adapting a gallery example moves to vega-altair; closes #274

Author: bast

content/plotting-matplotlib.md

@@ -392,116 +392,6 @@ ax.tick_params(labelsize=15)
 ```
 ````
 
-````{challenge} Exercise Customization-3: adapting a gallery example
-**This is a great exercise which is very close to real life.**
-
-- Your task is to select one visualization library (some need to be installed first - in
-  doubt choose Matplotlib or Seaborn since they are part of Anaconda installation):
-  - [Matplotlib](https://matplotlib.org/stable/gallery/index.html):
-    probably the most standard and most widely used
-  - [Seaborn](https://seaborn.pydata.org/examples/index.html):
-    high-level interface to Matplotlib, statistical functions built in
-  - [Vega-Altair](https://altair-viz.github.io/gallery/index.html):
-    declarative visualization, statistics built in
-    (we have an [entire lesson about data visualization using Vega-Altair](https://coderefinery.github.io/data-visualization-python/))
-  - [Plotly](https://plotly.com/python/):
-    interactive graphs
-  - [Bokeh](https://demo.bokeh.org/):
-    also here good for interactivity
-  - [plotnine](https://plotnine.readthedocs.io/):
-    implementation of a grammar of graphics in Python, it is based on [ggplot2](https://ggplot2.tidyverse.org/)
-  - [ggplot](https://yhat.github.io/ggpy/):
-    R users will be more at home
-  - [PyNGL](https://www.pyngl.ucar.edu/Examples/gallery.shtml):
-    used in the weather forecast community
-  - [K3D](https://k3d-jupyter.org/gallery/index.html):
-    Jupyter Notebook extension for 3D visualization
-
-- Browse the various example galleries (links above).
-- Select one example that is close to your recent visualization project or simply interests you.
-- Note that you might need to install additional Python packages in order make use of the libraries.
-  This could be the visualization library itself, and in addition also any required dependency package.
-- First try to reproduce this example in the Jupyter Notebook.
-- Then try to print out the data that is used in this example just before the call of the plotting function
-  to learn about its structure. Is it a pandas dataframe? Is it a NumPy array? Is it a dictionary? A list?
-  a list of lists?
-- Then try to modify the data a bit.
-- If you have time, try to feed it different, simplified data.
-  This will be key for adapting the examples to your projects.
-
-Example "solution" for such an exploration below.
-````
-
-````{solution} An example exploration
-- Let us imagine we were browsing <https://seaborn.pydata.org/examples/index.html>
-- And this example plot caught our eye: <https://seaborn.pydata.org/examples/simple_violinplots.html>
-- Try to run it in the notebook.
-- The `d` seems to be the data. Right before the call to `sns.violinplot`, add a `print(d)`:
-  ```{code-block} python
-  ---
-  emphasize-lines: 12
-  ---
-  import numpy as np
-  import seaborn as sns
-
-  sns.set_theme()
-
-  # Create a random dataset across several variables
-  rs = np.random.default_rng(0)
-  n, p = 40, 8
-  d = rs.normal(0, 2, (n, p))
-  d += np.log(np.arange(1, p + 1)) * -5 + 10
-
-  print(d)
-
-  # Show each distribution with both violins and points
-  sns.violinplot(data=d, palette="light:g", inner="points", orient="h")
-  ```
-- The print reveals that `d` is a NumPy array and looks like a two-dimensional list:
-  ```text
-  [[10.25146044  6.27005437  5.78778386  3.27832843  0.88147169  1.76439276  2.87844934  1.49695422]
-   [ 8.59252953  4.00342116  3.26038963  3.15118015 -2.69725111  0.60361933 -2.22137264 -1.86174242]
-   ... many more lines ...
-   [12.45950762  4.32352988  6.56724895  3.42215312  0.34419915  0.46123886 -1.56953795  0.95292133]]
-  ```
-- Now let's try with a much simplified two-dimensional list:
-  ```{code-block} python
-  ---
-  emphasize-lines: 12, 13
-  ---
-  # import numpy as np
-  import seaborn as sns
-
-  sns.set_theme()
-
-  # # Create a random dataset across several variables
-  # rs = np.random.default_rng(0)
-  # n, p = 40, 8
-  # d = rs.normal(0, 2, (n, p))
-  # d += np.log(np.arange(1, p + 1)) * -5 + 10
-
-  d = [[1.0, 2.0, 2.0, 3.0, 3.0, 3.0],
-       [1.0, 1.0, 1.0, 2.0, 2.0, 3.0]]
-
-  # Show each distribution with both violins and points
-  sns.violinplot(data=d, palette="light:g", inner="points", orient="h")
-  ```
-- Seems to work! And finally we arrive at a working example with our own data with all
-  the "clutter" removed:
-  ```python
-  import seaborn as sns
-
-  # l1 and l2 are note great names but they will do for a quick test
-  l1 = [1.0, 2.0, 2.0, 3.0, 3.0, 3.0]
-  l2 = [1.0, 1.0, 1.0, 2.0, 2.0, 3.0]
-
-  sns.violinplot(data=[l1, l2], palette="light:g", inner="points", orient="h")
-  ```
-- And now we can focus the rest of our work to read our real data.
-- Finally we can customize the plot, e.g. web search for "seaborn violin plot axis labels"
-  and add `ax.set_yticklabels(['dataset 1', 'dataset 2'])`.
-````
-
 ---
 
 ```{discussion}

content/plotting-vega-altair.md

@@ -413,14 +413,229 @@ remember everything so this strategy is useful to practice:
 - Select one example that is close to your current/recent visualization project
   or simply interests you.
 - First try to reproduce this example, as-is, in the Jupyter Notebook.
+- **If you get the error "ModuleNotFoundError: No module named
+  'vega_datasets'", then try one of these examples:** (they do not need the "vega_datasets" module)
+  - [Slider cutoff](https://altair-viz.github.io/gallery/slider_cutoff.html)
+    (**below you can find a walk-through for this example**)
+  - [Multi-Line tooltip](https://altair-viz.github.io/gallery/multiline_tooltip_standard.html)
+  - [Heatmap](https://altair-viz.github.io/gallery/simple_heatmap.html)
+  - [Layered histogram](https://altair-viz.github.io/gallery/layered_histogram.html)
 - Then try to print out the data that is used in this example just before the call of the plotting function
   to learn about its structure.
 - Then try to modify the data a bit.
 - If you have time, try to feed it different, simplified data.
-  This will be key for adapting the examples to your projects.
+  **This will be key for adapting the examples to your projects.**
 
-:::{solution} Example walk-through
-(work in progress)
+:::{solution} Example walk-through for the slider cutoff example
+In this walk-through I imagine browsing: <https://altair-viz.github.io/gallery/index.html>
+
+Then this example caught my eye: <https://altair-viz.github.io/gallery/slider_cutoff.html>
+
+I then copy-paste the example code into a notebook and try to run it and I get
+the same result.
+
+If you get stuck below, **you can also browse all the steps** in a [notebook
+using
+nbviewer](https://nbviewer.org/github/AaltoSciComp/python-for-scicomp/blob/master/resources/notebooks/plotting-exercise-2.ipynb).
+
+Next, there is a lot of code that I don't (need to) understand yet but my eyes are trying to find
+`alt.Chart` which tells me that the data must be the "df" in `alt.Chart(df)`:
+```{code-block} python
+---
+emphasize-lines: 15
+---
+import altair as alt
+import pandas as pd
+import numpy as np
+
+rand = np.random.RandomState(42)
+
+df = pd.DataFrame({
+    'xval': range(100),
+    'yval': rand.randn(100).cumsum()
+})
+
+slider = alt.binding_range(min=0, max=100, step=1)
+cutoff = alt.param(bind=slider, value=50)
+
+alt.Chart(df).mark_point().encode(
+    x='xval',
+    y='yval',
+    color=alt.condition(
+        alt.datum.xval < cutoff,
+        alt.value('red'), alt.value('blue')
+    )
+).add_params(
+    cutoff
+)
+```
+
+My next step will be to print out the data `df` just before the call to `alt.Chart`:
+```{code-block} python
+---
+emphasize-lines: 15
+---
+import altair as alt
+import pandas as pd
+import numpy as np
+
+rand = np.random.RandomState(42)
+
+df = pd.DataFrame({
+    'xval': range(100),
+    'yval': rand.randn(100).cumsum()
+})
+
+slider = alt.binding_range(min=0, max=100, step=1)
+cutoff = alt.param(bind=slider, value=50)
+
+print(df)
+
+alt.Chart(df).mark_point().encode(
+    x='xval',
+    y='yval',
+    color=alt.condition(
+        alt.datum.xval < cutoff,
+        alt.value('red'), alt.value('blue')
+    )
+).add_params(
+    cutoff
+    )
+```
+
+The print reveals that `df` is a dataframe which contains x and y values:
+```text
+    xval       yval
+0      0   0.496714
+1      1   0.358450
+2      2   1.006138
+3      3   2.529168
+4      4   2.295015
+..   ...        ...
+95    95 -10.712354
+96    96 -10.416233
+97    97 -10.155178
+98    98 -10.150065
+99    99 -10.384652
+
+[100 rows x 2 columns]
+```
+
+The next thing that often helps me is to save the data to a comma-separated
+values (CSV) file:
+```python
+import pandas as pd
+
+df.to_csv("data.csv", index=False)
+```
+
+I then open the file in an editor and see that it contains 100 rows:
+```text
+xval,yval
+0,0.4967141530112327
+1,0.358449851840048
+2,1.0061383899407406
+3,2.5291682463487657
+4,2.2950148716254297
+5,2.060877914676249
+6,3.6400907301836405
+7,4.407525459336549
+8,3.938051073401597
+9,4.4806111169875615
+...
+```
+
+Saving the data to file often helps me to see the structure of the data and now
+I am in a position to replace this with my own data. I create a file called
+"mydata.csv" and there I use the maximum temperatures for months 1-10 from the
+Tromso monthly data which we used further up:
+```text
+xval,yval
+01,7.7
+02,6.6
+03,4.5
+04,9.8
+05,17.7
+06,25.4
+07,26.7
+08,25.1
+09,19.3
+10,9.8
+```
+
+In the notebook I then verify that the reading of the data works:
+```python
+mydata = pd.read_csv("mydata.csv")
+
+mydata
+```
+
+Now I can replace the example with my own data (note how I could comment out
+some code that I don't need any longer):
+```{code-block} python
+---
+emphasize-lines: 16
+---
+import altair as alt
+import pandas as pd
+# import numpy as np
+
+# rand = np.random.RandomState(42)
+
+# df = pd.DataFrame({
+#     'xval': range(100),
+#     'yval': rand.randn(100).cumsum()
+# })
+
+slider = alt.binding_range(min=0, max=100, step=1)
+cutoff = alt.param(bind=slider, value=50)
+
+# print(df)
+df = pd.read_csv("mydata.csv")
+
+alt.Chart(df).mark_point().encode(
+    x='xval',
+    y='yval',
+    color=alt.condition(
+        alt.datum.xval < cutoff,
+        alt.value('red'), alt.value('blue')
+    )
+).add_params(
+    cutoff
+    )
+```
+
+Seems to work! I then make few more adjustments (I want the slider to work on
+the y-axis and have a more reasonable default):
+```{code-block} python
+---
+emphasize-lines: 4,5,13
+---
+import altair as alt
+import pandas as pd
+
+slider = alt.binding_range(min=0, max=30, step=1)
+cutoff = alt.param(bind=slider, value=15)
+
+df = pd.read_csv("mydata.csv")
+
+alt.Chart(df).mark_point().encode(
+    x='xval',
+    y='yval',
+    color=alt.condition(
+        alt.datum.yval < cutoff,
+        alt.value('red'), alt.value('blue')
+    )
+).add_params(
+    cutoff
+    )
+```
+
+My next steps would then be to change axis titles, display the month names, add
+a legend, and refine from here.
+
+**You can also browse all the steps** in a [notebook using
+nbviewer](https://nbviewer.org/github/AaltoSciComp/python-for-scicomp/blob/master/resources/notebooks/plotting-exercise-2.ipynb).
 :::
 ::::