replicate_row.replicate] = {\n",
-    "        \"variants_df\" : mut_df_replicates,\n",
-    "        \"wildtype_df\" : model.wildtype_df,\n",
-    "        \"epistasis_shape\" : shape,\n",
-    "        \"condition_colors\" : model.data.condition_colors\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "f09023eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys([1, 2])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "replicate_data.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0348eef2-a50e-4b71-a277-848a0b480b4a",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Plot the observed functional scores of a random sample of all variants (20%), as function of both latent phenotype prediction (top), and functional score phenotype prediction (bottom)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "ba43db23-d144-445e-8962-fe9faa887440",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=\"global_epistasis_and_prediction_correlations\"\n",
-    "fig, ax = plt.subplots(2,2, figsize=[6.4,6], sharey='row')    \n",
-    "\n",
-    "row=0\n",
-    "for replicate, data in replicate_data.items():\n",
-    "\n",
-    "    iter_ax = ax[row, replicate-1]\n",
-    "    sns.scatterplot(\n",
-    "        data=data[\"variants_df\"].sample(frac=0.2),\n",
-    "        x=\"predicted_latent\",\n",
-    "        y=f\"func_score\",\n",
-    "        hue=\"condition\",\n",
-    "        palette=model.data.condition_colors,\n",
-    "        ax=iter_ax,\n",
-    "        legend=False,\n",
-    "        size=5,\n",
-    "        alpha=0.1,\n",
-    "        lw=3\n",
-    "    )\n",
-    "    \n",
-    "    for condition, values in data[\"wildtype_df\"].iterrows():\n",
-    "        iter_ax.axvline(\n",
-    "            values.predicted_latent,\n",
-    "            label=condition,\n",
-    "            c=model.data.condition_colors[condition],\n",
-    "            lw=2,\n",
-    "        )\n",
-    "    \n",
-    "    iter_ax.plot(*data[\"epistasis_shape\"], color=\"k\", lw=2)\n",
-    "    \n",
-    "    xlb, xub = [-1, 1] + np.quantile(data[\"variants_df\"].predicted_latent, [0.05, 1.0])\n",
-    "    ylb, yub = [-1, 1] + np.quantile(data[\"variants_df\"].func_score, [0.05, 1.0])\n",
-    "    iter_ax.set_xlim([xlb, xub])\n",
-    "    iter_ax.set_ylim([ylb, yub])\n",
-    "    iter_ax.set_title(f\"replicate {replicate+1}\")\n",
-    "    iter_ax.set_ylabel(\"observed\\nfunctional score\")\n",
-    "    iter_ax.set_xlabel(\"predicted latent phenotype ($\\phi$)\")\n",
-    "\n",
-    "row=1\n",
-    "for replicate, data in replicate_data.items():\n",
-    "\n",
-    "    iter_ax = ax[row, replicate-1]\n",
-    "    sns.scatterplot(\n",
-    "        data=data[\"variants_df\"].sample(frac=0.1),\n",
-    "        x=\"predicted_func_score\",\n",
-    "        y=f\"func_score\",\n",
-    "        hue=\"condition\",\n",
-    "        palette=model.data.condition_colors,\n",
-    "        ax=iter_ax,\n",
-    "        legend=False,\n",
-    "        size=5,\n",
-    "        alpha=0.1\n",
-    "    )\n",
-    "    \n",
-    "    for condition, values in data[\"wildtype_df\"].iterrows():\n",
-    "        iter_ax.axvline(\n",
-    "            values.predicted_latent,\n",
-    "            label=condition,\n",
-    "            c=model.data.condition_colors[condition],\n",
-    "            lw=2,\n",
-    "        )\n",
-    "    \n",
-    "    iter_ax.set_ylabel(\"observed\\nfunctional score\")\n",
-    "    iter_ax.set_xlabel(\"predicted functional score\")\n",
-    "\n",
-    "    start_y = 0.9\n",
-    "    for c, cdf in data[\"variants_df\"].groupby(\"condition\"):\n",
-    "        r = pearsonr(\n",
-    "            cdf[\"predicted_func_score\"],\n",
-    "            cdf[\"func_score\"]\n",
-    "        )[0]\n",
-    "        iter_ax.annotate(\n",
-    "            f\"$r = {r:.2f}$\",\n",
-    "            (0.1, start_y),\n",
-    "            xycoords=\"axes fraction\",\n",
-    "            fontsize=12,\n",
-    "            c=model.data.condition_colors[c],\n",
-    "        )\n",
-    "        start_y += -0.1\n",
-    "\n",
-    "\n",
-    "elements = [\n",
-    "    mlines.Line2D([], [], color=color, marker='o', linestyle='None',markersize=5, label=condition)\n",
-    "    for condition, color in replicate_data[1][\"condition_colors\"].items()\n",
-    "]\n",
-    "\n",
-    "\n",
-    "ax[0, 0].legend(\n",
-    "    handles=elements, \n",
-    "    bbox_to_anchor = (0., .99), \n",
-    "    loc='upper left', \n",
-    "    frameon=False, \n",
-    "    fontsize=9\n",
-    ")\n",
-    "    \n",
-    "    \n",
-    "plt.tight_layout()\n",
-    "fig.subplots_adjust(wspace=0.05)\n",
-    "\n",
-    "ax[0,0].text(\n",
-    "    -0.1, 1.00, \n",
-    "    f\"A\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=ax[0,0].transAxes\n",
-    ")\n",
-    "ax[1,0].text(\n",
-    "    -0.1, 1.00, \n",
-    "    f\"B\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=ax[1,0].transAxes\n",
-    ")\n",
-    "\n",
-    "\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5dc5e8b-f236-4b17-8104-eeaa4b53a63f",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Shifted mutations (interactive altair chart)\n",
-    "\n",
-    "The easiest way to view shifted mutations is to create an interactive `altair` chart using `multidms.plot.mut_shift_plot`. This function can take a single model, or a collection of models in a dictionary if you want to visualize the aggregated (mean) results of shared mutations between models. Toggle the drop down for the cell below to see details on using this function. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "5c3ffdf7-acdc-4028-ae11-80628782da5b",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function mut_param_heatmap in module multidms.model_collection:\n",
-      "\n",
-      "mut_param_heatmap(self, query=None, mut_param='shift', aggregate_func='mean', inner_merge_dataset_muts=True, times_seen_threshold=0, phenotype_as_effect=True, **kwargs)\n",
-      "    Create lineplot and heatmap altair chart\n",
-      "    across replicate datasets.\n",
-      "    This function optionally applies a given `pandas.query`\n",
-      "    on the fit_models dataframe that should result in a subset of\n",
-      "    fit's which make sense to aggregate mutational data across, e.g.\n",
-      "    replicate datasets.\n",
-      "    It then computes the mean or median mutational parameter value\n",
-      "    (\"beta\", \"shift\", or \"predicted_func_score\")\n",
-      "    between the remaining fits. and creates an interactive altair chart.\n",
-      "    \n",
-      "    \n",
-      "    Note that this will throw an error if the queried fits have more\n",
-      "    than one unique hyper-parameter besides \"dataset_name\".\n",
-      "    \n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    query : str\n",
-      "        The query to apply to the fit_models dataframe. This should be\n",
-      "        used to subset the fits to only those which make sense to aggregate\n",
-      "        mutational data across, e.g. replicate datasets.\n",
-      "        For example, if you have a collection of\n",
-      "        fits with different epistatic models, you may want to query\n",
-      "        for only those fits with the same epistatic model. e.g.\n",
-      "        `query=\"epistatic_model == 'Sigmoid'\"`. For more on the query\n",
-      "        syntax, see the\n",
-      "        `pandas.query <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html>`_\n",
-      "        documentation.\n",
-      "    mut_param : str, optional\n",
-      "        The mutational parameter to plot. The default is \"shift\".\n",
-      "        Must be one of \"shift\", \"predicted_func_score\", or \"beta\".\n",
-      "    aggregate_func : str, optional\n",
-      "        The function to aggregate the mutational parameter values\n",
-      "        between dataset fits. The default is \"mean\".\n",
-      "    inner_merge_dataset_muts : bool, optional\n",
-      "        Whether to toss mutations which are _not_ shared across all datasets\n",
-      "        before aggregation of group mutation parameter values.\n",
-      "        The default is True.\n",
-      "    times_seen_threshold : int, optional\n",
-      "        The minimum number of times a mutation must be seen across\n",
-      "        all conditions within a single fit to be included in the\n",
-      "        aggregation. The default is 0.\n",
-      "    phenotype_as_effect : bool, optional\n",
-      "        Passed to `Model.get_mutations_df()`,\n",
-      "        Only applies if `mut_param=\"predicted_func_score\"`.\n",
-      "    **kwargs : dict\n",
-      "        Keyword arguments to pass to\n",
-      "        :func:`multidms.plot._lineplot_and_heatmap`.\n",
-      "    \n",
-      "    Returns\n",
-      "    -------\n",
-      "    altair.Chart\n",
-      "        A chart object which can be displayed in a jupyter notebook\n",
-      "        or saved to a file.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(multidms.ModelCollection.mut_param_heatmap)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54c5f17c-4e75-4a6c-8e11-703ddbdd9ffb",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Here, we create the interactive chart by feeding the function a dictionary containing the two replicate models, and specifying:\n",
-    "\n",
-    "1. times_seen_threshold = 1, meaning for a mutation to be included, it must be seen at least once in every condition\n",
-    "2. inlcude_beta = False, we only wish to see the shifted parameters visualized, not the respective _effect_ (beta) parameters. (note that respective effect values will be added as a tooltip when hovering over any shift mutation).\n",
-    "\n",
-    "To view the chart, toggle the output of the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "dd89416e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mc = multidms.ModelCollection(models.drop(columns=\"replicate\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "6d450c2b-2f9e-4313-ad40-6c8221beb8f2",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cache miss - this could take a moment\n"
-     ]
-    },
-    {
-     "data": {
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\"_stat_site_order\": 1196}, {\"site\": 1215, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 1202}, {\"site\": 160, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 149}, {\"site\": 170, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 159}, {\"site\": 200, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 189}, {\"site\": 204, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 193}, {\"site\": 248, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 236}, {\"site\": 265, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 253}, {\"site\": 266, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 254}, {\"site\": 269, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 257}, {\"site\": 279, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 267}, {\"site\": 28, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 24}, {\"site\": 313, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 301}, {\"site\": 351, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 339}, {\"site\": 365, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 353}, {\"site\": 369, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 357}, {\"site\": 37, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 33}, {\"site\": 38, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 34}, {\"site\": 380, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 367}, {\"site\": 396, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 383}, {\"site\": 421, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 408}, {\"site\": 423, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 410}, {\"site\": 449, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 436}, {\"site\": 451, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 438}, {\"site\": 453, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 440}, {\"site\": 473, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 460}, {\"site\": 489, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 476}, {\"site\": 495, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 482}, {\"site\": 501, \"wildtype\": \"Y\", \"wildtype_Delta\": \"N\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 488}, {\"site\": 508, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 495}, {\"site\": 612, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 599}, {\"site\": 636, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 623}, {\"site\": 655, \"wildtype\": \"Y\", \"wildtype_Delta\": \"H\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 642}, {\"site\": 660, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 647}, {\"site\": 674, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 661}, {\"site\": 695, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 682}, {\"site\": 707, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 694}, {\"site\": 741, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 728}, {\"site\": 756, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 743}, {\"site\": 789, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 776}, {\"site\": 796, \"wildtype\": \"Y\", \"wildtype_Delta\": \"D\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 783}, {\"site\": 837, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 824}, {\"site\": 873, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 860}, {\"site\": 904, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 891}, {\"site\": 91, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 85}, {\"site\": 917, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 904}, {\"site\": 422, \"wildtype\": \"N\", \"wildtype_Delta\": \"N\", \"wildtype_Omicron_BA2\": \"N\", \"_stat_site_order\": 409}, {\"site\": 818, \"wildtype\": \"I\", \"wildtype_Delta\": \"I\", \"wildtype_Omicron_BA2\": \"I\", \"_stat_site_order\": 805}, {\"site\": 898, \"wildtype\": \"F\", \"wildtype_Delta\": \"F\", \"wildtype_Omicron_BA2\": \"F\", \"_stat_site_order\": 885}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.VConcatChart(...)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart = mc.mut_param_heatmap(query=f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\", times_seen_threshold=times_seen_threshold)\n",
-    "chart.save(f\"{output_dir}/interactive_shift_chart.html\")\n",
-    "chart"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49e626f6-9cbe-4d40-ad9c-d7e8d56538d2",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Shifted mutations (manually queried)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "50d97f84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combine_replicate_muts(fit_dict, predicted_func_scores = False, how=\"inner\", **kwargs):\n",
-    "    \"\"\"\n",
-    "    Take a dictionary of fit objects, with key's as the prefix for individual\n",
-    "    replicate values, and merge then such that all individual and average mutation\n",
-    "    values are present in both.\n",
-    "    \"\"\"\n",
-    "    # obtain and curate each of the replicate mutational dataframes\n",
-    "    mutations_dfs = []\n",
-    "    for replicate, fit in fit_dict.items():\n",
-    "        fit_mut_df = fit.get_mutations_df(**kwargs)\n",
-    "        # drop all \"predicted_func_score\" and \"times seen\" columns\n",
-    "        fit_mut_df = fit_mut_df.drop(\n",
-    "            [c for c in fit_mut_df.columns if \"times_seen\" in c], \n",
-    "            axis=1\n",
-    "        )\n",
-    "\n",
-    "        new_column_name_map = {c: f\"{replicate}_{c}\" for c in fit_mut_df.columns}\n",
-    "        fit_mut_df = fit_mut_df.rename(new_column_name_map, axis=1)\n",
-    "\n",
-    "        mutations_dfs.append(fit_mut_df)\n",
-    "\n",
-    "    # merge each of the replicate mutational dataframes\n",
-    "    mut_df = reduce(\n",
-    "        lambda left, right: pd.merge(\n",
-    "            left, right, left_index=True, right_index=True, how=how\n",
-    "        ),\n",
-    "        mutations_dfs,\n",
-    "    )\n",
-    "\n",
-    "    column_order = []\n",
-    "    # now compute replicate averages\n",
-    "    for c in fit.mutations_df.columns:\n",
-    "\n",
-    "        if not predicted_func_scores and \"predicted_func_score\" in c:\n",
-    "            continue\n",
-    "\n",
-    "        if c == \"mutation\" or \"times_seen\" in c: # or \"predicted_func_score\" in c:\n",
-    "            continue\n",
-    "        \n",
-    "        cols_to_combine = [f\"{replicate}_{c}\" for replicate in fit_dict.keys()]\n",
-    "\n",
-    "        # just keep one replicate wt, site, mut .. as they are shared.\n",
-    "        if c in [\"wts\", \"sites\", \"muts\"]:\n",
-    "            mut_df[c] = mut_df[cols_to_combine[0]]\n",
-    "            mut_df.drop(cols_to_combine, axis=1, inplace=True)\n",
-    "\n",
-    "        # take the average.\n",
-    "        else:\n",
-    "            mut_df[f\"avg_{c}\"] = mut_df[cols_to_combine].mean(axis=1)\n",
-    "            column_order += cols_to_combine + [f\"avg_{c}\"]\n",
-    "\n",
-    "    return mut_df.loc[:, [\"wts\", \"sites\", \"muts\"] + column_order]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "dfc964eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wts</th>\n",
-       "      <th>sites</th>\n",
-       "      <th>muts</th>\n",
-       "      <th>1_beta</th>\n",
-       "      <th>2_beta</th>\n",
-       "      <th>avg_beta</th>\n",
-       "      <th>1_shift_Delta</th>\n",
-       "      <th>2_shift_Delta</th>\n",
-       "      <th>avg_shift_Delta</th>\n",
-       "      <th>1_shift_Omicron_BA2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>avg_shift_Omicron_BA2</th>\n",
-       "      <th>1_predicted_func_score_Delta</th>\n",
-       "      <th>2_predicted_func_score_Delta</th>\n",
-       "      <th>avg_predicted_func_score_Delta</th>\n",
-       "      <th>1_predicted_func_score_Omicron_BA1</th>\n",
-       "      <th>2_predicted_func_score_Omicron_BA1</th>\n",
-       "      <th>avg_predicted_func_score_Omicron_BA1</th>\n",
-       "      <th>1_predicted_func_score_Omicron_BA2</th>\n",
-       "      <th>2_predicted_func_score_Omicron_BA2</th>\n",
-       "      <th>avg_predicted_func_score_Omicron_BA2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>M</td>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-2.924932</td>\n",
-       "      <td>-4.256726</td>\n",
-       "      <td>-3.590829</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-3.162696</td>\n",
-       "      <td>-3.348626</td>\n",
-       "      <td>-3.255661</td>\n",
-       "      <td>-3.009065</td>\n",
-       "      <td>-3.236155</td>\n",
-       "      <td>-3.122610</td>\n",
-       "      <td>-3.085516</td>\n",
-       "      <td>-3.409368</td>\n",
-       "      <td>-3.247442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>L</td>\n",
-       "      <td>0.200928</td>\n",
-       "      <td>0.207115</td>\n",
-       "      <td>0.204021</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.204654</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.102327</td>\n",
-       "      <td>0.287689</td>\n",
-       "      <td>0.366644</td>\n",
-       "      <td>0.327167</td>\n",
-       "      <td>0.405955</td>\n",
-       "      <td>0.487242</td>\n",
-       "      <td>0.446598</td>\n",
-       "      <td>-0.200109</td>\n",
-       "      <td>0.107339</td>\n",
-       "      <td>-0.046385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.194773</td>\n",
-       "      <td>-0.074343</td>\n",
-       "      <td>0.060215</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.275053</td>\n",
-       "      <td>-0.286178</td>\n",
-       "      <td>-0.005562</td>\n",
-       "      <td>0.393345</td>\n",
-       "      <td>-0.166355</td>\n",
-       "      <td>0.113495</td>\n",
-       "      <td>0.195843</td>\n",
-       "      <td>-0.525584</td>\n",
-       "      <td>-0.164871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.239144</td>\n",
-       "      <td>-0.030672</td>\n",
-       "      <td>0.104236</td>\n",
-       "      <td>-0.086489</td>\n",
-       "      <td>-0.153066</td>\n",
-       "      <td>-0.119778</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.188819</td>\n",
-       "      <td>-0.521577</td>\n",
-       "      <td>-0.166379</td>\n",
-       "      <td>0.484417</td>\n",
-       "      <td>-0.069216</td>\n",
-       "      <td>0.207601</td>\n",
-       "      <td>0.286191</td>\n",
-       "      <td>-0.431950</td>\n",
-       "      <td>-0.072880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>V</td>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>-0.007044</td>\n",
-       "      <td>-0.047157</td>\n",
-       "      <td>-0.027101</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.001301</td>\n",
-       "      <td>-0.133532</td>\n",
-       "      <td>-0.225989</td>\n",
-       "      <td>-0.179760</td>\n",
-       "      <td>-0.013975</td>\n",
-       "      <td>-0.106083</td>\n",
-       "      <td>-0.060029</td>\n",
-       "      <td>-0.206590</td>\n",
-       "      <td>-0.473092</td>\n",
-       "      <td>-0.339841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252T</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>T</td>\n",
-       "      <td>-0.132241</td>\n",
-       "      <td>-0.189524</td>\n",
-       "      <td>-0.160882</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.074971</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.037485</td>\n",
-       "      <td>-0.379343</td>\n",
-       "      <td>-0.533716</td>\n",
-       "      <td>-0.456529</td>\n",
-       "      <td>-0.258642</td>\n",
-       "      <td>-0.414259</td>\n",
-       "      <td>-0.336450</td>\n",
-       "      <td>-0.586871</td>\n",
-       "      <td>-0.763869</td>\n",
-       "      <td>-0.675370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252V</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.161672</td>\n",
-       "      <td>0.177089</td>\n",
-       "      <td>0.169381</td>\n",
-       "      <td>0.262923</td>\n",
-       "      <td>-0.185348</td>\n",
-       "      <td>0.038788</td>\n",
-       "      <td>-0.044192</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.080998</td>\n",
-       "      <td>0.750125</td>\n",
-       "      <td>-0.138738</td>\n",
-       "      <td>0.305693</td>\n",
-       "      <td>0.325693</td>\n",
-       "      <td>0.414606</td>\n",
-       "      <td>0.370149</td>\n",
-       "      <td>0.039893</td>\n",
-       "      <td>-0.233608</td>\n",
-       "      <td>-0.096858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252W</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>W</td>\n",
-       "      <td>0.046494</td>\n",
-       "      <td>0.283281</td>\n",
-       "      <td>0.164887</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.018787</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.015843</td>\n",
-       "      <td>-0.026414</td>\n",
-       "      <td>0.553441</td>\n",
-       "      <td>0.263513</td>\n",
-       "      <td>0.092733</td>\n",
-       "      <td>0.674209</td>\n",
-       "      <td>0.383471</td>\n",
-       "      <td>-0.064218</td>\n",
-       "      <td>0.168365</td>\n",
-       "      <td>0.052073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252Y</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>0.349203</td>\n",
-       "      <td>0.464681</td>\n",
-       "      <td>0.406942</td>\n",
-       "      <td>-0.103307</td>\n",
-       "      <td>-0.228062</td>\n",
-       "      <td>-0.165685</td>\n",
-       "      <td>-0.029801</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.107036</td>\n",
-       "      <td>0.380213</td>\n",
-       "      <td>0.438552</td>\n",
-       "      <td>0.409383</td>\n",
-       "      <td>0.711687</td>\n",
-       "      <td>1.132873</td>\n",
-       "      <td>0.922280</td>\n",
-       "      <td>0.450806</td>\n",
-       "      <td>0.282706</td>\n",
-       "      <td>0.366756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252*</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>*</td>\n",
-       "      <td>-0.069944</td>\n",
-       "      <td>-0.002437</td>\n",
-       "      <td>-0.036191</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.071298</td>\n",
-       "      <td>0.035649</td>\n",
-       "      <td>0.113153</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.020770</td>\n",
-       "      <td>-0.257914</td>\n",
-       "      <td>0.038034</td>\n",
-       "      <td>-0.109940</td>\n",
-       "      <td>-0.137814</td>\n",
-       "      <td>-0.005529</td>\n",
-       "      <td>-0.071672</td>\n",
-       "      <td>-0.107910</td>\n",
-       "      <td>-0.524964</td>\n",
-       "      <td>-0.316437</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5934 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
-       "mutation                                                                \n",
-       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
-       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
-       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
-       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
-       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
-       "...       ..    ...  ...       ...       ...       ...            ...   \n",
-       "S1252T     S   1252    T -0.132241 -0.189524 -0.160882      -0.000000   \n",
-       "S1252V     S   1252    V  0.161672  0.177089  0.169381       0.262923   \n",
-       "S1252W     S   1252    W  0.046494  0.283281  0.164887       0.000000   \n",
-       "S1252Y     S   1252    Y  0.349203  0.464681  0.406942      -0.103307   \n",
-       "S1252*     S   1252    * -0.069944 -0.002437 -0.036191       0.000000   \n",
-       "\n",
-       "          2_shift_Delta  avg_shift_Delta  1_shift_Omicron_BA2  ...  \\\n",
-       "mutation                                                       ...   \n",
-       "M1I            0.000000         0.000000            -0.000000  ...   \n",
-       "F2L           -0.000000         0.000000            -0.204654  ...   \n",
-       "F2S            0.000000         0.000000             0.000000  ...   \n",
-       "F2V           -0.153066        -0.119778             0.000000  ...   \n",
-       "V3A           -0.000000         0.000000            -0.000000  ...   \n",
-       "...                 ...              ...                  ...  ...   \n",
-       "S1252T         0.000000         0.000000            -0.074971  ...   \n",
-       "S1252V        -0.185348         0.038788            -0.044192  ...   \n",
-       "S1252W         0.000000         0.000000             0.018787  ...   \n",
-       "S1252Y        -0.228062        -0.165685            -0.029801  ...   \n",
-       "S1252*         0.071298         0.035649             0.113153  ...   \n",
-       "\n",
-       "          avg_shift_Omicron_BA2  1_predicted_func_score_Delta  \\\n",
-       "mutation                                                        \n",
-       "M1I                    0.000000                     -3.162696   \n",
-       "F2L                   -0.102327                      0.287689   \n",
-       "F2S                    0.000000                      0.275053   \n",
-       "F2V                    0.000000                      0.188819   \n",
-       "V3A                   -0.001301                     -0.133532   \n",
-       "...                         ...                           ...   \n",
-       "S1252T                -0.037485                     -0.379343   \n",
-       "S1252V                -0.080998                      0.750125   \n",
-       "S1252W                -0.015843                     -0.026414   \n",
-       "S1252Y                -0.107036                      0.380213   \n",
-       "S1252*                 0.020770                     -0.257914   \n",
-       "\n",
-       "          2_predicted_func_score_Delta  avg_predicted_func_score_Delta  \\\n",
-       "mutation                                                                 \n",
-       "M1I                          -3.348626                       -3.255661   \n",
-       "F2L                           0.366644                        0.327167   \n",
-       "F2S                          -0.286178                       -0.005562   \n",
-       "F2V                          -0.521577                       -0.166379   \n",
-       "V3A                          -0.225989                       -0.179760   \n",
-       "...                                ...                             ...   \n",
-       "S1252T                       -0.533716                       -0.456529   \n",
-       "S1252V                       -0.138738                        0.305693   \n",
-       "S1252W                        0.553441                        0.263513   \n",
-       "S1252Y                        0.438552                        0.409383   \n",
-       "S1252*                        0.038034                       -0.109940   \n",
-       "\n",
-       "          1_predicted_func_score_Omicron_BA1  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.009065   \n",
-       "F2L                                 0.405955   \n",
-       "F2S                                 0.393345   \n",
-       "F2V                                 0.484417   \n",
-       "V3A                                -0.013975   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.258642   \n",
-       "S1252V                              0.325693   \n",
-       "S1252W                              0.092733   \n",
-       "S1252Y                              0.711687   \n",
-       "S1252*                             -0.137814   \n",
-       "\n",
-       "          2_predicted_func_score_Omicron_BA1  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.236155   \n",
-       "F2L                                 0.487242   \n",
-       "F2S                                -0.166355   \n",
-       "F2V                                -0.069216   \n",
-       "V3A                                -0.106083   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.414259   \n",
-       "S1252V                              0.414606   \n",
-       "S1252W                              0.674209   \n",
-       "S1252Y                              1.132873   \n",
-       "S1252*                             -0.005529   \n",
-       "\n",
-       "          avg_predicted_func_score_Omicron_BA1  \\\n",
-       "mutation                                         \n",
-       "M1I                                  -3.122610   \n",
-       "F2L                                   0.446598   \n",
-       "F2S                                   0.113495   \n",
-       "F2V                                   0.207601   \n",
-       "V3A                                  -0.060029   \n",
-       "...                                        ...   \n",
-       "S1252T                               -0.336450   \n",
-       "S1252V                                0.370149   \n",
-       "S1252W                                0.383471   \n",
-       "S1252Y                                0.922280   \n",
-       "S1252*                               -0.071672   \n",
-       "\n",
-       "          1_predicted_func_score_Omicron_BA2  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.085516   \n",
-       "F2L                                -0.200109   \n",
-       "F2S                                 0.195843   \n",
-       "F2V                                 0.286191   \n",
-       "V3A                                -0.206590   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.586871   \n",
-       "S1252V                              0.039893   \n",
-       "S1252W                             -0.064218   \n",
-       "S1252Y                              0.450806   \n",
-       "S1252*                             -0.107910   \n",
-       "\n",
-       "          2_predicted_func_score_Omicron_BA2  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.409368   \n",
-       "F2L                                 0.107339   \n",
-       "F2S                                -0.525584   \n",
-       "F2V                                -0.431950   \n",
-       "V3A                                -0.473092   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.763869   \n",
-       "S1252V                             -0.233608   \n",
-       "S1252W                              0.168365   \n",
-       "S1252Y                              0.282706   \n",
-       "S1252*                             -0.524964   \n",
-       "\n",
-       "          avg_predicted_func_score_Omicron_BA2  \n",
-       "mutation                                        \n",
-       "M1I                                  -3.247442  \n",
-       "F2L                                  -0.046385  \n",
-       "F2S                                  -0.164871  \n",
-       "F2V                                  -0.072880  \n",
-       "V3A                                  -0.339841  \n",
-       "...                                        ...  \n",
-       "S1252T                               -0.675370  \n",
-       "S1252V                               -0.096858  \n",
-       "S1252W                                0.052073  \n",
-       "S1252Y                                0.366756  \n",
-       "S1252*                               -0.316437  \n",
-       "\n",
-       "[5934 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mut_df_replicates = combine_replicate_muts(\n",
-    "    {\n",
-    "        f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
-    "        for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
-    "    },\n",
-    "    predicted_func_scores=True,\n",
-    "    how=\"inner\",\n",
-    "    times_seen_threshold=times_seen_threshold\n",
-    ")\n",
-    "mut_df_replicates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bb4df58-4301-4356-874d-544b1f6d6837",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "We need the sitemap of inferred wildtype amino acids at each site, for each condition. This is easily accessible via the `Model.data` attribute "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "acd8e8e4-2270-4d4d-a9b6-49ace3ae67bc",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Delta</th>\n",
-       "      <th>Omicron_BA1</th>\n",
-       "      <th>Omicron_BA2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>L</td>\n",
-       "      <td>L</td>\n",
-       "      <td>L</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>V</td>\n",
-       "      <td>V</td>\n",
-       "      <td>V</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>S</td>\n",
-       "      <td>S</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>S</td>\n",
-       "      <td>S</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q</td>\n",
-       "      <td>Q</td>\n",
-       "      <td>Q</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>V</td>\n",
-       "      <td>V</td>\n",
-       "      <td>V</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>N</td>\n",
-       "      <td>N</td>\n",
-       "      <td>N</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>L</td>\n",
-       "      <td>L</td>\n",
-       "      <td>L</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>R</td>\n",
-       "      <td>T</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>T</td>\n",
-       "      <td>T</td>\n",
-       "      <td>T</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Delta Omicron_BA1 Omicron_BA2\n",
-       "10     L           L           L\n",
-       "11     V           V           V\n",
-       "12     S           S           S\n",
-       "13     S           S           S\n",
-       "14     Q           Q           Q\n",
-       "15     C           C           C\n",
-       "16     V           V           V\n",
-       "17     N           N           N\n",
-       "18     L           L           L\n",
-       "19     R           T           I\n",
-       "20     T           T           T"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "site_map = model.data.site_map\n",
-    "site_map.loc[10:20, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "253b1f06-f9a2-4d89-ae66-9df6fa3e8356",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x900 with 15 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# some renaming and wrangling\n",
-    "mut_df_replicates[\"sense\"] = [\"stop\" if mut == \"*\" else \"nonsynonymous\" for mut in mut_df_replicates.muts]\n",
-    "rename_omicron = {\n",
-    "    c:\"_\".join(c.split(\"_\")[:2]+[c.split(\"_\")[3]])\n",
-    "    for c in mut_df_replicates.columns if \"Omicron\" in c\n",
-    "}\n",
-    "mut_df_replicates.rename(rename_omicron, axis=1, inplace=True)\n",
-    "site_map = site_map.reset_index().rename(\n",
-    "    {\"index\":\"sites\", \"Omicron_BA2\": \"BA2\", \"Omicron_BA1\":\"BA1\"}, axis=1\n",
-    ").set_index(\"sites\")\n",
-    "\n",
-    "saveas = \"shift_by_site_heatmap_zoom\"\n",
-    "\n",
-    "site_ranges = {\n",
-    "    \"zoom1\" : [137, 142],\n",
-    "    \"zoom2\" : [416, 421],\n",
-    "    \"zoom3\" : [568, 573],\n",
-    "    \"zoom4\" : [843, 856],\n",
-    "    \"zoom5\" : [1019, 1029]\n",
-    "}\n",
-    "\n",
-    "# heatmap ax width ratios\n",
-    "width_ratios = [(end-start) for key, (start, end) in site_ranges.items()]\n",
-    "\n",
-    "# make the first one a little bigger for the color bar\n",
-    "width_ratios[0] += width_ratios[0] * 0.5\n",
-    "\n",
-    "\n",
-    "# Plot domain architecture in primary sequence\n",
-    "# based on structure papers (Gobeil et al., 2022, Molecular Cell)\n",
-    "# (Duan et al., 2020, Frotiers in Immunology)\n",
-    "domain_dict = {\n",
-    "    'NTD' : [13, 293],\n",
-    "    'RBD' : [330, 528],\n",
-    "    'SD1' : [528, 590],\n",
-    "    'SD2' : [590, 685],\n",
-    "    'FP' : [815, 834],\n",
-    "    'HR1' : [907, 985],\n",
-    "    'CH' : [985, 1035],\n",
-    "    'CD' : [1075, 1162],\n",
-    "    'HR2' : [1162, 1211],\n",
-    "    'TM' : [1211, 1234],\n",
-    "}\n",
-    "\n",
-    "sort_order = [\n",
-    "    \"R\",\"K\",\"H\",\"D\",\"E\",\"Q\",\"N\",\"S\",\n",
-    "    \"T\",\"Y\",\"W\",\"F\",\"A\",\"I\",\"L\",\"M\",\n",
-    "    \"V\",\"G\",\"P\",\"C\",\"-\",\"*\",\n",
-    "]\n",
-    "\n",
-    "fig = plt.figure( figsize=[6.4, 9])\n",
-    "axs = fig.subplot_mosaic(\n",
-    "    [\n",
-    "        [\"Annotation\"]*5,\n",
-    "        [\"Delta\"]*5,\n",
-    "        [f\"{k}_Delta\" for k in list(site_ranges.keys())],\n",
-    "        [f\"{k}_Delta\" for k in list(site_ranges.keys())],\n",
-    "        [\".\"]*5,\n",
-    "        [\"BA2\"]*5,\n",
-    "        [f\"{k}_BA2\" for k in list(site_ranges.keys())],\n",
-    "        [f\"{k}_BA2\" for k in list(site_ranges.keys())]\n",
-    "    ],\n",
-    "    \n",
-    "    height_ratios=[\n",
-    "        1.5, \n",
-    "        2, \n",
-    "        2, \n",
-    "        2,\n",
-    "        0.3,\n",
-    "        2,\n",
-    "        2, \n",
-    "        2\n",
-    "    ],\n",
-    "    empty_sentinel=\".\",\n",
-    "    # set the width ratios between the columns\n",
-    "    width_ratios=width_ratios,\n",
-    "    gridspec_kw={\n",
-    "        \"wspace\": 0.20,\n",
-    "        \"hspace\": 0.4,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "# derived from\n",
-    "# https://matplotlib.org/stable/gallery/subplots_axes_and_figures/axes_zoom_effect.html\n",
-    "def connect_bbox(bbox1, bbox2,\n",
-    "                 loc1a, loc2a, loc1b, loc2b,\n",
-    "                 prop_lines, prop_patches=None):\n",
-    "    if prop_patches is None:\n",
-    "        prop_patches = {\n",
-    "            **prop_lines,\n",
-    "            \"alpha\": prop_lines.get(\"alpha\", 1) * 0.2,\n",
-    "            \"clip_on\": False,\n",
-    "        }\n",
-    "\n",
-    "    c1 = BboxConnector(\n",
-    "        bbox1, bbox2, loc1=loc1a, loc2=loc2a, clip_on=False, **prop_lines)\n",
-    "    c2 = BboxConnector(\n",
-    "        bbox1, bbox2, loc1=loc1b, loc2=loc2b, clip_on=False, **prop_lines)\n",
-    "\n",
-    "    bbox_patch1 = BboxPatch(bbox1, **prop_patches)\n",
-    "    bbox_patch2 = BboxPatch(bbox2, **prop_patches)\n",
-    "\n",
-    "    p = BboxConnectorPatch(bbox1, bbox2,\n",
-    "                           loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b,\n",
-    "                           clip_on=False,\n",
-    "                           **prop_patches)\n",
-    "\n",
-    "    return c1, c2, bbox_patch1, bbox_patch2, p\n",
-    "\n",
-    "def zoom_effect03(ax1, ax2, xmin, xmax, **kwargs):\n",
-    "\n",
-    "    mybbox1 = ax1.bbox\n",
-    "    \n",
-    "    bbox = Bbox.from_extents(xmin, 0, xmax, 1)\n",
-    "    mybbox2 = TransformedBbox(bbox, ax2.get_xaxis_transform())\n",
-    "\n",
-    "    prop_patches = {**kwargs, \"ec\": \"none\", \"alpha\": 0.2}\n",
-    "\n",
-    "    c1, c2, bbox_patch1, bbox_patch2, p = connect_bbox(\n",
-    "        mybbox1, mybbox2,\n",
-    "        loc1a=2, loc2a=3, loc1b=1, loc2b=4,\n",
-    "        prop_lines=kwargs, prop_patches=prop_patches)\n",
-    "\n",
-    "    ax2.add_patch(c1)\n",
-    "    ax2.add_patch(c2)\n",
-    "    ax2.add_patch(p)\n",
-    "\n",
-    "    return c1, c2, bbox_patch1, bbox_patch2, p\n",
-    "\n",
-    "#############\n",
-    "# sitewise\n",
-    "#############\n",
-    "\n",
-    "\n",
-    "pal = sns.color_palette('colorblind')\n",
-    "cs = ['darkviolet', 'orange']\n",
-    "cs = {\n",
-    "    'BA2' : 'darkviolet', \n",
-    "    'Delta' : 'orange'\n",
-    "}\n",
-    "\n",
-    "# Plot per-site metric\n",
-    "metric_prefix = 'max_abs_shift_'\n",
-    "for (i, homolog) in enumerate(['BA2', 'Delta']):\n",
-    "\n",
-    "    sns.scatterplot(\n",
-    "        x='sites', \n",
-    "        y=f'avg_shift_{homolog}',\n",
-    "        data=mut_df_replicates, \n",
-    "        s=15,\n",
-    "        alpha=0.7,\n",
-    "        edgecolor=\"grey\",\n",
-    "        linewidth=0.05,\n",
-    "        ax=axs[homolog], \n",
-    "        color=cs[homolog],\n",
-    "        label=\"\"\n",
-    "    )\n",
-    "    \n",
-    "    nis = site_map.query(f\"{homolog} != BA1\")\n",
-    "    sns.scatterplot(\n",
-    "        x='sites', \n",
-    "        y=np.repeat(2.9, len(nis)),\n",
-    "        data=nis, \n",
-    "        s=30,\n",
-    "        ax=axs[homolog],\n",
-    "        marker='v',\n",
-    "        facecolor=cs[homolog], \n",
-    "        edgecolor=\"k\"\n",
-    "    )\n",
-    "\n",
-    "    axs[homolog].grid()\n",
-    "    axs[homolog].set(\n",
-    "        xlim=[-10,1260],\n",
-    "        ylim=[-2, 3],\n",
-    "        yticks=[-2, -1, 0, 1, 2],\n",
-    "    )\n",
-    "    sns.despine(ax=axs[homolog])\n",
-    "    \n",
-    "    axs[homolog].tick_params(\n",
-    "        axis='x', \n",
-    "        bottom=False,\n",
-    "        labelbottom=False,\n",
-    "        labeltop=True if homolog == \"Delta\" else False,\n",
-    "        top=True if homolog == \"Delta\" else False,\n",
-    "        size=9\n",
-    "    )\n",
-    "    axs[homolog].set_xlabel(None)\n",
-    "    axs[homolog].set_ylabel('shift ($\\Delta_{d,m}$)', size=10)\n",
-    "\n",
-    "\n",
-    "axs[\"BA2\"]._shared_axes['x'].join(axs[\"BA2\"], axs[\"Delta\"])\n",
-    "\n",
-    "plot_rectangles = True\n",
-    "for zoom, site_range in site_ranges.items():\n",
-    "    if not plot_rectangles:\n",
-    "        continue\n",
-    "    (site_i, site_j) = site_range\n",
-    "    for (i, homolog) in enumerate(['BA2', 'Delta']):\n",
-    "        rect = patches.Rectangle(\n",
-    "            (site_i-5, -2), site_j-site_i+11, 4,\n",
-    "            edgecolor='none', facecolor='0.75', zorder=0\n",
-    "        )\n",
-    "        axs[homolog].add_patch(rect)\n",
-    "        \n",
-    "        \n",
-    "#############\n",
-    "# Annotation\n",
-    "#############\n",
-    "\n",
-    "for (domain, (start, end)) in domain_dict.items():\n",
-    "    rectangle = patches.Rectangle((start, 1), end-start, 2, edgecolor='0.25', facecolor='white')\n",
-    "    axs[\"Annotation\"].add_patch(rectangle)\n",
-    "    rx, ry = rectangle.get_xy()\n",
-    "    cx = rx + rectangle.get_width()/2.0\n",
-    "    cy = ry - 0.05 + rectangle.get_height()/2.0\n",
-    "    if domain in ['FP', 'TM']:\n",
-    "        cy += 2\n",
-    "    axs[\"Annotation\"].annotate(\n",
-    "        domain, (cx, cy), color='black', ha='center', va='center',\n",
-    "        fontsize=7\n",
-    "    )\n",
-    "\n",
-    "axs[\"Annotation\"].set(ylim=[-0.5,4], yticks=[])\n",
-    "sns.despine(left=True, bottom=True, ax=axs[\"Annotation\"])\n",
-    "\n",
-    "axs[\"Annotation\"].sharex(axs[\"BA2\"])\n",
-    "axs[\"Annotation\"].axhline(2, c='0.25', zorder=0)\n",
-    "axs[\"Annotation\"].xaxis.set_tick_params(which='both', bottom=False, labelbottom=False, labeltop=False)\n",
-    "\n",
-    "#############\n",
-    "# Heatmap\n",
-    "#############\n",
-    "\n",
-    "for (i, homolog) in enumerate(['Delta', 'BA2']):\n",
-    "    \n",
-    "    df_shifts_wide = mut_df_replicates.pivot(\n",
-    "        index='muts', \n",
-    "        columns='sites', \n",
-    "        values=f'avg_shift_{homolog}'\n",
-    "    ).loc[sort_order, :]\n",
-    "\n",
-    "    for zoom, (start, end) in site_ranges.items():\n",
-    "        \n",
-    "        iter_ax = axs[f\"{zoom}_{homolog}\"]\n",
-    "        iter_ax.set_facecolor(\"lightgrey\")\n",
-    "        sites = [s for s in list(range(start, end+1)) if s in df_shifts_wide.columns]\n",
-    "        \n",
-    "        sns.heatmap(\n",
-    "            df_shifts_wide.loc[:, sites], \n",
-    "            cbar=True if zoom == \"zoom1\" else False,\n",
-    "            cbar_kws={\n",
-    "                \"shrink\": 0.5, \n",
-    "                \"location\":'left',\n",
-    "                'anchor': (-1.5, 0.5),\n",
-    "                'label' : None\n",
-    "            },\n",
-    "            ax = iter_ax,\n",
-    "            linewidth=.5, \n",
-    "            linecolor=\"darkgrey\",\n",
-    "            center=0,\n",
-    "            cmap='RdBu',\n",
-    "            vmin=-2.0,\n",
-    "            vmax=2.0,\n",
-    "            xticklabels=False,\n",
-    "            yticklabels=False,\n",
-    "        )\n",
-    "\n",
-    "        for i, site in enumerate(sites):\n",
-    "            for j, mut in enumerate(sort_order):\n",
-    "                \n",
-    "                is_ref_wt = True if mut == site_map.loc[site,\"BA1\"] else False\n",
-    "                if is_ref_wt:\n",
-    "                    iter_ax.scatter(\n",
-    "                        [i+0.5], \n",
-    "                        [j+0.5], \n",
-    "                        marker=\"x\", \n",
-    "                        s=8, \n",
-    "                        c=\"black\"\n",
-    "                    )\n",
-    "                is_nis = (\n",
-    "                    True \n",
-    "                    if mut ==  site_map.loc[site,homolog] and\n",
-    "                    mut !=  site_map.loc[site,\"BA1\"]\n",
-    "                    else False\n",
-    "                )\n",
-    "                \n",
-    "                if is_nis:\n",
-    "                    iter_ax.scatter(\n",
-    "                        [i+0.5], \n",
-    "                        [j+0.5], \n",
-    "                        marker=\"o\", \n",
-    "                        s=12, \n",
-    "                        facecolors=cs[homolog],\n",
-    "                        edgecolors=\"black\"\n",
-    "                    )\n",
-    "\n",
-    "        if zoom != \"zoom1\":\n",
-    "            axs[f\"{zoom}_{homolog}\"].tick_params(axis='y', left=False, labelleft=False)\n",
-    "            sns.despine(left=True, bottom=True, ax=axs[f\"{zoom}_{homolog}\"])  \n",
-    "        else:\n",
-    "            axs[f\"{zoom}_{homolog}\"].set_yticks(\n",
-    "                [s+0.5 for s in range(len(sort_order))],\n",
-    "                labels=sort_order, \n",
-    "                va=\"center\",\n",
-    "                size=6\n",
-    "            )\n",
-    "            \n",
-    "        axs[f\"{zoom}_{homolog}\"].set_ylabel(None)\n",
-    "            \n",
-    "        if homolog != \"Delta\":\n",
-    "            axs[f\"{zoom}_{homolog}\"].sharex(axs[f\"{zoom}_Delta\"])\n",
-    "        axs[f\"{zoom}_{homolog}\"].set_xticks(\n",
-    "            [s+0.5 for s in range(len(sites))],\n",
-    "             labels=sites, \n",
-    "             ha=\"center\",\n",
-    "            rotation=90,\n",
-    "            size=7\n",
-    "        )\n",
-    "        axs[f\"{zoom}_{homolog}\"].set_xlabel(None)\n",
-    "\n",
-    "for zoom, (start, end) in site_ranges.items():\n",
-    "    for homolog in ['Delta', 'BA2']:\n",
-    "        zoom_effect03(axs[f\"{zoom}_{homolog}\"], axs[homolog], start, end, alpha= 0.2)\n",
-    "\n",
-    "fig.text(\n",
-    "    0.5, 0.05, 'sites',\n",
-    "    ha='center'\n",
-    ")\n",
-    "\n",
-    "axs[\"Delta\"].text(\n",
-    "    -0.1, 1.25, \n",
-    "    f\"A\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"Delta\"].transAxes\n",
-    ")\n",
-    "axs[\"Delta\"].text(\n",
-    "    0.035, 1.15, \n",
-    "    f\"Delta\", \n",
-    "    ha=\"left\", va=\"center\", \n",
-    "    size=12,\n",
-    "    transform=axs[\"Delta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "axs[\"BA2\"].text(\n",
-    "    -0.1, 1.25, \n",
-    "    f\"B\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"BA2\"].transAxes\n",
-    ")\n",
-    "axs[\"BA2\"].text(\n",
-    "    0.035, 1.15, \n",
-    "    f\"BA.2\", \n",
-    "    ha=\"left\", va=\"center\", \n",
-    "    size=12,\n",
-    "    transform=axs[\"BA2\"].transAxes\n",
-    ")\n",
-    "\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9187c02e-51a5-4c66-9d62-5422319dc433",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Comparison of results to the naive approach (independent-condition fits)\n",
-    "\n",
-    "here we fit a model to each homolog individually, so that we may compare the results to our joint fitting process"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "45cfc79e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>func_score</th>\n",
-       "      <th>aa_substitutions</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>replicate</th>\n",
-       "      <th>n_subs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>272676</th>\n",
-       "      <td>0.4616</td>\n",
-       "      <td>N87T L517F T1006A</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>272787</th>\n",
-       "      <td>1.5096</td>\n",
-       "      <td>D80L</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>272779</th>\n",
-       "      <td>-0.7202</td>\n",
-       "      <td>A1026V D1168Y</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>272786</th>\n",
-       "      <td>1.1890</td>\n",
-       "      <td>G75W K1154E</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>272781</th>\n",
-       "      <td>-1.2116</td>\n",
-       "      <td>T307S S803L A893V</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>930492</th>\n",
-       "      <td>-3.5000</td>\n",
-       "      <td>K182R N405L S408* T941S D1163Y</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>930493</th>\n",
-       "      <td>-0.7129</td>\n",
-       "      <td>P82S S112T D138T K1038N</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>930494</th>\n",
-       "      <td>-0.8500</td>\n",
-       "      <td>L179P A222T G261H N405D</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>930495</th>\n",
-       "      <td>-2.3933</td>\n",
-       "      <td>S27F G413E</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>930497</th>\n",
-       "      <td>0.6685</td>\n",
-       "      <td>A222V S1242I</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>689433 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        func_score                aa_substitutions    condition  replicate  \\\n",
-       "272676      0.4616               N87T L517F T1006A        Delta          1   \n",
-       "272787      1.5096                            D80L        Delta          1   \n",
-       "272779     -0.7202                   A1026V D1168Y        Delta          1   \n",
-       "272786      1.1890                     G75W K1154E        Delta          1   \n",
-       "272781     -1.2116               T307S S803L A893V        Delta          1   \n",
-       "...            ...                             ...          ...        ...   \n",
-       "930492     -3.5000  K182R N405L S408* T941S D1163Y  Omicron_BA2          2   \n",
-       "930493     -0.7129         P82S S112T D138T K1038N  Omicron_BA2          2   \n",
-       "930494     -0.8500         L179P A222T G261H N405D  Omicron_BA2          2   \n",
-       "930495     -2.3933                      S27F G413E  Omicron_BA2          2   \n",
-       "930497      0.6685                    A222V S1242I  Omicron_BA2          2   \n",
-       "\n",
-       "        n_subs  \n",
-       "272676       3  \n",
-       "272787       1  \n",
-       "272779       2  \n",
-       "272786       2  \n",
-       "272781       3  \n",
-       "...        ...  \n",
-       "930492       5  \n",
-       "930493       4  \n",
-       "930494       4  \n",
-       "930495       2  \n",
-       "930497       2  \n",
-       "\n",
-       "[689433 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "func_score_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "dc7b6777-c3ca-4455-b9d9-a68c8cc3d7f1",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
-    },
-    {
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/28515 [00:00<?, ?it/s]"
-      ]
-     },
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
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-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3565), Label(value='0 / 3565'))), …"
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [],\n",
-      "dropping: 0 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Delta\n",
-      "is reference, skipping\n",
-      "inferring site map for Omicron_BA1\n"
-     ]
-    },
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-       "  0%|          | 0/70597 [00:00<?, ?it/s]"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
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-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [],\n",
-      "dropping: 0 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "inferring site map for Omicron_BA2\n"
-     ]
-    },
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
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-     "text": [
-      "unknown cond wildtype at sites: [],\n",
-      "dropping: 0 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
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-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7550), Label(value='0 / 7550'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Omicron_BA2\n",
-      "is reference, skipping\n",
-      "inferring site map for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "64d3abb182874d82b08591a29203cb49",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/29158 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "07070e80086b49a1b2a41a32514c5dcc",
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-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3645), Label(value='0 / 3645'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [],\n",
-      "dropping: 0 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Delta\n",
-      "is reference, skipping\n",
-      "inferring site map for Omicron_BA1\n"
-     ]
-    },
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/62129 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
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-       "model_id": "73a8af7a52d5486fb0c8bb11cc859b16",
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [],\n",
-      "dropping: 0 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "inferring site map for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "002c466bfe884707ab2b46c1d77193d3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/57719 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 8 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "61343d29a80a4b93b62e43fde3cef172",
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-      },
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-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7215), Label(value='0 / 7215'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [],\n",
-      "dropping: 0 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be3e7f60dbed4d1ebab2cdf600891171",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7215), Label(value='0 / 7215'))), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Omicron_BA2\n",
-      "is reference, skipping\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_condition_datasets = []\n",
-    "for (replicate, condition), condition_fsdf in func_score_df.groupby([\"replicate\", \"condition\"]):\n",
-    "    start = time.time()\n",
-    "    data = multidms.Data(\n",
-    "        condition_fsdf,\n",
-    "        collapse_identical_variants=\"mean\",\n",
-    "        alphabet=multidms.AAS_WITHSTOP_WITHGAP,\n",
-    "        reference=condition,\n",
-    "        assert_site_integrity=False,\n",
-    "        verbose=True,\n",
-    "        nb_workers=8,\n",
-    "        name=f\"{replicate}-{condition}\"\n",
-    "    )\n",
-    "    data.condition_colors = cc\n",
-    "    single_condition_datasets.append(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "0aae707a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Data(1-Delta),\n",
-       " Data(1-Omicron_BA1),\n",
-       " Data(1-Omicron_BA2),\n",
-       " Data(2-Delta),\n",
-       " Data(2-Omicron_BA1),\n",
-       " Data(2-Omicron_BA2)]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "single_condition_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "ca644ee0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "single_condition_fit_params = {\n",
-    "    'init_beta_naught' : [0.0],\n",
-    "    'epistatic_model' : [\"Sigmoid\"],\n",
-    "    'output_activation' : [\"Identity\"],\n",
-    "    'tol':[1e-6],\n",
-    "    'num_training_steps': [3],\n",
-    "    'iterations_per_step':[1000]\n",
-    "}\n",
-    "single_condition_fit_params[\"dataset\"] = single_condition_datasets\n",
-    "_, _, naive_models = multidms.fit_models(single_condition_fit_params, n_threads=-1)     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "3b691b07-81ce-4552-b2fc-0b9fb2572fb2",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>step_loss</th>\n",
-       "      <th>epistatic_model</th>\n",
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-       "      <th>scale_coeff_ridge_gamma</th>\n",
-       "      <th>scale_coeff_ridge_alpha_d</th>\n",
-       "      <th>...</th>\n",
-       "      <th>gamma_corrected</th>\n",
-       "      <th>alpha_d</th>\n",
-       "      <th>init_beta_naught</th>\n",
-       "      <th>lock_beta_naught_at</th>\n",
-       "      <th>tol</th>\n",
-       "      <th>num_training_steps</th>\n",
-       "      <th>iterations_per_step</th>\n",
-       "      <th>n_hidden_units</th>\n",
-       "      <th>lower_bound</th>\n",
-       "      <th>PRNGKey</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Model(Model-0)</td>\n",
-       "      <td>1-Delta</td>\n",
-       "      <td>[1.6401410102844238, 0.3213399648666382, 0.238...</td>\n",
-       "      <td>Sigmoid</td>\n",
-       "      <td>Identity</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Model(Model-0)</td>\n",
-       "      <td>1-Omicron_BA1</td>\n",
-       "      <td>[1.498695969581604, 0.2580853998661041, 0.2273...</td>\n",
-       "      <td>Sigmoid</td>\n",
-       "      <td>Identity</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Model(Model-0)</td>\n",
-       "      <td>1-Omicron_BA2</td>\n",
-       "      <td>[1.3041640520095825, 0.21561458706855774, 0.19...</td>\n",
-       "      <td>Sigmoid</td>\n",
-       "      <td>Identity</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Model(Model-0)</td>\n",
-       "      <td>2-Delta</td>\n",
-       "      <td>[1.4836487770080566, 0.2918327748775482, 0.251...</td>\n",
-       "      <td>Sigmoid</td>\n",
-       "      <td>Identity</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Model(Model-0)</td>\n",
-       "      <td>2-Omicron_BA1</td>\n",
-       "      <td>[1.414493441581726, 0.19964061677455902, 0.177...</td>\n",
-       "      <td>Sigmoid</td>\n",
-       "      <td>Identity</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>3</td>\n",
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-       "      <td>5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Model(Model-0)</td>\n",
-       "      <td>2-Omicron_BA2</td>\n",
-       "      <td>[1.3287856578826904, 0.20366378128528595, 0.18...</td>\n",
-       "      <td>Sigmoid</td>\n",
-       "      <td>Identity</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0.000001</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            model   dataset_name  \\\n",
-       "0  Model(Model-0)        1-Delta   \n",
-       "1  Model(Model-0)  1-Omicron_BA1   \n",
-       "2  Model(Model-0)  1-Omicron_BA2   \n",
-       "3  Model(Model-0)        2-Delta   \n",
-       "4  Model(Model-0)  2-Omicron_BA1   \n",
-       "5  Model(Model-0)  2-Omicron_BA2   \n",
-       "\n",
-       "                                           step_loss epistatic_model  \\\n",
-       "0  [1.6401410102844238, 0.3213399648666382, 0.238...         Sigmoid   \n",
-       "1  [1.498695969581604, 0.2580853998661041, 0.2273...         Sigmoid   \n",
-       "2  [1.3041640520095825, 0.21561458706855774, 0.19...         Sigmoid   \n",
-       "3  [1.4836487770080566, 0.2918327748775482, 0.251...         Sigmoid   \n",
-       "4  [1.414493441581726, 0.19964061677455902, 0.177...         Sigmoid   \n",
-       "5  [1.3287856578826904, 0.20366378128528595, 0.18...         Sigmoid   \n",
-       "\n",
-       "  output_activation scale_coeff_lasso_shift scale_coeff_ridge_beta  \\\n",
-       "0          Identity                 0.00002                      0   \n",
-       "1          Identity                 0.00002                      0   \n",
-       "2          Identity                 0.00002                      0   \n",
-       "3          Identity                 0.00002                      0   \n",
-       "4          Identity                 0.00002                      0   \n",
-       "5          Identity                 0.00002                      0   \n",
-       "\n",
-       "  scale_coeff_ridge_shift scale_coeff_ridge_gamma scale_coeff_ridge_alpha_d  \\\n",
-       "0                       0                       0                         0   \n",
-       "1                       0                       0                         0   \n",
-       "2                       0                       0                         0   \n",
-       "3                       0                       0                         0   \n",
-       "4                       0                       0                         0   \n",
-       "5                       0                       0                         0   \n",
-       "\n",
-       "   ... gamma_corrected alpha_d init_beta_naught lock_beta_naught_at       tol  \\\n",
-       "0  ...           False   False              0.0                None  0.000001   \n",
-       "1  ...           False   False              0.0                None  0.000001   \n",
-       "2  ...           False   False              0.0                None  0.000001   \n",
-       "3  ...           False   False              0.0                None  0.000001   \n",
-       "4  ...           False   False              0.0                None  0.000001   \n",
-       "5  ...           False   False              0.0                None  0.000001   \n",
-       "\n",
-       "  num_training_steps iterations_per_step n_hidden_units lower_bound PRNGKey  \n",
-       "0                  3                1000              5        None       0  \n",
-       "1                  3                1000              5        None       0  \n",
-       "2                  3                1000              5        None       0  \n",
-       "3                  3                1000              5        None       0  \n",
-       "4                  3                1000              5        None       0  \n",
-       "5                  3                1000              5        None       0  \n",
-       "\n",
-       "[6 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "naive_models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fe42406-43b4-413d-af53-fd33024c8049",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Combine the results of the individual models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "e5284e45-f066-408b-8f59-5e9bc2065137",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wts</th>\n",
-       "      <th>sites</th>\n",
-       "      <th>muts</th>\n",
-       "      <th>1-Delta_beta</th>\n",
-       "      <th>1-Omicron_BA1_beta</th>\n",
-       "      <th>1-Omicron_BA2_beta</th>\n",
-       "      <th>2-Delta_beta</th>\n",
-       "      <th>2-Omicron_BA1_beta</th>\n",
-       "      <th>2-Omicron_BA2_beta</th>\n",
-       "      <th>avg_beta</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>M</td>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-1.870671</td>\n",
-       "      <td>-4.543005</td>\n",
-       "      <td>-5.079311</td>\n",
-       "      <td>-6.021095</td>\n",
-       "      <td>-4.960052</td>\n",
-       "      <td>-6.756710</td>\n",
-       "      <td>-4.871808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>L</td>\n",
-       "      <td>0.719424</td>\n",
-       "      <td>0.616395</td>\n",
-       "      <td>-0.345233</td>\n",
-       "      <td>1.548375</td>\n",
-       "      <td>0.809705</td>\n",
-       "      <td>0.415978</td>\n",
-       "      <td>0.627441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.886682</td>\n",
-       "      <td>0.227816</td>\n",
-       "      <td>1.413247</td>\n",
-       "      <td>-0.468354</td>\n",
-       "      <td>-0.126621</td>\n",
-       "      <td>-0.354802</td>\n",
-       "      <td>0.262995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>V</td>\n",
-       "      <td>1.321443</td>\n",
-       "      <td>1.497329</td>\n",
-       "      <td>0.099748</td>\n",
-       "      <td>-0.062049</td>\n",
-       "      <td>-0.162207</td>\n",
-       "      <td>0.666150</td>\n",
-       "      <td>0.560069</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>V</td>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.152784</td>\n",
-       "      <td>0.079664</td>\n",
-       "      <td>-0.049184</td>\n",
-       "      <td>-0.023562</td>\n",
-       "      <td>0.038327</td>\n",
-       "      <td>-0.134961</td>\n",
-       "      <td>0.010511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252T</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>T</td>\n",
-       "      <td>-0.340671</td>\n",
-       "      <td>-0.268261</td>\n",
-       "      <td>-0.571357</td>\n",
-       "      <td>-0.691662</td>\n",
-       "      <td>-0.331198</td>\n",
-       "      <td>-0.534978</td>\n",
-       "      <td>-0.456354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252V</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.542526</td>\n",
-       "      <td>0.436843</td>\n",
-       "      <td>0.308891</td>\n",
-       "      <td>0.242965</td>\n",
-       "      <td>0.544752</td>\n",
-       "      <td>0.226868</td>\n",
-       "      <td>0.383807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252W</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>W</td>\n",
-       "      <td>1.142103</td>\n",
-       "      <td>0.211932</td>\n",
-       "      <td>0.312270</td>\n",
-       "      <td>2.329360</td>\n",
-       "      <td>0.828580</td>\n",
-       "      <td>0.878538</td>\n",
-       "      <td>0.950464</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252Y</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>0.990768</td>\n",
-       "      <td>0.969647</td>\n",
-       "      <td>0.879357</td>\n",
-       "      <td>1.086219</td>\n",
-       "      <td>1.231161</td>\n",
-       "      <td>0.910776</td>\n",
-       "      <td>1.011322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252*</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>*</td>\n",
-       "      <td>-0.159378</td>\n",
-       "      <td>-0.199520</td>\n",
-       "      <td>0.032886</td>\n",
-       "      <td>-0.186606</td>\n",
-       "      <td>0.224432</td>\n",
-       "      <td>-0.334765</td>\n",
-       "      <td>-0.103825</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5635 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         wts  sites muts  1-Delta_beta  1-Omicron_BA1_beta  \\\n",
-       "mutation                                                     \n",
-       "M1I        M      1    I     -1.870671           -4.543005   \n",
-       "F2L        F      2    L      0.719424            0.616395   \n",
-       "F2S        F      2    S      0.886682            0.227816   \n",
-       "F2V        F      2    V      1.321443            1.497329   \n",
-       "V3A        V      3    A      0.152784            0.079664   \n",
-       "...       ..    ...  ...           ...                 ...   \n",
-       "S1252T     S   1252    T     -0.340671           -0.268261   \n",
-       "S1252V     S   1252    V      0.542526            0.436843   \n",
-       "S1252W     S   1252    W      1.142103            0.211932   \n",
-       "S1252Y     S   1252    Y      0.990768            0.969647   \n",
-       "S1252*     S   1252    *     -0.159378           -0.199520   \n",
-       "\n",
-       "          1-Omicron_BA2_beta  2-Delta_beta  2-Omicron_BA1_beta  \\\n",
-       "mutation                                                         \n",
-       "M1I                -5.079311     -6.021095           -4.960052   \n",
-       "F2L                -0.345233      1.548375            0.809705   \n",
-       "F2S                 1.413247     -0.468354           -0.126621   \n",
-       "F2V                 0.099748     -0.062049           -0.162207   \n",
-       "V3A                -0.049184     -0.023562            0.038327   \n",
-       "...                      ...           ...                 ...   \n",
-       "S1252T             -0.571357     -0.691662           -0.331198   \n",
-       "S1252V              0.308891      0.242965            0.544752   \n",
-       "S1252W              0.312270      2.329360            0.828580   \n",
-       "S1252Y              0.879357      1.086219            1.231161   \n",
-       "S1252*              0.032886     -0.186606            0.224432   \n",
-       "\n",
-       "          2-Omicron_BA2_beta  avg_beta  \n",
-       "mutation                                \n",
-       "M1I                -6.756710 -4.871808  \n",
-       "F2L                 0.415978  0.627441  \n",
-       "F2S                -0.354802  0.262995  \n",
-       "F2V                 0.666150  0.560069  \n",
-       "V3A                -0.134961  0.010511  \n",
-       "...                      ...       ...  \n",
-       "S1252T             -0.534978 -0.456354  \n",
-       "S1252V              0.226868  0.383807  \n",
-       "S1252W              0.878538  0.950464  \n",
-       "S1252Y              0.910776  1.011322  \n",
-       "S1252*             -0.334765 -0.103825  \n",
-       "\n",
-       "[5635 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit_dict = {row.dataset_name:row.model for _, row in naive_models.iterrows()}\n",
-    "naive_mut_df = combine_replicate_muts(fit_dict,how=\"inner\",times_seen_threshold=times_seen_threshold)\n",
-    "naive_mut_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74209b8d-e99d-4c11-a0b0-786e4c6657c0",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Using BA.1 as a reference fit, compute the difference of betas. Which is the naive approach to computing \"shifts\" in mutation effect between experiments without using the multidms joint modeling infrastructure. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "e85041f5-30de-4722-aad3-61e2b6b63cec",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wts</th>\n",
-       "      <th>sites</th>\n",
-       "      <th>muts</th>\n",
-       "      <th>1-Delta_beta</th>\n",
-       "      <th>1-Omicron_BA1_beta</th>\n",
-       "      <th>1-Omicron_BA2_beta</th>\n",
-       "      <th>2-Delta_beta</th>\n",
-       "      <th>2-Omicron_BA1_beta</th>\n",
-       "      <th>2-Omicron_BA2_beta</th>\n",
-       "      <th>avg_beta</th>\n",
-       "      <th>1-Delta_S</th>\n",
-       "      <th>2-Delta_S</th>\n",
-       "      <th>1-Omicron_BA2_S</th>\n",
-       "      <th>2-Omicron_BA2_S</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>M</td>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-1.870671</td>\n",
-       "      <td>-4.543005</td>\n",
-       "      <td>-5.079311</td>\n",
-       "      <td>-6.021095</td>\n",
-       "      <td>-4.960052</td>\n",
-       "      <td>-6.756710</td>\n",
-       "      <td>-4.871808</td>\n",
-       "      <td>2.672334</td>\n",
-       "      <td>-1.061042</td>\n",
-       "      <td>-0.536307</td>\n",
-       "      <td>-1.796658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>L</td>\n",
-       "      <td>0.719424</td>\n",
-       "      <td>0.616395</td>\n",
-       "      <td>-0.345233</td>\n",
-       "      <td>1.548375</td>\n",
-       "      <td>0.809705</td>\n",
-       "      <td>0.415978</td>\n",
-       "      <td>0.627441</td>\n",
-       "      <td>0.103029</td>\n",
-       "      <td>0.738670</td>\n",
-       "      <td>-0.961628</td>\n",
-       "      <td>-0.393727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.886682</td>\n",
-       "      <td>0.227816</td>\n",
-       "      <td>1.413247</td>\n",
-       "      <td>-0.468354</td>\n",
-       "      <td>-0.126621</td>\n",
-       "      <td>-0.354802</td>\n",
-       "      <td>0.262995</td>\n",
-       "      <td>0.658866</td>\n",
-       "      <td>-0.341734</td>\n",
-       "      <td>1.185431</td>\n",
-       "      <td>-0.228181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>V</td>\n",
-       "      <td>1.321443</td>\n",
-       "      <td>1.497329</td>\n",
-       "      <td>0.099748</td>\n",
-       "      <td>-0.062049</td>\n",
-       "      <td>-0.162207</td>\n",
-       "      <td>0.666150</td>\n",
-       "      <td>0.560069</td>\n",
-       "      <td>-0.175887</td>\n",
-       "      <td>0.100159</td>\n",
-       "      <td>-1.397581</td>\n",
-       "      <td>0.828358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>V</td>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.152784</td>\n",
-       "      <td>0.079664</td>\n",
-       "      <td>-0.049184</td>\n",
-       "      <td>-0.023562</td>\n",
-       "      <td>0.038327</td>\n",
-       "      <td>-0.134961</td>\n",
-       "      <td>0.010511</td>\n",
-       "      <td>0.073120</td>\n",
-       "      <td>-0.061889</td>\n",
-       "      <td>-0.128848</td>\n",
-       "      <td>-0.173288</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252T</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>T</td>\n",
-       "      <td>-0.340671</td>\n",
-       "      <td>-0.268261</td>\n",
-       "      <td>-0.571357</td>\n",
-       "      <td>-0.691662</td>\n",
-       "      <td>-0.331198</td>\n",
-       "      <td>-0.534978</td>\n",
-       "      <td>-0.456354</td>\n",
-       "      <td>-0.072410</td>\n",
-       "      <td>-0.360464</td>\n",
-       "      <td>-0.303095</td>\n",
-       "      <td>-0.203781</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252V</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.542526</td>\n",
-       "      <td>0.436843</td>\n",
-       "      <td>0.308891</td>\n",
-       "      <td>0.242965</td>\n",
-       "      <td>0.544752</td>\n",
-       "      <td>0.226868</td>\n",
-       "      <td>0.383807</td>\n",
-       "      <td>0.105683</td>\n",
-       "      <td>-0.301787</td>\n",
-       "      <td>-0.127952</td>\n",
-       "      <td>-0.317884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252W</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>W</td>\n",
-       "      <td>1.142103</td>\n",
-       "      <td>0.211932</td>\n",
-       "      <td>0.312270</td>\n",
-       "      <td>2.329360</td>\n",
-       "      <td>0.828580</td>\n",
-       "      <td>0.878538</td>\n",
-       "      <td>0.950464</td>\n",
-       "      <td>0.930171</td>\n",
-       "      <td>1.500780</td>\n",
-       "      <td>0.100338</td>\n",
-       "      <td>0.049959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252Y</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>0.990768</td>\n",
-       "      <td>0.969647</td>\n",
-       "      <td>0.879357</td>\n",
-       "      <td>1.086219</td>\n",
-       "      <td>1.231161</td>\n",
-       "      <td>0.910776</td>\n",
-       "      <td>1.011322</td>\n",
-       "      <td>0.021121</td>\n",
-       "      <td>-0.144942</td>\n",
-       "      <td>-0.090290</td>\n",
-       "      <td>-0.320385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252*</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>*</td>\n",
-       "      <td>-0.159378</td>\n",
-       "      <td>-0.199520</td>\n",
-       "      <td>0.032886</td>\n",
-       "      <td>-0.186606</td>\n",
-       "      <td>0.224432</td>\n",
-       "      <td>-0.334765</td>\n",
-       "      <td>-0.103825</td>\n",
-       "      <td>0.040142</td>\n",
-       "      <td>-0.411038</td>\n",
-       "      <td>0.232406</td>\n",
-       "      <td>-0.559197</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5635 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         wts  sites muts  1-Delta_beta  1-Omicron_BA1_beta  \\\n",
-       "mutation                                                     \n",
-       "M1I        M      1    I     -1.870671           -4.543005   \n",
-       "F2L        F      2    L      0.719424            0.616395   \n",
-       "F2S        F      2    S      0.886682            0.227816   \n",
-       "F2V        F      2    V      1.321443            1.497329   \n",
-       "V3A        V      3    A      0.152784            0.079664   \n",
-       "...       ..    ...  ...           ...                 ...   \n",
-       "S1252T     S   1252    T     -0.340671           -0.268261   \n",
-       "S1252V     S   1252    V      0.542526            0.436843   \n",
-       "S1252W     S   1252    W      1.142103            0.211932   \n",
-       "S1252Y     S   1252    Y      0.990768            0.969647   \n",
-       "S1252*     S   1252    *     -0.159378           -0.199520   \n",
-       "\n",
-       "          1-Omicron_BA2_beta  2-Delta_beta  2-Omicron_BA1_beta  \\\n",
-       "mutation                                                         \n",
-       "M1I                -5.079311     -6.021095           -4.960052   \n",
-       "F2L                -0.345233      1.548375            0.809705   \n",
-       "F2S                 1.413247     -0.468354           -0.126621   \n",
-       "F2V                 0.099748     -0.062049           -0.162207   \n",
-       "V3A                -0.049184     -0.023562            0.038327   \n",
-       "...                      ...           ...                 ...   \n",
-       "S1252T             -0.571357     -0.691662           -0.331198   \n",
-       "S1252V              0.308891      0.242965            0.544752   \n",
-       "S1252W              0.312270      2.329360            0.828580   \n",
-       "S1252Y              0.879357      1.086219            1.231161   \n",
-       "S1252*              0.032886     -0.186606            0.224432   \n",
-       "\n",
-       "          2-Omicron_BA2_beta  avg_beta  1-Delta_S  2-Delta_S  1-Omicron_BA2_S  \\\n",
-       "mutation                                                                        \n",
-       "M1I                -6.756710 -4.871808   2.672334  -1.061042        -0.536307   \n",
-       "F2L                 0.415978  0.627441   0.103029   0.738670        -0.961628   \n",
-       "F2S                -0.354802  0.262995   0.658866  -0.341734         1.185431   \n",
-       "F2V                 0.666150  0.560069  -0.175887   0.100159        -1.397581   \n",
-       "V3A                -0.134961  0.010511   0.073120  -0.061889        -0.128848   \n",
-       "...                      ...       ...        ...        ...              ...   \n",
-       "S1252T             -0.534978 -0.456354  -0.072410  -0.360464        -0.303095   \n",
-       "S1252V              0.226868  0.383807   0.105683  -0.301787        -0.127952   \n",
-       "S1252W              0.878538  0.950464   0.930171   1.500780         0.100338   \n",
-       "S1252Y              0.910776  1.011322   0.021121  -0.144942        -0.090290   \n",
-       "S1252*             -0.334765 -0.103825   0.040142  -0.411038         0.232406   \n",
-       "\n",
-       "          2-Omicron_BA2_S  \n",
-       "mutation                   \n",
-       "M1I             -1.796658  \n",
-       "F2L             -0.393727  \n",
-       "F2S             -0.228181  \n",
-       "F2V              0.828358  \n",
-       "V3A             -0.173288  \n",
-       "...                   ...  \n",
-       "S1252T          -0.203781  \n",
-       "S1252V          -0.317884  \n",
-       "S1252W           0.049959  \n",
-       "S1252Y          -0.320385  \n",
-       "S1252*          -0.559197  \n",
-       "\n",
-       "[5635 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reference = \"Omicron_BA1\"\n",
-    "for i, condition in enumerate([\"Delta\", \"Omicron_BA2\"]): \n",
-    "    for replicate in [1, 2]:\n",
-    "        reference_betas = naive_mut_df[f\"{replicate}-{reference}_beta\"]\n",
-    "        condition_betas = naive_mut_df[f\"{replicate}-{condition}_beta\"]\n",
-    "        naive_mut_df[f\"{replicate}-{condition}_S\"] = condition_betas - reference_betas\n",
-    "naive_mut_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "753fe01e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mut_df_replicates = combine_replicate_muts(\n",
-    "    {\n",
-    "        f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
-    "        for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
-    "    },\n",
-    "    predicted_func_scores=False,\n",
-    "    how=\"inner\",\n",
-    "    times_seen_threshold=times_seen_threshold\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "2b29adfb-24d3-4ddf-9459-cab064570d62",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x900 with 15 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=\"shift_distribution_correlation_naive\"\n",
-    "pal = sns.color_palette('colorblind')\n",
-    "fig = plt.figure(figsize=[6.4, 9])\n",
-    "\n",
-    "# the ratio of plot to divider between \n",
-    "dist_sf = 3\n",
-    "\n",
-    "# three rows of distributions\n",
-    "dist_rows = [[] for _ in range(3)]\n",
-    "for row, mut_type in enumerate([\"m\", \"i\", \"n\"]):\n",
-    "    dist_rows[row].extend([f\"dist_{mut_type}_beta\"]*dist_sf)\n",
-    "    dist_rows[row].append(\".\")\n",
-    "    for param in [\"shift_Delta\", \"shift_Omicron_BA2\"]:\n",
-    "        dist_rows[row].extend([f\"dist_{mut_type}_{param}\"]*dist_sf)\n",
-    "\n",
-    "# empty space row\n",
-    "number_of_rows = len(dist_rows[0])\n",
-    "empty_row = [\".\"] * len(dist_rows[0])\n",
-    "\n",
-    "# correlation plot row\n",
-    "corr_row = [\"corr_beta\"]*dist_sf+ [\".\"]\n",
-    "for param in [\"shift_Delta\", \"shift_Omicron_BA2\"]:\n",
-    "    corr_row.extend([f\"corr_{param}\"]*dist_sf)\n",
-    "    \n",
-    "# niave correlation plot row\n",
-    "naive_corr_row = [\"naive_corr_Omicron_BA1_beta\"]*dist_sf+ [\".\"]\n",
-    "for param in [\"Delta_S\", \"Omicron_BA2_S\"]:\n",
-    "    naive_corr_row.extend([f\"naive_corr_{param}\"]*dist_sf)\n",
-    "\n",
-    "axs = fig.subplot_mosaic(\n",
-    "    dist_rows+[empty_row, corr_row, empty_row, naive_corr_row],\n",
-    "    height_ratios=[0.3, 0.3, 0.3] + [0.38, 0.7, 0.55, 0.7],\n",
-    "    empty_sentinel=\".\",\n",
-    "    gridspec_kw={\n",
-    "        \"wspace\": 0.05,\n",
-    "        \"hspace\": 0.05,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "query_dict = {\n",
-    "    \"m\" : \"muts != '*' and muts != '-'\",\n",
-    "    \"i\" : \"muts == '-'\",\n",
-    "    \"n\" : \"muts == '*'\"\n",
-    "}\n",
-    "\n",
-    "bins_dict = {\n",
-    "    \"beta\" : np.arange(-5.25, 1.0, 0.5),\n",
-    "    \"shift_Delta\" : np.arange(-3.25, 3.25, 0.5),\n",
-    "    \"shift_Omicron_BA2\" : np.arange(-3.25, 3.25, 0.5)\n",
-    "}\n",
-    "\n",
-    "#################\n",
-    "# DISTRIBUTIONS\n",
-    "#################\n",
-    "mut_df_replicates[\"sense\"] = [\"stop\" if mut == \"*\" else \"nonsynonymous\" for mut in mut_df_replicates.muts]\n",
-    "\n",
-    "prefix = \"avg\"\n",
-    "bins = np.arange(-5.25, 1.0, 0.5)\n",
-    "# df = mut_df_replicates.copy()\n",
-    "mut_df_replicates[\"avg_beta\"].clip(lower=-5, inplace=True)\n",
-    "\n",
-    "for col, param in enumerate([\"beta\", \"shift_Delta\", \"shift_Omicron_BA2\"]):\n",
-    "    for row, mut_type in enumerate([\"m\", \"i\", \"n\"]):\n",
-    "        \n",
-    "        iter_ax = axs[f\"dist_{mut_type}_{param}\"]\n",
-    "        sns.histplot(\n",
-    "            mut_df_replicates.query(query_dict[mut_type]), \n",
-    "            x=f\"{prefix}_{param}\", \n",
-    "            ax=iter_ax,\n",
-    "            stat='probability',\n",
-    "            bins=bins_dict[param],\n",
-    "            label=\"stop\",\n",
-    "            color=\"red\" if mut_type == \"n\" else pal.as_hex()[row],\n",
-    "            alpha=0.5\n",
-    "        )\n",
-    "        iter_ax.set_ylim(-0.05,1.05)\n",
-    "        \n",
-    "        # remove the ylabel from all but the first column\n",
-    "        if col != 0: \n",
-    "            iter_ax.tick_params(axis='y', labelleft=False)\n",
-    "        iter_ax.set_yticks(\n",
-    "            [0.0, 0.5], [0.0, 0.5], rotation=0, ha=\"right\",size=9\n",
-    "        )\n",
-    "            \n",
-    "        if col != 0 or row != 1:\n",
-    "            iter_ax.set_ylabel(None)\n",
-    "        else:\n",
-    "            iter_ax.set_ylabel(\"probability\")\n",
-    "        \n",
-    "        # remove the x labels from all but bottom\n",
-    "        if row != 2:\n",
-    "            iter_ax.tick_params(axis='x', labelbottom=False)\n",
-    "            iter_ax.set_xlabel(None)\n",
-    "\n",
-    "        iter_ax.set_xlabel(None)\n",
-    "        \n",
-    "        sns.despine(ax=iter_ax)\n",
-    "        \n",
-    "axs[\"dist_n_beta\"].set_xlabel(r\"mut. effect $(\\beta_{m})$\")\n",
-    "axs[\"dist_n_beta\"].xaxis.set_label_coords(0.5, -.5)\n",
-    "\n",
-    "axs[\"dist_m_beta\"].text(\n",
-    "    0.1, 0.9, \n",
-    "    f\"nonsynonymous\\nmuts\", \n",
-    "    ha=\"left\", va=\"top\", \n",
-    "    size=9,\n",
-    "    transform=axs[\"dist_m_beta\"].transAxes\n",
-    ")\n",
-    "axs[\"dist_i_beta\"].text(\n",
-    "    0.1, 0.9, \n",
-    "    f\"in-frame codon\\ndeletion muts\", \n",
-    "    ha=\"left\", va=\"top\", \n",
-    "    size=9,\n",
-    "    transform=axs[\"dist_i_beta\"].transAxes\n",
-    ")\n",
-    "axs[\"dist_n_beta\"].text(\n",
-    "    0.1, 0.9, \n",
-    "    f\"stop muts\", \n",
-    "    ha=\"left\", va=\"top\", \n",
-    "    size=9,\n",
-    "    transform=axs[\"dist_n_beta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "axs[\"dist_n_shift_Delta\"].set_xlabel('shift ($\\Delta_{d,m}$)')\n",
-    "axs[\"dist_n_shift_Delta\"].xaxis.set_label_coords(1.0, -.5)\n",
-    "\n",
-    "axs[\"dist_m_shift_Delta\"].set_title(\"Delta\")\n",
-    "axs[\"dist_m_shift_Omicron_BA2\"].set_title(\"BA.2\")\n",
-    "\n",
-    "#################\n",
-    "# CORRELATIONS\n",
-    "#################\n",
-    "\n",
-    "data = mut_df_replicates.dropna().copy()\n",
-    "data['1_beta'].clip(lower=-5, inplace=True)\n",
-    "data['2_beta'].clip(lower=-5, inplace=True)\n",
-    "# plot the correlations of parameters\n",
-    "for col, param in enumerate([\"beta\", \"shift_Delta\", \"shift_Omicron_BA2\"]):\n",
-    "    iter_ax = axs[f\"corr_{param}\"]\n",
-    "    x, y = data[f\"1_{param}\"], data[f\"2_{param}\"]\n",
-    "    sns.scatterplot(\n",
-    "        data = data,\n",
-    "        x = f\"1_{param}\",\n",
-    "        y = f\"2_{param}\",\n",
-    "        ax=iter_ax,\n",
-    "        s=60, \n",
-    "        alpha=0.3,     \n",
-    "        c='0.25'\n",
-    "    )\n",
-    "    \n",
-    "    # remove y labels from all but first column\n",
-    "    if col != 0: \n",
-    "        \n",
-    "        if col == 2:\n",
-    "            iter_ax.tick_params(axis='y', labelleft=False)\n",
-    "        iter_ax.set_ylabel(None)\n",
-    "        \n",
-    "        lim = [-2.8, 2.8]\n",
-    "        ticks = [-2, 0, 2]\n",
-    "        \n",
-    "        iter_ax.set_ylim(lim)\n",
-    "        iter_ax.set_xlim(lim)\n",
-    "        iter_ax.set_yticks(\n",
-    "            ticks, ticks, size=12\n",
-    "        )        \n",
-    "        iter_ax.set_xticks(\n",
-    "            ticks, ticks, rotation=0, size=12\n",
-    "        )\n",
-    "            # line of equivilence\n",
-    "        iter_ax.plot(\n",
-    "            lim, \n",
-    "            lim,\n",
-    "            linestyle=\"--\", \n",
-    "            lw=2,\n",
-    "            c='royalblue'\n",
-    "        )\n",
-    "        \n",
-    "    else:\n",
-    "        lim = [-6, 3]\n",
-    "        ticks = [-4, -2, 0, 2]\n",
-    "        \n",
-    "        iter_ax.set_ylim(lim)\n",
-    "        iter_ax.set_xlim(lim)\n",
-    "        iter_ax.set_yticks(\n",
-    "            ticks, ticks, size=12\n",
-    "        )        \n",
-    "        iter_ax.set_xticks(\n",
-    "            ticks, ticks, rotation=0, size=12\n",
-    "        )\n",
-    "            # line of equivilence\n",
-    "        iter_ax.plot(\n",
-    "            lim, \n",
-    "            lim,\n",
-    "            linestyle=\"--\", \n",
-    "            lw=2,\n",
-    "            c='royalblue'\n",
-    "        )\n",
-    "        \n",
-    "        iter_ax.set_ylabel(\"replicate 2\")\n",
-    "        \n",
-    "    iter_ax.set_xlabel(\"replicate 1\")\n",
-    "    iter_ax.grid()\n",
-    "\n",
-    "    \n",
-    "    corr = pearsonr(x, y)[0]**2\n",
-    "    iter_ax.annotate(\n",
-    "        f\"$R^2 = {corr:.2f}$\", \n",
-    "        (0.07, 0.8), \n",
-    "        xycoords=\"axes fraction\", \n",
-    "        fontsize=11\n",
-    "    )\n",
-    "    sns.despine(ax=iter_ax)\n",
-    "    \n",
-    "#################\n",
-    "# NAIVE CORRELATIONS\n",
-    "#################\n",
-    "\n",
-    "data = naive_mut_df.dropna().copy()\n",
-    "data[\"1-Omicron_BA1_beta\"].clip(lower=-10, inplace=True)\n",
-    "data[\"2-Omicron_BA1_beta\"].clip(lower=-10, inplace=True)\n",
-    "\n",
-    "# plot the correlations of parameters\n",
-    "for col, param in enumerate([\"Omicron_BA1_beta\", \"Delta_S\", \"Omicron_BA2_S\"]):\n",
-    "    iter_ax = axs[f\"naive_corr_{param}\"]\n",
-    "    x, y = data[f\"1-{param}\"], data[f\"2-{param}\"]\n",
-    "    sns.scatterplot(\n",
-    "        data = data,\n",
-    "        x = f\"1-{param}\",\n",
-    "        y = f\"2-{param}\",\n",
-    "        ax=iter_ax,\n",
-    "        s=60, \n",
-    "        alpha=0.3,     \n",
-    "        c='0.25'\n",
-    "    )\n",
-    "    \n",
-    "    # remove y labels from all but first column\n",
-    "    if col != 0: \n",
-    "        \n",
-    "        if col == 2:\n",
-    "            iter_ax.tick_params(axis='y', labelleft=False)\n",
-    "        iter_ax.set_ylabel(None)\n",
-    "        \n",
-    "        lim = [-8.2, 12.2]\n",
-    "        ticks = range(-8, 12, 4)\n",
-    "        \n",
-    "        iter_ax.set_ylim(lim)\n",
-    "        iter_ax.set_xlim(lim)\n",
-    "        iter_ax.set_yticks(\n",
-    "            ticks, ticks, size=12\n",
-    "        )        \n",
-    "        iter_ax.set_xticks(\n",
-    "            ticks, ticks, rotation=0, size=12\n",
-    "        )\n",
-    "            # line of equivilence\n",
-    "        iter_ax.plot(\n",
-    "            lim, \n",
-    "            lim,\n",
-    "            linestyle=\"--\", \n",
-    "            lw=2,\n",
-    "            c='royalblue'\n",
-    "        )\n",
-    "        \n",
-    "    else:\n",
-    "        lim = [-11, 6]\n",
-    "        ticks = range(-10, 6, 5)\n",
-    "        \n",
-    "        iter_ax.set_ylim(lim)\n",
-    "        iter_ax.set_xlim(lim)\n",
-    "        iter_ax.set_yticks(\n",
-    "            ticks, ticks, size=12\n",
-    "        )        \n",
-    "        iter_ax.set_xticks(\n",
-    "            ticks, ticks, rotation=0, size=12\n",
-    "        )\n",
-    "            # line of equivilence\n",
-    "        iter_ax.plot(\n",
-    "            lim, \n",
-    "            lim,\n",
-    "            linestyle=\"--\", \n",
-    "            lw=2,\n",
-    "            c='royalblue'\n",
-    "        )\n",
-    "        \n",
-    "        iter_ax.set_ylabel(\"replicate 2\")\n",
-    "        \n",
-    "    iter_ax.set_xlabel(\"replicate 1\")\n",
-    "    iter_ax.grid()\n",
-    "\n",
-    "    \n",
-    "    corr = pearsonr(x, y)[0]**2\n",
-    "    iter_ax.annotate(\n",
-    "        f\"$R^{2} = {corr:.2f}$\", \n",
-    "        (0.07, 0.8), \n",
-    "        xycoords=\"axes fraction\", \n",
-    "        fontsize=11\n",
-    "    )\n",
-    "    sns.despine(ax=iter_ax)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Add subpanel labels\n",
-    "axs[\"dist_m_beta\"].text(\n",
-    "    -0.2, 1.05, \n",
-    "    f\"A\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"dist_m_beta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "\n",
-    "axs[\"dist_m_shift_Delta\"].text(\n",
-    "    -0.15, 1.05, \n",
-    "    f\"B\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"dist_m_shift_Delta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "axs[\"corr_beta\"].text(\n",
-    "    -0.2, 1.05, \n",
-    "    f\"C\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"corr_beta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "axs[\"corr_shift_Delta\"].text(\n",
-    "    -0.15, 1.05, \n",
-    "    f\"D\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"corr_shift_Delta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "axs[\"naive_corr_Omicron_BA1_beta\"].text(\n",
-    "    -0.2, 1.05, \n",
-    "    f\"E\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"naive_corr_Omicron_BA1_beta\"].transAxes\n",
-    ")\n",
-    "\n",
-    "axs[\"naive_corr_Delta_S\"].text(\n",
-    "    -0.15, 1.05, \n",
-    "    f\"F\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"naive_corr_Delta_S\"].transAxes\n",
-    ")\n",
-    "\n",
-    "# titles\n",
-    "axs[\"dist_m_beta\"].set_title(\"BA.1\")\n",
-    "axs[\"corr_beta\"].set_title(r\"mut. effect $(\\beta_{m})$\", size=10)\n",
-    "axs[\"corr_shift_Delta\"].set_title(\"shift ($\\Delta_{Delta, m}$)\", size=10)\n",
-    "axs[\"corr_shift_Omicron_BA2\"].set_title(\"shift ($\\Delta_{BA.2, m}$)\", size=10)\n",
-    "\n",
-    "axs[\"naive_corr_Omicron_BA1_beta\"].set_title(\"mut. effect in BA.1\", size=10)\n",
-    "axs[\"naive_corr_Delta_S\"].set_title(\"shift in mut. effect\\n(Delta - BA.1)\", size=10)\n",
-    "axs[\"naive_corr_Omicron_BA2_S\"].set_title(\"shift in mut. effect\\n(BA.2 - BA.1)\", size=10)\n",
-    "\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "\n",
-    "plt.show()  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00006ef8",
-   "metadata": {},
-   "source": [
-    "## Comparison to linear model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "d5861707",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit_params_linear = fit_params.copy()\n",
-    "fit_params_linear[\"dataset\"] = datasets\n",
-    "fit_params_linear[\"epistatic_model\"] = [\"Identity\"]\n",
-    "\n",
-    "_, _, linear_models = multidms.fit_models(fit_params_linear, n_threads=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "093863d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_collection_linear = multidms.ModelCollection(linear_models)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "1a4dd7f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cache miss - this could take a moment\n",
-      "  dataset_name  scale_coeff_lasso_shift       mut_type    mut_param  sparsity\n",
-      "0        rep-1                 0.000000  nonsynonymous  shift_Delta  0.234082\n",
-      "1        rep-1                 0.000000           stop  shift_Delta  0.247678\n",
-      "2        rep-1                 0.000005  nonsynonymous  shift_Delta  0.320816\n",
-      "3        rep-1                 0.000005           stop  shift_Delta  0.318885\n",
-      "4        rep-1                 0.000010  nonsynonymous  shift_Delta  0.413163\n"
-     ]
-    },
-    {
-     "data": {
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\"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.30275510204081635}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.24148606811145512}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.39683673469387754}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.37770897832817335}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.5281632653061225}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.5758513931888545}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.7018367346938775}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.8111455108359134}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.8541836734693877}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9256965944272446}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.953265306122449}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9969040247678018}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9888775510204082}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9968367346938776}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1483673469387755}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1826625386996904}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.2553061224489796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.30340557275541796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3754081632653061}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.4674922600619195}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.5646938775510204}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7585139318885449}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7470408163265306}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8978328173374613}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8965306122448979}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9876160990712074}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9703061224489796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9931632653061224}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9993877551020408}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1526530612244898}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.17647058823529413}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.2530612244897959}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.33746130030959753}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3673469387755102}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.4613003095975232}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.5478571428571428}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7554179566563467}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7328571428571429}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.913312693498452}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8863265306122449}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9814241486068112}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9679591836734693}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9933673469387755}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9990816326530613}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.FacetChart(...)"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart, sparsity_df = model_collection_linear.shift_sparsity(return_data=True, height_scalar=100) # TODO raise issue to fix height scalar\n",
-    "print(sparsity_df.head())\n",
-    "chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "2801f2bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      datasets mut_param  correlation  scale_coeff_lasso_shift\n",
-      "0  rep-1,rep-2      beta     0.723252                 0.000000\n",
-      "0  rep-1,rep-2      beta     0.742715                 0.000005\n",
-      "0  rep-1,rep-2      beta     0.746827                 0.000010\n",
-      "0  rep-1,rep-2      beta     0.737302                 0.000020\n",
-      "0  rep-1,rep-2      beta     0.726406                 0.000040\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "  #altair-viz-288cd02d5e4440b786abcdf23b125946.vega-embed {\n",
-       "    width: 100%;\n",
-       "    display: flex;\n",
-       "  }\n",
-       "\n",
-       "  #altair-viz-288cd02d5e4440b786abcdf23b125946.vega-embed details,\n",
-       "  #altair-viz-288cd02d5e4440b786abcdf23b125946.vega-embed details summary {\n",
-       "    position: relative;\n",
-       "  }\n",
-       "</style>\n",
-       "<div id=\"altair-viz-288cd02d5e4440b786abcdf23b125946\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-       "  (function(spec, embedOpt){\n",
-       "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-288cd02d5e4440b786abcdf23b125946\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-288cd02d5e4440b786abcdf23b125946\");\n",
-       "    }\n",
-       "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.15.1?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
-       "    };\n",
-       "\n",
-       "    function maybeLoadScript(lib, version) {\n",
-       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-       "      return (VEGA_DEBUG[key] == version) ?\n",
-       "        Promise.resolve(paths[lib]) :\n",
-       "        new Promise(function(resolve, reject) {\n",
-       "          var s = document.createElement('script');\n",
-       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "          s.async = true;\n",
-       "          s.onload = () => {\n",
-       "            VEGA_DEBUG[key] = version;\n",
-       "            return resolve(paths[lib]);\n",
-       "          };\n",
-       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-       "          s.src = paths[lib];\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function showError(err) {\n",
-       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-       "      throw err;\n",
-       "    }\n",
-       "\n",
-       "    function displayChart(vegaEmbed) {\n",
-       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-       "    }\n",
-       "\n",
-       "    if(typeof define === \"function\" && define.amd) {\n",
-       "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-       "    } else {\n",
-       "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.15.1\"))\n",
-       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
-       "        .catch(showError)\n",
-       "        .then(() => displayChart(vegaEmbed));\n",
-       "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-b62eeb94c60f2ef3e0b2755633b4fc5e\"}, \"facet\": {\"column\": {\"field\": \"datasets\", \"title\": \"Experiment comparison\", \"type\": \"nominal\"}}, \"spec\": {\"layer\": [{\"mark\": {\"type\": \"point\"}, \"encoding\": {\"color\": {\"field\": \"mut_param\", \"title\": \"Parameter\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"datasets\", \"type\": \"nominal\"}, {\"field\": \"correlation\", \"type\": \"quantitative\"}, {\"field\": \"mut_param\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"field\": \"correlation\", \"title\": \"Correlation\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"line\"}, \"encoding\": {\"color\": {\"field\": \"mut_param\", \"title\": \"Parameter\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"datasets\", \"type\": \"nominal\"}, {\"field\": \"correlation\", \"type\": \"quantitative\"}, {\"field\": \"mut_param\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"field\": \"correlation\", \"title\": \"Correlation\", \"type\": \"quantitative\"}}}], \"height\": 200, \"width\": 200}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.15.1.json\", \"datasets\": {\"data-b62eeb94c60f2ef3e0b2755633b4fc5e\": [{\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7232518036966645, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7427151626096438, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.746827190440104, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7373015985890201, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.726406185802199, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7166822417521634, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7109316807016348, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7079732944810495, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7052664194131558, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.5461508896362519, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.5778961723672983, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.6028470054441032, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.6367925199488867, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.6801142260168166, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.6999004838612, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7069337572287419, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7072927788351033, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7052239734411834, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7232518073041926, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7427151621476121, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7468271946958948, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7373015947537663, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7264061844161868, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7166822403508754, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7109316799561909, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7079732878500581, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7052664174967669, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.6260249542137526, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.6625960786837645, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.6882333140191729, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7048258276418253, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7106087199686132, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.711837959569212, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7097347978403256, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7077474216005084, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7052315149449219, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.3609446176893114, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.3838436429115677, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.4031193130568265, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.4407312910656222, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.5179240996765818, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6015798222149723, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6468273491478964, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6694203079489227, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.8754983696991615, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.2993643256933147, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.32937124289948083, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.36227721605135715, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.4355317608040999, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.5224288314801461, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.6408989719229061, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.7101979537174019, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.7594567199257061, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.9835332722377108, \"scale_coeff_lasso_shift\": 0.00064}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.FacetChart(...)"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart, corr_df = model_collection_linear.mut_param_dataset_correlation(width_scalar=200, return_data=True)\n",
-    "print(corr_df.head())\n",
-    "chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "41c62ccc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit_params_linear[\"dataset\"] = train \n",
-    "_, _, linear_models_cv = multidms.model_collection.fit_models(fit_params_linear, n_threads = -1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "3ab400e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_mc = multidms.model_collection.ModelCollection(linear_models_cv)\n",
-    "linear_mc.add_validation_loss(test, overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "9ee9e06c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>scale_coeff_lasso_shift</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>loss</th>\n",
-       "      <th>split</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.234156</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.235351</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.00001</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.237773</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.00002</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.244865</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>0.00004</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.262903</td>\n",
-       "      <td>training</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  dataset_name scale_coeff_lasso_shift condition      loss     split\n",
-       "0        rep-1                     0.0     Delta  0.234156  training\n",
-       "1        rep-1                0.000005     Delta  0.235351  training\n",
-       "2        rep-1                 0.00001     Delta  0.237773  training\n",
-       "3        rep-1                 0.00002     Delta  0.244865  training\n",
-       "4        rep-1                 0.00004     Delta  0.262903  training"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cross_validation_df = linear_mc.get_conditional_loss_df()\n",
-    "cross_validation_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "0aeb5c5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x750 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=\"shrinkage_analysis_linear_models\"\n",
-    "\n",
-    "fig, ax = plt.subplots(3, figsize=[4.5, 7.5], sharex=True)\n",
-    "\n",
-    "# replicate correlation\n",
-    "iter_ax = ax[0]\n",
-    "sns.lineplot(\n",
-    "    data=(\n",
-    "        corr_df\n",
-    "        .query(\"mut_param.str.contains('shift')\")\n",
-    "        .rename({\"mut_param\":\"shift params\"}, axis=1)\n",
-    "        # .replace({\"Data-1\":\"rep-1\", \"Data-2\":\"rep-2\"})\n",
-    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
-    "        .assign(\n",
-    "            scale_coeff_lasso_shift = [\n",
-    "                f\"{l:.1e}\" \n",
-    "                for l in corr_df.query(\"mut_param.str.contains('shift')\").scale_coeff_lasso_shift\n",
-    "            ],\n",
-    "            correlation = lambda x: x.correlation**2\n",
-    "        )\n",
-    "        .reset_index(drop=True)\n",
-    "    ),\n",
-    "    x=\"scale_coeff_lasso_shift\",\n",
-    "    y=\"correlation\",\n",
-    "    style=\"shift params\",\n",
-    "    markers=True,\n",
-    "    ax=iter_ax,\n",
-    "    linewidth=3,\n",
-    "    color=\"black\"\n",
-    ")\n",
-    "iter_ax.set_ylabel(\"rep1 v. rep2\\nshift $(R^2)$\")\n",
-    "# move legend outside of plot\n",
-    "iter_ax.legend(\n",
-    "    bbox_to_anchor = (1, 1), \n",
-    "    loc='upper left', \n",
-    "    frameon=False\n",
-    ")\n",
-    "\n",
-    "\n",
-    "\n",
-    "# plot loss\n",
-    "iter_ax = ax[1]\n",
-    "sns.lineplot(\n",
-    "    data = (\n",
-    "        cross_validation_df.query(\"condition=='total'\")\n",
-    "        .assign(\n",
-    "            # lasso_strength = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift]\n",
-    "            # lasso_strength = lambda x: f\"{x.scale_coeff_lasso_shift:.1e}\"\n",
-    "            lasso_strength = lambda x: x['scale_coeff_lasso_shift'].apply(lambda y: f'{y:.1e}')\n",
-    "        )\n",
-    "    ),\n",
-    "    x=\"lasso_strength\",\n",
-    "    y=\"loss\",\n",
-    "    ax=iter_ax,\n",
-    "    hue=\"split\",\n",
-    "    style=\"dataset_name\",\n",
-    "    palette={\"training\":\"slategrey\", \"validation\":\"#2CA02C\"},\n",
-    "    markers=True,\n",
-    "    linewidth=3\n",
-    ")\n",
-    "# move legend outside of plot\n",
-    "iter_ax.legend(\n",
-    "    bbox_to_anchor = (1, 1), \n",
-    "    loc='upper left', \n",
-    "    frameon=False\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# plot sparsity\n",
-    "iter_ax = ax[2]\n",
-    "sns.lineplot(\n",
-    "    data=(\n",
-    "        sparsity_df\n",
-    "        .rename({\"dataset_name\":\"replicate\"}, axis=1)\n",
-    "        .rename({\"mut_param\":\"shift params\", \"mut_type\":\"mutation type\"}, axis=1)\n",
-    "        # .replace({\"Data-0\":\"rep-1\", \"Data-1\":\"rep-2\"})\n",
-    "        .replace({\"nonsynonymous\":\"nonsynonymous\", \"stop\":\"stop\"})\n",
-    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
-    "        .assign(\n",
-    "            scale_coeff_lasso_shift = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift],\n",
-    "            sparsity_percent = lambda x: x.sparsity * 100,\n",
-    "        )\n",
-    "    ),\n",
-    "    x=\"scale_coeff_lasso_shift\",\n",
-    "    y=\"sparsity_percent\",\n",
-    "    hue=\"mutation type\",\n",
-    "    style=\"replicate\",\n",
-    "    palette={\"nonsynonymous\":\"grey\", \"stop\":\"#E377C2\"},\n",
-    "    markers=True,\n",
-    "    legend=True,\n",
-    "    ax=iter_ax,\n",
-    "    linewidth=3\n",
-    ")\n",
-    "# move legend outside of plot\n",
-    "iter_ax.legend(\n",
-    "    bbox_to_anchor = (1, 1), \n",
-    "    loc='upper left', \n",
-    "    frameon=False\n",
-    ")\n",
-    "# rotate x labels\n",
-    "iter_ax.set_xticklabels(\n",
-    "    iter_ax.get_xticklabels(), \n",
-    "    rotation=90, \n",
-    "    ha='center'\n",
-    ")\n",
-    "iter_ax.set_ylabel(\"sparsity\\n$(\\%\\Delta=0)$\")\n",
-    "iter_ax.set_xlabel(f\"lasso regularization strength ($\\lambda$)\")\n",
-    "\n",
-    "for axes in ax:\n",
-    "    axes.axvline(\n",
-    "        f\"{chosen_lasso_strength:.1e}\", \n",
-    "        color=\"grey\",\n",
-    "        linewidth=10,\n",
-    "        alpha=0.35\n",
-    "    )\n",
-    "\n",
-    "sns.despine(fig)\n",
-    "plt.tight_layout()\n",
-    "# plt.tight_layout()\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "# plt.show()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e73c8774-b6b5-4bf1-98c0-e8f9633da993",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Validation mutations\n",
-    "\n",
-    "Next, we compare the results of the model to mutations tested individually in _in-vitro_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "0ce65a40",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wts</th>\n",
-       "      <th>sites</th>\n",
-       "      <th>muts</th>\n",
-       "      <th>1_beta</th>\n",
-       "      <th>2_beta</th>\n",
-       "      <th>avg_beta</th>\n",
-       "      <th>1_shift_Delta</th>\n",
-       "      <th>2_shift_Delta</th>\n",
-       "      <th>avg_shift_Delta</th>\n",
-       "      <th>1_shift_Omicron_BA2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>avg_shift_Omicron_BA2</th>\n",
-       "      <th>1_predicted_func_score_Delta</th>\n",
-       "      <th>2_predicted_func_score_Delta</th>\n",
-       "      <th>avg_predicted_func_score_Delta</th>\n",
-       "      <th>1_predicted_func_score_Omicron_BA1</th>\n",
-       "      <th>2_predicted_func_score_Omicron_BA1</th>\n",
-       "      <th>avg_predicted_func_score_Omicron_BA1</th>\n",
-       "      <th>1_predicted_func_score_Omicron_BA2</th>\n",
-       "      <th>2_predicted_func_score_Omicron_BA2</th>\n",
-       "      <th>avg_predicted_func_score_Omicron_BA2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>M</td>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-2.924932</td>\n",
-       "      <td>-4.256726</td>\n",
-       "      <td>-3.590829</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-3.162696</td>\n",
-       "      <td>-3.348626</td>\n",
-       "      <td>-3.255661</td>\n",
-       "      <td>-3.009065</td>\n",
-       "      <td>-3.236155</td>\n",
-       "      <td>-3.122610</td>\n",
-       "      <td>-3.085516</td>\n",
-       "      <td>-3.409368</td>\n",
-       "      <td>-3.247442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>L</td>\n",
-       "      <td>0.200928</td>\n",
-       "      <td>0.207115</td>\n",
-       "      <td>0.204021</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.204654</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.102327</td>\n",
-       "      <td>0.287689</td>\n",
-       "      <td>0.366644</td>\n",
-       "      <td>0.327167</td>\n",
-       "      <td>0.405955</td>\n",
-       "      <td>0.487242</td>\n",
-       "      <td>0.446598</td>\n",
-       "      <td>-0.200109</td>\n",
-       "      <td>0.107339</td>\n",
-       "      <td>-0.046385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.194773</td>\n",
-       "      <td>-0.074343</td>\n",
-       "      <td>0.060215</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.275053</td>\n",
-       "      <td>-0.286178</td>\n",
-       "      <td>-0.005562</td>\n",
-       "      <td>0.393345</td>\n",
-       "      <td>-0.166355</td>\n",
-       "      <td>0.113495</td>\n",
-       "      <td>0.195843</td>\n",
-       "      <td>-0.525584</td>\n",
-       "      <td>-0.164871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.239144</td>\n",
-       "      <td>-0.030672</td>\n",
-       "      <td>0.104236</td>\n",
-       "      <td>-0.086489</td>\n",
-       "      <td>-0.153066</td>\n",
-       "      <td>-0.119778</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.188819</td>\n",
-       "      <td>-0.521577</td>\n",
-       "      <td>-0.166379</td>\n",
-       "      <td>0.484417</td>\n",
-       "      <td>-0.069216</td>\n",
-       "      <td>0.207601</td>\n",
-       "      <td>0.286191</td>\n",
-       "      <td>-0.431950</td>\n",
-       "      <td>-0.072880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>V</td>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>-0.007044</td>\n",
-       "      <td>-0.047157</td>\n",
-       "      <td>-0.027101</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.001301</td>\n",
-       "      <td>-0.133532</td>\n",
-       "      <td>-0.225989</td>\n",
-       "      <td>-0.179760</td>\n",
-       "      <td>-0.013975</td>\n",
-       "      <td>-0.106083</td>\n",
-       "      <td>-0.060029</td>\n",
-       "      <td>-0.206590</td>\n",
-       "      <td>-0.473092</td>\n",
-       "      <td>-0.339841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252T</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>T</td>\n",
-       "      <td>-0.132241</td>\n",
-       "      <td>-0.189524</td>\n",
-       "      <td>-0.160882</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.074971</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.037485</td>\n",
-       "      <td>-0.379343</td>\n",
-       "      <td>-0.533716</td>\n",
-       "      <td>-0.456529</td>\n",
-       "      <td>-0.258642</td>\n",
-       "      <td>-0.414259</td>\n",
-       "      <td>-0.336450</td>\n",
-       "      <td>-0.586871</td>\n",
-       "      <td>-0.763869</td>\n",
-       "      <td>-0.675370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252V</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.161672</td>\n",
-       "      <td>0.177089</td>\n",
-       "      <td>0.169381</td>\n",
-       "      <td>0.262923</td>\n",
-       "      <td>-0.185348</td>\n",
-       "      <td>0.038788</td>\n",
-       "      <td>-0.044192</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.080998</td>\n",
-       "      <td>0.750125</td>\n",
-       "      <td>-0.138738</td>\n",
-       "      <td>0.305693</td>\n",
-       "      <td>0.325693</td>\n",
-       "      <td>0.414606</td>\n",
-       "      <td>0.370149</td>\n",
-       "      <td>0.039893</td>\n",
-       "      <td>-0.233608</td>\n",
-       "      <td>-0.096858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252W</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>W</td>\n",
-       "      <td>0.046494</td>\n",
-       "      <td>0.283281</td>\n",
-       "      <td>0.164887</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.018787</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.015843</td>\n",
-       "      <td>-0.026414</td>\n",
-       "      <td>0.553441</td>\n",
-       "      <td>0.263513</td>\n",
-       "      <td>0.092733</td>\n",
-       "      <td>0.674209</td>\n",
-       "      <td>0.383471</td>\n",
-       "      <td>-0.064218</td>\n",
-       "      <td>0.168365</td>\n",
-       "      <td>0.052073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252Y</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>0.349203</td>\n",
-       "      <td>0.464681</td>\n",
-       "      <td>0.406942</td>\n",
-       "      <td>-0.103307</td>\n",
-       "      <td>-0.228062</td>\n",
-       "      <td>-0.165685</td>\n",
-       "      <td>-0.029801</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.107036</td>\n",
-       "      <td>0.380213</td>\n",
-       "      <td>0.438552</td>\n",
-       "      <td>0.409383</td>\n",
-       "      <td>0.711687</td>\n",
-       "      <td>1.132873</td>\n",
-       "      <td>0.922280</td>\n",
-       "      <td>0.450806</td>\n",
-       "      <td>0.282706</td>\n",
-       "      <td>0.366756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252*</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>*</td>\n",
-       "      <td>-0.069944</td>\n",
-       "      <td>-0.002437</td>\n",
-       "      <td>-0.036191</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.071298</td>\n",
-       "      <td>0.035649</td>\n",
-       "      <td>0.113153</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.020770</td>\n",
-       "      <td>-0.257914</td>\n",
-       "      <td>0.038034</td>\n",
-       "      <td>-0.109940</td>\n",
-       "      <td>-0.137814</td>\n",
-       "      <td>-0.005529</td>\n",
-       "      <td>-0.071672</td>\n",
-       "      <td>-0.107910</td>\n",
-       "      <td>-0.524964</td>\n",
-       "      <td>-0.316437</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5934 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
-       "mutation                                                                \n",
-       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
-       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
-       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
-       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
-       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
-       "...       ..    ...  ...       ...       ...       ...            ...   \n",
-       "S1252T     S   1252    T -0.132241 -0.189524 -0.160882      -0.000000   \n",
-       "S1252V     S   1252    V  0.161672  0.177089  0.169381       0.262923   \n",
-       "S1252W     S   1252    W  0.046494  0.283281  0.164887       0.000000   \n",
-       "S1252Y     S   1252    Y  0.349203  0.464681  0.406942      -0.103307   \n",
-       "S1252*     S   1252    * -0.069944 -0.002437 -0.036191       0.000000   \n",
-       "\n",
-       "          2_shift_Delta  avg_shift_Delta  1_shift_Omicron_BA2  ...  \\\n",
-       "mutation                                                       ...   \n",
-       "M1I            0.000000         0.000000            -0.000000  ...   \n",
-       "F2L           -0.000000         0.000000            -0.204654  ...   \n",
-       "F2S            0.000000         0.000000             0.000000  ...   \n",
-       "F2V           -0.153066        -0.119778             0.000000  ...   \n",
-       "V3A           -0.000000         0.000000            -0.000000  ...   \n",
-       "...                 ...              ...                  ...  ...   \n",
-       "S1252T         0.000000         0.000000            -0.074971  ...   \n",
-       "S1252V        -0.185348         0.038788            -0.044192  ...   \n",
-       "S1252W         0.000000         0.000000             0.018787  ...   \n",
-       "S1252Y        -0.228062        -0.165685            -0.029801  ...   \n",
-       "S1252*         0.071298         0.035649             0.113153  ...   \n",
-       "\n",
-       "          avg_shift_Omicron_BA2  1_predicted_func_score_Delta  \\\n",
-       "mutation                                                        \n",
-       "M1I                    0.000000                     -3.162696   \n",
-       "F2L                   -0.102327                      0.287689   \n",
-       "F2S                    0.000000                      0.275053   \n",
-       "F2V                    0.000000                      0.188819   \n",
-       "V3A                   -0.001301                     -0.133532   \n",
-       "...                         ...                           ...   \n",
-       "S1252T                -0.037485                     -0.379343   \n",
-       "S1252V                -0.080998                      0.750125   \n",
-       "S1252W                -0.015843                     -0.026414   \n",
-       "S1252Y                -0.107036                      0.380213   \n",
-       "S1252*                 0.020770                     -0.257914   \n",
-       "\n",
-       "          2_predicted_func_score_Delta  avg_predicted_func_score_Delta  \\\n",
-       "mutation                                                                 \n",
-       "M1I                          -3.348626                       -3.255661   \n",
-       "F2L                           0.366644                        0.327167   \n",
-       "F2S                          -0.286178                       -0.005562   \n",
-       "F2V                          -0.521577                       -0.166379   \n",
-       "V3A                          -0.225989                       -0.179760   \n",
-       "...                                ...                             ...   \n",
-       "S1252T                       -0.533716                       -0.456529   \n",
-       "S1252V                       -0.138738                        0.305693   \n",
-       "S1252W                        0.553441                        0.263513   \n",
-       "S1252Y                        0.438552                        0.409383   \n",
-       "S1252*                        0.038034                       -0.109940   \n",
-       "\n",
-       "          1_predicted_func_score_Omicron_BA1  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.009065   \n",
-       "F2L                                 0.405955   \n",
-       "F2S                                 0.393345   \n",
-       "F2V                                 0.484417   \n",
-       "V3A                                -0.013975   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.258642   \n",
-       "S1252V                              0.325693   \n",
-       "S1252W                              0.092733   \n",
-       "S1252Y                              0.711687   \n",
-       "S1252*                             -0.137814   \n",
-       "\n",
-       "          2_predicted_func_score_Omicron_BA1  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.236155   \n",
-       "F2L                                 0.487242   \n",
-       "F2S                                -0.166355   \n",
-       "F2V                                -0.069216   \n",
-       "V3A                                -0.106083   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.414259   \n",
-       "S1252V                              0.414606   \n",
-       "S1252W                              0.674209   \n",
-       "S1252Y                              1.132873   \n",
-       "S1252*                             -0.005529   \n",
-       "\n",
-       "          avg_predicted_func_score_Omicron_BA1  \\\n",
-       "mutation                                         \n",
-       "M1I                                  -3.122610   \n",
-       "F2L                                   0.446598   \n",
-       "F2S                                   0.113495   \n",
-       "F2V                                   0.207601   \n",
-       "V3A                                  -0.060029   \n",
-       "...                                        ...   \n",
-       "S1252T                               -0.336450   \n",
-       "S1252V                                0.370149   \n",
-       "S1252W                                0.383471   \n",
-       "S1252Y                                0.922280   \n",
-       "S1252*                               -0.071672   \n",
-       "\n",
-       "          1_predicted_func_score_Omicron_BA2  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.085516   \n",
-       "F2L                                -0.200109   \n",
-       "F2S                                 0.195843   \n",
-       "F2V                                 0.286191   \n",
-       "V3A                                -0.206590   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.586871   \n",
-       "S1252V                              0.039893   \n",
-       "S1252W                             -0.064218   \n",
-       "S1252Y                              0.450806   \n",
-       "S1252*                             -0.107910   \n",
-       "\n",
-       "          2_predicted_func_score_Omicron_BA2  \\\n",
-       "mutation                                       \n",
-       "M1I                                -3.409368   \n",
-       "F2L                                 0.107339   \n",
-       "F2S                                -0.525584   \n",
-       "F2V                                -0.431950   \n",
-       "V3A                                -0.473092   \n",
-       "...                                      ...   \n",
-       "S1252T                             -0.763869   \n",
-       "S1252V                             -0.233608   \n",
-       "S1252W                              0.168365   \n",
-       "S1252Y                              0.282706   \n",
-       "S1252*                             -0.524964   \n",
-       "\n",
-       "          avg_predicted_func_score_Omicron_BA2  \n",
-       "mutation                                        \n",
-       "M1I                                  -3.247442  \n",
-       "F2L                                  -0.046385  \n",
-       "F2S                                  -0.164871  \n",
-       "F2V                                  -0.072880  \n",
-       "V3A                                  -0.339841  \n",
-       "...                                        ...  \n",
-       "S1252T                               -0.675370  \n",
-       "S1252V                               -0.096858  \n",
-       "S1252W                                0.052073  \n",
-       "S1252Y                                0.366756  \n",
-       "S1252*                               -0.316437  \n",
-       "\n",
-       "[5934 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mut_df_replicates = combine_replicate_muts(\n",
-    "    {\n",
-    "        f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
-    "        for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
-    "    },\n",
-    "    predicted_func_scores=True,\n",
-    "    phenotype_as_effect=True,\n",
-    "    how=\"inner\",\n",
-    "    times_seen_threshold=times_seen_threshold\n",
-    ")\n",
-    "mut_df_replicates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "535e949f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>phenotypic_effect_Delta</th>\n",
-       "      <th>phenotypic_effect_Omicron_BA2</th>\n",
-       "      <th>phenotypic_effect_Omicron_BA1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>5934.000000</td>\n",
-       "      <td>5934.000000</td>\n",
-       "      <td>5934.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.685844</td>\n",
-       "      <td>0.555077</td>\n",
-       "      <td>0.691773</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.333441</td>\n",
-       "      <td>0.300639</td>\n",
-       "      <td>0.398528</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.092687</td>\n",
-       "      <td>0.094148</td>\n",
-       "      <td>0.101826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.468865</td>\n",
-       "      <td>0.278595</td>\n",
-       "      <td>0.306873</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.742053</td>\n",
-       "      <td>0.595659</td>\n",
-       "      <td>0.748459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.909277</td>\n",
-       "      <td>0.786314</td>\n",
-       "      <td>0.983936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>4.126436</td>\n",
-       "      <td>2.176377</td>\n",
-       "      <td>2.776442</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       phenotypic_effect_Delta  phenotypic_effect_Omicron_BA2  \\\n",
-       "count              5934.000000                    5934.000000   \n",
-       "mean                  0.685844                       0.555077   \n",
-       "std                   0.333441                       0.300639   \n",
-       "min                   0.092687                       0.094148   \n",
-       "25%                   0.468865                       0.278595   \n",
-       "50%                   0.742053                       0.595659   \n",
-       "75%                   0.909277                       0.786314   \n",
-       "max                   4.126436                       2.176377   \n",
-       "\n",
-       "       phenotypic_effect_Omicron_BA1  \n",
-       "count                    5934.000000  \n",
-       "mean                        0.691773  \n",
-       "std                         0.398528  \n",
-       "min                         0.101826  \n",
-       "25%                         0.306873  \n",
-       "50%                         0.748459  \n",
-       "75%                         0.983936  \n",
-       "max                         2.776442  "
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mut_df = (\n",
-    "    mut_df_replicates\n",
-    "    .assign(\n",
-    "        phenotypic_effect_Delta = 2**mut_df_replicates[\"avg_predicted_func_score_Delta\"],\n",
-    "        phenotypic_effect_Omicron_BA2 = 2**mut_df_replicates[\"avg_predicted_func_score_Omicron_BA2\"],\n",
-    "        phenotypic_effect_Omicron_BA1 = 2**mut_df_replicates[\"avg_predicted_func_score_Omicron_BA1\"]\n",
-    "    )\n",
-    "    .reset_index()\n",
-    ")\n",
-    "mut_df[[c for c in mut_df.columns if \"phenotypic_effect\" in c]].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "7d31b330-1b7c-47cf-abe6-385faa435cf6",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x700 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=\"validation_titer_fold_change\"\n",
-    "\n",
-    "row1 = ['titer', 'titer', '.']\n",
-    "row2 = ['D142L', 'A419S', 'A570D'] \n",
-    "row3 = ['K854N', 'T1027I', 'legend']\n",
-    "empty_row = [\".\"] * 3\n",
-    "\n",
-    "fig = plt.figure( figsize=[6.4, 7])\n",
-    "axs = fig.subplot_mosaic(\n",
-    "    [row1, empty_row, row2, row3],\n",
-    "    height_ratios=[\n",
-    "        1, 0.39, 0.7, 0.7\n",
-    "    ],\n",
-    "    empty_sentinel=\".\",\n",
-    "    gridspec_kw={\n",
-    "        \"wspace\": 0.20,\n",
-    "        \"hspace\": 0.4,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "#############\n",
-    "# TITERS\n",
-    "#############\n",
-    "\n",
-    "# Read in data\n",
-    "titers_df = pd.read_csv('data/viral_titers.csv')\n",
-    "titers_df.rename(\n",
-    "    columns={'RLUperuL':'titer', 'background':'homolog'},\n",
-    "    inplace=True\n",
-    ")\n",
-    "\n",
-    "# Add a column giving the replicate and mutation\n",
-    "titers_df['replicate'] = titers_df['virus'].apply(lambda x: x[-1])\n",
-    "titers_df['mutation'] = titers_df['virus'].str.extract(r'_(\\S+)_')\n",
-    "titers_df['mutation'].fillna('unmutated', inplace=True)\n",
-    "titers_df['mutation'].replace('142L', 'D142L', inplace=True)\n",
-    "\n",
-    "# Plot data for a given mutation\n",
-    "validation_mutations = ['D142L', 'A419S', 'A570D', 'K854N', 'T1027I']\n",
-    "homologs = ['Delta', 'BA.1', 'BA.2']\n",
-    "replicates = ['1', '2', '3']\n",
-    "xticklabels = ['unmutated'] + validation_mutations\n",
-    "pal = sns.color_palette('colorblind')\n",
-    "hex_codes = pal.as_hex()\n",
-    "for (i, homolog) in enumerate(homologs):\n",
-    "    \n",
-    "    data = titers_df[(titers_df['homolog'] == homolog)]\n",
-    "    \n",
-    "    sns.stripplot(\n",
-    "        x='mutation', y='titer', data=data, ax=axs['titer'],\n",
-    "        order=xticklabels, s=10, alpha=0.5,\n",
-    "        hue='homolog', hue_order=['Delta', 'BA.1', 'BA.2'],\n",
-    "    )\n",
-    "    sns.boxplot(\n",
-    "        x='mutation', y='titer', data=data, ax=axs['titer'],\n",
-    "        order=xticklabels,\n",
-    "        showfliers=False, showbox=False, showcaps=False,\n",
-    "        medianprops={'visible': False}, #dict(color=hex_codes[i]),\n",
-    "        whiskerprops={'visible': False},\n",
-    "    )\n",
-    "\n",
-    "handles, labels = axs['titer'].get_legend_handles_labels()\n",
-    "by_label = dict(zip(labels, handles))\n",
-    "axs['titer'].legend(by_label.values(), by_label.keys(), bbox_to_anchor=[1,0.5])\n",
-    "\n",
-    "axs['titer'].set_yscale('log')\n",
-    "axs['titer'].set_yticks([1e2, 1e4, 1e6])\n",
-    "axs['titer'].set_xticklabels(axs['titer'].get_xticklabels(), rotation = 90)\n",
-    "axs['titer'].set_ylabel(r'viral titer (RLU/$\\mu$L)')\n",
-    "axs['titer'].set_xlabel('')\n",
-    "axs['titer'].grid()\n",
-    "sns.despine(ax = axs['titer'])\n",
-    "\n",
-    "\n",
-    "#############\n",
-    "# FOLD CHANGE\n",
-    "#############\n",
-    "\n",
-    "# Read in data\n",
-    "val_df = pd.read_csv('data/spike_validation_data.csv')\n",
-    "\n",
-    "# Restructure the data\n",
-    "val_dict = {\n",
-    "    key : []\n",
-    "    for key in [\n",
-    "        'mutation', 'fold_change', 'homolog', 'replicate',\n",
-    "        'predicted_beta'\n",
-    "    ]\n",
-    "}\n",
-    "validation_mutations = ['D142L', 'A419S', 'A570D', 'K854N', 'T1027I']\n",
-    "for i, row in val_df.iterrows():\n",
-    "    for mutation in validation_mutations:\n",
-    "        homolog = row['background'].replace('.', '')\n",
-    "        homolog = \"Omicron_\" + homolog if \"BA\" in homolog else homolog\n",
-    "\n",
-    "        val_dict['mutation'].append(mutation)\n",
-    "        val_dict['fold_change'].append(row[mutation])\n",
-    "        val_dict['homolog'].append(homolog)\n",
-    "        val_dict['replicate'].append(row['replicate'])\n",
-    "\n",
-    "        predicted_beta = float(mut_df[\n",
-    "            mut_df['mutation'] == mutation\n",
-    "        ][f'phenotypic_effect_{homolog}'].values[0])\n",
-    "        val_dict['predicted_beta'].append(predicted_beta)\n",
-    "\n",
-    "val_df = pd.DataFrame(val_dict)\n",
-    "val_df['site'] = val_df['mutation'].apply(lambda x: int(x[1:-1]))\n",
-    "val_df['homolog'].replace('Omicron_BA1', 'BA.1', inplace=True)\n",
-    "val_df['homolog'].replace('Omicron_BA2', 'BA.2', inplace=True)\n",
-    "val_df.sort_values('site', inplace=True)\n",
-    "\n",
-    "for (i, mutation) in enumerate(validation_mutations):\n",
-    "    data = val_df[val_df['mutation'] == mutation]\n",
-    "    iter_ax = axs[mutation]\n",
-    "    sns.scatterplot(\n",
-    "        x='predicted_beta', y='fold_change', data=data,\n",
-    "        hue='homolog', ax=iter_ax, s=100, alpha=0.7,\n",
-    "        hue_order=['Delta', 'BA.1', 'BA.2']\n",
-    "    )\n",
-    "    iter_ax.set(\n",
-    "        title=mutation, xlabel='', ylabel='',\n",
-    "        yscale='log', ylim=[1e-5,2], yticks=[1, 1e-2, 1e-4],\n",
-    "    )\n",
-    "    iter_ax.set_xscale('log', base=2)\n",
-    "    iter_ax.set_xlim([0.1, 1.3])\n",
-    "    iter_ax.set_xticks([2**-3, 2**-2, 2**-1, 2**0])\n",
-    "\n",
-    "    iter_ax.grid()\n",
-    "    iter_ax.get_legend().remove()\n",
-    "    sns.despine(ax=iter_ax)\n",
-    "    \n",
-    "    if mutation in ['D142L', 'A419S']:\n",
-    "        iter_ax.tick_params(axis=\"x\", labelbottom=False)\n",
-    "    \n",
-    "    if mutation not in ['D142L', 'K854N']:\n",
-    "        iter_ax.tick_params(axis=\"y\", labelleft=False)\n",
-    "\n",
-    "fig.text(\n",
-    "    0.5, 0.02, \"predicted enrichment ratio in DMS experiment \\n(mutant : unmutated)\",\n",
-    "    ha='center'\n",
-    ")\n",
-    "fig.text(\n",
-    "    0.000, 0.31, 'fold change in viral titer\\n (mutant : unmutated)',\n",
-    "    va='center', rotation='vertical'\n",
-    ")\n",
-    "axs[\"legend\"].set_axis_off()\n",
-    "\n",
-    "axs[\"titer\"].text(\n",
-    "    -0.05, 1.15, \n",
-    "    f\"A\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"titer\"].transAxes\n",
-    ")\n",
-    "axs[\"D142L\"].text(\n",
-    "    -0.1, 1.25, \n",
-    "    f\"B\", \n",
-    "    ha=\"right\", va=\"center\", \n",
-    "    size=15,\n",
-    "    weight=\"bold\",\n",
-    "    transform=axs[\"D142L\"].transAxes\n",
-    ")\n",
-    "\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d5b17fe-bfa8-4f4e-9b06-f2e704adc275",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Model-reference choice comparison\n",
-    "\n",
-    "Here, we fit each of the replicate dataset as before, but we also fit models where using Delta and BA.2 as a reference to show the model is robust to choice of reference "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "fac1a21d-bc2c-4f20-b768-2af1869af792",
-   "metadata": {
-    "editable": true,
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5554d47fbaba4d90a6f01762531cad05",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA1\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99cd8fe401d74825a6a34aeefadf500d",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA2\n"
-     ]
-    },
-    {
-     "data": {
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-       "model_id": "5f8c02a1103f42408a8915311520c401",
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-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 16 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd917d6f76294b11b24d1c0abcf586a3",
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-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9970), Label(value='0 / 9970'))), …"
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-     },
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
-      "dropping: 10983 variantswhich have mutations at those sites.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dfc94c93e7bb4fc9ac2505c9adc29690",
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-      "text/plain": [
-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9283), Label(value='0 / 9283'))), …"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [371, 376], dropping 3499 variants\n",
-      "Converting mutations for Delta\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA1\n"
-     ]
-    },
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-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=4049), Label(value='0 / 4049'))), …"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA2\n"
-     ]
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA1\n"
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-     "name": "stdout",
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-      "inferring site map for Omicron_BA2\n"
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 16 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9313), Label(value='0 / 9313'))), …"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
-      "dropping: 10129 variantswhich have mutations at those sites.\n"
-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [371], dropping 1873 variants\n",
-      "Converting mutations for Delta\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA1\n"
-     ]
-    },
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA2\n"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
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-     "output_type": "stream",
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-      "inferring site map for Omicron_BA1\n"
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-      "inferring site map for Omicron_BA2\n"
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 16 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
-      "dropping: 10983 variantswhich have mutations at those sites.\n"
-     ]
-    },
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [371], dropping 2041 variants\n",
-      "Converting mutations for Delta\n"
-     ]
-    },
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA2\n"
-     ]
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
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-    },
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-     "output_type": "stream",
-     "text": [
-      "inferring site map for Omicron_BA1\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "inferring site map for Omicron_BA2\n"
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-     "text": [
-      "INFO: Pandarallel will run on 16 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
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-     "text": [
-      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
-      "dropping: 10129 variantswhich have mutations at those sites.\n"
-     ]
-    },
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-     "text": [
-      "invalid non-identical-sites: [371], dropping 1873 variants\n",
-      "Converting mutations for Delta\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA1\n",
-      "is reference, skipping\n",
-      "Converting mutations for Omicron_BA2\n"
-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inferring site map for Delta\n"
-     ]
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-      "inferring site map for Omicron_BA1\n"
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-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 16 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
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-     "text": [
-      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
-      "dropping: 10983 variantswhich have mutations at those sites.\n"
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-     "text": [
-      "invalid non-identical-sites: [376], dropping 1486 variants\n",
-      "Converting mutations for Delta\n"
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-      "Converting mutations for Omicron_BA2\n",
-      "is reference, skipping\n",
-      "inferring site map for Delta\n"
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-      "inferring site map for Omicron_BA1\n"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Pandarallel will run on 16 workers.\n",
-      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
-      "dropping: 10129 variantswhich have mutations at those sites.\n"
-     ]
-    },
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "invalid non-identical-sites: [], dropping 0 variants\n",
-      "Converting mutations for Delta\n"
-     ]
-    },
-    {
-     "data": {
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA1\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-     "output_type": "stream",
-     "text": [
-      "Converting mutations for Omicron_BA2\n",
-      "is reference, skipping\n"
-     ]
-    }
-   ],
-   "source": [
-    "variable_reference_datasets = []\n",
-    "\n",
-    "for reference in [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]:\n",
-    "    for replicate, rep_fsdf in func_score_df.groupby(\"replicate\"):\n",
-    "        \n",
-    "        start = time.time()\n",
-    "\n",
-    "        # initialize data object\n",
-    "        data = multidms.Data(\n",
-    "            rep_fsdf,\n",
-    "            collapse_identical_variants=\"mean\",\n",
-    "            alphabet=multidms.AAS_WITHSTOP_WITHGAP,\n",
-    "            reference=reference,\n",
-    "            assert_site_integrity=False,\n",
-    "            verbose=True,\n",
-    "            nb_workers=16,\n",
-    "            name=f\"{replicate}-{reference}\"\n",
-    "        )\n",
-    "        data.condition_colors = cc\n",
-    "        variable_reference_datasets.append(data)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "2cccd793",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "variable_reference_fit_params = fit_params.copy()\n",
-    "variable_reference_fit_params[\"dataset\"] = variable_reference_datasets\n",
-    "variable_reference_fit_params[\"scale_coeff_lasso_shift\"] = [chosen_lasso_strength]\n",
-    "_, _, variable_reference_models = multidms.fit_models(variable_reference_fit_params, n_threads=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "19cfad8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "variable_reference_models = (\n",
-    "    variable_reference_models\n",
-    "    .assign(\n",
-    "        reference=lambda x: x.dataset_name.str.split(\"-\").str[-1],\n",
-    "        replicate=lambda x: x.dataset_name.str.split(\"-\").str[0]\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ad5bffb-b049-438c-bc0e-ffc77498d830",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "For each of the reference fits, compute parameters relative to a BA.1 reference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "cdd5d948-b2c8-4c23-aab5-192dc19233a8",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>beta_Omicron_BA1</th>\n",
-       "      <th>shift_Delta</th>\n",
-       "      <th>shift_Omicron_BA2</th>\n",
-       "      <th>reference</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>-3.362432</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>Delta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>0.232434</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.066369</td>\n",
-       "      <td>Delta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>0.142001</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>Delta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>0.277621</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>Delta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>-0.026383</td>\n",
-       "      <td>-0.045533</td>\n",
-       "      <td>-0.045533</td>\n",
-       "      <td>Delta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252T</th>\n",
-       "      <td>-0.155242</td>\n",
-       "      <td>-0.036178</td>\n",
-       "      <td>-0.036178</td>\n",
-       "      <td>BA2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252V</th>\n",
-       "      <td>0.166300</td>\n",
-       "      <td>0.030589</td>\n",
-       "      <td>-0.078923</td>\n",
-       "      <td>BA2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252W</th>\n",
-       "      <td>0.165440</td>\n",
-       "      <td>0.014676</td>\n",
-       "      <td>0.014676</td>\n",
-       "      <td>BA2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252Y</th>\n",
-       "      <td>0.420877</td>\n",
-       "      <td>-0.176302</td>\n",
-       "      <td>-0.121661</td>\n",
-       "      <td>BA2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252*</th>\n",
-       "      <td>-0.061469</td>\n",
-       "      <td>0.114527</td>\n",
-       "      <td>0.062062</td>\n",
-       "      <td>BA2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17804 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          beta_Omicron_BA1  shift_Delta  shift_Omicron_BA2 reference\n",
-       "mutation                                                            \n",
-       "M1I              -3.362432     0.000000           0.000000     Delta\n",
-       "F2L               0.232434     0.000000          -0.066369     Delta\n",
-       "F2S               0.142001     0.000000           0.000000     Delta\n",
-       "F2V               0.277621     0.000000           0.000000     Delta\n",
-       "V3A              -0.026383    -0.045533          -0.045533     Delta\n",
-       "...                    ...          ...                ...       ...\n",
-       "S1252T           -0.155242    -0.036178          -0.036178       BA2\n",
-       "S1252V            0.166300     0.030589          -0.078923       BA2\n",
-       "S1252W            0.165440     0.014676           0.014676       BA2\n",
-       "S1252Y            0.420877    -0.176302          -0.121661       BA2\n",
-       "S1252*           -0.061469     0.114527           0.062062       BA2\n",
-       "\n",
-       "[17804 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "relative_params = pd.DataFrame()\n",
-    "homologs = [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]\n",
-    "\n",
-    "# For each respective model fit \n",
-    "for reference, replicate_models in variable_reference_models.groupby(\"reference\"):\n",
-    "    \n",
-    "    # combine the replicate mutational \n",
-    "    mut_df = combine_replicate_muts(\n",
-    "        {\n",
-    "            f\"rep_{row.replicate}\":row[\"model\"] \n",
-    "            for idx, row in replicate_models.iterrows()\n",
-    "        },\n",
-    "        times_seen_threshold = times_seen_threshold\n",
-    "    )\n",
-    "    \n",
-    "    mut_df = mut_df.copy()[[c for c in mut_df.columns if \"avg\" in c]]\n",
-    "    \n",
-    "    # Compute mut effect (beta+shift) relative to each homolog\n",
-    "    for homolog in homologs:\n",
-    "        if homolog == reference:\n",
-    "            mut_df[f\"beta_{homolog}\"] = mut_df[\"avg_beta\"]\n",
-    "        else:\n",
-    "            mut_df[f\"beta_{homolog}\"] = mut_df[\"avg_beta\"] + mut_df[f\"avg_shift_{homolog}\"]\n",
-    "       \n",
-    "    # Compute shifts relative to BA1 (betas_h - beta_BA1)\n",
-    "    for homolog in homologs:\n",
-    "        mut_df[f\"shift_{homolog}\"] = mut_df[f\"beta_{homolog}\"] - mut_df[f\"beta_Omicron_BA1\"]\n",
-    "    \n",
-    "    # drop un-neccessary columns\n",
-    "    mut_df.drop([c for c in mut_df.columns if \"avg\" in c], axis=1, inplace=True)\n",
-    "    \n",
-    "    mut_df = mut_df.assign(reference = reference)\n",
-    "    \n",
-    "    relative_params = pd.concat([relative_params, mut_df])\n",
-    "\n",
-    "    \n",
-    "relative_params.drop([\"beta_Delta\", \"beta_Omicron_BA2\", \"shift_Omicron_BA1\"], axis=1, inplace=True)\n",
-    "relative_params.reference.replace({\"Omicron_BA2\":\"BA2\", \"Omicron_BA1\": \"BA1\"}, inplace=True)\n",
-    "relative_params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "a6e371d1-09de-4225-b503-7117c3ab5894",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x800 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas = \"reference_model_comparison_params_scatter\"\n",
-    "parameters = [\"beta_Omicron_BA1\", \"shift_Delta\", \"shift_Omicron_BA2\"]\n",
-    "\n",
-    "\n",
-    "fig = plt.figure( figsize=[6.4, 8])\n",
-    "axs = fig.subplot_mosaic(\n",
-    "    [[f\"{param}_{col}\" for col in range(3)] for param in parameters],\n",
-    "    gridspec_kw={\n",
-    "        \"wspace\": 0.20,\n",
-    "        \"hspace\": 1.0,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "param_limits = {\n",
-    "    \"beta_Omicron_BA1\" : range(-8, 2, 2),\n",
-    "    \"shift_Delta\" : range(-2, 3),\n",
-    "    \"shift_Omicron_BA2\" : range(-2, 3) \n",
-    "}\n",
-    "\n",
-    "param_titles = {\n",
-    "    \"beta_Omicron_BA1\" : r\"Mutation effect in BA.1\",\n",
-    "    \"shift_Delta\" : \"shift in Delta, relative to BA.1\",\n",
-    "    \"shift_Omicron_BA2\" : \"shift in BA.1, relative to BA.1\"\n",
-    "}\n",
-    "\n",
-    "\n",
-    "for row, param in enumerate(parameters):\n",
-    "    \n",
-    "    # pivot to each fit on columns, drop NIS.\n",
-    "    data = relative_params.pivot(\n",
-    "        columns = \"reference\",\n",
-    "        values = param\n",
-    "    ).dropna()\n",
-    "\n",
-    "    # plot each combination of fits\n",
-    "    for col, (x, y) in enumerate(combinations(data.columns, 2)):\n",
-    "        iter_ax = axs[f\"{param}_{col}\"]\n",
-    "        \n",
-    "        sns.scatterplot(\n",
-    "            data = data,\n",
-    "            x = x,\n",
-    "            y = y,\n",
-    "            ax = iter_ax,\n",
-    "            alpha=0.3,\n",
-    "            c='0.25'\n",
-    "        )\n",
-    "        \n",
-    "        corr = pearsonr(data[x], data[y])[0]**2\n",
-    "        iter_ax.annotate(\n",
-    "            f\"$R^2 = {corr:.2f}$\", \n",
-    "            (0.07, 0.8), \n",
-    "            xycoords=\"axes fraction\", \n",
-    "            fontsize=11\n",
-    "        )\n",
-    "        \n",
-    "        limits = param_limits[param]\n",
-    "        iter_ax.set_yticks(limits)\n",
-    "        iter_ax.set_xticks(limits)\n",
-    "        mmin, mmax = min(limits), max(limits)\n",
-    "        iter_ax.plot([mmin, mmax], [mmin, mmax], \"--\", lw=2, c=\"royalblue\")\n",
-    "        label_fn = lambda x: x if x == \"Delta\" else f\"{x[0]}{x[1]}.{x[2]}\"\n",
-    "        \n",
-    "        \n",
-    "        xl = f\"{label_fn(x)}\"\n",
-    "        yl = f\"{label_fn(y)}\"\n",
-    "        if col == 1: xl += \"\\nmodel fit reference\"\n",
-    "        if col == 0: yl = \"model fit reference\\n\" + yl\n",
-    "        iter_ax.set_xlabel(xl)\n",
-    "        iter_ax.set_ylabel(yl)            \n",
-    "            \n",
-    "        \n",
-    "        \n",
-    "        iter_ax.grid()\n",
-    "        sns.despine(ax = iter_ax)\n",
-    "        \n",
-    "        if col != 0:\n",
-    "            iter_ax.tick_params(\"y\", labelleft=False)\n",
-    "            \n",
-    "        if col == 1:\n",
-    "            iter_ax.set_title(param_titles[param], size=13)\n",
-    "            \n",
-    "\n",
-    "\n",
-    "for param, sub_anno in zip(parameters, [\"A\", \"B\", \"C\"]):\n",
-    "    iter_ax = axs[f\"{param}_0\"]    \n",
-    "    axs[f\"{param}_0\"].text(\n",
-    "        -0.2, 1.15, \n",
-    "        sub_anno, \n",
-    "        ha=\"right\", va=\"center\", \n",
-    "        size=15,\n",
-    "        weight=\"bold\",\n",
-    "        transform=iter_ax.transAxes\n",
-    "    )\n",
-    "\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "746aac9d-d84d-40fc-bb66-f4f3fd181a2e",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Cumulative distribution of model sparsity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "3c9dc700",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>replicate</th>\n",
-       "      <th>mutation</th>\n",
-       "      <th>sense</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>shift</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>A1015D</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.316587</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>A1015S</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>A1015T</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.117340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>A1016D</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>rep-1</td>\n",
-       "      <td>A1016S</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Delta</td>\n",
-       "      <td>-0.081105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25609</th>\n",
-       "      <td>rep-2</td>\n",
-       "      <td>Y917C</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25610</th>\n",
-       "      <td>rep-2</td>\n",
-       "      <td>Y917F</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>-0.001024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25611</th>\n",
-       "      <td>rep-2</td>\n",
-       "      <td>Y917H</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25612</th>\n",
-       "      <td>rep-2</td>\n",
-       "      <td>Y91C</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25613</th>\n",
-       "      <td>rep-2</td>\n",
-       "      <td>Y91H</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "      <td>Omicron_BA2</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>25614 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      replicate mutation          sense    condition     shift\n",
-       "0         rep-1   A1015D  nonsynonymous        Delta  0.316587\n",
-       "1         rep-1   A1015S  nonsynonymous        Delta  0.000000\n",
-       "2         rep-1   A1015T  nonsynonymous        Delta  0.117340\n",
-       "3         rep-1   A1016D  nonsynonymous        Delta  0.000000\n",
-       "4         rep-1   A1016S  nonsynonymous        Delta -0.081105\n",
-       "...         ...      ...            ...          ...       ...\n",
-       "25609     rep-2    Y917C  nonsynonymous  Omicron_BA2  0.000000\n",
-       "25610     rep-2    Y917F  nonsynonymous  Omicron_BA2 -0.001024\n",
-       "25611     rep-2    Y917H  nonsynonymous  Omicron_BA2  0.000000\n",
-       "25612     rep-2     Y91C  nonsynonymous  Omicron_BA2  0.000000\n",
-       "25613     rep-2     Y91H  nonsynonymous  Omicron_BA2  0.000000\n",
-       "\n",
-       "[25614 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tall_mut_df_chosen = (\n",
-    "    mc.split_apply_combine_muts(query=f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\", times_seen_threshold=times_seen_threshold)\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"dataset_name\":\"replicate\"})\n",
-    "    .assign(sense=lambda x: [\"stop\" if \"*\" in mut else \"nonsynonymous\" for mut in x.mutation])\n",
-    "    .melt(\n",
-    "        id_vars=[\"replicate\", \"mutation\", \"sense\"],\n",
-    "        value_vars=[\"shift_Delta\", \"shift_Omicron_BA2\"],\n",
-    "        var_name=\"condition\",\n",
-    "        value_name=\"shift\"\n",
-    "    )\n",
-    "    .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"Omicron_BA2\"})\n",
-    ")\n",
-    "tall_mut_df_chosen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "47784572-2636-4707-81ea-873a633ad642",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x350 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas = \"percent_shifts_under_x_lineplot\"\n",
-    "fig, ax = plt.subplots(1,2, figsize=[6.4,3.5], sharey='row')\n",
-    "\n",
-    "condition_col = {\n",
-    "    \"Delta\" : 0,\n",
-    "    \"Omicron_BA2\" : 1\n",
-    "}\n",
-    "\n",
-    "replicate_line_style = {\n",
-    "    \"rep-1\" : \"-\",\n",
-    "    \"rep-2\" : \"--\"\n",
-    "}\n",
-    "\n",
-    "sense_colors = {\n",
-    "    \"nonsynonymous\" : \"darkgrey\",\n",
-    "    \"stop\" : \"red\"\n",
-    "}\n",
-    "\n",
-    "t_points = np.linspace(0, 2.5, 100)\n",
-    "def perc_abs_lte(x, t):\n",
-    "    abs_x = np.abs(x)\n",
-    "    return len(abs_x[abs_x<=t]) / len(x) #)*100\n",
-    "\n",
-    "for (condition, replicate, sense), mut_df_replicates in tall_mut_df_chosen.groupby([\"condition\", \"replicate\", \"sense\"]):\n",
-    "    iter_ax = ax[condition_col[condition]]\n",
-    "    lt_percentages = [perc_abs_lte(mut_df_replicates[\"shift\"], t)*100 for t in t_points]\n",
-    "    iter_ax.plot(\n",
-    "        t_points, \n",
-    "        lt_percentages,\n",
-    "        linestyle = replicate_line_style[replicate],\n",
-    "        color = sense_colors[sense],\n",
-    "        linewidth = 3\n",
-    "    )\n",
-    "    iter_ax.set_xticks(np.linspace(0,2.5,6),np.linspace(0,2.5,6), rotation=0, ha=\"center\",size=10)\n",
-    "    sns.despine(ax=iter_ax)\n",
-    "    iter_ax.grid(visible=True)\n",
-    "    \n",
-    "ax[condition_col[\"Delta\"]].set_title(\"Delta\")\n",
-    "ax[condition_col[\"Omicron_BA2\"]].set_title(\"BA.2\")\n",
-    "\n",
-    "ax[0].set_ylabel(\"percent \\n$|\\Delta_{d,m}| <= x$\")\n",
-    "ax[0].set_xlabel(\"$x$\")\n",
-    "ax[1].set_xlabel(\"$x$\")\n",
-    "\n",
-    "black_line = mlines.Line2D([], [], color='black', linestyle='-',\n",
-    "                          markersize=5, label='rep 1')\n",
-    "black_dashed = mlines.Line2D([], [], color='black',linestyle='--',\n",
-    "                          markersize=5, label='rep 2')\n",
-    "red_line = mlines.Line2D([], [], color='red', linewidth=2,linestyle='-',markersize=5, label='stop')\n",
-    "grey_line = mlines.Line2D([], [], color='grey',linewidth=2, linestyle='-',markersize=5, label='nonsynonymous')\n",
-    "ax[1].legend(\n",
-    "    handles=[black_line, black_dashed, red_line, grey_line], \n",
-    "    bbox_to_anchor = (1, 0), \n",
-    "    loc='lower right', \n",
-    "    frameon=True, \n",
-    "    fontsize=9\n",
-    ")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "fig.subplots_adjust(wspace=0.05)\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3511314a-0ce8-478a-850e-e5f07447e7e4",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Correlation of BA2 and Delta Shifts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "4f644e9e-e4ff-4e30-a7c4-b5c3147fab9b",
-   "metadata": {
-    "editable": true,
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wts</th>\n",
-       "      <th>sites</th>\n",
-       "      <th>muts</th>\n",
-       "      <th>1_beta</th>\n",
-       "      <th>2_beta</th>\n",
-       "      <th>avg_beta</th>\n",
-       "      <th>1_shift_Delta</th>\n",
-       "      <th>2_shift_Delta</th>\n",
-       "      <th>avg_shift_Delta</th>\n",
-       "      <th>1_shift_Omicron_BA2</th>\n",
-       "      <th>2_shift_Omicron_BA2</th>\n",
-       "      <th>avg_shift_Omicron_BA2</th>\n",
-       "      <th>sense</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>M</td>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-2.924932</td>\n",
-       "      <td>-4.256726</td>\n",
-       "      <td>-3.590829</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>L</td>\n",
-       "      <td>0.200928</td>\n",
-       "      <td>0.207115</td>\n",
-       "      <td>0.204021</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.204654</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.102327</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.194773</td>\n",
-       "      <td>-0.074343</td>\n",
-       "      <td>0.060215</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.239144</td>\n",
-       "      <td>-0.030672</td>\n",
-       "      <td>0.104236</td>\n",
-       "      <td>-0.086489</td>\n",
-       "      <td>-0.153066</td>\n",
-       "      <td>-0.119778</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>V</td>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>-0.007044</td>\n",
-       "      <td>-0.047157</td>\n",
-       "      <td>-0.027101</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.002601</td>\n",
-       "      <td>-0.001301</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
-       "mutation                                                                \n",
-       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
-       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
-       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
-       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
-       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
-       "\n",
-       "          2_shift_Delta  avg_shift_Delta  1_shift_Omicron_BA2  \\\n",
-       "mutation                                                        \n",
-       "M1I            0.000000         0.000000            -0.000000   \n",
-       "F2L           -0.000000         0.000000            -0.204654   \n",
-       "F2S            0.000000         0.000000             0.000000   \n",
-       "F2V           -0.153066        -0.119778             0.000000   \n",
-       "V3A           -0.000000         0.000000            -0.000000   \n",
-       "\n",
-       "          2_shift_Omicron_BA2  avg_shift_Omicron_BA2          sense  \n",
-       "mutation                                                             \n",
-       "M1I                  0.000000               0.000000  nonsynonymous  \n",
-       "F2L                  0.000000              -0.102327  nonsynonymous  \n",
-       "F2S                 -0.000000               0.000000  nonsynonymous  \n",
-       "F2V                  0.000000               0.000000  nonsynonymous  \n",
-       "V3A                 -0.002601              -0.001301  nonsynonymous  "
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mut_df_replicates = (\n",
-    "    combine_replicate_muts(\n",
-    "        {\n",
-    "            f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
-    "            for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
-    "        },\n",
-    "        times_seen_threshold=times_seen_threshold,\n",
-    "        how=\"inner\"\n",
-    "    )\n",
-    "    .assign(\n",
-    "        sense=lambda x: [\"stop\" if \"*\" in mut else \"nonsynonymous\" for mut in x.muts]\n",
-    "    )\n",
-    ")\n",
-    "mut_df_replicates.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "c96eab17-2c6b-47aa-ad7a-00f35dfa89b4",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# data = mut_df_replicates.dropna()\n",
-    "saveas=\"shift_corr_Delta_BA2\"\n",
-    "fig, ax = plt.subplots(1,figsize=[4,4])\n",
-    "lim = [-1.6, 2.6]\n",
-    "ticks = range(-1, 2)\n",
-    "sns.scatterplot(\n",
-    "    data=mut_df_replicates,\n",
-    "    x=\"avg_shift_Delta\",\n",
-    "    y=\"avg_shift_Omicron_BA2\",\n",
-    "#     hue = \"sense\",\n",
-    "    alpha = 0.35,\n",
-    "    ax=ax,\n",
-    "    c='0.25'\n",
-    ")\n",
-    "ax.plot()\n",
-    "ax.plot(\n",
-    "    [-1.5, 2.5], \n",
-    "    [-1.5, 2.5],\n",
-    "    linestyle=\"--\", \n",
-    "    lw=2,\n",
-    "    c='royalblue'\n",
-    ")\n",
-    "corr = pearsonr(mut_df_replicates[\"avg_shift_Delta\"], mut_df_replicates[\"avg_shift_Omicron_BA2\"])[0]**2\n",
-    "ax.annotate(\n",
-    "    f\"$R^2 = {corr:.2f}$\", \n",
-    "    (0.05, 0.8), \n",
-    "    xycoords=\"axes fraction\", \n",
-    "    fontsize=12\n",
-    ")\n",
-    "ax.set_ylim(lim)\n",
-    "ax.set_xlim(lim)\n",
-    "ax.set_yticks(ticks, labels=ticks)\n",
-    "ax.set_xticks(ticks, labels=ticks)\n",
-    "ax.set_ylabel(\"BA.2 shift ($\\Delta_{d,m}$)\")\n",
-    "ax.set_xlabel(\"Delta shift ($\\Delta_{d,m}$)\")\n",
-    "# ax.set(xticks=np.linspace(-1.5, 2.5, 5))\n",
-    "ax.grid()\n",
-    "sns.despine(ax=ax)\n",
-    "\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31de53e4-f1d9-4e7f-9cf4-a12c6c420e66",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## Structural Analysis\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "95ae0f57-512a-4478-8cc5-131f624db3b1",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# mut_df_replicates = pd.read_csv(f\"{output_dir}/mutations_df.csv\")\n",
-    "mut_df_replicates.rename(\n",
-    "    columns={\n",
-    "        col : col.replace('Omicron_BA2', 'BA2')\n",
-    "        for col in mut_df_replicates\n",
-    "    }, \n",
-    "    inplace=True\n",
-    ")\n",
-    "\n",
-    "# mut_df_replicates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "e244ce31",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wts</th>\n",
-       "      <th>sites</th>\n",
-       "      <th>muts</th>\n",
-       "      <th>1_beta</th>\n",
-       "      <th>2_beta</th>\n",
-       "      <th>avg_beta</th>\n",
-       "      <th>1_shift_Delta</th>\n",
-       "      <th>2_shift_Delta</th>\n",
-       "      <th>avg_shift_Delta</th>\n",
-       "      <th>1_shift_BA2</th>\n",
-       "      <th>2_shift_BA2</th>\n",
-       "      <th>avg_shift_BA2</th>\n",
-       "      <th>sense</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mutation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>M1I</th>\n",
-       "      <td>M</td>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-2.924932</td>\n",
-       "      <td>-4.256726</td>\n",
-       "      <td>-3.590829</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2L</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>L</td>\n",
-       "      <td>0.200928</td>\n",
-       "      <td>0.207115</td>\n",
-       "      <td>0.204021</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.204654</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.102327</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2S</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0.194773</td>\n",
-       "      <td>-0.074343</td>\n",
-       "      <td>0.060215</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F2V</th>\n",
-       "      <td>F</td>\n",
-       "      <td>2</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.239144</td>\n",
-       "      <td>-0.030672</td>\n",
-       "      <td>0.104236</td>\n",
-       "      <td>-0.086489</td>\n",
-       "      <td>-0.153066</td>\n",
-       "      <td>-0.119778</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V3A</th>\n",
-       "      <td>V</td>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>-0.007044</td>\n",
-       "      <td>-0.047157</td>\n",
-       "      <td>-0.027101</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.002601</td>\n",
-       "      <td>-0.001301</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252T</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>T</td>\n",
-       "      <td>-0.132241</td>\n",
-       "      <td>-0.189524</td>\n",
-       "      <td>-0.160882</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.074971</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.037485</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252V</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>V</td>\n",
-       "      <td>0.161672</td>\n",
-       "      <td>0.177089</td>\n",
-       "      <td>0.169381</td>\n",
-       "      <td>0.262923</td>\n",
-       "      <td>-0.185348</td>\n",
-       "      <td>0.038788</td>\n",
-       "      <td>-0.044192</td>\n",
-       "      <td>-0.117804</td>\n",
-       "      <td>-0.080998</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252W</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>W</td>\n",
-       "      <td>0.046494</td>\n",
-       "      <td>0.283281</td>\n",
-       "      <td>0.164887</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.018787</td>\n",
-       "      <td>-0.050473</td>\n",
-       "      <td>-0.015843</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252Y</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>0.349203</td>\n",
-       "      <td>0.464681</td>\n",
-       "      <td>0.406942</td>\n",
-       "      <td>-0.103307</td>\n",
-       "      <td>-0.228062</td>\n",
-       "      <td>-0.165685</td>\n",
-       "      <td>-0.029801</td>\n",
-       "      <td>-0.184271</td>\n",
-       "      <td>-0.107036</td>\n",
-       "      <td>nonsynonymous</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1252*</th>\n",
-       "      <td>S</td>\n",
-       "      <td>1252</td>\n",
-       "      <td>*</td>\n",
-       "      <td>-0.069944</td>\n",
-       "      <td>-0.002437</td>\n",
-       "      <td>-0.036191</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.071298</td>\n",
-       "      <td>0.035649</td>\n",
-       "      <td>0.113153</td>\n",
-       "      <td>-0.071614</td>\n",
-       "      <td>0.020770</td>\n",
-       "      <td>stop</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5934 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
-       "mutation                                                                \n",
-       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
-       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
-       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
-       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
-       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
-       "...       ..    ...  ...       ...       ...       ...            ...   \n",
-       "S1252T     S   1252    T -0.132241 -0.189524 -0.160882      -0.000000   \n",
-       "S1252V     S   1252    V  0.161672  0.177089  0.169381       0.262923   \n",
-       "S1252W     S   1252    W  0.046494  0.283281  0.164887       0.000000   \n",
-       "S1252Y     S   1252    Y  0.349203  0.464681  0.406942      -0.103307   \n",
-       "S1252*     S   1252    * -0.069944 -0.002437 -0.036191       0.000000   \n",
-       "\n",
-       "          2_shift_Delta  avg_shift_Delta  1_shift_BA2  2_shift_BA2  \\\n",
-       "mutation                                                             \n",
-       "M1I            0.000000         0.000000    -0.000000     0.000000   \n",
-       "F2L           -0.000000         0.000000    -0.204654     0.000000   \n",
-       "F2S            0.000000         0.000000     0.000000    -0.000000   \n",
-       "F2V           -0.153066        -0.119778     0.000000     0.000000   \n",
-       "V3A           -0.000000         0.000000    -0.000000    -0.002601   \n",
-       "...                 ...              ...          ...          ...   \n",
-       "S1252T         0.000000         0.000000    -0.074971    -0.000000   \n",
-       "S1252V        -0.185348         0.038788    -0.044192    -0.117804   \n",
-       "S1252W         0.000000         0.000000     0.018787    -0.050473   \n",
-       "S1252Y        -0.228062        -0.165685    -0.029801    -0.184271   \n",
-       "S1252*         0.071298         0.035649     0.113153    -0.071614   \n",
-       "\n",
-       "          avg_shift_BA2          sense  \n",
-       "mutation                                \n",
-       "M1I            0.000000  nonsynonymous  \n",
-       "F2L           -0.102327  nonsynonymous  \n",
-       "F2S            0.000000  nonsynonymous  \n",
-       "F2V            0.000000  nonsynonymous  \n",
-       "V3A           -0.001301  nonsynonymous  \n",
-       "...                 ...            ...  \n",
-       "S1252T        -0.037485  nonsynonymous  \n",
-       "S1252V        -0.080998  nonsynonymous  \n",
-       "S1252W        -0.015843  nonsynonymous  \n",
-       "S1252Y        -0.107036  nonsynonymous  \n",
-       "S1252*         0.020770           stop  \n",
-       "\n",
-       "[5934 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mut_df_replicates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5467b38-01a6-400c-ab43-ba8c572284f0",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Compute site-wise summary statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "cf871238-2934-4284-ae88-50f0f0eebc02",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sites</th>\n",
-       "      <th>max_abs_S_BA2</th>\n",
-       "      <th>max_abs_S_Delta</th>\n",
-       "      <th>mean_abs_S_BA2</th>\n",
-       "      <th>mean_abs_S_Delta</th>\n",
-       "      <th>mean_S_BA2</th>\n",
-       "      <th>mean_S_Delta</th>\n",
-       "      <th>sum_S_BA2</th>\n",
-       "      <th>sum_S_Delta</th>\n",
-       "      <th>res_n</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0.102327</td>\n",
-       "      <td>0.119778</td>\n",
-       "      <td>0.034109</td>\n",
-       "      <td>0.039926</td>\n",
-       "      <td>-0.034109</td>\n",
-       "      <td>-0.039926</td>\n",
-       "      <td>-0.102327</td>\n",
-       "      <td>-0.119778</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0.115220</td>\n",
-       "      <td>0.104436</td>\n",
-       "      <td>0.042435</td>\n",
-       "      <td>0.017570</td>\n",
-       "      <td>0.033281</td>\n",
-       "      <td>0.017242</td>\n",
-       "      <td>0.199683</td>\n",
-       "      <td>0.103452</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0.060115</td>\n",
-       "      <td>0.107191</td>\n",
-       "      <td>0.023524</td>\n",
-       "      <td>0.052339</td>\n",
-       "      <td>-0.023524</td>\n",
-       "      <td>-0.019122</td>\n",
-       "      <td>-0.070573</td>\n",
-       "      <td>-0.057365</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0.686307</td>\n",
-       "      <td>0.291834</td>\n",
-       "      <td>0.089656</td>\n",
-       "      <td>0.090932</td>\n",
-       "      <td>0.041643</td>\n",
-       "      <td>-0.086132</td>\n",
-       "      <td>0.707937</td>\n",
-       "      <td>-1.464238</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sites  max_abs_S_BA2  max_abs_S_Delta  mean_abs_S_BA2  mean_abs_S_Delta  \\\n",
-       "0      1       0.000000         0.000000        0.000000          0.000000   \n",
-       "1      2       0.102327         0.119778        0.034109          0.039926   \n",
-       "2      3       0.115220         0.104436        0.042435          0.017570   \n",
-       "3      4       0.060115         0.107191        0.023524          0.052339   \n",
-       "4      5       0.686307         0.291834        0.089656          0.090932   \n",
-       "\n",
-       "   mean_S_BA2  mean_S_Delta  sum_S_BA2  sum_S_Delta  res_n  \n",
-       "0    0.000000      0.000000   0.000000     0.000000      1  \n",
-       "1   -0.034109     -0.039926  -0.102327    -0.119778      2  \n",
-       "2    0.033281      0.017242   0.199683     0.103452      3  \n",
-       "3   -0.023524     -0.019122  -0.070573    -0.057365      4  \n",
-       "4    0.041643     -0.086132   0.707937    -1.464238      5  "
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols_to_collapse = [col for col in mut_df_replicates.columns if \"avg_\" in col]\n",
-    "cols_to_collapse.extend(['sites']) #, 'BA1_wt', 'BA2_wt', 'Delta_wt', 'is_BA2_wt', 'is_Delta_wt'])\n",
-    "shifts_by_site = mut_df_replicates[cols_to_collapse].groupby(\"sites\").agg(\n",
-    "    max_abs_S_BA2 = ('avg_shift_BA2', lambda x: np.max(np.abs(x))),\n",
-    "    max_abs_S_Delta = ('avg_shift_Delta', lambda x: np.max(np.abs(x))),\n",
-    "    \n",
-    "    mean_abs_S_BA2 = ('avg_shift_BA2', lambda x: np.mean(np.abs(x))),\n",
-    "    mean_abs_S_Delta = ('avg_shift_Delta', lambda x: np.mean(np.abs(x))),\n",
-    "    \n",
-    "    mean_S_BA2 = ('avg_shift_BA2', np.mean),\n",
-    "    mean_S_Delta = ('avg_shift_Delta', np.mean),\n",
-    "    \n",
-    "    sum_S_BA2 = ('avg_shift_BA2',  np.sum),\n",
-    "    sum_S_Delta = ('avg_shift_Delta', np.sum),\n",
-    ")\n",
-    "\n",
-    "shifts_by_site.reset_index(inplace=True)\n",
-    "shifts_by_site[\"res_n\"] = shifts_by_site[\"sites\"].astype(int)\n",
-    "shifts_by_site.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d4e47b3-1bf4-4f64-846d-6fd4a36d7e9e",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Read alignment of homologs, identify nonidentical sites in Delta and BA.2 relative to BA.1, using WH1 as the numbering reference, and add to data frame with site-level data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "d67ac38a-d4f8-4229-ad8d-40dafe70e156",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Delta\n",
-      "n_diffs 43 0.9657\n",
-      "BA.2\n",
-      "n_diffs 27 0.9785\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Read in alignment\n",
-    "alignment_file = 'data/clustalo-I20230702-193723-0021-19090519-p1m.clustal_num'\n",
-    "align_dict = {}\n",
-    "with open(alignment_file) as handle:\n",
-    "    for record in SeqIO.parse(handle, \"clustal\"):\n",
-    "        align_dict[record.id] = record.seq\n",
-    "        \n",
-    "# Identify non-identical sites realtive to BA.1\n",
-    "ref_seq = align_dict['WH1_QHD43416.1']\n",
-    "homologs = ['Delta', 'BA.2']\n",
-    "align_nis_dict = {\n",
-    "    homolog : []\n",
-    "    for homolog in homologs\n",
-    "}\n",
-    "align_gaps_dict = {\n",
-    "    homolog : []\n",
-    "    for homolog in homologs\n",
-    "}\n",
-    "for homolog in homologs:\n",
-    "    print(homolog)\n",
-    "    ref_n = 1\n",
-    "    n_diffs = 0\n",
-    "    for (n, (ref, i, j)) in enumerate(zip(ref_seq, align_dict['BA.1'], align_dict[homolog])):\n",
-    "        if i != j:\n",
-    "            \n",
-    "            # If the mut is an indel, then record it in the gap dict\n",
-    "            if (i == '-' or j == '-'):\n",
-    "                if True: # ref_n not in align_gaps_dict[homolog]:\n",
-    "                    align_gaps_dict[homolog].append(ref_n)\n",
-    "                pass # continue\n",
-    "            \n",
-    "            # Record the mut in the nis dict\n",
-    "            #print(ref_n, i, j)\n",
-    "            align_nis_dict[homolog].append(ref_n)\n",
-    "            n_diffs += 1\n",
-    "        if ref != '-':\n",
-    "            ref_n += 1\n",
-    "    print('n_diffs', n_diffs, 1-round(n_diffs/1254, 4))\n",
-    "    \n",
-    "# Add columns to shifts_by_site that indicate if a site is nonidentical\n",
-    "for homolog in ['Delta', 'BA.2']:\n",
-    "    hn = homolog.replace('.', '')\n",
-    "    shifts_by_site[f\"is_{hn}_nis\"] = shifts_by_site['res_n'].apply(lambda x: x in align_nis_dict[homolog])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "842938aa-3c91-4a23-b611-f0ce4578f213",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Define function for getting distance matrix from structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "28974a14-fead-4750-86b0-a85fdc649cef",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "def get_distance_matrix(structure, d_metric='interatomic'):\n",
-    "\n",
-    "    # Get a list of residues, ignorning heteroatoms\n",
-    "    residues = [r for r in structure.get_residues() if r.get_id()[0] == \" \"]\n",
-    "    res_ns = []\n",
-    "    res_ids = []\n",
-    "    for res in residues:\n",
-    "        full_id = res.get_full_id()\n",
-    "        chain = full_id[2]\n",
-    "        res_n = ''.join(map(str, full_id[3][1:])).strip()\n",
-    "        if (res_n not in res_ns) and chain == 'A':\n",
-    "            res_ns.append(res_n)\n",
-    "        res_ids.append(f'{chain}_{res_n}')\n",
-    "        \n",
-    "    # Compute a distance matrix between all pairs of C-alpha carbons\n",
-    "    dist_dict = {\n",
-    "        key : []\n",
-    "        for key in ['res_n', 'res_id'] + res_ids\n",
-    "    }\n",
-    "    for res_i in residues:\n",
-    "        full_id_i = res_i.get_full_id()\n",
-    "        chain_i = full_id_i[2]\n",
-    "        res_n_i = ''.join(map(str, full_id_i[3][1:])).strip()\n",
-    "        dist_dict['res_n'].append(res_n_i)\n",
-    "        dist_dict['res_id'].append(f'{chain_i}_{res_n_i}')\n",
-    "        \n",
-    "        #print(res_n_i)\n",
-    "        \n",
-    "        for res_j in residues:\n",
-    "            full_id_j = res_j.get_full_id()\n",
-    "            chain_j = full_id_j[2]\n",
-    "            res_n_j = ''.join(map(str, full_id_j[3][1:])).strip()\n",
-    "            \n",
-    "            if d_metric == 'CA':\n",
-    "                xyz_i = res_i[\"CA\"].get_coord()\n",
-    "                xyz_j = res_j[\"CA\"].get_coord()\n",
-    "                d = np.linalg.norm(xyz_j-xyz_i)\n",
-    "\n",
-    "            elif d_metric == 'interatomic':\n",
-    "                interatomic_dists = []\n",
-    "                for atom_i in res_i:\n",
-    "                    for atom_j in res_j:\n",
-    "                        interatomic_dists.append(atom_i-atom_j)\n",
-    "                d = min(interatomic_dists)\n",
-    "            \n",
-    "            else:\n",
-    "                raise ValueError(f'{d_metric} not recognized')\n",
-    "            \n",
-    "            dist_dict[f'{chain_j}_{res_n_j}'].append(d)\n",
-    "    dist_df = pd.DataFrame(dist_dict)\n",
-    "    \n",
-    "    return dist_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62809f1c-a9fe-41ef-8345-32fb378e1647",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "For each input PDB, compute a distance matrix that quantifies the minimum inter-atomic distance between all atom pairs from a given pair of residues. Since this takes a long time, save the results to a file and only rerun if the file doesn't exist."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "0e4f24e2-f119-49b5-a7a8-1c8b03533197",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7tf8\n",
-      "Downloading PDB structure '7tf8'...\n",
-      "Writing distance matrix for 7tf8 to results/spike_analysis/7tf8_dist_matrix.csv\n",
-      "7tl9\n",
-      "Downloading PDB structure '7tl9'...\n",
-      "Writing distance matrix for 7tl9 to results/spike_analysis/7tl9_dist_matrix.csv\n",
-      "7tge\n",
-      "Downloading PDB structure '7tge'...\n",
-      "Writing distance matrix for 7tge to results/spike_analysis/7tge_dist_matrix.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Make a list of PDBs to analyze, one per spike conformation\n",
-    "pdbs = ['7tf8', '7tl9', '7tge']\n",
-    "\n",
-    "# Loop over PDBs and compute distance matrices for each\n",
-    "dfs = []\n",
-    "for pdb in pdbs:\n",
-    "\n",
-    "    # Download structure\n",
-    "    print(pdb)\n",
-    "    pdbl = PDBList()\n",
-    "    pdbl.retrieve_pdb_file(pdb, pdir='results/', file_format='pdb')\n",
-    "    shutil.copy(f'results/pdb{pdb}.ent', f'results/pdb{pdb}.pdb')\n",
-    "\n",
-    "    # Read in structure\n",
-    "    parser = PDBParser()\n",
-    "    structure = parser.get_structure(pdb, f'results/pdb{pdb}.ent')\n",
-    "    \n",
-    "    # Compute distance matrix and write to output file\n",
-    "    output_file = os.path.join(output_dir, f'{pdb}_dist_matrix.csv')\n",
-    "    if not os.path.isfile(output_file):\n",
-    "        mut_df_replicates = get_distance_matrix(structure, d_metric='interatomic')\n",
-    "        print(f'Writing distance matrix for {pdb} to {output_file}')\n",
-    "        mut_df_replicates.to_csv(output_file, index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61b2fe9e-baca-41fe-b663-a78bd323e3c1",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Read in the distance matrices generated above. Then make an NxN matrix with the minimum distance between each pair of residues, considering all instances of a residue in a given trimer, and across all conformations being analyzed. Finally, Compute summary statistics quantifying the level of shifts in a neighborhood around a residue and compute the nearest distance to a non-identical site."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "803f763f-bd3a-48d7-9150-7e6ffa4162e2",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1050 1050\n",
-      "1224 1050 1033\n",
-      "Using a neighbor distance cutoff of 5 Angstroms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Read in data\n",
-    "dfs = []\n",
-    "for pdb in pdbs:\n",
-    "    output_file = os.path.join(output_dir, f'{pdb}_dist_matrix.csv')\n",
-    "    mut_df_replicates = pd.read_csv(output_file)\n",
-    "    dfs.append(mut_df_replicates)\n",
-    "dist_df = pd.concat(dfs)\n",
-    "\n",
-    "# Ignore subset of sites\n",
-    "dist_df = dist_df[\n",
-    "    ~dist_df['res_n'].isin(['214A', '214B', '214C'])\n",
-    "].copy()\n",
-    "dist_df['res_n'] = dist_df['res_n'].astype(int)\n",
-    "\n",
-    "# First, collapse rows by site (ignoring chain),\n",
-    "# taking the min value across common entries\n",
-    "min_dist_df = dist_df.groupby('res_n').min()\n",
-    "min_dist_df.index.name = None\n",
-    "del min_dist_df['res_id']\n",
-    "\n",
-    "# Then, transpose the dataframe and do that same\n",
-    "# as above, ignoring the same subset as above\n",
-    "min_dist_df = min_dist_df.T\n",
-    "min_dist_df['res_n'] = min_dist_df.apply(\n",
-    "    lambda row: row.name[2:],\n",
-    "    axis=1\n",
-    ")\n",
-    "min_dist_df = min_dist_df[\n",
-    "    ~min_dist_df['res_n'].isin(['214A', '214B', '214C'])\n",
-    "].copy()\n",
-    "min_dist_df['res_n'] = min_dist_df['res_n'].astype(int)\n",
-    "\n",
-    "min_dist_df = min_dist_df.groupby('res_n').min()\n",
-    "print(len(min_dist_df.columns.values), len(min_dist_df.index))\n",
-    "res_ns = sorted(min_dist_df.index.unique())\n",
-    "# Merge per-site data with distance matrix\n",
-    "data = shifts_by_site.merge(\n",
-    "    min_dist_df, on='res_n', how='inner'\n",
-    ")\n",
-    "print(len(shifts_by_site), len(min_dist_df), len(data))\n",
-    "\n",
-    "# For each site, compute the average metric among\n",
-    "# all neighbors\n",
-    "metric_prefix =\"max_abs_S\"\n",
-    "nbr_dist_cutoff = 5\n",
-    "print(f'Using a neighbor distance cutoff of {nbr_dist_cutoff} Angstroms')\n",
-    "metrics = [f'{metric_prefix}_{h}' for h in ['Delta', 'BA2']]\n",
-    "nbr_score_dict = {\n",
-    "    key : []\n",
-    "    for key in ['res_n', 'n_nbrs'] + [f'nbr_{metric}' for metric in metrics]\n",
-    "}\n",
-    "for res_n in list(set(res_ns)):\n",
-    "    nbr_data = data[\n",
-    "        (data[res_n] < nbr_dist_cutoff) &\n",
-    "        (data['res_n'] != res_n)\n",
-    "    ]\n",
-    "    nbr_score_dict['res_n'].append(res_n)\n",
-    "    nbr_score_dict['n_nbrs'].append(len(nbr_data))\n",
-    "    for metric in metrics:\n",
-    "        nbr_score_dict[f'nbr_{metric}'].append(nbr_data[metric].mean())\n",
-    "\n",
-    "nbr_score_df = pd.DataFrame(nbr_score_dict)\n",
-    "nbr_score_df = nbr_score_df.merge(data, on='res_n')\n",
-    "\n",
-    "for homolog in ['BA2', 'Delta']:\n",
-    "    \n",
-    "    # Get a list of non-identical sites\n",
-    "    nis = nbr_score_df.query(f\"is_{homolog}_nis == True\")[\"res_n\"]\n",
-    "    \n",
-    "    # Add a column that gives the minimum distance to non-identical\n",
-    "    # sites for a given homolog, not counting the query site, which\n",
-    "    # we achieve by ignoring entries of 0 in the distance matrix\n",
-    "    nbr_score_df[f\"dist_nearest_{homolog}_nis\"] = nbr_score_df.replace(0, np.nan)[nis].min(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "495843e2-8df7-42f4-af19-fc7e54c2e459",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "Make plots analyzing the distribution shifts on the structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "4e2443a8-ebb0-49b5-b285-25d6d35405f8",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saveas=f\"structural_statistics_{metric_prefix}\"\n",
-    "\n",
-    "fig = plt.figure(figsize=[4, 5])\n",
-    "axs = fig.subplot_mosaic(\n",
-    "    \"\"\"\n",
-    "    bbbBBB\n",
-    "    cccCCC\n",
-    "    \"\"\",\n",
-    "    gridspec_kw={\n",
-    "        \"hspace\": 0.75,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "for homolog, iter_ax in zip([\"Delta\", \"BA2\"], [\"b\", \"B\"]):\n",
-    "\n",
-    "    sns.regplot(\n",
-    "        data = nbr_score_df,\n",
-    "        y = f\"{metric_prefix}_{homolog}\",\n",
-    "        x = f\"nbr_{metric_prefix}_{homolog}\",\n",
-    "        ax=axs[iter_ax],\n",
-    "        scatter_kws = {\n",
-    "            \"color\":'royalblue' if homolog == \"Delta\" else \"magenta\",\n",
-    "            \"s\":35,\n",
-    "            \"alpha\":0.2,\n",
-    "        },\n",
-    "        line_kws={\"color\": \"0.25\"}\n",
-    "    )\n",
-    "\n",
-    "    corr, p = scipy.stats.pearsonr(\n",
-    "        nbr_score_df[f\"nbr_{metric_prefix}_{homolog}\"], \n",
-    "        nbr_score_df[f\"{metric_prefix}_{homolog}\"]\n",
-    "    )\n",
-    "    p = round(p, 3)\n",
-    "    p = f\"p={p}\" if p != 0 else \"p<0.001\"\n",
-    "    axs[iter_ax].annotate(\n",
-    "        f\"$r = {corr:.2f}$\\n${p}$\", \n",
-    "        (0.55, 0.8), \n",
-    "        xycoords=\"axes fraction\", \n",
-    "        fontsize=11\n",
-    "    )\n",
-    "    \n",
-    "    axs[iter_ax].set_ylim([-0.2, 2.5])\n",
-    "    axs[iter_ax].set_yticks([0, 1,2], labels = [0, 1,2])\n",
-    "    axs[iter_ax].set_xlim([-0.2, 1.52])\n",
-    "    axs[iter_ax].set_xticks([0, 0.5, 1.0, 1.5], labels = [0, 0.5, 1.0, 1.5])\n",
-    "    axs[iter_ax].set_title(homolog+\"\\n\" if homolog == \"Delta\" else \"BA.2\\n\")\n",
-    "    \n",
-    "    if homolog == \"Delta\":\n",
-    "        xlabel=f'{metric_prefix} \\naveraged across neighboring sites'\n",
-    "        xlabel=f'max. abs. shift \\naveraged across neighboring sites'\n",
-    "        axs[iter_ax].set_xlabel(xlabel)\n",
-    "        axs[iter_ax].xaxis.set_label_coords(1.0, -0.22)\n",
-    "        axs[iter_ax].set_ylabel(\"max. abs. shift\")\n",
-    "    else:\n",
-    "        axs[iter_ax].set_yticklabels([])\n",
-    "        axs[iter_ax].set_xlabel(None)\n",
-    "        axs[iter_ax].set_ylabel(None)\n",
-    "        \n",
-    "    sns.despine(ax=axs[iter_ax])\n",
-    "    axs[iter_ax].grid()\n",
-    "\n",
-    "for homolog, iter_ax in zip([\"Delta\", \"BA2\"], [\"c\", \"C\"]):\n",
-    "    \n",
-    "    data = nbr_score_df.query(f\"dist_nearest_{homolog}_nis != 0\").copy()\n",
-    "    data[f\"dist_nearest_{homolog}_nis\"].clip(upper=50, inplace=True)\n",
-    "    sns.regplot(\n",
-    "        data = data,\n",
-    "        y = f\"{metric_prefix}_{homolog}\",\n",
-    "        x = f\"dist_nearest_{homolog}_nis\",\n",
-    "        ax=axs[iter_ax],\n",
-    "        scatter_kws = {\n",
-    "            \"color\":'royalblue' if homolog == \"Delta\" else \"magenta\",\n",
-    "            \"s\":35,\n",
-    "            \"alpha\":0.2,\n",
-    "        },\n",
-    "        line_kws={\"color\": \"0.25\"}\n",
-    "    )\n",
-    "    corr, p = scipy.stats.pearsonr(\n",
-    "        nbr_score_df.query(f\"dist_nearest_{homolog}_nis != 0\")[f\"dist_nearest_{homolog}_nis\"], \n",
-    "        nbr_score_df.query(f\"dist_nearest_{homolog}_nis != 0\")[f\"{metric_prefix}_{homolog}\"]\n",
-    "    )\n",
-    "    p = round(p, 2)\n",
-    "    p = f\"p={p}\" if p != 0 else \"p<0.001\"\n",
-    "    axs[iter_ax].annotate(\n",
-    "        f\"$r={corr:.2f}$\\n${p}$\", \n",
-    "        (0.55, 0.8),\n",
-    "        xycoords=\"axes fraction\", \n",
-    "        fontsize=11\n",
-    "    )\n",
-    "\n",
-    "    axs[iter_ax].set_ylim([-0.2, 2.5])\n",
-    "    axs[iter_ax].set_yticks([0, 1,2], labels = [0, 1,2])\n",
-    "    \n",
-    "    axs[iter_ax].set_xlim([-1, 55])\n",
-    "    axs[iter_ax].set_xticks([0, 25, 50], labels = [0, 25, 50])\n",
-    "    \n",
-    "    \n",
-    "    if homolog == \"Delta\":\n",
-    "        xlabel = 'distance to nearest non-identical site ($\\AA$)'\n",
-    "        axs[iter_ax].set_xlabel(xlabel)\n",
-    "        axs[iter_ax].xaxis.set_label_coords(1.0, -0.18)\n",
-    "        axs[iter_ax].set_ylabel(metric_prefix)\n",
-    "        axs[iter_ax].set_ylabel(\"max. abs. shift\")\n",
-    "        \n",
-    "    else:\n",
-    "        axs[iter_ax].set_yticklabels([])\n",
-    "        axs[iter_ax].set_xlabel(None)\n",
-    "        axs[iter_ax].set_ylabel(None)\n",
-    "        \n",
-    "    sns.despine(ax=axs[iter_ax])\n",
-    "    axs[iter_ax].grid()\n",
-    "\n",
-    "    \n",
-    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
-    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  }
- ],
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