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docs: Fix a few typos
There are small typos in: - doc/devel/documenting.rst - quantities/decorators.py - quantities/quantity.py - quantities/umath.py Fixes: - Should read `updating` rather than `updateing`. - Should read `replace` rather than `repalce`. - Should read `quantities` rather than `quanitities`. - Should read `necessary` rather than `necesssary`. - Should read `mimics` rather than `mimicks`.
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doc/devel/documenting.rst

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@@ -257,7 +257,7 @@ and refer to it using the standard reference syntax::
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Keep in mind that we may want to reorganize the contents later, so
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let's avoid top level names in references like ``user`` or ``devel``
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or ``faq`` unless necesssary, because for example the FAQ "what is a
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or ``faq`` unless necessary, because for example the FAQ "what is a
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backend?" could later become part of the users guide, so the label::
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.. _what-is-a-backend
@@ -286,7 +286,7 @@ Emacs helpers
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There is an emacs mode `rst.el
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<http://docutils.sourceforge.net/tools/editors/emacs/rst.el>`_ which
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automates many important ReST tasks like building and updateing
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automates many important ReST tasks like building and updating
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table-of-contents, and promoting or demoting section headings. Here
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is the basic ``.emacs`` configuration::
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quantities/decorators.py

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@@ -104,7 +104,7 @@ def wrapped_function(*args , **kwargs):
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#convert the list back to a tuple so it can be used as an output
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args = tuple (args)
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#repalce all the quantities in the keyword argument
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#replace all the quantities in the keyword argument
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#dictionary with ndarrays
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for i in kwargs:
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#test if the argument is a quantity

quantities/quantity.py

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@@ -11,7 +11,7 @@
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from .registry import unit_registry
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from .decorators import with_doc
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PREFERRED = [] # List of preferred quanitities for each symbol,
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PREFERRED = [] # List of preferred quantities for each symbol,
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# e.g. PREFERRED = [pq.mV, pq.pA, pq.UnitQuantity('femtocoulomb', 1e-15*pq.C, 'fC')]
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# Intended to be overwritten in down-stream packages
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quantities/umath.py

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@@ -57,7 +57,7 @@ def ediff1d(ary, to_end=None, to_begin=None):
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@with_doc(np.gradient)
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def gradient(f, *varargs):
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# if no sample distances are specified, use dimensionless 1
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# this mimicks the behavior of np.gradient, but perhaps we should
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# this mimics the behavior of np.gradient, but perhaps we should
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# remove this default behavior
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# removed for now::
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#

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