improved error handling in the program and significant rewrite

linalg-practice.py

@@ -3,6 +3,8 @@ import time
 import random
 import readline
 
+errs = (ValueError, TypeError)
+
 def genmatrix(rowcol, intmax, dif) :
     # generate a random matrices
     a = sp.randMatrix(rowcol, rowcol, 0, intmax)
@@ -21,7 +23,11 @@ def inmat(rowcol) :
         mat[i]=input().split(" ")
         for j in range(len(mat[i])) :
             # convert list using nsimplify in order to take rational number symbolically
+            try :
                 mat[i][j] = sp.nsimplify(mat[i][j])
+            except errs : return False
+    try : mat = sp.Matrix(mat)
+    except errs : return False
     return mat
 
 # multcheck function to check if two matrices are multiplied together
@@ -29,39 +35,41 @@ def multcheck(a, b, rowcol, intmax) :
     sp.pprint(b)
     print("Multiply these two matrices together")
     # return bool based on if input equals the two matrices multiplied together
-    return (sp.Matrix(inmat(rowcol)) == a*b)
+    return (inmat(rowcol) == a*b)
 
 # detcheck function to check if the determinant of the matrix is correct
 def detcheck(a, b, rowcol, intmax):
     det = sp.det(a)
     # return bool based on if input equals determinant
-    return (det == sp.nsimplify(input("What is the determinant of this matrix?: ")))
+    try : d = sp.nsimplify(input("What is the determinant of this matrix?: "))
+    except errs : return False
+    return (det == d)
 
 # invcheck function to check if the inverse of the matrix is correct
 def invcheck(a, b, rowcol, intmax):
     # return bool based on if input equals inverse of matrix
     print("What is the inverse of this matrix?")
-    return (sp.Matrix(inmat(rowcol)) == a.inv())
+    return (inmat(rowcol) == a.inv())
 
 # eigcheck function to check if the eigenvalues are correct
 def eigcheck(a, b, rowcol, intmax):
     eigs = a.eigenvals()
     for i in range(len(eigs)) :
+        try :
             val = sp.nsimplify(input("Input eigenvalue: "))
-        if not (val in eigs and eigs[val] == sp.nsimplify(input("Input its algebraic multiplicity: "))) :
+            algm = sp.nsimplify(input("Input its algebraic multiplicity: "))
+        except errs : return False
+        if not (val in eigs and eigs[val] == algm) :
             # return false if the eigenvalue not in dictionary and wrong alg multiplicity
             return False
     return True
 
 def diagcheck(a, b, rowcol, intmax):
-    return (a.diagonalize()[1] == sp.Matrix(inmat(rowcol)))
+    return (a.diagonalize()[1] == inmat(rowcol))
 
 # practice function
 def practice(t) :
     count = 0
-    rowcol = int(input("What size of matrix do you want to practice with? "))
-    intmax = int(input("What maximum size of integer do you want the matrix to be made out of? "))
-    dif = float(input("What difficulty (probability for a matrix of rational values between 0, 1) do you want? "))
 
     # choose the function of the program that you want
     if t == "mult" :
@@ -77,6 +85,15 @@ def practice(t) :
     else :
         exit()
 
+    while True :
+        try :
+            rowcol = abs(int(input("What size of matrix do you want to practice with? ")))
+            intmax = abs(int(input("What maximum size of integer do you want the matrix to be made out of? ")))
+            dif = float(input("What difficulty (probability for a matrix of rational values between 0, 1) do you want? "))
+            break
+        except errs:
+            continue
+
     # initialise time measurement
     tic = time.perf_counter()