This problem has nothing to do with magic values it is a matter of how transforms are made, modified, and used in the autoscaling, when dealing with collections. Tick_tups = įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py', line 944, in įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py', line 889, in iter_ticksįile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/ticker.py', line 867, in set_locs Ticks_to_draw = self._update_ticks(renderer)įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py', line 944, in _update_ticks Mimage._draw_list_compositing_images(renderer, self, dsu)įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py', line 1111, in draw Renderer, self, dsu, self.suppressComposite)įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/image.py', line 139, in _draw_list_compositing_imagesįile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axes/_base.py', line 2381, in draw Return draw(artist, renderer, *args, **kwargs)įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/figure.py', line 1262, in draw > /home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py(684)_set_scale()įile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/backends/backend_qt5agg.py', line 183, in _draw_idle_aggįile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/backends/backend_agg.py', line 464, in drawįile '/home/conner/Documents/GitHub/matplotlib/lib/matplotlib/artist.py', line 68, in draw_wrapper > self._t_default_locators_and_formatters(self) > /home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py(682)_set_scale() > self._scale = mscale.scale_factory(value, self, **kwargs) > /home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py(681)_set_scale() > def _set_scale(self, value, **kwargs): > /home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axis.py(680)_set_scale() home/conner/Documents/GitHub/matplotlib/lib/matplotlib/axes/_base.py(3161)set_yscale() X, y = xmin + (xmax-xmin)*xran, ymin + (ymax-ymin)*yran Xran, yran = ((rans-lowers) / (uppers-lowers)).T # make sure you hit the target min/max Maxes = (5*np.random.random(2*N)) + mins # 0 to 5 moreįor xmin, xmax, ymin, ymax in zip(xmins, xmaxes, ymins, ymaxes):

Matplotlib scatter plot log scale