Standardize plotly rendering function

Author: choldgraf

book/cloud.md

@@ -50,15 +50,15 @@ import datetime
 from pathlib import Path
 import plotly.express as px
 import plotly.graph_objects as go
-import plotly.io as pio
 from plotly.subplots import make_subplots
 from textwrap import dedent
-
-pio.renderers.default = "notebook"
 from IPython.display import Markdown, display
 import requests
 from rich.progress import track
+import twoc
 from twoc import colors
+
+twoc.set_plotly_defaults()
 ```
 
 +++ {"editable": true, "slideshow": {"slide_type": ""}}
@@ -172,13 +172,7 @@ slideshow:
   slide_type: ''
 tags: [remove-cell]
 ---
-import pandas as pd
-import pandas as pd
-import plotly.express as px
-import numpy as np
 from plotly.express.colors import qualitative
-import plotly.io as pio
-pio.renderers.default = "notebook"
 ```
 
 ```{code-cell} ipython3
@@ -479,7 +473,7 @@ for i, scale in enumerate(scale_ordering, 1):
     fig_bins.update_xaxes(title_text=f"{scale.capitalize()} Active Users", tickangle=-45, row=1, col=i)
     fig_bins.update_yaxes(title_text="Number of communities", range=[0, max_y_bins], row=1, col=i)
 fig_bins.update_layout(title_text="Number of communities in bins of active users", showlegend=False)
-display(fig_bins)
+fig_bins.show()
 
 # Create subplots for percentage breakdown
 fig_perc = make_subplots(rows=1, cols=len(scale_ordering), subplot_titles=[s.capitalize() for s in scale_ordering])
@@ -500,7 +494,7 @@ for i, scale in enumerate(scale_ordering, 1):
     fig_perc.update_xaxes(title_text="Bin", tickangle=-45, row=1, col=i)
     fig_perc.update_yaxes(title_text="% of users", tickformat='.0%', range=[0, 1], row=1, col=i)
 fig_perc.update_layout(title_text="% Total Active Users by community size", showlegend=False)
-display(fig_perc)
+fig_perc.show()
 ```
 
 ### Date of First Value

book/finances.md

@@ -61,9 +61,6 @@ import twoc
 from twoc.dates import round_to_nearest_month
 
 twoc.set_plotly_defaults()
-import plotly.io as pio
-# Use notebook renderer to output HTML for Jupyter Book
-pio.renderers.default = "notebook"
 
 # This just suppresses a warning
 pd.set_option("future.no_silent_downcasting", True)
@@ -367,6 +364,7 @@ for fig in figures.values():
     fig.show()
 ```
 
+(undertanding-revenue)=
 ## Understanding revenue
 
 The plots above show our revenue projections under different assumptions, going 18 months into the future:

book/marketing.md

@@ -31,10 +31,9 @@ import xml.etree.ElementTree as ET
 from datetime import datetime
 import plotly.express as px
 import pandas as pd
-import plotly.io as pio
+import twoc
 
-# Set plotly renderer for static HTML output
-pio.renderers.default = "plotly_mimetype+notebook"
+twoc.set_plotly_defaults()
 
 # Fetch and parse RSS feed
 response = requests.get("https://2i2c.org/blog/index.xml")

book/people.md

@@ -202,9 +202,10 @@ slideshow:
   slide_type: ''
 tags: [remove-input]
 ---
-import plotly.io as pio
 import plotly.express as px
-pio.renderers.default = "notebook"
+import twoc
+
+twoc.set_plotly_defaults()
 
 df["last day plus one"] = df["last day"] + timedelta(days=1)
 

book/twoc/__init__.py

@@ -2,24 +2,39 @@
 A helper module to quickly generate visualizations that use 2i2c's colors and style.
 Importing this modifies plotly defaults to use these colors.
 """
+
 colors = dict(
-    bigblue = "#1D4EF5",
-    paleblue = "#F2F5FC",
-    midnight = "#230344",
-    mauve = "#B86BFC",
-    forest = "#057761",
-    lightgreen = "#0CEFAE",
-    magenta = "#C60A76",
-    pink = "#FF808B",
-    coral = "#FF4E4F",
-    yellow = "#FFDE17",
+    bigblue="#1D4EF5",
+    paleblue="#F2F5FC",
+    midnight="#230344",
+    mauve="#B86BFC",
+    forest="#057761",
+    lightgreen="#0CEFAE",
+    magenta="#C60A76",
+    pink="#FF808B",
+    coral="#FF4E4F",
+    yellow="#FFDE17",
 )
+
+
 def set_plotly_defaults():
-    """Update plotly defaults to use these colors."""
+    """Update plotly defaults to use 2i2c colors and notebook renderer for Jupyter Book."""
     import plotly.io as pio
+
+    # Use notebook renderer for HTML output in Jupyter Book
+    pio.renderers.default = "notebook"
     pio.templates.default = "plotly_white"
     custom_template = pio.templates["plotly_white"].layout.template
-    custom_template.layout.update(plot_bgcolor=colors["paleblue"], paper_bgcolor=colors["paleblue"])
-    custom_template.layout.colorway = [colors["bigblue"], colors["coral"], colors["lightgreen"], colors["magenta"], colors["pink"], colors["yellow"]]
+    custom_template.layout.update(
+        plot_bgcolor=colors["paleblue"], paper_bgcolor=colors["paleblue"]
+    )
+    custom_template.layout.colorway = [
+        colors["bigblue"],
+        colors["coral"],
+        colors["lightgreen"],
+        colors["magenta"],
+        colors["pink"],
+        colors["yellow"],
+    ]
     pio.templates["custom_theme"] = custom_template
-    pio.templates.default = "custom_theme"
+    pio.templates.default = "custom_theme"

book/upstream.md

@@ -38,12 +38,12 @@ from subprocess import run
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
-import plotly.io as pio
 from IPython.display import Markdown, display
+import twoc
 from twoc import colors as twoc_colors
 from yaml import safe_load
 
-pio.renderers.default = "notebook"
+twoc.set_plotly_defaults()
 
 # Colors we'll re-use
 TWOC_PALETTE = [