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Remove mov_nanvar_cumsum() and mov_nanstd_cumsum().
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The cumsum method can give negative var.
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@kwgoodman
kwgoodman committed Nov 6, 2010
1 parent e4fccfd commit cd15272
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Showing 2 changed files with 9 additions and 119 deletions.
122 changes: 6 additions & 116 deletions la/farray/mov.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def mov_sum(arr, window, axis=-1, method='filter'):
elif method == 'loop':
y = mov_func_loop(np.sum, arr, window, axis=axis)
else:
msg = "`method` must be 'filter', 'cumsum', 'strides', or 'loop'."
msg = "`method` must be 'filter', 'strides', or 'loop'."
raise ValueError, msg
return y

Expand Down Expand Up @@ -154,7 +154,7 @@ def mov_mean(arr, window, axis=-1, method='filter'):
elif method == 'loop':
y = mov_func_loop(np.mean, arr, window, axis=axis)
else:
msg = "`method` must be 'filter', 'cumsum', 'strides', or 'loop'."
msg = "`method` must be 'filter', 'strides', or 'loop'."
raise ValueError, msg
return y

Expand Down Expand Up @@ -282,21 +282,19 @@ def mov_var(arr, window, axis=-1, method='filter'):
elif method == 'loop':
y = mov_func_loop(np.var, arr, window, axis=axis)
else:
msg = "`method` must be 'filter', 'cumsum', 'strides', or 'loop'."
msg = "`method` must be 'filter', 'strides', or 'loop'."
raise ValueError, msg
return y

def mov_nanvar(arr, window, axis=-1, method='filter'):
if method == 'filter':
y = mov_nanvar_filter(arr, window, axis=axis)
elif method == 'cumsum':
y = mov_nanvar_cumsum(arr, window, axis=axis)
elif method == 'strides':
y = mov_func_strides(nanvar, arr, window, axis=axis)
elif method == 'loop':
y = mov_func_loop(nanvar, arr, window, axis=axis)
else:
msg = "`method` must be 'filter', 'cumsum', 'strides', or 'loop'."
msg = "`method` must be 'filter', 'strides', or 'loop'."
raise ValueError, msg
return y

Expand Down Expand Up @@ -345,83 +343,6 @@ def mov_nanvar_filter(arr, window, axis=-1):
arr[nrr == window] = np.nan
return arr

def mov_nanvar_cumsum(arr, window, axis=-1):
"""
A moving window variance along the specified axis, ignoring NaNs.
Parameters
----------
arr : ndarray
Input array.
window : int
The number of elements in the moving window.
axis : int, optional
The axis over which to find the moving variance. By default the moving
variance is taken over the last axis (-1).
Returns
-------
y : ndarray
The moving variance of the input array along the specified axis. The
output has the same shape as the input.
Examples
--------
TODO: examples
"""

# Check input
if window < 1:
raise ValueError, 'window must be at least 1.'
if window > arr.shape[axis]:
raise ValueError, 'Window is too big.'

# Set missing values to 0
m = ismissing(arr)
arr = arr.astype(float)
arr[m] = 0

# Cumsum
csx = arr.cumsum(axis)
arr *= arr
csx2 = arr.cumsum(axis)

# Set up indexes
index1 = [slice(None)] * arr.ndim
index2 = list(index1)
index3 = list(index1)
index1[axis] = slice(window - 1, None)
index2[axis] = slice(None, -window)
index3[axis] = slice(1, None)

# Make moving sum
msx = csx[index1]
msx[index3] = msx[index3] - csx[index2]
csm = (~m).cumsum(axis)
msm = csm[index1]
msm[index3] = msm[index3] - csm[index2]

# Fill in windows with all missing values
msx[msm == 0] = np.nan

# Make moving sum
msx2 = csx2[index1]
msx2[index3] = msx2[index3] - csx2[index2]

# Calc
msx *= msx
msx /= msm
msx2 -= msx
msx2 /= msm
msx2[msx2 < 0] = 0

# Pad to get back to original shape
arr.fill(np.nan)
arr[index1] = msx2

return arr

# STD -----------------------------------------------------------------------

def mov_std(arr, window, axis=-1, method='filter'):
Expand All @@ -432,21 +353,19 @@ def mov_std(arr, window, axis=-1, method='filter'):
elif method == 'loop':
y = mov_func_loop(np.std, arr, window, axis=axis)
else:
msg = "`method` must be 'filter', 'cumsum', 'strides', or 'loop'."
msg = "`method` must be 'filter', 'strides', or 'loop'."
raise ValueError, msg
return y

def mov_nanstd(arr, window, axis=-1, method='filter'):
if method == 'filter':
y = mov_nanstd_filter(arr, window, axis=axis)
elif method == 'cumsum':
y = mov_nanstd_cumsum(arr, window, axis=axis)
elif method == 'strides':
y = mov_func_strides(nanstd, arr, window, axis=axis)
elif method == 'loop':
y = mov_func_loop(nanstd, arr, window, axis=axis)
else:
msg = "`method` must be 'filter', 'cumsum', 'strides', or 'loop'."
msg = "`method` must be 'filter', 'strides', or 'loop'."
raise ValueError, msg
return y

Expand All @@ -472,35 +391,6 @@ def mov_nanstd_filter(arr, window, axis=-1):
np.sqrt(y, y)
return y

def mov_nanstd_cumsum(arr, window, axis=-1):
"""
Moving window standard deviation along the specified axis, ignoring NaNs.
Parameters
----------
arr : ndarray
Input array.
window : int
The number of elements in the moving window.
axis : int, optional
The axis over which to find the moving standard deviation. By default
the moving standard deviation is taken over the last axis (-1).
Returns
-------
y : ndarray
The moving standard deviation of the input array along the specified
axis. The output has the same shape as the input.
Examples
--------
TODO: examples
"""
y = mov_nanvar_cumsum(arr, window, axis=axis)
np.sqrt(y, y)
return y

# MIN -----------------------------------------------------------------------

def mov_min(arr, window, axis=-1, method='filter'):
Expand Down
6 changes: 3 additions & 3 deletions la/farray/tests/mov_test.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ def mov_unit_maker(func, arrfunc, methods):
else:
d = func(arr, window=w, axis=axis, method=method)
err_msg = msg % (func.__name__, method, arr.ndim, w, axis)
assert_array_almost_equal(actual, d, 7, err_msg)
assert_array_almost_equal(actual, d, 10, err_msg)

def test_mov_sum():
"Test mov_sum."
Expand Down Expand Up @@ -66,7 +66,7 @@ def test_mov_var():

def test_mov_nanvar():
"Test mov_nanvar."
methods = ('cumsum', 'filter', 'strides', 'func_loop', 'func_strides')
methods = ('filter', 'strides', 'func_loop', 'func_strides')
yield mov_unit_maker, mov_nanvar, nanvar, methods

def test_mov_std():
Expand All @@ -76,7 +76,7 @@ def test_mov_std():

def test_mov_nanstd():
"Test mov_nanstd."
methods = ('cumsum', 'filter', 'strides', 'func_loop', 'func_strides')
methods = ('filter', 'strides', 'func_loop', 'func_strides')
yield mov_unit_maker, mov_nanstd, nanstd, methods

def test_mov_max():
Expand Down

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