PYME.contrib.pad.pad module¶
The pad.py module contains a group of functions to pad values onto the edges of an n-dimensional array.
- PYME.contrib.pad.pad.with_constant(matrix, pad_width=(1,), constant_values=(0,))¶
Pads with a constant value.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- constant_values{tuple of N tuples(before, after), tuple(both,)},
optional
The values to set the padded values to. ((before_len, after_len),) * np.rank(matrix) (len,) is a shortcut for before = after = len for all dimensions
None
uses the entire vector. Default isNone
.
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_minimum
pad.with_median
pad.with_mean
pad.with_linear_ramp
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_constant(a, (2,3), (4,6)) array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
- PYME.contrib.pad.pad.with_linear_ramp(matrix, pad_width=(1,), end_value=(0,))¶
Pads with the linear ramp between end_value and the begining/end of the vector along each axis.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- end_value{tuple of N tuples(before, after), tuple(both,)}, optional
What value should the padded values end with. ((before_len, after_len),) * np.rank(matrix) (len,) is a shortcut for before = after = len for all dimensions
None
uses the entire vector. Default isNone
.
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_minimum
pad.with_median
pad.with_mean
pad.with_constant
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_linear_ramp(a, (2,3), (5,-4)) array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
- PYME.contrib.pad.pad.with_maximum(matrix, pad_width=(1,), stat_len=None)¶
Pads with the maximum value of all or part of the vector along each axis.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(pad,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- stat_len{tuple of N tuples(before, after), tuple(len,)}, optional
How many values at each end of vector to determine the statistic. ((before_len, after_len),) * np.rank(matrix) (len,) is a shortcut for before = after = len for all dimensions
None
uses the entire vector. Default isNone
.
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_minimum
pad.with_median
pad.with_mean
pad.with_constant
pad.with_linear_ramp
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_maximum(a, (2,)) array([5, 5, 1, 2, 3, 4, 5, 5, 5])
>>> pad.with_maximum(a, (1, 7)) array([5, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5])
>>> pad.with_maximum(a, (0, 7)) array([1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5])
- PYME.contrib.pad.pad.with_mean(matrix, pad_width=(1,), stat_len=None)¶
Pads with the mean value of all or part of the vector along each axis.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- stat_len{tuple of N tuples(before, after), tuple(both,)}, optional
How many values at each end of vector to determine the statistic. ((before_len, after_len),) * np.rank(matrix) (len,) is a shortcut for before = after = len for all dimensions
None
uses the entire vector. Default isNone
.
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_minimum
pad.with_median
pad.with_constant
pad.with_linear_ramp
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_mean(a, (2,)) array([3, 3, 1, 2, 3, 4, 5, 3, 3])
- PYME.contrib.pad.pad.with_median(matrix, pad_width=(1,), stat_len=None)¶
Pads with the median value of all or part of the vector along each axis.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- stat_len{tuple of N tuples(before, after), tuple(both,)}, optional
How many values at each end of vector to determine the statistic. ((before_len, after_len),) * np.rank(matrix) (len,) is a shortcut for before = after = len for all dimensions
None
uses the entire vector. Default isNone
.
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_minimum
pad.with_mean
pad.with_constant
pad.with_linear_ramp
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_median(a, (2,)) array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> pad.with_median(a, (4, 0)) array([3, 3, 3, 3, 1, 2, 3, 4, 5])
- PYME.contrib.pad.pad.with_minimum(matrix, pad_width=(1,), stat_len=None)¶
Pads with the minimum value of all or part of the vector along each axis.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- stat_len{tuple of N tuples(before, after), tuple(both,)}, optional
How many values at each end of vector to determine the statistic. ((before_len, after_len),) * np.rank(matrix) (len,) is a shortcut for before = after = len for all dimensions
None
uses the entire vector. Default isNone
.
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_median
pad.with_mean
pad.with_constant
pad.with_linear_ramp
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5, 6] >>> pad.with_minimum(a, (2,)) array([1, 1, 1, 2, 3, 4, 5, 6, 1, 1])
>>> pad.with_minimum(a, (4, 2)) array([1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 1, 1])
>>> a = [[1,2], [3,4]] >>> pad.with_minimum(a, ((3, 2), (2, 3))) array([[1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [3, 3, 3, 4, 3, 3, 3], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1]])
- PYME.contrib.pad.pad.with_reflect(matrix, pad_width=(1,))¶
Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_minimum
pad.with_median
pad.with_mean
pad.with_constant
pad.with_linear_ramp
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_reflect(a, (2,3)) array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
- PYME.contrib.pad.pad.with_wrap(matrix, pad_width=(1,))¶
Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning.
- Parameters
- matrixarray_like of rank N
Input array
- pad_width{tuple of N tuples(before, after), tuple(both,)}, optional
How many values padded to each end of the vector for each axis. ((before, after),) * np.rank(matrix) (pad,) is a shortcut for before = after = pad for all axes Default is (1, ).
- Returns
- outndarray of rank N
Padded array.
See also
pad.with_maximum
pad.with_minimum
pad.with_median
pad.with_mean
pad.with_constant
pad.with_linear_ramp
pad.with_reflect
pad.with_wrap
Examples
>>> import pad >>> a = [1, 2, 3, 4, 5] >>> pad.with_wrap(a, (2,3)) array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])