Calling eprh.hfss_report_full_convergence sometimes results in ValueError: Data has no positive values, and therefore can not be log-scaled.
In this case, the simulations did not converge into tight criterion, but behaved normally
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [14], in <cell line: 1>()
----> 1 eprh.hfss_report_full_convergence()
File c:\users\iqm\pyepr\pyEPR\core_distributed_analysis.py:1637, in DistributedAnalysis.hfss_report_full_convergence(self, fig, _display)
1633 plot_convergence_solved_elem(ax0t, convergence_t.iloc[:, 0])
1634 plot_convergence_maxdf_vs_sol(axs[2], convergence_t.iloc[:, 1],
1635 convergence_t.iloc[:, 0])
-> 1637 fig.tight_layout(w_pad=0.1) # pad=0.0, w_pad=0.1, h_pad=1.0)
1639 if _display:
1640 from IPython.display import display
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\figure.py:3197, in Figure.tight_layout(self, pad, h_pad, w_pad, rect)
3195 renderer = _get_renderer(self)
3196 with getattr(renderer, "_draw_disabled", nullcontext)():
-> 3197 kwargs = get_tight_layout_figure(
3198 self, self.axes, subplotspec_list, renderer,
3199 pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
3200 if kwargs:
3201 self.subplots_adjust(**kwargs)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\tight_layout.py:320, in get_tight_layout_figure(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)
315 return {}
316 span_pairs.append((
317 slice(ss.rowspan.start * div_row, ss.rowspan.stop * div_row),
318 slice(ss.colspan.start * div_col, ss.colspan.stop * div_col)))
--> 320 kwargs = _auto_adjust_subplotpars(fig, renderer,
321 shape=(max_nrows, max_ncols),
322 span_pairs=span_pairs,
323 subplot_list=subplot_list,
324 ax_bbox_list=ax_bbox_list,
325 pad=pad, h_pad=h_pad, w_pad=w_pad)
327 # kwargs can be none if tight_layout fails...
328 if rect is not None and kwargs is not None:
329 # if rect is given, the whole subplots area (including
330 # labels) will fit into the rect instead of the
(...)
334 # auto_adjust_subplotpars twice, where the second run
335 # with adjusted rect parameters.
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\tight_layout.py:82, in _auto_adjust_subplotpars(fig, renderer, shape, span_pairs, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)
80 if ax.get_visible():
81 try:
---> 82 bb += [ax.get_tightbbox(renderer, for_layout_only=True)]
83 except TypeError:
84 bb += [ax.get_tightbbox(renderer)]
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axes\_base.py:4628, in _AxesBase.get_tightbbox(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)
4626 if self.yaxis.get_visible():
4627 try:
-> 4628 bb_yaxis = self.yaxis.get_tightbbox(
4629 renderer, for_layout_only=for_layout_only)
4630 except TypeError:
4631 # in case downstream library has redefined axis:
4632 bb_yaxis = self.yaxis.get_tightbbox(renderer)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1103, in Axis.get_tightbbox(self, renderer, for_layout_only)
1100 if not self.get_visible():
1101 return
-> 1103 ticks_to_draw = self._update_ticks()
1105 self._update_label_position(renderer)
1107 # go back to just this axis's tick labels
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1045, in Axis._update_ticks(self)
1040 def _update_ticks(self):
1041 """
1042 Update ticks (position and labels) using the current data interval of
1043 the axes. Return the list of ticks that will be drawn.
1044 """
-> 1045 major_locs = self.get_majorticklocs()
1046 major_labels = self.major.formatter.format_ticks(major_locs)
1047 major_ticks = self.get_major_ticks(len(major_locs))
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1277, in Axis.get_majorticklocs(self)
1275 def get_majorticklocs(self):
1276 """Return this Axis' major tick locations in data coordinates."""
-> 1277 return self.major.locator()
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\ticker.py:2292, in LogLocator.__call__(self)
2290 """Return the locations of the ticks."""
2291 vmin, vmax = self.axis.get_view_interval()
-> 2292 return self.tick_values(vmin, vmax)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\ticker.py:2317, in LogLocator.tick_values(self, vmin, vmax)
2314 vmin = self.axis.get_minpos()
2316 if vmin <= 0.0 or not np.isfinite(vmin):
-> 2317 raise ValueError(
2318 "Data has no positive values, and therefore can not be "
2319 "log-scaled.")
2321 _log.debug('vmin %s vmax %s', vmin, vmax)
2323 if vmax < vmin:
ValueError: Data has no positive values, and therefore can not be log-scaled.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib_inline\backend_inline.py:43, in show(close, block)
39 try:
40 for figure_manager in Gcf.get_all_fig_managers():
41 display(
42 figure_manager.canvas.figure,
---> 43 metadata=_fetch_figure_metadata(figure_manager.canvas.figure)
44 )
45 finally:
46 show._to_draw = []
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib_inline\backend_inline.py:231, in _fetch_figure_metadata(fig)
228 # determine if a background is needed for legibility
229 if _is_transparent(fig.get_facecolor()):
230 # the background is transparent
--> 231 ticksLight = _is_light([label.get_color()
232 for axes in fig.axes
233 for axis in (axes.xaxis, axes.yaxis)
234 for label in axis.get_ticklabels()])
235 if ticksLight.size and (ticksLight == ticksLight[0]).all():
236 # there are one or more tick labels, all with the same lightness
237 return {'needs_background': 'dark' if ticksLight[0] else 'light'}
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib_inline\backend_inline.py:234, in <listcomp>(.0)
228 # determine if a background is needed for legibility
229 if _is_transparent(fig.get_facecolor()):
230 # the background is transparent
231 ticksLight = _is_light([label.get_color()
232 for axes in fig.axes
233 for axis in (axes.xaxis, axes.yaxis)
--> 234 for label in axis.get_ticklabels()])
235 if ticksLight.size and (ticksLight == ticksLight[0]).all():
236 # there are one or more tick labels, all with the same lightness
237 return {'needs_background': 'dark' if ticksLight[0] else 'light'}
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1249, in Axis.get_ticklabels(self, minor, which)
1247 if minor:
1248 return self.get_minorticklabels()
-> 1249 return self.get_majorticklabels()
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1201, in Axis.get_majorticklabels(self)
1199 def get_majorticklabels(self):
1200 """Return this Axis' major tick labels, as a list of `~.text.Text`."""
-> 1201 ticks = self.get_major_ticks()
1202 labels1 = [tick.label1 for tick in ticks if tick.label1.get_visible()]
1203 labels2 = [tick.label2 for tick in ticks if tick.label2.get_visible()]
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1371, in Axis.get_major_ticks(self, numticks)
1369 r"""Return the list of major `.Tick`\s."""
1370 if numticks is None:
-> 1371 numticks = len(self.get_majorticklocs())
1373 while len(self.majorTicks) < numticks:
1374 # Update the new tick label properties from the old.
1375 tick = self._get_tick(major=True)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axis.py:1277, in Axis.get_majorticklocs(self)
1275 def get_majorticklocs(self):
1276 """Return this Axis' major tick locations in data coordinates."""
-> 1277 return self.major.locator()
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\ticker.py:2292, in LogLocator.__call__(self)
2290 """Return the locations of the ticks."""
2291 vmin, vmax = self.axis.get_view_interval()
-> 2292 return self.tick_values(vmin, vmax)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\ticker.py:2317, in LogLocator.tick_values(self, vmin, vmax)
2314 vmin = self.axis.get_minpos()
2316 if vmin <= 0.0 or not np.isfinite(vmin):
-> 2317 raise ValueError(
2318 "Data has no positive values, and therefore can not be "
2319 "log-scaled.")
2321 _log.debug('vmin %s vmax %s', vmin, vmax)
2323 if vmax < vmin:
ValueError: Data has no positive values, and therefore can not be log-scaled.
Calling
eprh.hfss_report_full_convergencesometimes results in ValueError: Data has no positive values, and therefore can not be log-scaled.In this case, the simulations did not converge into tight criterion, but behaved normally