def sdf_from_xyz(df, x_col, y_col, z_col=None, sr=None):
"""builds a SpatialDataFrame from DataFrame with
x, y, and z columns
args:
df - the dataframe
x_col - the dataframe column corresponding to x coordinate
y_col - the dataframe column corresponding to y coordinate
z_col - optional, the dataframe column corresponding to z coordinate
sr - the spatial reference for the spatial data frame
"""
if not z_col:
return SpatialDataFrame.from_xy(df, x_col, y_col, sr)
def point_for_row(x, y, z, sr):
return Point({'x': x, 'y': y, 'z': z, "spatialReference": sr})
if sr is None:
sr = SpatialReference({'wkid': 4326})
df_geom = df.apply(
lambda row: point_for_row(row[x_col], row[y_col], row[z_col], sr),
axis=1)
return SpatialDataFrame(data=df, geometry=df_geom, sr=sr)
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
def build_ways_sdf(o_response, g_type):
# Extract Relevant Way Elements from OSM Response
if g_type == 'polygon':
ways = [
e for e in o_response
if e['type'] == 'way' and e['nodes'][0] == e['nodes'][-1]
]
else:
ways = [
e for e in o_response
if e['type'] == 'way' and e['nodes'][0] != e['nodes'][-1]
]
# Dictionary For Geometries & IDs
geo_dict = {'geo': []}
val_dict = {'osm_id': []}
# Dictionary For Incoming Tags
for w in ways:
w_tags = w['tags'].keys()
for tag in w_tags:
if tag not in val_dict.keys():
val_dict[tag] = []
# Build Lists
for w in ways:
try:
# Populate Tags
for tag in [key for key in val_dict.keys() if key != 'osm_id']:
val_dict[tag].append(str(w['tags'].get(tag, 'Null')))
# Populate Geometries & IDs
coords = [[e['lon'], e['lat']] for e in w.get('geometry')]
if g_type == 'polygon':
poly = Polygon({
"rings": [coords],
"spatialReference": {
"wkid": 4326
}
})
else:
poly = Polyline({
"paths": [coords],
"spatialReference": {
"wkid": 4326
}
})
geo_dict['geo'].append(poly)
val_dict['osm_id'].append(str(w['id']))
except Exception as ex:
print('Way ID {0} Raised Exception: {1}'.format(w['id'], str(ex)))
try:
return SpatialDataFrame(val_dict, geometry=geo_dict['geo'])
except TypeError:
raise Exception('Ensure ArcPy is Included in Python Interpreter')
def _from_xy(df, x_column, y_column, sr=None):
"""
"""
from arcgis.geometry import SpatialReference, Geometry
from arcgis.features import SpatialDataFrame
if sr is None:
sr = SpatialReference({'wkid': 4326})
if not isinstance(sr, SpatialReference):
if isinstance(sr, dict):
sr = SpatialReference(sr)
elif isinstance(sr, int):
sr = SpatialReference({'wkid': sr})
elif isinstance(sr, str):
sr = SpatialReference({'wkt': sr})
geoms = []
for idx, row in df.iterrows():
geoms.append(
Geometry({
'x': row[x_column],
'y': row[y_column],
'spatialReference': sr
}))
df['SHAPE'] = geoms
return SpatialDataFrame(data=df, sr=sr)
def build_node_sdf(n_list, excludedattributes):
'''
Function to convert returned OSM point data to Esri SpatialDataFrame.
Returns an ESRI SpatialDataFrame.
@param n_list: The list of nodes as returned by th get_osm_elements function
@param excludedattributes: The attributes exluded in the configuration file osmconfig.json
'''
# Dictionary For Geometries & IDs
geo_dict = {"geo": []}
val_dict = {'osm_id': [], 'timestamp': []}
# Dictionary For Incoming Tags
for n in n_list:
n_tags = n['tags'].keys()
for tag in n_tags:
if tag not in val_dict.keys() and tag not in excludedattributes:
tagname = tag
val_dict[tagname] = []
print('Constructing points...')
p = 0
pbar = createpbar(len(n_list))
# Build Lists
for n in n_list:
try:
p = updatepbar(p, pbar)
# Populate Tags
for tag in [
key for key in val_dict.keys()
if key not in ['osm_id', 'timestamp']
and key not in excludedattributes
]:
val_dict[tag].append(n['tags'].get(str(tag), ''))
# Populate Geometries & IDs
point = Point({
"x": n['lon'],
"y": n['lat'],
"spatialReference": {
"wkid": 4326
}
})
geo_dict['geo'].append(point)
val_dict['osm_id'].append(str(n['id']))
val_dict['timestamp'].append(
dt.strptime(n['timestamp'], '%Y-%m-%dT%H:%M:%SZ'))
except Exception as ex:
print('Node ID {0} Raised Exception: {1}'.format(n['id'], str(ex)))
try:
val_dict = {k: v for k, v in val_dict.items() if v is not None}
return SpatialDataFrame(val_dict, geometry=geo_dict['geo'])
except TypeError:
raise Exception('Ensure ArcPy is Included in Python Interpreter')
def bb():
try:
# script to update the status of the blue bikes
r = requests.get('https://gbfs.bluebikes.com/gbfs/en/station_status.json')
r = r.json()['data']['stations']
df = pd.DataFrame.from_dict(json_normalize(r), orient='columns')
df = df.rename(columns={'last_reported':'last_reported','station_id':'description', 'num_bikes_available':'bikes_available',
'num_docks_available':'docks_available'})
bb_table = df[['last_reported','description', 'bikes_available', 'docks_available']]
bb_features = gis.content.get(fc_id).layers[7]
bb_features
bb_fset = SpatialDataFrame.from_layer(bb_features)
bb_fset = bb_features.query(where="category_subtype = 'Blue Bikes'") #querying without any conditions returns all the features
overlap_rows = pd.merge(left = bb_fset.sdf, right = bb_table, how='inner', on = 'description')
#print (overlap_rows.head())
features_for_update = [] #list containing corrected features
all_features = bb_fset.features
# inspect one of the features
#print (all_features[2])
# update all features that were joined
for root_id in overlap_rows['description']:
# get the feature to be updated
original_feature = [f for f in all_features if f.attributes['description'] == root_id][0]
feature_to_be_updated = deepcopy(original_feature)
# get the matching row from csv
matching_row = bb_table.where(bb_table.description == root_id).dropna()
#print('snr' + tablenumber + '', 'snr' + tablenumber + 'average', float(matching_row['snrval'].values))
timestamp = matching_row['last_reported'].values[0]
#print (str(datetime.fromtimestamp(timestamp)))
feature_to_be_updated.attributes['use_type'] = matching_row['bikes_available'].values[0]
feature_to_be_updated.attributes['source_name'] = matching_row['docks_available'].values[0]
#feature_to_be_updated.attributes['source_type'] = str(datetime.fromtimestamp(matching_row['last_reported'].values[0]))
feature_to_be_updated.attributes['source_type'] = str(datetime.fromtimestamp(matching_row['last_reported'].values[0]).strftime("%I:%M %p"))
features_for_update.append(feature_to_be_updated)
#print (features_for_update)
bb_features.edit_features(updates= features_for_update)
except:
print("Something went wrong")
os._exit(0)
def _get_sdf(self, path_directory):
"""
Load the photos into a spatial data frame.
:param path_directory: Path to directory containing photos.
:return: SpatialDataFrame
"""
# load up the photos as a list of dictionaries
photo_list = [
Photo(file).dictionary for file in os.listdir(path_directory)
if file.lower().endswith('.jpg')
]
# convert this list of dictionaries into a SpatialDataFrame
photo_sdf = SpatialDataFrame(photo_list)
# set the geometry field property
photo_sdf.set_geometry('SHAPE',
inplace=True,
sr=SpatialReference(wkid=4326))
# reindex all the data and return the result
return photo_sdf.reset_index(drop=True)
def build_node_sdf(n_list):
# Dictionary For Geometries & IDs
geo_dict = {"geo": []}
val_dict = {'osm_id': []}
# Dictionary For Incoming Tags
for n in n_list:
n_tags = n['tags'].keys()
for tag in n_tags:
if tag not in val_dict.keys():
val_dict[tag] = []
# Build Lists
for n in n_list:
try:
# Populate Tags
for tag in [key for key in val_dict.keys() if key != 'osm_id']:
val_dict[tag].append(str(n['tags'].get(tag, 'Null')))
# Populate Geometries & IDs
point = Point({
"x": n['lon'],
"y": n['lat'],
"spatialReference": {
"wkid": 4326
}
})
geo_dict['geo'].append(point)
val_dict['osm_id'].append(str(n['id']))
except Exception as ex:
print('Node ID {0} Raised Exception: {1}'.format(n['id'], str(ex)))
try:
return SpatialDataFrame(val_dict, geometry=geo_dict['geo'])
except TypeError:
raise Exception('Ensure ArcPy is Included in Python Interpreter')
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,
def spatial_join(df1,
df2,
left_tag="_left",
right_tag="_right",
keep_all=True):
"""
Joins two spatailly enabled dataframes based on spatial location based
on if the two geometries are intersected.
Parameters:
:df1: left spatial dataframe
:df2: right spatial dataframe
:left_tag: if the same column is in the left and right dataframe,
this will append that string value to the field
:right_tag: if the same column is in the left and right dataframe,
this will append that string value to the field
:keep_all: if set to true all df1 will be kept regardless of spatial
matches
:output:
Spatial Dataframe
"""
import numpy as np
import pandas as pd
from arcgis.features import SpatialDataFrame
if not isinstance(df1, SpatialDataFrame):
raise ValueError("df1 must be a spatial dataframe")
if not isinstance(df2, SpatialDataFrame):
raise ValueError("df2 must be a spatial dataframe")
right_index = df2.sindex
join_idx = []
if not df1.geometry is None:
geom_field = df1.geometry.name
else:
raise ValueError("df1 is missing a geometry column")
if not df2.geometry is None:
geom_field2 = df2.geometry.name
else:
raise ValueError("df2 is missing a geometry column")
for idx, row in df1.iterrows():
geom = row[geom_field]
if isinstance(geom.extent, tuple):
ext = (geom.extent[0], geom.extent[1], geom.extent[2],
geom.extent[3])
else:
ext = (geom.extent.XMin, geom.exten.YMin, geom.extent.XMax,
geom.extent.YMax)
select_idx = right_index.intersect(ext)
if len(select_idx) > 0:
sub = df2.loc[select_idx]
res = sub[sub.disjoint(geom) == False]
if len(res) > 0:
for idx2, row2 in res.iterrows():
join_idx.append([idx, idx2])
del idx2, row2
elif len(res) == 0 and keep_all:
join_idx.append([idx, None])
del sub, res
elif len(select_idx) == 0 and \
keep_all:
join_idx.append([idx, None])
del geom
del ext
del select_idx
del idx
join_field_names = ["TARGET_OID", "JOIN_OID"]
df2 = df2.copy()
del df2[df2.geometry.name]
join_df = pd.DataFrame(data=join_idx, columns=join_field_names)
join_df = join_df.merge(df1,
left_on=join_field_names[0],
right_index=True,
how='left',
suffixes=(left_tag, right_tag))
join_df = join_df.merge(df2,
left_on=join_field_names[1],
right_index=True,
how='left',
suffixes=(left_tag, right_tag),
copy=True)
join_df = SpatialDataFrame(join_df)
join_df.geometry = join_df[df1.geometry.name]
del join_idx
join_df.reset_index(drop=True, inplace=True)
return join_df
from arcgis import GIS
import pandas as pd
from arcgis.features import GeoAccessor, GeoSeriesAccessor
from arcgis.geocoding import batch_geocode
from arcgis.features import SpatialDataFrame
gis = GIS('https://siarcgisweb01.trssllc.com/portal/home', 'sazdrake')
map1 = gis.map('New York, NY')
map1.basemap = "osm"
map1
address_frame = pd.read_csv('data/city_of_new_york.csv')
address_shp = SpatialDataFrame.from_xy(address_frame, 'LON', 'LAT')
address_shp.spatial.plot(map_widget=map1)
def completeness(out_sdf, df_list, osm_sdf):
print('Running Completeness')
for idx, row in enumerate(out_sdf.iterrows()):
before_val = None
geom = Geometry(row[1].SHAPE)
# Unpack Geom Extent as OSM Expects
bbox = (geom.extent[1], geom.extent[0], geom.extent[3], geom.extent[2])
# Fetch OSM SpatialDataFrame
osm_sdf = gen_osm_sdf('line', bbox, osm_tag='highway')
data_sdf = df_list[idx]
if len(data_sdf) == 0:
before_val = 0
else:
sq = data_sdf[data_sdf.geometry.notnull()].geometry.disjoint(
geom) == False
df_before = data_sdf[sq].copy()
geoms_before = df_before.clip(geom.extent)
geoms_before_sdf = SpatialDataFrame(geometry=geoms_before)
q_before = geoms_before_sdf['SHAPE'] == {"paths": []}
geoms_before_sdf = geoms_before_sdf[~q_before].copy()
geoms_before_sdf.reset_index(inplace=True, drop=True)
geometry_type = osm_sdf.geometry_type
sq = osm_sdf[osm_sdf.geometry.notnull()].geometry.disjoint(
geom) == False
df_after = osm_sdf[sq].copy()
geoms_after = df_after.clip(geom.extent)
geoms_after_sdf = SpatialDataFrame(geometry=geoms_after)
#geoms_after_sdf = SpatialDataFrame({'Pass': '******'}, geometry=geoms_after, index=[0])
q_after = geoms_after_sdf['SHAPE'] == {"paths": []}
geoms_after_sdf = geoms_after_sdf[~q_after].copy()
geoms_after_sdf.reset_index(inplace=True, drop=True)
# This Need Work
if geometry_type == "Polygon":
if before_val == None:
before_val = geoms_before_sdf.geometry.project_as(
4326).get_area('GEODESIC', 'SQUAREKILOMETERS').sum()
after_val = geoms_after_sdf.geometry.project_as(4326).get_area(
'GEODESIC', 'SQUAREKILOMETERS').sum()
if after_val > 0:
score = get_cp_score(ratio=before_val / after_val,
baseVal=before_val,
inputVal=after_val)
else:
score = get_cp_score(0, before_val, after_val)
out_sdf.set_value(idx,
field_schema.get('cmpl')[0],
round(before_val, 1))
out_sdf.set_value(idx,
field_schema.get('cmpl')[1], round(after_val, 1))
out_sdf.set_value(idx,
field_schema.get('cmpl')[3],
round(before_val - after_val, 1))
out_sdf.set_value(idx, field_schema.get('cmpl')[2], score)
elif geometry_type == "Polyline":
if before_val == None:
geom = geoms_before_sdf.geometry
geom_projected = geoms_before_sdf.geometry.project_as(3857)
before_val = int(sum(geom_projected.length.tolist()))
geom_projected = geoms_before_sdf.geometry.project_as(3857)
after_val = int(sum(geom_projected.length.tolist()))
if after_val > 0:
score = get_cp_score(ratio=before_val / after_val,
baseVal=before_val,
inputVal=after_val)
else:
score = get_cp_score(0, before_val, after_val)
out_sdf.set_value(idx,
field_schema.get('cmpl')[0],
round(before_val, 1))
out_sdf.set_value(idx,
field_schema.get('cmpl')[1], round(after_val, 1))
out_sdf.set_value(idx,
field_schema.get('cmpl')[3],
round(before_val - after_val, 1))
out_sdf.set_value(idx, field_schema.get('cmpl')[2], score)
else:
if before_val == None:
before_count = len(geoms_before_sdf)
else:
before_count = 0
after_count = len(geoms_after_sdf)
if after_count > 0:
score = get_cp_score(ratio=before_count / after_count,
baseVal=before_count,
inputVal=after_count)
else:
score = get_cp_score(ratio=0,
baseVal=before_count,
inputVal=after_count)
out_sdf.set_value(idx, field_schema.get('cmpl')[0], before_count)
out_sdf.set_value(idx, field_schema.get('cmpl')[1], after_count)
out_sdf.set_value(idx,
field_schema.get('cmpl')[3],
before_count - after_count)
out_sdf.set_value(idx, field_schema.get('cmpl')[2], score)
del sq
del df_after
del geom
if before_val != None:
print(before_val)
# del df_before
return out_sdf
from arcgis.gis import GIS
from arcgis.features import SpatialDataFrame
from IPython.display import display
import passwords
gis = GIS("https://www.arcgis.com", passwords.user_name, passwords.password)
search_results = gis.content.search(
query='title: "Park*" AND type: "Feature Service"')
display(search_results)
# In[2]:
feature_service_item = search_results[0]
feature_layer = feature_service_item.layers[0]
display(feature_layer)
# In[3]:
# Search for an item and construct a Spatial Data Frame
# In[4]:
sdf = SpatialDataFrame.from_layer(feature_layer)
sdf.head()
# In[ ]:
# sdf.to_featureclass(out_location = 'path/to/save/data', out_name = 'file.shp')
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)
def build_ways_sdf_toline(o_response, excludedattributes):
'''
Function to convert returned OSM polyline data to Esri SpatialDataFrame.
Returns an ESRI SpatialDataFrame.
@param o_response: The valid response data from the OSM server containing the way elements
@param excludedattributes: The attributes exluded in the configuration file osmconfig.json
'''
# Extract Relevant Way Elements from OSM Response
ways = [
e for e in o_response
if e['type'] == 'way' and e['nodes'][0] != e['nodes'][-1]
]
# Dictionary For Geometries & IDs
geo_dict = {'geo': []}
val_dict = {'osm_id': [], 'timestamp': []}
# Dictionary For Incoming Tags
for w in ways:
w_tags = w['tags'].keys()
for tag in w_tags:
if tag not in val_dict.keys() and tag not in excludedattributes:
tagname = tag
val_dict[tagname] = []
print('Constructing lines...')
p = 0
pbar = createpbar(len(ways))
# Build Lists
for w in ways:
try:
p = updatepbar(p, pbar)
# Populate Tags
for tag in [
key for key in val_dict.keys()
if key not in ['osm_id', 'timestamp']
and key not in excludedattributes
]:
val_dict[tag].append(w['tags'].get(str(tag), ''))
# Populate Geometries & IDs
coords = [[e['lon'], e['lat']] for e in w.get('geometry')]
poly = Polyline({
"paths": [coords],
"spatialReference": {
"wkid": 4326
}
})
geo_dict['geo'].append(poly)
val_dict['osm_id'].append(str(w['id']))
val_dict['timestamp'].append(
dt.strptime(w['timestamp'], '%Y-%m-%dT%H:%M:%SZ'))
except Exception as ex:
print('Way ID {0} Raised Exception: {1}'.format(w['id'], str(ex)))
try:
geo_dict = geo_dict['geo']
val_dict = {k: v for k, v in val_dict.items() if v is not None}
return SpatialDataFrame(val_dict, geometry=geo_dict)
except TypeError:
raise Exception('Ensure ArcPy is Included in Python Interpreter')
def build_ways_sdf_topoly(o_response, excludedattributes, o_r_response=None):
'''
Function to convert returned OSM polygon data to Esri SpatialDataFrame.
Returns an ESRI SpatialDataFrame.
@param o_response: The valid response data from the OSM server containing the way elements
@param excludedattributes: The attributes exluded in the configuration file osmconfig.json
@param o_r_response: The optional valid response data from the OSM server containing the relation elements
'''
# Extract relevant relations and way elements from OSM response
if o_r_response:
relations = [e for e in o_r_response if e['type'] == 'relation']
ways = [
e for e in o_response
if e['type'] == 'way' and e['nodes'][0] == e['nodes'][-1]
]
# Dictionary for geometries & IDs
geo_dict_r = {'geo': []}
val_dict_r = {'osm_id': [], 'timestamp': []}
geo_dict_w = {'geo': []}
val_dict_w = {'osm_id': [], 'timestamp': []}
invalid_rel_idx_list = []
for r in range(len(relations)):
nodesinrel = []
nodesinrel = [
relations[r]['members'].index(item)
for item in filter(lambda n: n.get('type') == 'node',
lget(relations, r)['members'])
]
if len(nodesinrel) > 0:
invalid_rel_idx_list.append(r)
relations = [
lget(relations, r) for r in range(len(relations))
if r not in invalid_rel_idx_list
]
# Dictionary for incoming relation tags
for r in relations:
r_tags = r['tags'].keys()
for tag in r_tags:
if tag not in val_dict_r.keys() and tag not in excludedattributes:
tagname = tag
val_dict_r[tagname] = []
valid_list = []
# Build Lists
print('Constructing complex polygons...')
p = 0
pbar = createpbar(len(relations))
for r in relations:
try:
p = updatepbar(p, pbar)
relation = r["members"]
relation = sorted(relation, key=lambda item: item['role'])
outerlist = [memb for memb in relation if memb['role'] == 'outer']
innerlist = [memb for memb in relation if memb['role'] == 'inner']
innerlistgeomtuple = []
outerlistgeomtuple = []
innerlistgeomtuple2 = []
outerlistgeomtuple2 = []
rngitn_i = False
rngitn_o = False
innerlistgeomtuple = [[(x['lon'], x['lat'])
for x in ol['geometry']]
for ol in innerlist]
if len(innerlistgeomtuple) > 1:
all_rings_connected_i = checkrings_connected(
innerlistgeomtuple, r)
if all_rings_connected_i and innerlistgeomtuple:
for ring in innerlistgeomtuple:
innerlistgeomtuple2 += ring
innerlistgeomtuple = []
innerlistgeomtuple.append(
l_ordered_remove_duplicates(innerlistgeomtuple2))
innerlistgeomtuple2 = innerlistgeomtuple
innerlistgeomtuple = []
all_rings_closed_i = checkrings_closed(innerlistgeomtuple, r)
if all_rings_closed_i and innerlistgeomtuple:
for ring in innerlistgeomtuple:
innerlistgeomtuple2.append(ring)
innerlistgeomtuple = []
innerlistgeomtuple3 = []
if innerlistgeomtuple2:
all_rings_connected_i = checkrings_connected(
innerlistgeomtuple2, r)
if not innerlistgeomtuple2:
all_rings_connected_i = checkrings_connected(
innerlistgeomtuple, r)
if innerlistgeomtuple and not all_rings_connected_i:
innerlistgeomtuple2 = innerlistgeomtuple
innerlistgeomtuple3.append(innerlistgeomtuple2[0])
startgeom = innerlistgeomtuple2[0][0]
for ridx in range(0, len(innerlistgeomtuple2)):
next_ring = innerlistgeomtuple3[-1][-1]
if next_ring == startgeom and ridx < len(
innerlistgeomtuple2) - 1:
next_ring = innerlistgeomtuple2[len(
innerlistgeomtuple3)][-1]
startgeom = next_ring
idx_next_ring = lfindgetsingleidx(innerlistgeomtuple2,
next_ring, ridx)
try:
if innerlistgeomtuple2[
idx_next_ring[0]] not in innerlistgeomtuple3:
if innerlistgeomtuple2[idx_next_ring[0]][
-1] == innerlistgeomtuple3[-1][-1]:
innerlistgeomtuple2[idx_next_ring[0]].reverse()
innerlistgeomtuple3.append(
innerlistgeomtuple2[idx_next_ring[0]])
rngitn_i = True
except:
continue
if rngitn_i:
innerlistgeomtuple4 = []
innerlistgeomtuple4 = merge_sublist_items(innerlistgeomtuple3)
rings = detect_rings(innerlistgeomtuple4)
if len(rings) > 1:
rings = [l_ordered_remove_duplicates(rng) for rng in rings]
all_rings_closed_i = checkrings_closed(rings, r)
else:
innerlistgeomtuple3 = l_ordered_remove_duplicates(
innerlistgeomtuple4)
all_rings_closed_i = checkrings_closed(
[innerlistgeomtuple3], r)
if innerlistgeomtuple and rngitn_i and all_rings_closed_i:
innerlistgeomtuple2 = []
all_rings_connected_i = True
if len(rings) < 2:
innerlistgeomtuple2.append([])
innerlistgeomtuple2[0] = innerlistgeomtuple3
else:
innerlistgeomtuple2 = rings
outerlistgeomtuple = [[(x['lon'], x['lat'])
for x in ol['geometry']]
for ol in outerlist]
if len(outerlistgeomtuple) > 1:
all_rings_connected_o = checkrings_connected(
outerlistgeomtuple, r)
if all_rings_connected_o:
for ring in outerlistgeomtuple:
outerlistgeomtuple2 += ring
outerlistgeomtuple = []
outerlistgeomtuple.append(
l_ordered_remove_duplicates(outerlistgeomtuple2))
outerlistgeomtuple2 = outerlistgeomtuple
outerlistgeomtuple = []
all_rings_closed_o = checkrings_closed(outerlistgeomtuple, r)
if all_rings_closed_o:
for ring in outerlistgeomtuple:
outerlistgeomtuple2.append(ring)
outerlistgeomtuple = []
outerlistgeomtuple3 = []
if outerlistgeomtuple2:
all_rings_connected_o = checkrings_connected(
outerlistgeomtuple2, r)
if not outerlistgeomtuple2:
all_rings_connected_o = checkrings_connected(
outerlistgeomtuple, r)
if not all_rings_connected_o:
outerlistgeomtuple2 = outerlistgeomtuple
outerlistgeomtuple3.append(outerlistgeomtuple2[0])
startgeom = outerlistgeomtuple2[0][0]
for ridx in range(0, len(outerlistgeomtuple2)):
next_ring = outerlistgeomtuple3[-1][-1]
if next_ring == startgeom and ridx < len(
outerlistgeomtuple2) - 1:
next_ring = outerlistgeomtuple2[len(
outerlistgeomtuple3)][-1]
startgeom = next_ring
idx_next_ring = lfindgetsingleidx(outerlistgeomtuple2,
next_ring, ridx)
try:
if outerlistgeomtuple2[
idx_next_ring[0]] not in outerlistgeomtuple3:
if outerlistgeomtuple2[idx_next_ring[0]][
-1] == outerlistgeomtuple3[-1][-1]:
outerlistgeomtuple2[idx_next_ring[0]].reverse()
outerlistgeomtuple3.append(
outerlistgeomtuple2[idx_next_ring[0]])
rngitn_o = True
except:
continue
if rngitn_o:
outerlistgeomtuple4 = []
outerlistgeomtuple4 = merge_sublist_items(outerlistgeomtuple3)
rings = detect_rings(outerlistgeomtuple4)
if len(rings) > 1:
rings = [l_ordered_remove_duplicates(rng) for rng in rings]
all_rings_closed_o = checkrings_closed(rings, r)
else:
outerlistgeomtuple3 = outerlistgeomtuple4
outerlistgeomtuple3 = l_ordered_remove_duplicates(
outerlistgeomtuple4)
all_rings_closed_o = checkrings_closed(
[outerlistgeomtuple3], r)
if outerlistgeomtuple and rngitn_o and all_rings_closed_o:
outerlistgeomtuple2 = []
all_rings_connected_o = True
if len(rings) < 2:
outerlistgeomtuple2.append([])
outerlistgeomtuple2[0] = outerlistgeomtuple3
else:
outerlistgeomtuple2 = rings
if innerlistgeomtuple or innerlistgeomtuple2:
innerlistgeomtuple = innerlistgeomtuple2
if outerlistgeomtuple2:
outerlistgeomtuple = outerlistgeomtuple2
l_polygon_rings = []
for subelement in range(len(outerlistgeomtuple)):
ncoordsouter = [(n[0], n[1])
for n in outerlistgeomtuple[subelement]]
temppolyarea = Polygon({
"rings": [ncoordsouter],
"spatialReference": {
"wkid": 4326
}
}).area
if temppolyarea < 0.0:
ncoordsouter.reverse()
if ncoordsouter:
l_polygon_rings.append(ncoordsouter)
for subelement in range(len(innerlistgeomtuple)):
if innerlistgeomtuple[subelement]:
ncoordsinner = [(n[0], n[1])
for n in innerlistgeomtuple[subelement]]
temppolyarea = Polygon({
"rings": [ncoordsinner],
"spatialReference": {
"wkid": 4326
}
}).area
if temppolyarea > 0.0:
ncoordsinner.reverse()
if ncoordsinner:
l_polygon_rings.append(ncoordsinner)
poly = Polygon({
"rings": l_polygon_rings,
"spatialReference": {
"wkid": 4326
}
})
valid_list.append([poly.is_valid, r['id']])
if poly.is_valid:
geo_dict_r['geo'].append(poly)
val_dict_r['osm_id'].append(str(r['id']))
val_dict_r['timestamp'].append(
dt.strptime(r['timestamp'], '%Y-%m-%dT%H:%M:%SZ'))
# Populate Relation tags
for tag in [
key for key in val_dict_r.keys()
if key not in ['osm_id', 'timestamp']
and key not in excludedattributes
]:
val_dict_r[tag].append(r['tags'].get(str(tag), ''))
except Exception as ex:
tb = traceback.format_exc()
print('Relation ID {0} Raised Exception: {1}'.format(
r['id'], str(tb)))
# Dictionary For Incoming Way Tags
for w in ways:
w_tags = w['tags'].keys()
for tag in w_tags:
if tag not in val_dict_w.keys() and tag not in excludedattributes:
tagname = tag
val_dict_w[tagname] = []
print('Constructing simple polygons...')
p = 0
pbar = createpbar(len(ways))
# Build Lists
for w in ways:
try:
p = updatepbar(p, pbar)
# Populate Tags
for tag in [
key for key in val_dict_w.keys()
if key not in ['osm_id', 'timestamp']
and key not in excludedattributes
]:
val_dict_w[tag].append(w['tags'].get(str(tag), ''))
# Populate Geometries & IDs
coords = [[e['lon'], e['lat']] for e in w.get('geometry')]
poly = Polygon({
"rings": [coords],
"spatialReference": {
"wkid": 4326
}
})
geo_dict_w['geo'].append(poly)
val_dict_w['osm_id'].append(str(w['id']))
val_dict_w['timestamp'].append(
dt.strptime(w['timestamp'], '%Y-%m-%dT%H:%M:%SZ'))
except Exception as ex:
print('Way ID {0} Raised Exception: {1}'.format(w['id'], str(ex)))
try:
val_dict = merge_dict_lists(val_dict_r, val_dict_w)
val_dict = {k: v for k, v in val_dict.items() if v is not None}
geo_dict = geo_dict_r['geo'] + geo_dict_w['geo']
len(valid_list)
return SpatialDataFrame(val_dict, geometry=geo_dict)
except TypeError:
raise Exception('Ensure ArcPy is Included in Python Interpreter')
#%%
df.to_csv(custAcctFile,header=False, index=True)
#%% [markdown]
# [```gis.features.SpatialDataFrame()```](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html?highlight=spatialdataframe#arcgis.features.SpatialDataFrame.from_xy)
#%%
from arcgis.features import SpatialDataFrame
from arcgis.gis import GIS
from getpass import getpass
from IPython.display import display
#%%
sdf = SpatialDataFrame.from_xy(df,"POINT_X","POINT_Y")
gis = GIS(arcpy.GetActivePortalURL(), username=input("Enter User Name "), password=(getpass()))
#gis = GIS()
#portalDesc = arcpy.GetPortalDescription()
# search and list all items owned by connected user
#query=f'owner:{portalDesc["user"]["username"]} AND title:CW BaseMap'
#itemType="Feature Layer"
#sortField="title"
#sortOrder="asc"
# default max__items is 10
#maxItems=100
#m = gis.content.search(query,itemType,sortField,sortOrder,maxItems)
#%%
consumptionLyr = gis.content.import_data(sdf)
def logical_consisitency(gis, template_fc, template_gdb, filename, tabname,
data_sdf, input_features, output_features,
grid_filter, geom, def_cnt_field, def_field):
try:
stList = set(data_sdf['F_CODE'].values)
fc = input_features
alias_table = get_field_alias(template_fc)
fc_domain_dict = get_fc_domains(template_gdb)
specificAttributeDict, attrCheck = create_attr_dict(filename, tabname)
temp_result_df = pd.DataFrame(columns=FIELDS) #, dtypes=DTYPES)
geoms = []
counter = 0
total_feature_count = len(data_sdf)
for idx, row in data_sdf.iterrows():
#print(row['F_CODE'])
if row['F_CODE'] in stList:
if row['F_CODE'] in specificAttributeDict:
vals = []
vals = [alias_table[i] for i in specificAttributeDict[row['F_CODE']] \
if row[i] in empty]
line = row['SHAPE']
def_count = len(vals)
polyline = Polyline(line)
geoms.append(polyline)
if def_count > 0:
fs = ",".join(vals)
oid = row['OBJECTID']
ERROR = str(fc) + r" | " + str(
fc_domain_dict[row['F_CODE']]) + r" | OID: " + str(
oid) + r" | " + fs
temp_result_df.set_value(counter, FIELDS[0], fs)
temp_result_df.set_value(counter, FIELDS[1], fc)
temp_result_df.set_value(
counter, FIELDS[2],
(fc_domain_dict[row['F_CODE']]))
temp_result_df.set_value(counter, FIELDS[3],
round(oid))
temp_result_df.set_value(counter, FIELDS[4], len(vals))
else:
temp_result_df.set_value(counter, FIELDS[0], 'N/A')
temp_result_df.set_value(counter, FIELDS[1], fc)
temp_result_df.set_value(
counter, FIELDS[2],
(fc_domain_dict[row['F_CODE']]))
temp_result_df.set_value(counter, FIELDS[3],
round(oid))
temp_result_df.set_value(counter, FIELDS[4], len(vals))
counter = counter + 1
assessed_feature_count = len(temp_result_df)
attr_sdf = SpatialDataFrame(temp_result_df, geometry=geoms)
out_fl = FeatureLayer(gis=gis, url=output_features)
out_sdf = out_fl.query(geometry_filter=grid_filter,
return_geometry=True,
return_all_records=True).df
df_current = attr_sdf
fcount = len(df_current)
error_field_count = def_cnt_field
error_field_def = def_field
errors = []
attrs = []
if fcount > 0: #len(df_current) > 0:
errors += df_current[error_field_count].tolist()
def process(x):
#print(x)
return [
va for va in x.replace(' ', '').split('|')[-1].split(',')
if len(va) > 1
]
for e in df_current[error_field_def].apply(process).tolist():
attrs += e
del e
results = get_answers(0, errors, attrs, fcount)
out_sdf[SUM_FIELDS[0]][0] = results[1]
out_sdf[SUM_FIELDS[1]][0] = results[2]
out_sdf[SUM_FIELDS[2]][0] = results[3]
out_sdf[SUM_FIELDS[3]][0] = results[4]
out_sdf[SUM_FIELDS[4]][0] = results[5]
out_sdf[SUM_FIELDS[5]][0] = results[6]
out_sdf[SUM_FIELDS[6]][0] = results[7]
out_sdf[SUM_FIELDS[7]][0] = results[8]
out_sdf[SUM_FIELDS[8]][0] = results[9]
out_sdf[SUM_FIELDS[9]][0] = results[10]
out_sdf[SUM_FIELDS[10]][0] = results[11]
out_sdf[SUM_FIELDS[11]][0] = results[12]
out_sdf[SUM_FIELDS[12]][0] = results[13]
out_sdf[SUM_FIELDS[13]][0] = results[14]
out_sdf[SUM_FIELDS[14]][0] = results[15]
out_sdf[SUM_FIELDS[15]][0] = results[16]
print(out_sdf.columns.values)
return out_sdf, out_fl
except arcpy.ExecuteError:
line, filename, synerror = trace()
except FunctionError as f_e:
messages = f_e.args[0]
except:
line, filename, synerror = trace()
#--------------------------------------------------------------------------
##if __name__ == "__main__":
## #env.overwriteOutput = True
## argv = tuple(arcpy.GetParameterAsText(i)
## for i in range(arcpy.GetArgumentCount()))
## main(*argv)
def plot(df,
map_widget=None,
name=None,
renderer_type=None,
symbol_type=None,
symbol_style=None,
col=None,
colors='jet',
alpha=1,
**kwargs):
"""
Plot draws the data on a web map. The user can describe in simple terms how to
renderer spatial data using symbol. To make the process simplier a pallette
for which colors are drawn from can be used instead of explicit colors.
====================== =========================================================
**Explicit Argument** **Description**
---------------------- ---------------------------------------------------------
df required SpatialDataFrame or GeoSeries. This is the data
to map.
---------------------- ---------------------------------------------------------
map_widget optional WebMap object. This is the map to display the
data on.
---------------------- ---------------------------------------------------------
colors optional string/dict. Color mapping. For simple renderer,
just provide a string. For more robust renderers like
unique renderer, a dictionary can be given.
---------------------- ---------------------------------------------------------
renderer_type optional string. Determines the type of renderer to use
for the provided dataset. The default is 's' which is for
simple renderers.
Allowed values:
+ 's' - is a simple renderer that uses one symbol only.
+ 'u' - unique renderer symbolizes features based on one
or more matching string attributes.
+ 'c' - A class breaks renderer symbolizes based on the
value of some numeric attribute.
+ 'h' - heatmap renders point data into a raster
visualization that emphasizes areas of higher
density or weighted values.
---------------------- ---------------------------------------------------------
symbol_type optional string. This is the type of symbol the user
needs to create. Valid inputs are: simple, picture, text,
or carto. The default is simple.
---------------------- ---------------------------------------------------------
symbol_type optional string. This is the symbology used by the
geometry. For example 's' for a Line geometry is a solid
line. And '-' is a dash line.
Allowed symbol types based on geometries:
**Point Symbols**
+ 'o' - Circle (default)
+ '+' - Cross
+ 'D' - Diamond
+ 's' - Square
+ 'x' - X
**Polyline Symbols**
+ 's' - Solid (default)
+ '-' - Dash
+ '-.' - Dash Dot
+ '-..' - Dash Dot Dot
+ '.' - Dot
+ '--' - Long Dash
+ '--.' - Long Dash Dot
+ 'n' - Null
+ 's-' - Short Dash
+ 's-.' - Short Dash Dot
+ 's-..' - Short Dash Dot Dot
+ 's.' - Short Dot
**Polygon Symbols**
+ 's' - Solid Fill (default)
+ '\' - Backward Diagonal
+ '/' - Forward Diagonal
+ '|' - Vertical Bar
+ '-' - Horizontal Bar
+ 'x' - Diagonal Cross
+ '+' - Cross
---------------------- ---------------------------------------------------------
col optional string/list. Field or fields used for heatmap,
class breaks, or unique renderers.
---------------------- ---------------------------------------------------------
colors optional string. The color map to draw from in order to
visualize the data. The default cmap is 'jet'. To get a
visual representation of the allowed color maps,use
the **display_colormaps** method.
---------------------- ---------------------------------------------------------
alpha optional float. This is a value between 0 and 1 with 1
being the default value. The alpha sets the transparancy
of the renderer when applicable.
====================== =========================================================
The kwargs parameter accepts all parameters of the create_symbol method and the
create_renderer method.
"""
if isinstance(df, GeoSeries):
fid = [[i] for i in range(len(df))]
sdf = SpatialDataFrame(data=fid, geometry=df)
plot(df=sdf,
map_widget=map_widget,
name=name,
renderer_type=renderer_type,
symbol_type=symbol_type,
symbol_style=symbol_style,
col=col,
colors=colors,
alpha=1,
**kwargs)
return
r = None
if isinstance(col, str):
col = [col]
map_exists = True
if symbol_type is None:
symbol_type = 'simple'
if name is None:
import uuid
name = uuid.uuid4().hex[:7]
if map_widget is None:
map_exists = False
map_widget = MapView()
fc = df.to_feature_collection(name=name)
if renderer_type in [None, 's']:
renderer_type = 's' # simple (default)
r = generate_renderer(geometry_type=df.geometry_type.lower(),
sdf_or_series=df,
label=name,
symbol_type=symbol_type,
symbol_style=symbol_style,
render_type=renderer_type,
colors=colors,
alpha=alpha,
**kwargs)
fc.layer['layerDefinition']['drawingInfo']['renderer'] = r
elif isinstance(col, str) and \
col not in df.columns:
raise ValueError("Columns %s does not exist." % col)
elif isinstance(col, (tuple, list, str)) and \
all([c in df.columns for c in col]) == True and \
renderer_type in ['u', 'c']:
if isinstance(col, str):
col = [col]
idx = 1
if renderer_type == 'u':
for c in col:
kwargs['field%s' % idx] = c
idx += 1
elif renderer_type == 'c':
kwargs['field'] = col[0]
r = generate_renderer(geometry_type=df.geometry_type.lower(),
sdf_or_series=df,
label=name,
symbol_type=symbol_type,
symbol_style=symbol_style,
render_type=renderer_type,
colors=colors,
alpha=alpha,
**kwargs)
fc.layer['layerDefinition']['drawingInfo']['renderer'] = r
elif renderer_type == 'h':
r = generate_renderer(geometry_type=df.geometry_type.lower(),
sdf_or_series=df,
label=name,
symbol_type=symbol_type,
symbol_style=symbol_style,
render_type=renderer_type,
colors=colors,
alpha=alpha,
**kwargs)
fc.layer['layerDefinition']['drawingInfo']['renderer'] = r
elif renderer_type == 'str':
r = generate_renderer(geometry_type=df.geometry_type.lower(),
sdf_or_series=df,
label=name,
symbol_type=None,
symbol_style=None,
render_type=renderer_type,
colors=colors,
alpha=alpha,
**kwargs)
fc.layer['layerDefinition']['drawingInfo']['renderer'] = r
elif renderer_type == 't':
r = generate_renderer(geometry_type=df.geometry_type.lower(),
sdf_or_series=df,
label=name,
symbol_type=None,
symbol_style=None,
render_type=renderer_type,
colors=colors,
alpha=alpha,
**kwargs)
fc.layer['layerDefinition']['drawingInfo']['renderer'] = r
if map_exists:
map_widget.add_layer(fc, options={'title': name})
else:
map_widget.add_layer(fc, options={'title': name})
return map_widget
gis = GIS("URLofESRIPortal", UserID, Password)
print("Connected")
siteMessages = []
for key, value in sites.items():
#print(value)
item = gis.content.get(value)
#print(item)
flayer = item.layers[0]
#print(flayer)
#Query produces a Feature Set, which is needed to call .features
fSet = flayer.query()
#Stores all features so that ones needing update can be copied
all_features = fSet.features
#Pulls data into Data Frame for manipulation with Pandas
ESRIdf = SpatialDataFrame.from_layer(flayer)
#print(ESRIdf.head())
features_for_update = []
#Iterate over ESRI Data Frame
print("Starting Query")
for index, row in ESRIdf.iterrows():
# skip rows that already have a gateway IP or don't contain a machine
if pd.notnull(row['gatewayip']) or row['followup'] != 'No':
#print("Skipped Row")
continue
# Skip rows without an asset tag
elif pd.isnull(row['asset']):
#print("Skipped " + str(row['objectid']))
continue
else:
def from_featureclass(filename, **kwargs):
"""
Returns a GeoDataFrame from a feature class.
Inputs:
filename: full path to the feature class
Optional Parameters:
sql_clause: sql clause to parse data down
where_clause: where statement
sr: spatial reference object
fields: list of fields to extract from the table
"""
from .. import SpatialDataFrame
from arcgis.geometry import _types
if HASARCPY:
sql_clause = kwargs.pop('sql_clause', (None,None))
where_clause = kwargs.pop('where_clause', None)
sr = kwargs.pop('sr', arcpy.Describe(filename).spatialReference or arcpy.SpatialReference(4326))
fields = kwargs.pop('fields', None)
desc = arcpy.Describe(filename)
if not fields:
fields = [field.name for field in arcpy.ListFields(filename) \
if field.type not in ['Geometry']]
if hasattr(desc, 'areaFieldName'):
afn = desc.areaFieldName
if afn in fields:
fields.remove(afn)
if hasattr(desc, 'lengthFieldName'):
lfn = desc.lengthFieldName
if lfn in fields:
fields.remove(lfn)
geom_fields = fields + ['SHAPE@']
flds = fields + ['SHAPE']
vals = []
geoms = []
geom_idx = flds.index('SHAPE')
shape_type = desc.shapeType
default_polygon = _types.Geometry(arcpy.Polygon(arcpy.Array([arcpy.Point(0,0)]* 3)))
default_polyline = _types.Geometry(arcpy.Polyline(arcpy.Array([arcpy.Point(0,0)]* 2)))
default_point = _types.Geometry(arcpy.PointGeometry(arcpy.Point()))
default_multipoint = _types.Geometry(arcpy.Multipoint(arcpy.Array([arcpy.Point()])))
with arcpy.da.SearchCursor(filename,
field_names=geom_fields,
where_clause=where_clause,
sql_clause=sql_clause,
spatial_reference=sr) as rows:
for row in rows:
row = list(row)
# Prevent curves/arcs
if row[geom_idx] is None:
row.pop(geom_idx)
g = {}
elif row[geom_idx].type in ['polyline', 'polygon']:
g = _types.Geometry(row.pop(geom_idx).generalize(0))
else:
g = _types.Geometry(row.pop(geom_idx))
if g == {}:
if shape_type.lower() == 'point':
g = default_point
elif shape_type.lower() == 'polygon':
g = default_polygon
elif shape_type.lower() == 'polyline':
g = default_point
elif shape_type.lower() == 'multipoint':
g = default_multipoint
geoms.append(g)
vals.append(row)
del row
del rows
df = pd.DataFrame(data=vals, columns=fields)
sdf = SpatialDataFrame(data=df, geometry=geoms)
sdf.reset_index(drop=True, inplace=True)
del df
if sdf.sr is None:
if sr is not None:
sdf.sr = sr
else:
sdf.sr = sdf.geometry[sdf.geometry.first_valid_index()].spatialReference
return sdf
elif HASARCPY == False and \
HASPYSHP == True and\
filename.lower().find('.shp') > -1:
geoms = []
records = []
reader = shapefile.Reader(filename)
fields = [field[0] for field in reader.fields if field[0] != 'DeletionFlag']
for r in reader.shapeRecords():
atr = dict(zip(fields, r.record))
g = r.shape.__geo_interface__
g = _geojson_to_esrijson(g)
geom = _types.Geometry(g)
atr['SHAPE'] = geom
records.append(atr)
del atr
del r, g
del geom
sdf = SpatialDataFrame(records)
sdf.set_geometry(col='SHAPE')
sdf.reset_index(inplace=True)
return sdf
elif HASARCPY == False and \
HASPYSHP == False and \
HASFIONA == True and \
(filename.lower().find('.shp') > -1 or \
os.path.dirname(filename).lower().find('.gdb') > -1):
is_gdb = os.path.dirname(filename).lower().find('.gdb') > -1
if is_gdb:
with fiona.drivers():
from arcgis.geometry import _types
fp = os.path.dirname(filename)
fn = os.path.basename(filename)
geoms = []
atts = []
with fiona.open(path=fp, layer=fn) as source:
meta = source.meta
cols = list(source.schema['properties'].keys())
for idx, row in source.items():
geoms.append(_types.Geometry(row['geometry']))
atts.append(list(row['properties'].values()))
del idx, row
df = pd.DataFrame(data=atts, columns=cols)
return SpatialDataFrame(data=df, geometry=geoms)
else:
with fiona.drivers():
from arcgis.geometry import _types
geoms = []
atts = []
with fiona.open(path=filename) as source:
meta = source.meta
cols = list(source.schema['properties'].keys())
for idx, row in source.items():
geoms.append(_types.Geometry(row['geometry']))
atts.append(list(row['properties'].values()))
del idx, row
df = pd.DataFrame(data=atts, columns=cols)
return SpatialDataFrame(data=df, geometry=geoms)
return
def completeness(gis, df_after, df_before, output_features, grid_filter, geom):
""" main driver of program """
try:
out_fl = FeatureLayer(gis=gis, url=output_features)
out_sdf = out_fl.query(geometry_filter=grid_filter,return_geometry=True,
return_all_records=True).df
geometry_type = df_after.geometry_type
sq = df_before[df_before.geometry.notnull()].geometry.disjoint(geom) == False
df_before = df_before[sq].copy()
before_count = len(df_before)
sq = df_after[df_after.geometry.notnull()].geometry.disjoint(geom) == False
df_after = df_after[sq].copy()
after_count = len(df_after)
geoms_after = df_after.clip(geom.extent)
geoms_before = df_before.clip(geom.extent)
geoms_before_sdf = SpatialDataFrame(geometry=geoms_before)
geoms_after_sdf = SpatialDataFrame(geometry=geoms_after)
q_after = geoms_after_sdf.geometry.JSON == '{"paths":[]}'
geoms_after_sdf = geoms_after_sdf[~q_after].copy()
geoms_after_sdf.reset_index(inplace=True, drop=True)
q_before = geoms_before_sdf.geometry.JSON == '{"paths":[]}'
geoms_before_sdf = geoms_before_sdf[~q_before].copy()
geoms_before_sdf.reset_index(inplace=True, drop=True)
if geometry_type == "Polygon":
before_val = geoms_before_sdf.geometry.get_area('GEODESIC','SQUAREKILOMETERS').sum()
after_val = geoms_after_sdf.geometry.get_area('GEODESIC','SQUAREKILOMETERS').sum()
if after_val > 0:
score = get_score(ratio=before_val/after_val,
baseVal=before_val,
inputVal=after_val)
else:
score = get_score(0, before_val, after_val)
out_sdf[FIELDS[0]][0] = round(before_val,1)
out_sdf[FIELDS[1]][0] = round(after_val,1)
out_sdf[FIELDS[3]][0] = round(before_val - after_val,1)
out_sdf[FIELDS[2]][0] = score
elif geometry_type == "Polyline":
before_val = geoms_before_sdf.geometry.get_length('GEODESIC','KILOMETERS').sum()
after_val = geoms_after_sdf.geometry.get_length('GEODESIC','KILOMETERS').sum()
if after_val > 0:
score = get_score(ratio=before_val/after_val,
baseVal=before_val,
inputVal=after_val)
else:
score = get_score(0, before_val, after_val)
out_sdf[FIELDS[0]][0] = round(before_val,1)
out_sdf[FIELDS[1]][0] = round(after_val,1)
out_sdf[FIELDS[3]][0] = round(before_val - after_val,1)
out_sdf[FIELDS[2]][0] = score
else:
before_count = len(geoms_before_sdf)
after_count = len(geoms_after_sdf)
if after_count > 0:
score = get_score(ratio=before_count/after_count,
baseVal=before_count,
inputVal=after_count)
else:
score = get_score(ratio=0,
baseVal=before_count,
inputVal=after_count)
out_sdf[FIELDS[0]][0] = before_count
out_sdf[FIELDS[1]][0] = after_count
out_sdf[FIELDS[3]][0] = before_count - after_count
out_sdf[FIELDS[2]][0] = score
del sq
del df_after
del df_before
del geom
return out_sdf, out_fl
#arcpy.SetParameterAsText(4, out_grid)
except FunctionError as f_e:
messages = f_e.args[0]
except:
line, filename, synerror = trace()
#--------------------------------------------------------------------------
##if __name__ == "__main__":
## #env.overwriteOutput = True
## argv = tuple(arcpy.GetParameterAsText(i)
## for i in range(arcpy.GetArgumentCount()))
## main(*argv)