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<|file_name|>F57.py<|end_file_name|><｜fim▁begin｜>import fechbase
class Records(fechbase.RecordsBase):
def <|fim_middle|>(self):
fechbase.RecordsBase.__init__(self)
self.fields = [
{'name': 'FORM TYPE', 'number': '1'},
{'name': 'FILER FEC CMTE ID', 'number': '2'},
{'name': 'ENTITY TYPE', 'number': '3'},
{'name': 'NAME (Payee)', 'number': '4'},
{'name': 'STREET 1', 'number': '5'},
{'name': 'STREET 2', 'number': '6'},
{'name': 'CITY', 'number': '7'},
{'name': 'STATE', 'number': '8'},
{'name': 'ZIP', 'number': '9'},
{'name': 'TRANSDESC', 'number': '10'},
{'name': 'Of Expenditure', 'number': '11-'},
{'name': 'AMOUNT', 'number': '12'},
{'name': 'SUPPORT/OPPOSE', 'number': '13'},
{'name': 'S/O FEC CAN ID NUMBER', 'number': '14'},
{'name': 'S/O CAN/NAME', 'number': '15'},
{'name': 'S/O CAN/OFFICE', 'number': '16'},
{'name': 'S/O CAN/STATE', 'number': '17'},
{'name': 'S/O CAN/DIST', 'number': '18'},
{'name': 'FEC COMMITTEE ID NUMBER', 'number': '19'},
{'name': 'Unused field', 'number': '20'},
{'name': 'Unused field', 'number': '21'},
{'name': 'Unused field', 'number': '22'},
{'name': 'Unused field', 'number': '23'},
{'name': 'Unused field', 'number': '24'},
{'name': 'CONDUIT NAME', 'number': '25'},
{'name': 'CONDUIT STREET 1', 'number': '26'},
{'name': 'CONDUIT STREET 2', 'number': '27'},
{'name': 'CONDUIT CITY', 'number': '28'},
{'name': 'CONDUIT STATE', 'number': '29'},
{'name': 'CONDUIT ZIP', 'number': '30'},
{'name': 'AMENDED CD', 'number': '31'},
{'name': 'TRAN ID', 'number': '32'},
]
self.fields_names = self.hash_names(self.fields)
<｜fim▁end｜> | __init__ |
<|file_name|>loggingFunctions.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
"""
Set up the logging
"""<｜fim▁hole｜>import logging
import tempfile
import os
def initialize_logging():
"""
Set up the screen and file logging.
:return: The log filename
"""
# set up DEBUG logging to file, INFO logging to STDERR
log_file = os.path.join(tempfile.gettempdir(), 'spfy.log')
formatter = logging.Formatter(
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=log_file,
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setFormatter(formatter)
console.setLevel(logging.INFO)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
return log_file<｜fim▁end｜> | |
<|file_name|>loggingFunctions.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
"""
Set up the logging
"""
import logging
import tempfile
import os
def initialize_logging():
<|fim_middle|>
<｜fim▁end｜> | """
Set up the screen and file logging.
:return: The log filename
"""
# set up DEBUG logging to file, INFO logging to STDERR
log_file = os.path.join(tempfile.gettempdir(), 'spfy.log')
formatter = logging.Formatter(
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=log_file,
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setFormatter(formatter)
console.setLevel(logging.INFO)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
return log_file |
<|file_name|>loggingFunctions.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
"""
Set up the logging
"""
import logging
import tempfile
import os
def <|fim_middle|>():
"""
Set up the screen and file logging.
:return: The log filename
"""
# set up DEBUG logging to file, INFO logging to STDERR
log_file = os.path.join(tempfile.gettempdir(), 'spfy.log')
formatter = logging.Formatter(
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=log_file,
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setFormatter(formatter)
console.setLevel(logging.INFO)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
return log_file
<｜fim▁end｜> | initialize_logging |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>from __future__ import absolute_import<｜fim▁hole｜>
from .base import WhiteNoise
__version__ = '2.0.3'
__all__ = ['WhiteNoise']<｜fim▁end｜> | |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''<｜fim▁hole｜><｜fim▁end｜> | if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
<|fim_middle|>
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
<|fim_middle|>
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
<|fim_middle|>
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']); |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
<|fim_middle|>
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
<|fim_middle|>
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
<|fim_middle|>
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
<|fim_middle|>
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
<|fim_middle|>
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
<|fim_middle|>
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
<|fim_middle|>
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
<|fim_middle|>
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
<|fim_middle|>
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
<|fim_middle|>
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | '''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
<|fim_middle|>
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
<|fim_middle|>
<｜fim▁end｜> | ''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
<|fim_middle|>
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | print('No unflooded training domain provided.')
return None |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
<|fim_middle|>
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | threshold = float(domain.algorithm_params['modis_diff_threshold']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
<|fim_middle|>
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | print('No unflooded training domain provided.')
return None |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
<|fim_middle|>
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | threshold = float(domain.algorithm_params['dartmouth_threshold']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
<|fim_middle|>
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | print('No unflooded training domain provided.')
return None |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
<|fim_middle|>
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | threshold = float(domain.algorithm_params['mod_ndwi_threshold']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
<|fim_middle|>
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | print('No unflooded training domain provided.')
return None |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
<|fim_middle|>
return get_fai(b).lte(threshold)
<｜fim▁end｜> | threshold = float(domain.algorithm_params['fai_threshold']) |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def <|fim_middle|>(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | dem_threshold |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def <|fim_middle|>(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | evi |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def <|fim_middle|>(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | xiao |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def <|fim_middle|>(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | get_diff |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def <|fim_middle|>(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | diff_learned |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def <|fim_middle|>(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | modis_diff |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def <|fim_middle|>(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | get_dartmouth |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def <|fim_middle|>(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | dart_learned |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def <|fim_middle|>(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | dartmouth |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def <|fim_middle|>(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | get_mod_ndwi |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def <|fim_middle|>(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | mod_ndwi_learned |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def <|fim_middle|>(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | mod_ndwi |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def <|fim_middle|>(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | get_fai |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def <|fim_middle|>(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def fai(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | fai_learned |
<|file_name|>simple_modis_algorithms.py<|end_file_name|><｜fim▁begin｜># -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# -----------------------------------------------------------------------------
import ee
import math
from cmt.mapclient_qt import addToMap
from cmt.util.miscUtilities import safe_get_info
import modis_utilities
'''
Contains implementations of several simple MODIS-based flood detection algorithms.
'''
#==============================================================
def dem_threshold(domain, b):
'''Just use a height threshold on the DEM!'''
heightLevel = float(domain.algorithm_params['dem_threshold'])
dem = domain.get_dem().image
return dem.lt(heightLevel).select(['elevation'], ['b1'])
#==============================================================
def evi(domain, b):
'''Simple EVI based classifier'''
#no_clouds = b['b3'].lte(2100).select(['sur_refl_b03'], ['b1'])
criteria1 = b['EVI'].lte(0.3).And(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1'])
criteria2 = b['EVI'].lte(0.05).And(b['LSWI'].lte(0.0)).select(['sur_refl_b02'], ['b1'])
#return no_clouds.And(criteria1.Or(criteria2))
return criteria1.Or(criteria2)
def xiao(domain, b):
'''Method from paper: Xiao, Boles, Frolking, et. al. Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sensing of Environment, 2006.
This method implements a very simple decision tree from several standard MODIS data products.
The default constants were tuned for (wet) rice paddy detection.
'''
return b['LSWI'].subtract(b['NDVI']).gte(0.05).Or(b['LSWI'].subtract(b['EVI']).gte(0.05)).select(['sur_refl_b02'], ['b1']);
#==============================================================
def get_diff(b):
'''Just the internals of the difference method'''
return b['b2'].subtract(b['b1']).select(['sur_refl_b02'], ['b1'])
def diff_learned(domain, b):
'''modis_diff but with the threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_diff(unflooded_b), water_mask, domain.bounds)
return modis_diff(domain, b, threshold)
def modis_diff(domain, b, threshold=None):
'''Compute (b2-b1) < threshold, a simple water detection index.
This method may be all that is needed in cases where the threshold can be hand tuned.
'''
if threshold == None: # If no threshold value passed in, load it based on the data set.
threshold = float(domain.algorithm_params['modis_diff_threshold'])
return get_diff(b).lte(threshold)
#==============================================================
def get_dartmouth(b):
A = 500
B = 2500
return b['b2'].add(A).divide(b['b1'].add(B)).select(['sur_refl_b02'], ['b1'])
def dart_learned(domain, b):
'''The dartmouth method but with threshold calculation included (training image required)'''
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_dartmouth(unflooded_b), water_mask, domain.bounds)
return dartmouth(domain, b, threshold)
def dartmouth(domain, b, threshold=None):
'''A flood detection method from the Dartmouth Flood Observatory.
This method is a refinement of the simple b2-b1 detection method.
'''
if threshold == None:
threshold = float(domain.algorithm_params['dartmouth_threshold'])
return get_dartmouth(b).lte(threshold)
#==============================================================
def get_mod_ndwi(b):
return b['b6'].subtract(b['b4']).divide(b['b4'].add(b['b6'])).select(['sur_refl_b06'], ['b1'])
def mod_ndwi_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_mod_ndwi(unflooded_b), water_mask, domain.bounds)
return mod_ndwi(domain, b, threshold)
def mod_ndwi(domain, b, threshold=None):
if threshold == None:
threshold = float(domain.algorithm_params['mod_ndwi_threshold'])
return get_mod_ndwi(b).lte(threshold)
#==============================================================
def get_fai(b):
'''Just the internals of the FAI method'''
return b['b2'].subtract(b['b1'].add(b['b5'].subtract(b['b1']).multiply((859.0 - 645) / (1240 - 645)))).select(['sur_refl_b02'], ['b1'])
def fai_learned(domain, b):
if domain.unflooded_domain == None:
print('No unflooded training domain provided.')
return None
unflooded_b = modis_utilities.compute_modis_indices(domain.unflooded_domain)
water_mask = modis_utilities.get_permanent_water_mask()
threshold = modis_utilities.compute_binary_threshold(get_fai(unflooded_b), water_mask, domain.bounds)
return fai(domain, b, threshold)
def <|fim_middle|>(domain, b, threshold=None):
''' Floating Algae Index. Method from paper: Feng, Hu, Chen, Cai, Tian, Gan,
Assessment of inundation changes of Poyang Lake using MODIS observations
between 2000 and 2010. Remote Sensing of Environment, 2012.
'''
if threshold == None:
threshold = float(domain.algorithm_params['fai_threshold'])
return get_fai(b).lte(threshold)
<｜fim▁end｜> | fai |
<|file_name|>__manifest__.py<|end_file_name|><｜fim▁begin｜>##############################################################################<｜fim▁hole｜>#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
{
'name': 'Products Management Group',
'version': '13.0.1.0.0',
'category': 'base.module_category_knowledge_management',
'author': 'ADHOC SA',
'website': 'www.adhoc.com.ar',
'license': 'AGPL-3',
'depends': [
'sale',
],
'data': [
'security/product_management_security.xml',
],
'installable': False,
}<｜fim▁end｜> | #
# Copyright (C) 2015 ADHOC SA (http://www.adhoc.com.ar)
# All Rights Reserved. |
<|file_name|>gd.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2016, Jianfeng Chen <[email protected]>
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights<｜fim▁hole｜># to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from __future__ import division
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b))
def GD(PF0, PFc):
up = 0
for i in PFc:
up += min([dist(i, j) for j in PF0])
return up**0.5 / (len(PFc))<｜fim▁end｜> | |
<|file_name|>gd.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2016, Jianfeng Chen <[email protected]>
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from __future__ import division
def dist(a, b):
<|fim_middle|>
def GD(PF0, PFc):
up = 0
for i in PFc:
up += min([dist(i, j) for j in PF0])
return up**0.5 / (len(PFc))
<｜fim▁end｜> | return sum((i-j)**2 for i, j in zip(a, b)) |
<|file_name|>gd.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2016, Jianfeng Chen <[email protected]>
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from __future__ import division
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b))
def GD(PF0, PFc):
<|fim_middle|>
<｜fim▁end｜> | up = 0
for i in PFc:
up += min([dist(i, j) for j in PF0])
return up**0.5 / (len(PFc)) |
<|file_name|>gd.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2016, Jianfeng Chen <[email protected]>
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from __future__ import division
def <|fim_middle|>(a, b):
return sum((i-j)**2 for i, j in zip(a, b))
def GD(PF0, PFc):
up = 0
for i in PFc:
up += min([dist(i, j) for j in PF0])
return up**0.5 / (len(PFc))
<｜fim▁end｜> | dist |
<|file_name|>gd.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2016, Jianfeng Chen <[email protected]>
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from __future__ import division
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b))
def <|fim_middle|>(PF0, PFc):
up = 0
for i in PFc:
up += min([dist(i, j) for j in PF0])
return up**0.5 / (len(PFc))
<｜fim▁end｜> | GD |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
argspec = hashivault_argspec()<｜fim▁hole｜> argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
module.exit_json(**result)
@hashiwrapper
def hashivault_approle_role_get(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
main()<｜fim▁end｜> | argspec['name'] = dict(required=True, type='str') |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
<|fim_middle|>
@hashiwrapper
def hashivault_approle_role_get(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
main()
<｜fim▁end｜> | argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
module.exit_json(**result) |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
module.exit_json(**result)
@hashiwrapper
def hashivault_approle_role_get(params):
<|fim_middle|>
if __name__ == '__main__':
main()
<｜fim▁end｜> | name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result} |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
<|fim_middle|>
else:
module.exit_json(**result)
@hashiwrapper
def hashivault_approle_role_get(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
main()
<｜fim▁end｜> | module.fail_json(**result) |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
<|fim_middle|>
@hashiwrapper
def hashivault_approle_role_get(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
main()
<｜fim▁end｜> | module.exit_json(**result) |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
module.exit_json(**result)
@hashiwrapper
def hashivault_approle_role_get(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
<|fim_middle|>
<｜fim▁end｜> | main() |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def <|fim_middle|>():
argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
module.exit_json(**result)
@hashiwrapper
def hashivault_approle_role_get(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
main()
<｜fim▁end｜> | main |
<|file_name|>hashivault_approle_role_get.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from ansible.module_utils.hashivault import hashivault_argspec
from ansible.module_utils.hashivault import hashivault_auth_client
from ansible.module_utils.hashivault import hashivault_init
from ansible.module_utils.hashivault import hashiwrapper
ANSIBLE_METADATA = {'status': ['stableinterface'], 'supported_by': 'community', 'version': '1.1'}
DOCUMENTATION = '''
---
module: hashivault_approle_role_get
version_added: "3.8.0"
short_description: Hashicorp Vault approle role get module
description:
- Module to get a approle role from Hashicorp Vault.
options:
name:
description:
- role name.
mount_point:
description:
- mount point for role
default: approle
extends_documentation_fragment: hashivault
'''
EXAMPLES = '''
---
- hosts: localhost
tasks:
- hashivault_approle_role_get:
name: 'ashley'
register: 'vault_approle_role_get'
- debug: msg="Role is {{vault_approle_role_get.role}}"
'''
def main():
argspec = hashivault_argspec()
argspec['name'] = dict(required=True, type='str')
argspec['mount_point'] = dict(required=False, type='str', default='approle')
module = hashivault_init(argspec)
result = hashivault_approle_role_get(module.params)
if result.get('failed'):
module.fail_json(**result)
else:
module.exit_json(**result)
@hashiwrapper
def <|fim_middle|>(params):
name = params.get('name')
client = hashivault_auth_client(params)
result = client.get_role(name, mount_point=params.get('mount_point'))
return {'role': result}
if __name__ == '__main__':
main()
<｜fim▁end｜> | hashivault_approle_role_get |
<|file_name|>A_evolve_outer_star_to_giant.py<|end_file_name|><｜fim▁begin｜>import os
import os.path
from amuse.units import units
from amuse.datamodel import Particle
from amuse.ext.star_to_sph import pickle_stellar_model
from amuse.community.mesa.interface import MESA as stellar_evolution_code
from xiTau_parameters import triple_parameters
def evolve_giant(giant, stop_radius):
stellar_evolution = stellar_evolution_code()
giant_in_code = stellar_evolution.particles.add_particle(giant)
while (giant_in_code.radius < 0.7 | units.AU):
giant_in_code.evolve_one_step()
print "Giant starts to ascend the giant branch, now saving model every step..."
print giant_in_code.as_set()
i = 0
while (giant_in_code.radius < stop_radius):
giant_in_code.evolve_one_step()
print giant_in_code.radius, giant_in_code.age
pickle_file_name = "./model_{0:=04}_".format(i) + "%0.1f"%(giant_in_code.radius.value_in(units.AU))
pickle_stellar_model(giant_in_code, pickle_file_name)
i += 1
if __name__ == "__main__":
model_directory = os.path.join("../../../../../BIGDATA/code/amuse-10.0", "giant_models")
if not os.path.exists(model_directory):
os.mkdir(model_directory)<｜fim▁hole｜> os.chdir(model_directory)
giant = Particle(mass = triple_parameters["mass_out"])
print "\nEvolving with", stellar_evolution_code.__name__
evolve_giant(giant, 1.0 | units.AU)
print "Done"<｜fim▁end｜> | |
<|file_name|>A_evolve_outer_star_to_giant.py<|end_file_name|><｜fim▁begin｜>import os
import os.path
from amuse.units import units
from amuse.datamodel import Particle
from amuse.ext.star_to_sph import pickle_stellar_model
from amuse.community.mesa.interface import MESA as stellar_evolution_code
from xiTau_parameters import triple_parameters
def evolve_giant(giant, stop_radius):
<|fim_middle|>
if __name__ == "__main__":
model_directory = os.path.join("../../../../../BIGDATA/code/amuse-10.0", "giant_models")
if not os.path.exists(model_directory):
os.mkdir(model_directory)
os.chdir(model_directory)
giant = Particle(mass = triple_parameters["mass_out"])
print "\nEvolving with", stellar_evolution_code.__name__
evolve_giant(giant, 1.0 | units.AU)
print "Done"
<｜fim▁end｜> | stellar_evolution = stellar_evolution_code()
giant_in_code = stellar_evolution.particles.add_particle(giant)
while (giant_in_code.radius < 0.7 | units.AU):
giant_in_code.evolve_one_step()
print "Giant starts to ascend the giant branch, now saving model every step..."
print giant_in_code.as_set()
i = 0
while (giant_in_code.radius < stop_radius):
giant_in_code.evolve_one_step()
print giant_in_code.radius, giant_in_code.age
pickle_file_name = "./model_{0:=04}_".format(i) + "%0.1f"%(giant_in_code.radius.value_in(units.AU))
pickle_stellar_model(giant_in_code, pickle_file_name)
i += 1 |
<|file_name|>A_evolve_outer_star_to_giant.py<|end_file_name|><｜fim▁begin｜>import os
import os.path
from amuse.units import units
from amuse.datamodel import Particle
from amuse.ext.star_to_sph import pickle_stellar_model
from amuse.community.mesa.interface import MESA as stellar_evolution_code
from xiTau_parameters import triple_parameters
def evolve_giant(giant, stop_radius):
stellar_evolution = stellar_evolution_code()
giant_in_code = stellar_evolution.particles.add_particle(giant)
while (giant_in_code.radius < 0.7 | units.AU):
giant_in_code.evolve_one_step()
print "Giant starts to ascend the giant branch, now saving model every step..."
print giant_in_code.as_set()
i = 0
while (giant_in_code.radius < stop_radius):
giant_in_code.evolve_one_step()
print giant_in_code.radius, giant_in_code.age
pickle_file_name = "./model_{0:=04}_".format(i) + "%0.1f"%(giant_in_code.radius.value_in(units.AU))
pickle_stellar_model(giant_in_code, pickle_file_name)
i += 1
if __name__ == "__main__":
<|fim_middle|>
<｜fim▁end｜> | model_directory = os.path.join("../../../../../BIGDATA/code/amuse-10.0", "giant_models")
if not os.path.exists(model_directory):
os.mkdir(model_directory)
os.chdir(model_directory)
giant = Particle(mass = triple_parameters["mass_out"])
print "\nEvolving with", stellar_evolution_code.__name__
evolve_giant(giant, 1.0 | units.AU)
print "Done" |
<|file_name|>A_evolve_outer_star_to_giant.py<|end_file_name|><｜fim▁begin｜>import os
import os.path
from amuse.units import units
from amuse.datamodel import Particle
from amuse.ext.star_to_sph import pickle_stellar_model
from amuse.community.mesa.interface import MESA as stellar_evolution_code
from xiTau_parameters import triple_parameters
def evolve_giant(giant, stop_radius):
stellar_evolution = stellar_evolution_code()
giant_in_code = stellar_evolution.particles.add_particle(giant)
while (giant_in_code.radius < 0.7 | units.AU):
giant_in_code.evolve_one_step()
print "Giant starts to ascend the giant branch, now saving model every step..."
print giant_in_code.as_set()
i = 0
while (giant_in_code.radius < stop_radius):
giant_in_code.evolve_one_step()
print giant_in_code.radius, giant_in_code.age
pickle_file_name = "./model_{0:=04}_".format(i) + "%0.1f"%(giant_in_code.radius.value_in(units.AU))
pickle_stellar_model(giant_in_code, pickle_file_name)
i += 1
if __name__ == "__main__":
model_directory = os.path.join("../../../../../BIGDATA/code/amuse-10.0", "giant_models")
if not os.path.exists(model_directory):
<|fim_middle|>
os.chdir(model_directory)
giant = Particle(mass = triple_parameters["mass_out"])
print "\nEvolving with", stellar_evolution_code.__name__
evolve_giant(giant, 1.0 | units.AU)
print "Done"
<｜fim▁end｜> | os.mkdir(model_directory) |
<|file_name|>A_evolve_outer_star_to_giant.py<|end_file_name|><｜fim▁begin｜>import os
import os.path
from amuse.units import units
from amuse.datamodel import Particle
from amuse.ext.star_to_sph import pickle_stellar_model
from amuse.community.mesa.interface import MESA as stellar_evolution_code
from xiTau_parameters import triple_parameters
def <|fim_middle|>(giant, stop_radius):
stellar_evolution = stellar_evolution_code()
giant_in_code = stellar_evolution.particles.add_particle(giant)
while (giant_in_code.radius < 0.7 | units.AU):
giant_in_code.evolve_one_step()
print "Giant starts to ascend the giant branch, now saving model every step..."
print giant_in_code.as_set()
i = 0
while (giant_in_code.radius < stop_radius):
giant_in_code.evolve_one_step()
print giant_in_code.radius, giant_in_code.age
pickle_file_name = "./model_{0:=04}_".format(i) + "%0.1f"%(giant_in_code.radius.value_in(units.AU))
pickle_stellar_model(giant_in_code, pickle_file_name)
i += 1
if __name__ == "__main__":
model_directory = os.path.join("../../../../../BIGDATA/code/amuse-10.0", "giant_models")
if not os.path.exists(model_directory):
os.mkdir(model_directory)
os.chdir(model_directory)
giant = Particle(mass = triple_parameters["mass_out"])
print "\nEvolving with", stellar_evolution_code.__name__
evolve_giant(giant, 1.0 | units.AU)
print "Done"
<｜fim▁end｜> | evolve_giant |
<|file_name|>0063_auto_20210511_2343.py<|end_file_name|><｜fim▁begin｜># Generated by Django 3.2 on 2021-05-11 13:43
from django.db import migrations<｜fim▁hole｜>
dependencies = [
('stock', '0062_auto_20210511_2151'),
]
operations = [
migrations.RemoveField(
model_name='stockitemtracking',
name='link',
),
migrations.RemoveField(
model_name='stockitemtracking',
name='quantity',
),
migrations.RemoveField(
model_name='stockitemtracking',
name='system',
),
migrations.RemoveField(
model_name='stockitemtracking',
name='title',
),
]<｜fim▁end｜> |
class Migration(migrations.Migration): |
<|file_name|>0063_auto_20210511_2343.py<|end_file_name|><｜fim▁begin｜># Generated by Django 3.2 on 2021-05-11 13:43
from django.db import migrations
class Migration(migrations.Migration):
<|fim_middle|>
<｜fim▁end｜> | dependencies = [
('stock', '0062_auto_20210511_2151'),
]
operations = [
migrations.RemoveField(
model_name='stockitemtracking',
name='link',
),
migrations.RemoveField(
model_name='stockitemtracking',
name='quantity',
),
migrations.RemoveField(
model_name='stockitemtracking',
name='system',
),
migrations.RemoveField(
model_name='stockitemtracking',
name='title',
),
] |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""<｜fim▁hole｜> self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)<｜fim▁end｜> | |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
<|fim_middle|>
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
<|fim_middle|>
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent) |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
<|fim_middle|>
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
<|fim_middle|>
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
<|fim_middle|>
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor) |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
<|fim_middle|>
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | self._changed_path() |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
<|fim_middle|>
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width() |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
<|fim_middle|>
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
<|fim_middle|>
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
<|fim_middle|>
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | """ Called whenever a change is made to the text of the document.
"""
self.changed = True |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
<|fim_middle|>
<｜fim▁end｜> | """ A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event) |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
<|fim_middle|>
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
<|fim_middle|>
<｜fim▁end｜> | """ Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event) |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
<|fim_middle|>
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | path = self.path |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
<|fim_middle|>
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | f = open(self.path, 'r')
text = f.read()
f.close() |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
<|fim_middle|>
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | text = '' |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
<|fim_middle|>
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | path = self.path |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
<|fim_middle|>
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width() |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
<|fim_middle|>
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | self.__editor.control.emit(QtCore.SIGNAL('lostFocus')) |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
<|fim_middle|>
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event) |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def <|fim_middle|>(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | __init__ |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def <|fim_middle|>(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | load |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def <|fim_middle|>(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | save |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def <|fim_middle|>(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | select_line |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def <|fim_middle|>(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | _path_changed |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def <|fim_middle|>(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | _show_line_numbers_changed |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def <|fim_middle|>(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | _create_control |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def <|fim_middle|>(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | _on_dirty_changed |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def <|fim_middle|>(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | _on_text_changed |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def <|fim_middle|>(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def eventFilter(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | __init__ |
<|file_name|>python_editor.py<|end_file_name|><｜fim▁begin｜>#------------------------------------------------------------------------------
# Copyright (c) 2007, Riverbank Computing Limited
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD license.
# However, when used with the GPL version of PyQt the additional terms described in the PyQt GPL exception also apply
#
# Author: Riverbank Computing Limited
# Description: <Enthought pyface package component>
#------------------------------------------------------------------------------
# Standard library imports.
import sys
# Major package imports.
from pyface.qt import QtCore, QtGui
# Enthought library imports.
from traits.api import Bool, Event, provides, Unicode
# Local imports.
from pyface.i_python_editor import IPythonEditor, MPythonEditor
from pyface.key_pressed_event import KeyPressedEvent
from pyface.widget import Widget
from pyface.ui.qt4.code_editor.code_widget import AdvancedCodeWidget
@provides(IPythonEditor)
class PythonEditor(MPythonEditor, Widget):
""" The toolkit specific implementation of a PythonEditor. See the
IPythonEditor interface for the API documentation.
"""
#### 'IPythonEditor' interface ############################################
dirty = Bool(False)
path = Unicode
show_line_numbers = Bool(True)
#### Events ####
changed = Event
key_pressed = Event(KeyPressedEvent)
###########################################################################
# 'object' interface.
###########################################################################
def __init__(self, parent, **traits):
super(PythonEditor, self).__init__(**traits)
self.control = self._create_control(parent)
###########################################################################
# 'PythonEditor' interface.
###########################################################################
def load(self, path=None):
""" Loads the contents of the editor.
"""
if path is None:
path = self.path
# We will have no path for a new script.
if len(path) > 0:
f = open(self.path, 'r')
text = f.read()
f.close()
else:
text = ''
self.control.code.setPlainText(text)
self.dirty = False
def save(self, path=None):
""" Saves the contents of the editor.
"""
if path is None:
path = self.path
f = open(path, 'w')
f.write(self.control.code.toPlainText())
f.close()
self.dirty = False
def select_line(self, lineno):
""" Selects the specified line.
"""
self.control.code.set_line_column(lineno, 0)
self.control.code.moveCursor(QtGui.QTextCursor.EndOfLine,
QtGui.QTextCursor.KeepAnchor)
###########################################################################
# Trait handlers.
###########################################################################
def _path_changed(self):
self._changed_path()
def _show_line_numbers_changed(self):
if self.control is not None:
self.control.code.line_number_widget.setVisible(
self.show_line_numbers)
self.control.code.update_line_number_width()
###########################################################################
# Private interface.
###########################################################################
def _create_control(self, parent):
""" Creates the toolkit-specific control for the widget.
"""
self.control = control = AdvancedCodeWidget(parent)
self._show_line_numbers_changed()
# Install event filter to trap key presses.
event_filter = PythonEditorEventFilter(self, self.control)
self.control.installEventFilter(event_filter)
self.control.code.installEventFilter(event_filter)
# Connect signals for text changes.
control.code.modificationChanged.connect(self._on_dirty_changed)
control.code.textChanged.connect(self._on_text_changed)
# Load the editor's contents.
self.load()
return control
def _on_dirty_changed(self, dirty):
""" Called whenever a change is made to the dirty state of the
document.
"""
self.dirty = dirty
def _on_text_changed(self):
""" Called whenever a change is made to the text of the document.
"""
self.changed = True
class PythonEditorEventFilter(QtCore.QObject):
""" A thin wrapper around the advanced code widget to handle the key_pressed
Event.
"""
def __init__(self, editor, parent):
super(PythonEditorEventFilter, self).__init__(parent)
self.__editor = editor
def <|fim_middle|>(self, obj, event):
""" Reimplemented to trap key presses.
"""
if self.__editor.control and obj == self.__editor.control and \
event.type() == QtCore.QEvent.FocusOut:
# Hack for Traits UI compatibility.
self.__editor.control.emit(QtCore.SIGNAL('lostFocus'))
elif self.__editor.control and obj == self.__editor.control.code and \
event.type() == QtCore.QEvent.KeyPress:
# Pyface doesn't seem to be Unicode aware. Only keep the key code
# if it corresponds to a single Latin1 character.
kstr = event.text()
try:
kcode = ord(str(kstr))
except:
kcode = 0
mods = event.modifiers()
self.key_pressed = KeyPressedEvent(
alt_down = ((mods & QtCore.Qt.AltModifier) ==
QtCore.Qt.AltModifier),
control_down = ((mods & QtCore.Qt.ControlModifier) ==
QtCore.Qt.ControlModifier),
shift_down = ((mods & QtCore.Qt.ShiftModifier) ==
QtCore.Qt.ShiftModifier),
key_code = kcode,
event = event)
return super(PythonEditorEventFilter, self).eventFilter(obj, event)
<｜fim▁end｜> | eventFilter |
<|file_name|>create_attn.py<|end_file_name|><｜fim▁begin｜>""" Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
def get_attn(attn_type):
if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type is not None:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network architecture blocks in addition to existing convolutions.
if attn_type == 'se':
module_cls = SEModule
elif attn_type == 'ese':
module_cls = EffectiveSEModule
elif attn_type == 'eca':
module_cls = EcaModule
elif attn_type == 'ecam':
module_cls = partial(EcaModule, use_mlp=True)
elif attn_type == 'ceca':
module_cls = CecaModule
elif attn_type == 'ge':
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
module_cls = LightCbamModule
# Attention / attention-like modules w/ significant params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'sk':
module_cls = SelectiveKernel
elif attn_type == 'splat':
module_cls = SplitAttn
# Self-attention / attention-like modules w/ significant compute and/or params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'lambda':
return LambdaLayer
elif attn_type == 'bottleneck':
return BottleneckAttn
elif attn_type == 'halo':<｜fim▁hole｜> return HaloAttn
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':
module_cls = BatNonLocalAttn
# Woops!
else:
assert False, "Invalid attn module (%s)" % attn_type
elif isinstance(attn_type, bool):
if attn_type:
module_cls = SEModule
else:
module_cls = attn_type
return module_cls
def create_attn(attn_type, channels, **kwargs):
module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None<｜fim▁end｜> | |
<|file_name|>create_attn.py<|end_file_name|><｜fim▁begin｜>""" Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
def get_attn(attn_type):
<|fim_middle|>
def create_attn(attn_type, channels, **kwargs):
module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None
<｜fim▁end｜> | if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type is not None:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network architecture blocks in addition to existing convolutions.
if attn_type == 'se':
module_cls = SEModule
elif attn_type == 'ese':
module_cls = EffectiveSEModule
elif attn_type == 'eca':
module_cls = EcaModule
elif attn_type == 'ecam':
module_cls = partial(EcaModule, use_mlp=True)
elif attn_type == 'ceca':
module_cls = CecaModule
elif attn_type == 'ge':
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
module_cls = LightCbamModule
# Attention / attention-like modules w/ significant params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'sk':
module_cls = SelectiveKernel
elif attn_type == 'splat':
module_cls = SplitAttn
# Self-attention / attention-like modules w/ significant compute and/or params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'lambda':
return LambdaLayer
elif attn_type == 'bottleneck':
return BottleneckAttn
elif attn_type == 'halo':
return HaloAttn
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':
module_cls = BatNonLocalAttn
# Woops!
else:
assert False, "Invalid attn module (%s)" % attn_type
elif isinstance(attn_type, bool):
if attn_type:
module_cls = SEModule
else:
module_cls = attn_type
return module_cls |
<|file_name|>create_attn.py<|end_file_name|><｜fim▁begin｜>""" Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
def get_attn(attn_type):
if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type is not None:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network architecture blocks in addition to existing convolutions.
if attn_type == 'se':
module_cls = SEModule
elif attn_type == 'ese':
module_cls = EffectiveSEModule
elif attn_type == 'eca':
module_cls = EcaModule
elif attn_type == 'ecam':
module_cls = partial(EcaModule, use_mlp=True)
elif attn_type == 'ceca':
module_cls = CecaModule
elif attn_type == 'ge':
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
module_cls = LightCbamModule
# Attention / attention-like modules w/ significant params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'sk':
module_cls = SelectiveKernel
elif attn_type == 'splat':
module_cls = SplitAttn
# Self-attention / attention-like modules w/ significant compute and/or params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'lambda':
return LambdaLayer
elif attn_type == 'bottleneck':
return BottleneckAttn
elif attn_type == 'halo':
return HaloAttn
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':
module_cls = BatNonLocalAttn
# Woops!
else:
assert False, "Invalid attn module (%s)" % attn_type
elif isinstance(attn_type, bool):
if attn_type:
module_cls = SEModule
else:
module_cls = attn_type
return module_cls
def create_attn(attn_type, channels, **kwargs):
<|fim_middle|>
<｜fim▁end｜> | module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None |
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