def resolution(x, zero=True):
"""
Compute the resolution of a data vector
Resolution is smallest non-zero distance between adjacent values
Parameters
----------
x : 1D array-like
zero : Boolean
Whether to include zero values in the computation
Result
------
res : resolution of x
If x is an integer array, then the resolution is 1
"""
x = np.asarray(x)
# (unsigned) integers or an effective range of zero
_x = x[~pd.isna(x)]
_x = (x.min(), x.max())
if x.dtype.kind in ('i', 'u') or zero_range(_x):
return 1
x = np.unique(x)
if zero:
x = np.unique(np.hstack([0, x]))
return np.min(np.diff(np.sort(x)))
def resolution(x, zero=True):
"""
Compute the resolution of a data vector
Resolution is smallest non-zero distance between adjacent values
Parameters
----------
x : 1D array-like
zero : Boolean
Whether to include zero values in the computation
Result
------
res : resolution of x
If x is an integer array, then the resolution is 1
"""
x = np.asarray(x)
# (unsigned) integers or an effective range of zero
_x = x[~np.isnan(x)]
_x = (x.min(), x.max())
if x.dtype.kind in ('i', 'u') or zero_range(_x):
return 1
x = np.unique(x)
if zero:
x = np.unique(np.hstack([0, x]))
return np.min(np.diff(np.sort(x)))
def get_breaks(self, limits=None, strict=False):
"""
Generate breaks for the axis or legend
Parameters
----------
limits : list-like | None
If None the self.limits are used
They are expected to be in transformed
space.
strict : bool
If True then the breaks gauranteed to fall within
the limits. e.g. when the legend uses this method.
Returns
-------
out : array-like
Notes
-----
Breaks are calculated in data space and
returned in transformed space since all
data is plotted in transformed space.
"""
if limits is None:
limits = self.limits
# To data space
_limits = self.inverse(limits)
if self.is_empty():
breaks = []
elif self.breaks is None or self.breaks is False:
breaks = []
elif zero_range(_limits):
breaks = [_limits[0]]
elif is_waive(self.breaks):
breaks = self.trans.breaks(_limits)
elif callable(self.breaks):
breaks = self.breaks(_limits)
else:
breaks = self.breaks
breaks = self.transform(breaks)
breaks = np.asarray(breaks)
# At this point, any breaks beyond the limits
# are kept since they may be used to calculate
# minor breaks
if strict:
cond = (breaks >= limits[0]) & (limits[1] >= breaks)
breaks = np.compress(cond, breaks)
return breaks
def test_zero_range():
c = np.array
eps = np.finfo(float).eps
assert zero_range(c((1, 1 + eps)))
assert zero_range(c((1, 1 + 99 * eps)))
# Crossed the tol threshold
# assert zero_range(c((1, 1 + 101 * eps))) is False
assert (not zero_range(c((1, 1 + 101 * eps))))
# Changed tol
assert (not zero_range(c((1, 1 + 2 * eps)), tol=eps))
# Scaling up or down all the values has no effect since
# the values are rescaled to 1 before checking against
# the tolerance
assert zero_range(100000 * c((1, 1 + eps)))
assert (not zero_range(100000 * c((1, 1 + 200 * eps))))
assert zero_range(.00001 * c((1, 1 + eps)))
assert (not zero_range(.00001 * c((1, 1 + 200 * eps))))
# NA values
assert zero_range((1, np.nan))
# Infinite values
assert (not zero_range((1, np.inf)))
assert (not zero_range((-np.inf, np.inf)))
assert zero_range((np.inf, np.inf))
# Single value
assert zero_range(1)
assert zero_range([1])
# length greater than 2
with pytest.raises(ValueError):
zero_range([1, 2, 3])
# datetime - pandas, cpython
x = datetime(2010, 1, 1), datetime(2010, 1, 1)
x2 = datetime(2010, 1, 1), datetime(2020, 1, 1)
x3 = (pd.Timestamp('2010-01-01', tz='US/Eastern'),
pd.Timestamp('2010-01-01', tz='US/Central'))
assert (zero_range(x))
assert (not zero_range(x2))
assert (not zero_range(x3))
# datetime - numpy
x = np.datetime64(7, 'D'), np.datetime64(7, 'D')
x2 = np.datetime64(7, 'D'), np.datetime64(1, 'W')
x3 = np.datetime64(7, 'D'), np.datetime64(1, 'D')
assert (zero_range(x))
assert (zero_range(x2))
assert (not zero_range(x3))
# timedelta - pandas, cpython
x = timedelta(seconds=2010), timedelta(seconds=2010)
x2 = (timedelta(seconds=2010,
microseconds=90), timedelta(seconds=2010, microseconds=34))
x3 = pd.Timedelta(200, 'D'), pd.Timedelta(203, 'D')
assert (zero_range(x))
assert (not zero_range(x2))
assert (not zero_range(x3))
# timedelta - numpy
x = np.timedelta64(7, 'D'), np.timedelta64(7, 'D')
x2 = np.timedelta64(7, 'D'), np.timedelta64(1, 'W')
x3 = np.timedelta64(7, 'D'), np.timedelta64(2, 'D')
assert (zero_range(x))
assert (zero_range(x2))
assert (not zero_range(x3))
# branches #
assert str(zero_range([4, float('nan')])) == 'nan'
assert (not zero_range([4, float('inf')]))
with pytest.raises(TypeError):
zero_range(['a', 'b'])
def test_zero_range():
c = np.array
eps = np.finfo(float).eps
assert zero_range(c((1, 1 + eps)))
assert zero_range(c((1, 1 + 99 * eps)))
# Crossed the tol threshold
# assert zero_range(c((1, 1 + 101 * eps))) is False
assert(not zero_range(c((1, 1 + 101 * eps))))
# Changed tol
assert(not zero_range(c((1, 1 + 2 * eps)), tol=eps))
# Scaling up or down all the values has no effect since
# the values are rescaled to 1 before checking against
# the tolerance
assert zero_range(100000 * c((1, 1 + eps)))
assert(not zero_range(100000 * c((1, 1 + 200 * eps))))
assert zero_range(.00001 * c((1, 1 + eps)))
assert(not zero_range(.00001 * c((1, 1 + 200 * eps))))
# NA values
assert zero_range((1, np.nan))
# Infinite values
assert(not zero_range((1, np.inf)))
assert(not zero_range((-np.inf, np.inf)))
assert zero_range((np.inf, np.inf))
# Single value
assert zero_range(1)
assert zero_range([1])
# length greater than 2
with pytest.raises(ValueError):
zero_range([1, 2, 3])
# datetime - pandas, cpython
x = datetime(2010, 1, 1), datetime(2010, 1, 1)
x2 = datetime(2010, 1, 1), datetime(2020, 1, 1)
x3 = (pd.Timestamp('2010-01-01', tz='US/Eastern'),
pd.Timestamp('2010-01-01', tz='US/Central'))
assert(zero_range(x))
assert(not zero_range(x2))
assert(not zero_range(x3))
# datetime - numpy
x = np.datetime64(7, 'D'), np.datetime64(7, 'D')
x2 = np.datetime64(7, 'D'), np.datetime64(1, 'W')
x3 = np.datetime64(7, 'D'), np.datetime64(1, 'D')
assert(zero_range(x))
assert(zero_range(x2))
assert(not zero_range(x3))
# timedelta - pandas, cpython
x = timedelta(seconds=2010), timedelta(seconds=2010)
x2 = (timedelta(seconds=2010, microseconds=90),
timedelta(seconds=2010, microseconds=34))
x3 = pd.Timedelta(200, 'D'), pd.Timedelta(203, 'D')
assert(zero_range(x))
assert(not zero_range(x2))
assert(not zero_range(x3))
# timedelta - numpy
x = np.timedelta64(7, 'D'), np.timedelta64(7, 'D')
x2 = np.timedelta64(7, 'D'), np.timedelta64(1, 'W')
x3 = np.timedelta64(7, 'D'), np.timedelta64(2, 'D')
assert(zero_range(x))
assert(zero_range(x2))
assert(not zero_range(x3))
# branches #
assert str(zero_range([4, float('nan')])) == 'nan'
assert(not zero_range([4, float('inf')]))
with pytest.raises(TypeError):
zero_range(['a', 'b'])
