def bmat(obj, ldict=None, gdict=None):
"""Build a matrix object from string, nested sequence, or array.
Ex: F = bmat('A, B; C, D')
F = bmat([[A,B],[C,D]])
F = bmat(r_[c_[A,B],c_[C,D]])
all produce the same Matrix Object [ A B ]
[ C D ]
if A, B, C, and D are appropriately shaped 2-d arrays.
"""
if isinstance(obj, str):
if gdict is None:
# get previous frame
frame = sys._getframe().f_back
glob_dict = frame.f_globals
loc_dict = frame.f_locals
else:
glob_dict = gdict
loc_dict = ldict
return matrix(_from_string(obj, glob_dict, loc_dict))
if isinstance(obj, (tuple, list)):
# [[A,B],[C,D]]
arr_rows = []
for row in obj:
if isinstance(row, N.ndarray): # not 2-d
return matrix(concatenate(obj,axis=-1))
else:
arr_rows.append(concatenate(row,axis=-1))
return matrix(concatenate(arr_rows,axis=0))
if isinstance(obj, N.ndarray):
return matrix(obj)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by `hsplit`.
Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays together.
hsplit : Split array along second axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)``
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrs = map(atleast_1d,tup)
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
else:
return _nx.concatenate(arrs, 1)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by `hsplit`.
Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays together.
hsplit : Split array along second axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)``
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrs = map(atleast_1d, tup)
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
else:
return _nx.concatenate(arrs, 1)
def post_process(self, data, start, dt_tau):
if dt_tau != self.cache_dt_tau or self.cache_tick_count > self.view.watcher.timelog.tick_count:
self.cache_dt_tau = dt_tau
self.clear_cache()
self.cache_tick_count = self.view.watcher.timelog.tick_count
forget = self.cache_tick_count - self.view.watcher.timelog.tick_limit
if forget in self.cache:
del self.cache[forget]
if not hasattr(self, 'vocab'):
return []
if len(self.vocab.keys) == 0:
return []
d = []
for i, dd in enumerate(data):
if dd is None:
d.append(None)
else:
index = i + start
v = self.cache.get((index, dt_tau), None)
if v is None:
if self.normalize or self.smooth_normalize:
dd = self.calc_normal(dd)
v = self.vocab.dot(dd)
if self.show_pairs:
v2 = self.vocab.dot_pairs(dd)
if v2 is not None:
v = numeric.concatenate((v, v2), 0)
self.cache[(index, dt_tau)] = v
d.append(v)
return d
def post_process(self, data, start, dt_tau):
if dt_tau != self.cache_dt_tau or self.cache_tick_count > self.view.watcher.timelog.tick_count:
self.cache_dt_tau = dt_tau
self.clear_cache()
self.cache_tick_count = self.view.watcher.timelog.tick_count
forget = self.cache_tick_count - self.view.watcher.timelog.tick_limit
if forget in self.cache:
del self.cache[forget]
if len(self.vocab.keys) == 0:
return []
d = []
for i, dd in enumerate(data):
if dd is None:
d.append(None)
else:
index = i + start
v = self.cache.get((index, dt_tau), None)
if v is None:
if self.normalize or self.smooth_normalize:
dd = self.calc_normal(dd)
v = self.vocab.dot(dd)
if self.show_pairs:
v2 = self.vocab.dot_pairs(dd)
if v2 is not None:
v = numeric.concatenate((v, v2), 0)
self.cache[(index, dt_tau)] = v
d.append(v)
return d
def resize(a, new_shape):
"""resize(a,new_shape) returns a new array with the specified shape.
The original array's total size can be any size. It
fills the new array with repeated copies of a.
Note that a.resize(new_shape) will fill array with 0's
beyond current definition of a.
"""
if isinstance(new_shape, (int, nt.integer)):
new_shape = (new_shape,)
a = ravel(a)
Na = len(a)
if not Na: return mu.zeros(new_shape, a.dtype.char)
total_size = um.multiply.reduce(new_shape)
n_copies = int(total_size / Na)
extra = total_size % Na
if total_size == 0:
return a[:0]
if extra != 0:
n_copies = n_copies+1
extra = Na-extra
a = concatenate( (a,)*n_copies)
if extra > 0:
a = a[:-extra]
return reshape(a, new_shape)
def _leading_trailing(a):
if a.ndim == 1:
if len(a) > 2*_summaryEdgeItems:
b = _nc.concatenate((a[:_summaryEdgeItems],
a[-_summaryEdgeItems:]))
else:
b = a
else:
if len(a) > 2*_summaryEdgeItems:
l = [_leading_trailing(a[i]) for i in range(
min(len(a), _summaryEdgeItems))]
l.extend([_leading_trailing(a[-i]) for i in range(
min(len(a), _summaryEdgeItems),0,-1)])
else:
l = [_leading_trailing(a[i]) for i in range(0, len(a))]
b = _nc.concatenate(tuple(l))
return b
def _from_string(str,gdict,ldict):
rows = str.split(';')
rowtup = []
for row in rows:
trow = row.split(',')
newrow = []
for x in trow:
newrow.extend(x.split())
trow = newrow
coltup = []
for col in trow:
col = col.strip()
try:
thismat = ldict[col]
except KeyError:
try:
thismat = gdict[col]
except KeyError:
raise KeyError, "%s not found" % (col,)
coltup.append(thismat)
rowtup.append(concatenate(coltup,axis=-1))
return concatenate(rowtup,axis=0)
def _from_string(str,gdict,ldict):
rows = str.split(';')
rowtup = []
for row in rows:
trow = row.split(',')
newrow = []
for x in trow:
newrow.extend(x.split())
trow = newrow
coltup = []
for col in trow:
col = col.strip()
try:
thismat = ldict[col]
except KeyError:
try:
thismat = gdict[col]
except KeyError:
raise KeyError, "%s not found" % (col,)
coltup.append(thismat)
rowtup.append(concatenate(coltup,axis=-1))
return concatenate(rowtup,axis=0)
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
Take a sequence of arrays and stack them vertically to make a single
array. Rebuild arrays divided by `vsplit`.
Parameters
----------
tup : sequence of ndarrays
Tuple containing arrays to be stacked. The arrays must have the same
shape along all but the first axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
concatenate : Join a sequence of arrays together.
vsplit : Split array into a list of multiple sub-arrays vertically.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
are at least 2-dimensional.
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate(map(atleast_2d,tup),0)
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
Take a sequence of arrays and stack them vertically to make a single
array. Rebuild arrays divided by `vsplit`.
Parameters
----------
tup : sequence of ndarrays
Tuple containing arrays to be stacked. The arrays must have the same
shape along all but the first axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
concatenate : Join a sequence of arrays together.
vsplit : Split array into a list of multiple sub-arrays vertically.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
are at least 2-dimensional.
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate(map(atleast_2d, tup), 0)
def resize(a, new_shape):
"""Return a new array with the specified shape.
The original array's total size can be any size. The new array is
filled with repeated copies of a.
Note that a.resize(new_shape) will fill the array with 0's beyond
current definition of a.
*Parameters*:
a : {array_like}
Array to be reshaped.
new_shape : {tuple}
Shape of reshaped array.
*Returns*:
reshaped_array : {array}
The new array is formed from the data in the old array, repeated if
necessary to fill out the required number of elements, with the new
shape.
"""
if isinstance(new_shape, (int, nt.integer)):
new_shape = (new_shape,)
a = ravel(a)
Na = len(a)
if not Na: return mu.zeros(new_shape, a.dtype.char)
total_size = um.multiply.reduce(new_shape)
n_copies = int(total_size / Na)
extra = total_size % Na
if total_size == 0:
return a[:0]
if extra != 0:
n_copies = n_copies+1
extra = Na-extra
a = concatenate( (a,)*n_copies)
if extra > 0:
a = a[:-extra]
return reshape(a, new_shape)
def __init__(self,view,name,config,func,args=(),label=None):
core.DataViewComponent.__init__(self,label)
self.view=view
self.name=name
self.func=func
self.data=self.view.watcher.watch(name,func,args=args)
self.border_top=20
self.border_left=20
self.border_right=20
self.border_bottom=20
self.config=config
self.start = 0
# self.setSize(440,440+self.label_offset)
self.setSize(240,240+self.label_offset)
self.max = 0.0001
self.min = -0.0001
self.grid_size = 50
self.image = None
self.counter = 0
######################################
## initialize function input grid
grid_size = self.grid_size
# construct input grid
row = array([map(float,range(grid_size))])
row = row / (self.grid_size-1) * 2 - 2 # normalized to [-1, 1]
gridX = dot(ones([grid_size,1]), row)
gridY = transpose(gridX)
# get function value
x1 = reshape(gridX,[1, grid_size*grid_size])
x2 = reshape(gridY,[1, grid_size*grid_size])
self.func_input = concatenate([x1, x2], 0)
def bmat(obj, ldict=None, gdict=None):
"""
Build a matrix object from a string, nested sequence, or array.
Parameters
----------
obj : string, sequence or array
Input data. Variables names in the current scope may
be referenced, even if `obj` is a string.
Returns
-------
out : matrix
Returns a matrix object, which is a specialized 2-D array.
See Also
--------
matrix
Examples
--------
>>> A = np.mat('1 1; 1 1')
>>> B = np.mat('2 2; 2 2')
>>> C = np.mat('3 4; 5 6')
>>> D = np.mat('7 8; 9 0')
All the following expressions construct the same block matrix:
>>> np.bmat([[A, B], [C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat('A,B; C,D')
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
"""
if isinstance(obj, str):
if gdict is None:
# get previous frame
frame = sys._getframe().f_back
glob_dict = frame.f_globals
loc_dict = frame.f_locals
else:
glob_dict = gdict
loc_dict = ldict
return matrix(_from_string(obj, glob_dict, loc_dict))
if isinstance(obj, (tuple, list)):
# [[A,B],[C,D]]
arr_rows = []
for row in obj:
if isinstance(row, N.ndarray): # not 2-d
return matrix(concatenate(obj,axis=-1))
else:
arr_rows.append(concatenate(row,axis=-1))
return matrix(concatenate(arr_rows,axis=0))
if isinstance(obj, N.ndarray):
return matrix(obj)
def paintComponent(self,g):
f,dimension,minx,maxx,miny,maxy, params, color, time_step = self.config
core.DataViewComponent.paintComponent(self,g)
width=self.size.width-self.border_left-self.border_right
height=self.size.height-self.border_top-self.border_bottom-self.label_offset
if width<2: return
dt_tau=None
if self.view.tau_filter>0:
dt_tau=self.view.dt/self.view.tau_filter
try:
data=self.data.get(start=self.view.current_tick,count=1,dt_tau=dt_tau)[0]
except:
return
if dimension == 2:
if self.image is None :
self.image = BI(width, height, BI.TYPE_INT_ARGB)
if self.counter != time_step:
self.counter += 1
g.drawImage( self.image, self.border_left, self.border_top, None)
else:
self.counter = 0
# currently only for fixed grid
grid_size = self.grid_size
# step_size = width / grid_size
coeffs=transpose(array([data]))
basis = array(f(self.func_input, params))
value = transpose(dot(transpose(basis),coeffs))
maxv = max(value[0])
minv = min(value[0])
if maxv > self.max:
self.max = maxv
if minv < self.min:
self.min = minv
pvalue = (value - self.min) / (self.max - self.min) # normalized pixel value
if color == 'g':
## gray
pvalue = array(map(int, array(pvalue[0]) * 0xFF))
pvalue = pvalue * 0x10000 + pvalue * 0x100 + pvalue
elif color == 'c':
##color
pvalue = map(int, array(pvalue[0]) * 0xFF * 2)
R = zeros(len(pvalue))
G = zeros(len(pvalue))
B = zeros(len(pvalue))
for i, v in enumerate(pvalue):
if v < 0xFF:
B[i] = 0xFF - v
G[i] = v
else:
G[i] = 2*0xFF - v
R[i] = v - 0xFF
pvalue = R * 0x10000 + G * 0x100 + B
pvalue = reshape(pvalue,[grid_size, grid_size])
rvalue = 0xFF000000 + pvalue
# expand pixel value from grid to raster size
# ratio = float(width) / grid_size
# indeces = map(int, (floor(array(range(width)) / ratio)))
## Tooooooo slow here!
# for i, ii in enumerate(indeces):
# for j, jj in enumerate(indeces) :
# rvalue[i,j] = pvalue[ii,jj]
for zoom in range(2):
zgrid_size = grid_size * (zoom + 1)
rvalue = reshape(rvalue, [zgrid_size * zgrid_size, 1])
rvalue = concatenate([rvalue, rvalue], 1)
rvalue = reshape(rvalue, [zgrid_size, zgrid_size* 2])
rvalue = repeat(rvalue, ones(zgrid_size) * 2)
# draw image
rvalue = reshape(rvalue, [1, width * height])
self.image.setRGB(0, 0, width, height, rvalue[0], 0, width)
g.drawImage( self.image, self.border_left, self.border_top, None)
elif dimension == 1:
g.color=Color(0.8,0.8,0.8)
g.drawRect(self.border_left,self.border_top+self.label_offset,width,height)
g.color=Color.black
txt='%4g'%maxx
bounds=g.font.getStringBounds(txt,g.fontRenderContext)
g.drawString(txt,self.size.width-self.border_right-bounds.width/2,self.size.height-self.border_bottom+bounds.height)
txt='%4g'%minx
bounds=g.font.getStringBounds(txt,g.fontRenderContext)
g.drawString(txt,self.border_left-bounds.width/2,self.size.height-self.border_bottom+bounds.height)
g.drawString('%6g'%maxy,0,10+self.border_top+self.label_offset)
g.drawString('%6g'%miny,0,self.size.height-self.border_bottom)
g.color=Color.black
pdftemplate=getattr(self.view.area,'pdftemplate',None)
if pdftemplate is not None:
pdf,scale=pdftemplate
pdf.setLineWidth(0.5)
steps=100
dx=float(maxx-minx)/(width*steps)
for i in range(width*steps):
x=minx+i*dx
value=sum([f(j,x)*d for j,d in enumerate(data)])
y=float((value-miny)*height/(maxy-miny))
xx=self.border_left+i/float(steps)
yy=self.height-self.border_bottom-y
if i==0:
pdf.moveTo((self.x+xx)*scale,800-(self.y+yy)*scale)
else:
if 0<y<height:
pdf.lineTo((self.x+xx)*scale,800-(self.y+yy)*scale)
pdf.setRGBColorStroke(g.color.red,g.color.green,g.color.blue)
pdf.stroke()
else:
dx=float(maxx-minx)/(width-1)
px,py=None,None
for i in range(width):
x=minx+i*dx
value=sum([f(j,x)*d for j,d in enumerate(data)])
y=int((value-miny)*height/(maxy-miny))
xx=self.border_left+i
yy=self.height-self.border_bottom-y
if px is not None and miny<value<maxy:
g.drawLine(px,py,xx,yy)
px,py=xx,yy