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standardizing naming conventions in code

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This commit is contained in:
Andy Eschbacher 2017-01-12 17:12:09 -05:00
parent 4b3481b1a6
commit 04bd067045
3 changed files with 13 additions and 13 deletions
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12
src/py/crankshaft/crankshaft/clustering/getis.py
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@ -31,13 +31,13 @@ class Getis(object):
# geometries with attributes that are null are ignored # geometries with attributes that are null are ignored
# resulting in a collection of not as near neighbors if kNN is chosen # resulting in a collection of not as near neighbors if kNN is chosen
qvals = OrderedDict([("id_col", id_col), params = OrderedDict([("id_col", id_col),
("attr1", attr), ("attr1", attr),
("geom_col", geom_col), ("geom_col", geom_col),
("subquery", subquery), ("subquery", subquery),
("num_ngbrs", num_ngbrs)]) ("num_ngbrs", num_ngbrs)])
result = self.data_provider.get_getis(w_type, qvals) result = self.data_provider.get_getis(w_type, params)
attr_vals = pu.get_attributes(result) attr_vals = pu.get_attributes(result)
# build PySAL weight object # build PySAL weight object

8
src/py/crankshaft/crankshaft/clustering/kmeans.py
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@ -20,12 +20,12 @@ class Kmeans(object):
"geom_col": "the_geom", "geom_col": "the_geom",
"id_col": "cartodb_id"} "id_col": "cartodb_id"}
data = self.data_provider.get_spatial_kmeans(params) result = self.data_provider.get_spatial_kmeans(params)
# Unpack query response # Unpack query response
xs = data[0]['xs'] xs = result[0]['xs']
ys = data[0]['ys'] ys = result[0]['ys']
ids = data[0]['ids'] ids = result[0]['ids']
km = KMeans(n_clusters=no_clusters, n_init=no_init) km = KMeans(n_clusters=no_clusters, n_init=no_init)
labels = km.fit_predict(zip(xs, ys)) labels = km.fit_predict(zip(xs, ys))

6
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
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@ -61,14 +61,14 @@ class Markov(object):
"subquery": subquery, "subquery": subquery,
"num_ngbrs": num_ngbrs} "num_ngbrs": num_ngbrs}
query_result = self.data_provider.get_markov(w_type, params) result = self.data_provider.get_markov(w_type, params)
# build weight # build weight
weights = pu.get_weight(query_result, w_type) weights = pu.get_weight(result, w_type)
weights.transform = 'r' weights.transform = 'r'
# prep time data # prep time data
t_data = get_time_data(query_result, time_cols) t_data = get_time_data(result, time_cols)
sp_markov_result = ps.Spatial_Markov(t_data, sp_markov_result = ps.Spatial_Markov(t_data,
weights, weights,