From 696fb8373f9f5c8a67fab01ff1243836c5b55c52 Mon Sep 17 00:00:00 2001
From: Jerome Kelleher <jk@well.ox.ac.uk>
Date: Thu, 24 Oct 2024 16:37:07 +0100
Subject: [PATCH] Add notebook for SARS-CoV-2

Update the notebook to compute diversity

Remove unused stuff
---
 notebooks/SARS-Cov-2.ipynb | 1579 ++++++++++++++++++++++++++++++++++++
 1 file changed, 1579 insertions(+)
 create mode 100644 notebooks/SARS-Cov-2.ipynb

diff --git a/notebooks/SARS-Cov-2.ipynb b/notebooks/SARS-Cov-2.ipynb
new file mode 100644
index 0000000..4a59965
--- /dev/null
+++ b/notebooks/SARS-Cov-2.ipynb
@@ -0,0 +1,1579 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4bd49be1-d0df-43e2-a132-988052c99ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "import collections\n",
+    "import zarr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm.notebook import tqdm\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baab8865-7b5c-49e3-bc92-29b9415cf944",
+   "metadata": {},
+   "source": [
+    "## Top level stats\n",
+    "\n",
+    "The dataset consists of 3,960,704 sequences of length 29903 base pairs along with 31 metadata columns encoding information about those samples. This full dataset is stored in a single 345MiB zip file, 241MiB of which is used by the alignment data. This gives an average compression ratio of 468X."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7b68b446-5b55-4073-8855-44163fd65e6e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45dc21cbbbc644bdbf166c62f9d1ec94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tree(nodes=(Node(disabled=True, name='/', nodes=(Node(disabled=True, icon='table', name='call_genotype (29903,…"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_path = \"../real_data/viridian_2024-04-29.zarr.zip\"\n",
+    "store = zarr.ZipStore(data_path)\n",
+    "root = zarr.open(store, mode=\"r\")\n",
+    "root.tree()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9324e8e1-b6d8-42ff-a8c7-86b3f3cc18cf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 jk jk 345M Nov 28 13:30 ../real_data/viridian_2024-04-29.zarr.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ls -lh ../real_data/viridian_2024-04-29.zarr.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "6998c34f-8f06-4f54-9606-5a5332dbc52a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_metadata_cols = len([k for k in root.keys() if k.startswith(\"sample_\")]) - 1\n",
+    "num_metadata_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d8113bd7-cef9-4d2d-a40e-cf9e2e16b921",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(29903, 3960704, 1)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "root[\"call_genotype\"].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab82a916-d65c-470e-93f2-7e5d37883d3e",
+   "metadata": {},
+   "source": [
+    "# Plot chunk size over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b41ac6ca-7eda-4340-ba2e-190e2a85e248",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = zipfile.ZipFile(data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "018d3904-75c7-4569-8bf8-16e36b221361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_chunk_sizes = collections.Counter()\n",
+    "for zi in z.infolist():\n",
+    "    if zi.filename.startswith(\"call_genotype\"):\n",
+    "        indexes = list(zi.filename.split(\"/\"))\n",
+    "        if len(indexes) == 4 and len(indexes[-1]) > 0:\n",
+    "            sample_chunk = int(indexes[2])\n",
+    "            sample_chunk_sizes[sample_chunk] += zi.file_size\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "424a08a6-93d0-483d-be1a-0e992a82b3c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "299030000"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute uncompressed size of a full sample chunk\n",
+    "call_genotype_array = root[\"call_genotype\"]\n",
+    "uncompressed_chunk_size = call_genotype_array.blocks[:, 0].nbytes\n",
+    "uncompressed_chunk_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2f5a87cd-18cd-4a3d-aa7a-ccc9a51ca18f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "252887096"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_genotypes_bytes = sum(sample_chunk_sizes.values())\n",
+    "total_genotypes_bytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a6dbd40-a9b1-4344-a91b-6a0b9b1905b2",
+   "metadata": {},
+   "source": [
+    "### Overal size\n",
+    "\n",
+    "Get the overall size of the genotypes from the zip and compression ratio:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "e2245b95-4bc6-4d4d-beb7-3ca590bf4b34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'241.2 MiB'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import humanize\n",
+    "humanize.naturalsize(total_genotypes_bytes, binary=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "7f1d1258-d721-460f-9dc6-71d8940a0dcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "468.3391663131756"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "call_genotype_array.nbytes / total_genotypes_bytes "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b237b2ea-82de-4af8-94fd-1a81ca249327",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute the midpoint date from each sample chunk\n",
+    "a = root[\"sample_Collection_date\"]\n",
+    "chunk_dates = {}\n",
+    "for k in range(4, a.cdata_shape[0]):\n",
+    "    c = a.blocks[k].astype(str)\n",
+    "    dates = c[np.char.str_len(c) == 10]\n",
+    "    dates = dates.astype(\"datetime64[D]\")\n",
+    "    chunk_dates[k] = dates[len(dates) // 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "a7b1a1cb-3b09-4001-a2fe-52a5b12b2d13",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>date</th>\n",
+       "      <th>diversity</th>\n",
+       "      <th>size</th>\n",
+       "      <th>compression_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-03-11</td>\n",
+       "      <td>0.001733</td>\n",
+       "      <td>802770</td>\n",
+       "      <td>372.497727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-03-24</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>713293</td>\n",
+       "      <td>419.224638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-04-02</td>\n",
+       "      <td>0.000392</td>\n",
+       "      <td>686073</td>\n",
+       "      <td>435.857409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-04-15</td>\n",
+       "      <td>0.000419</td>\n",
+       "      <td>691695</td>\n",
+       "      <td>432.314821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-05-02</td>\n",
+       "      <td>0.000459</td>\n",
+       "      <td>702424</td>\n",
+       "      <td>425.711536</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>388</th>\n",
+       "      <td>2022-12-19</td>\n",
+       "      <td>0.001334</td>\n",
+       "      <td>734649</td>\n",
+       "      <td>407.037919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2022-12-30</td>\n",
+       "      <td>0.001524</td>\n",
+       "      <td>753628</td>\n",
+       "      <td>396.787274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>2023-01-15</td>\n",
+       "      <td>0.001602</td>\n",
+       "      <td>779796</td>\n",
+       "      <td>383.472088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>2023-02-06</td>\n",
+       "      <td>0.001565</td>\n",
+       "      <td>629517</td>\n",
+       "      <td>475.014972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>2023-02-17</td>\n",
+       "      <td>0.001687</td>\n",
+       "      <td>177629</td>\n",
+       "      <td>1683.452589</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>393 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date  diversity    size  compression_ratio\n",
+       "0   2020-03-11   0.001733  802770         372.497727\n",
+       "1   2020-03-24   0.000381  713293         419.224638\n",
+       "2   2020-04-02   0.000392  686073         435.857409\n",
+       "3   2020-04-15   0.000419  691695         432.314821\n",
+       "4   2020-05-02   0.000459  702424         425.711536\n",
+       "..         ...        ...     ...                ...\n",
+       "388 2022-12-19   0.001334  734649         407.037919\n",
+       "389 2022-12-30   0.001524  753628         396.787274\n",
+       "390 2023-01-15   0.001602  779796         383.472088\n",
+       "391 2023-02-06   0.001565  629517         475.014972\n",
+       "392 2023-02-17   0.001687  177629        1683.452589\n",
+       "\n",
+       "[393 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = []\n",
+    "for j, date in chunk_dates.items():\n",
+    "    data.append({\n",
+    "        \"date\": date,\n",
+    "        \"diversity\": chunk_diversity[j],\n",
+    "        \"size\": sample_chunk_sizes[j]})\n",
+    "\n",
+    "df_chunk_size = pd.DataFrame(data)\n",
+    "df_chunk_size[\"compression_ratio\"] = uncompressed_chunk_size / df_chunk_size[\"size\"] \n",
+    "df_chunk_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c61ce18b-7589-4e9b-beb5-4e6cf6a3a84c",
+   "metadata": {},
+   "source": [
+    "## Compute diversity in chunks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "71199498-d63b-44ed-a96c-b06c5251bbaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import allel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "e9e4290a-410f-4297-bd6c-2edb7ff85c29",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49c8177d11494f048c139c75e2e52dae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/397 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gt_array = root[\"call_genotype\"]\n",
+    "# Make a Zarr array with the same chunk size to make indexing by block easy\n",
+    "chunk_diversity = np.zeros(shape=gt_array.cdata_shape[1])\n",
+    "\n",
+    "for s_chunk in tqdm(range(gt_array.cdata_shape[1])): \n",
+    "    h = allel.HaplotypeArray(gt_array.blocks[:, s_chunk].squeeze())\n",
+    "    ac = h.count_alleles()\n",
+    "    pi = allel.mean_pairwise_difference(ac)\n",
+    "    chunk_diversity[s_chunk] = np.mean(pi[~np.isnan(pi)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72019e81-5704-40c8-9d43-59ba5defbdf4",
+   "metadata": {},
+   "source": [
+    "## Compute the Scorpio prevalance over time from metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "45c01ffe-8027-4918-96db-c7379d5319fd",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>date</th>\n",
+       "      <th>scorpio</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>.</td>\n",
+       "      <td>Delta (B.1.617.2-like)</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>.</td>\n",
+       "      <td>Delta (B.1.617.2-like)</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>.</td>\n",
+       "      <td>Delta (B.1.617.2-like)</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3960699</th>\n",
+       "      <td>not applicable</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3960700</th>\n",
+       "      <td>not applicable</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3960701</th>\n",
+       "      <td>not applicable</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3960702</th>\n",
+       "      <td>not applicable</td>\n",
+       "      <td>Omicron (BA.2-like)</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3960703</th>\n",
+       "      <td>not applicable</td>\n",
+       "      <td>Omicron (BA.2-like)</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3960704 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   date                 scorpio  count\n",
+       "0                     .                       .    1.0\n",
+       "1                     .                       .    1.0\n",
+       "2                     .  Delta (B.1.617.2-like)    1.0\n",
+       "3                     .  Delta (B.1.617.2-like)    1.0\n",
+       "4                     .  Delta (B.1.617.2-like)    1.0\n",
+       "...                 ...                     ...    ...\n",
+       "3960699  not applicable                       .    1.0\n",
+       "3960700  not applicable                       .    1.0\n",
+       "3960701  not applicable                       .    1.0\n",
+       "3960702  not applicable     Omicron (BA.2-like)    1.0\n",
+       "3960703  not applicable     Omicron (BA.2-like)    1.0\n",
+       "\n",
+       "[3960704 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_scorpio = pd.DataFrame({\n",
+    "    \"date\": root[\"sample_Collection_date\"],\n",
+    "    \"scorpio\": root[\"sample_Viridian_scorpio\"],\n",
+    "    \"count\": np.ones(root[\"sample_id\"].shape[0])})\n",
+    "df_scorpio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a14935e2-46d1-4cb1-b6ea-252f4962c8dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subset down to things that have a precise date and convert to datetime\n",
+    "df_scorpio = df_scorpio[df_scorpio.date.str.len() == 10].copy()\n",
+    "df_scorpio[\"date\"] = np.array(df_scorpio[\"date\"], dtype=\"datetime64[D]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "232059d1-b765-447c-a2b2-87ea0023d2d3",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>scorpio</th>\n",
+       "      <th>.</th>\n",
+       "      <th>A.23.1-like</th>\n",
+       "      <th>A.23.1-like+E484K</th>\n",
+       "      <th>AV.1-like</th>\n",
+       "      <th>Alpha (B.1.1.7-like)</th>\n",
+       "      <th>B.1.1.318-like</th>\n",
+       "      <th>B.1.1.7-like+E484K</th>\n",
+       "      <th>B.1.617.1-like</th>\n",
+       "      <th>B.1.617.3-like</th>\n",
+       "      <th>Beta (B.1.351-like)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Probable Omicron (BA.1-like)</th>\n",
+       "      <th>Probable Omicron (BA.2-like)</th>\n",
+       "      <th>Probable Omicron (BA.3-like)</th>\n",
+       "      <th>Probable Omicron (BA.5-like)</th>\n",
+       "      <th>Probable Omicron (Unassigned)</th>\n",
+       "      <th>Probable Omicron (XBB-like)</th>\n",
+       "      <th>Probable Omicron (XBB.1-like)</th>\n",
+       "      <th>Probable Omicron (XBB.1.5-like)</th>\n",
+       "      <th>Theta (P.3-like)</th>\n",
+       "      <th>Zeta (P.2-like)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</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>1900-01-01</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1922-05-19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1931-08-27</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001-07-30</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008-08-23</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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>2023-02-17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-20</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-21</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1134 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scorpio       .  A.23.1-like  A.23.1-like+E484K  AV.1-like  \\\n",
+       "date                                                         \n",
+       "1900-01-01  0.0          0.0                0.0        0.0   \n",
+       "1922-05-19  0.0          0.0                0.0        0.0   \n",
+       "1931-08-27  0.0          0.0                0.0        0.0   \n",
+       "2001-07-30  0.0          0.0                0.0        0.0   \n",
+       "2008-08-23  0.0          0.0                0.0        0.0   \n",
+       "...         ...          ...                ...        ...   \n",
+       "2023-02-17  0.0          0.0                0.0        0.0   \n",
+       "2023-02-18  0.0          0.0                0.0        0.0   \n",
+       "2023-02-19  0.0          0.0                0.0        0.0   \n",
+       "2023-02-20  0.0          0.0                0.0        0.0   \n",
+       "2023-02-21  0.0          0.0                0.0        0.0   \n",
+       "\n",
+       "scorpio     Alpha (B.1.1.7-like)  B.1.1.318-like  B.1.1.7-like+E484K  \\\n",
+       "date                                                                   \n",
+       "1900-01-01                   0.0             0.0                 0.0   \n",
+       "1922-05-19                   0.0             0.0                 0.0   \n",
+       "1931-08-27                   0.0             0.0                 0.0   \n",
+       "2001-07-30                   0.0             0.0                 0.0   \n",
+       "2008-08-23                   0.0             0.0                 0.0   \n",
+       "...                          ...             ...                 ...   \n",
+       "2023-02-17                   0.0             0.0                 0.0   \n",
+       "2023-02-18                   0.0             0.0                 0.0   \n",
+       "2023-02-19                   0.0             0.0                 0.0   \n",
+       "2023-02-20                   0.0             0.0                 0.0   \n",
+       "2023-02-21                   0.0             0.0                 0.0   \n",
+       "\n",
+       "scorpio     B.1.617.1-like  B.1.617.3-like  Beta (B.1.351-like)  ...  \\\n",
+       "date                                                             ...   \n",
+       "1900-01-01             0.0             0.0                  0.0  ...   \n",
+       "1922-05-19             0.0             0.0                  0.0  ...   \n",
+       "1931-08-27             0.0             0.0                  0.0  ...   \n",
+       "2001-07-30             0.0             0.0                  0.0  ...   \n",
+       "2008-08-23             0.0             0.0                  0.0  ...   \n",
+       "...                    ...             ...                  ...  ...   \n",
+       "2023-02-17             0.0             0.0                  0.0  ...   \n",
+       "2023-02-18             0.0             0.0                  0.0  ...   \n",
+       "2023-02-19             0.0             0.0                  0.0  ...   \n",
+       "2023-02-20             0.0             0.0                  0.0  ...   \n",
+       "2023-02-21             0.0             0.0                  0.0  ...   \n",
+       "\n",
+       "scorpio     Probable Omicron (BA.1-like)  Probable Omicron (BA.2-like)  \\\n",
+       "date                                                                     \n",
+       "1900-01-01                           0.0                           0.0   \n",
+       "1922-05-19                           0.0                           0.0   \n",
+       "1931-08-27                           0.0                           0.0   \n",
+       "2001-07-30                           0.0                           0.0   \n",
+       "2008-08-23                           0.0                           0.0   \n",
+       "...                                  ...                           ...   \n",
+       "2023-02-17                           0.0                           0.0   \n",
+       "2023-02-18                           0.0                           0.0   \n",
+       "2023-02-19                           0.0                           0.0   \n",
+       "2023-02-20                           0.0                           0.0   \n",
+       "2023-02-21                           0.0                           0.0   \n",
+       "\n",
+       "scorpio     Probable Omicron (BA.3-like)  Probable Omicron (BA.5-like)  \\\n",
+       "date                                                                     \n",
+       "1900-01-01                           0.0                           0.0   \n",
+       "1922-05-19                           0.0                           0.0   \n",
+       "1931-08-27                           0.0                           0.0   \n",
+       "2001-07-30                           0.0                           0.0   \n",
+       "2008-08-23                           0.0                           0.0   \n",
+       "...                                  ...                           ...   \n",
+       "2023-02-17                           0.0                           0.0   \n",
+       "2023-02-18                           0.0                           0.0   \n",
+       "2023-02-19                           0.0                           0.0   \n",
+       "2023-02-20                           0.0                           0.0   \n",
+       "2023-02-21                           0.0                           0.0   \n",
+       "\n",
+       "scorpio     Probable Omicron (Unassigned)  Probable Omicron (XBB-like)  \\\n",
+       "date                                                                     \n",
+       "1900-01-01                            0.0                          0.0   \n",
+       "1922-05-19                            0.0                          0.0   \n",
+       "1931-08-27                            0.0                          0.0   \n",
+       "2001-07-30                            0.0                          0.0   \n",
+       "2008-08-23                            0.0                          0.0   \n",
+       "...                                   ...                          ...   \n",
+       "2023-02-17                            0.0                          0.0   \n",
+       "2023-02-18                            0.0                          0.0   \n",
+       "2023-02-19                            0.0                          0.0   \n",
+       "2023-02-20                            0.0                          0.0   \n",
+       "2023-02-21                            0.0                          0.0   \n",
+       "\n",
+       "scorpio     Probable Omicron (XBB.1-like)  Probable Omicron (XBB.1.5-like)  \\\n",
+       "date                                                                         \n",
+       "1900-01-01                            0.0                              0.0   \n",
+       "1922-05-19                            0.0                              0.0   \n",
+       "1931-08-27                            0.0                              0.0   \n",
+       "2001-07-30                            0.0                              0.0   \n",
+       "2008-08-23                            0.0                              0.0   \n",
+       "...                                   ...                              ...   \n",
+       "2023-02-17                            0.0                              0.0   \n",
+       "2023-02-18                            0.0                              0.0   \n",
+       "2023-02-19                            0.0                              0.0   \n",
+       "2023-02-20                            0.0                              0.0   \n",
+       "2023-02-21                            0.0                              0.0   \n",
+       "\n",
+       "scorpio     Theta (P.3-like)  Zeta (P.2-like)  \n",
+       "date                                           \n",
+       "1900-01-01               0.0              0.0  \n",
+       "1922-05-19               0.0              0.0  \n",
+       "1931-08-27               0.0              0.0  \n",
+       "2001-07-30               0.0              0.0  \n",
+       "2008-08-23               0.0              0.0  \n",
+       "...                      ...              ...  \n",
+       "2023-02-17               0.0              0.0  \n",
+       "2023-02-18               0.0              0.0  \n",
+       "2023-02-19               0.0              0.0  \n",
+       "2023-02-20               0.0              0.0  \n",
+       "2023-02-21               0.0              0.0  \n",
+       "\n",
+       "[1134 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pivot so we get counts of Scorpio values per day\n",
+    "df_pivot = df_scorpio.pivot_table(\n",
+    "    columns=\"scorpio\", index=\"date\", values=\"count\", aggfunc=\"sum\", fill_value=0)\n",
+    "df_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "18fc83c2-3055-4d47-873a-37ecebc44f0d",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Other</th>\n",
+       "      <th>Alpha</th>\n",
+       "      <th>Delta</th>\n",
+       "      <th>BA.1</th>\n",
+       "      <th>BA.2</th>\n",
+       "      <th>Other_Omicron</th>\n",
+       "      <th>BA.4</th>\n",
+       "      <th>BA.5</th>\n",
+       "      <th>XBB</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</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>1900-01-01</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1922-05-19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1931-08-27</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001-07-30</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008-08-23</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>145.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-20</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-21</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1134 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Other  Alpha  Delta  BA.1  BA.2  Other_Omicron  BA.4  BA.5    XBB\n",
+       "date                                                                         \n",
+       "1900-01-01    0.0    0.0    1.0   0.0   0.0            0.0   0.0   0.0    0.0\n",
+       "1922-05-19    0.0    0.0    0.0   0.0   1.0            0.0   0.0   0.0    0.0\n",
+       "1931-08-27    0.0    0.0    0.0   0.0   0.0            0.0   1.0   0.0    0.0\n",
+       "2001-07-30    0.0    0.0    1.0   0.0   0.0            0.0   0.0   0.0    0.0\n",
+       "2008-08-23    0.0    0.0    0.0   0.0   0.0            0.0   0.0   0.0    1.0\n",
+       "...           ...    ...    ...   ...   ...            ...   ...   ...    ...\n",
+       "2023-02-17    0.0    0.0    0.0   0.0  97.0            1.0   0.0  75.0  145.0\n",
+       "2023-02-18    0.0    0.0    0.0   0.0  49.0            0.0   0.0  13.0   24.0\n",
+       "2023-02-19    0.0    0.0    0.0   0.0  14.0            0.0   0.0   7.0    6.0\n",
+       "2023-02-20    0.0    0.0    0.0   0.0  32.0            0.0   0.0  17.0   39.0\n",
+       "2023-02-21    0.0    0.0    0.0   0.0   0.0            0.0   0.0   1.0    0.0\n",
+       "\n",
+       "[1134 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def scorpio_to_major_voc(scorpio):\n",
+    "    keys = {\"Alpha\", \"Delta\", \"BA.1\", \"BA.2\", \"BA.4\", \"BA.5\", \"BQ.1\", \"XBB\"}\n",
+    "    for k in keys:\n",
+    "        if k in scorpio:\n",
+    "            return k\n",
+    "    if \"Omicron\" in scorpio:\n",
+    "        return \"Other_Omicron\"\n",
+    "    return \"Other\"\n",
+    "\n",
+    "def merge_scorpio_columns(df):\n",
+    "    out_cols = {}\n",
+    "    for col, values in df.items():\n",
+    "        voc = scorpio_to_major_voc(col)\n",
+    "        if voc not in out_cols:\n",
+    "            out_cols[voc] = values.copy()\n",
+    "        else:\n",
+    "            out_cols[voc] += values\n",
+    "    return pd.DataFrame(out_cols)\n",
+    "\n",
+    "df_major_voc = merge_scorpio_columns(df_pivot)\n",
+    "df_major_voc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d98cc937-409f-4040-8ecf-33f7370a1c8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_fractions(df):\n",
+    "    sum_col = sum(col for _, col in df.items())\n",
+    "    #print(df.copy)\n",
+    "    copy = df.copy()\n",
+    "    #print(copy)\n",
+    "    for k, v in df.items():\n",
+    "        copy[k] = v / sum_col\n",
+    "    return copy\n",
+    "\n",
+    "df_major_voc = compute_fractions(df_major_voc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b352b74-009a-4d65-82a0-85bc7a76aba5",
+   "metadata": {},
+   "source": [
+    "## Make the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "8813c37a-cee3-4ec1-833c-b12ffc3ca307",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start = np.array([\"2021-01\"], dtype=\"datetime64\")[0]\n",
+    "end = np.array([\"2023-01\"], dtype=\"datetime64\")[0]\n",
+    "\n",
+    "df_voc = df_major_voc[(start <= df_major_voc.index) & (df_major_voc.index < end)]\n",
+    "df_cs = df_chunk_size[(start <= df_chunk_size.date) & (df_chunk_size.date < end)]\n",
+    "\n",
+    "fig, ax = plt.subplots(1, figsize=(16, 4))\n",
+    "\n",
+    "ax3 = ax.twinx()\n",
+    "ax3.stackplot(df_voc.index, \n",
+    "    *[df_voc[voc] for voc in df_voc], \n",
+    "    labels=[\" \".join(k.split(\"_\")) for k in df_voc], \n",
+    "    alpha=0.7)\n",
+    "ax2 = ax.twinx()\n",
+    "ax2.set_ylabel(\"Diversity\")\n",
+    "ax2.plot(df_cs[\"date\"], df_cs[\"diversity\"], color=\"pink\")\n",
+    "ax.plot(df_cs[\"date\"], df_cs[\"compression_ratio\"], color=\"black\")\n",
+    "ax.set_ylabel(\"Compression ratio\")\n",
+    "ax3.legend(loc=\"upper left\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbb53ffc-d0eb-4749-9550-ef6465c86341",
+   "metadata": {},
+   "source": [
+    "## NOTES\n",
+    "\n",
+    "Note: this isn't quite what i'd been expecting. One would assume that peak compression would correspond with the lowest diversity, but this is not what we're seeing. Needs some more looking at."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4dca91b-9020-4bad-a671-83db144f76d4",
+   "metadata": {},
+   "source": [
+    "# Performing calculations\n",
+    "\n",
+    "## Compute non-ACGT values per sample\n",
+    "\n",
+    "We want to know how many bases per sample are Ns, gaps or ambiguous. The IUPAC alleles are encoded as ACGT = (0, 1, 2, 3) so we can find anything that's not these by excluding missing values (-1) or anything >= 4.\n",
+    "\n",
+    "This takes 3 minutes to compute in a single thread. Accessing the haplotype for a single sample in the middle of the matrix required 163 ms and accessing the genotypes for a given site required 163ms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "3e34a300-52ee-4d97-8db0-ffba7b1d9cd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fcb02f4799445fe83311463c10882c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/397 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 34s, sys: 23.6 s, total: 2min 57s\n",
+      "Wall time: 2min 57s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "gt_array = root[\"call_genotype\"]\n",
+    "# Make a Zarr array with the same chunk size to make indexing by block easy\n",
+    "missing_count = zarr.zeros(shape=gt_array.shape[1], chunks=gt_array.chunks[1], dtype=int)\n",
+    "\n",
+    "for s_chunk in tqdm(range(gt_array.cdata_shape[1])): \n",
+    "    H = gt_array.blocks[:, s_chunk].squeeze(axis=2).T\n",
+    "    missing_count.blocks[s_chunk] = np.sum((H < 0) | (H >= 4), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "69f63ea6-ecf8-46f6-a02f-2a611a0a7e01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(missing_count[:]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa766767-0bf3-4298-bac1-47cf7251a9b9",
+   "metadata": {},
+   "source": [
+    "## Get a single sample's haplotype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "33337e6c-abfb-445c-a5c0-d8dd71bfd0cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 172 ms, sys: 16.5 ms, total: 188 ms\n",
+      "Wall time: 163 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "k = gt_array.shape[1] // 2\n",
+    "h = gt_array[:, k].squeeze(axis=1).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "419254e8-5e64-422a-b9d5-4f61613844dc",
+   "metadata": {},
+   "source": [
+    "## Get genotypes for a single site\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "8f650a05-ae03-49da-b14e-494be123ee73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 203 ms, sys: 7.21 ms, total: 210 ms\n",
+      "Wall time: 209 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "j = gt_array.shape[0] // 2\n",
+    "g = gt_array[j].squeeze(axis=1).T"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}