def get_geojson_cyclone(request, iso_alpha3=None):
collection = None
with GeoDashDatabaseConnection() as geodash_conn:
# Admin 2 Districts
collection = data_local_country_admin().get(cursor=geodash_conn.cursor,
iso_alpha3=iso_alpha3,
level=2)
# Vam Data
vam_by_admin1 = get_vam_by_admin1(request, iso_alpha3=iso_alpha3)
# Population at Risk Data
rows_popatrisk = geodash_conn.exec_query_multiple(
get_template("sparc2/sql/_cyclone.sql").render({
'admin2_popatrisk':
'cyclone.admin2_popatrisk',
'iso_alpha3':
iso_alpha3
}))
popatrisk_by_admin2 = {}
for row in rows_popatrisk:
newRow = json.loads(row[0]) if (type(row[0])
is not dict) else row[0]
admin2_code = newRow.pop(u"admin2_code", None)
newRow.pop(u"iso3", None)
if admin2_code not in popatrisk_by_admin2:
popatrisk_by_admin2[admin2_code] = []
popatrisk_by_admin2[admin2_code].append(newRow)
context_by_admin2 = get_context_by_admin2(geodash_conn=geodash_conn,
iso_alpha3=iso_alpha3)
for feature in collection["features"]:
admin1_code = str(feature["properties"]["admin1_code"])
admin2_code = str(feature["properties"]["admin2_code"])
if admin2_code in context_by_admin2:
feature["properties"]["ldi"] = context_by_admin2[admin2_code][
"ldi"]
feature["properties"]["delta_negative"] = context_by_admin2[
admin2_code]["delta_negative"]
feature["properties"][
"erosion_propensity"] = context_by_admin2[admin2_code][
"erosion_propensity"]
feature["properties"].update({
"FCS": 0,
"FCS_border": 0,
"FCS_acceptable": 0,
"CSI_no": 0,
"CSI_low": 0,
"CSI_med": 0,
"CSI_high": 0
})
feature["properties"]["addinfo"] = popatrisk_by_admin2[
admin2_code] if admin2_code in popatrisk_by_admin2 else []
if admin1_code in vam_by_admin1:
feature["properties"].update(vam_by_admin1[admin1_code])
return collection
def get_geojson_vam(request, iso_alpha3=None):
collection = None
with GeoDashDatabaseConnection() as geodash_conn:
collection = data_local_country_admin().get(cursor=geodash_conn.cursor,
iso_alpha3=iso_alpha3,
level=1)
for feature in collection["features"]:
response = requests.get(url=URL_VAM["FCS"].format(
admin0=feature["properties"]["admin0_code"],
admin1=feature["properties"]["admin1_code"]))
vam_data_fcs = response.json()
response = requests.get(url=URL_VAM["CSI"].format(
admin0=feature["properties"]["admin0_code"],
admin1=feature["properties"]["admin1_code"]))
vam_data_csi = response.json()
vam = {}
if vam_data_fcs:
vam_data_fcs = vam_data_fcs[0]
vam["fcs"] = {
"year": vam_data_fcs["FCS_year"],
"month": vam_data_fcs["FCS_month"],
"source": vam_data_fcs["FCS_dataSource"],
"poor": vam_data_fcs["FCS_poor"],
"borderline": vam_data_fcs["FCS_borderline"],
"acceptable": vam_data_fcs["FCS_acceptable"]
}
if vam_data_csi:
vam_data_csi = vam_data_csi[0]
vam["csi"] = {
"year": vam_data_csi["CSI_rYear"],
"month": vam_data_csi["CSI_rMonth"],
"source": vam_data_csi["CSI_rDataSource"],
"no": vam_data_csi["CSI_rNoCoping"],
"low": vam_data_csi["CSI_rLowCoping"],
"medium": vam_data_csi["CSI_rMediumCoping"],
"high": vam_data_csi["CSI_rHighCoping"]
}
feature["properties"]["vam"] = vam
return collection
def get_geojson_context(request, iso_alpha3=None):
collection = None
with GeoDashDatabaseConnection() as geodash_conn:
collection = data_local_country_admin().get(cursor=geodash_conn.cursor,
iso_alpha3=iso_alpha3,
level=2)
rows_context = geodash_conn.exec_query_multiple(
get_template("sparc2/sql/_context.sql").render({
'admin2_context':
'context.admin2_context',
'iso_alpha3':
iso_alpha3
}))
if "features" in collection:
for feature in collection["features"]:
for row_context in rows_context:
json_context = json.loads(row_context[0]) if (type(
row_context[0]) is not dict) else row_context[0]
if int(json_context["admin2_code"]
) == feature["properties"]["admin2_code"]:
feature["properties"].update(json_context)
return collection
def get_summary_drought(table_popatrisk=None, iso_alpha3=None):
now = datetime.datetime.now()
current_month = now.strftime("%b").lower()
if (not table_popatrisk) or (not iso_alpha3):
raise Exception("Missing table_popatrisk or iso3 for get_summary_drought.")
summary = None
prob_classes = [
{ "min": 0.01, "max": .05, "label": "1-5%" },
{ "min": .06, "max": .10, "label": "5-10%" },
{ "min": .11, "max": .19, "label": "10-20%" },
{ "min": .2, "max": 1.0, "label": "20-100%" }
]
values_all = []
values_float = []
natural = []
natural_adjusted = []
values_by_prob_class = {}
values_by_prob_class_by_month = {}
values_by_admin2_by_prob_class_by_month = {}
with GeoDashDatabaseConnection() as geodash_conn:
for prob_class in prob_classes:
values = geodash_conn.exec_query_single_aslist(
get_template("sparc2/sql/_drought_data_all_at_admin2_for_probclass.sql").render({
'admin2_popatrisk': table_popatrisk,
'prob_min': prob_class["min"],
'prob_max': prob_class["max"],
'iso_alpha3': iso_alpha3}))
values_by_prob_class[prob_class['label']] = values
for prob_class in prob_classes:
values_all = values_all + values_by_prob_class[prob_class['label']]
values_float = [float(x) for x in values_all]
num_breakpoints = 5
natural = calc_breaks_natural(values_float, num_breakpoints)
natural_adjusted = natural
for prob_class in prob_classes:
rows = geodash_conn.exec_query_multiple(
get_template("sparc2/sql/_drought_data_all_at_admin2_by_month_for_probclass.sql").render({
'admin2_popatrisk': table_popatrisk,
'prob_min': prob_class["min"],
'prob_max': prob_class["max"],
'iso_alpha3': iso_alpha3}))
if prob_class["label"] not in values_by_prob_class_by_month:
values_by_prob_class_by_month[prob_class["label"]] = {}
for r in rows:
month, value = r
values_by_prob_class_by_month[prob_class["label"]][month] = value
rows = geodash_conn.exec_query_multiple(
get_template("sparc2/sql/_drought_data_by_admin2_by_month_for_probclass.sql").render({
'admin2_popatrisk': table_popatrisk,
'prob_min': prob_class["min"],
'prob_max': prob_class["max"],
'iso_alpha3': iso_alpha3}))
for r in rows:
admin2_code, month, value = r
if admin2_code not in values_by_admin2_by_prob_class_by_month:
values_by_admin2_by_prob_class_by_month[admin2_code] = {}
if prob_class["label"] not in values_by_admin2_by_prob_class_by_month[admin2_code]:
values_by_admin2_by_prob_class_by_month[admin2_code][prob_class["label"]] = {}
values_by_admin2_by_prob_class_by_month[admin2_code][prob_class["label"]][month] = value
max_values = 0
if len(values_float) != 0:
max_values = int(max(values_float))
summary = {
'all': {
"max": {
'at_country_month': None,
'at_admin2_month': max_values
},
'breakpoints': {
'natural': natural,
'natural_adjusted': [0] + natural_adjusted
}
},
"prob_class": {},
"admin2": {}
}
for prob_class in prob_classes:
if prob_class["label"] not in summary["prob_class"]:
summary["prob_class"][prob_class["label"]] = {
"by_month": []
}
summary["prob_class"][prob_class["label"]]["by_month"] = [values_by_prob_class_by_month[prob_class["label"]].get(x, 0) for x in MONTHS_SHORT3]
for admin2_code in values_by_admin2_by_prob_class_by_month:
for prob_class in values_by_admin2_by_prob_class_by_month[admin2_code]:
values_by_month = [values_by_admin2_by_prob_class_by_month[admin2_code][prob_class].get(x, 0) for x in MONTHS_SHORT3]
summary["admin2"] = insertIntoObject(
summary["admin2"],
[admin2_code, "prob_class", prob_class, 'by_month'],
values_by_month)
summary['header'] = {
'all_breakpoints_natural': len(summary["all"]["breakpoints"]["natural"]),
'all_breakpoints_natural_adjusted': len(summary["all"]["breakpoints"]["natural_adjusted"]),
'admin2': len(summary["admin2"].keys()),
'prob_classes': prob_classes
}
return summary
def get_summary_cyclone(table_popatrisk=None, iso_alpha3=None):
now = datetime.datetime.now()
current_month = now.strftime("%b").lower()
if (not table_popatrisk) or (not iso_alpha3):
raise Exception(
"Missing table_popatrisk or iso_alpha3 for get_summary_cyclone.")
summary = None
with GeoDashDatabaseConnection() as geodash_conn:
values = geodash_conn.exec_query_single_aslist(
get_template("sparc2/sql/_cyclone_data_all_at_admin2.sql").render({
'admin2_popatrisk':
table_popatrisk,
'iso_alpha3':
iso_alpha3
}))
values = [float(x) for x in values]
num_breakpoints = 5
natural = calc_breaks_natural(values, num_breakpoints)
natural_adjusted = natural
summary = {
'all': {
"max": {
'at_country_month': None,
'at_admin2_month': None
},
'breakpoints': {
'natural': natural,
'natural_adjusted': [0] + natural_adjusted
}
},
"prob_class": {},
"admin2": {}
}
max_value = 0
if len(values) != 0:
max_value = max(values)
summary["all"]["max"]["at_admin2_month"] = max_value
prob_classes = [
"0.01-0.1", "0.1-0.2", "0.2-0.3", "0.3-0.4", "0.4-0.5", "0.5-0.6",
"0.6-0.7", "0.7-0.8", "0.8-0.9", "0.9-1.0"
]
for prob_class in prob_classes:
values = geodash_conn.exec_query_single_aslist(
get_template(
"sparc2/sql/_cyclone_data_by_prob_class_month.sql").render(
{
'admin2_popatrisk': table_popatrisk,
'iso_alpha3': iso_alpha3,
'prob_class': prob_class
}))
summary["prob_class"][prob_class] = {}
summary["prob_class"][prob_class]['by_month'] = [
float(x) for x in values
]
rows = geodash_conn.exec_query_multiple(
get_template(
"sparc2/sql/_cyclone_data_by_group_prob_class_month.sql").
render({
'admin2_popatrisk': table_popatrisk,
'iso_alpha3': iso_alpha3,
'prob_class': prob_class,
'group': 'admin2_code'
}))
for row in rows:
admin2_code, values = row
if admin2_code not in summary["admin2"]:
summary["admin2"][admin2_code] = {"prob_class": {}}
if "prob_class" not in summary["admin2"][admin2_code]:
summary["admin2"][admin2_code]["prob_class"] = {}
if prob_class not in summary["admin2"][admin2_code][
"prob_class"]:
summary["admin2"][admin2_code]["prob_class"][
prob_class] = {}
summary["admin2"][admin2_code]["prob_class"][prob_class][
'by_month'] = [float(x) for x in values.split(",")]
summary['header'] = {
'all_breakpoints_natural':
len(summary["all"]["breakpoints"]["natural"]),
'all_breakpoints_natural_adjusted':
len(summary["all"]["breakpoints"]["natural_adjusted"]),
'admin2':
len(summary["admin2"].keys()),
'prob_classes':
prob_classes
}
return summary
def get_geojson_landslide(request, iso_alpha3=None):
collection = None
prob_classes = [{
"code": 1,
"label": "low"
}, {
"code": 2,
"label": "medium"
}, {
"code": 3,
"label": "high"
}, {
"code": 4,
"label": "very_high"
}]
with GeoDashDatabaseConnection() as geodash_conn:
# Admin 2 Districts
collection = data_local_country_admin().get(cursor=geodash_conn.cursor,
iso_alpha3=iso_alpha3,
level=2)
# Vam Data
vam_by_admin1 = get_vam_by_admin1(request, iso_alpha3=iso_alpha3)
# Context Data
context_by_admin2 = get_context_by_admin2(geodash_conn=geodash_conn,
iso_alpha3=iso_alpha3)
# Population at Risk Data
popatrisk_by_admin2_probclass_month = {}
for prob_class in prob_classes:
rows_popatrisk = geodash_conn.exec_query_multiple(
get_template(
"sparc2/sql/_landslide_data_by_admin2_for_probclass.sql").
render({
'admin2_popatrisk': 'landslide.admin2_popatrisk',
'prob_min': str(prob_class["code"]),
'prob_max': str(prob_class["code"]),
'iso_alpha3': iso_alpha3
}))
for r in rows_popatrisk:
admin2_code, month, value = r
if admin2_code not in popatrisk_by_admin2_probclass_month:
popatrisk_by_admin2_probclass_month[admin2_code] = {}
if prob_class[
"label"] not in popatrisk_by_admin2_probclass_month[
admin2_code]:
popatrisk_by_admin2_probclass_month[admin2_code][
prob_class["label"]] = {}
popatrisk_by_admin2_probclass_month[admin2_code][
prob_class["label"]][month] = value
for feature in collection["features"]:
admin1_code = str(feature["properties"]["admin1_code"])
admin2_code = str(feature["properties"]["admin2_code"])
if admin2_code in context_by_admin2:
feature["properties"]["ldi"] = context_by_admin2[admin2_code][
"ldi"]
feature["properties"]["delta_negative"] = context_by_admin2[
admin2_code]["delta_negative"]
feature["properties"][
"erosion_propensity"] = context_by_admin2[admin2_code][
"erosion_propensity"]
feature["properties"].update({
"FCS": 0,
"FCS_border": 0,
"FCS_acceptable": 0,
"CSI_no": 0,
"CSI_low": 0,
"CSI_med": 0,
"CSI_high": 0
})
if admin1_code in vam_by_admin1:
feature["properties"].update(vam_by_admin1[admin1_code])
feature["properties"]["addinfo"] = []
for prob_class in prob_classes:
includeX = False
x = {
"prob_class": prob_class["label"],
"prob_class_min": str(prob_class["code"]),
"prob_class_max": str(prob_class["code"])
}
for month in MONTHS_SHORT3:
if admin2_code in popatrisk_by_admin2_probclass_month:
if prob_class[
"label"] in popatrisk_by_admin2_probclass_month[
admin2_code]:
x[month] = popatrisk_by_admin2_probclass_month[
admin2_code][prob_class["label"]].get(
month, 0)
includeX = True
if includeX:
feature["properties"]["addinfo"].append(x)
return collection
def get_geojson_flood(request, iso_alpha3=None):
collection = None
with GeoDashDatabaseConnection() as geodash_conn:
# Admin 2 Districts
collection = data_local_country_admin().get(cursor=geodash_conn.cursor,
iso_alpha3=iso_alpha3,
level=2)
# Vam Data
vam_by_admin1 = get_vam_by_admin1(request, iso_alpha3=iso_alpha3)
# Population at Risk Data
returnPeriods = [25, 50, 100, 200, 500, 1000]
for rp in returnPeriods:
rows = geodash_conn.exec_query_multiple(
get_template(
"sparc2/sql/_flood_data_by_admin2_rp_month_asjson.sql").
render({
'admin2_popatrisk': 'flood.admin2_popatrisk',
'iso_alpha3': iso_alpha3,
'rp': rp
}))
values_by_admin2 = {}
for row in rows:
admin2_code, data = row
data.pop(u"admin2_code")
values_by_admin2[str(admin2_code)] = data
context_by_admin2 = get_context_by_admin2(
geodash_conn=geodash_conn, iso_alpha3=iso_alpha3)
for feature in collection["features"]:
admin1_code = str(feature["properties"]["admin1_code"])
admin2_code = str(feature["properties"]["admin2_code"])
values = values_by_admin2.get(admin2_code, None)
if values:
feature["properties"]["RP" + str(rp)] = values
else:
feature["properties"]["RP" + str(rp)] = {
x: 0
for x in MONTHS_SHORT3
}
if admin2_code in context_by_admin2:
feature["properties"]["ldi"] = context_by_admin2[
admin2_code]["ldi"]
feature["properties"]["delta_mean"] = context_by_admin2[
admin2_code]["delta_mean"]
feature["properties"][
"delta_positive"] = context_by_admin2[admin2_code][
"delta_positive"]
feature["properties"][
"delta_negative"] = context_by_admin2[admin2_code][
"delta_negative"]
feature["properties"]["delta_crop"] = context_by_admin2[
admin2_code]["delta_crop"]
feature["properties"]["delta_forest"] = context_by_admin2[
admin2_code]["delta_forest"]
feature["properties"][
"erosion_propensity"] = context_by_admin2[admin2_code][
"erosion_propensity"]
if admin1_code in vam_by_admin1:
feature["properties"].update(vam_by_admin1[admin1_code])
return collection
