def write_tree(self, file_name):
# Deannotate the tree
objectify.deannotate(self.tree)
etree.cleanup_namespaces(self.tree)
# Ensure the newly created XML validates against the schema
utilities.validate_xml(self.tree, self.xml_schema_file)
# Write out the tree
self.tree.write(file_name, pretty_print=True)
def write_tree(self, file_name):
# Deannotate the tree
objectify.deannotate(self.tree)
etree.cleanup_namespaces(self.tree)
# Ensure the newly created XML validates against the schema
utilities.validate_xml(self.tree, self.xml_schema_file)
# Write out the tree
self.tree.write(file_name, pretty_print=True)
def create_report_metadata(self):
"""
Create the XML file containing metadata to be written into
the accuracy assessment report
Parameters
----------
None
Returns
-------
None
"""
p = self.parameter_parser
# Connect to the lemma web database
web_db = web_database.WebDatabase(p.model_project,
p.model_region, p.web_dsn)
# Create the XML
xml_schema_file = \
'http://lemma.forestry.oregonstate.edu/xml/report_metadata.xsd'
root_str = """
<report_metadata
xmlns:xsi="%s"
xsi:noNamespaceSchemaLocation="%s"/>
"""
root_str = root_str % (
'http://www.w3.org/2001/XMLSchema-instance',
xml_schema_file
)
#root_str = "<report_metadata/>"
root_elem = objectify.fromstring(root_str)
# Get the model region overview
mr_overview = web_db.get_model_region_info()
field_names = mr_overview.dtype.names
overview_elem = etree.SubElement(root_elem, 'overview')
for f in field_names:
child = etree.SubElement(overview_elem, f.lower())
overview_elem[child.tag] = getattr(mr_overview[0], f)
# Get contact info for people associated with this project
people_info = web_db.get_people_info()
field_names = people_info.dtype.names
people_elem = etree.SubElement(root_elem, 'contact_information')
for person in people_info:
person_elem = etree.SubElement(people_elem, 'contact')
for f in field_names:
child = etree.SubElement(person_elem, f.lower())
person_elem[child.tag] = getattr(person, f)
# Store list of plot IDs into a string if this variable hasn't
# yet been created
if not hasattr(self, 'id_str'):
self.id_str = self._get_id_string()
# Subset the string of plot IDs to thin to one plot at a
# location just for locations that have the exact same spectral
# values for all plot measurements (i.e. places where the
# imagery has been stabilized
delete_list = self.plot_db.get_duplicate_plots_to_remove(self.id_str)
if len(delete_list) > 0:
id_list_subset = [int(x) for x in self.id_str.split(",")]
for id in delete_list:
try:
id_list_subset.remove(id)
# if the ID is not in the list, go on to the next ID
except ValueError:
continue
# turn subsetted id_list into a string
id_str_subset = ','.join(map(str, id_list_subset))
else:
id_str_subset = self.id_str
# Get the plot data sources
data_sources = self.plot_db.get_plot_data_source_summary(id_str_subset)
field_names = data_sources.dtype.names
data_sources_elem = etree.SubElement(root_elem, 'plot_data_sources')
# Create subelements for each unique plot data source
for ds in np.unique(data_sources.DATA_SOURCE):
data_source_elem = \
etree.SubElement(data_sources_elem, 'plot_data_source')
child = etree.SubElement(data_source_elem, 'data_source')
data_source_elem[child.tag] = ds
child = etree.SubElement(data_source_elem, 'description')
descriptions = \
data_sources[np.where(data_sources.DATA_SOURCE == ds)]
description = np.unique(descriptions)
data_source_elem[child.tag] = description['DESCRIPTION'][0]
years_elem = etree.SubElement(data_source_elem, 'assessment_years')
recs = data_sources[np.where(data_sources.DATA_SOURCE == ds)]
# Create subelements for each plot assessment years for
# this data source
for rec in recs:
year_elem = etree.SubElement(years_elem, 'year')
child = etree.SubElement(year_elem, 'assessment_year')
year_elem[child.tag] = getattr(rec, 'ASSESSMENT_YEAR')
child = etree.SubElement(year_elem, 'plot_count')
year_elem[child.tag] = getattr(rec, 'PLOT_COUNT')
# Get the species scientific and common names
species_names = \
self.plot_db.get_species_names(self.id_str, p.lump_table)
field_names = species_names.dtype.names
species_names_elem = etree.SubElement(root_elem, 'species_names')
for species_name in species_names:
species_name_elem = etree.SubElement(species_names_elem, 'species')
for f in field_names:
child = etree.SubElement(species_name_elem, f.lower())
species_name_elem[child.tag] = getattr(species_name, f)
# Get the ordination variable descriptions
ordination_vars = ','.join(p.get_ordination_variable_names())
ordination_descr = \
self.plot_db.get_ordination_variable_descriptions(ordination_vars)
field_names = ordination_descr.dtype.names
ord_vars_elem = etree.SubElement(root_elem, 'ordination_variables')
for ord_var in ordination_descr:
ord_var_elem = \
etree.SubElement(ord_vars_elem, 'ordination_variable')
for f in field_names:
child = etree.SubElement(ord_var_elem, f.lower())
ord_var_elem[child.tag] = getattr(ord_var, f)
tree = root_elem.getroottree()
objectify.deannotate(tree)
etree.cleanup_namespaces(tree)
# Ensure that this tree validates against the schema file
utilities.validate_xml(tree, xml_schema_file)
# Write XML to file
report_metadata_file = p.report_metadata_file
aa_dir = os.path.dirname(report_metadata_file)
if not os.path.exists(aa_dir):
os.makedirs(aa_dir)
tree.write(report_metadata_file, pretty_print=True)
def create_attribute_metadata(self, field_names):
"""
Create the attribute metadata based on the field_names parameter
Parameters
----------
field_names: list
Field names for which to get metadata
Returns
-------
None
"""
p = self.parameter_parser
# Get the metadata associated with the attribute data
structure_fields, structure_codes = \
self.plot_db.get_structure_metadata(p.model_project)
species_fields = \
self.plot_db.get_species_metadata()
# Create the metadata XML
xml_schema_file = \
'http://lemma.forestry.oregonstate.edu/xml/stand_attributes.xsd'
root_str = """
<attributes
xmlns:xsi="%s"
xsi:noNamespaceSchemaLocation="%s"/>
"""
root_str = root_str % (
'http://www.w3.org/2001/XMLSchema-instance',
xml_schema_file
)
root_elem = objectify.fromstring(root_str)
for n in field_names:
n = n.upper()
other_fields = {}
try:
r = structure_fields[structure_fields.FIELD_NAME == n][0]
other_fields['SPECIES_ATTR'] = 0
other_fields['PROJECT_ATTR'] = r.PROJECT_ATTR
other_fields['ACCURACY_ATTR'] = r.ACCURACY_ATTR
except IndexError:
try:
r = species_fields[species_fields.FIELD_NAME == n][0]
other_fields['SPECIES_ATTR'] = 1
other_fields['PROJECT_ATTR'] = 1
other_fields['ACCURACY_ATTR'] = 1
except IndexError:
err_msg = n + ' has no metadata'
print err_msg
continue
# Add the attribute element
attribute_elem = etree.SubElement(root_elem, 'attribute')
# Add all metadata common to both structure and species recarrays
fields = ('FIELD_NAME', 'FIELD_TYPE', 'UNITS', 'DESCRIPTION',
'SHORT_DESCRIPTION')
for f in fields:
child = etree.SubElement(attribute_elem, f.lower())
attribute_elem[child.tag] = getattr(r, f)
# Add special fields customized for structure and species
fields = ('SPECIES_ATTR', 'PROJECT_ATTR', 'ACCURACY_ATTR')
for f in fields:
child = etree.SubElement(attribute_elem, f.lower())
attribute_elem[child.tag] = other_fields[f]
# Print out codes if they exist
if r.CODED == True:
codes_elem = etree.SubElement(attribute_elem, 'codes')
try:
c_records = \
structure_codes[structure_codes.FIELD_NAME == n]
except IndexError:
#try:
# c_records = \
# species_codes[species_codes.FIELD_NAME == n]
#except IndexError:
err_msg = 'Codes were not found for ' + n
print err_msg
continue
for c_rec in c_records:
code_elem = etree.SubElement(codes_elem, 'code')
c_fields = ('CODE_VALUE', 'DESCRIPTION', 'LABEL')
for c in c_fields:
child = etree.SubElement(code_elem, c.lower())
code_elem[child.tag] = getattr(c_rec, c)
tree = root_elem.getroottree()
objectify.deannotate(tree)
etree.cleanup_namespaces(tree)
# Ensure that this tree validates against the schema file
utilities.validate_xml(tree, xml_schema_file)
# Write out this metadata file
metadata_file = p.stand_metadata_file
tree.write(metadata_file, pretty_print=True)
def create_report_metadata(self):
"""
Create the XML file containing metadata to be written into
the accuracy assessment report
Parameters
----------
None
Returns
-------
None
"""
p = self.parameter_parser
# Connect to the lemma web database
web_db = web_database.WebDatabase(p.model_project, p.model_region,
p.web_dsn)
# Create the XML
xml_schema_file = \
'http://lemma.forestry.oregonstate.edu/xml/report_metadata.xsd'
root_str = """
<report_metadata
xmlns:xsi="%s"
xsi:noNamespaceSchemaLocation="%s"/>
"""
root_str = root_str % ('http://www.w3.org/2001/XMLSchema-instance',
xml_schema_file)
#root_str = "<report_metadata/>"
root_elem = objectify.fromstring(root_str)
# Get the model region overview
mr_overview = web_db.get_model_region_info()
field_names = mr_overview.dtype.names
overview_elem = etree.SubElement(root_elem, 'overview')
for f in field_names:
child = etree.SubElement(overview_elem, f.lower())
overview_elem[child.tag] = getattr(mr_overview[0], f)
# Get contact info for people associated with this project
people_info = web_db.get_people_info()
field_names = people_info.dtype.names
people_elem = etree.SubElement(root_elem, 'contact_information')
for person in people_info:
person_elem = etree.SubElement(people_elem, 'contact')
for f in field_names:
child = etree.SubElement(person_elem, f.lower())
person_elem[child.tag] = getattr(person, f)
# Store list of plot IDs into a string if this variable hasn't
# yet been created
if not hasattr(self, 'id_str'):
self.id_str = self._get_id_string()
# Subset the string of plot IDs to thin to one plot at a
# location just for locations that have the exact same spectral
# values for all plot measurements (i.e. places where the
# imagery has been stabilized
delete_list = self.plot_db.get_duplicate_plots_to_remove(self.id_str)
if len(delete_list) > 0:
id_list_subset = [int(x) for x in self.id_str.split(",")]
for id in delete_list:
try:
id_list_subset.remove(id)
# if the ID is not in the list, go on to the next ID
except ValueError:
continue
# turn subsetted id_list into a string
id_str_subset = ','.join(map(str, id_list_subset))
else:
id_str_subset = self.id_str
# Get the plot data sources
data_sources = self.plot_db.get_plot_data_source_summary(id_str_subset)
field_names = data_sources.dtype.names
data_sources_elem = etree.SubElement(root_elem, 'plot_data_sources')
# Create subelements for each unique plot data source
for ds in np.unique(data_sources.DATA_SOURCE):
data_source_elem = \
etree.SubElement(data_sources_elem, 'plot_data_source')
child = etree.SubElement(data_source_elem, 'data_source')
data_source_elem[child.tag] = ds
child = etree.SubElement(data_source_elem, 'description')
descriptions = \
data_sources[np.where(data_sources.DATA_SOURCE == ds)]
description = np.unique(descriptions)
data_source_elem[child.tag] = description['DESCRIPTION'][0]
years_elem = etree.SubElement(data_source_elem, 'assessment_years')
recs = data_sources[np.where(data_sources.DATA_SOURCE == ds)]
# Create subelements for each plot assessment years for
# this data source
for rec in recs:
year_elem = etree.SubElement(years_elem, 'year')
child = etree.SubElement(year_elem, 'assessment_year')
year_elem[child.tag] = getattr(rec, 'ASSESSMENT_YEAR')
child = etree.SubElement(year_elem, 'plot_count')
year_elem[child.tag] = getattr(rec, 'PLOT_COUNT')
# Get the species scientific and common names
species_names = \
self.plot_db.get_species_names(self.id_str, p.lump_table)
field_names = species_names.dtype.names
species_names_elem = etree.SubElement(root_elem, 'species_names')
for species_name in species_names:
species_name_elem = etree.SubElement(species_names_elem, 'species')
for f in field_names:
child = etree.SubElement(species_name_elem, f.lower())
species_name_elem[child.tag] = getattr(species_name, f)
# Get the ordination variable descriptions
ordination_vars = ','.join(p.get_ordination_variable_names())
ordination_descr = \
self.plot_db.get_ordination_variable_descriptions(ordination_vars)
field_names = ordination_descr.dtype.names
ord_vars_elem = etree.SubElement(root_elem, 'ordination_variables')
for ord_var in ordination_descr:
ord_var_elem = \
etree.SubElement(ord_vars_elem, 'ordination_variable')
for f in field_names:
child = etree.SubElement(ord_var_elem, f.lower())
ord_var_elem[child.tag] = getattr(ord_var, f)
tree = root_elem.getroottree()
objectify.deannotate(tree)
etree.cleanup_namespaces(tree)
# Ensure that this tree validates against the schema file
utilities.validate_xml(tree, xml_schema_file)
# Write XML to file
report_metadata_file = p.report_metadata_file
aa_dir = os.path.dirname(report_metadata_file)
if not os.path.exists(aa_dir):
os.makedirs(aa_dir)
tree.write(report_metadata_file, pretty_print=True)
def create_attribute_metadata(self, field_names):
"""
Create the attribute metadata based on the field_names parameter
Parameters
----------
field_names: list
Field names for which to get metadata
Returns
-------
None
"""
p = self.parameter_parser
# Get the metadata associated with the attribute data
structure_fields, structure_codes = \
self.plot_db.get_structure_metadata(p.model_project)
species_fields = \
self.plot_db.get_species_metadata()
# Create the metadata XML
xml_schema_file = \
'http://lemma.forestry.oregonstate.edu/xml/stand_attributes.xsd'
root_str = """
<attributes
xmlns:xsi="%s"
xsi:noNamespaceSchemaLocation="%s"/>
"""
root_str = root_str % ('http://www.w3.org/2001/XMLSchema-instance',
xml_schema_file)
root_elem = objectify.fromstring(root_str)
for n in field_names:
n = n.upper()
other_fields = {}
try:
r = structure_fields[structure_fields.FIELD_NAME == n][0]
other_fields['SPECIES_ATTR'] = 0
other_fields['PROJECT_ATTR'] = r.PROJECT_ATTR
other_fields['ACCURACY_ATTR'] = r.ACCURACY_ATTR
except IndexError:
try:
r = species_fields[species_fields.FIELD_NAME == n][0]
other_fields['SPECIES_ATTR'] = 1
other_fields['PROJECT_ATTR'] = 1
other_fields['ACCURACY_ATTR'] = 1
except IndexError:
err_msg = n + ' has no metadata'
print err_msg
continue
# Add the attribute element
attribute_elem = etree.SubElement(root_elem, 'attribute')
# Add all metadata common to both structure and species recarrays
fields = ('FIELD_NAME', 'FIELD_TYPE', 'UNITS', 'DESCRIPTION',
'SHORT_DESCRIPTION')
for f in fields:
child = etree.SubElement(attribute_elem, f.lower())
attribute_elem[child.tag] = getattr(r, f)
# Add special fields customized for structure and species
fields = ('SPECIES_ATTR', 'PROJECT_ATTR', 'ACCURACY_ATTR')
for f in fields:
child = etree.SubElement(attribute_elem, f.lower())
attribute_elem[child.tag] = other_fields[f]
# Print out codes if they exist
if r.CODED == True:
codes_elem = etree.SubElement(attribute_elem, 'codes')
try:
c_records = \
structure_codes[structure_codes.FIELD_NAME == n]
except IndexError:
#try:
# c_records = \
# species_codes[species_codes.FIELD_NAME == n]
#except IndexError:
err_msg = 'Codes were not found for ' + n
print err_msg
continue
for c_rec in c_records:
code_elem = etree.SubElement(codes_elem, 'code')
c_fields = ('CODE_VALUE', 'DESCRIPTION', 'LABEL')
for c in c_fields:
child = etree.SubElement(code_elem, c.lower())
code_elem[child.tag] = getattr(c_rec, c)
tree = root_elem.getroottree()
objectify.deannotate(tree)
etree.cleanup_namespaces(tree)
# Ensure that this tree validates against the schema file
utilities.validate_xml(tree, xml_schema_file)
# Write out this metadata file
metadata_file = p.stand_metadata_file
tree.write(metadata_file, pretty_print=True)
def create_model_xml(self, model_directory, model_region, model_year):
"""
Create an XML string from prototype XML specialized for the
model directory, model region, and model year
Parameters
----------
model_directory : str
Model directory for this model
model_region : int
Modeling region with which to specialize this XML
model_year : int
Year (4-digit) with which to specialize this XML
Returns
-------
out_xml : StringIO
XML string to be serialized
"""
# Make a deep copy of this instance
obj = deepcopy(self)
# Switch the parameter_set tag to now be 'FULL'
obj.parameter_set = 'FULL'
# Replace the necessary elements with the model directory,
# model region and year
obj.model_directory = model_directory
obj.model_region = model_region
obj.model_year = model_year
# Create a PlotDatabase instance for filling in many elements
# Note that we pass obj.model_region and obj.model_year as
# specified rather than the prototype's values; everything else
# can come from the prototype
plot_db = \
plot_database.PlotDatabase(self.model_type, obj.model_region,
self.buffer, obj.model_year, self.summary_level,
self.image_source, self.image_version, dsn=self.plot_dsn)
# Model boundary_raster and region extent
rec = (plot_db.get_model_region_window())[0]
obj.boundary_raster = rec.BOUNDARY_RASTER
obj.envelope = [rec.X_MIN, rec.Y_MIN, rec.X_MAX, rec.Y_MAX]
# Plot image crosswalk
#
# For model types that use imagery, we need to match plot assessment
# years to available image years. First look for the presence of a
# keyword tag in the <plot_image_crosswalk> block and if it exists,
# query the database for the plot assessment years and available
# image years and return the formatted XML. Otherwise, skip this
# section as the crosswalk has already been defined.
value = self.plot_image_crosswalk
if value and isinstance(value, str):
pi_data = plot_db.get_plot_image_pairs(value)
obj.plot_image_crosswalk = pi_data
# If the plot_image_crosswalk tag is missing, we need to populate
# the plot_years tag in non-imagery models
else:
obj.plot_years = plot_db.get_plot_years()
# Ordination variables
#
# First look for the presence of the keyword tag in the
# 'ordination_variables' block and if it exists, query the database
# for the allowed spatial variables. Otherwise, skip over this section
# as the spatial variables have already been specified
value = self.get_ordination_variables()
if value and isinstance(value, str):
ord_vars = plot_db.get_ordination_variable_list(
value, self.variable_filter)
obj.set_ordination_variables(ord_vars)
# Accuracy assessment report name
if self.accuracy_assessment_report:
mr_str = 'mr' + str(obj.model_region)
prefix = '_'.join(
(mr_str, self.model_type, str(obj.model_year), 'aa'))
obj.accuracy_assessment_report = prefix + '.pdf'
# Deannotate the tree
objectify.deannotate(obj.tree)
etree.cleanup_namespaces(obj.tree)
# Ensure the newly created XML validates against the schema
utilities.validate_xml(obj.tree, self.xml_schema_file)
# Return the tree for serializing
return obj
def validate(self):
# Validate the XML schema - if the current tree doesn't validate
# against the XML schema, this will raise an exception
utilities.validate_xml(self.xml_tree, self.xml_schema_file)
def create_model_xml(self, model_directory, model_region, model_year):
"""
Create an XML string from prototype XML specialized for the
model directory, model region, and model year
Parameters
----------
model_directory : str
Model directory for this model
model_region : int
Modeling region with which to specialize this XML
model_year : int
Year (4-digit) with which to specialize this XML
Returns
-------
out_xml : StringIO
XML string to be serialized
"""
# Make a deep copy of this instance
obj = deepcopy(self)
# Switch the parameter_set tag to now be 'FULL'
obj.parameter_set = 'FULL'
# Replace the necessary elements with the model directory,
# model region and year
obj.model_directory = model_directory
obj.model_region = model_region
obj.model_year = model_year
# Create a PlotDatabase instance for filling in many elements
# Note that we pass obj.model_region and obj.model_year as
# specified rather than the prototype's values; everything else
# can come from the prototype
plot_db = \
plot_database.PlotDatabase(self.model_type, obj.model_region,
self.buffer, obj.model_year, self.summary_level,
self.image_source, self.image_version, dsn=self.plot_dsn)
# Model boundary_raster and region extent
rec = (plot_db.get_model_region_window())[0]
obj.boundary_raster = rec.BOUNDARY_RASTER
obj.envelope = [rec.X_MIN, rec.Y_MIN, rec.X_MAX, rec.Y_MAX]
# Plot image crosswalk
#
# For model types that use imagery, we need to match plot assessment
# years to available image years. First look for the presence of a
# keyword tag in the <plot_image_crosswalk> block and if it exists,
# query the database for the plot assessment years and available
# image years and return the formatted XML. Otherwise, skip this
# section as the crosswalk has already been defined.
value = self.plot_image_crosswalk
if value and isinstance(value, str):
pi_data = plot_db.get_plot_image_pairs(value)
obj.plot_image_crosswalk = pi_data
# If the plot_image_crosswalk tag is missing, we need to populate
# the plot_years tag in non-imagery models
else:
obj.plot_years = plot_db.get_plot_years()
# Ordination variables
#
# First look for the presence of the keyword tag in the
# 'ordination_variables' block and if it exists, query the database
# for the allowed spatial variables. Otherwise, skip over this section
# as the spatial variables have already been specified
value = self.get_ordination_variables()
if value and isinstance(value, str):
ord_vars = plot_db.get_ordination_variable_list(
value, self.variable_filter)
obj.set_ordination_variables(ord_vars)
# Accuracy assessment report name
if self.accuracy_assessment_report:
mr_str = 'mr' + str(obj.model_region)
prefix = '_'.join(
(mr_str, self.model_type, str(obj.model_year), 'aa'))
obj.accuracy_assessment_report = prefix + '.pdf'
# Deannotate the tree
objectify.deannotate(obj.tree)
etree.cleanup_namespaces(obj.tree)
# Ensure the newly created XML validates against the schema
utilities.validate_xml(obj.tree, self.xml_schema_file)
# Return the tree for serializing
return obj
