def get_country_indicators(country, questions, agencies_data):
indicator_data = calc_country_indicators(country)
indicators = {}
for indicator in indicator_data:
ind = indicators[indicator] = {}
data = indicator_data[indicator][0]
ind["baseline_value"] = none_num(data[0])
ind["baseline_year"] = data[1]
ind["latest_value"] = none_num(data[2])
ind["latest_year"] = data[3]
if indicators["3G"]["latest_value"] != NA_STR:
indicators["3G"]["hs_budget_gap"] = 15 - indicators["3G"]["latest_value"]
else:
indicators["3G"]["hs_budget_gap"] = None
indicators["other"] = {}
other_indicators = indicators["other"]
baseline_denom = questions["18"]["baseline_value"] / 10000.0
latest_denom = questions["18"]["latest_value"] / 10000.0
# Outpatient Visits
other_indicators["outpatient_visits_baseline"] = questions["19"]["baseline_value"] / baseline_denom
other_indicators["outpatient_visits_latest"] = questions["19"]["latest_value"] / latest_denom
other_indicators["outpatient_visits_change"], other_indicators["outpatient_visits_change_dir"] = calc_change(other_indicators["outpatient_visits_latest"], other_indicators["outpatient_visits_baseline"])
# Skilled Personnel
other_indicators["skilled_personnel_baseline"] = questions["17"]["baseline_value"] / baseline_denom
other_indicators["skilled_personnel_latest"] = questions["17"]["latest_value"] / latest_denom
other_indicators["skilled_personnel_change"], other_indicators["skilled_personnel_change_dir"] = calc_change(other_indicators["skilled_personnel_latest"], other_indicators["skilled_personnel_baseline"])
# Health Workforce
other_indicators["health_workforce_perc_of_budget_baseline"] = questions["20"]["baseline_value"] / questions["7"]["baseline_value"]
other_indicators["health_workforce_perc_of_budget_latest"] = questions["20"]["latest_value"] / questions["7"]["latest_value"]
other_indicators["health_workforce_spent_change"], other_indicators["health_workforce_spent_change_dir"] = calc_change(questions["20"]["latest_value"], questions["20"]["baseline_value"])
other_indicators["pfm_diff"] = questions["9"]["latest_value"] - questions["9"]["baseline_value"]
def sum_agency_values(question_number, field):
sum = 0
for agency in agencies_data:
if question_number in agencies_data[agency]:
try:
sum += float(agencies_data[agency][question_number][field])
except ValueError:
pass
return sum
coordinated_programmes = sum_agency_values("5", "latest_value") - sum_agency_values("4", "latest_value")
if coordinated_programmes > 0.51:
other_indicators["coordinated_programmes"] = models.Rating.TICK
elif coordinated_programmes >= 0.11:
other_indicators["coordinated_programmes"] = models.Rating.ARROW
else:
other_indicators["coordinated_programmes"] = models.Rating.CROSS
return indicators
def get_country_questions(country):
questions = {}
for question in models.GovQuestion.objects.filter(submission__country=country):
qvals = questions[question.question_number] = {}
qvals["baseline_year"] = question.baseline_year
qvals["latest_year"] = question.latest_year
qvals["comments"] = question.comments
qvals["baseline_value"] = none_num(question.base_val)
qvals["latest_value"] = none_num(question.cur_val)
return questions
