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Hello,
I'm using mpl_toolkits.axisartist.floating_axes.GridHelperCurveLinear
for creating half-polar plots from 180 degree measurements for receive 
sensitivity.
Working with the measurement values itself is no problem if I let the 
values scaling start at zero.
If I use normalized values I can plot it also, but if I transform it 
into the dB scale I got a segfault in this lib.
I provide an example. For my problems I would like to have a solution 
that I can either use r limit from -30 to 0 (f3) or changing the tick 
labels in figure f2.
And by the way is there a possibility that the if i want to plot data in 
the range from 80 to 120, that rlim(80,120) would set the 80 to the 
centerpoint? At the moment I got only a small stripe.
[code]
"""Demo of polar plot of arbitrary theta. This is a workaround for MPL's 
polar plot limitation
to a full 360 deg.
Based on 
http://matplotlib.org/mpl_toolkits/axes_grid/examples/demo_floating_axes.py
get from 
https://github.com/neuropy/neuropy/blob/master/neuropy/scripts/polar_demo.py
TODO: license / copyright
"""
from __future__ import division
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
from matplotlib.projections import PolarAxes
from mpl_toolkits.axisartist import angle_helper
from mpl_toolkits.axisartist.grid_finder import MaxNLocator
from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear, 
FloatingSubplot
def fractional_polar_axes(f, thlim=(0, 180), rlim=(0, 1), step=(30, 0.2),
 thlabel='theta', rlabel='r', ticklabels=True, 
theta_offset=0):
 """Return polar axes that adhere to desired theta (in deg) and r 
limits. steps for theta
 and r are really just hints for the locators."""
 th0, th1 = thlim # deg
 r0, r1 = rlim
 thstep, rstep = step
 tr_rotate = Affine2D().translate(theta_offset, 0)
 # scale degrees to radians:
 tr_scale = Affine2D().scale(np.pi/180., 1.)
 #pa = axes(polar="true") # Create a polar axis
 pa = PolarAxes
 tr = tr_rotate + tr_scale + pa.PolarTransform()
 theta_grid_locator = angle_helper.LocatorDMS((th1-th0)//thstep)
 r_grid_locator = MaxNLocator((r1-r0)//rstep)
 theta_tick_formatter = angle_helper.FormatterDMS()
 grid_helper = GridHelperCurveLinear(tr,
 extremes=(th0, th1, r0, r1),
 grid_locator1=theta_grid_locator,
 grid_locator2=r_grid_locator,
 
tick_formatter1=theta_tick_formatter,
 tick_formatter2=None)
 a = FloatingSubplot(f, 111, grid_helper=grid_helper)
 f.add_subplot(a)
 # adjust x axis (theta):
 a.axis["bottom"].set_visible(False)
 a.axis["top"].set_axis_direction("bottom") # tick direction
 a.axis["top"].toggle(ticklabels=ticklabels, label=bool(thlabel))
 a.axis["top"].major_ticklabels.set_axis_direction("top")
 a.axis["top"].label.set_axis_direction("top")
 # adjust y axis (r):
 a.axis["left"].set_axis_direction("bottom") # tick direction
 a.axis["right"].set_axis_direction("top") # tick direction
 a.axis["left"].toggle(ticklabels=ticklabels, label=bool(rlabel))
 # add labels:
 a.axis["top"].label.set_text(thlabel)
 a.axis["left"].label.set_text(rlabel)
 # create a parasite axes whose transData is theta, r:
 auxa = a.get_aux_axes(tr)
 # make aux_ax to have a clip path as in a?:
 auxa.patch = a.patch
 # this has a side effect that the patch is drawn twice, and 
possibly over some other
 # artists. So, we decrease the zorder a bit to prevent this:
 a.patch.zorder = -2
 # add sector lines for both dimensions:
 thticks = grid_helper.grid_info['lon_info'][0]
 rticks = grid_helper.grid_info['lat_info'][0]
 for th in thticks[1:-1]: # all but the first and last
 auxa.plot([th, th], [r0, r1], '--', c='grey', zorder=-1)
 for ri, r in enumerate(rticks):
 # plot first r line as axes border in solid black only if it 
isn't at r=0
 if ri == 0 and r != 0:
 ls, lw, color = 'solid', 2, 'black'
 else:
 ls, lw, color = 'dashed', 1, 'grey'
 # From http://stackoverflow.com/a/19828753/2020363
 auxa.add_artist(plt.Circle([0, 0], radius=r, ls=ls, lw=lw, 
color=color, fill=False,
 transform=auxa.transData._b, zorder=-1))
 return auxa
if __name__ == '__main__':
 f1 = plt.figure(facecolor='white', figsize=(16/2.54, 12/2.54), dpi=600)
 a1 = fractional_polar_axes(f1, thlim=(-90, 90),step=(10, 
0.2),theta_offset=90)
 # example spiral plot:
 thstep = 10
 th = np.arange(-90, 90+thstep, thstep) # deg
 rstep = 1/(len(th)-1)
 r = np.arange(0, 1+rstep, rstep)
 a1.plot(th, r, 'b')
 f1.show()
 f2 = plt.figure(facecolor='white', figsize=(16/2.54, 12/2.54), dpi=600)
 a2 = fractional_polar_axes(f2, thlim=(-90, 
90),rlim=(0,30),step=(10, 8),theta_offset=90)
 # example spiral plot:
 r2 = 20 * np.log10(r) +30
 a2.plot(th, r2, 'b')
 f2.show()
 f3 = plt.figure(facecolor='white', figsize=(16/2.54, 12/2.54), dpi=600)
 a3 = fractional_polar_axes(f2, thlim=(-90, 90),rlim=(-30,0), 
step=(10, 8),theta_offset=90)
 # example spiral plot:
 r3 = 20 * np.log10(r)
 a3.plot(th, r2, 'b')
 f2.show()
[\code]
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