}
params = owsConnections['default']
## END USER SETTINGS
import os
import grass.script as gscript
from grass.pygrass.modules import Module
from grass.pygrass.modules.shortcuts import raster as r, vector as v, general as g, display as d
params['username'] += username
params['password'] += password
dsn = params['dsn'] + ' user=%s password=%s' %(username,password)
g.mapset(flags='c',mapset='Gemeinde_'+bfsnum)
v.in_ogr(dsn=dsn, layer=params['layers']['gemeinde'], output='region',
where='bfs_nummer = 2461',overwrite=True)
g.region(vect='region',flags='a',res='2')
g.region(flags='p')
v.in_ogr(dsn=dsn, layer=params['layers']['fieldblocks'], output='fieldblocks', flags='r')
rinwcs = Module("r.in.wcs")
rinwcs.inputs.url=params['url']
rinwcs.inputs.username=username
rinwcs.inputs.password=password
rinwcs(coverage=params['coverages']['elevation'])
rinwcs(coverage=params['coverages']['rfactor'])
from grass.pygrass.modules.shortcuts import general as g
from grass.pygrass.modules.shortcuts import vector as v
from grass.pygrass.modules.shortcuts import raster as r
#---------------------------------------
# The code below assumes you have already loaded the treecover and deforestation datasets
# from Global Forest Watch (Hansen, 2000), loaded the tiles in a GRASS GIS location, in the
# mapset PERMANENT, and made a mosaic out of each of them.
#
# The following steps will be made in a mapset called "cut_variables_using_buffers".
#---------------------------------------
# Change to a new mapset
mapset_name = 'cut_variables_using_buffers'
g.mapset(mapset = mapset_name, flags = 'c') #-c to create
#---------------------------------------
# Import vector of points and make buffer
#
# Each point correspond to the sampling location of an ecological community
# We load them and make a 5km buffer around each point
# folder
folder_path = r'D:\bernardo\00_academico\01_artigos\ms_Lucas_world_landscape_metrics\world-landscape-metrics-repo\01_data\community_locations'
os.chdir(folder_path) # Change to this folder
# import points
v.in_ogr(input = 'comm_data_neotro_checked_2020_d11_06.shp',
output = 'comm_data_neotro_checked_2020_d11_06', overwrite = True)
grass.run_command('v.import', input = i, output = name, overwrite = True) # Import maps
#------------------------------------------
# Location utm 23N
# list maps in cgs-location
all_maps = grass.read_command('r.proj',
location='newLocation_wgs84',
mapset='variables_cut',
flags='l').replace('\r', '').split('\n')
all_regions = grass.read_command('v.proj',
location='newLocation_wgs84',
mapset='variables_cut',
flags='l').replace('\r', '').split('\n')
# change location
g.mapset(mapset="PERMANENT", location="location_S_zone_23")
# get only those for zone 23
maps = [i for i in all_maps if 'zone_23' in i]
regions = [
i for i in all_regions if i.endswith('zone_23') and i.startswith('region')
]
# loop to reproject regions
for i in regions:
v.proj(location='newLocation_wgs84',
mapset='variables_cut',
input=i,
output=i,
overwrite=True)
def extract_points(self, vect_name, fields, na_rm=True, as_df=False):
"""Samples a list of GRASS rasters using a point dataset
Parameters
----------
vect_name : str
Name of GRASS GIS vector containing point features.
fields : list, str
Name of attribute(s) containing the vect_name variable(s).
na_rm : bool (opt). Default is True
Whether to remove samples containing NaNs.
as_df : bool (opt). Default is False.
Whether to return the extracted RasterStack values as a Pandas
DataFrame.
Returns
-------
X : ndarray
2d array containing the extracted raster values with the dimensions
ordered by (n_samples, n_features).
y : ndarray
1d or 2d array of labels with the dimensions ordered by
(n_samples, n_fields).
df : pandas.DataFrame
Extracted raster values as Pandas DataFrame if as_df = True.
"""
# some checks
try:
vname, mapset = vect_name.split("@")
except ValueError:
vname = vect_name
mapset = (g.mapset(flags="p", stdout_=PIPE).outputs.stdout.split(
os.linesep)[0])
if VectorTopo(name=vname, mapset=mapset).exist() is False:
gs.fatal("The supplied vector map does not exist")
if isinstance(fields, str):
fields = [fields]
# open grass vector
with VectorTopo(name=vname, mapset=mapset, mode="r") as points:
# retrieve key column
key_col = points.table.key
# read attribute table (ignores region)
df = pd.read_sql_query(
sql="select * from {name}".format(name=points.table.name),
con=points.table.conn)
for i in fields:
if i not in df.columns.tolist():
gs.fatal(i + " not present in the attribute table")
df = df.loc[:, fields + [points.table.key]]
# extract raster data
Xs = []
for name, layer in self.loc.items():
rast_data = v.what_rast(
map=vect_name,
raster=layer.fullname(),
flags="p",
quiet=True,
stdout_=PIPE,
).outputs.stdout.strip().split(os.linesep)
with RasterRow(layer.fullname()) as src:
if src.mtype == "CELL":
nodata = self._cell_nodata
dtype = pd.Int64Dtype()
else:
nodata = np.nan
dtype = np.float32
if len(list(itertools.chain(*rast_data))) == 0:
gs.fatal("There are no training point geometries in "
"the supplied vector dataset")
X = [
k.split("|")[1] if k.split("|")[1] != "*" else nodata
for k in rast_data
]
X = np.asarray(X)
cat = np.asarray([int(k.split("|")[0]) for k in rast_data])
if src.mtype == "CELL":
X = [int(i) for i in X]
else:
X = [float(i) for i in X]
X = pd.DataFrame(data=np.column_stack((X, cat)),
columns=[name, key_col])
X[name] = X[name].astype(dtype)
Xs.append(X)
for X in Xs:
df = df.merge(X, on=key_col)
# set any grass integer nodata values to NaN
df = df.replace(self._cell_nodata, np.nan)
# remove rows with missing response data
df = df.dropna(subset=fields)
# remove samples containing NaNs
if na_rm is True:
gs.message("Removing samples with NaN values in the raster "
"feature variables...")
df = df.dropna()
if as_df is False:
if len(fields) == 1:
fields = fields[0]
X = df.loc[:, df.columns.isin(self.loc.keys())].values
y = np.asarray(df.loc[:, fields].values)
cat = np.asarray(df.loc[:, key_col].values)
return X, y, cat
return df
python
# import modules
import os, fnmatch, imp
import numpy as np
import subprocess
import grass.script as grass
from grass.pygrass.modules.shortcuts import general as g
from grass.pygrass.modules.shortcuts import vector as v
from grass.pygrass.modules.shortcuts import raster as r
#---------------------------------------
# Change to a new mapset
g.mapset(mapset = 'akde', flags = 'c') #-c to create
#---------------------------------------
# Import buffers
# akde 95
folder_path = r'E:\_neojaguardatabase\other_limits_cut_variables\akde\95_per'
os.chdir(folder_path) # Change to this folder
files = os.listdir(folder_path) # List files in the folder
for i in files:
if i.endswith('shp'): # Select shp files
print i
name = 'akde_ind'+i.replace('.shp', '').replace('.', '_').replace('%', '_')
grass.run_command('v.import', input = i, output = name, overwrite = True) # Import maps
owsConnections = {}
owsConnections['default'] = {
'dsn' : 'PG:host=523.riedackerhof.ch dbname=gis user=gis password=gisHoch3',
'url' : 'https://523.riedackerhof.ch:4433/ows?',
'username' :'gis',
'password' : 'gisHoch3',
'layers' : {
'cultures' : 'ch_gelan_kulturen_2014',
'fieldblocks' : 'ch_blw_erk2_feldblockkarte',
'borders' : 'ch_swisstopo_kantone'
},
}
params = owsConnections['default']
dsn = params['dsn']
g.mapset(flags='c',mapset='solothurn')
v.in_ogr(dsn=dsn, layer=params['layers']['borders'], output='region',
where="kantonsnum = 11")
g.region(vect='region')
g.region(flags='p')
v.in_ogr(dsn=dsn, layer=params['layers']['cultures'], output='cultures', flags='r')
v.in_ogr(dsn=dsn, layer=params['layers']['fieldblocks'],output='fieldblocks', flags='r')
v.overlay(ainput='fieldblocks', binput = 'cultures', operator='not', output='fff')
python
# import modules
import os, fnmatch, imp
import subprocess
import numpy as np
import grass.script as grass
from grass.pygrass.modules.shortcuts import general as g
from grass.pygrass.modules.shortcuts import vector as v
from grass.pygrass.modules.shortcuts import raster as r
#---------------------------------------
# Create a single mapset for each environmental variables to be cut, still in lat-lon, before being reprojected to UTM
# This means that all the data for all correspondent UTm zones will be in a single mapset, in lat-lon
# The next step will be to reproject these
g.mapset(mapset="variables_cut", flags="c")
#---------------------------------------
# Import buffers
folder_path = r'E:\_neojaguardatabase\Buffer70_zones\buffers'
os.chdir(folder_path) # Change to this folder
files = os.listdir(folder_path) # List files in the folder
for i in files:
if i.endswith('gpkg'): # Select tif files
print i
name = i.replace('.gpkg', '')
v.in_ogr(input=i, output=name, overwrite=True) # Import maps
#------
# cut for buffers