def get_population_class(values_int, values_ext):
bats_in_colonies = sum(get_values(values_int, 'NR_INDIVIZI'))
isolated_bats = sum(get_values(values_int, 'NR_INDIVIZI_IZOLATI'))
obs_spots = len(get_values(values_ext, 'NR_INDIVIZI'))
obs_dates = get_values(values_int, 'DATA') + get_values(values_ext, 'DATA')
years = len(set([get_year(d) for d in obs_dates]))
return (bats_in_colonies + 30 * isolated_bats + 100 * obs_spots) / years
def get_LL_habitat_trend(specnum, region):
where_query = "SPECIE='%s' AND REG_BIOGEO='%s'" % (specnum, region)
url = _get_species_url(LL, TREND_HAB)
data = generic_rest_call(url, where_query) or []
values = [r['attributes'] for r in data]
if not values:
return
hab_q = most_common(get_values(values, 'CALITATE_HAB'))
cons = most_common(get_values(values, 'STARE_CONS'))
return hab_q, cons
def get_LL_range_trend(specnum, region):
where_query = "SPECIE='%s' AND REG_BIOGEO='%s'" % (specnum, region)
url = _get_species_url(LL, TREND)
data = generic_rest_call(url, where_query) or []
values = [r['attributes'] for r in data]
if not values:
return
trend = most_common(get_values(values, 'TREND'))
cons = most_common(get_values(values, 'STARE_CONS'))
rang = most_common(get_values(values, 'RANG'))
return trend, cons, rang
def get_PS_trend(habcode, region):
where_query = "HABITAT='%s' AND REG_BIOGEO='%s'" % (habcode, region)
url = _get_habitat_url(PS, TREND)
data = generic_rest_call(url, where_query) or []
values = [r['attributes'] for r in data]
if not values:
return
grad = most_common(get_values(values, 'GRAD'))
rang = most_common(get_values(values, 'RANG'))
morf_fields = ['MORFOLOGIE_LOC_ST', 'UMPLUTURA', 'REGIM_HIDRO',
'ACUMULARE_APA', 'IVIRE_APA', 'SURSA_MICRO_PICATURI']
morf_frequencies = []
for field in morf_fields:
morf_values = get_values(values, field)
if not morf_values:
continue
most_common_value = most_common(morf_values)
frequency = (morf_values.count(most_common_value) /
float(len(morf_values) or 1))
morf_frequencies.append(frequency)
morf_frequency = average(morf_frequencies)
env_fields = ['TEMP_INT', 'UMITIDATE_REL']
seasonal_averages = get_seasonal_avg(values, env_fields, average)
url = _get_habitat_url(PS, LAST_10)
data = generic_rest_call(url, where_query) or []
values = [r['attributes'] for r in data]
hist_seasonal_intervals = get_seasonal_intervals(values, env_fields)
env_characteristics = 0
for field, seasonal_values in seasonal_averages.iteritems():
for season, value in seasonal_values.iteritems():
min, max = hist_seasonal_intervals[field][season]
if min <= value <= max:
env_characteristics += 1
else:
env_characteristics -= 1
return grad, rang, (env_characteristics >= 0), morf_frequency
def get_population_trend(values_int, values_ext):
trend = most_common(get_values(values_ext, 'TREND_EVAL_POP_AR'))
if not trend:
return 'x'
years_dict = get_years_dict(values_int, values_ext)
years = sorted(years_dict.keys())
diff = 0
if len(years) > 1:
for year in years[:-1]:
pop_class = get_class(years_dict[year])
next_year = years[years.index(year) + 1]
next_pop_class = get_class(years_dict[next_year])
diff += next_pop_class - pop_class
if trend in ('X', '+') and diff > 0:
return '+'
elif trend in ('X', '-') and diff < 0:
return '-'
return '0'
def get_ref_population(values_int, values_ext):
bats_in_colonies = sum(get_values(values_int, 'NR_INDIVIZI'))
isolated_bats = sum(get_values(values_ext, 'NR_INDIVIZI'))
return bats_in_colonies + 100 * isolated_bats
def get_max_population(values_int, values_ext):
bats_in_colonies = sum(get_values(values_int, 'NR_INDIVIZI'))
isolated_bats = sum(get_values(values_int, 'NR_INDIVIZI_IZOLATI'))
obs_spots = len(get_values(values_ext, 'NR_INDIVIZI'))
return bats_in_colonies + 30 * isolated_bats + 100 * obs_spots
def get_min_population(values_int):
return len(get_values(values_int, 'NR_INDIVIZI')) * 100