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)