WIP: Intros / Tutorials / Gallery: Include folder in colormap name

doc/contributing.md

@@ -689,7 +689,7 @@ def test_my_plotting_case():
     Test that my plotting method works.
     """
     fig_ref, fig_test = Figure(), Figure()
-    fig_ref.grdimage("@earth_relief_01d_g", projection="W120/15c", cmap="geo")
-    fig_test.grdimage(grid, projection="W120/15c", cmap="geo")
+    fig_ref.grdimage("@earth_relief_01d_g", projection="W120/15c", cmap="gmt/geo")
+    fig_test.grdimage(grid, projection="W120/15c", cmap="gmt/geo")
     return fig_ref, fig_test
 ```

doc/techref/justification_codes.md

@@ -105,7 +105,7 @@ Script showing justification codes for plot embellishments, e.g., a colorbar.
 fig = pygmt.Figure()
 fig.basemap(projection="X10c/2c", region=[-size, size, -size, size], frame=0)
 
-fig.colorbar(cmap="buda", frame=0, position="jMC+w10c/2c+h")
+fig.colorbar(cmap="SCM/buda", frame=0, position="jMC+w10c/2c+h")
 
 for code in codes:
     fig.text(

examples/gallery/3d_plots/grdview_surface.py

@@ -52,7 +52,7 @@ def ackley(x, y):
     projection=f"x{SCALE}c",
     zscale=f"{SCALE}c",
     surftype="s",
-    cmap="roma",
+    cmap="SCM/roma",
     perspective=[135, 30],  # Azimuth southeast (135°), at elevation 30°
     shading="+a45",
 )

examples/gallery/3d_plots/scatter3d.py

@@ -54,7 +54,7 @@
 # palette "cubhelix" in categorical format and add the species names as
 # annotations for the colorbar
 pygmt.makecpt(
-    cmap="cubhelix",
+    cmap="cpt-city/cubhelix",
     # Use the minimum and maximum of the categorical number code
     # to set the lowest_value and the highest_value of the CPT
     series=(df.species.cat.codes.min(), df.species.cat.codes.max(), 1),

examples/gallery/basemaps/ternary.py

@@ -22,7 +22,7 @@
 
 # Define a colormap to be used for the values given in the fourth column
 # of the input dataset
-pygmt.makecpt(cmap="batlow", series=[0, 80, 10])
+pygmt.makecpt(cmap="SCM/batlow", series=[0, 80, 10])
 
 fig.ternary(
     data,

examples/gallery/embellishments/colorbar.py

@@ -35,12 +35,12 @@
 # Colorbar is placed at Bottom Center (BC) by default if no position is given
 # Add quantity and unit as labels ("+l") to the x and y axes
 # Add annotations ("+a") in steps of 0.5 and ticks ("+f") in steps of 0.1
-fig.colorbar(cmap="roma", frame=["xa0.5f0.1+lVelocity", "y+lm/s"])
+fig.colorbar(cmap="SCM/roma", frame=["xa0.5f0.1+lVelocity", "y+lm/s"])
 
 # ============
 # Create a colorbar showing the scientific rainbow - batlow
 fig.colorbar(
-    cmap="batlow",
+    cmap="SCM/batlow",
     # Colorbar positioned at map coordinates (g) longitude/latitude 0.3/8.7,
     # with a length/width (+w) of 4 cm by 0.5 cm, and plotted horizontally (+h)
     position="g0.3/8.7+w4c/0.5c+h",
@@ -52,7 +52,7 @@
 # ============
 # Create a colorbar suitable for surface topography - oleron
 fig.colorbar(
-    cmap="oleron",
+    cmap="SCM/oleron",
     # Colorbar placed outside the plot bounding box (J) at Middle Right (MR),
     # offset (+o) by 1 cm horizontally and 0 cm vertically from anchor point,
     # with a length/width (+w) of 7 cm by 0.5 cm and a box for NaN values (+n)
@@ -68,7 +68,7 @@
 # Create a colorbar suitable for categorical data - hawaii
 # Set up the colormap
 pygmt.makecpt(
-    cmap="hawaii",
+    cmap="SCM/hawaii",
     series=[0, 3, 1],
     # Comma-separated string for the annotations of the colorbar
     color_model="+cA,B,C,D",

examples/gallery/embellishments/colorbars_multiple.py

@@ -28,15 +28,15 @@
     # Activate the first panel so that the colormap created by the makecpt
     # function is a panel-level CPT
     with fig.set_panel(panel=0):
-        pygmt.makecpt(cmap="geo", series=[-8000, 8000])
+        pygmt.makecpt(cmap="gmt/geo", series=[-8000, 8000])
         # "R?" means Winkel Tripel projection with map width automatically
         # determined from the subplot width.
         fig.grdimage(grid=grid_globe, projection="R?", region="g", frame="a")
         fig.colorbar(frame=["a4000f2000", "x+lElevation", "y+lm"])
     # Activate the second panel so that the colormap created by the makecpt
     # function is a panel-level CPT
     with fig.set_panel(panel=1):
-        pygmt.makecpt(cmap="globe", series=[-6000, 3000])
+        pygmt.makecpt(cmap="gmt/globe", series=[-6000, 3000])
         # "M?" means Mercator projection with map width also automatically
         # determined from the subplot width.
         fig.grdimage(grid=grid_subset, projection="M?", region=subset_region, frame="a")

examples/gallery/histograms/blockm.py

@@ -33,7 +33,7 @@
     grid=grd,
     region=region,
     frame=["af", "+tMean earthquake depth inside each block"],
-    cmap="batlow",
+    cmap="SCM/batlow",
 )
 # Plot slightly transparent landmasses on top
 fig.coast(land="darkgray", transparency=40)
@@ -52,7 +52,7 @@
     grid=grd,
     region=region,
     frame=["af", "+tNumber of points inside each block"],
-    cmap="batlow",
+    cmap="SCM/batlow",
 )
 fig.coast(land="darkgray", transparency=40)
 fig.plot(x=data.longitude, y=data.latitude, style="c0.3c", fill="white", pen="1p,black")

examples/gallery/images/cross_section.py

@@ -37,7 +37,7 @@
 grid_map = pygmt.datasets.load_earth_relief(resolution="10m", region=region_map)
 
 # Plot the downloaded grid with color-coding based on the elevation
-fig.grdimage(grid=grid_map, cmap="oleron")
+fig.grdimage(grid=grid_map, cmap="SCM/oleron")
 
 # Add a colorbar for the elevation
 fig.colorbar(

examples/gallery/images/grdclip.py

@@ -25,7 +25,7 @@
     projection="M12c",
     frame=["WSne+toriginal grid", "xa5f1", "ya2f1"],
 )
-fig.grdimage(grid=grid, cmap="oleron")
+fig.grdimage(grid=grid, cmap="SCM/oleron")
 
 # Shift plot origin of the second map by "width of the first map + 0.5 cm"
 # in x-direction

examples/gallery/images/grdgradient.py

@@ -30,7 +30,7 @@
 
 # --------------- plotting the original Data Elevation Model -----------
 
-pygmt.makecpt(cmap="gray", series=[200, 4000, 10])
+pygmt.makecpt(cmap="gmt/gray", series=[200, 4000, 10])
 fig.grdimage(
     grid=grid,
     projection="M12c",
@@ -45,7 +45,7 @@
 # Shift plot origin of the second map by 12.5 cm in x-direction
 fig.shift_origin(xshift="12.5c")
 
-pygmt.makecpt(cmap="gray", series=[-1.5, 0.3, 0.01])
+pygmt.makecpt(cmap="gmt/gray", series=[-1.5, 0.3, 0.01])
 fig.grdimage(
     grid=dgrid,
     projection="M12c",

examples/gallery/images/grdgradient_shading.py

@@ -27,7 +27,7 @@
 fig = pygmt.Figure()
 
 # Define a colormap to be used for topography
-pygmt.makecpt(cmap="terra", series=[-7000, 7000])
+pygmt.makecpt(cmap="gmt/terra", series=[-7000, 7000])
 
 # Define figure configuration
 pygmt.config(FONT_TITLE="10p,5", MAP_TITLE_OFFSET="1p", MAP_FRAME_TYPE="plain")

examples/gallery/images/grdlandmask.py

@@ -30,7 +30,7 @@
 # use color_model="+cwater,land" to write the discrete color palette
 # "batlow" in categorical format and add water/land as annotations for the
 # colorbar.
-pygmt.makecpt(cmap="batlow", series=(0, 1, 1), color_model="+cwater,land")
+pygmt.makecpt(cmap="SCM/batlow", series=(0, 1, 1), color_model="+cwater,land")
 
 fig.grdimage(grid=grid, cmap=True)
 fig.colorbar(position="JMR+o0.5c/0c+w8c")

examples/gallery/images/track_sampling.py

@@ -29,15 +29,15 @@
 # Plot the earth relief grid on Cylindrical Stereographic projection, masking
 # land areas
 fig.basemap(region="g", projection="Cyl_stere/150/-20/15c", frame=True)
-fig.grdimage(grid=grid, cmap="gray")
+fig.grdimage(grid=grid, cmap="gmt/gray")
 fig.coast(land="#666666")
 # Plot the sampled bathymetry points using circles (c) of 0.15 cm size
 # Points are colored using elevation values (normalized for visual purposes)
 fig.plot(
     x=track.longitude,
     y=track.latitude,
     style="c0.15c",
-    cmap="terra",
+    cmap="gmt/terra",
     fill=(track.bathymetry - track.bathymetry.mean()) / track.bathymetry.std(),
 )
 fig.show()

examples/gallery/lines/line_custom_cpt.py

@@ -25,7 +25,7 @@
 # Create a custom CPT with the batlow CPT and 10 discrete z-values (colors),
 # use color_model="+c0-9" to write the color palette in categorical format and
 # add labels (0) to (9) for the colorbar legend
-pygmt.makecpt(cmap="batlow", series=[0, 9, 1], color_model="+c0-9")
+pygmt.makecpt(cmap="SCM/batlow", series=[0, 9, 1], color_model="+c0-9")
 
 # Plot 10 lines and set a different z-value for each line
 for zvalue in range(10):

examples/gallery/maps/choropleth_map.py

@@ -29,7 +29,7 @@
 fig.basemap(region=[-19.5, 53, -37.5, 38], projection="M10c", frame="+n")
 
 # First, we define the colormap to fill the polygons based on the "POP_EST" column.
-pygmt.makecpt(cmap="acton", series=(0, 100), reverse=True)
+pygmt.makecpt(cmap="SCM/acton", series=(0, 100), reverse=True)
 
 # Next, we plot the polygons and fill them using the defined colormap. The target column
 # is defined by the aspatial parameter.

examples/gallery/symbols/bars.py

@@ -24,7 +24,7 @@
 
 fig = pygmt.Figure()
 
-pygmt.makecpt(cmap="roma", series=[0, 4, 1])
+pygmt.makecpt(cmap="SCM/roma", series=[0, 4, 1])
 
 with fig.subplot(
     nrows=2,

examples/gallery/symbols/points_categorical.py

@@ -64,7 +64,7 @@
 # palette "inferno" in categorical format and add the species names as
 # annotations for the colorbar
 pygmt.makecpt(
-    cmap="inferno",
+    cmap="matplotlib/inferno",
     # Use the minimum and maximum of the categorical number code
     # to set the lowest_value and the highest_value of the CPT
     series=(df.species.cat.codes.min(), df.species.cat.codes.max(), 1),

examples/intro/02_contour_map.py

@@ -43,7 +43,7 @@
 # "oleron" is used; a full list of CPTs can be found at :gmt-docs:`reference/cpts.html`.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.show()
 
 
@@ -64,7 +64,7 @@
 # :meth:`pygmt.Figure.grdimage`.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])
 fig.show()
 
@@ -85,7 +85,7 @@
 # lines can be adjusted (separately) by specifying the desired ``pen``.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.grdcontour(grid=grid, levels=500, annotation=1000)
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])
 fig.show()
@@ -100,7 +100,7 @@
 # the ``shorelines`` parameter draws a border around the islands.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.grdcontour(grid=grid, levels=500, annotation=1000)
 fig.coast(shorelines="2p", land="lightgray")
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])

examples/tutorials/advanced/3d_perspective_image.py

@@ -50,7 +50,7 @@
     # Set the surftype to "surface"
     surftype="s",
     # Set the CPT to "geo"
-    cmap="geo",
+    cmap="gmt/geo",
 )
 fig.show()
 
@@ -66,7 +66,7 @@
     projection="M15c",
     zsize="1.5c",
     surftype="s",
-    cmap="geo",
+    cmap="gmt/geo",
     plane=1000,  # Set the plane elevation to 1,000 meters
     facade_fill="gray",  # Color the facade in "gray"
 )
@@ -89,7 +89,7 @@
     projection="M15c",
     zsize="1.5c",
     surftype="s",
-    cmap="geo",
+    cmap="gmt/geo",
     plane=1000,
     facade_fill="gray",
     # Set the contour pen thickness to 0.1 points

examples/tutorials/advanced/contour_map.py

@@ -84,7 +84,7 @@
 # :meth:`pygmt.Figure.colorbar` method.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, cmap="haxby", projection="M10c", frame=True)
+fig.grdimage(grid=grid, cmap="gmt/haxby", projection="M10c", frame=True)
 fig.grdcontour(grid=grid, annotation=1000, levels=250, limit=[-4000, -2000])
 fig.colorbar(frame=["x+lelevation", "y+lm"])
 fig.show()

examples/tutorials/advanced/draping_on_3d_surface.py

@@ -50,7 +50,7 @@
 
 # Set up colormap for the crustal age
 pygmt.config(COLOR_NAN="lightgray")
-pygmt.makecpt(cmap="batlow", series=[0, 200, 1], reverse=True, overrule_bg=True)
+pygmt.makecpt(cmap="SCM/batlow", series=[0, 200, 1], reverse=True, overrule_bg=True)
 
 fig.grdview(
     projection="M12c",  # Mercator projection with a width of 12 centimeters

examples/tutorials/advanced/earth_relief.py

@@ -52,14 +52,14 @@
 # Using the *geo* CPT:
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, projection="R12c", cmap="geo")
+fig.grdimage(grid=grid, projection="R12c", cmap="gmt/geo")
 fig.show()
 
 # %%
 # Using the *relief* CPT:
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, projection="R12c", cmap="relief")
+fig.grdimage(grid=grid, projection="R12c", cmap="gmt/relief")
 fig.show()
 
 
@@ -76,7 +76,7 @@
 # ``"y+lm"`` sets the y-axis label.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, projection="R12c", cmap="geo")
+fig.grdimage(grid=grid, projection="R12c", cmap="gmt/geo")
 fig.colorbar(frame=["a2500", "x+lElevation", "y+lm"])
 fig.show()
 
@@ -96,7 +96,7 @@
 
 grid = pygmt.datasets.load_earth_relief(resolution="10m", region=[-14, 30, 35, 60])
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, projection="M15c", frame="a", cmap="geo")
+fig.grdimage(grid=grid, projection="M15c", frame="a", cmap="gmt/geo")
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])
 fig.show()
 

examples/tutorials/advanced/focal_mechanisms.py

@@ -308,7 +308,7 @@
 fig.coast(region="d", projection="N10c", land="lightgray", frame=True)
 
 # Set up colormap and colorbar for hypocentral depth
-pygmt.makecpt(cmap="lajolla", series=[0, 20])
+pygmt.makecpt(cmap="SCM/lajolla", series=[0, 20])
 fig.colorbar(frame=["x+lhypocentral depth", "y+lkm"])
 
 fig.meca(

examples/tutorials/advanced/grid_equalization.py

@@ -35,7 +35,7 @@
 # Define figure configuration
 pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
 # Define the colormap for the figure
-pygmt.makecpt(series=[500, 3540], cmap="turku")
+pygmt.makecpt(series=[500, 3540], cmap="SCM/turku")
 # Setup subplots with two panels
 with fig.subplot(
     nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Digital Elevation Model"
@@ -104,7 +104,7 @@
 # Define figure configuration
 pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
 # Define the colormap for the figure
-pygmt.makecpt(series=[0, divisions, 1], cmap="lajolla")
+pygmt.makecpt(series=[0, divisions, 1], cmap="SCM/lajolla")
 # Setup subplots with two panels
 with fig.subplot(
     nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Linear distribution"
@@ -154,7 +154,7 @@
 # Define figure configuration
 pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
 # Define the colormap for the figure
-pygmt.makecpt(series=[-4.5, 4.5], cmap="vik")
+pygmt.makecpt(series=[-4.5, 4.5], cmap="SCM/vik")
 # Setup subplots with two panels
 with fig.subplot(
     nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Normal distribution"
@@ -218,7 +218,7 @@
 # Define figure configuration
 pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
 # Define the colormap for the figure
-pygmt.makecpt(series=[0, divisions, 1], cmap="lajolla")
+pygmt.makecpt(series=[0, divisions, 1], cmap="SCM/lajolla")
 # Setup subplots with two panels
 with fig.subplot(
     nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Quadratic distribution"

examples/tutorials/advanced/working_with_panel.py

@@ -123,7 +123,7 @@ def view(central_lon):
     fig = pygmt.Figure()
     # Set up a colormap for the elevation in meters
     pygmt.makecpt(
-        cmap="oleron",
+        cmap="SCM/oleron",
         # minimum, maximum, step
         series=[int(grd_relief.data.min()) - 1, int(grd_relief.data.max()) + 1, 100],
     )