/usr/lib64/python2.6/site-packages/matplotlib
Edit: /usr/lib64/python2.6/site-packages/matplotlib/ticker.py (40428B)
"""
Tick locating and formatting
============================
This module contains classes to support completely configurable tick
locating and formatting. Although the locators know nothing about
major or minor ticks, they are used by the Axis class to support major
and minor tick locating and formatting. Generic tick locators and
formatters are provided, as well as domain specific custom ones..
Tick locating
-------------
The Locator class is the base class for all tick locators. The
locators handle autoscaling of the view limits based on the data
limits, and the choosing of tick locations. A useful semi-automatic
tick locator is MultipleLocator. You initialize this with a base, eg
10, and it picks axis limits and ticks that are multiples of your
base.
The Locator subclasses defined here are
:class:`NullLocator`
No ticks
:class:`FixedLocator`
Tick locations are fixed
:class:`IndexLocator`
locator for index plots (eg. where x = range(len(y)))
:class:`LinearLocator`
evenly spaced ticks from min to max
:class:`LogLocator`
logarithmically ticks from min to max
:class:`MultipleLocator`
ticks and range are a multiple of base;
either integer or float
:class:`OldAutoLocator`
choose a MultipleLocator and dyamically reassign it for
intelligent ticking during navigation
:class:`MaxNLocator`
finds up to a max number of ticks at nice locations
:class:`AutoLocator`
:class:`MaxNLocator` with simple defaults. This is the default
tick locator for most plotting.
There are a number of locators specialized for date locations - see
the dates module
You can define your own locator by deriving from Locator. You must
override the __call__ method, which returns a sequence of locations,
and you will probably want to override the autoscale method to set the
view limits from the data limits.
If you want to override the default locator, use one of the above or a
custom locator and pass it to the x or y axis instance. The relevant
methods are::
ax.xaxis.set_major_locator( xmajorLocator )
ax.xaxis.set_minor_locator( xminorLocator )
ax.yaxis.set_major_locator( ymajorLocator )
ax.yaxis.set_minor_locator( yminorLocator )
The default minor locator is the NullLocator, eg no minor ticks on by
default.
Tick formatting
---------------
Tick formatting is controlled by classes derived from Formatter. The
formatter operates on a single tick value and returns a string to the
axis.
:class:`NullFormatter`
no labels on the ticks
:class:`IndexFormatter`
set the strings from a list of labels
:class:`FixedFormatter`
set the strings manually for the labels
:class:`FuncFormatter`
user defined function sets the labels
:class:`FormatStrFormatter`
use a sprintf format string
:class:`ScalarFormatter`
default formatter for scalars; autopick the fmt string
:class:`LogFormatter`
formatter for log axes
You can derive your own formatter from the Formatter base class by
simply overriding the ``__call__`` method. The formatter class has access
to the axis view and data limits.
To control the major and minor tick label formats, use one of the
following methods::
ax.xaxis.set_major_formatter( xmajorFormatter )
ax.xaxis.set_minor_formatter( xminorFormatter )
ax.yaxis.set_major_formatter( ymajorFormatter )
ax.yaxis.set_minor_formatter( yminorFormatter )
See :ref:`pylab_examples-major_minor_demo1` for an example of setting
major an minor ticks. See the :mod:`matplotlib.dates` module for
more information and examples of using date locators and formatters.
"""
from __future__ import division
import math
import numpy as np
from matplotlib import rcParams
from matplotlib import cbook
from matplotlib import transforms as mtransforms
class TickHelper:
axis = None
class DummyAxis:
def __init__(self):
self.dataLim = mtransforms.Bbox.unit()
self.viewLim = mtransforms.Bbox.unit()
def get_view_interval(self):
return self.viewLim.intervalx
def set_view_interval(self, vmin, vmax):
self.viewLim.intervalx = vmin, vmax
def get_data_interval(self):
return self.dataLim.intervalx
def set_data_interval(self, vmin, vmax):
self.dataLim.intervalx = vmin, vmax
def set_axis(self, axis):
self.axis = axis
def create_dummy_axis(self):
if self.axis is None:
self.axis = self.DummyAxis()
def set_view_interval(self, vmin, vmax):
self.axis.set_view_interval(vmin, vmax)
def set_data_interval(self, vmin, vmax):
self.axis.set_data_interval(vmin, vmax)
def set_bounds(self, vmin, vmax):
self.set_view_interval(vmin, vmax)
self.set_data_interval(vmin, vmax)
class Formatter(TickHelper):
"""
Convert the tick location to a string
"""
# some classes want to see all the locs to help format
# individual ones
locs = []
def __call__(self, x, pos=None):
'Return the format for tick val x at position pos; pos=None indicated unspecified'
raise NotImplementedError('Derived must overide')
def format_data(self,value):
return self.__call__(value)
def format_data_short(self,value):
'return a short string version'
return self.format_data(value)
def get_offset(self):
return ''
def set_locs(self, locs):
self.locs = locs
def fix_minus(self, s):
"""
some classes may want to replace a hyphen for minus with the
proper unicode symbol as described `here
`_.
The default is to do nothing
Note, if you use this method, eg in :meth`format_data` or
call, you probably don't want to use it for
:meth:`format_data_short` since the toolbar uses this for
interative coord reporting and I doubt we can expect GUIs
across platforms will handle the unicode correctly. So for
now the classes that override :meth:`fix_minus` should have an
explicit :meth:`format_data_short` method
"""
return s
class IndexFormatter:
"""
format the position x to the nearest i-th label where i=int(x+0.5)
"""
def __init__(self, labels):
self.labels = labels
self.n = len(labels)
def __call__(self, x, pos=None):
'Return the format for tick val x at position pos; pos=None indicated unspecified'
i = int(x+0.5)
if i<0:
return ''
elif i>=self.n:
return ''
else:
return self.labels[i]
class NullFormatter(Formatter):
'Always return the empty string'
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
return ''
class FixedFormatter(Formatter):
'Return fixed strings for tick labels'
def __init__(self, seq):
"""
*seq* is a sequence of strings. For positions ``i < len(seq)`` return
*seq[i]* regardless of *x*. Otherwise return ''
"""
self.seq = seq
self.offset_string = ''
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
if pos is None or pos>=len(self.seq): return ''
else: return self.seq[pos]
def get_offset(self):
return self.offset_string
def set_offset_string(self, ofs):
self.offset_string = ofs
class FuncFormatter(Formatter):
"""
User defined function for formatting
"""
def __init__(self, func):
self.func = func
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
return self.func(x, pos)
class FormatStrFormatter(Formatter):
"""
Use a format string to format the tick
"""
def __init__(self, fmt):
self.fmt = fmt
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
return self.fmt % x
class OldScalarFormatter(Formatter):
"""
Tick location is a plain old number.
"""
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
xmin, xmax = self.axis.get_view_interval()
d = abs(xmax - xmin)
return self.pprint_val(x,d)
def pprint_val(self, x, d):
#if the number is not too big and it's an int, format it as an
#int
if abs(x)<1e4 and x==int(x): return '%d' % x
if d < 1e-2: fmt = '%1.3e'
elif d < 1e-1: fmt = '%1.3f'
elif d > 1e5: fmt = '%1.1e'
elif d > 10 : fmt = '%1.1f'
elif d > 1 : fmt = '%1.2f'
else: fmt = '%1.3f'
s = fmt % x
#print d, x, fmt, s
tup = s.split('e')
if len(tup)==2:
mantissa = tup[0].rstrip('0').rstrip('.')
sign = tup[1][0].replace('+', '')
exponent = tup[1][1:].lstrip('0')
s = '%se%s%s' %(mantissa, sign, exponent)
else:
s = s.rstrip('0').rstrip('.')
return s
class ScalarFormatter(Formatter):
"""
Tick location is a plain old number. If useOffset==True and the data range
is much smaller than the data average, then an offset will be determined
such that the tick labels are meaningful. Scientific notation is used for
data < 1e-3 or data >= 1e4.
"""
def __init__(self, useOffset=True, useMathText=False):
# useOffset allows plotting small data ranges with large offsets:
# for example: [1+1e-9,1+2e-9,1+3e-9]
# useMathText will render the offset and scientific notation in mathtext
self._useOffset = useOffset
self._usetex = rcParams['text.usetex']
self._useMathText = useMathText
self.offset = 0
self.orderOfMagnitude = 0
self.format = ''
self._scientific = True
self._powerlimits = rcParams['axes.formatter.limits']
def fix_minus(self, s):
'use a unicode minus rather than hyphen'
if rcParams['text.usetex'] or not rcParams['axes.unicode_minus']: return s
else: return s.replace('-', u'\u2212')
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
if len(self.locs)==0:
return ''
else:
s = self.pprint_val(x)
return self.fix_minus(s)
def set_scientific(self, b):
'''True or False to turn scientific notation on or off
see also :meth:`set_powerlimits`
'''
self._scientific = bool(b)
def set_powerlimits(self, lims):
'''
Sets size thresholds for scientific notation.
e.g. ``xaxis.set_powerlimits((-3, 4))`` sets the pre-2007 default in
which scientific notation is used for numbers less than
1e-3 or greater than 1e4.
See also :meth:`set_scientific`.
'''
assert len(lims) == 2, "argument must be a sequence of length 2"
self._powerlimits = lims
def format_data_short(self,value):
'return a short formatted string representation of a number'
return '%-12g'%value
def format_data(self,value):
'return a formatted string representation of a number'
s = self._formatSciNotation('%1.10e'% value)
return self.fix_minus(s)
def get_offset(self):
"""Return scientific notation, plus offset"""
if len(self.locs)==0: return ''
s = ''
if self.orderOfMagnitude or self.offset:
offsetStr = ''
sciNotStr = ''
if self.offset:
offsetStr = self.format_data(self.offset)
if self.offset > 0: offsetStr = '+' + offsetStr
if self.orderOfMagnitude:
if self._usetex or self._useMathText:
sciNotStr = self.format_data(10**self.orderOfMagnitude)
else:
sciNotStr = '1e%d'% self.orderOfMagnitude
if self._useMathText:
if sciNotStr != '':
sciNotStr = r'\times\mathdefault{%s}' % sciNotStr
s = ''.join(('$',sciNotStr,r'\mathdefault{',offsetStr,'}$'))
elif self._usetex:
if sciNotStr != '':
sciNotStr = r'\times%s' % sciNotStr
s = ''.join(('$',sciNotStr,offsetStr,'$'))
else:
s = ''.join((sciNotStr,offsetStr))
return self.fix_minus(s)
def set_locs(self, locs):
'set the locations of the ticks'
self.locs = locs
if len(self.locs) > 0:
vmin, vmax = self.axis.get_view_interval()
d = abs(vmax-vmin)
if self._useOffset: self._set_offset(d)
self._set_orderOfMagnitude(d)
self._set_format()
def _set_offset(self, range):
# offset of 20,001 is 20,000, for example
locs = self.locs
if locs is None or not len(locs) or range == 0:
self.offset = 0
return
ave_loc = np.mean(locs)
if ave_loc: # dont want to take log10(0)
ave_oom = math.floor(math.log10(np.mean(np.absolute(locs))))
range_oom = math.floor(math.log10(range))
if np.absolute(ave_oom-range_oom) >= 3: # four sig-figs
if ave_loc < 0:
self.offset = math.ceil(np.max(locs)/10**range_oom)*10**range_oom
else:
self.offset = math.floor(np.min(locs)/10**(range_oom))*10**(range_oom)
else: self.offset = 0
def _set_orderOfMagnitude(self,range):
# if scientific notation is to be used, find the appropriate exponent
# if using an numerical offset, find the exponent after applying the offset
if not self._scientific:
self.orderOfMagnitude = 0
return
locs = np.absolute(self.locs)
if self.offset: oom = math.floor(math.log10(range))
else:
if locs[0] > locs[-1]: val = locs[0]
else: val = locs[-1]
if val == 0: oom = 0
else: oom = math.floor(math.log10(val))
if oom <= self._powerlimits[0]:
self.orderOfMagnitude = oom
elif oom >= self._powerlimits[1]:
self.orderOfMagnitude = oom
else:
self.orderOfMagnitude = 0
def _set_format(self):
# set the format string to format all the ticklabels
# The floating point black magic (adding 1e-15 and formatting
# to 8 digits) may warrant review and cleanup.
locs = (np.asarray(self.locs)-self.offset) / 10**self.orderOfMagnitude+1e-15
sigfigs = [len(str('%1.8f'% loc).split('.')[1].rstrip('0')) \
for loc in locs]
sigfigs.sort()
self.format = '%1.' + str(sigfigs[-1]) + 'f'
if self._usetex:
self.format = '$%s$' % self.format
elif self._useMathText:
self.format = '$\mathdefault{%s}$' % self.format
def pprint_val(self, x):
xp = (x-self.offset)/10**self.orderOfMagnitude
if np.absolute(xp) < 1e-8: xp = 0
return self.format % xp
def _formatSciNotation(self, s):
# transform 1e+004 into 1e4, for example
tup = s.split('e')
try:
significand = tup[0].rstrip('0').rstrip('.')
sign = tup[1][0].replace('+', '')
exponent = tup[1][1:].lstrip('0')
if self._useMathText or self._usetex:
if significand == '1':
# reformat 1x10^y as 10^y
significand = ''
if exponent:
exponent = '10^{%s%s}'%(sign, exponent)
if significand and exponent:
return r'%s{\times}%s'%(significand, exponent)
else:
return r'%s%s'%(significand, exponent)
else:
s = ('%se%s%s' %(significand, sign, exponent)).rstrip('e')
return s
except IndexError, msg:
return s
class LogFormatter(Formatter):
"""
Format values for log axis;
if attribute *decadeOnly* is True, only the decades will be labelled.
"""
def __init__(self, base=10.0, labelOnlyBase = True):
"""
*base* is used to locate the decade tick,
which will be the only one to be labeled if *labelOnlyBase*
is ``False``
"""
self._base = base+0.0
self.labelOnlyBase=labelOnlyBase
self.decadeOnly = True
def base(self,base):
'change the *base* for labeling - warning: should always match the base used for :class:`LogLocator`'
self._base=base
def label_minor(self,labelOnlyBase):
'switch on/off minor ticks labeling'
self.labelOnlyBase=labelOnlyBase
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
vmin, vmax = self.axis.get_view_interval()
d = abs(vmax - vmin)
b=self._base
if x == 0.0:
return '0'
sign = np.sign(x)
# only label the decades
fx = math.log(abs(x))/math.log(b)
isDecade = self.is_decade(fx)
if not isDecade and self.labelOnlyBase: s = ''
elif x>10000: s= '%1.0e'%x
elif x<1: s = '%1.0e'%x
else : s = self.pprint_val(x,d)
if sign == -1:
s = '-%s' % s
return self.fix_minus(s)
def format_data(self,value):
self.labelOnlyBase = False
value = cbook.strip_math(self.__call__(value))
self.labelOnlyBase = True
return value
def format_data_short(self,value):
'return a short formatted string representation of a number'
return '%-12g'%value
def is_decade(self, x):
n = self.nearest_long(x)
return abs(x-n)<1e-10
def nearest_long(self, x):
if x==0: return 0L
elif x>0: return long(x+0.5)
else: return long(x-0.5)
def pprint_val(self, x, d):
#if the number is not too big and it's an int, format it as an
#int
if abs(x)<1e4 and x==int(x): return '%d' % x
if d < 1e-2: fmt = '%1.3e'
elif d < 1e-1: fmt = '%1.3f'
elif d > 1e5: fmt = '%1.1e'
elif d > 10 : fmt = '%1.1f'
elif d > 1 : fmt = '%1.2f'
else: fmt = '%1.3f'
s = fmt % x
#print d, x, fmt, s
tup = s.split('e')
if len(tup)==2:
mantissa = tup[0].rstrip('0').rstrip('.')
sign = tup[1][0].replace('+', '')
exponent = tup[1][1:].lstrip('0')
s = '%se%s%s' %(mantissa, sign, exponent)
else:
s = s.rstrip('0').rstrip('.')
return s
class LogFormatterExponent(LogFormatter):
"""
Format values for log axis; using ``exponent = log_base(value)``
"""
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
d = abs(vmax-vmin)
b=self._base
if x == 0:
return '0'
sign = np.sign(x)
# only label the decades
fx = math.log(abs(x))/math.log(b)
isDecade = self.is_decade(fx)
if not isDecade and self.labelOnlyBase: s = ''
#if 0: pass
elif fx>10000: s= '%1.0e'%fx
#elif x<1: s = '$10^{%d}$'%fx
#elif x<1: s = '10^%d'%fx
elif fx<1: s = '%1.0e'%fx
else : s = self.pprint_val(fx,d)
if sign == -1:
s = '-%s' % s
return self.fix_minus(s)
class LogFormatterMathtext(LogFormatter):
"""
Format values for log axis; using ``exponent = log_base(value)``
"""
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
b = self._base
# only label the decades
if x == 0:
return '$0$'
sign = np.sign(x)
fx = math.log(abs(x))/math.log(b)
isDecade = self.is_decade(fx)
usetex = rcParams['text.usetex']
if sign == -1:
sign_string = '-'
else:
sign_string = ''
if not isDecade and self.labelOnlyBase: s = ''
elif not isDecade:
if usetex:
s = r'$%s%d^{%.2f}$'% (sign_string, b, fx)
else:
s = '$\mathdefault{%s%d^{%.2f}}$'% (sign_string, b, fx)
else:
if usetex:
s = r'$%s%d^{%d}$'% (sign_string, b, self.nearest_long(fx))
else:
s = r'$\mathdefault{%s%d^{%d}}$'% (sign_string, b, self.nearest_long(fx))
return s
class Locator(TickHelper):
"""
Determine the tick locations;
Note, you should not use the same locator between different :class:`~matplotlib.axis.Axis`
because the locator stores references to the Axis data and view
limits
"""
def __call__(self):
'Return the locations of the ticks'
raise NotImplementedError('Derived must override')
def view_limits(self, vmin, vmax):
"""
select a scale for the range from vmin to vmax
Normally This will be overridden.
"""
return mtransforms.nonsingular(vmin, vmax)
def autoscale(self):
'autoscale the view limits'
return self.view_limits(*self.axis.get_view_interval())
def pan(self, numsteps):
'Pan numticks (can be positive or negative)'
ticks = self()
numticks = len(ticks)
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
if numticks>2:
step = numsteps*abs(ticks[0]-ticks[1])
else:
d = abs(vmax-vmin)
step = numsteps*d/6.
vmin += step
vmax += step
self.axis.set_view_interval(vmin, vmax, ignore=True)
def zoom(self, direction):
"Zoom in/out on axis; if direction is >0 zoom in, else zoom out"
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
interval = abs(vmax-vmin)
step = 0.1*interval*direction
self.axis.set_view_interval(vmin + step, vmax - step, ignore=True)
def refresh(self):
'refresh internal information based on current lim'
pass
class IndexLocator(Locator):
"""
Place a tick on every multiple of some base number of points
plotted, eg on every 5th point. It is assumed that you are doing
index plotting; ie the axis is 0, len(data). This is mainly
useful for x ticks.
"""
def __init__(self, base, offset):
'place ticks on the i-th data points where (i-offset)%base==0'
self._base = base
self.offset = offset
def __call__(self):
'Return the locations of the ticks'
dmin, dmax = self.axis.get_data_interval()
return np.arange(dmin + self.offset, dmax+1, self._base)
class FixedLocator(Locator):
"""
Tick locations are fixed. If nbins is not None,
the array of possible positions will be subsampled to
keep the number of ticks <= nbins +1.
The subsampling will be done so as to include the smallest
absolute value; for example, if zero is included in the
array of possibilities, then it is guaranteed to be one of
the chosen ticks.
"""
def __init__(self, locs, nbins=None):
self.locs = np.asarray(locs)
self.nbins = nbins
if self.nbins is not None:
self.nbins = max(self.nbins, 2)
def __call__(self):
'Return the locations of the ticks'
if self.nbins is None:
return self.locs
step = max(int(0.99 + len(self.locs) / float(self.nbins)), 1)
ticks = self.locs[::step]
for i in range(1,step):
ticks1 = self.locs[i::step]
if np.absolute(ticks1).min() < np.absolute(ticks).min():
ticks = ticks1
return ticks
class NullLocator(Locator):
"""
No ticks
"""
def __call__(self):
'Return the locations of the ticks'
return []
class LinearLocator(Locator):
"""
Determine the tick locations
The first time this function is called it will try to set the
number of ticks to make a nice tick partitioning. Thereafter the
number of ticks will be fixed so that interactive navigation will
be nice
"""
def __init__(self, numticks = None, presets=None):
"""
Use presets to set locs based on lom. A dict mapping vmin, vmax->locs
"""
self.numticks = numticks
if presets is None:
self.presets = {}
else:
self.presets = presets
def __call__(self):
'Return the locations of the ticks'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
if vmax0)
self._base = base
def lt(self, x):
'return the largest multiple of base < x'
d,m = divmod(x, self._base)
if closeto(m,0) and not closeto(m/self._base,1):
return (d-1)*self._base
return d*self._base
def le(self, x):
'return the largest multiple of base <= x'
d,m = divmod(x, self._base)
if closeto(m/self._base,1): # was closeto(m, self._base)
#looks like floating point error
return (d+1)*self._base
return d*self._base
def gt(self, x):
'return the smallest multiple of base > x'
d,m = divmod(x, self._base)
if closeto(m/self._base,1):
#looks like floating point error
return (d+2)*self._base
return (d+1)*self._base
def ge(self, x):
'return the smallest multiple of base >= x'
d,m = divmod(x, self._base)
if closeto(m,0) and not closeto(m/self._base,1):
return d*self._base
return (d+1)*self._base
def get_base(self):
return self._base
class MultipleLocator(Locator):
"""
Set a tick on every integer that is multiple of base in the
view interval
"""
def __init__(self, base=1.0):
self._base = Base(base)
def __call__(self):
'Return the locations of the ticks'
vmin, vmax = self.axis.get_view_interval()
if vmax 0:
ex = divmod(math.log10(meanv), 1)[0]
offset = 10**ex
else:
ex = divmod(math.log10(-meanv), 1)[0]
offset = -10**ex
ex = divmod(math.log10(dv/n), 1)[0]
scale = 10**ex
return scale, offset
class MaxNLocator(Locator):
"""
Select no more than N intervals at nice locations.
"""
def __init__(self, nbins = 10, steps = None,
trim = True,
integer=False,
symmetric=False,
prune=None):
"""
Keyword args:
*prune*
Remove edge ticks -- useful for stacked or ganged plots
where the upper tick of one axes overlaps with the lower
tick of the axes above it. One of 'lower' | 'upper' |
'both' | None. If prune=='lower', the smallest tick will
be removed. If prune=='upper', the largest tick will be
removed. If prune=='both', the largest and smallest ticks
will be removed. If prune==None, no ticks will be removed.
"""
self._nbins = int(nbins)
self._trim = trim
self._integer = integer
self._symmetric = symmetric
self._prune = prune
if steps is None:
self._steps = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10]
else:
if int(steps[-1]) != 10:
steps = list(steps)
steps.append(10)
self._steps = steps
if integer:
self._steps = [n for n in self._steps if divmod(n,1)[1] < 0.001]
def bin_boundaries(self, vmin, vmax):
nbins = self._nbins
scale, offset = scale_range(vmin, vmax, nbins)
if self._integer:
scale = max(1, scale)
vmin -= offset
vmax -= offset
raw_step = (vmax-vmin)/nbins
scaled_raw_step = raw_step/scale
best_vmax = vmax
best_vmin = vmin
for step in self._steps:
if step < scaled_raw_step:
continue
step *= scale
best_vmin = step*divmod(vmin, step)[0]
best_vmax = best_vmin + step*nbins
if (best_vmax >= vmax):
break
if self._trim:
extra_bins = int(divmod((best_vmax - vmax), step)[0])
nbins -= extra_bins
return (np.arange(nbins+1) * step + best_vmin + offset)
def __call__(self):
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
locs = self.bin_boundaries(vmin, vmax)
#print 'locs=', locs
prune = self._prune
if prune=='lower':
locs = locs[1:]
elif prune=='upper':
locs = locs[:-1]
elif prune=='both':
locs = locs[1:-1]
return locs
def view_limits(self, dmin, dmax):
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander = 0.05)
return np.take(self.bin_boundaries(dmin, dmax), [0,-1])
def decade_down(x, base=10):
'floor x to the nearest lower decade'
lx = math.floor(math.log(x)/math.log(base))
return base**lx
def decade_up(x, base=10):
'ceil x to the nearest higher decade'
lx = math.ceil(math.log(x)/math.log(base))
return base**lx
def is_decade(x,base=10):
lx = math.log(x)/math.log(base)
return lx==int(lx)
class LogLocator(Locator):
"""
Determine the tick locations for log axes
"""
def __init__(self, base=10.0, subs=[1.0]):
"""
place ticks on the location= base**i*subs[j]
"""
self.base(base)
self.subs(subs)
self.numticks = 15
def base(self,base):
"""
set the base of the log scaling (major tick every base**i, i interger)
"""
self._base=base+0.0
def subs(self,subs):
"""
set the minor ticks the log scaling every base**i*subs[j]
"""
if subs is None:
self._subs = None # autosub
else:
self._subs = np.asarray(subs)+0.0
def _set_numticks(self):
self.numticks = 15 # todo; be smart here; this is just for dev
def __call__(self):
'Return the locations of the ticks'
b=self._base
vmin, vmax = self.axis.get_view_interval()
if vmin <= 0.0:
vmin = self.axis.get_minpos()
if vmin <= 0.0:
raise ValueError(
"Data has no positive values, and therefore can not be log-scaled.")
vmin = math.log(vmin)/math.log(b)
vmax = math.log(vmax)/math.log(b)
if vmax10: subs = np.array([1.0])
elif numdec>6: subs = np.arange(2.0, b, 2.0)
else: subs = np.arange(2.0, b)
else:
subs = self._subs
stride = 1
while numdec/stride+1 > self.numticks:
stride += 1
decades = np.arange(math.floor(vmin),
math.ceil(vmax)+stride, stride)
if len(subs) > 1 or (len(subs == 1) and subs[0] != 1.0):
ticklocs = []
for decadeStart in b**decades:
ticklocs.extend( subs*decadeStart )
else:
ticklocs = b**decades
return np.array(ticklocs)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
if vmax10: subs = np.array([1.0])
elif numdec>6: subs = np.arange(2.0, b, 2.0)
else: subs = np.arange(2.0, b)
else:
subs = np.asarray(self._subs)
stride = 1
while numdec/stride+1 > self.numticks:
stride += 1
decades = np.arange(math.floor(vmin), math.ceil(vmax)+stride, stride)
if len(subs) > 1 or subs[0] != 1.0:
ticklocs = []
for decade in decades:
ticklocs.extend(subs * (np.sign(decade) * b ** np.abs(decade)))
else:
ticklocs = np.sign(decades) * b ** np.abs(decades)
return np.array(ticklocs)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._transform.base
if vmax vmax:
vmin,vmax = vmax,vmin
return locs[(vmin < locs) & (locs < vmax)]
class OldAutoLocator(Locator):
"""
On autoscale this class picks the best MultipleLocator to set the
view limits and the tick locs.
"""
def __init__(self):
self._locator = LinearLocator()
def __call__(self):
'Return the locations of the ticks'
self.refresh()
return self._locator()
def refresh(self):
'refresh internal information based on current lim'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
d = abs(vmax-vmin)
self._locator = self.get_locator(d)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
d = abs(vmax-vmin)
self._locator = self.get_locator(d)
return self._locator.view_limits(vmin, vmax)
def get_locator(self, d):
'pick the best locator based on a distance'
d = abs(d)
if d<=0:
locator = MultipleLocator(0.2)
else:
try: ld = math.log10(d)
except OverflowError:
raise RuntimeError('AutoLocator illegal data interval range')
fld = math.floor(ld)
base = 10**fld
#if ld==fld: base = 10**(fld-1)
#else: base = 10**fld
if d >= 5*base : ticksize = base
elif d >= 2*base : ticksize = base/2.0
else : ticksize = base/5.0
locator = MultipleLocator(ticksize)
return locator
__all__ = ('TickHelper', 'Formatter', 'FixedFormatter',
'NullFormatter', 'FuncFormatter', 'FormatStrFormatter',
'ScalarFormatter', 'LogFormatter', 'LogFormatterExponent',
'LogFormatterMathtext', 'Locator', 'IndexLocator',
'FixedLocator', 'NullLocator', 'LinearLocator',
'LogLocator', 'AutoLocator', 'MultipleLocator',
'MaxNLocator', )