HI, -I'm using scipy_test/testing.py as a 1st try to add tests units into my scripts and reuse some of it in scipy.optimize. -When dealing with something like optimize.leastsq, I needed to compare its resulted array (i.e. optimize.leastsq(...)[0]) against its accuracy. I didn't find anything I could use for this matter in 'testing.py'. I was looking for a n-dimensional (n>1) version of assert_approx_equal. So I wrote one (in fact, i just extented 'assert_approx_equal' for n-arrays); I'm not sure if there is similar code lying around somewhere in scipy_test. -Bellow goes the patch against testing.py (cvs): *** testing.py 2004-10-06 20:03:34.000000000 -0300 --- testing.py 2004-10-23 02:06:34.000000000 -0300 *************** *** 10,16 **** # If Numeric and scipy_base are not available, then some of the # functions below will not be available. from Numeric import alltrue,equal,shape,ravel,around,zeros,Float64,asarray,\ ! less_equal,array2string,less import scipy_base.fastumath as math except ImportError: pass --- 10,16 ---- # If Numeric and scipy_base are not available, then some of the # functions below will not be available. from Numeric import alltrue,equal,shape,ravel,around,zeros,Float64,asarray,\ ! less_equal,array2string,less,floor,fabs,log10,Float import scipy_base.fastumath as math except ImportError: pass *************** *** 689,694 **** --- 689,735 ---- print x, y raise ValueError, 'arrays are not almost equal' + __all__.append('assert_array_approx_equal') + def assert_array_approx_equal(x,y,significant=7,err_msg=''): + x, y = asarray((x, y),Float) + msg = '\nItems are not equal to %d significant digits:\n' % significant + msg += err_msg + + try: + cond = alltrue(equal(shape(x),shape(y))) + if not cond: + msg = msg + ' (shapes mismatch):\n\t'\ + 'Shape of array 1: %s\n\tShape of array 2: %s' % (shape(x),shape(y)) + assert cond, msg + '\n\t' + err_msg + + # Normalized the numbers to be in range (-10.0,10.0) + scale = pow(10,floor(log10(0.5*(abs(x)+abs(y))))) + try: + sc_x = x/scale + except ZeroDivisionError: + sc_x = zeros(shape(x)) + try: + sc_y = y/scale + except ZeroDivisionError: + sc_y = zeros(shape(y)) + + reduced = fabs(sc_x - sc_y) <= pow(10.,-1*significant) + cond = alltrue(reduced) + if not cond: + s1 = array2string(x,precision=significant+1) + s2 = array2string(y,precision=significant+1) + if len(s1)>120: s1 = s1[:120] + '...' + if len(s2)>120: s2 = s2[:120] + '...' + match = 100-100.0*reduced.tolist().count(1)/len(reduced) + msg = msg + ' (mismatch %s%%):\n\tArray 1: %s\n\tArray 2: %s' % (match,s1,s2) + assert cond,\ + msg + '\n\t' + err_msg + except ValueError: + print sys.exc_value + print shape(x),shape(y) + print x, y + raise ValueError, 'arrays are not approximately equal' + __all__.append('assert_array_less') def assert_array_less(x,y,err_msg=''): x,y = asarray(x), asarray(y) regards, -- roberto (krivilli)