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1. egaudry commented on Sep 30, 2016

   

   egaudry

   on Sep 30, 2016

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   The attached patch allows to draw ticks/ticklabels in case no decade (or only one) was selected based on vmin/vmax detected values.
   There is however still an issue in the colorbar height.

   Index: lib/matplotlib/colorbar.py
   ===================================================================
   --- lib/matplotlib/colorbar.py  (revision 97805)
   +++ lib/matplotlib/colorbar.py  (working copy)
   @@ -592,12 +592,14 @@
            formatter.set_data_interval(*intv)
   
            b = np.array(locator())
   -        if isinstance(locator, ticker.LogLocator):
   +        if isinstance(locator, ticker.LogLocator) or isinstance(locator, ticker.SymmetricalLogLocator):
                eps = 1e-10
                b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
            else:
                eps = (intv[1] - intv[0]) * 1e-10
                b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
   +        if len(b)<2:
   +            b = np.array(locator())
            ticks = self._locate(b)
            formatter.set_locs(b)
            ticklabels = [formatter(t, i) for i, t in enumerate(b)]
   Index: lib/matplotlib/ticker.py
   ===================================================================
   --- lib/matplotlib/ticker.py    (revision 97805)
   +++ lib/matplotlib/ticker.py    (working copy)
   @@ -1722,6 +1722,8 @@
                    a_range = get_log_range(t, -vmin + 1)
                else:
                    a_range = get_log_range(-vmax, -vmin + 1)
   +                if vmin<0 and vmax<0:
   +                  a_range = ( a_range[0], a_range[1]+1 )
            else:
                a_range = (0, 0)
   
   
2. egaudry commented on Sep 30, 2016

   

   egaudry

   on Sep 30, 2016

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   might be linked to #7146
3. tacaswell commented on Oct 3, 2016

   

   tacaswell

   on Oct 3, 2016

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   @egaudry Can you open a pull request with that patch?

   attn @efiring
4. NelleV commented on Oct 24, 2016

   

   NelleV

   on Oct 24, 2016

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   import numpy as np
   import matplotlib.pyplot as plt
   from matplotlib import colors
   from matplotlib import ticker
   
   X = -1 * np.random.randint(10, 90, size=(100, 100))
   
   fig, ax = plt.subplots()
   m = ax.imshow(X, norm=colors.SymLogNorm(1))
   formatter = ticker.LogFormatter(10)
   cb = fig.colorbar(m, format=formatter)
   ax.set_title("label only base")
5. NelleV

   added

   Release criticalFor bugs that make the library unusable (segfaults, incorrect plots, etc) and major regressions.For bugs that make the library unusable (segfaults, incorrect plots, etc) and major regressions.

   on Oct 25, 2016
6. NelleV

   added this to the This milestone has been deleted milestone on Oct 25, 2016
7. tacaswell commented on Oct 31, 2016

   

   tacaswell

   on Oct 31, 2016

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   import numpy as np
   import matplotlib.pyplot as plt
   from matplotlib import colors
   from matplotlib import ticker
   
   plt.ion()
   
   X = np.random.randint(15, 90, size=(100, 100))
   
   fig, ax = plt.subplots()
   m = ax.imshow(X, norm=colors.LogNorm())
   formatter = ticker.LogFormatter(10)
   cb = fig.colorbar(m, format=formatter)
   ax.set_title("label only base")
   

   
8. NelleV commented on Oct 31, 2016

   

   NelleV

   on Oct 31, 2016

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   So… The behaviour is not the same for log and symlog Norm… minorticks appear with symlog but not log.
   I'll do more tests.
9. NelleV commented on Nov 5, 2016

   

   NelleV

   on Nov 5, 2016

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   @efiring @tacaswell @dopplershift

   Following monday's discussion, I've ran some tests. By default, Log formatter behave extremely poorly with colorbars. The current behaviour is as follows

   

   Disabling labelOnlyBase does not change anything:
   

   Disabling the sublabels only feature (introduced in august) does not change anything:

   

   To have a behaviour that seems reasonable for colorbars we need to modify both the sublabels filtering and the labelOnlyBase:

   

   Now going to using the LogFormatter on a log scaled plot while disabling the sublabels filtering does not change anything on this example: http://matplotlib.org/devdocs/examples/scales/scales.html

   With sublabels:
   

   Without sublabels:
   

   From our discussion, I undersand that the labelOnlyBase=True is mostly use for minor ticks. I propose to change the default to have labelOnlyBase = False to remove or disable the sublabel filtering by default. I have yet to find a case where this new filtering improved the readibility of the figure, and I believe this may be due to improvements in the defaults of max number of ticks and placements of those ticks.

   I have to tweak matplotlib's code to add an option to disable the sublabels, that you can find in https://github.com/NelleV/matplotlib/tree/test_7202

   But here are some tests scripts I wrote:

   1. Colorbars

   import numpy as np
   
   import matplotlib.pyplot as plt
   from matplotlib import ticker, colors
   
   disable_sublabel = True
   
   
   X_1 = np.random.randint(10000, 90000, size=(100, 100))
   X_2 = np.random.randint(10000, 1000000, size=(100, 100))
   
   formatter = ticker.LogFormatter(
       10, disable_sublabel=disable_sublabel,
       labelOnlyBase=False)
   
   X = [X_1, X_2]
   
   fig, axes = plt.subplots(ncols=2, figsize=(10, 4))
   for i, x in enumerate(X):
       ax = axes[i]
       m = ax.imshow(x, norm=colors.SymLogNorm(1))
       ax.set_title("Disabling sublabels & labelOnlyBase")
       cb = fig.colorbar(m, format=formatter, ax=ax, shrink=0.9, )
   fig.savefig("images/log_colorbar_proposed_new_default.png")
   
   formatter = ticker.LogFormatter(
       10, disable_sublabel=disable_sublabel,
       labelOnlyBase=True)
   
   fig, axes = plt.subplots(ncols=2, figsize=(10, 4))
   for i, x in enumerate(X):
       ax = axes[i]
       m = ax.imshow(x, norm=colors.SymLogNorm(1))
       ax.set_title("Disable sublabel only")
       cb = fig.colorbar(m, format=formatter, ax=ax, shrink=0.9)
   fig.savefig("images/log_colorbar_disable_sublabels_only.png")
   
   formatter = ticker.LogFormatter(10, labelOnlyBase=False)
   
   fig, axes = plt.subplots(ncols=2, figsize=(10, 4))
   for i, x in enumerate(X):
       ax = axes[i]
       m = ax.imshow(x, norm=colors.SymLogNorm(1))
       ax.set_title("Log formatter labelOnlyBase=False")
       cb = fig.colorbar(m, ax=ax, format=formatter, shrink=0.9)
   fig.savefig("images/log_colorbar_disable_labelonlybase.png")
   
   formatter = ticker.LogFormatter(10)
   
   fig, axes = plt.subplots(ncols=2, figsize=(10, 4))
   for i, x in enumerate(X):
       ax = axes[i]
       m = ax.imshow(x, norm=colors.SymLogNorm(1))
       ax.set_title("Current Default")
       cb = fig.colorbar(m, ax=ax, format=formatter, shrink=0.9)
   fig.savefig("images/log_colorbar_default.png")

   2. the scales example

   import numpy as np
   from matplotlib import ticker
   import matplotlib.pyplot as plt
   
   disable_sublabel = True
   
   old_formatter = ticker.LogFormatterSciNotation(
       10, labelOnlyBase=False)
   formatter = ticker.LogFormatterSciNotation(
       10, disable_sublabel=True,
       labelOnlyBase=False)
   
   np.random.seed(19680801)
   # make up some data in the interval ]0, 1[
   y = np.random.normal(loc=0.5, scale=0.4, size=1000)
   y = y[(y > 0) & (y < 1)]
   y.sort()
   x = np.arange(len(y))
   
   for i, fmt in enumerate([old_formatter, formatter]):
       # plot with various axes scales
       fig, axs = plt.subplots(2, 2)
   
       # linear
       ax = axs[0, 0]
       ax.plot(x, y)
       ax.set_yscale('linear')
       ax.set_title('linear')
       ax.grid(True)
   
       # log
       ax = axs[0, 1]
       ax.plot(x, y)
       ax.set_yscale('log')
       ax.set_title('log')
       ax.yaxis.set_major_formatter(fmt)
   
       ax.grid(True)
   
       # symmetric log
       ax = axs[1, 1]
       ax.plot(x, y - y.mean())
       ax.set_yscale('symlog', linthreshy=0.05)
       ax.set_title('symlog')
       ax.yaxis.set_major_formatter(fmt)
       ax.grid(True)
   
       # logit
       ax = axs[1, 0]
       ax.plot(x, y)
       ax.set_yscale('logit')
       ax.set_title('logit')
       #ax.yaxis.set_major_formatter(formatter)
       ax.yaxis.set_minor_formatter(ticker.NullFormatter())
       ax.grid(True)
   
       plt.show()
       if i == 0:
           fig.savefig("images/example_old_formatter.png")
       else:
           fig.savefig("images/example_disabling_sublabels.png")

   3. some random tests

   import numpy as np
   
   import matplotlib.pyplot as plt
   from matplotlib import ticker, colors
   
   formatter = ticker.LogFormatterSciNotation(
       10, disable_sublabel=True,
       labelOnlyBase=False)
   
   X_1 = np.random.randint(10000, 90000, size=(1000,))
   X_1.sort()
   X_2 = np.random.randint(1e5, 1e12, size=(1000,))
   X_2.sort()
   X_3 = np.random.randint(1e5, 1e16, size=(1000,))
   X_3.sort()
   
   X = [X_1, X_2, X_3]
   
   fig, ax = plt.subplots(ncols=len(X), nrows=2, figsize=(12, 6))
   for i, x in enumerate(X):
       ax[0, i].plot(x)
       ax[0, i].set_yscale("symlog")
   
       ax[1, i].plot(x)
       ax[1, i].set_yscale("symlog")
       ax[1, i].yaxis.set_major_formatter(formatter)
   
   
   ax[0, 1].set_title("Symmetrical log")
   
   fig, ax = plt.subplots(ncols=len(X), nrows=2, figsize=(12, 6))
   for i, x in enumerate(X):
       ax[0, i].plot(x)
       ax[0, i].set_yscale("log")
       ax[1, i].plot(x)
       ax[1, i].set_yscale("log")
       ax[1, i].yaxis.set_major_formatter(formatter)
   
   ax[0, 1].set_title("Log")
   l_f = ax[0, 1].yaxis.get_major_formatter()
   
   
10. NelleV commented on Nov 5, 2016

    

    NelleV

    on Nov 5, 2016

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    Note that by default, labelOnlyBase is True for LogFormatter but False for LogFormatterSciNotation.
11. tacaswell

    closed this as completedin 9b98582on Dec 2, 2016