-
Notifications
You must be signed in to change notification settings - Fork 5
/
makeWatersheds.py
286 lines (251 loc) · 12.9 KB
/
makeWatersheds.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
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 15 15:29:55 2017
@author: RHill04
@author: mweber
"""
import geopandas as gpd
import pandas as pd
import numpy as np
import time
import os
import sys
import glob
import fiona as fiona
from pyogrio import read_dataframe
def watershed_onnet(focal_com, uids, lengths, up, basins_shp, agg_ws_shp):
try:
# focal_uid = uids_trns[np.in1d(comids_trns, focal_com)]
focal_uid = focal_com
x = np.ndarray.item(lengths[np.in1d(uids, focal_uid)])
lngth = lengths[: np.ndarray.item(np.where(np.in1d(uids, focal_uid))[0])]
start = np.sum(lngth)
uplist = up[start : start + x]
except:
uplist = [focal_uid]
tmp_basin = basins_shp.loc[basins_shp["FEATUREID"].isin(uplist)]
local_basin = basins_shp.loc[basins_shp["FEATUREID"]==focal_com]
tmp_basin = pd.concat([tmp_basin, local_basin], ignore_index=True)
tmp_basin.is_copy = False
tmp_basin["COMID"] = int(focal_com)
tmp_basin = tmp_basin[["geometry", "COMID"]]
tmp_intervpu = agg_ws_shp.loc[agg_ws_shp["COMID"].isin(uplist)]
tmp_intervpu.is_copy = False
tmp_intervpu["COMID"] = int(focal_com)
tmp_basin = pd.concat([tmp_basin, tmp_intervpu], ignore_index=True)
# tmp_basin['geometry'] = tmp_basin.buffer(0)
# start_time2 = time.time()
# tmp_basin = tmp_basin.dissolve(by='COMID')
# print("--- %s seconds ---" % (time.time() - start_time2))
return tmp_basin
def watershed_offnet(focal_com, uids, lengths, up, uids_trns, comids_trns, basins_shp):
try:
focal_uid = uids_trns[np.in1d(comids_trns, focal_com)]
l = np.asscalar(lengths[np.in1d(uids, focal_uid)])
start = np.sum(lengths[:np.asscalar(np.where(np.in1d(uids, focal_uid))[0])])
uplist = up[start:start+l]
except:
uplist = [focal_uid]
tmp_basin = basins_shp.loc[basins_shp['UID'].isin(uplist)]
tmp_basin.is_copy = False
tmp_basin['COMID'] = int(focal_com)
tmp_basin = tmp_basin[['geometry', 'COMID']]
tmp_basin = tmp_basin.dissolve(by='COMID')
return tmp_basin
def WBCOMID_COMID(nhd_dir, hydro, vpu, nhdcats):
pre = "%s/NHDPlus%s/NHDPlus%s" % (nhd_dir, hydro, vpu)
fl = dbf2DF("%s/NHDSnapshot/Hydrography/NHDFlowline.dbf"%(pre))[['COMID', 'WBAREACOMI']]
cat = gpd.read_file('%s/NHDPlusCatchment/Catchment.shp'%(pre)).drop(['GRIDCODE', 'SOURCEFC'], axis=1)
cat.columns = cat.columns[:-1].str.upper().tolist() + ['geometry']
vaa = dbf2DF('%s/NHDPlusAttributes/PlusFlowlineVAA.dbf'%(pre))[['COMID','HYDROSEQ']]
# merge
df = pd.merge(cat.drop('geometry', axis=1),fl,left_on='FEATUREID',
right_on='COMID',how='inner')
df = df[df['WBAREACOMI']!=0]
df = pd.merge(df,vaa, on='COMID',how='left')
df = df.loc[df.groupby('WBAREACOMI')['HYDROSEQ'].idxmin()]
# initialize containers for on-net lakes
df = df[['WBAREACOMI','COMID']]
df.columns = ['WBAREACOMID','COMID']
return df
#Define directories
nhd_dir = 'G:/NHDPlusV21/'
lakecat_dir = 'O:/PRIV/CPHEA/PESD/COR/CORFiles/Geospatial_Library_Projects/LakeCat/'
ws_dir = lakecat_dir + 'Watersheds_Framework/'
np_dir = 'E:/GitProjects/StreamCat/accum_npy/'
comid_wbcomid_lookups = r'O:\PRIV\CPHEA\PESD\COR\CORFILES\Geospatial_Library_Projects\LakeCat\LakeCat_Framework\joinTables'
out_file = "O:/PRIV/CPHEA/PESD/COR/CORFILES/Geospatial_Library_Projects/LakeCat/OnNetLakeWatersheds/OnNetWorkWatersheds.gpkg"
# np_dir = 'O:/PRIV/CPHEA/PESD/COR/CORFiles/Geospatial_Library_Projects/LakeCat/LakeCat_Framework/LakeCat_npy/children/'
#Read in COMIDs of interest of select certain COMIDs
#nla17_dir = 'O:/PRIV/CPHEA/PESD/COR/CORFiles/Geospatial_Library_Projects/NLA/NLA2017LandscapeMetrics/'
#lakes_df = pd.read_csv(nla17_dir + 'NLA17_missing.csv')
# nla22_dir = 'E:/WorkingData/NLA22_Watersheds/'
# lakes_df = pd.read_excel(nla22_dir + 'NLA22_SiteListmac.xlsx',
# 'NLA22_SiteListmac (sorted)')
# lakes_df = pd.read_csv(nla22_dir + 'missing_lakes.csv')
lakes_df = pd.read_csv(lakecat_dir + 'FTP_Staging/FinalTables/Dams.csv')
# lakes_df = lakes_df[lakes_df['NHDPlusV2 COMID'].notnull()]
# lakes_df = pd.read_csv(nla22_dir + 'missing_lakes.csv')
lakes_df = lakes_df[lakes_df['inStreamCat'] == 1 & lakes_df['COMID'].notnull()]
# lakes_df = lakes_df[lakes_df['COMID'].notnull()]
# lakes_df = lakes_df.rename(columns={'NHDPlusV2 COMID': 'COMID'})
lakes = np.array(lakes_df.COMID).astype(int)
# coms = np.array(19268286).astype(int)
#Read in on-network numpy files
tmp_np = np.load(ws_dir + 'onNetFramework.npz')
on_uids = tmp_np['uids']
#on_lengths = tmp_np['lengths']
#on_up = tmp_np['upstream']
on_vpus = tmp_np['vpus']
on_uids_trns = tmp_np['on_uids_trns']
on_comids_trns = tmp_np['on_comids_trns']
vectunit = tmp_np['vectunit'].astype(str)
hydreg = tmp_np['hydreg'].astype(str)
del tmp_np
# Create on-net lake watersheds - this creates an array
# of NHDPlus waterbody COMIDS in 'onnet'
onnet = lakes[np.in1d(lakes, on_comids_trns)]
# This next line translates the waterbody COMIDs to NHDPlus
# flowline COMIDS which we need to process StreamCat features
onuids = on_uids_trns[np.in1d(on_comids_trns, onnet)]
onuids = on_uids[np.in1d(on_uids, onuids)]
vpus = on_vpus[np.in1d(on_uids, onuids)].astype(str)
vpu_list = np.unique(vpus).astype(str)
intervpu = gpd.read_file(ws_dir + 'interVPUs.shp')
mask = np.isin(onuids, full['COMID'], invert=True)
missing = onuids[mask]
i=0
for vpu in vpu_list:
print(vpu)
hydro = hydreg[np.in1d(vectunit,vpu)]
onuids_vpu = onuids[np.in1d(vpus, vpu)]
# onnet_vpu = onnet[np.in1d(vpus, vpu)]
nhdcats = gpd.read_file(nhd_dir + '/NHDPlus' + hydro[0] + '/NHDPlus' + vpu + '/NHDPlusCatchment/Catchment.shp')
tmp_np = np.load(np_dir + 'accum_' + str(vpu) + '.npz')
#nhdcats['dummy'] = 1
for lake in onuids_vpu:
if lake in missing:
print(lake)
start_time2 = time.time()
if i==0:
out_ws = watershed_onnet(lake, tmp_np['comids'], tmp_np['lengths'], tmp_np['upstream'], nhdcats, intervpu)
out_ws = out_ws.to_crs(epsg=5070)
out_ws['geometry'] = out_ws.buffer(0.01)
out_ws = out_ws.dissolve(by='COMID')
out_ws['COMID'] = out_ws.index
# out_ws['SITE_ID'] = lakes_df.loc[lakes_df.index[i],'SITE_ID']
else:
if not int(lake) in out_ws['COMID'].values:
temp_ws = watershed_onnet(lake, tmp_np['comids'], tmp_np['lengths'], tmp_np['upstream'], nhdcats, intervpu)
temp_ws = temp_ws.to_crs(epsg=5070)
temp_ws['COMID'] = lake
temp_ws['geometry'] = temp_ws.buffer(0.01)
temp_ws = temp_ws.dissolve(by='COMID')
temp_ws['COMID'] = temp_ws.index
# temp_ws['SITE_ID'] = lakes_df.loc[lakes_df.index[i],'SITE_ID']
out_ws = pd.concat([out_ws, temp_ws], ignore_index=True)
# out_ws = out_ws.append(temp_ws, ignore_index=True)
i+=1
print("--- %s seconds ---" % (time.time() - start_time2))
#world.to_file(filename=temp_shp,driver='ESRI Shapefile',crs_wkt=prj)
print('------------------------------------')
# tmp = int(on_comids_trns[np.in1d(on_uids_trns, comid)])
# out_ws['WBCOMID'] = on_comids_trns[np.in1d(on_uids_trns, out_ws['COMID'])]
# for vals in out_ws['COMID']:
# tmp = int(on_comids_trns[np.in1d(on_uids_trns, vals)])
# out_ws.loc[(out_ws['COMID']==vals),'WBCOMID'] = tmp
# out_ws[['WBCOMID']] = out_ws[['WBCOMID']].astype(int)
# out_ws[['COMID']] = out_ws[['COMID']].astype(int)
lookup_files = glob.glob(os.path.join(comid_wbcomid_lookups, "*.csv")) # advisable to use os.path.join as this makes concatenation OS independent
df_from_each_file = (pd.read_csv(f) for f in lookup_files)
lookup = pd.concat(df_from_each_file, ignore_index=True)
lookup = lookup.rename(columns={'catCOMID': 'COMID', 'wbCOMID': 'WBCOMID'})
out_ws = pd.merge(out_ws, lookup, how='inner', on='COMID')
out_ws[['WBCOMID']] = out_ws[['WBCOMID']].astype(int)
out_ws[['COMID']] = out_ws[['COMID']].astype(int)
# out_ws = out_ws[['COMID','geometry']]
# out_ws = out_ws[['SITE_ID','COMID','geometry']]
# # Add SITE_ID
lakes_df = lakes_df[['SITE_ID','COMID','LAT_DD83','LON_DD83',
'UNIQUE_ID','PSTL_CODE','NES_SITE','Reachcode',
'GNIS_ID','GNIS_NAME']]
lakes_df[['COMID']] = lakes_df[['COMID']].astype(int)
# out_ws = out_ws.merge(lakes_df, how='left')
# out_ws = out_ws[['SITE_ID','COMID','CAT_COMID','geometry']]
# out_ws.to_file(nla17_dir + 'Missing_OnNetLakes.shp', driver = 'ESRI Shapefile')
extra = extra[~extra.isin(out_ws['COMID'])]
extra = extra.drop(columns=['CatAreaSqKm_y'])
extra = extra.rename(columns={'CatAreaSqKm_x': 'CatAreaSqKm'})
extra = extra[['COMID', 'CatAreaSqKm', 'WBCOMID', 'geometry']]
full = pd.concat([full, out_ws])
for i, df in enumerate(np.array_split(out_ws, 20)):
print(f"OnNetWatersheds{i+1}")
df.info()
df.to_file(out_file, layer=f"OnNetWatersheds{i+1}",driver="GPKG", mode='w')
out_ws.to_file("O:/PRIV/CPHEA/PESD/COR/CORFILES/Geospatial_Library_Projects/LakeCat/OnNetLakeWatersheds/OnNetWatersheds.gpkg", layer='OnNetWatersheds', driver="GPKG", mode='w')
out_ws = out_ws.merge(lakes_df, how='left',on='COMID')
out_ws.to_file(nla22_dir + 'OnNetWatersheds.shp', driver = 'ESRI Shapefile')
#Off-Net Lakes
#Read in off-network numpy files
off_np = np.load('L:/Priv/CORFiles/Geospatial_Library_Projects/LakeCat/Watersheds_Framework/offNetFramework.npz')
#Create off-net lake watersheds
offnet = lakes[np.in1d(lakes, off_np['off_comids_trns'])]
basins = gpd.read_file(ws_dir + '/allBasins.shp')
i=0
startTime = time.time()
for lake in offnet:
print(lake)
if i==0:
out_ws = watershed_offnet(lake, off_np['uids'], off_np['lengths'], off_np['upstream'], off_np['off_uids_trns'],
off_np['off_comids_trns'], basins)
out_ws = out_ws.to_crs(epsg=5070)
out_ws['geometry'] = out_ws.buffer(0.01)
out_ws = out_ws.dissolve(by='COMID')
out_ws['COMID'] = out_ws.index
out_ws['SITE_ID'] = lakes_df.loc[lakes_df.index[i],'SITE_ID']
else:
temp_ws = watershed_offnet(lake, off_np['uids'], off_np['lengths'], off_np['upstream'], off_np['off_uids_trns'],
off_np['off_comids_trns'], basins)
temp_ws = temp_ws.to_crs(epsg=5070)
temp_ws.index.name = None
temp_ws['COMID'] = lake
temp_ws['geometry'] = temp_ws.buffer(0.01)
# temp_ws['COMID'] = temp_ws.index
temp_ws = temp_ws.dissolve(by='COMID')
temp_ws['SITE_ID'] = lakes_df.loc[lakes_df.index[i],'SITE_ID']
out_ws = out_ws.append(temp_ws, ignore_index=True)
i+=1
print("--- %s seconds ---" % (time.time() - startTime))
#world.to_file(filename=temp_shp,driver='ESRI Shapefile',crs_wkt=prj)
print('------------------------------------')
# out_ws = out_ws[['COMID','geometry']]
# out_ws = out_ws.merge(lakes_df, how='left')
out_ws = out_ws[['SITE_ID','COMID','geometry']]
out_ws.to_file(nla22_dir + 'OffNetWatersheds.shp', driver = 'ESRI Shapefile')
off_net_inNHD = out_ws
# Combind on and off network lakes with standard columns
# off_net = gpd.read_file('L:/Priv/CORFiles/Geospatial_Library_Projects/NLA/NLA2017LandscapeMetrics/Off_Network_Lakes/Off_Network_NLA17Lakes.shp')
# on_net = gpd.read_file('L:/Priv/CORFiles/Geospatial_Library_Projects/NLA/NLA2017LandscapeMetrics/On_Network_Lakes/OnNetLakes.shp')
off_net = gpd.read_file(nla22_dir + 'OffNetWatersheds.shp')
on_net = gpd.read_file(nla22_dir + 'OnNetWatersheds.shp')
layers = fiona.listlayers('E:/WorkingData/NLA22_Watersheds/NLA22_mac.gdb')
not_in_nhdplus = geodata = gpd.read_file(nla22_dir + 'NLA22_mac.gdb', driver='fileGDB', layer='wspolys1')
# dissolve on SITE_ID - extra features in 'not_in_nhdplus'
not_in_nhdplus = not_in_nhdplus.dissolve(by='SITE_ID')
# off_net = off_net[['comid','geometry']]
# off_net.columns = ['COMID','geometry']
# off_net = off_net.merge(lakes_df, how='left')
on_net[['COMID']] = on_net[['COMID']].astype(int)
off_net[['COMID']] = off_net[['COMID']].astype(int)
not_in_nhdplus['COMID'] = np.nan
on_net = on_net[['SITE_ID','COMID','geometry']]
on_net = on_net.dissolve(by='SITE_ID')
off_net = off_net[['SITE_ID','COMID','geometry']]
off_net = off_net.dissolve(by='SITE_ID')
not_in_nhdplus = not_in_nhdplus[['SITE_ID','COMID','geometry']]
lake_wats = pd.concat([on_net, off_net, not_in_nhdplus])
# and then the InNHD off-network
# lake_wats = lake_wats.append(off_net_inNHD, ignore_index=True)
# lake_wats.to_file('L:/Priv/CORFiles/Geospatial_Library_Projects/NLA/NLA2017LandscapeMetrics/NLA17_Watersheds.shp', driver = 'ESRI Shapefile')
out_dir='O:/PRIV/CPHEA/PESD/COR/CORFILES/Geospatial_Library_Resource/Physical/WATERSHEDS/NLA2022_Basins/'
lake_wats.to_file(out_dir + "NLA22_Basins.gpkg", layer='lake_wats', driver="GPKG")