Merge pull request #343 from UBC-DSCI/main

PDF fixes production update

Author: trevorcampbell

.github/workflows/update_build_environment.yml

@@ -2,6 +2,8 @@ name: Rebuild and publish new ubcdsci/py-intro-to-ds image on DockerHub
 on:
   pull_request:
     types: [opened, synchronize]
+    branches:
+      - 'main'
 
 jobs:
   rebuild-docker:

source/classification1.md

@@ -16,6 +16,7 @@ kernelspec:
 :tags: [remove-cell]
 from chapter_preamble import *
 from IPython.display import HTML
+from IPython.display import Image
 from sklearn.metrics.pairwise import euclidean_distances
 import numpy as np
 import plotly.express as px
@@ -281,6 +282,7 @@ perimeter and concavity variables. Recall that the default palette in `altair`
 is colorblind-friendly, so we can stick with that here.
 
 ```{code-cell} ipython3
+:tags: ["remove-output"]
 perim_concav = alt.Chart(cancer).mark_circle().encode(
     x=alt.X("Perimeter").title("Perimeter (standardized)"),
     y=alt.Y("Concavity").title("Concavity (standardized)"),
@@ -289,12 +291,16 @@ perim_concav = alt.Chart(cancer).mark_circle().encode(
 perim_concav
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+glue("fig:05-scatter", perim_concav)
+```
+
+:::{glue:figure} fig:05-scatter
 :name: fig:05-scatter
-:figclass: caption-hack
 
 Scatter plot of concavity versus perimeter colored by diagnosis label.
-```
+:::
 
 +++
 
@@ -855,7 +861,11 @@ for neighbor_df in neighbor_df_list:
 # tight layout
 fig.update_layout(margin=dict(l=0, r=0, b=0, t=1), template="plotly_white")
 
-glue("fig:05-more", fig)
+# if HTML, use the plotly 3d image; if PDF, use static image
+if "BOOK_BUILD_TYPE" in os.environ and os.environ["BOOK_BUILD_TYPE"] == "PDF":
+    glue("fig:05-more", Image("img/classification1/plot3d_knn_classification.png"))
+else:
+    glue("fig:05-more", fig)
 ```
 
 ```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
@@ -1432,6 +1442,7 @@ The new imbalanced data is shown in {numref}`fig:05-unbalanced`,
 and we print the counts of the classes using the `value_counts` function.
 
 ```{code-cell} ipython3
+:tags: ["remove-output"]
 rare_cancer = pd.concat((
     cancer[cancer["Class"] == "Benign"],
     cancer[cancer["Class"] == "Malignant"].head(3)
@@ -1445,12 +1456,16 @@ rare_plot = alt.Chart(rare_cancer).mark_circle().encode(
 rare_plot
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+glue("fig:05-unbalanced", rare_plot)
+```
+
+:::{glue:figure} fig:05-unbalanced
 :name: fig:05-unbalanced
-:figclass: caption-hack
 
 Imbalanced data.
-```
+:::
 
 ```{code-cell} ipython3
 rare_cancer["Class"].value_counts()
@@ -1947,16 +1962,15 @@ unscaled_plot + prediction_plot
 ```
 
 ```{code-cell} ipython3
-:tags: [remove-input]
+:tags: [remove-cell]
 glue("fig:05-workflow-plot", (unscaled_plot + prediction_plot))
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+:::{glue:figure} fig:05-workflow-plot
 :name: fig:05-workflow-plot
-:figclass: caption-hack
 
 Scatter plot of smoothness versus area where background color indicates the decision of the classifier.
-```
+:::
 
 +++
 
@@ -1974,6 +1988,7 @@ found in {numref}`Chapter %s <move-to-your-own-machine>`. This will ensure that
 and guidance that the worksheets provide will function as intended.
 
 +++
+
 ## References
 
 ```{bibliography}

source/classification2.md

@@ -395,20 +395,20 @@ the `random_state` argument that is available in many `pandas` and `scikit-learn
 functions. Those functions will then use your `Generator` to generate random numbers instead of
 `numpy`'s default generator. For example, we can reproduce our earlier example by using a `Generator`
 object with the `seed` value set to 1; we get the same lists of numbers once again.
-```{code}
+```python
 from numpy.random import Generator, PCG64
 rng = Generator(PCG64(seed=1))
 random_numbers1_third = nums_0_to_9.sample(n=10, random_state=rng).to_list()
 random_numbers1_third
 ```
-```{code}
+```text
 array([2, 9, 6, 4, 0, 3, 1, 7, 8, 5])
 ```
-```{code}
+```python
 random_numbers2_third = nums_0_to_9.sample(n=10, random_state=rng).to_list()
 random_numbers2_third
 ```
-```{code}
+```text
 array([9, 5, 3, 0, 8, 4, 2, 1, 6, 7])
 ```
 
@@ -432,6 +432,7 @@ You will also notice that we set the random seed using the `np.random.seed` func
 as described in {numref}`randomseeds`.
 
 ```{code-cell} ipython3
+:tags: ["remove-output"]
 # load packages
 import altair as alt
 import pandas as pd
@@ -462,11 +463,18 @@ perim_concav = alt.Chart(cancer).mark_circle().encode(
 perim_concav
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+glue("fig:06-precode", perim_concav)
+```
+
+:::{glue:figure} fig:06-precode
 :name: fig:06-precode
 
 Scatter plot of tumor cell concavity versus smoothness colored by diagnosis label.
-```
+:::
+
+
 
 +++
 
@@ -2205,10 +2213,10 @@ and guidance that the worksheets provide will function as intended.
   text, it requires a bit more mathematical background than we require.
 
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```

source/clustering.md

@@ -1063,10 +1063,10 @@ and guidance that the worksheets provide will function as intended.
   learning, it covers *principal components analysis (PCA)*, which is a very
   popular technique for reducing the number of predictors in a data set.
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```