Add categorical colorbars for plot, plot3d and line colors gallery examples

examples/gallery/3d_plots/scatter3d.py

@@ -17,7 +17,7 @@
 import pandas as pd
 import pygmt
 
-# Load sample iris data, and convert 'species' column to categorical dtype
+# Load sample iris data and convert 'species' column to categorical dtype
 df = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
 df.species = df.species.astype(dtype="category")
 
@@ -36,9 +36,11 @@
 
 # Define a colormap to be used for three categories, define the range of the
 # new discrete CPT using series=(lowest_value, highest_value, interval),
-# use color_model="+c" to write the discrete color palette "cubhelix" in
-# categorical format
-pygmt.makecpt(cmap="cubhelix", color_model="+c", series=(0, 3, 1))
+# use color_model="+csetosa,versicolor,virginica" to write the discrete color palette
+# "cubhelix" in categorical format and add the species names as annotations
+pygmt.makecpt(
+    cmap="cubhelix", color_model="+csetosa,versicolor,virginica", series=(0, 2, 1)
+)
 
 fig.plot3d(
     # Use petal width, sepal length and petal length as x, y and z data input,
@@ -58,7 +60,7 @@
     region=region,
     # Set frame parameters
     frame=[
-        "WsNeZ3",  # z axis label positioned on 3rd corner
+        'WsNeZ3+t"Iris flower data set"',  # z axis label positioned on 3rd corner, add title
         'xafg+l"Petal Width"',
         'yafg+l"Sepal Length"',
         'zafg+l"Petal Length"',
@@ -68,4 +70,8 @@
     # Vertical exaggeration factor
     zscale=1.5,
 )
+
+# Add colorbar legend
+fig.colorbar(xshift=3.1)
+
 fig.show()

examples/gallery/lines/line_custom_cpt.py

@@ -21,8 +21,12 @@
 fig = pygmt.Figure()
 fig.basemap(frame=["WSne", "af"], region=[20, 30, -10, 10])
 
-# Create a custom CPT with the batlow CPT and 10 discrete z-values (colors)
-pygmt.makecpt(cmap="batlow", series=[0, 10, 1])
+# Create a custom CPT with the batlow CPT and 10 discrete z-values (colors),
+# use color_model="+c" + ','.join(map(str, range(1,11))) to write the color
+# palette in categorical format and add labels (1) to (10) for the colorbar legend
+pygmt.makecpt(
+    cmap="batlow", series=[0, 9, 1], color_model="+c" + ",".join(map(str, range(1, 11)))
+)
 
 # Plot 10 lines and set a different z-value for each line
 for zvalue in range(0, 10):

examples/gallery/symbols/points_categorical.py

@@ -14,8 +14,13 @@
 import pandas as pd
 import pygmt
 
-# Load sample penguins data and convert 'species' column to categorical dtype
+# Load sample penguins data
 df = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/penguins.csv")
+
+# Extract species names for colorbar legend
+species = ",".join(df.species.unique())
+
+# Convert 'species' column to categorical dtype
 df.species = df.species.astype(dtype="category")
 
 # Use pygmt.info to get region bounds (xmin, xmax, ymin, ymax)
@@ -44,9 +49,9 @@
 
 # Define a colormap to be used for three categories, define the range of the
 # new discrete CPT using series=(lowest_value, highest_value, interval),
-# use color_model="+c" to write the discrete color palette "inferno" in
-# categorical format
-pygmt.makecpt(cmap="inferno", series=(0, 3, 1), color_model="+c")
+# use color_model="+c" + species to write the discrete color palette "inferno" in
+# categorical format and add the species names extracted above as annotations
+pygmt.makecpt(cmap="inferno", series=(0, 2, 1), color_model="+c" + species)
 
 fig.plot(
     # Use bill length and bill depth as x and y data input, respectively
@@ -66,7 +71,7 @@
     transparency=40,
 )
 
-# A colorbar displaying the different penguin species types will be added
-# once GMT 6.2.0 is released.
+# Add colorbar legend
+fig.colorbar()
 
 fig.show()