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ENH: _StringFuncParser to get numerical functions callables from strings #7464

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Merged
merged 11 commits into from
Dec 15, 2016
Merged

ENH: _StringFuncParser to get numerical functions callables from strings #7464

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0ad3710
First commit
alvarosg Nov 15, 2016
509ed9f
Creation of class _FuncInfo
alvarosg Nov 15, 2016
bb042f6
Added parameter check, and other feedback from the PR
Nov 16, 2016
4fa16f3
Removed np.cbrt for compatibility with older numpy versions
alvarosg Nov 16, 2016
c5770fd
Corrections from @efiring
alvarosg Dec 13, 2016
69f2a04
Solved compatibility error with python 3+
alvarosg Dec 13, 2016
94a1af0
Feedback from @story645
alvarosg Dec 14, 2016
331d5d3
Tiny change missing @story645
alvarosg Dec 14, 2016
a45f217
Changed exception message
alvarosg Dec 14, 2016
3bd901e
Fixed typo
alvarosg Dec 14, 2016
c143e75
Implemented feedback from @QuLogic
alvarosg Dec 15, 2016
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@alvarosg
alvarosg committed Nov 15, 2016
commit 0ad3710ca6f2c6b6052cbd8ccf347fa1dd11e945
78 changes: 78 additions & 0 deletions 78 lib/matplotlib/cbook.py
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Expand Up @@ -2661,3 +2661,81 @@ def __exit__(self, exc_type, exc_value, traceback):
os.rmdir(path)
except OSError:
pass


class _StringFuncParser(object):
# Each element has:
# -The direct function,
# -The inverse function,
# -A boolean indicating whether the function
# is bounded in the interval 0-1

funcs = {'linear': (lambda x: x, lambda x: x, True),
'quadratic': (lambda x: x**2, lambda x: x**(1. / 2), True),
'cubic': (lambda x: x**3, lambda x: x**(1. / 3), True),
'sqrt': (lambda x: x**(1. / 2), lambda x: x**2, True),
'cbrt': (lambda x: x**(1. / 3), lambda x: x**3, True),
'log10': (lambda x: np.log10(x), lambda x: (10**(x)), False),
'log': (lambda x: np.log(x), lambda x: (np.exp(x)), False),
'power{a}': (lambda x, a: x**a,
lambda x, a: x**(1. / a), True),
'root{a}': (lambda x, a: x**(1. / a),
lambda x, a: x**a, True),
'log10(x+{a})': (lambda x, a: np.log10(x + a),
lambda x, a: 10**x - a, True),
'log(x+{a})': (lambda x, a: np.log(x + a),
lambda x, a: np.exp(x) - a, True)}

def __init__(self, str_func):
self.str_func = str_func

def is_string(self):
return not hasattr(self.str_func, '__call__')

def get_func(self):
return self._get_element(0)

def get_invfunc(self):
return self._get_element(1)

def is_bounded_0_1(self):
return self._get_element(2)

def _get_element(self, ind):
if not self.is_string():
raise ValueError("The argument passed is not a string.")

str_func = six.text_type(self.str_func)
# Checking if it comes with a parameter
param = None
regex = '\{(.*?)\}'
search = re.search(regex, str_func)
if search is not None:
parstring = search.group(1)

try:
param = float(parstring)
except:
raise ValueError("'a' in parametric function strings must be "
"replaced by a number that is not "
"zero, e.g. 'log10(x+{0.1})'.")
if param == 0:
raise ValueError("'a' in parametric function strings must be "
"replaced by a number that is not "
"zero.")
str_func = re.sub(regex, '{a}', str_func)

try:
output = self.funcs[str_func][ind]
if param is not None:
output = (lambda x, output=output: output(x, param))

return output
except KeyError:
raise ValueError("%s: invalid function. The only strings "
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You should raise KeyError if you're explicitly catching the KeyError, otherwise I don't see the point in differentiating the value errors.

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Well, actually from the point of view of the dictionary it is a key error, but from the point of view of the user, it may be seen as a value error in one of the input arguments, as he does not need to know that this is stored in a dictionary and access with the string as a key. In any case, I am happy to switch to KeyError.

The reason I have two cases was to differentiate from TypeError, because in this case it may not be as safe to cast the input as a string for printing the error message. The way the code is now there is no chance it will not be a string, so yes, I am happy to just have a single except.

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So yeah, then I vote for use one for now and split if a need arises.

"recognized as functions are %s." %
(str_func, self.funcs.keys()))
except:
raise ValueError("Invalid function. The only strings recognized "
"as functions are %s." %
(self.funcs.keys()))
33 changes: 33 additions & 0 deletions 33 lib/matplotlib/tests/test_cbook.py
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Expand Up @@ -517,3 +517,36 @@ def test_flatiter():

assert 0 == next(it)
assert 1 == next(it)


class TestFuncParser(object):
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if you insist on writing your own lambdas instead of using numpy, then please test that the lambda functions are correct (no stray typos or the like)

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There is a parametric test for each possible case, and it tests both the direct and the inverse, so it should be ok.

x_test = np.linspace(0.01, 0.5, 3)
validstrings = ['linear', 'quadratic', 'cubic', 'sqrt', 'cbrt',
'log', 'log10', 'power{1.5}', 'root{2.5}',
'log(x+{0.5})', 'log10(x+{0.1})']
results = [(lambda x: x),
(lambda x: x**2),
(lambda x: x**3),
(lambda x: x**(1. / 2)),
(lambda x: x**(1. / 3)),
(lambda x: np.log(x)),
(lambda x: np.log10(x)),
(lambda x: x**1.5),
(lambda x: x**(1 / 2.5)),
(lambda x: np.log(x + 0.5)),
(lambda x: np.log10(x + 0.1))]

@pytest.mark.parametrize("string", validstrings, ids=validstrings)
def test_inverse(self, string):
func_parser = cbook._StringFuncParser(string)
f = func_parser.get_func()
finv = func_parser.get_invfunc()
assert_array_almost_equal(finv(f(self.x_test)), self.x_test)

@pytest.mark.parametrize("string, func",
zip(validstrings, results),
ids=validstrings)
def test_values(self, string, func):
func_parser = cbook._StringFuncParser(string)
f = func_parser.get_func()
assert_array_almost_equal(f(self.x_test), func(self.x_test))
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