# "longitude_fuzz": -123.882,
# "calc_slope": 59.5,
# "stand_age": 15.0
# }
# }
data = json.loads(txt)
# default is defined in plots.py
# default weight dict
weight_dict = None
ps, num_candidates = get_nearest_neighbors(
data['plot_attrs'],
data['tree_list'],
data['variant'],
weight_dict,
k=20,
verbose=True)
print ".... Total Candidates: ", num_candidates
print
print "----- Top candidate"
pseries = ps[0]
#print pseries
cond_id = pseries.name
print "ID::", cond_id
print "Certainty::", pseries['_certainty']
print "NN Distance::", pseries['_kdtree_distance']
print "NONSPEC_TPA::", pseries['NONSPEC_TPA']
print "NONSPEC_BA::", pseries['NONSPEC_BA']
def impute_nearest_neighbor(stand_results, savetime):
# import here to avoid circular dependencies
from trees.models import Stand, IdbSummary
from trees.plots import get_nearest_neighbors
# you can pass the output of impute_rasters OR a stand id
try:
stand_id = stand_results['stand_id']
except TypeError:
stand_id = int(stand_results)
try:
stand_qs = Stand.objects.filter(id=stand_id)
stand = stand_qs[0]
except (Stand.DoesNotExist, IndexError):
exc = Exception("Cant run nearest neighbor; Stand %s does not exist." %
stand_id)
raise impute_nearest_neighbor.retry(exc=exc)
if stand.is_locked:
if stand.cond_id != stand.locked_cond_id:
# should never happen; fix it here
stand_qs.filter(nn_savetime__lt=savetime).update(
cond_id=stand.locked_cond_id, nn_savetime=savetime)
stand.invalidate_cache()
return {'stand_id': stand_id, 'cond_id': stand.cond_id}
# Do we have the required attributes yet?
if not (stand.strata and stand.elevation and stand.aspect and stand.slope
and stand.geometry_final):
# if not, retry it
exc = Exception(
"Cant run nearest neighbor; missing required attributes.")
raise impute_nearest_neighbor.retry(exc=exc)
# get variant code
variant = stand.collection.variant.code
stand_list = stand.strata.stand_list
geom = stand.geometry_final.transform(4326, clone=True)
site_cond = {
'latitude_fuzz': geom.centroid[1],
'longitude_fuzz': geom.centroid[0],
}
if stand.aspect:
site_cond['calc_aspect'] = stand.aspect
if stand.elevation:
site_cond['elev_ft'] = stand.elevation
if stand.slope:
site_cond['calc_slope'] = stand.slope
if stand.strata.search_age:
site_cond['stand_age'] = float(stand.strata.search_age)
weight_dict = None # take the default defined in plots.py
ps, num_candidates = get_nearest_neighbors(site_cond,
stand_list['classes'],
variant=variant,
weight_dict=weight_dict,
k=2)
# Take the top match
cond_id = int(ps[0].name)
# TODO confirm that cond_id exists in the idbsummary and fvsaggregate tables?
# update the database
# stuff might have changed, we don't want a wholesale update of all fields like save()
# use the timestamp to make sure we don't clobber a more recent request
stand_qs.filter(nn_savetime__lt=savetime).update(cond_id=cond_id,
nn_savetime=savetime)
stand.invalidate_cache()
return {'stand_id': stand_id, 'cond_id': cond_id}
stand_list = [tuple(tl.treelist) for tl in tls]
idb = IdbSummary.objects.get(cond_id=cond)
site_cond = {
'calc_aspect': idb.calc_aspect,
'elev_ft': idb.elev_ft,
'latitude_fuzz': idb.latitude_fuzz,
'longitude_fuzz': idb.longitude_fuzz,
'calc_slope': idb.calc_slope,
'stand_age': idb.stand_age
}
weight_dict = None
try:
ps, num_candidates = get_nearest_neighbors(
site_cond, stand_list, variant, weight_dict, k=4)
except:
print "Error: ", site_cond
continue
if num_candidates < 2:
print "DID NOT MATCH ANYTHING BUT ITSELF"
continue
for pseries in ps[1:4]: # top 3
# ['original_condition', 'variant', 'matched_condition', 'certainty']
certainty = pseries['_certainty']
if certainty < 0.75: # ... that are at least 75% certain
continue
data = [cond, variant, pseries.name, certainty]
fh.write(','.join([str(x) for x in data]))
# "latitude_fuzz": 42.394,
# "longitude_fuzz": -123.882,
# "calc_slope": 59.5,
# "stand_age": 15.0
# }
# }
data = json.loads(txt)
# default is defined in plots.py
# default weight dict
weight_dict = None
ps, num_candidates = get_nearest_neighbors(data['plot_attrs'],
data['tree_list'],
data['variant'],
weight_dict,
k=20,
verbose=True)
print ".... Total Candidates: ", num_candidates
print
print "----- Top candidate"
pseries = ps[0]
#print pseries
cond_id = pseries.name
print "ID::", cond_id
print "Certainty::", pseries['_certainty']
print "NN Distance::", pseries['_kdtree_distance']
print "NONSPEC_TPA::", pseries['NONSPEC_TPA']
print "NONSPEC_BA::", pseries['NONSPEC_BA']
print "PLOT_BA::", pseries['PLOT_BA']
idb = IdbSummary.objects.get(cond_id=cond)
site_cond = {
'calc_aspect': idb.calc_aspect,
'elev_ft': idb.elev_ft,
'latitude_fuzz': idb.latitude_fuzz,
'longitude_fuzz': idb.longitude_fuzz,
'calc_slope': idb.calc_slope,
'stand_age': idb.stand_age
}
weight_dict = None
try:
ps, num_candidates = get_nearest_neighbors(site_cond,
stand_list,
variant,
weight_dict,
k=4)
except:
print "Error: ", site_cond
continue
if num_candidates < 2:
print "DID NOT MATCH ANYTHING BUT ITSELF"
continue
for pseries in ps[1:4]: # top 3
# ['original_condition', 'variant', 'matched_condition', 'certainty']
certainty = pseries['_certainty']
if certainty < 0.75: # ... that are at least 75% certain
continue
def impute_nearest_neighbor(stand_results, savetime):
# import here to avoid circular dependencies
from trees.models import Stand, IdbSummary
from trees.plots import get_nearest_neighbors
# you can pass the output of impute_rasters OR a stand id
try:
stand_id = stand_results['stand_id']
except TypeError:
stand_id = int(stand_results)
try:
stand_qs = Stand.objects.filter(id=stand_id)
stand = stand_qs[0]
except (Stand.DoesNotExist, IndexError):
exc = Exception("Cant run nearest neighbor; Stand %s does not exist." % stand_id)
raise impute_nearest_neighbor.retry(exc=exc)
# Do we have the required attributes yet?
if not (stand.strata and stand.elevation and stand.aspect and stand.slope and stand.geometry_final):
# if not, retry it
exc = Exception("Cant run nearest neighbor; missing required attributes.")
raise impute_nearest_neighbor.retry(exc=exc)
# get variant code
variant = stand.collection.variant.code
stand_list = stand.strata.stand_list
geom = stand.geometry_final.transform(4326, clone=True)
site_cond = {
'latitude_fuzz': geom.centroid[1],
'longitude_fuzz': geom.centroid[0],
}
if stand.aspect:
site_cond['calc_aspect'] = stand.aspect
if stand.elevation:
site_cond['elev_ft'] = stand.elevation
if stand.slope:
site_cond['calc_slope'] = stand.slope
if stand.strata.search_age:
site_cond['stand_age'] = float(stand.strata.search_age)
weight_dict = None # take the default defined in plots.py
ps, num_candidates = get_nearest_neighbors(
site_cond, stand_list['classes'], variant=variant,
weight_dict=weight_dict, k=2
)
# Take the top match
cond_id = int(ps[0].name)
# just confirm that it exists
# TODO confirm that cond_id exists in the fvsaggregate table
IdbSummary.objects.get(cond_id=cond_id)
# update the database
# stuff might have changed, we dont want a wholesale update of all fields like save()
# use the timestamp to make sure we don't clobber a more recent request
stand_qs.filter(nn_savetime__lt=savetime).update(cond_id=cond_id, nn_savetime=savetime)
stand.invalidate_cache()
return {'stand_id': stand_id, 'cond_id': cond_id}