This function creates a metadata dictionary
meant to be stored in a newly created xarray.DataArray's
global attributes. The initial metadata created stores
scale type of the axis (linear, enumerated or none).

This commit changes how the linear and enumerated axes
are assigned. We now look for the "imagej" key in the
xarray's global attributes. If the key is present we look for
dim + "_axis_scale" to assign linear or enumerated axes.

Although the only calibrated axes used by nearly everyone are
DefaultLinearAxis and EnumeratedAxis, I added metadata support
for all CalibratedAxis types (e.g. PolynomialAxis etc...) just to
be thorough. This metadata will be used for matching the Calibrated
Axis type when going back to ImageJ/Java land.

This commit refactors dims._assign_axes() to use
imagej specific metadata attached to a given
xarray.DataArray. If the "imagej" attribute is present
in the xarray's global attributes then information
like scale, origin and the type of CalibratedAxis
(e.g. DefaultLinearAxis, EnumeratedAxis, etc...) can be used
to assign the correct calibrated axis per given dimension
during the net.imagej.Dataset conversion process. If the
desired calibrated axis is not available or the axis is
unknown then we fall back to a DefaultLinearAxis and attempt
to get scale/origin data from the coordinates.

The new "imagej" metadata attribute may not
always be present (depending on the origin of
the xarray). Checking for all attributes makes this
kind of hard, so lets just check for the key we put in!

If a singleton dimension is detected, assign a scale/slope
of 1.

This commit adds a new metadata submodule to handle
all image related metadata functions (e.g. creating/updating the
xarray.DataArray metadata attribute). All metadata related
functions should exist in this submodule.

Gabe suggested using a nice one-liner to get
the CalibratedAxis from the dict instead of
the if/else statement.

The axis submodule has also been streamlined to drop
the CalibratedAxis dict and instead uses a list of Strings.
This avoids Java import errors if a user imports the axis submodule
before initializing ImageJ.

The docstring wasn't clear about what kind of string to send
to and from the calibrated axis helper functions.

The axis data are stored in the scifio.metadata.image
map under the "axes" key. Converting this to a string
is unecessary.

This commit uses the metadata module to create the
metadata for both xarray and java images. Images
can now be transferred back and forth between Java
and Python without loosing the metadata.

I missed this one :(.

This module contains the xarray accessors that
extend the xarray.DataArrays with additional methods.
This module must be imported before the accessors are found.
No other code is needed to attach these accessors.

Once the metadata has been set call the tree()
method to print a dict tree.

This preserves the metadata dict between slices.

The _update() method runs any time the MetadataAccerssor is accessed.
This allows us to update the metadata base by checking the state
of the backing xarray.DataArray. So for example if dimensions change
order or are dropped the "scifio.metadata.image" (if present) metadata
should reflect these changes where appropriate.

The axes were still in ImageJ order. They now match and check
against the dimension order of the parent xarray.DataArray.

Note that the metadata must be updated manually after creation
to update the order from Java to Python.

Use the MetadataAccessor class of the xarray to assign
the correct axes. Fallback to a DefaultLinearAxis if the metadata
is not available.

This is no longer in needed.