Python Spread
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Python Spread
Last Updated :
08 May, 2020
# Import Image from wand.image module
# Read image using Image() function
with Image(filename = "koala.jpeg" ) as img:
# Generate spread image using spread() function
img.save(filename = "spreadkoala.jpg" )
# Import Image from wand.image module
# Read image using Image() function
with Image(filename = "koala.jpeg" ) as img:
# Generate spread image using spread() function
img.save(filename = "spreadkoala2.jpg" )
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Spread replaces each pixel with the random pixel value found nearby. spread() function is used to apply Spread effect to the image. The size of the area to search for a new pixel can be controlled by defining a radius.
link
brightness_4
code
Example 2: Increase radius value in spread() method.
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brightness_4
code
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Distance a pixel can be displaced from source. Default value is 0.0, which will allow ImageMagick to auto select a radius.
Interpolation method. Only available with ImageMagick-7. Optional parameter.
Object spread operator for Python
Python - spread () method in Wand - GeeksforGeeks
Python spread примеры, mysticmathmeasures. spread Python ... - HotExamples
GitHub - open-rsx/ spread - python 3: Python 3 port of the spread bindings
spread in python Code Example
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Пространство имен/Пакет: mysticmathmeasures
Файл:
discrete.py
Проект:
agamdua/mystic
def bounded_mean ( mean_x , samples , xmin , xmax , wts = None ) :
from mystic . math . measures import impose_mean , impose_spread
from mystic . math . measures import spread , mean
from numpy import asarray
a = impose_mean ( mean_x , samples , wts )
if min ( a ) < xmin : # maintain the bound
#print "violate lo(a)"
s = spread ( a ) - 2 * ( xmin - min ( a ) ) #XXX: needs compensation (as below) ?
a = impose_mean ( mean_x , impose_spread ( s , samples , wts ) , wts )
if max ( a ) > xmax : # maintain the bound
#print "violate hi(a)"
s = spread ( a ) + 2 * ( xmax - max ( a ) ) #XXX: needs compensation (as below) ?
a = impose_mean ( mean_x , impose_spread ( s , samples , wts ) , wts )
return asarray ( a )
Файл:
test_constraints.py
Проект:
uqfoundation/mystic
Файл:
test_coupler.py
Проект:
uqfoundation/mystic
Файл:
test_symbolic.py
Проект:
uqfoundation/mystic
Файл:
test_symbolic.py
Проект:
cdeil/mystic
Файл:
test_constraints.py
Проект:
uqfoundation/mystic
def test_as_constraint ( ) :
from mystic . math . measures import mean , spread
def mean_constraint ( x , target ) :
return mean ( x ) - target
def range_constraint ( x , target ) :
return spread ( x ) - target
@quadratic_equality ( condition = range_constraint , kwds = { 'target' : 5.0 } )
@quadratic_equality ( condition = mean_constraint , kwds = { 'target' : 5.0 } )
def penalty ( x ) :
return 0.0
ndim = 3
constraints = as_constraint ( penalty , solver = 'fmin' )
#XXX: this is expensive to evaluate, as there are nested optimizations
from numpy import arange
x = arange ( ndim )
_x = constraints ( x )
assert round ( mean ( _x ) ) == 5.0
assert round ( spread ( _x ) ) == 5.0
assert round ( penalty ( _x ) ) == 0.0
def cost ( x ) :
return abs ( sum ( x ) - 5.0 )
npop = ndim * 3
from mystic . solvers import diffev
y = diffev ( cost , x , npop , constraints = constraints , disp = False , gtol = 10 )
assert round ( mean ( y ) ) == 5.0
assert round ( spread ( y ) ) == 5.0
assert round ( cost ( y ) ) == 5.0 * ( ndim - 1 )
Файл:
test_constraints.py
Проект:
uqfoundation/mystic
Файл:
test_constraints.py
Проект:
uqfoundation/mystic
Файл:
distance.py
Проект:
Magellen/mystic
def graphical_distance ( model , points , ** kwds ) :
"""find the radius(x') that minimizes the graph between reality, y = G(x),
and an approximating function, y' = F(x')
Inputs:
model = the model function, y' = F(x'), that approximates reality, y = G(x)
points = object of type 'datapoint' to validate against; defines y = G(x)
Additional Inputs:
ytol = maximum acceptable difference |y - F(x')|; a single value
xtol = maximum acceptable difference |x - x'|; an iterable or single value
cutoff = zero out distances less than cutoff; typically: ytol, 0.0, or None
hausdorff = norm; where if given, ytol = |y - F(x')| + |x - x'|/norm
Returns:
radius = minimum distance from x,G(x) to x',F(x') for each x
Notes:
xtol defines the n-dimensional base of a pilar of height ytol, centered at
each point. The region inside the pilar defines the space where a "valid"
model must intersect. If xtol is not specified, then the base of the pilar
will be a dirac at x' = x. This function performs an optimization for each
x to find an appropriate x'. While cutoff and ytol are very tightly related,
they play a distinct role; ytol is used to set the optimization termination
for an acceptable |y - F(x')|, while cutoff is applied post-optimization.
If we are using the hausdorff norm, then ytol will set the optimization
termination for an acceptable |y - F(x')| + |x - x'|/norm, where the x
values are normalized by norm = hausdorff.
""" #FIXME: update docs to show normalization in y
#NotImplemented:
#L = list of lipschitz constants, for use when lipschitz metric is desired
#constraints = constraints function for finding minimum distance
from mystic . math . legacydata import dataset
from numpy import asarray , sum , isfinite , zeros , seterr
from mystic . solvers import diffev2 , fmin_powell
from mystic . monitors import Monitor , VerboseMonitor
# ensure target xe and ye is a dataset
target = dataset ( )
target . load ( * _get_xy ( points ) )
nyi = target . npts # y's are target.values
nxi = len ( target . coords [ - 1 ] ) # nxi = len(x) / len(y)
# NOTE: the constraints function is a function over a single xe,ye
# because each underlying optimization is over a single xe,ye.
# thus, we 'pass' on using constraints at this time...
constraints = None # default is no constraints
if 'constraints' in kwds : constraints = kwds . pop ( 'constraints' )
if not constraints : # if None (default), there are no constraints
constraints = lambda x : x
# get tolerance in y and wiggle room in x
ytol = kwds . pop ( 'ytol' , 0.0 )
xtol = kwds . pop ( 'xtol' , 0.0 ) # default is to not allow 'wiggle room' in x
cutoff = ytol # default is to zero out distances less than tolerance
if 'cutoff' in kwds : cutoff = kwds . pop ( 'cutoff' )
if cutoff is True : cutoff = ytol
elif cutoff is False : cutoff = None
ipop = kwds . pop ( 'ipop' , min ( 20 , 3 * nxi ) ) #XXX: tune ipop?
imax = kwds . pop ( 'imax' , 1000 ) #XXX: tune imax?
# get range for the dataset (normalization for hausdorff distance)
hausdorff = kwds . pop ( 'hausdorff' , False )
if not hausdorff : # False, (), None, ...
ptp = [ 0.0 ] * nxi
yptp = 1.0
elif hausdorff is True :
from mystic . math . measures import spread
ptp = [ spread ( xi ) for xi in zip ( * target . coords ) ]
yptp = spread ( target . values ) #XXX: this can lead to bad bad things...
else :
try : #iterables
if len ( hausdorff ) < nxi + 1 :
hausdorff = list ( hausdorff ) + [ 0.0 ] * ( nxi - len ( hausdorff ) ) + [ 1.0 ]
ptp = hausdorff [ : - 1 ] # all the x
yptp = hausdorff [ - 1 ] # just the y
except TypeError : #non-iterables
ptp = [ hausdorff ] * nxi
yptp = hausdorff
#########################################################################
def radius ( model , point , ytol = 0.0 , xtol = 0.0 , ipop = None , imax = None ) :
"""graphical distance between a single point x,y and a model F(x')"""
# given a single point x,y: find the radius = |y - F(x')| + delta
# radius is just a minimization over x' of |y - F(x')| + delta
# where we apply a constraints function (of box constraints) of
# |x - x'| <= xtol (for each i in x)
#
# if hausdorff = some iterable, delta = |x - x'|/hausdorff
# if hausdorff = True, delta = |x - x'|/spread(x); using the dataset range
# if hausdorff = False, delta = 0.0
#
# if ipop, then DE else Powell; ytol is used in VTR(ytol)
# and will terminate when cost <= ytol
x , y = _get_xy ( point )
y = asarray ( y )
# catch cases where yptp or y will cause issues in normalization
#if not isfinite(yptp): return 0.0 #FIXME: correct? shouldn't happen
#if yptp == 0: from numpy import inf; return inf #FIXME: this is bad
# build the cost function
if hausdorff : # distance in all directions
def cost ( rv ) :
'''cost = |y - F(x')| + |x - x'| for each x,y (point in dataset)'''
_y = model ( rv )
if not isfinite ( _y ) : return abs ( _y )
errs = seterr ( invalid = 'ignore' , divide = 'ignore' ) # turn off warning
z = abs ( ( asarray ( x ) - rv ) / ptp ) # normalize by range
m = abs ( y - _y ) / yptp # normalize by range
seterr ( invalid = errs [ 'invalid' ] , divide = errs [ 'divide' ] ) # turn on warning
return m + sum ( z [ isfinite ( z ) ] )
else : # vertical distance only
def cost ( rv ) :
'''cost = |y - F(x')| for each x,y (point in dataset)'''
return abs ( y - model ( rv ) )
if debug :
print ( "rv: %s" % str ( x ) )
print ( "cost: %s" % cost ( x ) )
# if xtol=0, radius is difference in x,y and x,F(x); skip the optimization
try :
if not imax or not max ( xtol ) : #iterables
return cost ( x )
except TypeError :
if not xtol : #non-iterables
return cost ( x )
# set the range constraints
xtol = asarray ( xtol )
bounds = list ( zip ( x - xtol , x + xtol ) )
if debug :
print ( "lower: %s" % str ( zip ( * bounds ) [ 0 ] ) )
print ( "upper: %s" % str ( zip ( * bounds ) [ 1 ] ) )
# optimize where initially x' = x
stepmon = Monitor ( )
if debug : stepmon = VerboseMonitor ( 1 )
#XXX: edit settings?
MINMAX = 1 #XXX: confirm MINMAX=1 is minimization
ftol = ytol
gtol = None # use VTRCOG
if ipop :
results = diffev2 ( cost , bounds , ipop , ftol = ftol , gtol = gtol , \
itermon = stepmon , maxiter = imax , bounds = bounds , \
full_output = 1 , disp = 0 , handler = False )
else :
results = fmin_powell ( cost , x , ftol = ftol , gtol = gtol , \
itermon = stepmon , maxiter = imax , bounds = bounds , \
full_output = 1 , disp = 0 , handler = False )
#solved = results[0] # x'
func_opt = MINMAX * results [ 1 ] # cost(x')
if debug :
print ( "solved: %s" % results [ 0 ] )
print ( "cost: %s" % func_opt )
# get the minimum distance |y - F(x')|
return func_opt
#return results[0], func_opt
#########################################################################
#XXX: better to do a single optimization rather than for each point ???
d = [ radius ( model , point , ytol , xtol , ipop , imax ) for point in target ]
return infeasibility ( d , cutoff )
Файл:
test_constraints.py
Проект:
uqfoundation/mystic
Файл:
discrete.py
Проект:
agamdua/mystic
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