def test2():
"""
tests todo
"""
print( 'testing ZIP data processing...')
fname = "HPI_AT_ZIP5.txt"
annual = indexTools.read_zip_house_price_data( "data/" + fname )
answer = dict()
answer[ "HPI_AT_ZIP5.txt 1994 2002" ] = \
[('80205', 13.660048147719017), ('02143', 11.966500098655452), ('96792', -2.854788072761205)]
answer[ "HPI_AT_ZIP5.txt 1995 1996" ] = \
[('02650', 60.7489425230157), ('46186', 26.705136694730868), ('13204', -35.465021912027275)]
answer[ "HPI_AT_ZIP5.txt 2006 2008" ] = \
[('52037', 20.656522375739293), ('47231', 16.33947277520282), ('93635', -35.20316697877354)]
for year in [ (1994,2002), (1995,1996), (2006,2008) ]:
print( '= ' * 27 )
trends = trending.calculate_trends( annual, year[0], year[1] )
results = [ trends[0], trends[9], trends[-1] ] # sampling
key = fname + " " + str(year[0]) + " " + str(year[1])
print( fname, str(year[0]), str(year[1]), ":", ( results == answer[ key ] ))
return
def test2():
"""
tests volatility calculations for zip code data sets.
"""
print('testing ZIP data processing...')
fname = "HPI_AT_ZIP5.txt"
annual = indexTools.read_zip_house_price_data("data/" + fname)
answer = \
[('95129', 623.4638267500322), ('95014', 573.4712827751273), ('95032', 571.0663306170637), ('54442', 1.9823622272430441), ('65035', 1.7312261550704462), ('65075', 1.3242144841376704)]
measures = volatility.measure_volatility(annual)
results = [measures[i] for i in [0, 1, 2, -3, -2, -1]]
if results == answer:
print(fname, ":", True)
else:
print(fname, ":", "incorrect", results)
return
def test1():
"""
The function reads a dataset file,
annualizes the dataset values, and
uses a canned sequence of names to plot to get plot data and
check it against expected values.
"""
# test with subset of data files
for fname in [ "HPI_PO_state.txt", "HPI_AT_ZIP5.txt"]:
print( "=" * 72 )
print( "\nReading", fname, "..." )
if "ZIP" in fname:
# read a house price index file with Zip code key.
# note: Zip data is already annualized
annual = indexTools.read_zip_house_price_data( 'data/' + fname )
# this keylist has data with some gaps
keylist = [ "04083", "14625", "48210", "12202" ]
else:
data = indexTools.read_state_house_price_data( 'data/' + fname )
# state data must be annualized for the timeline plots.
annual = indexTools.annualize( data )
keylist = [ "NY", "IL", "MA", "VT", "MS" ]
# filter the data to get a subrange
annual = timelinePlot.filter_years( annual, 1988, 2008 )
# call the functions for plotting
timelinePlot.plot_HPI( annual, keylist)
timelinePlot.plot_whiskers( annual, keylist)
return
def test2():
"""
tests periodRanking.annual_data()
"""
print( 'testing ZIP data processing...')
fname = "HPI_AT_ZIP5.txt"
data = indexTools.read_zip_house_price_data( "data/" + fname )
answer = dict()
answer["HPI_AT_ZIP5.txt 1996"] = [('95129', 580.09), ('94086', 533.61)]
answer["HPI_AT_ZIP5.txt 2003"] = [('93117', 1156.19), ('94112', 1056.11)]
answer["HPI_AT_ZIP5.txt 2006"] = [('95129', 1592.33), ('95127', 1535.7)]
answer["HPI_AT_ZIP5.txt 2014"] = [('95014', 1999.64), ('94110', 1765.75)]
for year in [ 1996, 2003, 2006, 2014]:
results = periodRanking.annual_data( data, year)
key = fname + " " + str(year)
#print( key )
if key in answer:
print( fname, str( year), ":", ( results[1:11:9] == answer[ key] ))
else:
print( fname, str( year), ":", "incorrect", results[1:11:9] )
return