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Fix for mid date interpolation #2757

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merged 6 commits into from
Jun 20, 2018
Merged

Fix for mid date interpolation #2757

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2418c56
Fix for mid date interpolation
douglasmacdonald Jun 1, 2018
65265f4
Improved handling of dates in interpolate_curve
philippjfr Jun 15, 2018
4fb1b01
Fixed array comparisons for datetimes
philippjfr Jun 15, 2018
ac1c9c9
Added unit tests for interpolate_curve with dates
philippjfr Jun 15, 2018
9866597
Further cleaned up interpolate_curve datetime handling
philippjfr Jun 15, 2018
38d4722
Correctly handle additional value dimensions in Curve interpolation
philippjfr Jun 20, 2018
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4 changes: 4 additions & 0 deletions holoviews/element/comparison.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,6 +251,10 @@ def compare_floats(cls, arr1, arr2, msg='Floats'):
@classmethod
def compare_arrays(cls, arr1, arr2, msg='Arrays'):
try:
if arr1.dtype.kind == 'M':
arr1 = arr1.astype('datetime64[ns]').astype('int64')
if arr2.dtype.kind == 'M':
arr2 = arr2.astype('datetime64[ns]').astype('int64')
assert_array_equal(arr1, arr2)
except:
try:
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82 changes: 53 additions & 29 deletions holoviews/operation/element.py
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Expand Up @@ -674,45 +674,70 @@ class interpolate_curve(Operation):
Controls the transition point of the step along the x-axis.""")

@classmethod
def pts_to_prestep(cls, x, y):
steps = np.zeros((2, 2 * len(x) - 1))
steps[0, 0::2] = x
steps[0, 1::2] = steps[0, 0:-2:2]
steps[1:, 0::2] = y
steps[1:, 1::2] = steps[1:, 2::2]
return steps
def pts_to_prestep(cls, x, values):
steps = np.zeros(2 * len(x) - 1)
value_steps = tuple(np.empty(2 * len(x) - 1, dtype=v.dtype) for v in values)

steps[0::2] = x
steps[1::2] = steps[0:-2:2]

val_arrays = []
for v, s in zip(values, value_steps):
s[0::2] = v
s[1::2] = s[2::2]
val_arrays.append(s)

return steps, tuple(val_arrays)

@classmethod
def pts_to_midstep(cls, x, y):
steps = np.zeros((2, 2 * len(x)))
x = np.asanyarray(x)
steps[0, 1:-1:2] = steps[0, 2::2] = (x[:-1] + x[1:]) / 2
steps[0, 0], steps[0, -1] = x[0], x[-1]
steps[1:, 0::2] = y
steps[1:, 1::2] = steps[1:, 0::2]
return steps
def pts_to_midstep(cls, x, values):
steps = np.zeros(2 * len(x))
value_steps = tuple(np.empty(2 * len(x), dtype=v.dtype) for v in values)

steps[1:-1:2] = steps[2::2] = x[:-1] + (x[1:] - x[:-1])/2
steps[0], steps[-1] = x[0], x[-1]

val_arrays = []
for v, s in zip(values, value_steps):
s[0::2] = v
s[1::2] = s[0::2]
val_arrays.append(s)

return steps, tuple(val_arrays)

@classmethod
def pts_to_poststep(cls, x, y):
steps = np.zeros((2, 2 * len(x) - 1))
steps[0, 0::2] = x
steps[0, 1::2] = steps[0, 2::2]
steps[1:, 0::2] = y
steps[1:, 1::2] = steps[1:, 0:-2:2]
return steps
def pts_to_poststep(cls, x, values):
steps = np.zeros(2 * len(x) - 1)
value_steps = tuple(np.empty(2 * len(x) - 1, dtype=v.dtype) for v in values)

steps[0::2] = x
steps[1::2] = steps[2::2]

val_arrays = []
for v, s in zip(values, value_steps):
s[0::2] = v
s[1::2] = s[0:-2:2]
val_arrays.append(s)

return steps, tuple(val_arrays)

def _process_layer(self, element, key=None):
INTERPOLATE_FUNCS = {'steps-pre': self.pts_to_prestep,
'steps-mid': self.pts_to_midstep,
'steps-post': self.pts_to_poststep}
if self.p.interpolation not in INTERPOLATE_FUNCS:
return element
x, y = element.dimension_values(0), element.dimension_values(1)
if 'f' in (x.dtype.kind, y.dtype.kind):
x, y = x.astype('float'), y.astype('float')
array = INTERPOLATE_FUNCS[self.p.interpolation](x, y)
dvals = tuple(element.dimension_values(d) for d in element.dimensions()[2:])
return element.clone((array[0, :].astype(x.dtype), array[1, :].astype(y.dtype))+dvals)
x = element.dimension_values(0)
dtype = x.dtype
is_datetime = dtype.kind == 'M' or isinstance(x[0], datetime_types)
if is_datetime:
dt_type = dtype if dtype.kind == 'M' else 'datetime64[ns]'
x = x.astype(dt_type).astype('int64')
dvals = tuple(element.dimension_values(d) for d in element.dimensions()[1:])
xs, dvals = INTERPOLATE_FUNCS[self.p.interpolation](x.astype('f'), dvals)
if is_datetime:
xs = xs.astype(dt_type)
return element.clone((xs,)+dvals)

def _process(self, element, key=None):
return element.map(self._process_layer, Element)
Expand Down Expand Up @@ -833,4 +858,3 @@ def _process(self, p, element, ranges={}):
datatype=['dataframe', 'dictionary'])
data[(d1.name, d2.name)] = el
return data

61 changes: 61 additions & 0 deletions tests/operation/testoperation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,16 +192,77 @@ def test_interpolate_curve_pre(self):
curve = Curve([(0, 0), (0, 0.5), (1, 0.5), (1, 1), (2, 1)])
self.assertEqual(interpolated, curve)

def test_interpolate_curve_pre_with_values(self):
interpolated = interpolate_curve(Curve([(0, 0, 'A'), (1, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']),
interpolation='steps-pre')
curve = Curve([(0, 0, 'A'), (0, 0.5, 'B'), (1, 0.5, 'B'), (1, 1, 'C'), (2, 1, 'C')], vdims=['y', 'z'])
self.assertEqual(interpolated, curve)

def test_interpolate_datetime_curve_pre(self):
dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M')
values = [0, 1, 2, 3]
interpolated = interpolate_curve(Curve((dates, values)), interpolation='steps-pre')
dates_interp = np.array([
'2017-01-01T00:00:16.364011520', '2017-01-01T00:00:16.364011520',
'2017-01-02T00:01:05.465745408', '2017-01-02T00:01:05.465745408',
'2017-01-02T23:59:37.128525824', '2017-01-02T23:59:37.128525824',
'2017-01-04T00:00:26.230259712'
], dtype='datetime64[ns]')
curve = Curve((dates_interp, [0, 1, 1, 2, 2, 3, 3]))
self.assertEqual(interpolated, curve)

def test_interpolate_curve_mid(self):
interpolated = interpolate_curve(Curve([0, 0.5, 1]), interpolation='steps-mid')
curve = Curve([(0, 0), (0.5, 0), (0.5, 0.5), (1.5, 0.5), (1.5, 1), (2, 1)])
self.assertEqual(interpolated, curve)

def test_interpolate_curve_mid_with_values(self):
interpolated = interpolate_curve(Curve([(0, 0, 'A'), (1, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']),
interpolation='steps-mid')
curve = Curve([(0, 0, 'A'), (0.5, 0, 'A'), (0.5, 0.5, 'B'),
(1.5, 0.5, 'B'), (1.5, 1, 'C'), (2, 1, 'C')],
vdims=['y', 'z'])
self.assertEqual(interpolated, curve)

def test_interpolate_datetime_curve_mid(self):
dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M')
values = [0, 1, 2, 3]
interpolated = interpolate_curve(Curve((dates, values)), interpolation='steps-mid')
dates_interp = np.array([
'2017-01-01T00:00:16.364011520', '2017-01-01T11:59:32.195401728',
'2017-01-01T11:59:32.195401728', '2017-01-02T12:00:21.297135616',
'2017-01-02T12:00:21.297135616', '2017-01-03T12:01:10.398869504',
'2017-01-03T12:01:10.398869504', '2017-01-04T00:00:26.230259712'
], dtype='datetime64[ns]')
curve = Curve((dates_interp, [0, 0, 1, 1, 2, 2, 3, 3]))
self.assertEqual(interpolated, curve)

def test_interpolate_curve_post(self):
interpolated = interpolate_curve(Curve([0, 0.5, 1]), interpolation='steps-post')
curve = Curve([(0, 0), (1, 0), (1, 0.5), (2, 0.5), (2, 1)])
self.assertEqual(interpolated, curve)

def test_interpolate_curve_post_with_values(self):
interpolated = interpolate_curve(Curve([(0, 0, 'A'), (1, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']),
interpolation='steps-post')
curve = Curve([(0, 0, 'A'), (1, 0, 'A'), (1, 0.5, 'B'),
(2, 0.5, 'B'), (2, 1, 'C')],
vdims=['y', 'z'])
self.assertEqual(interpolated, curve)

def test_interpolate_datetime_curve_post(self):
dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M')
values = [0, 1, 2, 3]
interpolated = interpolate_curve(Curve((dates, values)), interpolation='steps-post')
dates_interp = np.array([
'2017-01-01T00:00:16.364011520', '2017-01-02T00:01:05.465745408',
'2017-01-02T00:01:05.465745408', '2017-01-02T23:59:37.128525824',
'2017-01-02T23:59:37.128525824', '2017-01-04T00:00:26.230259712',
'2017-01-04T00:00:26.230259712'
], dtype='datetime64[ns]')
curve = Curve((dates_interp, [0, 0, 1, 1, 2, 2, 3]))
self.assertEqual(interpolated, curve)

def test_stack_area_overlay(self):
areas = Area([1, 2, 3]) * Area([1, 2, 3])
stacked = Area.stack(areas)
Expand Down