Source code for regparse.esri_feature
"""
An ESRI feature "parser" (really the requests library does most of the actual parsing).
Most of the utility functions are exposed but most applications won't use them
:func:make_node is generally the only point of interest here.
"""
import requests, metadata
[docs]def make_grid_col( **kw ):
"""
Generate a RAMP compliant datagrid column object with the following defaults:
fieldName ''
isSortable False
sortType 'string'
alignment 0
:param kw: Takes keyword arguments and just fills in the defaults
:returns: dict -- a dictionary with the defaults applied
"""
d = { 'fieldName':'', 'orderable':False, 'type':'string', 'alignment':0 }
d.update(kw)
return d
[docs]def make_extent( json_data ):
"""
Extracts the extent for the layer from ESRI's JSON config.
:param json_data: A dictionary containing scraped data from an ESRI feature service endpoint
:type json_data: dict
:returns: dict -- A dictionary with the same data as the ESRI layerExtent node
"""
return json_data['extent']
[docs]def make_data_grid( json_data ):
"""
Generate a RAMP datagrid by walking through the attributes.
Iterates over all entries in *fields* that do not have a type of *esriFieldGeometry*
:param json_data: A dictionary containing scraped data from an ESRI feature service endpoint
:type json_data: dict
:returns: dict -- A dictionary with a single entry *gridColumns* containing an array of datagrid objects
"""
g = []
g.append( make_grid_col(id="iconCol", width="50px", title="Icon", columnTemplate="graphic_icon") )
g.append( make_grid_col(id="detailsCol", width="60px", title="Details", columnTemplate="details_button") )
g.extend( [ make_grid_col(id=attrib['name'], fieldName=attrib['name'], width="400px",
orderable=True, alignment=1, title=attrib['name'],
columnTemplate="unformatted_grid_value")
for attrib in json_data['fields'] if attrib['type'] != 'esriFieldGeometry' ] )
return { 'gridColumns':g }
[docs]def get_legend_url( feature_service_url ):
"""
Converts a feature service URL into a legend request. Handles the optional '/' at the end of requests.
:param feature_service_url: A URL pointing to an ESRI feature service
:type feature_service_url: str
:returns: str -- A URL pointing to a legend request
"""
if feature_service_url.endswith('/'):
feature_service_url = feature_service_url[:-1]
return feature_service_url[:feature_service_url.rfind('/')] + '/legend?f=json'
[docs]def get_legend_mapping( data, layer_id ):
"""
Generates a mapping of layer labels to image data URLs.
:param data: The initial payload to RCS (should contain a 'service_url' entry)
:type data: dict
:param layer_id: The id of the layer to create the mapping for.
:returns: dict -- a mapping of 'label' => 'data URI encoded image'
"""
legend_json = requests.get( get_legend_url( data['service_url'] ) ).json()
for layer in legend_json['layers']:
if layer['layerId'] == layer_id:
break
return { x['label']:'data:'+x['contentType']+';base64,'+x['imageData'] for x in layer['legend'] }
[docs]def make_symbology( json_data, data ):
"""
Generates a symbology node for the RAMP configuration. Handles simple,
unique value and class break renders; prefetches all symbology images.
:param json_data: A dictionary containing scraped data from an ESRI feature service endpoint
:type json_data: dict
:param data: The initial payload to RCS (should contain a 'service_url' entry)
:type data: dict
:returns: dict -- a symbology node
"""
render_json = json_data['drawingInfo']['renderer']
symb = { 'type':render_json['type'] }
label_map = get_legend_mapping( data, json_data['id'] )
if render_json['type'] == 'simple':
symb['imageUrl'] = label_map[render_json['label']]
elif render_json['type'] == 'uniqueValue':
if render_json.get('defaultLabel',None) and render_json['defaultLabel'] in label_map:
symb['defaultImageUrl'] = label_map[render_json['defaultLabel']]
for field in 'field1 field2 field3'.split():
symb[field] = render_json[field]
val_maps = [ dict( value= u['value'], imageUrl= label_map[u['label']] )
for u in render_json['uniqueValueInfos'] ]
symb['valueMaps'] = val_maps
elif render_json['type'] == 'classBreaks':
if render_json.get('defaultLabel',None) and render_json['defaultLabel'] in label_map:
symb['defaultImageUrl'] = label_map[render_json['defaultLabel']]
symb['field'] = render_json['field']
symb['minValue'] = render_json['minValue']
range_maps = [ dict(maxValue=u['classMaxValue'], imageUrl=label_map[u['label']])
for u in render_json['classBreakInfos'] ]
symb['rangeMaps'] = range_maps
return symb
[docs]def make_node( data, id, config ):
"""
Generate a RAMP layer entry for an ESRI feature service.
:param data: The initial payload to RCS (should contain a 'service_url' entry)
:type data: dict
:param id: An identifier for the layer (as this is unique it is generally supplied from :module:rcs )
:type id: str
:returns: dict -- a RAMP configuration fragment representing the ESRI layer
"""
node = { 'id': id }
r = requests.get( data['service_url'] + '?f=json' )
svc_data = r.json()
node['url'] = data['service_url']
node['displayName'] = data.get('service_name',None)
node['nameField'] = data.get('display_field',None)
if node.get('displayName',None) is None:
node['displayName'] = svc_data['name']
if node.get('nameField',None) is None:
node['nameField'] = svc_data['displayField']
metadata_url, catalogue_url = metadata.get_url( data, config )
if metadata_url:
node['metadataUrl'] = metadata_url
node['catalogueUrl'] = catalogue_url
node['minScale'] = svc_data.get('minScale',0)
node['maxScale'] = svc_data.get('maxScale',0)
node['datagrid'] = make_data_grid( svc_data )
node['layerExtent'] = make_extent( svc_data )
node['symbology'] = make_symbology( svc_data, data )
return node