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Merge pull request #181 from bacetiner/master
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Added option for HAZUS required building attributes for earthquake analysis
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@bacetiner
bacetiner authored Jan 29, 2024
2 parents 570df8a + 809d73e commit 1ddb508
Showing 1 changed file with 134 additions and 83 deletions.
217 changes: 134 additions & 83 deletions brails/InventoryGenerator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,20 +40,18 @@
# Satish Rao
#
# Last updated:
# 01-26-2024
# 01-29-2024

import random
import sys
import pandas as pd
import os
from shapely.geometry import Polygon
import json
from shapely.geometry import Polygon
from datetime import datetime
#import os
#import shutil
from importlib.metadata import version

#import brails.models as models
import brails
from brails.modules import (ChimneyDetector, FacadeParser, GarageDetector,
NFloorDetector, RoofClassifier,
OccupancyClassifier, RoofCoverClassifier,
Expand Down Expand Up @@ -180,33 +178,136 @@ def write_to_dataframe(df,predictions,column,imtype='street_images'):

return df

# Pre-process the attribute entries such that incorrect entries are
# removed:
if isinstance(attributes,str) and attributes=='all':
self.attributes = self.enabledAttributes[:]
elif isinstance(attributes,list):
self.attributes = [attribute.lower() for attribute in attributes]
ignore_entries = []
for attribute in self.attributes:
if attribute not in self.enabledAttributes:
ignore_entries.append(attribute)
self.attributes.remove(attribute)
if len(ignore_entries)==1:
print('Found an entry in attributes that was not enabled in ' +
'InventoryGenerator.\nIgnoring entry: ' +
', '.join(ignore_entries))
elif len(ignore_entries)>1:
print('Found entries in attributes that were not enabled in ' +
'InventoryGenerator.\nIgnoring entries: ' +
', '.join(ignore_entries))

if len(self.attributes)==0:
sys.exit('Defined list of attributes does not contain a ' +
'correct attribute entry. Attribute entries enabled' +
' are: ' + ', '.join(self.enabledAttributes))
def parse_attribute_input(attrIn,attrEnabled):
"""
Function that pre-processes the user attribute entries to
InventoryGenerator().generate method such that pre-defined
attribute sets are correctly parsed and incorrect entries are
removed:
Inputs:
df: list
A list of string descriptions for the user requested
building attributes
attrEnabled: list
A list of string descriptions for the building attributes
currently enabled in InventoryGenerator().generate method
Output: A list of string descriptions for the user requested
building attributes that were determined to valid for use
with InventoryGenerator().generate method
"""

if isinstance(attrIn,str) and attrIn=='all':
attrOut = attrEnabled[:]
elif isinstance(attrIn,str) and attrIn=='hazuseq':
attrOut = ['numstories']
elif isinstance(attrIn,list):
attrOut = [attribute.lower() for attribute in attrIn]
ignore_entries = []
for attribute in attrOut:
if attribute not in attrEnabled:
ignore_entries.append(attribute)
attrOut.remove(attribute)
if len(ignore_entries)==1:
print('An entry in attributes is not enabled.'
f'\nIgnoring entry: {ignore_entries[0]}')
elif len(ignore_entries)>1:
print('Several entries in attributes are not enabled.'
'\nIgnoring entries: ' + ', '.join(ignore_entries))

if len(attrOut)==0:
sys.exit('Defined list of attributes does not contain a ' +
'correct attribute entry. Attribute entries enabled' +
' are: ' + ', '.join(attrEnabled))

# Remove duplicate attribute entries:
attrOut = sorted(list(set(attrOut)))

return attrOut

def write_inventory_output(inventorydf,outFile):
"""
Function that writes the data in inventorydf DataFrame into a CSV
or GeoJSON file based on the file name defined in the outFile.
Inputs:
df: DataFrame
A DataFrame containing building attributes. If this
DataFrame contains columns for satellite_images and
street_images, these columns are removed before the
output file is created.
outFile: string
String for the name of the output file, e.g., out.geojson.
This function writes data only in CSV and GeoJSON formats.
If an output format different than these two formats is
defined in the file extension, the function defaults to
GeoJSON format.
"""

# Create a new table with does not list the image names
# corresponding to each building but includes building ID, latitude,
# and longitudecolumns added:
dfout = inventorydf.copy(deep=True)
dfout = dfout.drop(columns=['satellite_images', 'street_images'],
errors='ignore')


for index, row in inventorydf.iterrows():
dfout.loc[index, 'Footprint'] = ('{"type":"Feature","geometry":' +
'{"type":"Polygon","coordinates":[' +
f"""{row['Footprint']}""" +
']},"properties":{}}')
centroid = Polygon(row['Footprint']).centroid
dfout.loc[index, 'Latitude'] = centroid.y
dfout.loc[index, 'Longitude'] = centroid.x

# Rearrange the column order of dfout such that the Footprint field is
# the last:
cols = [col for col in dfout.columns if col!='Footprint']
new_cols = ['Latitude','Longitude'] + cols[:-2] + ['Footprint']
dfout = dfout[new_cols]

# If the inventory is desired in CSV format, write dfout to a CSV:
if '.csv' in outFile.lower():
dfout.to_csv(outFile, index=True, index_label='id')

# Else write the inventory into a GeoJSON file:
else:
if '.geojson' not in outFile.lower():
print('Output format unimplemented!')
outFile = outFile.replace(outFile.split('.')[-1],'geojson')
geojson = {'type':'FeatureCollection',
'generated':str(datetime.now()),
'brails_version': version('BRAILS'),
"crs": {"type": "name",
"properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84" }},
'units': {"length": lengthUnit},
'features':[]}

attrs = dfout.columns.values.tolist()
attrs.remove('Footprint')
for index, row in dfout.iterrows():
feature = {'type':'Feature',
'properties':{},
'geometry':{'type':'Polygon',
'coordinates':[]}}
fp = dfout.loc[index, 'Footprint'].split('"coordinates":')[-1]
fp = fp.replace('},"properties":{}}','')
feature['geometry']['coordinates'] = json.loads(fp)
feature['properties']['id'] = index
for attr in attrs:
feature['properties'][attr] = row[attr]
geojson['features'].append(feature)

with open(outFile, 'w') as output_file:
json.dump(geojson, output_file, indent=2)

print(f'\nFinal inventory data available in {outFile} in {os.getcwd()}')

# Remove duplicate attribute entries:
self.attributes = sorted(list(set(self.attributes)))
# Parse/correct the list of user requested building attributes:
self.attributes = parse_attribute_input(attributes, self.enabledAttributes)

# Create a list of footprints for easier module calls:
footprints = self.inventory['Footprint'].values.tolist()
Expand Down Expand Up @@ -353,63 +454,13 @@ def write_to_dataframe(df,predictions,column,imtype='street_images'):
if 'roofeaveheight' in self.attributes:
self.inventory['roofeaveheight'] = self.inventory['roofeaveheight'].apply(lambda x: x*0.3048)

# Remove the columns that list the image names corresponding to each
# building from the inventory DataFrame, add an ID column, and print
# the resulting table to the output file titled inventory.csv:
dfout = self.inventory.copy(deep=True)
dfout = dfout.drop(columns=['satellite_images', 'street_images'],
errors='ignore')
dfout2merge = dfout.copy(deep=True)

for index, row in self.inventory.iterrows():
dfout.loc[index, 'Footprint'] = ('{"type":"Feature","geometry":' +
'{"type":"Polygon","coordinates":[' +
f"""{row['Footprint']}""" +
']},"properties":{}}')
centroid = Polygon(row['Footprint']).centroid
dfout.loc[index, 'Latitude'] = centroid.y
dfout.loc[index, 'Longitude'] = centroid.x

cols = [col for col in dfout.columns if col!='Footprint']
new_cols = ['Latitude','Longitude'] + cols[:-2] + ['Footprint']
dfout = dfout[new_cols]

if '.csv' in outFile.lower():
dfout.to_csv(outFile, index=True, index_label='id')
print(f'\nFinal inventory data available in {outFile} in {os.getcwd()}')
else:
if '.geojson' not in outFile.lower():
print('Output format unimplemented!')
outFile = outFile.replace(outFile.split('.')[-1],'geojson')
geojson = {'type':'FeatureCollection',
'generated':str(datetime.now()),
'brails_version': brails.__version__,
"crs": {"type": "name",
"properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84" }},
'units': {"length": lengthUnit},
'features':[]}

attrs = dfout.columns.values.tolist()
attrs.remove('Footprint')
for index, row in dfout.iterrows():
feature = {'type':'Feature',
'properties':{},
'geometry':{'type':'Polygon',
'coordinates':[]}}
feature['geometry']['coordinates'] = json.loads(dfout.loc[index, 'Footprint'].split('"coordinates":')[-1].replace('},"properties":{}}',''))
for attr in attrs:
feature['properties'][attr] = row[attr]
geojson['features'].append(feature)

with open(outFile, 'w') as output_file:
json.dump(geojson, output_file, indent=2)

print(f'\nFinal inventory data available in {outFile} in {os.getcwd()}')

# Write the genereated inventory in outFile:
write_inventory_output(self.inventory,outFile)

# Merge the DataFrame of predicted attributes with the DataFrame of
# incomplete inventory and print the resulting table to the output file
# titled IncompleteInventory.csv:
# dfout2merge = dfout.copy(deep=True)
# dfout2merge['fp_as_string'] = dfout2merge['Footprint'].apply(lambda x: "".join(str(x)))

# dfout_incomp = self.incompleteInventory.copy(deep=True)
Expand Down

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