Coverage for python/lsst/analysis/tools/actions/plot/plotUtils.py: 14%
279 statements
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1# This file is part of analysis_tools.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
21from __future__ import annotations
23__all__ = ("PanelConfig",)
25from typing import TYPE_CHECKING, Iterable, List, Mapping, Tuple
27import matplotlib
28import matplotlib.pyplot as plt
29import numpy as np
30from lsst.geom import Box2D, SpherePoint, degrees
31from lsst.pex.config import Config, Field
32from matplotlib import colors
33from matplotlib.collections import PatchCollection
34from matplotlib.patches import Rectangle
35from scipy.stats import binned_statistic_2d
37from ...math import nanMedian, nanSigmaMad
39if TYPE_CHECKING: 39 ↛ 40line 39 didn't jump to line 40, because the condition on line 39 was never true
40 from matplotlib.figure import Figure
42null_formatter = matplotlib.ticker.NullFormatter()
45def parsePlotInfo(dataId, runName, tableName, bands, plotName, SN):
46 """Parse plot info from the dataId.
48 Parameters
49 ----------
50 dataId : `dict`
51 The dataId of the data to be plotted.
52 runName : `str`
53 The name of the run.
54 tableName : `str`
55 The name of the table.
56 bands : `list` [`str`]
57 The bands to be plotted.
58 plotName : `str`
59 The name of the plot.
60 SN : `str`
61 The signal to noise of the data.
63 Returns
64 -------
65 plotInfo : `dict`
66 A dictionary of the plot information.
67 """
68 plotInfo = {"run": runName, "tableName": tableName, "plotName": plotName, "SN": SN}
70 for dataInfo in dataId:
71 plotInfo[dataInfo.name] = dataId[dataInfo.name]
73 bandStr = ""
74 for band in bands:
75 bandStr += ", " + band
76 plotInfo["bands"] = bandStr[2:]
78 if "tract" not in plotInfo.keys():
79 plotInfo["tract"] = "N/A"
80 if "visit" not in plotInfo.keys():
81 plotInfo["visit"] = "N/A"
83 return plotInfo
86def generateSummaryStats(data, skymap, plotInfo):
87 """Generate a summary statistic in each patch or detector.
89 Parameters
90 ----------
91 data : `dict`
92 A dictionary of the data to be plotted.
93 skymap : `lsst.skymap.ringsSkyMap.RingsSkyMap`
94 The skymap associated with the data.
95 plotInfo : `dict`
96 A dictionary of the plot information.
98 Returns
99 -------
100 patchInfoDict : `dict`
101 A dictionary of the patch information.
102 """
103 # TODO: what is the more generic type of skymap?
104 tractInfo = skymap.generateTract(plotInfo["tract"])
105 tractWcs = tractInfo.getWcs()
107 # For now also convert the gen 2 patchIds to gen 3
108 if "y" in data.keys():
109 yCol = "y"
110 elif "yStars" in data.keys():
111 yCol = "yStars"
112 elif "yGalaxies" in data.keys():
113 yCol = "yGalaxies"
114 elif "yUnknowns" in data.keys():
115 yCol = "yUnknowns"
117 patchInfoDict = {}
118 maxPatchNum = tractInfo.num_patches.x * tractInfo.num_patches.y
119 patches = np.arange(0, maxPatchNum, 1)
120 for patch in patches:
121 if patch is None:
122 continue
123 # Once the objectTable_tract catalogues are using gen 3 patches
124 # this will go away
125 onPatch = data["patch"] == patch
126 if sum(onPatch) == 0:
127 stat = np.nan
128 else:
129 stat = nanMedian(data[yCol][onPatch])
130 try:
131 patchTuple = (int(patch.split(",")[0]), int(patch.split(",")[-1]))
132 patchInfo = tractInfo.getPatchInfo(patchTuple)
133 gen3PatchId = tractInfo.getSequentialPatchIndex(patchInfo)
134 except AttributeError:
135 # For native gen 3 tables the patches don't need converting
136 # When we are no longer looking at the gen 2 -> gen 3
137 # converted repos we can tidy this up
138 gen3PatchId = patch
139 patchInfo = tractInfo.getPatchInfo(patch)
141 corners = Box2D(patchInfo.getInnerBBox()).getCorners()
142 skyCoords = tractWcs.pixelToSky(corners)
144 patchInfoDict[gen3PatchId] = (skyCoords, stat)
146 tractCorners = Box2D(tractInfo.getBBox()).getCorners()
147 skyCoords = tractWcs.pixelToSky(tractCorners)
148 patchInfoDict["tract"] = (skyCoords, np.nan)
150 return patchInfoDict
153def generateSummaryStatsVisit(cat, colName, visitSummaryTable):
154 """Generate a summary statistic in each patch or detector.
156 Parameters
157 ----------
158 cat : `pandas.core.frame.DataFrame`
159 A dataframe of the data to be plotted.
160 colName : `str`
161 The name of the column to be plotted.
162 visitSummaryTable : `pandas.core.frame.DataFrame`
163 A dataframe of the visit summary table.
165 Returns
166 -------
167 visitInfoDict : `dict`
168 A dictionary of the visit information.
169 """
170 visitInfoDict = {}
171 for ccd in cat.detector.unique():
172 if ccd is None:
173 continue
174 onCcd = cat["detector"] == ccd
175 stat = nanMedian(cat[colName].values[onCcd])
177 sumRow = visitSummaryTable["id"] == ccd
178 corners = zip(visitSummaryTable["raCorners"][sumRow][0], visitSummaryTable["decCorners"][sumRow][0])
179 cornersOut = []
180 for ra, dec in corners:
181 corner = SpherePoint(ra, dec, units=degrees)
182 cornersOut.append(corner)
184 visitInfoDict[ccd] = (cornersOut, stat)
186 return visitInfoDict
189# Inspired by matplotlib.testing.remove_ticks_and_titles
190def get_and_remove_axis_text(ax) -> Tuple[List[str], List[np.ndarray]]:
191 """Remove text from an Axis and its children and return with line points.
193 Parameters
194 ----------
195 ax : `plt.Axis`
196 A matplotlib figure axis.
198 Returns
199 -------
200 texts : `List[str]`
201 A list of all text strings (title and axis/legend/tick labels).
202 line_xys : `List[numpy.ndarray]`
203 A list of all line ``_xy`` attributes (arrays of shape ``(N, 2)``).
204 """
205 line_xys = [line._xy for line in ax.lines]
206 texts = [text.get_text() for text in (ax.title, ax.xaxis.label, ax.yaxis.label)]
207 ax.set_title("")
208 ax.set_xlabel("")
209 ax.set_ylabel("")
211 try:
212 texts_legend = ax.get_legend().texts
213 texts.extend(text.get_text() for text in texts_legend)
214 for text in texts_legend:
215 text.set_alpha(0)
216 except AttributeError:
217 pass
219 for idx in range(len(ax.texts)):
220 texts.append(ax.texts[idx].get_text())
221 ax.texts[idx].set_text("")
223 ax.xaxis.set_major_formatter(null_formatter)
224 ax.xaxis.set_minor_formatter(null_formatter)
225 ax.yaxis.set_major_formatter(null_formatter)
226 ax.yaxis.set_minor_formatter(null_formatter)
227 try:
228 ax.zaxis.set_major_formatter(null_formatter)
229 ax.zaxis.set_minor_formatter(null_formatter)
230 except AttributeError:
231 pass
232 for child in ax.child_axes:
233 texts_child, lines_child = get_and_remove_axis_text(child)
234 texts.extend(texts_child)
236 return texts, line_xys
239def get_and_remove_figure_text(figure: Figure):
240 """Remove text from a Figure and its Axes and return with line points.
242 Parameters
243 ----------
244 figure : `matplotlib.pyplot.Figure`
245 A matplotlib figure.
247 Returns
248 -------
249 texts : `List[str]`
250 A list of all text strings (title and axis/legend/tick labels).
251 line_xys : `List[numpy.ndarray]`, (N, 2)
252 A list of all line ``_xy`` attributes (arrays of shape ``(N, 2)``).
253 """
254 texts = [str(figure._suptitle)]
255 lines = []
256 figure.suptitle("")
258 texts.extend(text.get_text() for text in figure.texts)
259 figure.texts = []
261 for ax in figure.get_axes():
262 texts_ax, lines_ax = get_and_remove_axis_text(ax)
263 texts.extend(texts_ax)
264 lines.extend(lines_ax)
266 return texts, lines
269def addPlotInfo(fig: Figure, plotInfo: Mapping[str, str]) -> Figure:
270 """Add useful information to the plot.
272 Parameters
273 ----------
274 fig : `matplotlib.figure.Figure`
275 The figure to add the information to.
276 plotInfo : `dict`
277 A dictionary of the plot information.
279 Returns
280 -------
281 fig : `matplotlib.figure.Figure`
282 The figure with the information added.
283 """
284 # TO DO: figure out how to get this information
285 photocalibDataset = "None"
286 astroDataset = "None"
288 fig.text(0.01, 0.99, plotInfo["plotName"], fontsize=7, transform=fig.transFigure, ha="left", va="top")
290 run = plotInfo["run"]
291 datasetsUsed = f"\nPhotoCalib: {photocalibDataset}, Astrometry: {astroDataset}"
292 tableType = f"\nTable: {plotInfo['tableName']}"
294 dataIdText = ""
295 if "tract" in plotInfo.keys():
296 dataIdText += f", Tract: {plotInfo['tract']}"
297 if "visit" in plotInfo.keys():
298 dataIdText += f", Visit: {plotInfo['visit']}"
300 bandText = ""
301 for band in plotInfo["bands"]:
302 bandText += band + ", "
303 bandsText = f", Bands: {bandText[:-2]}"
304 infoText = f"\n{run}{datasetsUsed}{tableType}{dataIdText}{bandsText}"
306 # Find S/N and mag keys, if present.
307 snKeys = []
308 magKeys = []
309 selectionKeys = []
310 selectionPrefix = "Selection: "
311 for key, value in plotInfo.items():
312 if "SN" in key or "S/N" in key:
313 snKeys.append(key)
314 elif "Mag" in key:
315 magKeys.append(key)
316 elif key.startswith(selectionPrefix):
317 selectionKeys.append(key)
318 # Add S/N and mag values to label, if present.
319 # TODO: Do something if there are multiple sn/mag keys. Log? Warn?
320 newline = "\n"
321 if snKeys:
322 infoText = f"{infoText}{newline if magKeys else ', '}{snKeys[0]}{plotInfo.get(snKeys[0])}"
323 if magKeys:
324 infoText = f"{infoText}, {magKeys[0]}{plotInfo.get(magKeys[0])}"
325 if selectionKeys:
326 nPrefix = len(selectionPrefix)
327 selections = ", ".join(f"{key[nPrefix:]}: {plotInfo[key]}" for key in selectionKeys)
328 infoText = f"{infoText}, Selections: {selections}"
330 fig.text(0.01, 0.984, infoText, fontsize=6, transform=fig.transFigure, alpha=0.6, ha="left", va="top")
332 return fig
335def mkColormap(colorNames):
336 """Make a colormap from the list of color names.
338 Parameters
339 ----------
340 colorNames : `list`
341 A list of strings that correspond to matplotlib named colors.
343 Returns
344 -------
345 cmap : `matplotlib.colors.LinearSegmentedColormap`
346 A colormap stepping through the supplied list of names.
347 """
348 nums = np.linspace(0, 1, len(colorNames))
349 blues = []
350 greens = []
351 reds = []
352 for num, color in zip(nums, colorNames):
353 r, g, b = colors.colorConverter.to_rgb(color)
354 blues.append((num, b, b))
355 greens.append((num, g, g))
356 reds.append((num, r, r))
358 colorDict = {"blue": blues, "red": reds, "green": greens}
359 cmap = colors.LinearSegmentedColormap("newCmap", colorDict)
360 return cmap
363def extremaSort(xs):
364 """Return the IDs of the points reordered so that those furthest from the
365 median, in absolute terms, are last.
367 Parameters
368 ----------
369 xs : `np.array`
370 An array of the values to sort
372 Returns
373 -------
374 ids : `np.array`
375 """
376 med = nanMedian(xs)
377 dists = np.abs(xs - med)
378 ids = np.argsort(dists)
379 return ids
382def sortAllArrays(arrsToSort, sortArrayIndex=0):
383 """Sort one array and then return all the others in the associated order.
385 Parameters
386 ----------
387 arrsToSort : `list` [`np.array`]
388 A list of arrays to be simultaneously sorted based on the array in
389 the list position given by ``sortArrayIndex`` (defaults to be the
390 first array in the list).
391 sortArrayIndex : `int`, optional
392 Zero-based index indicating the array on which to base the sorting.
394 Returns
395 -------
396 arrsToSort : `list` [`np.array`]
397 The list of arrays sorted on array in list index ``sortArrayIndex``.
398 """
399 ids = extremaSort(arrsToSort[sortArrayIndex])
400 for i, arr in enumerate(arrsToSort):
401 arrsToSort[i] = arr[ids]
402 return arrsToSort
405def addSummaryPlot(fig, loc, sumStats, label):
406 """Add a summary subplot to the figure.
408 Parameters
409 ----------
410 fig : `matplotlib.figure.Figure`
411 The figure that the summary plot is to be added to.
412 loc : `matplotlib.gridspec.SubplotSpec` or `int` or `(int, int, index`
413 Describes the location in the figure to put the summary plot,
414 can be a gridspec SubplotSpec, a 3 digit integer where the first
415 digit is the number of rows, the second is the number of columns
416 and the third is the index. This is the same for the tuple
417 of int, int, index.
418 sumStats : `dict`
419 A dictionary where the patchIds are the keys which store the R.A.
420 and the dec of the corners of the patch, along with a summary
421 statistic for each patch.
422 label : `str`
423 The label to be used for the colorbar.
425 Returns
426 -------
427 fig : `matplotlib.figure.Figure`
428 """
429 # Add the subplot to the relevant place in the figure
430 # and sort the axis out
431 axCorner = fig.add_subplot(loc)
432 axCorner.yaxis.tick_right()
433 axCorner.yaxis.set_label_position("right")
434 axCorner.xaxis.tick_top()
435 axCorner.xaxis.set_label_position("top")
436 axCorner.set_aspect("equal")
438 # Plot the corners of the patches and make the color
439 # coded rectangles for each patch, the colors show
440 # the median of the given value in the patch
441 patches = []
442 colors = []
443 for dataId in sumStats.keys():
444 (corners, stat) = sumStats[dataId]
445 ra = corners[0][0].asDegrees()
446 dec = corners[0][1].asDegrees()
447 xy = (ra, dec)
448 width = corners[2][0].asDegrees() - ra
449 height = corners[2][1].asDegrees() - dec
450 patches.append(Rectangle(xy, width, height))
451 colors.append(stat)
452 ras = [ra.asDegrees() for (ra, dec) in corners]
453 decs = [dec.asDegrees() for (ra, dec) in corners]
454 axCorner.plot(ras + [ras[0]], decs + [decs[0]], "k", lw=0.5)
455 cenX = ra + width / 2
456 cenY = dec + height / 2
457 if dataId != "tract":
458 axCorner.annotate(dataId, (cenX, cenY), color="k", fontsize=4, ha="center", va="center")
460 # Set the bad color to transparent and make a masked array
461 cmapPatch = plt.cm.coolwarm.copy()
462 cmapPatch.set_bad(color="none")
463 colors = np.ma.array(colors, mask=np.isnan(colors))
464 collection = PatchCollection(patches, cmap=cmapPatch)
465 collection.set_array(colors)
466 axCorner.add_collection(collection)
468 # Add some labels
469 axCorner.set_xlabel("R.A. (deg)", fontsize=7)
470 axCorner.set_ylabel("Dec. (deg)", fontsize=7)
471 axCorner.tick_params(axis="both", labelsize=6, length=0, pad=1.5)
472 axCorner.invert_xaxis()
474 # Add a colorbar
475 pos = axCorner.get_position()
476 yOffset = (pos.y1 - pos.y0) / 3
477 cax = fig.add_axes([pos.x0, pos.y1 + yOffset, pos.x1 - pos.x0, 0.025])
478 plt.colorbar(collection, cax=cax, orientation="horizontal")
479 cax.text(
480 0.5,
481 0.48,
482 label,
483 color="k",
484 transform=cax.transAxes,
485 rotation="horizontal",
486 horizontalalignment="center",
487 verticalalignment="center",
488 fontsize=6,
489 )
490 cax.tick_params(
491 axis="x", labelsize=6, labeltop=True, labelbottom=False, bottom=False, top=True, pad=0.5, length=2
492 )
494 return fig
497def shorten_list(numbers: Iterable[int], *, range_indicator: str = "-", range_separator: str = ",") -> str:
498 """Shorten an iterable of integers.
500 Parameters
501 ----------
502 numbers : `~collections.abc.Iterable` [`int`]
503 Any iterable (list, set, tuple, numpy.array) of integers.
504 range_indicator : `str`, optional
505 The string to use to indicate a range of numbers.
506 range_separator : `str`, optional
507 The string to use to separate ranges of numbers.
509 Returns
510 -------
511 result : `str`
512 A shortened string representation of the list.
514 Examples
515 --------
516 >>> shorten_list([1,2,3,5,6,8])
517 "1-3,5-6,8"
519 >>> shorten_list((1,2,3,5,6,8,9,10,11), range_separator=", ")
520 "1-3, 5-6, 8-11"
522 >>> shorten_list(range(4), range_indicator="..")
523 "0..3"
524 """
525 # Sort the list in ascending order.
526 numbers = sorted(numbers)
528 if not numbers: # empty container
529 return ""
531 # Initialize an empty list to hold the results to be returned.
532 result = []
534 # Initialize variables to track the current start and end of a list.
535 start = 0
536 end = 0 # initialize to 0 to handle single element lists.
538 # Iterate through the sorted list of numbers
539 for end in range(1, len(numbers)):
540 # If the current number is the same or consecutive to the previous
541 # number, skip to the next iteration.
542 if numbers[end] > numbers[end - 1] + 1: # > is used to handle duplicates, if any.
543 # If the current number is not consecutive to the previous number,
544 # add the current range to the result and reset the start to end.
545 if start == end - 1:
546 result.append(str(numbers[start]))
547 else:
548 result.append(range_indicator.join((str(numbers[start]), str(numbers[end - 1]))))
550 # Update start.
551 start = end
553 # Add the final range to the result.
554 if start == end:
555 result.append(str(numbers[start]))
556 else:
557 result.append(range_indicator.join((str(numbers[start]), str(numbers[end]))))
559 # Return the shortened string representation.
560 return range_separator.join(result)
563class PanelConfig(Config):
564 """Configuration options for the plot panels used by DiaSkyPlot.
566 The defaults will produce a good-looking single panel plot.
567 The subplot2grid* fields correspond to matplotlib.pyplot.subplot2grid.
568 """
570 topSpinesVisible = Field[bool](
571 doc="Draw line and ticks on top of panel?",
572 default=False,
573 )
574 bottomSpinesVisible = Field[bool](
575 doc="Draw line and ticks on bottom of panel?",
576 default=True,
577 )
578 leftSpinesVisible = Field[bool](
579 doc="Draw line and ticks on left side of panel?",
580 default=True,
581 )
582 rightSpinesVisible = Field[bool](
583 doc="Draw line and ticks on right side of panel?",
584 default=True,
585 )
586 subplot2gridShapeRow = Field[int](
587 doc="Number of rows of the grid in which to place axis.",
588 default=10,
589 )
590 subplot2gridShapeColumn = Field[int](
591 doc="Number of columns of the grid in which to place axis.",
592 default=10,
593 )
594 subplot2gridLocRow = Field[int](
595 doc="Row of the axis location within the grid.",
596 default=1,
597 )
598 subplot2gridLocColumn = Field[int](
599 doc="Column of the axis location within the grid.",
600 default=1,
601 )
602 subplot2gridRowspan = Field[int](
603 doc="Number of rows for the axis to span downwards.",
604 default=5,
605 )
606 subplot2gridColspan = Field[int](
607 doc="Number of rows for the axis to span to the right.",
608 default=5,
609 )
612def plotProjectionWithBinning(
613 ax,
614 xs,
615 ys,
616 zs,
617 cmap,
618 xMin,
619 xMax,
620 yMin,
621 yMax,
622 xNumBins=45,
623 yNumBins=None,
624 fixAroundZero=False,
625 nPointBinThresh=5000,
626 isSorted=False,
627 vmin=None,
628 vmax=None,
629 showExtremeOutliers=True,
630 scatPtSize=7,
631):
632 """Plot color-mapped data in projection and with binning when appropriate.
634 Parameters
635 ----------
636 ax : `matplotlib.axes.Axes`
637 Axis on which to plot the projection data.
638 xs, ys : `np.array`
639 Arrays containing the x and y positions of the data.
640 zs : `np.array`
641 Array containing the scaling value associated with the (``xs``, ``ys``)
642 positions.
643 cmap : `matplotlib.colors.Colormap`
644 Colormap for the ``zs`` values.
645 xMin, xMax, yMin, yMax : `float`
646 Data limits within which to compute bin sizes.
647 xNumBins : `int`, optional
648 The number of bins along the x-axis.
649 yNumBins : `int`, optional
650 The number of bins along the y-axis. If `None`, this is set to equal
651 ``xNumBins``.
652 nPointBinThresh : `int`, optional
653 Threshold number of points above which binning will be implemented
654 for the plotting. If the number of data points is lower than this
655 threshold, a basic scatter plot will be generated.
656 isSorted : `bool`, optional
657 Whether the data have been sorted in ``zs`` (the sorting is to
658 accommodate the overplotting of points in the upper and lower
659 extrema of the data).
660 vmin, vmax : `float`, optional
661 The min and max limits for the colorbar.
662 showExtremeOutliers: `bool`, default True
663 Use overlaid scatter points to show the x-y positions of the 15%
664 most extreme values.
665 scatPtSize : `float`, optional
666 The point size to use if just plotting a regular scatter plot.
668 Returns
669 -------
670 plotOut : `matplotlib.collections.PathCollection`
671 The plot object with ``ax`` updated with data plotted here.
672 """
673 med = nanMedian(zs)
674 mad = nanSigmaMad(zs)
675 if vmin is None:
676 vmin = med - 2 * mad
677 if vmax is None:
678 vmax = med + 2 * mad
679 if fixAroundZero:
680 scaleEnd = np.max([np.abs(vmin), np.abs(vmax)])
681 vmin = -1 * scaleEnd
682 vmax = scaleEnd
684 yNumBins = xNumBins if yNumBins is None else yNumBins
686 xBinEdges = np.linspace(xMin, xMax, xNumBins + 1)
687 yBinEdges = np.linspace(yMin, yMax, yNumBins + 1)
688 binnedStats, xEdges, yEdges, binNums = binned_statistic_2d(
689 xs, ys, zs, statistic="median", bins=(xBinEdges, yBinEdges)
690 )
691 if len(xs) >= nPointBinThresh:
692 s = min(10, max(0.5, nPointBinThresh / 10 / (len(xs) ** 0.5)))
693 lw = (s**0.5) / 10
694 plotOut = ax.imshow(
695 binnedStats.T,
696 cmap=cmap,
697 extent=[xEdges[0], xEdges[-1], yEdges[-1], yEdges[0]],
698 vmin=vmin,
699 vmax=vmax,
700 )
701 if not isSorted:
702 sortedArrays = sortAllArrays([zs, xs, ys])
703 zs, xs, ys = sortedArrays[0], sortedArrays[1], sortedArrays[2]
704 if len(xs) > 1:
705 if showExtremeOutliers:
706 # Find the most extreme 15% of points. The list is ordered
707 # by the distance from the median, this is just the
708 # head/tail 15% of points.
709 extremes = int(np.floor((len(xs) / 100)) * 85)
710 plotOut = ax.scatter(
711 xs[extremes:],
712 ys[extremes:],
713 c=zs[extremes:],
714 s=s,
715 cmap=cmap,
716 vmin=vmin,
717 vmax=vmax,
718 edgecolor="white",
719 linewidths=lw,
720 )
721 else:
722 plotOut = ax.scatter(
723 xs,
724 ys,
725 c=zs,
726 cmap=cmap,
727 s=scatPtSize,
728 vmin=vmin,
729 vmax=vmax,
730 edgecolor="white",
731 linewidths=0.2,
732 )
733 return plotOut