def read_concerns(fp, id_col):
"""
Turns a json file of spatial only features into a pandas dataframe
"""
items = json.load(open(fp))
grouped_by_source = group_json_by_field(items, 'source')
data_frames = []
for source, items in grouped_by_source.items():
results, grouped = group_json_by_location(items)
segments = [k for k in list(grouped.keys()) if k]
results = {source: [grouped[k]['count'] for k in segments]}
df = pd.DataFrame(results, index=segments)
data_frames.append((source, df))
return data_frames
# util.find_nearest needs. Eventually this should be cleaned up
inproj = pyproj.Proj(init='epsg:4326')
outproj = pyproj.Proj(init='epsg:3857')
re_point = pyproj.transform(inproj, outproj, address[2],
address[1])
point = Point(re_point)
record = [{'point': point, 'properties': {}}]
util.find_nearest(record, combined_seg, segments_index, 20)
if record[0]['properties']['near_id']:
near_id = record[0]['properties']['near_id']
crashes, crash_data = util.group_json_by_location(
crash_items
) #years=[2015, 2016, 2017], yearfield='Date Time')
import ipdb
ipdb.set_trace()
if str(near_id) in list(crash_data.keys()):
print(
str(crash_data[str(near_id)]['count']) +
" crashes found")
elif args.date:
print(parse(args.date))
results = [
crash for crash in crash_items
if parse(crash['dateOccurred']).date() == parse(args.date).date()
]