def test1():
"""
tests periodRanking.quarter_data() and periodRanking.annual_data().
"""
print( 'testing state data processing...')
fname = "HPI_PO_state.txt"
data = indexTools.read_state_house_price_data( "data/" + fname )
answer = dict()
answer["HPI_PO_state.txt 1993 1"] = [('UT', 117.69), ('OR', 116.94)]
answer["HPI_PO_state.txt 1993 3"] = [('UT', 128.49), ('CO', 125.16)]
answer["HPI_PO_state.txt 1993 None"] = [('UT', 125.77499999999999), ('CO', 122.3775)]
answer["HPI_PO_state.txt 1997 1"] = [('OR', 162.61), ('MT', 162.09)]
answer["HPI_PO_state.txt 1997 3"] = [('OR', 166.34), ('CO', 162.8)]
answer["HPI_PO_state.txt 1997 None"] = [('OR', 164.875), ('MT', 162.20499999999998)]
answer["HPI_PO_state.txt 2010 1"] = [('MT', 298.92), ('WY', 281.91)]
answer["HPI_PO_state.txt 2010 3"] = [('MT', 293.55), ('WY', 281.33)]
answer["HPI_PO_state.txt 2010 None"] = [('MT', 292.9875), ('WY', 281.6325)]
for year in [ 1993, 1997, 2010]:
for qtr in [ 1, 3, None]:
if qtr != None:
results = periodRanking.quarter_data( data, year, qtr )
else:
results = periodRanking.annual_data( indexTools.annualize( data), year )
key = fname + " " + str(year) + " " + str(qtr)
#print( key )
#if key in answer:
print( fname, year, qtr, ":", ( results[1:3] == answer[ key] ))
#else:
# print( fname, year, qtr, ":", "incorrect", results[1:3] )
return
def test1():
"""
tests state-keyed data
"""
print( 'testing state data processing...')
fname = "HPI_PO_state.txt"
data = indexTools.read_state_house_price_data( "data/" + fname )
annual = indexTools.annualize( data)
answer = dict()
answer[ "HPI_PO_state.txt 1994 2002" ] = \
[('MA', 8.927726257507196), ('NJ', 6.07279996720127), ('HI', 1.1529363923406422)]
answer[ "HPI_PO_state.txt 1995 1996" ] = \
[('MI', 8.165561829936996), ('SD', 4.714654541250818), ('HI', -5.63621724111062)]
answer[ "HPI_PO_state.txt 2006 2008" ] = \
[('WY', 4.268849486063586), ('WV', 1.450495211395464), ('CA', -17.73558710889268)]
for year in [ (1994,2002), (1995,1996), (2006,2008) ]:
print( '= ' * 27 )
# compute growth rates only for annual averages of HPI values.
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 main():
"""
docstring for main function here
:return:
"""
# Prompt user to input filename, region variable, and year variable
# filenameVariable = input("Filename: ")
# regionVariable = input("State abbreviation or ZIP Code: ")
# yearVariable = input("Year: ")
# Check to see if the ZIP exists in the filename
# if "ZIP" in filenameVariable:
# data = indexTools.read_zip_house_price_data(filenameVariable)
# print("ZIP Data")
# print()
dataFilesPeriodRanking = []
filename = ''
year = ''
if len(sys.argv) != 3:
filename = input("Enter region-based house price index filename: ")
year = int(input("Enter year of interest for house price: "))
else:
filename = sys.argv[1]
year = int(sys.argv[2])
dataFiles = indexTools.read_state_house_price_data(filename)
dataFilesAnnualized = indexTools.annualize(dataFiles)
pythonList = []
for key in dataFilesAnnualized:
entry = dataFilesAnnualized[key]
for entryValue in entry:
if entryValue.year == year:
pythonList.append((key, entryValue))
pythonList = sorted(pythonList, key=lambda x:x[1].index,reverse=True)
print(year, " Annual Ranking")
print("The Top 10:")
for i,listValue in enumerate(pythonList[:10]):
print(i+1, " : (", listValue[0], ", ", listValue[1].index, ")")
pythonList.reverse()
lowest = pythonList[:10]
lowest.reverse()
print("The Bottom 10:")
for i,reverseValue in enumerate(lowest):
print(i+1, " : (", reverseValue[0], ", ", reverseValue[1].index, ")")
def test1():
"""
tests volatility calculations for state data sets.
"""
print('testing state data processing...')
fname = "HPI_PO_state.txt"
data = indexTools.read_state_house_price_data("data/" + fname)
# compute growth rates only for annual averages of HPI values.
annual = indexTools.annualize(data)
answer = \
[('DC', 130.95338408932818), ('MT', 77.11321243277986), ('OR', 75.93526891291103), ('MS', 30.164122833810534), ('IN', 22.794615225619044), ('OH', 22.511935480767455)]
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", str(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