28609932887

Committed 02 Jul 2026 05:40PM UTC coverage: 86.835% (-2.0%) from 88.798%

Build # 28609932887

Build Type

Pull #358

github

Committed by

web-flow

Commit Message

Merge 245f6c3af into 6042d4ac0

Pull Request Pull Request #358: Add py3.14

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5 of 6 new or added lines in 2 files covered. (83.33%)

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61.74

/smpl/plot2d.py

import matplotlib.pyplot as plt
import numpy as np
import smplr
from matplotlib import colors
from matplotlib.image import NonUniformImage

from smpl import doc, util
from smpl import plot as splot

default = {
    "title": [None, "Plot title"],
    "xlabel": [None, "."],
    "ylabel": [None, "."],
    "zlabel": [None, "."],
    "logz": [True, "Colorbar in logarithmic scale."],
    "style": [
        "pcolormesh",
        "Plot via an image ('image') or scatter ('scatter') or mesh ('pcolormesh').",
    ],
    "interpolation": [
        "nearest",
        "Only 'nearest' or 'bilinear' for nonuniformimage. Check https://matplotlib.org/stable/gallery/images_contours_and_fields/interpolation_methods.html#interpolations-for-imshow",
    ],
    "cmap": [
        "viridis",
        "Good default color map for missing datapoints since it does not include white.",
    ],
    # 'zscale' : [None,"Rescale z values."],
}

# @doc.insert_str("\tDefault kwargs\n\n\t")


@doc.append_str(doc.array_table(default, init=False))
@doc.append_str(
    doc.array_table({"plot2d_kwargs": ["default", "description"]}, bottom=False)
)
def plot2d_kwargs(kwargs):
    """Set default plot2d_kwargs if not set."""
    for k, v in default.items():
        if k not in kwargs:
            kwargs[k] = v[0]
    return kwargs


def plot2d(datax, datay, dataz, **kwargs):
    """
    Creates a 2D-Plot.

    Parameters
    ----------
    **kwargs : optional
        see :func:`plot2d_kwargs`.
    """
    kwargs = plot2d_kwargs(kwargs)
    if "xaxis" in kwargs and ("xlabel" not in kwargs or not kwargs["xlabel"]):
        # warnings.warn("xaxis is deprecated. Use xlabel instead.", DeprecationWarning, 2)
        kwargs["xlabel"] = kwargs["xaxis"]
    if "yaxis" in kwargs and ("ylabel" not in kwargs or not kwargs["ylabel"]):
        # warnings.warn("yaxis is deprecated. Use ylabel instead.", DeprecationWarning, 2)
        kwargs["ylabel"] = kwargs["yaxis"]
    if "zaxis" in kwargs and ("zlabel" not in kwargs or not kwargs["zlabel"]):
        # warnings.warn("zaxis is deprecated. Use zlabel instead.", DeprecationWarning, 2)
        kwargs["zlabel"] = kwargs["zaxis"]

    if util.has("axes", kwargs) and kwargs["axes"] is not None:
        plt.sca(kwargs["axes"])
    if kwargs["style"] == "pcolormesh":
        pcolormesh_vplot(datax, datay, dataz, **kwargs)
    elif kwargs["style"] == "image":
        map_vplot(datax, datay, dataz, **kwargs)
    elif kwargs["style"] == "scatter":
        scatter_vplot(datax, datay, dataz, **kwargs)


def sort_xyz(x, y, z):
    p1 = x.argsort(kind="stable")
    x = np.copy(x[p1])
    y = np.copy(y[p1])
    z = np.copy(z[p1])
    p2 = y.argsort(kind="stable")
    x = x[p2]
    y = y[p2]
    z = z[p2]
    return x, y, z


def pcolormesh_vplot(
    tvx,
    tvy,
    tvz,
    xlabel=None,
    ylabel=None,
    zlabel=None,
    logz=True,
    zscale=1.0,
    **kwargs,
):
    """
    Advantage over matplotlibs pcolor(mesh) is that does not require a meshgrid. Instead it uses the data points directly in three lists.
    """
    vx = np.copy(tvx)
    vy = np.copy(tvy)
    vz = np.copy(tvz)
    assert vx.shape == vy.shape == vz.shape

    if len(vz.shape) < 2:
        mesh = np.meshgrid(np.unique(vx), np.unique(vy))
        X, Y = mesh
        # set Z to values of vz on the meshgrid
        Z = np.empty(mesh[0].shape)
        Z[:] = np.nan
        for i, _ in enumerate(vx):
            Z[(mesh[0] == vx[i]) & (mesh[1] == vy[i])] = splot.unv(vz[i])
        Z[:] *= zscale
    else:
        X = vx
        Y = vy
        Z = vz * zscale

    plt.pcolormesh(
        X, Y, Z, norm=colors.LogNorm() if logz else None, cmap=kwargs["cmap"]
    )

    # ax.set_xlim(xl, xm)
    # ax.set_ylim(yl, ym)

    cb = plt.colorbar()
    cb.set_label(zlabel)
    smplr.style_plot2d(xlabel=xlabel, ylabel=ylabel, **kwargs)


def map_vplot(
    tvx,
    tvy,
    tvz,
    xlabel=None,
    ylabel=None,
    zlabel=None,
    logz=True,
    sort=True,
    fill_missing=True,
    zscale=1.0,
    **kwargs,
):
    """ """
    vx = np.copy(tvx)
    vy = np.copy(tvy)
    vz = np.copy(tvz)
    if fill_missing:
        # TODO speed up
        for x in vx:
            for y in vy:
                ex = np.any(np.logical_and((vx == x), (vy == y)))
                if not ex:
                    vx = np.append(vx, x)
                    vy = np.append(vy, y)
                    vz = np.append(vz, 0)
    if sort:
        vx, vy, vz = sort_xyz(vx, vy, vz)

    s = 1
    while vy[s] == vy[s - 1]:
        s = s + 1
    if s == 1:
        # print("flipped x y ")
        while vx[s] == vx[s - 1]:
            s = s + 1
        if s == 1:
            print("error too small map")
            return
        # x, y = y, x
        xlabel, ylabel = ylabel, xlabel
        vx, vy = vy, vx

    grid = splot.unv(vz).reshape((int(np.rint(np.size(vx) / s)), s)) * zscale

    _, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True)
    im = None
    xl = vx.min() + (vx.min() / 2) - vx[vx != vx.min()].min() / 2
    xm = vx.max() + (vx.max() / 2) - vx[vx != vx.max()].max() / 2
    yl = vy.min() + (vy.min() / 2) - vy[vy != vy.min()].min() / 2
    ym = vy.max() + (vy.max() / 2) - vy[vy != vy.max()].max() / 2
    im = NonUniformImage(
        ax,
        origin="lower",
        cmap=kwargs["cmap"],
        interpolation=kwargs["interpolation"],
        extent=(xl, xm, yl, ym),
        norm=colors.LogNorm() if logz else None,
    )

    im.set_data(np.unique(vx), np.unique(vy), grid)
    ax.add_image(im)
    # ax.images.append(im)
    ax.set_xlim(xl, xm)
    ax.set_ylim(yl, ym)

    cb = plt.colorbar(im)
    cb.set_label(zlabel)
    smplr.style_plot2d(xlabel=xlabel, ylabel=ylabel, **kwargs)


def scatter_vplot(
    vx,
    vy,
    vz,
    xlabel=None,
    ylabel=None,
    zlabel=None,
    logz=True,
    sort=True,
    **kwargs,
):
    if sort:
        vx, vy, vz = sort_xyz(vx, vy, vz)

    _, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True)
    xl = vx.min() + (vx.min() / 2) - vx[vx != vx.min()].min() / 2
    xm = vx.max() + (vx.max() / 2) - vx[vx != vx.max()].max() / 2
    yl = vy.min() + (vy.min() / 2) - vy[vy != vy.min()].min() / 2
    ym = vy.max() + (vy.max() / 2) - vy[vy != vy.max()].max() / 2

    s = plt.scatter(
        np.concatenate((vx, vx, vx)),
        np.concatenate((vy, vy, vy)),
        c=np.concatenate(
            (
                splot.unv(vz) + splot.usd(vz),
                splot.unv(vz) - splot.usd(vz),
                splot.unv(vz),
            )
        ),
        s=np.concatenate(
            (
                [(3 * plt.rcParams["lines.markersize"]) ** 2 for i in range(len(vx))],
                [(2 * plt.rcParams["lines.markersize"]) ** 2 for i in range(len(vx))],
                [(plt.rcParams["lines.markersize"]) ** 2 for i in range(len(vx))],
            )
        ),
        norm=colors.LogNorm() if logz else None,
        cmap=kwargs["cmap"],
    )

    ax.set_xlim(xl, xm)
    ax.set_ylim(yl, ym)

    cb = plt.colorbar(s)
    cb.set_label(zlabel)
    smplr.style_plot2d(xlabel=xlabel, ylabel=ylabel, **kwargs)

1	import matplotlib.pyplot as plt	2✔
2	import numpy as np	2✔
3	import smplr	2✔
4	from matplotlib import colors	2✔
5	from matplotlib.image import NonUniformImage	2✔
6
7	from smpl import doc, util	2✔
8	from smpl import plot as splot	2✔
9
10	default = {	2✔
11	"title": [None, "Plot title"],
12	"xlabel": [None, "."],
13	"ylabel": [None, "."],
14	"zlabel": [None, "."],
15	"logz": [True, "Colorbar in logarithmic scale."],
16	"style": [
17	"pcolormesh",
18	"Plot via an image ('image') or scatter ('scatter') or mesh ('pcolormesh').",
19	],
20	"interpolation": [
21	"nearest",
22	"Only 'nearest' or 'bilinear' for nonuniformimage. Check https://matplotlib.org/stable/gallery/images_contours_and_fields/interpolation_methods.html#interpolations-for-imshow",
23	],
24	"cmap": [
25	"viridis",
26	"Good default color map for missing datapoints since it does not include white.",
27	],
28	# 'zscale' : [None,"Rescale z values."],
29	}
30
31	# @doc.insert_str("\tDefault kwargs\n\n\t")
32
33
34	@doc.append_str(doc.array_table(default, init=False))	2✔
35	@doc.append_str(	2✔
36	doc.array_table({"plot2d_kwargs": ["default", "description"]}, bottom=False)
37	)
38	def plot2d_kwargs(kwargs):	2✔
39	"""Set default plot2d_kwargs if not set."""
40	for k, v in default.items():	2✔
41	if k not in kwargs:	2✔
42	kwargs[k] = v[0]	2✔
43	return kwargs	2✔
44
45
46	def plot2d(datax, datay, dataz, **kwargs):	2✔
47	"""
48	Creates a 2D-Plot.
49
50	Parameters
51	----------
52	**kwargs : optional
53	see :func:`plot2d_kwargs`.
54	"""
55	kwargs = plot2d_kwargs(kwargs)	2✔
56	if "xaxis" in kwargs and ("xlabel" not in kwargs or not kwargs["xlabel"]):	2✔
57	# warnings.warn("xaxis is deprecated. Use xlabel instead.", DeprecationWarning, 2)
58	kwargs["xlabel"] = kwargs["xaxis"]	×
59	if "yaxis" in kwargs and ("ylabel" not in kwargs or not kwargs["ylabel"]):	2✔
60	# warnings.warn("yaxis is deprecated. Use ylabel instead.", DeprecationWarning, 2)
61	kwargs["ylabel"] = kwargs["yaxis"]	×
62	if "zaxis" in kwargs and ("zlabel" not in kwargs or not kwargs["zlabel"]):	2✔
63	# warnings.warn("zaxis is deprecated. Use zlabel instead.", DeprecationWarning, 2)
64	kwargs["zlabel"] = kwargs["zaxis"]	×
65
66	if util.has("axes", kwargs) and kwargs["axes"] is not None:	2✔
67	plt.sca(kwargs["axes"])	×
68	if kwargs["style"] == "pcolormesh":	2✔
69	pcolormesh_vplot(datax, datay, dataz, **kwargs)	2✔
70	elif kwargs["style"] == "image":	2✔
UNCOV 71	map_vplot(datax, datay, dataz, **kwargs)	×
72	elif kwargs["style"] == "scatter":	2✔
73	scatter_vplot(datax, datay, dataz, **kwargs)	2✔
74
75
76	def sort_xyz(x, y, z):	2✔
77	p1 = x.argsort(kind="stable")	2✔
78	x = np.copy(x[p1])	2✔
79	y = np.copy(y[p1])	2✔
80	z = np.copy(z[p1])	2✔
81	p2 = y.argsort(kind="stable")	2✔
82	x = x[p2]	2✔
83	y = y[p2]	2✔
84	z = z[p2]	2✔
85	return x, y, z	2✔
86
87
88	def pcolormesh_vplot(	2✔
89	tvx,
90	tvy,
91	tvz,
92	xlabel=None,
93	ylabel=None,
94	zlabel=None,
95	logz=True,
96	zscale=1.0,
97	**kwargs,
98	):
99	"""
100	Advantage over matplotlibs pcolor(mesh) is that does not require a meshgrid. Instead it uses the data points directly in three lists.
101	"""
102	vx = np.copy(tvx)	2✔
103	vy = np.copy(tvy)	2✔
104	vz = np.copy(tvz)	2✔
105	assert vx.shape == vy.shape == vz.shape	2✔
106
107	if len(vz.shape) < 2:	2✔
108	mesh = np.meshgrid(np.unique(vx), np.unique(vy))	2✔
109	X, Y = mesh	2✔
110	# set Z to values of vz on the meshgrid
111	Z = np.empty(mesh[0].shape)	2✔
112	Z[:] = np.nan	2✔
113	for i, _ in enumerate(vx):	2✔
114	Z[(mesh[0] == vx[i]) & (mesh[1] == vy[i])] = splot.unv(vz[i])	2✔
115	Z[:] *= zscale	2✔
116	else:
117	X = vx	2✔
118	Y = vy	2✔
119	Z = vz * zscale	2✔
120
121	plt.pcolormesh(	2✔
122	X, Y, Z, norm=colors.LogNorm() if logz else None, cmap=kwargs["cmap"]
123	)
124
125	# ax.set_xlim(xl, xm)
126	# ax.set_ylim(yl, ym)
127
128	cb = plt.colorbar()	2✔
129	cb.set_label(zlabel)	2✔
130	smplr.style_plot2d(xlabel=xlabel, ylabel=ylabel, **kwargs)	2✔
131
132
133	def map_vplot(	2✔
134	tvx,
135	tvy,
136	tvz,
137	xlabel=None,
138	ylabel=None,
139	zlabel=None,
140	logz=True,
141	sort=True,
142	fill_missing=True,
143	zscale=1.0,
144	**kwargs,
145	):
146	""" """
UNCOV 147	vx = np.copy(tvx)	×
UNCOV 148	vy = np.copy(tvy)	×
UNCOV 149	vz = np.copy(tvz)	×
UNCOV 150	if fill_missing:	×
151	# TODO speed up
UNCOV 152	for x in vx:	×
UNCOV 153	for y in vy:	×
UNCOV 154	ex = np.any(np.logical_and((vx == x), (vy == y)))	×
UNCOV 155	if not ex:	×
UNCOV 156	vx = np.append(vx, x)	×
UNCOV 157	vy = np.append(vy, y)	×
UNCOV 158	vz = np.append(vz, 0)	×
UNCOV 159	if sort:	×
UNCOV 160	vx, vy, vz = sort_xyz(vx, vy, vz)	×
161
UNCOV 162	s = 1	×
UNCOV 163	while vy[s] == vy[s - 1]:	×
UNCOV 164	s = s + 1	×
UNCOV 165	if s == 1:	×
166	# print("flipped x y ")
167	while vx[s] == vx[s - 1]:	×
168	s = s + 1	×
169	if s == 1:	×
170	print("error too small map")	×
171	return	×
172	# x, y = y, x
173	xlabel, ylabel = ylabel, xlabel	×
174	vx, vy = vy, vx	×
175
UNCOV 176	grid = splot.unv(vz).reshape((int(np.rint(np.size(vx) / s)), s)) * zscale	×
177
UNCOV 178	_, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True)	×
UNCOV 179	im = None	×
UNCOV 180	xl = vx.min() + (vx.min() / 2) - vx[vx != vx.min()].min() / 2	×
UNCOV 181	xm = vx.max() + (vx.max() / 2) - vx[vx != vx.max()].max() / 2	×
UNCOV 182	yl = vy.min() + (vy.min() / 2) - vy[vy != vy.min()].min() / 2	×
UNCOV 183	ym = vy.max() + (vy.max() / 2) - vy[vy != vy.max()].max() / 2	×
UNCOV 184	im = NonUniformImage(	×
185	ax,
186	origin="lower",
187	cmap=kwargs["cmap"],
188	interpolation=kwargs["interpolation"],
189	extent=(xl, xm, yl, ym),
190	norm=colors.LogNorm() if logz else None,
191	)
192
UNCOV 193	im.set_data(np.unique(vx), np.unique(vy), grid)	×
UNCOV 194	ax.add_image(im)	×
195	# ax.images.append(im)
UNCOV 196	ax.set_xlim(xl, xm)	×
UNCOV 197	ax.set_ylim(yl, ym)	×
198
UNCOV 199	cb = plt.colorbar(im)	×
UNCOV 200	cb.set_label(zlabel)	×
UNCOV 201	smplr.style_plot2d(xlabel=xlabel, ylabel=ylabel, **kwargs)	×
202
203
204	def scatter_vplot(	2✔
205	vx,
206	vy,
207	vz,
208	xlabel=None,
209	ylabel=None,
210	zlabel=None,
211	logz=True,
212	sort=True,
213	**kwargs,
214	):
215	if sort:	2✔
216	vx, vy, vz = sort_xyz(vx, vy, vz)	2✔
217
218	_, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True)	2✔
219	xl = vx.min() + (vx.min() / 2) - vx[vx != vx.min()].min() / 2	2✔
220	xm = vx.max() + (vx.max() / 2) - vx[vx != vx.max()].max() / 2	2✔
221	yl = vy.min() + (vy.min() / 2) - vy[vy != vy.min()].min() / 2	2✔
222	ym = vy.max() + (vy.max() / 2) - vy[vy != vy.max()].max() / 2	2✔
223
224	s = plt.scatter(	2✔
225	np.concatenate((vx, vx, vx)),
226	np.concatenate((vy, vy, vy)),
227	c=np.concatenate(
228	(
229	splot.unv(vz) + splot.usd(vz),
230	splot.unv(vz) - splot.usd(vz),
231	splot.unv(vz),
232	)
233	),
234	s=np.concatenate(
235	(
236	[(3 * plt.rcParams["lines.markersize"]) ** 2 for i in range(len(vx))],
237	[(2 * plt.rcParams["lines.markersize"]) ** 2 for i in range(len(vx))],
238	[(plt.rcParams["lines.markersize"]) ** 2 for i in range(len(vx))],
239	)
240	),
241	norm=colors.LogNorm() if logz else None,
242	cmap=kwargs["cmap"],
243	)
244
245	ax.set_xlim(xl, xm)	2✔
246	ax.set_ylim(yl, ym)	2✔
247
248	cb = plt.colorbar(s)	2✔
249	cb.set_label(zlabel)	2✔
250	smplr.style_plot2d(xlabel=xlabel, ylabel=ylabel, **kwargs)	2✔

APN-Pucky / smpl / 28609932887

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