BREAKING STUFF!

Rename fluents to laydi.

laydi/lib/cx_utils.py

@@ -0,0 +1,115 @@
+from scipy import apply_along_axis,newaxis,zeros,\
+     median,round_,nonzero,dot,argmax,any,sqrt,ndarray,\
+     trace,zeros_like,sign,sort,real,argsort,rand,array,\
+     matrix,nan
+from scipy.linalg import norm,svd,inv,eig
+from scipy.stats import median,mean
+
+def normalise(a, axis=0, return_scales=False):
+    s = apply_along_axis(norm, axis, a)
+    if axis==0:
+        s = s[newaxis]
+    else:
+        s = s[:,newaxis]
+	    
+    a_s = a/s
+
+    if return_scales:
+       return a_s, s
+
+    return a_s
+
+def sub2ind(shape, i, j):
+	"""Indices from subscripts. Only support for 2d"""
+	row,col = shape
+	ind = []
+	for k in xrange(len(i)):
+		for m in xrange(len(j)):
+			ind.append(i[k]*col + j[m])
+	return ind
+
+
+def sorted_eig(a, b=None,sort_by='sm'):
+    """
+    Just eig with real part of output sorted:
+    This is for convenience only, not general!
+    
+    sort_by='sm': return the eigenvectors by eigenvalues
+                  of smallest magnitude first. (default)
+            'lm': returns largest eigenvalues first      
+
+    output: just as eig with 2 outputs
+            -- s,v (eigvals,eigenvectors)
+    (This is reversed output compared to matlab)
+    
+    """
+    s,v = eig(a, b)
+    s = real(s) # dont expect any imaginary part
+    v = real(v)
+    ind = argsort(s)
+    if sort_by=='lm':
+        ind = ind[::-1]
+    v = v.take(ind, 1)
+    s = s.take(ind)
+
+    return s,v
+
+def str2num(string_number):
+    """Convert input (string number) into number, if float(string_number) fails, a nan is inserted. 
+    """
+    missings = ['','nan','NaN','NA']
+    try:
+        num = float(string_number)
+    except:
+        if string_number in missings:
+            num = nan
+        else:
+            print "Found strange entry: %s" %string_number
+            raise
+    return num
+
+def randperm(n):
+  r = rand(n)
+  dict={}
+  for i in range(n):
+     dict[r[i]] = i
+  r = sort(r)
+  out = zeros(n)
+  for i in range(n):
+     out[i] = dict[r[i]]
+  return array(out).astype('i')
+
+def mat_center(X,axis=0,ret_mn=False):
+    """Mean center matrix along axis.
+    
+        X -- matrix, data
+        axis -- dim,
+        ret_mn -- bool, return mean
+
+    output:
+            Xc, [mnX]
+            
+    NB: axis = 1 is column-centering, axis=0=row-centering
+    default is row centering (axis=0)
+    """
+
+    try:
+        rows,cols = X.shape
+    except ValueError:
+        print "The X data needs to be two-dimensional"
+        
+    if axis==0:
+        mnX = mean(X,axis)[newaxis]
+        Xs = X - mnX
+    
+    elif axis==1:
+        mnX = mean(X,axis)[newaxis]
+        Xs = (X.T - mnX).T
+    if ret_mn:
+        return Xs,mnX
+    else:
+        return Xs
+
+def m_shape(array):
+	"""Returns the array shape on the form of a numpy.matrix."""
+	return matrix(array).shape