Conditional Plot Rendering for PDF Format (#34)

Data visualizations are not showing up in the PDF file. Because plotly creates interactive plots that don't translate to PDF / book format. So we are conditionally rendering a static PNG image when the format is PDF.

Some unrelated additional updates for PDF formatting include:
  +  preventing request URLs from overflowing the page on PDF
  + updating table widths to look good in the PDF

Some future updates include:
  + Customizing the image heights #36

Author: s2t2

.gitignore

@@ -3,6 +3,9 @@
 
 .jupyter_cache
 
+__pycache__
+
+
 # rendered files:
 docs/.quarto
 docs/_build

.vscode/settings.json

@@ -0,0 +1,8 @@
+{
+  "editor.rulers": [
+    78
+  ],
+  "files.insertFinalNewline": true,
+  "files.trimFinalNewlines": true,
+  "files.trimTrailingWhitespace": true,
+}

docs/_quarto.yml

@@ -259,7 +259,8 @@ format:
     theme: #cosmo
       light: cosmo # default #flatly
       dark: cyborg # vapor # superhero # darkly
-    css: styles/my-style.css
+    css: [styles/my-style.css]
+    #css: [styles/my-style.css, styles/wrap.css]
     code-copy: true
     #code-fold: false #show
     ##code-line-numbers: true
@@ -281,6 +282,8 @@ format:
 
   pdf:
 
+    #output-file: index.pdf
+
     # use XeLaTeX or LuaLaTeX so fontspec works:
     latex-engine: xelatex
 
@@ -290,8 +293,9 @@ format:
     #fontsize: 11pt
     #linestretch: 1.2
 
-    includes:
-      in-header: _pdf_styles.tex
+    # this sets the margin, but still overflows
+    #margin-right: 2in
 
-  #PrettyPDF-pdf:
-  #  keep-tex: true
+    includes:
+      in-header:
+        - _pdf_styles.tex

docs/notes/dataviz/candlesticks.qmd

@@ -9,20 +9,56 @@ execute:
 
 # Candlestick Charts with `plotly`
 
-In financial applications, we often have access to OHLC data (containing the open, high, low, and close price on each day). We can use a candlestick chart can help us see the movement of the price within each day.
+In financial applications, we may have access to OHLC data (containing the open, high, low, and closing prices on each day).
 
+With OHLC data, we can use a [candlestick chart](https://plotly.com/python/candlestick-charts/) to display the price movements within each day.
 
-To implement a [candlestick chart](https://plotly.com/python/candlestick-charts/), we can use the [`Candlestick` class](https://plotly.github.io/plotly.py-docs/generated/plotly.graph_objects.Candlestick.html) from plotly's Graph Objects sub-library.
+To implement a candlestick chart, we can use the [`Candlestick` class](https://plotly.github.io/plotly.py-docs/generated/plotly.graph_objects.Candlestick.html) from plotly's Graph Objects sub-library.
 
 We start with some OHLC data:
 
 ```{python}
 ohlc_data = [
-    {"date": "2030-03-16", "open": 236.2800, "high": 240.0550, "low": 235.9400, "close": 237.7100, "volume": 28092196},
-    {"date": "2030-03-15", "open": 234.9600, "high": 235.1850, "low": 231.8100, "close": 234.8100, "volume": 26042669},
-    {"date": "2030-03-12", "open": 234.0100, "high": 235.8200, "low": 233.2300, "close": 235.7500, "volume": 22653662},
-    {"date": "2030-03-11", "open": 234.9600, "high": 239.1700, "low": 234.3100, "close": 237.1300, "volume": 29907586},
-    {"date": "2030-03-10", "open": 237.0000, "high": 237.0000, "low": 232.0400, "close": 232.4200, "volume": 29746812}
+    {
+        "date": "2030-03-16",
+        "open": 237.8000,
+        "high": 239.2000,
+        "low": 237.0000,
+        "close": 238.6500,
+        "volume": 26800000
+    },
+    {
+        "date": "2030-03-15",
+        "open": 235.2500,
+        "high": 238.9000,
+        "low": 235.0000,
+        "close": 237.8000,
+        "volume": 27400000
+    },
+    {
+        "date": "2030-03-12",
+        "open": 236.5000,
+        "high": 237.7500,
+        "low": 234.8000,
+        "close": 235.2500,
+        "volume": 24800000
+    },
+    {
+        "date": "2030-03-11",
+        "open": 234.1000,
+        "high": 238.0000,
+        "low": 233.0000,
+        "close": 236.5000,
+        "volume": 31200000
+    },
+    {
+        "date": "2030-03-10",
+        "open": 232.0000,
+        "high": 235.5000,
+        "low": 230.2500,
+        "close": 234.1000,
+        "volume": 29500000
+    }
 ]
 ```
 
@@ -44,10 +80,12 @@ for item in ohlc_data:
 
 ```
 
-Finally, creating the chart:
+Finally, creating and displaying the chart:
 
 
 ```{python}
+#| output: false
+
 from plotly.graph_objects import Figure, Candlestick
 
 stick = Candlestick(x=dates, open=opens, high=highs, low=lows, close=closes)
@@ -56,3 +94,17 @@ fig = Figure(data=[stick])
 fig.update_layout(title="Example Candlestick Chart")
 fig.show()
 ```
+
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::

docs/notes/dataviz/overview.qmd

@@ -56,6 +56,8 @@ print(prices)
 ```
 
 ```{python}
+#| output: false
+
 from plotly.express import line
 
 fig = line(x=dates, y=prices, height=350,
@@ -66,6 +68,21 @@ fig.show()
 ```
 
 
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::
+
+
 ### Bar Charts
 
 Starting with some example data:
@@ -98,6 +115,8 @@ print(viewers)
 ```
 
 ```{python}
+#| output: false
+
 from plotly.express import bar
 
 fig = bar(x=genres, y=viewers, height=350,
@@ -107,6 +126,20 @@ fig = bar(x=genres, y=viewers, height=350,
 fig.show()
 ```
 
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::
+
 
 #### Horizontal Bar Chart
 
@@ -133,13 +166,29 @@ When sorting the data, we have to sort BEFORE mapping, to ensure the two resulti
 With horizontal bar, we use the `orientation` parameter, and also flip the X and Y references:
 
 ```{python}
+#| output: false
+
 fig = bar(y=genres, x=viewers, orientation="h", height=350,
           title="Viewership by Genre",
           labels={"y": "Genre", "x": "Viewers"}
 )
 fig.show()
 ```
 
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::
+
 ### Scatter Plots
 
 We can use a scatter plot to examine the relationship between two variables (`x` and `y`).
@@ -174,6 +223,8 @@ print(expectancies)
 ```
 
 ```{python}
+#| output: false
+
 from plotly.express import scatter
 
 fig = scatter(x=incomes, y=expectancies, height=350,
@@ -184,6 +235,20 @@ fig.show()
 ```
 
 
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::
+
 
 
 ### Pie Charts
@@ -214,6 +279,8 @@ print(market_shares)
 ```
 
 ```{python}
+#| output: false
+
 from plotly.express import pie
 
 fig = pie(labels=companies, values=market_shares, height=350,
@@ -223,6 +290,19 @@ fig.show()
 ```
 
 
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::
 
 
 ### Histograms
@@ -251,6 +331,8 @@ print(opinions)
 ```
 
 ```{python}
+#| output: false
+
 from plotly.express import histogram
 
 fig = histogram(x=opinions, height=350, nbins=5,
@@ -259,3 +341,18 @@ fig = histogram(x=opinions, height=350, nbins=5,
 )
 fig.show()
 ```
+
+
+::: {.content-visible when-format="html"}
+```{python}
+#| echo: false
+fig.show()
+```
+:::
+
+::: {.content-visible when-format="pdf"}
+```{python}
+#| echo: false
+fig.show(renderer="png")
+```
+:::