def _logticks(self, lower, upper):
#lower,upper = map(_Numeric.log10,[lower,upper])
#print 'logticks',lower,upper
ticks = []
mag = _Numeric.power(10,_Numeric.floor(lower))
if upper-lower > 6:
t = _Numeric.power(10,_Numeric.ceil(lower))
base = _Numeric.power(10,_Numeric.floor((upper-lower)/6))
def inc(t):
return t*base-t
else:
t = _Numeric.ceil(_Numeric.power(10,lower)/mag)*mag
def inc(t):
return 10**int(_Numeric.floor(_Numeric.log10(t)+1e-16))
majortick = int(_Numeric.log10(mag))
while t <= pow(10,upper):
if majortick != int(_Numeric.floor(_Numeric.log10(t)+1e-16)):
majortick = int(_Numeric.floor(_Numeric.log10(t)+1e-16))
ticklabel = '1e%d'%majortick
else:
if upper-lower < 2:
minortick = int(t/pow(10,majortick)+.5)
ticklabel = '%de%d'%(minortick,majortick)
else:
ticklabel = ''
ticks.append((_Numeric.log10(t), ticklabel))
t += inc(t)
if len(ticks) == 0:
ticks = [(0,'')]
return ticks
def _ticks(self, lower, upper):
ideal = (upper-lower)/7.
log = _Numeric.log10(ideal)
power = _Numeric.floor(log)
fraction = log-power
factor = 1.
error = fraction
for f, lf in self._multiples:
e = _Numeric.fabs(fraction-lf)
if e < error:
error = e
factor = f
grid = factor * 10.**power
if power > 4 or power < -4:
format = '%+7.1e'
elif power >= 0:
digits = max(1, int(power))
format = '%' + `digits`+'.0f'
else:
digits = -int(power)
format = '%'+`digits+2`+'.'+`digits`+'f'
ticks = []
t = -grid*_Numeric.floor(-lower/grid)
while t <= upper:
ticks.append( (t, format % (t,)) )
t = t + grid
return ticks
def _quantize(self, vec):
"""
Given a point in space, partition space into little cubes
so that when the time comes, it will be quick to locate and
search the cubes right around a point.
"""
maxradius = self._maxradius
return (int(floor(vec[0] / maxradius)), int(floor(vec[1] / maxradius)),
int(floor(vec[2] / maxradius)))
def _quantize(self, vec):
"""
Given a point in space, partition space into little cubes
so that when the time comes, it will be quick to locate and
search the cubes right around a point.
"""
maxradius = self._maxradius
return (int(floor(vec[0] / maxradius)),
int(floor(vec[1] / maxradius)),
int(floor(vec[2] / maxradius)))
def _axisInterval(self, spec, lower, upper):
"""Returns sensible axis range for given spec"""
if spec == 'none' or spec == 'min':
if lower == upper:
return lower-0.5, upper+0.5
else:
return lower, upper
elif spec == 'auto':
range = upper-lower
if range == 0.:
return lower-0.5, upper+0.5
log = _Numeric.log10(range)
power = _Numeric.floor(log)
fraction = log-power
if fraction <= 0.05:
power = power-1
grid = 10.**power
lower = lower - lower % grid
mod = upper % grid
if mod != 0:
upper = upper - mod + grid
return lower, upper
elif type(spec) == type(()):
lower, upper = spec
if lower <= upper:
return lower, upper
else:
return upper, lower
else:
raise ValueError, str(spec) + ': illegal axis specification'
def histogram(data, nbins, range=None):
"""
Create a histogram.
Comes from Konrad Hinsen: Scientific Python
@param data: data list or array
@type data: [any]
@param nbins: number of bins
@type nbins: int
@param range: data range to create histogram from (min val, max val)
@type range: (float, float) OR None
@return: array (2 x len(data) ) with start of bin and witdh of bin.
@rtype: array
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(
data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data, min)))
bin_width = (max - min) / nbins
data = Numeric.floor((data - min) / bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(
Numeric.equal(Numeric.arange(nbins)[:, Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width * (Numeric.arange(nbins) + 0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def histogram(data, nbins, range=None):
"""
Comes from Konrad Hinsen: Scientific Python
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(
data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data, min)))
# end if
bin_width = (max - min) / nbins
data = Numeric.floor((data - min) / bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(
Numeric.equal(Numeric.arange(nbins)[:, Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width * (Numeric.arange(nbins) + 0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def _arrayToImage(self, inArray):
"""
Generate a 1-channel PIL Image from an array of values from 0 to 1.0.
Values larger than 1.0 are clipped, after adding them to the total
clipped_pixels. Returns a one-channel (monochrome) Image.
"""
# PIL 'L' Images use a range of 0 to 255, so we scale the
# input array to match. The pixels are scaled by 255, not
# 256, so that 1.0 maps to fully white.
max_pixel_value=255
inArray = (Numeric.floor(inArray * max_pixel_value)).astype(Numeric.Int)
# Clip any values that are still larger than max_pixel_value
to_clip = (Numeric.greater(inArray.ravel(),max_pixel_value)).sum()
if (to_clip>0):
# CEBALERT: no explanation of why clipped pixel count is
# being accumulated.
self.clipped_pixels = self.clipped_pixels + to_clip
inArray.clip(0,max_pixel_value,out=inArray)
self.verbose("Bitmap: clipped",to_clip,"image pixels that were out of range")
r,c = inArray.shape
# The size is (width,height), so we swap r and c:
newImage = Image.new('L',(c,r),None)
newImage.putdata(inArray.ravel())
return newImage
def histogram(data, nbins, range = None):
"""
Create a histogram.
Comes from Konrad Hinsen: Scientific Python
@param data: data list or array
@type data: [any]
@param nbins: number of bins
@type nbins: int
@param range: data range to create histogram from (min val, max val)
@type range: (float, float) OR None
@return: array (2 x len(data) ) with start of bin and witdh of bin.
@rtype: array
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data, min)))
bin_width = (max-min)/nbins
data = Numeric.floor((data - min)/bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(Numeric.equal(
Numeric.arange(nbins)[:,Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width*(Numeric.arange(nbins)+0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def histogram(data, nbins, range = None):
"""
Comes from Konrad Hinsen: Scientific Python
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data,
min)))
# end if
bin_width = (max-min)/nbins
data = Numeric.floor((data - min)/bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(Numeric.equal(
Numeric.arange(nbins)[:,Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width*(Numeric.arange(nbins)+0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def _arrayToImage(self, inArray):
"""
Generate a 1-channel PIL Image from an array of values from 0 to 1.0.
Values larger than 1.0 are clipped, after adding them to the total
clipped_pixels. Returns a one-channel (monochrome) Image.
"""
# PIL 'L' Images use a range of 0 to 255, so we scale the
# input array to match. The pixels are scaled by 255, not
# 256, so that 1.0 maps to fully white.
max_pixel_value = 255
inArray = (Numeric.floor(inArray * max_pixel_value)).astype(
Numeric.Int)
# Clip any values that are still larger than max_pixel_value
to_clip = (Numeric.greater(inArray.ravel(), max_pixel_value)).sum()
if (to_clip > 0):
# CEBALERT: no explanation of why clipped pixel count is
# being accumulated.
self.clipped_pixels = self.clipped_pixels + to_clip
inArray.clip(0, max_pixel_value, out=inArray)
self.verbose("Bitmap: clipped", to_clip,
"image pixels that were out of range")
r, c = inArray.shape
# The size is (width,height), so we swap r and c:
newImage = Image.new('L', (c, r), None)
newImage.putdata(inArray.ravel())
return newImage
def __call__(self,**params_to_override):
p = ParamOverrides(self,params_to_override)
xsize,ysize = SheetCoordinateSystem(p.bounds,p.xdensity,p.ydensity).shape
xsize,ysize = int(round(xsize)),int(round(ysize))
xdisparity = int(round(xsize*p.xdisparity))
ydisparity = int(round(xsize*p.ydisparity))
dotsize = int(round(xsize*p.dotsize))
bigxsize = 2*xsize
bigysize = 2*ysize
ndots=int(round(p.dotdensity * (bigxsize+2*dotsize) * (bigysize+2*dotsize) /
min(dotsize,xsize) / min(dotsize,ysize)))
halfdot = floor(dotsize/2)
# Choose random colors and locations of square dots
random_seed = p.random_seed
random_array.seed(random_seed*12,random_seed*99)
col=where(random_array.random((ndots))>=0.5, 1.0, -1.0)
random_array.seed(random_seed*122,random_seed*799)
xpos=floor(random_array.random((ndots))*(bigxsize+2*dotsize)) - halfdot
random_array.seed(random_seed*1243,random_seed*9349)
ypos=floor(random_array.random((ndots))*(bigysize+2*dotsize)) - halfdot
# Construct arrays of points specifying the boundaries of each
# dot, cropping them by the big image size (0,0) to (bigxsize,bigysize)
x1=xpos.astype(Int) ; x1=choose(less(x1,0),(x1,0))
y1=ypos.astype(Int) ; y1=choose(less(y1,0),(y1,0))
x2=(xpos+(dotsize-1)).astype(Int) ; x2=choose(greater(x2,bigxsize),(x2,bigxsize))
y2=(ypos+(dotsize-1)).astype(Int) ; y2=choose(greater(y2,bigysize),(y2,bigysize))
# Draw each dot in the big image, on a blank background
bigimage = zeros((bigysize,bigxsize))
for i in range(ndots):
bigimage[y1[i]:y2[i]+1,x1[i]:x2[i]+1] = col[i]
result = p.offset + p.scale*bigimage[ (ysize/2)+ydisparity:(3*ysize/2)+ydisparity ,
(xsize/2)+xdisparity:(3*xsize/2)+xdisparity ]
for of in p.output_fns:
of(result)
return result
def __call__(self,**params_to_override):
p = ParamOverrides(self,params_to_override)
xsize,ysize = SheetCoordinateSystem(p.bounds,p.xdensity,p.ydensity).shape
xsize,ysize = int(round(xsize)),int(round(ysize))
xdisparity = int(round(xsize*p.xdisparity))
ydisparity = int(round(xsize*p.ydisparity))
dotsize = int(round(xsize*p.dotsize))
bigxsize = 2*xsize
bigysize = 2*ysize
ndots=int(round(p.dotdensity * (bigxsize+2*dotsize) * (bigysize+2*dotsize) /
min(dotsize,xsize) / min(dotsize,ysize)))
halfdot = floor(dotsize/2)
# Choose random colors and locations of square dots
random_seed = p.random_seed
random_array.seed(random_seed*12,random_seed*99)
col=where(random_array.random((ndots))>=0.5, 1.0, -1.0)
random_array.seed(random_seed*122,random_seed*799)
xpos=floor(random_array.random((ndots))*(bigxsize+2*dotsize)) - halfdot
random_array.seed(random_seed*1243,random_seed*9349)
ypos=floor(random_array.random((ndots))*(bigysize+2*dotsize)) - halfdot
# Construct arrays of points specifying the boundaries of each
# dot, cropping them by the big image size (0,0) to (bigxsize,bigysize)
x1=xpos.astype(Int) ; x1=choose(less(x1,0),(x1,0))
y1=ypos.astype(Int) ; y1=choose(less(y1,0),(y1,0))
x2=(xpos+(dotsize-1)).astype(Int) ; x2=choose(greater(x2,bigxsize),(x2,bigxsize))
y2=(ypos+(dotsize-1)).astype(Int) ; y2=choose(greater(y2,bigysize),(y2,bigysize))
# Draw each dot in the big image, on a blank background
bigimage = zeros((bigysize,bigxsize))
for i in range(ndots):
bigimage[y1[i]:y2[i]+1,x1[i]:x2[i]+1] = col[i]
result = p.offset + p.scale*bigimage[ (ysize/2)+ydisparity:(3*ysize/2)+ydisparity ,
(xsize/2)+xdisparity:(3*xsize/2)+xdisparity ]
for of in p.output_fns:
of(result)
return result
def __init__(self, shp, ptlist, origin, selSense, **opts):
"""
ptlist is a list of 3d points describing a selection.
origin is the center of view, and normal gives the direction
of the line of light. Form a structure for telling whether
arbitrary points fall inside the curve from the point of view.
"""
# bruce 041214 rewrote some of this method
simple_shape_2d.__init__( self, shp, ptlist, origin, selSense, opts)
# bounding rectangle, in integers (scaled 8 to the angstrom)
ibbhi = array(map(int, ceil(8 * self.bboxhi)+2))
ibblo = array(map(int, floor(8 * self.bboxlo)-2))
bboxlo = self.bboxlo
# draw the curve in these matrices and fill it
# [bruce 041214 adds this comment: this might be correct but it's very
# inefficient -- we should do it geometrically someday. #e]
mat = zeros(ibbhi - ibblo)
mat1 = zeros(ibbhi - ibblo)
mat1[0,:] = 1
mat1[-1,:] = 1
mat1[:,0] = 1
mat1[:,-1] = 1
pt2d = self.pt2d
pt0 = pt2d[0]
for pt in pt2d[1:]:
l = ceil(vlen(pt - pt0)*8)
if l<0.01: continue
v=(pt - pt0)/l
for i in range(1 + int(l)):
ij = 2 + array(map(int, floor((pt0 + v * i - bboxlo)*8)))
mat[ij]=1
pt0 = pt
mat1 += mat
fill(mat1, array([1, 1]),1)
mat1 -= mat #Which means boundary line is counted as inside the shape.
# boolean raster of filled-in shape
self.matrix = mat1 ## For any element inside the matrix, if it is 0, then it's inside.
# where matrix[0, 0] is in x, y space
self.matbase = ibblo
# axes of the plane; only used for debugging
self.x = self.right
self.y = self.up
self.z = self.normal
def _hashAtomPos(self, pos):
return int(dot(V(1000000, 1000, 1), floor(pos * 1.2)))
def _hashAtomPos(self, pos):
return int(dot(V(1000000, 1000, 1), floor(pos * 1.2)))
def inc(t):
return 10**int(_Numeric.floor(_Numeric.log10(t)+1e-16))