from nbdev.showdoc import *
from fastcore.nb_imports import *

Simple test functions

We can check that code raises an exception when that's expected (test_fail). To test for equality or inequality (with different types of things) we define a simple function test that compares two object with a given cmp operator.

test_fail[source]

test_fail(f, msg='', contains='', args=None, kwargs=None)

def _fail(): raise Exception("foobar")
test_fail(_fail, contains="foo")

def _fail(): raise Exception()
test_fail(_fail)

We can also pass args and kwargs to function to check if it fails with special inputs.

def _fail_args(a):
    if a == 5:
        raise ValueError
test_fail(_fail_args, args=(5,))
test_fail(_fail_args, kwargs=dict(a=5))

test[source]

test(a, b, cmp, cname=None)

assert that cmp(a,b); display inputs and cname or cmp.__name__ if it fails

test([1,2],[1,2], operator.eq)
test_fail(lambda: test([1,2],[1], operator.eq))
test([1,2],[1],   operator.ne)
test_fail(lambda: test([1,2],[1,2], operator.ne))

all_equal[source]

all_equal(a, b)

Compares whether a and b are the same length and have the same contents

test(['abc'], ['abc'],  all_equal)

equals[source]

equals(a, b)

Compares a and b for equality; supports sublists, tensors and arrays too

test([['abc'],['a']], [['abc'],['a']],  equals)

nequals[source]

nequals(a, b)

Compares a and b for not equals

test(['abc'], ['ab' ], nequals)

test_eq test_ne, etc...

Just use test_eq/test_ne to test for ==/!=. test_eq_type checks things are equal and of the same type. We define them using test:

test_eq[source]

test_eq(a, b)

test that a==b

test_eq([1,2],[1,2])
test_eq([1,2],map(int,[1,2]))
test_eq(array([1,2]),array([1,2]))
test_eq(array([1,2]),array([1,2]))
test_eq([array([1,2]),3],[array([1,2]),3])
test_eq(dict(a=1,b=2), dict(b=2,a=1))
test_fail(lambda: test_eq([1,2], 1), contains="==")
test_fail(lambda: test_eq(None, np.array([1,2])), contains="==")
test_eq({'a', 'b', 'c'}, {'c', 'a', 'b'})

df1 = pd.DataFrame(dict(a=[1,2],b=['a','b']))
df2 = pd.DataFrame(dict(a=[1,2],b=['a','b']))
df3 = pd.DataFrame(dict(a=[1,2],b=['a','c']))

test_eq(df1,df2)
test_eq(df1.a,df2.a)
test_fail(lambda: test_eq(df1,df3), contains='==')
class T(pd.Series): pass
test_eq(df1.iloc[0], T(df2.iloc[0]))

test_eq(torch.zeros(10), torch.zeros(10, dtype=torch.float64))
test_eq(torch.zeros(10), torch.ones(10)-1)
test_fail(lambda:test_eq(torch.zeros(10), torch.ones(1, 10)), contains='==')
test_eq(torch.zeros(3), [0,0,0])

test_eq_type[source]

test_eq_type(a, b)

test that a==b and are same type

test_eq_type(1,1)
test_fail(lambda: test_eq_type(1,1.))
test_eq_type([1,1],[1,1])
test_fail(lambda: test_eq_type([1,1],(1,1)))
test_fail(lambda: test_eq_type([1,1],[1,1.]))

test_ne[source]

test_ne(a, b)

test that a!=b

test_ne([1,2],[1])
test_ne([1,2],[1,3])
test_ne(array([1,2]),array([1,1]))
test_ne(array([1,2]),array([1,1]))
test_ne([array([1,2]),3],[array([1,2])])
test_ne([3,4],array([3]))
test_ne([3,4],array([3,5]))
test_ne(dict(a=1,b=2), ['a', 'b'])
test_ne(['a', 'b'], dict(a=1,b=2))

is_close[source]

is_close(a, b, eps=1e-05)

Is a within eps of b

test_close[source]

test_close(a, b, eps=1e-05)

test that a is within eps of b

test_close(1,1.001,eps=1e-2)
test_fail(lambda: test_close(1,1.001))
test_close([-0.001,1.001], [0.,1.], eps=1e-2)
test_close(np.array([-0.001,1.001]), np.array([0.,1.]), eps=1e-2)
test_close(array([-0.001,1.001]), array([0.,1.]), eps=1e-2)

test_is[source]

test_is(a, b)

test that a is b

test_fail(lambda: test_is([1], [1]))
a = [1]
test_is(a, a)

test_shuffled[source]

test_shuffled(a, b)

test that a and b are shuffled versions of the same sequence of items

a = list(range(50))
b = copy(a)
random.shuffle(b)
test_shuffled(a,b)
test_fail(lambda:test_shuffled(a,a))

a = 'abc'
b = 'abcabc'
test_fail(lambda:test_shuffled(a,b))

a = ['a', 42, True] 
b = [42, True, 'a']
test_shuffled(a,b)

test_stdout[source]

test_stdout(f, exp, regex=False)

Test that f prints exp to stdout, optionally checking as regex

test_stdout(lambda: print('hi'), 'hi')
test_fail(lambda: test_stdout(lambda: print('hi'), 'ho'))
test_stdout(lambda: 1+1, '')
test_stdout(lambda: print('hi there!'), r'^hi.*!$', regex=True)

test_warns[source]

test_warns(f, show=False)

test_warns(lambda: warnings.warn("Oh no!"), {})
test_fail(lambda: test_warns(lambda: 2+2))

test_warns(lambda: warnings.warn("Oh no!"), show=True)

<class 'UserWarning'>: Oh no!

im = Image.open(TEST_IMAGE).resize((128,128)); im

im = Image.open(TEST_IMAGE_BW).resize((128,128)); im

test_fig_exists[source]

test_fig_exists(ax)

Test there is a figure displayed in ax

fig,ax = plt.subplots()
ax.imshow(array(im));

test_fig_exists(ax)

class ExceptionExpected[source]

ExceptionExpected(ex=Exception, regex='')

Context manager that tests if an exception is raised

def _tst_1(): assert False, "This is a test"
def _tst_2(): raise SyntaxError

with ExceptionExpected(): _tst_1()
with ExceptionExpected(ex=AssertionError, regex="This is a test"): _tst_1()
with ExceptionExpected(ex=SyntaxError): _tst_2()

exception is an abbreviation for ExceptionExpected().

with exception: _tst_1()