def __call__(self, value):
vmin = self.vmin
vmax = self.vmax
if type(value) in [IntType, FloatType]:
vtype = 'scalar'
val = array([value])
else:
vtype = 'array'
val = asarray(value)
if vmin is None or vmax is None:
rval = ravel(val)
if vmin is None: vmin = nxmin(rval)
if vmax is None: vmax = nxmax(rval)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return 0.*value
else:
val = where(val<vmin, vmin, val)
val = where(val>vmax, vmax, val)
result = (1.0/(vmax-vmin))*(val-vmin)
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value):
vmin = self.vmin
vmax = self.vmax
if type(value) in [IntType, FloatType]:
vtype = "scalar"
val = array([value])
else:
vtype = "array"
val = asarray(value)
if vmin is None or vmax is None:
rval = ravel(val)
if vmin is None:
vmin = amin(rval)
if vmax is None:
vmax = amax(rval)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin == vmax:
return 0.0 * value
else:
val = where(val < vmin, vmin, val)
val = where(val > vmax, vmax, val)
result = (1.0 / (vmax - vmin)) * (val - vmin)
if vtype == "scalar":
result = result[0]
return result
def __call__(self, X, alpha=1.0):
"""
X is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). Any values
that are outside the 0,1 interval are clipped to that
interval before generating rgb values.
Alpha must be a scalar
"""
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
if type(X) in [IntType, FloatType]:
vtype = 'scalar'
xa = array([X])
else:
vtype = 'array'
xa = array(X)
xa = where(xa>1.,1.,xa)
xa = where(xa<0.,0.,xa)
xa = (xa *(self.N-1)).astype(Int)
rgba = zeros(xa.shape+(4,), Float)
rgba[...,0] = take(self._red_lut, xa)
rgba[...,1] = take(self._green_lut, xa)
rgba[...,2] = take(self._blue_lut, xa)
rgba[...,3] = alpha
if vtype == 'scalar':
rgba = tuple(rgba[0,:])
return rgba
def __call__(self, X, alpha=1.0):
"""
X is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). Any values
that are outside the 0,1 interval are clipped to that
interval before generating rgb values.
Alpha must be a scalar
"""
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
if type(X) in [IntType, FloatType]:
vtype = 'scalar'
xa = array([X])
else:
vtype = 'array'
xa = array(X)
xa = where(xa>1.,1.,xa)
xa = where(xa<0.,0.,xa)
xa = (xa *(self.N-1)).astype(Int)
rgba = zeros(xa.shape+(4,), Float)
rgba[...,0] = take(self._red_lut, xa)
rgba[...,1] = take(self._green_lut, xa)
rgba[...,2] = take(self._blue_lut, xa)
rgba[...,3] = alpha
if vtype == 'scalar':
rgba = tuple(rgba[0,:])
return rgba
def __call__(self, value):
vmin = self.vmin
vmax = self.vmax
if type(value) in [IntType, FloatType]:
vtype = 'scalar'
val = array([value])
else:
vtype = 'array'
val = array(value)
if vmin is None or vmax is None:
rval = ravel(val)
if vmin is None:
vmin = min(rval)
if vmax is None:
vmax = max(rval)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return 0.*value
else:
val = where(val<vmin, vmin, val)
val = where(val>vmax, vmax, val)
result = divide(val-vmin, vmax-vmin)
if vtype == 'scalar':
result = result[0]
return result
def soft(data, value, substitute=0):
mvalue = -value
cond_less = numerix.less(data, value)
cond_greater = numerix.greater(data, mvalue)
data = numerix.where(cond_less & cond_greater, substitute, data)
data = numerix.where(cond_less, data + value, data)
data = numerix.where(cond_greater, data - value, data)
return data
def soft(data, value, substitute=0):
mvalue = -value
cond_less = numerix.less(data, value)
cond_greater = numerix.greater(data, mvalue)
data = numerix.where(cond_less & cond_greater, substitute, data)
data = numerix.where(cond_less, data + value, data)
data = numerix.where(cond_greater, data - value, data)
return data
def makeMappingArray(N, data):
"""Create an N-element 1-d lookup table
data represented by a list of x,y0,y1 mapping correspondences.
Each element in this list represents how a value between 0 and 1
(inclusive) represented by x is mapped to a corresponding value
between 0 and 1 (inclusive). The two values of y are to allow
for discontinuous mapping functions (say as might be found in a
sawtooth) where y0 represents the value of y for values of x
<= to that given, and y1 is the value to be used for x > than
that given). The list must start with x=0, end with x=1, and
all values of x must be in increasing order. Values between
the given mapping points are determined by simple linear interpolation.
The function returns an array "result" where result[x*(N-1)]
gives the closest value for values of x between 0 and 1.
"""
try:
adata = array(data)
except:
raise TypeError("data must be convertable to an array")
shape = adata.shape
if len(shape) != 2 and shape[1] != 3:
raise ValueError("data must be nx3 format")
x = adata[:,0]
y0 = adata[:,1]
y1 = adata[:,2]
if x[0] != 0. or x[-1] != 1.0:
raise ValueError(
"data mapping points must start with x=0. and end with x=1")
if sometrue(sort(x)-x):
raise ValueError(
"data mapping points must have x in increasing order")
# begin generation of lookup table
x = x * (N-1)
lut = zeros((N,), Float)
xind = arange(float(N))
ind = searchsorted(x, xind)[1:-1]
lut[1:-1] = ( divide(xind[1:-1] - take(x,ind-1),
take(x,ind)-take(x,ind-1) )
*(take(y0,ind)-take(y1,ind-1)) + take(y1,ind-1))
lut[0] = y1[0]
lut[-1] = y0[-1]
# ensure that the lut is confined to values between 0 and 1 by clipping it
lut = where(lut > 1., 1., lut)
lut = where(lut < 0., 0., lut)
return lut
def makeMappingArray(N, data):
"""Create an N-element 1-d lookup table
data represented by a list of x,y0,y1 mapping correspondences.
Each element in this list represents how a value between 0 and 1
(inclusive) represented by x is mapped to a corresponding value
between 0 and 1 (inclusive). The two values of y are to allow
for discontinuous mapping functions (say as might be found in a
sawtooth) where y0 represents the value of y for values of x
<= to that given, and y1 is the value to be used for x > than
that given). The list must start with x=0, end with x=1, and
all values of x must be in increasing order. Values between
the given mapping points are determined by simple linear interpolation.
The function returns an array "result" where result[x*(N-1)]
gives the closest value for values of x between 0 and 1.
"""
try:
adata = array(data)
except:
raise TypeError("data must be convertable to an array")
shape = adata.shape
if len(shape) != 2 and shape[1] != 3:
raise ValueError("data must be nx3 format")
x = adata[:,0]
y0 = adata[:,1]
y1 = adata[:,2]
if x[0] != 0. or x[-1] != 1.0:
raise ValueError(
"data mapping points must start with x=0. and end with x=1")
if sometrue(sort(x)-x):
raise ValueError(
"data mapping points must have x in increasing order")
# begin generation of lookup table
x = x * (N-1)
lut = zeros((N,), Float)
xind = arange(float(N))
ind = searchsorted(x, xind)[1:-1]
lut[1:-1] = ( divide(xind[1:-1] - take(x,ind-1),
take(x,ind)-take(x,ind-1) )
*(take(y0,ind)-take(y1,ind-1)) + take(y1,ind-1))
lut[0] = y1[0]
lut[-1] = y0[-1]
# ensure that the lut is confined to values between 0 and 1 by clipping it
lut = where(lut > 1., 1., lut)
lut = where(lut < 0., 0., lut)
return lut
def hard(data, value, substitute=0):
mvalue = -value
cond = numerix.less(data, value)
cond &= numerix.greater(data, mvalue)
return numerix.where(cond, substitute, data)
def hard(data, value, substitute=0):
mvalue = -value
cond = numerix.less(data, value)
cond &= numerix.greater(data, mvalue)
return numerix.where(cond, substitute, data)
def contour_rectgrid(x_grid, y_grid, z, levels):
'''calculate the contours of z on the rectangular grid x_grid, y_grid at
levels.
xarr and yarr are 1d arrays which hold the x/y coordinates of the grid.
They do not need to be equispaced nor of same length.
z[y, x] holds the value of some scalar function on that
grid.
levels is a list of float values for which the contours shall be
calculated.
Returns a list of equal length to levels. Each entry is a set of contour
lines for the corresponding level value.
A set of contour lines is a list which contains 2-element lists
[xarr, yarr]. xarr and yarr hold the points of one contour line in cartesic
coordinates.
xarr = result[levidx][lineidx][0]
yarr = result[levidx][lineidx][1]'''
result = []
dx_grid = x_grid[1:] - x_grid[:-1] # x_n+1 - x_n
dy_grid = y_grid[1:] - y_grid[:-1] # y_n+1 - y_n
for level in levels:
lines = contour_graph(z, level)
translated_lines = []
for line in lines:
interp_y, xind, yind, weights = map(array, line)
xcoord = where(interp_y,
x_grid[xind],
x_grid[xind] + weights*dx_grid[xind-1])
ycoord = where(interp_y,
y_grid[yind] + weights*dy_grid[yind-1],
y_grid[yind])
translated_lines.append([xcoord, ycoord])
result.append(translated_lines)
return result
def __call__(self, X, alpha=1.0):
"""
X is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). If the X-values
are integers, then they are used as indices into the array.
If they are floating point, then they must be in the
interval (0.0, 1.0).
Alpha must be a scalar.
"""
if not self._isinit: self._init()
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
self._lut[:-3, -1] = alpha
mask_bad = None
if isinstance(X, (int, float)):
vtype = 'scalar'
xa = array([X])
else:
vtype = 'array'
xma = ma.asarray(X)
xa = xma.filled(0)
mask_bad = ma.getmaskorNone(xma)
if typecode(xa) in typecodes['Float']:
xa = where(xa == 1.0, 0.9999999, xa) # Tweak so 1.0 is in range.
xa = (xa * self.N).astype(Int)
mask_under = xa < 0
mask_over = xa > self.N-1
xa = where(mask_under, self._i_under, xa)
xa = where(mask_over, self._i_over, xa)
if mask_bad is not None: # and sometrue(mask_bad):
xa = where(mask_bad, self._i_bad, xa)
#print 'types', typecode(self._lut), typecode(xa), xa.shape
rgba = take(self._lut, xa)
if vtype == 'scalar':
rgba = tuple(rgba[0,:])
#print rgba[0,1:10,:] # Now the same for numpy, numeric...
return rgba
def __call__(self, X, alpha=1.0):
"""
X is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). If the X-values
are integers, then they are used as indices into the array.
If they are floating point, then they must be in the
interval (0.0, 1.0).
Alpha must be a scalar.
"""
if not self._isinit: self._init()
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
self._lut[:-3, -1] = alpha
mask_bad = None
if isinstance(X, (int, float)):
vtype = 'scalar'
xa = array([X])
else:
vtype = 'array'
xma = ma.asarray(X)
xa = xma.filled(0)
mask_bad = ma.getmaskorNone(xma)
if typecode(xa) in typecodes['Float']:
xa = where(xa == 1.0, 0.9999999, xa) # Tweak so 1.0 is in range.
xa = (xa * self.N).astype(Int)
mask_under = xa < 0
mask_over = xa > self.N - 1
xa = where(mask_under, self._i_under, xa)
xa = where(mask_over, self._i_over, xa)
if mask_bad is not None: # and sometrue(mask_bad):
xa = where(mask_bad, self._i_bad, xa)
#print 'types', typecode(self._lut), typecode(xa), xa.shape
rgba = take(self._lut, xa)
if vtype == 'scalar':
rgba = tuple(rgba[0, :])
#print rgba[0,1:10,:] # Now the same for numpy, numeric...
return rgba
def prctile(x, p = (0.0, 25.0, 50.0, 75.0, 100.0)):
"""
Return the percentiles of x. p can either be a sequence of
percentil values or a scalar. If p is a sequence the i-th element
of the return sequence is the p(i)-th percentile of x
"""
x = sort(ravel(x))
Nx = len(x)
if not iterable(p):
return x[int(p*Nx/100.0)]
p = multiply(array(p), Nx/100.0)
ind = p.astype(Int)
ind = where(ind>=Nx, Nx-1, ind)
return take(x, ind)
def prctile(x, p=(0.0, 25.0, 50.0, 75.0, 100.0)):
"""
Return the percentiles of x. p can either be a sequence of
percentil values or a scalar. If p is a sequence the i-th element
of the return sequence is the p(i)-th percentile of x
"""
x = sort(ravel(x))
Nx = len(x)
if not iterable(p):
return x[int(p * Nx / 100.0)]
p = multiply(array(p), Nx / 100.0)
ind = p.astype(Int)
ind = where(ind >= Nx, Nx - 1, ind)
return take(x, ind)
def contour_gencoords(x_grid, y_grid, z, levels):
'''calculate the contours of z on the coordinate grid x_grid, y_grid at
levels.
Let the 2d plane be parametrized by p and q.
x_grid[p, q] and y_grid[p,q] have to contain the cartesic coordinates of
the point (p, q). z[p, q] holds the value of some scalar function on that
grid. levels is a list of float values for which the contours shall be
calculated.
Returns a list of equal length to levels. Each entry is a set of contour
lines for the corresponding level value.
A set of contour lines is a list which contains 2-element lists
[xarr, yarr]. xarr and yarr hold the points of one contour line in cartesic
coordinates.
xarr = result[levidx][lineidx][0]
yarr = result[levidx][lineidx][1]'''
result = []
maxy, maxx = z.shape
for level in levels:
lines = contour_graph(z, level)
coordinates = []
for line in lines:
interp_y, x, y, weights = map(array, line)
line_translated = []
for grid in x_grid, y_grid:
line_translated.append(where(interp_y,
# The % operation does not affect the result!
# It will avoid index errors but the corresponding
# entries will be eaten by where.
grid[y,x]*(1-weights) + grid[(y+1)%maxy,x]*weights,
grid[y,x]*(1-weights) + grid[y,(x+1)%maxx]*weights ))
coordinates.append(line_translated)
result.append(coordinates)
return result # result[lev][line][0 or 1] -> x / y (interpol. x_grid / y_grid)
def less(data, value, substitute=0):
return numerix.where(numerix.greater(data, value), substitute, data)
def pyxplotcontour(canvas, data, x=None, y=None, levels=8,
colors='map', colmap=None, color=None,
labels=False, labelsize=0.4,
**kwargs):
"""do contour plot.
data[y, x]: 2d real array containing z values
x, y: None - equidistant point spacing
1d array: rectangular non-equidistant grid
2d array: arbitrary coordinates
levels: int (level count) or list of z values
default: 8 equispaced levels
colors: "map": use colmap (ColorMapper)
"map_invert": use colmap but invert color
(max visibility on top of bitmap plot)
"color_array": color and levels must be arrays of the same
length, level i get's the color i
"color": use color (as in pyxgraph.pyxplot)
labels: draw text labels designating the level value of each contour
labelsize: label text size (relative to normal text size)
kwargs: are passed to pyxplot
"""
zmin, zmax = data.min(), data.max()
# define a function zcolor(z) that returns the color to use
if 'map' in colors:
if colmap==None:
colmap=ColMapper.ColorMapper("gauss")
if 'inv' in colors:
def zcolor(z):
r, g, b = colmap.colfct((z-zmin)/(zmax-zmin))
return 1-r, 1-g, 1-b
else:
def zcolor(z):
return colmap.colfct((z-zmin)/(zmax-zmin))
elif colors == "color_array":
levels = array(levels)
assert hasattr(color, "__iter__") # for this color and levels
assert hasattr(levels, "__iter__") # have to be lists of the
assert len(color)==len(levels) # same length
def zcolor(z):
return color[argmin(abs(levels - z))]
# return most suitable color
else: #use color
def zcolor(z):
return color
# calculate level list
if isinstance(levels, int):
levels = (arange(levels)+0.5)*(zmax-zmin)/levels + zmin
# calculate the levels
if x is None or y is None:
clines = contour_nogrid(data, levels)
elif len(x.shape) == 1 and len(y.shape) == 1:
clines = contour_rectgrid(x, y, data, levels)
elif x.shape == y.shape == data.shape:
clines = contour_gencoord(x, y, data, levels)
else:
raise ValueError, 'pyxplotcontour: x and/or y misshaped'
# clines is clines[levidx][lineidx][0 or 1 for x/y]
# set default args
if 'linetype' not in kwargs and 'lt' not in kwargs:
kwargs['linetype']=pyx.style.linestyle.solid
if 'style' not in kwargs:
kwargs['style'] = 'lines'
# plot
for levidx, level in enumerate(levels):
llabel = '%0.4g'%level
levelcolor = zcolor(level)
for line in clines[levidx]:
canvas.pyxplot((line[0], line[1]),
color=levelcolor, title=False, **kwargs)
if labels:
# find label position
x, y = line
xy = map(canvas.pos, x, y)
canvas_x = array([i[0] for i in xy])
canvas_y = array([i[1] for i in xy])
dcx = canvas_x[1:] - canvas_x[:-1]
dcy = canvas_y[1:] - canvas_y[:-1]
dcx = where(dcx==0, min(dcx)/10, dcx) # avoid singularity problems
ind = argmin(abs(dcy/dcx))
# try to avoid placing labels on plot borders
# FIXME: find better method for that?
relx = canvas_x[ind] - canvas.xpos
if not (labelsize < relx < canvas.width-labelsize): # label at left or right border
#if ind < 3 or ind > len(x)-4: # probably a line ending at border
ind = len(x) / 2
# label position (graphcoords)
lx, ly = x[ind], y[ind]
angle = arctan(dcy[ind]/dcx[ind])*180/pi
if abs(angle) < 5:
angle = 0
# FIXME: do something against labels exceeding plot area
canvas.pyxlabel((lx, ly), llabel, graphcoords=True,
style=[pyx.trafo.translate(0, 0.05),
pyx.trafo.rotate(angle),
pyx.trafo.scale(labelsize),
pyx.text.halign.center,
pyx.text.valign.bottom])
def less(data, value, substitute=0):
return numerix.where(numerix.greater(data, value), substitute, data)
def contour_graph(z, level):
'''calculate contour of z at level.
z[y,x] has to be a 2d array.
Returns a list of 4-element lists. Each entry represents one contour.
Each entry is [interp_y, xind, yind, weight] where all four are 1d lists
of equal size.
Imagine a rectangular grid where the crossings are the points of z. All
points of the result are located on this grid. For point n:
if interp_y[n] is True, the point is on a vertical line, else on a
horizontal line.
If it is on a vertical line, xind[n] gives the corresponding index into z.
The y position is between yind[n] and yind[n]+1. weight[n] is a number
between 0.0 and 1.0 giving the position: 0 means the point is on yind[n]
exactly, 1.0 means the point is on yind[n]+1 exactly.
If the point is on a horizontal line, weight[n] denotes the position
between xind[n] and xind[n]+1.
result[lineidx][0..3][pointidx]
'''
bw = (z>level) # "black and white" image of z
horiz = (bw[:,:-1] != bw[:,1:]) # True where a contour crosses rows
vert = (bw[:-1] != bw[1:]) # True where a contour crosses columns
# for later readability
up, down, left, right = 1, 4, 2, 8
# adjacency & up means crosspoint at y (top), & left: at x (left),
# & down: at y+1, & right: at x+1
# adjacency[y, x] is the cell between x...x+1 and y...y+1
adjacency = (horiz[:-1,:]*up | vert[:,:-1]*left |
horiz [1:,:]*down | vert[:,1:]*right)
# calculate interpolation weights and store in horiz, vert
# weight = (level - z[n])/(z[n+1]-z[n])
# -1 => dummy value (no crossing)
horiz = where(horiz, (level-z[:,:-1]) / (z[:,1:]-z[:,:-1]), -1)
vert = where(vert, (level-z[:-1]) / (z[1:]-z[:-1]), -1)
result = []
def _appendpoint(line, x, y, direction):
'''append the point adjacency[y,x], direction translated into grid
coordinates and remove from adjacency.
returns tuple (bool, x, y, weight)
bool: False means on horizontal grid, True on vertical grid
weight is the position between x and x+1 resp y and y+1'''
adjacency[y, x] ^= direction # XOR out direction
if direction & (up|down):
if direction & down:
y += 1
assert horiz[y,x] > -1 # for debugging
line[0].append(False)
line[1].append(x)
line[2].append(y)
line[3].append(horiz[y, x])
else:
# direction is left or right
if direction & right:
x += 1
assert vert[y, x] > -1 # for debugging
line[0].append(True)
line[1].append(x)
line[2].append(y)
line[3].append(vert[y,x])
# end def
opposite = {up:down, down:up, left:right, right:left}
while any(adjacency != 0):
# first look if there are any contours ending at the borders of z
border = True
# top border
if any(adjacency[0] & up):
x0 = nonzero(adjacency[0] & up)[0][0] # just take first one
y0 = 0
orig = up # where we come from
# bottom
elif any(adjacency[-1] & down):
x0 = nonzero(adjacency[-1] & down)[0][0]
y0 = adjacency.shape[0] - 1
orig = down
# left
elif any(adjacency[:,0] & left):
x0 = 0
y0 = nonzero(adjacency[:,0] & left)[0][0]
orig = left
# right border
elif any(adjacency[:,-1] & right):
x0 = adjacency.shape[1] - 1
y0 = nonzero(adjacency[:,-1] & right)[0][0]
orig = right
else: # pick an arbitrary point (all contours are closed loops now)
m = adjacency.argmax()
# FIXME: This is not very pretty! forward compatible?!
x0, y0 = m%adjacency.shape[1], m/adjacency.shape[1]
border = False # did not start from border
adj = adjacency[y0, x0]
if adj & up:
orig = up
elif adj & down:
orig = down
elif adj & left:
orig = left
else:
# There can be only 2 or 4 ends, so adj&right only is an error
raise 'Dead end at r%d,c%d -- should be impossible!'%(y0,x0)
# okay now we have starting cell x0,y0 and direction orig.
endofcontour = False # set when border reached or closed
line = [[], [], [], []]
while not endofcontour:
# append last point
_appendpoint(line, x0, y0, orig)
# find next point
adj = adjacency[y0, x0]
if (adj | orig) == 15: # all directions possible, hm...
# lets connect cross-wise
# this might create weird results with multiple contours
cont = opposite[orig]
else: # only one continuation - no problem :-D
cont = adj
newx0 = x0 + {up:0, down:0, left:-1, right:1}[cont]
newy0 = y0 + {up:-1, down:1, left:0, right:0}[cont]
orig = opposite[cont]
# out of bounds
if border and ((not 0<=newx0<adjacency.shape[1]) or
(not 0<=newy0<adjacency.shape[0])):
_appendpoint(line, x0, y0, cont)
endofcontour = True
else:
adjacency[y0, x0] ^= cont
x0, y0 = newx0, newy0
# loop closed
if adjacency[y0, x0] & orig == 0:
_appendpoint(line, x0, y0, orig)
adjacency[y0, x0] ^= orig
endofcontour = True
result.append(line)
return result