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-rw-r--r--numpy/ma/morestats.py16
1 files changed, 8 insertions, 8 deletions
diff --git a/numpy/ma/morestats.py b/numpy/ma/morestats.py
index 9816608b7..7dbc844b0 100644
--- a/numpy/ma/morestats.py
+++ b/numpy/ma/morestats.py
@@ -59,7 +59,7 @@ Returns
Notes
-----
The function is restricted to 2D arrays.
-
+
"""
def _hd_1D(data,prob,var):
"Computes the HD quantiles for a 1D array. Returns nan for invalid data."
@@ -114,7 +114,7 @@ Parameters
Axis along which to compute the quantiles. If None, use a flattened array.
var : boolean
Whether to return the variance of the estimate.
-
+
"""
result = hdquantiles(data,[0.5], axis=axis, var=var)
return result.squeeze()
@@ -137,7 +137,7 @@ Parameters
Notes
-----
The function is restricted to 2D arrays.
-
+
"""
def _hdsd_1D(data,prob):
"Computes the std error for 1D arrays."
@@ -192,7 +192,7 @@ Parameters
Confidence level of the intervals.
axis : int
Axis along which to cut. If None, uses a flattened version of the input.
-
+
"""
data = masked_array(data, copy=False)
trimmed = trim_both(data, proportiontocut=proportiontocut, axis=axis)
@@ -215,7 +215,7 @@ Parameters
Sequence of quantiles to compute.
axis : int
Axis along which to compute the quantiles. If None, use a flattened array.
-
+
"""
def _mjci_1D(data, p):
data = data.compressed()
@@ -345,7 +345,7 @@ def rank_data(data, axis=None, use_missing=False):
along the given axis.
If some values are tied, their rank is averaged.
- If some values are masked, their rank is set to 0 if use_missing is False,
+ If some values are masked, their rank is set to 0 if use_missing is False,
or set to the average rank of the unmasked values if use_missing is True.
Parameters
@@ -353,8 +353,8 @@ def rank_data(data, axis=None, use_missing=False):
data : sequence
Input data. The data is transformed to a masked array
axis : integer
- Axis along which to perform the ranking.
- If None, the array is first flattened. An exception is raised if
+ Axis along which to perform the ranking.
+ If None, the array is first flattened. An exception is raised if
the axis is specified for arrays with a dimension larger than 2
use_missing : boolean
Whether the masked values have a rank of 0 (False) or equal to the