def tbl_fromdb_todb(from_db, to_db, tables, qForTbl=None, api='pandas'):
"""
Send PGSQL Tables from one database to other
"""
from gasp.pyt import obj_to_lst
api = 'pandas' if api != 'pandas' and api != 'psql' else api
tables = obj_to_lst(tables)
if api == 'pandas':
from gasp.sql.fm import q_to_obj
for table in tables:
if not qForTbl:
tblDf = q_to_obj(from_db, "SELECT * FROM {}".format(
table), db_api='psql')
else:
if table not in qForTbl:
tblDf = q_to_obj(from_db, "SELECT * FROM {}".format(
table), db_api='psql')
else:
tblDf = q_to_obj(from_db, qForTbl[table], db_api='psql')
df_to_db(to_db, tblDf, table, api='psql')
else:
import os
from gasp.pyt.oss import mkdir, del_folder
from gasp.sql.fm import dump_tbls
from gasp.sql.to import restore_tbls
tmpFolder = mkdir(
os.path.dirname(os.path.abspath(__file__)), randName=True
)
# Dump
sqlScript = dump_tbls(from_db, tables, os.path.join(
tmpFolder, "tables_data.sql"
))
# Restore
restore_tbls(to_db, sqlScript, tables)
del_folder(tmpFolder)
def split_table_by_col_distinct(db, tbl, col):
"""
Create a new table for each value in one column
"""
from gasp.sql.fm import q_to_obj
from gasp.sql.i import cols_type
from gasp.sql.to import q_to_ntbl
fields_types = cols_type(db, tbl)
# Get unique values
VALUES = q_to_obj(db,
"SELECT {col} FROM {t} GROUP BY {col}".format(
col=col, t=tbl
), db_api='psql'
)[col].tolist()
whr = '{}=\'{}\'' if fields_types[col] == str else '{}={}'
for row in VALUES:
q_to_ntbl(
db, '{}_{}'.format(tbl, str(row[0])),
"SELECT * FROM {} WHERE {}".format(
tbl, whr.format(col, str(row[0]))
), api='psql')
def thereIsRowsSameTimeInt(row):
whr = []
for c in COLS:
if COLS_TYPE[c] == str:
whr.append("{}='{}'".format(c, row[c]))
else:
whr.append("{}={}".format(c, row[c]))
hourRows = q_to_obj(db,
"SELECT {} FROM {} WHERE {}".format(
hour_decimal_field, table, " AND ".join(whr)),
db_api='psql')[hour_decimal_field].tolist()
for i in range(len(hourRows)):
for e in range(i + 1, len(hourRows)):
dif = abs(hourRows[i][0] - hourRows[e][0])
if dif < TIME_TOLERANCE:
break
if dif < TIME_TOLERANCE:
break
if dif < TIME_TOLERANCE:
row['time_difference'] = 1
else:
row['time_difference'] = 0
return row
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
"""
import os
from gasp.sql.fm import q_to_obj
from gasp.sql.i import lst_tbl
from gasp.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 bar_chart_by_query(db, query, xaxis_col, chart_data_fld_name, series,
outxls):
"""
Execute some query for each serie in series.
Produce a bar chart with the data returned.
Each serie will be new bar in the chart.
So, the query statment must have a white space
to put each serie in series... E.g.:
"SELECT fld_name FROM t WHERE series_fld={}".format(serie in series)
"""
from gasp.sql.fm import q_to_obj
from gasp.pyt.df.joins import combine_dfs
from gasp.pyt.xls.charts import barchart_in_xls_from_df
dataDf = []
for serie in series:
data = q_to_obj(db, query.format(serie), db_api='psql')
data.rename(columns={chart_data_fld_name: serie}, inplace=True)
dataDf.append(data)
main_df = dataDf[0]
main_df = combine_dfs(main_df, dataDf[1:], xaxis_col)
outxls = barchart_in_xls_from_df(main_df, outxls, series, xaxis_col)
return outxls
def apndtbl_in_otherdb(db_a, db_b, tblA, tblB, mapCols,
geomCol=None, srsEpsg=None):
"""
Append data of one table to another table in other database.
"""
from gasp.sql.fm import q_to_obj
if geomCol and srsEpsg:
df = q_to_obj(db_a, "SELECT {} FROM {}".format(
", ".join(list(mapCols.keys())), tblA
), db_api='psql', geomCol=geomCol, epsg=srsEpsg)
else:
df = q_to_obj(db_b, "SELECT {} FROM {}".format(
", ".join(list(mapCols.keys())), tblA
), db_api='psql', geomCol=None, epsg=None)
# Change Names
df.rename(columns=mapCols, inplace=True)
if geomCol:
for k in mapCols:
if geomCol == k:
geomCol = mapCols[k]
break
# Get Geom Type
# Send data to other database
if geomCol and srsEpsg:
from gasp.gt.prop.feat import get_gtype
gType = get_gtype(df, geomCol=geomCol, gisApi='pandas')
df_to_db(
db_b, df, tblB, append=True, api='psql', epsg=srsEpsg,
geomType=gType, colGeom=geomCol
)
else:
df_to_db(db_b, df, tblB, append=True, api='psql')
return tblB
def tbl_geomtype(db, table, geomCol='geom'):
"""
Return the number of geometry types in table
"""
from gasp.sql.fm import q_to_obj
return int(q_to_obj(db, (
"SELECT COUNT(*) AS row_count FROM ("
"SELECT ST_GeometryType((ST_Dump({})).geom) AS cnt_geom "
"FROM {} GROUP BY ST_GeometryType((ST_Dump({})).geom)"
") AS foo"
).format(geomCol, table, geomCol), db_api='psql').iloc[0].row_count)
def split_table_entity_number(db, table, entity_field, entity_number):
"""
Split tables in several using as reference a number of entities per table
If a table has 1 000 000 entities and the entity_number is 250 000,
this method will create four tables, each one with 250 000 entities.
250 000 entities, not rows. Don't forget that the main table may have
more than one reference to the same entity.
"""
import pandas
from gasp.sql.fm import q_to_obj
from gasp.sql.i import cols_type
from gasp.sql.to import q_to_ntbl
# Select entities in table
entities = q_to_obj(db, "SELECT {c} FROM {t} GROUP BY {c}".format(
c=entity_field, t=table
), db_api='psql')
# Split entities into groups acoording entity_number
entityGroup = []
lower = 0
high = entity_number
while lower <= len(entities.index):
if high > len(entities.index):
high = len(entities.index)
entityGroup.append(entities.iloc[lower : high])
lower += entity_number
high += entity_number
# For each dataframe, create a new table
COLS_TYPE = cols_type(db, table)
c = 0
for df in entityGroup:
if COLS_TYPE[entity_field] != str:
df[entity_field] = '{}='.format(entity_field) + df[entity_field].astype(str)
else:
df[entity_field] = '{}=\''.format(entity_field) + df[entity_field].astype(str) + '\''
whr = ' OR '.join(df[entity_field])
q_to_ntbl(db, '{}_{}'.format(table, str(c)), (
"SELECT * FROM {} WHERE {}"
).format(table, whr), api='psql')
c += 1
def build12_torst(buildTbl):
LulcCls = q_to_obj(
osmdb,
"SELECT cls FROM {} GROUP BY cls".format(buildTbl),
db_api='psql' if apidb == 'POSTGIS' else 'sqlite').cls.tolist()
for lulc_cls in LulcCls:
time_m = dt.datetime.now().replace(microsecond=0)
# To SHP
if apidb == 'SQLITE':
shpB = sel_by_attr(osmdb,
"SELECT * FROM {} WHERE cls={}".format(
buildTbl, str(lulc_cls)),
os.path.join(
folder,
'nshp_build_{}.shp'.format(lulc_cls)),
api_gis='ogr')
else:
shpB = sel_by_attr(osmdb,
"SELECT * FROM {} WHERE cls={}".format(
buildTbl, str(lulc_cls)),
"geometry",
os.path.join(
folder,
'nshp_build_{}.shp'.format(lulc_cls)),
api='pgsql2shp',
tableIsQuery=True)
time_n = dt.datetime.now().replace(microsecond=0)
# To RST
brst = shp_to_rst(shpB,
None,
cells,
0,
os.path.join(
folder,
'nrst_build_{}.tif'.format(lulc_cls)),
srscode,
rstT,
api='gdal')
time_o = dt.datetime.now().replace(microsecond=0)
resLyr[int(lulc_cls)] = [brst]
timeGasto[int(lulc_cls)] = ('to_shp_{}'.format(str(lulc_cls)),
time_n - time_m)
timeGasto[int(lulc_cls) + 1] = ('to_rst_n_{}'.format(
str(lulc_cls)), time_o - time_n)
def count_entity_periods_with_certain_duration(db,
PERIOD_INTERVAL,
PGTABLE,
TIME_FIELD,
ENTITY_FIELD,
OUT_TABLE,
filterWhere=None):
"""
Count rows in a pgtable for a given period of X minutes for each
interest entity
PERIOD_INTERVAL = "01:00:00"
"""
import pandas
from gasp.pyt.tm import day_to_intervals2
from gasp.pyt.df.joins import combine_dfs
# Get Intervals
INTERVALS = day_to_intervals2(PERIOD_INTERVAL)
# For each interval/period, count the number of rows by entity
counting = []
for _int in INTERVALS:
Q = ("SELECT {entityCol}, COUNT({entityCol}) AS {cntCol} "
"FROM {table} WHERE "
"TO_TIMESTAMP({timeCol}, 'HH24:MI:SS') >= "
"TO_TIMESTAMP('{minLower}', 'HH24:MI:SS') AND "
"TO_TIMESTAMP({timeCol}, 'HH24:MI:SS') < "
"TO_TIMESTAMP('{minUpper}', 'HH24:MI:SS'){whr} "
"GROUP BY {entityCol}").format(cntCol="s{}_e{}".format(
_int[0][:5], _int[1][:5]).replace(":", "_"),
table=PGTABLE,
timeCol=TIME_FIELD,
entityCol=ENTITY_FIELD,
minLower=_int[0],
minUpper=_int[1],
whr="" if not filterWhere else
" AND ({}) ".format(filterWhere))
count = q_to_obj(db, Q, db_api='psql')
counting.append(count)
mainDf = combine_dfs(counting[0], counting[1:], ENTITY_FIELD)
obj_to_tbl(mainDf, OUT_TABLE)
return OUT_TABLE
def distinct_val(db, pgtable, column):
"""
Get distinct values in one column of one pgtable
"""
from gasp.pyt import obj_to_lst
from gasp.sql.fm import q_to_obj
data = q_to_obj(db,
"SELECT {col} FROM {t} GROUP BY {col};".format(
col=", ".join(obj_to_lst(column)), t=pgtable
), db_api='psql'
).to_dict(orient="records")
return data
def check_last_id(db, pk, table):
"""
Check last ID of a given table
return 0 if there is no data
"""
from gasp.sql.fm import q_to_obj
q = "SELECT MAX({}) AS fid FROM {}".format(pk, table)
d = q_to_obj(db, q, db_api='psql').fid.tolist()
if not d[0]:
return 0
else:
return d[0]
def grs_rst(db, polyTbl, api='SQLITE'):
"""
Simple selection, convert result to Raster
"""
import datetime
from gasp.sql.fm import q_to_obj
from gasp.gt.toshp.db import dbtbl_to_shp as db_to_grs
from gasp.gt.torst import shp_to_rst
# Get Classes
time_a = datetime.datetime.now().replace(microsecond=0)
lulcCls = q_to_obj(db, (
"SELECT selection FROM {} "
"WHERE selection IS NOT NULL "
"GROUP BY selection"
).format(polyTbl), db_api='psql' if api == 'POSTGIS' else 'sqlite').selection.tolist()
time_b = datetime.datetime.now().replace(microsecond=0)
timeGasto = {0 : ('check_cls', time_b - time_a)}
# Import data into GRASS and convert it to raster
clsRst = {}
tk = 1
for cls in lulcCls:
time_x = datetime.datetime.now().replace(microsecond=0)
grsVect = db_to_grs(
db, polyTbl, "geometry", "rule1_{}".format(str(cls)),
inDB='psql' if api == 'POSTGIS' else 'sqlite',
where="selection = {}".format(str(cls)), notTable=True,
filterByReg=True, outShpIsGRASS=True
)
time_y = datetime.datetime.now().replace(microsecond=0)
grsRst = shp_to_rst(
grsVect, int(cls), None, None, "rst_rule1_{}".format(str(cls)),
api='grass'
)
time_z = datetime.datetime.now().replace(microsecond=0)
clsRst[int(cls)] = grsRst
timeGasto[tk] = ('import_{}'.format(cls), time_y - time_x)
timeGasto[tk+1] = ('torst_{}'.format(cls), time_z - time_y)
tk += 2
return clsRst, timeGasto
def count_by_periods_with_certain_duration(db,
PERIOD_INTERVAL,
pgtable,
TIME_FIELD,
outTable,
filterWhere=None):
"""
Count rows in a pgtable by periods of X minutes
PERIOD_INTERVAL = "01:00:00"
"""
import pandas
from gasp.pyt.tm import day_to_intervals2
# Get Intervals
INTERVALS = day_to_intervals2(PERIOD_INTERVAL)
# For each interval/period, count the number of rows
counting = None
for _int_ in INTERVALS:
QUERY = ("SELECT COUNT(*) AS count FROM {table} WHERE "
"TO_TIMESTAMP({timeCol}, 'HH24:MI:SS') >= "
"TO_TIMESTAMP('{minLower}', 'HH24:MI:SS') AND "
"TO_TIMESTAMP({timeCol}, 'HH24:MI:SS') < "
"TO_TIMESTAMP('{minUpper}', 'HH24:MI:SS'){whr}").format(
table=pgtable,
timeCol=TIME_FIELD,
minLower=_int_[0],
minUpper=_int_[1],
whr=""
if not filterWhere else " AND ({})".format(filterWhere))
count = q_to_obj(db, QUERY, db_api='psql')
count.rename(index={0: "{}-{}".format(_int_[0][:5], _int_[1][:5])},
inplace=True)
if type(counting) != pandas.DataFrame:
counting = count.copy()
else:
counting = counting.append(count, ignore_index=False)
obj_to_tbl(counting, outTable)
return outTable
def show_duplicates_in_xls(db_name, table, pkCols, outFile, tableIsQuery=None):
"""
Find duplicates and write these objects in a table
"""
import pandas
from gasp.pyt import obj_to_lst
from gasp.sql.fm import q_to_obj
from gasp.to import obj_to_tbl
pkCols = obj_to_lst(pkCols)
if not pkCols:
raise ValueError("pkCols value is not valid")
if not tableIsQuery:
q = ("SELECT {t}.* FROM {t} INNER JOIN ("
"SELECT {cls}, COUNT({cnt}) AS conta FROM {t} "
"GROUP BY {cls}"
") AS foo ON {rel} "
"WHERE conta > 1").format(t=table,
cls=", ".join(pkCols),
cnt=pkCols[0],
rel=" AND ".join([
"{t}.{c} = foo.{c}".format(t=table,
c=col)
for col in pkCols
]))
else:
q = ("SELECT foo.* FROM ({q_}) AS foo INNER JOIN ("
"SELECT {cls}, COUNT({cnt}) AS conta "
"FROM ({q_}) AS foo2 GROUP BY {cls}"
") AS jt ON {rel} "
"WHERE conta > 1").format(q_=table,
cls=", ".join(pkCols),
cnt=pkCols[0],
rel=" AND ".join([
"foo.{c} = jt.{c}".format(c=x)
for x in pkCols
]))
data = q_to_obj(db_name, q, db_api='psql')
obj_to_tbl(data, outFile)
return outFile
def row_num(db, table, where=None, api='psql'):
"""
Return the number of rows in Query
API's Available:
* psql;
* sqlite;
"""
from gasp.sql.fm import q_to_obj
if not table.startswith('SELECT '):
Q = "SELECT COUNT(*) AS nrows FROM {}{}".format(
table, "" if not where else " WHERE {}".format(where))
else:
Q = "SELECT COUNT(*) AS nrows FROM ({}) AS foo".format(table)
d = q_to_obj(db, Q, db_api=api)
return int(d.iloc[0].nrows)
def cols_name(dbname, table, sanitizeSpecialWords=True, api='psql'):
"""
Return the columns names of a table in one Database
"""
if api == 'psql':
c = sqlcon(dbname, sqlAPI='psql')
cursor = c.cursor()
cursor.execute("SELECT * FROM {} LIMIT 1;".format(table))
colnames = [desc[0] for desc in cursor.description]
if sanitizeSpecialWords:
from gasp.cons.psql import PG_SPECIAL_WORDS
for i in range(len(colnames)):
if colnames[i] in PG_SPECIAL_WORDS:
colnames[i] = '"{}"'.format(colnames[i])
elif api == 'sqlite':
import sqlite3
con = sqlite3.connect(dbname)
cursor = con.execute("SELECT * FROM {} LIMIT 1".format(table))
colnames = list(map(lambda x: x[0], cursor.description))
elif api == 'mysql':
from gasp.sql.fm import q_to_obj
data = q_to_obj(dbname,
"SELECT * FROM {} LIMIT 1".format(table),
db_api='mysql')
colnames = data.columns.values
else:
raise ValueError('API {} is not available'.format(api))
return colnames
def lst_views(db, schema='public', basename=None):
"""
List Views in database
"""
from gasp.pyt import obj_to_lst
from gasp.sql.fm import q_to_obj
basename = obj_to_lst(basename)
basenameStr = "" if not basename else "{}".format(" OR ".join(
["{} LIKE '%%{}%%'".format("table_name", b) for b in basename]))
views = q_to_obj(
db,
("SELECT table_name FROM information_schema.views "
"WHERE table_schema='{}'{}").format(
schema, "" if not basename else " AND ({})".format(basenameStr)),
db_api='psql')
return views.table_name.tolist()
def tbl_ext(db, table, geomCol):
"""
Return extent of the geometries in one pgtable
"""
from gasp.sql.fm import q_to_obj
q = (
"SELECT MIN(ST_X(pnt_geom)) AS eleft, MAX(ST_X(pnt_geom)) AS eright, "
"MIN(ST_Y(pnt_geom)) AS bottom, MAX(ST_Y(pnt_geom)) AS top "
"FROM ("
"SELECT (ST_DumpPoints({geomcol})).geom AS pnt_geom "
"FROM {tbl}"
") AS foo"
).format(tbl=table, geomcol=geomCol)
ext = q_to_obj(db, q, db_api='psql').to_dict(orient='index')[0]
return [
ext['eleft'], ext['bottom'], ext['eright'], ext['top']
]
def get_osm_feat_by_rule(nomenclature):
"""
Return OSM Features By rule
"""
from gasp.sql.fm import q_to_obj
Q = (
"SELECT jtbl.{rellulccls} AS {rellulccls}, "
"{osmfeat}.{key} AS {key}, {osmfeat}.{val} AS {val}, "
"jtbl.{ruleName} AS {ruleName}, jtbl.{bufferCol} "
"AS {bufferCol}, jtbl.{areaCol} AS {areaCol} "
"FROM {osmfeat} INNER JOIN ("
"SELECT {osmrel}.{relosmid}, {osmrel}.{rellulccls}, "
"{rules}.{ruleID}, {rules}.{ruleName}, "
"{osmrel}.{bufferCol}, {osmrel}.{areaCol} "
"FROM {osmrel} "
"INNER JOIN {rules} ON {osmrel}.{_ruleID} = {rules}.{ruleID} "
") AS jtbl ON {osmfeat}.{osmid} = jtbl.{relosmid}"
).format(
osmfeat = DB_SCHEMA["OSM_FEATURES"]["NAME"],
osmid = DB_SCHEMA["OSM_FEATURES"]["OSM_ID"],
key = DB_SCHEMA["OSM_FEATURES"]["OSM_KEY"],
val = DB_SCHEMA["OSM_FEATURES"]["OSM_VALUE"],
osmrel = DB_SCHEMA[nomenclature]["OSM_RELATION"],
relosmid = DB_SCHEMA[nomenclature]["OSM_FK"],
rellulccls = DB_SCHEMA[nomenclature]["CLS_FK"],
_ruleID = DB_SCHEMA[nomenclature]["RULE_FK"],
rules = DB_SCHEMA["RULES"]["NAME"],
ruleID = DB_SCHEMA["RULES"]["RULE_ID"],
ruleName = DB_SCHEMA["RULES"]["RULE_NAME"],
bufferCol = DB_SCHEMA[nomenclature]["RULES_FIELDS"]["BUFFER"],
areaCol = DB_SCHEMA[nomenclature]["RULES_FIELDS"]["AREA"]
)
return q_to_obj(PROCEDURE_DB, Q, db_api='sqlite')
def get_not_used_tags(OSM_FILE, OUT_TBL):
"""
Use a file OSM to detect tags not considered in the
OSM2LULC procedure
"""
import os
from gasp.to import obj_to_tbl
from gasp.gt.attr import sel_by_attr
from gasp.sql.fm import q_to_obj
from gasp.pyt.df.split import df_split
from gasp.pyt.oss import fprop
from gasp.gt.toshp.osm import osm_to_gpkg
OSM_TAG_MAP = {
"DB":
os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
'osmtolulc.sqlite'),
"OSM_FEAT":
"osm_features",
"KEY_COL":
"key",
"VALUE_COL":
"value",
"GEOM_COL":
"geom"
}
WORKSPACE = os.path.dirname(OUT_TBL)
sqdb = osm_to_gpkg(
OSM_FILE, os.path.join(WORKSPACE,
fprop(OSM_FILE, 'fn') + '.gpkg'))
# Get Features we are considering
ourOSMFeatures = q_to_obj(
OSM_TAG_MAP["DB"],
("SELECT {key} AS key_y, {value} AS value_y, {geom} AS geom_y "
"FROM {tbl}").format(key=OSM_TAG_MAP["KEY_COL"],
value=OSM_TAG_MAP["VALUE_COL"],
geom=OSM_TAG_MAP["GEOM_COL"],
tbl=OSM_TAG_MAP["OSM_FEAT"]),
db_api='sqlite')
# Get Features in File
TABLES_TAGS = {
'points': ['highway', 'man_made', 'building'],
'lines':
['highway', 'waterway', 'aerialway', 'barrier', 'man_made', 'railway'],
'multipolygons': [
'aeroway', 'amenity', 'barrier', 'building', 'craft', 'historic',
'land_area', ''
'landuse', 'leisure', 'man_made', 'military', 'natural', 'office',
'place', 'shop', 'sport', 'tourism', 'waterway', 'power',
'railway', 'healthcare', 'highway'
]
}
Qs = [
" UNION ALL ".join([(
"SELECT '{keycol}' AS key, {keycol} AS value, "
"'{geomtype}' AS geom FROM {tbl} WHERE "
"{keycol} IS NOT NULL"
).format(
keycol=c, geomtype='Point' if table == 'points' else 'Line' \
if table == 'lines' else 'Polygon',
tbl=table
) for c in TABLES_TAGS[table]]) for table in TABLES_TAGS
]
fileOSMFeatures = q_to_obj(sqdb,
("SELECT key, value, geom FROM ({}) AS foo "
"GROUP BY key, value, geom").format(
" UNION ALL ".join(Qs)),
db_api='sqlite')
_fileOSMFeatures = fileOSMFeatures.merge(
ourOSMFeatures,
how='outer',
left_on=["key", "value", "geom"],
right_on=["key_y", "value_y", "geom_y"])
# Select OSM Features of file without correspondence
_fileOSMFeatures["isnew"] = _fileOSMFeatures.key_y.fillna(value='nenhum')
newTags = _fileOSMFeatures[_fileOSMFeatures.isnew == 'nenhum']
newTags["value"] = newTags.value.str.replace("'", "''")
newTags["whr"] = newTags.key + "='" + newTags.value + "'"
# Export tags not being used to new shapefile
def to_regular_str(row):
san_str = row.whr
row["whr_san"] = san_str
return row
for t in TABLES_TAGS:
if t == 'points':
filterDf = newTags[newTags.geom == 'Point']
elif t == 'lines':
filterDf = newTags[newTags.geom == 'Line']
elif t == 'multipolygons':
filterDf = newTags[newTags.geom == 'Polygon']
if filterDf.shape[0] > 500:
dfs = df_split(filterDf, 500, nrows=True)
else:
dfs = [filterDf]
Q = "SELECT * FROM {} WHERE {}".format(
t, filterDf.whr.str.cat(sep=" OR "))
i = 1
for df in dfs:
fn = t + '.shp' if len(dfs) == 1 else '{}_{}.shp'.format(t, str(i))
try:
shp = sel_by_attr(sqdb,
Q.format(t, df.whr.str.cat(sep=" OR ")),
os.path.join(WORKSPACE, fn),
api_gis='ogr')
except:
__df = df.apply(lambda x: to_regular_str(x), axis=1)
shp = sel_by_attr(sqdb,
Q.format(t, __df.whr.str.cat(sep=" OR ")),
os.path.join(WORKSPACE, fn))
i += 1
# Export OUT_TBL with tags not being used
newTags.drop(['key_y', 'value_y', 'geom_y', 'isnew', 'whr'],
axis=1,
inplace=True)
obj_to_tbl(newTags, OUT_TBL, sheetsName="new_tags", sanitizeUtf8=True)
return OUT_TBL
def basic_buffer(osmdb, lineTable, dataFolder, apidb='SQLITE'):
"""
Data from Lines table to Polygons using a basic buffering stratagie
"""
import datetime
from gasp.sql.fm import q_to_obj
if apidb == 'POSTGIS':
from gasp.gql.prox import st_buffer
else:
from gasp.gql.prox import splite_buffer as st_buffer
from gasp.gt.torst import shp_to_rst
from gasp.gt.toshp.cff import shp_to_grs
time_a = datetime.datetime.now().replace(microsecond=0)
lulcCls = q_to_obj(
osmdb,
("SELECT basic_buffer FROM {} WHERE basic_buffer IS NOT NULL "
"GROUP BY basic_buffer").format(lineTable),
db_api='psql'
if apidb == 'POSTGIS' else 'sqlite').basic_buffer.tolist()
time_b = datetime.datetime.now().replace(microsecond=0)
timeGasto = {0: ('check_cls', time_b - time_a)}
clsRst = {}
tk = 1
for cls in lulcCls:
# Run BUFFER Tool
time_x = datetime.datetime.now().replace(microsecond=0)
bb_file = st_buffer(osmdb,
lineTable,
"bf_basic_buffer",
"geometry",
os.path.join(
dataFolder,
'bb_rule5_{}.shp'.format(str(int(cls)))),
whrClause="basic_buffer={}".format(str(int(cls))),
outTblIsFile=True,
dissolve="ALL",
cols_select="basic_buffer")
time_y = datetime.datetime.now().replace(microsecond=0)
# Data TO GRASS
grsVect = shp_to_grs(bb_file,
"bb_{}".format(int(cls)),
asCMD=True,
filterByReg=True)
time_z = datetime.datetime.now().replace(microsecond=0)
# Data to Raster
rstVect = shp_to_rst(grsVect,
int(cls),
None,
None,
"rbb_{}".format(int(cls)),
api="grass")
time_w = datetime.datetime.now().replace(microsecond=0)
clsRst[int(cls)] = rstVect
timeGasto[tk] = ('do_buffer_{}'.format(cls), time_y - time_x)
timeGasto[tk + 1] = ('import_{}'.format(cls), time_z - time_y)
timeGasto[tk + 2] = ('torst_{}'.format(cls), time_w - time_z)
tk += 3
return clsRst, timeGasto
def num_base_buffer(osmdb,
lineTbl,
folder,
cells,
srscode,
rtemplate,
api='SQLITE'):
"""
Data from Lines to Polygons
"""
import datetime
from threading import Thread
from gasp.sql.fm import q_to_obj
if api == 'SQLITE':
from gasp.gql.prox import splite_buffer as st_buffer
else:
from gasp.gql.prox import st_buffer
from gasp.gt.torst import shp_to_rst
# Get LULC Classes to be selected
time_a = datetime.datetime.now().replace(microsecond=0)
lulcCls = q_to_obj(
osmdb,
("SELECT basic_buffer FROM {} WHERE basic_buffer IS NOT NULL "
"GROUP BY basic_buffer").format(lineTbl),
db_api='psql' if api == 'POSTGIS' else 'sqlite').basic_buffer.tolist()
time_b = datetime.datetime.now().replace(microsecond=0)
timeGasto = {0: ('check_cls', time_b - time_a)}
clsRst = {}
def exportAndBufferB(CLS, cnt):
# Run BUFFER Tool
time_x = datetime.datetime.now().replace(microsecond=0)
bb_file = st_buffer(osmdb,
lineTbl,
"bf_basic_buffer",
"geometry",
os.path.join(
folder,
'bb_rule5_{}.shp'.format(str(int(CLS)))),
whrClause="basic_buffer={}".format(str(int(CLS))),
outTblIsFile=True,
dissolve=None,
cols_select="basic_buffer")
time_y = datetime.datetime.now().replace(microsecond=0)
# To raster
rstCls = shp_to_rst(bb_file,
None,
cells,
0,
os.path.join(folder,
'rst_bbfr_{}.tif'.format(CLS)),
epsg=srscode,
rst_template=rtemplate,
api='gdal')
time_z = datetime.datetime.now().replace(microsecond=0)
clsRst[CLS] = rstCls
timeGasto[cnt + 1] = ('buffer_{}'.format(str(CLS)), time_y - time_x)
timeGasto[cnt + 2] = ('torst_{}'.format(str(CLS)), time_z - time_y)
thrds = [
Thread(name="r5-{}".format(lulcCls[i]),
target=exportAndBufferB,
args=(lulcCls[i], (i + 1) * 10)) for i in range(len(lulcCls))
]
for t in thrds:
t.start()
for t in thrds:
t.join()
return clsRst, timeGasto
def num_selection(osmdb, polyTbl, folder,
cellsize, srscode, rstTemplate, api='SQLITE'):
"""
Select and Convert to Raster
"""
import datetime; import os
from threading import Thread
if api == 'SQLITE':
from gasp.gt.attr import sel_by_attr
else:
from gasp.gt.toshp.db import dbtbl_to_shp as sel_by_attr
from gasp.sql.fm import q_to_obj
from gasp.gt.torst import shp_to_rst
# Get classes in data
time_a = datetime.datetime.now().replace(microsecond=0)
classes = q_to_obj(osmdb, (
"SELECT selection FROM {} "
"WHERE selection IS NOT NULL "
"GROUP BY selection"
).format(
polyTbl
), db_api='psql' if api == 'POSTGIS' else 'sqlite').selection.tolist()
time_b = datetime.datetime.now().replace(microsecond=0)
timeGasto = {0 : ('check_cls', time_b - time_a)}
# Select and Export
clsRst = {}
SQL_Q = "SELECT {lc} AS cls, geometry FROM {tbl} WHERE selection={lc}"
def FilterAndExport(CLS, cnt):
time_x = datetime.datetime.now().replace(microsecond=0)
if api == 'SQLITE':
shp = sel_by_attr(
osmdb, SQL_Q.format(lc=str(CLS), tbl=polyTbl),
os.path.join(folder, 'sel_{}.shp'.format(str(CLS))),
api_gis='ogr'
)
else:
shp = sel_by_attr(
osmdb, SQL_Q.format(lc=str(CLS), tbl=polyTbl), "geometry",
os.path.join(folder, 'sel_{}.shp'.format(str(CLS))),
api='pgsql2shp', tableIsQuery=True
)
time_y = datetime.datetime.now().replace(microsecond=0)
rstCls = shp_to_rst(
shp, None, cellsize, 0,
os.path.join(folder, 'sel_{}.tif'.format(str(CLS))),
epsg=srscode, rst_template=rstTemplate, api='gdal'
)
time_z = datetime.datetime.now().replace(microsecond=0)
clsRst[int(CLS)] = rstCls
timeGasto[cnt + 1] = ('toshp_{}'.format(str(CLS)), time_y - time_x)
timeGasto[cnt + 2] = ('torst_{}'.format(str(CLS)), time_z - time_y)
trs = []
for i in range(len(classes)):
trs.append(Thread(
name="lll{}".format(str(classes[i])),
target=FilterAndExport, args=(classes[i], (i+1) * 10)
))
for t in trs: t.start()
for t in trs: t.join()
return clsRst, timeGasto
def run_query_for_values_in_col(db, query,
table_interest_col, interest_col,
outworkspace):
"""
Execute a query for each value in one column
In each iteration, the values may participate in the query.
Export the several tables to excel
Example:
ID_PERCURSO | PARAGEM | DIA | GEOM
0 | 255 |'2018-01-01 | xxxx
0 | 255 |'2018-01-01 | xxxx
0 | 254 |'2018-01-01 | xxxx
0 | 254 |'2018-01-01 | xxxx
0 | 255 |'2018-01-02 | xxxx
0 | 255 |'2018-01-02 | xxxx
0 | 254 |'2018-01-02 | xxxx
0 | 254 |'2018-01-02 | xxxx
For a query as:
SELECT ID_PERCURSO, PARAGEM, GEOM, DIA, COUNT(PARAGEM) AS conta FROM
table WHERE DIA={} GROUP BY PARAGEM, GEOM, DIA;
This method will generate two tables:
First table:
ID_PERCURSO | PARAGEM | DIA | GEOM | conta
0 | 255 |'2018-01-01 | xxxx | 2
0 | 254 |'2018-01-01 | xxxx | 2
Second table:
ID_PERCURSO | PARAGEM | DIA | GEOM | conta
0 | 255 |'2018-01-02 | xxxx | 2
0 | 254 |'2018-01-02 | xxxx | 2
{} will be replaced for every value in the interest_column that will
be iterated one by one
"""
import os
from gasp.sql.fm import q_to_obj
from gasp.sql.i import cols_type
from gasp.to import obj_to_tbl
fields_types = cols_type(db, table_interest_col)
# Get unique values
VALUES = q_to_obj(db,
"SELECT {col} FROM {t} GROUP BY {col}".format(
col=interest_col, t=table_interest_col
), db_api='psql'
)[interest_col].tolist()
# Aplly query for every value in VALUES
# Write data in excel
for value in VALUES:
data = q_to_obj(db, query.format(
str(value[0]) if fields_types[interest_col] != str else \
"'{}'".format(str(value[0]))
), db_api='psql')
obj_to_tbl(data, os.path.join(outworkspace, '{}_{}.xlsx'.format(
table_interest_col, str(value[0])
)))
def rst_pnt_to_build(osmdb, pntTable, polyTable, api_db='SQLITE'):
"""
Replace buildings with tag yes using the info in the Points Layer
Only used for URBAN ATLAS and CORINE LAND COVER
"""
import datetime as dt
from gasp.sql.i import row_num as cnt_row
from gasp.sql.fm import q_to_obj
from gasp.gt.toshp.db import dbtbl_to_shp as db_to_shp
from gasp.gql.ovly import feat_within, feat_not_within
from gasp.gt.torst import shp_to_rst
time_a = dt.datetime.now().replace(microsecond=0)
new_build = feat_within(
osmdb,
("(SELECT buildings AS pnt_build, geometry AS pnt_geom "
"FROM {} WHERE buildings IS NOT NULL)").format(pntTable),
"pnt_geom",
("(SELECT buildings AS poly_build, geometry AS poly_geom "
"FROM {} WHERE buildings IS NOT NULL)").format(polyTable),
"poly_geom",
"new_buildings",
inTblCols="pnt_build AS cls",
withinCols="poly_geom AS geometry",
outTblIsFile=None,
apiToUse="OGR_SPATIALITE" if api_db != "POSTGIS" else api_db,
geom_col="geometry")
time_b = dt.datetime.now().replace(microsecond=0)
yes_build = feat_not_within(
osmdb,
("(SELECT buildings AS poly_build, geometry AS poly_geom "
"FROM {} WHERE buildings IS NOT NULL)").format(polyTable),
"poly_geom",
("(SELECT buildings AS pnt_build, geometry AS pnt_geom "
"FROM {} WHERE buildings IS NOT NULL)").format(pntTable),
"pnt_geom",
"yes_builds",
inTblCols="poly_geom AS geometry, 11 AS cls",
outTblIsFile=None,
apiToUse="OGR_SPATIALITE" if api_db != "POSTGIS" else api_db,
geom_col="geometry")
time_c = dt.datetime.now().replace(microsecond=0)
resLayers = {}
N11 = cnt_row(osmdb,
yes_build,
api='psql' if api_db == 'POSTGIS' else 'sqlite')
time_d = dt.datetime.now().replace(microsecond=0)
if N11:
# Data to GRASS GIS
grsBuild11 = db_to_shp(
osmdb,
yes_build,
"geometry",
"yes_builds",
notTable=True,
filterByReg=True,
inDB='psql' if api_db == 'POSTGIS' else 'sqlite',
outShpIsGRASS=True)
time_f = dt.datetime.now().replace(microsecond=0)
# To raster
rstBuild11 = shp_to_rst(grsBuild11,
11,
None,
None,
"rst_builds11",
api="grass")
time_g = dt.datetime.now().replace(microsecond=0)
resLayers[11] = [rstBuild11]
else:
time_f = None
time_g = None
# Add data into GRASS GIS
lulcCls = q_to_obj(
osmdb,
"SELECT cls FROM {} GROUP BY cls".format(new_build),
db_api='psql' if api_db == 'POSTGIS' else 'sqlite').cls.tolist()
timeGasto = {
0: ('intersect', time_b - time_a),
1: ('disjoint', time_c - time_b),
2: ('count_b11', time_d - time_c),
3: None if not time_f else ('import_b11', time_f - time_d),
4: None if not time_g else ('torst_b11', time_g - time_f),
}
tk = 5
for cls in lulcCls:
time_x = dt.datetime.now().replace(microsecond=0)
shp = db_to_shp(osmdb,
new_build,
"geometry",
"nbuild_{}".format(str(cls)),
"cls = {}".format(cls),
notTable=True,
filterByReg=True,
outShpIsGRASS=True)
time_y = dt.datetime.now().replace(microsecond=0)
rstb = shp_to_rst(shp,
int(cls),
None,
None,
"rst_nbuild_{}".format(str(cls)),
api="grass")
time_z = dt.datetime.now().replace(microsecond=0)
if int(cls) == 11 and int(cls) in resLayers:
resLayers[int(cls)].append(rstb)
else:
resLayers[int(cls)] = [rstb]
timeGasto[tk] = ('import_bn{}'.format(cls), time_y - time_x)
timeGasto[tk + 1] = ('torst_bn{}'.format(cls), time_z - time_y)
tk += 2
return resLayers, timeGasto
def tbl_to_area_mtx(db, tbl, cola, colb, result):
"""
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 | | | |
cola = rows
colb = cols
"""
from gasp.sql.to import q_to_ntbl
from gasp.sql.fm import q_to_obj
ref_val = q_to_obj(db,
("SELECT fcol FROM ("
"SELECT CAST({map1_cls} AS text) AS fcol FROM {tbl} "
"GROUP BY {map1_cls} "
"UNION ALL "
"SELECT CAST({map2_cls} AS text) AS fcol FROM {tbl} "
"GROUP BY {map2_cls}"
") AS foo GROUP BY fcol ORDER BY fcol").format(
tbl=tbl,
map1_cls=cola,
map2_cls=colb,
),
db_api='psql').fcol.tolist()
mtx_tbl = q_to_ntbl(db,
result,
("SELECT * FROM crosstab('"
"SELECT CASE "
"WHEN foo.{map1_cls} IS NOT NULL "
"THEN foo.{map1_cls} ELSE jtbl.flyr "
"END AS lulc1_cls, CASE "
"WHEN foo.{map2_cls} IS NOT NULL "
"THEN foo.{map2_cls} ELSE jtbl.slyr "
"END AS lulc2_cls, CASE "
"WHEN foo.garea IS NOT NULL "
"THEN round(CAST(foo.garea / 1000000 AS numeric)"
", 3) ELSE 0 "
"END AS garea FROM ("
"SELECT CAST({map1_cls} AS text) AS {map1_cls}, "
"CAST({map2_cls} AS text) AS {map2_cls}, "
"SUM(ST_Area(geom)) AS garea "
"FROM {tbl} GROUP BY {map1_cls}, {map2_cls}"
") AS foo FULL JOIN ("
"SELECT f.flyr, s.slyr FROM ("
"SELECT CAST({map1_cls} AS text) AS flyr "
"FROM {tbl} GROUP BY {map1_cls}"
") AS f, ("
"SELECT CAST({map2_cls} AS text) AS slyr "
"FROM {tbl} GROUP BY {map2_cls}"
") AS s"
") AS jtbl "
"ON foo.{map1_cls} = jtbl.flyr AND "
"foo.{map2_cls} = jtbl.slyr "
"ORDER BY 1,2"
"') AS ct("
"lulc_cls text, {crossCols}"
")").format(crossCols=", ".join(
["cls_{} numeric".format(c) for c in ref_val]),
tbl=tbl,
map1_cls=cola,
map2_cls=colb),
api='psql')
return mtx_tbl
def dbtbl_to_shp(db, tbl, geom_col, outShp, where=None, inDB='psql',
notTable=None, filterByReg=None, outShpIsGRASS=None,
tableIsQuery=None, api='psql', epsg=None):
"""
Database Table to Feature Class file
idDB Options:
* psql
* sqlite
api Options:
* psql
* sqlite
* pgsql2shp
if outShpIsGRASS if true, the method assumes that outShp is
a GRASS Vector. That implies that a GRASS Session was been
started already.
"""
from gasp.gt.toshp import df_to_shp
if outShpIsGRASS:
from gasp import exec_cmd
from gasp.cons.psql import con_psql
db_con = con_psql()
whr = "" if not where else " where=\"{}\"".format(where)
cmd_str = (
"v.in.ogr input=\"PG:host={} dbname={} user={} password={} "
"port={}\" output={} layer={} geometry={}{}{}{} -o --overwrite --quiet"
).format(
db_con["HOST"], db, db_con["USER"], db_con["PASSWORD"],
db_con["PORT"], outShp, tbl, geom_col, whr,
" -t" if notTable else "", " -r" if filterByReg else ""
) if inDB == 'psql' else (
"v.in.ogr -o input={} layer={} output={}{}{}{}"
).format(db, tbl, outShp, whr,
" -t" if notTable else "", " -r" if filterByReg else ""
) if inDB == 'sqlite' else None
rcmd = exec_cmd(cmd_str)
else:
if api == 'pgsql2shp':
from gasp import exec_cmd
from gasp.cons.psql import con_psql
db_con = con_psql()
outcmd = exec_cmd((
'pgsql2shp -f {out} -h {hst} -u {usr} -p {pt} -P {pas}{geom} '
'{bd} {t}'
).format(
hst=db_con['HOST'], usr=db_con["USER"], pt=db_con["PORT"],
pas=db_con['PASSWORD'], bd=db, out=outShp,
t=tbl if not tableIsQuery else '"{}"'.format(tbl),
geom="" if not geom_col else " -g {}".format(geom_col)
))
elif api == 'psql' or api == 'sqlite':
from gasp.sql.fm import q_to_obj
q = "SELECT * FROM {}".format(tbl) if not tableIsQuery else tbl
df = q_to_obj(db, q, db_api=api, geomCol=geom_col, epsg=epsg)
outsh = df_to_shp(df, outShp)
else:
raise ValueError((
'api value must be \'psql\', \'sqlite\' or \'pgsql2shp\''))
return outShp
def ID_rows_with_temporal_proximity_by_entities(db, table, entity_field,
day_field, hour_field,
hour_decimal_field,
time_tolerance, outXlsPath):
"""
Retrieve rows from one pgtable with some temporal proximity
Table structure should be
entity | day | hour | hour_decimal
0 | 2018-01-02 | 5 | 5,10
0 | 2018-01-03 | 4 | 4,15
0 | 2018-01-02 | 5 | 5,12
0 | 2018-01-02 | 5 | 5,8
1 | 2018-01-02 | 4 | 4,10
1 | 2018-01-02 | 5 | 5,12
1 | 2018-01-02 | 4 | 4,20
1 | 2018-01-02 | 4 | 4,12
1 | 2018-01-02 | 4 | 4,6
For a time_tolerance of 5 minutes, the output table will have
the rows with a temporal difference inside/bellow that time tolerance
entity_field could be more than one field
This method only identifies if one entity, for one day, has rows
very close of each others, in terms of time.
Not a good strategy for large tables. For large tables, SQL based methods
are needed
"""
import pandas
from gasp.pyt import obj_to_lst
from gasp.sql.fm import q_to_obj
from gasp.sql.i import cols_type
from gasp.to import obj_to_tbl
entity_field = obj_to_lst(entity_field)
COLS = entity_field + [day_field, hour_field]
COLS_TYPE = cols_type(db, table)
# TIME TOLERANCE IN HOURS
TIME_TOLERANCE = time_tolerance / 60.0
def thereIsRowsSameTimeInt(row):
whr = []
for c in COLS:
if COLS_TYPE[c] == str:
whr.append("{}='{}'".format(c, row[c]))
else:
whr.append("{}={}".format(c, row[c]))
hourRows = q_to_obj(db,
"SELECT {} FROM {} WHERE {}".format(
hour_decimal_field, table, " AND ".join(whr)),
db_api='psql')[hour_decimal_field].tolist()
for i in range(len(hourRows)):
for e in range(i + 1, len(hourRows)):
dif = abs(hourRows[i][0] - hourRows[e][0])
if dif < TIME_TOLERANCE:
break
if dif < TIME_TOLERANCE:
break
if dif < TIME_TOLERANCE:
row['time_difference'] = 1
else:
row['time_difference'] = 0
return row
# Count entity occourrences for one day and hour
countsByEntityTime = q_to_obj(
db,
("SELECT {scols}, conta FROM "
"(SELECT {scols}, COUNT({ent}) AS conta FROM {tbl} "
"GROUP BY {scols}) AS foo WHERE conta > 1").format(
scols=', '.join(COLS), ent=entity_field[0], tbl=table),
db_api='psql')
# For each row in the last count, When count is > 1
# Check time difference between rows for one day and hour
countsByEntityTime = countsByEntityTime.apply(
lambda x: thereIsRowsSameTimeInt(x), axis=1)
obj_to_tbl(countsByEntityTime, outXlsPath)
return outXlsPath
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 gasp.sql.fm import q_to_obj
from gasp.to import db_to_tbl
from gasp.sql.to import df_to_db
from gasp.gt.toshp.cff import shp_to_shp
from gasp.gt.toshp.db import dbtbl_to_shp
from gasp.gt.toshp.rst import rst_to_polyg
from gasp.gql.to import shp_to_psql
from gasp.gql.tomtx import tbl_to_area_mtx
from gasp.gt.prop.ff import check_isRaster
from gasp.pyt.oss import fprop
from gasp.sql.db import create_db
from gasp.sql.tbl import tbls_to_tbl
from gasp.sql.to import q_to_ntbl
from gasp.gql.cln import fix_geom
from gasp.to import db_to_tbl
# Check if folder exists, if not create it
if not os.path.exists(OUT_FOLDER):
from gasp.pyt.oss import mkdir
mkdir(OUT_FOLDER, overwrite=None)
else:
raise ValueError('{} already exists!'.format(OUT_FOLDER))
from gasp.gt.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 gasp.gt.toshp.cff import shp_to_grs, grs_to_shp
from gasp.gt.torst import rst_to_grs
from gasp.gt.tbl.fld 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
