def format_impact_summary(self):
"""Format impact summary.
:returns: The impact summary.
:rtype: safe.messaging.Message
"""
message = m.Message(style_class='container')
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
if 'headings' in self.impact_summary.keys():
row = m.Row()
row.add(m.Cell(self.impact_summary['headings'][0], header=True))
row.add(
m.Cell(self.impact_summary['headings'][1],
header=True,
align='right'))
table.add(row)
for category in self.impact_summary['fields']:
row = m.Row()
row.add(m.Cell(category[0], header=True))
row.add(m.Cell(self.format_int(category[1]), align='right'))
# For value field, if existed
if len(category) > 2:
row.add(m.Cell(self.format_int(category[2]), align='right'))
table.add(row)
message.add(table)
return message
def _add_field_to_table(field, table):
row = m.Row()
row.add(m.Cell(field['name']))
row.add(m.Cell(field['field_name']))
field_types = None
if not isinstance(field['type'], list):
field_types = '%s' % _type_to_string(field['type'])
else:
# List of field types are supported but the user will only care
# about the simple types so we turn them into simple names (whole
# number, decimal number etc. and then strip out the duplicates
unique_list = []
# First iterate the types found in the definition to get english names
for field_type in field['type']:
field_type_string = _type_to_string(field_type)
if field_type_string not in unique_list:
unique_list.append(field_type_string)
# now iterate the unque list and write to a sentence
for field_type in unique_list:
if field_types:
field_types += ', %s' % unicode(field_type)
else:
field_types = unicode(field_type)
row.add(m.Cell(field_types))
row.add(m.Cell(field['precision']))
table.add(row)
# Description goes in its own row with spanning
row = m.Row()
row.add(m.Cell(field['description'] + ' ' + field['help_text'], span=6))
table.add(row)
def _start_glossary_table(group):
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
row.add(m.Cell(tr('Term')), header_flag=True)
row.add(m.Cell(tr('Description')), header_flag=True)
table.add(row)
return table
def minimum_needs_breakdown(self):
"""Breakdown by population.
:returns: The population breakdown report.
:rtype: list
"""
message = m.Message(style_class='container')
message.add(
m.Heading(tr('Evacuated population minimum needs'),
**styles.INFO_STYLE))
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
total_needs = self.total_needs
for frequency, needs in total_needs.items():
row = m.Row()
row.add(
m.Cell(tr('Relief items to be provided %s' % frequency),
header=True))
row.add(m.Cell(tr('Total'), header=True, align='right'))
table.add(row)
for resource in needs:
row = m.Row()
row.add(m.Cell(tr(resource['table name'])))
row.add(
m.Cell(tr(format_int(resource['amount'])), align='right'))
table.add(row)
message.add(table)
return message
def hazard_table(self, hazard_table):
""" Return updated hazard table.
:param hazard_table: hazard table.
:type hazard_table: Table
:returns hazard_table: Updated Hazard Table
:rtype area_name: Table
"""
hazard_table.caption = None
for key, value in self.hazard_levels.iteritems():
name = self.hazard_class_mapping[key][0]
# This skips reporting people not affected in No zone
if key == 'wet':
row = m.Row()
row.add(
m.Cell(
tr('People within hazard field ("%s") of value "%s"') %
(self.hazard_class_field, name),
header=True))
value = format_int(population_rounding(value))
row.add(m.Cell(value, align='right'))
elif key == 'dry':
continue
else:
row = m.Row()
row.add(m.Cell(name, header=True))
value = format_int(population_rounding(value))
row.add(m.Cell(value, align='right'))
hazard_table.add(row)
# Total affected population
row = m.Row()
row.add(m.Cell(tr('Total affected people'), header=True))
affected = format_int(
population_rounding(self.total_affected_population))
row.add(m.Cell(affected, align='right'))
hazard_table.add(row)
# Non affected population
row = m.Row()
unaffected = format_int(population_rounding(
self.unaffected_population))
row.add(m.Cell(tr('Unaffected people'), header=True))
row.add(m.Cell(unaffected, align='right'))
hazard_table.add(row)
# Total Population
row = m.Row()
total_population = format_int(
population_rounding(self.total_population))
row.add(m.Cell(tr('Total people'), header=True))
row.add(m.Cell(total_population, align='right'))
hazard_table.add(row)
return hazard_table
def _start_glossary_table(group):
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
# first col for icons if present
row.add(m.Cell(tr('Term'), header=True))
row.add(m.Cell(tr('Description'), header=True))
row.add(m.Cell(tr(''), header=True))
table.add(row)
return table
def _create_fields_table():
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
row.add(m.Cell(tr('Name'), header=True))
row.add(m.Cell(tr('Field Name'), header=True))
row.add(m.Cell(tr('Type'), header=True))
row.add(m.Cell(tr('Length'), header=True))
row.add(m.Cell(tr('Precision'), header=True))
table.add(row)
return table
def _dict_to_row(keyword_value):
"""Helper to make a message row from a keyword where value is a dict.
.. versionadded:: 3.2
Use this when constructing a table from keywords to display as
part of a message object. This variant will unpack the dict and
present it nicely in the keyword value area as a nested table in the
cell.
We are expecting keyword value would be something like this:
"{'high': ['Kawasan Rawan Bencana III'], "
"'medium': ['Kawasan Rawan Bencana II'], "
"'low': ['Kawasan Rawan Bencana I']}"
Or by passing a python dict object with similar layout to above.
i.e. A string representation of a dict where the values are lists.
:param keyword_value: Value of the keyword to be rendered. This must
be a string representation of a dict, or a dict.
:type keyword_value: basestring, dict
:returns: A table to be added into a cell in the keywords table.
:rtype: safe.messaging.items.table
"""
if isinstance(keyword_value, basestring):
keyword_value = literal_eval(keyword_value)
table = m.Table(style_class='table table-condensed')
for key, value in keyword_value.items():
row = m.Row()
# First the heading
if definition(key):
name = definition(key)['name']
else:
name = tr(key.capitalize())
row.add(m.Cell(m.ImportantText(name)))
# Then the value. If it contains more than one element we
# present it as a bullet list, otherwise just as simple text
if isinstance(value, (tuple, list, dict, set)):
if len(value) > 1:
bullets = m.BulletedList()
for item in value:
bullets.add(item)
row.add(m.Cell(bullets))
elif len(value) == 0:
row.add(m.Cell(""))
else:
row.add(m.Cell(value[0]))
else:
row.add(m.Cell(value))
table.add(row)
return table
def _create_post_processor_subtable(item_list):
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
row.add(m.Cell(tr('Name'), header=True))
row.add(m.Cell(tr('Description'), header=True))
table.add(row)
for item in item_list:
row = m.Row()
row.add(m.Cell(item['key']))
row.add(m.Cell(item['description']))
table.add(row)
return table
def _make_defaults_exposure_table():
"""Build headers for a table related to exposure classes.
:return: A table with headers.
:rtype: m.Table
"""
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
row.add(m.Cell(tr('Name'), header=True))
row.add(m.Cell(tr('Default values'), header=True))
table.add(row)
return table
def impact_summary(self):
"""The impact summary as per category.
:returns: The impact summary.
:rtype: safe.messaging.Message
"""
affect_types = self._impact_breakdown
message = m.Message(style_class='container')
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
row = m.Row()
row.add(m.Cell('', header=True)) # intentionally empty top left cell
row.add(m.Cell('Buildings affected', header=True))
for (category, building_breakdown) in self.affected_buildings.items():
total_affected = [0] * len(affect_types)
for affected_breakdown in building_breakdown.values():
for affect_type, number_affected in affected_breakdown.items():
count = affect_types.index(affect_type)
total_affected[count] += number_affected
row = m.Row()
row.add(m.Cell(tr(category), header=True))
for affected in total_affected:
row.add(m.Cell(format_int(affected), align='right'))
table.add(row)
if len(self._affected_categories) > 1:
row = m.Row()
row.add(m.Cell(tr('Affected buildings'), header=True))
row.add(
m.Cell(format_int(self.total_affected_buildings),
align='right'))
table.add(row)
# Only show not affected building row if the IF does not use custom
# affected categories
if self._affected_categories == self.affected_buildings.keys():
row = m.Row()
row.add(m.Cell(tr('Not affected buildings'), header=True))
row.add(
m.Cell(format_int(self.total_unaffected_buildings),
align='right'))
table.add(row)
row = m.Row()
row.add(m.Cell(tr('Total'), header=True))
row.add(m.Cell(format_int(self.total_buildings), align='right'))
table.add(row)
message.add(table)
return message
def _tabulate_zero_impact(self):
thresholds = self.parameters['thresholds'].value
message = m.Message()
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
label = m.ImportantText(
tr('People in %.1f m of water') % thresholds[-1])
content = '%s' % format_int(self.total_evacuated)
row.add(m.Cell(label))
row.add(m.Cell(content))
table.add(row)
table.caption = self.question
message.add(table)
message = message.to_html(suppress_newlines=True)
return message
def format_postprocessing(self):
"""Format postprocessing.
:returns: The postprocessing.
:rtype: safe.messaging.Message
"""
# List of post processor that can't be sum up. Issue #3118
no_total = ['Age', 'Gender', 'MinimumNeeds']
if not self.postprocessing:
return False
message = m.Message()
for postprocessor, v in self.postprocessing.items():
table = m.Table(style_class='table table-condensed table-striped')
table.caption = v['caption']
attributes = v['attributes']
if attributes:
header = m.Row()
# Bold and align left the 1st one.
header.add(m.Cell(attributes[0], header=True, align='left'))
for attribute in attributes[1:]:
# Bold and align right.
header.add(m.Cell(attribute, header=True, align='right'))
if postprocessor not in no_total:
header.add(m.Cell('Total', header=True, align='right'))
table.add(header)
for field in v['fields']:
row = m.Row()
# First column is string
row.add(m.Cell(field[0]))
total = 0
for value in field[1:]:
try:
val = int(value)
total += val
# Align right integers.
row.add(m.Cell(self.format_int(val),
align='right'))
except ValueError:
# Catch no data value. Align left strings.
row.add(m.Cell(value, align='left'))
if postprocessor not in no_total:
row.add(
m.Cell(self.format_int(round(total)),
align='right'))
table.add(row)
message.add(table)
for note in v['notes']:
message.add(m.EmphasizedText(note))
return message
def impact_summary(self):
"""The impact summary as per category
:returns: The impact summary.
:rtype: safe.messaging.Message
"""
message = m.Message(style_class='container')
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
row = m.Row()
row = self.head_row(row)
table.add(row)
second_row = m.Row()
second_row.add(m.Cell(tr('All')))
second_row = self.total_row(second_row)
table.add(second_row)
break_row = m.Row()
break_row.add(
m.Cell(tr('Breakdown by Area'), header=True, align='right'))
# intentionally empty right cells
break_row.add(m.Cell('', header=True))
break_row.add(m.Cell('', header=True))
break_row.add(m.Cell('', header=True))
break_row.add(m.Cell('', header=True))
break_row.add(m.Cell('', header=True))
break_row.add(m.Cell('', header=True))
table.add(break_row)
table = self.impact_calculation(table)
last_row = m.Row()
last_row.add(m.Cell(tr('Total')))
table.add(self.total_row(last_row))
hazard_table = m.Table(
style_class='table table-condensed table-striped')
hazard_table = self.hazard_table(hazard_table)
message.add(hazard_table)
message.add(table)
return message
def no_population_impact_message(question):
"""Create a message that indicates that no population were impacted.
:param question: A question sentence that will be used as the table
caption.
:type question: basestring
:returns: An html document containing a nice message saying nobody was
impacted.
:rtype: basestring
"""
message = m.Message()
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
label = m.ImportantText(tr('People impacted'))
content = 0
row.add(m.Cell(label))
row.add(m.Cell(content))
table.add(row)
table.caption = question
message.add(table)
message = message.to_html(suppress_newlines=True)
return message
def impact_summary(self):
"""The impact summary as per category
:returns: The impact summary.
:rtype: safe.message.Message
"""
affected_categories = self.affected_road_categories
message = m.Message(style_class='container')
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
row = m.Row()
row.add(m.Cell(tr('Breakdown by road type'), header=True))
row.add(m.Cell('', header=True)) # intentionally empty top left cell
row.add(m.Cell('', header=True)) # intentionally empty top left cell
table.add(row)
row = m.Row()
row.add(m.Cell(tr('Road Type'), header=True))
for affected_category in affected_categories:
row.add(m.Cell(affected_category, header=True, align='right'))
row.add(m.Cell(tr('Total (m)'), header=True, align='right'))
table.add(row)
total_affected = [0] * len(affected_categories)
for (category, road_breakdown) in self.affected_road_lengths.items():
number_affected = sum(road_breakdown.values())
count = affected_categories.index(category)
total_affected[count] = format_int(int(number_affected))
row = m.Row()
row.add(m.Cell(tr('All')))
for total_affected_value in total_affected:
row.add(m.Cell(total_affected_value, align='right'))
row.add(m.Cell(format_int(int(self.total_road_length)), align='right'))
table.add(row)
message.add(table)
return message
def _make_defaults_table():
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
# first row is for colour - we dont use a header here as some tables
# do not have colour...
row.add(m.Cell(tr('')), header_flag=True)
row.add(m.Cell(tr('Name')), header_flag=True)
row.add(m.Cell(tr('Affected')), header_flag=True)
row.add(m.Cell(tr('Displacement rate')), header_flag=True)
row.add(m.Cell(tr('Default values')), header_flag=True)
row.add(m.Cell(tr('Default min')), header_flag=True)
row.add(m.Cell(tr('Default max')), header_flag=True)
table.add(row)
return table
def impact_summary(self):
"""The impact summary as per category
:returns: The impact summary.
:rtype: safe.messaging.Message
"""
message = m.Message(style_class='container')
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
row = m.Row()
row.add(
m.Cell(tr('Population needing evacuation <sup>1</sup>'),
header=True))
evacuated = format_int(population_rounding(self.total_evacuated))
row.add(m.Cell(evacuated, align='right'))
table.add(row)
if len(self.impact_category_ordering):
table.add(m.Row()) # add a blank line
row = m.Row()
row.add(m.Cell(tr('Total affected population'), header=True))
affected = format_int(
population_rounding(self.total_affected_population))
row.add(m.Cell(affected, align='right'))
table.add(row)
for category in self.impact_category_ordering:
population_in_category = self.lookup_category(category)
population_in_category = format_int(
population_rounding(population_in_category))
row = m.Row()
row.add(m.Cell(tr(category), header=True))
row.add(m.Cell(population_in_category, align='right'))
table.add(row)
table.add(m.Row()) # add a blank line
row = m.Row()
unaffected = format_int(population_rounding(
self.unaffected_population))
row.add(m.Cell(tr('Unaffected population'), header=True))
row.add(m.Cell(unaffected, align='right'))
table.add(row)
message.add(table)
return message
def format_impact_table(self):
"""Impact detailed report.
:returns: The detailed report.
:rtype: safe.messaging.Message
"""
message = m.Message(style_class='container')
table = m.Table(style_class='table table-condensed table-striped')
table.caption = None
# Table header
row = m.Row()
attributes = self.impact_table['attributes']
# Bold and align left the 1st one.
row.add(m.Cell(attributes[0], header=True, align='left'))
for attribute in attributes[1:]:
# Bold and align right.
row.add(m.Cell(attribute, header=True, align='right'))
table.add(row)
# Fields
for record in self.impact_table['fields'][:-1]:
row = m.Row()
# Bold and align left the 1st one.
row.add(m.Cell(record[0], header=True, align='left'))
for content in record[1:-1]:
# Align right.
row.add(m.Cell(format_int(int(content)), align='right'))
# Bold and align right the last one.
row.add(
m.Cell(format_int(int(record[-1])), header=True,
align='right'))
table.add(row)
# Total Row
row = m.Row()
last_row = self.impact_table['fields'][-1]
# Bold and align left the 1st one.
row.add(m.Cell(last_row[0], header=True, align='left'))
for content in last_row[1:]:
# Bold and align right.
row.add(
m.Cell(format_int(int(content)), header=True, align='right'))
table.add(row)
message.add(table)
return message
def generate_analysis_result_html(self):
"""Return a HTML table of the analysis result
:return: A file path to the html file saved to disk.
"""
message = m.Message(style_class='report')
# Table for affected population
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
total_people = self.tr('%s') % format_int(
population_rounding(self.impact_data.total_affected_population))
estimates_idp = self.tr('%s') % format_int(
population_rounding(self.impact_data.estimates_idp))
row.add(
m.Cell(self.tr('Total affected population (people)'), header=True))
row.add(m.Cell(total_people, style_class="text-right"))
table.add(row)
row = m.Row()
row.add(m.Cell(self.tr('Estimates of IDP (people)'), header=True))
row.add(m.Cell(estimates_idp, style_class="text-right"))
table.add(row)
message.add(table)
# Table for minimum needs
for k, v in self.impact_data.minimum_needs.iteritems():
section = self.tr('Relief items to be provided %s :') % k
# text = m.Text(section)
row = m.Row(style_class='alert-info')
row.add(m.Cell(section, header=True, attributes='colspan=2'))
# message.add(text)
table = m.Table(header=row,
style_class='table table-condensed table-striped')
for e in v:
row = m.Row()
need_name = self.tr(e['name'])
need_number = format_int(population_rounding(e['amount']))
need_unit = self.tr(e['unit']['abbreviation'])
if need_unit:
need_string = '%s (%s)' % (need_name, need_unit)
else:
need_string = need_name
row.add(m.Cell(need_string, header=True))
row.add(m.Cell(need_number, style_class="text-right"))
table.add(row)
message.add(table)
path = self.write_html_table('impact_analysis_report.html', message)
return path
def _make_defaults_hazard_table():
"""Build headers for a table related to hazard classes.
:return: A table with headers.
:rtype: m.Table
"""
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
# first row is for colour - we dont use a header here as some tables
# do not have colour...
row.add(m.Cell(tr(''), header=True))
row.add(m.Cell(tr('Name'), header=True))
row.add(m.Cell(tr('Affected'), header=True))
row.add(m.Cell(tr('Fatality rate'), header=True))
row.add(m.Cell(tr('Displacement rate'), header=True))
row.add(m.Cell(tr('Default values'), header=True))
row.add(m.Cell(tr('Default min'), header=True))
row.add(m.Cell(tr('Default max'), header=True))
table.add(row)
return table
def head_row(self, row):
"""Set and return header row in impact summary
:param row: The empty header row
:type row: Row
:return Header row with content
:rtype Row
"""
row.add(m.Cell(tr('Area Name'), header=True, align='right'))
row.add(m.Cell(tr('Affected Area (ha)'), header=True, align='right'))
row.add(m.Cell(tr('Affected Area (%)'), header=True, align='right'))
row.add(m.Cell(tr('Total (ha)'), header=True, align='right'))
row.add(m.Cell(tr('Affected People'), header=True, align='right'))
row.add(m.Cell(tr('Affected People(%)'), header=True, align='right'))
row.add(
m.Cell(tr('Total Number of People'), header=True, align='right'))
return row
def impact_row(self, area_id, affected, percent_affected,
single_total_area, number_people_affected,
percent_people_affected):
"""Adds the calculated results into respective impact row
:param area_id: Area id
:type area_id: int
:param affected: table with first and second row
:type affected: Table
:param percent_affected: percentage of affected area
:type percent_affected:float
:param single_total_area: total area of the land
:type single_total_area:float
:param number_people_affected: number of people affected
:type number_people_affected:float
:param percent_people_affected: percentage of people affected
in the area
:type percent_people_affected:float
:return row: the new impact row
:rtype row: Row
"""
area_name = self.area_name(area_id)
row = m.Row()
row.add(m.Cell(area_name))
row.add(m.Cell(format_int(int(affected)), align='right'))
row.add(m.Cell("%.1f%%" % percent_affected, align='right'))
row.add(m.Cell(format_int(int(single_total_area)), align='right'))
row.add(m.Cell(format_int(int(number_people_affected)), align='right'))
row.add(m.Cell("%.1f%%" % percent_people_affected, align='right'))
row.add(
m.Cell(format_int(int(self.areas_population[area_id])),
align='right'))
return row
def row(self, row, total_affected_area, percentage_affected_area,
total_area, total_affected_population, total_population):
"""Adds values to the second row columns
:param row: an empty row
:type row: Row
:param total_affected_area: total affected area
:type total_affected_area: float
:param percentage_affected_area: percentage of
affected area
:type percentage_affected_area: float
:param total_area: total area
:type total_area: float
:param total_affected_population: total affected
population
:type total_affected_population: float
:param total_population: total population
:type total_population:float
:return row
:rtype Row
"""
row.add(m.Cell(format_int(int(total_affected_area)), align='right'))
row.add(m.Cell("%.0f%%" % percentage_affected_area, align='right'))
row.add(m.Cell(format_int(int(total_area)), align='right'))
row.add(
m.Cell(format_int(int(total_affected_population)), align='right'))
row.add(m.Cell("%.0f%%" % percentage_affected_area, align='right'))
row.add(m.Cell(format_int(int(total_population)), align='right'))
return row
def show_current_state(self):
"""Setup the UI for QTextEdit to show the current state."""
right_panel_heading = QLabel(tr('Status'))
right_panel_heading.setFont(big_font)
right_panel_heading.setSizePolicy(
QSizePolicy.Maximum, QSizePolicy.Maximum)
self.right_layout.addWidget(right_panel_heading)
message = m.Message()
if self.layer_mode == layer_mode_continuous:
title = tr('Thresholds')
else:
title = tr('Value maps')
message.add(m.Heading(title, **INFO_STYLE))
for i in range(len(self.exposures)):
message.add(m.Text(self.exposure_labels[i]))
classification = self.get_classification(
self.exposure_combo_boxes[i])
if self.layer_mode == layer_mode_continuous:
thresholds = self.thresholds.get(self.exposures[i]['key'])
if not thresholds or not classification:
message.add(m.Paragraph(tr('No classifications set.')))
continue
table = m.Table(
style_class='table table-condensed table-striped')
header = m.Row()
header.add(m.Cell(tr('Class name')))
header.add(m.Cell(tr('Minimum')))
header.add(m.Cell(tr('Maximum')))
table.add(header)
classes = classification.get('classes')
# Sort by value, put the lowest first
classes = sorted(classes, key=lambda k: k['value'])
for the_class in classes:
threshold = thresholds[classification['key']]['classes'][
the_class['key']]
row = m.Row()
row.add(m.Cell(the_class['name']))
row.add(m.Cell(threshold[0]))
row.add(m.Cell(threshold[1]))
table.add(row)
else:
value_maps = self.value_maps.get(self.exposures[i]['key'])
if not value_maps or not classification:
message.add(m.Paragraph(tr('No classifications set.')))
continue
table = m.Table(
style_class='table table-condensed table-striped')
header = m.Row()
header.add(m.Cell(tr('Class name')))
header.add(m.Cell(tr('Value')))
table.add(header)
classes = classification.get('classes')
# Sort by value, put the lowest first
classes = sorted(classes, key=lambda k: k['value'])
for the_class in classes:
value_map = value_maps[classification['key']][
'classes'].get(the_class['key'], [])
row = m.Row()
row.add(m.Cell(the_class['name']))
row.add(m.Cell(', '.join([str(v) for v in value_map])))
table.add(row)
message.add(table)
# status_text_edit = QTextBrowser(None)
status_text_edit = QWebView(None)
status_text_edit.setSizePolicy(
QSizePolicy.Ignored,
QSizePolicy.Ignored)
status_text_edit.page().mainFrame().setScrollBarPolicy(
Qt.Horizontal,
Qt.ScrollBarAlwaysOff)
html_string = html_header() + message.to_html() + html_footer()
status_text_edit.setHtml(html_string)
self.right_layout.addWidget(status_text_edit)
def field_mapping_help_content():
"""Helper method that returns just the content in extent mode.
This method was added so that the text could be reused in the
wizard.
:returns: A message object without brand element.
:rtype: safe.messaging.message.Message
"""
message = m.Message()
paragraph = m.Paragraph(
tr('Field mapping describes the process of matching one or more fields '
'in an attribute table to a concept in InaSAFE. The field mappings '
'tool InaSAFE allows you to match concepts such as "elderly", '
'"disabled people", "pregnant" and so on to their counterpart fields '
'in either an aggregation layer or an exposure population vector '
'layer.'))
message.add(paragraph)
paragraph = m.Paragraph(
m.ImportantText(
'Note: It is not possible to use this tool with raster population '
'exposure data, but ratios defined in aggregation layers will be '
'used when raster exposure population data is used.'))
message.add(paragraph)
paragraph = m.Paragraph(m.Image('file:///%s/img/screenshots/'
'demographic-concepts-screenshot.png' %
resources_path()),
style_class='text-center')
message.add(paragraph)
paragraph = m.Paragraph(
tr('The illustration above shows the principle behind InaSAFE\'s '
'demographic breakdown reporting system. The idea here is to support '
'the production of a detailed demographic breakdown when carrying out '
'an analysis with a population exposure vector dataset. So for '
'example instead of simply reporting on the total number of people '
'exposed to a hazard, we want to break down the affected population '
'into distinct demographic groups. In InaSAFE by default we consider '
'three groups:'))
message.add(paragraph)
bullets = m.BulletedList()
bullets.add(
m.Paragraph(
m.ImportantText(tr('Gender: ')),
tr('The gender group reports on gender specific demographics '
'including things like the number of women of child bearing age, '
'number of pregnant women, number of lactating women and so on.'
)))
bullets.add(
m.Paragraph(
m.ImportantText(tr('Age: ')),
tr('The age group reports on age specific demographics including '
'things like the number of infants, children, young adults, '
'adults elderly people and so on.')))
bullets.add(
m.Paragraph(
m.ImportantText(tr('Vulnerable people: ')),
tr('The vulnerable people group reports on specific demographics '
'relating to vulnerability including things like the number of '
'infants, elderly people, disabled people and so on.')))
message.add(bullets)
paragraph = m.Paragraph(
tr('In the diagram above, you can see that we have an "age" group '
'(column on the right) which, for purposes of illustration, has two '
'age classes: "infant" and "child" (center column). These age classes '
'are defined in InaSAFE metadata and there are actually five classes '
'in a default installation. In the left hand column you can see a '
'number of columns listed from the attribute table. In this example '
'our population data contains columns for different age ranges ('
'0-1, 1-2, 2-4, 4-6). The field mapping tool can be used in order '
'to combine the data in the "0 - 1" and "1 - 2" columns into a '
'new column called "infant". In the next section of this document we '
'enumerate the different groups and concepts that InaSAFE supports '
'when generating demographic breakdowns.'))
message.add(paragraph)
paragraph = m.Paragraph(
tr('When the tool is used, it will write additional data to the '
'exposure or aggregation layer keywords so that your preferred '
'concept mappings will be used when reports are generated after the '
'analysis is carried out. You should note the following special '
'characteristics of the field mapping tool when used for aggregation '
'datasets versus when used for vector population exposure datasets:'
))
message.add(paragraph)
paragraph = m.Paragraph(
m.ImportantText(tr('Aggregation datasets: ')),
tr('For aggregation datasets, the field mapping tool uses global '
'defaults (see the InaSAFE Options Dialog documentation for more '
'details) or dataset level defaults to determine which ratios '
'should be used to calculate concept values. For example, in the '
'age group the aggregation dataset may specify that infants '
'should by calculated as a ratio of 0.1% of the total population. '
'Note that for aggregation datasets you can only use ratios, '
'not counts.'))
message.add(paragraph)
paragraph = m.Paragraph(
m.ImportantText(tr('Vector population exposure datasets: ')),
tr('For exposure datasets, ratios are not supported, only counts. '
'The field mappings carried out here will be used to generate '
'new columns during a pre-processing step before the actual '
'analysis is carried out.'))
message.add(paragraph)
paragraph = m.Paragraph(
tr('The interplay between default ratios, aggregation layer '
'provided ratios and population exposure layers is illustrated '
'in the table below.'))
message.add(paragraph)
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
row.add(m.Cell(tr('Aggregation'), header=True))
row.add(m.Cell(tr('Raster'), header=True))
row.add(m.Cell(tr('Vector, no counts'), header=True))
row.add(m.Cell(tr('Vector with counts'), header=True))
row.add(m.Cell(tr('Notes'), header=True))
table.add(row)
row = m.Row([
tr('No aggregation'),
tr('Use global default ratio'),
tr('Use global default ratio'),
tr('Use count to determine ratio'),
tr(''),
])
table.add(row)
row = m.Row([
tr('Aggregation, ratio not set'),
tr('Use global default ratio'),
tr('Do nothing'),
tr('Use count to determine ratio'),
tr(''),
])
table.add(row)
row = m.Row([
tr('Aggregation, ratio value set'),
tr('Use aggregation layer ratio'),
tr('Use aggregation layer ratio'),
tr('Use count to determine ratio'),
tr(''),
])
table.add(row)
row = m.Row([
tr('Aggregation, ratio field mapping set'),
tr('Use aggregation layer ratio'),
tr('Use aggregation layer ratio'),
tr('Use count to determine ratio'),
tr(''),
])
table.add(row)
message.add(table)
return message
def _value_maps_row(value_maps_keyword):
"""Helper to make a message row from a value maps.
Expected keywords:
'value_maps': {
'structure': {
'ina_structure_flood_hazard_classification': {
'classes': {
'low': [1, 2, 3],
'medium': [4],
'high': [5, 6]
},
'active': True
},
'ina_structure_flood_hazard_4_class_classification':
{
'classes': {
'low': [1],
'medium': [2, 3, 4],
'high': [5, 6, 7],
'very_high': [8]
},
'active': False
}
},
'population': {
'ina_population_flood_hazard_classification': {
'classes': {
'low': [1],
'medium': [2, 3],
'high': [4, 5, 6]
},
'active': False
},
'ina_population_flood_hazard_4_class_classification':
{
'classes': {
'low': [1, 2],
'medium': [3, 4],
'high': [4, 5, 6],
'very_high': [6, 7, 8]
},
'active': True
}
},
}
:param value_maps_keyword: Value of the keyword to be rendered. This
must be a string representation of a dict, or a dict.
:type value_maps_keyword: basestring, dict
:returns: A table to be added into a cell in the keywords table.
:rtype: safe.messaging.items.table
"""
if isinstance(value_maps_keyword, basestring):
value_maps_keyword = literal_eval(value_maps_keyword)
table = m.Table(style_class='table table-condensed table-striped')
i = 0
for exposure_key, classifications in value_maps_keyword.items():
i += 1
exposure = definition(exposure_key)
exposure_row = m.Row()
exposure_row.add(m.Cell(m.ImportantText(tr('Exposure'))))
exposure_row.add(m.Cell(exposure['name']))
table.add(exposure_row)
classification_row = m.Row()
classification_row.add(
m.Cell(m.ImportantText(tr('Classification'))))
active_classification = None
for classification, value in classifications.items():
if value.get('active'):
active_classification = definition(classification)
if active_classification.get('name'):
classification_row.add(
m.Cell(active_classification['name']))
break
if not active_classification:
classification_row.add(m.Cell(tr('No classifications set.')))
continue
table.add(classification_row)
header = m.Row()
header.add(m.Cell(tr('Class name')))
header.add(m.Cell(tr('Values')))
table.add(header)
classes = active_classification.get('classes')
# Sort by value, put the lowest first
classes = sorted(classes, key=lambda k: k['value'])
for the_class in classes:
value_map = classifications[
active_classification['key']]['classes'].get(
the_class['key'], [])
row = m.Row()
row.add(m.Cell(the_class['name']))
row.add(m.Cell(', '.join([str(v) for v in value_map])))
table.add(row)
if i < len(value_maps_keyword):
# Empty row
empty_row = m.Row()
empty_row.add(m.Cell(''))
empty_row.add(m.Cell(''))
table.add(empty_row)
return table
def _threshold_to_row(thresholds_keyword):
"""Helper to make a message row from a threshold
We are expecting something like this:
{
'thresholds': {
'structure': {
'ina_structure_flood_hazard_classification': {
'classes': {
'low': [1, 2],
'medium': [3, 4],
'high': [5, 6]
},
'active': True
},
'ina_structure_flood_hazard_4_class_classification':
{
'classes': {
'low': [1, 2],
'medium': [3, 4],
'high': [5, 6],
'very_high': [7, 8]
},
'active': False
}
},
'population': {
'ina_population_flood_hazard_classification': {
'classes': {
'low': [1, 2.5],
'medium': [2.5, 4.5],
'high': [4.5, 6]
},
'active': False
},
'ina_population_flood_hazard_4_class_classification':
{
'classes': {
'low': [1, 2.5],
'medium': [2.5, 4],
'high': [4, 6],
'very_high': [6, 8]
},
'active': True
}
},
},
Each value is a list with exactly two element [a, b], where a <= b.
:param thresholds_keyword: Value of the keyword to be rendered. This
must be a string representation of a dict, or a dict.
:type thresholds_keyword: basestring, dict
:returns: A table to be added into a cell in the keywords table.
:rtype: safe.messaging.items.table
"""
if isinstance(thresholds_keyword, basestring):
thresholds_keyword = literal_eval(thresholds_keyword)
for k, v in thresholds_keyword.items():
# If the v is not dictionary, it should be the old value maps.
# To handle thresholds in the Impact Function.
if not isinstance(v, dict):
table = m.Table(style_class='table table-condensed')
for key, value in thresholds_keyword.items():
row = m.Row()
name = definition(key)['name'] if definition(key) else key
row.add(m.Cell(m.ImportantText(name)))
pretty_value = tr('%s to %s' % (value[0], value[1]))
row.add(m.Cell(pretty_value))
table.add(row)
return table
table = m.Table(style_class='table table-condensed table-striped')
i = 0
for exposure_key, classifications in thresholds_keyword.items():
i += 1
exposure = definition(exposure_key)
exposure_row = m.Row()
exposure_row.add(m.Cell(m.ImportantText('Exposure')))
exposure_row.add(m.Cell(m.Text(exposure['name'])))
exposure_row.add(m.Cell(''))
table.add(exposure_row)
active_classification = None
classification_row = m.Row()
classification_row.add(m.Cell(m.ImportantText('Classification')))
for classification, value in classifications.items():
if value.get('active'):
active_classification = definition(classification)
classification_row.add(
m.Cell(active_classification['name']))
classification_row.add(m.Cell(''))
break
if not active_classification:
classification_row.add(m.Cell(tr('No classifications set.')))
classification_row.add(m.Cell(''))
continue
table.add(classification_row)
header = m.Row()
header.add(m.Cell(tr('Class name')))
header.add(m.Cell(tr('Minimum')))
header.add(m.Cell(tr('Maximum')))
table.add(header)
classes = active_classification.get('classes')
# Sort by value, put the lowest first
classes = sorted(classes, key=lambda k: k['value'])
for the_class in classes:
threshold = classifications[
active_classification['key']]['classes'][the_class['key']]
row = m.Row()
row.add(m.Cell(the_class['name']))
row.add(m.Cell(threshold[0]))
row.add(m.Cell(threshold[1]))
table.add(row)
if i < len(thresholds_keyword):
# Empty row
empty_row = m.Row()
empty_row.add(m.Cell(''))
empty_row.add(m.Cell(''))
table.add(empty_row)
return table
def _keyword_to_row(self, keyword, value, wrap_slash=False):
"""Helper to make a message row from a keyword.
.. versionadded:: 3.2
Use this when constructing a table from keywords to display as
part of a message object.
:param keyword: The keyword to be rendered.
:type keyword: str
:param value: Value of the keyword to be rendered.
:type value: basestring
:param wrap_slash: Whether to replace slashes with the slash plus the
html <wbr> tag which will help to e.g. wrap html in small cells if
it contains a long filename. Disabled by default as it may cause
side effects if the text contains html markup.
:type wrap_slash: bool
:returns: A row to be added to a messaging table.
:rtype: safe.messaging.items.row.Row
"""
row = m.Row()
# Translate titles explicitly if possible
if keyword == 'title':
value = tr(value)
# # See #2569
if keyword == 'url':
if isinstance(value, QUrl):
value = value.toString()
if keyword == 'date':
if isinstance(value, QDateTime):
value = value.toString('d MMM yyyy')
elif isinstance(value, datetime):
value = value.strftime('%d %b %Y')
# we want to show the user the concept name rather than its key
# if possible. TS
keyword_definition = definition(keyword)
if keyword_definition is None:
keyword_definition = tr(keyword.capitalize().replace('_', ' '))
else:
try:
keyword_definition = keyword_definition['name']
except KeyError:
# Handling if name is not exist.
keyword_definition = keyword_definition['key'].capitalize()
keyword_definition = keyword_definition.replace('_', ' ')
# We deal with some special cases first:
# In this case the value contains a DICT that we want to present nicely
if keyword in [
'value_map', 'inasafe_fields', 'inasafe_default_values'
]:
value = self._dict_to_row(value)
elif keyword == 'value_maps':
value = self._value_maps_row(value)
elif keyword == 'thresholds':
value = self._threshold_to_row(value)
# In these KEYWORD cases we show the DESCRIPTION for
# the VALUE keyword_definition
elif keyword in ['classification']:
# get the keyword_definition for this class from definitions
value = definition(value)
value = value['description']
# In these VALUE cases we show the DESCRIPTION for
# the VALUE keyword_definition
elif value in []:
# get the keyword_definition for this class from definitions
value = definition(value)
value = value['description']
# In these VALUE cases we show the NAME for the VALUE
# keyword_definition
elif value in [
'multiple_event', 'single_event', 'point', 'line', 'polygon'
'field'
]:
# get the name for this class from definitions
value = definition(value)
value = value['name']
# otherwise just treat the keyword as literal text
else:
# Otherwise just directly read the value
value = get_string(value)
key = m.ImportantText(keyword_definition)
row.add(m.Cell(key))
row.add(m.Cell(value, wrap_slash=wrap_slash))
return row
def content():
"""Helper method that returns just the content.
This method was added so that the text could be reused in the
other contexts.
.. versionadded:: 3.2.2
:returns: A message object without brand element.
:rtype: safe.messaging.message.Message
"""
message = m.Message()
paragraph = m.Paragraph(
m.Image(
'file:///%s/img/screenshots/'
'batch-calculator-screenshot.png' % resources_path()),
style_class='text-center'
)
message.add(paragraph)
message.add(m.Paragraph(tr(
'With this tool you can set up numerous scenarios and run them all in '
'one go. A typical use case may be where you define a number of e.g. '
'flood impact scenarios all using a standard data set e.g. '
'flood.shp. As new flood data becomes available you replace flood.shp '
'and rerun the scenarios using the batch runner. Using this approach '
'you can quickly produce regional contingency plans as your '
'understanding of hazards changes. When you run the batch of '
'scenarios, pdf reports are generated automatically and all placed in '
'a single common directory making it easy for you to browse and '
'disseminate the reports produced.')))
message.add(m.Paragraph(tr(
'When the batch process completes, it will also produce a summary '
'report like this:')))
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row(m.Cell(tr('InaSAFE Batch Report File')), header=True)
table.add(row)
table.add(m.Row(m.Cell('P: gempa bumi Sumatran fault (Mw7.8)')))
table.add(m.Row(m.Cell('P: gempa di Yogya tahun 2006')))
table.add(m.Row(m.Cell('P: banjir jakarta 2007')))
table.add(m.Row(m.Cell('P: Tsunami di Maumere (Mw 8.1)')))
table.add(m.Row(m.Cell('P: gempa Mw6.5 Palu-Koro Fault')))
table.add(m.Row(m.Cell('P: gunung merapi meletus')))
table.add(m.Row(m.Cell('-----------------------------')))
table.add(m.Row(m.Cell(tr('Total passed: 6'))))
table.add(m.Row(m.Cell(tr('Total failed: 0'))))
table.add(m.Row(m.Cell(tr('Total tasks: 6'))))
message.add(table)
# message.add(m.Paragraph(tr(
# 'For advanced users there is also the ability to batch run python '
# 'scripts using this tool, but this should be considered an '
# 'experimental</strong> feature still at this stage.')))
message.add(m.Paragraph(tr(
'Before running the Batch Runner you might want to use the \'save '
'scenario\' tool to first save some scenarios on which you '
'can let the batch runner do its work. This tool lets you run saved '
'scenarios in one go. It lets you select scenarios or let run all '
'scenarios in one go.')))
return message
