def txts_to_db(folder, db, delimiter, __encoding='utf-8', apidb='psql',
rewrite=None):
"""
Executes tbl_to_db for every file in a given folder
The file name will be the table name
"""
from glass.pys.oss import lst_ff, fprop
from glass.ng.prop.sql import db_exists
if not db_exists(db):
# Create database
from glass.ng.sql.db import create_db
create_db(db, api=apidb, overwrite=None)
else:
if rewrite:
from glass.ng.sql.db import create_db
create_db(db, api=db, overwrite=True)
__files = lst_ff(folder, file_format=['.txt', '.csv', '.tsv'])
"""
Send data to DB using Pandas
"""
for __file in __files:
tbl_to_db(
__file, db, fprop(__file, 'fn'),
delimiter=delimiter, encoding_=__encoding, api_db=apidb
)
def osm_to_psql(osmXml, osmdb):
"""
Use GDAL to import osmfile into PostGIS database
"""
from glass.pys import execmd
from glass.cons.psql import con_psql
from glass.ng.prop.sql import db_exists
is_db = db_exists(osmdb)
if not is_db:
from glass.ng.sql.db import create_db
create_db(osmdb, api='psql')
con = con_psql()
cmd = ("ogr2ogr -f PostgreSQL \"PG:dbname='{}' host='{}' port='{}' "
"user='******' password='******'\" {} -lco COLUM_TYPES=other_tags=hstore"
).format(osmdb, con["HOST"], con["PORT"], con["USER"],
con["PASSWORD"], osmXml)
cmdout = execmd(cmd)
return osmdb
def remove_deadend(inShp, outShp, db=None):
"""
Remove deadend
"""
from glass.pys.oss import fprop
from glass.ng.sql.db import create_db
from glass.g.it.db import shp_to_psql
from glass.g.gp.cln.sql import rm_deadend
from glass.g.it.shp import dbtbl_to_shp
# Create DB
if not db:
db = create_db(fprop(inShp, 'fn', forceLower=True), api='psql')
else:
from glass.ng.prop.sql import db_exists
isDb = db_exists(db)
if not isDb:
create_db(db, api='psql')
# Send data to Database
inTbl = shp_to_psql(db, inShp, api="shp2pgsql", encoding="LATIN1")
# Produce result
out_tbl = rm_deadend(db, inTbl, fprop(
outShp, 'fn', forceLower=True))
# Export result
return dbtbl_to_shp(
db, out_tbl, "geom", outShp, inDB='psql', tableIsQuery=None,
api="pgsql2shp"
)
def break_lines_on_points(lineShp,
pntShp,
outShp,
lnhidonpnt,
api='shply',
db=None):
"""
Break lines on points location
api's available:
- shply (shapely);
- psql (postgis);
"""
if api == 'shply':
result = shply_break_lines_on_points(lineShp, pntShp, lnhidonpnt,
outShp)
elif api == 'psql':
from glass.pys.oss import fprop
from glass.ng.sql.db import create_db
from glass.g.it.db import shp_to_psql
from glass.g.it.shp import dbtbl_to_shp
from glass.g.gp.brk.sql import split_lines_on_pnt
# Create DB
if not db:
db = create_db(fprop(lineShp, 'fn', forceLower=True), api='psql')
else:
from glass.ng.prop.sql import db_exists
isDb = db_exists(db)
if not isDb:
db = create_db(db, api='psql')
# Send Data to BD
lnhTbl = shp_to_psql(db, lineShp, api="shp2pgsql")
pntTbl = shp_to_psql(db, pntShp, api="shp2pgsql")
# Get result
outTbl = split_lines_on_pnt(db, lnhTbl, pntTbl,
fprop(outShp, 'fn', forceLower=True),
lnhidonpnt, 'gid')
# Export result
result = dbtbl_to_shp(db,
outTbl,
"geom",
outShp,
inDB='psql',
tableIsQuery=None,
api="pgsql2shp")
else:
raise ValueError("API {} is not available".format(api))
return result
def db_to_db(db_a, db_b, typeDBA, typeDBB):
"""
All tables in one Database to other database
Useful when we want to migrate a SQLITE DB to a PostgreSQL DB
typesDB options:
* sqlite
* psql
"""
from glass.g.wt.sql import df_to_db
from glass.ng.sql.q import q_to_obj
from glass.ng.prop.sql import lst_tbl
from glass.ng.sql.db import create_db
# List Tables in DB A
tbls = lst_tbl(db_a, excludeViews=True, api=typeDBA)
# Create database B
db_b = create_db(db_b, overwrite=False, api=typeDBB)
# Table to Database B
for tbl in tbls:
df = q_to_obj(
db_a, "SELECT * FROM {}".format(tbl), db_api=typeDBA
)
df_to_db(db_b, df, tbl, append=None, api=typeDBB)
def get_stop_words(inTbl, fidCol, txtCol, outFile,
lang='portuguese', inSheet=None, db=None):
"""
Pick a text column and save it in a new column only with the stop words.
Uses PostgreSQL dictionaries to get stop words
"""
from glass.pys.oss import fprop
from glass.ng.prop.sql import cols_name
from glass.ng.sql.db import create_db
from glass.ng.it.sql import tbl_to_db
from glass.ng.it import db_to_tbl
FILENAME = fprop(inTbl, 'fn')
# Create Temp database
db = create_db("db_" + FILENAME if not db else db)
# Send table to PostgreSQL
tbl = tbl_to_db(inTbl, db, FILENAME, sheet=inSheet, api_db='psql')
cols = cols_name(db, tbl, sanitizeSpecialWords=None, api='psql')
# Sanitize data and create a new column only with stop words
Q1 = (
"(SELECT *, to_tsvector('{_lang}', regexp_replace("
"regexp_replace(lower(unaccent({txt_c})), 'http://[^:\s]+(\S+)', "
"' ', 'g'), '[^\w]+', ' ', 'g')) "
"AS txt_data FROM {t}) AS stop_table"
).format(_lang=lang, txt_c=txtCol, t=tbl)
Q2 = (
"SELECT {selCols}, ARRAY_TO_STRING(array_agg("
"word ORDER BY word_index), ' ', '*') AS {outCol}, "
"REPLACE(CAST(STRIP("
"stop_table.txt_data) AS text), '''', '') AS no_duplicated "
"FROM ("
"SELECT fid, word, CAST(UNNEST(word_index) AS integer) "
"AS word_index FROM ("
"SELECT fid, SPLIT_PART(tst, ';', 1) AS word, "
"STRING_TO_ARRAY(SPLIT_PART(tst, ';', 2), ',') AS word_index FROM ("
"SELECT {fid} AS fid, REPLACE(REPLACE(REPLACE(REPLACE(REPLACE("
"CAST(UNNEST(txt_data) AS text), "
"',{{', ',\"{{'), ',\"{{', ';'), '}}\"', ''), "
"'(', ''), '}}', '') AS tst "
"FROM {tbl}"
") AS foo"
") AS foo2"
") AS foo3 INNER JOIN {tbl} ON foo3.fid = stop_table.{fid} "
"GROUP BY {selCols}, stop_table.txt_data"
).format(
outCol="clean_" + txtCol, tbl=Q1, fid=fidCol,
selCols=", ".join(["stop_table.{}".format(i) for i in cols])
)
# Export new table
return db_to_tbl(db, Q2, outFile, sheetsNames=inSheet)
def matrix_od_mean_dist_by_group(MATRIX_OD, ORIGIN_COL, GROUP_ORIGIN_ID,
GROUP_ORIGIN_NAME, GROUP_DESTINA_ID,
GROUP_DESTINA_NAME, TIME_COL, epsg, db,
RESULT_MATRIX):
"""
Calculate Mean GROUP distance from OD Matrix
OD MATRIX EXAMPLE
| origin_entity | origin_group | destina_entity | destina_group | distance
| XXXX | XXXX | XXXX | XXX | XXX
OUTPUT EXAMPLE
| origin_group | destina_group | mean_distance
| XXXX | XXXX | XXXX
"""
from glass.pys.oss import fprop
from glass.g.it.db import shp_to_psql
from glass.ng.sql.db import create_db
from glass.ng.sql.q import q_to_ntbl
from glass.ng.it import db_to_tbl
db = create_db(fprop(MATRIX_OD, 'fn'), overwrite=True, api='psql')
TABLE = shp_to_psql(db,
MATRIX_OD,
pgTable="tbl_{}".format(db),
api="pandas",
srsEpsgCode=epsg)
OUT_TABLE = q_to_ntbl(
db,
fprop(RESULT_MATRIX, 'fn'),
("SELECT {groupOriginCod}, {groupOriginName}, {groupDestCod}, "
"{groupDestName}, AVG(mean_time) AS mean_time FROM ("
"SELECT {origin}, {groupOriginCod}, {groupOriginName}, "
"{groupDestCod}, {groupDestName}, "
"AVG({timeCol}) AS mean_time FROM {t} "
"GROUP BY {origin}, {groupOriginCod}, {groupOriginName}, "
"{groupDestCod}, {groupDestName}"
") AS foo "
"GROUP BY {groupOriginCod}, {groupOriginName}, "
"{groupDestCod}, {groupDestName} "
"ORDER BY {groupOriginCod}, {groupDestCod}").format(
groupOriginCod=GROUP_ORIGIN_ID,
groupOriginName=GROUP_ORIGIN_NAME,
groupDestCod=GROUP_DESTINA_ID,
groupDestName=GROUP_DESTINA_NAME,
origin=ORIGIN_COL,
timeCol=TIME_COL,
t=TABLE),
api='psql')
return db_to_tbl(db,
"SELECT * FROM {}".format(OUT_TABLE),
RESULT_MATRIX,
sheetsNames="matrix",
dbAPI='psql')
def line_intersect_to_pnt(inShp, outShp, db=None):
"""
Get Points where two line features of the same feature class
intersects.
"""
from glass.pys.oss import fprop
from glass.g.it.shp import dbtbl_to_shp
from glass.ng.sql.db import create_db
from glass.g.it.db import shp_to_psql
from glass.g.gp.ovl.sql import line_intersection_pnt
# Create DB if necessary
if not db:
db = create_db(fprop(inShp, 'fn', forceLower=True), api='psql')
else:
from glass.ng.prop.sql import db_exists
isDb = db_exists(db)
if not isDb:
create_db(db, api='psql')
# Send data to DB
inTbl = shp_to_psql(db, inShp, api="shp2pgsql")
# Get result
outTbl = line_intersection_pnt(db, inTbl,
fprop(outShp, 'fn', forceLower=True))
# Export data from DB
outShp = dbtbl_to_shp(db,
outTbl,
"geom",
outShp,
inDB='psql',
tableIsQuery=None,
api="pgsql2shp")
return outShp
def check_autofc_overlap(checkShp, epsg, dbname, outOverlaps):
"""
Check if the features of one Feature Class overlaps each other
"""
import os
from glass.ng.sql.db import create_db
from glass.ng.sql.q import q_to_ntbl
from glass.g.it.db import shp_to_psql
from glass.g.it.shp import dbtbl_to_shp
create_db(dbname, api='psql')
# Send data to postgresql
table = shp_to_psql(dbname, checkShp, srsEpsgCode=epsg, api="pandas")
# Produce result
q = (
"SELECT foo.* FROM ("
"SELECT * FROM {t}"
") AS foo, ("
"SELECT cat AS relcat, geom AS tst_geom FROM {t}"
") AS foo2 "
"WHERE ("
"ST_Overlaps(geom, tst_geom) IS TRUE OR "
"ST_Equals(geom, tst_geom) IS TRUE OR "
"ST_Contains(geom, tst_geom) IS TRUE"
") AND cat <> relcat"
).format(t=table)
resultTable = os.path.splitext(os.path.basename(outOverlaps))[0]
q_to_ntbl(dbname, resultTable, q, api='psql')
dbtbl_to_shp(
dbname, resultTable, "geom", outOverlaps, api='psql', epsg=epsg)
return outOverlaps
def v_break_at_points(workspace, loc, lineShp, pntShp, db, srs, out_correct,
out_tocorrect):
"""
Break lines at points - Based on GRASS GIS v.edit
Use PostGIS to sanitize the result
TODO: Confirm utility
Problem: GRASS GIS always uses the first line to break.
"""
import os
from glass.g.it.db import shp_to_psql
from glass.g.it.shp import dbtbl_to_shp
from glass.g.wenv.grs import run_grass
from glass.pys.oss import fprop
from glass.ng.sql.db import create_db
from glass.ng.sql.q import q_to_ntbl
tmpFiles = os.path.join(workspace, loc)
gbase = run_grass(workspace, location=loc, srs=srs)
import grass.script as grass
import grass.script.setup as gsetup
gsetup.init(gbase, workspace, loc, 'PERMANENT')
from glass.g.it.shp import shp_to_grs, grs_to_shp
grsLine = shp_to_grs(lineShp, fprop(lineShp, 'fn', forceLower=True))
vedit_break(grsLine, pntShp, geomType='line')
LINES = grs_to_shp(grsLine, os.path.join(tmpFiles, grsLine + '_v1.shp'),
'line')
# Sanitize output of v.edit.break using PostGIS
create_db(db, overwrite=True, api='psql')
LINES_TABLE = shp_to_psql(db,
LINES,
srsEpsgCode=srs,
pgTable=fprop(LINES, 'fn', forceLower=True),
api="shp2pgsql")
# Delete old/original lines and stay only with the breaked one
Q = ("SELECT {t}.*, foo.cat_count FROM {t} INNER JOIN ("
"SELECT cat, COUNT(cat) AS cat_count, "
"MAX(ST_Length(geom)) AS max_len "
"FROM {t} GROUP BY cat"
") AS foo ON {t}.cat = foo.cat "
"WHERE foo.cat_count = 1 OR foo.cat_count = 2 OR ("
"foo.cat_count = 3 AND ST_Length({t}.geom) <= foo.max_len)").format(
t=LINES_TABLE)
CORR_LINES = q_to_ntbl(db,
"{}_corrected".format(LINES_TABLE),
Q,
api='psql')
# TODO: Delete Rows that have exactly the same geometry
# Highlight problems that the user must solve case by case
Q = ("SELECT {t}.*, foo.cat_count FROM {t} INNER JOIN ("
"SELECT cat, COUNT(cat) AS cat_count FROM {t} GROUP BY cat"
") AS foo ON {t}.cat = foo.cat "
"WHERE foo.cat_count > 3").format(t=LINES_TABLE)
ERROR_LINES = q_to_ntbl(db,
"{}_not_corr".format(LINES_TABLE),
Q,
api='psql')
dbtbl_to_shp(db, CORR_LINES, "geom", out_correct, api="pgsql2shp")
dbtbl_to_shp(db, ERROR_LINES, "geom", out_tocorrect, api="pgsql2shp")
def osm_to_relationaldb(osmData,
inSchema,
osmGeoTbl,
osmCatTbl,
osmRelTbl,
outSQL=None,
db_name=None):
"""
PostgreSQL - OSM Data to Relational Model
E.g.
osmData = '/mnt/d/mystuff/flainar/portugal-latest.osm.pbf'
inSchema = {
"TBL" : ['points', 'lines', 'multipolygons'],
'FID' : 'ogc_fid',
"COLS" : [
'name', 'osm_id',
#"ST_X(wkb_geometry) AS longitude",
#"ST_Y(wkb_geometry) AS latitude",
"wkb_geometry AS geom",
"NULL AS featurecategoryid",
"NULL AS flainarcategoryid",
"NULL AS createdby",
"NOW() AS createdon",
"NULL AS updatedon",
"NULL AS deletedon"
],
"NOT_KEYS" : [
'ogc_fid', 'osm_id', 'name', "wkb_geometry",
'healthcare2', 'other_tags', 'osm_way_id',
'ref', 'sidewalk', 'z_order', 'is_in', 'cuisine',
'barrier', 'busway'
]
}
osmGeoTbl = {
"points" : {"TBL" : 'osm_position', "FID" : 'positionid'},
"multipolygons" : {"TBL" : "osm_polygons", "FID" : 'polygonid'},
"lines" : {"TBL" : 'osm_lines', "FID" : 'lineid'}
}
osmCatTbl = {
"TBL" : 'osmcategory', "FID" : "osmcategoryid",
"KEY_COL" : "key", "VAL_COL" : "value",
"COLS" : [
"NULL AS createdby", "NOW() AS createdon",
"NULL AS updatedon", "NULL AS deletedon"
]
}
osmRelTbl = {
"points" : {"TBL" : "position_osmcat", "FID" : 'pososmcatid'},
"multipolygons" : {"TBL" : "polygons_osmcat", "FID" : 'polygoncatid'},
"lines" : {"TBL" : "lines_osmcat", "FID" : 'linecatid'},
}
outSQL = '/mnt/d/mystuff/flainar/portugal-osmdb.sql'
db_name='osm_pt'
"""
from glass.pys import obj_to_lst
from glass.pys.oss import fprop
from glass.ng.prop.sql import cols_name
from glass.ng.sql.q import q_to_ntbl
from glass.ng.sql.db import create_db
from glass.g.it.db import osm_to_psql
inSchema["TBL"] = obj_to_lst(inSchema["TBL"])
# Create DB
osm_fn = fprop(osmData, 'fn')
osm_fn = osm_fn.replace('-', '').replace('.', '')
db = create_db(osm_fn if not db_name else db_name, api='psql')
# Send OSM data to Database
osm_to_psql(osmData, db)
# Get KEYS COLUMNS
transcols = {}
for tbl in inSchema["TBL"]:
transcols[tbl] = [
c for c in cols_name(db, tbl, sanitizeSpecialWords=None)
if c not in inSchema["NOT_KEYS"]
]
# Create osmGeoTbl
osmgeotbl = [
q_to_ntbl(db,
osmGeoTbl[tbl]['TBL'],
("SELECT {} AS {}, {} FROM {}").format(
inSchema["FID"], osmGeoTbl[tbl]["FID"],
", ".join(inSchema["COLS"]), tbl),
api='psql') for tbl in inSchema["TBL"]
]
# Create OSM categories table
qs = []
for tbl in inSchema["TBL"]:
qs.extend([
("SELECT '{keyV}' AS {keyC}, CAST({t}.{keyV} AS text) AS {valC} "
"FROM {t} WHERE {t}.{keyV} IS NOT NULL "
"GROUP BY {t}.{keyV}").format(keyV=c,
t=tbl,
keyC=osmCatTbl["KEY_COL"],
valC=osmCatTbl["VAL_COL"])
for c in transcols[tbl]
])
osmcatbl = q_to_ntbl(
db,
osmCatTbl["TBL"],
("SELECT row_number() OVER(ORDER BY {keyC}) "
"AS {osmcatid}, {keyC}, {valC}{ocols} "
"FROM ({q}) AS foo").format(
q="SELECT {k}, {v} FROM ({t}) AS kvtbl GROUP BY {k}, {v}".format(
k=osmCatTbl["KEY_COL"],
v=osmCatTbl["VAL_COL"],
t=" UNION ALL ".join(qs),
) if len(inSchema["TBL"]) > 1 else " UNION ALL ".join(qs),
keyC=osmCatTbl["KEY_COL"],
osmcatid=osmCatTbl["FID"],
valC=osmCatTbl["VAL_COL"],
ocols="" if "COLS" not in osmCatTbl else ", {}".format(", ".join(
osmCatTbl["COLS"]))),
api='psql')
# Create relation table
osmreltbl = []
for tbl in inSchema["TBL"]:
qs = [(
"SELECT {fid}, '{keyV}' AS key, CAST({t}.{keyV} AS text) AS osmval "
"FROM {t} WHERE {t}.{keyV} IS NOT NULL").format(
fid=inSchema["FID"], keyV=c, t=tbl) for c in transcols[tbl]]
osmreltbl.append(
q_to_ntbl(
db,
osmRelTbl[tbl]["TBL"],
("SELECT foo.{fid} AS {nfid}, catbl.{osmcatfid} "
"FROM ({mtbl}) AS foo INNER JOIN {catTbl} AS catbl "
"ON foo.key = catbl.{catkey} AND foo.osmval = catbl.{catval}"
).format(mtbl=" UNION ALL ".join(qs),
fid=inSchema["FID"],
nfid=osmRelTbl[tbl]["FID"],
catTbl=osmCatTbl["TBL"],
osmcatfid=osmCatTbl["FID"],
catkey=osmCatTbl["KEY_COL"],
catval=osmCatTbl["VAL_COL"]),
api='psql'))
if not outSQL:
return osmgeotbl, osmcatbl, osmreltbl
else:
from glass.ng.sql.bkup import dump_tbls
return dump_tbls(db, osmgeotbl + [osmcatbl] + osmreltbl, outSQL)
def dsn_data_collection_by_multibuffer(inBuffers,
workspace,
db,
datasource,
keywords=None):
"""
Extract Digital Social Network Data for each sub-buffer in buffer.
A sub-buffer is a buffer with a radius equals to the main buffer radius /2
and with a central point at North, South, East, West, Northeast, Northwest,
Southwest and Southeast of the main buffer central point.
inBuffers = {
"lisbon" : {
'x' : -89004.994779, # in meters
'y' : -102815.866054, # in meters
'radius' : 10000,
'epsg' : 3763
},
"london : {
'x' : -14210.551441, # in meters
'y' : 6711542.47559, # in meters
'radius' : 10000,
'epsg' : 3857
}
}
or
inBuffers = {
"lisbon" : {
"path" : /path/to/file.shp,
"epsg" : 3763
}
}
keywords = ['flood', 'accident', 'fire apartment', 'graffiti', 'homeless']
datasource = 'facebook' or datasource = 'flickr'
TODO: Only works for Flickr and Facebook
"""
import os
from osgeo import ogr
from glass.pys import obj_to_lst
from glass.ng.sql.db import create_db
from glass.ng.sql.q import q_to_ntbl
from glass.g.wt.sql import df_to_db
from glass.g.it.db import shp_to_psql
from glass.g.it.shp import dbtbl_to_shp
from glass.g.gp.prox.bfing import get_sub_buffers, dic_buffer_array_to_shp
if datasource == 'flickr':
from glass.g.acq.dsn.flickr import photos_location
elif datasource == 'facebook':
from glass.g.acq.dsn.fb.places import places_by_query
keywords = obj_to_lst(keywords)
keywords = ["None"] if not keywords else keywords
# Create Database to Store Data
create_db(db, overwrite=True, api='psql')
for city in inBuffers:
# Get Smaller Buffers
if "path" in inBuffers[city]:
# Get X, Y and Radius
from glass.g.prop.feat.bf import bf_prop
__bfprop = bf_prop(inBuffers[city]["path"],
inBuffers[city]["epsg"],
isFile=True)
inBuffers[city]["x"] = __bfprop["X"]
inBuffers[city]["y"] = __bfprop["Y"]
inBuffers[city]["radius"] = __bfprop["R"]
inBuffers[city]["list_buffer"] = [{
'X': inBuffers[city]["x"],
'Y': inBuffers[city]["y"],
'RADIUS': inBuffers[city]['radius'],
'cardeal': 'major'
}] + get_sub_buffers(inBuffers[city]["x"], inBuffers[city]["y"],
inBuffers[city]["radius"])
# Smaller Buffers to File
multiBuffer = os.path.join(workspace, 'buffers_{}.shp'.format(city))
dic_buffer_array_to_shp(inBuffers[city]["list_buffer"],
multiBuffer,
inBuffers[city]['epsg'],
fields={'cardeal': ogr.OFTString})
# Retrive data for each keyword and buffer
# Record these elements in one dataframe
c = None
tblData = None
for bf in inBuffers[city]["list_buffer"]:
for k in keywords:
if datasource == 'flickr':
tmpData = photos_location(
bf,
inBuffers[city]["epsg"],
keyword=k if k != 'None' else None,
epsg_out=inBuffers[city]["epsg"],
onlySearchAreaContained=False)
elif datasource == 'facebook':
tmpData = places_by_query(
bf,
inBuffers[city]["epsg"],
keyword=k if k != 'None' else None,
epsgOut=inBuffers[city]["epsg"],
onlySearchAreaContained=False)
if type(tmpData) == int:
print("NoData finded for buffer '{}' and keyword '{}'".
format(bf['cardeal'], k))
continue
tmpData["keyword"] = k
tmpData["buffer_or"] = bf["cardeal"]
if not c:
tblData = tmpData
c = 1
else:
tblData = tblData.append(tmpData, ignore_index=True)
inBuffers[city]["data"] = tblData
# Get data columns names
cols = inBuffers[city]["data"].columns.values
dataColumns = [
c for c in cols if c != 'geom' and c != 'keyword' \
and c != 'buffer_or' and c != 'geometry'
]
# Send data to PostgreSQL
if 'geometry' in cols:
cgeom = 'geometry'
else:
cgeom = 'geom'
inBuffers[city]["table"] = 'tbldata_{}'.format(city)
df_to_db(db,
inBuffers[city]["data"],
inBuffers[city]["table"],
api='psql',
epsg=inBuffers[city]["epsg"],
geomType='POINT',
colGeom=cgeom)
# Send Buffers data to PostgreSQL
inBuffers[city]["pg_buffer"] = shp_to_psql(
db,
multiBuffer,
pgTable='buffers_{}'.format(city),
api="shp2pgsql",
srsEpsgCode=inBuffers[city]["epsg"])
inBuffers[city]["filter_table"] = q_to_ntbl(
db,
"filter_{}".format(inBuffers[city]["table"]),
("SELECT srcdata.*, "
"array_agg(buffersg.cardeal ORDER BY buffersg.cardeal) "
"AS intersect_buffer FROM ("
"SELECT {cols}, keyword, geom, "
"array_agg(buffer_or ORDER BY buffer_or) AS extracted_buffer "
"FROM {pgtable} "
"GROUP BY {cols}, keyword, geom"
") AS srcdata, ("
"SELECT cardeal, geom AS bfg FROM {bftable}"
") AS buffersg "
"WHERE ST_Intersects(srcdata.geom, buffersg.bfg) IS TRUE "
"GROUP BY {cols}, keyword, geom, extracted_buffer").format(
cols=", ".join(dataColumns),
pgtable=inBuffers[city]["table"],
bftable=inBuffers[city]["pg_buffer"]),
api='psql')
inBuffers[city]["outside_table"] = q_to_ntbl(
db,
"outside_{}".format(inBuffers[city]["table"]),
("SELECT * FROM ("
"SELECT srcdata.*, "
"array_agg(buffersg.cardeal ORDER BY buffersg.cardeal) "
"AS not_intersect_buffer FROM ("
"SELECT {cols}, keyword, geom, "
"array_agg(buffer_or ORDER BY buffer_or) AS extracted_buffer "
"FROM {pgtable} "
"GROUP BY {cols}, keyword, geom"
") AS srcdata, ("
"SELECT cardeal, geom AS bfg FROM {bftable}"
") AS buffersg "
"WHERE ST_Intersects(srcdata.geom, buffersg.bfg) IS NOT TRUE "
"GROUP BY {cols}, keyword, geom, extracted_buffer"
") AS foo WHERE array_length(not_intersect_buffer, 1) = 9"
).format(cols=", ".join(dataColumns),
pgtable=inBuffers[city]["table"],
bftable=inBuffers[city]["pg_buffer"]),
api='psql')
# Union these two tables
inBuffers[city]["table"] = q_to_ntbl(
db,
"data_{}".format(city),
("SELECT * FROM {intbl} UNION ALL "
"SELECT {cols}, keyword, geom, extracted_buffer, "
"CASE WHEN array_length(not_intersect_buffer, 1) = 9 "
"THEN '{array_symbol}' ELSE not_intersect_buffer END AS "
"intersect_buffer FROM {outbl}").format(
intbl=inBuffers[city]["filter_table"],
outbl=inBuffers[city]["outside_table"],
cols=", ".join(dataColumns),
array_symbol='{' + '}'),
api='psql')
"""
Get Buffers table with info related:
-> pnt_obtidos = nr pontos obtidos usando esse buffer
-> pnt_obtidos_fora = nt pontos obtidos fora desse buffer, mas
obtidos com ele
-> pnt_intersect = nt pontos que se intersectam com o buffer
-> pnt_intersect_non_obtain = nr pontos que se intersectam mas nao
foram obtidos como buffer
"""
inBuffers[city]["pg_buffer"] = q_to_ntbl(
db,
"dt_{}".format(inBuffers[city]["pg_buffer"]),
("SELECT main.*, get_obtidos.pnt_obtidos, "
"obtidos_fora.pnt_obtidos_fora, intersecting.pnt_intersect, "
"int_not_obtained.pnt_intersect_non_obtain "
"FROM {bf_table} AS main "
"LEFT JOIN ("
"SELECT gid, cardeal, COUNT(gid) AS pnt_obtidos "
"FROM {bf_table} AS bf "
"INNER JOIN {dt_table} AS dt "
"ON bf.cardeal = ANY(dt.extracted_buffer) "
"GROUP BY gid, cardeal"
") AS get_obtidos ON main.gid = get_obtidos.gid "
"LEFT JOIN ("
"SELECT gid, cardeal, COUNT(gid) AS pnt_obtidos_fora "
"FROM {bf_table} AS bf "
"INNER JOIN {dt_table} AS dt "
"ON bf.cardeal = ANY(dt.extracted_buffer) "
"WHERE ST_Intersects(bf.geom, dt.geom) IS NOT TRUE "
"GROUP BY gid, cardeal"
") AS obtidos_fora ON main.gid = obtidos_fora.gid "
"LEFT JOIN ("
"SELECT gid, cardeal, COUNT(gid) AS pnt_intersect "
"FROM {bf_table} AS bf "
"INNER JOIN {dt_table} AS dt "
"ON bf.cardeal = ANY(dt.intersect_buffer) "
"GROUP BY gid, cardeal"
") AS intersecting ON main.gid = intersecting.gid "
"LEFT JOIN ("
"SELECT gid, cardeal, COUNT(gid) AS pnt_intersect_non_obtain "
"FROM {bf_table} AS bf "
"INNER JOIN {dt_table} AS dt "
"ON bf.cardeal = ANY(dt.intersect_buffer) "
"WHERE NOT (bf.cardeal = ANY(dt.extracted_buffer)) "
"GROUP BY gid, cardeal"
") AS int_not_obtained "
"ON main.gid = int_not_obtained.gid "
"ORDER BY main.gid").format(bf_table=inBuffers[city]["pg_buffer"],
dt_table=inBuffers[city]["table"]),
api='psql')
"""
Get Points table with info related:
-> nobtido = n vezes um ponto foi obtido
-> obtido_e_intersect = n vezes um ponto foi obtido usando um buffer
com o qual se intersecta
-> obtido_sem_intersect = n vezes um ponto foi obtido usando um buffer
com o qual nao se intersecta
-> nintersect = n vezes que um ponto se intersecta com um buffer
-> intersect_sem_obtido = n vezes que um ponto nao foi obtido apesar
de se intersectar com o buffer
"""
inBuffers[city]["table"] = q_to_ntbl(
db,
"info_{}".format(city),
("SELECT {cols}, dt.keyword, dt.geom, "
"CAST(dt.extracted_buffer AS text) AS extracted_buffer, "
"CAST(dt.intersect_buffer AS text) AS intersect_buffer, "
"array_length(extracted_buffer, 1) AS nobtido, "
"SUM(CASE WHEN ST_Intersects(bf.geom, dt.geom) IS TRUE "
"THEN 1 ELSE 0 END) AS obtido_e_intersect, "
"(array_length(extracted_buffer, 1) - SUM("
"CASE WHEN ST_Intersects(bf.geom, dt.geom) IS TRUE "
"THEN 1 ELSE 0 END)) AS obtido_sem_intersect, "
"array_length(intersect_buffer, 1) AS nintersect, "
"(array_length(intersect_buffer, 1) - SUM("
"CASE WHEN ST_Intersects(bf.geom, dt.geom) IS TRUE "
"THEN 1 ELSE 0 END)) AS intersect_sem_obtido "
"FROM {dt_table} AS dt "
"INNER JOIN {bf_table} AS bf "
"ON bf.cardeal = ANY(dt.extracted_buffer) "
"GROUP BY {cols}, dt.keyword, dt.geom, "
"dt.extracted_buffer, dt.intersect_buffer").format(
dt_table=inBuffers[city]["table"],
bf_table=inBuffers[city]["pg_buffer"],
cols=", ".join(["dt.{}".format(x) for x in dataColumns])),
api='psql')
# Export Results
dbtbl_to_shp(db,
inBuffers[city]["table"],
'geom',
os.path.join(workspace,
"{}.shp".format(inBuffers[city]["table"])),
api='psql',
epsg=inBuffers[city]["epsg"])
dbtbl_to_shp(db,
inBuffers[city]["pg_buffer"],
'geom',
os.path.join(
workspace,
"{}.shp".format(inBuffers[city]["pg_buffer"])),
api='psql',
epsg=inBuffers[city]["epsg"])
return inBuffers
def raster_based(osmdata, nomenclature, refRaster, lulcRst,
overwrite=None, dataStore=None, roadsAPI='POSTGIS'):
"""
Convert OSM Data into Land Use/Land Cover Information
An raster based approach.
TODO: Add detailed description
"""
# ************************************************************************ #
# Python Modules from Reference Packages #
# ************************************************************************ #
import datetime; import os; import pandas; import copy
# ************************************************************************ #
# glass dependencies #
# ************************************************************************ #
from glass.pys.oss import mkdir, fprop
from glass.g.prop import check_isRaster
from glass.g.prop.prj import get_rst_epsg
from glass.g.wenv.grs import run_grass
if roadsAPI == 'POSTGIS':
from glass.ng.sql.db import create_db
from glass.g.it.db import osm_to_psql
from glass.ete.osm2lulc.mod2 import roads_sqdb
from glass.ng.sql.bkup import dump_db
from glass.ng.sql.db import drop_db
else:
from glass.g.it.osm import osm_to_sqdb
from glass.ete.osm2lulc.mod2 import grs_rst_roads
from glass.ete.osm2lulc.utils import osm_project, add_lulc_to_osmfeat, osmlulc_rsttbl
from glass.ete.osm2lulc.utils import get_ref_raster
from glass.ete.osm2lulc.mod1 import grs_rst
from glass.ete.osm2lulc.m3_4 import rst_area
from glass.ete.osm2lulc.mod5 import basic_buffer
from glass.ete.osm2lulc.mod6 import rst_pnt_to_build
# ************************************************************************ #
# Global Settings #
# ************************************************************************ #
# Check if input parameters exists!
if not os.path.exists(os.path.dirname(lulcRst)):
raise ValueError('{} does not exist!'.format(os.path.dirname(lulcRst)))
if not os.path.exists(osmdata):
raise ValueError('File with OSM DATA ({}) does not exist!'.format(osmdata))
if not os.path.exists(refRaster):
raise ValueError('File with reference area ({}) does not exist!'.format(refRaster))
# Check if Nomenclature is valid
nomenclature = "URBAN_ATLAS" if nomenclature != "URBAN_ATLAS" and \
nomenclature != "CORINE_LAND_COVER" and \
nomenclature == "GLOBE_LAND_30" else nomenclature
time_a = datetime.datetime.now().replace(microsecond=0)
workspace = os.path.join(os.path.dirname(
lulcRst), 'osmtolulc') if not dataStore else dataStore
# Check if workspace exists
if os.path.exists(workspace):
if overwrite:
mkdir(workspace)
else:
raise ValueError('Path {} already exists'.format(workspace))
else:
mkdir(workspace)
# Get Ref Raster
refRaster, epsg = get_ref_raster(refRaster, workspace, cellsize=2)
from glass.ete.osm2lulc import PRIORITIES, osmTableData, LEGEND
__priorites = PRIORITIES[nomenclature]
__legend = LEGEND[nomenclature]
time_b = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Convert OSM file to SQLITE DB or to POSTGIS DB #
# ************************************************************************ #
if roadsAPI == 'POSTGIS':
osm_db = create_db(fprop(
osmdata, 'fn', forceLower=True), overwrite=True)
osm_db = osm_to_psql(osmdata, osm_db)
else:
osm_db = osm_to_sqdb(osmdata, os.path.join(workspace, 'osm.sqlite'))
time_c = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Add Lulc Classes to OSM_FEATURES by rule #
# ************************************************************************ #
add_lulc_to_osmfeat(osm_db, osmTableData, nomenclature, api=roadsAPI)
time_d = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Transform SRS of OSM Data #
# ************************************************************************ #
osmTableData = osm_project(
osm_db, epsg, api=roadsAPI,
isGlobeLand=None if nomenclature != 'GLOBE_LAND_30' else True
)
time_e = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Start a GRASS GIS Session #
# ************************************************************************ #
grass_base = run_grass(
workspace, grassBIN='grass78', location='grloc', srs=epsg)
import grass.script as grass
import grass.script.setup as gsetup
gsetup.init(grass_base, workspace, 'grloc', 'PERMANENT')
# ************************************************************************ #
# IMPORT SOME glass MODULES FOR GRASS GIS #
# ************************************************************************ #
from glass.g.it.rst import rst_to_grs, grs_to_rst
from glass.g.rst.mos import rsts_to_mosaic
from glass.g.wenv.grs import rst_to_region
# ************************************************************************ #
# SET GRASS GIS LOCATION EXTENT #
# ************************************************************************ #
extRst = rst_to_grs(refRaster, 'extent_raster')
rst_to_region(extRst)
time_f = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# MapResults #
mergeOut = {}
# ************************************************************************ #
# ************************************************************************ #
# 1 - Selection Rule #
# ************************************************************************ #
"""
selOut = {
cls_code : rst_name, ...
}
"""
selOut, timeCheck1 = grs_rst(osm_db, osmTableData['polygons'], api=roadsAPI)
for cls in selOut:
mergeOut[cls] = [selOut[cls]]
time_g = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# 2 - Get Information About Roads Location #
# ************************************************************************ #
"""
roads = {
cls_code : rst_name, ...
}
"""
if roadsAPI != 'POSTGIS':
roads, timeCheck2 = grs_rst_roads(
osm_db, osmTableData['lines'], osmTableData['polygons'],
workspace, 1221 if nomenclature != "GLOBE_LAND_30" else 801
)
else:
roadCls = 1221 if nomenclature != "GLOBE_LAND_30" else 801
roads, timeCheck2 = roads_sqdb(
osm_db, osmTableData['lines'], osmTableData['polygons'],
apidb='POSTGIS', asRst=roadCls
)
roads = {roadCls : roads}
for cls in roads:
if cls not in mergeOut:
mergeOut[cls] = [roads[cls]]
else:
mergeOut[cls].append(roads[cls])
time_h = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# 3 - Area Upper than #
# ************************************************************************ #
"""
auOut = {
cls_code : rst_name, ...
}
"""
if nomenclature != 'GLOBE_LAND_30':
auOut, timeCheck3 = rst_area(
osm_db, osmTableData['polygons'], UPPER=True, api=roadsAPI
)
for cls in auOut:
if cls not in mergeOut:
mergeOut[cls] = [auOut[cls]]
else:
mergeOut[cls].append(auOut[cls])
time_l = datetime.datetime.now().replace(microsecond=0)
else:
timeCheck3 = None
time_l = None
# ************************************************************************ #
# 4 - Area Lower than #
# ************************************************************************ #
"""
alOut = {
cls_code : rst_name, ...
}
"""
if nomenclature != 'GLOBE_LAND_30':
alOut, timeCheck4 = rst_area(
osm_db, osmTableData['polygons'], UPPER=None, api=roadsAPI
)
for cls in alOut:
if cls not in mergeOut:
mergeOut[cls] = [alOut[cls]]
else:
mergeOut[cls].append(alOut[cls])
time_j = datetime.datetime.now().replace(microsecond=0)
else:
timeCheck4 = None
time_j = None
# ************************************************************************ #
# 5 - Get data from lines table (railway | waterway) #
# ************************************************************************ #
"""
bfOut = {
cls_code : rst_name, ...
}
"""
bfOut, timeCheck5 = basic_buffer(
osm_db, osmTableData['lines'], workspace, apidb=roadsAPI
)
for cls in bfOut:
if cls not in mergeOut:
mergeOut[cls] = [bfOut[cls]]
else:
mergeOut[cls].append(bfOut[cls])
time_m = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# 7 - Assign untagged Buildings to tags #
# ************************************************************************ #
if nomenclature != "GLOBE_LAND_30":
buildsOut, timeCheck7 = rst_pnt_to_build(
osm_db, osmTableData['points'], osmTableData['polygons'],
api_db=roadsAPI
)
for cls in buildsOut:
if cls not in mergeOut:
mergeOut[cls] = buildsOut[cls]
else:
mergeOut[cls] += buildsOut[cls]
time_n = datetime.datetime.now().replace(microsecond=0)
else:
timeCheck7 = None
time_n = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Produce LULC Map #
# ************************************************************************ #
"""
Merge all results for one cls into one raster
mergeOut = {
cls_code : [rst_name, rst_name, ...], ...
}
into
mergeOut = {
cls_code : patched_raster, ...
}
"""
for cls in mergeOut:
if len(mergeOut[cls]) == 1:
mergeOut[cls] = mergeOut[cls][0]
else:
mergeOut[cls] = rsts_to_mosaic(
mergeOut[cls], 'mosaic_{}'.format(str(cls)), api="grass"
)
time_o = datetime.datetime.now().replace(microsecond=0)
"""
Merge all Class Raster using a priority rule
"""
__priorities = PRIORITIES[nomenclature]
lst_rst = []
for cls in __priorities:
if cls not in mergeOut:
continue
else:
lst_rst.append(mergeOut[cls])
outGrs = rsts_to_mosaic(lst_rst, os.path.splitext(
os.path.basename(lulcRst))[0], api="grass"
)
time_p = datetime.datetime.now().replace(microsecond=0)
# Ceck if lulc Rst has an valid format
outIsRst = check_isRaster(lulcRst)
if not outIsRst:
from glass.pys.oss import fprop
lulcRst = os.path.join(
os.path.dirname(lulcRst),
fprop(lulcRst, 'fn') + '.tif'
)
grs_to_rst(outGrs, lulcRst, as_cmd=True)
osmlulc_rsttbl(nomenclature, os.path.join(
os.path.dirname(lulcRst), os.path.basename(lulcRst) + '.vat.dbf'
))
time_q = datetime.datetime.now().replace(microsecond=0)
# Dump Database if PostGIS was used
# Drop Database if PostGIS was used
if roadsAPI == 'POSTGIS':
dump_db(osm_db, os.path.join(
workspace, osm_db + '.sql'
), api='psql')
drop_db(osm_db)
return lulcRst, {
0 : ('set_settings', time_b - time_a),
1 : ('osm_to_sqdb', time_c - time_b),
2 : ('cls_in_sqdb', time_d - time_c),
3 : ('proj_data', time_e - time_d),
4 : ('set_grass', time_f - time_e),
5 : ('rule_1', time_g - time_f, timeCheck1),
6 : ('rule_2', time_h - time_g, timeCheck2),
7 : None if not timeCheck3 else ('rule_3', time_l - time_h, timeCheck3),
8 : None if not timeCheck4 else ('rule_4', time_j - time_l, timeCheck4),
9 : ('rule_5', time_m - time_j if timeCheck4 else time_m - time_h, timeCheck5),
10 : None if not timeCheck7 else ('rule_7', time_n - time_m, timeCheck7),
11 : ('merge_rst', time_o - time_n),
12 : ('priority_rule', time_p - time_o),
13 : ('export_rst', time_q - time_p)
}
def dsnsearch_by_cell(GRID_PNT, EPSG, RADIUS, DATA_SOURCE, db, OUTPUT_TABLE):
"""
Search for data in DSN and other platforms by cell
"""
import time
from glass.g.rd.shp import shp_to_obj
from glass.ng.sql.db import create_db
from glass.g.acq.dsn.fb.places import places_by_query
from glass.g.prj.obj import df_prj
from glass.ng.pd import merge_df
from glass.g.it.shp import dbtbl_to_shp
from glass.ng.sql.q import q_to_ntbl
from glass.g.wt.sql import df_to_db
# Open GRID SHP
GRID_DF = shp_to_obj(GRID_PNT)
GRID_DF = df_prj(GRID_DF, 4326) if EPSG != 4326 else GRID_DF
GRID_DF["lng"] = GRID_DF.geometry.x.astype(float)
GRID_DF["lat"] = GRID_DF.geometry.y.astype(float)
GRID_DF["grid_id"] = GRID_DF.index
# GET DATA
RESULTS = []
def get_data(row, datasrc):
if datasrc == 'facebook':
d = places_by_query({
'x': row.lng,
'y': row.lat,
'r': RADIUS
},
4326,
keyword=None,
epsgOut=EPSG,
_limit='100',
onlySearchAreaContained=None)
else:
raise ValueError(
'{} as datasource is not a valid value'.format(datasrc))
if type(d) == int:
return
d['grid_id'] = row.grid_id
RESULTS.append(d)
time.sleep(5)
GRID_DF.apply(lambda x: get_data(x, DATA_SOURCE), axis=1)
RT = merge_df(RESULTS)
# Create DB
create_db(db, overwrite=True, api='psql')
# Send Data to PostgreSQL
df_to_db(db,
RT,
"{}_data".format(DATA_SOURCE),
EPSG,
"POINT",
colGeom='geometry' if 'geometry' in RT.columns.values else 'geom')
COLS = [
x for x in RT.columns.values if x != "geometry" and \
x != 'geom' and x != "grid_id"
] + ["geom"]
GRP_BY_TBL = q_to_ntbl(
db,
"{}_grpby".format(DATA_SOURCE),
("SELECT {cols}, CAST(array_agg(grid_id) AS text) AS grid_id "
"FROM {dtsrc}_data GROUP BY {cols}").format(cols=", ".join(COLS),
dtsrc=DATA_SOURCE),
api='psql')
dbtbl_to_shp(db, GRP_BY_TBL, "geom", OUTPUT_TABLE, api="psql", epsg=EPSG)
return OUTPUT_TABLE
def shps_to_shp(shps, outShp, api="ogr2ogr", fformat='.shp',
dbname=None):
"""
Get all features in several Shapefiles and save them in one file
api options:
* ogr2ogr;
* psql;
* pandas;
* psql;
* grass;
"""
import os
if type(shps) != list:
# Check if is dir
if os.path.isdir(shps):
from glass.pys.oss import lst_ff
# List shps in dir
shps = lst_ff(shps, file_format=fformat)
else:
raise ValueError((
'shps should be a list with paths for Feature Classes or a path to '
'folder with Feature Classes'
))
if api == "ogr2ogr":
from glass.pys import execmd
from glass.g.prop import drv_name
out_drv = drv_name(outShp)
# Create output and copy some features of one layer (first in shps)
cmdout = execmd('ogr2ogr -f "{}" {} {}'.format(
out_drv, outShp, shps[0]
))
# Append remaining layers
lcmd = [execmd(
'ogr2ogr -f "{}" -update -append {} {}'.format(
out_drv, outShp, shps[i]
)
) for i in range(1, len(shps))]
elif api == 'pandas':
"""
Merge SHP using pandas
"""
from glass.g.rd.shp import shp_to_obj
from glass.g.wt.shp import df_to_shp
if type(shps) != list:
raise ValueError('shps should be a list with paths for Feature Classes')
dfs = [shp_to_obj(shp) for shp in shps]
result = dfs[0]
for df in dfs[1:]:
result = result.append(df, ignore_index=True, sort=True)
df_to_shp(result, outShp)
elif api == 'psql':
import os
from glass.ng.sql.tbl import tbls_to_tbl, del_tables
from glass.g.it.db import shp_to_psql
if not dbname:
from glass.ng.sql.db import create_db
create_db(dbname, api='psql')
pg_tbls = shp_to_psql(
dbname, shps, api="shp2pgsql"
)
if os.path.isfile(outShp):
from glass.pys.oss import fprop
outbl = fprop(outShp, 'fn')
else:
outbl = outShp
tbls_to_tbl(dbname, pg_tbls, outbl)
if outbl != outShp:
from glass.g.it.shp import dbtbl_to_shp
dbtbl_to_shp(
dbname, outbl, 'geom', outShp, inDB='psql',
api="pgsql2shp"
)
del_tables(dbname, pg_tbls)
elif api == 'grass':
from glass.g.wenv.grs import run_grass
from glass.pys.oss import fprop, lst_ff
from glass.g.prop.prj import get_shp_epsg
lshps = lst_ff(shps, file_format='.shp')
epsg = get_shp_epsg(lshps[0])
gwork = os.path.dirname(outShp)
outshpname = fprop(outShp, "fn")
loc = f'loc_{outshpname}'
gbase = run_grass(gwork, loc=loc, srs=epsg)
import grass.script.setup as gsetup
gsetup.init(gbase, gwork, loc, 'PERMANENT')
from glass.g.it.shp import shp_to_grs, grs_to_shp
# Import data
gshps = [shp_to_grs(s, fprop(s, 'fn'), asCMD=True) for s in lshps]
patch = vpatch(gshps, outshpname)
grs_to_shp(patch, outShp, "area")
else:
raise ValueError(
"{} API is not available"
)
return outShp
def lnh_to_polygons(inShp, outShp, api='saga', db=None):
"""
Line to Polygons
API's Available:
* saga;
* grass;
* pygrass;
* psql;
"""
if api == 'saga':
"""
http://www.saga-gis.org/saga_tool_doc/7.0.0/shapes_polygons_3.html
Converts lines to polygons. Line arcs are closed to polygons simply
by connecting the last point with the first. Optionally parts of
polylines can be merged into one polygon optionally.
"""
from glass.pys import execmd
rcmd = execmd(("saga_cmd shapes_polygons 3 -POLYGONS {} "
"LINES {} -SINGLE 1 -MERGE 1").format(outShp, inShp))
elif api == 'grass' or api == 'pygrass':
# Do it using GRASS GIS
import os
from glass.g.wenv.grs import run_grass
from glass.pys.oss import fprop
# Create GRASS GIS Session
wk = os.path.dirname(outShp)
lo = fprop(outShp, 'fn', forceLower=True)
gs = run_grass(wk, lo, srs=inShp)
import grass.script as grass
import grass.script.setup as gsetup
gsetup.init(gs, wk, lo, 'PERMANENT')
# Import Packages
from glass.g.it.shp import shp_to_grs, grs_to_shp
# Send data to GRASS GIS
lnh_shp = shp_to_grs(inShp,
fprop(inShp, 'fn', forceLower=True),
asCMD=True if api == 'grass' else None)
# Build Polylines
pol_lnh = line_to_polyline(lnh_shp,
"polylines",
asCmd=True if api == 'grass' else None)
# Polyline to boundary
bound = geomtype_to_geomtype(pol_lnh,
'bound_shp',
'line',
'boundary',
cmd=True if api == 'grass' else None)
# Boundary to Area
areas_shp = boundary_to_areas(bound,
lo,
useCMD=True if api == 'grass' else None)
# Export data
outShp = grs_to_shp(areas_shp,
outShp,
'area',
asCMD=True if api == 'grass' else None)
elif api == 'psql':
""" Do it using PostGIS """
from glass.pys.oss import fprop
from glass.ng.sql.db import create_db
from glass.g.it.db import shp_to_psql
from glass.g.it.shp import dbtbl_to_shp
from glass.g.dp.cg.sql import lnh_to_polg
from glass.g.prop.prj import get_shp_epsg
# Create DB
if not db:
db = create_db(fprop(inShp, 'fn', forceLower=True), api='psql')
else:
from glass.ng.prop.sql import db_exists
isDB = db_exists(db)
if not isDB:
create_db(db, api='psql')
# Send data to DB
in_tbl = shp_to_psql(db, inShp, api="shp2pgsql")
# Get Result
result = lnh_to_polg(db, in_tbl, fprop(outShp, 'fn', forceLower=True))
# Export Result
outshp = dbtbl_to_shp(db,
result,
"geom",
outShp,
api='psql',
epsg=get_shp_epsg(inShp))
else:
raise ValueError("API {} is not available".format(api))
return outShp
def run_viewshed_by_cpu(tid, db, obs, dem, srs,
vis_basename='vis', maxdst=None, obselevation=None):
# Create Database
new_db = create_db("{}_{}".format(db, str(tid)), api='psql')
# Points to Database
pnt_tbl = df_to_db(
new_db, obs, 'pnt_tbl', api='psql',
epsg=srs, geomType='Point', colGeom='geometry')
# Create GRASS GIS Session
workspace = mkdir(os.path.join(
os.path.dirname(dem), 'work_{}'.format(str(tid))
))
loc_name = 'vis_loc'
gbase = run_grass(workspace, location=loc_name, srs=dem)
# Start GRASS GIS Session
import grass.script as grass
import grass.script.setup as gsetup
gsetup.init(gbase, workspace, loc_name, 'PERMANENT')
from glass.g.it.rst import rst_to_grs, grs_to_rst
from glass.g.rst.surf import grs_viewshed
from glass.g.deldt import del_rst
# Send DEM to GRASS GIS
grs_dem = rst_to_grs(dem, 'grs_dem', as_cmd=True)
# Produce Viewshed for each point in obs
for idx, row in obs.iterrows():
# Get Viewshed raster
vrst = grs_viewshed(
grs_dem, (row.geometry.x, row.geometry.y),
'{}_{}'.format(vis_basename, str(row[obs_id])),
max_dist=maxdst, obs_elv=obselevation
)
# Export Raster to File
frst = grs_to_rst(vrst, os.path.join(workspace, vrst + '.tif'))
# Raster to Array
img = gdal.Open(frst)
num = img.ReadAsArray()
# Two Dimension to One Dimension
# Reshape Array
numone = num.reshape(num.shape[0] * num.shape[1])
# Get Indexes with visibility
visnum = np.arange(numone.shape[0]).astype(np.uint32)
visnum = visnum[numone == 1]
# Get Indexes intervals
visint = get_minmax_fm_seq_values(visnum)
# Get rows indexes
_visint = visint.reshape(visint.shape[0] * visint.shape[1])
visrow = _visint / num.shape[1]
visrow = visrow.astype(np.uint32)
# Get cols indexes
viscol = _visint - (visrow * num.shape[1])
# Reshape
visrow = visrow.reshape(visint.shape)
viscol = viscol.reshape(visint.shape)
# Split array
irow, erow = np.vsplit(visrow.T, 1)[0]
icol, ecol = np.vsplit(viscol.T, 1)[0]
# Visibility indexes to Pandas DataFrame
idxnum = np.full(irow.shape, row[obs_id])
visdf = pd.DataFrame({
'pntid' : idxnum, 'rowi' : irow, 'rowe' : erow,
'coli': icol, 'cole' : ecol
})
# Pandas DF to database
# Create Visibility table
df_to_db(
new_db, visdf, vis_basename,
api='psql', colGeom=None,
append=None if not idx else True
)
# Delete all variables
numone = None
visnum = None
visint = None
_visint = None
visrow = None
viscol = None
irow = None
erow = None
icol = None
ecol = None
idxnum = None
visdf = None
del img
# Delete GRASS GIS File
del_rst(vrst)
# Delete TIFF File
del_file(frst)
frst = None
# Create ZIP Table
("CREATE TABLE zip_vistoburn AS "
"SELECT rowi, coli, array_agg(pntid) AS pntid "
"FROM vistoburn GROUP BY rowi, coli"),
# Delete vistoburn
"DROP TABLE IF EXISTS vistoburn"
]
import os
from glass.ng.sql import psql_cmd
from glass.pys.oss import lst_ff, fprop
from glass.ng.sql.q import exec_write_q
from glass.ng.sql.bkup import dump_db
from glass.ng.sql.db import create_db, drop_db
sqls = lst_ff(sql_fld)
for sql in sqls:
# Restore database
new_db = create_db(fprop(sql, 'fn'))
psql_cmd(new_db, sql)
# Execute queries
exec_write_q(new_db, QS)
# Dump Database
dump_db(new_db, os.path.join(outfld, os.path.basename(sql)), api='psql')
# Drop Database
drop_db(new_db)
def psql_to_djgdb(sql_dumps,
db_name,
djg_proj=None,
mapTbl=None,
userDjgAPI=None):
"""
Import PGSQL database in a SQL Script into the database
controlled by one Django Project
To work, the name of a model instance of type foreign key should be
equal to the name of the 'db_column' clause.
"""
import os
from glass.pys import __import
from glass.pys import obj_to_lst
from glass.ng.sql.db import restore_tbls
from glass.ng.sql.db import create_db, drop_db
from glass.ng.prop.sql import lst_tbl
from glass.ng.sql.q import q_to_obj
from glass.webg.djg.mdl.rel import order_mdl_by_rel
from glass.webg.djg.mdl.i import lst_mdl_proj
# Global variables
TABLES_TO_EXCLUDE = [
'geography_columns', 'geometry_columns', 'spatial_ref_sys',
'raster_columns', 'raster_columns', 'raster_overviews',
'pointcloud_formats', 'pointcloud_columns'
]
# Several SQL Files are expected
sql_scripts = obj_to_lst(sql_dumps)
# Create Database
tmp_db_name = db_name + '_xxxtmp'
create_db(tmp_db_name)
# Restore tables in SQL files
for sql in sql_scripts:
restore_tbls(tmp_db_name, sql)
# List tables in the database
tables = [x for x in lst_tbl(tmp_db_name, excludeViews=True, api='psql')
] if not mapTbl else mapTbl
# Open Django Project
if djg_proj:
from glass.webg.djg import get_djgprj
application = get_djgprj(djg_proj)
# List models in project
app_mdls = lst_mdl_proj(djg_proj, thereIsApp=True, returnClassName=True)
data_tbl = {}
for t in tables:
if t == 'auth_user' or t == 'auth_group' or t == 'auth_user_groups':
data_tbl[t] = t
elif t.startswith('auth') or t.startswith('django'):
continue
elif t not in app_mdls or t in TABLES_TO_EXCLUDE:
continue
else:
data_tbl["{}.models.{}".format(t.split('_')[0], app_mdls[t])] = t
from django.contrib.gis.db import models
mdl_cls = [
"{}.models.{}".format(m.split('_')[0], app_mdls[m]) for m in app_mdls
]
orderned_table = order_mdl_by_rel(mdl_cls)
# Add default tables of Django
def_djg_tbl = []
if 'auth_group' in data_tbl:
def_djg_tbl.append('auth_group')
if 'auth_user' in data_tbl:
def_djg_tbl.append('auth_user')
if 'auth_user_groups' in data_tbl:
def_djg_tbl.append('auth_user_groups')
orderned_table = def_djg_tbl + orderned_table
if userDjgAPI:
for table in orderned_table:
# Map pgsql table data
tableData = q_to_obj(tmp_db_name, data_tbl[table], of='dict')
# Table data to Django Model
if table == 'auth_user':
mdl_cls = __import('django.contrib.auth.models.User')
elif table == 'auth_group':
mdl_cls = __import('django.contrib.auth.models.Group')
else:
mdl_cls = __import(table)
__mdl = mdl_cls()
for row in tableData:
for col in row:
# Check if field is a foreign key
field_obj = mdl_cls._meta.get_field(col)
if not isinstance(field_obj, models.ForeignKey):
# If not, use the value
# But first check if value is nan (special type of float)
if row[col] != row[col]:
row[col] = None
setattr(__mdl, col, row[col])
else:
# If yes, use the model instance of the related table
# Get model of the table related com aquela cujos dados
# estao a ser restaurados
related_name = field_obj.related_model.__name__
related_model = __import('{a}.models.{m}'.format(
a=table.split('_')[0], m=related_name))
# If NULL, continue
if not row[col]:
setattr(__mdl, col, row[col])
continue
related_obj = related_model.objects.get(
pk=int(row[col]))
setattr(__mdl, col, related_obj)
__mdl.save()
else:
import pandas as pd
from glass.ng.sql.q import q_to_obj
from glass.g.wt.sql import df_to_db
for tbl in orderned_table:
if tbl not in data_tbl:
continue
data = q_to_obj(tmp_db_name,
"SELECT * FROM {}".format(data_tbl[tbl]))
if tbl == 'auth_user':
data['last_login'] = pd.to_datetime(data.last_login, utc=True)
data['date_joined'] = pd.to_datetime(data.date_joined,
utc=True)
df_to_db(db_name, data, data_tbl[tbl], append=True)
drop_db(tmp_db_name)
def proj(inShp,
outShp,
outEPSG,
inEPSG=None,
gisApi='ogr',
sql=None,
db_name=None):
"""
Project Geodata using GIS
API's Available:
* ogr;
* ogr2ogr;
* pandas;
* ogr2ogr_SQLITE;
* psql;
"""
import os
if gisApi == 'ogr':
"""
Using ogr Python API
"""
if not inEPSG:
raise ValueError(
'To use ogr API, you should specify the EPSG Code of the'
' input data using inEPSG parameter')
from osgeo import ogr
from glass.g.lyr.fld import copy_flds
from glass.g.prop.feat import get_gtype
from glass.g.prop import drv_name
from glass.g.prop.prj import get_sref_from_epsg, get_trans_param
from glass.pys.oss import fprop
def copyShp(out, outDefn, lyr_in, trans):
for f in lyr_in:
g = f.GetGeometryRef()
g.Transform(trans)
new = ogr.Feature(outDefn)
new.SetGeometry(g)
for i in range(0, outDefn.GetFieldCount()):
new.SetField(
outDefn.GetFieldDefn(i).GetNameRef(), f.GetField(i))
out.CreateFeature(new)
new.Destroy()
f.Destroy()
# ####### #
# Project #
# ####### #
transP = get_trans_param(inEPSG, outEPSG)
inData = ogr.GetDriverByName(drv_name(inShp)).Open(inShp, 0)
inLyr = inData.GetLayer()
out = ogr.GetDriverByName(drv_name(outShp)).CreateDataSource(outShp)
outlyr = out.CreateLayer(fprop(outShp, 'fn'),
get_sref_from_epsg(outEPSG),
geom_type=get_gtype(inShp,
name=None,
py_cls=True,
gisApi='ogr'))
# Copy fields to the output
copy_flds(inLyr, outlyr)
# Copy/transform features from the input to the output
outlyrDefn = outlyr.GetLayerDefn()
copyShp(outlyr, outlyrDefn, inLyr, transP)
inData.Destroy()
out.Destroy()
elif gisApi == 'ogr2ogr':
"""
Transform SRS of any OGR Compilant Data. Save the transformed data
in a new file
"""
if not inEPSG:
from glass.g.prop.prj import get_shp_epsg
inEPSG = get_shp_epsg(inShp)
if not inEPSG:
raise ValueError('To use ogr2ogr, you must specify inEPSG')
from glass.pys import execmd
from glass.g.prop import drv_name
cmd = ('ogr2ogr -f "{}" {} {}{} -s_srs EPSG:{} -t_srs EPSG:{}').format(
drv_name(outShp), outShp, inShp,
'' if not sql else ' -dialect sqlite -sql "{}"'.format(sql),
str(inEPSG), str(outEPSG))
outcmd = execmd(cmd)
elif gisApi == 'ogr2ogr_SQLITE':
"""
Transform SRS of a SQLITE DB table. Save the transformed data in a
new table
"""
from glass.pys import execmd
if not inEPSG:
raise ValueError(
('With ogr2ogr_SQLITE, the definition of inEPSG is '
'demandatory.'))
# TODO: Verify if database is sqlite
db, tbl = inShp['DB'], inShp['TABLE']
sql = 'SELECT * FROM {}'.format(tbl) if not sql else sql
outcmd = execmd(
('ogr2ogr -update -append -f "SQLite" {db} -nln "{nt}" '
'-dialect sqlite -sql "{_sql}" -s_srs EPSG:{inepsg} '
'-t_srs EPSG:{outepsg} {db}').format(db=db,
nt=outShp,
_sql=sql,
inepsg=str(inEPSG),
outepsg=str(outEPSG)))
elif gisApi == 'pandas':
# Test if input Shp is GeoDataframe
from glass.g.rd.shp import shp_to_obj
from glass.g.wt.shp import df_to_shp
df = shp_to_obj(inShp)
# Project df
newDf = df.to_crs('EPSG:{}'.format(str(outEPSG)))
# Save as file
return df_to_shp(df, outShp)
elif gisApi == 'psql':
from glass.ng.sql.db import create_db
from glass.pys.oss import fprop
from glass.g.it.db import shp_to_psql
from glass.g.it.shp import dbtbl_to_shp
from glass.g.prj.sql import sql_proj
# Create Database
if not db_name:
db_name = create_db(fprop(outShp, 'fn', forceLower=True),
api='psql')
else:
from glass.ng.prop.sql import db_exists
isDb = db_exists(db_name)
if not isDb:
create_db(db_name, api='psql')
# Import Data
inTbl = shp_to_psql(db_name, inShp, api='shp2pgsql', encoding="LATIN1")
# Transform
oTbl = sql_proj(db_name,
inTbl,
fprop(outShp, 'fn', forceLower=True),
outEPSG,
geomCol='geom',
newGeom='geom')
# Export
outShp = dbtbl_to_shp(db_name,
oTbl,
'geom',
outShp,
api='psql',
epsg=outEPSG)
else:
raise ValueError('Sorry, API {} is not available'.format(gisApi))
return outShp
def osm2lulc(osmdata,
nomenclature,
refRaster,
lulcRst,
overwrite=None,
dataStore=None,
roadsAPI='POSTGIS'):
"""
Convert OSM data into Land Use/Land Cover Information
A matrix based approach
roadsAPI Options:
* SQLITE
* POSTGIS
"""
# ************************************************************************ #
# Python Modules from Reference Packages #
# ************************************************************************ #
import os
import numpy
import datetime
from threading import Thread
from osgeo import gdal
# ************************************************************************ #
# Dependencies #
# ************************************************************************ #
from glass.g.rd.rst import rst_to_array
from glass.g.prop import check_isRaster
from glass.g.prop.rst import get_cellsize
from glass.pys.oss import mkdir, copy_file
from glass.pys.oss import fprop
if roadsAPI == 'POSTGIS':
from glass.ng.sql.db import create_db
from glass.g.it.db import osm_to_psql
from glass.ete.osm2lulc.mod2 import pg_num_roads
from glass.ng.sql.bkup import dump_db
from glass.ng.sql.db import drop_db
else:
from glass.g.it.osm import osm_to_sqdb
from glass.ete.osm2lulc.mod2 import num_roads
from glass.ete.osm2lulc.utils import osm_project, add_lulc_to_osmfeat
from glass.ete.osm2lulc.utils import osmlulc_rsttbl
from glass.ete.osm2lulc.utils import get_ref_raster
from glass.ete.osm2lulc.mod1 import num_selection
from glass.ete.osm2lulc.m3_4 import num_selbyarea
from glass.ete.osm2lulc.mod5 import num_base_buffer
from glass.ete.osm2lulc.mod6 import num_assign_builds
from glass.g.wt.rst import obj_to_rst
# ************************************************************************ #
# Global Settings #
# ************************************************************************ #
# Check if input parameters exists!
if not os.path.exists(os.path.dirname(lulcRst)):
raise ValueError('{} does not exist!'.format(os.path.dirname(lulcRst)))
if not os.path.exists(osmdata):
raise ValueError(
'File with OSM DATA ({}) does not exist!'.format(osmdata))
if not os.path.exists(refRaster):
raise ValueError(
'File with reference area ({}) does not exist!'.format(refRaster))
# Check if Nomenclature is valid
nomenclature = "URBAN_ATLAS" if nomenclature != "URBAN_ATLAS" and \
nomenclature != "CORINE_LAND_COVER" and \
nomenclature == "GLOBE_LAND_30" else nomenclature
time_a = datetime.datetime.now().replace(microsecond=0)
workspace = os.path.join(os.path.dirname(lulcRst),
'num_osmto') if not dataStore else dataStore
# Check if workspace exists:
if os.path.exists(workspace):
if overwrite:
mkdir(workspace, overwrite=True)
else:
raise ValueError('Path {} already exists'.format(workspace))
else:
mkdir(workspace, overwrite=None)
# Get Ref Raster and EPSG
refRaster, epsg = get_ref_raster(refRaster, workspace, cellsize=2)
CELLSIZE = get_cellsize(refRaster, gisApi='gdal')
from glass.ete.osm2lulc import osmTableData, PRIORITIES
time_b = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Convert OSM file to SQLITE DB or to POSTGIS DB #
# ************************************************************************ #
if roadsAPI == 'POSTGIS':
osm_db = create_db(fprop(osmdata, 'fn', forceLower=True),
overwrite=True)
osm_db = osm_to_psql(osmdata, osm_db)
else:
osm_db = osm_to_sqdb(osmdata, os.path.join(workspace, 'osm.sqlite'))
time_c = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Add Lulc Classes to OSM_FEATURES by rule #
# ************************************************************************ #
add_lulc_to_osmfeat(osm_db, osmTableData, nomenclature, api=roadsAPI)
time_d = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Transform SRS of OSM Data #
# ************************************************************************ #
osmTableData = osm_project(
osm_db,
epsg,
api=roadsAPI,
isGlobeLand=None if nomenclature != "GLOBE_LAND_30" else True)
time_e = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# MapResults #
# ************************************************************************ #
mergeOut = {}
timeCheck = {}
RULES = [1, 2, 3, 4, 5, 7]
def run_rule(ruleID):
time_start = datetime.datetime.now().replace(microsecond=0)
_osmdb = copy_file(
osm_db,
os.path.splitext(osm_db)[0] +
'_r{}.sqlite'.format(ruleID)) if roadsAPI == 'SQLITE' else None
# ******************************************************************** #
# 1 - Selection Rule #
# ******************************************************************** #
if ruleID == 1:
res, tm = num_selection(_osmdb if _osmdb else osm_db,
osmTableData['polygons'],
workspace,
CELLSIZE,
epsg,
refRaster,
api=roadsAPI)
# ******************************************************************** #
# 2 - Get Information About Roads Location #
# ******************************************************************** #
elif ruleID == 2:
res, tm = num_roads(
_osmdb, nomenclature, osmTableData['lines'],
osmTableData['polygons'], workspace, CELLSIZE, epsg,
refRaster) if _osmdb else pg_num_roads(
osm_db, nomenclature, osmTableData['lines'],
osmTableData['polygons'], workspace, CELLSIZE, epsg,
refRaster)
# ******************************************************************** #
# 3 - Area Upper than #
# ******************************************************************** #
elif ruleID == 3:
if nomenclature != "GLOBE_LAND_30":
res, tm = num_selbyarea(osm_db if not _osmdb else _osmdb,
osmTableData['polygons'],
workspace,
CELLSIZE,
epsg,
refRaster,
UPPER=True,
api=roadsAPI)
else:
return
# ******************************************************************** #
# 4 - Area Lower than #
# ******************************************************************** #
elif ruleID == 4:
if nomenclature != "GLOBE_LAND_30":
res, tm = num_selbyarea(osm_db if not _osmdb else _osmdb,
osmTableData['polygons'],
workspace,
CELLSIZE,
epsg,
refRaster,
UPPER=False,
api=roadsAPI)
else:
return
# ******************************************************************** #
# 5 - Get data from lines table (railway | waterway) #
# ******************************************************************** #
elif ruleID == 5:
res, tm = num_base_buffer(osm_db if not _osmdb else _osmdb,
osmTableData['lines'],
workspace,
CELLSIZE,
epsg,
refRaster,
api=roadsAPI)
# ******************************************************************** #
# 7 - Assign untagged Buildings to tags #
# ******************************************************************** #
elif ruleID == 7:
if nomenclature != "GLOBE_LAND_30":
res, tm = num_assign_builds(osm_db if not _osmdb else _osmdb,
osmTableData['points'],
osmTableData['polygons'],
workspace,
CELLSIZE,
epsg,
refRaster,
apidb=roadsAPI)
else:
return
time_end = datetime.datetime.now().replace(microsecond=0)
mergeOut[ruleID] = res
timeCheck[ruleID] = {'total': time_end - time_start, 'detailed': tm}
thrds = []
for r in RULES:
thrds.append(
Thread(name="to_{}".format(str(r)), target=run_rule, args=(r, )))
for t in thrds:
t.start()
for t in thrds:
t.join()
# Merge all results into one Raster
compileResults = {}
for rule in mergeOut:
for cls in mergeOut[rule]:
if cls not in compileResults:
if type(mergeOut[rule][cls]) == list:
compileResults[cls] = mergeOut[rule][cls]
else:
compileResults[cls] = [mergeOut[rule][cls]]
else:
if type(mergeOut[rule][cls]) == list:
compileResults[cls] += mergeOut[rule][cls]
else:
compileResults[cls].append(mergeOut[rule][cls])
time_m = datetime.datetime.now().replace(microsecond=0)
# All Rasters to Array
arrayRst = {}
for cls in compileResults:
for raster in compileResults[cls]:
if not raster:
continue
array = rst_to_array(raster)
if cls not in arrayRst:
arrayRst[cls] = [array.astype(numpy.uint8)]
else:
arrayRst[cls].append(array.astype(numpy.uint8))
time_n = datetime.datetime.now().replace(microsecond=0)
# Sum Rasters of each class
for cls in arrayRst:
if len(arrayRst[cls]) == 1:
sumArray = arrayRst[cls][0]
else:
sumArray = arrayRst[cls][0]
for i in range(1, len(arrayRst[cls])):
sumArray = sumArray + arrayRst[cls][i]
arrayRst[cls] = sumArray
time_o = datetime.datetime.now().replace(microsecond=0)
# Apply priority rule
__priorities = PRIORITIES[nomenclature + "_NUMPY"]
for lulcCls in __priorities:
__lulcCls = rstcls_map(lulcCls)
if __lulcCls not in arrayRst:
continue
else:
numpy.place(arrayRst[__lulcCls], arrayRst[__lulcCls] > 0, lulcCls)
for i in range(len(__priorities)):
lulc_i = rstcls_map(__priorities[i])
if lulc_i not in arrayRst:
continue
else:
for e in range(i + 1, len(__priorities)):
lulc_e = rstcls_map(__priorities[e])
if lulc_e not in arrayRst:
continue
else:
numpy.place(arrayRst[lulc_e],
arrayRst[lulc_i] == __priorities[i], 0)
time_p = datetime.datetime.now().replace(microsecond=0)
# Merge all rasters
startCls = 'None'
for i in range(len(__priorities)):
lulc_i = rstcls_map(__priorities[i])
if lulc_i in arrayRst:
resultSum = arrayRst[lulc_i]
startCls = i
break
if startCls == 'None':
return 'NoResults'
for i in range(startCls + 1, len(__priorities)):
lulc_i = rstcls_map(__priorities[i])
if lulc_i not in arrayRst:
continue
resultSum = resultSum + arrayRst[lulc_i]
# Save Result
outIsRst = check_isRaster(lulcRst)
if not outIsRst:
from glass.pys.oss import fprop
lulcRst = os.path.join(os.path.dirname(lulcRst),
fprop(lulcRst, 'fn') + '.tif')
numpy.place(resultSum, resultSum == 0, 1)
obj_to_rst(resultSum, lulcRst, refRaster, noData=1)
osmlulc_rsttbl(
nomenclature + "_NUMPY",
os.path.join(os.path.dirname(lulcRst),
os.path.basename(lulcRst) + '.vat.dbf'))
time_q = datetime.datetime.now().replace(microsecond=0)
# Dump Database if PostGIS was used
# Drop Database if PostGIS was used
if roadsAPI == 'POSTGIS':
dump_db(osm_db, os.path.join(workspace, osm_db + '.sql'), api='psql')
drop_db(osm_db)
return lulcRst, {
0: ('set_settings', time_b - time_a),
1: ('osm_to_sqdb', time_c - time_b),
2: ('cls_in_sqdb', time_d - time_c),
3: ('proj_data', time_e - time_d),
4: ('rule_1', timeCheck[1]['total'], timeCheck[1]['detailed']),
5: ('rule_2', timeCheck[2]['total'], timeCheck[2]['detailed']),
6:
None if 3 not in timeCheck else
('rule_3', timeCheck[3]['total'], timeCheck[3]['detailed']),
7:
None if 4 not in timeCheck else
('rule_4', timeCheck[4]['total'], timeCheck[4]['detailed']),
8: ('rule_5', timeCheck[5]['total'], timeCheck[5]['detailed']),
9:
None if 7 not in timeCheck else
('rule_7', timeCheck[7]['total'], timeCheck[7]['detailed']),
10: ('rst_to_array', time_n - time_m),
11: ('sum_cls', time_o - time_n),
12: ('priority_rule', time_p - time_o),
13: ('merge_rst', time_q - time_p)
}
def tbl_to_areamtx(inShp, col_a, col_b, outXls, db=None, with_metrics=None):
"""
Table to Matrix
Table as:
FID | col_a | col_b | geom
0 | 1 | A | A | ....
0 | 2 | A | B | ....
0 | 3 | A | A | ....
0 | 4 | A | C | ....
0 | 5 | A | B | ....
0 | 6 | B | A | ....
0 | 7 | B | A | ....
0 | 8 | B | B | ....
0 | 9 | B | B | ....
0 | 10 | C | A | ....
0 | 11 | C | B | ....
0 | 11 | C | D | ....
To:
classe | A | B | C | D
A | | | |
B | | | |
C | | | |
D | | | |
col_a = rows
col_b = cols
api options:
* pandas;
* psql;
"""
# TODO: check if col_a and col_b exists in table
if not db:
import pandas as pd
import numpy as np
from glass.g.rd.shp import shp_to_obj
from glass.ng.wt import obj_to_tbl
# Open data
df = shp_to_obj(inShp)
# Remove nan values
df = df[pd.notnull(df[col_a])]
df = df[pd.notnull(df[col_b])]
# Get Area
df['realarea'] = df.geometry.area / 1000000
# Get rows and Cols
rows = df[col_a].unique()
cols = df[col_b].unique()
refval = list(np.sort(np.unique(np.append(rows, cols))))
# Produce matrix
outDf = []
for row in refval:
newCols = [row]
for col in refval:
newDf = df[(df[col_a] == row) & (df[col_b] == col)]
if not newDf.shape[0]:
newCols.append(0)
else:
area = newDf.realarea.sum()
newCols.append(area)
outDf.append(newCols)
outcols = ['class'] + refval
outDf = pd.DataFrame(outDf, columns=outcols)
if with_metrics:
from glass.ng.cls.eval import get_measures_for_mtx
out_df = get_measures_for_mtx(outDf, 'class')
return obj_to_tbl(out_df, outXls)
# Export to Excel
return obj_to_tbl(outDf, outXls)
else:
from glass.pys.oss import fprop
from glass.ng.sql.db import create_db
from glass.ng.prop.sql import db_exists
from glass.g.it.db import shp_to_psql
from glass.g.dp.tomtx.sql import tbl_to_area_mtx
from glass.ng.it import db_to_tbl
# Create database if not exists
is_db = db_exists(db)
if not is_db:
create_db(db, api='psql')
# Add data to database
tbl = shp_to_psql(db, inShp, api='shp2pgsql')
# Create matrix
mtx = tbl_to_area_mtx(db, tbl, col_a, col_b, fprop(outXls, 'fn'))
# Export result
return db_to_tbl(db, mtx, outXls, sheetsNames='matrix')
def check_shape_diff(SHAPES_TO_COMPARE, OUT_FOLDER, REPORT, DB,
GRASS_REGION_TEMPLATE):
"""
Script to check differences between pairs of Feature Classes
Suponha que temos diversas Feature Classes (FC) e que cada uma delas
possui um determinado atributo; imagine tambem que,
considerando todos os pares possiveis entre estas FC,
se pretende comparar as diferencas na distribuicao dos valores
desse atributo para cada par.
* Dependencias:
- GRASS;
- PostgreSQL;
- PostGIS.
"""
import datetime
import os
import pandas
from glass.ng.sql.q import q_to_obj
from glass.ng.it import db_to_tbl
from glass.g.wt.sql import df_to_db
from glass.g.dp.rst.toshp import rst_to_polyg
from glass.g.it.db import shp_to_psql
from glass.g.dp.tomtx import tbl_to_area_mtx
from glass.g.prop import check_isRaster
from glass.pys.oss import fprop
from glass.ng.sql.db import create_db
from glass.ng.sql.tbl import tbls_to_tbl
from glass.ng.sql.q import q_to_ntbl
# Check if folder exists, if not create it
if not os.path.exists(OUT_FOLDER):
from glass.pys.oss import mkdir
mkdir(OUT_FOLDER, overwrite=None)
else:
raise ValueError('{} already exists!'.format(OUT_FOLDER))
from glass.g.wenv.grs import run_grass
gbase = run_grass(OUT_FOLDER,
grassBIN='grass78',
location='shpdif',
srs=GRASS_REGION_TEMPLATE)
import grass.script as grass
import grass.script.setup as gsetup
gsetup.init(gbase, OUT_FOLDER, 'shpdif', 'PERMANENT')
from glass.g.it.shp import shp_to_grs, grs_to_shp
from glass.g.it.rst import rst_to_grs
from glass.g.tbl.col import rn_cols
# Convert to SHAPE if file is Raster
i = 0
_SHP_TO_COMPARE = {}
for s in SHAPES_TO_COMPARE:
isRaster = check_isRaster(s)
if isRaster:
# To GRASS
rstName = fprop(s, 'fn')
inRst = rst_to_grs(s, "rst_" + rstName, as_cmd=True)
# To Vector
d = rst_to_polyg(inRst,
rstName,
rstColumn="lulc_{}".format(i),
gisApi="grass")
# Export Shapefile
shp = grs_to_shp(d, os.path.join(OUT_FOLDER, d + '.shp'), "area")
_SHP_TO_COMPARE[shp] = "lulc_{}".format(i)
else:
# To GRASS
grsV = shp_to_grs(s, fprop(s, 'fn'), asCMD=True)
# Change name of column with comparing value
ncol = "lulc_{}".format(str(i))
rn_cols(grsV, {SHAPES_TO_COMPARE[s]: "lulc_{}".format(str(i))},
api="grass")
# Export
shp = grs_to_shp(grsV, os.path.join(OUT_FOLDER, grsV + '_rn.shp'),
"area")
_SHP_TO_COMPARE[shp] = "lulc_{}".format(str(i))
i += 1
SHAPES_TO_COMPARE = _SHP_TO_COMPARE
__SHAPES_TO_COMPARE = SHAPES_TO_COMPARE
# Create database
create_db(DB, api='psql')
""" Union SHAPEs """
UNION_SHAPE = {}
FIX_GEOM = {}
SHPS = list(__SHAPES_TO_COMPARE.keys())
for i in range(len(SHPS)):
for e in range(i + 1, len(SHPS)):
# Optimized Union
print("Union between {} and {}".format(SHPS[i], SHPS[e]))
time_a = datetime.datetime.now().replace(microsecond=0)
__unShp = optimized_union_anls(
SHPS[i],
SHPS[e],
os.path.join(OUT_FOLDER, "un_{}_{}.shp".format(i, e)),
GRASS_REGION_TEMPLATE,
os.path.join(OUT_FOLDER, "work_{}_{}".format(i, e)),
multiProcess=True)
time_b = datetime.datetime.now().replace(microsecond=0)
print(time_b - time_a)
# Rename cols
unShp = rn_cols(
__unShp, {
"a_" + __SHAPES_TO_COMPARE[SHPS[i]]:
__SHAPES_TO_COMPARE[SHPS[i]],
"b_" + __SHAPES_TO_COMPARE[SHPS[e]]:
__SHAPES_TO_COMPARE[SHPS[e]]
})
UNION_SHAPE[(SHPS[i], SHPS[e])] = unShp
# Send data to postgresql
SYNTH_TBL = {}
for uShp in UNION_SHAPE:
# Send data to PostgreSQL
union_tbl = shp_to_psql(DB, UNION_SHAPE[uShp], api='shp2pgsql')
# Produce table with % of area equal in both maps
areaMapTbl = q_to_ntbl(
DB,
"{}_syn".format(union_tbl),
("SELECT CAST('{lulc_1}' AS text) AS lulc_1, "
"CAST('{lulc_2}' AS text) AS lulc_2, "
"round("
"CAST(SUM(g_area) / 1000000 AS numeric), 4"
") AS agree_area, round("
"CAST((SUM(g_area) / MIN(total_area)) * 100 AS numeric), 4"
") AS agree_percentage, "
"round("
"CAST(MIN(total_area) / 1000000 AS numeric), 4"
") AS total_area FROM ("
"SELECT {map1_cls}, {map2_cls}, ST_Area(geom) AS g_area, "
"CASE "
"WHEN {map1_cls} = {map2_cls} "
"THEN 1 ELSE 0 "
"END AS isthesame, total_area FROM {tbl}, ("
"SELECT SUM(ST_Area(geom)) AS total_area FROM {tbl}"
") AS foo2"
") AS foo WHERE isthesame = 1 "
"GROUP BY isthesame").format(
lulc_1=fprop(uShp[0], 'fn'),
lulc_2=fprop(uShp[1], 'fn'),
map1_cls=__SHAPES_TO_COMPARE[uShp[0]],
map2_cls=__SHAPES_TO_COMPARE[uShp[1]],
tbl=union_tbl),
api='psql')
# Produce confusion matrix for the pair in comparison
matrixTbl = tbl_to_area_mtx(DB, union_tbl,
__SHAPES_TO_COMPARE[uShp[0]],
__SHAPES_TO_COMPARE[uShp[1]],
union_tbl + '_mtx')
SYNTH_TBL[uShp] = {"TOTAL": areaMapTbl, "MATRIX": matrixTbl}
# UNION ALL TOTAL TABLES
total_table = tbls_to_tbl(DB, [SYNTH_TBL[k]["TOTAL"] for k in SYNTH_TBL],
'total_table')
# Create table with % of agreement between each pair of maps
mapsNames = q_to_obj(
DB,
("SELECT lulc FROM ("
"SELECT lulc_1 AS lulc FROM {tbl} GROUP BY lulc_1 "
"UNION ALL "
"SELECT lulc_2 AS lulc FROM {tbl} GROUP BY lulc_2"
") AS lu GROUP BY lulc ORDER BY lulc").format(tbl=total_table),
db_api='psql').lulc.tolist()
FLDS_TO_PIVOT = ["agree_percentage", "total_area"]
Q = ("SELECT * FROM crosstab('"
"SELECT CASE "
"WHEN foo.lulc_1 IS NOT NULL THEN foo.lulc_1 ELSE jtbl.tmp1 "
"END AS lulc_1, CASE "
"WHEN foo.lulc_2 IS NOT NULL THEN foo.lulc_2 ELSE jtbl.tmp2 "
"END AS lulc_2, CASE "
"WHEN foo.{valCol} IS NOT NULL THEN foo.{valCol} ELSE 0 "
"END AS agree_percentage FROM ("
"SELECT lulc_1, lulc_2, {valCol} FROM {tbl} UNION ALL "
"SELECT lulc_1, lulc_2, {valCol} FROM ("
"SELECT lulc_1 AS lulc_2, lulc_2 AS lulc_1, {valCol} "
"FROM {tbl}"
") AS tst"
") AS foo FULL JOIN ("
"SELECT lulc_1 AS tmp1, lulc_2 AS tmp2 FROM ("
"SELECT lulc_1 AS lulc_1 FROM {tbl} GROUP BY lulc_1 "
"UNION ALL "
"SELECT lulc_2 AS lulc_1 FROM {tbl} GROUP BY lulc_2"
") AS tst_1, ("
"SELECT lulc_1 AS lulc_2 FROM {tbl} GROUP BY lulc_1 "
"UNION ALL "
"SELECT lulc_2 AS lulc_2 FROM {tbl} GROUP BY lulc_2"
") AS tst_2 WHERE lulc_1 = lulc_2 GROUP BY lulc_1, lulc_2"
") AS jtbl ON foo.lulc_1 = jtbl.tmp1 AND foo.lulc_2 = jtbl.tmp2 "
"ORDER BY lulc_1, lulc_2"
"') AS ct("
"lulc_map text, {crossCols}"
")")
TOTAL_AGREE_TABLE = None
TOTAL_AREA_TABLE = None
for f in FLDS_TO_PIVOT:
if not TOTAL_AGREE_TABLE:
TOTAL_AGREE_TABLE = q_to_ntbl(
DB,
"agreement_table",
Q.format(tbl=total_table,
valCol=f,
crossCols=", ".join([
"{} numeric".format(map_) for map_ in mapsNames
])),
api='psql')
else:
TOTAL_AREA_TABLE = q_to_ntbl(DB,
"area_table",
Q.format(tbl=total_table,
valCol=f,
crossCols=", ".join([
"{} numeric".format(map_)
for map_ in mapsNames
])),
api='psql')
# Union Mapping
UNION_MAPPING = pandas.DataFrame(
[[k[0], k[1], fprop(UNION_SHAPE[k], 'fn')] for k in UNION_SHAPE],
columns=['shp_a', 'shp_b', 'union_shp'])
UNION_MAPPING = df_to_db(DB, UNION_MAPPING, 'union_map', api='psql')
# Export Results
TABLES = [UNION_MAPPING, TOTAL_AGREE_TABLE, TOTAL_AREA_TABLE
] + [SYNTH_TBL[x]["MATRIX"] for x in SYNTH_TBL]
SHEETS = ["union_map", "agreement_percentage", "area_with_data_km"] + [
"{}_{}".format(fprop(x[0], 'fn')[:15],
fprop(x[1], 'fn')[:15]) for x in SYNTH_TBL
]
db_to_tbl(DB, ["SELECT * FROM {}".format(x) for x in TABLES],
REPORT,
sheetsNames=SHEETS,
dbAPI='psql')
return REPORT
def merge_dbs(destinationDb, dbs,
tbls_to_merge=None, ignoretbls=None):
"""
Put several database into one
For now works only with PostgreSQL
"""
import os
from glass.pys.oss import fprop, del_file
from glass.ng.sql import psql_cmd
from glass.ng.prop.sql import db_exists, lst_tbl
from glass.ng.sql.db import create_db, drop_db
from glass.ng.sql.tbl import rename_tbl, tbls_to_tbl
from glass.ng.sql.bkup import dump_tbls
from glass.ng.sql.db import restore_tbls
from glass.ng.sql.tbl import distinct_to_table, del_tables
# Prepare database
fdb = fprop(destinationDb, ['fn', 'ff'])
if fdb['fileformat'] != '':
if fdb['fileformat'] == '.sql':
newdb = create_db(fdb['filename'],
overwrite=None, api='psql')
psql_cmd(newdb, destinationDb)
destinationDb = newdb
else:
raise ValueError((
'destinationDb is a file but is not correct. The file must be'
' a SQL Script'
))
else:
if os.path.isdir(destinationDb):
raise ValueError(
'destinationdb is a dir. It must be a DB name or a SQL Script'
)
# Check if destination db exists
if not db_exists(destinationDb):
create_db(destinationDb, overwrite=None, api='psql')
# Check if dbs is a list or a dir
if type(dbs) == list:
dbs = dbs
elif os.path.isdir(dbs):
# list SQL files
from glass.pys.oss import lst_ff
dbs = lst_ff(dbs, file_format='.sql')
else:
raise ValueError(
'''
dbs value should be a list with paths
to sql files or a dir with sql files inside
'''
)
TABLES = {}
for i in range(len(dbs)):
# Check if DB is a file or a db
fp = fprop(dbs[i], ['fn', 'ff'])
DB_NAME = fp['filename']
if fp['fileformat'] == '.sql':
# Create DB
create_db(DB_NAME, overwrite=True, api='psql')
# Restore DB
psql_cmd(DB_NAME, dbs[i])
# List Tables
if not tbls_to_merge:
tbls__ = lst_tbl(DB_NAME, excludeViews=True, api='psql')
tbls = tbls__ if not ignoretbls else [t for t in tbls__ if t not in ignoretbls]
else:
tbls = tbls_to_merge
# Rename Tables
newTbls = rename_tbl(DB_NAME, {tbl : "{}_{}".format(
tbl, str(i)) for tbl in tbls})
for t in range(len(tbls)):
if tbls[t] not in TABLES:
TABLES[tbls[t]] = ["{}_{}".format(tbls[t], str(i))]
else:
TABLES[tbls[t]].append("{}_{}".format(tbls[t], str(i)))
# Dump Tables
SQL_DUMP = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
'tbl_{}.sql'.format(DB_NAME)
); dump_tbls(DB_NAME, newTbls, SQL_DUMP)
# Restore Tables in the destination Database
restore_tbls(destinationDb, SQL_DUMP, newTbls)
# Delete Temp Database
if fp['fileformat'] == '.sql':
drop_db(DB_NAME)
# Delete SQL File
del_file(SQL_DUMP)
# Union of all tables
max_len = max([len(TABLES[t]) for t in TABLES])
for tbl in TABLES:
# Rename original table
NEW_TBL = "{}_{}".format(tbl, max_len)
rename_tbl(destinationDb, {tbl : NEW_TBL})
TABLES[tbl].append(NEW_TBL)
# Union
tbls_to_tbl(destinationDb, TABLES[tbl], tbl + '_tmp')
# Group By
distinct_to_table(destinationDb, tbl + '_tmp', tbl, cols=None)
# Drop unwanted tables
del_tables(destinationDb, TABLES[tbl] + [tbl + '_tmp'])
return destinationDb
def vector_based(osmdata, nomenclature, refRaster, lulcShp,
overwrite=None, dataStore=None, RoadsAPI='POSTGIS'):
"""
Convert OSM Data into Land Use/Land Cover Information
An vector based approach.
TODO: Add a detailed description.
RoadsAPI Options:
* GRASS
* SQLITE
* POSTGIS
"""
# ************************************************************************ #
# Python Modules from Reference Packages #
# ************************************************************************ #
import datetime; import os; import copy
# ************************************************************************ #
# glass dependencies #
# ************************************************************************ #
from glass.pys.oss import fprop, mkdir
from glass.g.wenv.grs import run_grass
if RoadsAPI == 'POSTGIS':
from glass.ng.sql.db import create_db
from glass.g.it.db import osm_to_psql
from glass.ng.sql.db import drop_db
from glass.ng.sql.bkup import dump_db
else:
from glass.g.it.osm import osm_to_sqdb
from glass.ete.osm2lulc.utils import osm_project, add_lulc_to_osmfeat, get_ref_raster
from glass.g.dp.mge import shps_to_shp
from glass.ete.osm2lulc.mod1 import grs_vector
if RoadsAPI == 'SQLITE' or RoadsAPI == 'POSTGIS':
from glass.ete.osm2lulc.mod2 import roads_sqdb
else:
from glass.ete.osm2lulc.mod2 import grs_vec_roads
from glass.ete.osm2lulc.m3_4 import grs_vect_selbyarea
from glass.ete.osm2lulc.mod5 import grs_vect_bbuffer
from glass.ete.osm2lulc.mod6 import vector_assign_pntags_to_build
from glass.g.dp.mge import same_attr_to_shp
from glass.g.prj import def_prj
# ************************************************************************ #
# Global Settings #
# ************************************************************************ #
# Check if input parameters exists!
if not os.path.exists(os.path.dirname(lulcShp)):
raise ValueError('{} does not exist!'.format(os.path.dirname(lulcShp)))
if not os.path.exists(osmdata):
raise ValueError('File with OSM DATA ({}) does not exist!'.format(osmdata))
if not os.path.exists(refRaster):
raise ValueError('File with reference area ({}) does not exist!'.format(refRaster))
# Check if Nomenclature is valid
nomenclature = "URBAN_ATLAS" if nomenclature != "URBAN_ATLAS" and \
nomenclature != "CORINE_LAND_COVER" and \
nomenclature == "GLOBE_LAND_30" else nomenclature
time_a = datetime.datetime.now().replace(microsecond=0)
# Create workspace for temporary files
workspace = os.path.join(os.path.dirname(
lulcShp), 'osmtolulc') if not dataStore else dataStore
# Check if workspace exists
if os.path.exists(workspace):
if overwrite:
mkdir(workspace)
else:
raise ValueError('Path {} already exists'.format(workspace))
else:
mkdir(workspace)
# Get Reference Raster
refRaster, epsg = get_ref_raster(refRaster, workspace, cellsize=10)
from glass.ete.osm2lulc import osmTableData, PRIORITIES, LEGEND
__priorities = PRIORITIES[nomenclature]
__legend = LEGEND[nomenclature]
time_b = datetime.datetime.now().replace(microsecond=0)
if RoadsAPI != 'POSTGIS':
# ******************************************************************** #
# Convert OSM file to SQLITE DB #
# ******************************************************************** #
osm_db = osm_to_sqdb(osmdata, os.path.join(workspace, 'osm.sqlite'))
else:
# Convert OSM file to POSTGRESQL DB #
osm_db = create_db(fprop(
osmdata, 'fn', forceLower=True), overwrite=True)
osm_db = osm_to_psql(osmdata, osm_db)
time_c = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Add Lulc Classes to OSM_FEATURES by rule #
# ************************************************************************ #
add_lulc_to_osmfeat(
osm_db, osmTableData, nomenclature,
api='SQLITE' if RoadsAPI != 'POSTGIS' else RoadsAPI
)
time_d = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Transform SRS of OSM Data #
# ************************************************************************ #
osmTableData = osm_project(
osm_db, epsg,
api='SQLITE' if RoadsAPI != 'POSTGIS' else RoadsAPI,
isGlobeLand=None if nomenclature != 'GLOBE_LAND_30' else True
)
time_e = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Start a GRASS GIS Session #
# ************************************************************************ #
grass_base = run_grass(workspace, grassBIN='grass78', location='grloc', srs=epsg)
#import grass.script as grass
import grass.script.setup as gsetup
gsetup.init(grass_base, workspace, 'grloc', 'PERMANENT')
# ************************************************************************ #
# IMPORT SOME glass MODULES FOR GRASS GIS #
# ************************************************************************ #
from glass.g.gp.ovl import erase
from glass.g.wenv.grs import rst_to_region
from glass.g.gp.gen import dissolve
from glass.g.tbl.grs import add_and_update, reset_table, update_table
from glass.g.tbl.col import add_fields
from glass.g.it.shp import shp_to_grs, grs_to_shp
from glass.g.it.rst import rst_to_grs
# ************************************************************************ #
# SET GRASS GIS LOCATION EXTENT #
# ************************************************************************ #
extRst = rst_to_grs(refRaster, 'extent_raster')
rst_to_region(extRst)
time_f = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# MapResults #
# ************************************************************************ #
osmShps = []
# ************************************************************************ #
# 1 - Selection Rule #
# ************************************************************************ #
ruleOneShp, timeCheck1 = grs_vector(
osm_db, osmTableData['polygons'], apidb=RoadsAPI
)
osmShps.append(ruleOneShp)
time_g = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# 2 - Get Information About Roads Location #
# ************************************************************************ #
ruleRowShp, timeCheck2 = roads_sqdb(
osm_db, osmTableData['lines'], osmTableData['polygons'], apidb=RoadsAPI
) if RoadsAPI == 'SQLITE' or RoadsAPI == 'POSTGIS' else grs_vec_roads(
osm_db, osmTableData['lines'], osmTableData['polygons'])
osmShps.append(ruleRowShp)
time_h = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# 3 - Area Upper than #
# ************************************************************************ #
if nomenclature != "GLOBE_LAND_30":
ruleThreeShp, timeCheck3 = grs_vect_selbyarea(
osm_db, osmTableData['polygons'], UPPER=True, apidb=RoadsAPI
)
osmShps.append(ruleThreeShp)
time_l = datetime.datetime.now().replace(microsecond=0)
else:
timeCheck3 = None
time_l = None
# ************************************************************************ #
# 4 - Area Lower than #
# ************************************************************************ #
if nomenclature != "GLOBE_LAND_30":
ruleFourShp, timeCheck4 = grs_vect_selbyarea(
osm_db, osmTableData['polygons'], UPPER=False, apidb=RoadsAPI
)
osmShps.append(ruleFourShp)
time_j = datetime.datetime.now().replace(microsecond=0)
else:
timeCheck4 = None
time_j = None
# ************************************************************************ #
# 5 - Get data from lines table (railway | waterway) #
# ************************************************************************ #
ruleFiveShp, timeCheck5 = grs_vect_bbuffer(
osm_db, osmTableData["lines"], api_db=RoadsAPI
)
osmShps.append(ruleFiveShp)
time_m = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# 7 - Assign untagged Buildings to tags #
# ************************************************************************ #
if nomenclature != "GLOBE_LAND_30":
ruleSeven11, ruleSeven12, timeCheck7 = vector_assign_pntags_to_build(
osm_db, osmTableData['points'], osmTableData['polygons'],
apidb=RoadsAPI
)
if ruleSeven11:
osmShps.append(ruleSeven11)
if ruleSeven12:
osmShps.append(ruleSeven12)
time_n = datetime.datetime.now().replace(microsecond=0)
else:
timeCheck7 = None
time_n = datetime.datetime.now().replace(microsecond=0)
# ************************************************************************ #
# Produce LULC Map #
# ************************************************************************ #
"""
Get Shps with all geometries related with one class - One Shape for Classe
"""
_osmShps = []
for i in range(len(osmShps)):
if not osmShps[i]: continue
_osmShps.append(grs_to_shp(
osmShps[i], os.path.join(workspace, osmShps[i] + '.shp'),
'auto', lyrN=1, asCMD=True, asMultiPart=None
))
for shp in _osmShps:
def_prj(os.path.splitext(shp)[0] + '.prj', epsg=epsg, api='epsgio')
_osmShps = same_attr_to_shp(
_osmShps, "cat", workspace, "osm_", resultDict=True
)
del osmShps
time_o = datetime.datetime.now().replace(microsecond=0)
"""
Merge all Classes into one feature class using a priority rule
"""
osmShps = {}
for cls in _osmShps:
if cls == '1':
osmShps[1221] = shp_to_grs(_osmShps[cls], "osm_1221", asCMD=True)
else:
osmShps[int(cls)] = shp_to_grs(_osmShps[cls], "osm_" + cls,
asCMD=True)
# Erase overlapping areas by priority
osmNameRef = copy.deepcopy(osmShps)
for e in range(len(__priorities)):
if e + 1 == len(__priorities): break
if __priorities[e] not in osmShps:
continue
else:
for i in range(e+1, len(__priorities)):
if __priorities[i] not in osmShps:
continue
else:
osmShps[__priorities[i]] = erase(
osmShps[__priorities[i]], osmShps[__priorities[e]],
"{}_{}".format(osmNameRef[__priorities[i]], e),
notTbl=True, api='pygrass'
)
time_p = datetime.datetime.now().replace(microsecond=0)
# Export all classes
lst_merge = []
a = None
for i in range(len(__priorities)):
if __priorities[i] not in osmShps:
continue
if not a:
reset_table(
osmShps[__priorities[i]],
{'cls' : 'varchar(5)', 'leg' : 'varchar(75)'},
{'cls' : str(__priorities[i]), 'leg' : str(__legend[__priorities[i]])}
)
a = 1
else:
add_and_update(
osmShps[__priorities[i]],
{'cls' : 'varchar(5)'},
{'cls' : str(__priorities[i])}
)
ds = dissolve(
osmShps[__priorities[i]],
'dl_{}'.format(str(__priorities[i])), 'cls', api="grass"
)
add_fields(ds, {'leg': 'varchar(75)'}, api="grass")
update_table(ds, 'leg', str(__legend[__priorities[i]]), 'leg is null')
lst_merge.append(grs_to_shp(
ds, os.path.join(
workspace, "lulc_{}.shp".format(str(__priorities[i]))
), 'auto', lyrN=1, asCMD=True, asMultiPart=None
))
time_q = datetime.datetime.now().replace(microsecond=0)
if fprop(lulcShp, 'ff') != '.shp':
lulcShp = os.path.join(
os.path.dirname(lulcShp), fprop(lulcShp, 'fn') + '.shp')
shps_to_shp(lst_merge, lulcShp, api='pandas')
# Check if prj of lulcShp exists and create it if necessary
prj_ff = os.path.splitext(lulcShp)[0] + '.prj'
if not os.path.exists(prj_ff):
def_prj(prj_ff, epsg=epsg, api='epsgio')
time_r = datetime.datetime.now().replace(microsecond=0)
# Dump Database if PostGIS was used
# Drop Database if PostGIS was used
if RoadsAPI == 'POSTGIS':
dump_db(osm_db, os.path.join(
workspace, osm_db + '.sql'
), api='psql')
drop_db(osm_db)
return lulcShp, {
0 : ('set_settings', time_b - time_a),
1 : ('osm_to_sqdb', time_c - time_b),
2 : ('cls_in_sqdb', time_d - time_c),
3 : ('proj_data', time_e - time_d),
4 : ('set_grass', time_f - time_e),
5 : ('rule_1', time_g - time_f, timeCheck1),
6 : ('rule_2', time_h - time_g, timeCheck2),
7 : None if not timeCheck3 else ('rule_3', time_l - time_h, timeCheck3),
8 : None if not timeCheck4 else ('rule_4', time_j - time_l, timeCheck4),
9 : ('rule_5',
time_m - time_j if timeCheck4 else time_m - time_h, timeCheck5),
10 : None if not timeCheck7 else ('rule_7', time_n - time_m, timeCheck7),
11 : ('disj_cls', time_o - time_n),
12 : ('priority_rule', time_p - time_o),
13 : ('export_cls', time_q - time_p),
14 : ('merge_cls', time_r - time_q)
}
