Minor tweaks to the documentation.

README.rst

@@ -101,9 +101,9 @@ You use *Quantity* to convert numbers and units in various forms to quantities:
    >>> print(Fhy)
    1.4204 GHz
 
-   >>> Rsense = Quantity('1e-4 Ohms')
+   >>> Rsense = Quantity('1e-4 Ω')
    >>> print(Rsense)
-   100 uOhms
+   100 uΩ
 
    >>> cost = Quantity('$11_200_000')
    >>> print(cost)
@@ -128,7 +128,7 @@ the quantity:
    22400000.0
 
    >>> Rsense.units
-   'Ohms'
+   'Ω'
 
    >>> str(Tboil)
    '100 °C'

doc/examples.rst

@@ -186,6 +186,10 @@ included a width specification, but in the second the desired unit of measure
 was specified (*B*), which caused the underlying value to be converted from bits 
 to bytes.
 
+Also it is important to recognize that *QuantiPhy* is using decimal rather than 
+binary scale factors. So 5 GB is 5 gigabyte and not 5 gibibyte.  In otherwords 
+5 GB represents 5×10⁹ B and not 5×2³⁰ B.
+
 
 .. _thermal voltage example:
 
@@ -399,32 +403,19 @@ produces an SVG version of the results using MatPlotLib.
     from matplotlib.ticker import FuncFormatter
     import matplotlib.pyplot as pl
     from quantiphy import Quantity
-    Quantity.set_prefs(prec=2)
-
-    # define the axis formatting routines
-    def freq_fmt(val, pos):
-        return Quantity(val, 'Hz').render()
-    freq_formatter = FuncFormatter(freq_fmt)
-
-    def volt_fmt(val, pos):
-        return Quantity(val, 'V').render()
-    volt_formatter = FuncFormatter(volt_fmt)
 
     # read the data from delta-sigma.smpl
     data = np.fromfile('delta-sigma.smpl', sep=' ')
     time, wave = data.reshape((2, len(data)//2), order='F')
 
     # print out basic information about the data
-    timestep = Quantity(time[1] - time[0], 's')
-    nonperiodicity = Quantity(wave[-1] - wave[0], 'V')
-    points = len(time)
-    period = Quantity(timestep * len(time), 's')
-    freq_res = Quantity(1/period, 'Hz')
-    print('timestep:', timestep)
-    print('nonperiodicity:', nonperiodicity)
-    print('timepoints:', points)
-    print('period:', period)
-    print('freq resolution:', freq_res)
+    timestep = Quantity(time[1] - time[0], name='Time step', units='s')
+    nonperiodicity = Quantity(wave[-1] - wave[0], name='Nonperiodicity', units='V')
+    points = Quantity(len(time), name='Time points')
+    period = Quantity(timestep * len(time), name='Period', units='s')
+    freq_res = Quantity(1/period, name='Frequency resolution', units='Hz')
+    with Quantity.prefs(show_label=True, prec=2):
+        print(timestep, nonperiodicity, points, period, freq_res, sep='\n')
 
     # create the window
     window = np.kaiser(len(time), 11)/0.37
@@ -436,6 +427,10 @@ produces an SVG version of the results using MatPlotLib.
     spectrum = 2*fftshift(fft(windowed))/len(time)
     freq = fftshift(fftfreq(len(wave), timestep))
 
+    # define the axis formatting routines
+    freq_formatter = FuncFormatter(lambda v, p: str(Quantity(v, 'Hz')))
+    volt_formatter = FuncFormatter(lambda v, p: str(Quantity(v, 'V')))
+
     # generate graphs of the resulting spectrum
     fig = pl.figure()
     ax = fig.add_subplot(111)
@@ -450,11 +445,11 @@ produces an SVG version of the results using MatPlotLib.
 
 This script produces the following textual output::
 
-    timestep: 20 ns
-    nonperiodicity: 2.3 pV
-    timepoints: 28k
-    period: 560 us
-    freq resolution: 1.79 kHz
+    Time step = 20 ns
+    Nonperiodicity = 2.3 pV
+    Time points = 28k
+    Period = 560 us
+    Frequency resolution = 1.79 kHz
 
 And the following is one of the two graphs produced:
 

doc/user.rst

@@ -231,15 +231,15 @@ If given as a string, the value may also be the name of a known constant:
     160.22e-21 C
 
 If you only specify a real number for the value, then the units, name, and 
-description do not get values. But even if given as a string or quantity the 
-value may not contain these extra attributes. This is where the second argument, 
-the model, helps.  It may be another quantity or it may be a string.  Any 
-attributes that are not provided by the first argument are taken from the second 
-if available.  If the second argument is a string, it is split.  If it contains 
-one value, that value is taken to be the units, if it contains two, those values 
-are taken to be the name and units, and it it contains more than two, the 
-remaining values are taken to be the description.  If the model is a quantity, 
-only the units are inherited. For example:
+description do not get values. Even if given as a string or quantity the value 
+may not contain these extra attributes. This is where the second argument, the 
+model, helps.  It may be another quantity or it may be a string.  Any attributes 
+that are not provided by the first argument are taken from the second if 
+available.  If the second argument is a string, it is split.  If it contains one 
+value, that value is taken to be the units, if it contains two, those values are 
+taken to be the name and units, and it it contains more than two, the remaining 
+values are taken to be the description.  If the model is a quantity, only the 
+units are inherited. For example:
 
 .. code-block:: python
 
@@ -361,13 +361,17 @@ You can also create your own converters using :class:`quantiphy.UnitConversion`:
     >>> UnitConversion('m', 'pc parsec', 3.0857e16)
     <...>
 
-    >>> d = Quantity('5 μpc', scale='m')
-    >>> print(d)
+    >>> d_sol = Quantity('5 μpc', scale='m')
+    >>> print(d_sol)
     154.28 Gm
 
 This unit conversion says, when converting units of 'm' to either 'pc' or 
 'parsec' multiply by 3.0857e16, when going the other way, divide by 3.0857e16.
 
+    >>> d_sol = Quantity('154.285 Gm', scale='pc')
+    >>> print(d_sol)
+    5 upc
+
 When using unit conversions it is important to only convert to units without 
 scale factors (such as those in the first column above) when creating 
 a quantity.  For example, it is better to convert to 'm' rather than 'cm'.  If