def shapefile_upload(request, job_id, index_name):
"""
Controller for uploading shapefiles.
"""
context = {}
user = str(request.user)
user.lower()
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
# Clear the workspace
gi_lib.clear_folder(userDir)
# Create a session
gsshapy_session = gsshapy_sessionmaker()
# Get the params
params = request.POST
files = request.FILES.getlist('shapefile_files')
shp_name = ''
for file in files:
if file.name.endswith('.shp'):
shp_name = file.name[:-4]
# Start Spatial Dataset Engine
dataset_engine = get_spatial_dataset_engine(name='gsshaindex_geoserver',
app_class=GSSHAIndex)
# Check to see if Spatial Dataset Engine Exists
workspace = gi_lib.check_workspace(dataset_engine)
# Clear the store and create a new feature resource
# store = gi_lib.clear_store(dataset_engine, user)
feature_resource = dataset_engine.create_shapefile_resource(
store_id='gsshaindex:' + user + '-' + shp_name,
shapefile_upload=files,
overwrite=True,
debug=True)
return redirect(
reverse('gsshaindex:shapefile_index',
kwargs={
'job_id': job_id,
'index_name': index_name,
'shapefile_name': shp_name
}))
def extract_existing_gssha(request, job_id):
'''
This takes the file name and id that were submitted and unzips the files.
'''
context = {}
user = str(request.user)
session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user, session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb',
app_class=GSSHAIndex)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
indexMapDir = os.path.join(userDir, 'index_maps')
# Clear the workspace
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(indexMapDir)
# Get url for the resource and extract the GSSHA file
url = job.original_url
extract_path, unique_dir = gi_lib.extract_zip_from_url(user, url, userDir)
# Create GSSHAPY Session
gsshapy_session = gsshapy_sessionmaker()
# Find the project file
for root, dirs, files in os.walk(userDir):
for file in files:
if file.endswith(".prj"):
project_name = file
project_path = os.path.join(root, file)
read_dir = os.path.dirname(project_path)
context['job_id'] = job_id
return redirect(
reverse('gsshaindex:select_index', kwargs={'job_id': job_id}))
def shapefile_upload(request, job_id, index_name):
"""
Controller for uploading shapefiles.
"""
context = {}
user = str(request.user)
user.lower()
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
# Clear the workspace
gi_lib.clear_folder(userDir)
# Create a session
gsshapy_session = gsshapy_sessionmaker()
# Get the params
params = request.POST
files = request.FILES.getlist('shapefile_files')
shp_name = ''
for file in files:
if file.name.endswith('.shp'):
shp_name = file.name[:-4]
# Start Spatial Dataset Engine
dataset_engine = get_spatial_dataset_engine(name='gsshaindex_geoserver', app_class=GSSHAIndex)
# Check to see if Spatial Dataset Engine Exists
workspace = gi_lib.check_workspace(dataset_engine)
# Clear the store and create a new feature resource
# store = gi_lib.clear_store(dataset_engine, user)
feature_resource = dataset_engine.create_shapefile_resource(store_id='gsshaindex:'+user+'-'+shp_name, shapefile_upload=files, overwrite=True, debug=True)
return redirect(reverse('gsshaindex:shapefile_index', kwargs={'job_id':job_id, 'index_name':index_name, 'shapefile_name':shp_name}))
def extract_existing_gssha(request, job_id):
'''
This takes the file name and id that were submitted and unzips the files.
'''
context = {}
user = str(request.user)
session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user,session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb', app_class=GSSHAIndex)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
indexMapDir = os.path.join(userDir, 'index_maps')
# Clear the workspace
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(indexMapDir)
# Get url for the resource and extract the GSSHA file
url = job.original_url
extract_path, unique_dir = gi_lib.extract_zip_from_url(user, url, userDir)
# Create GSSHAPY Session
gsshapy_session = gsshapy_sessionmaker()
# Find the project file
for root, dirs, files in os.walk(userDir):
for file in files:
if file.endswith(".prj"):
project_name = file
project_path = os.path.join(root, file)
read_dir = os.path.dirname(project_path)
context['job_id'] = job_id
return redirect(reverse('gsshaindex:select_index', kwargs={'job_id':job_id}))
def results(request, job_id, view_type):
context = {}
# Get the user id
user = str(request.user)
# Get the job from the database and delete
session = jobs_sessionmaker()
job = session.query(Jobs).\
filter(Jobs.user_id == user).\
filter(Jobs.original_id == job_id).one()
# Get the run result urls
result_files = job.result_urls
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
jobDir = os.path.join(userDir,str(job_id))
fakeDir = os.path.join(userDir,'fake')
newResultsPath = os.path.join(jobDir, 'new_results')
originalResultsPath = os.path.join(jobDir, 'original_results')
# Check to see if the otl folder for this job is already downloaded and has values and get a handle on them if it does
if os.path.exists(jobDir) and os.listdir(newResultsPath)!=[] and os.listdir(originalResultsPath)!=[]:
# Get the otl files
new_otl_file = gi_lib.find_otl(newResultsPath)
original_otl_file = gi_lib.find_otl(originalResultsPath)
# If it doesn't, get the otl files
else:
# Clear the results folder
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(jobDir)
gi_lib.clear_folder(newResultsPath)
gi_lib.clear_folder(originalResultsPath)
# Get the otl files
new_otl_file = gi_lib.extract_otl(result_files['new'], newResultsPath)
original_otl_file = gi_lib.extract_otl(result_files['original'], originalResultsPath)
# Format the values for display with high charts
new_values = []
originalValues = []
new_values = gi_lib.get_otl_values(new_otl_file, new_values)
originalValues = gi_lib.get_otl_values(original_otl_file, originalValues)
# Set up for high charts hydrograph
highcharts_object = {
'chart': {
'type': 'spline'
},
'title': {
'text': 'Comparison Hydrograph'
},
'subtitle': {
'text': 'Display of the two model results'
},
'legend': {
'layout': 'vertical',
'align': 'right',
'verticalAlign': 'middle',
'borderWidth': 0
},
'xAxis': {
'title': {
'enabled': True,
'text': 'Time (hours)'
},
'labels': {
'formatter': 'function () { return this.value + " hr"; }'
}
},
'yAxis': {
'title': {
'enabled': True,
'text': 'Discharge (cfs)'
},
'labels': {
'formatter': 'function () { return this.value + " cfs"; }'
}
},
'tooltip': {
'headerFormat': '{series.name}',
'pointFormat': '{point.x} hours: {point.y} cfs'
},
'series': [{
'name': job.original_name.replace("_", " "),
'color': '#0066ff',
'dashStyle': 'ShortDash',
'marker' : {'enabled': False},
'data': originalValues
},{
'name': job.new_name.replace("_", " "),
'marker' : {'enabled': False},
'color': '#ff6600',
'data': new_values}
]}
hydrograph = {'highcharts_object': highcharts_object,
'width': '500px',
'height': '500px'}
google_map = {'height': '600px',
'width': '100%',
'reference_kml_action': '/apps/gsshaindex/'+ job_id + '/get-depth-map/' + view_type}
session.close()
kml_link = ''
title = ''
if view_type == 'originalMax':
title = job.original_name.replace("_", " ") + ' Maximum Depth'
kml_link = job.originalMax
elif view_type == 'newMax':
title = job.new_name.replace("_", " ") + ' Maximum Depth'
kml_link = job.newMax
elif view_type == 'newTime':
title = job.new_name.replace("_", " ") + ' Time Series'
kml_link = job.newTime
elif view_type == 'originalTime':
title = job.original_name.replace("_", " ") + ' Time Series'
kml_link = job.originalTime
else:
kml_link = ''
title = ''
context['map_title'] = title
context['kml_link'] = kml_link
context['hydrograph'] = hydrograph
context['google_map'] = google_map
context['map_type'] = view_type
context['original_name'] = job.original_name.replace("_", " ")
context['new_name'] = job.new_name.replace("_", " ")
context['original_file'] = job.result_urls['original']
context['new_file'] = job.result_urls['new']
context['job_id'] = job_id
return render(request, 'gsshaindex/results.html', context)
def fly(request, job_id):
context = {}
# Get the user id
user = str(request.user)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb', app_class=GSSHAIndex)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
resultsPath = os.path.join(userDir, 'results')
originalFileRunPath = os.path.join(userDir, "preRun")
writeFile = os.path.join(userDir, "writeFile")
zipPath = os.path.join(userDir, "zipPath")
#Create session
gsshapy_session = gsshapy_sessionmaker()
# Clear the results folder
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(resultsPath)
gi_lib.clear_folder(originalFileRunPath)
gi_lib.clear_folder(writeFile)
# Get the jobs from the database
session = jobs_sessionmaker()
job = session.query(Jobs).\
filter(Jobs.user_id == user).\
filter(Jobs.original_id == job_id).one()
# Get the urls and names for the analysis
run_urls = job.run_urls
arguments={'new': {'url':run_urls['new']['url'], 'name':run_urls['new']['name']}, 'original':{'url':run_urls['original']['url'], 'name':run_urls['original']['name']}}
# Set up for fly GSSHA
job.status = "processing"
session.commit()
status = 'complete'
results = []
# results_urls = []
results_urls = {}
count = 0
GSSHA_dataset = gi_lib.check_dataset("gssha-models", CKAN_engine)
# Try running the web service
# try:
for k in arguments:
url = str(arguments[k]['url'])
if k == 'original' and job.original_certification=="Certified":
results_urls['original']=url
count +=1
continue
elif k == 'original' and job.original_certification=="Missing gfl":
# Need to download from url, add gfl, zip, send to ckan, run, and save the url
downloaded_project = gi_lib.extract_zip_from_url(user, url, originalFileRunPath)
# Create an empty Project File Object
# Find the project file
for root, dirs, files in os.walk(originalFileRunPath):
for file in files:
if file.endswith(".prj"):
project_name = file
project_path = os.path.join(root, file)
read_dir = os.path.dirname(project_path)
project = ProjectFile()
project.readInput(directory=read_dir,
projectFileName=project_name,
session = gsshapy_session,
spatial=True)
if project.getCard("FLOOD_GRID") == None:
max_depth_card = ProjectCard("FLOOD_GRID", '"{0}.gfl"'.format(project_name[:-4]))
project_cards = project.projectCards.append(max_depth_card)
gsshapy_session.commit()
# Need to format so that it will work for the file I just did
# Get all the project files
project.writeInput(session=gsshapy_session, directory=writeFile, name=project_name[:-4])
# Make a list of the project files
writeFile_list = os.listdir(writeFile)
# Add each project file to the zip folder
with zipfile.ZipFile(zipPath, "w") as gssha_zip:
for item in writeFile_list:
abs_path = os.path.join(writeFile, item)
archive_path = os.path.join(project_name, item)
gssha_zip.write(abs_path, archive_path)
GSSHA_dataset = gi_lib.check_dataset("gssha-models", CKAN_engine)
description = job.original_description + "with a gfl added"
pretty_date= time.strftime("%A %B %d, %Y %I:%M:%S %p")
# Add the zipped GSSHA file to the public ckan
results, success = gi_lib.add_zip_GSSHA(GSSHA_dataset, zipPath, CKAN_engine, project_name[:-4] + " with gfl", description, pretty_date, user)
job.original_url = results['url']
url = job.original_url
resultsFile = os.path.join(resultsPath, arguments[k]['name'].replace(" ","_")+datetime.now().strftime('%Y%d%m%H%M%S'))
gi_lib.flyGssha(str(url), resultsFile)
# Push file to ckan dataset
resource_name = ' '.join((arguments[k]['name'], '-Run',datetime.now().strftime('%b %d %y %H:%M:%S')))
pretty_date= time.strftime("%A %B %d, %Y %I:%M:%S %p")
result, success = gi_lib.add_zip_GSSHA(GSSHA_dataset, resultsFile, CKAN_engine, resource_name, "", pretty_date, user, certification="Certified")
# Save the new url as the original_url and run
job.original_certification = "Certified"
# Publish link to table
results_urls['original']=result['url']
count +=1
else:
resultsFile = os.path.join(resultsPath, arguments[k]['name'].replace(" ","_")+datetime.now().strftime('%Y%d%m%H%M%S'))
gi_lib.flyGssha(url, resultsFile)
# Push file to ckan dataset
resource_name = ' '.join((arguments[k]['name'], '-Run',datetime.now().strftime('%b %d %y %H:%M:%S')))
pretty_date= time.strftime("%A %B %d, %Y %I:%M:%S %p")
result, success = gi_lib.add_zip_GSSHA(GSSHA_dataset, resultsFile, CKAN_engine, resource_name, "", pretty_date, user, certification="Certified")
# Publish link to table
if k=='original':
results_urls['original']=result['url']
job.original_certification = "Certified"
else:
results_urls['new']=result['url']
count +=1
if (count == 2):
print results_urls
else:
status = 'failed'
# except:
# status = 'failed'
job.status = status
job.result_urls = results_urls
session.commit()
session.close()
gsshapy_session.commit()
gsshapy_session.close()
return redirect(reverse('gsshaindex:status'))
def zip_file(request, job_id):
'''
This zips up the GSSHA files in preparation of their being run
'''
context = {}
# Get the job id and user id
job_id = job_id
user = str(request.user)
session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user,session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb', app_class=GSSHAIndex)
project_file_id = job.new_model_id
# Get the name and description from the submission
params=request.POST
not_clean_name = params['new_name']
new_description = params['new_description']
# Reformat the name by removing bad characters
# bad_char = "',.<>()[]{}=+-/\"|:;\\^?!~`@#$%&* "
bad_char = "',.<>[]{}=+-/\"|:;\\^?!~`@#$%&*"
for char in bad_char:
new_name = not_clean_name.replace(char,"_")
#Create session
gsshapy_session = gsshapy_sessionmaker()
# Get project from the database
projectFileAll = gsshapy_session.query(ProjectFile).get(project_file_id)
# Create name for files
project_name = projectFileAll.name
if project_name.endswith('.prj'):
project_name = project_name[:-4]
pretty_date= time.strftime("%A %B %d, %Y %I:%M:%S %p")
# Set depth map
if projectFileAll.getCard("FLOOD_GRID") == None:
max_depth_card = ProjectCard("FLOOD_GRID", '"{0}.gfl"'.format(new_name))
project_cards = projectFileAll.projectCards.append(max_depth_card)
gsshapy_session.commit()
job.original_certification = "Missing gfl"
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
newFileDir = os.path.join(userDir, 'newFile')
writeFile = os.path.join(newFileDir, new_name)
zipPath = os.path.join(newFileDir, new_name + "_zip")
# Clear workspace folders
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(newFileDir)
gi_lib.clear_folder(writeFile)
# Get all the project files
projectFileAll.writeInput(session=gsshapy_session, directory=writeFile, name=new_name)
# Make a list of the project files
writeFile_list = os.listdir(writeFile)
# Add each project file to the zip folder
with zipfile.ZipFile(zipPath, "w") as gssha_zip:
for item in writeFile_list:
abs_path = os.path.join(writeFile, item)
archive_path = os.path.join(new_name, item)
gssha_zip.write(abs_path, archive_path)
GSSHA_dataset = gi_lib.check_dataset("gssha-models", CKAN_engine)
# Add the zipped GSSHA file to the public ckan
results, success = gi_lib.add_zip_GSSHA(GSSHA_dataset, zipPath, CKAN_engine, new_name, new_description, pretty_date, user)
# If the file zips correctly, get information and store it in the database
if success == True:
new_url = results['url']
new_name = results['name']
original_url = job.original_url
original_name = job.original_name
model_data = {'original': {'url':original_url, 'name':original_name}, 'new':{'url':new_url, 'name':new_name}}
job.run_urls = model_data
job.new_name = new_name
job.status = "ready to run"
session.commit()
return redirect(reverse('gsshaindex:status'))
def extract_gssha(request, job_id):
'''
This takes the file name and id that were submitted and unzips the files, finds the index maps, and creates kmls.
'''
context = {}
user = str(request.user)
session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user,session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb', app_class=GSSHAIndex)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
indexMapDir = os.path.join(userDir, 'index_maps')
# Clear the workspace
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(indexMapDir)
# Get url for the resource and extract the GSSHA file
url = job.original_url
extract_path, unique_dir = gi_lib.extract_zip_from_url(user, url, userDir)
# Create GSSHAPY Session
gsshapy_session = gsshapy_sessionmaker()
# Find the project file
for root, dirs, files in os.walk(userDir):
for file in files:
if file.endswith(".prj"):
project_name = file
project_path = os.path.join(root, file)
read_dir = os.path.dirname(project_path)
# Create an empty Project File Object
project = ProjectFile()
project.readInput(directory=read_dir,
projectFileName=project_name,
session = gsshapy_session,
spatial=True)
# Create empty dictionary to hold the kmls from this session
current_kmls = {}
# Store model information
job.new_model_name = project.name
job.new_model_id = project.id
job.created = datetime.now()
# Get index maps
index_list = gsshapy_session.query(IndexMap).filter(IndexMap.mapTableFile == project.mapTableFile).all()
# Loop through the index
for current_index in index_list:
# Create kml file name and path
current_time = time.strftime("%Y%m%dT%H%M%S")
resource_name = current_index.name + "_" + str(user) + "_" + current_time
kml_ext = resource_name + '.kml'
clusterFile = os.path.join(indexMapDir, kml_ext)
# Generate color ramp
current_index.getAsKmlClusters(session=gsshapy_session,
path = clusterFile,
colorRamp = ColorRampEnum.COLOR_RAMP_HUE,
alpha=0.6)
index_map_dataset = gi_lib.check_dataset("index-maps", CKAN_engine)
resource, status = gi_lib.add_kml_CKAN(index_map_dataset, CKAN_engine, clusterFile, resource_name)
# If the kml is added correctly, create an entry for the current_kmls with the name as the index name
if status == True:
current_kmls[current_index.name] = {'url':resource['url'], 'full_name':resource['name']}
# Add the kmls with their url to the database
job.current_kmls = json.dumps(current_kmls)
session.commit()
session.close()
gsshapy_session.close()
context['job_id'] = job_id
return redirect(reverse('gsshaindex:select_index', kwargs={'job_id':job_id}))
def get_mask_map(request, file_id):
"""
This action is used to pass the kml data to the google map.
It must return a JSON response with a Python dictionary that
has the key 'kml_links'.
"""
kml_links = []
session = jobs_sessionmaker()
user = str(request.user)
job, success = gi_lib.get_new_job(file_id, user,session)
if job.kml_url != None:
kml_links.append(job.kml_url)
#TODO Need some way to check and see if the link works or if it's broken
return JsonResponse({'kml_links': kml_links})
else:
# Check that there's a package to store kmls
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb', app_class=GSSHAIndex)
present = gi_lib.check_package('kmls', CKAN_engine)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
# Clear the workspace
gi_lib.clear_folder(userDir)
url = job.original_url
maskMapDir = os.path.join(userDir, 'mask_maps')
extractPath = os.path.join(maskMapDir, file_id)
mask_file = gi_lib.extract_mask(url, extractPath)
if mask_file == "blank":
job.kml_url = ''
session.commit()
return JsonResponse({'kml_links': ''})
else:
projection_file = gi_lib.extract_projection(url,extractPath)
# Set up kml file name and save location
name = job.original_name
norm_name = name.replace(" ","")
current_time = time.strftime("%Y%m%dT%H%M%S")
kml_name = norm_name + "_" + user + "_" + current_time
kml_ext = kml_name + ".kml"
kml_file = os.path.join(extractPath, kml_ext)
colors = [(237,9,222),(92,245,61),(61,184,245),(171,61,245),(250,245,105),(245,151,44),(240,37,14),(88,5,232),(5,232,190),(11,26,227)]
color = [random.choice(colors)]
# Extract mask map and create kml
gsshapy_session = gsshapy_sessionmaker()
if projection_file != "blank":
srid = ProjectionFile.lookupSpatialReferenceID(extractPath, projection_file)
else:
srid = 4302
mask_map = RasterMapFile()
mask_map.read(directory=extractPath, filename=mask_file, session=gsshapy_session, spatial=True, spatialReferenceID=srid)
mask_map.getAsKmlClusters(session=gsshapy_session, path=kml_file, colorRamp={'colors':color, 'interpolatedPoints':1})
mask_map_dataset = gi_lib.check_dataset("mask-maps", CKAN_engine)
# Add mask kml to CKAN for viewing
resource, success = gi_lib.add_kml_CKAN(mask_map_dataset, CKAN_engine, kml_file, kml_name)
# Check to ensure the resource was added and save it to database by adding "kml_url"
if success == True:
job.kml_url = resource['url']
session.commit()
kml_links.append(job.kml_url)
return JsonResponse({'kml_links': kml_links})
def replace_index_with_shapefile(request, job_id, index_name, shapefile_name):
"""
Controller to replace the index map with the selected shapefile.
"""
context = {}
user = str(request.user)
geojson = get_geojson_from_geoserver(user, shapefile_name)
# Get the job from the database
job_session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user, job_session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb',
app_class=GSSHAIndex)
# Get project file id
project_file_id = job.new_model_id
# Create a session
gsshapy_session = gsshapy_sessionmaker()
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
indexMapDir = os.path.join(userDir, 'index_maps')
# Clear the workspace
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(indexMapDir)
# Use project id to link to original map table file
project_file = gsshapy_session.query(ProjectFile).filter(
ProjectFile.id == project_file_id).one()
index_raster = gsshapy_session.query(IndexMap).filter(
IndexMap.mapTableFile == project_file.mapTableFile).filter(
IndexMap.name == index_name).one()
mapTables = index_raster.mapTables
if geojson['success'] != False:
geojson_result = geojson['geojson']
# Get existing indices
index_raster_indices = index_raster.indices
srid_name = geojson['crs']
project_file_srid = project_file.srid
id = 200
# Loop through each geometry
for object in geojson_result:
index_present = False
object_id = object['id']
# Check to see if the index is present
for index in index_raster_indices:
if object_id == index.index:
index_present = True
break
# Create new index value if it doesn't exist and add the number of ids
if index_present == False:
new_indice = MTIndex(id, object_id, "")
new_indice.indexMap = index_raster
for mapping_table in mapTables:
distinct_vars = gsshapy_session.query(distinct(MTValue.variable)).\
filter(MTValue.mapTable == mapping_table).\
order_by(MTValue.variable).\
all()
variables = []
for var in distinct_vars:
variables.append(var[0])
for variable in variables:
new_value = MTValue(variable, 0)
new_value.mapTable = mapping_table
new_value.index = new_indice
gsshapy_session.commit()
geom = object['geometry']
geom['crs'] = srid_name
geom_full = json.dumps(geom)
# Change values in the index map
change_index_values = "SELECT ST_SetValue(raster,1,ST_Transform(ST_GeomFromGeoJSON('{0}'), {1}),{2}) " \
"FROM idx_index_maps " \
"WHERE id = {3};".format(str(geom_full), project_file_srid, id, index_raster.id)
result = gi_lib.timeout(gi_lib.draw_update_index,
args=(change_index_values,
index_raster.id),
kwargs={},
timeout=10,
result_can_be_pickled=True,
default=None)
# If there is a timeout
if result == None:
messages.error(request,
'The submission timed out. Please try again.')
job_session.close()
gsshapy_session.close()
context['index_name'] = index_name
context['job_id'] = job_id
return redirect(
reverse('gsshaindex:shapefile_index',
kwargs={
'job_id': job_id,
'index_name': index_name,
'shapefile_name': shapefile_name
}))
id += 1
# Get the values in the index map
statement3 = '''SELECT (pvc).*
FROM (SELECT ST_ValueCount(raster,1,true) As pvc
FROM idx_index_maps WHERE id = ''' + unicode(
index_raster.id) + ''') AS foo
ORDER BY (pvc).value;
'''
result3 = gsshapy_engine.execute(statement3)
numberIDs = 0
ids = []
for row in result3:
numberIDs += 1
ids.append(row.value)
map_table_count = 0
for mapping_table in mapTables:
index_raster.mapTables[map_table_count].numIDs = numberIDs
indices = gsshapy_session.query(distinct(MTIndex.index), MTIndex.id, MTIndex.description1, MTIndex.description2).\
join(MTValue).\
filter(MTValue.mapTable == mapping_table).\
order_by(MTIndex.index).\
all()
for index in indices:
if not int(index[0]) in ids:
bob = gsshapy_session.query(MTIndex).get(index.id)
for val in bob.values:
gsshapy_session.delete(val)
gsshapy_session.delete(bob)
gsshapy_session.commit()
map_table_count += 1
index_raster = gsshapy_session.query(IndexMap).filter(
IndexMap.mapTableFile == project_file.mapTableFile).filter(
IndexMap.name == index_name).one()
# Create kml file name and path
current_time = time.strftime("%Y%m%dT%H%M%S")
resource_name = index_raster.name + "_" + str(
user) + "_" + current_time
kml_ext = resource_name + '.kml'
clusterFile = os.path.join(indexMapDir, kml_ext)
# Generate color ramp
index_raster.getAsKmlClusters(session=gsshapy_session,
path=clusterFile,
colorRamp=ColorRampEnum.COLOR_RAMP_HUE,
alpha=0.6)
index_map_dataset = gi_lib.check_dataset("index-maps", CKAN_engine)
resource, status = gi_lib.add_kml_CKAN(index_map_dataset, CKAN_engine,
clusterFile, resource_name)
temp_list = json.loads(job.current_kmls)
if status == True:
for item in temp_list:
if item == index_name:
del temp_list[item]
temp_list[index_name] = {
'url': resource['url'],
'full_name': resource['name']
}
break
job.current_kmls = json.dumps(temp_list)
job_session.commit()
job_session.close()
gsshapy_session.close()
context['index_name'] = index_name
context['job_id'] = job_id
return redirect(
reverse('gsshaindex:edit_index',
kwargs={
'job_id': job_id,
'index_name': index_name
}))
def replace_index_with_shapefile(request, job_id, index_name, shapefile_name):
"""
Controller to replace the index map with the selected shapefile.
"""
context = {}
user = str(request.user)
geojson = get_geojson_from_geoserver(user, shapefile_name)
# Get the job from the database
job_session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user, job_session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb', app_class=GSSHAIndex)
# Get project file id
project_file_id = job.new_model_id
# Create a session
gsshapy_session = gsshapy_sessionmaker()
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir,'public')
userDir = os.path.join(publicDir, str(user))
indexMapDir = os.path.join(userDir, 'index_maps')
# Clear the workspace
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(indexMapDir)
# Use project id to link to original map table file
project_file = gsshapy_session.query(ProjectFile).filter(ProjectFile.id == project_file_id).one()
index_raster = gsshapy_session.query(IndexMap).filter(IndexMap.mapTableFile == project_file.mapTableFile).filter(IndexMap.name == index_name).one()
mapTables = index_raster.mapTables
if geojson['success'] != False:
geojson_result = geojson['geojson']
# Get existing indices
index_raster_indices = index_raster.indices
srid_name = geojson['crs']
project_file_srid = project_file.srid
id = 200
# Loop through each geometry
for object in geojson_result:
index_present = False
object_id = object['id']
# Check to see if the index is present
for index in index_raster_indices:
if object_id == index.index:
index_present = True
break
# Create new index value if it doesn't exist and add the number of ids
if index_present == False:
new_indice = MTIndex(id, object_id,"")
new_indice.indexMap = index_raster
for mapping_table in mapTables:
distinct_vars = gsshapy_session.query(distinct(MTValue.variable)).\
filter(MTValue.mapTable == mapping_table).\
order_by(MTValue.variable).\
all()
variables = []
for var in distinct_vars:
variables.append(var[0])
for variable in variables:
new_value = MTValue(variable, 0)
new_value.mapTable = mapping_table
new_value.index = new_indice
gsshapy_session.commit()
geom = object['geometry']
geom['crs'] = srid_name
geom_full = json.dumps(geom)
# Change values in the index map
change_index_values = "SELECT ST_SetValue(raster,1,ST_Transform(ST_GeomFromGeoJSON('{0}'), {1}),{2}) " \
"FROM idx_index_maps " \
"WHERE id = {3};".format(str(geom_full), project_file_srid, id, index_raster.id)
result = gi_lib.timeout(gi_lib.draw_update_index, args=(change_index_values,index_raster.id), kwargs={}, timeout=10, result_can_be_pickled=True, default=None)
# If there is a timeout
if result == None:
messages.error(request, 'The submission timed out. Please try again.')
job_session.close()
gsshapy_session.close()
context['index_name'] = index_name
context['job_id'] = job_id
return redirect(reverse('gsshaindex:shapefile_index', kwargs={'job_id':job_id, 'index_name':index_name, 'shapefile_name':shapefile_name}))
id += 1
# Get the values in the index map
statement3 = '''SELECT (pvc).*
FROM (SELECT ST_ValueCount(raster,1,true) As pvc
FROM idx_index_maps WHERE id = '''+ unicode(index_raster.id) +''') AS foo
ORDER BY (pvc).value;
'''
result3 = gsshapy_engine.execute(statement3)
numberIDs = 0
ids = []
for row in result3:
numberIDs +=1
ids.append(row.value)
map_table_count = 0
for mapping_table in mapTables:
index_raster.mapTables[map_table_count].numIDs = numberIDs
indices = gsshapy_session.query(distinct(MTIndex.index), MTIndex.id, MTIndex.description1, MTIndex.description2).\
join(MTValue).\
filter(MTValue.mapTable == mapping_table).\
order_by(MTIndex.index).\
all()
for index in indices:
if not int(index[0]) in ids:
bob = gsshapy_session.query(MTIndex).get(index.id)
for val in bob.values:
gsshapy_session.delete(val)
gsshapy_session.delete(bob)
gsshapy_session.commit()
map_table_count +=1
index_raster = gsshapy_session.query(IndexMap).filter(IndexMap.mapTableFile == project_file.mapTableFile).filter(IndexMap.name == index_name).one()
# Create kml file name and path
current_time = time.strftime("%Y%m%dT%H%M%S")
resource_name = index_raster.name + "_" + str(user) + "_" + current_time
kml_ext = resource_name + '.kml'
clusterFile = os.path.join(indexMapDir, kml_ext)
# Generate color ramp
index_raster.getAsKmlClusters(session=gsshapy_session, path=clusterFile, colorRamp=ColorRampEnum.COLOR_RAMP_HUE, alpha=0.6)
index_map_dataset = gi_lib.check_dataset("index-maps", CKAN_engine)
resource, status = gi_lib.add_kml_CKAN(index_map_dataset, CKAN_engine, clusterFile, resource_name)
temp_list = json.loads(job.current_kmls)
if status == True:
for item in temp_list:
if item == index_name:
del temp_list[item]
temp_list[index_name] = {'url':resource['url'], 'full_name':resource['name']}
break
job.current_kmls = json.dumps(temp_list)
job_session.commit()
job_session.close()
gsshapy_session.close()
context['index_name'] = index_name
context['job_id'] = job_id
return redirect(reverse('gsshaindex:edit_index', kwargs={'job_id':job_id, 'index_name':index_name}))
def results(request, job_id, view_type):
context = {}
# Get the user id
user = str(request.user)
# Get the job from the database and delete
session = jobs_sessionmaker()
job = session.query(Jobs).\
filter(Jobs.user_id == user).\
filter(Jobs.original_id == job_id).one()
# Get the run result urls
result_files = job.result_urls
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
jobDir = os.path.join(userDir, str(job_id))
fakeDir = os.path.join(userDir, 'fake')
newResultsPath = os.path.join(jobDir, 'new_results')
originalResultsPath = os.path.join(jobDir, 'original_results')
# Check to see if the otl folder for this job is already downloaded and has values and get a handle on them if it does
if os.path.exists(jobDir) and os.listdir(
newResultsPath) != [] and os.listdir(originalResultsPath) != []:
# Get the otl files
new_otl_file = gi_lib.find_otl(newResultsPath)
original_otl_file = gi_lib.find_otl(originalResultsPath)
# If it doesn't, get the otl files
else:
# Clear the results folder
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(jobDir)
gi_lib.clear_folder(newResultsPath)
gi_lib.clear_folder(originalResultsPath)
# Get the otl files
new_otl_file = gi_lib.extract_otl(result_files['new'], newResultsPath)
original_otl_file = gi_lib.extract_otl(result_files['original'],
originalResultsPath)
# Format the values for display with high charts
new_values = []
originalValues = []
new_values = gi_lib.get_otl_values(new_otl_file, new_values)
originalValues = gi_lib.get_otl_values(original_otl_file, originalValues)
# Set up for high charts hydrograph
highcharts_object = {
'chart': {
'type': 'spline'
},
'title': {
'text': 'Comparison Hydrograph'
},
'subtitle': {
'text': 'Display of the two model results'
},
'legend': {
'layout': 'vertical',
'align': 'right',
'verticalAlign': 'middle',
'borderWidth': 0
},
'xAxis': {
'title': {
'enabled': True,
'text': 'Time (hours)'
},
'labels': {
'formatter': 'function () { return this.value + " hr"; }'
}
},
'yAxis': {
'title': {
'enabled': True,
'text': 'Discharge (cfs)'
},
'labels': {
'formatter': 'function () { return this.value + " cfs"; }'
}
},
'tooltip': {
'headerFormat': '{series.name}',
'pointFormat': '{point.x} hours: {point.y} cfs'
},
'series': [{
'name': job.original_name.replace("_", " "),
'color': '#0066ff',
'dashStyle': 'ShortDash',
'marker': {
'enabled': False
},
'data': originalValues
}, {
'name': job.new_name.replace("_", " "),
'marker': {
'enabled': False
},
'color': '#ff6600',
'data': new_values
}]
}
hydrograph = {
'highcharts_object': highcharts_object,
'width': '500px',
'height': '500px'
}
google_map = {
'height':
'600px',
'width':
'100%',
'reference_kml_action':
'/apps/gsshaindex/' + job_id + '/get-depth-map/' + view_type
}
session.close()
kml_link = ''
title = ''
if view_type == 'originalMax':
title = job.original_name.replace("_", " ") + ' Maximum Depth'
kml_link = job.originalMax
elif view_type == 'newMax':
title = job.new_name.replace("_", " ") + ' Maximum Depth'
kml_link = job.newMax
elif view_type == 'newTime':
title = job.new_name.replace("_", " ") + ' Time Series'
kml_link = job.newTime
elif view_type == 'originalTime':
title = job.original_name.replace("_", " ") + ' Time Series'
kml_link = job.originalTime
else:
kml_link = ''
title = ''
context['map_title'] = title
context['kml_link'] = kml_link
context['hydrograph'] = hydrograph
context['google_map'] = google_map
context['map_type'] = view_type
context['original_name'] = job.original_name.replace("_", " ")
context['new_name'] = job.new_name.replace("_", " ")
context['original_file'] = job.result_urls['original']
context['new_file'] = job.result_urls['new']
context['job_id'] = job_id
return render(request, 'gsshaindex/results.html', context)
def fly(request, job_id):
context = {}
# Get the user id
user = str(request.user)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb',
app_class=GSSHAIndex)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
resultsPath = os.path.join(userDir, 'results')
originalFileRunPath = os.path.join(userDir, "preRun")
writeFile = os.path.join(userDir, "writeFile")
zipPath = os.path.join(userDir, "zipPath")
#Create session
gsshapy_session = gsshapy_sessionmaker()
# Clear the results folder
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(resultsPath)
gi_lib.clear_folder(originalFileRunPath)
gi_lib.clear_folder(writeFile)
# Get the jobs from the database
session = jobs_sessionmaker()
job = session.query(Jobs).\
filter(Jobs.user_id == user).\
filter(Jobs.original_id == job_id).one()
# Get the urls and names for the analysis
run_urls = job.run_urls
arguments = {
'new': {
'url': run_urls['new']['url'],
'name': run_urls['new']['name']
},
'original': {
'url': run_urls['original']['url'],
'name': run_urls['original']['name']
}
}
# Set up for fly GSSHA
job.status = "processing"
session.commit()
status = 'complete'
results = []
# results_urls = []
results_urls = {}
count = 0
GSSHA_dataset = gi_lib.check_dataset("gssha-models", CKAN_engine)
# Try running the web service
# try:
for k in arguments:
url = str(arguments[k]['url'])
if k == 'original' and job.original_certification == "Certified":
results_urls['original'] = url
count += 1
continue
elif k == 'original' and job.original_certification == "Missing gfl":
# Need to download from url, add gfl, zip, send to ckan, run, and save the url
downloaded_project = gi_lib.extract_zip_from_url(
user, url, originalFileRunPath)
# Create an empty Project File Object
# Find the project file
for root, dirs, files in os.walk(originalFileRunPath):
for file in files:
if file.endswith(".prj"):
project_name = file
project_path = os.path.join(root, file)
read_dir = os.path.dirname(project_path)
project = ProjectFile()
project.readInput(directory=read_dir,
projectFileName=project_name,
session=gsshapy_session,
spatial=True)
if project.getCard("FLOOD_GRID") == None:
max_depth_card = ProjectCard(
"FLOOD_GRID", '"{0}.gfl"'.format(project_name[:-4]))
project_cards = project.projectCards.append(max_depth_card)
gsshapy_session.commit()
# Need to format so that it will work for the file I just did
# Get all the project files
project.writeInput(session=gsshapy_session,
directory=writeFile,
name=project_name[:-4])
# Make a list of the project files
writeFile_list = os.listdir(writeFile)
# Add each project file to the zip folder
with zipfile.ZipFile(zipPath, "w") as gssha_zip:
for item in writeFile_list:
abs_path = os.path.join(writeFile, item)
archive_path = os.path.join(project_name, item)
gssha_zip.write(abs_path, archive_path)
GSSHA_dataset = gi_lib.check_dataset("gssha-models", CKAN_engine)
description = job.original_description + "with a gfl added"
pretty_date = time.strftime("%A %B %d, %Y %I:%M:%S %p")
# Add the zipped GSSHA file to the public ckan
results, success = gi_lib.add_zip_GSSHA(
GSSHA_dataset, zipPath, CKAN_engine,
project_name[:-4] + " with gfl", description, pretty_date,
user)
job.original_url = results['url']
url = job.original_url
resultsFile = os.path.join(
resultsPath, arguments[k]['name'].replace(" ", "_") +
datetime.now().strftime('%Y%d%m%H%M%S'))
gi_lib.flyGssha(str(url), resultsFile)
# Push file to ckan dataset
resource_name = ' '.join(
(arguments[k]['name'], '-Run',
datetime.now().strftime('%b %d %y %H:%M:%S')))
pretty_date = time.strftime("%A %B %d, %Y %I:%M:%S %p")
result, success = gi_lib.add_zip_GSSHA(GSSHA_dataset,
resultsFile,
CKAN_engine,
resource_name,
"",
pretty_date,
user,
certification="Certified")
# Save the new url as the original_url and run
job.original_certification = "Certified"
# Publish link to table
results_urls['original'] = result['url']
count += 1
else:
resultsFile = os.path.join(
resultsPath, arguments[k]['name'].replace(" ", "_") +
datetime.now().strftime('%Y%d%m%H%M%S'))
gi_lib.flyGssha(url, resultsFile)
# Push file to ckan dataset
resource_name = ' '.join(
(arguments[k]['name'], '-Run',
datetime.now().strftime('%b %d %y %H:%M:%S')))
pretty_date = time.strftime("%A %B %d, %Y %I:%M:%S %p")
result, success = gi_lib.add_zip_GSSHA(GSSHA_dataset,
resultsFile,
CKAN_engine,
resource_name,
"",
pretty_date,
user,
certification="Certified")
# Publish link to table
if k == 'original':
results_urls['original'] = result['url']
job.original_certification = "Certified"
else:
results_urls['new'] = result['url']
count += 1
if (count == 2):
print results_urls
else:
status = 'failed'
# except:
# status = 'failed'
job.status = status
job.result_urls = results_urls
session.commit()
session.close()
gsshapy_session.commit()
gsshapy_session.close()
return redirect(reverse('gsshaindex:status'))
def zip_file(request, job_id):
'''
This zips up the GSSHA files in preparation of their being run
'''
context = {}
# Get the job id and user id
job_id = job_id
user = str(request.user)
session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user, session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb',
app_class=GSSHAIndex)
project_file_id = job.new_model_id
# Get the name and description from the submission
params = request.POST
not_clean_name = params['new_name']
new_description = params['new_description']
# Reformat the name by removing bad characters
# bad_char = "',.<>()[]{}=+-/\"|:;\\^?!~`@#$%&* "
bad_char = "',.<>[]{}=+-/\"|:;\\^?!~`@#$%&*"
for char in bad_char:
new_name = not_clean_name.replace(char, "_")
#Create session
gsshapy_session = gsshapy_sessionmaker()
# Get project from the database
projectFileAll = gsshapy_session.query(ProjectFile).get(project_file_id)
# Create name for files
project_name = projectFileAll.name
if project_name.endswith('.prj'):
project_name = project_name[:-4]
pretty_date = time.strftime("%A %B %d, %Y %I:%M:%S %p")
# Set depth map
if projectFileAll.getCard("FLOOD_GRID") == None:
max_depth_card = ProjectCard("FLOOD_GRID",
'"{0}.gfl"'.format(new_name))
project_cards = projectFileAll.projectCards.append(max_depth_card)
gsshapy_session.commit()
job.original_certification = "Missing gfl"
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
newFileDir = os.path.join(userDir, 'newFile')
writeFile = os.path.join(newFileDir, new_name)
zipPath = os.path.join(newFileDir, new_name + "_zip")
# Clear workspace folders
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(newFileDir)
gi_lib.clear_folder(writeFile)
# Get all the project files
projectFileAll.writeInput(session=gsshapy_session,
directory=writeFile,
name=new_name)
# Make a list of the project files
writeFile_list = os.listdir(writeFile)
# Add each project file to the zip folder
with zipfile.ZipFile(zipPath, "w") as gssha_zip:
for item in writeFile_list:
abs_path = os.path.join(writeFile, item)
archive_path = os.path.join(new_name, item)
gssha_zip.write(abs_path, archive_path)
GSSHA_dataset = gi_lib.check_dataset("gssha-models", CKAN_engine)
# Add the zipped GSSHA file to the public ckan
results, success = gi_lib.add_zip_GSSHA(GSSHA_dataset, zipPath,
CKAN_engine, new_name,
new_description, pretty_date, user)
# If the file zips correctly, get information and store it in the database
if success == True:
new_url = results['url']
new_name = results['name']
original_url = job.original_url
original_name = job.original_name
model_data = {
'original': {
'url': original_url,
'name': original_name
},
'new': {
'url': new_url,
'name': new_name
}
}
job.run_urls = model_data
job.new_name = new_name
job.status = "ready to run"
session.commit()
return redirect(reverse('gsshaindex:status'))
def extract_gssha(request, job_id):
'''
This takes the file name and id that were submitted and unzips the files, finds the index maps, and creates kmls.
'''
context = {}
user = str(request.user)
session = jobs_sessionmaker()
job, success = gi_lib.get_pending_job(job_id, user, session)
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb',
app_class=GSSHAIndex)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
indexMapDir = os.path.join(userDir, 'index_maps')
# Clear the workspace
gi_lib.clear_folder(userDir)
gi_lib.clear_folder(indexMapDir)
# Get url for the resource and extract the GSSHA file
url = job.original_url
extract_path, unique_dir = gi_lib.extract_zip_from_url(user, url, userDir)
# Create GSSHAPY Session
gsshapy_session = gsshapy_sessionmaker()
# Find the project file
for root, dirs, files in os.walk(userDir):
for file in files:
if file.endswith(".prj"):
project_name = file
project_path = os.path.join(root, file)
read_dir = os.path.dirname(project_path)
# Create an empty Project File Object
project = ProjectFile()
project.readInput(directory=read_dir,
projectFileName=project_name,
session=gsshapy_session,
spatial=True)
# Create empty dictionary to hold the kmls from this session
current_kmls = {}
# Store model information
job.new_model_name = project.name
job.new_model_id = project.id
job.created = datetime.now()
# Get index maps
index_list = gsshapy_session.query(IndexMap).filter(
IndexMap.mapTableFile == project.mapTableFile).all()
# Loop through the index
for current_index in index_list:
# Create kml file name and path
current_time = time.strftime("%Y%m%dT%H%M%S")
resource_name = current_index.name + "_" + str(
user) + "_" + current_time
kml_ext = resource_name + '.kml'
clusterFile = os.path.join(indexMapDir, kml_ext)
# Generate color ramp
current_index.getAsKmlClusters(session=gsshapy_session,
path=clusterFile,
colorRamp=ColorRampEnum.COLOR_RAMP_HUE,
alpha=0.6)
index_map_dataset = gi_lib.check_dataset("index-maps", CKAN_engine)
resource, status = gi_lib.add_kml_CKAN(index_map_dataset, CKAN_engine,
clusterFile, resource_name)
# If the kml is added correctly, create an entry for the current_kmls with the name as the index name
if status == True:
current_kmls[current_index.name] = {
'url': resource['url'],
'full_name': resource['name']
}
# Add the kmls with their url to the database
job.current_kmls = json.dumps(current_kmls)
session.commit()
session.close()
gsshapy_session.close()
context['job_id'] = job_id
return redirect(
reverse('gsshaindex:select_index', kwargs={'job_id': job_id}))
def get_mask_map(request, file_id):
"""
This action is used to pass the kml data to the google map.
It must return a JSON response with a Python dictionary that
has the key 'kml_links'.
"""
kml_links = []
session = jobs_sessionmaker()
user = str(request.user)
job, success = gi_lib.get_new_job(file_id, user, session)
if job.kml_url != None:
kml_links.append(job.kml_url)
#TODO Need some way to check and see if the link works or if it's broken
return JsonResponse({'kml_links': kml_links})
else:
# Check that there's a package to store kmls
CKAN_engine = get_dataset_engine(name='gsshaindex_ciwweb',
app_class=GSSHAIndex)
present = gi_lib.check_package('kmls', CKAN_engine)
# Specify the workspace
controllerDir = os.path.abspath(os.path.dirname(__file__))
gsshaindexDir = os.path.abspath(os.path.dirname(controllerDir))
publicDir = os.path.join(gsshaindexDir, 'public')
userDir = os.path.join(publicDir, str(user))
# Clear the workspace
gi_lib.clear_folder(userDir)
url = job.original_url
maskMapDir = os.path.join(userDir, 'mask_maps')
extractPath = os.path.join(maskMapDir, file_id)
mask_file = gi_lib.extract_mask(url, extractPath)
if mask_file == "blank":
job.kml_url = ''
session.commit()
return JsonResponse({'kml_links': ''})
else:
projection_file = gi_lib.extract_projection(url, extractPath)
# Set up kml file name and save location
name = job.original_name
norm_name = name.replace(" ", "")
current_time = time.strftime("%Y%m%dT%H%M%S")
kml_name = norm_name + "_" + user + "_" + current_time
kml_ext = kml_name + ".kml"
kml_file = os.path.join(extractPath, kml_ext)
colors = [(237, 9, 222), (92, 245, 61), (61, 184, 245),
(171, 61, 245), (250, 245, 105), (245, 151, 44),
(240, 37, 14), (88, 5, 232), (5, 232, 190),
(11, 26, 227)]
color = [random.choice(colors)]
# Extract mask map and create kml
gsshapy_session = gsshapy_sessionmaker()
if projection_file != "blank":
srid = ProjectionFile.lookupSpatialReferenceID(
extractPath, projection_file)
else:
srid = 4302
mask_map = RasterMapFile()
mask_map.read(directory=extractPath,
filename=mask_file,
session=gsshapy_session,
spatial=True,
spatialReferenceID=srid)
mask_map.getAsKmlClusters(session=gsshapy_session,
path=kml_file,
colorRamp={
'colors': color,
'interpolatedPoints': 1
})
mask_map_dataset = gi_lib.check_dataset("mask-maps", CKAN_engine)
# Add mask kml to CKAN for viewing
resource, success = gi_lib.add_kml_CKAN(mask_map_dataset,
CKAN_engine, kml_file,
kml_name)
# Check to ensure the resource was added and save it to database by adding "kml_url"
if success == True:
job.kml_url = resource['url']
session.commit()
kml_links.append(job.kml_url)
return JsonResponse({'kml_links': kml_links})