.. examples
Examples
========
Altitude vs Pressure
--------------------
This is a simple scatter plot of a standard atmospheric pressure vs altitude.
.. image:: ./_static/alt_vs_press_excel.png
:width: 50%
`Click images to see full size`
:download:`download alt_vs_press.ods <./_static/alt_vs_press.ods>`
:download:`download alt_vs_press.py <./_static/alt_vs_press.py>`
The python code below generates the plot shown above. The basic approach is to create
a ``list_of_rows`` containing the data labels, units and values. Make a data sheet with
the ``add_sheet`` command. Create a scatter plot with the ``add_scatter`` command. And finally,
save as an ``ods`` file with the ``save`` command.
The ``list_of_rows`` input variable has the following format.
* Row 1 holds all of the labels of each column
* Row 2 holds the units for each column (use '' for no units)
* Rows 3 through N hold data values.
After saving, the spreadsheet can be launched with the ``launch_application`` command and either
Excel, LibreOffice or OpenOffice will open depending on which is linked to ods files on your system. (Note that the
``save`` command also has a ``launch=True`` option, shown in the next example.)
.. code:: python
from odscharts.spreadsheet import SpreadSheet
mySprSht = SpreadSheet()
list_of_rows = [['Altitude','Pressure'], ['feet','psia'],
[0, 14.7], [5000, 12.23], [10000, 10.11],
[30000, 4.36],[60000, 1.04]]
mySprSht.add_sheet('Alt_Data', list_of_rows)
mySprSht.add_scatter( 'Alt_P_Plot', 'Alt_Data',
title='Pressure vs Altitude',
xlabel='Altitude',
ylabel='Pressure',
xcol=1,
ycolL=[2])
mySprSht.save( filename='alt_vs_press' )
mySprSht.launch_application()
Trig Functions
--------------
The trig function example builds a large ``list_of_rows`` variable to display sine and cosine functions.
See :ref:`internal_trig_example` on the QuickStart page for further details of the Trig Function example.
:download:`download trig_plot.ods <./_static/trig_plot.ods>`
:download:`download trig_funcs.py <./_static/trig_funcs.py>`
Line Styles
-----------
There are 10 dashed line styles in ``ODSCharts``. This example shows them all.
Notice that OpenOffice renders the various line styles more distinctly than Excel.
(i.e. A number of the styles are hard to distinguish from one another in Excel.)
.. raw:: html
| Excel Output | OpenOffice Output |
|
|
`Click images to see full size`
:download:`download line_style_plot.ods <./_static/line_style_plot.ods>`
:download:`download line_styles.py <./_static/line_styles.py>`
The python code below generates the plots shown above.
.. code:: python
"""
This example demonstrates the use of line styles
"""
from math import *
from odscharts.spreadsheet import SpreadSheet
from odscharts.line_styles import LINE_STYLE_LIST as sL
mySprSht = SpreadSheet()
rev_orderL = list( range(10,-1,-1) )
toprow = ['Line Style Index']
unitsrow = ['']
toprow.extend( ['%i) %s'%(i,sL[i]) for i in rev_orderL] )
unitsrow.extend( ['' for i in rev_orderL] )
xbegrow = [0]
xbegrow.extend( [i for i in rev_orderL] )
xendrow = [1]
xendrow.extend( [i for i in rev_orderL] )
list_of_rows = [toprow, unitsrow, xbegrow, xendrow]
mySprSht.add_sheet('Line_Style_Data', list_of_rows)
mySprSht.add_scatter( 'Line_Style', 'Line_Style_Data',
title='Line Styles',
xlabel='',
ylabel='Index of Line Style',
xcol=1,
ycolL=[2,3,4,5,6,7,8,9,10,11,12],
lineStyleL=[i for i in rev_orderL],
lineThkL = [1.5],
showMarkerL=[0])
mySprSht.setYrange( ymin=-1, ymax=None, plot_sheetname=None)
mySprSht.save( filename='line_style_plot', launch=True)
Metabolic Rate vs Body Mass
---------------------------
This example demonstrates log plots using body_mass vs metabolic_rate
data taken from: http://www.datacarpentry.org/semester-biology/assignments/r-4/
(Data Carpentry for Biologists)
``XY_Math`` was used to fit the data to a power curve.
See the ``XY_Math`` docs at: ``_.
It resulted in the following curve fit.::
Fitting Non-Linear Equation to dataset with Percent Error.
y = A*x**c
A = 0.022110831039
c = 0.745374452331
x = Body Mass (g)
y = Metabolic Rate (mLO2/hr)
Correlation Coefficient = 0.935357443771
Standard Deviation = 31.8545944339
Percent Standard Deviation = 20.5192091584%
y = 0.022110831039*x**0.745374452331
When I think of data that spans many orders of magnitude, I think of the historical plots of
metabolic rate vs body mass (see plot lower right)
ODSCharts is used here to create a similar plot spanning a mere 3 orders of magnitude as compared
to the 18 orders of magnitude in the historical plot.
.. image:: ./_static/metabolic_excel.png
:width: 50%
.. image:: ./_static/metabolicRateAnimals2.jpg
:width: 40%
`Click images to see full size`
:download:`download mass_vs_rate.ods <./_static/mass_vs_rate.ods>`
:download:`download metabolic.py <./_static/metabolic.py>`
The more interesting ODSCharts features used in this plot include
* Adding a column of calculated values to the data
* Setting the ``logx`` and ``logy`` flags
* Showing markers-only for the data and line-only for the curve fit
The python code is shown below.
.. code:: python
# Grams
body_mass = [32000, 37800, 347000, 4200, 196500, 100000, 4290,
32000, 65000, 69125, 9600, 133300, 150000, 407000, 115000, 67000,
325000, 21500, 58588, 65320, 85000, 135000, 20500, 1613, 1618]
# mLO2/hr
metabolic_rate = [49.984, 51.981, 306.770, 10.075, 230.073,
148.949, 11.966, 46.414, 123.287, 106.663, 20.619, 180.150,
200.830, 224.779, 148.940, 112.430, 286.847, 46.347, 142.863,
106.670, 119.660, 104.150, 33.165, 4.900, 4.865]
dataL = []
for bm, mr in zip(body_mass, metabolic_rate):
dataL.append( [bm,mr] )
dataL.sort()
# create a list of curve fit data
for row in dataL:
bm, mr = row
row.append( 0.022110831039*bm**0.745374452331 )
list_of_rows = [['Body Mass','Rate Data','Fitted Rate'], ['g','mLO2/hr','mLO2/hr']]
list_of_rows.extend( dataL )
mySprSht = SpreadSheet()
mySprSht.add_sheet('MassRate_Data', list_of_rows)
mySprSht.add_scatter( 'MassRate_Plot', 'MassRate_Data',
title='Metabolic Rate vs Body Mass',
xlabel='Body Mass',
ylabel='Metabolic Rate',
xcol=1, logx=1, logy=1,
showLineL=[0,1], showMarkerL=[1,0],
ycolL=[2,3])
mySprSht.setXrange(xmin=1000)
mySprSht.save( filename='mass_vs_rate', launch=True )
Body Mass Index (BMI)
---------------------
This example takes some body mass index (BMI) data from the World Health Organizations's (WHO) website
http://knoema.com/WHOGDOBMIMay/who-global-database-on-body-mass-index-bmi
and compares the percentage of the US and Australial population that have a body mass index
in the normal, overweight and underweight categories.
What makes this chart interesting from an ``ODSCharts`` perspective is the combining of data
from different data sheet tabs within the spreadsheet onto the same chart.
A portion of the ``bmi_index.py`` example is shown below.
* The first two lines create two tabbed sheets of data within the spreadsheet called "Aussie_Data" and "USA_Data".
* The call to ``add_scatter`` creates a scatter plot called "Combo2_Plot" using the "Aussie_Data".
* The call to ``add_curve`` adds the "USA_Data" to "Combo2_Plot".
Notice that on setting properties like ``showMarkerL`` or ``lineThkL`` that simply filling in the first
value will cause all three curves to use that first value. For example, ``lineStyleL=[1,]`` will
cause all of the curves to use Dot line styles.
.. code:: python
mySprSht.add_sheet('Aussie_Data', aussieLL)
mySprSht.add_sheet('USA_Data', usaLL)
.
.
.
mySprSht.add_scatter( 'Combo2_Plot', 'Aussie_Data',
title='Australian and USA BMI', xlabel='Year',
ylabel='Percent of Aussie Population',
y2label='Percent of USA Population',
xcol=1,
ycolL=[2,3,4],
showMarkerL=[0,],
lineStyleL=[1,],
lineThkL=[2,],
colorL=['r','g','b'])
mySprSht.add_curve('Combo2_Plot', 'USA_Data',
xcol=1,
ycol2L=[2,3,4],
showMarker2L=[0,],
lineThk2L=[2,],
color2L=['r','g','b'])
:download:`download bmi_index.ods <./_static/bmi_index.ods>`
:download:`download bmi_index.py <./_static/bmi_index.py>`
.. raw:: html
| Excel Output | OpenOffice Output |
|
|
`Click images to see full size`