def crossCorrelateValuesForPlotting(data1, data2, countryIndex, yearOffset):
# pprint.pprint(data2[countryIndex])
d1, d2, years = RADS.yearCorrelate(data1[countryIndex], data2[countryIndex], yearOffset=yearOffset)
d1 = np.array(d1)
d2 = np.array(d2)
if (len(d1) > 1) and (len(d2) > 1):
return d1, d2
else:
# print 'Country ', countryIndex, ' has no data for given indicator.'
return d1, d2
d1_all = []; d2_all = []
for country in country_list:
#print 'Country is: ', country
#d1, d2 = crossCorrelateValuesForPlotting(dependent, independent, country_index)
depSet=False
indSet=False
for valueSet in dependent:
if valueSet[0] == country:
depSet = valueSet
for valueSet in independent:
if valueSet[0]==country:
indSet = valueSet
if (depSet!=False) and (indSet!=False):
d1, d2, years = RADS.yearCorrelate(depSet, indSet, 0)
d1 = np.array(d1)
d2 = np.array(d2)
m, b, r_value, p_value, std_err = scipy.stats.linregress(d1,d2)
if (len(d1)>1):
currentLenD1 = len(d1)
d1_all.append(d1); d2_all.append(d2)
plt.plot(d1,d2,
marker='h',
ls='.',
markersize=15,
color = colorArray[country_list.index(country)],
alpha=.99,
label = country )
