def road_make(feats, fp):
""" Makes road feature df, intersections + non-intersections
Args:
feats - list of features to be included
fp - geojson file for intersections and non intersections
Returns:
dataframe consisting of features given (if they exist)
"""
# Read in segments data (geojson)
print("reading ", fp)
segments = read_geojson(fp)
df = pd.DataFrame([x.properties for x in segments])
df.set_index('id', inplace=True)
# Check for missing features
missing_feats = [x for x in feats if x not in df.columns]
feats = [x for x in feats if x in df.columns]
if missing_feats:
warnings.warn(
str(len(missing_feats))
+ " feature(s) missing, skipping (" +
', '.join(missing_feats)
+ ")")
return df[feats]
def get_buffer(filename, lat, lon, radius):
"""
Given a geojson file, latitude and longitude in 4326 projection,
and a radius, write to file (as geojson) all the LineStrings and
Points that overlap a circular buffer around the coordinates.
Args:
filename
lat
lon
radius
Returns:
A list of overlapping geojson features 4326 projection
"""
segments = read_geojson(filename)
# Calculate the bounding circle
overlapping = []
point = get_reproject_point(lat, lon, transformer_4326_to_3857)
buffered_poly = point.buffer(radius)
for segment in segments:
if segment.geometry.intersects(buffered_poly):
if type(segment.geometry) == LineString:
coords = [x for x in segment.geometry.coords]
overlapping.append({
'geometry': {
'coordinates': coords,
'type': 'LineString'
},
'type': 'Feature',
'properties': segment.properties
})
elif type(segment.geometry) == Point:
overlapping.append({
'geometry': {
'coordinates':
[segment.geometry.x, segment.geometry.y],
'type': 'Point'
},
'properties': segment.properties
})
elif type(segment.geometry) == MultiLineString:
overlapping.append({
'geometry': {
'coordinates':
[[y for y in x.coords] for x in segment.geometry],
'type':
'MultiLineString'
},
'properties': segment.properties
})
else:
print("{} not implemented yet, skipping...".format(
type(segment.geometry)))
if overlapping:
overlapping = prepare_geojson(overlapping)
return overlapping
def road_make(feats, inters_fp, non_inters_fp, agg='max'):
""" Makes road feature df, intersections + non-intersections
agg : aggregation type (default is max)
IMPORTANT: if the aggregation type changes, need to also update
how aggregation is calculated in src/data/add_map.py
"""
# Read in inters data (json), turn into df with inter index
df_index = []
df_records = []
print("reading ", inters_fp)
with open(inters_fp, 'r') as f:
inters = json.load(f)
# Append each index to dataframe
for idx, lines in inters.items():
# Each intersection has more than one segment
# Add each segment's properties to df_records
df_records.extend(lines)
df_index.extend([idx] * len(lines))
inters_df = pd.DataFrame(df_records, index=df_index)
# Read in non_inters data:
print("reading ", non_inters_fp)
non_inters = read_geojson(non_inters_fp)
non_inters_df = pd.DataFrame([x[1] for x in non_inters])
non_inters_df.set_index('id', inplace=True)
# Combine inter + non_inter
combined = pd.concat([inters_df, non_inters_df], sort=True)
missing_feats = [x for x in feats if x not in combined.columns]
feats = [x for x in feats if x in combined.columns]
if missing_feats:
warnings.warn(
str(len(missing_feats)) + " feature(s) missing, skipping (" +
', '.join(missing_feats) + ")")
# Since there are multiple segments per intersection,
# aggregating inters data = apply aggregation (default is max)
aggregated = getattr(combined[feats].groupby(combined.index), agg)
# return aggregation and adjacency info (orig_id)
return (aggregated(), combined['orig_id'])
