Xtykutl

I am having trouble with some coding of using a specialized colorscale and the basemap

    levels=[35,45,55,65,75,85,95,105,115]

    CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a','#f781bf', '#a65628', 

    '#984ea3','#999999', '#e41a1c', '#dede00']

    fig=plt.figure(501)

  m=Basemap(projection='merc',llcrnrlat=27,urcrnrlat=47,llcrnrlon=234,urcrnrlon=285,resolution='h')



    px,py=N.meshgrid(lon+360,lat,sparse="True")

    X,Y=m(px,py)





   m.drawcoastlines()

   m.drawcountries()

   m.drawstates()

   m.fillcontinents(color='gray',alpha=0.1,lake_color='aqua')

   m.drawcounties()





    #plotting contours



 cs=m.contourf(lon+360,lat,tempsfc,levels=levels,cmap=CB_color_cycle,latlon=True,extend='both')

cs2=m.contour(lon+360,lat,tempsfc,levels=levels,latlon=True,colors='k',linestyles='solid')



   cbar=plt.colorbar(cs)

   cbar.add_lines(cs2)

   cbar.ax.set_ylabel('Temp F')



   plt.suptitle("SFC Temperature June 26,2017 at 19Z")

   plt.show()

But I got this error although I have experience with Python, I have never had to deal with this before as I am trying to use a special color scale.

C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmpl_toolkitsbasemap__init__.py:3608: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.

  b = ax.ishold()

C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmpl_toolkitsbasemap__init__.py:3675: MatplotlibDeprecationWarning: axes.hold is deprecated.

    See the API Changes document (http://matplotlib.org/api/api_changes.html)

    for more details.

  ax.hold(b)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

C:UsersstratusDocumentsTestforMattsPaper.py in <module>()

     36     #plotting contours

     37 

---> 38 cs=m.contourf(lon+360,lat,tempsfc,levels=levels,cmap=CB_color_cycle,latlon=True,extend='both')

     39 cs2=m.contour(lon+360,lat,tempsfc,levels=levels,latlon=True,colors='k',linestyles='solid')

     40 



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmpl_toolkitsbasemap__init__.pyc in with_transform(self, x, y, data, *args, **kwargs)

    519             # convert lat/lon coords to map projection coords.

    520             x, y = self(x,y)

--> 521         return plotfunc(self,x,y,data,*args,**kwargs)

    522     return with_transform

    523 



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmpl_toolkitsbasemap__init__.pyc in contourf(self, x, y, data, *args, **kwargs)

   3671                 mask = np.logical_or(ma.getmaskarray(data),xymask)

   3672                 data = ma.masked_array(data,mask=mask)

-> 3673                 CS = ax.contourf(x,y,data,*args,**kwargs)

   3674         except:

   3675             ax.hold(b)



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmatplotlib__init__.pyc in inner(ax, *args, **kwargs)

   1890                     warnings.warn(msg % (label_namer, func.__name__),

   1891                                   RuntimeWarning, stacklevel=2)

-> 1892             return func(ax, *args, **kwargs)

   1893         pre_doc = inner.__doc__

   1894         if pre_doc is None:



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmatplotlibaxes_axes.pyc in contourf(self, *args, **kwargs)

   5827             self.cla()

   5828         kwargs['filled'] = True

-> 5829         contours = mcontour.QuadContourSet(self, *args, **kwargs)

   5830         self.autoscale_view()

   5831         return contours



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmatplotlibcontour.pyc in __init__(self, ax, *args, **kwargs)

    862         self._transform = kwargs.get('transform', None)

    863 

--> 864         self._process_args(*args, **kwargs)

    865         self._process_levels()

    866 



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmatplotlibcontour.pyc in _process_args(self, *args, **kwargs)

   1427                 self._corner_mask = mpl.rcParams['contour.corner_mask']

   1428 

-> 1429             x, y, z = self._contour_args(args, kwargs)

   1430 

   1431             _mask = ma.getmask(z)



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmatplotlibcontour.pyc in _contour_args(self, args, kwargs)

   1518             warnings.warn('Log scale: values of z <= 0 have been masked')

   1519             self.zmin = float(z.min())

-> 1520         self._contour_level_args(z, args)

   1521         return (x, y, z)

   1522 



C:UsersstratusAppDataLocalEnthoughtCanopyUserlibsite-packagesmatplotlibcontour.pyc in _contour_level_args(self, z, args)

   1184                 warnings.warn("Contour levels are not increasing")

   1185             else:

-> 1186                 raise ValueError("Contour levels must be increasing")

   1187 

   1188     def _process_levels(self):



ValueError: Contour levels must be increasing

The indenting is not the problem but rather the CB_color_cycle since I am trying to help a friend with coding for colorblindness.

CB_color_cycle is just a list of colors, so use the colors keyword instead of cmap:

cs = m.contourf(lon+360, lat, tempsfc, levels=levels, colors=CB_color_cycle, latlon=True, extend='both')

Here's what it says about the colors keyword in the contourf docs:

colors : color string or sequence of colors, optional
The colors of the levels, i.e. the lines for contour and the areas for contourf.

The sequence is cycled for the levels in ascending order. If the sequence is shorter than the number of levels, it's repeated.

As a shortcut, single color strings may be used in place of one-element lists, i.e. 'red' instead of ['red'] to color all levels with the same color. This shortcut does only work for color strings, not for other ways of specifying colors.

By default (value None), the colormap specified by cmap will be used.

If you were actually trying to do something fancier involving a real ColorMap, see the documentation for ListedColorMap or LinearSegmentedColormap (and maybe also this SO answer) for details on converting a list of colors into a proper ColorMap.