00001 """
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
* NullLocator - No ticks
* FixedLocator - Tick locations are fixed
* IndexLocator - locator for index plots (eg where x = range(len(y))
* LinearLocator - evenly spaced ticks from min to max
* LogLocator - logarithmically ticks from min to max
* MultipleLocator - ticks and range are a multiple of base;
either integer or float
* OldAutoLocator - choose a MultipleLocator and dyamically reassign
it for intelligent ticking during navigation
* MaxNLocator - finds up to a max number of ticks at nice
locations
* AutoLocator - 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.
* NullFormatter - no labels on the ticks
* FixedFormatter - set the strings manually for the labels
* FuncFormatter - user defined function sets the labels
* FormatStrFormatter - use a sprintf format string
* ScalarFormatter - default formatter for scalars; autopick the fmt string
* 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 examples/major_minor_demo1.py for an example of setting major an
minor ticks. See the matplotlib.dates module for more information and
examples of using date locators and formatters.
DEVELOPERS NOTE
If you are implementing your own class or modifying one of these, it
is critical that you use viewlim and dataInterval READ ONLY MODE so
multiple axes can share the same locator w/o side effects!
"""
from __future__ import division
import sys, os, re, time, math, warnings
import numpy as npy
import matplotlib as mpl
from matplotlib import verbose, rcParams
from matplotlib import cbook
from matplotlib import transforms as mtrans
class TickHelper:
viewInterval = None
dataInterval = None
def verify_intervals(self):
if self.dataInterval is None:
raise RuntimeError("You must set the data interval to use this function")
if self.viewInterval is None:
raise RuntimeError("You must set the view interval to use this function")
def set_view_interval(self, interval):
self.viewInterval = interval
def set_data_interval(self, interval):
self.dataInterval = interval
def set_bounds(self, vmin, vmax):
'''
Set dataInterval and viewInterval from numeric vmin, vmax.
This is for stand-alone use of Formatters and/or
Locators that require these intervals; that is, for
cases where the Intervals do not need to be updated
automatically.
'''
self.dataInterval = mtrans.Interval(mtrans.Value(vmin), mtrans.Value(vmax))
self.viewInterval = mtrans.Interval(mtrans.Value(vmin), mtrans.Value(vmax))
00155 class Formatter(TickHelper):
"""
Convert the tick location to a string
"""
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
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
00207 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)
00219 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
00230 class OldScalarFormatter(Formatter):
"""
Tick location is a plain old number. If viewInterval is set, the
formatter will use %d, %1.#f or %1.ef as appropriate. If it is
not set, the formatter will do str conversion
"""
def __call__(self, x, pos=None):
'Return the format for tick val x at position pos'
self.verify_intervals()
d = abs(self.viewInterval.span())
return self.pprint_val(x,d)
def pprint_val(self, x, d):
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
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
00268 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):
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 __call__(self, x, pos=None):
'Return the format for tick val x at position pos'
if len(self.locs)==0:
return ''
else:
return self.pprint_val(x)
00295 def set_scientific(self, b):
'''True or False to turn scientific notation on or off
see also set_powerlimits()
'''
self._scientific = bool(b)
00301 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 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 '%1.3g'%value
def format_data(self,value):
'return a formatted string representation of a number'
return self._formatSciNotation('%1.10e'% value)
00321 def get_offset(self):
"""Return scientific notation, plus offset"""
if len(self.locs)==0: return ''
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 = r'{\times}'+self.format_data(10**self.orderOfMagnitude)
else:
sciNotStr = u'\xd7'+'1e%d'% self.orderOfMagnitude
if self._useMathText:
return ''.join(('$\mathdefault{',sciNotStr,offsetStr,'}$'))
elif self._usetex:
return ''.join(('$',sciNotStr,offsetStr,'$'))
else:
return ''.join((sciNotStr,offsetStr))
else: return ''
def set_locs(self, locs):
'set the locations of the ticks'
self.locs = locs
if len(self.locs) > 0:
self.verify_intervals()
d = abs(self.viewInterval.span())
if self._useOffset: self._set_offset(d)
self._set_orderOfMagnitude(d)
self._set_format()
def _set_offset(self, range):
locs = self.locs
if locs is None or not len(locs) or range == 0:
self.offset = 0
return
ave_loc = npy.mean(locs)
if ave_loc:
ave_oom = math.floor(math.log10(npy.mean(npy.absolute(locs))))
range_oom = math.floor(math.log10(range))
if npy.absolute(ave_oom-range_oom) >= 3:
if ave_loc < 0:
self.offset = math.ceil(npy.max(locs)/10**range_oom)*10**range_oom
else:
self.offset = math.floor(npy.min(locs)/10**(range_oom))*10**(range_oom)
else: self.offset = 0
def _set_orderOfMagnitude(self,range):
if not self._scientific:
self.orderOfMagnitude = 0
return
locs = npy.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):
locs = (npy.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 npy.absolute(xp) < 1e-8: xp = 0
return self.format % xp
def _formatSciNotation(self, s):
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':
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:
return ('%se%s%s' %(significand, sign, exponent)).rstrip('e')
except IndexError, msg:
return s
00434 class LogFormatter(Formatter):
"""
Format values for log axis;
if attribute decadeOnly is True, only the decades will be labelled.
"""
00440 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 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'
self.verify_intervals()
d = abs(self.viewInterval.span())
b=self._base
fx = math.log(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)
return s
def format_data(self,value):
self.labelOnlyBase = False
value = cbook.strip_math(self.__call__(value))
self.labelOnlyBase = True
return 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 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
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
00511 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'
self.verify_intervals()
d = abs(self.viewInterval.span())
b=self._base
fx = math.log(x)/math.log(b)
isDecade = self.is_decade(fx)
if not isDecade and self.labelOnlyBase: s = ''
elif fx>10000: s= '%1.0e'%fx
elif fx<1: s = '%1.0e'%fx
else : s = self.pprint_val(fx,d)
return s
00534 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'
self.verify_intervals()
b = self._base
fx = math.log(x)/math.log(b)
isDecade = self.is_decade(fx)
usetex = rcParams['text.usetex']
if not isDecade and self.labelOnlyBase: s = ''
elif not isDecade:
if usetex:
s = r'$%d^{%.2f}$'% (b, fx)
else:
s = '$\mathdefault{%d^{%.2f}}$'% (b, fx)
else:
if usetex:
s = r'$%d^{%d}$'% (b, self.nearest_long(fx))
else:
s = r'$\mathdefault{%d^{%d}}$'% (b, self.nearest_long(fx))
return s
00567 class Locator(TickHelper):
"""
Determine the tick locations;
Note, you should not use the same locator between different 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 autoscale(self):
'autoscale the view limits'
self.verify_intervals()
return mtrans.nonsingular(*self.dataInterval.get_bounds())
def pan(self, numsteps):
'Pan numticks (can be positive or negative)'
ticks = self()
numticks = len(ticks)
if numticks>2:
step = numsteps*abs(ticks[0]-ticks[1])
else:
step = numsteps*self.viewInterval.span()/6
self.viewInterval.shift(step)
def zoom(self, direction):
"Zoom in/out on axis; if direction is >0 zoom in, else zoom out"
vmin, vmax = self.viewInterval.get_bounds()
interval = self.viewInterval.span()
step = 0.1*interval*direction
self.viewInterval.set_bounds(vmin + step, vmax - step)
def refresh(self):
'refresh internal information based on current lim'
pass
00609 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.dataInterval.get_bounds()
return npy.arange(dmin + self.offset, dmax+1, self._base)
00627 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.
"""
def __init__(self, locs, nbins=None):
self.locs = 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)
return self.locs[::step]
00650 class NullLocator(Locator):
"""
No ticks
"""
def __call__(self):
'Return the locations of the ticks'
return []
00659 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
"""
00670 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'
self.verify_intervals()
vmin, vmax = self.viewInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
if self.presets.has_key((vmin, vmax)):
return self.presets[(vmin, vmax)]
if self.numticks is None:
self._set_numticks()
if self.numticks==0: return []
ticklocs = npy.linspace(vmin, vmax, self.numticks)
return ticklocs
def _set_numticks(self):
self.numticks = 11
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
if vmin==vmax:
vmin-=1
vmax+=1
exponent, remainder = divmod(math.log10(vmax - vmin), 1)
if remainder < 0.5:
exponent -= 1
scale = 10**(-exponent)
vmin = math.floor(scale*vmin)/scale
vmax = math.ceil(scale*vmax)/scale
return mtrans.nonsingular(vmin, vmax)
def closeto(x,y):
if abs(x-y)<1e-10: return True
else: return False
class Base:
'this solution has some hacks to deal with floating point inaccuracies'
def __init__(self, base):
assert(base>0)
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):
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):
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
00771 class MultipleLocator(Locator):
"""
Set a tick on every integer that is multiple of base in the
viewInterval
"""
def __init__(self, base=1.0):
self._base = Base(base)
def __call__(self):
'Return the locations of the ticks'
self.verify_intervals()
vmin, vmax = self.viewInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
vmin = self._base.ge(vmin)
base = self._base.get_base()
n = (vmax - vmin + 0.001*base)//base
locs = vmin + npy.arange(n+1) * base
return locs
00794 def autoscale(self):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
self.verify_intervals()
dmin, dmax = self.dataInterval.get_bounds()
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin==vmax:
vmin -=1
vmax +=1
return mtrans.nonsingular(vmin, vmax)
def scale_range(vmin, vmax, n = 1, threshold=100):
dv = abs(vmax - vmin)
maxabsv = max(abs(vmin), abs(vmax))
if maxabsv == 0 or dv/maxabsv < 1e-12:
return 1.0, 0.0
meanv = 0.5*(vmax+vmin)
if abs(meanv)/dv < threshold:
offset = 0
elif meanv > 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
00831 class MaxNLocator(Locator):
"""
Select no more than N intervals at nice locations.
"""
def __init__(self, nbins = 10, steps = None, trim = True, integer=False):
self._nbins = int(nbins)
self._trim = trim
self._integer = integer
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
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 (npy.arange(nbins+1) * step + best_vmin + offset)
def __call__(self):
self.verify_intervals()
vmin, vmax = self.viewInterval.get_bounds()
vmin, vmax = mtrans.nonsingular(vmin, vmax, expander = 0.05)
return self.bin_boundaries(vmin, vmax)
def autoscale(self):
self.verify_intervals()
dmin, dmax = self.dataInterval.get_bounds()
dmin, dmax = mtrans.nonsingular(dmin, dmax, expander = 0.05)
return npy.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)
00902 class LogLocator(Locator):
"""
Determine the tick locations for log axes
"""
00907 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
00915 def base(self,base):
"""
set the base of the log scaling (major tick every base**i, i interger)
"""
self._base=base+0.0
00921 def subs(self,subs):
"""
set the minor ticks the log scaling every base**i*subs[j]
"""
if subs is None:
self._subs = None
else:
self._subs = npy.asarray(subs)+0.0
def _set_numticks(self):
self.numticks = 15
def __call__(self):
'Return the locations of the ticks'
self.verify_intervals()
b=self._base
vmin, vmax = self.viewInterval.get_bounds()
vmin = math.log(vmin)/math.log(b)
vmax = math.log(vmax)/math.log(b)
if vmax<vmin:
vmin, vmax = vmax, vmin
ticklocs = []
numdec = math.floor(vmax)-math.ceil(vmin)
if self._subs is None:
if numdec>10: subs = npy.array([1.0])
elif numdec>6: subs = npy.arange(2.0, b, 2.0)
else: subs = npy.arange(2.0, b)
else:
subs = self._subs
stride = 1
while numdec/stride+1 > self.numticks:
stride += 1
for decadeStart in b**npy.arange(math.floor(vmin),
math.ceil(vmax)+stride, stride):
ticklocs.extend( subs*decadeStart )
return npy.array(ticklocs)
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
minpos = self.dataInterval.minpos()
if minpos<=0:
raise RuntimeError('No positive data to plot')
if vmin<=0:
vmin = minpos
if not is_decade(vmin,self._base): vmin = decade_down(vmin,self._base)
if not is_decade(vmax,self._base): vmax = decade_up(vmax,self._base)
if vmin==vmax:
vmin = decade_down(vmin,self._base)
vmax = decade_up(vmax,self._base)
return mtrans.nonsingular(vmin, vmax)
class AutoLocator(MaxNLocator):
def __init__(self):
MaxNLocator.__init__(self, nbins=9, steps=[1, 2, 5, 10])
00990 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'
d = self.viewInterval.span()
self._locator = self.get_locator(d)
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
d = abs(self.dataInterval.span())
self._locator = self.get_locator(d)
return self._locator.autoscale()
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 dataInterval range')
fld = math.floor(ld)
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)
locator.set_view_interval(self.viewInterval)
locator.set_data_interval(self.dataInterval)
return locator
__all__ = ('TickHelper', 'Formatter', 'FixedFormatter',
'NullFormatter', 'FuncFormatter', 'FormatStrFormatter',
'ScalarFormatter', 'LogFormatter', 'LogFormatterExponent',
'LogFormatterMathtext', 'Locator', 'IndexLocator',
'FixedLocator', 'NullLocator', 'LinearLocator',
'LogLocator', 'AutoLocator', 'MultipleLocator',
'MaxNLocator', )