def CreateNewFile(self):
get_lib_path('g.gui.tangible')
dlg = wx.FileDialog(self, message="Create a new file with analyses",
wildcard="Python source (*.py)|*.py",
style=wx.SAVE | wx.FD_OVERWRITE_PROMPT)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
orig = os.path.join(get_lib_path('g.gui.tangible'), 'current_analyses.py')
if not os.path.exists(orig):
self.giface.WriteError("File with analyses not found: {}".format(orig))
else:
copyfile(orig, path)
self.selectAnalyses.SetValue(path)
self.settings['analyses']['file'] = path
dlg.Destroy()
def CreateNewFile(self):
get_lib_path('g.gui.tangible')
dlg = wx.FileDialog(self, message="Create a new file with analyses",
wildcard="Python source (*.py)|*.py",
style=wx.SAVE | wx.FD_OVERWRITE_PROMPT)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
orig = os.path.join(get_lib_path('g.gui.tangible'), 'current_analyses.py')
if not os.path.exists(orig):
self.giface.WriteError("File with analyses not found: {}".format(orig))
else:
copyfile(orig, path)
self.selectAnalyses.SetValue(path)
self.settings['analyses']['file'] = path
dlg.Destroy()
def get_lib_path(modname, libname=None):
"""Return the path of the libname contained in the module.
.. deprecated:: 7.1
Use :func:`grass.script.utils.get_lib_path` instead.
"""
from grass.script.utils import get_lib_path
return get_lib_path(modname=modname, libname=libname)
def get_lib_path(modname, libname=None):
"""Return the path of the libname contained in the module.
.. deprecated:: 7.1
Use :func:`grass.script.utils.get_lib_path` instead.
"""
from grass.script.utils import get_lib_path
return get_lib_path(modname=modname, libname=libname)
#% type: integer
#% label: Number of pixels to pass to the prediction method
#% description: Number of pixels to pass to the prediction method. GRASS GIS reads raster by-row so chunksize is rounded down based on the number of columns
#% answer: 100000
#% guisection: Optional
#%end
import sys
import grass.script as gs
import numpy as np
import math
from grass.script.utils import get_lib_path
from grass.pygrass.gis.region import Region
from grass.pygrass.modules.shortcuts import raster as r
path = get_lib_path(modname="r.learn.ml2")
if path is None:
gs.fatal("Not able to find the r.learn.ml2 library directory")
sys.path.append(path)
from raster import RasterStack
def string_to_rules(string):
"""Converts a string to a file for input as a GRASS Rules File"""
tmp = gs.tempfile()
f = open("%s" % (tmp), "wt")
f.write(string)
f.close()
return tmp
#% label: Backbone architecture
#% required: no
#% multiple: no
#% answer: resnet101
#% options: resnet50,resnet101
#% guisection: Training parameters
#%end
import grass.script as gscript
from grass.script.utils import get_lib_path
import os
import sys
from random import shuffle
path = get_lib_path(modname="maskrcnn", libname="model")
if path is None:
grass.script.fatal("Not able to find the maskrcnn library directory.")
sys.path.append(path)
def main(options, flags):
from config import ModelConfig
import utils
import model as modellib
try:
dataset = options["training_dataset"]
initialWeights = options["model"]
classes = options["classes"]
import os
from shutil import copyfile
import sys
from io import BytesIO
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import grass.script as gscript
from grass.script.utils import get_lib_path
import grass.script.array as garray
from osgeo import gdal, osr
path = get_lib_path(modname='maskrcnn', libname='model')
if path is None:
gscript.fatal('Not able to find the maskrcnn library directory.')
sys.path.append(path)
def main(options, flags):
import model as modellib
from config import ModelConfig
try:
imagesDir = options['images_directory']
modelPath = options['model']
classes = options['classes'].split(',')
if options['band1']:
def createPDF(self, save=True):
self.story = self._parseMDOWS()
self.doc = Pdf('Metadata file', 'GRASS GIS')
self.doc.set_theme(MyTheme)
lib_name = 'config'
logo_path = get_lib_path("wx.metadata", lib_name)
logo_path = os.path.join(logo_path, lib_name, 'logo_variant_bg.png')
self.doc.add_image(logo_path, 57, 73, LEFT)
if self.map is None:
self.doc.add_header(self.filename, T1)
else:
self.doc.add_header(self.map, T1)
self.doc.add_header(self.filename, T2)
if self.type == 'vector':
name = 'vector map'
self.doc.add_header(name, T2)
else:
name = 'raster map'
self.doc.add_header(name, T2)
self.doc.add_header("%s metadata profile" % self.profile, T3)
self.doc.add_spacer(2)
if map is not None:
mapPic = self.getMapPic()
self.doc.add_image(mapPic, 200, 200, CENTER)
self.textFactory(title="Keywords",
name='Keywords',
tag='identification',
multiple=-1)
self.textFactory(title="Abstract",
name='Abstract',
tag='identification')
# #################### metadata #################################
self.doc.add_spacer(25)
self.doc.add_header("Metadata on metadata", H1)
head = ['Organization name', 'E-mail',
'Role'] # this is the header row
self.tableFactory("Metadata point of contact", head, 'contact')
self.textFactory(title="Metadata date",
name='Datestamp',
tag='datestamp')
self.textFactory(title="Metadata language",
name='Language',
tag='languagecode')
# #################### identification #################################
self.doc.add(PageBreak())
self.doc.add_header("Identification", H1)
self.textFactory(title="Restource title",
name='Title',
tag='identification')
self.textFactory(title="Identifier",
name='Identifier',
tag='identifier')
# self.textFactory(title="Resource locator", name='Identifier',tag='identifier')#TODO linkage is missing
self.tableFactory("Resource language", ['Language'], 'identification')
# head = ['Organization name', 'E-mail','Role']
#.tableFactory("identifier",head,'contact')
##################### Keywords ################################## TODO
##################### Geographic ##################################
self.doc.add_spacer(25)
self.doc.add_header('Geographic Location', H1)
maxy = float(self.findItem(self.story['identification'], 'maxy', 0))
maxx = float(self.findItem(self.story['identification'], 'maxx', 0))
miny = float(self.findItem(self.story['identification'], 'miny', 0))
minx = float(self.findItem(self.story['identification'], 'minx', 0))
head = [[
'North Bound Latitude', 'East Bound Longitude',
'South Bound Latitude', 'West Bound Longitude'
]]
head.append([maxx, maxy, minx, miny])
self.doc.add_table(head, self.TABLE_WIDTH)
self.doc.add_spacer(25)
mapPath = MapBBFactory([[maxx, minx], [maxy, miny]], )
##################### Add google picture(extend) ##################################
try:
gmap = Image(mapPath.link1, 200, 200)
gmap1 = Image(mapPath.link2, 200, 200)
self.doc.add(Table([[gmap1, gmap]]))
self.doc.add(PageBreak())
except:
GWarning(
"Cannot download metadata picture of extend provided by Google API. Please check internet connection."
)
##################### Temporal ##################################
self.doc.add_spacer(25)
self.doc.add_header('Temporal reference', H1)
self.textFactory(title="Temporal extend start",
name='Temporal extend start',
tag='identification')
self.textFactory(title="Temporal extend end",
name='Temporal extend end',
tag='identification')
##################### Quality ##################################
self.doc.add_spacer(25)
self.doc.add_header('Quality a validity', H1)
self.textFactory(title="Lineage", name='Lineage', tag='dataquality')
#self.textFactory(title="Temporal extend start", name='Temporal extend start',tag='identification')
# TODO md.identification.denominators
######################Conformity ########################
self.doc.add_spacer(25)
self.doc.add_header('Conformity', H1)
head = ['Conformance date', "Conformance date type", 'Specification']
self.tableFactory("Conformity", head, 'dataquality')
###################### Constraints ########################
self.doc.add_spacer(25)
self.doc.add_header('Constraints', H1)
self.tableFactory("Condition applying to use", ["Use limitation"],
'identification')
self.tableFactory("Condition applying to access",
["Access constraints"], 'identification')
self.tableFactory("Limitation on public access",
["Other constraintrs"], 'identification')
###################### Responsible party ########################
self.doc.add_spacer(25)
self.doc.add_header('Responsible party', H1)
header = ["Organization name", "E-mail", "Role"]
self.tableFactory(
"Organisations responsible for the establishment, management, maintenance and distribution of spatial data sets and services",
header, 'identification')
text = self.doc.render()
# http://www.reportlab.com/docs/reportlab-userguide.pdf
if save and self.pdf_file is not None:
path = self.savePDF(text)
return path
return text
from grass.script.utils import get_lib_path
from grass.pygrass.raster import RasterRow
from copy import deepcopy
from grass.pygrass.gis.region import Region
from grass.pygrass.modules.grid import split
import multiprocessing as mltp
from itertools import chain
import time
import numpy as np
import sys
path = get_lib_path('r.learn.ml')
sys.path.append(path)
from raster import RasterStack
reg = Region()
stack = RasterStack(rasters=["lsat5_1987_10", "lsat5_1987_20", "lsat5_1987_30", "lsat5_1987_40",
"lsat5_1987_50", "lsat5_1987_70"])
X, y, crd = stack.extract_points(vect_name='landclass96_roi', fields=['value', 'cat'])
df = stack.extract_points(vect_name='landclass96_roi', fields='value', as_df=True)
df = stack.extract_pixels(response='landclass96_roi', as_df=True)
X, y, crd = stack.extract_pixels(response='landclass96_roi')
stack.head()
stack.tail()
df = stack.to_pandas()
df = stack.to_pandas(res=500)
df = df.melt(id_vars=['x', 'y'])
#% exclusive: load_training,save_training
#%end
from __future__ import absolute_import, print_function
import atexit
import os
import sys
import warnings
import numpy as np
from copy import deepcopy
import grass.script as gs
from grass.script.utils import get_lib_path
path = get_lib_path(modname='r.learn.ml')
if path is None:
gs.fatal('Not able to find the r.learn library directory')
sys.path.append(path)
from model_selection import cross_val_scores
from utils import (model_classifiers, load_training_data, save_training_data,
option_to_list, scoring_metrics)
from raster import RasterStack
tmp_rast = []
def cleanup():
for rast in tmp_rast:
