diff --git a/scripts/convert_ipynb_to_mdx.py b/scripts/convert_ipynb_to_mdx.py
index a7ca4771e6..2701f969ff 100644
--- a/scripts/convert_ipynb_to_mdx.py
+++ b/scripts/convert_ipynb_to_mdx.py
@@ -39,8 +39,6 @@
"text/markdown",
"image/png", # matplotlib output.
"application/vnd.jupyter.widget-view+json", # tqdm progress bars.
- "application/vnd.bokehjs_load.v0+json", # Bokeh loading output.
- "application/vnd.bokehjs_exec.v0+json", # Bokeh `show` outputs.
"application/vnd.plotly.v1+json", # Plotly
"text/html",
"stream",
@@ -158,7 +156,7 @@ def create_imports() -> str:
plot_out = "@site/src/components/Plotting.jsx"
imports = f'import LinkButtons from "{link_btn}";\n'
imports += f'import CellOutput from "{cell_out}";\n'
- imports += f'import {{BokehFigure, PlotlyFigure}} from "{plot_out}";\n'
+ imports += f'import {{PlotlyFigure}} from "{plot_out}";\n'
return f"{imports}\n"
@@ -404,110 +402,6 @@ def handle_cell_input(cell: NotebookNode, language: str) -> str:
return f"```{language}\n{cell_source}\n```\n\n"
-def transform_bokeh_json(json_data: Dict[str, Any]) -> Dict[str, Any]:
- """
- Transform Bokeh JSON found in a cell output to something BokehJS can consume.
-
- Args:
- json_data (Dict[str, Any]): JSON data found in a notebook's cell output that is
- for Bokeh.
-
- Returns:
- Dict[str, Any]: Reorganized JSON output for BokehJS.
- """
- key = list(json_data.keys())[0]
- data = json_data[key]
- json_tx = {}
- json_tx["target_id"] = key
- json_tx["root_id"] = data["roots"]["root_ids"][0]
- json_tx["doc"] = {
- "defs": data["defs"],
- "roots": data["roots"],
- "title": data["title"],
- "version": data["version"],
- }
- json_tx["version"] = data["version"]
- return json_tx
-
-
-def handle_bokeh(
- values: List[Dict[str, Union[int, str, NotebookNode]]],
- plot_data_folder: Path,
-) -> List[Tuple[int, str]]:
- """
- Convert Bokeh `show` outputs and Applications to MDX.
-
- Args:
- values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell
- outputs.
- plot_data_folder (Path): Path to the folder where plot data should be
- stored.
-
- Returns:
- List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is
- the index where the output occurred from the cell, and the second entry of
- the tuple is the MDX formatted string.
- """
- output = []
- for value in values:
- index = int(value["index"])
- data = str(value["data"])
- app_flag = data.startswith("")
- json_data = {}
- # Handle Bokeh `show` outputs.
- if not app_flag:
- # Parse the JavaScript for the Bokeh JSON data. The BokehJS output is
- # standardized, so we can make the following assumption for finding the
- # right spot to for the JSON data. Also, this is pure JavaScript so
- # parsing it with lxml is not an option.
- json_string = list(
- filter(
- lambda line: line.startswith("const docs_json = "),
- [line.strip() for line in data.splitlines() if line],
- ),
- )[0]
-
- # Ignore the `const` definition and the ending `;` from the line.
- json_string = json_string[len("const docs_json = ") : -1]
- json_data = json.loads(json_string)
-
- # Handle Bokeh Applications.
- if app_flag:
- # Bokeh Application objects are rendered in the notebook as HTML. This
- # HTML is saved in the output cell, which we parse below using lxml and
- # xpaths.
- doc = etree.HTML(data) # pyre-ignore
- scripts = doc.xpath("//body/script[@type='application/json']")
- script = scripts[0]
- script = "".join(script.itertext())
- # Unescape characters. If we skip this step, then the JSON read in by
- # the React BokehFigure object will error in the browser.
- script = script.replace("&", "&")
- script = script.replace("<", "<")
- script = script.replace(">", ">")
- script = script.replace(""", '"')
- script = script.replace("'", "'")
- script = script.replace("`", "`")
- json_data = json.loads(script)
-
- # Shuffle the data so we can save it in a format BokehJS will be able to
- # consume later.
- js = transform_bokeh_json(json_data)
- file_name = js["target_id"]
- # Save the Bokeh JSON data to disk. It will be read by React when loaded in
- # Docusaurus.
- file_path = plot_data_folder / f"{file_name}.json"
- with file_path.open("w") as f:
- json.dump(js, f, indent=2)
-
- # Add the Bokeh figure to the MDX output.
- path_to_data = f"./assets/plot_data/{file_name}.json"
- output.append(
- (index, f"\n\n"),
- )
- return output
-
-
def handle_image(
values: List[Dict[str, Union[int, str, NotebookNode]]],
) -> List[Tuple[int, str]]:
@@ -713,8 +607,6 @@ def aggregate_mdx(
for key, values in cell_outputs_to_process.items():
if not values:
continue
- if key == "bokeh":
- processed_mdx.extend(handle_bokeh(values, plot_data_folder))
if key == "image":
processed_mdx.extend(handle_image(values))
if key == "markdown":
@@ -772,39 +664,6 @@ def prioritize_dtypes(
return prioritized_cell_output_dtypes, plotly_flags
-def aggregate_bokeh(
- prioritized_data_dtype: str,
- cell_output: NotebookNode,
- data: NotebookNode,
- cell_outputs_to_process: CELL_OUTPUTS_TO_PROCESS,
- i: int,
-) -> None:
- """
- Aggregate Bokeh cell outputs.
-
- Args:
- prioritized_data_dtype (str): The prioritized cell output data type.
- cell_output (NotebookNode): The actual cell output from the notebook.
- data (NotebookNode): The data of the cell output.
- cell_outputs_to_process (CELL_OUTPUTS_TO_PROCESS): Dictionary containing
- aggregated cell output objects.
- i (int): Index for the cell output in the list of cell output objects.
-
- Returns:
- None: Does not return anything, instead adds values to the
- cell_outputs_to_process if applicable.
- """
- if prioritized_data_dtype == "application/vnd.bokehjs_load.v0+json":
- pass
- # Bokeh `show` outputs.
- if prioritized_data_dtype == "application/vnd.bokehjs_exec.v0+json":
- data = cell_output["data"]["application/javascript"]
- cell_outputs_to_process["bokeh"].append({"index": i, "data": data})
- # Bokeh applications.
- if prioritized_data_dtype == "text/html" and "Bokeh Application" in data:
- cell_outputs_to_process["bokeh"].append({"index": i, "data": data})
-
-
def aggregate_images_and_plotly(
prioritized_data_dtype: str,
cell_output: NotebookNode,
@@ -887,7 +746,6 @@ def aggregate_output_types(cell_outputs: List[NotebookNode]) -> CELL_OUTPUTS_TO_
prioritized_cell_output_dtypes, plotly_flags = prioritize_dtypes(cell_outputs)
cell_outputs_to_process = {
- "bokeh": [],
"image": [],
"markdown": [],
"pandas": [],
@@ -908,17 +766,6 @@ def aggregate_output_types(cell_outputs: List[NotebookNode]) -> CELL_OUTPUTS_TO_
if "text" in cell_output
else cell_output["evalue"]
)
- bokeh_check = "bokeh" in prioritized_data_dtype or (
- prioritized_data_dtype == "text/html" and "Bokeh Application" in data
- )
- if bokeh_check:
- aggregate_bokeh(
- prioritized_data_dtype,
- cell_output,
- data,
- cell_outputs_to_process,
- i,
- )
image_check = (
prioritized_data_dtype.startswith("image")
or "plotly" in prioritized_data_dtype
diff --git a/website/src/components/Plotting.jsx b/website/src/components/Plotting.jsx
index 8d96cbd657..c80511c0f8 100644
--- a/website/src/components/Plotting.jsx
+++ b/website/src/components/Plotting.jsx
@@ -8,26 +8,6 @@
import React from 'react';
import Loadable from 'react-loadable';
import BrowserOnly from '@docusaurus/BrowserOnly';
-import '../css/bokeh.css';
-
-export const BokehFigure = React.memo(({data}) => {
- const targetId = data['target_id'];
- return (
-
- loading...
}>
- {() => {
- {
- window.Bokeh.embed.embed_item(data, targetId);
- }
- }}
-
-
- );
-});
// const Plotly = Loadable({
// loader: () => import(`react-plotly.js`),
diff --git a/website/src/css/bokeh.css b/website/src/css/bokeh.css
deleted file mode 100644
index 5d74d372d3..0000000000
--- a/website/src/css/bokeh.css
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
- .bk.bm-tool-loading {
- overflow: hidden;
- }
- .bk.bm-tool-loading:before {
- position: absolute;
- height: 100%;
- width: 100%;
- content: '';
- z-index: 1000;
- background-color: rgb(255, 255, 255, 0.75);
- border-color: lightgray;
- background-repeat: no-repeat;
- background-position: center;
- background-size: auto 50%;
- border-width: 1px;
- cursor: wait;
- }
- .bk.bm-tool-loading.arcs:hover:before {
- content: 'Please select a Query from the Select menu above.';
- font: x-large Arial, sans-serif;
- color: black;
- cursor: progress;
- display: flex;
- justify-content: center;
- align-items: center;
- }