# Matplotlib Plot throws an error **URL:** \ **Category:** W&B Help\ **Created:** [January 9, 2023, 11:59am UTC](https://community.wandb.ai/t/matplotlib-plot-throws-an-error/3654 "2023-01-09T11:59:32Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![cyberhawk-ai](https://avatars.discourse-cdn.com/v4/letter/c/f1d935/32.png) [@cyberhawk-ai](https://community.wandb.ai/u/cyberhawk-ai)\ **Post date:** [January 9, 2023, 11:59am UTC](https://community.wandb.ai/t/matplotlib-plot-throws-an-error/3654/1 "2023-01-09T11:59:32Z")
Hi, I’m trying to log a matplotlib graph on Wandb and it throws the following error. ```bash AttributeError: 'XAxis' object has no attribute '_gridOnMajor' ``` Steps to reproduce: Following example: [Create and track plots from experiments - Weights & Biases Documentation](https://docs.wandb.ai/guides/track/log/plots#matplotlib-and-plotly-plots) ## Exact steps: ### imports ```python import wandb import random import math ``` ### Login: ```python wandb.login() ``` ### Create and upload graph ```auto # 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: ```auto 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 () 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..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..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: ```bash { "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\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\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 8\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----> 9\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 11\u001b[0m \u001b[39m# Finally, end the run. We only need this ine in Jupyter notebooks.\u001b[39;00m\n\u001b[1;32m 12\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..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..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 🙂 ---
**Author:** ![artsiom](https://sea2.discourse-cdn.com/flex020/user_avatar/community.wandb.ai/artsiom/32/1001_2.png) [@artsiom](https://community.wandb.ai/u/artsiom)\ **Post date:** [January 9, 2023, 8:00pm UTC](https://community.wandb.ai/t/matplotlib-plot-throws-an-error/3654/2 "2023-01-09T20:00:00Z")
Hi Kostas! Very Strange, using your code on my machine, I don’t get the same error. Are you running this in google collab? Also just to double check is this the exact code you are using? Also, also, can you see if you are running into the exact same issue if wandb is disabled/commented out? Cheers, Artsiom ![](https://weightsandbiases.zendesk.com/attachments/token/dZf5iFTNBqk15pt4FISNCaj9y/?name=image.png) ---
**Author:** ![cyberhawk-ai](https://avatars.discourse-cdn.com/v4/letter/c/f1d935/32.png) [@cyberhawk-ai](https://community.wandb.ai/u/cyberhawk-ai)\ **Post date:** [January 10, 2023, 4:35pm UTC](https://community.wandb.ai/t/matplotlib-plot-throws-an-error/3654/3 "2023-01-10T16:35:48Z")
Hi Artsiom, I’m running on my local environment. Yes, that’s the exact code that I’m using. I’ve tried without Wandb and it works fine. I’ve also tried today the same code with Wandb and works as expected :S , very weird … Thanks, Kostas ---
**Author:** ![artsiom](https://sea2.discourse-cdn.com/flex020/user_avatar/community.wandb.ai/artsiom/32/1001_2.png) [@artsiom](https://community.wandb.ai/u/artsiom)\ **Post date:** [January 12, 2023, 9:21pm UTC](https://community.wandb.ai/t/matplotlib-plot-throws-an-error/3654/4 "2023-01-12T21:21:17Z")
Hi Kostas, That is very strange :0 But now that it works fine, you are not seeing any errors that are popping up anymore? Cheers! Artsiom ---
**Author:** ![system](https://us1.discourse-cdn.com/flex020/uploads/wandb/original/1X/366b649231631dbab896843020da0056074ac79d.png) [@system](https://community.wandb.ai/u/system)\ **Post date:** [March 11, 2023, 4:35pm UTC](https://community.wandb.ai/t/matplotlib-plot-throws-an-error/3654/5 "2023-03-11T16:35:51Z")
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