Bulk format

docs/source/_patch/sphinx_gallery_scrapers.py

@@ -45,7 +45,7 @@ def alt_matplotlib_scraper(block, block_vars, gallery_conf, **kwargs):
                 save_at=Path(image_path).parent,
                 name=Path(image_path).stem,
                 save_fmts=["png"],
-                **kwargs.copy()
+                **kwargs.copy(),
             )
             image_paths.append(image_path)
 

docs/source/conf.py

@@ -288,7 +288,7 @@ def reset_mpl(gallery_conf, fname):
     # "jupyterlite": {"use_jupyter_lab": True},
     "first_notebook_cell": "%matplotlib inline\n",
     "reset_modules": (reset_mpl),
-    "nested_sections": False
+    "nested_sections": False,
 }
 # Remove matplotlib agg warnings from generated doc when using plt.show
 warnings.filterwarnings(
@@ -345,9 +345,7 @@ def reset_mpl(gallery_conf, fname):
     <img src="https://zenodo.org/badge/137172322.svg" alt="Archive">
     </a>
 
-""".format(
-    rc=refcomps, year=str(date.today().year), version=version
-)
+""".format(rc=refcomps, year=str(date.today().year), version=version)
 
 rst_prolog += """
 

docs/source/gallery/data/aitchison.py

@@ -11,6 +11,7 @@
 (all returning a :class:`~pandas.DataFrame`).
 
 """
+
 from pyrolite.data.Aitchison import load_boxite, load_coxite, load_hongite, load_kongite
 
 df = load_boxite()

docs/source/gallery/data/radii.py

@@ -9,6 +9,7 @@
 to get radii for individual elements, using a :code:`source` keyword argument to swap
 between the datasets:
 """
+
 import matplotlib.pyplot as plt
 import pandas as pd
 

docs/source/gallery/data/refcomp.py

@@ -7,6 +7,7 @@
 contain extended descriptions and notes for some of the compositions and associated
 references.
 """
+
 import matplotlib.pyplot as plt
 
 from pyrolite.geochem.norm import all_reference_compositions, get_reference_composition

docs/source/gallery/data/timescale.py

@@ -8,6 +8,7 @@
 specific geological ages, to look up times for known geological age names
 and to access a reference table for all of these.
 """
+
 from pyrolite.util.time import Timescale, age_name
 
 ts = Timescale()

docs/source/gallery/examples/comp/logratiomeans.py

@@ -2,6 +2,7 @@
 Log Ratio Means
 -----------------
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/comp/sphericalcoords.py

@@ -24,6 +24,7 @@
 First let's create some example mineralogical abundance data, where at least
 one of the minerals might occasionally have zero abundance:
 """
+
 import numpy as np
 
 from pyrolite.util.synthetic import normal_frame

docs/source/gallery/examples/geochem/CIPW.py

@@ -25,9 +25,10 @@
 Verma's SINCLAS/IgRoCS program. Here we can use this file to demonstrate the use
 of the CIPW Norm and verify that the results should generally be comparable
 between Verma's original implementation and the :mod:`pyrolite` implementation.
-We import this file and do a little cleaning and registration of geochemical 
+We import this file and do a little cleaning and registration of geochemical
 components so we can work with it in the sections to follow:
 """
+
 import warnings
 
 import matplotlib.pyplot as plt
@@ -198,7 +199,6 @@ def compare_NORMs(SINCLAS_outputs, NORM_outputs, name=""):
 volcanic_filter = df.loc[:, "ROCK_TYPE"].str.lower().str.startswith("volc")
 fig, ax = compare_NORMs(df.loc[volcanic_filter, :], NORM.loc[volcanic_filter])
 
-
 ########################################################################################
 # And everything else:
 #

docs/source/gallery/examples/geochem/convert_chemistry.py

@@ -7,6 +7,7 @@
 :func:`~pyrolite.geochem.transform.convert_chemistry` function. Note that by default
 pyrolite assumes that data are in the same units.
 """
+
 import pandas as pd
 
 import pyrolite.geochem

docs/source/gallery/examples/geochem/indexes_selectors.py

@@ -3,6 +3,7 @@
 ==================================
 
 """
+
 import pandas as pd
 
 import pyrolite.geochem

docs/source/gallery/examples/geochem/ionic_radii.py

@@ -8,6 +8,7 @@
 the :func:`~pyrolite.geochem.ind.get_ionic_radii` function. The function can be used
 to get radii for individual elements:
 """
+
 from pyrolite.geochem.ind import REE, get_ionic_radii
 
 Cu_radii = get_ionic_radii("Cu")

docs/source/gallery/examples/geochem/lambdas.py

@@ -21,6 +21,7 @@
 Here we generate some example data, reduce these to lambda values, and visualise the
 results.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/geochem/mineral_endmembers.py

@@ -7,6 +7,7 @@
 pyrolite includes some utilities to achieve this and a limited mineral database
 for looking up endmember compositions.
 """
+
 import numpy as np
 import pandas as pd
 

docs/source/gallery/examples/geochem/mineral_lattice.py

@@ -25,6 +25,7 @@
 generates plagioclase's hallmark 'europium anomaly', and the effects of variable
 europium oxidation state on bulk europium partitioning.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

docs/source/gallery/examples/geochem/mineral_mindb.py

@@ -5,6 +5,7 @@
 pyrolite includes a limited mineral database which is useful for looking up endmember
 compositions.
 """
+
 import pandas as pd
 
 from pyrolite.mineral.mindb import (

docs/source/gallery/examples/geochem/normalization.py

@@ -6,6 +6,7 @@
 accessed with :func:`pyrolite.geochem.norm.get_reference_composition` (see the list
 at the bottom of the page for a complete list):
 """
+
 import matplotlib.pyplot as plt
 import pandas as pd
 

docs/source/gallery/examples/geochem/scaling.py

@@ -2,6 +2,7 @@
 Unit Scaling
 =============
 """
+
 import numpy as np
 import pandas as pd
 
@@ -88,9 +89,9 @@
 )
 converted_wt_pct.head(2)
 ########################################################################################
-# Here we rename the columns before we export them, just so we know explicitly 
+# Here we rename the columns before we export them, just so we know explicitly
 # what the units are:
-converted_wt_pct = converted_wt_pct.rename(  
+converted_wt_pct = converted_wt_pct.rename(
     columns={c: c + "_wt%" for c in converted_wt_pct.pyrochem.list_oxides}
 )
 converted_wt_pct.head(2)

docs/source/gallery/examples/plotting/REE_v_radii.py

@@ -2,6 +2,7 @@
 REE Radii Plots
 ============================
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/plotting/density.py

@@ -9,6 +9,7 @@
 and evaluation over a grid. The below examples highlight some of the currently
 implemented features.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/plotting/heatscatter.py

@@ -10,6 +10,7 @@
 represents the best of both worlds. A version inspired by similar existing
 visualisations is implemented with :func:`~pyrolite.plot.pyroplot.heatscatter`.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/plotting/parallel.py

@@ -6,6 +6,7 @@
 higher dimensional data. pyrolite now includes an implementation of this which allows
 a handy quick exploratory visualisation.
 """
+
 import matplotlib.axes
 import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1 import make_axes_locatable
@@ -44,18 +45,18 @@
 compdata[comp] = CLRTransform().transform(compdata[comp])
 ax = compdata.loc[:, comp].pyroplot.parallel(color=compdata.Depth.values, cmap=cmap)
 divider = make_axes_locatable(ax)
-cax = divider.append_axes('right', size='5%', pad=0.05)
+cax = divider.append_axes("right", size="5%", pad=0.05)
 
 # we can add a meaningful colorbar to indicate one variable also, here Depth
 sm = plt.cm.ScalarMappable(cmap=cmap)
 sm.set_array(df.Depth)
-plt.colorbar(sm, cax=cax, orientation='vertical')
+plt.colorbar(sm, cax=cax, orientation="vertical")
 plt.show()
 ########################################################################################
 ax = compdata.loc[:, comp].pyroplot.parallel(
     rescale=True, color=compdata.Depth.values, cmap=cmap
 )
 divider = make_axes_locatable(ax)
-cax = divider.append_axes('right', size='5%', pad=0.05)
-plt.colorbar(sm, cax=cax, orientation='vertical')
+cax = divider.append_axes("right", size="5%", pad=0.05)
+plt.colorbar(sm, cax=cax, orientation="vertical")
 plt.show()

docs/source/gallery/examples/plotting/spider.py

@@ -2,6 +2,7 @@
 Spiderplots & Density Spiderplots
 ==================================
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -265,7 +266,7 @@
         unity_line=True,
         index_order="incompatibility",
         *args,
-        **kwargs
+        **kwargs,
     )
 
 plt.tight_layout()

docs/source/gallery/examples/plotting/stem.py

@@ -8,6 +8,7 @@
 so (such that the sampling frequency along this axis is important, which is not
 emphasised by a scatter plot).
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/plotting/templates.py

@@ -6,6 +6,7 @@
 be used to create new plots, or add a template to an existing
 :class:`matplotlib.axes.Axes`.
 """
+
 import matplotlib.pyplot as plt
 
 ########################################################################################

docs/source/gallery/examples/plotting/ternary.py

@@ -2,6 +2,7 @@
 Ternary Plots
 =============
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd

docs/source/gallery/examples/plotting/ternary_color.py

@@ -8,6 +8,7 @@
 well with some of the plot templates (:mod:`pyrolite.plot.templates`) and
 associated classifiers (:mod:`pyrolite.util.classification`).
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

docs/source/gallery/examples/util/TAS.py

@@ -5,6 +5,7 @@
 Some simple discrimination methods are implemented,
 including the Total Alkali-Silica (TAS) classification.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -54,10 +55,20 @@
 
 fig, ax = plt.subplots(1)
 
-cm.add_to_axes(ax, alpha=0.5, linewidth=0.0, zorder=-2, add_labels=False,
-               which_ids=np.unique(df["TAS"]), fill=True, facecolor=[0.9, 0.8, 1.0])
+cm.add_to_axes(
+    ax,
+    alpha=0.5,
+    linewidth=0.0,
+    zorder=-2,
+    add_labels=False,
+    which_ids=np.unique(df["TAS"]),
+    fill=True,
+    facecolor=[0.9, 0.8, 1.0],
+)
 cm.add_to_axes(ax, alpha=0.5, linewidth=0.5, zorder=-1, add_labels=True)
-df[["SiO2", "Na2O + K2O"]].pyroplot.scatter(ax=ax, c=df["TAS"], alpha=0.7, axlabels=False)
+df[["SiO2", "Na2O + K2O"]].pyroplot.scatter(
+    ax=ax, c=df["TAS"], alpha=0.7, axlabels=False
+)
 
 ########################################################################################
 # Variations of the Diagram

docs/source/gallery/examples/util/manifold_vis.py

@@ -8,6 +8,7 @@
 predictions and relative prediction certainty (using entropy across predicted
 probability for each individual class) for a toy :mod:`sklearn` dataset.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import sklearn.datasets

docs/source/gallery/examples/util/timescale.py

@@ -12,6 +12,7 @@
     `The ICS International Chronostratigraphic Chart <https://stratigraphy.org/chart>`__.
     Episodes 36, 199–204.
 """
+
 ########################################################################################
 # First we'll create a timescale:
 #

docs/source/gallery/tutorials/logo.py

@@ -2,6 +2,7 @@
 Making the Logo
 ==================================
 """
+
 #######################################################################################
 # Having some funky ellipses in a simplex inspired some interest when I put the logo
 # together for pyrolite, so I put together a cleaned-up example of how you can create

docs/source/gallery/tutorials/plot_formatting.py

@@ -10,6 +10,7 @@
 (and also :mod:`mpltern` for ternary diagrams), you should also check out their
 documentation pages for more in-depth guides, examples and API documentation.
 """
+
 #######################################################################################
 # First let's pull in a simple dataset to use throughout these examples:
 #
@@ -209,6 +210,11 @@
 
 ax = df[["CaO", "MgO", "Al2O3"]].pyroplot.scatter()
 ax.set_ternary_lim(
-    0.1, 0.5, 0.2, 0.6, 0.3, 0.7  # tmin  # tmax  # lmin  # lmax  # rmin  # rmax
+    0.1,
+    0.5,
+    0.2,
+    0.6,
+    0.3,
+    0.7,  # tmin  # tmax  # lmin  # lmax  # rmin  # rmax
 )
 plt.show()

docs/source/gallery/tutorials/templates.py

@@ -7,6 +7,7 @@
 * Add plot.templates file
 
 """
+
 #######################################################################################
 # First let's pull in a simple dataset to use throughout these examples:
 #

docs/source/gallery/tutorials/ternary_density.py

@@ -10,6 +10,7 @@
 irregualr triangulated grid and log transforms, and how this avoids some of the
 potential issues of the methods mentioned above.
 """
+
 #######################################################################################
 # Let's first get some data to deal with. :mod:`mpltern` has a conventient dataset
 # which we can use here:

pyrolite/__init__.py

@@ -47,5 +47,6 @@ def load_extensions(base="pyrolite_", replace=["util"]):
             n = n.replace(r, "")
         setattr(extensions, n, m)
 
+
 # _export_pyrolite_mplstyle() should be called in .plot import regardless
 matplotlib.style.use("pyrolite")