Author: tlinnet
Date: Sat May 10 18:57:16 2014
New Revision: 23149
URL: http://svn.gna.org/viewcvs/relax?rev=23149&view=rev
Log:
Moved numpy_future from lib to extern.
Extern is special package for external software or code that is bundled with
relax.
task #7792: (https://gna.org/task/?7792) Make the dx.map write suggest chi
surface values.
Added:
trunk/extern/numpy_future.py
- copied, changed from r23148, trunk/lib/numpy_future.py
Removed:
trunk/lib/numpy_future.py
Modified:
trunk/extern/__init__.py
trunk/lib/__init__.py
trunk/pipe_control/opendx.py
Modified: trunk/extern/__init__.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/extern/__init__.py?rev=23149&r1=23148&r2=23149&view=diff
==============================================================================
--- trunk/extern/__init__.py (original)
+++ trunk/extern/__init__.py Sat May 10 18:57:16 2014
@@ -23,5 +23,6 @@
"""External Python modules bundled with relax."""
__all__ = [
+ 'numpy_future',
'sobol'
]
Copied: trunk/extern/numpy_future.py (from r23148, trunk/lib/numpy_future.py)
URL:
http://svn.gna.org/viewcvs/relax/trunk/extern/numpy_future.py?p2=trunk/extern/numpy_future.py&p1=trunk/lib/numpy_future.py&r1=23148&r2=23149&rev=23149&view=diff
==============================================================================
(empty)
Modified: trunk/lib/__init__.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/lib/__init__.py?rev=23149&r1=23148&r2=23149&view=diff
==============================================================================
--- trunk/lib/__init__.py (original)
+++ trunk/lib/__init__.py Sat May 10 18:57:16 2014
@@ -43,7 +43,6 @@
'mathematics',
'model_selection',
'nmr',
- 'numpy_future',
'optimisation',
'order',
'periodic_table',
Removed: trunk/lib/numpy_future.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/lib/numpy_future.py?rev=23148&view=auto
==============================================================================
--- trunk/lib/numpy_future.py (original)
+++ trunk/lib/numpy_future.py (removed)
@@ -1,178 +0,0 @@
-#####################################################################################################################################################
-#
#
-# Copyright (c) 2005-2013, NumPy Developers.
#
-#
#
-# All rights reserved.
#
-#
#
-# Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
#
-#
#
-# 1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
#
-# 2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions #
-# and the following disclaimer in the documentation and/or other
materials provided with the distribution.
#
-# 3. Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived from this
#
-# software without specific prior written permission.
#
-#
#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT #
-# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT #
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON #
-# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE #
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
-#
#
-# The full license for NumPy is provided at:
http://www.numpy.org/license.html.
#
-# This license is known under the terms: 3-clause license ("Revised BSD
License", "New BSD License", or "Modified BSD License"). #
-# This can be found here: http://opensource.org/licenses/BSD-3-Clause
#
-#
#
-####################################################################################################################################################
-
-# Module docstring.
-"""Module for implementing numpy function code from higher versions of
numpy. The relax dependencies listed at
-U{the download page of
relax<http://www.nmr-relax.com/download.html#Source_code_release>}, currently
only requires 1.0.4."""
-
-# Python module imports.
-import numpy as np
-from numpy import add, array, isscalar, sort
-
-#####################################################################
-# This is from source code of Numpy v. 1.6.1. #
-#####################################################################
-
-def percentile(a, q, axis=None, out=None, overwrite_input=False):
- """
- Compute the qth percentile of the data along the specified axis.
-
- Returns the qth percentile of the array elements.
-
- Parameters
- ----------
- a : array_like
- Input array or object that can be converted to an array.
- q : float in range of [0,100] (or sequence of floats)
- Percentile to compute which must be between 0 and 100 inclusive.
- axis : int, optional
- Axis along which the percentiles are computed. The default (None)
- is to compute the median along a flattened version of the array.
- out : ndarray, optional
- Alternative output array in which to place the result. It must
- have the same shape and buffer length as the expected output,
- but the type (of the output) will be cast if necessary.
- overwrite_input : bool, optional
- If True, then allow use of memory of input array `a` for
- calculations. The input array will be modified by the call to
- median. This will save memory when you do not need to preserve
- the contents of the input array. Treat the input as undefined,
- but it will probably be fully or partially sorted.
- Default is False. Note that, if `overwrite_input` is True and the
- input is not already an array, an error will be raised.
-
- Returns
- -------
- pcntile : ndarray
- A new array holding the result (unless `out` is specified, in
- which case that array is returned instead). If the input contains
- integers, or floats of smaller precision than 64, then the output
- data-type is float64. Otherwise, the output data-type is the same
- as that of the input.
-
- See Also
- --------
- mean, median
-
- Notes
- -----
- Given a vector V of length N, the qth percentile of V is the qth ranked
- value in a sorted copy of V. A weighted average of the two nearest
- neighbors is used if the normalized ranking does not match q exactly.
- The same as the median if ``q=0.5``, the same as the minimum if ``q=0``
- and the same as the maximum if ``q=1``.
-
- Examples
- --------
- >>> a = np.array([[10, 7, 4], [3, 2, 1]])
- >>> a
- array([[10, 7, 4],
- [ 3, 2, 1]])
- >>> np.percentile(a, 50)
- 3.5
- >>> np.percentile(a, 0.5, axis=0)
- array([ 6.5, 4.5, 2.5])
- >>> np.percentile(a, 50, axis=1)
- array([ 7., 2.])
-
- >>> m = np.percentile(a, 50, axis=0)
- >>> out = np.zeros_like(m)
- >>> np.percentile(a, 50, axis=0, out=m)
- array([ 6.5, 4.5, 2.5])
- >>> m
- array([ 6.5, 4.5, 2.5])
-
- >>> b = a.copy()
- >>> np.percentile(b, 50, axis=1, overwrite_input=True)
- array([ 7., 2.])
- >>> assert not np.all(a==b)
- >>> b = a.copy()
- >>> np.percentile(b, 50, axis=None, overwrite_input=True)
- 3.5
-
- """
- a = np.asarray(a)
-
- if q == 0:
- return a.min(axis=axis, out=out)
- elif q == 100:
- return a.max(axis=axis, out=out)
-
- if overwrite_input:
- if axis is None:
- sorted = a.ravel()
- sorted.sort()
- else:
- a.sort(axis=axis)
- sorted = a
- else:
- sorted = sort(a, axis=axis)
- if axis is None:
- axis = 0
-
- return _compute_qth_percentile(sorted, q, axis, out)
-
-
-#####################################################################
-# This is from source code of Numpy v. 1.6.1. #
-#####################################################################
-
-# handle sequence of q's without calling sort multiple times
-def _compute_qth_percentile(sorted, q, axis, out):
- if not isscalar(q):
- p = [_compute_qth_percentile(sorted, qi, axis, None)
- for qi in q]
-
- if out is not None:
- out.flat = p
-
- return p
-
- q = q / 100.0
- if (q < 0) or (q > 1):
- raise ValueError, "percentile must be either in the range [0,100]"
-
- indexer = [slice(None)] * sorted.ndim
- Nx = sorted.shape[axis]
- index = q*(Nx-1)
- i = int(index)
- if i == index:
- indexer[axis] = slice(i, i+1)
- weights = array(1)
- sumval = 1.0
- else:
- indexer[axis] = slice(i, i+2)
- j = i + 1
- weights = array([(j - index), (index - i)],float)
- wshape = [1]*sorted.ndim
- wshape[axis] = 2
- weights.shape = wshape
- sumval = weights.sum()
-
- # Use add.reduce in both cases to coerce data type as well as
- # check and use out array.
- return add.reduce(sorted[indexer]*weights, axis=axis, out=out)/sumval
Modified: trunk/pipe_control/opendx.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/pipe_control/opendx.py?rev=23149&r1=23148&r2=23149&view=diff
==============================================================================
--- trunk/pipe_control/opendx.py (original)
+++ trunk/pipe_control/opendx.py Sat May 10 18:57:16 2014
@@ -30,7 +30,7 @@
# relax module imports.
from lib.errors import RelaxError
from lib.io import open_write_file
-from lib.numpy_future import percentile
+from extern.numpy_future import percentile
from lib.software.opendx.files import write_config, write_general,
write_point, write_program
from pipe_control import value
from specific_analyses.api import return_api
r23149 - in /trunk: extern/__init__.py extern/numpy_future.py lib/__init__.py lib/numpy_future.py pipe_control/opendx.py