Masked array operations¶
Constants¶
| ma.MaskType | Numpy’s Boolean type. |
Creation¶
From existing data¶
| ma.masked_array | An array class with possibly masked values. |
| ma.array(data[, dtype, copy, order, mask, ...]) | An array class with possibly masked values. |
| ma.copy | copy |
| ma.frombuffer(buffer[, dtype, count, offset]) | Interpret a buffer as a 1-dimensional array. |
| ma.fromfunction(function, shape, **kwargs) | Construct an array by executing a function over each coordinate. |
| ma.MaskedArray.copy([order]) | Return a copy of the array. |
Ones and zeros¶
| ma.empty(shape[, dtype, order]) | Return a new array of given shape and type, without initializing entries. |
| ma.empty_like(a[, dtype, order, subok]) | Return a new array with the same shape and type as a given array. |
| ma.masked_all(shape[, dtype]) | Empty masked array with all elements masked. |
| ma.masked_all_like(arr) | Empty masked array with the properties of an existing array. |
| ma.ones(shape[, dtype, order]) | Return a new array of given shape and type, filled with ones. |
| ma.zeros(shape[, dtype, order]) | Return a new array of given shape and type, filled with zeros. |
Inspecting the array¶
| ma.all(self[, axis, out]) | Check if all of the elements of a are true. |
| ma.any(self[, axis, out]) | Check if any of the elements of a are true. |
| ma.count(a[, axis]) | Count the non-masked elements of the array along the given axis. |
| ma.count_masked(arr[, axis]) | Count the number of masked elements along the given axis. |
| ma.getmask(a) | Return the mask of a masked array, or nomask. |
| ma.getmaskarray(arr) | Return the mask of a masked array, or full boolean array of False. |
| ma.getdata(a[, subok]) | Return the data of a masked array as an ndarray. |
| ma.nonzero(self) | Return the indices of unmasked elements that are not zero. |
| ma.shape(obj) | Return the shape of an array. |
| ma.size(obj[, axis]) | Return the number of elements along a given axis. |
| ma.is_masked(x) | Determine whether input has masked values. |
| ma.is_mask(m) | Return True if m is a valid, standard mask. |
| ma.MaskedArray.data | Return the current data, as a view of the original underlying data. |
| ma.MaskedArray.mask | Mask |
| ma.MaskedArray.recordmask | Return the mask of the records. |
| ma.MaskedArray.all([axis, out]) | Check if all of the elements of a are true. |
| ma.MaskedArray.any([axis, out]) | Check if any of the elements of a are true. |
| ma.MaskedArray.count([axis]) | Count the non-masked elements of the array along the given axis. |
| ma.MaskedArray.nonzero() | Return the indices of unmasked elements that are not zero. |
| ma.shape(obj) | Return the shape of an array. |
| ma.size(obj[, axis]) | Return the number of elements along a given axis. |
Manipulating a MaskedArray¶
Changing the shape¶
| ma.ravel(self[, order]) | Returns a 1D version of self, as a view. |
| ma.reshape(a, new_shape[, order]) | Returns an array containing the same data with a new shape. |
| ma.resize(x, new_shape) | Return a new masked array with the specified size and shape. |
| ma.MaskedArray.flatten([order]) | Return a copy of the array collapsed into one dimension. |
| ma.MaskedArray.ravel([order]) | Returns a 1D version of self, as a view. |
| ma.MaskedArray.reshape(*s, **kwargs) | Give a new shape to the array without changing its data. |
| ma.MaskedArray.resize(newshape[, refcheck, ...]) |
Modifying axes¶
| ma.swapaxes | swapaxes |
| ma.transpose(a[, axes]) | Permute the dimensions of an array. |
| ma.MaskedArray.swapaxes(axis1, axis2) | Return a view of the array with axis1 and axis2 interchanged. |
| ma.MaskedArray.transpose(*axes) | Returns a view of the array with axes transposed. |
Changing the number of dimensions¶
| ma.atleast_1d(*arys) | Convert inputs to arrays with at least one dimension. |
| ma.atleast_2d(*arys) | View inputs as arrays with at least two dimensions. |
| ma.atleast_3d(*arys) | View inputs as arrays with at least three dimensions. |
| ma.expand_dims(x, axis) | Expand the shape of an array. |
| ma.squeeze(a[, axis]) | Remove single-dimensional entries from the shape of an array. |
| ma.MaskedArray.squeeze([axis]) | Remove single-dimensional entries from the shape of a. |
| ma.column_stack(tup) | Stack 1-D arrays as columns into a 2-D array. |
| ma.concatenate(arrays[, axis]) | Concatenate a sequence of arrays along the given axis. |
| ma.dstack(tup) | Stack arrays in sequence depth wise (along third axis). |
| ma.hstack(tup) | Stack arrays in sequence horizontally (column wise). |
| ma.hsplit(ary, indices_or_sections) | Split an array into multiple sub-arrays horizontally (column-wise). |
| ma.mr_ | Translate slice objects to concatenation along the first axis. |
| ma.row_stack(tup) | Stack arrays in sequence vertically (row wise). |
| ma.vstack(tup) | Stack arrays in sequence vertically (row wise). |
Joining arrays¶
| ma.column_stack(tup) | Stack 1-D arrays as columns into a 2-D array. |
| ma.concatenate(arrays[, axis]) | Concatenate a sequence of arrays along the given axis. |
| ma.append(a, b[, axis]) | Append values to the end of an array. |
| ma.dstack(tup) | Stack arrays in sequence depth wise (along third axis). |
| ma.hstack(tup) | Stack arrays in sequence horizontally (column wise). |
| ma.vstack(tup) | Stack arrays in sequence vertically (row wise). |
Operations on masks¶
Creating a mask¶
| ma.make_mask(m[, copy, shrink, dtype]) | Create a boolean mask from an array. |
| ma.make_mask_none(newshape[, dtype]) | Return a boolean mask of the given shape, filled with False. |
| ma.mask_or(m1, m2[, copy, shrink]) | Combine two masks with the logical_or operator. |
| ma.make_mask_descr(ndtype) | Construct a dtype description list from a given dtype. |
Accessing a mask¶
| ma.getmask(a) | Return the mask of a masked array, or nomask. |
| ma.getmaskarray(arr) | Return the mask of a masked array, or full boolean array of False. |
| ma.masked_array.mask | Mask |
Finding masked data¶
| ma.flatnotmasked_contiguous(a) | Find contiguous unmasked data in a masked array along the given axis. |
| ma.flatnotmasked_edges(a) | Find the indices of the first and last unmasked values. |
| ma.notmasked_contiguous(a[, axis]) | Find contiguous unmasked data in a masked array along the given axis. |
| ma.notmasked_edges(a[, axis]) | Find the indices of the first and last unmasked values along an axis. |
| ma.clump_masked(a) | Returns a list of slices corresponding to the masked clumps of a 1-D array. |
| ma.clump_unmasked(a) | Return list of slices corresponding to the unmasked clumps of a 1-D array. |
Modifying a mask¶
| ma.mask_cols(a[, axis]) | Mask columns of a 2D array that contain masked values. |
| ma.mask_or(m1, m2[, copy, shrink]) | Combine two masks with the logical_or operator. |
| ma.mask_rowcols(a[, axis]) | Mask rows and/or columns of a 2D array that contain masked values. |
| ma.mask_rows(a[, axis]) | Mask rows of a 2D array that contain masked values. |
| ma.harden_mask(self) | Force the mask to hard. |
| ma.soften_mask(self) | Force the mask to soft. |
| ma.MaskedArray.harden_mask() | Force the mask to hard. |
| ma.MaskedArray.soften_mask() | Force the mask to soft. |
| ma.MaskedArray.shrink_mask() | Reduce a mask to nomask when possible. |
| ma.MaskedArray.unshare_mask() | Copy the mask and set the sharedmask flag to False. |
Conversion operations¶
> to a masked array¶
| ma.asarray(a[, dtype, order]) | Convert the input to a masked array of the given data-type. |
| ma.asanyarray(a[, dtype]) | Convert the input to a masked array, conserving subclasses. |
| ma.fix_invalid(a[, mask, copy, fill_value]) | Return input with invalid data masked and replaced by a fill value. |
| ma.masked_equal(x, value[, copy]) | Mask an array where equal to a given value. |
| ma.masked_greater(x, value[, copy]) | Mask an array where greater than a given value. |
| ma.masked_greater_equal(x, value[, copy]) | Mask an array where greater than or equal to a given value. |
| ma.masked_inside(x, v1, v2[, copy]) | Mask an array inside a given interval. |
| ma.masked_invalid(a[, copy]) | Mask an array where invalid values occur (NaNs or infs). |
| ma.masked_less(x, value[, copy]) | Mask an array where less than a given value. |
| ma.masked_less_equal(x, value[, copy]) | Mask an array where less than or equal to a given value. |
| ma.masked_not_equal(x, value[, copy]) | Mask an array where not equal to a given value. |
| ma.masked_object(x, value[, copy, shrink]) | Mask the array x where the data are exactly equal to value. |
| ma.masked_outside(x, v1, v2[, copy]) | Mask an array outside a given interval. |
| ma.masked_values(x, value[, rtol, atol, ...]) | Mask using floating point equality. |
| ma.masked_where(condition, a[, copy]) | Mask an array where a condition is met. |
> to a ndarray¶
| ma.compress_cols(a) | Suppress whole columns of a 2-D array that contain masked values. |
| ma.compress_rowcols(x[, axis]) | Suppress the rows and/or columns of a 2-D array that contain masked values. |
| ma.compress_rows(a) | Suppress whole rows of a 2-D array that contain masked values. |
| ma.compressed(x) | Return all the non-masked data as a 1-D array. |
| ma.filled(a[, fill_value]) | Return input as an array with masked data replaced by a fill value. |
| ma.MaskedArray.compressed() | Return all the non-masked data as a 1-D array. |
| ma.MaskedArray.filled([fill_value]) | Return a copy of self, with masked values filled with a given value. |
> to another object¶
| ma.MaskedArray.tofile(fid[, sep, format]) | Save a masked array to a file in binary format. |
| ma.MaskedArray.tolist([fill_value]) | Return the data portion of the masked array as a hierarchical Python list. |
| ma.MaskedArray.torecords() | Transforms a masked array into a flexible-type array. |
| ma.MaskedArray.tobytes([fill_value, order]) | Return the array data as a string containing the raw bytes in the array. |
Pickling and unpickling¶
| ma.dump(a, F) | Pickle a masked array to a file. |
| ma.dumps(a) | Return a string corresponding to the pickling of a masked array. |
| ma.load(F) | Wrapper around cPickle.load which accepts either a file-like object or a filename. |
| ma.loads(strg) | Load a pickle from the current string. |
Filling a masked array¶
| ma.common_fill_value(a, b) | Return the common filling value of two masked arrays, if any. |
| ma.default_fill_value(obj) | Return the default fill value for the argument object. |
| ma.maximum_fill_value(obj) | Return the minimum value that can be represented by the dtype of an object. |
| ma.maximum_fill_value(obj) | Return the minimum value that can be represented by the dtype of an object. |
| ma.set_fill_value(a, fill_value) | Set the filling value of a, if a is a masked array. |
| ma.MaskedArray.get_fill_value() | Return the filling value of the masked array. |
| ma.MaskedArray.set_fill_value([value]) | Set the filling value of the masked array. |
| ma.MaskedArray.fill_value | Filling value. |
Masked arrays arithmetics¶
Arithmetics¶
| ma.anom(self[, axis, dtype]) | Compute the anomalies (deviations from the arithmetic mean) along the given axis. |
| ma.anomalies(self[, axis, dtype]) | Compute the anomalies (deviations from the arithmetic mean) along the given axis. |
| ma.average(a[, axis, weights, returned]) | Return the weighted average of array over the given axis. |
| ma.conjugate(x[, out]) | Return the complex conjugate, element-wise. |
| ma.corrcoef(x[, y, rowvar, bias, ...]) | Return Pearson product-moment correlation coefficients. |
| ma.cov(x[, y, rowvar, bias, allow_masked, ddof]) | Estimate the covariance matrix. |
| ma.cumsum(self[, axis, dtype, out]) | Return the cumulative sum of the elements along the given axis. |
| ma.cumprod(self[, axis, dtype, out]) | Return the cumulative product of the elements along the given axis. |
| ma.mean(self[, axis, dtype, out]) | Returns the average of the array elements. |
| ma.median(a[, axis, out, overwrite_input]) | Compute the median along the specified axis. |
| ma.power(a, b[, third]) | Returns element-wise base array raised to power from second array. |
| ma.prod(self[, axis, dtype, out]) | Return the product of the array elements over the given axis. |
| ma.std(self[, axis, dtype, out, ddof]) | Compute the standard deviation along the specified axis. |
| ma.sum(self[, axis, dtype, out]) | Return the sum of the array elements over the given axis. |
| ma.var(self[, axis, dtype, out, ddof]) | Compute the variance along the specified axis. |
| ma.MaskedArray.anom([axis, dtype]) | Compute the anomalies (deviations from the arithmetic mean) along the given axis. |
| ma.MaskedArray.cumprod([axis, dtype, out]) | Return the cumulative product of the elements along the given axis. |
| ma.MaskedArray.cumsum([axis, dtype, out]) | Return the cumulative sum of the elements along the given axis. |
| ma.MaskedArray.mean([axis, dtype, out]) | Returns the average of the array elements. |
| ma.MaskedArray.prod([axis, dtype, out]) | Return the product of the array elements over the given axis. |
| ma.MaskedArray.std([axis, dtype, out, ddof]) | Compute the standard deviation along the specified axis. |
| ma.MaskedArray.sum([axis, dtype, out]) | Return the sum of the array elements over the given axis. |
| ma.MaskedArray.var([axis, dtype, out, ddof]) | Compute the variance along the specified axis. |
Minimum/maximum¶
| ma.argmax(a[, axis, fill_value]) | Returns array of indices of the maximum values along the given axis. |
| ma.argmin(a[, axis, fill_value]) | Return array of indices to the minimum values along the given axis. |
| ma.max(obj[, axis, out, fill_value]) | Return the maximum along a given axis. |
| ma.min(obj[, axis, out, fill_value]) | Return the minimum along a given axis. |
| ma.ptp(obj[, axis, out, fill_value]) | Return (maximum - minimum) along the the given dimension (i.e. |
| ma.MaskedArray.argmax([axis, fill_value, out]) | Returns array of indices of the maximum values along the given axis. |
| ma.MaskedArray.argmin([axis, fill_value, out]) | Return array of indices to the minimum values along the given axis. |
| ma.MaskedArray.max([axis, out, fill_value]) | Return the maximum along a given axis. |
| ma.MaskedArray.min([axis, out, fill_value]) | Return the minimum along a given axis. |
| ma.MaskedArray.ptp([axis, out, fill_value]) | Return (maximum - minimum) along the the given dimension (i.e. |
Sorting¶
| ma.argsort(a[, axis, kind, order, fill_value]) | Return an ndarray of indices that sort the array along the specified axis. |
| ma.sort(a[, axis, kind, order, endwith, ...]) | Sort the array, in-place :Parameters: a : array_like Array to be sorted. |
| ma.MaskedArray.argsort([axis, kind, order, ...]) | Return an ndarray of indices that sort the array along the specified axis. |
| ma.MaskedArray.sort([axis, kind, order, ...]) | Sort the array, in-place :Parameters: a : array_like Array to be sorted. |
Algebra¶
| ma.diag(v[, k]) | Extract a diagonal or construct a diagonal array. |
| ma.dot(a, b[, strict]) | Return the dot product of two arrays. |
| ma.identity(n[, dtype]) | Return the identity array. |
| ma.inner(a, b) | Inner product of two arrays. |
| ma.innerproduct(a, b) | Inner product of two arrays. |
| ma.outer(a, b) | Compute the outer product of two vectors. |
| ma.outerproduct(a, b) | Compute the outer product of two vectors. |
| ma.trace(self[, offset, axis1, axis2, ...]) | Return the sum along diagonals of the array. |
| ma.transpose(a[, axes]) | Permute the dimensions of an array. |
| ma.MaskedArray.trace([offset, axis1, axis2, ...]) | Return the sum along diagonals of the array. |
| ma.MaskedArray.transpose(*axes) | Returns a view of the array with axes transposed. |
Polynomial fit¶
| ma.vander(x[, n]) | Generate a Vandermonde matrix. |
| ma.polyfit(x, y, deg[, rcond, full, w, cov]) | Least squares polynomial fit. |
Clipping and rounding¶
| ma.around | Round an array to the given number of decimals. |
| ma.clip(a, a_min, a_max[, out]) | Clip (limit) the values in an array. |
| ma.round(a[, decimals, out]) | Return a copy of a, rounded to ‘decimals’ places. |
| ma.MaskedArray.clip([min, max, out]) | Return an array whose values are limited to [min, max]. |
| ma.MaskedArray.round([decimals, out]) | Return a with each element rounded to the given number of decimals. |
Miscellanea¶
| ma.allequal(a, b[, fill_value]) | Return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked. |
| ma.allclose(a, b[, masked_equal, rtol, atol]) | Returns True if two arrays are element-wise equal within a tolerance. |
| ma.apply_along_axis(func1d, axis, arr, ...) | Apply a function to 1-D slices along the given axis. |
| ma.arange([start,] stop[, step,][, dtype]) | Return evenly spaced values within a given interval. |
| ma.choose(indices, choices[, out, mode]) | Use an index array to construct a new array from a set of choices. |
| ma.ediff1d(arr[, to_end, to_begin]) | Compute the differences between consecutive elements of an array. |
| ma.indices(dimensions[, dtype]) | Return an array representing the indices of a grid. |
| ma.where(condition[, x, y]) | Return a masked array with elements from x or y, depending on condition. |