5564819928

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Build # 5564819928

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morganjwilliams

Commit Message

Merge branch 'release/0.3.3' into main

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94.0

/pyrolite/plot/density/ternary.py

import inspect

import numpy as np

from ...comp.codata import ILR, close, inverse_ILR
from ...util.distributions import sample_kde
from ...util.log import Handle
from ...util.math import flattengrid
from ...util.plot.grid import bin_centres_to_edges

logger = Handle(__name__)


def ternary_heatmap(
    data,
    bins=20,
    mode="density",
    transform=ILR,
    inverse_transform=inverse_ILR,
    ternary_min_value=0.0001,  # 0.01%
    grid_border_frac=0.1,  # 110% range for grid
    grid=None,
    **kwargs
):
    """
    Heatmap for ternary diagrams. This invokes a 3D to 2D transform such as a
    log transform prior to creating a grid.

    Parameters
    -----------
    data : :class:`numpy.ndarray`
        Ternary data to obtain heatmap coords from.
    bins : :class:`int`
        Number of bins for the grid.
    mode : :class:`str`, :code:`{'histogram', 'density'}`
        Which mode to render the histogram/KDE in.
    transform : :class:`callable` | :class:`sklearn.base.TransformerMixin`
        Callable function or Transformer class.
    inverse_transform : :class:`callable`
        Inverse function for `transform`, necessary if transformer class not specified.
    ternary_min_value : :class:`float`
        Optional specification of minimum values within a ternary diagram to draw the
        transformed grid.
    grid_border_frac : :class:`float`
        Size of border around the grid, expressed as a fraction of the total grid range.
    grid : :class:`numpy.ndarray`
        Grid coordinates to sample at, if already calculated. For the density mode,
        this is a (nsamples, 2) array. For histograms, this is a two-member list of
        bin edges.

    Returns
    -------
    t, l, r : :class:`tuple` of :class:`numpy.ndarray`
        Ternary coordinates for the heatmap.
    H : :class:`numpy.ndarray`
        Histogram/density estimates for the coordinates.
    data : :class:`dict`
        Data dictonary with grid arrays and relevant information.

    Notes
    -----

    Zeros will not render in this heatmap, consider replacing zeros with small values
    or imputing them if they must be incorporated.
    """
    arr = close(data)  # should remove zeros/nans
    arr = arr[np.isfinite(arr).all(axis=1)]

    if inspect.isclass(transform):
        # TransformerMixin
        tcls = transform()
        tfm = tcls.transform
        itfm = tcls.inverse_transform
    else:
        # callable
        tfm = transform
        assert callable(inverse_transform)
        itfm = inverse_transform

    if grid is None:
        # minimum bounds on triangle
        mins = np.max(
            np.vstack([np.ones(3) * ternary_min_value, np.nanmin(arr, axis=0)]), axis=0
        )
        maxs = np.min(
            np.vstack([1 - np.ones(3) * ternary_min_value, np.nanmax(arr, axis=0)]),
            axis=0,
        )

        # let's construct a bounding triangle
        # three points defining the edges of what will be rendered
        ternbounds = np.vstack([mins, maxs]).T
        ternbound_points = np.array(
            [
                [
                    (1 - np.sum([ternbounds[i, 0] for z in range(3) if z != ix]))
                    if i == ix
                    else ternbounds[i, 0]
                    for i in range(3)
                ]
                for ix in range(3)
            ]
        )
        ternbound_points_tfmd = tfm(ternbound_points)

        tdata = tfm(arr)
        tfm_min, tfm_max = np.min(tdata, axis=0), np.max(tdata, axis=0)
        trng = tfm_max - tfm_min
        brdr = grid_border_frac / 2
        tfm_bin_centres = [
            np.linspace(
                np.nanmin(tdata[:, dim]) - brdr * trng[dim],  # small step back
                np.nanmax(tdata[:, dim]) + brdr * trng[dim],  # small step forward
                bins,
            )
            for dim in range(2)
        ]
        tfm_bin_edges = [bin_centres_to_edges(b) for b in tfm_bin_centres]

        tfm_centregrid = np.meshgrid(*tfm_bin_centres)
        tfm_edgegrid = np.meshgrid(*tfm_bin_edges)

        tern_centre_grid = itfm(flattengrid(tfm_centregrid))
        tern_edge_grid = itfm(flattengrid(tfm_edgegrid))

    if mode == "density":
        dgrid = grid or flattengrid(tfm_edgegrid)
        H = sample_kde(tdata, dgrid)
        H = H.reshape(tfm_edgegrid[0].shape)
        coords = tern_edge_grid
    elif "hist" in mode:
        hgrid = grid or tfm_bin_edges
        H, _ = np.histogramdd(tdata, bins=hgrid)
        H = H.T
        coords = tern_centre_grid
    elif "hex" in mode:
        raise NotImplementedError
    else:
        raise NotImplementedError

    data = dict(
        tfm_centres=tfm_centregrid,
        tfm_edges=tfm_edgegrid,
        tern_edges=tern_edge_grid,
        tern_centres=tern_centre_grid,
        tern_bound_points=ternbound_points,
        tfm_tern_bound_points=ternbound_points_tfmd,
        grid_transform=tfm,
        grid_inverse_transform=itfm,
    )
    H[~np.isfinite(H)] = 0
    return coords, H, data

1	import inspect	12✔
2
3	import numpy as np	12✔
4
5	from ...comp.codata import ILR, close, inverse_ILR	12✔
6	from ...util.distributions import sample_kde	12✔
7	from ...util.log import Handle	12✔
8	from ...util.math import flattengrid	12✔
9	from ...util.plot.grid import bin_centres_to_edges	12✔
10
11	logger = Handle(__name__)	12✔
12
13
14	def ternary_heatmap(	12✔
15	data,
16	bins=20,
17	mode="density",
18	transform=ILR,
19	inverse_transform=inverse_ILR,
20	ternary_min_value=0.0001, # 0.01%
21	grid_border_frac=0.1, # 110% range for grid
22	grid=None,
23	**kwargs
24	):
25	"""
26	Heatmap for ternary diagrams. This invokes a 3D to 2D transform such as a
27	log transform prior to creating a grid.
28
29	Parameters
30	-----------
31	data : :class:`numpy.ndarray`
32	Ternary data to obtain heatmap coords from.
33	bins : :class:`int`
34	Number of bins for the grid.
35	mode : :class:`str`, :code:`{'histogram', 'density'}`
36	Which mode to render the histogram/KDE in.
37	transform : :class:`callable` \| :class:`sklearn.base.TransformerMixin`
38	Callable function or Transformer class.
39	inverse_transform : :class:`callable`
40	Inverse function for `transform`, necessary if transformer class not specified.
41	ternary_min_value : :class:`float`
42	Optional specification of minimum values within a ternary diagram to draw the
43	transformed grid.
44	grid_border_frac : :class:`float`
45	Size of border around the grid, expressed as a fraction of the total grid range.
46	grid : :class:`numpy.ndarray`
47	Grid coordinates to sample at, if already calculated. For the density mode,
48	this is a (nsamples, 2) array. For histograms, this is a two-member list of
49	bin edges.
50
51	Returns
52	-------
53	t, l, r : :class:`tuple` of :class:`numpy.ndarray`
54	Ternary coordinates for the heatmap.
55	H : :class:`numpy.ndarray`
56	Histogram/density estimates for the coordinates.
57	data : :class:`dict`
58	Data dictonary with grid arrays and relevant information.
59
60	Notes
61	-----
62
63	Zeros will not render in this heatmap, consider replacing zeros with small values
64	or imputing them if they must be incorporated.
65	"""
66	arr = close(data) # should remove zeros/nans	12✔
67	arr = arr[np.isfinite(arr).all(axis=1)]	12✔
68
69	if inspect.isclass(transform):	12✔
70	# TransformerMixin
71	tcls = transform()	12✔
72	tfm = tcls.transform	12✔
73	itfm = tcls.inverse_transform	12✔
74	else:
75	# callable
76	tfm = transform	12✔
77	assert callable(inverse_transform)	12✔
78	itfm = inverse_transform	12✔
79
80	if grid is None:	12✔
81	# minimum bounds on triangle
82	mins = np.max(	12✔
83	np.vstack([np.ones(3) * ternary_min_value, np.nanmin(arr, axis=0)]), axis=0
84	)
85	maxs = np.min(	12✔
86	np.vstack([1 - np.ones(3) * ternary_min_value, np.nanmax(arr, axis=0)]),
87	axis=0,
88	)
89
90	# let's construct a bounding triangle
91	# three points defining the edges of what will be rendered
92	ternbounds = np.vstack([mins, maxs]).T	12✔
93	ternbound_points = np.array(	12✔
94	[
95	[
96	(1 - np.sum([ternbounds[i, 0] for z in range(3) if z != ix]))
97	if i == ix
98	else ternbounds[i, 0]
99	for i in range(3)
100	]
101	for ix in range(3)
102	]
103	)
104	ternbound_points_tfmd = tfm(ternbound_points)	12✔
105
106	tdata = tfm(arr)	12✔
107	tfm_min, tfm_max = np.min(tdata, axis=0), np.max(tdata, axis=0)	12✔
108	trng = tfm_max - tfm_min	12✔
109	brdr = grid_border_frac / 2	12✔
110	tfm_bin_centres = [	12✔
111	np.linspace(
112	np.nanmin(tdata[:, dim]) - brdr * trng[dim], # small step back
113	np.nanmax(tdata[:, dim]) + brdr * trng[dim], # small step forward
114	bins,
115	)
116	for dim in range(2)
117	]
118	tfm_bin_edges = [bin_centres_to_edges(b) for b in tfm_bin_centres]	12✔
119
120	tfm_centregrid = np.meshgrid(*tfm_bin_centres)	12✔
121	tfm_edgegrid = np.meshgrid(*tfm_bin_edges)	12✔
122
123	tern_centre_grid = itfm(flattengrid(tfm_centregrid))	12✔
124	tern_edge_grid = itfm(flattengrid(tfm_edgegrid))	12✔
125
126	if mode == "density":	12✔
127	dgrid = grid or flattengrid(tfm_edgegrid)	12✔
128	H = sample_kde(tdata, dgrid)	12✔
129	H = H.reshape(tfm_edgegrid[0].shape)	12✔
130	coords = tern_edge_grid	12✔
131	elif "hist" in mode:	12✔
132	hgrid = grid or tfm_bin_edges	12✔
133	H, _ = np.histogramdd(tdata, bins=hgrid)	12✔
134	H = H.T	12✔
135	coords = tern_centre_grid	12✔
136	elif "hex" in mode:	×
UNCOV 137	raise NotImplementedError	×
138	else:
UNCOV 139	raise NotImplementedError	×
140
141	data = dict(	12✔
142	tfm_centres=tfm_centregrid,
143	tfm_edges=tfm_edgegrid,
144	tern_edges=tern_edge_grid,
145	tern_centres=tern_centre_grid,
146	tern_bound_points=ternbound_points,
147	tfm_tern_bound_points=ternbound_points_tfmd,
148	grid_transform=tfm,
149	grid_inverse_transform=itfm,
150	)
151	H[~np.isfinite(H)] = 0	12✔
152	return coords, H, data	12✔

morganjwilliams / pyrolite / 5564819928

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