Merge branch 'develop' into docs

abhijitm08 · Mar 21, 2019 · 6977565 · 6977565
2 parents 6b8a3be + e8f5417
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diff --git a/HISTORY.rst b/HISTORY.rst
@@ -2,7 +2,12 @@
 History
 =======
 
-0.0.0 (2018-03-22)
+0.3.0 (2019-03-20)
+------------------
+
+Beta stage and first pip release
+
+0.0.1 (2018-03-22)
 ------------------
 
 * First creation of the package.
diff --git a/README.rst b/README.rst
@@ -1,8 +1,10 @@
+
 ===============================
 zfit: scalable pythonic fitting
 ===============================
 
 
+
 .. image:: https://img.shields.io/pypi/v/zfit.svg
         :target: https://pypi.python.org/pypi/zfit
 
@@ -78,6 +80,7 @@ The domain of the PDF is defined by an *observable space*, which is created usin
 
     obs = zfit.Space('x', limits=(-10, 10))
 
+
 Using this domain, we can now create a simple Gaussian PDF. To do this, we define its parameters and their limits using the ``zfit.Parameter`` class and we instantiate the PDF from the zfit library:
 
 .. code-block:: python
@@ -146,10 +149,10 @@ Contributions are always welcome, please have a look at the `Contributing guide`
 
 .. _Contributing guide: CONTRIBUTING.rst
 
-
 Acknowledgements
 ----------------
 
 zfit has been developed with support from the University of Zürich and the Swiss National Science Foundation (SNSF) under contracts 168169 and 174182.
 
-The zfit structure is based on the `TensorFlowAnalysis <https://gitlab.cern.ch/poluekt/TensorFlowAnalysis>`_ framework developed by Anton Poluektov using the TensorFlow open source library.
+The idea of zfit is inspired by the `TensorFlowAnalysis <https://gitlab.cern.ch/poluekt/TensorFlowAnalysis>`_ framework developed by Anton Poluektov using the TensorFlow open source library.
+
diff --git a/setup.py b/setup.py
@@ -29,35 +29,37 @@
 test_requirements = requirements_dev[requirements_dev_split + 1:]  # +1: skip empty line
 
 setup(
-    author="zfit",
-    author_email='zfit@physik.uzh.ch',
+    author="Jonas Eschle",
+    # author_email='zfit@physik.uzh.ch',
     classifiers=[
-        'Development Status :: 2 - Pre-Alpha',
+        'Development Status :: 4 - Beta',
         'Intended Audience :: Science/Research',
         'License :: OSI Approved :: BSD License',
         'Natural Language :: English',
         'Operating System :: MacOS',
         'Operating System :: Unix',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Topic :: Scientific/Engineering :: Physics',
         ],
-    description="Pythonic, scalable, fitting library for high energy physics",
+
+    maintainer="zfit",
+    maintainer_email='zfit@physik.uzh.ch',
+    description="Scalable, pythonic fitting for high energy physics",
     install_requires=requirements,
     license="BSD 3-Clause",
     long_description=readme + '\n\n' + history,
     include_package_data=True,
-    keywords='zfit',
+    keywords='TensorFlow, model, fitting, scalable, HEP',
     name='zfit',
-    python_requires=">=3.5",
+    python_requires=">=3.6",
     packages=find_packages(include=['zfit', 'zfit.ztf',
                                     'zfit.util', 'zfit.core', "zfit.minimizers", 'zfit.models']),
     setup_requires=setup_requirements,
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/zfit/zfit',
-    version='0.0.0',
+    version='0.3.0',
     zip_safe=False,
     )
diff --git a/zfit/__init__.py b/zfit/__init__.py
@@ -1,20 +1,31 @@
 # -*- coding: utf-8 -*-
 """Top-level package for zfit."""
 
-__author__ = """zfit"""
+__version__ = '0.3.0'
+
+__license__ = "BSD 3-Clause"
+__copyright__ = "Copyright 2018, zfit"
+__status__ = "Beta"
+
+__author__ = "Jonas Eschle"
+__maintainer__ = "zfit"
 __email__ = 'zfit@physik.uzh.ch'
-__version__ = '0.0.0'
+__credits__ = ["Jonas Eschle <jonas.eschle@cern.ch>",
+               "Albert Puig <albert.puig@cern.ch",
+               "Rafael Silva Coutinho <rafael.silva.coutinho@cern.ch>", ]
 
 __all__ = ["ztf", "constraint", "pdf", "minimize", "loss", "core", "data", "func",
            "Parameter", "ComposedParameter", "ComplexParameter", "convert_to_parameter",
            "Space", "convert_to_space", "supports",
            "run", "settings"]
 
 from . import ztf
+from .settings import ztypes
 
 import tensorflow as tf
 
 tf.get_variable_scope().set_use_resource(True)
+tf.get_variable_scope().set_dtype(ztypes.float)
 
 from . import constraint, pdf, minimize, loss, core, data, func
 from .core.parameter import Parameter, ComposedParameter, ComplexParameter, convert_to_parameter

diff --git a/zfit/core/basemodel.py b/zfit/core/basemodel.py
@@ -296,7 +296,13 @@ def integrate(self, limits: ztyping.LimitsType, norm_range: ztyping.LimitsType =
         limits = self._check_input_limits(limits=limits)
         integral = self._single_hook_integrate(limits=limits, norm_range=norm_range, name=name)
         if isinstance(integral, tf.Tensor):
-            assert integral.shape.as_list() == [], "Error in integral creation, should return with shape ()"
+            if not integral.shape.as_list() == []:
+                raise ShapeIncompatibleError("Error in integral creation, should return an integral "
+                                             "with shape () (resp. [] as list), current shape "
+                                             "{}. If you registered an analytic integral which is used"
+                                             "now, make sure to return a scalar and not a tensor "
+                                             "(typically: shape is (1,) insead of () -> return tensor[0] "
+                                             "instead of tensor)".format(integral.shape.as_list()))
         return integral
 
     def _single_hook_integrate(self, limits, norm_range, name):