Added note on the circular dependence of CMV on accurate field positi…

…ons to the demos.

docs/sphinx/source/demos/field_demo.ipynb

@@ -593,7 +593,11 @@
   },
   {
    "cell_type": "markdown",
-   "source": "It's necessary to provide the CMV for each time period. These precomputed values for the time periods in the data file are provided here. It's also possible to compute them directly using methods in `solarspatialtools.cmv`. Note that it's necessary for the CMVs to be roughly perpendicular, because the matrix calculation associated with triangulating the predicted position becomes singular for CMVs that are too close to parallel (or anti-parallel).",
+   "source": [
+    "It's necessary to provide the CMV for each time period. These precomputed values for the time periods in the data file are provided here. It's also possible to compute them directly using methods in `solarspatialtools.cmv`. Note that it's necessary for the CMVs to be roughly perpendicular, because the matrix calculation associated with triangulating the predicted position becomes singular for CMVs that are too close to parallel (or anti-parallel).\n",
+    "\n",
+    "One other potential issue with CMVs is that the `spatialsolartools.cmv` module is also based on accurate knowledge of the combiner positions. In our experience, even plants with small numbers of scrambled combiners can still produce useful CMVs, because the position errors are averaged out when including all the combiners in the entire plant. A completely scrambled plant with no confidence in any of the combiner positions would likely require special handling, or CMVs from an independent source. But users should be aware of this as a potential limitation of the technique for very low confidence plant maps.  "
+   ],
    "metadata": {
     "collapsed": false
    },

docs/sphinx/source/demos/field_demo.py

@@ -13,7 +13,13 @@
 # two time periods A) and B) that represent two different CMV periods
 datafile = "data/sample_plant_2.h5"
 
-# Input the CMVs, see cmv_demo.py for examples of how to calculate these
+# Input the CMVs, see cmv_demo.py for examples of how to calculate these. It's
+# important to note that the CMVs as calculated using the CMV module would be
+# dependant on accurate field positions. So it's important in this step that
+# the CMVs either come from an independent source, or that the errors in the
+# field positions are isolated incidents, and the location data for the plant
+# as a whole is somewhat accurate. Plants that were completely scrambled would
+# likely require special treatment that we haven't tested the method on.
 cmv_a = spatial.pol2rect(9.52, 0.62)
 cmv_b = spatial.pol2rect(8.47, 2.17)