We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
JupyterViz: the default grid space drawer doesn't scale to large size
**Describe the bug**
<!-- A clear and concise description the bug -->
Here is Schelling space for 60x60:
![2023-07-24-102909_468x350_scrot](https://github.com/projectmesa/mesa/assets/395821/0949b124-dfeb-48ff-9205-4d242e99c833)

**Expected behavior**
<!-- A clear and concise description of what you expected to happen -->
Should either scale down the circle marker size automatically, or scale up the figure size automatically.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `mesa/experimental/components/matplotlib.py`
Content:
```
1 from typing import Optional
2 
3 import networkx as nx
4 import solara
5 from matplotlib.figure import Figure
6 from matplotlib.ticker import MaxNLocator
7 
8 import mesa
9 
10 
11 @solara.component
12 def SpaceMatplotlib(model, agent_portrayal, dependencies: Optional[list[any]] = None):
13     space_fig = Figure()
14     space_ax = space_fig.subplots()
15     space = getattr(model, "grid", None)
16     if space is None:
17         # Sometimes the space is defined as model.space instead of model.grid
18         space = model.space
19     if isinstance(space, mesa.space.NetworkGrid):
20         _draw_network_grid(space, space_ax, agent_portrayal)
21     elif isinstance(space, mesa.space.ContinuousSpace):
22         _draw_continuous_space(space, space_ax, agent_portrayal)
23     else:
24         _draw_grid(space, space_ax, agent_portrayal)
25     solara.FigureMatplotlib(space_fig, format="png", dependencies=dependencies)
26 
27 
28 def _draw_grid(space, space_ax, agent_portrayal):
29     def portray(g):
30         x = []
31         y = []
32         s = []  # size
33         c = []  # color
34         for i in range(g.width):
35             for j in range(g.height):
36                 content = g._grid[i][j]
37                 if not content:
38                     continue
39                 if not hasattr(content, "__iter__"):
40                     # Is a single grid
41                     content = [content]
42                 for agent in content:
43                     data = agent_portrayal(agent)
44                     x.append(i)
45                     y.append(j)
46                     if "size" in data:
47                         s.append(data["size"])
48                     if "color" in data:
49                         c.append(data["color"])
50         out = {"x": x, "y": y}
51         if len(s) > 0:
52             out["s"] = s
53         if len(c) > 0:
54             out["c"] = c
55         return out
56 
57     space_ax.set_xlim(-1, space.width)
58     space_ax.set_ylim(-1, space.height)
59     space_ax.scatter(**portray(space))
60 
61 
62 def _draw_network_grid(space, space_ax, agent_portrayal):
63     graph = space.G
64     pos = nx.spring_layout(graph, seed=0)
65     nx.draw(
66         graph,
67         ax=space_ax,
68         pos=pos,
69         **agent_portrayal(graph),
70     )
71 
72 
73 def _draw_continuous_space(space, space_ax, agent_portrayal):
74     def portray(space):
75         x = []
76         y = []
77         s = []  # size
78         c = []  # color
79         for agent in space._agent_to_index:
80             data = agent_portrayal(agent)
81             _x, _y = agent.pos
82             x.append(_x)
83             y.append(_y)
84             if "size" in data:
85                 s.append(data["size"])
86             if "color" in data:
87                 c.append(data["color"])
88         out = {"x": x, "y": y}
89         if len(s) > 0:
90             out["s"] = s
91         if len(c) > 0:
92             out["c"] = c
93         return out
94 
95     # Determine border style based on space.torus
96     border_style = "solid" if not space.torus else (0, (5, 10))
97 
98     # Set the border of the plot
99     for spine in space_ax.spines.values():
100         spine.set_linewidth(1.5)
101         spine.set_color("black")
102         spine.set_linestyle(border_style)
103 
104     width = space.x_max - space.x_min
105     x_padding = width / 20
106     height = space.y_max - space.y_min
107     y_padding = height / 20
108     space_ax.set_xlim(space.x_min - x_padding, space.x_max + x_padding)
109     space_ax.set_ylim(space.y_min - y_padding, space.y_max + y_padding)
110 
111     # Portray and scatter the agents in the space
112     space_ax.scatter(**portray(space))
113 
114 
115 @solara.component
116 def PlotMatplotlib(model, measure, dependencies: Optional[list[any]] = None):
117     fig = Figure()
118     ax = fig.subplots()
119     df = model.datacollector.get_model_vars_dataframe()
120     if isinstance(measure, str):
121         ax.plot(df.loc[:, measure])
122         ax.set_ylabel(measure)
123     elif isinstance(measure, dict):
124         for m, color in measure.items():
125             ax.plot(df.loc[:, m], label=m, color=color)
126         fig.legend()
127     elif isinstance(measure, (list, tuple)):
128         for m in measure:
129             ax.plot(df.loc[:, m], label=m)
130         fig.legend()
131     # Set integer x axis
132     ax.xaxis.set_major_locator(MaxNLocator(integer=True))
133     solara.FigureMatplotlib(fig, dependencies=dependencies)
134
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/mesa/experimental/components/matplotlib.py b/mesa/experimental/components/matplotlib.py
--- a/mesa/experimental/components/matplotlib.py
+++ b/mesa/experimental/components/matplotlib.py
@@ -48,6 +48,9 @@
                     if "color" in data:
                         c.append(data["color"])
         out = {"x": x, "y": y}
+        # This is the default value for the marker size, which auto-scales
+        # according to the grid area.
+        out["s"] = (180 / min(g.width, g.height)) ** 2
         if len(s) > 0:
             out["s"] = s
         if len(c) > 0:

{"golden_diff": "diff --git a/mesa/experimental/components/matplotlib.py b/mesa/experimental/components/matplotlib.py\n--- a/mesa/experimental/components/matplotlib.py\n+++ b/mesa/experimental/components/matplotlib.py\n@@ -48,6 +48,9 @@\n                     if \"color\" in data:\n                         c.append(data[\"color\"])\n         out = {\"x\": x, \"y\": y}\n+        # This is the default value for the marker size, which auto-scales\n+        # according to the grid area.\n+        out[\"s\"] = (180 / min(g.width, g.height)) ** 2\n         if len(s) > 0:\n             out[\"s\"] = s\n         if len(c) > 0:\n", "issue": "JupyterViz: the default grid space drawer doesn't scale to large size\n**Describe the bug**\r\n<!-- A clear and concise description the bug -->\r\nHere is Schelling space for 60x60:\r\n![2023-07-24-102909_468x350_scrot](https://github.com/projectmesa/mesa/assets/395821/0949b124-dfeb-48ff-9205-4d242e99c833)\r\n\r\n**Expected behavior**\r\n<!-- A clear and concise description of what you expected to happen -->\r\nShould either scale down the circle marker size automatically, or scale up the figure size automatically.\n", "before_files": [{"content": "from typing import Optional\n\nimport networkx as nx\nimport solara\nfrom matplotlib.figure import Figure\nfrom matplotlib.ticker import MaxNLocator\n\nimport mesa\n\n\n@solara.component\ndef SpaceMatplotlib(model, agent_portrayal, dependencies: Optional[list[any]] = None):\n    space_fig = Figure()\n    space_ax = space_fig.subplots()\n    space = getattr(model, \"grid\", None)\n    if space is None:\n        # Sometimes the space is defined as model.space instead of model.grid\n        space = model.space\n    if isinstance(space, mesa.space.NetworkGrid):\n        _draw_network_grid(space, space_ax, agent_portrayal)\n    elif isinstance(space, mesa.space.ContinuousSpace):\n        _draw_continuous_space(space, space_ax, agent_portrayal)\n    else:\n        _draw_grid(space, space_ax, agent_portrayal)\n    solara.FigureMatplotlib(space_fig, format=\"png\", dependencies=dependencies)\n\n\ndef _draw_grid(space, space_ax, agent_portrayal):\n    def portray(g):\n        x = []\n        y = []\n        s = []  # size\n        c = []  # color\n        for i in range(g.width):\n            for j in range(g.height):\n                content = g._grid[i][j]\n                if not content:\n                    continue\n                if not hasattr(content, \"__iter__\"):\n                    # Is a single grid\n                    content = [content]\n                for agent in content:\n                    data = agent_portrayal(agent)\n                    x.append(i)\n                    y.append(j)\n                    if \"size\" in data:\n                        s.append(data[\"size\"])\n                    if \"color\" in data:\n                        c.append(data[\"color\"])\n        out = {\"x\": x, \"y\": y}\n        if len(s) > 0:\n            out[\"s\"] = s\n        if len(c) > 0:\n            out[\"c\"] = c\n        return out\n\n    space_ax.set_xlim(-1, space.width)\n    space_ax.set_ylim(-1, space.height)\n    space_ax.scatter(**portray(space))\n\n\ndef _draw_network_grid(space, space_ax, agent_portrayal):\n    graph = space.G\n    pos = nx.spring_layout(graph, seed=0)\n    nx.draw(\n        graph,\n        ax=space_ax,\n        pos=pos,\n        **agent_portrayal(graph),\n    )\n\n\ndef _draw_continuous_space(space, space_ax, agent_portrayal):\n    def portray(space):\n        x = []\n        y = []\n        s = []  # size\n        c = []  # color\n        for agent in space._agent_to_index:\n            data = agent_portrayal(agent)\n            _x, _y = agent.pos\n            x.append(_x)\n            y.append(_y)\n            if \"size\" in data:\n                s.append(data[\"size\"])\n            if \"color\" in data:\n                c.append(data[\"color\"])\n        out = {\"x\": x, \"y\": y}\n        if len(s) > 0:\n            out[\"s\"] = s\n        if len(c) > 0:\n            out[\"c\"] = c\n        return out\n\n    # Determine border style based on space.torus\n    border_style = \"solid\" if not space.torus else (0, (5, 10))\n\n    # Set the border of the plot\n    for spine in space_ax.spines.values():\n        spine.set_linewidth(1.5)\n        spine.set_color(\"black\")\n        spine.set_linestyle(border_style)\n\n    width = space.x_max - space.x_min\n    x_padding = width / 20\n    height = space.y_max - space.y_min\n    y_padding = height / 20\n    space_ax.set_xlim(space.x_min - x_padding, space.x_max + x_padding)\n    space_ax.set_ylim(space.y_min - y_padding, space.y_max + y_padding)\n\n    # Portray and scatter the agents in the space\n    space_ax.scatter(**portray(space))\n\n\n@solara.component\ndef PlotMatplotlib(model, measure, dependencies: Optional[list[any]] = None):\n    fig = Figure()\n    ax = fig.subplots()\n    df = model.datacollector.get_model_vars_dataframe()\n    if isinstance(measure, str):\n        ax.plot(df.loc[:, measure])\n        ax.set_ylabel(measure)\n    elif isinstance(measure, dict):\n        for m, color in measure.items():\n            ax.plot(df.loc[:, m], label=m, color=color)\n        fig.legend()\n    elif isinstance(measure, (list, tuple)):\n        for m in measure:\n            ax.plot(df.loc[:, m], label=m)\n        fig.legend()\n    # Set integer x axis\n    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n    solara.FigureMatplotlib(fig, dependencies=dependencies)\n", "path": "mesa/experimental/components/matplotlib.py"}], "after_files": [{"content": "from typing import Optional\n\nimport networkx as nx\nimport solara\nfrom matplotlib.figure import Figure\nfrom matplotlib.ticker import MaxNLocator\n\nimport mesa\n\n\n@solara.component\ndef SpaceMatplotlib(model, agent_portrayal, dependencies: Optional[list[any]] = None):\n    space_fig = Figure()\n    space_ax = space_fig.subplots()\n    space = getattr(model, \"grid\", None)\n    if space is None:\n        # Sometimes the space is defined as model.space instead of model.grid\n        space = model.space\n    if isinstance(space, mesa.space.NetworkGrid):\n        _draw_network_grid(space, space_ax, agent_portrayal)\n    elif isinstance(space, mesa.space.ContinuousSpace):\n        _draw_continuous_space(space, space_ax, agent_portrayal)\n    else:\n        _draw_grid(space, space_ax, agent_portrayal)\n    solara.FigureMatplotlib(space_fig, format=\"png\", dependencies=dependencies)\n\n\ndef _draw_grid(space, space_ax, agent_portrayal):\n    def portray(g):\n        x = []\n        y = []\n        s = []  # size\n        c = []  # color\n        for i in range(g.width):\n            for j in range(g.height):\n                content = g._grid[i][j]\n                if not content:\n                    continue\n                if not hasattr(content, \"__iter__\"):\n                    # Is a single grid\n                    content = [content]\n                for agent in content:\n                    data = agent_portrayal(agent)\n                    x.append(i)\n                    y.append(j)\n                    if \"size\" in data:\n                        s.append(data[\"size\"])\n                    if \"color\" in data:\n                        c.append(data[\"color\"])\n        out = {\"x\": x, \"y\": y}\n        # This is the default value for the marker size, which auto-scales\n        # according to the grid area.\n        out[\"s\"] = (180 / min(g.width, g.height)) ** 2\n        if len(s) > 0:\n            out[\"s\"] = s\n        if len(c) > 0:\n            out[\"c\"] = c\n        return out\n\n    space_ax.set_xlim(-1, space.width)\n    space_ax.set_ylim(-1, space.height)\n    space_ax.scatter(**portray(space))\n\n\ndef _draw_network_grid(space, space_ax, agent_portrayal):\n    graph = space.G\n    pos = nx.spring_layout(graph, seed=0)\n    nx.draw(\n        graph,\n        ax=space_ax,\n        pos=pos,\n        **agent_portrayal(graph),\n    )\n\n\ndef _draw_continuous_space(space, space_ax, agent_portrayal):\n    def portray(space):\n        x = []\n        y = []\n        s = []  # size\n        c = []  # color\n        for agent in space._agent_to_index:\n            data = agent_portrayal(agent)\n            _x, _y = agent.pos\n            x.append(_x)\n            y.append(_y)\n            if \"size\" in data:\n                s.append(data[\"size\"])\n            if \"color\" in data:\n                c.append(data[\"color\"])\n        out = {\"x\": x, \"y\": y}\n        if len(s) > 0:\n            out[\"s\"] = s\n        if len(c) > 0:\n            out[\"c\"] = c\n        return out\n\n    # Determine border style based on space.torus\n    border_style = \"solid\" if not space.torus else (0, (5, 10))\n\n    # Set the border of the plot\n    for spine in space_ax.spines.values():\n        spine.set_linewidth(1.5)\n        spine.set_color(\"black\")\n        spine.set_linestyle(border_style)\n\n    width = space.x_max - space.x_min\n    x_padding = width / 20\n    height = space.y_max - space.y_min\n    y_padding = height / 20\n    space_ax.set_xlim(space.x_min - x_padding, space.x_max + x_padding)\n    space_ax.set_ylim(space.y_min - y_padding, space.y_max + y_padding)\n\n    # Portray and scatter the agents in the space\n    space_ax.scatter(**portray(space))\n\n\n@solara.component\ndef PlotMatplotlib(model, measure, dependencies: Optional[list[any]] = None):\n    fig = Figure()\n    ax = fig.subplots()\n    df = model.datacollector.get_model_vars_dataframe()\n    if isinstance(measure, str):\n        ax.plot(df.loc[:, measure])\n        ax.set_ylabel(measure)\n    elif isinstance(measure, dict):\n        for m, color in measure.items():\n            ax.plot(df.loc[:, m], label=m, color=color)\n        fig.legend()\n    elif isinstance(measure, (list, tuple)):\n        for m in measure:\n            ax.plot(df.loc[:, m], label=m)\n        fig.legend()\n    # Set integer x axis\n    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n    solara.FigureMatplotlib(fig, dependencies=dependencies)\n", "path": "mesa/experimental/components/matplotlib.py"}]}