clean up matrix.py & square matrices

[matrixnullspace.com.git] / matrix.py

diff --git a/matrix.py b/matrix.py
index 5ae2eba..41713e2 100755 (executable)
--- a/matrix.py
+++ b/matrix.py
@@ -1,47 +1,39 @@
-import urlparse
+import cgi
+from scipy import compress, transpose
+from scipy.linalg import svd, det
 import json
 
-import scipy
-import scipy.linalg
-from scipy import *
-
-def null(A, eps=1e-15):
-    u, s, vh = scipy.linalg.svd(A)
+def calculate_nullspace(A, eps=1e-15):
+    u, s, vh = svd(A)
     null_mask = (s <= eps)
-    null_space = scipy.compress(null_mask, vh, axis=0)
-    return scipy.transpose(null_space)
+    null_space = compress(null_mask, vh, axis=0)
+    return transpose(null_space)
 
-def eigenvalues(A, eps=1e-15):
-    u, s, vh = scipy.linalg.svd(A)
+def calculate_eigenvalues(A, eps=1e-15):
+    u, s, vh = svd(A)
     return s
 
 def application(environ, start_response):
-    if environ['REQUEST_METHOD'] == 'POST':
-        post_data = environ['wsgi.input'].read()
+    parameters = cgi.parse_qs(environ.get('QUERY_STRING', ''))
 
-       post_data = urlparse.parse_qsl(post_data, True, True)
+    query_string = str(parameters['matrix'][0])
+    matrix = query_string.splitlines()
+    for index, row in enumerate(matrix):
+        matrix[index] = row.split()
 
-        data_string = str(post_data[0][1])
-        data_rows = data_string.splitlines()
-        for index, row in enumerate(data_rows):
-            data_rows[index] = row.split()
+    while len(matrix) < len(matrix[0]):
+        matrix.append([0]*len(matrix[0]))
 
-       given_matrix = data_rows
-        null_space = null(given_matrix)
-        try:
-            null_space = null_space / null_space.max()
-        except:
-            pass
+    nullspace = calculate_nullspace(matrix)
+    if nullspace.max() != 0:
+        nullspace = nullspace / nullspace.max()
 
-#        la, v = scipy.linalg.eig(given_matrix)
-#      output += "eigenvalues:<br>"
-#        for l in la:
-#            output += str(l)
-        determinant = scipy.linalg.det(given_matrix)
+    eigenvalues = calculate_eigenvalues(matrix)
+    determinant = det(matrix)
 
     output = json.dumps({
-        'nullspace' : str(null_space),
-        'eigenvalues' : str(eigenvalues(given_matrix)),
+        'nullspace' : str(nullspace),
+        'eigenvalues' : str(eigenvalues),
         'determinant' : str(determinant)
         })