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scale.py
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scale.py
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import math
import types
import numbers
import bisect
import numpy as np
def is_iterable(x):
try:
iter(x)
return True
except TypeError:
return False
class quantitative(object):
"""Implement abstract quantitative scale."""
def __init__(self, *args):
self._domain = [0,1]
self._range = [0,1]
self._transform = lambda x: x
self._inverse = lambda y: y
self.domain(*args)
def _in_domain(self,x):
return (x >= min(self._domain)) and (x <= max(self._domain))
def _in_range(self,y):
return (y >= min(self._range)) and (y <= max(self._range))
def __call__(self,x):
if not self._in_domain(x):
raise ValueError, "outside domain"
segment = bisect.bisect_right(self._domain,x) - 1
if segment + 1 == len(self._domain): segment -= 1 # deal with extra endpoint (fully closed interval), e.g., [0,1) [1,2) [2,3]
return (self._transform(x) - self._transform(self._domain[segment])) / (self._transform(self._domain[segment+1]) - self._transform(self._domain[segment])) * (self._range[segment+1] - self._range[segment]) + self._range[segment]
def domain(self,*args):
if len(args) == 0:
return self._domain
elif is_iterable(args[0]): # given array of data from which to determine domain
if len(args[0]) < 2: raise ValueError, "domain specification needs at least two numbers"
self._domain = [np.min(args[0]),np.max(args[0])]
else: # given explicit values for piecewise domain
if len(args) != len(set(args)):
raise ValueError, "domain values must be unique"
if list(args) != sorted(list(args)) and list(args)[::-1] != sorted(list(args)): # FIGURE THIS OUT
raise ValueError, "domain values must be sorted"
self._domain = args
self._domain = map(float,self._domain)
map(self._transform,self._domain) # test that transform is defined on domain
return self
def range(self,*args):
if len(args) == 0:
return self._range
elif is_iterable(args[0]): # given array of data from which to determine range
if len(args[0]) != len(self._domain): raise ValueError, "range specification needs at least two numbers"
self._range = [np.min(args[0]),np.max(args[0])]
else: # given explicit values for piecewise range
if len(args) != len(set(args)):
raise ValueError, "range values must be unique"
if list(args) != sorted(list(args)) and list(args)[::-1] != sorted(list(args)): # FIGURE THIS OUT
raise ValueError, "range values must be sorted"
self._range = args
if len(args) != len(self._domain):
raise ValueError, "range specification must have same number of points as domain"
return self
def invert(self,y):
if not self._in_range(x):
raise ValueError, "outside range"
segment = bisect.bisect_right(self._range,y) - 1
if segment == len(self._range): segment -= 1 # deal with extra endpoint (fully closed interval), e.g., [0,1) [1,2) [2,3]
return self._inverse((y - self._range[segment]) / (self._range[segment+1] - self._range[segment]) * (self._transform(self._domain[segment+1]) - self._transform(self._domain[segment])) + self._transform(self._domain[segment]))
linear = quantitative
class log(quantitative):
"""Implementation of log scale"""
def __init__(self, *args):
self._domain = [1,10]
quantitative.__init__(self,*args)
self.base(10)
def base(self,*args):
if len(args) == 0:
return self._base
else:
self._base = args[0]
self._logbase = math.log(self._base)
self._transform = lambda x: math.log(x) / self._logbase
self._inverse = lambda y: self._base ** y
return self
class root(quantitative):
"""root scale"""
def __init__(self, *args):
quantitative.__init__(self,*args)
self.power(2)
def power(self,*args):
if len(args) == 0:
return self._power
else:
self._power = args[0]
self._transform = lambda x: x**(1./self._power)
self._inverse = lambda y: y**self._power
return self
# class ordinal(object):
# """Implementation for ordinal scale"""
#
# def __init__(self, *args):
# Scale.__init__(self)
# self._domain = []
# self._indices = {}
# self._range = []
# self._band = 0
# self.domain(*args)
# return self
#
# def scale(self,x):
# if x not in self._indices:
# self._domain.append(x)
# self._indices[x] = len(self._domain) - 1
# return self._range[ self._indices[x] % len(self._range) ]
#
# def domain(self,*args):
# if len(args) == 0:
# return self._domain
#
# try:
# iter(args[0]) # test for array type
# array = args[0]
# if len(args) > 1:
# array = map(args[1],array)
# except TypeError:
# array = args
#
# self._domain = list(set(array))
# self._indices = pv.numerate(self._domain)
#
# return self
#
# def range(self,*args):
# if len(args) == 0:
# return self._range
#
# try:
# iter(args[0]) # test for array type
# array = args[0]
# if len(args) > 1:
# array = map(args[1],array)
# except TypeError:
# array = args
#
# if isinstance(array[0],types.StringType):
# array = map(pv.color,array)
#
# self._range = array
#
# return self
#
# def split(self,_min,_max):
# step = float(_max - _min) / length(self.domain())
# self._range = range(_min + step / 2., _max, step)
# return self
#
# def splitFlush(self,_min,_max):
# n = len(self.domain())
# step = float(_max - _min) / (n - 1)
# if n == 1:
# self._range = (_min + _max) / 2.
# else:
# self._range = range(_min, _max + step / 2., step)
# return self
#
# def splitBanded(self,_min,_max,band=1):
# if band < 0:
# n = len(self.domain())
# total = -band * n
# remaining = _max - _min - total
# padding = remaining / float(n + 1)
# self._range = range(_min + padding, _max, padding - band)
# self._band = -band
# else:
# step = float(_max - _min) / (len(self.domain()) + (1 - band))
# self._range = range(_min + step * (1 - band), _max, step)
# self._band = step * band
# return self
#
# def by(self,f):
# raise NotImplementedError
#
# class quantile(Scale):
# """quantile scale"""
#
# def __init__(self, *args):
# Scale.__init__(self)
# self._num_quantiles = -1
# self._max_quantile_index = -1
# self._quantile_boundaries = []
# self._domain = []
# self._y = linear() # the range
# self.domain(*args)
# return self
#
# def scale(self,x):
# return self._y(max(0, min(self._max_quantile_index, bisect.bisect_right(self._quantile_boundaries, x) - 1)) / float(self._max_quantile_index))
#
# def quantiles(self,*args):
# if len(args) == 0:
# return self._quantile_boundaries
#
# self._num_quantiles = int(args[0])
#
# if self._num_quantiles < 0:
# self._quantile_boundaries = [self._domain[0]] + self._domain
# self._max_quantile_index = len(self._domain) - 1
# else:
# self._quantile_boundaries = [self._domain[0]]
# for i in range(1,self._num_quantiles+1):
# self._quantile_boundaries.append( self._domain[ int(float(i) * (len(self._domain) - 1) / self._num_quantiles) ] )
# self._max_quantile_index = self._num_quantiles - 1
#
# return self
#
# def domain(self,*args):
# if len(args) == 0:
# return self._domain
#
# try:
# iter(args[0])
# array = args[0]
# if len(args) > 1:
# array = map(args[1],array)
# except TypeError:
# array = args
#
# self._domain = array
# self._domain.sort()
# self.quantiles(self._num_quantiles)
# return self
#
# def range(self,*args):
# if len(args) == 0:
# return self._y.range()
#
# self._y.range(*args)
# return self
#
# def by(self,f):
# raise NotImplementedError
#