fix links to apply_norm (#423)

Author: cvanelteren

docs/2dplots.py

@@ -77,9 +77,10 @@
 #    setting and the :ref:`user guide <ug_1dstd>`).
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 state = np.random.RandomState(51423)
 x = y = np.array([-10, -5, 0, 5, 10])
@@ -110,9 +111,10 @@
     axs[3].contourf(xedges, yedges, data)
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 cmap = "turku_r"
 state = np.random.RandomState(51423)
@@ -184,9 +186,9 @@
 #    `~pint.UnitRegistry.setup_matplotlib` so that the axes become unit-aware.
 
 # %%
-import xarray as xr
 import numpy as np
 import pandas as pd
+import xarray as xr
 
 # DataArray
 state = np.random.RandomState(51423)
@@ -261,13 +263,14 @@
 # ``diverging=True``, ``cyclic=True``, or ``qualitative=True`` to any plotting
 # command. If the colormap type is not explicitly specified, `sequential` is
 # used with the default linear normalizer when data is strictly positive
-# or negative, and `diverging` is used with the :ref:`diverging normalizer <ug_norm>`
+# or negative, and `diverging` is used with the :ref:`diverging normalizer <ug_apply_norm>`
 # when the data limits or colormap levels cross zero (see :ref:`below <ug_autonorm>`).
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 N = 18
 state = np.random.RandomState(51423)
@@ -294,9 +297,10 @@
 uplt.rc.reset()
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 N = 20
 state = np.random.RandomState(51423)
@@ -322,9 +326,10 @@
     colorbar="b",
 )
 
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 N = 20
 state = np.random.RandomState(51423)
@@ -347,7 +352,7 @@
 # Special normalizers
 # -------------------
 #
-# UltraPlot includes two new :ref:`"continuous" normalizers <ug_norm>`. The
+# UltraPlot includes two new :ref:`"continuous" normalizers <ug_apply_norm>`. The
 # `~ultraplot.colors.SegmentedNorm` normalizer provides even color gradations with respect
 # to index for an arbitrary monotonically increasing or decreasing list of levels. This
 # is automatically applied if you pass unevenly spaced `levels` to a plotting command,
@@ -372,9 +377,10 @@
 # affect the interpretation of different datasets.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 state = np.random.RandomState(51423)
 data = 11 ** (2 * state.rand(20, 20).cumsum(axis=0) / 7)
@@ -395,9 +401,10 @@
     )
     ax.format(title=norm.title() + " normalizer")
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 state = np.random.RandomState(51423)
 data1 = (state.rand(20, 20) - 0.485).cumsum(axis=1).cumsum(axis=0)
@@ -434,7 +441,7 @@
 # commands (e.g., :func:`~ultraplot.axes.PlotAxes.contourf`, :func:`~ultraplot.axes.PlotAxes.pcolor`).
 # This is analogous to `matplotlib.colors.BoundaryNorm`, except
 # `~ultraplot.colors.DiscreteNorm` can be paired with arbitrary
-# continuous normalizers specified by `norm` (see :ref:`above <ug_norm>`).
+# continuous normalizers specified by `norm` (see :ref:`above <ug_apply_norm>`).
 # Discrete color levels can help readers discern exact numeric values and
 # tend to reveal qualitative structure in the data. `~ultraplot.colors.DiscreteNorm`
 # also repairs the colormap end-colors by ensuring the following conditions are met:
@@ -463,9 +470,10 @@
 # the zero level (useful for single-color :func:`~ultraplot.axes.PlotAxes.contour` plots).
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 state = np.random.RandomState(51423)
 data = 10 + state.normal(0, 1, size=(33, 33)).cumsum(axis=0).cumsum(axis=1)
@@ -485,9 +493,10 @@
 axs[2].format(title="Imshow plot\ndiscrete=False (default)", yformatter="auto")
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 # Sample data
 state = np.random.RandomState(51423)
 data = (20 * (state.rand(20, 20) - 0.4).cumsum(axis=0).cumsum(axis=1)) % 360
@@ -547,7 +556,7 @@
 # the 2D :class:`~ultraplot.axes.PlotAxes` commands will apply the diverging colormap
 # :rc:`cmap.diverging` (rather than :rc:`cmap.sequential`) and the diverging
 # normalizer `~ultraplot.colors.DivergingNorm` (rather than :class:`~matplotlib.colors.Normalize`
-# -- see :ref:`above <ug_norm>`) if the following conditions are met:
+# -- see :ref:`above <ug_apply_norm>`) if the following conditions are met:
 #
 # #. If discrete levels are enabled (see :ref:`above <ug_discrete>`) and the
 #    level list includes at least 2 negative and 2 positive values.
@@ -560,9 +569,10 @@
 # setting :rcraw:`cmap.autodiverging` to ``False``.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 N = 20
 state = np.random.RandomState(51423)
 data = N * 2 + (state.rand(N, N) - 0.45).cumsum(axis=0).cumsum(axis=1) * 10
@@ -605,9 +615,10 @@
 # command documentation for details.
 
 # %%
-import ultraplot as uplt
-import pandas as pd
 import numpy as np
+import pandas as pd
+
+import ultraplot as uplt
 
 # Sample data
 state = np.random.RandomState(51423)
@@ -663,10 +674,11 @@
 # `~ultraplot.axes.CartesianAxes`.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 import pandas as pd
 
+import ultraplot as uplt
+
 # Covariance data
 state = np.random.RandomState(51423)
 data = state.normal(size=(10, 10)).cumsum(axis=0)

docs/colorbars_legends.py

@@ -78,9 +78,10 @@
 #    complex arrangements of subplots, colorbars, and legends.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 state = np.random.RandomState(51423)
 fig = uplt.figure(share=False, refwidth=2.3)
 
@@ -183,9 +184,10 @@
 )
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 N = 10
 state = np.random.RandomState(51423)
 fig, axs = uplt.subplots(
@@ -232,9 +234,10 @@
 # and the tight layout padding can be controlled with the `pad` keyword.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 state = np.random.RandomState(51423)
 fig, axs = uplt.subplots(ncols=3, nrows=3, refwidth=1.4)
 for ax in axs:
@@ -254,9 +257,10 @@
 fig.colorbar(m, label="colorbar with length <1", ticks=0.1, loc="r", length=0.7)
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 state = np.random.RandomState(51423)
 fig, axs = uplt.subplots(
     ncols=2, nrows=2, order="F", refwidth=1.7, wspace=2.5, share=False
@@ -299,7 +303,7 @@
 #   will build the required `~matplotlib.cm.ScalarMappable` on-the-fly. Lists
 #   of :class:`~matplotlib.artist.Artists`\ s are used when you use the `colorbar`
 #   keyword with :ref:`1D commands <ug_1dplots>` like :func:`~ultraplot.axes.PlotAxes.plot`.
-# * The associated :ref:`colormap normalizer <ug_norm>` can be specified with the
+# * The associated :ref:`colormap normalizer <ug_apply_norm>` can be specified with the
 #   `vmin`, `vmax`, `norm`, and `norm_kw` keywords. The `~ultraplot.colors.DiscreteNorm`
 #   levels can be specified with `values`, or UltraPlot will infer them from the
 #   :class:`~matplotlib.artist.Artist` labels (non-numeric labels will be applied to
@@ -332,9 +336,10 @@
 # See :func:`~ultraplot.axes.Axes.colorbar` for details.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 fig = uplt.figure(share=False, refwidth=2)
 
 # Colorbars from lines
@@ -427,9 +432,10 @@
 # (or use the `handle_kw` keyword). See `ultraplot.axes.Axes.legend` for details.
 
 # %%
-import ultraplot as uplt
 import numpy as np
 
+import ultraplot as uplt
+
 uplt.rc.cycle = "538"
 fig, axs = uplt.subplots(ncols=2, span=False, share="labels", refwidth=2.3)
 labels = ["a", "bb", "ccc", "dddd", "eeeee"]

docs/why.rst

@@ -501,7 +501,7 @@ like :func:`~ultraplot.axes.PlotAxes.pcolor` and :func:`~ultraplot.axes.PlotAxes
 * The :class:`~ultraplot.colors.DivergingNorm` normalizer is perfect for data with a
   :ref:`natural midpoint <ug_apply_norm>` and offers both "fair" and "unfair" scaling.
   The :class:`~ultraplot.colors.SegmentedNorm` normalizer can generate
-  uneven color gradations useful for :ref:`unusual data distributions <ug_norm>`.
+  uneven color gradations useful for :ref:`unusual data distributions <ug_apply_norm>`.
 * The :func:`~ultraplot.axes.PlotAxes.heatmap` command invokes
   :func:`~ultraplot.axes.PlotAxes.pcolormesh` then applies an `equal axes apect ratio
   <https://matplotlib.org/stable/gallery/subplots_axes_and_figures/axis_equal_demo.html>`__,