def insert_new_results(selected_auth_fl, selected_res_fl, authoritative_fc,
schema):
selected_auth_sdf = SpatialDataFrame.from_featureclass(selected_auth_fl)
print(len(selected_auth_sdf))
selected_res_sdf = SpatialDataFrame.from_featureclass(selected_res_fl)
print(len(selected_res_sdf))
fields = field_schema.get(schema)
#for f in fields:
# print(f)
# Write this function
dtypes = dts.get(schema)
fields = field_schema.get(schema)
for idx, sel_auth_row in enumerate(selected_auth_sdf.iterrows()):
geom = sel_auth_row[1].SHAPE.buffer(-.01)
oid = sel_auth_row[1].OBJECTID
# print(oid)
ext = geom.extent
sq = selected_res_sdf['SHAPE'].disjoint(geom) == False
df_current = selected_res_sdf[sq].copy()
df_current.reset_index(inplace=True)
#print(df_current.head())
if len(df_current) > 0:
# print("Here")
#['MEAN_CE90']
for f in fields:
try:
cur_val = df_current.loc[0].at[f]
#print(cur_val)
selected_auth_sdf.at[idx, f] = cur_val
except:
# break
print("Field doesn't exist")
insert_df = selected_auth_sdf.drop(['SHAPE'], axis=1, inplace=False)
records = insert_df.to_records(index=False)
rows = np.array(records, dtype=dtypes)
array = rows # np.array(rows, dtypes)
da.ExtendTable(authoritative_fc, "OID@", array, "_ID", False)
return authoritative_fc
from arcgis.gis import GIS
from arcgis.features import SpatialDataFrame
from arcgis.raster import ImageryLayer
from arcgis.geometry import Polygon
from arcgis.geometry import Geometry
import sys
import json
import arcgis_config
# type of coordinate referrence system
crs_id = 3857
gis = GIS("https://www.arcgis.com", arcgis_config.username, arcgis_config.password)
shp_file = 'raw/bottom_part.shp'
building_data = SpatialDataFrame.from_featureclass(shp_file)
# print(type(building_data.geometry))
# print(df.dtypes)
# print(df.shape)
# naip = gis.content.search('Views', 'Imagery Layer', outside_org=True)
naip = gis.content.get('3f8d2d3828f24c00ae279db4af26d566')
# for layer in naip.layers:
# print(layer)
naip_image_layer = naip.layers[0]
# naip_image_layer = apply(naip_image_layer, 'FalseColorComposite')
# print(naip_image_layer.extent)
# redefine occupancy type to be residential(1) and non-residential(2)
with open('residential_occupancy_types.json', 'r') as f:
res_types = json.load(f)
fc_parcels = r"I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb\TEST_parcels_for_mixIndex_point2"
out_csv = r'I:\Projects\Darren\PPA_V2_GIS\CSV\Simpson_div_idx_x_taz{}.csv'.format(
dt_suffix)
csv_lutypes_lookup = r"I:\Projects\Darren\PPA_V2_GIS\CSV\lutypes_lookup.csv"
col_taz = 'TAZ07'
col_netarea = 'GISAc'
col_lutype = 'LUTYPE'
col_lutype_rev = 'LUTYPE_rev'
excl_lutypes = 'EXCLUDE'
cols = [col_taz, col_netarea, col_lutype]
sdf_parcel = SDF.from_featureclass(fc_parcels)
df_x_taz_and_type = sdf_parcel.groupby(
[col_taz, col_lutype])[col_netarea].sum() #total TAZ area x land use type
dfpp = df_x_taz_and_type.reset_index()
#lookup of land use type sto consolidate similar land use types
df_lutypes_lookup = pd.read_csv(csv_lutypes_lookup)
dfpp = dfpp.merge(df_lutypes_lookup, on=col_lutype)
#pivot: cols = land use types, rows = TAZ IDs, values = total net acres of that land use on that TAZ
df_pivot = dfpp.pivot_table(values=col_netarea,
index=col_taz,
columns=col_lutype_rev,