standardizing naming conventions in code
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@ -31,13 +31,13 @@ class Getis(object):
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# geometries with attributes that are null are ignored
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# resulting in a collection of not as near neighbors if kNN is chosen
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qvals = OrderedDict([("id_col", id_col),
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("attr1", attr),
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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params = OrderedDict([("id_col", id_col),
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("attr1", attr),
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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result = self.data_provider.get_getis(w_type, qvals)
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result = self.data_provider.get_getis(w_type, params)
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attr_vals = pu.get_attributes(result)
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# build PySAL weight object
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@ -20,12 +20,12 @@ class Kmeans(object):
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"geom_col": "the_geom",
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"id_col": "cartodb_id"}
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data = self.data_provider.get_spatial_kmeans(params)
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result = self.data_provider.get_spatial_kmeans(params)
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# Unpack query response
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xs = data[0]['xs']
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ys = data[0]['ys']
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ids = data[0]['ids']
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xs = result[0]['xs']
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ys = result[0]['ys']
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ids = result[0]['ids']
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km = KMeans(n_clusters=no_clusters, n_init=no_init)
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labels = km.fit_predict(zip(xs, ys))
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@ -61,14 +61,14 @@ class Markov(object):
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"subquery": subquery,
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"num_ngbrs": num_ngbrs}
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query_result = self.data_provider.get_markov(w_type, params)
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result = self.data_provider.get_markov(w_type, params)
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# build weight
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weights = pu.get_weight(query_result, w_type)
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weights = pu.get_weight(result, w_type)
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weights.transform = 'r'
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# prep time data
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t_data = get_time_data(query_result, time_cols)
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t_data = get_time_data(result, time_cols)
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sp_markov_result = ps.Spatial_Markov(t_data,
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weights,
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