Repository: xgi-org/xgi

Issue:
default settings in `draw()` ignored if one of them is passed as argument
Running 

```python
H = xgi.random_hypergraph(10, ps=[0.6, 0.05, 0.001], seed=3)
settings = {"min_node_size": 5}
xgi.draw_nodes(H, settings=settings)
```

raises `KeyError: 'node_fc_cmap'` for `settings["node_fc_cmap"]`. 

This is because the function starts with 

```python
if settings is None:
        settings = {
            "min_node_size": 10.0,
            "max_node_size": 30.0,
            "min_node_lw": 1.0,
            "max_node_lw": 5.0,
            "node_fc_cmap": cm.Reds,
            "node_ec_cmap": cm.Greys,
        }
```

so that if you pass a `settings` argument with a single one of those (or that is just not `None`), all other default values are never defined. 

I guess default settings should always be defined, and only updated by the arguments?



Discussion:
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Attention: `1 lines` in your changes are missing coverage. Please review.
> Comparison is base [(`31b0fc8`)](https://app.codecov.io/gh/xgi-org/xgi/commit/31b0fc870cbd92c1d4c9db1d8e02221aa2834f8a?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=xgi-org) 92.05% compared to head [(`f178c23`)](https://app.codecov.io/gh/xgi-org/xgi/pull/476?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=xgi-org) 92.04%.

<details><summary>Additional details and impacted files</summary>


```diff
@@            Coverage Diff             @@
##             main     #476      +/-   ##
==========================================
- Coverage   92.05%   92.04%   -0.01%     
==========================================
  Files          60       60              
  Lines        4392     4387       -5     
==========================================
- Hits         4043     4038       -5     
  Misses        349      349              
```


| [Files](https://app.codecov.io/gh/xgi-org/xgi/pull/476?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=xgi-org) | Coverage Δ | |
|---|---|---|
| [xgi/drawing/draw\_utils.py](https://app.codecov.io/gh/xgi-org/xgi/pull/476?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=xgi-org#diff-eGdpL2RyYXdpbmcvZHJhd191dGlscy5weQ==) | `88.61% <100.00%> (ø)` | |
| [xgi/drawing/draw.py](https://app.codecov.io/gh/xgi-org/xgi/pull/476?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=xgi-org#diff-eGdpL2RyYXdpbmcvZHJhdy5weQ==) | `75.98% <90.00%> (-0.34%)` | :arrow_down: |


</details>

[:umbrella: View full report in Codecov by Sentry](https://app.codecov.io/gh/xgi-org/xgi/pull/476?src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=xgi-org).   
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---

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maximelucas: Good points Thomas, thanks! All addressed now I think.

Patch:
diff --git a/docs/source/api/recipes/recipes.ipynb b/docs/source/api/recipes/recipes.ipynb
index c6cdebb..07c3ed5 100644
--- a/docs/source/api/recipes/recipes.ipynb
+++ b/docs/source/api/recipes/recipes.ipynb
@@ -631,7 +631,7 @@
     }
    ],
    "source": [
-    "edge_color = [colors[i-2] for i in H.edges.filterby(\"order\", 1, \"gt\").size.aslist()]\n",
+    "edge_color = [colors[i - 2] for i in H.edges.filterby(\"order\", 1, \"gt\").size.aslist()]\n",
     "\n",
     "xgi.draw(H, pos=pos, dyad_color=link_color, edge_fc=edge_color);"
    ]
@@ -661,7 +661,7 @@
     }
    ],
    "source": [
-    "color_dict = {idx : colors[i-2] for idx, i in H.edges.size.asdict().items()}\n",
+    "color_dict = {idx: colors[i - 2] for idx, i in H.edges.size.asdict().items()}\n",
     "\n",
     "xgi.draw(H, pos=pos, dyad_color=color_dict, edge_fc=color_dict);"
    ]
@@ -692,9 +692,12 @@
    ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
+    "\n",
     "cmap = ListedColormap(colors[1:])\n",
     "\n",
-    "_, (node_collection, dyad_collection, edge_collection) = xgi.draw(H, pos=pos, dyad_color=link_color, edge_fc_cmap=cmap)\n",
+    "_, (node_collection, dyad_collection, edge_collection) = xgi.draw(\n",
+    "    H, pos=pos, dyad_color=link_color, edge_fc_cmap=cmap\n",
+    ")\n",
     "\n",
     "plt.colorbar(edge_collection, label=\"edges\")\n",
     "\n",
diff --git a/docs/source/api/tutorials/In Depth 1 - Drawing nodes.ipynb b/docs/source/api/tutorials/In Depth 1 - Drawing nodes.ipynb
index dc68b11..7cb44e2 100644
--- a/docs/source/api/tutorials/In Depth 1 - Drawing nodes.ipynb
+++ b/docs/source/api/tutorials/In Depth 1 - Drawing nodes.ipynb
@@ -5,7 +5,7 @@
    "id": "2f6ce3a3",
    "metadata": {},
    "source": [
-    "# In depth: drawing nodes\n",
+    "# In Depth 1 - Drawing nodes\n",
     "\n",
     "Here we show the fuctionalities and parameters of `xgi.draw_nodes()`.  It is similar to the [networkx](https://networkx.org/documentation/stable/reference/drawing.html) corresponding function (+ some bonus) and heavily relies on [matplotlib](https://matplotlib.org/)'s scatter function."
    ]
@@ -533,7 +533,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -547,7 +547,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.16"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/api/tutorials/In Depth 2 - Drawing hyperedges.ipynb b/docs/source/api/tutorials/In Depth 2 - Drawing hyperedges.ipynb
index 1f1ffa5..c2d1439 100644
--- a/docs/source/api/tutorials/In Depth 2 - Drawing hyperedges.ipynb
+++ b/docs/source/api/tutorials/In Depth 2 - Drawing hyperedges.ipynb
@@ -447,10 +447,8 @@
    ],
    "source": [
     "ax, (dyad_collection, edge_collection) = xgi.draw_hyperedges(\n",
-    "    H,\n",
-    "    pos=pos,\n",
-    "    dyad_color=[3, 1, 4],\n",
-    "    edge_fc=H.edges.size)\n",
+    "    H, pos=pos, dyad_color=[3, 1, 4], edge_fc=H.edges.size\n",
+    ")\n",
     "\n",
     "ax, node_collection = xgi.draw_nodes(H, pos=pos, node_fc=H.nodes.degree, zorder=3)\n",
     "\n",
@@ -458,9 +456,7 @@
     "plt.colorbar(dyad_collection, label=\"dyads\")\n",
     "plt.colorbar(edge_collection, label=\"edges\")\n",
     "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "    "
+    "plt.tight_layout()"
    ]
   }
  ],
diff --git a/docs/source/api/tutorials/In Depth 3 - Drawing DiHypergraphs.ipynb b/docs/source/api/tutorials/In Depth 3 - Drawing DiHypergraphs.ipynb
new file mode 100644
index 0000000..757cd32
--- /dev/null
+++ b/docs/source/api/tutorials/In Depth 3 - Drawing DiHypergraphs.ipynb
@@ -0,0 +1,705 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e1c5005b",
+   "metadata": {},
+   "source": [
+    "# In Depth 3 - Drawing DiHypergraphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea8aad6a",
+   "metadata": {},
+   "source": [
+    "Here we show the fuctionalities and parameters of `xgi.draw_dihypergraph()`.  It is similar to the [networkx](https://networkx.org/documentation/stable/reference/drawing.html) corresponding function (+ some bonus) and heavily relies on [matplotlib](https://matplotlib.org/)'s Collection plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b762703f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgi\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f252027f",
+   "metadata": {},
+   "source": [
+    "Les us first create a small toy hypergraph containing edges of different sizes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0a032899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diedges = [({0, 1}, {2}), ({1}, {4}), ({2, 3}, {4,5})]\n",
+    "DH = xgi.DiHypergraph(diedges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "444f90d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x148456700>,\n",
+       "  <matplotlib.collections.PathCollection at 0x148456af0>))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5336d3",
+   "metadata": {},
+   "source": [
+    "## Basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12d7fa53",
+   "metadata": {},
+   "source": [
+    "Notice that this function returns a tuple `(ax, collections)` where `collections` is a tuple `(node_collection, edge_node_collection)`. The collections can be used to plot colorbars as we will see later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e879854",
+   "metadata": {},
+   "source": [
+    "The color, linewidth, transparancy, and style of the hyperedges can all be customised, for example with single values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a351af81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x1486ae340>,\n",
+       "  <matplotlib.collections.PathCollection at 0x1486ae970>))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(\n",
+    "    DH,\n",
+    "    node_shape=\"h\",\n",
+    "    node_fc=\"grey\",\n",
+    "    node_ec=\"r\",\n",
+    "    node_size=20,\n",
+    "    edge_marker_size=20,\n",
+    "    edge_marker=\"p\",\n",
+    "    lines_lw=2,\n",
+    "    arrowsize=10,\n",
+    "    edge_marker_fc=\"b\",\n",
+    "    lines_fc=\"b\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eb2b443",
+   "metadata": {},
+   "source": [
+    "Or with multiple values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7a86466",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/maxime/Dropbox (ISI Foundation)/WORK/SCIENCE/xgi/xgi/drawing/draw.py:436: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
+      "  node_collection = ax.scatter(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x1486dfee0>,\n",
+       "  <matplotlib.collections.PathCollection at 0x14870bf70>))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(\n",
+    "    DH,\n",
+    "    node_fc=[\"g\", \"grey\", \"purple\", \"coral\", \"r\", \"b\"],\n",
+    "    arrowsize=20,\n",
+    "    edge_marker_fc=[\"violet\", \"lightgreen\", \"skyblue\"],\n",
+    "    lines_fc=\"k\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13b388b0",
+   "metadata": {},
+   "source": [
+    "Adding node and edge labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7541fa6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x14879fc70>,\n",
+       "  <matplotlib.collections.PathCollection at 0x14879f8b0>))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH, node_labels=True, hyperedge_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "310d67df",
+   "metadata": {},
+   "source": [
+    "## Arrays of floats and colormaps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92d32d0d",
+   "metadata": {},
+   "source": [
+    "In XGI, you can easily color hyperedges according to an EdgeStat, or just an array or a dict with float values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "13c8efbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x148671fd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax, collections = xgi.draw_dihypergraph(\n",
+    "    DH, node_fc=DH.nodes.degree, edge_marker_fc=DH.edges.size, node_size=DH.nodes.degree\n",
+    ")\n",
+    "\n",
+    "node_coll, edge_coll = collections\n",
+    "\n",
+    "plt.colorbar(node_coll, label=\"nodes\")\n",
+    "plt.colorbar(edge_coll, label=\"edges\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56966a12",
+   "metadata": {},
+   "source": [
+    "By default, the colormaps used are \"crest_r\" and \"Reds\". These can be changed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c9566bd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x1483be520>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax, collections = xgi.draw_dihypergraph(\n",
+    "    DH,\n",
+    "    node_fc=DH.nodes.degree,\n",
+    "    edge_marker_fc=DH.edges.size,\n",
+    "    node_fc_cmap=\"viridis\",\n",
+    "    edge_marker_fc_cmap=\"tab10\",\n",
+    ")\n",
+    "\n",
+    "node_coll, edge_coll = collections\n",
+    "\n",
+    "plt.colorbar(node_coll, label=\"nodes\")\n",
+    "plt.colorbar(edge_coll, label=\"edges\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40e0587c",
+   "metadata": {},
+   "source": [
+    "## Styling of arrows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64362ef0",
+   "metadata": {},
+   "source": [
+    "By default, the `arrowstyle` used is `\"-|>\"`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f50cd066",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x1595c6f70>,\n",
+       "  <matplotlib.collections.PathCollection at 0x1595aa3d0>))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH, arrowstyle=\"-|>\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90962beb",
+   "metadata": {},
+   "source": [
+    "Other styles can be used, see the full list from [Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.FancyArrowPatch.html#matplotlib.patches.FancyArrowPatch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "27cdcec3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "styles = [\"simple\", \"fancy\", \"]->\"]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, len(styles), figsize=(10, 3))\n",
+    "\n",
+    "for i, style in enumerate(styles):\n",
+    "    ax = axs[i]\n",
+    "    xgi.draw_dihypergraph(DH, arrowstyle=style, ax=ax)\n",
+    "    ax.set_title(f\"{style}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13324569",
+   "metadata": {},
+   "source": [
+    "There is a second argument to style arrows: `connectionstyle`. The default value is `\"arc3\"`, but other values can be used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4ce35e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "styles = [\"angle3\", \"bar\", \"angle\"]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, len(styles), figsize=(12, 4))\n",
+    "\n",
+    "for i, style in enumerate(styles):\n",
+    "    ax = axs[i]\n",
+    "    xgi.draw_dihypergraph(DH, connectionstyle=style, ax=ax)\n",
+    "    ax.set_title(f\"{style}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51e00b61",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af6eecb5",
+   "metadata": {},
+   "source": [
+    "## Layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb4942d5",
+   "metadata": {},
+   "source": [
+    "The initial layout is computed based on the original nodes only. After that, the layout can be recomputed based on those original positions, with the edge-nodes starting at the barycenter, to avoid overlap:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0b1dd42f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x16504e430>,\n",
+       "  <matplotlib.collections.PathCollection at 0x16505ebe0>))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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k0tdUBkRErsMwDPYc/5CDp0oAyEpeyKT4gVcIqs9XUlzzHm5vJ2HWCLKSF2gAUwhRGRARuYGuQvARB0/tBSAreQGT4qebnMo/3N5Odtft4MjZcgCGRyeQO+4+YiMHm5xM+pPKgIhIDxiGwd7jH/PJqT0AzEm+i7T4GSan6p2zrSfIP/Y2LR2NADgSspk2OhurxWZyMulvGjokItIDFouFzDG3YwEqTu1hV+12gKAsBIZhUHFyN/vqCzDwER0ey21j72Nk3Bizo4lJdGZAROQmGIZBSX0+FSd3AzA76S4mjwyeQuDqbKGg+h1ONtcBkDJkEtkpC4kIizI5mZhJZUBE5CYZhsG++nzKuwvBfCaPnGluqB6ovXCIour36PS2E2YNZ3byfMYPS8disZgdTUymMiAicgu6CsFOyk/uAmBW0p1MGZkJgMfrpt3jCphFeB6fmz11H3ZvxDQseiS3jb2PQVFDTU4mgUJlQETkFhmGwf76AspOOoGuQpA8ZALvVf4XrZ0t3D/1rxhiH25qxvOu0+QffYumjvMATB01m+mj52GzapGgfEplQESkFwzDYH9DAWUnugpBhC2KTm87AOmjZjNzzO2m5Tp4ai8l9TvxGV7s4THMS11MwqAUU/JIYNPdBCIivWCxWJg+eh5ubyeVp/d1FwGA6vNVzEjM7fdr8m3uVgqOvcuJ5hoAkgaPJzt1EVFh9n7NIcFDZUBEpJfa3C3UNx674vHWzibOuU4xPGZUv2U53niUwuo8Ojxt2CxhzEq6g4kjpmmRoFyXyoCISC/tPf4xLZ2NV32u5nxVv5QBr8/D3uMfd++0OMQ+gtyx9zHY5DULEhy0F6WISC8lD5lIVFj0VZ87dLaUvl6adaHtDG9/8vvuIjB55EzunfxFFQHpMS0gFBHxA5/h40xrAzXnD1F7oYo2d2v3cxOsswn3Xb0s3Iq4uDgmTZqEYRhUnt7P3uMf4TO8RIXZyUldTOLgsX57LQkNKgMiIn5mGAZnWhvY31DIkUNHWfHAt/3+GgfK9nEm4hj1TUcBGD0olZzUe7CHx/j9tWTg05oBERE/s1gsxMcmcvekRxna3LWx0bP/58skjuv92oH6oydZ+73f8vaBP5I4KR6rxUbmmNtJi5+hRYJyy1QGRET6QeK4UYybmuy343V42hkcNYzbxt7H0Oh4vx1XQpPKgIhIEEodmsa9U/6SMKvexqX3dDeBiEgQSk+YoyIgfqMyICIiEuJUBkREREKcyoCIiEiIUxkQEREJcSoDIiIiIU5lQEREJMSpDIiIiIQ4lQEREZEQpzIgIiIS4lQGREREQpzKgIiISIhTGRARCULn286YHUEGEO1yISLSSz7DR4enjXa3izZ3K6dbG4iNGMSEEY4+e83CY+9iH2MwO+lOIsPsffY6EhpUBkREboHX5+WjI3/mnOskHZ42DIwrPiZ12OQ+zXDs3CecaKpmTvICUoZO6tPXkoFNlwlERG5Bp7ed+qajtHtcVy0CsZGD+3SL4Zyx9zA4ahjtnjY+PvomHx3ZSpu7tc9eTwY2nRkQEbkF9vAY0uJnUHl63xXPWS1WFkxYdtlj9UdP+uV1Lx1nqD2eGVPupuyEk7ITu6i9cJgTzXXMTrqTccOmYrFY/PJ6EhpUBkREboHH6+YqJwQAcCRkExc1BIC4uDgA1n7vt359/bi4OGzWMKYnziN5yESKarZxznWKwuo8qs9Xkp28kJjIQX59TRm4LIZhXOPHWUREruZ44xGctdtxdTZf8Vxc5BAemPolbJ+5RFBVVUVz85Ufe6vi4uKYNOnyNQI+w8cnJ/ewv6EQn+ElzBrOjMRc0uKn6yyB3JDKgIhID7k6W9hdt53aC4cBiImIY3bSXRw6c4D6pmMALJz4CAmDUkzL2NR+nqLqbZxurQcgPiaRuamLGBQ11LRMEvhUBkREbsBn+Kg6vZ999QV4fJ1YsDBl1CymJcwlzBZOa0cT2w+/wehBKcxKutPsuBiGQdWZ/ZQcz8fjc2O12Jg+Oocpo2ZhtWjduFxJZUBE5DrOuU5SXPM+51ynABgek0B28kKGRsebnOzGWjuaKK59n4amagCG2keSk7ooKLJL/1IZEBG5Cre3k/31BVSe3oeBQbgtgpmJuUwcMS2orsEbhsHRcxXsqfuQTm8HFqykJ8whIyHrsnUNEtpUBkREPsMwDOoaD7Ordgdt7hYAUoemMSvpTuzhMSanu3Vt7lZ21X7Qvd5hUNQwclIXMSJmtMnJJBCoDIiIXNTa2cSu2u0cbzwKQGzEYOakLCBxUKq5wfyo5nwVu2q30+5xATB55ExmjL6NMFu4ucHEVCoDIhLyfIaPg6dKONBQeHHBnZWpo2bjSMju0ymCZunwtLOn7kOOnqsAukpPdurdJMQlm5xMzKIyICIh7UzrCYpr3uPCxV0A42MSyU5ZyGD7cJOT9b36xmMU17yH6+LlkAnDHWQm3UGELdLkZNLfVAZEJCR1ejvYd3wnVWf2AxBhiyRzzO2MH+4IqgWCveX2dlByPJ+qMweArjHLWckLSRoy3uRk0p9UBkQkpBiGQc2FKnbX7ui+bj522BRmjbmDqPBok9OZ51TzcQpr8mjpaAS6Fk3OTpof0v9PQonKgIiEjJaORpy1H3Tfdx8XOYSslIW6Vn6Rx+fhQH0hn5zag4FBZJid2UnzSR2aFlJnS0KRyoCIDHg+w0vFyT2UNhTjNTxYLTYcCXNIHzVH99pfxdnWExRVb+NC+1kAxgweT1byAqIjYk1OJn1FZUBEBrTTLfUU17xP48VfbKNik8hKWahZ/Tfg9XkpP7mLshPF+Awf4bYIMsfcwYQQW1MRKlQGRGRA6vC0U3I8n8NnSwGIDLOTOeYOxg2bol9mN+FC2xmKqrdx1nUSgFFxycxNuZvYyMEmJxN/UhkQkQHFMAyOnT/InroP6fC0ATB+eDqZY24nMsxucrrgdGkOw/76AryGB5s1jBmJt5EWP0MbHw0QKgMiMmA0tZ9nV+0HnGiuBbpG7mYnL2Rk3BiTkw0Mze0XKKp5j1MtdQCMiBnN3JRFDLYPMzmZ9JbKgIgEPa/PQ/nJ3ZSdcOIzvNgsNhwJ2UwdNRub1WZ2vAHFMAwOnSll7/GP8fg6sVpsZCRkk54wG6tF/6+DlcqAiAS1k811OGvep6njPAAJcSlkpSwgLnKIucEGuNbOZpw171PfdAyAIfYR5KTew7DokeYGk1uiMiAiQand00ZJ3UccuThfPyosmllJd+qe+H7UvT6jdgcd3nYsWJg6ajbTRs/VLZtBRmVARIKKYRgcPVfB3rqP6PC2AzBxRAYzE3OJCIsyOV1oane72FW7nZoLVUDXMKe5qYsYGau1GsFCZUBEgkZj+zmcNe9zquU4AIOjhpOdspD42ESTkwlA7YXD7Kr9gDZ3KwBp8TOYkXgb4bYIk5PJjagMiEjA8/o8lJ1wUn5yFz7Dh80SxrTRc5kyKlOL1gJMp6edPcc/4sjZcgBiIuLITrmb0YNSTU4m16MyICIB7URTDc7aD2juuABA4qCxzEleQGzkIHODyXU1NFVTXPM+rZ1NAIwfNpVZSXfqUk6AUhkQkYDU7naxp+5Djp0/CHRtrTs7aT7JQyZqgWCQcHs72VdfQOXpEqBrkWdWygKSh0w0N5hcQWVARAKKYRgcPlvG3uMf4/Z2AF3XnqcnziPCFmlyOrkVp1vqKare1n37Z8qQScxOno89PMbkZHKJyoCIBIwLbWdw1rzP6dYGAIba48lKWciImASTk0lveX0eDjQUUXFyNwYGEbYoZifNZ+ywyTrTEwBUBkTEdB6fm9KGYipO7sHAR5g1nOmjc0gbOVOz7weYc65TFFVv43zbaaBrDUhWykJiIuJMThbaVAZExFT1jcdw1n7QvdAsafAEZifP1y+HAcxneKk4uZsDDcX4DC9h1ggyx+QyccQ0nSUwicqAiJiizd3K7tod3YNqosNjmZ18F8lDJpicTPpLY9s5imq2cebiZaGRsWOYm7KIuKgh5gYLQSoDItKvfIaPQ2cOsO/4Tty+TixYSBs5k+mjczScJgT5DB+Vp/exr34nXp8Hm8XG9MR5TB6ZqUtE/UhlQET6zXnXaYpr3uOs6yQAw6JHkp1ytza3EVo6GimqeY+TF7efHhY9ipzURQyxjzA5WWhQGRCRPuf2dnKgoYiDp/ZiYBBmjWBG4m1Mip+mv/6km2EYHDlbxp7jH+H2dmK1WHEkZJE+KktbUfcxlQER6VN1F46wq247rs5moOse81lJdxIdEWtyMglUrs4WnLUfcLzxCNC1B0VO6iKG6xbTPqMyICJ9wtXZzO66HdReOAx0zaifk7yAMYPHmZxMgoFhGNScr2JX3XY6PG1YsDBlZCbTEnMIs4abHW/AURkQEb+6tCBsf30BHp+760181CymJcwlzKY3cbk57Z42dtfuoPriWOrYyMHMTVnEqLgkk5MNLCoDIuI351wnKap+n/NtpwAYHpNAdvJChkbHm5xMgl3dhSM4az+gzd0CwKQR05g5Jpdwjaj2C5UBEek1t7eDffWFVJ3eh4FBuC2SmYm5TByRoSEy4jed3g721n3M4bOlQNdsiuyUu0kcPNbcYAOAyoCI3DLDMKi7cJhddTu6/2JLHTqZWUl3aBMa6TMnmmsprn6Pls5GAMYOm8LspDuJDLObnCx4qQyIyC1p7WhiV912jjceBbqu5WYlL2D0oFSTk0ko8Hjd7Gso4OCpvQBEhdmZk7yAlKGTTE4WnFQGROSm+AwfB0+VsL+hAK/Pg9ViZeqo2TgSsgmzhpkdT0LMmdYGiqq30dh+DoDkIROYk7xAZ6ZuksqAiPTYmdYTFNe8x4W2MwDExyaSnbyQwfbhJieTUOb1eSg74aTsxC4MfITbIpmddCfjhk3VmpUeUhkQkRvq9HSwrz6fqjMHAIiwRZE55nbGD0/Xm60EjPOu0xTVbOOcq+tultGDUslOXkhM5CCTkwU+lQERuaZLg1921+2g3eMCYNywqWSOuZ2o8GiT04lcyWf4+OTkHvY3FF7cHjmcGYm5pMVPV3G9DpUBEbmqlo5GnLUf0NBUDUBc5BCyUxYyKi7Z5GQiN9bUfp6i6m2cbq0HID4mkbmpixgUNbT7YzxeNx6fW8UWlQER+Ryf4aXi5B5KG4rwGl6sFhuOhDmkj5qDTQsEJYgYhkHVmf2UHM/H43NjtdiYPjqHKaNm4TN8vPPJ72npbOTeyV8M+d0RVQZEpNvplnqKa96nsf0sAKNik8hKWXjZX1Miwaa1o4mimvc40VwDwFD7SIbYh3P0XAUAo+KSWDjx0ZC+jKAyICJ0eNopOZ7fPdktMszOrDF3MHbYlJB+g5SBwzAMjp6rYHfdh7i9HVc8f/u4B647o8AwDPbu3YvT6aSsrIy2tjbsdjsOh4OsrCwyMzOD+t+KyoBICDMMg2PnDrLn+Id0eNoAmDDcwcwxtxMZFmVyOhH/a+1o4s2K/8Dt67zs8eiIOB5M/8oVOyK63W7Wr1/PmjVrqKiowGazkZaWRlxcHM3NzVRWVuL1epk6dSqrV6/mqaeeIjw8+DbkspodQETM0dR+nvcPbaag+h06PG0MjhrGorTlzE1dpCIgA9aRcxVXFAHo2nK7rMF52WOlpaXk5OTw3HPP4XA4yMvLo7m5mfLycoqKiigvL6e5uZm8vDwcDgfPPfccOTk5lJaW9teX4zc6MyASYrw+D+Und1N2wonP8GKz2MgYnc2UkbOxWW1mxxPpU1vKNtLcceGazz8w9csMsQ9nx44dLFmyhNTUVDZu3MicOXNueGyn08kTTzxBTU0NW7duZf78+X5M3rdUBkRCyMnmOpw179PUcR6AhLgUslIWEBc5xNxgIv3kZHMtNeeraO1sweVuxtXZQqe3vfv53LH301zfSU5ODvPmzeP1118nJqbno41bW1tZtmwZhYWFFBQUkJGR0Rdfht+pDIiEgHZPGyV1H3Hk4urpqLBoZiXdSerQtKBe9CTiDx6vm6aOC3i8HQyNGkVOTg7t7e0UFxffVBG4pLW1lezsbOx2OwUFBUGxhkA3DYsMYIZhcORcOSV1H9Nx8a+fiSOmMTMxl4iwSJPTiQSGMFs4w6LjAVi7di0lJSUUFRXdUhEAiImJYcOGDeTk5LB+/XqeffZZf8btEzozIDJANbafw1nzPqdajgMwxD6C7JSFjIgZbXIykcBkGAYOhwOHw8GmTZt6fbzly5dTUVFBaWlpwJ+B05kBkQHm0g5u5Sd34TN82KxhTBudw5SRM7FatEBQ5Fr27t1LRUUFL730kl+Ot3LlShYvXkxJSQmZmZl+OWZfURkQCQD+GmhyoqmG4tr3aeloBCBx0FjmJC8gVru2idyQ0+nEZrORm5vrl+Pl5uZis9lwOp0qAyJybTcaaLJ27doeDTRpd7vYU/chx84fBMAeHsPspPkkD5kY8KcnRQJFWVkZaWlp2O12vxwvOjqatLQ0ysrK/HK8vqShQyIm8cdAE8MwOHSmlC3lv+kuAmnxM3gw/SukDJ2kIiByE9ra2oiLi/PrMWNjY3G5XH49Zl/QmQERE3x2oElRUdE1B5rY7XYWLVrEokWLugeazJs3j61btzIj24Gz5n1OtzYAXZuvZKcsZHjMqP78UkQGDLvdTnNzs1+P2dLSQnR04G+RrDMDIv2stLSUJUuWMG/ePIqLi3s02QwgKyuL4uJicnJyWLLkAV7d8lNOtzYQZg1nVtKd3DvliyoCIr3gcDiorKykra3NL8dzuVxUVlbicDj8cry+pDIg0o/cbjePP/44qampNz3ZDLruX3799ddJSUlh3Qv/SUJ0KkvSv8KUkZlYLfrnLNIbWVlZeL1e8vPz/XK8/Px8vF4vWVlZfjleX9K7h0g/Wr9+PSUlJWzcuLFXA002bvwNxz6p45P364mJ8O81TpFQlZmZydSpU1m3bp1fjrdu3TrS09OZOXOmX47Xl1QGRPqJYRisWbOGRx99tMeXBq4lKyuLRx55hJ/97GdobpiIf1gsFlavXs1rr72G0+m88Sdch9PpZPPmzaxatSooFvJqAqFIP9mzZw+zZ88mLy+PRYsW9fp4eXl5LF68mD179gT8PcwiwcLtdvt1b4LCwkLCwgJ/rb7ODIj0k74caCIi/hEeHs7GjRupqalh2bJltLa23tTnX9q1sKamhg0bNgRFEQCVAZF+E8oDTUQCndfro9hZxZce/zd++KNt/OlPf6KwsJDs7OweF26n00l2djaFhYVs3bo1aLYvBpUBkX4TygNNRAKRYRiUldfyz//yBosf+D98feUr7D9QQ2l5LdOmz6GgoAC73U5OTg7Lly9n27ZtV9x26HK5yMvLY/ny5eTk5HRvWzx//nyTvqpbExznL0QGgFAeaCISaD7YUcq/vLiVY9Wnr3hu9OihxI8YRPyIDAoKCrpHht9zzz3dI8NjY2NpaWmhsrISr9dLeno6L7/88jVHhgc6lQGRfuJwOFi7dm33JkS9dWmgyfPPP9/7cCIh5g9/3HnVIgDwhUfmdv93eHg4zz77LM888wwlJSXdm4m5XC6io6N5/vnnycrKYubMmUFx18C1qAyI9JPPDjTxx90EwTTQRCTQ/M3zSyn/pI7z569cIHj3gmlXPGaxWMjMzBywd+5ozYBIPwnlgSYigSZt0mgeeSj7isdTU0YwYXzojfVWGRDpJ6E80EQk0Pz+j/n8auMHAMTERHY/vnDBtJD8N6WhQyL9KFQHmogEktdeL+L7//hHAJ782t08+kg2Tz79CidOXOAP//FNpkweY3LC/qcyINLPSktLmTdvHjk5OTe9WdGlgSaFhYUUFBQE1X3MIoFgy593893/9TsMw+ArX7qTv/2bh7BYLLhcHZw63cTY1HizI5pClwlE+llGRgZbt24NqYEmIoHgnbx9/MP3u4rAFx+7rbsIAERHR4ZsEQCVARFTzJ8/P6QGmoiY7f3tpfyP7/4Wn8/gkYez+fu/eyQk1wZciy4TiJjI7XZ3DzSpqKi47kCTVatWBe1AExEzfZRfwTe++Ws8Hi8PPjCbH7zwF9hs+lv4s1QGREx2+PQZfvruByweNYxTRw5fNtDE4XAMiIEmImYpLKpk1V//ks5OD4vvmcGPf/glwsJsZscKOFqGLGKiprZ2Vvz7H2lqb6fN7eHXK1aYHUlkwNi1+zDPffNXdHZ6WHCXgx/9QEXgWnSeRMQknR4Pf/tfb9DU3g7AeW04JOI3+/ZXs+qvf0l7u5vc26bwkx99lfBwFYFrURkQMYHPMHhh6zvsqanrfuxEUzMdHo+JqUQGhrLyWp5Z/SouVwdzsyby4k+eICJCJ8KvR2VAxAQ/2/4x75YfvOwxr89H5clTJiUSGRgOVtaz4tl1NLe0M2vmOF568b8TFaVFtzeiMiDSz94tP8hvCq4+W6C8/mQ/pxEZOA4fOcGKZ9bR1NTG9IwUfvbSk0TbI2/8iaIyINLfTjQ2XfO5soYT/ZhEZOCorjnNk0+/wrnzLUydksTPf7aC2Ngos2MFDV1EEelnX8mZw4ykRPIqDvKHXSWXPVd3/oIpmUSCWd3xs3x95c85c6aZSRNH8+raFQyKs5sdK6ioDIj0M4vFwozkMRw6fQaAGUmJfC13Lvvr6skem2JyOpHg0tBwnidXvsLJk42MGzuS9a+sZMiQm98ALNSpDIiYpPhoDQA548eSO2EcuRPGmZxIJLicOt3Ik0+/wvH6c6Qkj+AX655m+LA4s2MFJa0ZEDGBzzDYXVMLoLMBIrfg7Llmnlz5CjW1ZxiTOIxfrnuakfGDzY4VtFQGRExQefIUjW3txEREkJ6YYHYckaBy4UIrTz29jqPHTjFq1GB+se5pEhKGmh0rqKkMiJjAeazrEkFmShJhVv0zFOmppuY2Vjz7KlWHGhgxIo5frnuGpDHDzY4V9PQuJGKCS+sFsnSJQKTHWlraeWbVq1R8UsewobH84pWnSU2JNzvWgKAyINLPOj0e9tYeB7ReQKSnXG0drPrGL9hfWsPgwdG8+vOVTBivS2z+ojIg0s8OHG+gw+NhWEw0E+J1elPkRtrb3Xzj+V+xp+QocbFRrPvZCianJZoda0BRGRDpZ5fWC2SlJmOxWExOIxLYOjs9PP/tDRQ5DxEdHcnP16zAkZ5sdqwBR2VApJ8VH9N6AZGecLs9fOs7G8nf+Qn2qAjWvvQkM6anmh1rQFIZEOlHLR0dlNd37T+QPU5vaiLX4vF4+bvv/gfbPywnIiKMl/71a8yeNd7sWAOWyoBIP9pbcxyvYZA0dDCjBw8yO45IQPJ6fXz3+78jb9t+wsNtvPjTJ8iZm2Z2rAFNZUCkH326XkCXCESuxufz8cIPNvHmW3sJC7Pykx9/lTtyp5oda8BTGRDpR8XHqgGtFxC5GsMw+L8/3szmPxVjtVr40Q+/zMK7MsyOFRJUBkT6ydmWVg6fPguoDIh8nmEY/NNP/sQfNu3EYrHwgxf+knvvmWF2rJChMiDST3ZVd21MlDYqniHR2mtd5BLDMHjx5T/z2999BMAL33uMpUtmm5wqtKgMiPQTp24pFLmqV159l19t+ACAf/ifX+CRZXNNThR6VAZE+oFhGN3zBTSCWORTv/j1e6xd9y4Af/s3D/HFx24zOVFoUhkQ6QfHLzTS0NhEmNVKZnKS2XFEAsJvfruDf3v5TQD++rkH+OqX55ucKHSpDIj0g0tnBaaNGY09ItzkNCLm+/0f8/nnf3kDgGdWLObJr91tcqLQpjIg0g+0XkDkU5tfL+KHP3oNgK9/bSHPrFxsciJRGRDpYz7D6C4DWi8goW7rm7v5/v/ZBMCX/+pO/nr1A9qwKwCoDIj0sapTp2lsayc6IhxHovZfl9D1Tt4+vvu/fodhGHzxsdv4zrceUhEIECoDIn3s0lmBzOQkwmw2k9OImOP97aX8j+/+Fp/P4JGHs/n7v3tERSCAqAyI9LHio1ovIKHto/wKvvWd3+Dx+Fhy/yy+/w+PYbXq108g0XdDpA+5vV5Kao8DKgMSmgqLKvnmtzfg8Xi5Z9F0fvDCX2Cz6VdPoNF3RKQPlR5voM3tZmi0nYkjR5gdR6Rf7d5zhG9889d0dHi4a76DH//wy4SF6VJZIAozO4DIQHZpvcCc1BSsuj4qQaCqqorm5uZeH6eyqoEf/H//RWcnLFw4j5/++KuEh6sIBCqVAZE+5DzWtTlR1thkk5OI3FhVVRVpaWl+P+5rf/w2ERH6dRPI9N0R6SOuzk4O1DcAkD0u1eQ0Ijd26YxAUur9REYN6/XxOtrPUVf9Fp2d7b0+lvQtlQGRPrK3pg6vz0fikMGMGTLY7DgiPRYZNQx79CizY0g/0gJCkT5SrEsEIhIkVAZE+kj3COJU3VIoIoFNZUCkD5xrdVF16jQAczRfQEQCnMqASB/YVd11iWDSyBEMi4k2OY2IyPWpDIj0AW1ZLCLBRGVApA+oDIhIMFEZEPGz4xcaOX6hEZvVSmZyktlxRERuSGVAxM8unRXISEwgJjLC5DQiIjemMiDiZ7pEICLBRmVAxI98hvHpfAGVAREJEioDIn50+PQZzrvaiAoPI2PMaLPjiIj0iMqAiB9dOiuQmZxEuE3btYpIcFAZEPEjrRcQkWCkMiDiJx6vlz01dQBkj1MZEJHgoTIg4idlDSdxdboZYrczaWS82XFERHpMZUDET4qPVgMwJzUZq8VichoRkZ4LMzuAyECh9QIyUHS0nwuo40jfUxkQ8YO2TjcHjjcAWi8gwaWlpZ33PjjAq7/cxtEjRwCoq37Lr68RFxfn1+OJ/6kMiPhBSd1xPD4fowcPYsyQwWbHEbmujg43H+d/wpvv7GXHh2V0dHgAsIUN4sf//FsWLZzqt9eKi4tj0qRJfjue9A2VARE/uLReIGtsMhatF5AAtmv3Yb7xN7+mubntiufCwqw8t+ox7HbtqRFqtIBQxA+cx2oBrReQwHfk6MmrFgGA+Xc4VARClMqASC9dcLVRefIUAFmpKgMS2L7wSA6PLsu+6nN3L8jo5zQSKFQGRHppd3UtBjAhfjjDY2PMjiNyXTablWnTUq94PMxm5c47001IJIFAZUCkl4p1S6EEkS1/3s0//uD/B2DK5DHdj8+ZM4HBg6LNiiUmUxkQ6aVPtyy+8q8tkUDyTt4+/uH7v8MwDL742G384T+e5+mn7iEyMoy/eCzX7HhiIothGIbZIUSC1YnGJpb+7BfYLBa2/c2zxEZGmh1J5Kre317Kt76zEY/HxyMPZ/O/v/cYVmvX34Nerw+bTX8bhjJ990V64dIlgvTEBBUBCVgf5Vfwre/8Bo/Hx5L7Z/H9f/i0CAAqAqIyINIbu6p1S6EEtsKiSp7/1gY8Hi+L75nBD174C/3ylyvoJ0KkF4ZG2wmzWrlnaprZUUSusGv3YZ775q/o7PSw4C4HP/rBlwgLs5kdSwKQ1gyIfEZVVRXNzc09/nifYdDp8RIVfvVhnhrFKmbZt7+aFc+uw+XqIPe2Kbz0L18jIkJDZ+Xq9JMhclFVVRVpaf7/C7+yslKFQPpVWXktz6x+FZerg7lZE3nxJ0+oCMh16adD5KJLZwTGPfZXRMWP6vXx2k+f5Oim/7ypMw0ivXWwsp4Vz66juaWdWZnjeenF/05UVLjZsSTAqQyIfE5U/ChixiSZHUPkph0+coIVz6yjqamN6dNSWfvS14m26y4XuTEtIBQRGQCqa07z5NOvcO58C1OnJPHzNU8RExNldiwJEioDIiJBru74Wb6+8uecOdPMpImjeXXtCgbF2c2OJUFEZUBEJIg1NJznyZWvcPJkI+PHjWL9KysZMkQbZsnNURkQEQlSp0438uTTr3C8/hwpySP4xStPM3xYnNmxJAipDIiIBKGz55p5cuUr1NSeYUziMH657mni4weZHUuClMqAiEiQuXChlaeeXsfRY6dISBjCL9Y9TULCULNjSRBTGRARCSJNzW2sePZVqg41ED9iEL945WmSxgw3O5YEOZUBEZEg0dLSzjOrXqXikzqGDY3lF688TWpKvNmxZABQGRARCQKutg5WfeMX7C+tYfDgaF79+UrGj+/9pEwR0ARCEVO0u92UN5zkwPF6DhxvoLz+BJkpSfxw2RKzo0kAam93843nf8WekqPExUax7mcrmJyWaHYsGUBUBkT6ic/n4/k/vk7VydOcc7nwfW7D0APHG0xKJoGss9PD89/eQJHzENHRkbzysxU40pPNjiUDjMqADGhnW1r584FyoiMiGBEbQ3xcDMNjYxkRE02YrX/3dT929jwFR45d8/lpY0b3XxgJCm63h2/93W/I3/kJ9qgI1r70JNOnpZodSwYglQEZ0H6/ay8bdhZf9Tmb1cpfZc3iG3ff2S9ZxscP54GMqbxZWnHV59+rqORMSyuzU5KYnZpMxpjRRIbpn2io8ni8/N13/4PtO8qIjAzj5Rf/O7NnjTc7lgxQeqeRAS0j8dp/bXt9PsrqT/RjGnjhofvJnTieF7a8TafXe3kew2BPTR17aupY/3EhETYbGWNGqxyEIK/Xx3e//zvytu0nPNzGiz/5GnOzJ5kdSwYwvbPIgOUzDOzhYUTYbFf84gUYN3wY//rFZf2ea3H6ZEYNiuPbm/7EhbY2AEbGxbL2r5azp6aO3TV17K6u5UxLq8pBCPL5fLzwg028+dZewsKs/PSfvsrtuVPMjiUDnN5JZMBpaGxi6/4yth4op/5C41U/ZtLIEaz78n8jOiKin9N1mZGUyK+f+Eue/8Nmqs+dJ2tsCqnDh5E6fBiPZE7HMAxqz19gd3WtykEIMQyD//vjzWz+UzFWq4Uf/98vs2B+htmxJATonUMGhHa3mx2Vh3ljXynOYzVcWqcfExlBzrhU3vukqvtjxwwZzEt/8ShxUebu9Z40dAi/evwvyas4yPy0iZc9Z7FYSBk2lJRhQ1UOQoRhGPzTT/7EHzbtxGKx8MN//EsWL5phdiwJEXqnkKBlGAblDSfZsr+Md8o+oaWjo/u5OanJLJ3hYOHkSUSFh/O1Df9Jaf0JhsVEs+Yvv8CI2NhrHrf99Em/5OvJcQbZo/jCrBu/4V+tHNScu8Dumlr2XCwIKgfByzAMXnz5z/z2dx8B8ML3HuPBB2abnEpCicUwPnezs0iAO9/q4q2yCt7YV8rh02e7H08YFMfS6Q6WTHcwZsjgyz5nX+1x/qN4NyvumMfEkVcf31pVVUVaWprf81ZWVjJpUt8u/rpWOfgslYPA9fN177B23bsAfO/vv8B/W36byYkk1KgMSFDw+HzsPHyULftK+ejQUbw+HwCRYTbumjyJh6ZnMGdsMlaLpVevU1VVRXNzsz8iAxAXF9fnReBqeloOpo0ZzezUZGalJKkcmOQXv36Pf3v5TQC+862H+cqX+udWV5HPUhmQgHb0zFm27C/jzwfKOdfq6n7ckZjA0ukOFqdPNv3afzD4fDnYVV3H2VaVA7P95rc7+Od/eQOA57+xhK8/sdDkRBKqVAYk4LS0d/BuxUG27i+7bETv0Gg7D0xLZ+l0BxPiR5iYMPipHJjv93/M54c/eg2AZ1cu5pmV95qcSEKZyoAEBJ9hsKe6ljf2l/H+J1V0eDwA2CwWcieO56EZDnInjOv3EcKhwjAMqs+dZ091HbtratmtctCnNr9exP/6xz8C8OTX7uYbq+/H0stLXCK9oTIgprrWTIBxI4azdLqD+zOmMiI2xsSEoUnloO9sfXM3f/+932EYBl/50p387d88pCIgplMZkH7X7nazvfIQW/aVXTET4N70KSyd7sCRmKA3yACicuAf7+Tt4zv/89/x+Qy++NhtfPd/PKqfcwkIKgPSL7pnAuwr5Z3yg1fMBHhoRgYLJk8kKjzcxJTSU5fKwe7q2q4RyioHN/T+9lK+9Z2NeDw+Hl2Wzff/4TGsVqvZsUQAlQHpY+daXbxV2jUT4MiZT2cCjB48iAenpV91JoAEH505uL6P8iv4xjd/jcfj5cEHZvODF/4Cm01FQAKHyoD4ncfrJf/wMbbuv3ImwILJk1jqp5kAErg+Xw52VddedmsodP08TBuTyKyLQ5AciQkDshwUFlWy+vlf0tHhYfE9M/jxD79EWJgWwkpgURkQvzly+ixb9pfyZmnFFTMBHpqewT3paZoJEKIMw6D67Pnuswa7a25cDjISE4gI8nKwe88Rnlm9nrb2Thbc5eCnP36c8HAVAQk8KgPSK5dmAmzZV0pp/Ynux4dFR3P/tKmaCSBXFYzlwDAM9u7di9PppKysjLa2Nux2Ow6Hg6ysLDIzMy9bDLhvfzUrnl2Hy9XB7blT+Leffo2IiOAuNzJwqQzITfMZBrura9lylZkAt08az9LpmgkgNyeQy4Hb7Wb9+vWsWbOGiooKbDYbaWlpxMXF0dzcTGVlJV6vl6lTp7J69WqeeuopKqtO8NTTr9Dc0s7c7EmsefHrREVpcawELpUB6bHumQD7y6hvbOp+fNyI4Tw0w8H9jqkM10wA8YNAKQelpaU8/vjjlJSU8Oijj7Jy5Upyc3Ox2+3dH9PW1kZ+fj7r1q3jtddeIz09g7CoHDzeWGZljufna54k2h7p11wi/qYyINd1aSbAG/vK2PW5mQD3pU9h6QwH6aM1E0D6lhnlYMeOHSxZsoTU1FQ2btzInDlzbvg5TqeTr371cQ4dPsL8BSv402v/REyM1slI4FMZkCt0zQQ4wZZ9ZVfMBMgam8JDMxzclaaZAGKevi4HpaWl5OTkMG/ePF5//XViYnp+xqu1tZWHH36YoqIiCgoKyMjIuKmvTcQMKgPS7WxLK2+VVfDGvjKOfm4mwNLpDpZMSydRMwEkAN1MOZidmszslCQc1ygHbrebnJwc2tvbKS4uvqkicElrayvZ2dnY7XYKCgoIV3GWAKcyEOIuzQTYsr+Uj68yE+ChGRnMTtVMAAkuvSkHa9eu5bnnnqOoqKhHlwauxel0kpOTw8svv8yzzz7b2y9JpE+pDISo7pkAByo45/r0TTIjMYGl0zNYnD6Z2CgtepKBoaflICNxNG//778nKzOTTZs29fp1ly9fTkVFBaWlpVpXIwFNZSCEtLR38G75Qd7YX0rZZ2cCxETzQMZUlk7PYHz8cBMTivSPa5WD1uN1VKz9V/Ly8li0aFGvXycvL4/FixezZ88eMjMz/ZBcpG9oAsYA5zMMdh2rZcv+Uj44WEWHxwuAzWrl9onjNBNAQpLFYmHsiGGMHTGML8ya0V0OfvSv/0qlzUZubq5fXic3NxebzYbT6VQZkICmMjBA1V9o7JoJcKCchs/MBBg/YjgPzcjg/oypDIuJNjGhSOC4VA4imhtJS0u7bI5Ab0RHR5OWlkZZWZlfjifSV1QGBpB2t5sPDh7ijX2l7Kqu7X48NjKSe9Mns3RGBumjR+napcg1tLW1ERcX59djxsbG4nK5bvyBIiZSGQhyl2YCvLGvjHc/MxPAQtdMgKWaCSDSY3a7nebmZr8es6WlhehonYWTwKYyEKTOtrTyZmkFW/ZfPhMgcfAgHpzu4MHpDkYPHmRiQpHg43A4WLt2bfcmRL3lcrmorKzk+eef7304kT6kMhBEumYCHOWNfWXkHzqC9+KNIJFhYSycMomHpjuYpZkAIrcsKysLr9dLfn6+X+4myM/Px+v1kpWV5Yd0In1HtxYGgcOnz7BlfxlvfW4mwLQxo3lwuoPFUzUTQMQfDMPA4XDgcDg0Z0BCis4MBKjm9nbeLT/Ilv1lV8wEWDItnQenOTQTQMTPLBYLq1ev5rnnnsPpdPbqL3qn08nmzZt5+eWXVQQk4OnMQAC5NBPgjf2lbP/cTIA7Jo5j6YwMbhs/VjMBRPqQv/cmKCwsJMzPWyuL+JvKQADQTACRwFJaWsq8efPIycm5pV0Lly1bRmFhoXYtlKChMmCSdreb9w9WsWVf2ZUzARxTWDrdoZkAIibasWMHDz74ICkpKWzYsKFHlwycTidPPPEENTU1bN26lfnz5/dDUpHeUxnoR4ZhUFZ/gjf2lfJuxUFaOzqBT2cCPDQjg/lpEzQTQCRAlJaW8sQTT7B3714eeeQRnn76aXJzcy+77dDlcpGfn8+6devYvHkzmZmZbNiwQWcEJKioDPSD7pkA+0o5evZc9+OJQwbz4LR0zQQQCWBut5v169ezZs0aKioqsNlspKWlERsbS0tLC5WVlXi9XtLT01m1ahVPPfUU4Sr0EmRUBvqIx+vl40NH2bL/ypkAd0+ZxFLNBBAJKoZhUFJSgtPppKysDJfLRXR0NA6Hg6ysLGbOnKnLehK0VAb87NCprpkAb5deORNg6XQH92gmgIiIBBjd7+IHl2YCvLGvlPKGk92PX5oJsHS6g3EjNBNAREQCk8rALfIZBs5jNWzZX3aVmQDjWTrDoZkAIiISFAZEGTAMg71793Zfy7u0ycila3mZmZl+u5Z3/OJMgD9/bibAhPiumQD3OTQTQEREgktQrxm41irfuLg4mpubu1f5Tp06ldWrV9/yKt9rzQSIi4rk3vQpLJ3hYGqCZgKIiEhwCtoyUFpayuOPP05JSQmPPvooK1euvOL+37a2tu77f1977TVmzpzJxo0be3T/r2EYlNY3sGVf2RUzAbLHpbJ0ukMzAUREZEAIyjKwY8cOlixZQmpqKhs3bmTOnDk3/JyeTgY709LKW6XlbNlXdsVMgKXTHTw4LZ0EzQQQEZEBJOjKQGlpKTk5OcybN89vM8MvzQR4Y38pOw8dvWwmwKKpaSyd7iAzJUkzAUREZEAKqjLg793E/v1PW3i7opK3Sss572rr/phpY0bz0IwMFk1NIzZSMwFERGRgC6q7CdavX09JSQlFRUW3VAQAYmJi2LBhAzk5Odz3zGpG5uQCMDwmhiXTprJ0egZjRwzzZ2wREZGAFjRnBgzDwOFw4HA42LRpU6+P9+gXvkDezgKefvnnPDRjGvMmjCXMavVDUhERkeASNL/99u7dS0VFBStXrvTL8Z55+mlaTjTwVxNSuGPSeBUBEREJWUHzG9DpdGKz2cjNzfXL8XJzc7HZbDidTr8cT0REJFgFTRkoKysjLS3tsjkCvREdHU1aWhplZWV+OZ6IiEiwCpoy0NbWRlxcnF+PGRsbi+szOwuKiIiEoqApA3a7nebmZr8es6Wlheho7SMgIiKhLWjKgMPhoLKykra2tht/cA+4XC4qKytxOBx+OZ6IiEiwCpoykJWVhdfrJT8/3y/Hy8/Px+v1kpWV5ZfjiYiIBKuQnTOwfPlyKioqKC0t1W6DIiIS0oLmzIDFYmH16tW89tprvb4d0Ol0snnzZlatWqUiICIiIS9ozgyA//cmKCwsJCwsqCYyi4iI+F3QnBkACA8PZ+PGjdTU1LBs2TJaW1tv6vMv7VpYU1PDhg0bVAREREQIsjIAkJGRwdatWyksLCQ7O7vHlwycTifZ2dkUFhaydevW7u2LRUREQl3QlQGA+fPnU1BQgN1uJycnh+XLl7Nt27Yrbjt0uVzk5eWxfPlycnJysNvtFBQUMH/+fJOSi4iIBJ6gWjPweW63m/Xr17NmzRoqKiqw2WykpaURGxtLS0sLlZWVeL1e0tPTWbVqFU899RTh4eFmxxYREQkoQV0GLjEMg5KSEpxOJ2VlZbhcLqKjo3E4HGRlZTFz5kzdNSAiInINA6IMiIiIyK0LyjUDIiIi4j8qAyIiIiFOZUBERCTEqQyIiIiEOJUBERGREKcyICIiEuJUBkREREKcyoCIiEiIUxkQEREJcSoDIiIiIU5lQEREJMSpDIiIiIQ4lQEREZEQpzIgIiIS4lQGREREQpzKgIiISIhTGRAREQlxKgMiIiIhTmVAREQkxKkMiIiIhDiVARERkRCnMiAiIhLiVAZERERCnMqAiIhIiFMZEBERCXEqAyIiIiFOZUBERCTEqQyIiIiEOJUBERGREPf/AMs0cj49Ny24AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH, iterations=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "99cec264",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x165089af0>,\n",
+       "  <matplotlib.collections.PathCollection at 0x16509a9a0>))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH, iterations=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aec52abb",
+   "metadata": {},
+   "source": [
+    "A larger example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e653f0ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = [\n",
+    "    [[8], [0]],\n",
+    "    [[1, 2], [0]],\n",
+    "    [[0, 3], [1]],\n",
+    "    [[1, 3], [2]],\n",
+    "    [[1, 5], [3]],\n",
+    "    [[2, 5], [4]],\n",
+    "    [[3, 4], [5, 6]],\n",
+    "    [[6, 7], [5]],\n",
+    "    [[5, 8], [6]],\n",
+    "    [[6, 8], [7]],\n",
+    "    [[6, 0], [8]],\n",
+    "    [[7, 0], [9]],\n",
+    "]\n",
+    "\n",
+    "DH = xgi.DiHypergraph(edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "eacb941c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x164ffb910>,\n",
+       "  <matplotlib.collections.PathCollection at 0x164a67220>))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH, iterations=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "37f790a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x1651a4790>,\n",
+       "  <matplotlib.collections.PathCollection at 0x164cfcd60>))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgi.draw_dihypergraph(DH, iterations=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a073dcb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_xgi",
+   "language": "python",
+   "name": "venv_xgi"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/api/tutorials/Tutorial 8 - Directed Hypergraphs.ipynb b/docs/source/api/tutorials/Tutorial 8 - Directed Hypergraphs.ipynb
index 087181a..9e18cc5 100644
--- a/docs/source/api/tutorials/Tutorial 8 - Directed Hypergraphs.ipynb
+++ b/docs/source/api/tutorials/Tutorial 8 - Directed Hypergraphs.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -19,7 +18,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -37,7 +35,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -80,7 +77,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -101,7 +97,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -125,7 +120,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -141,7 +135,9 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: >"
+       "(<Axes: >,\n",
+       " (<matplotlib.collections.PathCollection at 0x1378a37c0>,\n",
+       "  <matplotlib.collections.PathCollection at 0x1378a3400>))"
       ]
      },
      "execution_count": 5,
@@ -150,7 +146,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -164,7 +160,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -214,7 +209,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -250,7 +244,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -271,8 +264,8 @@
       "{'one'}\n",
       "\n",
       "All node memberships:\n",
-      "{0: (set(), {'one'}), 1: ({'one', 2}, {0, 1, 'one'}), 2: ({'one'}, {0, 1, 'two'}), 3: ({0, 2}, {0, 'two'}), 4: ({0}, {2, 'two'}), 5: ({1}, set()), 6: ({1}, set()), 7: (set(), set())}\n",
-      "{0: {'one'}, 1: {'one', 0, 2, 1}, 2: {'one', 0, 'two', 1}, 3: {0, 2, 'two'}, 4: {0, 2, 'two'}, 5: {1}, 6: {1}, 7: set()}\n"
+      "{0: (set(), {'one'}), 1: ({2, 'one'}, {0, 1, 'one'}), 2: ({'one'}, {0, 1, 'two'}), 3: ({0, 2}, {0, 'two'}), 4: ({0}, {'two', 2}), 5: ({1}, set()), 6: ({1}, set()), 7: (set(), set())}\n",
+      "{0: {'one'}, 1: {0, 1, 2, 'one'}, 2: {0, 1, 'one', 'two'}, 3: {0, 2, 'two'}, 4: {0, 'two', 2}, 5: {1}, 6: {1}, 7: set()}\n"
      ]
     }
    ],
@@ -287,7 +280,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -328,7 +320,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -355,7 +346,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -392,7 +383,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -412,7 +403,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -441,7 +431,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -460,9 +449,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "hyper",
+   "display_name": "venv_xgi",
    "language": "python",
-   "name": "python3"
+   "name": "venv_xgi"
   },
   "language_info": {
    "codemirror_mode": {
@@ -476,7 +465,19 @@
    "pygments_lexer": "ipython3",
    "version": "3.9.13"
   },
-  "orig_nbformat": 4
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/docs/source/api/tutorials/in_depth.rst b/docs/source/api/tutorials/in_depth.rst
index ecfd3b3..ac600a9 100644
--- a/docs/source/api/tutorials/in_depth.rst
+++ b/docs/source/api/tutorials/in_depth.rst
@@ -1,9 +1,10 @@
 ##################
-In depth tutorials
+In Depth tutorials
 ##################
 
 .. toctree::
    :maxdepth: 1
 
    In Depth 1 - Drawing nodes
-   In Depth 2 - Drawing hyperedges
\ No newline at end of file
+   In Depth 2 - Drawing hyperedges
+   In Depth 3 - Drawing DiHypergraphs
diff --git a/xgi/core/hypergraph.py b/xgi/core/hypergraph.py
index 3d1539b..d43ed5a 100644
--- a/xgi/core/hypergraph.py
+++ b/xgi/core/hypergraph.py
@@ -862,7 +862,7 @@ class Hypergraph:
             try:
                 for e, value in values.items():
                     try:
-                        self._edge_attr[id][name] = value
+                        self._edge_attr[e][name] = value
                     except IDNotFound:
                         warn(f"Edge {e} does not exist!")
             except AttributeError:
diff --git a/xgi/drawing/draw.py b/xgi/drawing/draw.py
index 8d944b4..0e2af93 100644
--- a/xgi/drawing/draw.py
+++ b/xgi/drawing/draw.py
@@ -1,13 +1,13 @@
 """Draw hypergraphs and simplicial complexes with matplotlib."""
 
 from inspect import signature
-from itertools import combinations
 
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sb  # for cmap "crest"
 from matplotlib import cm
-from matplotlib.patches import FancyArrow
+from matplotlib.patches import FancyArrowPatch
 from mpl_toolkits.mplot3d.art3d import (
     Line3DCollection,
     LineCollection,
@@ -202,8 +202,6 @@ def draw(
         "max_dyad_lw": 10,
         "min_node_lw": 0,
         "max_node_lw": 5,
-        "edge_fc_cmap": "crest_r",  # for compatibility with simplices until update
-        "dyad_color_cmap": cm.Greys,  # for compatibility with simplices until update
     }
 
     settings.update(kwargs)
@@ -432,7 +430,7 @@ def draw_nodes(
             node_lw, settings["min_node_lw"], settings["max_node_lw"]
         )
 
-    node_size = node_size**2
+    node_size = np.array(node_size) ** 2
 
     # plot
     node_collection = ax.scatter(
@@ -593,10 +591,8 @@ def draw_hyperedges(
     dyad_lw = _draw_arg_to_arr(dyad_lw)
 
     # parse colors
-    dyad_color, dyad_c_mapped = _parse_color_arg(
-        dyad_color, dyad_color_cmap, list(dyads)
-    )
-    edge_fc, edge_c_mapped = _parse_color_arg(edge_fc, edge_fc_cmap, list(edges))
+    dyad_color, dyad_c_mapped = _parse_color_arg(dyad_color, list(dyads))
+    edge_fc, edge_c_mapped = _parse_color_arg(edge_fc, list(edges))
 
     # check validity of input values
     if np.any(dyad_lw < 0):
@@ -851,6 +847,7 @@ def draw_node_labels(
     verticalalignment_nodes="center",
     ax_nodes=None,
     clip_on_nodes=True,
+    zorder=None,
 ):
     """Draw node labels on the hypergraph or simplicial complex.
 
@@ -887,6 +884,9 @@ def draw_node_labels(
     clip_on_nodes : bool, optional
         Turn on clipping of node labels at axis boundaries.
         By default, True.
+    zorder : int, optional
+        The vertical order on which to draw the labels. By default, None, 
+        in which case it is plotted above the last plotted object.
 
     Returns
     -------
@@ -910,7 +910,8 @@ def draw_node_labels(
         node_labels = {id: id for id in H.nodes}
 
     # Plot the labels in the last layer
-    zorder = max_edge_order(H) + 1
+    if zorder is None:
+        zorder = max_edge_order(H) + 1
 
     text_items = {}
     for idx, label in node_labels.items():
@@ -1076,14 +1077,17 @@ def _draw_hull(node_pos, ax, edges_ec, facecolor, alpha, zorder, radius):
     Parameters
     ----------
     node_pos : np.array
-        nx2 dimensional array containing positions of the nodes
+        Array of dimension (n, 2) containing node positions
     ax : matplotlib.pyplot.axes
+        Axis to plot on
     edges_ec : str
         Color of the border of the convex hull
     facecolor : str
         Filling color of the convex hull
     alpha : float
         Transparency of the convex hull
+    zorder : float
+        Vertical order on which to plot
     radius : float
         Radius of the convex hull in the vicinity of the nodes.
 
@@ -1435,6 +1439,13 @@ def draw_multilayer(
     -------
     ax : matplotlib Axes3DSubplot
         The subplot with the multilayer network visualization.
+
+
+    Notes
+    -----
+    The effect of the `sep` parameter is limited by the `height` of the figure.
+    If `sep` is larger than a certain value depending on `height`, no additional
+    effect will be seen.
     """
     settings = {
         "min_node_size": 10.0,
@@ -1604,23 +1615,31 @@ def draw_multilayer(
 def draw_dihypergraph(
     DH,
     ax=None,
-    lines_fc=None,
-    lines_lw=1.5,
-    line_head_width=0.05,
+    pos=None,
     node_fc="white",
     node_ec="black",
     node_lw=1,
     node_size=15,
+    node_shape="o",
+    node_fc_cmap="Reds",
+    lines_fc=None,
+    lines_lw=1.5,
+    arrowsize=10,
+    arrowstyle="-|>",
+    connectionstyle="arc3",
     edge_marker_toggle=True,
     edge_marker_fc=None,
-    edge_marker_ec=None,
+    edge_marker_ec="black",
     edge_marker="s",
     edge_marker_lw=1,
     edge_marker_size=15,
+    edge_marker_fc_cmap="crest_r",
     max_order=None,
     node_labels=False,
     hyperedge_labels=False,
     settings=None,
+    rescale_sizes=True,
+    iterations=50,
     **kwargs,
 ):
     """Draw a directed hypergraph
@@ -1631,18 +1650,6 @@ def draw_dihypergraph(
         The directed hypergraph to draw.
     ax : matplotlib.pyplot.axes, optional
         Axis to draw on. If None (default), get the current axes.
-    lines_fc : str, dict, iterable, optional
-        Color of the hyperedges (lines). If str, use the same color for all hyperedges.
-        If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
-        assume the colors are specified in the same order as the hyperedges are found
-        in DH.edges. If None (default), use the size of the hyperedges.
-    lines_lw : int, float, dict, iterable, optional
-        Line width of the hyperedges (lines). If int or float, use the same width for
-        all hyperedges. If a dict, must contain (hyperedge_id: width) pairs. If other
-        iterable, assume the widths are specified in the same order as the hyperedges
-        are found in DH.edges. By default, 1.5.
-    line_head_width : float, optional
-        Length of arrows' heads. By default, 0.05
     node_fc : str, dict, iterable, or NodeStat, optional
         Color of the nodes.  If str, use the same color for all nodes.  If a dict, must
         contain (node_id: color_str) pairs.  If other iterable, assume the colors are
@@ -1665,6 +1672,29 @@ def draw_dihypergraph(
         the radiuses are specified in the same order as the nodes are found in
         H.nodes. If NodeStat, use a monotonic linear interpolation defined between
         min_node_size and max_node_size. By default, 15.
+    node_fc_cmap : colormap
+        Colormap for mapping node colors. By default, "Reds". Ignored, if `node_fc` is
+        a str (single color).
+    lines_fc : str, dict, iterable, optional
+        Color of the hyperedges (lines). If str, use the same color for all hyperedges.
+        If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
+        assume the colors are specified in the same order as the hyperedges are found
+        in DH.edges. If None (default), use the size of the hyperedges.
+    lines_lw : int, float, dict, iterable, optional
+        Line width of the hyperedges (lines). If int or float, use the same width for
+        all hyperedges. If a dict, must contain (hyperedge_id: width) pairs. If other
+        iterable, assume the widths are specified in the same order as the hyperedges
+        are found in DH.edges. By default, 1.5.
+    arrowstyle : str, optional
+        By default: '-\|>'. See `matplotlib.patches.ArrowStyle` for more options.
+    arrowsize : int (default=10)
+        Size of the arrow head's length and width. See `matplotlib.patches.FancyArrowPatch`
+        for attribute `mutation_scale` for more info.
+    connectionstyle : string (default="arc3")
+        Pass the connectionstyle parameter to create curved arc of rounding
+        radius rad. For example, connectionstyle='arc3,rad=0.2'.
+        See `matplotlib.patches.ConnectionStyle` and
+        `matplotlib.patches.FancyArrowPatch` for more info.
     edge_marker_toggle: bool, optional
         If True then marker representing the hyperedges are drawn. By default True.
     edge_marker_fc: str, dict, iterable, optional
@@ -1687,20 +1717,27 @@ def draw_dihypergraph(
     hyperedge_labels : bool or dict, optional
         If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
         pairs.  By default, False.
+    rescale_sizes: bool, optional
+        If True, linearly interpolate `node_size` and between min/max values
+        that can be changed in the other argument `params`.
+        If `node_size` is a single value, this is ignored. By default, True.
+    iterations : int, optional
+        Maximum number of iterations taken to recompute the layout.
+        An original partial layout is computed solely based on the original nodes.
+        A full initial layout is then computed by simply adding the "edge" nodes 
+        at the barycenters. This initial layout may suffer from overlap between edge-nodes.
+        After that, a spring layout is ran starting from the
+        initial layout, and each iteration makes all of the nodes overlap less.
     **kwargs : optional args
         Alternate default values. Values that can be overwritten are the following:
-        * min_node_size
-        * max_node_size
-        * min_node_lw
-        * max_node_lw
-        * node_fc_cmap
-        * node_ec_cmap
-        * min_lines_lw
-        * max_lines_lw
-        * lines_fc_cmap
-        * edge_fc_cmap
-        * edge_marker_fc_cmap
-        * edge_marker_ec_cmap
+        * "min_node_size" (default: 5)
+        * "max_node_size" (default: 30)
+        * "min_node_lw" (default: 0)
+        * "max_node_lw" (default: 5)
+        * "min_lines_lw" (default: 0)
+        * "max_lines_lw" (default: 50)
+        * "min_source_margin" (default: 0)
+        * "min_target_margin" (default: 0)
 
     Returns
     -------
@@ -1721,140 +1758,90 @@ def draw_dihypergraph(
     if not isinstance(DH, DiHypergraph):
         raise XGIError("The input must be a DiHypergraph")
 
-    if settings is None:
-        settings = {
-            "min_node_size": 10.0,
-            "max_node_size": 30.0,
-            "min_node_lw": 1.0,
-            "max_node_lw": 5.0,
-            "node_fc_cmap": cm.Reds,
-            "node_ec_cmap": cm.Greys,
-            "min_lines_lw": 2.0,
-            "max_lines_lw": 10.0,
-            "lines_fc_cmap": cm.Blues,
-            "edge_marker_fc_cmap": cm.Blues,
-            "edge_marker_ec_cmap": cm.Greys,
-        }
+    settings = {
+        "min_node_size": 5,
+        "max_node_size": 30,
+        "min_node_lw": 0,
+        "max_node_lw": 5,
+        "min_lines_lw": 0,
+        "max_lines_lw": 50,
+        "min_source_margin": 0,
+        "min_target_margin": 0,
+    }
 
     settings.update(kwargs)
 
-    if ax is None:
-        ax = plt.gca()
-
-    ax.get_xaxis().set_ticks([])
-    ax.get_yaxis().set_ticks([])
-    ax.axis("off")
-
     # convert to hypergraph in order to use the augmented projection function
     H_conv = convert.convert_to_hypergraph(DH)
 
+    ax, _ = _draw_init(H_conv, ax, True)
+
     if not max_order:
         max_order = max_edge_order(H_conv)
 
-    lines_lw = _scalar_arg_to_dict(
-        lines_lw, H_conv.edges, settings["min_lines_lw"], settings["max_lines_lw"]
-    )
-
+    # set default colors
     if lines_fc is None:
         lines_fc = H_conv.edges.size
 
-    lines_fc = _color_arg_to_dict(lines_fc, H_conv.edges, settings["lines_fc_cmap"])
-
     if edge_marker_fc is None:
         edge_marker_fc = H_conv.edges.size
 
-    edge_marker_fc = _color_arg_to_dict(
-        edge_marker_fc, H_conv.edges, settings["edge_marker_fc_cmap"]
-    )
+    # convert all formats to ndarray
+    node_size = _draw_arg_to_arr(node_size)
+    edge_marker_size = _draw_arg_to_arr(edge_marker_size)
+    lines_lw = _draw_arg_to_arr(lines_lw)
 
-    if edge_marker_ec is None:
-        edge_marker_ec = H_conv.edges.size
+    # interpolate if needed
+    if rescale_sizes and isinstance(node_size, np.ndarray):
+        node_size = _interp_draw_arg(
+            node_size, settings["min_node_size"], settings["max_node_size"]
+        )
+    if rescale_sizes and isinstance(edge_marker_size, np.ndarray):
+        edge_marker_size = _interp_draw_arg(
+            edge_marker_size, settings["min_node_size"], settings["max_node_size"]
+        )
+    if rescale_sizes and isinstance(lines_lw, np.ndarray):
+        lines_lw = _interp_draw_arg(
+            lines_lw, settings["min_lines_lw"], settings["max_lines_lw"]
+        )
 
-    edge_marker_ec = _color_arg_to_dict(
-        edge_marker_ec, H_conv.edges, settings["edge_marker_ec_cmap"]
-    )
+    # parse colors
+    lines_fc, lines_c_mapped = _parse_color_arg(lines_fc, H_conv.edges)
 
-    node_size = _scalar_arg_to_dict(
-        node_size, H_conv.nodes, settings["min_node_size"], settings["max_node_size"]
-    )
+    # convert numbers to colors for FancyArrowPatch
+    if lines_c_mapped:
+        norm = mpl.colors.Normalize()
+        m = cm.ScalarMappable(norm=norm, cmap=edge_marker_fc_cmap)
+        lines_fc = m.to_rgba(lines_fc)
 
+    # compute the augmented projection
+    # this is a networkx Graph as a bipartite representation of H_conv
+    # nodes and hyperedges > 2 are represented by nodes
     G_aug = _augmented_projection(H_conv)
-    for dyad in H_conv.edges.filterby("size", 2).members():
-        try:
-            index = max(n for n in G_aug.nodes if isinstance(n, int)) + 1
-        except ValueError:
-            # The list of node-labels has no integers, so I start from 0
-            index = 0
-        G_aug.add_edges_from([[list(dyad)[0], index], [list(dyad)[1], index]])
 
     phantom_nodes = [n for n in list(G_aug.nodes) if n not in list(H_conv.nodes)]
-    pos = spring_layout(G_aug)
 
-    for id, he in DH.edges.members(dtype=dict).items():
-        d = len(he) - 1
-        if d > 0:
-            # identify the center of the edge in the augemented projection
-            center = [n for n in phantom_nodes if set(G_aug.neighbors(n)) == he][0]
-            x_center, y_center = pos[center]
-            for node in DH.edges.dimembers(id)[0]:
-                x_coords = [pos[node][0], x_center]
-                y_coords = [pos[node][1], y_center]
-                line = plt.Line2D(
-                    x_coords,
-                    y_coords,
-                    color=lines_fc[id],
-                    lw=lines_lw[id],
-                    zorder=max_order - d,
-                )
-                ax.add_line(line)
-            for node in DH.edges.dimembers(id)[1]:
-                dx, dy = pos[node][0] - x_center, pos[node][1] - y_center
-                # the following to avoid the point of the arrow overlapping the node
-                distance = np.hypot(dx, dy)
-                direction_vector = np.array([dx, dy]) / distance
-                shortened_distance = (
-                    distance - node_size[node] * 0.003
-                )  # Calculate the shortened length
-                dx = direction_vector[0] * shortened_distance
-                dy = direction_vector[1] * shortened_distance
-                arrow = FancyArrow(
-                    x_center,
-                    y_center,
-                    dx,
-                    dy,
-                    color=lines_fc[id],
-                    width=lines_lw[id] * 0.001,
-                    length_includes_head=True,
-                    head_width=line_head_width,
-                    zorder=max_order - d,
-                )
-                ax.add_patch(arrow)
-            if edge_marker_toggle:
-                ax.scatter(
-                    x=x_center,
-                    y=y_center,
-                    marker=edge_marker,
-                    s=edge_marker_size**2,
-                    c=edge_marker_fc[id],
-                    edgecolors=edge_marker_ec[id],
-                    linewidths=edge_marker_lw,
-                    zorder=max_order,
-                )
+    if pos is None:  # compute positions, first based on original nodes
+        pos_nodes = barycenter_spring_layout(H_conv)
+        # then compute the barycenter positions for the "edge" phantom nodes
 
-    if hyperedge_labels:
-        # Get all valid keywords by inspecting the signatures of draw_node_labels
-        valid_label_kwds = signature(draw_hyperedge_labels).parameters.keys()
-        # Remove the arguments of this function (draw_networkx)
-        valid_label_kwds = valid_label_kwds - {"H", "pos", "ax", "hyperedge_labels"}
-        if any([k not in valid_label_kwds for k in kwargs]):
-            invalid_args = ", ".join([k for k in kwargs if k not in valid_label_kwds])
-            raise ValueError(f"Received invalid argument(s): {invalid_args}")
-        label_kwds = {k: v for k, v in kwargs.items() if k in valid_label_kwds}
-        if "font_size_edges" not in label_kwds:
-            label_kwds["font_size_edges"] = 6
-        draw_hyperedge_labels(H_conv, pos, hyperedge_labels, ax_edges=ax, **label_kwds)
+        centers = {}
+        for idx, members in DH.edges.members(dtype=dict).items():
+            center = np.zeros((2,))
+            edge_bi_id = phantom_nodes[idx]
+            for node in members:
+                center += pos_nodes[node]
+            centers[edge_bi_id] = center / len(members)
+
+        # merge both pos dictionaries
+        pos_nodes.update(centers)
+        pos = pos_nodes
+
+        # recompute positions from the original positions
+        pos = spring_layout(G_aug, pos=pos, iterations=iterations)
 
-    draw_nodes(
+    # draw "node" nodes
+    ax, node_collection = draw_nodes(
         H=H_conv,
         pos=pos,
         ax=ax,
@@ -1862,13 +1849,166 @@ def draw_dihypergraph(
         node_ec=node_ec,
         node_lw=node_lw,
         node_size=node_size,
-        zorder=max_order,
+        node_fc_cmap=node_fc_cmap,
+        node_shape=node_shape,
+        zorder=4,
         params=settings,
         node_labels=node_labels,
-        **kwargs,
+        rescale_sizes=rescale_sizes,
     )
 
+    # draw "edge" phantom nodes
+    if edge_marker_toggle:
+
+        HG_phantom = Hypergraph()
+        HG_phantom.add_nodes_from(phantom_nodes)  # dummy HG
+
+        ax, phantom_node_collection = draw_nodes(
+            H=HG_phantom,
+            pos=pos,
+            ax=ax,
+            node_fc=edge_marker_fc,
+            node_ec=edge_marker_ec,
+            node_lw=edge_marker_lw,
+            node_size=edge_marker_size,
+            node_shape=edge_marker,
+            node_fc_cmap=edge_marker_fc_cmap,
+            zorder=2,
+            params=settings,
+            rescale_sizes=rescale_sizes,
+        )
+
+        if hyperedge_labels:
+            labels = {i: ii for i, ii in zip(phantom_nodes, H_conv.edges)}
+            draw_node_labels(
+                HG_phantom,
+                pos=pos,
+                node_labels=labels,
+                ax_nodes=ax,
+                font_color_nodes="white",
+                zorder=3,
+            )
+
+    else:
+        phantom_node_collection = None
+
+    for dyad in H_conv.edges.filterby("size", 2).members():
+        try:
+            index = max(n for n in G_aug.nodes if isinstance(n, int)) + 1
+        except ValueError:
+            # The list of node-labels has no integers, so I start from 0
+            index = 0
+        G_aug.add_edges_from([[list(dyad)[0], index], [list(dyad)[1], index]])
+
+    edges = DH.edges.filterby("size", 2, "geq")
+
+    nodes_id = list(DH.nodes)  # in original order, to find index for arrays
+
+    def _arrow_shrink(source="node", target="edge"):
+        """Compute the shrink factor for the arrows based on node sizes."""
+
+        def to_marker_edge(marker_size, marker):
+            # from networkx
+            # https://networkx.org/documentation/stable/_modules/networkx/drawing/nx_pylab.html#draw_networkx_edges
+            if marker in "s^>v<d":  # `large` markers need extra space
+                return marker_size / 1.6
+            else:
+                return marker_size / 2
+
+        shrink_source = 0  # space from source to tail
+        shrink_target = 0  # space from  head to target
+
+        if isinstance(node_size, np.ndarray):  # many node sizes
+            source_node_size = node_size[nodes_id.index(node)]
+            shrink_source = to_marker_edge(source_node_size, node_shape)
+        else:
+            shrink_source = to_marker_edge(node_size, node_shape)
+
+        if isinstance(edge_marker_size, np.ndarray):
+            target_node_size = edge_marker_size[idx]
+            shrink_target = to_marker_edge(target_node_size, edge_marker)
+        else:
+            shrink_target = to_marker_edge(edge_marker_size, edge_marker)
+
+        if shrink_source < settings["min_source_margin"]:
+            shrink_source = settings["min_source_margin"]
+
+        if shrink_target < settings["min_target_margin"]:
+            shrink_target = settings["min_target_margin"]
+
+        if source == "node" and target == "edge":
+            return shrink_source, shrink_target
+        elif source == "edge" and target == "node":
+            return shrink_target, shrink_source
+        else:
+            raise ValueError("Wrong input arguments.")
+
+    # We are using single patches rather than a PatchCollection of arrows
+    # because Matplotlib has an old bug: FancyArrowPatch has incompatibilities
+    # with PatchCollection (https://github.com/matplotlib/matplotlib/issues/2341)
+    # We are thus following the approach used by Netoworkx
+    # https://github.com/networkx/networkx/pull/2760
+
+    patches = []
+    for idx, dimembers in edges.dimembers(dtype=dict).items():
+
+        edge_bi_id = phantom_nodes[idx]
+
+        for node in dimembers[0]:  # lines going towards the center
+
+            xy_source = pos[node]
+            xy_target = pos[edge_bi_id]
+
+            shrink_source, shrink_target = _arrow_shrink(source="node", target="edge")
+            if lines_c_mapped:
+                lines_color = lines_fc[idx]
+            else:
+                lines_color = lines_fc
+
+            patch = FancyArrowPatch(
+                xy_source,
+                xy_target,
+                arrowstyle=arrowstyle,
+                shrinkA=shrink_source,
+                shrinkB=shrink_target,
+                mutation_scale=arrowsize,
+                linewidth=lines_lw,
+                zorder=0,
+                color=lines_color,
+                connectionstyle=connectionstyle,
+            )  # arrows go behind nodes
+
+            patches.append(patch)
+            ax.add_patch(patch)
+
+        for node in dimembers[1]:  # lines going out from the center
+
+            xy_source = pos[edge_bi_id]
+            xy_target = pos[node]
+
+            shrink_source, shrink_target = _arrow_shrink(source="edge", target="node")
+            if lines_c_mapped:
+                lines_color = lines_fc[idx]
+            else:
+                lines_color = lines_fc
+
+            patch = FancyArrowPatch(
+                xy_source,
+                xy_target,
+                arrowstyle=arrowstyle,
+                shrinkA=shrink_source,
+                shrinkB=shrink_target,
+                mutation_scale=arrowsize,
+                linewidth=lines_lw,
+                zorder=0,
+                color=lines_color,
+                connectionstyle=connectionstyle,
+            )  # arrows go behind nodes
+
+            patches.append(patch)
+            ax.add_patch(patch)
+
     # compute axis limits
-    _update_lims(pos, ax)
+    _update_lims(pos_nodes, ax)
 
-    return ax
+    return ax, (node_collection, phantom_node_collection)
diff --git a/xgi/drawing/draw_utils.py b/xgi/drawing/draw_utils.py
index 7d7ee39..5444b95 100644
--- a/xgi/drawing/draw_utils.py
+++ b/xgi/drawing/draw_utils.py
@@ -1,9 +1,7 @@
 """Draw hypergraphs and simplicial complexes with matplotlib."""
 
 from collections.abc import Iterable
-from numbers import Number
 
-import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sb  # for cmap "crest"
@@ -84,7 +82,7 @@ def _update_lims(pos, ax):
     ax.autoscale_view()
 
 
-def _parse_color_arg(colors, cmap, ids, id_kind="edges"):
+def _parse_color_arg(colors, ids, id_kind="edges"):
     """
     Parse and process color arguments for plotting.
 
@@ -106,8 +104,6 @@ def _parse_color_arg(colors, cmap, ids, id_kind="edges"):
         * array of floats
         * dict of floats containing the `ids` as keys
         * IDStat containing the `ids` as keys
-    cmap : matplotlib colormap or None
-        The colormap to use for mapping numerical values to colors.
     ids : array-like or None
         The IDs of the elements being plotted.
     id_kind : str, optional
@@ -157,7 +153,7 @@ def _parse_color_arg(colors, cmap, ids, id_kind="edges"):
             colors = np.asanyarray(colors, dtype=float)
             colors_are_mapped = True
         except:
-            raise ValueError("Invalud input format for colors.")
+            raise ValueError("Invalid input format for colors.")
 
     if not is_color_like(colors) and len(colors) != xsize:
         raise ValueError(
diff --git a/xgi/drawing/layout.py b/xgi/drawing/layout.py
index 9d3033d..e39e06e 100644
--- a/xgi/drawing/layout.py
+++ b/xgi/drawing/layout.py
@@ -7,7 +7,6 @@ import numpy as np
 from numpy.linalg import inv, svd
 
 from .. import convert
-from ..algorithms import max_edge_order
 from ..core import SimplicialComplex
 
 __all__ = [
@@ -163,14 +162,7 @@ def _augmented_projection(H, weighted=False):
     G = nx.Graph()
 
     # Adding real nodes
-    G.add_nodes_from(list(H.nodes))
-
-    # Adding links (edges composed by two nodes only, for which we don't use phantom nodes
-    for i, j in H.edges.filterby("order", 1).members():
-        if weighted == True:
-            G.add_edge(i, j, weight=1)
-        else:
-            G.add_edge(i, j)
+    G.add_nodes_from(list(H.nodes), bipartite="node")
 
     # Adding phantom nodes and connections therein
     # I will start from the first int node-label available
@@ -180,18 +172,19 @@ def _augmented_projection(H, weighted=False):
         # The list of node-labels has no integers, so I start from 0
         phantom_node_id = 0
 
-    # Looping over the hyperedges of different order (from triples up)
-    for d in range(2, max_edge_order(H) + 1):
-        # Hyperedges of order d (d=2: triplets, etc.)
-        for he_id, members in H.edges.filterby("order", d).members(dtype=dict).items():
-            # Adding one phantom node for each hyperedge and linking it to the nodes of
-            # the hyperedge
-            for n in members:
-                if weighted == True:
-                    G.add_edge(phantom_node_id, n, weight=d)
-                else:
-                    G.add_edge(phantom_node_id, n)
-            phantom_node_id += 1
+    edges = H.edges.filterby("order", 1, "geq")
+    # Looping over the hyperedges of different order
+    for he_id, members in edges.members(dtype=dict).items():
+        d = len(members) - 1
+        # Adding one phantom node for each hyperedge
+        G.add_node(phantom_node_id, bipartite="hyperedge")
+        # and linking it to the nodes of the hyperedge
+        for n in members:
+            if weighted:
+                G.add_edge(phantom_node_id, n, weight=d)
+            else:
+                G.add_edge(phantom_node_id, n)
+        phantom_node_id += 1
     return G
 
 
diff --git a/xgi/stats/__init__.py b/xgi/stats/__init__.py
index 1bcf571..8b1f707 100644
--- a/xgi/stats/__init__.py
+++ b/xgi/stats/__init__.py
@@ -92,6 +92,9 @@ class IDStat:
         out += ")"
         return out
 
+    def __len__(self):
+        return len(self.view)
+
     @property
     def name(self):
         """Name of this stat.


Test patch:
diff --git a/tests/core/test_hypergraph.py b/tests/core/test_hypergraph.py
index dcc7f17..74fbe74 100644
--- a/tests/core/test_hypergraph.py
+++ b/tests/core/test_hypergraph.py
@@ -742,6 +742,18 @@ def test_set_edge_attributes(edgelist1):
         H3.set_edge_attributes({"test": {2: "weight"}})
 
 
+def test_480(edgelist1):
+    H1 = xgi.Hypergraph(edgelist1)
+    attr_dict1 = {
+        0: 1,
+        1: 2,
+        2: 3,
+        3: -1,
+    }
+    H1.set_edge_attributes(attr_dict1, "weight")
+    assert H1.edges.attrs("weight").asdict() == attr_dict1
+
+
 def test_cleanup():
     H = xgi.Hypergraph()
     H.add_edges_from([["a", "b", "c"], ["a", "b", "c"], ["e", "f"]])
diff --git a/tests/drawing/test_draw.py b/tests/drawing/test_draw.py
index 7c365a5..748dc6f 100644
--- a/tests/drawing/test_draw.py
+++ b/tests/drawing/test_draw.py
@@ -461,30 +461,81 @@ def test_correct_number_of_collections_draw_multilayer(edgelist8):
 def test_draw_dihypergraph(diedgelist2, edgelist8):
     DH = xgi.DiHypergraph(diedgelist2)
 
-    fig, ax1 = plt.subplots()
-    ax1 = xgi.draw_dihypergraph(DH, ax=ax1)
+    fig1, ax1 = plt.subplots()
+    ax1, collections = xgi.draw_dihypergraph(DH, ax=ax1)
+    node_coll, phantom_node_coll = collections
+    fig2, ax2 = plt.subplots()
+    ax2, collections2 = xgi.draw_dihypergraph(
+        DH,
+        ax=ax2,
+        node_fc="red",
+        node_ec="blue",
+        node_lw=2,
+        node_size=20,
+        lines_fc="blue",
+        lines_lw=2,
+        edge_marker_fc="red",
+        edge_marker_lw=2,
+        edge_marker_size=20,
+    )
+    node_coll2, phantom_node_coll2 = collections2
 
     # number of elements
-    assert len(ax1.lines) == 7  # number of source nodes
-    assert len(ax1.patches) == 4  # number of target nodes
-    assert len(ax1.collections) == DH.num_edges + 1 - len(
-        DH.edges.filterby("size", 1)
-    )  # hyperedges markers + nodes
+    assert len(node_coll.get_offsets()) == 6  # number of original nodes
+    assert len(phantom_node_coll.get_offsets()) == 3  # number of original edges
+    assert len(ax1.patches) == 11  # number of lines
+
+    # node face colors
+    assert np.all(node_coll.get_facecolor() == np.array([[1, 1, 1, 1]]))  # white
+    assert np.all(node_coll2.get_facecolor() == np.array([[1, 0, 0, 1]]))  # red
+
+    # node edge colors
+    assert np.all(node_coll.get_edgecolor() == np.array([[0, 0, 0, 1]]))  # black
+    assert np.all(node_coll2.get_edgecolor() == np.array([[0, 0, 1, 1]]))  # blue
+
+    # node_lw
+    assert np.all(node_coll.get_linewidth() == np.array([1]))
+    assert np.all(node_coll2.get_linewidth() == np.array([2]))
+
+    # node_size
+    assert np.all(node_coll.get_sizes() == np.array([15**2]))
+    assert np.all(node_coll2.get_sizes() == np.array([20**2]))
+
+    # edge face colors
+    assert np.all(phantom_node_coll2.get_facecolor() == np.array([[1, 0, 0, 1]]))  # red
+
+    # edge _lw
+    assert np.all(phantom_node_coll.get_linewidth() == np.array([1]))
+    assert np.all(phantom_node_coll2.get_linewidth() == np.array([2]))
+
+    # edge_size
+    assert np.all(phantom_node_coll.get_sizes() == np.array([15**2]))
+    assert np.all(phantom_node_coll2.get_sizes() == np.array([20**2]))
+
+    # line lw
+    for patch in ax1.patches:  # lines
+        assert np.all(patch.get_linewidth() == np.array([1.5]))
+    for patch in ax2.patches:  # lines
+        assert np.all(patch.get_linewidth() == np.array([2]))
+
+    # line fc
+    for patch in ax2.patches:  # lines
+        assert np.all(patch.get_facecolor() == np.array([[0, 0, 1, 1]]))
 
     # zorder
-    for line, z in zip(ax1.lines, [1, 1, 1, 1, 0, 0, 0]):  # lines for source nodes
-        assert line.get_zorder() == z
-    for patch, z in zip(ax1.patches, [1, 1, 0, 0]):  # arrows for target nodes
-        assert patch.get_zorder() == z
-    for collection in ax1.collections:
-        assert collection.get_zorder() == 3  # nodes and hyperedges markers
+    assert node_coll.get_zorder() == 4
+    assert phantom_node_coll.get_zorder() == 2
+    for patch in ax1.patches:  # lines
+        assert patch.get_zorder() == 0
 
-    plt.close()
+    plt.close("all")
 
     # test toggle for edges
     fig, ax2 = plt.subplots()
-    ax2 = xgi.draw_dihypergraph(DH, edge_marker_toggle=False, ax=ax2)
+    ax2, collections = xgi.draw_dihypergraph(DH, edge_marker_toggle=False, ax=ax2)
+    node_coll, phantom_node_coll = collections
     assert len(ax2.collections) == 1
+    assert phantom_node_coll is None
 
     plt.close()
 
@@ -498,18 +549,17 @@ def test_draw_dihypergraph(diedgelist2, edgelist8):
 
 def test_draw_dihypergraph_with_str_labels_and_isolated_nodes():
     DH1 = xgi.DiHypergraph()
+    DH1.add_nodes_from(["one", "two", "three", "four", "five", "six"])
     DH1.add_edges_from(
         [
             [{"one"}, {"two", "three"}],
             [{"two", "three"}, {"four", "five"}],
-            [{"six"}, {}],
         ]
     )
 
     fig, ax4 = plt.subplots()
-    ax4 = xgi.draw_dihypergraph(DH1, ax=ax4)
-    assert len(ax4.lines) == 3
-    assert len(ax4.patches) == 4
-    assert len(ax4.collections) == DH1.num_edges + 1 - len(
-        DH1.edges.filterby("size", 1)
-    )
+    ax4, collections4 = xgi.draw_dihypergraph(DH1, ax=ax4)
+    node_coll4, phantom_node_coll4 = collections4
+    assert len(node_coll4.get_offsets()) == 6  # number of original nodes
+    assert len(phantom_node_coll4.get_offsets()) == 2  # number of original edges
+    assert len(ax4.patches) == 7  # number of lines