-
Notifications
You must be signed in to change notification settings - Fork 0
/
load.py
198 lines (157 loc) · 6.3 KB
/
load.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
import os
import io
import zipfile
import psycopg2
from dotenv import load_dotenv
import geopandas as gpd
from geopandas import GeoDataFrame
import pandas as pd
from sqlalchemy import create_engine
import requests
import math
import numpy as np
from urllib.parse import urlparse
load_dotenv()
host = os.getenv("HOST")
database = os.getenv("DB")
user = os.getenv("USER")
password = os.getenv("PASSWORD")
port = os.getenv("PORT")
portal = {
"username": os.getenv("PORTAL_USERNAME"),
"password": os.getenv("PORTAL_PASSWORD"),
"client": os.getenv("PORTAL_CLIENT"),
"referer": os.getenv("PORTAL_URL"),
"expiration": int(os.getenv("PORTAL_EXPIRATION")),
"f": os.getenv("PORTAL_F")
}
def explode_gdf_if_multipart(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
"""
Check if the GeoDataFrame has multipart geometries.
If so, explode them.
"""
gdf = gdf[gdf.geometry.notnull()]
if gdf.geom_type.str.contains('Multi').any():
gdf = gdf.explode(index_parts=False)
return gdf
def fetch_portal_token():
"""
Generate a token for ArcGIS server.
"""
try:
response = requests.post("https://arcgis.dvrpc.org/dvrpc/sharing/rest/generateToken", data=portal)
response.raise_for_status()
response_obj = response.json()
token = response_obj.get("token")
if not token:
raise ValueError("Failed to retrieve token.")
return token
except requests.RequestException as e:
raise SystemError(f"An error occurred while fetching the token: {e}")
def load_gis_data(dbname, target_schema, url_key, url, crs):
"""
Loads the data from feature services into database.
"""
print("\t -> Loading GIS data...")
engine = create_engine(f"postgresql://{user}:{password}@{host}:{port}/{dbname}")
if 'opendata.arcgis.com/' in url.lower():
print(f"\t \t -> Loading direct GeoJSON for {url_key}...")
response = requests.get(url)
gdf = gpd.read_file(response.content)
gdf = gdf.to_crs(crs)
else:
if url.startswith("https://arcgis.dvrpc.org"):
token = fetch_portal_token()
else:
token = None
parsed_url = urlparse(url)
path_parts = parsed_url.path.split("/")
base_url = url.split('?')[0]
count_url = f"{base_url}?where=1=1&returnCountOnly=true"
if token:
count_url += f"&token={token}"
count_url += "&f=json"
count_response = requests.get(count_url)
total_features = count_response.json().get("count")
gdf_list = []
total_chunks = math.ceil(total_features / 2000) # 2000 default esri record limit on feature services
print(f"\t \t -> {url_key}...")
for chunk in range(total_chunks):
offset = chunk * 2000
query_url = f"{url}&resultOffset={offset}&resultRecordCount=2000"
if token:
query_url += f"&token={token}"
response = requests.get(query_url)
data = response.json()
chunk_gdf = gpd.GeoDataFrame.from_features(data['features'])
gdf_list.append(chunk_gdf)
gdf = pd.concat(gdf_list, ignore_index=True)
if 'geometry' not in gdf.columns:
# no geometry service
gdf.columns = map(str.lower, gdf.columns)
gdf.to_sql(url_key.lower(), engine, schema=target_schema, if_exists='replace', index=False)
else:
# geometries
gdf.columns = map(str.lower, gdf.columns)
gdf.crs = crs
gdf.to_postgis(url_key.lower(), engine, schema=target_schema, if_exists='replace', index=False)
def download_and_load_gtfs(dbname, gtfs_url):
"""
downloads, extracts, loads septa gtfs into the db
"""
print("\t -> Loading SEPTA GTFS data...")
response = requests.get(gtfs_url)
zip_content = io.BytesIO(response.content)
with zipfile.ZipFile(zip_content, 'r') as zip_ref:
zip_ref.extractall('gtfs')
zip_files = [file for file in os.listdir('gtfs') if file.endswith(".zip")]
for zip in zip_files:
path = os.path.join('gtfs', zip)
file_name = os.path.splitext(path)[0]
os.mkdir(file_name)
with zipfile.ZipFile(path, 'r') as zip_ref:
zip_ref.extractall(os.path.join(file_name))
schemas = {'google_bus', 'google_rail'}
engine = create_engine(f"postgresql://{user}:{password}@{host}:{port}/{dbname}")
conn = psycopg2.connect(
host=host, port=port, database=dbname, user=user, password=password
)
cur = conn.cursor()
conn.autocommit = True
for schema in schemas:
cur.execute(f"SELECT 1 FROM pg_namespace WHERE nspname='{schema}'")
schema_exists = bool(cur.rowcount)
if not schema_exists:
cur.execute(f"CREATE SCHEMA {schema};")
for file_name in os.listdir(os.path.join('gtfs', schema)):
if file_name.endswith('.txt'):
file_path = os.path.join('gtfs', schema, file_name)
table_name = os.path.splitext(file_name)[0]
df = pd.read_csv(file_path)
df.to_sql(table_name, engine, schema=schema, if_exists='replace', index=False)
def csv_table(dbname, target_schema, csv):
"""
Loads the csv into database.
"""
engine = create_engine(f"postgresql://{user}:{password}@{host}:{port}/{dbname}")
df = pd.read_csv(csv)
df.columns = map(str.lower, df.columns)
table_name = os.path.splitext(os.path.basename(csv))[0]
print(f"Loading {table_name}.csv...\n")
df.to_sql(table_name, con=engine, schema=target_schema, if_exists='replace', index=False)
def load_matrix(csv_path_i, csv_path_o, dbname, target_schema, table_name, minutes=45):
"""
Find zones in matrix tables w/in 45 minutes and output to database for analysis
"""
engine = create_engine(f"postgresql://{user}:{password}@{host}:{port}/{dbname}")
df_i = pd.read_csv(csv_path_i, index_col=0)
df_o = pd.read_csv(csv_path_o, index_col=0)
total_time = df_i.values + df_o.values
within_threshold_mask = total_time <= minutes
o_taz, d_taz = np.where(within_threshold_mask)
df = pd.DataFrame({
'o_taz': df_i.index[o_taz],
'd_taz': df_i.columns[d_taz],
'total_time': total_time[o_taz, d_taz]
})
df.to_sql(table_name, engine, schema=target_schema, if_exists='replace', index=False)