def plan_designation_county_etl():
"""Run ETL for county plan designations."""
with arcetl.ArcETL("County Plan Designations") as etl:
etl.extract(dataset.PLAN_DESIGNATION_COUNTY.path("maint"))
transform.add_missing_fields(etl,
dataset.PLAN_DESIGNATION_COUNTY,
tags=["pub"])
etl.transform(arcetl.attributes.update_by_value,
field_name="planjuris",
value="LC")
for new_name, old_name in [("plandes", "ZONE_"),
("plandesnam", "ZONE_NAME")]:
etl.transform(
arcetl.attributes.update_by_function,
field_name=new_name,
function=(lambda x: x),
field_as_first_arg=False,
arg_field_names=[old_name],
)
# Remove county designations where city ones exist.
etl.transform(
arcetl.features.erase,
erase_dataset_path=dataset.PLAN_DESIGNATION_CITY.path("pub"),
)
transform.clean_whitespace(
etl,
field_names=["planjuris", "plandes", "plandesnam", "finalorder"])
etl.transform(arcetl.features.delete,
dataset_where_sql="plandes is null")
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=dataset.PLAN_DESIGNATION_COUNTY.field_names,
tolerance=TOLERANCE["xy"],
)
etl.load(dataset.PLAN_DESIGNATION_COUNTY.path("pub"))
def tax_code_area_etl():
"""Run ETL for tax code areas."""
with arcetl.ArcETL("Tax Code Areas") as etl:
etl.extract(dataset.TAX_CODE_AREA.path("maint"))
transform.clean_whitespace(
etl, field_names=["taxcode", "source", "ordinance", "schooldist"])
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=dataset.TAX_CODE_AREA.field_names,
tolerance=TOLERANCE["xy"],
)
etl.load(dataset.TAX_CODE_AREA.path("pub"))
def facility_etl():
"""Run ETL for facilities.
Currently only undertaken for other ETL purposes--not publication.
"""
with arcetl.ArcETL("Facilities") as etl:
etl.extract(dataset.FACILITY.path("maint"))
etl.transform(
arcetl.dataset.rename_field,
field_name="geofeat_id",
new_field_name="address_intid",
)
# Clean maintenance values.
transform.clear_nonpositive(etl, field_names=["address_intid"])
transform.clean_whitespace(
etl, field_names=["label", "label_full", "type", "type_full"])
transform.force_lowercase(etl, field_names=["type"])
transform.force_uppercase(etl, field_names=["label"])
transform.add_missing_fields(etl, dataset.FACILITY, tags=["pub"])
# Assign geometry attributes.
coordinate_system_xy_keys = {
2914: {
"x": "x_coordinate",
"y": "y_coordinate"
},
4326: {
"x": "longitude",
"y": "latitude"
},
}
for spatial_reference_id, xy_key in coordinate_system_xy_keys.items():
for axis, key in xy_key.items():
etl.transform(
arcetl.attributes.update_by_geometry,
field_name=key,
spatial_reference_item=spatial_reference_id,
geometry_properties=["centroid", axis],
)
etl.transform(
arcetl.attributes.update_by_mapping,
field_name="address_uuid",
mapping=address_intid_to_uuid_map,
key_field_names=["address_intid"],
)
etl.load(dataset.FACILITY.path("pub"))
def plat_etl():
"""Run ETL for plats."""
with arcetl.ArcETL("Plats") as etl:
etl.extract(dataset.PLAT.path("maint"))
transform.clean_whitespace(etl, field_names=["platname", "docnumber"])
transform.force_uppercase(etl, field_names=["platname"])
transform.clear_nonpositive(etl, field_names=["agencydocn"])
pub_field_names = {
field["name"]
for field in dataset.PLAT.fields if "pub" in field["tags"]
}
etl.transform(
arcetl.features.delete,
dataset_where_sql=" and ".join("{} is null".format(name)
for name in pub_field_names),
)
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=pub_field_names,
tolerance=TOLERANCE["xy"],
)
etl.load(dataset.PLAT.path("pub"))
def plss_dlc_etl():
"""Run ETL for PLSS donation land claims."""
with arcetl.ArcETL("PLSS Donation Land Claims") as etl:
etl.extract(dataset.PLSS_DLC.path("maint"))
transform.clean_whitespace(etl, field_names=["name", "trs"])
transform.add_missing_fields(etl, dataset.PLSS_DLC, tags=["pub"])
etl.transform(
arcetl.attributes.update_by_function,
field_name="dlcname",
function=(lambda x: x),
field_as_first_arg=False,
arg_field_names=["NAME"],
)
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=[
field["name"] for field in dataset.PLSS_DLC.fields
if "pub" in field["tags"]
],
tolerance=TOLERANCE["xy"],
)
etl.load(dataset.PLSS_DLC.path("pub"))
def zoning_county_etl():
"""Run ETL for county zoning."""
overlay_field_names = [
name for name in dataset.ZONING_COUNTY.field_names
if name.lower().startswith("over")
]
with arcetl.ArcETL("County Zoning") as etl:
etl.extract(dataset.ZONING_COUNTY.path("maint"))
etl.transform(
arcetl.features.insert_from_path,
insert_dataset_path=dataset.ZONING_COUNTY.path("insert"),
)
transform.add_missing_fields(etl, dataset.ZONING_COUNTY, tags=["pub"])
for new_name, old_name in [("zonecode", "ZONE_"),
("zonename", "ZONE_NAME")]:
etl.transform(
arcetl.attributes.update_by_function,
field_name=new_name,
function=(lambda x: x),
field_as_first_arg=False,
arg_field_names=[old_name],
)
# UGB zoning has slightly different names. We want to standardize on the main
# zoning dataset names.
etl.transform(
arcetl.attributes.update_by_mapping,
field_name="zonename",
mapping=county_zone_name_map,
key_field_names="zonecode",
)
# Clean maintenance values.
transform.clean_whitespace(etl, field_names=["zonecode", "zonename"])
etl.transform(arcetl.features.delete,
dataset_where_sql="zonecode is null")
# Remove county zoning where city ones exist.
etl.transform(arcetl.features.erase,
erase_dataset_path=dataset.ZONING_CITY.path("pub"))
# Assign zoning overlays.
identity_kwargs = [
{
"field_name":
"coastalzonecode",
"identity_field_name":
"TYPE",
"identity_dataset_path":
os.path.join(LANE_ZONING_STAGING_PATH, "coastal_zones.shp"),
},
{
"field_name":
"overas",
"identity_field_name":
"AIRPORT",
"identity_dataset_path":
os.path.join(LANE_ZONING_STAGING_PATH, "aszone.shp"),
"replacement_value":
"Y",
},
{
"field_name":
"overcas",
"identity_field_name":
"AIRPORT",
"identity_dataset_path":
os.path.join(LANE_ZONING_STAGING_PATH, "caszone.shp"),
"replacement_value":
"Y",
},
{
"field_name":
"overdms",
"identity_field_name":
"TYPE",
"identity_dataset_path":
os.path.join(LANE_ZONING_STAGING_PATH, "dredge_sites.shp"),
"replacement_value":
"Y",
},
{
"field_name":
"overbd",
"identity_field_name":
"Shape_Leng",
"identity_dataset_path":
os.path.join(LANE_ZONING_STAGING_PATH, "beach_dune.shp"),
"replacement_value":
"Y",
},
{
"field_name":
"overu",
"identity_field_name":
"urban",
"identity_dataset_path":
os.path.join(LANE_ZONING_STAGING_PATH, "interim_urban.shp"),
"replacement_value":
"Y",
},
]
for kwargs in identity_kwargs:
etl.transform(arcetl.geoset.identity, **kwargs)
# Clean identity values.
transform.clean_whitespace(etl, field_names=["coastalzonecode"])
etl.transform(arcetl.attributes.update_by_value,
field_name="zonejuris",
value="LC")
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=[
field["name"] for field in dataset.ZONING_COUNTY.fields
if "pub" in field["tags"]
],
tolerance=TOLERANCE["xy"],
)
# Assign the overlay flags dependent on coastal zone code.
for code in ["CE", "DE", "MD", "NE", "NRC", "PW", "RD", "SN"]:
etl.transform(
arcetl.attributes.update_by_function,
field_name="over{}".format(code.lower()),
function=(lambda czc, c=code: "Y" if czc == c else "N"),
field_as_first_arg=False,
arg_field_names=["coastalzonecode"],
)
transform.force_uppercase(etl, overlay_field_names)
transform.force_yn(etl, overlay_field_names, default="N")
etl.transform(
arcetl.attributes.update_by_function,
field_name="alloverlays",
function=concatenate_zoning_overlays,
field_as_first_arg=False,
kwarg_field_names=overlay_field_names,
)
etl.load(dataset.ZONING_COUNTY.path("pub"))
def zoning_city_etl():
"""Run ETL for city zoning."""
overlay_field_names = [
name for name in dataset.ZONING_CITY.field_names
if name.lower().startswith("over")
]
with arcetl.ArcETL("City Zoning") as etl:
etl.init_schema(dataset.ZONING_CITY.path("pub"))
for _path in dataset.ZONING_CITY.path("inserts"):
etl.transform(arcetl.features.insert_from_path,
insert_dataset_path=_path)
# Clean maintenance values.
transform.force_uppercase(etl, overlay_field_names)
transform.force_yn(etl, overlay_field_names, default="N")
etl.transform(arcetl.features.delete,
dataset_where_sql="zonecode is null")
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=dataset.ZONING_CITY.field_names,
tolerance=TOLERANCE["xy"],
)
juris_domain = {
"COB": "CoburgZoning",
"COT": "CottageGroveZoning",
"CRE": "CreswellZoning",
"DUN": "DunesCityZoning",
"EUG": "EugeneZoning",
"FLO": "FlorenceZoning",
"JUN": "JunctionCityZoning",
"LOW": "LowellZoning",
"OAK": "OakridgeZoning",
"SPR": "SpringfieldZoning",
"VEN": "VenetaZoning",
"WES": "WestfirZoning",
}
for juris_code, domain_name in juris_domain.items():
etl.transform(
arcetl.attributes.update_by_domain_code,
field_name="zonename",
code_field_name="zonecode",
domain_name=domain_name,
domain_workspace_path=database.LCOGGEO.path,
dataset_where_sql="zonejuris = '{}'".format(juris_code),
)
etl.transform(
arcetl.attributes.update_by_domain_code,
field_name="subareaname",
code_field_name="subarea",
domain_name="EugeneZoningSubarea",
domain_workspace_path=database.LCOGGEO.path,
)
# Clean domain-derived values.
transform.clean_whitespace(etl,
field_names=["zonename", "subareaname"])
etl.transform(
arcetl.attributes.update_by_function,
field_name="alloverlays",
function=concatenate_zoning_overlays,
field_as_first_arg=False,
kwarg_field_names=overlay_field_names,
)
etl.load(dataset.ZONING_CITY.path("pub"))
def address_point_etl():
"""Run ETL for address points."""
with arcetl.ArcETL("Address Points") as etl:
etl.extract(dataset.TILLAMOOK_ADDRESS_POINT.path("maint"))
# Remove addresses flagged in validationas "not OK to publish".
etl.transform(
arcetl.dataset.join_field,
join_dataset_path=dataset.TILLAMOOK_ADDRESS_POINT_ISSUES.path(),
join_field_name="ok_to_publish",
on_field_name="address_id",
on_join_field_name="address_id",
)
etl.transform(arcetl.features.delete,
dataset_where_sql="ok_to_publish = 0")
etl.transform(arcetl.dataset.delete_field, field_name="ok_to_publish")
# Clean maintenance values.
transform.clear_nonpositive(etl, field_names=["stnum"])
transform.clean_whitespace(
etl,
field_names=[
"stnumsuf",
"predir",
"name",
"type",
"sufdir",
"unit_type",
"unit",
"postcomm",
"zip",
"county",
],
)
transform.force_uppercase(
etl,
field_names=[
"stnumsuf",
"predir",
"name",
"type",
"unit_type",
"unit",
"postcomm",
"county",
"valid",
"archived",
"confidence",
],
)
transform.clear_non_numeric_text(etl, field_names=["zip"])
transform.force_yn(etl, field_names=["archived"], default="N")
transform.force_yn(etl, field_names=["valid"], default="Y")
transform.add_missing_fields(etl,
dataset.TILLAMOOK_ADDRESS_POINT,
tags=["pub"])
# Assign geometry attributes.
for x_name, y_name, srid in [("lon", "lat", 4326)]:
for name, axis in [(x_name, "x"), (y_name, "y")]:
etl.transform(
arcetl.attributes.update_by_geometry,
field_name=name,
spatial_reference_item=srid,
geometry_properties=["centroid", axis],
)
# Assign joined values.
etl.transform(
arcetl.attributes.update_by_joined_value,
field_name="join_id",
join_dataset_path=dataset.TILLAMOOK_ALTERNATE_STREET_NAME.path(),
join_field_name="join_id",
on_field_pairs=[
("predir", "prime_predir"),
("name", "prime_name"),
("type", "prime_type"),
("sufdir", "prime_sufdir"),
],
)
# Assign overlays.
overlay_kwargs = [
{
"field_name": "city_limit",
"overlay_field_name": "city",
"overlay_dataset_path": dataset.TILLAMOOK_CITY_LIMITS.path(),
},
{
"field_name": "ems",
"overlay_field_name": "district",
"overlay_dataset_path": dataset.TILLAMOOK_EMS.path(),
},
{
"field_name":
"esn",
"overlay_field_name":
"esn",
"overlay_dataset_path":
dataset.TILLAMOOK_EMERGENCY_SERVICE_ZONE.path(),
},
{
"field_name": "fire",
"overlay_field_name": "district",
"overlay_dataset_path": dataset.TILLAMOOK_FIRE.path(),
},
{
"field_name": "police",
"overlay_field_name": "district",
"overlay_dataset_path": dataset.TILLAMOOK_POLICE.path(),
},
]
for kwargs in overlay_kwargs:
etl.transform(arcetl.attributes.update_by_overlay,
overlay_central_coincident=True,
**kwargs)
# Build values: Constants.
value_kwargs = [{"field_name": "state", "value": "OR"}]
transform.update_attributes_by_values(etl, value_kwargs)
# Build values: Concatenations.
etl.transform(
arcetl.attributes.update_by_function,
field_name="address",
function=concatenate_arguments,
field_as_first_arg=False,
arg_field_names=[
"stnum",
"stnumsuf",
"predir",
"name",
"type",
"sufdir",
"unit_type",
"unit",
],
)
etl.load(dataset.TILLAMOOK_ADDRESS_POINT.path("pub"))
def site_address_etl():
"""Run ETL for site addresses."""
with arcetl.ArcETL("Site Addresses") as etl:
etl.extract(dataset.SITE_ADDRESS.path("maint"))
# Clean maintenance values.
transform.clear_nonpositive(etl, field_names=["house_nbr"])
transform.clean_whitespace(
etl,
field_names=[
"house_suffix_code",
"pre_direction_code",
"street_name",
"street_type_code",
"unit_type_code",
"unit_id",
"city_name",
"landuse",
"maptaxlot",
"account",
],
)
transform.force_uppercase(
etl,
field_names=[
"house_suffix_code",
"pre_direction_code",
"street_name",
"street_type_code",
"unit_type_code",
"unit_id",
"maptaxlot",
"valid",
"archived",
],
)
transform.clear_non_numeric_text(etl, field_names=["account"])
etl.transform(
arcetl.attributes.update_by_function,
field_name="landuse",
function=(lambda x: x if is_numeric(x) else "0"),
)
transform.force_yn(etl, field_names=["archived"], default="N")
transform.force_yn(etl, field_names=["valid"], default="Y")
transform.add_missing_fields(etl, dataset.SITE_ADDRESS, tags=["pub"])
# Assign geometry attributes.
coordinate_system_xy_keys = {
2914: {
"x": "x_coordinate",
"y": "y_coordinate"
},
4326: {
"x": "longitude",
"y": "latitude"
},
}
for spatial_reference_id, xy_key in coordinate_system_xy_keys.items():
for axis, key in xy_key.items():
etl.transform(
arcetl.attributes.update_by_geometry,
field_name=key,
spatial_reference_item=spatial_reference_id,
geometry_properties=["centroid", axis],
)
# Assign overlays.
overlay_kwargs = [
# City attributes.
{
"field_name": "geocity",
"overlay_field_name": "inccityabbr",
"overlay_dataset_path":
dataset.INCORPORATED_CITY_LIMITS.path(),
},
{
"field_name": "annexhist",
"overlay_field_name": "annexnum",
"overlay_dataset_path": dataset.ANNEXATION_HISTORY.path("pub"),
},
# Have to do overlay rather than join because some lack codes.
{
"field_name": "yearanx",
"overlay_field_name": "annexyear",
"overlay_dataset_path": dataset.ANNEXATION_HISTORY.path("pub"),
},
{
"field_name": "ugb",
"overlay_field_name": "ugbcity",
"overlay_dataset_path": dataset.UGB.path("pub"),
},
# Planning & zoning attributes.
{
"field_name":
"greenwy",
"overlay_field_name":
"greenway",
"overlay_dataset_path":
dataset.WILLAMETTE_RIVER_GREENWAY.path("pub"),
},
{
"field_name": "nodaldev",
"overlay_field_name": "nodearea",
"overlay_dataset_path":
dataset.NODAL_DEVELOPMENT_AREA.path("pub"),
},
{
"field_name": "plandes_id",
"overlay_field_name": "plandes_id",
"overlay_dataset_path": dataset.PLAN_DESIGNATION.path("pub"),
},
{
"field_name": "sprsvcbndy",
"overlay_field_name": "is_inside",
"overlay_dataset_path":
dataset.SPRINGFIELD_HANSEN_EXTENT.path(),
},
# Public safety attributes.
{
"field_name": "ambulance_district",
"overlay_field_name": "asacode",
"overlay_dataset_path":
dataset.AMBULANCE_SERVICE_AREA.path("pub"),
},
{
"field_name": "firedist",
"overlay_field_name": "fireprotprov",
"overlay_dataset_path":
dataset.FIRE_PROTECTION_AREA.path("pub"),
},
{
"field_name":
"police_beat",
"overlay_field_name":
"CAD",
"overlay_dataset_path":
os.path.join(
path.LCOG_GIS_PROJECTS,
"Public_Safety\\PSAPS\\CLPSAP\\SunGard_CAD\\Maintained_Layers",
"Maintained_Layers.gdb\\Fire_Law_Tow\\law_beat",
),
},
{
"field_name": "psap_code",
"overlay_field_name": "psap_code",
"overlay_dataset_path": dataset.PSAP_AREA.path("pub"),
},
# Election attributes.
{
"field_name": "electionpr",
"overlay_field_name": "precntnum",
"overlay_dataset_path": dataset.ELECTION_PRECINCT.path("pub"),
},
{
"field_name": "ccward",
"overlay_field_name": "ward",
"overlay_dataset_path": dataset.CITY_WARD.path(),
},
{
"field_name":
"clpud_subdivision",
"overlay_field_name":
"SUBDIVISIO",
"overlay_dataset_path":
os.path.join(
path.LCOG_GIS_PROJECTS,
"UtilityDistricts\\CentralLincolnPUD\\Redistricting2012",
"CLPUD_Subdivisions.shp",
),
},
{
"field_name":
"cocommdist",
"overlay_field_name":
"commrdist",
"overlay_dataset_path":
(dataset.COUNTY_COMMISSIONER_DISTRICT.path("pub")),
},
{
"field_name": "epud",
"overlay_field_name": "boardid",
"overlay_dataset_path": dataset.EPUD_SUBDISTRICT.path("pub"),
},
{
"field_name":
"hwpud_subdivision",
"overlay_field_name":
"BoardZone",
"overlay_dataset_path":
os.path.join(
path.LCOG_GIS_PROJECTS,
"UtilityDistricts\\HecetaWaterPUD\\NewBoardSubzones",
"HecetaData.gdb",
"ScenarioB",
),
},
{
"field_name": "lcczone",
"overlay_field_name": "lccbrdzone",
"overlay_dataset_path": dataset.LCC_BOARD_ZONE.path("pub"),
},
{
"field_name": "senatedist",
"overlay_field_name": "sendist",
"overlay_dataset_path":
dataset.STATE_SENATOR_DISTRICT.path("pub"),
},
{
"field_name":
"strepdist",
"overlay_field_name":
"repdist",
"overlay_dataset_path":
(dataset.STATE_REPRESENTATIVE_DISTRICT.path("pub")),
},
{
"field_name":
"swcd",
"overlay_field_name":
"swcdist",
"overlay_dataset_path":
(dataset.SOIL_WATER_CONSERVATION_DISTRICT.path("pub")),
},
{
"field_name":
"swcdzone",
"overlay_field_name":
"swczone",
"overlay_dataset_path":
(dataset.SOIL_WATER_CONSERVATION_DISTRICT.path("pub")),
},
# Education attributes.
{
"field_name": "schooldist",
"overlay_field_name": "district",
"overlay_dataset_path": dataset.SCHOOL_DISTRICT.path("pub"),
},
{
"field_name": "elem",
"overlay_field_name": "attend",
"overlay_dataset_path":
dataset.ELEMENTARY_SCHOOL_AREA.path("pub"),
},
{
"field_name": "middle",
"overlay_field_name": "attend",
"overlay_dataset_path": dataset.MIDDLE_SCHOOL_AREA.path("pub"),
},
{
"field_name": "high",
"overlay_field_name": "attend",
"overlay_dataset_path": dataset.HIGH_SCHOOL_AREA.path("pub"),
},
# Transportation attributes.
{
"field_name":
"ltddist",
"overlay_field_name":
"LTD",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"transport\\ltd\\2012 LTD Boundary.shp"),
},
{
"field_name":
"ltdridesrc",
"overlay_field_name":
"LTD",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"transport\\ltd\\2015 RideSource Boundary.shp"),
},
{
"field_name":
"cats",
"overlay_field_name":
"CATSBNDY",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"transport\\eug\\catsbndy.shp"),
},
{
"field_name":
"trans_analysis_zone",
"overlay_field_name":
"TAZ_NUM",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA, "transport\\MTAZ16.shp"),
},
# Natural attributes.
{
"field_name":
"firmnumber",
"overlay_field_name":
"firm_pan",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\flood\\Flood.gdb\\FIRMPanel"),
},
{
"field_name":
"soilkey",
"overlay_field_name":
"mukey",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\soils\\Soils.gdb\\Soil"),
},
{
"field_name":
"wetland",
"overlay_field_name":
"WET_TYPE",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\eug\\Wetland\\wetlands.shp"),
},
# Census attributes.
{
"field_name":
"ctract",
"overlay_field_name":
"TRACT",
"overlay_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"federal\\census\\lane\\2010",
"lc_census2010.gdb\\lc_tracts2010",
),
},
{
"field_name":
"blockgr",
"overlay_field_name":
"BlockGroup",
"overlay_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"federal\\census\\lane\\2010",
"lc_census2010.gdb\\lc_blockgroups2010",
),
},
# Other district attributes.
{
"field_name":
"neighbor",
"overlay_field_name":
"NEIBORHD",
"overlay_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"boundary\\districts\\eug",
"Boundary.gdb\\EugNeighborhoods",
),
},
]
for kwargs in overlay_kwargs:
etl.transform(arcetl.attributes.update_by_overlay,
overlay_central_coincident=True,
**kwargs)
# Override overlays for special cases.
for override in OVERRIDE_ATTRS:
for kwargs in OVERRIDE_ATTRS[override].get("overlay_kwargs", []):
etl.transform(arcetl.attributes.update_by_value,
dataset_where_sql=OVERRIDE_ATTRS[override].get(
"where_sql"),
**kwargs)
# Clean overlay values.
transform.clean_whitespace(etl,
field_names=[
"police_beat", "wetland", "ctract",
"blockgr", "neighbor"
])
transform.force_uppercase(
etl, field_names=["cats", "ltddist", "ltdridesrc"])
# Set default overlay values where missing.
transform.force_yn(
etl,
field_names=[
"greenwy", "sprsvcbndy", "cats", "ltddist", "ltdridesrc"
],
default="N",
)
# Remove invalid overlay values.
transform.clear_nonpositive(etl, field_names=["ctract", "blockgr"])
etl.transform(
arcetl.attributes.update_by_function,
field_name="neighbor",
function=(lambda x: x if x and int(x) != 99 else None),
)
# Assign joinable field values after overlays.
join_kwargs = [
# Core attributes.
{
"field_name": "pre_direction",
"join_field_name": "description",
"join_dataset_path": dataset.STREET_DIRECTION.path(),
"on_field_pairs": [("pre_direction_code", "code")],
},
{
"field_name": "street_type",
"join_field_name": "description",
"join_dataset_path": dataset.STREET_TYPE.path(),
"on_field_pairs": [("street_type_code", "code")],
},
{
"field_name": "unit_type",
"join_field_name": "description",
"join_dataset_path": dataset.UNIT_TYPE.path(),
"on_field_pairs": [("unit_type_code", "code")],
},
{
"field_name": "city_name_abbr",
"join_field_name": "CityNameAbbr",
"join_dataset_path": dataset.CITY.path(),
"on_field_pairs": [("city_name", "CityName")],
},
# Extended attributes.
{
"field_name": "five_digit_zip_code",
"join_field_name": "zip_code",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
# Any addresses not assigned zip from USPS gets an overlay zip.
{
"field_name": "five_digit_zip_code",
"dataset_where_sql": "five_digit_zip_code is null",
"join_field_name": "zip_code_overlay",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "four_digit_zip_code",
"join_field_name": "plus_four_code",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "usps_delivery_point_code",
"join_field_name": "delivery_point_code",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "postal_carrier_route",
"join_field_name": "carrier_route",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "usps_is_cmra",
"join_field_name": "is_cmra",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "usps_is_vacant",
"join_field_name": "is_vacant",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "usps_has_mail_service",
"join_field_name": "has_mail_service",
"join_dataset_path": dataset.ADDRESS_POSTAL_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
{
"field_name": "landuse_desc",
"join_field_name": "ludesc",
"join_dataset_path":
dataset.LAND_USE_CODES_DETAILED.path("pub"),
"on_field_pairs": [("landuse", "landusec")],
},
{
"field_name": "usecode",
"join_field_name": "usecode",
"join_dataset_path":
dataset.LAND_USE_CODES_DETAILED.path("pub"),
"on_field_pairs": [("landuse", "landusec")],
},
{
"field_name": "usedesc",
"join_field_name": "ucname",
"join_dataset_path":
dataset.LAND_USE_CODES_USE_CODES.path("pub"),
"on_field_pairs": [("usecode", "usecode")],
},
# A&T attributes.
{
"field_name": "tca",
"join_field_name": "tax_code_overlay",
"join_dataset_path": dataset.ADDRESS_ASSESS_TAX_INFO.path(),
"on_field_pairs": [("geofeat_id", "geofeat_id")],
},
# City attributes.
{
"field_name": "geocity_name",
"join_field_name": "inccityname",
"join_dataset_path": dataset.INCORPORATED_CITY_LIMITS.path(),
"on_field_pairs": [("geocity", "inccityabbr")],
},
{
"field_name": "ugb_city_name",
"join_field_name": "ugbcityname",
"join_dataset_path": dataset.UGB.path("pub"),
"on_field_pairs": [("ugb", "ugbcity")],
},
# Planning & zoning attributes.
{
"field_name": "nodaldev_name",
"join_field_name": "nodename",
"join_dataset_path":
dataset.NODAL_DEVELOPMENT_AREA.path("pub"),
"on_field_pairs": [("nodaldev", "nodearea")],
},
{
"field_name": "plandesjuris",
"join_field_name": "planjuris",
"join_dataset_path": dataset.PLAN_DESIGNATION.path("pub"),
"on_field_pairs": [("plandes_id", "plandes_id")],
},
{
"field_name": "plandes",
"join_field_name": "plandes",
"join_dataset_path": dataset.PLAN_DESIGNATION.path("pub"),
"on_field_pairs": [("plandes_id", "plandes_id")],
},
{
"field_name": "plandesdesc",
"join_field_name": "plandesnam",
"join_dataset_path": dataset.PLAN_DESIGNATION.path("pub"),
"on_field_pairs": [("plandes_id", "plandes_id")],
},
# Public safety attributes.
{
"field_name": "ambulance_service_area",
"join_field_name": "asa",
"join_dataset_path":
dataset.AMBULANCE_SERVICE_AREA.path("pub"),
"on_field_pairs": [("ambulance_district", "asacode")],
},
{
"field_name": "ambulance_service_provider",
"join_field_name": "provider",
"join_dataset_path":
dataset.AMBULANCE_SERVICE_AREA.path("pub"),
"on_field_pairs": [("ambulance_district", "asacode")],
},
{
"field_name": "fire_protection_provider",
"join_field_name": "fpprovname",
"join_dataset_path": dataset.FIRE_PROTECTION_AREA.path("pub"),
"on_field_pairs": [("firedist", "fireprotprov")],
},
{
"field_name": "psap_name",
"join_field_name": "psap_name",
"join_dataset_path": dataset.PSAP_AREA.path("pub"),
"on_field_pairs": [("psap_code", "psap_code")],
},
{
"field_name":
"emergency_service_number",
"join_field_name":
"emergency_service_number",
"join_dataset_path":
dataset.EMERGENCY_SERVICE_NUMBER.path(),
"on_field_pairs": [
# City used as proxy for police.
("geocity", "city_limits"),
("ambulance_district", "asa_code"),
("firedist", "fire_district"),
("psap_code", "psap_code")
],
},
{
"field_name":
"emergency_service_number",
"join_field_name":
"emergency_service_number",
"join_dataset_path":
dataset.EMERGENCY_SERVICE_NUMBER.path(),
"on_field_pairs": [
# City used as proxy for police.
("geocity", "city_limits"),
("ambulance_district", "asa_code"),
("firedist", "fire_district"),
],
"dataset_where_sql":
"emergency_service_number is null",
},
# Election attributes.
{
"field_name": "city_councilor",
"join_field_name": "councilor",
"join_dataset_path": dataset.CITY_WARD.path(),
"on_field_pairs": [("ccward", "ward")],
},
{
"field_name":
"cocommdist_name",
"join_field_name":
"cmdistname",
"join_dataset_path":
dataset.COUNTY_COMMISSIONER_DISTRICT.path("pub"),
"on_field_pairs": [("cocommdist", "commrdist")],
},
{
"field_name":
"county_commissioner",
"join_field_name":
"commrname",
"join_dataset_path":
dataset.COUNTY_COMMISSIONER_DISTRICT.path("pub"),
"on_field_pairs": [("cocommdist", "commrdist")],
},
{
"field_name": "eweb_commissioner_name",
"join_field_name": "eweb_commissioner_name",
"join_dataset_path": dataset.EWEB_COMMISSIONER.path("pub"),
"on_field_pairs": [("ccward", "city_council_ward")],
},
{
"field_name":
"state_representative",
"join_field_name":
"repname",
"join_dataset_path":
dataset.STATE_REPRESENTATIVE_DISTRICT.path("pub"),
"on_field_pairs": [("strepdist", "repdist")],
},
{
"field_name": "state_senator",
"join_field_name": "senname",
"join_dataset_path":
dataset.STATE_SENATOR_DISTRICT.path("pub"),
"on_field_pairs": [("senatedist", "sendist")],
},
# Education attributes.
{
"field_name": "schooldist_name",
"join_field_name": "names",
"join_dataset_path": dataset.SCHOOL_DISTRICT.path("pub"),
"on_field_pairs": [("schooldist", "district")],
},
{
"field_name": "elem_name",
"join_field_name": "elem_school",
"join_dataset_path":
dataset.ELEMENTARY_SCHOOL_AREA.path("pub"),
"on_field_pairs": [("elem", "attend")],
},
{
"field_name": "middle_name",
"join_field_name": "middle_school",
"join_dataset_path": dataset.MIDDLE_SCHOOL_AREA.path("pub"),
"on_field_pairs": [("middle", "attend")],
},
{
"field_name": "high_name",
"join_field_name": "high_school",
"join_dataset_path": dataset.HIGH_SCHOOL_AREA.path("pub"),
"on_field_pairs": [("high", "attend")],
},
# Natural attributes.
{
"field_name":
"firmprinted",
"join_field_name":
"panel_printed",
"join_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\flood\\Flood.gdb\\FIRMPanel"),
"on_field_pairs": [("firmnumber", "firm_pan")],
},
{
"field_name":
"firm_community_id",
"join_field_name":
"com_nfo_id",
"join_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\flood\\Flood.gdb\\CommunityInfo"),
"on_field_pairs": [("geocity", "community_code")],
},
{
"field_name":
"firm_community_post_firm_date",
"join_field_name":
"in_frm_dat",
"join_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\flood\\Flood.gdb\\CommunityInfo"),
"on_field_pairs": [("geocity", "community_code")],
},
{
"field_name":
"soiltype",
"join_field_name":
"musym",
"join_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\soils\\Soils.gdb\\MUAggAtt"),
"on_field_pairs": [("soilkey", "mukey")],
},
# Other district attributes.
{
"field_name":
"neighborhood_name",
"join_field_name":
"NAME",
"join_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"boundary\\districts\\eug\\Boundary.gdb\\EugNeighborhoods",
),
"on_field_pairs": [("neighbor", "NEIBORHD")],
},
]
for kwargs in join_kwargs:
etl.transform(arcetl.attributes.update_by_joined_value, **kwargs)
# Clean join values.
transform.clean_whitespace(etl, field_names=["neighborhood_name"])
# Remove Metro Plan designations, per City of Eugene request.
transform.clear_all_values(
etl,
field_names=["plandes", "plandesdesc"],
dataset_where_sql="plandesjuris = 'MTP'",
)
# Remove +4 ZIP where initial ZIP is missing.
transform.clear_all_values(
etl,
field_names=["four_digit_zip_code"],
dataset_where_sql="five_digit_zip_code is null",
)
# Assign constants.
constant_kwargs = [
{
"field_name": "state_code",
"value": "OR"
},
{
"field_name": "state_name",
"value": "Oregon"
},
{
"field_name": "county_name",
"value": "Lane"
},
]
for kwargs in constant_kwargs:
etl.transform(arcetl.attributes.update_by_value, **kwargs)
# Override constants for special cases.
for override in OVERRIDE_ATTRS:
for kwargs in OVERRIDE_ATTRS[override].get("constant_kwargs", []):
etl.transform(arcetl.attributes.update_by_value,
dataset_where_sql=OVERRIDE_ATTRS[override].get(
"where_sql"),
**kwargs)
# Build values from functions.
function_kwargs = [
{
"field_name":
"street_name_full",
"function":
concatenate_arguments,
"arg_field_names": [
"pre_direction_code",
"street_name",
"street_type_code",
],
},
{
"field_name":
"city_state_zip",
"function":
city_state_zip,
"kwarg_field_names":
["city_name", "state_code", "five_digit_zip_code"],
},
{
"field_name":
"concat_address_no_unit",
"function":
concatenate_arguments,
"arg_field_names": [
"house_nbr",
"house_suffix_code",
"street_name_full",
],
},
{
"field_name":
"concat_address",
"function":
concatenate_arguments,
"arg_field_names": [
"concat_address_no_unit",
"unit_type_code",
"unit_id",
],
},
{
"field_name":
"concat_address_no_direction",
"function":
concatenate_arguments,
"arg_field_names": [
"house_nbr",
"house_suffix_code",
"street_name",
"street_type_code",
"unit_type_code",
"unit_id",
],
},
{
"field_name":
"concat_address_full",
"function":
concat_address_full,
"kwarg_field_names": [
"concat_address",
"city_name",
"state_code",
"five_digit_zip_code",
"four_digit_zip_code",
],
},
{
"field_name": "mapnumber",
"function": (lambda x: x[:8] if x else None),
"arg_field_names": ["maptaxlot"],
},
{
"field_name": "taxlot",
"function": (lambda x: x[-5:] if x else None),
"arg_field_names": ["maptaxlot"],
},
{
"field_name": "maptaxlot_hyphen",
"function": maptaxlot_separated,
"arg_field_names": ["maptaxlot"],
},
]
for kwargs in function_kwargs:
etl.transform(arcetl.attributes.update_by_function,
field_as_first_arg=False,
**kwargs)
# Take care of addresses flagged not to update in publication.
ids = {}
id_set_kwargs = {
"in_publication": {
"dataset_path": dataset.SITE_ADDRESS.path("pub")
},
"in_transform": {
"dataset_path": etl.transform_path
},
"no_update": {
"dataset_path": dataset.ADDRESS_ISSUES.path(),
"dataset_where_sql": "update_publication = 0",
},
}
for key, kwargs in id_set_kwargs.items():
ids[key] = set(_id for _id, in arcetl.attributes.as_iters(
field_names="site_address_gfid", **kwargs))
ids["rollback"] = ids["no_update"] & ids["in_transform"] & ids[
"in_publication"]
ids["hold"] = ids["no_update"] & (ids["in_transform"] -
ids["in_publication"])
rollback_features = [
feat for feat in arcetl.attributes.as_dicts(
dataset.SITE_ADDRESS.path("pub"))
if feat["site_address_gfid"] in ids["rollback"]
]
# Strip OIDs (not part of update).
for feat in rollback_features:
del feat["oid@"]
if rollback_features:
etl.transform(
arcetl.features.update_from_dicts,
update_features=rollback_features,
id_field_names="site_address_gfid",
field_names=rollback_features[0].keys(),
delete_missing_features=False,
)
etl.transform(
arcetl.features.delete_by_id,
delete_ids=ids["hold"],
id_field_names="site_address_gfid",
)
LOG.info("%s addresses held from publication", len(ids["hold"]))
LOG.info("%s addresses rolled-back from publication",
len(ids["rollback"]))
if any([ids["hold"], ids["rollback"]]):
send_publication_issues_message()
etl.load(dataset.SITE_ADDRESS.path("pub"))
send_new_lincom_address_message()
def land_use_area_etl():
"""Run ETL for land use areas."""
with arcetl.ArcETL("Land Use Areas") as etl:
etl.extract(dataset.LAND_USE_AREA.path("maint"))
# Clean maintenance values.
transform.clean_whitespace(etl, field_names=["maptaxlot"])
transform.clear_non_numeric_text(etl, field_names=["maptaxlot"])
etl.transform(
arcetl.attributes.update_by_function,
field_name="landuse",
function=(lambda x: 0 if x is None or x < 0 else x),
)
# Remove features with missing core identifiers.
for name in dataset.LAND_USE_AREA.id_field_names:
etl.transform(arcetl.features.delete,
dataset_where_sql="{} is null".format(name))
# Dissolve on core maintenance fields that are used in publication.
etl.transform(
arcetl.features.dissolve,
dissolve_field_names=dataset.LAND_USE_AREA.id_field_names,
tolerance=TOLERANCE["xy"],
)
transform.add_missing_fields(etl, dataset.LAND_USE_AREA, tags=["pub"])
# Assign geometry attributes.
coordinate_system_xy_keys = {
2914: {
"x": "xcoord",
"y": "ycoord"
},
4326: {
"x": "longitude",
"y": "latitude"
},
}
for spatial_reference_id, xy_key in coordinate_system_xy_keys.items():
for axis, key in xy_key.items():
etl.transform(
arcetl.attributes.update_by_geometry,
field_name=key,
spatial_reference_item=spatial_reference_id,
geometry_properties=["centroid", axis],
)
# Assign overlays.
overlay_kwargs = [
# City attributes.
{
"field_name": "geocity",
"overlay_field_name": "inccityabbr",
"overlay_dataset_path":
dataset.INCORPORATED_CITY_LIMITS.path(),
},
{
"field_name": "yearanx",
"overlay_field_name": "annexyear",
"overlay_dataset_path": dataset.ANNEXATION_HISTORY.path("pub"),
},
{
"field_name": "ugb",
"overlay_field_name": "ugbcity",
"overlay_dataset_path": dataset.UGB.path("pub"),
},
# Planning & zoning attributes.
{
"field_name":
"greenwy",
"overlay_field_name":
"greenway",
"overlay_dataset_path":
dataset.WILLAMETTE_RIVER_GREENWAY.path("pub"),
},
# Public safety attributes.
{
"field_name": "firedist",
"overlay_field_name": "fireprotprov",
"overlay_dataset_path":
dataset.FIRE_PROTECTION_AREA.path("pub"),
},
# Election attributes.
{
"field_name": "lcczone",
"overlay_field_name": "lccbrdzone",
"overlay_dataset_path": dataset.LCC_BOARD_ZONE.path("pub"),
},
# Education attributes.
{
"field_name": "elem",
"overlay_field_name": "attend",
"overlay_dataset_path":
dataset.ELEMENTARY_SCHOOL_AREA.path("pub"),
},
{
"field_name": "middle",
"overlay_field_name": "attend",
"overlay_dataset_path": dataset.MIDDLE_SCHOOL_AREA.path("pub"),
},
{
"field_name": "high",
"overlay_field_name": "attend",
"overlay_dataset_path": dataset.HIGH_SCHOOL_AREA.path("pub"),
},
# Transportation attributes.
{
"field_name":
"ltddist",
"overlay_field_name":
"LTD",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"transport\\ltd\\2012 LTD Boundary.shp"),
},
# Natural attributes.
{
"field_name":
"flood",
"overlay_field_name":
"fld_zone",
"overlay_dataset_path":
os.path.join(path.REGIONAL_DATA,
"natural\\flood\\Flood.gdb\\FloodHazardArea"),
},
# Census attributes.
{
"field_name":
"ctract",
"overlay_field_name":
"TRACT",
"overlay_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"federal\\census\\lane\\2010\\lc_census2010.gdb\\lc_tracts2010",
),
},
{
"field_name":
"blockgr",
"overlay_field_name":
"BlockGroup",
"overlay_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"federal\\census\\lane\\2010\\lc_census2010.gdb",
"lc_blockgroups2010",
),
},
# Other district attributes.
{
"field_name":
"neighbor",
"overlay_field_name":
"NEIBORHD",
"overlay_dataset_path":
os.path.join(
path.REGIONAL_DATA,
"boundary\\districts\\eug\\Boundary.gdb\\EugNeighborhoods",
),
},
]
for kwargs in overlay_kwargs:
etl.transform(arcetl.attributes.update_by_overlay,
overlay_central_coincident=True,
**kwargs)
# Clean overlay values.
transform.clean_whitespace(
etl, field_names=["ctract", "blockgr", "neighbor"])
transform.force_uppercase(etl, field_names=["ltddist"])
# Set default overlay values where missing.
transform.force_yn(etl,
field_names=["greenwy", "ltddist"],
default="N")
# Remove invalid overlay values.
transform.clear_nonpositive(etl, field_names=["ctract", "blockgr"])
etl.transform(
arcetl.attributes.update_by_function,
field_name="neighbor",
function=(lambda x: x if x and int(x) != 99 else None),
)
# Assign joinable field values after overlays.
join_kwargs = [
# Core attributes.
{
"field_name": "landusedes",
"join_field_name": "ludesc",
"join_dataset_path":
dataset.LAND_USE_CODES_DETAILED.path("pub"),
"on_field_pairs": [("landuse", "landuse")],
},
{
"field_name": "usecode",
"join_field_name": "usecode",
"join_dataset_path":
dataset.LAND_USE_CODES_DETAILED.path("pub"),
"on_field_pairs": [("landuse", "landuse")],
},
{
"field_name": "usecodedes",
"join_field_name": "ucname",
"join_dataset_path":
dataset.LAND_USE_CODES_USE_CODES.path("pub"),
"on_field_pairs": [("usecode", "usecode")],
},
]
for kwargs in join_kwargs:
etl.transform(arcetl.attributes.update_by_joined_value, **kwargs)
# Build values from functions.
function_kwargs = [
{
"field_name": "mapnumber",
"function": (lambda x: x[:8] if x else None),
"arg_field_names": ["maptaxlot"],
},
{
"field_name": "taxlot",
"function": (lambda x: x[-5:] if x else None),
"arg_field_names": ["maptaxlot"],
},
{
"field_name": "maptaxlot_hyphen",
"function": maptaxlot_separated,
"arg_field_names": ["maptaxlot"],
},
]
for kwargs in function_kwargs:
etl.transform(arcetl.attributes.update_by_function,
field_as_first_arg=False,
**kwargs)
# Build values from mappings.
mapping_kwargs = [
{
"field_name": "units",
"mapping": total_units,
"key_field_names": ["maptaxlot", "landuse"],
},
{
"field_name": "acres",
"mapping": total_acres,
"key_field_names": ["maptaxlot", "landuse"],
},
]
for kwargs in mapping_kwargs:
etl.transform(arcetl.attributes.update_by_mapping, **kwargs)
etl.transform(
arcetl.attributes.update_by_feature_match,
field_name="landusecount",
id_field_names=["maptaxlot"],
update_type="match_count",
)
etl.load(dataset.LAND_USE_AREA.path("pub"))
def taxlot_petition_document_etl():
"""Run ETL for taxlot/petition document cross-reference."""
keys = {"taxlot": ["maptaxlot", "maptaxlot_hyphen", "map", "taxlot"]}
with arcetl.ArcETL("Taxlot Petition Documents") as etl:
etl.init_schema(dataset.TAXLOT_PETITION_DOCUMENT.path())
# To avoid memory/topoengine errors when processing, run ETL on subsets.
subsets = taxlot_subset_temp_copies(REAL_LOT_SQL,
field_names=keys["taxlot"])
petition_documents = petition_documents_map()
for subset in subsets:
with subset:
arcetl.dataset.add_field(subset.path,
field_name="petition_id",
field_type="text")
arcetl.geoset.overlay(
dataset_path=subset.path,
field_name="petition_id",
overlay_dataset_path=PATH["petition"],
overlay_field_name="ID_NUM",
overlay_central_coincident=True,
)
arcetl.attributes.update_by_function(
dataset_path=subset.path,
field_name="petition_id",
function=clean_whitespace,
)
# Remove features without overlay.
arcetl.features.delete(dataset_path=subset.path,
dataset_where_sql="petition_id is null")
petition_document_rows = []
for petition in arcetl.attributes.as_dicts(
dataset_path=subset.path,
field_names=keys["taxlot"] + ["petition_id"],
):
petition.update({
"document_name": None,
"document_type": None
})
# If petition has no documents, add a document-less row.
if petition["petition_id"] not in petition_documents:
petition_document_rows.append(petition)
continue
for document in petition_documents[
petition["petition_id"]]:
row = copy(petition)
row.update(document)
petition_document_rows.append(row)
if petition_document_rows:
etl.transform(
arcetl.features.insert_from_dicts,
insert_features=petition_document_rows,
field_names=petition_document_rows[0].keys(),
)
# Set petition jurisdiction (only Eugene petitions at the moment).
etl.transform(
arcetl.attributes.update_by_value,
field_name="petition_jurisdiction_code",
value="EUG",
)
# Add temp field for convertable string values from petition lots.
etl.transform(
arcetl.dataset.add_field,
field_name="petition_date_string",
field_type="text",
field_length=32,
)
# Assign joinable attributes.
join_kwargs = [
{
"field_name": "petition_number",
"join_field_name": "PETNUM"
},
{
"field_name": "petition_type_code",
"join_field_name": "PET_TYPE"
},
{
"field_name": "petition_date_string",
"join_field_name": "DATE"
},
{
"field_name": "is_active",
"join_field_name": "ACTIVE"
},
{
"field_name": "alley_petition",
"join_field_name": "ALY"
},
{
"field_name": "bikepath_petition",
"join_field_name": "BP"
},
{
"field_name": "paving_petition",
"join_field_name": "PAV"
},
{
"field_name": "pedway_petition",
"join_field_name": "PED"
},
{
"field_name": "rehab_petition",
"join_field_name": "RHB"
},
{
"field_name": "sanitary_petition",
"join_field_name": "SAN"
},
{
"field_name": "sidewalk_petition",
"join_field_name": "CW"
},
{
"field_name": "storm_petition",
"join_field_name": "STM"
},
{
"field_name": "streetlight_petition",
"join_field_name": "SL"
},
]
for kwargs in join_kwargs:
etl.transform(arcetl.attributes.update_by_joined_value,
join_dataset_path=PATH["petition"],
on_field_pairs=[("petition_id", "ID_NUM")],
**kwargs)
petition_fields = [
"alley_petition",
"bikepath_petition",
"paving_petition",
"pedway_petition",
"rehab_petition",
"sanitary_petition",
"sidewalk_petition",
"storm_petition",
"streetlight_petition",
]
# Clean added values from sources of unknown maintenance.
transform.clean_whitespace(
etl,
field_names=petition_fields + [
"petition_number",
"petition_type_code",
"petition_date_string",
"is_active",
],
)
# RLID uses Y/N flags, convert these Yes/No ones.
for field_name in petition_fields + ["is_active"]:
etl.transform(
arcetl.attributes.update_by_function,
field_name=field_name,
function=(lambda x: "Y"
if x and x.upper() in ["Y", "YES"] else "N"),
)
# Update petition_date from the string value version.
etl.transform(
arcetl.attributes.update_by_function,
field_name="petition_date",
function=datetime_from_string,
field_as_first_arg=False,
arg_field_names=["petition_date_string"],
)
etl.transform(arcetl.dataset.delete_field,
field_name="petition_date_string")
# Add values derived from other values.
petition_type = {
"A": "Prepaid Assessment",
"I": "Irrevocable Petition",
"P": "Poll",
"S": "Survey",
"V": "Voluntary Petition",
"X": "Adjacent to Unimproved Street or Alley",
}
etl.transform(
arcetl.attributes.update_by_function,
field_name="petition_type_description",
function=petition_type.get,
field_as_first_arg=False,
arg_field_names=["petition_type_code"],
)
# Build URL values.
for field_name, ext in [
("rlid_document_url", ".pdf"),
("rlid_image_url", ".tif"),
]:
etl.transform(
arcetl.attributes.update_by_function,
field_name=field_name,
function=(url.RLID_IMAGE_SHARE + "/petitions/" + "{}" +
ext).format,
field_as_first_arg=False,
arg_field_names=["document_name"],
dataset_where_sql="document_name is not null",
)
etl.load(dataset.TAXLOT_PETITION_DOCUMENT.path())