diff options
Diffstat (limited to 'numpy/ma/mstats.py')
| -rw-r--r-- | numpy/ma/mstats.py | 38 |
1 files changed, 19 insertions, 19 deletions
diff --git a/numpy/ma/mstats.py b/numpy/ma/mstats.py index 7dc5a7cc3..093215e30 100644 --- a/numpy/ma/mstats.py +++ b/numpy/ma/mstats.py @@ -33,9 +33,9 @@ __all__ = ['cov','meppf','plotting_positions','meppf','mquantiles', def winsorize(data, alpha=0.2): """Returns a Winsorized version of the input array. - - The (alpha/2.) lowest values are set to the (alpha/2.)th percentile, - and the (alpha/2.) highest values are set to the (1-alpha/2.)th + + The (alpha/2.) lowest values are set to the (alpha/2.)th percentile, + and the (alpha/2.) highest values are set to the (1-alpha/2.)th percentile. Masked values are skipped. @@ -44,7 +44,7 @@ def winsorize(data, alpha=0.2): data : ndarray Input data to Winsorize. The data is first flattened. alpha : float - Percentage of total Winsorization: alpha/2. on the left, + Percentage of total Winsorization: alpha/2. on the left, alpha/2. on the right """ @@ -57,8 +57,8 @@ def winsorize(data, alpha=0.2): #.............................................................................. def trim_both(data, proportiontocut=0.2, axis=None): - """Trims the data by masking the int(trim*n) smallest and int(trim*n) - largest values of data along the given axis, where n is the number + """Trims the data by masking the int(trim*n) smallest and int(trim*n) + largest values of data along the given axis, where n is the number of unmasked values. Parameters @@ -66,11 +66,11 @@ def trim_both(data, proportiontocut=0.2, axis=None): data : ndarray Data to trim. proportiontocut : float - Percentage of trimming. If n is the number of unmasked values + Percentage of trimming. If n is the number of unmasked values before trimming, the number of values after trimming is: (1-2*trim)*n. axis : int - Axis along which to perform the trimming. + Axis along which to perform the trimming. If None, the input array is first flattened. Notes @@ -99,7 +99,7 @@ def trim_both(data, proportiontocut=0.2, axis=None): #.............................................................................. def trim_tail(data, proportiontocut=0.2, tail='left', axis=None): - """Trims the data by masking int(trim*n) values from ONE tail of the + """Trims the data by masking int(trim*n) values from ONE tail of the data along the given axis, where n is the number of unmasked values. Parameters @@ -107,16 +107,16 @@ def trim_tail(data, proportiontocut=0.2, tail='left', axis=None): data : ndarray Data to trim. proportiontocut : float - Percentage of trimming. If n is the number of unmasked values - before trimming, the number of values after trimming is + Percentage of trimming. If n is the number of unmasked values + before trimming, the number of values after trimming is (1-trim)*n. tail : string - Trimming direction, in ('left', 'right'). - If left, the ``proportiontocut`` lowest values are set to the - corresponding percentile. If right, the ``proportiontocut`` + Trimming direction, in ('left', 'right'). + If left, the ``proportiontocut`` lowest values are set to the + corresponding percentile. If right, the ``proportiontocut`` highest values are used instead. axis : int - Axis along which to perform the trimming. + Axis along which to perform the trimming. If None, the input array is first flattened. Notes @@ -158,7 +158,7 @@ def trim_tail(data, proportiontocut=0.2, tail='left', axis=None): #.............................................................................. def trimmed_mean(data, proportiontocut=0.2, axis=None): - """Returns the trimmed mean of the data along the given axis. + """Returns the trimmed mean of the data along the given axis. Trimming is performed on both ends of the distribution. Parameters @@ -169,7 +169,7 @@ def trimmed_mean(data, proportiontocut=0.2, axis=None): Proportion of the data to cut from each side of the data . As a result, (2*proportiontocut*n) values are actually trimmed. axis : int - Axis along which to perform the trimming. + Axis along which to perform the trimming. If None, the input array is first flattened. """ @@ -188,7 +188,7 @@ def trimmed_stde(data, proportiontocut=0.2, axis=None): Proportion of the data to cut from each side of the data . As a result, (2*proportiontocut*n) values are actually trimmed. axis : int - Axis along which to perform the trimming. + Axis along which to perform the trimming. If None, the input array is first flattened. Notes @@ -222,7 +222,7 @@ median along the given axis. data : ndarray Data to trim. axis : int - Axis along which to perform the trimming. + Axis along which to perform the trimming. If None, the input array is first flattened. """ |