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# -*- coding: utf-8 -*-
from __future__ import print_function, division
import math, sys
import itertools, operator
from mutatorMath import __version__
_EPSILON = sys.float_info.epsilon
__all__ = ["Location", "sortLocations"]
def numberToString(value):
# return a nicely formatted string of this value
# return tuples as a tuple-looking string with formatted numbers
# return ints as ints, no commas
# return floats as compact rounded value
if value is None:
return "None"
if type(value)==tuple:
t = []
for v in value:
t.append(numberToString(v))
return "(%s)"%(",".join(t))
if int(value) == value:
# it is an int
return "%d"%(value)
return "%3.3f"%value
class Location(dict):
"""
A object subclassed from dict to store n-dimensional locations.
- key is dimension or axis name
- value is the coordinate.
- Location objects behave like numbers.
- If a specific dimension is missing, assume it is zero.
- Convert to and from dict, tuple.
::
>>> l = Location(pop=1, snap=-100)
>>> print(l)
<Location pop:1, snap:-100 >
Location objects can be used as math objects:
::
>>> l = Location(pop=1)
>>> l * 2
<Location pop:2 >
>>> 2 * l
<Location pop:2 >
>>> l / 2
<Location pop:0.500 >
>>> l = Location(pop=1)
>>> m = Location(pop=10)
>>> l + m
<Location pop:11 >
>>> l = Location(pop=1)
>>> m = Location(pop=10)
>>> l - m
<Location pop:-9 >
"""
def __repr__(self):
t = ["<%s"%self.__class__.__name__]
t.append(self.asString())
t.append(">")
return " ".join(t)
def __lt__(self, other):
if len(self) < len(other):
return True
elif len(self) > len(other):
return False
self_keys = sorted(self.keys())
other_keys = sorted(other.keys())
for i, key in enumerate(self_keys):
if key < other_keys[i]:
return True
elif key > other_keys[i]:
return False
if self[key] < other[key]:
return True
return False
def expand(self, axisNames):
"""
Expand the location with zero values for all axes in axisNames that aren't filled in the current location.
::
>>> l = Location(pop=1)
>>> l.expand(['snap', 'crackle'])
>>> print(l)
<Location crackle:0, pop:1, snap:0 >
"""
for k in axisNames:
if k not in self:
self[k] = 0
def copy(self):
"""
Return a copy of this location.
::
>>> l = Location(pop=1, snap=0)
>>> l.copy()
<Location pop:1, snap:0 >
"""
new = self.__class__()
new.update(self)
return new
def fromTuple(self, locationTuple):
"""
Read the coordinates from a tuple.
::
>>> t = (('pop', 1), ('snap', -100))
>>> l = Location()
>>> l.fromTuple(t)
>>> print(l)
<Location pop:1, snap:-100 >
"""
for key, value in locationTuple:
try:
self[key] = float(value)
except TypeError:
self[key] = tuple([float(v) for v in value])
def asTuple(self):
"""Return the location as a tuple.
Sort the dimension names alphabetically.
::
>>> l = Location(pop=1, snap=-100)
>>> l.asTuple()
(('pop', 1), ('snap', -100))
"""
t = []
k = sorted(self.keys())
for key in k:
t.append((key, self[key]))
return tuple(t)
def getType(self, short=False):
"""Return a string describing the type of the location, i.e. origin, on axis, off axis etc.
::
>>> l = Location()
>>> l.getType()
'origin'
>>> l = Location(pop=1)
>>> l.getType()
'on-axis, pop'
>>> l = Location(pop=1, snap=1)
>>> l.getType()
'off-axis, pop snap'
>>> l = Location(pop=(1,2))
>>> l.getType()
'on-axis, pop, split'
"""
if self.isOrigin():
return "origin"
t = []
onAxis = self.isOnAxis()
if onAxis is False:
if short:
t.append("off-axis")
else:
t.append("off-axis, "+ " ".join(self.getActiveAxes()))
else:
if short:
t.append("on-axis")
else:
t.append("on-axis, %s"%onAxis)
if self.isAmbivalent():
t.append("split")
return ', '.join(t)
def getActiveAxes(self):
"""
Return a list of names of axes which are not zero
::
>>> l = Location(pop=1, snap=0, crackle=1)
>>> l.getActiveAxes()
['crackle', 'pop']
"""
names = sorted(k for k in self.keys() if self[k]!=0)
return names
def asString(self, strict=False):
"""
Return the location as a string.
::
>>> l = Location(pop=1, snap=(-100.0, -200))
>>> l.asString()
'pop:1, snap:(-100.000,-200.000)'
"""
if len(self.keys())==0:
return "origin"
v = []
n = []
try:
for name, value in self.asTuple():
s = ''
if value is None:
s = "None"
elif type(value) == tuple or type(value) == list:
s = "(%.3f,%.3f)"%(value[0], value[1])
elif int(value) == value:
s = "%d"%(int(value))
else:
s = "%.3f"%(value)
if s != '':
n.append("%s:%s"%(name, s))
return ", ".join(n)
except TypeError:
import traceback
print("Location value error:", name, value)
for key, value in self.items():
print("\t\tkey:", key)
print("\t\tvalue:", value)
traceback.print_exc()
return "error"
def asDict(self):
"""
Return the location as a plain python dict.
::
>>> l = Location(pop=1, snap=-100)
>>> l.asDict()['snap']
-100
>>> l.asDict()['pop']
1
"""
new = {}
new.update(self)
return new
def asSortedStringDict(self, roundValue=False):
""" Return the data in a dict with sorted names and column titles.
::
>>> l = Location(pop=1, snap=(1,10))
>>> l.asSortedStringDict()[0]['value']
'1'
>>> l.asSortedStringDict()[0]['axis']
'pop'
>>> l.asSortedStringDict()[1]['axis']
'snap'
>>> l.asSortedStringDict()[1]['value']
'(1,10)'
"""
data = []
names = sorted(self.keys())
for n in names:
data.append({'axis':n, 'value':numberToString(self[n])})
return data
def strip(self):
""" Remove coordinates that are zero, the opposite of expand().
::
>>> l = Location(pop=1, snap=0)
>>> l.strip()
<Location pop:1 >
"""
result = []
for k, v in self.items():
if isinstance(v, tuple):
if v > (_EPSILON, ) * len(v) or v < (-_EPSILON, ) * len(v):
result.append((k, v))
elif v > _EPSILON or v < -_EPSILON:
result.append((k, v))
return self.__class__(result)
def common(self, other):
"""
Return two objects with the same dimensions if they lie in the same orthogonal plane.
::
>>> l = Location(pop=1, snap=2)
>>> m = Location(crackle=1, snap=3)
>>> l.common(m)
(<Location snap:2 >, <Location snap:3 >)
"""
selfDim = set(self.keys())
otherDim = set(other.keys())
dims = selfDim | otherDim
newSelf = None
newOther = None
for dim in dims:
sd = self.get(dim, None)
od = other.get(dim, None)
if sd is None or od is None:
# axis is missing in one or the other
continue
if -_EPSILON < sd < _EPSILON and -_EPSILON < od < _EPSILON:
# values are both zero
continue
if newSelf is None:
newSelf = self.__class__()
if newOther is None:
newOther = self.__class__()
newSelf[dim] = self[dim]
newOther[dim] = other[dim]
return newSelf, newOther
#
#
# tests
#
#
def isOrigin(self):
"""
Return True if the location is at the origin.
::
>>> l = Location(pop=1)
>>> l.isOrigin()
False
>>> l = Location()
>>> l.isOrigin()
True
"""
for name, value in self.items():
if isinstance(value, tuple):
if (value < (-_EPSILON,) * len(value)
or value > (_EPSILON,) * len(value)):
return False
if value < -_EPSILON or value > _EPSILON:
return False
return True
def isOnAxis(self):
"""
Returns statements about this location:
* False if the location is not on-axis
* The name of the axis if it is on-axis
* None if the Location is at the origin
Note: this is only valid for an unbiased location.
::
>>> l = Location(pop=1)
>>> l.isOnAxis()
'pop'
>>> l = Location(pop=1, snap=1)
>>> l.isOnAxis()
False
>>> l = Location()
>>> l.isOnAxis() is None
True
"""
new = self.__class__()
new.update(self)
s = new.strip()
dims = list(s.keys())
if len(dims)> 1:
return False
elif len(dims)==1:
return dims[0]
return None
def isAmbivalent(self, dim=None):
"""
Return True if any of the factors are in fact tuples.
If a dimension name is given only that dimension is tested.
::
>>> l = Location(pop=1)
>>> l.isAmbivalent()
False
>>> l = Location(pop=1, snap=(100, -100))
>>> l.isAmbivalent()
True
"""
if dim is not None:
try:
return isinstance(self[dim], tuple)
except KeyError:
# dimension is not present, it should be 0, so not ambivalent
return False
for dim, val in self.items():
if isinstance(val, tuple):
return True
return False
def split(self):
"""
Split an ambivalent location into 2. One for the x, the other for the y.
::
>>> l = Location(pop=(-5,5))
>>> l.split()
(<Location pop:-5 >, <Location pop:5 >)
"""
x = self.__class__()
y = self.__class__()
for dim, val in self.items():
if isinstance(val, tuple):
x[dim] = val[0]
y[dim] = val[1]
else:
x[dim] = val
y[dim] = val
return x, y
def spliceX(self):
"""
Return a copy with the x values preferred for ambivalent locations.
::
>>> l = Location(pop=(-5,5))
>>> l.spliceX()
<Location pop:-5 >
"""
new = self.__class__()
for dim, val in self.items():
if isinstance(val, tuple):
new[dim] = val[0]
else:
new[dim] = val
return new
def spliceY(self):
"""
Return a copy with the y values preferred for ambivalent locations.
::
>>> l = Location(pop=(-5,5))
>>> l.spliceY()
<Location pop:5 >
"""
new = self.__class__()
for dim, val in self.items():
if isinstance(val, tuple):
new[dim] = val[1]
else:
new[dim] = val
return new
def distance(self, other=None):
"""Return the geometric distance to the other location.
If no object is provided, this will calculate the distance to the origin.
::
>>> l = Location(pop=100)
>>> m = Location(pop=200)
>>> l.distance(m)
100.0
>>> l = Location()
>>> m = Location(pop=200)
>>> l.distance(m)
200.0
>>> l = Location(pop=3, snap=5)
>>> m = Location(pop=7, snap=8)
>>> l.distance(m)
5.0
"""
t = 0
if other is None:
other = self.__class__()
for axisName in set(self.keys()) | set(other.keys()):
t += (other.get(axisName,0)-self.get(axisName,0))**2
return math.sqrt(t)
def sameAs(self, other):
"""
Check if this is the same location.
::
>>> l = Location(pop=5, snap=100)
>>> m = Location(pop=5.0, snap=100.0)
>>> l.sameAs(m)
0
>>> l = Location(pop=5, snap=100)
>>> m = Location(pop=5.0, snap=100.0001)
>>> l.sameAs(m)
-1
"""
if not hasattr(other, "get"):
return -1
d = self.distance(other)
if d < _EPSILON:
return 0
return -1
# math operators
def __add__(self, other):
new = self.__class__()
new.update(self)
new.update(other)
selfDim = set(self.keys())
otherDim = set(other.keys())
for key in selfDim & otherDim:
ts = type(self[key])!=tuple
to = type(other[key])!=tuple
if ts:
sx = sy = self[key]
else:
sx = self[key][0]
sy = self[key][1]
if to:
ox = oy = other[key]
else:
ox = other[key][0]
oy = other[key][1]
x = sx+ox
y = sy+oy
if x==y:
new[key] = x
else:
new[key] = x,y
return new
def __sub__(self, other):
new = self.__class__()
new.update(self)
for key, value in other.items():
try:
new[key] = -value
except TypeError:
new[key] = (-value[0], -value[1])
selfDim = set(self.keys())
otherDim = set(other.keys())
for key in selfDim & otherDim:
ts = type(self[key])!=tuple
to = type(other[key])!=tuple
if ts:
sx = sy = self[key]
else:
sx = self[key][0]
sy = self[key][1]
if to:
ox = oy = other[key]
else:
ox = other[key][0]
oy = other[key][1]
x = sx-ox
y = sy-oy
if x==y:
new[key] = x
else:
new[key] = x,y
return new
def __mul__(self, factor):
new = self.__class__()
if isinstance(factor, tuple):
for key, value in self.items():
if type(value) == tuple:
new[key] = factor[0] * value[0], factor[1] * value[1]
else:
new[key] = factor[0] * value, factor[1] * value
else:
for key, value in self.items():
if type(value) == tuple:
new[key] = factor * value[0], factor * value[1]
else:
new[key] = factor * value
return new
__rmul__ = __mul__
def __truediv__(self, factor):
if factor == 0:
raise ZeroDivisionError
if isinstance(factor, tuple):
if factor[0] == 0 or factor[1] == 0:
raise ZeroDivisionError
return self * (1.0/factor[0]) + self * (1.0/factor[1])
return self * (1.0/factor)
__div__ = __truediv__
def transform(self, transformDict):
if transformDict is None:
return self
new = self.__class__()
for dim, (offset, scale) in transformDict.items():
new[dim] = (self.get(dim,0)+offset)*scale
return new
def sortLocations(locations):
""" Sort the locations by ranking:
1. all on-axis points
2. all off-axis points which project onto on-axis points
these would be involved in master to master interpolations
necessary for patching. Projecting off-axis masters have
at least one coordinate in common with an on-axis master.
3. non-projecting off-axis points, 'wild' off axis points
These would be involved in projecting limits and need to be patched.
"""
onAxis = []
onAxisValues = {}
offAxis = []
offAxis_projecting = []
offAxis_wild = []
# first get the on-axis points
for l in locations:
if l.isOrigin():
continue
if l.isOnAxis():
onAxis.append(l)
for axis in l.keys():
if axis not in onAxisValues:
onAxisValues[axis] = []
onAxisValues[axis].append(l[axis])
else:
offAxis.append(l)
for l in offAxis:
ok = False
for axis in l.keys():
if axis not in onAxisValues:
continue
if l[axis] in onAxisValues[axis]:
ok = True
if ok:
offAxis_projecting.append(l)
else:
offAxis_wild.append(l)
return onAxis, offAxis_projecting, offAxis_wild
def biasFromLocations(locs, preferOrigin=True):
"""
Find the vector that translates the whole system to the origin.
"""
dims = {}
locs.sort()
for l in locs:
for d in l.keys():
if not d in dims:
dims[d] = []
v = l[d]
if type(v)==tuple:
dims[d].append(v[0])
dims[d].append(v[1])
else:
dims[d].append(v)
candidate = Location()
for k in dims.keys():
dims[k].sort()
v = mostCommon(dims[k])
if dims[k].count(v) > 1:
# add the dimension with two or more hits
candidate[k] = mostCommon(dims[k])
matches = []
# 1. do we have an exact match?
for l in locs:
if candidate == l:
return l
# 2. find a location that matches candidate (but has more dimensions)
for l in locs:
ok = True
for k, v in candidate.items():
if l.get(k)!=v:
ok = False
break
if ok:
if not l in matches:
matches.append(l)
matches.sort()
if len(matches)>0:
if preferOrigin:
for c in matches:
if c.isOrigin():
return c
return matches[0]
# 3. no matches. Find the best from the available locations
results = {}
for bias in locs:
rel = []
for l in locs:
rel.append((l - bias).isOnAxis())
c = rel.count(False)
if not c in results:
results[c] = []
results[c].append(bias)
if results:
candidates = results[min(results.keys())]
if preferOrigin:
for c in candidates:
if c.isOrigin():
return c
candidates.sort()
return candidates[0]
return Location()
def mostCommon(L):
"""
# http://stackoverflow.com/questions/1518522/python-most-common-element-in-a-list
>>> mostCommon([1, 2, 2, 3])
2
>>> mostCommon([1, 2, 3])
1
>>> mostCommon([-1, 2, 3])
-1
>>> mostCommon([-1, -2, -3])
-1
>>> mostCommon([-1, -2, -3, -1])
-1
>>> mostCommon([-1, -1, -2, -2])
-1
>>> mostCommon([0, 0.125, 0.275, 1])
0
>>> mostCommon([0, 0.1, 0.4, 0.4])
0.4
"""
# get an iterable of (item, iterable) pairs
SL = sorted((x, i) for i, x in enumerate(L))
# print 'SL:', SL
groups = itertools.groupby(SL, key=operator.itemgetter(0))
# auxiliary function to get "quality" for an item
def _auxfun(g):
item, iterable = g
count = 0
min_index = len(L)
for _, where in iterable:
count += 1
min_index = min(min_index, where)
# print 'item %r, count %r, minind %r' % (item, count, min_index)
return count, -min_index
# pick the highest-count/earliest item
return max(groups, key=_auxfun)[0]
if __name__ == "__main__":
import doctest
sys.exit(doctest.testmod().failed)
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