xbob -> bob

.travis.yml

@@ -25,8 +25,8 @@ install:
   - "python bootstrap.py"
   - "CFLAGS=-coverage ./bin/buildout"
 script:
-  - "./bin/python -c 'from xbob.measure import get_config; print(get_config())'"
-  - "./bin/coverage run --source=xbob.measure ./bin/nosetests -sv"
+  - "./bin/python -c 'from bob.measure import get_config; print(get_config())'"
+  - "./bin/coverage run --source=bob.measure ./bin/nosetests -sv"
   - "./bin/sphinx-build -b doctest doc sphinx"
   - "./bin/sphinx-build -b html doc sphinx"
 after_success:

MANIFEST.in

@@ -1,4 +1,4 @@
 include LICENSE README.rst bootstrap.py buildout.cfg
 recursive-include doc conf.py *.rst
-recursive-include xbob *.cpp
-recursive-include xbob/measure/data *.*
+recursive-include bob *.cpp
+recursive-include bob/measure/data *.*

README.rst

@@ -2,16 +2,16 @@
 .. Andre Anjos <andre.anjos@idiap.ch>
 .. Thu 29 Aug 2013 16:07:57 CEST
 
-.. image:: https://travis-ci.org/bioidiap/xbob.measure.svg?branch=master
-   :target: https://travis-ci.org/bioidiap/xbob.measure
-.. image:: https://coveralls.io/repos/bioidiap/xbob.measure/badge.png
-   :target: https://coveralls.io/r/bioidiap/xbob.measure
-.. image:: http://img.shields.io/github/tag/bioidiap/xbob.measure.png
-   :target: https://github.com/bioidiap/xbob.measure
-.. image:: http://img.shields.io/pypi/v/xbob.measure.png
-   :target: https://pypi.python.org/pypi/xbob.measure
-.. image:: http://img.shields.io/pypi/dm/xbob.measure.png
-   :target: https://pypi.python.org/pypi/xbob.measure
+.. image:: https://travis-ci.org/bioidiap/bob.measure.svg?branch=master
+   :target: https://travis-ci.org/bioidiap/bob.measure
+.. image:: https://coveralls.io/repos/bioidiap/bob.measure/badge.png
+   :target: https://coveralls.io/r/bioidiap/bob.measure
+.. image:: http://img.shields.io/github/tag/bioidiap/bob.measure.png
+   :target: https://github.com/bioidiap/bob.measure
+.. image:: http://img.shields.io/pypi/v/bob.measure.png
+   :target: https://pypi.python.org/pypi/bob.measure
+.. image:: http://img.shields.io/pypi/dm/bob.measure.png
+   :target: https://pypi.python.org/pypi/bob.measure
 
 =================================
  Python bindings for bob.measure
@@ -42,7 +42,7 @@ Testing
 
 You can run a set of tests using the nose test runner::
 
-  $ nosetests -sv xbob.measure
+  $ nosetests -sv bob.measure
 
 .. warning::
 
@@ -59,7 +59,7 @@ You can run our documentation tests using sphinx itself::
 
 You can test overall test coverage with::
 
-  $ nosetests --with-coverage --cover-package=xbob.measure
+  $ nosetests --with-coverage --cover-package=bob.measure
 
 The ``coverage`` egg must be installed for this to work properly.
 

xbob/measure/__init__.py → bob/measure/__init__.py

@@ -74,7 +74,7 @@ def recognition_rate(cmc_scores):
   The input has a specific format, which is a list of two-element tuples.  Each
   of the tuples contains the negative and the positive scores for one test
   item.  To read the lists from score files in 4 or 5 column format, please use
-  the :py:func:`xbob.measure.load.cmc_four_column` or
+  the :py:func:`bob.measure.load.cmc_four_column` or
   :py:func:`bob.measure.load.cmc_five_column` function.
 
   The recognition rate is defined as the number of test items, for which the
@@ -100,7 +100,7 @@ def cmc(cmc_scores):
   The input has a specific format, which is a list of two-element tuples.  Each
   of the tuples contains the negative and the positive scores for one test
   item.  To read the lists from score files in 4 or 5 column format, please use
-  the :py:func:`xbob.measure.load.cmc_four_column` or
+  the :py:func:`bob.measure.load.cmc_four_column` or
   :py:func:`bob.measure.load.cmc_five_column` function.
 
   For each test item the probability that the rank r of the positive score is

xbob/measure/calibration.py → bob/measure/calibration.py

@@ -23,7 +23,7 @@ def cllr(negatives, positives):
 def min_cllr(negatives, positives):
   """Computes the 'minimum cost of log likelihood ratio' measure as given in the bosaris toolkit"""
 
-  from xbob.math import pavx
+  from bob.math import pavx
 
   # first, sort both scores
   neg = sorted(negatives)

xbob/measure/data/make-examples.py → bob/measure/data/make-examples.py

@@ -10,7 +10,7 @@
 For separability information, please consult: http://en.wikipedia.org/wiki/File:Anderson%27s_Iris_data_set.png
 """
 
-import xbob.io
+import bob.io
 
 iris_columns = {
     'sepal.length': 0,
@@ -45,8 +45,8 @@ def example1():
   Separation threshold is about 3.
   """
   data = loaddata('iris.data', 'petal.length')
-  xbob.io.save(data['setosa'], 'linsep-negatives.hdf5')
-  xbob.io.save(data['virginica'],'linsep-positives.hdf5')
+  bob.io.save(data['setosa'], 'linsep-negatives.hdf5')
+  bob.io.save(data['virginica'],'linsep-positives.hdf5')
 
 def example2():
   """In the second example we will get a non-linearly separable set of scores:
@@ -58,8 +58,8 @@ def example2():
   Separation threshold is about 5 (min. HTER).
   """
   data = loaddata('iris.data', 'sepal.length')
-  xbob.io.save(data['setosa'], 'nonsep-negatives.hdf5')
-  xbob.io.save(data['versicolor'], 'nonsep-positives.hdf5')
+  bob.io.save(data['setosa'], 'nonsep-negatives.hdf5')
+  bob.io.save(data['versicolor'], 'nonsep-positives.hdf5')
 
 def main():
   """Generates data for all examples."""

xbob/measure/load.py → bob/measure/load.py

@@ -86,7 +86,7 @@ def cmc_four_column(filename):
   more are allowed.
 
   The result of this function can directly be passed to, e.g., the
-  :py:func:`xbob.measure.cmc` function.
+  :py:func:`bob.measure.cmc` function.
   """
 
   # read four column list
@@ -204,7 +204,7 @@ def cmc_five_column(filename):
   more are allowed.
 
   The result of this function can directly be passed to, e.g., the
-  :py:func:`xbob.measure.cmc` function.
+  :py:func:`bob.measure.cmc` function.
   """
 
   # read four column list

xbob/measure/main.cpp → bob/measure/main.cpp

@@ -8,8 +8,8 @@
 #ifdef NO_IMPORT_ARRAY
 #undef NO_IMPORT_ARRAY
 #endif
-#include <xbob.blitz/cppapi.h>
-#include <xbob.blitz/cleanup.h>
+#include <bob.blitz/cppapi.h>
+#include <bob.blitz/cleanup.h>
 #include <bob/measure/error.h>
 
 static int double1d_converter(PyObject* o, PyBlitzArrayObject** a) {
@@ -41,7 +41,7 @@ half-total error rate at that position.\n\
 The EPC curve plots the HTER on the test set for various values of\n\
 'cost'. For each value of 'cost', a threshold is found that provides\n\
 the minimum weighted error (see\n\
-:py:func:`xbob.measure.min_weighted_error_rate_threshold()`)\n\
+:py:func:`bob.measure.min_weighted_error_rate_threshold()`)\n\
 on the development set. Each threshold is consecutively applied to\n\
 the test set and the resulting HTER values are plotted in the EPC.\n\
 \n\
@@ -1077,7 +1077,7 @@ PyDoc_STRVAR(module_docstr, "Bob metrics and performance figures");
 #if PY_VERSION_HEX >= 0x03000000
 static PyModuleDef module_definition = {
   PyModuleDef_HEAD_INIT,
-  XBOB_EXT_MODULE_NAME,
+  BOB_EXT_MODULE_NAME,
   module_docstr,
   -1,
   module_methods,
@@ -1090,23 +1090,23 @@ static PyObject* create_module (void) {
 # if PY_VERSION_HEX >= 0x03000000
   PyObject* m = PyModule_Create(&module_definition);
 # else
-  PyObject* m = Py_InitModule3(XBOB_EXT_MODULE_NAME, module_methods, module_docstr);
+  PyObject* m = Py_InitModule3(BOB_EXT_MODULE_NAME, module_methods, module_docstr);
 # endif
   if (!m) return 0;
   auto m_ = make_safe(m); ///< protects against early returns
 
-  if (PyModule_AddStringConstant(m, "__version__", XBOB_EXT_MODULE_VERSION) < 0)
+  if (PyModule_AddStringConstant(m, "__version__", BOB_EXT_MODULE_VERSION) < 0)
     return 0;
 
-  /* imports xbob.blitz C-API + dependencies */
-  if (import_xbob_blitz() < 0) return 0;
+  /* imports bob.blitz C-API + dependencies */
+  if (import_bob_blitz() < 0) return 0;
 
   Py_INCREF(m);
   return m;
 
 }
 
-PyMODINIT_FUNC XBOB_EXT_ENTRY_NAME (void) {
+PyMODINIT_FUNC BOB_EXT_ENTRY_NAME (void) {
 # if PY_VERSION_HEX >= 0x03000000
   return
 # endif

xbob/measure/plot.py → bob/measure/plot.py

@@ -231,26 +231,26 @@ def det(negatives, positives, npoints=100, axisfontsize='x-small', **kwargs):
     If you wish to reset axis zooming, you must use the gaussian scale rather
     than the visual marks showed at the plot, which are just there for
     displaying purposes. The real axis scale is based on the
-    xbob.measure.ppndf() method. For example, if you wish to set the x and y
+    bob.measure.ppndf() method. For example, if you wish to set the x and y
     axis to display data between 1% and 40% here is the recipe:
 
     .. code-block:: python
 
-      import xbob.measure
+      import bob.measure
       import matplotlib.pyplot as mpl
-      xbob.measure.plot.det(...) #call this as many times as you need
+      bob.measure.plot.det(...) #call this as many times as you need
       #AFTER you plot the DET curve, just set the axis in this way:
-      mpl.axis([xbob.measure.ppndf(k/100.0) for k in (1, 40, 1, 40)])
+      mpl.axis([bob.measure.ppndf(k/100.0) for k in (1, 40, 1, 40)])
 
     We provide a convenient way for you to do the above in this module. So,
-    optionally, you may use the xbob.measure.plot.det_axis() method like this:
+    optionally, you may use the bob.measure.plot.det_axis() method like this:
 
     .. code-block:: python
 
-      import xbob.measure
-      xbob.measure.plot.det(...)
+      import bob.measure
+      bob.measure.plot.det(...)
       # please note we convert percentage values in det_axis()
-      xbob.measure.plot.det_axis([1, 40, 1, 40])
+      bob.measure.plot.det_axis([1, 40, 1, 40])
 
   Return value is the matplotlib line that was added as defined by the
   matplotlib.pyplot.plot() command.
@@ -308,8 +308,8 @@ def det_axis(v, **kwargs):
   """Sets the axis in a DET plot.
 
   This method wraps the matplotlib.pyplot.axis() by calling
-  xbob.measure.ppndf() on the values passed by the user so they are meaningful
-  in a DET plot as performed by xbob.measure.plot.det().
+  bob.measure.ppndf() on the values passed by the user so they are meaningful
+  in a DET plot as performed by bob.measure.plot.det().
 
   Keyword parameters:
 

xbob/measure/script/apply_threshold.py → bob/measure/script/apply_threshold.py

@@ -52,7 +52,7 @@ def get_options(user_input):
   parser.add_argument('-t', '--threshold', dest='thres', default=None,
       type=float, help="The threshold value to apply", metavar="FLOAT")
   parser.add_argument('-p', '--parser', dest="parser", default="4column",
-      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of xbob.measure.load.split_four_column() for details", metavar="NAME.FUNCTION")
+      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of bob.measure.load.split_four_column() for details", metavar="NAME.FUNCTION")
 
   # This option is not normally shown to the user...
   parser.add_argument("--self-test",

xbob/measure/script/compute_perf.py → bob/measure/script/compute_perf.py

@@ -157,7 +157,7 @@ def get_options(user_input):
   parser.add_argument('-x', '--no-plot', dest="doplot", default=True,
       action='store_false', help="If set, then I'll execute no plotting")
   parser.add_argument('-p', '--parser', dest="parser", default="4column",
-      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of xbob.measure.load.split_four_column() for details", metavar="NAME.FUNCTION")
+      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of bob.measure.load.split_four_column() for details", metavar="NAME.FUNCTION")
 
   # This option is not normally shown to the user...
   parser.add_argument("--self-test",

xbob/measure/script/eval_threshold.py → bob/measure/script/eval_threshold.py

@@ -77,7 +77,7 @@ def get_options(user_input):
   parser.add_argument('-w', '--cost', dest='cost', default=0.5,
       type=float, help="The value w of the cost when minimizing using the minimum weighter error rate (mwer) criterium. This value is ignored for eer or mhter criteria.", metavar="FLOAT")
   parser.add_argument('-p', '--parser', dest="parser", default="4column",
-      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of xbob.measure.load.split_four_column() for details", metavar="NAME.FUNCTION")
+      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of bob.measure.load.split_four_column() for details", metavar="NAME.FUNCTION")
 
   # This option is not normally shown to the user...
   parser.add_argument("--self-test",

xbob/measure/script/recurse_results.py → bob/measure/script/recurse_results.py

@@ -62,7 +62,7 @@ def get_args():
   parser.add_argument('--self-test', action='store_true', help=argparse.SUPPRESS)
 
   parser.add_argument('-p', '--parser', dest="parser", default="4column", metavar="NAME.FUNCTION",
-      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of xbob.measure.load.split_four_column() for details")
+      help="Name of a known parser or of a python-importable function that can parse your input files and return a tuple (negatives, positives) as blitz 1-D arrays of 64-bit floats. Consult the API of bob.measure.load.split_four_column() for details")
 
   # parse arguments
   args = parser.parse_args()