Renamed package from 'scidata' to 'xray'

Author: shoyer

README.md

@@ -1,10 +1,13 @@
-# scidata: objects for working with scientific data in Python
+# xray: transparently manipulate scientific datasets in Python
 
-**scidata** is a Python package for working with aligned sets of homogeneous,
+**xray** is a Python package for working with aligned sets of homogeneous,
 n-dimensional arrays. It implements flexible array operations and dataset
 manipulation for in-memory datasets within the [Common Data Model][cdm] widely
 used for self-describing scientific data (netCDF, OpenDAP, etc.).
 
+***Warning: xray is still in its early development phase. Expect the API to
+change.***
+
 ## Main Feaures
 
   - A `DatasetArray` object that is compatible with NumPy's ndarray and ufuncs
@@ -34,14 +37,14 @@ used for self-describing scientific data (netCDF, OpenDAP, etc.).
 
   - [Iris][iris] (supported by the UK Met office) is a similar package
     designed for working with geophysical datasets in Python. Iris provided
-    much of the inspiration for scidata (e.g., scidata's `DatasetArray` is
-    largely based on the Iris `Cube`), but it has several limitations that led
-    us to build scidata instead of extending Iris:
+    much of the inspiration for xray (e.g., xray's `DatasetArray` is largely
+    based on the Iris `Cube`), but it has several limitations that led us to
+    build xray instead of extending Iris:
     1. Iris has essentially one first-class object (the `Cube`) on which it
        attempts to build all functionality (`Coord` supports a much more
-       limited set of functionality). scidata has its equivalent of the Cube
+       limited set of functionality). xray has its equivalent of the Cube
        (the `DatasetArray` object), but it is only a thin wrapper on the more
-       primitive building blocks of Dataset and Variable objects.
+       primitive building blocks of Dataset and Array objects.
     2. Iris has a strict interpretation of [CF conventions][cf], which,
        although a principled choice, we have found to be impractical for
        everyday uses. With Iris, every quantity has physical (SI) units, all
@@ -55,14 +58,14 @@ used for self-describing scientific data (netCDF, OpenDAP, etc.).
        models of how Iris functions work. Moreover, it means that a lot of
        logic (e.g., constraint handling) uses non-vectorized operations. For
        example, extracting all times within a range can be surprisingly slow
-       (e.g., 0.3 seconds vs 3 milliseconds in scidata to select along a time
+       (e.g., 0.3 seconds vs 3 milliseconds in xray to select along a time
        dimension with 10000 elements).
   - [pandas][pandas] is fast and powerful but oriented around working with
     tabular datasets. pandas has experimental N-dimensional panels, but they
     don't support aligned math with other objects. We believe the
     `DatasetArray`/ `Cube` model is better suited to working with scientific
-    datasets. We use pandas internally in scidata to support fast indexing.
-  - [netCDF4-python][nc4] provides scidata's primary interface for working with
+    datasets. We use pandas internally in xray to support fast indexing.
+  - [netCDF4-python][nc4] provides xray's primary interface for working with
     netCDF and OpenDAP datasets.
 
 [pandas]: http://pandas.pydata.org/

doc/conf.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# scidata documentation build configuration file, created by
+# xray documentation build configuration file, created by
 # sphinx-quickstart on Thu Feb  6 18:57:54 2014.
 #
 # This file is execfile()d with the current directory set to its
@@ -53,7 +53,7 @@
 master_doc = 'index'
 
 # General information about the project.
-project = u'scidata'
+project = u'xray'
 copyright = u'2014, Stephan Hoyer and Alex Kleeman'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -186,7 +186,7 @@
 #html_file_suffix = None
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'scidatadoc'
+htmlhelp_basename = 'xraydoc'
 
 
 # -- Options for LaTeX output ---------------------------------------------
@@ -206,7 +206,7 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-  ('index', 'scidata.tex', u'scidata Documentation',
+  ('index', 'xray.tex', u'xray Documentation',
    u'Stephan Hoyer and Alex Kleeman', 'manual'),
 ]
 
@@ -236,7 +236,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    ('index', 'scidata', u'scidata Documentation',
+    ('index', 'xray', u'xray Documentation',
      [u'Stephan Hoyer and Alex Kleeman'], 1)
 ]
 
@@ -250,8 +250,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-  ('index', 'scidata', u'scidata Documentation',
-   u'Stephan Hoyer and Alex Kleeman', 'scidata', 'One line description of project.',
+  ('index', 'xray', u'xray Documentation',
+   u'Stephan Hoyer and Alex Kleeman', 'xray', 'One line description of project.',
    'Miscellaneous'),
 ]
 

doc/index.rst

@@ -3,8 +3,8 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-SciData reference
-=================
+xray reference
+==============
 
 Contents:
 

setup.py

@@ -5,7 +5,7 @@
 except:
     from distutils.core import setup
 
-setup(name='scidata',
+setup(name='xray',
       version='0.1-dev',
       description='Objects for holding self describing scientific data in python',
       author='Stephan Hoyer, Alex Kleeman',
@@ -15,5 +15,5 @@
       tests_require=['nose >= 1.0'],
       url='https://github.com/akleeman/scidata',
       test_suite='nose.collector',
-      packages=['scidata'],
+      packages=['xray'],
       package_dir={'': 'src'})

src/scidata/__init__.py renamed to src/xray/__init__.py

@@ -224,7 +224,7 @@ def __repr__(self):
             contents = ' (%s): %s' % (dim_summary, self.dtype)
         else:
             contents = ': %s' % self.data
-        return '<scidata.%s%s>' % (type(self).__name__, contents)
+        return '<xray.%s%s>' % (type(self).__name__, contents)
 
     def indexed_by(self, **indexers):
         """Return a new variable indexed along the specified dimension(s)
@@ -591,7 +591,7 @@ def _broadcast_variable_data(self, other):
     if isinstance(other, dataset.Dataset):
         raise TypeError('datasets do not support mathematical operations')
     elif all(hasattr(other, attr) for attr in ['dimensions', 'data', 'shape']):
-        # `other` satisfies the scidata.Array API
+        # `other` satisfies the xray.Array API
         new_self, new_other = broadcast_variables(self, other)
         self_data = new_self.data
         other_data = new_other.data

src/scidata/array_.py renamed to src/xray/array_.py

@@ -139,7 +139,7 @@ def __repr__(self):
                              (k, str(v).replace(
                                   '\n', '\n' + ' ' * (len(k) + 4)))
                              for k, v in self.items())
-        return ("<class 'scidata.data.%s' (dict-like)>\n%s"
+        return ("<class 'xray.data.%s' (dict-like)>\n%s"
                 % (type(self).__name__, contents))
 
 
@@ -432,9 +432,8 @@ def __str__(self):
     def __repr__(self):
         dim_summary = ', '.join('%s%s: %s' % ('@' if k in self else '', k, v)
                                 for k, v in self.dimensions.iteritems())
-        return '<scidata.%s (%s): %s>' % (type(self).__name__,
-                                          dim_summary,
-                                          ' '.join(self.noncoordinates))
+        return '<xray.%s (%s): %s>' % (type(self).__name__, dim_summary,
+                                       ' '.join(self.noncoordinates))
 
     def create_variable(self, name, dims, data, attributes=None):
         """Create a new variable and add it to this dataset

src/scidata/backends.py renamed to src/xray/backends.py

@@ -46,7 +46,7 @@ def __init__(self, dataset, focus):
         """
         Parameters
         ----------
-        dataset : scidata.Dataset
+        dataset : xray.Dataset
             The dataset on which to build this data view.
         focus : str
             The name of the "focus variable" in `dataset` on which this object
@@ -155,7 +155,7 @@ def __repr__(self):
             contents = ' (%s): %s' % (dim_summary, self.dtype)
         else:
             contents = ': %s' % self.data
-        return '<scidata.%s %r%s>' % (type(self).__name__, self.focus, contents)
+        return '<xray.%s %r%s>' % (type(self).__name__, self.focus, contents)
 
     def indexed_by(self, **indexers):
         """Return a new dataset array whose dataset is given by indexing along

src/scidata/common.py renamed to src/xray/common.py

@@ -2,7 +2,7 @@
 
 from numpy.testing import assert_array_equal
 
-from scidata import utils
+from xray import utils
 
 
 class TestCase(unittest.TestCase):

src/scidata/conventions.py renamed to src/xray/conventions.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from scidata import Array, Dataset
+from xray import Array, Dataset
 from . import TestCase
 
 
@@ -41,7 +41,7 @@ def test_properties(self):
 
     def test_repr(self):
         v = Array(['time', 'x'], self.d)
-        self.assertEqual('<scidata.Array (time: 10, x: 3): float64>',
+        self.assertEqual('<xray.Array (time: 10, x: 3): float64>',
                          repr(v))
 
     def test_items(self):

src/scidata/dataset.py renamed to src/xray/dataset.py

@@ -8,7 +8,7 @@
 import numpy as np
 import pandas as pd
 
-from scidata import Dataset, DatasetArray, Array, backends, open_dataset
+from xray import Dataset, DatasetArray, Array, backends, open_dataset
 from . import TestCase
 
 
@@ -42,7 +42,7 @@ def get_store(self):
 
     def test_repr(self):
         data = create_test_data(self.get_store())
-        self.assertEqual('<scidata.Dataset (time: 1000, @dim1: 100, '
+        self.assertEqual('<xray.Dataset (time: 1000, @dim1: 100, '
                          '@dim2: 50, @dim3: 10): var1 var2 var3>', repr(data))
 
     def test_init(self):

src/scidata/dataset_array.py renamed to src/xray/dataset_array.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from scidata import Dataset, DatasetArray, Array, intersection
+from xray import Dataset, DatasetArray, Array, intersection
 from . import TestCase, ReturnItem
 
 

src/scidata/groupby.py renamed to src/xray/groupby.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 
-from scidata import utils
+from xray import utils
 from . import TestCase, ReturnItem