testing array outputs
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@ -62,6 +62,11 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
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## rebin
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## rebin
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t_data = rebin_data(t_data, int(num_time_per_bin))
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t_data = rebin_data(t_data, int(num_time_per_bin))
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print 'shape of t_data %d, %d' % t_data.shape
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print 'number of weight objects: %d, %d' % (weights.sparse).shape
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print 'first num elements: %f' % t_data[0, 0]
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# ls = ps.lag_spatial(weights, t_data)
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sp_markov_result = ps.Spatial_Markov(t_data,
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sp_markov_result = ps.Spatial_Markov(t_data,
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weights,
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weights,
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k=7,
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k=7,
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@ -88,7 +93,7 @@ def get_time_data(markov_data, time_cols):
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"""
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"""
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num_attrs = len(time_cols)
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num_attrs = len(time_cols)
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return np.array([[x['attr' + str(i)] for x in markov_data]
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return np.array([[x['attr' + str(i)] for x in markov_data]
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for i in range(1, num_attrs+1)], dtype=float).T
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for i in range(1, num_attrs+1)], dtype=float).transpose()
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def rebin_data(time_data, num_time_per_bin):
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def rebin_data(time_data, num_time_per_bin):
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"""
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"""
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