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
#'Tanzania',
#'Togo',
'Uganda',
'Zambia',
'Zimbabwe']
def generate_list_of_Countries(data_list):
countryList = []
for data in data_list:
countryList.append( data[0] )
return countryList
dependent = RADS.ourCountries(dependent, country_list)
independent = RADS.ourCountries(independent, country_list)
## Re-order the countries in the dependent caraible to the order of the independent variable:
dependent_new=[]
for country_set in independent:
country = country_set[0]
for country_set_dependent in dependent:
if country_set_dependent[0] == country:
dependent_new.append(country_set_dependent)
dependent = dependent_new
## Get a list of countries present in each data set:
dependent_country_list = generate_list_of_Countries(dependent)
independent_country_list = generate_list_of_Countries(independent)
little_plots = True # :Plot the little plots of the scatter data with different time offsets
axies_scale = "linear" # :Plot nad compute the values with log/log axies. Options include: 'log_log'
variable_name_plot_dependent = "Improved Water Source (% access)" #'Improved Sanitation Facilities (% access)'#
variable_name_plot_independent = "% Gross Female Secondary School Enrollment"
# [(Country, indicator, [values],[years]), (Country, indicator, [values],[years]),...]
dependent = pickle.load(open(data_values[variable_name_plot_dependent], "rb"))
independent = pickle.load(open(data_values[variable_name_plot_independent], "rb"))
country_list = RADCL.countryList()
dependent = RADS.ourCountries(dependent, country_list)
independent = RADS.ourCountries(independent, country_list)
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
