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 )