Revision: 5824
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5824&view=rev
Author: dmkaplan
Date: 2008-07-23 14:18:41 +0000 (Wed, 23 Jul 2008)
Log Message:
-----------
Fix to is_scalar plus additional functions in cbook.py:
less_simple_linear_interpolation
isvector
vector_lengths
distances_along_curve
path_length
is_closed_polygon
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/cbook.py
Modified: trunk/matplotlib/lib/matplotlib/cbook.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/cbook.py 2008-07-23 13:28:32 UTC (rev
5823)
+++ trunk/matplotlib/lib/matplotlib/cbook.py 2008-07-23 14:18:41 UTC (rev
5824)
@@ -287,7 +287,7 @@
def is_scalar(obj):
'return true if *obj* is not string like and is not iterable'
- return not is_string_like(obj) or not iterable(obj)
+ return not is_string_like(obj) and not iterable(obj)
def is_numlike(obj):
'return true if *obj* looks like a number'
@@ -1156,6 +1156,46 @@
return result
+def less_simple_linear_interpolation( x, y, xi, extrap=False ):
+ """
+ This function provides simple (but somewhat less so than
+ simple_linear_interpolation) linear interpolation. This is very
+ inefficient linear interpolation meant to be used only for a small
+ number of points in relatively non-intensive use cases.
+
+ Call signature::
+
+ yi = less_simple_linear_interpolation(x,y,xi)
+ """
+ if is_scalar(xi): xi = [xi]
+
+ x = np.asarray(x)
+ y = np.asarray(y)
+ xi = np.asarray(xi)
+
+ s = list(y.shape)
+ s[0] = len(xi)
+ yi = np.tile( np.nan, s )
+
+ for ii,xx in enumerate(xi):
+ bb = x == xx
+ if np.any(bb):
+ jj, = np.nonzero(bb)
+ yi[ii] = y[jj[0]]
+ elif xx<x[0]:
+ if extrap:
+ yi[ii] = y[0]
+ elif xx>x[-1]:
+ if extrap:
+ yi[ii] = y[-1]
+ else:
+ jj, = np.nonzero(x<xx)
+ jj = max(jj)
+
+ yi[ii] = y[jj] + (xx-x[jj])/(x[jj+1]-x[jj]) * (y[jj+1]-y[jj])
+
+ return yi
+
def recursive_remove(path):
if os.path.isdir(path):
for fname in glob.glob(os.path.join(path, '*')) +
glob.glob(os.path.join(path, '.*')):
@@ -1294,7 +1334,76 @@
ic1 = breakpoints
return np.concatenate((ic0[:, np.newaxis], ic1[:, np.newaxis]), axis=1)
+def isvector(X):
+ """
+ Like the Matlab (TM) function with the same name, returns true if
+ the supplied numpy array or matrix looks like a vector, meaning it
+ has a one non-singleton axis (i.e., it can have multiple axes, but
+ all must have length 1, except for one of them).
+ If you just want to see if the array has 1 axis, use X.ndim==1
+
+ Call signature::
+ isvector(X)
+ """
+ return np.prod(X.shape)==np.max(X.shape)
+
+def vector_lengths( X, P=2., axis=None ):
+ """
+ Finds the length of a set of vectors in n dimensions. This is
+ like the mlab.norm function for vectors, but has the ability to
+ work over a particular axis of the supplied array or matrix.
+
+ Call signature::
+
+ vector_lengths( X, P=2., axis=None )
+
+ Computes (sum((x_i)^P))^(1/P) for each {x_i} being the elements of X along
+ the given axis. If *axis* is *None*, compute over all elements of X.
+ """
+ X = np.asarray(X)
+ return (np.sum(X**(P),axis=axis))**(1./P)
+
+def distances_along_curve( X ):
+ """
+ Computes the distance between a set of successive points in N dimensions.
+
+ Call signature::
+
+ distances_along_curve(X)
+
+ where X is an MxN array or matrix. The distances between successive rows
+ is computed. Distance is the standard Euclidean distance.
+ """
+ X = np.diff( X, axis=0 )
+ return vector_lengths(X,axis=1)
+
+def path_length(X):
+ """
+ Computes the distance travelled along a polygonal curve in N dimensions.
+
+ Call signature::
+
+ path_length(X)
+
+ where X is an MxN array or matrix. Returns an array of length M consisting
+ of the distance along the curve at each point (i.e., the rows of X).
+ """
+ X = distances_along_curve(X)
+ return np.concatenate( (np.zeros(1), np.cumsum(X)) )
+
+def is_closed_polygon(X):
+ """
+ Tests whether first and last object in a sequence are the same. These are
+ presumably coordinates on a polygonal curve, in which case this function
+ tests if that curve is closed.
+
+ Call signature::
+
+ is_closed_polygon(X)
+ """
+ return np.all(X[0] == X[-1])
+
# a dict to cross-map linestyle arguments
_linestyles = [('-', 'solid'),
('--', 'dashed'),
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