Made variable state shared across codeblocks and cleaned up previous fixes to imports/variables missing in subsequent code blocks.

Author: daexs

bin/run_markdown.py

@@ -206,11 +206,15 @@ def _report(condition, message):
 def _run_all_blocks(args, code_blocks, stem=None, block_number=None):
     """Run blocks found in a file."""
     execution_results = []
+    env = {
+            "__name__": "__main__",
+            "__file__": "<markdown_code>",
+        }
     figure_counter = 0
     for i, block in enumerate(code_blocks):
         if block_number is None:
             _report(args.verbose > 1, f"- Executing block {i}/{len(code_blocks)}")
-            figure_counter, result = _run_code(block["code"], args.outdir, figure_counter, stem)
+            figure_counter, result = _run_code(block["code"], args.outdir, figure_counter, stem, env)
             execution_results.append(result)
             _report(args.verbose > 0 and bool(result["error"]), f"  - Warning: block {i} had an error")
             _report(args.verbose > 1 and bool(result["images"]), f"  - Generated {len(result['images'])} image(s)")
@@ -224,7 +228,7 @@ def _run_all_blocks(args, code_blocks, stem=None, block_number=None):
     return execution_results
 
 
-def _run_code(code, output_dir, figure_counter, stem):
+def _run_code(code, output_dir, figure_counter, stem, exec_globals):
     """Execute code capturing output and generated files."""
     # Capture stdout and stderr
     stdout_buffer = io.StringIO()
@@ -239,10 +243,10 @@ def _run_code(code, output_dir, figure_counter, stem):
     try:
 
         # Create a namespace for code execution
-        exec_globals = {
-            "__name__": "__main__",
-            "__file__": "<markdown_code>",
-        }
+        # exec_globals = {
+        #     "__name__": "__main__",
+        #     "__file__": "<markdown_code>",
+        # }
 
         # Execute the code with output capture
         with redirect_stdout(stdout_buffer), redirect_stderr(stderr_buffer):

doc/python/creating-and-updating-figures.md

@@ -372,10 +372,6 @@ fig.show()
 Note that the following `update_layout()` operations are equivalent:
 
 ```python
-import plotly.graph_objects as go
-
-fig = go.Figure(data=go.Bar(x=[1, 2, 3], y=[1, 3, 2]))
-
 fig.update_layout(title_text="update_layout() Syntax Example",
                   title_font_size=30)
 

doc/python/ecdf-plots.md

@@ -96,7 +96,6 @@ In `standard` mode (the default), the right-most point is at 1 (or the total cou
 
 ```python
 import plotly.express as px
-df = px.data.tips()
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="standard",
               title="ecdfmode='standard' (Y=fraction at or below X value, this the default)")
 fig.show()
@@ -106,7 +105,6 @@ In `reversed` mode, the right-most point is at 1 (or the total count/sum, depend
 
 ```python
 import plotly.express as px
-df = px.data.tips()
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="reversed",
               title="ecdfmode='reversed' (Y=fraction at or above X value)")
 fig.show()
@@ -116,7 +114,6 @@ In `complementary` mode, the right-most point is at 0 and no points are at 1 (or
 
 ```python
 import plotly.express as px
-df = px.data.tips()
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="complementary",
               title="ecdfmode='complementary' (Y=fraction above X value)")
 fig.show()

doc/python/figure-introspection.md

@@ -117,7 +117,6 @@ print(full_fig.data[0])
 We see that this is an instance of `go.Scatter` (as expected, given the input) and that it has an attribute we've maybe never heard of called `cliponaxis` which by default seems to be set to `True` in this case. Let's find out more about this attribute using the built-in Python `help()` function
 
 ```python
-import plotly.graph_objects as go
 help(go.Scatter.cliponaxis)
 ```
 
@@ -131,7 +130,6 @@ fig.show()
 We can use this technique (of making a figure, and querying Plotly.js for the "full" version of that figure, and then exploring the attributes that are automatically set for us) to learn more about the range of possibilities that the figure schema makes available. We can drill down into `layout` attributes also:
 
 ```python
-import plotly.graph_objects as go
 help(go.layout.XAxis.autorange)
 ```
 

doc/python/figurewidget.md

@@ -56,26 +56,14 @@ f
 Add traces or update the layout and then watch the output above update in real time.
 
 ```python
-import plotly.graph_objects as go
-f = go.FigureWidget()
-
 f.add_scatter(y=[2, 1, 4, 3])
 ```
 
 ```python
-import plotly.graph_objects as go
-f = go.FigureWidget()
-f.add_scatter(y=[2, 1, 4, 3])
-
 f.add_bar(y=[1, 4, 3, 2])
 ```
 
 ```python
-import plotly.graph_objects as go
-f = go.FigureWidget()
-f.add_scatter(y=[2, 1, 4, 3])
-f.add_bar(y=[1, 4, 3, 2])
-
 f.layout.title = 'Hello FigureWidget'
 ```
 
@@ -85,42 +73,18 @@ f.layout.title = 'Hello FigureWidget'
 #### Update the Data and the Layout
 
 ```python
-import plotly.graph_objects as go
-f = go.FigureWidget()
-f.add_scatter(y=[2, 1, 4, 3])
-f.add_bar(y=[1, 4, 3, 2])
-f.layout.title = 'Hello FigureWidget'
-
 # update scatter data
 scatter = f.data[0]
 scatter.y = [3, 1, 4, 3]
 ```
 
 ```python
-import plotly.graph_objects as go
-f = go.FigureWidget()
-f.add_scatter(y=[2, 1, 4, 3])
-f.add_bar(y=[1, 4, 3, 2])
-f.layout.title = 'Hello FigureWidget'
-scatter = f.data[0]
-scatter.y = [3, 1, 4, 3]
-
 # update bar data
 bar = f.data[1]
 bar.y = [5, 3, 2, 8]
 ```
 
 ```python
-import plotly.graph_objects as go
-f = go.FigureWidget()
-f.add_scatter(y=[2, 1, 4, 3])
-f.add_bar(y=[1, 4, 3, 2])
-f.layout.title = 'Hello FigureWidget'
-scatter = f.data[0]
-scatter.y = [3, 1, 4, 3]
-bar = f.data[1]
-bar.y = [5, 3, 2, 8]
-
 f.layout.title.text = 'This is a new title'
 ```
 
@@ -150,7 +114,5 @@ f2
 See [these Jupyter notebooks](https://github.com/jonmmease/plotly_ipywidget_notebooks) for even more FigureWidget examples.
 
 ```python
-import plotly.graph_objects as go
-
 help(go.FigureWidget)
 ```

doc/python/network-graphs.md

@@ -57,13 +57,8 @@ G = nx.random_geometric_graph(200, 0.125)
 Add edges as disconnected lines in a single trace and nodes as a scatter trace
 
 ```python
-import plotly.graph_objects as go
-import networkx as nx
-
-G = nx.random_geometric_graph(200, 0.125)
 edge_x = []
 edge_y = []
-
 for edge in G.edges():
     x0, y0 = G.nodes[edge[0]]['pos']
     x1, y1 = G.nodes[edge[1]]['pos']
@@ -120,16 +115,11 @@ Another option would be to size points by the number of connections
 i.e. ```node_trace.marker.size = node_adjacencies```
 
 ```python
-import networkx as nx
-
-G = nx.random_geometric_graph(200, 0.125)
 node_adjacencies = []
 node_text = []
-
 for node, adjacencies in enumerate(G.adjacency()):
     node_adjacencies.append(len(adjacencies[1]))
     node_text.append('# of connections: '+str(len(adjacencies[1])))
-
 node_trace.marker.color = node_adjacencies
 node_trace.text = node_text
 ```

doc/python/px-arguments.md

@@ -55,8 +55,6 @@ df.head()
 By default `px.data` functions return a pandas `DataFrame` object, but you can specify an alternative dataframe type using `return_type`. `pandas`, `polars`, `pyarrow`, `modin`, and `cuDF` are supported return types.
 
 ```python
-import plotly.express as px
-
 df = px.data.iris(return_type='polars')
 df.head()
 ```

doc/python/static-image-export.md

@@ -204,23 +204,13 @@ Here's the bytes object displayed using `IPython.display.Image`:
 
 ```python
 from IPython.display import Image
-import plotly.express as px
-data_canada = px.data.gapminder().query("country == 'Canada'")
-fig = px.bar(data_canada, x='year', y='pop')
-img_bytes = fig.to_image(format="png")
-
 Image(img_bytes)
 ```
 
 ## Specify Image Dimensions and Scale
 In addition to the image format, the `to_image` and `write_image` functions provide arguments to specify the image `width` and `height` in logical pixels. They also provide a `scale` parameter that can be used to increase (`scale` > 1) or decrease (`scale` < 1) the physical resolution of the resulting image.
 
 ```python
-from IPython.display import Image
-import plotly.express as px
-data_canada = px.data.gapminder().query("country == 'Canada'")
-fig = px.bar(data_canada, x='year', y='pop')
-
 img_bytes = fig.to_image(format="png", width=600, height=350, scale=2)
 Image(img_bytes)
 ```

doc/python/ternary-contour.md

@@ -128,9 +128,6 @@ fig.show()
 Two modes are available in order to interpolate between data points: interpolation in Cartesian space (`interp_mode='cartesian'`) or interpolation using the [isometric log-ratio transformation](https://link.springer.com/article/10.1023/A:1023818214614) (see also [preprint](https://www.researchgate.net/profile/Leon_Parent2/post/What_is_the_best_approach_for_diagnosing_nutrient_disorders_and_formulating_fertilizer_recommendations/attachment/59d62a69c49f478072e9cf3f/AS%3A272541220835360%401441990298625/download/Egozcue+et+al+2003.pdf)),  `interp_mode='ilr'`. The `ilr` transformation preserves metrics in the [simplex](https://en.wikipedia.org/wiki/Simplex) but is not defined on its edges.
 
 ```python
-import numpy as np
-import plotly.figure_factory as ff
-
 a, b = np.mgrid[0:1:20j, 0:1:20j]
 mask = a + b <= 1
 a, b = a[mask], b[mask]
@@ -141,9 +138,6 @@ fig.show()
 ```
 
 ```python
-import numpy as np
-import plotly.figure_factory as ff
-
 a, b = np.mgrid[0:1:20j, 0:1:20j]
 mask = a + b <= 1
 a, b = a[mask], b[mask]

doc/python/ternary-scatter-contour.md

@@ -70,10 +70,6 @@ print(clean_data({'L1': ['.03 0.5 0.47','0.4 0.5 0.1']}))
 
 ```python
 import plotly.graph_objects as go
-import pandas as pd
-
-scatter_raw_data = pd.read_json('https://raw.githubusercontent.com/plotly/datasets/master/scatter_data.json')
-scatter_data =  scatter_raw_data['Data']
 
 a_list = []
 b_list = []
@@ -117,9 +113,7 @@ fig.show()
 
 ```python
 import plotly.graph_objects as go
-import pandas as pd
 
-contour_raw_data = pd.read_json('https://raw.githubusercontent.com/plotly/datasets/master/contour_data.json')
 contour_dict = contour_raw_data['Data']
 
 # Defining a colormap: