pylint suggested changes
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@ -47,8 +47,8 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
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try:
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query_result = plpy.execute(query)
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except:
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plpy.notice('** Query failed: %s' % query)
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except plpy.SPIError, err:
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plpy.notice('** Query failed with exception %s: %s' % (err, query))
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plpy.error('Spatial Markov failed: check the input parameters')
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return zip([None], [None], [None], [None], [None])
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@ -75,20 +75,13 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
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prob_dist = get_prob_dist(sp_markov_result.P, lag_classes, sp_markov_result.classes[:, -1])
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## find the ups and down and overall distribution of each cell
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trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
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trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
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sp_markov_result.classes[:, -1])
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## output the results
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return zip(trend, trend_up, trend_down, volatility, weights.id_order)
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def spatial_markov_predict(subquery, time_cols, num_time_per_bin,
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permutations, geom_col, id_col, w_type, num_ngbrs):
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"""
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Filler for this future function
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"""
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return None
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def get_time_data(markov_data, time_cols):
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"""
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Extract the time columns and bin appropriately
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@ -131,7 +124,7 @@ def rebin_data(time_data, num_time_per_bin):
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n_max = time_data.shape[1] / num_time_per_bin + 1
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return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
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for i in range(n_max)]).T
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for i in range(n_max)]).T
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def get_prob_dist(transition_matrix, lag_indices, unit_indices):
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"""
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Given an array of transition matrices, look up the probability
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