From 6327cb3ecd498a75fa1a9719784507b7212106c6 Mon Sep 17 00:00:00 2001
From: silastittes <silas.tittes@gmail.com>
Date: Tue, 3 Sep 2024 09:55:49 -0700
Subject: [PATCH] fixes for summary stats and runnign it

---
 notebooks/DFE_figures.ipynb   | 1122 +++++++++++++++++++++++++++++++++
 workflows/summary_stats.sh    |   12 +
 workflows/summary_stats.snake |   28 +-
 3 files changed, 1140 insertions(+), 22 deletions(-)
 create mode 100644 notebooks/DFE_figures.ipynb
 create mode 100644 workflows/summary_stats.sh

diff --git a/notebooks/DFE_figures.ipynb b/notebooks/DFE_figures.ipynb
new file mode 100644
index 0000000..dd8a9c6
--- /dev/null
+++ b/notebooks/DFE_figures.ipynb
@@ -0,0 +1,1122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import stdpopsim\n",
+    "from matplotlib import pyplot as plt\n",
+    "# Update default font sizes in rcParams\n",
+    "plt.rcParams.update({\n",
+    "    'font.size': 14,         # Default text size\n",
+    "    'axes.titlesize': 18,    # Title font size\n",
+    "    'axes.labelsize': 16,    # X and Y axis labels font size\n",
+    "    'xtick.labelsize': 12,   # X tick labels font size\n",
+    "    'ytick.labelsize': 12,   # Y tick labels font size\n",
+    "    'legend.fontsize': 14,   # Legend font size\n",
+    "    'figure.titlesize': 20   # Figure title font size\n",
+    "})\n",
+    "\n",
+    "from glob import glob\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from dataclasses import dataclass\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass\n",
+    "class SpeciesInfo:\n",
+    "    \"\"\"\n",
+    "    Class to store information about species, dfe, demog, and annotation\n",
+    "\n",
+    "    species: str - species name\n",
+    "    neu_prop: float - proportion of neutral sites\n",
+    "    nonneu_prop: float - proportion of non-neutral sites\n",
+    "    basedir: str - directory where results are stored\n",
+    "    demog: str - demographic model\n",
+    "    dfe: str - dfe model\n",
+    "    annot: str - annotation used\n",
+    "\n",
+    "    \"\"\"\n",
+    "    species: str\n",
+    "    basedir: str\n",
+    "    demog: str\n",
+    "    dfe: str\n",
+    "    annot: str\n",
+    "    neu_prop: float = 0.3\n",
+    "    nonneu_prop: float = 0.7\n",
+    "\n",
+    "    def __post_init__(self):\n",
+    "        self.get_species_info()\n",
+    "        self.make_dfe_dataframe()\n",
+    "\n",
+    "    def get_species_info(self):\n",
+    "        species = stdpopsim.get_species(self.species)\n",
+    "        \n",
+    "        if self.annot == \"all_sites\":\n",
+    "            seq_len = sum([g.length for g in species.genome.chromosomes if g.id not in [\"Y\", \"MT\"]])\n",
+    "            annotations = np.array([[0,g.length] for g in species.genome.chromosomes if g.id not in [\"Y\", \"MT\"]])\n",
+    "        else:\n",
+    "            annotations = species.get_annotations(self.annot)\n",
+    "            seq_len = 0\n",
+    "            for g in species.genome.chromosomes:\n",
+    "                if g.id not in [\"Y\", \"MT\"]:        \n",
+    "                    annot_intervals = annotations.get_chromosome_annotations(g.id)\n",
+    "                    seq_len+= sum([abs(i[1]-i[0]) for i in annot_intervals])\n",
+    "        dfe = species.get_dfe(self.dfe)\n",
+    "        try:\n",
+    "            model = species.get_demographic_model(self.demog)\n",
+    "            if model.mutation_rate is not None:\n",
+    "                mutation_rate = model.mutation_rate\n",
+    "            else:\n",
+    "                mutation_rate = species.genome.mean_mutation_rate\n",
+    "        except:\n",
+    "            mutation_rate = species.genome.mean_mutation_rate\n",
+    "            \n",
+    "        dfe_params = dfe.mutation_types[-1].distribution_args\n",
+    "        self.dfe_params = dfe_params\n",
+    "        self.mutation_rate = mutation_rate\n",
+    "        self.seq_len = seq_len\n",
+    "        self.species_id = species\n",
+    "\n",
+    "    def get_loss(self):\n",
+    "        def mae(variable, truth):\n",
+    "            return np.mean(np.abs(variable - truth))\n",
+    "\n",
+    "        Es, shape = self.dfe_params\n",
+    "        Es = np.abs(Es)\n",
+    "        mae_Es = self.all_DFE.groupby(['method'])[['Es']].agg(lambda x: mae(x, Es)).reset_index()\n",
+    "        mae_shape = self.all_DFE.groupby(['method'])[['shape']].agg(lambda x: mae(x, shape)).reset_index()\n",
+    "        loss_df = pd.merge(mae_Es, mae_shape, on = 'method')\n",
+    "        lenz = len(loss_df)\n",
+    "        return pd.DataFrame({'MAE E|s|': loss_df['Es'].values,\n",
+    "                'MAE shape': loss_df['shape'].values,\n",
+    "                'method': loss_df['method'].values,\n",
+    "                'species ID': [self.species]*lenz,\n",
+    "                'demography': [self.demog]*lenz,\n",
+    "                'annotation': [self.annot]*lenz})\n",
+    "\n",
+    "    ## Rescaling functions\n",
+    "    def grapes_rescale(self, df):\n",
+    "        Ne = df['theta'] / (4 * self.mutation_rate)\n",
+    "        df['Es'] = df['Es'] / Ne\n",
+    "        df['method'] = 'grapes'\n",
+    "        return df\n",
+    "\n",
+    "    def dfe_alpha_rescale(self, df):\n",
+    "        df['shape'] = df['b']\n",
+    "        df['Es'] = abs(df['Es'])\n",
+    "        df['method'] = \"DFE-alpha\"\n",
+    "        return df\n",
+    "\n",
+    "    def polydfe_rescale(self, df):\n",
+    "        df['shape'] = df['b']\n",
+    "        df['Es'] = 2 * abs(self.mutation_rate * df['S_d'] / df['theta_bar'])\n",
+    "        df['method'] = \"polyDFE\" \n",
+    "        return df \n",
+    "\n",
+    "    def dadi_rescale(self, df):\n",
+    "        df['Es'] = df['shape'] * df['scale'] / (df['theta'] / (4 * self.mutation_rate * self.seq_len * self.nonneu_prop))\n",
+    "        df['method'] = 'dadi'\n",
+    "        return df\n",
+    "\n",
+    "    def make_dfe(self, method):\n",
+    "        input_files = glob(f\"../{self.basedir}/inference/{self.demog}/{method}/{self.dfe}/{self.annot}/*/*/*bestfit\")\n",
+    "        df = pd.concat([pd.read_csv(i, sep=\"\\t\") for i in input_files])\n",
+    "        if method == \"grapes\":\n",
+    "            df = self.grapes_rescale(df)\n",
+    "        elif method == \"DFE-alpha\":\n",
+    "            df = self.dfe_alpha_rescale(df)\n",
+    "        elif method == \"polyDFE\":\n",
+    "            df = self.polydfe_rescale(df)\n",
+    "        elif method == \"dadi\":\n",
+    "            df = self.dadi_rescale(df)\n",
+    "        else:\n",
+    "            raise ValueError(f\"Method {method} not recognized\")\n",
+    "        return df\n",
+    "    \n",
+    "    def make_dfe_dataframe(self):\n",
+    "        if self.demog == \"Constant\":\n",
+    "            pop_dict = {f\"pop{0}\":0}\n",
+    "        else:\n",
+    "            model = self.species_id.get_demographic_model(self.demog)\n",
+    "            pop_dict = {f\"pop{i}\":m.name for i,m in enumerate(model.populations[:3])}\n",
+    "\n",
+    "        all_DFE = pd.concat([self.make_dfe(m) for m in [\"grapes\", \"polyDFE\", \"dadi\"]])\n",
+    "        #make new name columns for viz\n",
+    "        all_DFE['pop'] = all_DFE['pop_id'].map(pop_dict)\n",
+    "        self.all_DFE = all_DFE\n",
+    "\n",
+    "\n",
+    "def make_some_plots(species_info: SpeciesInfo, output_path = None):\n",
+    "    if output_path is None:\n",
+    "        output_path = f\"{species_info.species}_{species_info.demog}_{species_info.dfe}_{species_info.annot}_DFE_plot.pdf\"\n",
+    "    \n",
+    "    fig, axes = plt.subplot_mosaic(\n",
+    "        '''\n",
+    "        AB\n",
+    "        ''',\n",
+    "        figsize=(13, 6)\n",
+    "    )\n",
+    "    plt.subplots_adjust(wspace=0.4, hspace=0.3)\n",
+    "\n",
+    "    #all_DFE = pd.concat([species_info.make_dfe(m) for m in [\"grapes\", \"polyDFE\", \"dadi\"]])\n",
+    "    #make new name columns for viz\n",
+    "    #all_DFE['pop'] = all_DFE['pop_id'].map(pop_dict)\n",
+    "    #seaborn boxplot method vs. Es split by pop_id no lines, no error bars\n",
+    "    import seaborn as sns\n",
+    "    sns.set_theme(style=\"whitegrid\")\n",
+    "    sns.boxplot(x=\"method\", y=\"Es\", hue = 'pop', data=species_info.all_DFE, ax = axes['A'])\n",
+    "    #horizontal line at 0 on axes A\n",
+    "    axes['A'].axhline(abs(species_info.dfe_params[0]), color='black', linestyle='--')\n",
+    "    axes['A'].set_ylabel('|E(s)|')  \n",
+    "    axes['A'].set_title(\"A\", loc = \"left\")\n",
+    "    #seaborn boxplot method vs. Es split by pop_id no lines, no error bars\n",
+    "    sns.set_theme(style=\"whitegrid\")\n",
+    "    sns.boxplot(x=\"method\", y=\"shape\", hue = 'pop', data=species_info.all_DFE,  ax = axes['B'])\n",
+    "    #horizontal line at 0\n",
+    "    axes['B'].axhline(abs(species_info.dfe_params[1]), color='black', linestyle='--')\n",
+    "    axes['B'].set_title(\"B\", loc = \"left\")\n",
+    "    plt.savefig(output_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hom_sap = SpeciesInfo(\n",
+    "    species = \"HomSap\",\n",
+    "    basedir = \"HomSap_results\",\n",
+    "    demog = \"OutOfAfricaArchaicAdmixture_5R19\",\n",
+    "    dfe = \"Gamma_K17\",\n",
+    "    annot = \"ensembl_havana_104_exons\"\n",
+    ")\n",
+    "\n",
+    "#model = stdpopsim.get_species(\"HomSap\").get_demographic_model(hom_sap.demog)\n",
+    "#pop_dict = {f\"pop{i}\":m.name for i,m in enumerate(model.populations[:3])}\n",
+    "make_some_plots(hom_sap)\n",
+    "\n",
+    "\n",
+    "loss_list = []\n",
+    "loss_list.append(hom_sap.get_loss())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>MAE E|s|</th>\n",
+       "      <th>MAE shape</th>\n",
+       "      <th>method</th>\n",
+       "      <th>species ID</th>\n",
+       "      <th>demography</th>\n",
+       "      <th>annotation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.040148</td>\n",
+       "      <td>0.030905</td>\n",
+       "      <td>dadi</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>OutOfAfricaArchaicAdmixture_5R19</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.021437</td>\n",
+       "      <td>0.051345</td>\n",
+       "      <td>grapes</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>OutOfAfricaArchaicAdmixture_5R19</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.033484</td>\n",
+       "      <td>0.024394</td>\n",
+       "      <td>polyDFE</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>OutOfAfricaArchaicAdmixture_5R19</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MAE E|s|  MAE shape   method species ID                        demography  \\\n",
+       "0  0.040148   0.030905     dadi     HomSap  OutOfAfricaArchaicAdmixture_5R19   \n",
+       "1  0.021437   0.051345   grapes     HomSap  OutOfAfricaArchaicAdmixture_5R19   \n",
+       "2  0.033484   0.024394  polyDFE     HomSap  OutOfAfricaArchaicAdmixture_5R19   \n",
+       "\n",
+       "                 annotation  \n",
+       "0  ensembl_havana_104_exons  \n",
+       "1  ensembl_havana_104_exons  \n",
+       "2  ensembl_havana_104_exons  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hom_sap\n",
+    "Es, shape = hom_sap.dfe_params\n",
+    "\n",
+    "def mae(variable, truth):\n",
+    "        return np.mean(np.abs(variable - truth))\n",
+    "\n",
+    "mae_Es = hom_sap.all_DFE.groupby(['method'])[['Es']].agg(lambda x: mae(x, Es)).reset_index()\n",
+    "mae_shape = hom_sap.all_DFE.groupby(['method'])[['shape']].agg(lambda x: mae(x, shape)).reset_index()\n",
+    "loss_df = pd.merge(mae_Es, mae_shape, on = 'method')\n",
+    "lenz = len(loss_df)\n",
+    "pd.DataFrame({'MAE E|s|': loss_df['Es'].values,\n",
+    "        'MAE shape': loss_df['shape'].values,\n",
+    "        'method': loss_df['method'].values,\n",
+    "        'species ID': [hom_sap.species]*lenz,\n",
+    "        'demography': [hom_sap.demog]*lenz,\n",
+    "        'annotation': [hom_sap.annot]*lenz})\n",
+    "\n",
+    "#print(hom_sap.all_DFE.query('method == \"polyDFE\"')['mae_Es'].reset_index().mean())\n",
+    "#hom_sap.all_DFE.query('method == \"dadi\"')['mae_Es'].reset_index().mean()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hom_sap = SpeciesInfo(\n",
+    "    species = \"HomSap\",\n",
+    "    basedir = \"HomSap_results\",\n",
+    "    demog = \"OutOfAfricaArchaicAdmixture_5R19\",\n",
+    "    dfe = \"Gamma_K17\",\n",
+    "    annot = \"all_sites\"\n",
+    ")\n",
+    "\n",
+    "model = stdpopsim.get_species(\"HomSap\").get_demographic_model(hom_sap.demog)\n",
+    "pop_dict = {f\"pop{i}\":m.name for i,m in enumerate(model.populations[:3])}\n",
+    "make_some_plots(hom_sap)\n",
+    "loss_list.append(hom_sap.get_loss())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hom_sap = SpeciesInfo(\n",
+    "    species = \"HomSap\",\n",
+    "    basedir = \"HomSap_results\",\n",
+    "    demog = \"Constant\",\n",
+    "    dfe = \"Gamma_K17\",\n",
+    "    annot = \"ensembl_havana_104_exons\"\n",
+    ")\n",
+    "\n",
+    "make_some_plots(hom_sap)\n",
+    "loss_list.append(hom_sap.get_loss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CrV27ttyFQWBgoObMmWM7xoULF/T666/r4Ycflp+fn1JSUmSxWPT222+rTZs2tv0PHTpUq1at0syZM3Xo0CEVFxfrmWeesd3vOWDAgPr5wkCSlJ+fr4ULF9r+nvv27auBAwcqJSVFEyZM0Nq1a/XAAw/oz3/+s6TLH9rnz5/XypUrNWHCBF1zzTXl9jl69Gi99NJLSktL0z333CNJ+uyzz3T8+HHdcccdDo2rffv2kqSTJ0/atd911112r++44w69+OKLttcFBQW64YYbyu1vzZo1uvXWWx06Nure0qVLdfPNN5d7j1i+fLmt5sr3lNLSUvXv31/p6enauHGj7ULitddeU4cOHfTKK6/Izc1NkuTh4aFnn322Ac8GQFPE9RIqw7USGgLXSs5DkIJySkpKdPjwYc2cOdOufciQIeUuDG655Ra7iwJJunjxopYvX65Nmzbp559/tksyz58/b/fBMHToULu+w4YN09KlS/Xdd9+pb9++2r17t2699Vb5+fnZUnCj0aibb75Z6enpki6vTO7m5qb4+Hjddddd6tu3r3x9fWv/hUClfH19bRcGkmQ2mxUWFqb9+/frwIEDKi4u1siRI+36jBo1SuvXr9fhw4d18803l9unj4+PoqKi9M4779guDt555x317t1bQUFBDo3LarVW2P7SSy/Z7aN169Z22z09PbV27dpy/cpSejS8kpISffPNN3r88cft2ocPH253cZCZman58+frq6++0qlTp2ztP/zwg+3/v/76aw0ePNh2YVC2Hy4OANQG10uoCtdKqG9cKzkXQQrKycvL06VLl8q9gZb9huNqbS+//LLefvttPfLII7rxxhvl6+ur7du3a+nSpbp48aLdhUFlxyh73Nrp06e1f//+ChPwssWOunTpomXLlmn58uV69NFHZTQaFRERoWeffVYdO3as5tnDEb/8e5Mu/9398MMPtinC7dq1s9vetm1bSZd/Q1OZu+66S+PGjdO3334rf39/7dixQ7Nnz3Z4XMePH6/w2EFBQVddQK2q7Wh4lb0PlX0fSdK5c+f04IMPqnXr1po1a5Y6duyoFi1a6Omnn9bFixdtdSdOnCj3XtWyZUu5u/MRCKDmuF5CVbhWQn3jWsm5+MqgnNatW8vd3b3cM+uvTDDLVHRP5NatW/XHP/5RkyZNsrVVtpBZZccoe3P38/PTgAED9Nhjj5XrazKZbP8fGRmpyMhInTt3Trt27dKcOXP05JNPVviYNtTeL//epMt/d+3atbM9Ku/kyZO69tprbdvLppBe+Si9X+rdu7e6d++ud955R506dZKHh4eioqIcHtenn34qk8lU4YUkGpfK3oeunIq8f/9+HTt2TMuXL9f1119vaz979qxt6rJ0+f3kl+9f+fn5Fd7rDQCO4noJVeFaCfWNayXn4qk9KMfNzU2/+c1vtH37drv2jz76yKH+Fy9etFvsqqSkRO+//36Ftdu2bbN7/eGHH8rLy0u//vWvJUn9+vVTZmamLSW/8k9wcHC5/fn4+GjkyJEaNWqUMjMzHRovqu/s2bP6/PPP7V7v2bNHPXv2VGhoqDw8PLRlyxa7Pps3b5a3t7dCQkKq3Pedd96pTZs26e2339bIkSMrvEe4Irt27dK2bds0ZswYeXt7V/+k4FLc3NwUEhJS7j3igw8+sP1/YWGhJNm933z55Zc6evSoXZ8ePXro448/VklJSYX7AYCa4HoJVeFaCfWNayXnYkYKKjR16lTFxMTo6aef1ogRI3To0CH985//lKRyK3j/Ur9+/fT222+rW7duat26td58800VFRVVWHvkyBE9+eSTGjlypA4dOqSVK1fq/vvvl5+fnyRpwoQJ2rRpk/70pz/p/vvvV8eOHZWXl6evv/5a1157rSZMmKB169bpq6++UmRkpNq1a6cff/xR7733nvr371+3XxTYtGzZUk899ZSmTZsmX19frVixQpI0fvx4tW7dWvfdd59Wr14tk8mkm266SZ9//rnWr1+v2NjYq35w//73v9ff/vY3nT592rZ43y/9/PPP2r9/v0pKSnTixAnt2rVL//znP9WzZ0898cQT1T6f0tJSu0cSlmndurVtSjQa3pQpUxQTE2N7jzh48KDS0tJs23v16iVvb289//zzmjRpko4fP64lS5bY/XZPkiZNmqQ77rhDjzzyiO6++279+OOPWrVqlUNPNwCAqnC9hMpwrYSGwLWS8xCkoEJDhgzR7NmztXz5cr333nvq2bOnnnvuOT388MO2ld4r88wzz+i5557TX/7yF3l5eWnMmDEaOnSonn766XK1cXFx2rdvnx577DG5ubnp7rvvVlxcnG17q1attH79ei1cuFDz5s1Tfn6+2rRpo549e9oWXgsODtbHH3+sOXPmKD8/X+3atdOoUaMqnN6KutGuXTvFx8dr7ty5OnLkiLp3765Vq1bZ7sl8/PHHZTab9fbbb9tWAZ81a5YmTJhw1X23bNlSffv21c8//6zevXtXWPPGG2/ojTfekIeHh1q2bKng4GA9//zz+sMf/lCjezkLCwv1xz/+sVz7mDFjlJSUVO39oW4MGTJEzz//vJYtW6b3339fPXv21N/+9jeNGzdO0uV7gBctWqS5c+cqJiZG1113nWbPnq2VK1fa7SckJESLFi3SvHnz9Oijj6p79+5asGCBQ9+PAFAVrpdQGa6V0BC4VnIeg7WypZuBX3j77bf19NNPa/v27frVr35Vq339+OOPGjJkiBYtWqQRI0bU0QjREGbNmlUu7a5L586d04ABAxQbG6sHH3ywXo4BAEB94XoJXCsBTR8zUlCh/Px8LVmyRGFhYbrmmmuUnp6uZcuWaciQIbW+KAAqcu7cOWVmZurvf/+7DAaDbr/9dmcPCQCAKnG9hIbEtRLgOghSUCF3d3fl5OTo/fffl8ViUatWrfT73/9e8fHxzh4amqhvvvlG999/vzp06KCXXnqpyhXrAQBwBVwv/b/27i0kijaO4/hvo7RMyCIlqZCUZoq0rMC1SNn2orypEIIiVhS9iZKKorqwaL2oiA6QCmUbEh2EhTLKm5DoAB1ooSK7iU4QZW6LUYFp0bLPexEtzGvB9FKWvt8PzMU88599Zufqz2+e2cVgolcC/h682gMAAAAAAOASf38MAAAAAADgEkEKAAAAAACASwQpAAAAAAAALhGkAAAAAAAAuESQAuCvc/ToUV2+fHnAeFtbm2zb1sOHD3/7NVRUVKiiouK3zwMAAPBf0C8Bfw5BCoC/TnNz83cbAwAAAHxFvwT8OQQpAAAAAAAALhGkAHCtsbFRtm3r0aNH2rBhg+bPn6+ioiLt3btX8Xhcz58/V01NjebOnSu/369QKOQ4v7e3V/v27ZPf71d+fr5KSkq0e/du9fX1JWts21ZfX5/Onz8v27Zl2/aAJaMfP37Url275PV65fV6VVtbqzdv3jhqEomEQqGQysrKlJ+frwULFmjbtm2KRqOOOmOMQqGQFi9erIKCApWXl+v69eu/+M4BAID/C/olYPgb+acvAMDQs2nTJi1fvlyrV6/WzZs3dfz4ccXjcd26dUtr1qxRTU2N2tvbdeDAAeXk5GjJkiXq7+9XIBBQNBrV2rVrZdu2njx5ooaGBj1+/FgnTpyQx+NROBxWZWWlvF6v1q1bJ0lKT093zL9jxw75fD4dPHhQ3d3d2r9/v7Zu3aqTJ08ma4LBoMLhsAKBgHw+n7q6unT48GFFIhG1tbVpwoQJkqSmpiY1NTVp5cqVWrp0qaLRqHbu3KlEIqFp06YN3k0FAADDCv0SMIwZAHCpoaHBWJZlWlpaHOMrVqwwlmWZjo6O5NiXL19McXGxqa2tNcYY09zcbGbMmGE6Ozsd5166dMlYlmWuXbuWHCssLDTbt28fMP+5c+eMZVkmGAw6xkOhkLEsy8RiMWOMMU+fPv1u3YMHD4xlWebQoUPGGGM+fPhgCgoKzPr16x11d+/eNZZlmUAg4Oq+AAAAfEO/BAx/vNoD4Kf5fD7Hfl5enjwej0pLS5NjI0eOVE5Ojrq6uiRJV69e1fTp0zVz5kzF4/HktmjRInk8HkUiEdfz+/1+x75t25Kk169fS5Lu3LkjSSovL3fUzZ49W3l5ebp9+7Yk6f79+/r8+bOWLVvmqJs3b54mT57s+noAAAD+jX4JGL54tQfATxs3bpxjf9SoURozZoxSU1MHjPf29kqS3r59qxcvXmjWrFnf/cx37965nj8jI8Oxn5KSIkn69OmTJOn9+/eSpKysrAHnZmVlJRuIb3UTJ04cUPe9MQAAALfol4DhiyAFwKAYP368UlNTtWfPnh8e/1W+NQ6xWEyTJk1yHIvFYsm5vtX19PQM+Iyenh6esgAAgEFFvwQMDbzaA2BQ+Hw+vXz5UhkZGSooKBiwTZkyJVmbkpKSfFryXxQXF0uSLl686Bjv7OzUs2fPkscLCwuVmpqq9vZ2R929e/eSS2wBAAAGC/0SMDSwIgXAoKisrFRHR4cCgYCqqqpk27YSiYS6u7t148YNVVdXa86cOZIky7IUiUR05coVZWZmauzYscrNzXU9V25urlatWqXTp09rxIgRKi0tTf4KfXZ2tqqqqiR9XXJbXV2tI0eOqK6uTmVlZYpGo2psbFRmZubvuA0AAAA/RL8EDA0EKQAGRVpams6cOaNjx44pHA7r1atXGj16tLKzs7Vw4ULHstC6ujrV19dr8+bN6u/vV1FRkU6dOvVT8wWDQU2dOlVnz55Va2ur0tPTVVJSoi1btjiWxW7cuFFpaWlqbW3VhQsXlJubq/r6erW0tPyy7w4AAOAG/RIwNHiMMeZPXwQAAAAAAMBQwG+kAAAAAAAAuESQAgAAAAAA4BJBCgAAAAAAgEsEKQAAAAAAAC4RpAAAAAAAALhEkAIAAAAAAOASQQoAAAAAAIBLBCkAAAAAAAAuEaQAAAAAAAC4RJACAAAAAADgEkEKAAAAAACASwQpAAAAAAAALv0DEIe5p5aLHDMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hom_sap = SpeciesInfo(\n",
+    "    species = \"HomSap\",\n",
+    "    basedir = \"HomSap_results\",\n",
+    "    demog = \"Constant\",\n",
+    "    dfe = \"Gamma_K17\",\n",
+    "    annot = \"all_sites\"\n",
+    ")\n",
+    "\n",
+    "make_some_plots(hom_sap)\n",
+    "loss_list.append(hom_sap.get_loss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pho_sin = SpeciesInfo(\n",
+    "    species = \"PhoSin\",\n",
+    "    basedir = \"PhoSin_results\",\n",
+    "    demog = \"Vaquita2Epoch_1R22\",\n",
+    "    dfe = \"Gamma_R22\",\n",
+    "    annot = \"Phocoena_sinus.mPhoSin1.pri.110_exons\"\n",
+    ")\n",
+    "\n",
+    "make_some_plots(pho_sin)\n",
+    "loss_list.append(pho_sin.get_loss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pho_sin = SpeciesInfo(\n",
+    "    species = \"PhoSin\",\n",
+    "    basedir = \"PhoSin_results\",\n",
+    "    demog = \"Vaquita2Epoch_1R22\",\n",
+    "    dfe = \"Gamma_R22\",\n",
+    "    annot = \"all_sites\"\n",
+    ")\n",
+    "\n",
+    "make_some_plots(pho_sin)\n",
+    "loss_list.append(pho_sin.get_loss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pho_sin = SpeciesInfo(\n",
+    "    species = \"PhoSin\",\n",
+    "    basedir = \"PhoSin_results\",\n",
+    "    demog = \"Constant\",\n",
+    "    dfe = \"Gamma_R22\",\n",
+    "    annot = \"Phocoena_sinus.mPhoSin1.pri.110_exons\"\n",
+    ")\n",
+    "make_some_plots(pho_sin)\n",
+    "loss_list.append(pho_sin.get_loss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pho_sin = SpeciesInfo(\n",
+    "    species = \"PhoSin\",\n",
+    "    basedir = \"PhoSin_results\",\n",
+    "    demog = \"Constant\",\n",
+    "    dfe = \"Gamma_R22\",\n",
+    "    annot = \"all_sites\"\n",
+    ")\n",
+    "make_some_plots(pho_sin)\n",
+    "loss_list.append(pho_sin.get_loss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x145d2ebaa0e0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplot_mosaic(\n",
+    "    '''\n",
+    "    AAE\n",
+    "    CBE\n",
+    "    '''\n",
+    ")\n",
+    "plt.subplots_adjust(wspace=0.4, hspace=0.3)\n",
+    "axes[\"A\"].scatter(1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Expected SFS')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "obs_exp = pd.read_csv(\"/gpfs/projects/kernlab/stittes/analysis2/playground/sel_exp_obs_allele_freqs.csv\", sep=\" \")\n",
+    "obs_exp.head()\n",
+    "plt.scatter(obs_exp['Observed-SFS'], obs_exp['Expected'])\n",
+    "#one-to-one line\n",
+    "plt.plot([0,np.max(obs_exp['Observed-SFS'])],[0,np.max(obs_exp['Observed-SFS'])], color = 'black')\n",
+    "plt.xlabel(\"Observed SFS\")\n",
+    "plt.ylabel(\"Expected SFS\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>MAE E|s|</th>\n",
+       "      <th>MAE shape</th>\n",
+       "      <th>method</th>\n",
+       "      <th>species ID</th>\n",
+       "      <th>demography</th>\n",
+       "      <th>annotation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000807</td>\n",
+       "      <td>0.008626</td>\n",
+       "      <td>grapes</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001152</td>\n",
+       "      <td>0.029568</td>\n",
+       "      <td>dadi</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001773</td>\n",
+       "      <td>0.014414</td>\n",
+       "      <td>polyDFE</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>all_sites</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.022866</td>\n",
+       "      <td>0.230358</td>\n",
+       "      <td>grapes</td>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Vaquita2Epoch_1R22</td>\n",
+       "      <td>Phocoena_sinus.mPhoSin1.pri.110_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.023417</td>\n",
+       "      <td>0.232308</td>\n",
+       "      <td>grapes</td>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Vaquita2Epoch_1R22</td>\n",
+       "      <td>all_sites</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.023567</td>\n",
+       "      <td>0.250249</td>\n",
+       "      <td>grapes</td>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>Phocoena_sinus.mPhoSin1.pri.110_exons</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MAE E|s|  MAE shape   method species ID          demography  \\\n",
+       "1  0.000807   0.008626   grapes     HomSap            Constant   \n",
+       "0  0.001152   0.029568     dadi     HomSap            Constant   \n",
+       "2  0.001773   0.014414  polyDFE     HomSap            Constant   \n",
+       "1  0.022866   0.230358   grapes     PhoSin  Vaquita2Epoch_1R22   \n",
+       "1  0.023417   0.232308   grapes     PhoSin  Vaquita2Epoch_1R22   \n",
+       "1  0.023567   0.250249   grapes     PhoSin            Constant   \n",
+       "\n",
+       "                              annotation  \n",
+       "1               ensembl_havana_104_exons  \n",
+       "0               ensembl_havana_104_exons  \n",
+       "2                              all_sites  \n",
+       "1  Phocoena_sinus.mPhoSin1.pri.110_exons  \n",
+       "1                              all_sites  \n",
+       "1  Phocoena_sinus.mPhoSin1.pri.110_exons  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "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>MAE E|s|</th>\n",
+       "      <th>MAE shape</th>\n",
+       "      <th>method</th>\n",
+       "      <th>species ID</th>\n",
+       "      <th>demography</th>\n",
+       "      <th>annotation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.002667</td>\n",
+       "      <td>0.000712</td>\n",
+       "      <td>dadi</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>all_sites</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002344</td>\n",
+       "      <td>0.006828</td>\n",
+       "      <td>polyDFE</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000807</td>\n",
+       "      <td>0.008626</td>\n",
+       "      <td>grapes</td>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.024136</td>\n",
+       "      <td>0.179650</td>\n",
+       "      <td>dadi</td>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Vaquita2Epoch_1R22</td>\n",
+       "      <td>Phocoena_sinus.mPhoSin1.pri.110_exons</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.024237</td>\n",
+       "      <td>0.183145</td>\n",
+       "      <td>polyDFE</td>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Vaquita2Epoch_1R22</td>\n",
+       "      <td>all_sites</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.024476</td>\n",
+       "      <td>0.187955</td>\n",
+       "      <td>polyDFE</td>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>all_sites</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MAE E|s|  MAE shape   method species ID          demography  \\\n",
+       "0  0.002667   0.000712     dadi     HomSap            Constant   \n",
+       "2  0.002344   0.006828  polyDFE     HomSap            Constant   \n",
+       "1  0.000807   0.008626   grapes     HomSap            Constant   \n",
+       "0  0.024136   0.179650     dadi     PhoSin  Vaquita2Epoch_1R22   \n",
+       "2  0.024237   0.183145  polyDFE     PhoSin  Vaquita2Epoch_1R22   \n",
+       "2  0.024476   0.187955  polyDFE     PhoSin            Constant   \n",
+       "\n",
+       "                              annotation  \n",
+       "0                              all_sites  \n",
+       "2               ensembl_havana_104_exons  \n",
+       "1               ensembl_havana_104_exons  \n",
+       "0  Phocoena_sinus.mPhoSin1.pri.110_exons  \n",
+       "2                              all_sites  \n",
+       "2                              all_sites  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\\\begin{tabular}{rrllll}\\n\\\\toprule\\nMAE E|s| & MAE shape & method & species ID & demography & annotation \\\\\\\\\\n\\\\midrule\\n0.001152 & 0.029568 & dadi & HomSap & Constant & ensembl_havana_104_exons \\\\\\\\\\n0.002667 & 0.000712 & dadi & HomSap & Constant & all_sites \\\\\\\\\\n0.000807 & 0.008626 & grapes & HomSap & Constant & ensembl_havana_104_exons \\\\\\\\\\n0.004931 & 0.028638 & grapes & HomSap & Constant & all_sites \\\\\\\\\\n0.001773 & 0.014414 & polyDFE & HomSap & Constant & all_sites \\\\\\\\\\n0.002344 & 0.006828 & polyDFE & HomSap & Constant & ensembl_havana_104_exons \\\\\\\\\\n0.011556 & 0.026502 & dadi & HomSap & OutOfAfricaArchaicAdmixture_5R19 & all_sites \\\\\\\\\\n0.013851 & 0.030905 & dadi & HomSap & OutOfAfricaArchaicAdmixture_5R19 & ensembl_havana_104_exons \\\\\\\\\\n0.004859 & 0.051345 & grapes & HomSap & OutOfAfricaArchaicAdmixture_5R19 & ensembl_havana_104_exons \\\\\\\\\\n0.007921 & 0.055497 & grapes & HomSap & OutOfAfricaArchaicAdmixture_5R19 & all_sites \\\\\\\\\\n0.007188 & 0.024394 & polyDFE & HomSap & OutOfAfricaArchaicAdmixture_5R19 & ensembl_havana_104_exons \\\\\\\\\\n0.007422 & 0.035366 & polyDFE & HomSap & OutOfAfricaArchaicAdmixture_5R19 & all_sites \\\\\\\\\\n0.024292 & 0.209159 & dadi & PhoSin & Constant & all_sites \\\\\\\\\\n0.024365 & 0.233357 & dadi & PhoSin & Constant & Phocoena_sinus.mPhoSin1.pri.110_exons \\\\\\\\\\n0.023567 & 0.250249 & grapes & PhoSin & Constant & Phocoena_sinus.mPhoSin1.pri.110_exons \\\\\\\\\\n0.023853 & 0.235067 & grapes & PhoSin & Constant & all_sites \\\\\\\\\\n0.024439 & 0.234464 & polyDFE & PhoSin & Constant & Phocoena_sinus.mPhoSin1.pri.110_exons \\\\\\\\\\n0.024476 & 0.187955 & polyDFE & PhoSin & Constant & all_sites \\\\\\\\\\n0.024136 & 0.179650 & dadi & PhoSin & Vaquita2Epoch_1R22 & Phocoena_sinus.mPhoSin1.pri.110_exons \\\\\\\\\\n0.024367 & 0.200058 & dadi & PhoSin & Vaquita2Epoch_1R22 & all_sites \\\\\\\\\\n0.022866 & 0.230358 & grapes & PhoSin & Vaquita2Epoch_1R22 & Phocoena_sinus.mPhoSin1.pri.110_exons \\\\\\\\\\n0.023417 & 0.232308 & grapes & PhoSin & Vaquita2Epoch_1R22 & all_sites \\\\\\\\\\n0.024026 & 0.205934 & polyDFE & PhoSin & Vaquita2Epoch_1R22 & Phocoena_sinus.mPhoSin1.pri.110_exons \\\\\\\\\\n0.024237 & 0.183145 & polyDFE & PhoSin & Vaquita2Epoch_1R22 & all_sites \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import display\n",
+    "#pandas to latex table\n",
+    "loss_df = pd.concat(loss_list)\n",
+    "\n",
+    "display(loss_df.sort_values(by = ['MAE E|s|', 'species ID', 'demography', 'method']).groupby('species ID').head(3))\n",
+    "print(\"\")\n",
+    "display(loss_df.sort_values(by = ['MAE shape', 'species ID', 'demography', 'method']).groupby('species ID').head(3))\n",
+    "\n",
+    "loss_df.to_latex()\n",
+    "print(\"\")\n",
+    "loss_df.sort_values(by = ['MAE E|s|'])\n",
+    "\n",
+    "\n",
+    "loss_df.sort_values(by = ['species ID', 'demography', 'method', 'MAE E|s|', 'MAE shape']).to_latex(index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>species ID</th>\n",
+       "      <th>demography</th>\n",
+       "      <th>annotation</th>\n",
+       "      <th>MAE E|s|_dadi</th>\n",
+       "      <th>MAE E|s|_grapes</th>\n",
+       "      <th>MAE E|s|_polyDFE</th>\n",
+       "      <th>MAE shape_dadi</th>\n",
+       "      <th>MAE shape_grapes</th>\n",
+       "      <th>MAE shape_polyDFE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>all_sites</td>\n",
+       "      <td>0.002667</td>\n",
+       "      <td>0.004931</td>\n",
+       "      <td>0.001773</td>\n",
+       "      <td>0.000712</td>\n",
+       "      <td>0.028638</td>\n",
+       "      <td>0.014414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "      <td>0.001152</td>\n",
+       "      <td>0.000807</td>\n",
+       "      <td>0.002344</td>\n",
+       "      <td>0.029568</td>\n",
+       "      <td>0.008626</td>\n",
+       "      <td>0.006828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>OutOfAfricaArchaicAdmixture_5R19</td>\n",
+       "      <td>all_sites</td>\n",
+       "      <td>0.011556</td>\n",
+       "      <td>0.007921</td>\n",
+       "      <td>0.007422</td>\n",
+       "      <td>0.026502</td>\n",
+       "      <td>0.055497</td>\n",
+       "      <td>0.035366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HomSap</td>\n",
+       "      <td>OutOfAfricaArchaicAdmixture_5R19</td>\n",
+       "      <td>ensembl_havana_104_exons</td>\n",
+       "      <td>0.013851</td>\n",
+       "      <td>0.004859</td>\n",
+       "      <td>0.007188</td>\n",
+       "      <td>0.030905</td>\n",
+       "      <td>0.051345</td>\n",
+       "      <td>0.024394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>Phocoena_sinus.mPhoSin1.pri.110_exons</td>\n",
+       "      <td>0.024365</td>\n",
+       "      <td>0.023567</td>\n",
+       "      <td>0.024439</td>\n",
+       "      <td>0.233357</td>\n",
+       "      <td>0.250249</td>\n",
+       "      <td>0.234464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Constant</td>\n",
+       "      <td>all_sites</td>\n",
+       "      <td>0.024292</td>\n",
+       "      <td>0.023853</td>\n",
+       "      <td>0.024476</td>\n",
+       "      <td>0.209159</td>\n",
+       "      <td>0.235067</td>\n",
+       "      <td>0.187955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Vaquita2Epoch_1R22</td>\n",
+       "      <td>Phocoena_sinus.mPhoSin1.pri.110_exons</td>\n",
+       "      <td>0.024136</td>\n",
+       "      <td>0.022866</td>\n",
+       "      <td>0.024026</td>\n",
+       "      <td>0.179650</td>\n",
+       "      <td>0.230358</td>\n",
+       "      <td>0.205934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PhoSin</td>\n",
+       "      <td>Vaquita2Epoch_1R22</td>\n",
+       "      <td>all_sites</td>\n",
+       "      <td>0.024367</td>\n",
+       "      <td>0.023417</td>\n",
+       "      <td>0.024237</td>\n",
+       "      <td>0.200058</td>\n",
+       "      <td>0.232308</td>\n",
+       "      <td>0.183145</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  species ID                        demography  \\\n",
+       "0     HomSap                          Constant   \n",
+       "1     HomSap                          Constant   \n",
+       "2     HomSap  OutOfAfricaArchaicAdmixture_5R19   \n",
+       "3     HomSap  OutOfAfricaArchaicAdmixture_5R19   \n",
+       "4     PhoSin                          Constant   \n",
+       "5     PhoSin                          Constant   \n",
+       "6     PhoSin                Vaquita2Epoch_1R22   \n",
+       "7     PhoSin                Vaquita2Epoch_1R22   \n",
+       "\n",
+       "                              annotation  MAE E|s|_dadi  MAE E|s|_grapes  \\\n",
+       "0                              all_sites       0.002667         0.004931   \n",
+       "1               ensembl_havana_104_exons       0.001152         0.000807   \n",
+       "2                              all_sites       0.011556         0.007921   \n",
+       "3               ensembl_havana_104_exons       0.013851         0.004859   \n",
+       "4  Phocoena_sinus.mPhoSin1.pri.110_exons       0.024365         0.023567   \n",
+       "5                              all_sites       0.024292         0.023853   \n",
+       "6  Phocoena_sinus.mPhoSin1.pri.110_exons       0.024136         0.022866   \n",
+       "7                              all_sites       0.024367         0.023417   \n",
+       "\n",
+       "   MAE E|s|_polyDFE  MAE shape_dadi  MAE shape_grapes  MAE shape_polyDFE  \n",
+       "0          0.001773        0.000712          0.028638           0.014414  \n",
+       "1          0.002344        0.029568          0.008626           0.006828  \n",
+       "2          0.007422        0.026502          0.055497           0.035366  \n",
+       "3          0.007188        0.030905          0.051345           0.024394  \n",
+       "4          0.024439        0.233357          0.250249           0.234464  \n",
+       "5          0.024476        0.209159          0.235067           0.187955  \n",
+       "6          0.024026        0.179650          0.230358           0.205934  \n",
+       "7          0.024237        0.200058          0.232308           0.183145  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\\\begin{tabular}{lllrrrrrr}\\n\\\\toprule\\nspecies ID & demography & annotation & MAE E|s|_dadi & MAE E|s|_grapes & MAE E|s|_polyDFE & MAE shape_dadi & MAE shape_grapes & MAE shape_polyDFE \\\\\\\\\\n\\\\midrule\\nHomSap & Constant & all_sites & 0.002667 & 0.004931 & 0.001773 & 0.000712 & 0.028638 & 0.014414 \\\\\\\\\\nHomSap & Constant & ensembl_havana_104_exons & 0.001152 & 0.000807 & 0.002344 & 0.029568 & 0.008626 & 0.006828 \\\\\\\\\\nHomSap & OutOfAfricaArchaicAdmixture_5R19 & all_sites & 0.011556 & 0.007921 & 0.007422 & 0.026502 & 0.055497 & 0.035366 \\\\\\\\\\nHomSap & OutOfAfricaArchaicAdmixture_5R19 & ensembl_havana_104_exons & 0.013851 & 0.004859 & 0.007188 & 0.030905 & 0.051345 & 0.024394 \\\\\\\\\\nPhoSin & Constant & Phocoena_sinus.mPhoSin1.pri.110_exons & 0.024365 & 0.023567 & 0.024439 & 0.233357 & 0.250249 & 0.234464 \\\\\\\\\\nPhoSin & Constant & all_sites & 0.024292 & 0.023853 & 0.024476 & 0.209159 & 0.235067 & 0.187955 \\\\\\\\\\nPhoSin & Vaquita2Epoch_1R22 & Phocoena_sinus.mPhoSin1.pri.110_exons & 0.024136 & 0.022866 & 0.024026 & 0.179650 & 0.230358 & 0.205934 \\\\\\\\\\nPhoSin & Vaquita2Epoch_1R22 & all_sites & 0.024367 & 0.023417 & 0.024237 & 0.200058 & 0.232308 & 0.183145 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_df = loss_df.pivot_table(index=['species ID', 'demography', 'annotation'], \n",
+    "                          columns='method', \n",
+    "                          values=['MAE E|s|', 'MAE shape'])\n",
+    "pivot_df.columns = [f'{val}_{col}' for val, col in pivot_df.columns]\n",
+    "pivot_df.reset_index(inplace=True)\n",
+    "display(pivot_df)\n",
+    "pivot_df.to_latex(index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0007116947786198017\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.17965044653508885"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(np.min(loss_df[loss_df['species ID'] == \"HomSap\"]['MAE shape']))\n",
+    "np.min(loss_df[loss_df['species ID'] == \"PhoSin\"]['MAE shape'])  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "analysis2",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/workflows/summary_stats.sh b/workflows/summary_stats.sh
new file mode 100644
index 0000000..6d18a7d
--- /dev/null
+++ b/workflows/summary_stats.sh
@@ -0,0 +1,12 @@
+#config=workflows/config/snakemake/production_config_HomSap.yml
+config=workflows/config/snakemake/production_config_PhoSin.yml
+
+for i in {1..20};
+do
+    snakemake \
+    --snakefile workflows/summary_stats.snake \
+    --profile workflows/config/snakemake/oregon_profile/ \
+    --configfile $config \
+    --batch all=$i/20 \
+    --groups run_diploshic_fvs=group0
+done
diff --git a/workflows/summary_stats.snake b/workflows/summary_stats.snake
index 97da9e6..f60ea25 100644
--- a/workflows/summary_stats.snake
+++ b/workflows/summary_stats.snake
@@ -26,7 +26,6 @@ output_dir = os.path.abspath(config["output_dir"])
 # The analysis species
 species = stdpopsim.get_species(config["species"])
 
-
 # The names of all chromosomes to simulate, separated by commas
 # Use "all" to simulate all chromsomes for the genome
 chrm_list = [chrom.id for chrom in species.genome.chromosomes]
@@ -47,22 +46,19 @@ for x in demo_model_array:
     else:
         model = species.get_demographic_model(x["id"])
     demo_sample_size_dict[x["id"]] = {f"{model.populations[i].name}": m for i, m in enumerate(x["num_samples_per_population"])}
-    demo_pop_ids[x["id"]] = [x.name for x in model.populations]
+    demo_pop_ids[x["id"]] = [x.name for x in model.populations[:len(x["num_samples_per_population"])]]
 
 # Select DFE model from catalog  
 dfe_list = config["dfe_list"]
 annotation_list = config["annotation_list"]
 mask_file = config["mask_file"]
 
-
-
 nchunks=100
 chunks = np.arange(nchunks)
 # ###############################################################################
 # GENERAL RULES & GLOBALS
 # ###############################################################################
 
-
 rule all:
     input:
         expand(
@@ -92,14 +88,9 @@ rule all:
             chrms=chrm_list,
         ),
 
-
-
- 
-
 rule make_vcf:
     input:
         output_dir + "/simulated_data/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.trees"
-
     output:
         output_dir + "/summaries/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.vcf"
     run:
@@ -128,10 +119,9 @@ def write_diploshic_ancestralAllelesFile(ts, filename):
 rule make_diploshic_inputs:
     input:
         output_dir + "/simulated_data/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.trees"
-
     output:
-        temp(output_dir + "/summaries/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.sampleToPopFile"),
-        temp(output_dir + "/summaries/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.ancestralAllelesFile")
+        output_dir + "/summaries/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.sampleToPopFile",
+        output_dir + "/summaries/{demog}/{dfes}/{annots}/{seeds}/sim_{chrms}.ancestralAllelesFile"
     run:
         ts = tskit.load(input[0])
         write_diploshic_sampleToPopFile(ts, output[0], wildcards.demog)
@@ -156,22 +146,17 @@ rule run_diploshic_fvs:
     input:
         rules.make_vcf.output,
         rules.make_diploshic_inputs.output
-
     output:
-        temp(
             expand(output_dir + "/summaries/{{demog}}/{{dfes}}/{{annots}}/{{seeds}}/sim_{{chrms}}.{{popid}}.{{chunk}}.diploshic.{ext}",
             chunk=chunks,
             ext=["fvec", "stats"],        
             )
-        )
-
     params:
         seq_len = lambda wildcards, input: int(species.get_contig(wildcards.chrms).length), 
         start = lambda wildcards, input: chunk_coords(wildcards.chrms, int(wildcards.chunk), nchunks)[0],
         end = lambda wildcards, input: chunk_coords(wildcards.chrms, int(wildcards.chunk), nchunks)[1],
         test = lambda wildcards, input: chunk_coords(wildcards.chrms, int(wildcards.chunk), nchunks)[2],
         popid = lambda wildcards, input: wildcards.popid,
-
     shell:
         """
         if [[ {params.test} -eq 1 && `diploSHIC fvecVcf haploid {input[0]} 1 {params.seq_len} {output[0]} --sampleToPopFileName {input[1]} --ancFileName {input[2]} --targetPop {params.popid} --statFileName {output[1]} --segmentStart {params.start} --segmentEnd {params.end}` == 0 ]]; 
@@ -181,8 +166,7 @@ rule run_diploshic_fvs:
                 touch {output[0]} && touch {output[1]}
         fi
         """
-
-
+        
 rule gather_diploshic_fvs:
     input:
          expand(output_dir + "/summaries/{{demog}}/{{dfes}}/{{annots}}/{{seeds}}/sim_{{chrms}}.{{popid}}.{chunk}.diploshic.{ext}",
@@ -222,7 +206,7 @@ rule plot_pi_individual:
         sns.lineplot(x="mid", y="pi", data=df, ax=ax, linewidth=2.5, palette="tab10")
         
         # plot annotations as rugplot
-        if wildcards.annots != "none":
+        if wildcards.annots not in ["none", "all_sites"]:
             exons = species.get_annotations(wildcards.annots)
             exon_intervals = exons.get_chromosome_annotations(wildcards.chrms)
             sns.rugplot(pd.DataFrame(data={'exons':exon_intervals[:,0]}), ax=ax, color="g", lw=1, alpha=0.05)
@@ -266,7 +250,7 @@ rule plot_pi_allseeds:
         for i, stat in enumerate(stat_names):
             sns.lineplot(data=stacked, x="mid", y=stat, hue="seed", alpha=0.5, ax=axs[i])
             # plot annotations as rugplot
-            if wildcards.annots != "none":
+            if wildcards.annots not in ["none", "all_sites"]:
                 exons = species.get_annotations(wildcards.annots)
                 exon_intervals = exons.get_chromosome_annotations(wildcards.chrms)
                 sns.rugplot(pd.DataFrame(data={'exons':exon_intervals[:,0]}),