diff --git a/EMBEDR/plots/sweep_lineplots.py b/EMBEDR/plots/sweep_lineplots.py
index 1532625..0bff06a 100644
--- a/EMBEDR/plots/sweep_lineplots.py
+++ b/EMBEDR/plots/sweep_lineplots.py
@@ -565,7 +565,7 @@ def _plot(self, rowNo, colNo):
                          self._hp_array.max() + xspan * 0.01)
 
         if self.title is None:
-            title = self.lab_proc[2][self.lab_proc[3][label]]
+            title = self.lab_proc[2][self.lab_proc[3][label]].title()
         else:
             title = self.title
         axis.set_title(title, fontsize=self.title_size,
diff --git a/projects/Figures/Figure_04v1_GlobalParameterSweep.ipynb b/projects/Figures/Figure_04v1_GlobalParameterSweep.ipynb
new file mode 100644
index 0000000..930e727
--- /dev/null
+++ b/projects/Figures/Figure_04v1_GlobalParameterSweep.ipynb
@@ -0,0 +1,1360 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "80bd160d-33c6-4eb5-828c-28f0e832d2d1",
+   "metadata": {},
+   "source": [
+    "# Figure 4: Global Quality over Parameter Sweep\n",
+    "\n",
+    "The immediate application of EMBEDR is to assess the quality of embeddings that are generated at different hyperparameter values (`perplexity`, `n_neighbors`, etc.).  In the paper we demonstrate that this information can usefully be summarized as a series of box plots.  This type of figure has been incorporated into the EMBEDR code to facilitate analysis, as is shown in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "051bcc0b-66c4-4afc-8005-795df5e27fa4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from EMBEDR import EMBEDR, EMBEDR_sweep\n",
+    "from EMBEDR.human_round import *\n",
+    "import EMBEDR.plots.EMBEDR_Figure_01v1_DimRed_Zoology as F01\n",
+    "import EMBEDR.plotting_utility as putl\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.gridspec as gs\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import pairwise_distances as pwd\n",
+    "import warnings\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", message=\"This figure includes Axes that\")\n",
+    "warnings.filterwarnings(\"ignore\", message=\"tight_layout not applied: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed8397cf-d2c3-418c-a33f-37e0fb703206",
+   "metadata": {},
+   "source": [
+    "### Load the data and metadata\n",
+    "\n",
+    "Here we load the Tabula Muris Marrow data set.  The data is stored as an `anndata` object, so we load it with the scanpy package.  The metadata are then stored as the `.obs` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "354dfd7f-82b6-4179-95d5-635cb8c43e0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4771, 50)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = sc.read_h5ad(\"../../data/tabula-muris/04_facs_processed_data/FACS/Processed_Marrow.h5ad\")\n",
+    "sc.tl.leiden(data)\n",
+    "\n",
+    "X = data.obsm['X_pca'].astype(float).copy()\n",
+    "print(X.shape)\n",
+    "\n",
+    "metadata = data.obs\n",
+    "\n",
+    "del data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7252325b-c289-418b-b9aa-84f1e146c50e",
+   "metadata": {},
+   "source": [
+    "### Set runtime parameters here...\n",
+    "\n",
+    "While many of these parameters can be ignored by users, we make them explicit here to show some of the options available when using EMBEDR.  Most important, however, is to give your project a **name** and a **directory** in which it can store intermediate results.  EMBEDR has been written to be efficient with memory and time, but for larger hyperparameter sweeps this can still result in saving dozens of files, so specifying this directory can help with project management down the road.\n",
+    "\n",
+    "In this case, we want to sweep t-SNE's `perplexity` parameter, so we set `sweep_type = 'perplexity'`.  We can then indicate the values of perplexity that we want to use, or the `EMBEDR_sweep` object will initialize an array for you.  The `generate_rounded_log_arr` function generates an array of integers that are roughly log-spaced.  In the paper, we use 25 data embeddings and 10 null embeddings, but we find that results hold for as few as 3 data and 1 null embedding.  The EMBEDR infrastructure tries to save as much time as possible, so that once a large sweep (such as the one shown here) is completed, results are cached in the project directory for faster loading later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a0874eed-42e7-465d-befc-d744313f0e67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep_type = 'perplexity'\n",
+    "perp_array = generate_rounded_log_arr(25, 10, len(X))[:-1]\n",
+    "\n",
+    "n_data_embed = 25\n",
+    "n_null_embed = 10\n",
+    "\n",
+    "n_jobs = -1\n",
+    "\n",
+    "project_name = \"TabulaMuris_Marrow_FACS_Sweep1\"\n",
+    "project_dir = \"/home/emj760/../../projects/b1042/ejohnson/projects/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bd774b3a-8982-425f-964c-bf955835867a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Initializing EMBEDR hyperparameter sweep!\n",
+      "\n",
+      "Sweeping over 22 values of the 'perplexity' parameter!\n",
+      "\n",
+      "Fitting 'perplexity' sweep!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 3700 (1 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 2900 (2 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 2200 (3 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 1700 (4 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 1300 (5 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 1000 (6 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 800 (7 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 600 (8 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 450 (9 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 350 (10 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 300 (11 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 200 (12 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 150 (13 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 100 (14 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 80 (15 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 60 (16 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 50 (17 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 40 (18 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 30 (19 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 20 (20 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 15 (21 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Fitting data with 'perplexity' = 10 (22 / 22)\n",
+      "\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 1 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 2 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 3 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 4 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 5 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 6 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 7 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 8 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 9 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n",
+      "\n",
+      "Generating null 10 / 10\n",
+      "\n",
+      "Initialized EMBEDR.tSNE_Embed object!\n"
+     ]
+    }
+   ],
+   "source": [
+    "sweepObj = EMBEDR_sweep(sweep_type='perplexity',\n",
+    "                        n_jobs=n_jobs,\n",
+    "                        n_data_embed=n_data_embed,\n",
+    "                        n_null_embed=n_null_embed,\n",
+    "                        sweep_values=perp_array,\n",
+    "                        project_name=project_name,\n",
+    "                        project_dir=project_dir,\n",
+    "                        verbose=1)  ## Set verbose to zero to silence output!\n",
+    "\n",
+    "sweepObj.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a05b4a7-6dbf-431f-afa6-395684a74eb9",
+   "metadata": {},
+   "source": [
+    "### Plot the results!\n",
+    "\n",
+    "The `EMBEDR_sweep` object will then automatically genereate the boxplots from Figure 4.  However, if we want, we can easily add a few example embeddings over the boxplots as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "81e79597-89c9-4ba9-89a0-6d482f5a540a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='$k_{\\\\mathrm{Eff}}$'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axis = sweepObj.sweep_boxplot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "050b527e-1098-4ea4-b33c-01aa85255f91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'fig_dir' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_22311/889809323.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0mfig_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Figure_04v1_GlobalParameterSweep_\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mproject_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m \u001b[0mputl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfig_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pdf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tiff'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'fig_dir' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 540x540 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Perplexities to display\n",
+    "perp_2_show = np.array([30, 100, 1000])\n",
+    "\n",
+    "## Figure-level parameters\n",
+    "my_dpi   = 400\n",
+    "fig_wid  = 7.5  ## inches (8 inch-wide paper minus margins)\n",
+    "fig_hgt  = 1.0 * fig_wid\n",
+    "fig_size = (fig_wid, fig_hgt)\n",
+    "fig_pad  = 0.4\n",
+    "fig_ppad = 0.01  ## Percent of fig to leave around edge.\n",
+    "            \n",
+    "## Set gridspec parameters\n",
+    "main_hspace   = 0.02\n",
+    "main_hratios  = [1.0, 1.3]\n",
+    "top_wspace    = 0.01\n",
+    "bot_wpad       = 0.0\n",
+    "bot_hpad       = 0.0\n",
+    "bot_labelsize  = 16\n",
+    "bot_xlim       = [-1, sweepObj.n_sweep_values]\n",
+    "\n",
+    "## Set the p-Value colorbar explicitly so that its consistent between panels.\n",
+    "pVal_cps  = [0, 1, 2, 3, 5]\n",
+    "pVal_cmap = putl.CategoricalFadingCMap(change_points=pVal_cps)\n",
+    "\n",
+    "## Create the figure!\n",
+    "fig = plt.figure(figsize=fig_size)\n",
+    "\n",
+    "## Set up top - bottom subplots\n",
+    "main_gs = fig.add_gridspec(2, 1,\n",
+    "                           hspace=main_hspace,\n",
+    "                           height_ratios=main_hratios)\n",
+    "\n",
+    "## Set up top axes\n",
+    "top_behind_ax = fig.add_subplot(main_gs[0])\n",
+    "top_behind_ax = putl.make_border_axes(top_behind_ax,\n",
+    "                                      spine_alpha=0)\n",
+    "\n",
+    "## Gridspec for select embeddings\n",
+    "top_gs = main_gs[0].subgridspec(nrows=1,\n",
+    "                                ncols=len(perp_2_show),\n",
+    "                                wspace=top_wspace)\n",
+    "\n",
+    "## Axes for select embeddings\n",
+    "top_axes = []\n",
+    "for ii in range(len(perp_2_show)):\n",
+    "    ax = fig.add_subplot(top_gs[ii])\n",
+    "    \n",
+    "    top_axes.append(ax)\n",
+    "\n",
+    "fig.tight_layout(pad=fig_pad)\n",
+    "\n",
+    "## Add the boxplots to the bottom\n",
+    "bot_axis = sweepObj.sweep_boxplot(fig=fig,\n",
+    "                                  gridspec=main_gs,\n",
+    "                                  gridspec_idx=1,  ## Which gridspec to use to make subplots\n",
+    "                                  params_2_highlight=perp_2_show,\n",
+    "                                  pVal_cmap=pVal_cmap)\n",
+    "\n",
+    "## Add text overlays on the highlighted perplexity values\n",
+    "tform = bot_axis.transAxes.inverted()\n",
+    "for pNo, perp in enumerate(sweepObj.sweep_values[::-1]):\n",
+    "    if perp not in perp_2_show:\n",
+    "        continue\n",
+    "\n",
+    "    bX0, bY0, bdX, bdY = bot_axis.artists[pNo].get_window_extent().bounds\n",
+    "    tX = bX0 + (bdX / 2.) - 2\n",
+    "    tY = bY0 + (bdY / 2.)\n",
+    "    tX, tY = tform.transform([tX, tY])\n",
+    "    kE = int(human_round(sweepObj.kEff[perp]))\n",
+    "    bot_axis.text(tX, tY, r\"$k_{Eff} \\approx $\" + f\"{kE}\",\n",
+    "                  va='center', ha='center', fontweight='bold',\n",
+    "                  color='w', rotation=-90, fontsize=10,\n",
+    "                  transform=bot_axis.transAxes)\n",
+    "\n",
+    "for ii, perp in enumerate(perp_2_show):\n",
+    "    axis = top_axes[ii]\n",
+    "    _ = sweepObj.plot_embedding(param_2_plot=perp,\n",
+    "                                fig=fig,\n",
+    "                                axis=axis,\n",
+    "                                axis_kwds={'spine_alpha': 1,\n",
+    "                                           'spine_width':0.5},\n",
+    "                                cmap=pVal_cmap,\n",
+    "                                scatter_s=2,\n",
+    "                                scatter_alpha=0.4,\n",
+    "                                order='asc',\n",
+    "                                show_cbar=False,\n",
+    "                                cite_EMBEDR=False)\n",
+    "    \n",
+    "    ylim = axis.get_ylim()\n",
+    "    _ = axis.set_ylim(ylim[0], 1.1 * ylim[1])\n",
+    "    \n",
+    "    kE = human_round(sweepObj.kEff[perp])\n",
+    "    _ = axis.set_xlabel(r\"$k_{Eff} \\approx $\" + f\"{kE}\", labelpad=-12)\n",
+    "    _ = axis.xaxis.set_label_position('top')\n",
+    "    \n",
+    "_ = putl.add_panel_number(top_axes[0], \"A\", edge_pad=7,\n",
+    "                          fontsize=10)\n",
+    "_ = putl.add_panel_number(top_axes[1], \"B\", edge_pad=7,\n",
+    "                          fontsize=10)\n",
+    "_ = putl.add_panel_number(top_axes[2], \"C\", edge_pad=7,\n",
+    "                          fontsize=10)\n",
+    "\n",
+    "bot_behind_axis = putl.add_panel_number(fig.get_axes()[4], \"D\", edge_pad=7, fontsize=10)\n",
+    "\n",
+    "fig_name = f\"Figure_04v1_GlobalParameterSweep_\" + project_name\n",
+    "putl.save_figure(fig, fig_name, fig_dir=fig_dir, formats=['pdf', 'tiff', 'png'], dpi=400)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3af0ea0e-d01a-4353-8b54-1a3cc0f624aa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecb3e4cf-4a71-40ef-b523-3ae3bfc56081",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "EMBEDR_kernel",
+   "language": "python",
+   "name": "embedr"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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