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show ()Įnhance your skills with courses on Python legend_elements (), list ( set ( df ))) plt. The assumption is that marker sizes are small enough that they don't have to be taken into account in the autoscaling.Fig = plt. It is handled correctly by plot because that yields a Line2D object which is much simpler than a collection, and has a hook that triggers the necessary recalculations. Switching to the log transform after this, however, does not properly undo the calculation done with the original linear transform, and redo it with the new log transform. This is handled correctly (or at least nearly so) when the log transform is in effect at the time the collection is added to the Axes. Autoscaling therefore requires figuring out the data range required for the objects to fit, and this depends on the data transform-and in a particularly tricky way when it is a log transform. The fundamental problem with collections is that they operate with two or more transforms, so that in the scatter case, for example, they can draw objects of a given physical size at locations given in data coordinates. I suspect that if you specify a much larger-than-default size for the scatter marker, you will see your -2.305 value change. I don't expect the solution to be a one-liner.

I'm not 100% sure, but it looks like a fairly fundamental limitation, not a simple bug. 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):