Bulk black format and isort

docs/source/_patch/_sphinx_gallery_patch.py

@@ -1,6 +1,7 @@
+from sphinx_gallery import gen_rst
+
 from .sphinx_gallery_binder import alt_gen_binder_rst
 from .sphinx_gallery_scrapers import alt_matplotlib_scraper
-from sphinx_gallery import gen_rst
 
 # modify single image arrangement
 gen_rst.SINGLE_IMAGE = """

docs/source/_patch/sphinx_gallery_binder.py

@@ -6,7 +6,7 @@ def alt_gen_binder_rst(
 ):
     """
     Generate the RST + link for the Binder badge.
-    
+
     Parameters
     ----------
     fpath: str

docs/source/_patch/sphinx_gallery_scrapers.py

@@ -1,11 +1,13 @@
 import re
 from pathlib import Path
+
 import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib import _pylab_helpers
+from sphinx_gallery import scrapers
+
 from pyrolite.util.plot import save_figure
 
-from sphinx_gallery import scrapers
 
 def alt_matplotlib_scraper(block, block_vars, gallery_conf, **kwargs):
     """Patched matplotlib scraper which won't close figures.
@@ -49,4 +51,5 @@ def alt_matplotlib_scraper(block, block_vars, gallery_conf, **kwargs):
 
     return scrapers.figure_rst(image_paths, gallery_conf["src_dir"])
 
+
 scrapers._scraper_dict.update(dict(altmatplot=alt_matplotlib_scraper))

docs/source/gallery/data/aitchison.py

@@ -17,6 +17,7 @@
 df.head()
 ########################################################################################
 import matplotlib.pyplot as plt
+
 import pyrolite.plot
 
 fig, ax = plt.subplots(1)

docs/source/gallery/data/radii.py

@@ -9,9 +9,10 @@
 to get radii for individual elements, using a :code:`source` keyword argument to swap
 between the datasets:
 """
-import pandas as pd
 import matplotlib.pyplot as plt
-from pyrolite.geochem.ind import get_ionic_radii, REE
+import pandas as pd
+
+from pyrolite.geochem.ind import REE, get_ionic_radii
 
 REE_radii = pd.Series(
     get_ionic_radii(REE(), coordination=8, charge=3, source="Whittaker"), index=REE()

docs/source/gallery/data/refcomp.py

@@ -8,7 +8,9 @@
 references.
 """
 import matplotlib.pyplot as plt
+
 from pyrolite.geochem.norm import all_reference_compositions, get_reference_composition
+
 # sphinx_gallery_thumbnail_number = 11
 
 refcomps = all_reference_compositions()

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

@@ -2,17 +2,20 @@
 Log Ratio Means
 -----------------
 """
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+
 import pyrolite.comp
-from pyrolite.comp.codata import ILR, inverse_ILR, close
-from pyrolite.util.synthetic import random_cov_matrix
-import matplotlib.pyplot as plt
+from pyrolite.comp.codata import ILR, close, inverse_ILR
 from pyrolite.plot import pyroplot
+from pyrolite.util.synthetic import random_cov_matrix
 
 # sphinx_gallery_thumbnail_number = 3
 
 np.random.seed(82)
+
+
 ########################################################################################
 def random_compositional_trend(m1, m2, c1, c2, resolution=20, size=1000):
     """

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

@@ -77,6 +77,7 @@
 # `label_mode` parameter, for example to get nice labels for plotting:
 #
 import matplotlib.pyplot as plt
+
 df.pyrocomp.ILR(label_mode="latex").iloc[:, 0:2].pyroplot.scatter()
 plt.show()
 ########################################################################################

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

@@ -25,6 +25,7 @@
 one of the minerals might occasionally have zero abundance:
 """
 import numpy as np
+
 from pyrolite.util.synthetic import normal_frame
 
 # sphinx_gallery_thumbnail_number = 2

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

@@ -36,6 +36,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+
 import pyrolite.geochem
 from pyrolite.util.meta import pyrolite_datafolder
 

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

@@ -8,7 +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 get_ionic_radii, REE
+from pyrolite.geochem.ind import REE, get_ionic_radii
 
 Cu_radii = get_ionic_radii("Cu")
 print(Cu_radii)

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

@@ -24,6 +24,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+
 import pyrolite.plot
 
 # sphinx_gallery_thumbnail_number = 2
@@ -234,6 +235,7 @@
 #
 lts.head(2)
 from pyrolite.geochem.ind import REE
+
 ########################################################################################
 # Below we'll look at some of the potential issues of fitting lambdas and tetrads
 # together - by examining the effects of i) fitting tetrads where there are none

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

@@ -7,8 +7,9 @@
 pyrolite includes some utilities to achieve this and a limited mineral database
 for looking up endmember compositions.
 """
-import pandas as pd
 import numpy as np
+import pandas as pd
+
 from pyrolite.mineral.mindb import get_mineral
 from pyrolite.mineral.normative import endmember_decompose
 
@@ -58,4 +59,4 @@
 ########################################################################################
 # To make sure these compositions are within 0.01 percent:
 #
-assert np.allclose(recombined.values, comp.values, rtol=10 ** -4)
+assert np.allclose(recombined.values, comp.values, rtol=10**-4)

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

@@ -25,8 +25,9 @@
 generates plagioclase's hallmark 'europium anomaly', and the effects of variable
 europium oxidation state on bulk europium partitioning.
 """
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+
 from pyrolite.geochem.ind import REE, get_ionic_radii
 from pyrolite.mineral.lattice import strain_coefficient
 
@@ -40,8 +41,8 @@
 D_Ca = 4.1  # Partition coefficient Plag-Melt
 Tc = 900  # Temperature, °C
 Tk = Tc + 273.15  # Temperature, K
-E_2 = 120 * 10 ** 9  # Youngs modulus for 2+ site, Pa
-E_3 = 135 * 10 ** 9  # Youngs modulus for 3+ site, Pa
+E_2 = 120 * 10**9  # Youngs modulus for 2+ site, Pa
+E_3 = 135 * 10**9  # Youngs modulus for 3+ site, Pa
 r02, r03 = 1.196, 1.294  # fictive ideal cation radii for these sites
 rCa = get_ionic_radii("Ca", charge=2, coordination=8)
 rLa = get_ionic_radii("La", charge=3, coordination=8)
@@ -80,7 +81,7 @@
 # Through a handy relationship, we can estimate :math:`D_{La}`
 # based on the easier measured :math:`D_{Ca}`, :math:`D_{Na}` and temperature [#ref_2]_:
 #
-D_La = (D_Ca ** 2 / D_Na) * np.exp((529 / Tk) - 3.705)
+D_La = (D_Ca**2 / D_Na) * np.exp((529 / Tk) - 3.705)
 D_La  # 0.48085
 ########################################################################################
 # Now :math:`D_{La}` is defined, we can use it as a reference for the other REE:

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

@@ -7,8 +7,13 @@
 """
 import pandas as pd
 
-from pyrolite.mineral.mindb import (get_mineral, get_mineral_group,
-                                    list_formulae, list_groups, list_minerals)
+from pyrolite.mineral.mindb import (
+    get_mineral,
+    get_mineral_group,
+    list_formulae,
+    list_groups,
+    list_minerals,
+)
 
 pd.set_option("display.precision", 3)  # smaller outputs
 ########################################################################################

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

@@ -6,11 +6,12 @@
 accessed with :func:`pyrolite.geochem.norm.get_reference_composition` (see the list
 at the bottom of the page for a complete list):
 """
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
+
 import pyrolite.plot
 from pyrolite.geochem.ind import REE
-from pyrolite.geochem.norm import get_reference_composition, all_reference_compositions
+from pyrolite.geochem.norm import all_reference_compositions, get_reference_composition
 
 ########################################################################################
 chondrite = get_reference_composition("Chondrite_PON")

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

@@ -2,9 +2,10 @@
 REE Radii Plots
 ============================
 """
-import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
 from pyrolite.plot import pyroplot
 
 # sphinx_gallery_thumbnail_number = 3

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

@@ -12,6 +12,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+
 from pyrolite.comp.codata import close
 from pyrolite.plot import pyroplot
 from pyrolite.plot.density import density

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

@@ -10,9 +10,10 @@
 represents the best of both worlds. A version inspired by similar existing
 visualisations is implemented with :func:`~pyrolite.plot.pyroplot.heatscatter`.
 """
-import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
 from pyrolite.plot import pyroplot
 
 np.random.seed(12)

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

@@ -6,12 +6,14 @@
 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
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.axes
-import pyrolite.plot
+
 import pyrolite.data.Aitchison
+import pyrolite.plot
+
 # sphinx_gallery_thumbnail_number = 3
 
 ########################################################################################

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

@@ -2,9 +2,9 @@
 Spiderplots & Density Spiderplots
 ==================================
 """
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
 
 # sphinx_gallery_thumbnail_number = 4
 

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

@@ -8,9 +8,10 @@
 so (such that the sampling frequency along this axis is important, which is not
 emphasised by a scatter plot).
 """
-import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
 from pyrolite.plot import pyroplot
 from pyrolite.plot.stem import stem
 

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

@@ -61,7 +61,7 @@
 # based on their whole-rock geochemistry (:func:`~pyrolite.plot.templates.Pettijohn`,
 # :func:`~pyrolite.plot.templates.Herron`):
 #
-from pyrolite.plot.templates import Pettijohn, Herron
+from pyrolite.plot.templates import Herron, Pettijohn
 
 fig, ax = plt.subplots(1, 2, figsize=(10, 4))
 share_axes(ax, which="x")  # these diagrams have the same x axis

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

@@ -2,9 +2,10 @@
 Ternary Plots
 =============
 """
-import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
 from pyrolite.plot import pyroplot
 
 np.random.seed(82)

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

@@ -62,11 +62,11 @@
 # Variations of the Diagram
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# To use a different variation of the TAS diagram, you can pass the relevant keyword 
+# To use a different variation of the TAS diagram, you can pass the relevant keyword
 # :code:`which_model` allowing you to access the other available variants.
 #
-# Currently, the Le Bas/Le Maitre alternative and a T1-T2 combined variant of it are 
-# available as alternatives to the Middlemost version. Each of these can be used as a 
+# Currently, the Le Bas/Le Maitre alternative and a T1-T2 combined variant of it are
+# available as alternatives to the Middlemost version. Each of these can be used as a
 # classifier model as for the default above.
 #
 fig, ax = plt.subplots(1, 3, figsize=(20, 4))
@@ -94,4 +94,4 @@
 #   of Terms : Recommendations of International Union of Geological
 #   ciences Subcommission on the Systematics of Igneous Rocks.
 #   Cambridge University Press, 236pp.
-#   `doi: 10.1017/CBO9780511535581 <https://doi.org/10.1017/CBO9780511535581>`__.
+#   `doi: 10.1017/CBO9780511535581 <https://doi.org/10.1017/CBO9780511535581>`__.

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

@@ -8,12 +8,13 @@
 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
-import matplotlib.pyplot as plt
+
+from pyrolite.util.plot import DEFAULT_DISC_COLORMAP
 from pyrolite.util.skl.pipeline import SVC_pipeline
 from pyrolite.util.skl.vis import plot_mapping
-from pyrolite.util.plot import DEFAULT_DISC_COLORMAP
 
 np.random.seed(82)
 ########################################################################################
@@ -30,9 +31,7 @@
 # %% Plot
 fig, ax = plt.subplots(1, 2, figsize=(8, 4))
 
-a, tfm, mapped = plot_mapping(
-    data, gs.best_estimator_, ax=ax[1], s=50, init="pca"
-)
+a, tfm, mapped = plot_mapping(data, gs.best_estimator_, ax=ax[1], s=50, init="pca")
 ax[0].scatter(*mapped.T, c=DEFAULT_DISC_COLORMAP(gs.predict(data)), s=50)
 
 ax[0].set_title("Predicted Classes")

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

@@ -16,6 +16,7 @@
 # First we'll create a timescale:
 #
 from pyrolite.util.time import Timescale
+
 # sphinx_gallery_thumbnail_number = 2
 ts = Timescale()