Hi,
I’m trying to log a matplotlib graph on Wandb and it throws the following error.
AttributeError: 'XAxis' object has no attribute '_gridOnMajor'
Steps to reproduce:
Following example: Log Plots - Documentation
Exact steps:
imports
import wandb
import random
import math
Login:
wandb.login()
Create and upload graph
# Start a new run
run = wandb.init(project='custom-charts')
offset = random.random()
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel("some interesting numbers")
wandb.log({"chart": plt})
# Finally, end the run. We only need this ine in Jupyter notebooks.
run.finish()
Summary error message:
AttributeError Traceback (most recent call last)
/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb Cell 4 in <cell line: 9>()
7 plt.plot([1, 2, 3, 4])
8 plt.ylabel("some interesting numbers")
----> 9 wandb.log({"chart": plt})
11 # Finally, end the run. We only need this ine in Jupyter notebooks.
12 run.finish()
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:256, in _run_decorator._noop.<locals>.wrapper(self, *args, **kwargs)
253 wandb.termwarn(message, repeat=False)
254 return cls.Dummy()
--> 256 return func(self, *args, **kwargs)
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:222, in _run_decorator._attach.<locals>.wrapper(self, *args, **kwargs)
220 raise e
221 cls._is_attaching = ""
--> 222 return func(self, *args, **kwargs)
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:1548, in Run.log(self, data, step, commit, sync)
1541 if sync is not None:
1542 deprecate.deprecate(
1543 field_name=deprecate.Deprecated.run__log_sync,
1544 warning_message=(
1545 "`sync` argument is deprecated and does not affect the behaviour of `wandb.log`"
1546 ),
1547 )
-> 1548 self._log(data=data, step=step, commit=commit)
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:1339, in Run._log(self, data, step, commit)
1336 if any(not isinstance(key, str) for key in data.keys()):
1337 raise ValueError("Key values passed to `wandb.log` must be strings.")
-> 1339 self._partial_history_callback(data, step, commit)
1341 if step is not None:
1342 if os.getpid() != self._init_pid or self._is_attached:
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:1228, in Run._partial_history_callback(self, row, step, commit)
1225 if self._backend and self._backend.interface:
1226 not_using_tensorboard = len(wandb.patched["tensorboard"]) == 0
-> 1228 self._backend.interface.publish_partial_history(
1229 row,
1230 user_step=self._step,
1231 step=step,
1232 flush=commit,
1233 publish_step=not_using_tensorboard,
1234 )
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/interface/interface.py:541, in InterfaceBase.publish_partial_history(self, data, user_step, step, flush, publish_step, run)
530 def publish_partial_history(
531 self,
532 data: dict,
(...)
537 run: Optional["Run"] = None,
538 ) -> None:
539 run = run or self._run
--> 541 data = history_dict_to_json(run, data, step=user_step, ignore_copy_err=True)
542 data.pop("_step", None)
544 partial_history = pb.PartialHistoryRequest()
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/data_types/utils.py:54, in history_dict_to_json(run, payload, step, ignore_copy_err)
50 payload[key] = history_dict_to_json(
51 run, val, step=step, ignore_copy_err=ignore_copy_err
52 )
53 else:
---> 54 payload[key] = val_to_json(
55 run, key, val, namespace=step, ignore_copy_err=ignore_copy_err
56 )
58 return payload
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/data_types/utils.py:82, in val_to_json(run, key, val, namespace, ignore_copy_err)
79 val = wandb.Table(dataframe=val)
81 elif util.is_matplotlib_typename(typename) or util.is_plotly_typename(typename):
---> 82 val = Plotly.make_plot_media(val)
83 elif isinstance(val, Sequence) and all(isinstance(v, WBValue) for v in val):
84 assert run
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/data_types/plotly.py:48, in Plotly.make_plot_media(cls, val)
46 if util.matplotlib_contains_images(val):
47 return Image(val)
---> 48 val = util.matplotlib_to_plotly(val)
49 return cls(val)
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/util.py:565, in matplotlib_to_plotly(obj)
557 obj = ensure_matplotlib_figure(obj)
558 tools = get_module(
559 "plotly.tools",
560 required=(
(...)
563 ),
564 )
--> 565 return tools.mpl_to_plotly(obj)
File ~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/tools.py:112, in mpl_to_plotly(fig, resize, strip_style, verbose)
110 if matplotlylib:
111 renderer = matplotlylib.PlotlyRenderer()
...
--> 246 if axis._gridOnMajor and len(gridlines) > 0:
247 color = export_color(gridlines[0].get_color())
248 alpha = gridlines[0].get_alpha()
AttributeError: 'XAxis' object has no attribute '_gridOnMajor'
Full Error message:
{
"name": "AttributeError",
"message": "'XAxis' object has no attribute '_gridOnMajor'",
"stack": "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)\n\u001b[1;32m/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb Cell 4\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bai_training_g5/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m plt\u001b[39m.\u001b[39mplot([\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m])\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bai_training_g5/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m\"\u001b[39m\u001b[39msome interesting numbers\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bai_training_g5/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8'>9</a>\u001b[0m wandb\u001b[39m.\u001b[39;49mlog({\u001b[39m\"\u001b[39;49m\u001b[39mchart\u001b[39;49m\u001b[39m\"\u001b[39;49m: plt})\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bai_training_g5/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m# Finally, end the run. We only need this ine in Jupyter notebooks.\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bai_training_g5/home/ubuntu/src/workspace/py-vision/src/research/nbs/automated_inventory_analysis/aia_africaai_baseline/wanb_plot_test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11'>12</a>\u001b[0m run\u001b[39m.\u001b[39mfinish()\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:256\u001b[0m, in \u001b[0;36m_run_decorator._noop.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 253\u001b[0m wandb\u001b[39m.\u001b[39mtermwarn(message, repeat\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 254\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mDummy()\n\u001b[0;32m--> 256\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:222\u001b[0m, in \u001b[0;36m_run_decorator._attach.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 221\u001b[0m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_is_attaching \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 222\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:1548\u001b[0m, in \u001b[0;36mRun.log\u001b[0;34m(self, data, step, commit, sync)\u001b[0m\n\u001b[1;32m 1541\u001b[0m \u001b[39mif\u001b[39;00m sync \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1542\u001b[0m deprecate\u001b[39m.\u001b[39mdeprecate(\n\u001b[1;32m 1543\u001b[0m field_name\u001b[39m=\u001b[39mdeprecate\u001b[39m.\u001b[39mDeprecated\u001b[39m.\u001b[39mrun__log_sync,\n\u001b[1;32m 1544\u001b[0m warning_message\u001b[39m=\u001b[39m(\n\u001b[1;32m 1545\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m`sync` argument is deprecated and does not affect the behaviour of `wandb.log`\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1546\u001b[0m ),\n\u001b[1;32m 1547\u001b[0m )\n\u001b[0;32m-> 1548\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_log(data\u001b[39m=\u001b[39;49mdata, step\u001b[39m=\u001b[39;49mstep, commit\u001b[39m=\u001b[39;49mcommit)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:1339\u001b[0m, in \u001b[0;36mRun._log\u001b[0;34m(self, data, step, commit)\u001b[0m\n\u001b[1;32m 1336\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(key, \u001b[39mstr\u001b[39m) \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m data\u001b[39m.\u001b[39mkeys()):\n\u001b[1;32m 1337\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mKey values passed to `wandb.log` must be strings.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 1339\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_partial_history_callback(data, step, commit)\n\u001b[1;32m 1341\u001b[0m \u001b[39mif\u001b[39;00m step \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1342\u001b[0m \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39mgetpid() \u001b[39m!=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_init_pid \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_is_attached:\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:1228\u001b[0m, in \u001b[0;36mRun._partial_history_callback\u001b[0;34m(self, row, step, commit)\u001b[0m\n\u001b[1;32m 1225\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39minterface:\n\u001b[1;32m 1226\u001b[0m not_using_tensorboard \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(wandb\u001b[39m.\u001b[39mpatched[\u001b[39m\"\u001b[39m\u001b[39mtensorboard\u001b[39m\u001b[39m\"\u001b[39m]) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m-> 1228\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend\u001b[39m.\u001b[39;49minterface\u001b[39m.\u001b[39;49mpublish_partial_history(\n\u001b[1;32m 1229\u001b[0m row,\n\u001b[1;32m 1230\u001b[0m user_step\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_step,\n\u001b[1;32m 1231\u001b[0m step\u001b[39m=\u001b[39;49mstep,\n\u001b[1;32m 1232\u001b[0m flush\u001b[39m=\u001b[39;49mcommit,\n\u001b[1;32m 1233\u001b[0m publish_step\u001b[39m=\u001b[39;49mnot_using_tensorboard,\n\u001b[1;32m 1234\u001b[0m )\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/interface/interface.py:541\u001b[0m, in \u001b[0;36mInterfaceBase.publish_partial_history\u001b[0;34m(self, data, user_step, step, flush, publish_step, run)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpublish_partial_history\u001b[39m(\n\u001b[1;32m 531\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 532\u001b[0m data: \u001b[39mdict\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 537\u001b[0m run: Optional[\u001b[39m\"\u001b[39m\u001b[39mRun\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 538\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 539\u001b[0m run \u001b[39m=\u001b[39m run \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run\n\u001b[0;32m--> 541\u001b[0m data \u001b[39m=\u001b[39m history_dict_to_json(run, data, step\u001b[39m=\u001b[39;49muser_step, ignore_copy_err\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m 542\u001b[0m data\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39m_step\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m 544\u001b[0m partial_history \u001b[39m=\u001b[39m pb\u001b[39m.\u001b[39mPartialHistoryRequest()\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/data_types/utils.py:54\u001b[0m, in \u001b[0;36mhistory_dict_to_json\u001b[0;34m(run, payload, step, ignore_copy_err)\u001b[0m\n\u001b[1;32m 50\u001b[0m payload[key] \u001b[39m=\u001b[39m history_dict_to_json(\n\u001b[1;32m 51\u001b[0m run, val, step\u001b[39m=\u001b[39mstep, ignore_copy_err\u001b[39m=\u001b[39mignore_copy_err\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 54\u001b[0m payload[key] \u001b[39m=\u001b[39m val_to_json(\n\u001b[1;32m 55\u001b[0m run, key, val, namespace\u001b[39m=\u001b[39;49mstep, ignore_copy_err\u001b[39m=\u001b[39;49mignore_copy_err\n\u001b[1;32m 56\u001b[0m )\n\u001b[1;32m 58\u001b[0m \u001b[39mreturn\u001b[39;00m payload\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/data_types/utils.py:82\u001b[0m, in \u001b[0;36mval_to_json\u001b[0;34m(run, key, val, namespace, ignore_copy_err)\u001b[0m\n\u001b[1;32m 79\u001b[0m val \u001b[39m=\u001b[39m wandb\u001b[39m.\u001b[39mTable(dataframe\u001b[39m=\u001b[39mval)\n\u001b[1;32m 81\u001b[0m \u001b[39melif\u001b[39;00m util\u001b[39m.\u001b[39mis_matplotlib_typename(typename) \u001b[39mor\u001b[39;00m util\u001b[39m.\u001b[39mis_plotly_typename(typename):\n\u001b[0;32m---> 82\u001b[0m val \u001b[39m=\u001b[39m Plotly\u001b[39m.\u001b[39;49mmake_plot_media(val)\n\u001b[1;32m 83\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(val, Sequence) \u001b[39mand\u001b[39;00m \u001b[39mall\u001b[39m(\u001b[39misinstance\u001b[39m(v, WBValue) \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m val):\n\u001b[1;32m 84\u001b[0m \u001b[39massert\u001b[39;00m run\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/sdk/data_types/plotly.py:48\u001b[0m, in \u001b[0;36mPlotly.make_plot_media\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[39mif\u001b[39;00m util\u001b[39m.\u001b[39mmatplotlib_contains_images(val):\n\u001b[1;32m 47\u001b[0m \u001b[39mreturn\u001b[39;00m Image(val)\n\u001b[0;32m---> 48\u001b[0m val \u001b[39m=\u001b[39m util\u001b[39m.\u001b[39;49mmatplotlib_to_plotly(val)\n\u001b[1;32m 49\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39m(val)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/wandb/util.py:565\u001b[0m, in \u001b[0;36mmatplotlib_to_plotly\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m 557\u001b[0m obj \u001b[39m=\u001b[39m ensure_matplotlib_figure(obj)\n\u001b[1;32m 558\u001b[0m tools \u001b[39m=\u001b[39m get_module(\n\u001b[1;32m 559\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mplotly.tools\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 560\u001b[0m required\u001b[39m=\u001b[39m(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 563\u001b[0m ),\n\u001b[1;32m 564\u001b[0m )\n\u001b[0;32m--> 565\u001b[0m \u001b[39mreturn\u001b[39;00m tools\u001b[39m.\u001b[39;49mmpl_to_plotly(obj)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/tools.py:112\u001b[0m, in \u001b[0;36mmpl_to_plotly\u001b[0;34m(fig, resize, strip_style, verbose)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[39mif\u001b[39;00m matplotlylib:\n\u001b[1;32m 111\u001b[0m renderer \u001b[39m=\u001b[39m matplotlylib\u001b[39m.\u001b[39mPlotlyRenderer()\n\u001b[0;32m--> 112\u001b[0m matplotlylib\u001b[39m.\u001b[39;49mExporter(renderer)\u001b[39m.\u001b[39;49mrun(fig)\n\u001b[1;32m 113\u001b[0m \u001b[39mif\u001b[39;00m resize:\n\u001b[1;32m 114\u001b[0m renderer\u001b[39m.\u001b[39mresize()\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/matplotlylib/mplexporter/exporter.py:51\u001b[0m, in \u001b[0;36mExporter.run\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m 50\u001b[0m plt\u001b[39m.\u001b[39mclose(fig)\n\u001b[0;32m---> 51\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcrawl_fig(fig)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/matplotlylib/mplexporter/exporter.py:118\u001b[0m, in \u001b[0;36mExporter.crawl_fig\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrenderer\u001b[39m.\u001b[39mdraw_figure(fig\u001b[39m=\u001b[39mfig,\n\u001b[1;32m 116\u001b[0m props\u001b[39m=\u001b[39mutils\u001b[39m.\u001b[39mget_figure_properties(fig)):\n\u001b[1;32m 117\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m fig\u001b[39m.\u001b[39maxes:\n\u001b[0;32m--> 118\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcrawl_ax(ax)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/matplotlylib/mplexporter/exporter.py:123\u001b[0m, in \u001b[0;36mExporter.crawl_ax\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcrawl_ax\u001b[39m(\u001b[39mself\u001b[39m, ax):\n\u001b[1;32m 121\u001b[0m \u001b[39m\"\"\"Crawl the axes and process all elements within\"\"\"\u001b[39;00m\n\u001b[1;32m 122\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrenderer\u001b[39m.\u001b[39mdraw_axes(ax\u001b[39m=\u001b[39max,\n\u001b[0;32m--> 123\u001b[0m props\u001b[39m=\u001b[39mutils\u001b[39m.\u001b[39;49mget_axes_properties(ax)):\n\u001b[1;32m 124\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m ax\u001b[39m.\u001b[39mlines:\n\u001b[1;32m 125\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdraw_line(ax, line)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/matplotlylib/mplexporter/utils.py:272\u001b[0m, in \u001b[0;36mget_axes_properties\u001b[0;34m(ax)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_axes_properties\u001b[39m(ax):\n\u001b[1;32m 265\u001b[0m props \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39maxesbg\u001b[39m\u001b[39m'\u001b[39m: export_color(ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39mget_facecolor()),\n\u001b[1;32m 266\u001b[0m \u001b[39m'\u001b[39m\u001b[39maxesbgalpha\u001b[39m\u001b[39m'\u001b[39m: ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39mget_alpha(),\n\u001b[1;32m 267\u001b[0m \u001b[39m'\u001b[39m\u001b[39mbounds\u001b[39m\u001b[39m'\u001b[39m: ax\u001b[39m.\u001b[39mget_position()\u001b[39m.\u001b[39mbounds,\n\u001b[1;32m 268\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdynamic\u001b[39m\u001b[39m'\u001b[39m: ax\u001b[39m.\u001b[39mget_navigate(),\n\u001b[1;32m 269\u001b[0m \u001b[39m'\u001b[39m\u001b[39maxison\u001b[39m\u001b[39m'\u001b[39m: ax\u001b[39m.\u001b[39maxison,\n\u001b[1;32m 270\u001b[0m \u001b[39m'\u001b[39m\u001b[39mframe_on\u001b[39m\u001b[39m'\u001b[39m: ax\u001b[39m.\u001b[39mget_frame_on(),\n\u001b[1;32m 271\u001b[0m \u001b[39m'\u001b[39m\u001b[39mpatch_visible\u001b[39m\u001b[39m'\u001b[39m:ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39mget_visible(),\n\u001b[0;32m--> 272\u001b[0m \u001b[39m'\u001b[39m\u001b[39maxes\u001b[39m\u001b[39m'\u001b[39m: [get_axis_properties(ax\u001b[39m.\u001b[39;49mxaxis),\n\u001b[1;32m 273\u001b[0m get_axis_properties(ax\u001b[39m.\u001b[39myaxis)]}\n\u001b[1;32m 275\u001b[0m \u001b[39mfor\u001b[39;00m axname \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m]:\n\u001b[1;32m 276\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(ax, axname \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39maxis\u001b[39m\u001b[39m'\u001b[39m)\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/matplotlylib/mplexporter/utils.py:236\u001b[0m, in \u001b[0;36mget_axis_properties\u001b[0;34m(axis)\u001b[0m\n\u001b[1;32m 233\u001b[0m props[\u001b[39m'\u001b[39m\u001b[39mfontsize\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 235\u001b[0m \u001b[39m# Get associated grid\u001b[39;00m\n\u001b[0;32m--> 236\u001b[0m props[\u001b[39m'\u001b[39m\u001b[39mgrid\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m get_grid_style(axis)\n\u001b[1;32m 238\u001b[0m \u001b[39m# get axis visibility\u001b[39;00m\n\u001b[1;32m 239\u001b[0m props[\u001b[39m'\u001b[39m\u001b[39mvisible\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39mget_visible()\n\nFile \u001b[0;32m~/anaconda3/envs/cyberhawk_ai/lib/python3.10/site-packages/plotly/matplotlylib/mplexporter/utils.py:246\u001b[0m, in \u001b[0;36mget_grid_style\u001b[0;34m(axis)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_grid_style\u001b[39m(axis):\n\u001b[1;32m 245\u001b[0m gridlines \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39mget_gridlines()\n\u001b[0;32m--> 246\u001b[0m \u001b[39mif\u001b[39;00m axis\u001b[39m.\u001b[39;49m_gridOnMajor \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(gridlines) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 247\u001b[0m color \u001b[39m=\u001b[39m export_color(gridlines[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mget_color())\n\u001b[1;32m 248\u001b[0m alpha \u001b[39m=\u001b[39m gridlines[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mget_alpha()\n\n\u001b[0;31mAttributeError\u001b[0m: 'XAxis' object has no attribute '_gridOnMajor'"
}
Any suggestions are welcomed,
Thanks 
