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move to class based markov
This commit is contained in:
Andy Eschbacher
parent
2738c1f29c
commit
224fbc2fc5
@ -22,10 +22,11 @@ CREATE OR REPLACE FUNCTION
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RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
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AS $$
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from crankshaft.space_time_dynamics import spatial_markov_trend
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from crankshaft.space_time_dynamics import Markov
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markov = Markov()
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## TODO: use named parameters or a dictionary
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return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
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return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu;
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-- input table format: identical to above but in a predictable format
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@ -8,92 +8,104 @@ import pysal as ps
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import plpy
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import crankshaft.pysal_utils as pu
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def spatial_markov_trend(subquery, time_cols, num_classes=7,
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w_type='knn', num_ngbrs=5, permutations=0,
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geom_col='the_geom', id_col='cartodb_id'):
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"""
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Predict the trends of a unit based on:
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1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
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2. average class of its neighbors
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Inputs:
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@param subquery string: e.g., SELECT the_geom, cartodb_id,
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interesting_time_column FROM table_name
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@param time_cols list of strings: list of strings of column names
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@param num_classes (optional): number of classes to break distribution
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of values into. Currently uses quantile bins.
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@param w_type string (optional): weight type ('knn' or 'queen')
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@param num_ngbrs int (optional): number of neighbors (if knn type)
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@param permutations int (optional): number of permutations for test
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stats
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@param geom_col string (optional): name of column which contains the
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geometries
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@param id_col string (optional): name of column which has the ids of
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the table
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class QueryRunner:
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def get_result(self, query):
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try:
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data = plpy.execute(query)
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Outputs:
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@param trend_up float: probablity that a geom will move to a higher
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class
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@param trend_down float: probablity that a geom will move to a lower
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class
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@param trend float: (trend_up - trend_down) / trend_static
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@param volatility float: a measure of the volatility based on
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probability stddev(prob array)
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"""
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if len(data) == 0:
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return zip([None], [None], [None], [None], [None])
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if len(time_cols) < 2:
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plpy.error('More than one time column needs to be passed')
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return data
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except plpy.SPIError, err:
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plpy.error('Analysis failed: %s' % err)
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qvals = {"id_col": id_col,
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"time_cols": time_cols,
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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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try:
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query_result = plpy.execute(
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pu.construct_neighbor_query(w_type, qvals)
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)
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if len(query_result) == 0:
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return zip([None], [None], [None], [None], [None])
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except plpy.SPIError, e:
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plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
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plpy.error('Analysis failed: %s' % e)
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return zip([None], [None], [None], [None], [None])
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class Markov:
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def __init__(self, query_runner=None):
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if query_runner is None:
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self.query_runner = QueryRunner()
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else:
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self.query_runner = query_runner
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## build weight
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weights = pu.get_weight(query_result, w_type)
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weights.transform = 'r'
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def spatial_trend(self, subquery, time_cols, num_classes=7,
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w_type='knn', num_ngbrs=5, permutations=0,
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geom_col='the_geom', id_col='cartodb_id'):
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"""
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Predict the trends of a unit based on:
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1. history of its transitions to different classes (e.g., 1st
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quantile -> 2nd quantile)
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2. average class of its neighbors
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## prep time data
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t_data = get_time_data(query_result, time_cols)
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Inputs:
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@param subquery string: e.g., SELECT the_geom, cartodb_id,
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interesting_time_column FROM table_name
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@param time_cols list of strings: list of strings of column names
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@param num_classes (optional): number of classes to break
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distribution of values into. Currently uses quantile bins.
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@param w_type string (optional): weight type ('knn' or 'queen')
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@param num_ngbrs int (optional): number of neighbors (if knn type)
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@param permutations int (optional): number of permutations for test
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stats
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@param geom_col string (optional): name of column which contains
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the geometries
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@param id_col string (optional): name of column which has the ids
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of the table
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plpy.debug('shape of t_data %d, %d' % t_data.shape)
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plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
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plpy.debug('first num elements: %f' % t_data[0, 0])
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Outputs:
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@param trend_up float: probablity that a geom will move to a higher
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class
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@param trend_down float: probablity that a geom will move to a
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lower class
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@param trend float: (trend_up - trend_down) / trend_static
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@param volatility float: a measure of the volatility based on
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probability stddev(prob array)
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"""
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sp_markov_result = ps.Spatial_Markov(t_data,
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weights,
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k=num_classes,
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fixed=False,
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permutations=permutations)
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if len(time_cols) < 2:
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plpy.error('More than one time column needs to be passed')
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## get lag classes
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lag_classes = ps.Quantiles(
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ps.lag_spatial(weights, t_data[:, -1]),
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k=num_classes).yb
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qvals = {"id_col": id_col,
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"time_cols": time_cols,
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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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## look up probablity distribution for each unit according to class and lag class
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prob_dist = get_prob_dist(sp_markov_result.P,
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lag_classes,
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sp_markov_result.classes[:, -1])
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query = pu.construct_neighbor_query(w_type, qvals)
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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,
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sp_markov_result.classes[:, -1])
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query_result = self.query_runner.get_result(query)
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# build weight
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weights = pu.get_weight(query_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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sp_markov_result = ps.Spatial_Markov(t_data,
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weights,
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k=num_classes,
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fixed=False,
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permutations=permutations)
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# get lag classes
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lag_classes = ps.Quantiles(
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ps.lag_spatial(weights, t_data[:, -1]),
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k=num_classes).yb
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# look up probablity distribution for each unit according to class and
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# lag class
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prob_dist = get_prob_dist(sp_markov_result.P,
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lag_classes,
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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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# output the results
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return zip(trend, trend_up, trend_down, volatility, weights.id_order)
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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 get_time_data(markov_data, time_cols):
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"""
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@ -103,7 +115,8 @@ def get_time_data(markov_data, time_cols):
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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).transpose()
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## not currently used
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# not currently used
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def rebin_data(time_data, num_time_per_bin):
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"""
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Convert an n x l matrix into an (n/m) x l matrix where the values are
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@ -131,14 +144,16 @@ def rebin_data(time_data, num_time_per_bin):
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"""
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if time_data.shape[1] % num_time_per_bin == 0:
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## if fit is perfect, then use it
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# if fit is perfect, then use it
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n_max = time_data.shape[1] / num_time_per_bin
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else:
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## fit remainders into an additional column
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# fit remainders into an additional column
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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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return np.array(
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[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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def get_prob_dist(transition_matrix, lag_indices, unit_indices):
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"""
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@ -157,6 +172,7 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
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return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
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for i in range(len(lag_indices))])
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def get_prob_stats(prob_dist, unit_indices):
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"""
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get the statistics of the probability distributions
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@ -179,11 +195,12 @@ def get_prob_stats(prob_dist, unit_indices):
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trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
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trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
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if prob_dist[i, unit_indices[i]] > 0.0:
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trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
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trend[i] = (trend_up[i] - trend_down[i]) / (
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prob_dist[i, unit_indices[i]])
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else:
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trend[i] = None
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## calculate volatility of distribution
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# calculate volatility of distribution
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volatility = prob_dist.std(axis=1)
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return trend_up, trend_down, trend, volatility
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@ -9,81 +9,100 @@ import unittest
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#
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# import sys
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# sys.modules['plpy'] = plpy
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from helper import plpy, fixture_file
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from helper import fixture_file
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from crankshaft.space_time_dynamics import Markov
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import crankshaft.space_time_dynamics as std
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from crankshaft import random_seeds
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from crankshaft.clustering import QueryRunner
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import json
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class FakeQueryRunner(QueryRunner):
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def __init__(self, data):
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self.mock_result = data
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def get_result(self, query):
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return self.mock_result
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class SpaceTimeTests(unittest.TestCase):
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"""Testing class for Markov Functions."""
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def setUp(self):
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plpy._reset()
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# plpy._reset()
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self.params = {"id_col": "cartodb_id",
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"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
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"subquery": "SELECT * FROM a_list",
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"geom_col": "the_geom",
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"num_ngbrs": 321}
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self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
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self.neighbors_data = json.loads(
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open(fixture_file('neighbors_markov.json')).read())
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self.markov_data = json.loads(open(fixture_file('markov.json')).read())
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self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
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self.time_data = np.array([i * np.ones(10, dtype=float)
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for i in range(10)]).T
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self.transition_matrix = np.array([
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[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
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[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
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[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
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[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
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[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
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[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
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[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
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[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
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[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
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[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
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[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
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[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
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[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
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[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
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[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
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[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
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[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
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[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
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[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
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[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
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[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
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[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
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[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
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[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
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[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
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[[0.96341463, 0.0304878, 0.00609756, 0., 0.],
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[0.06040268, 0.83221477, 0.10738255, 0., 0.],
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[0., 0.14, 0.74, 0.12, 0.],
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[0., 0.03571429, 0.32142857, 0.57142857, 0.07142857],
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[0., 0., 0., 0.16666667, 0.83333333]],
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[[0.79831933, 0.16806723, 0.03361345, 0., 0.],
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[0.0754717, 0.88207547, 0.04245283, 0., 0.],
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[0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.],
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[0., 0., 0.06372549, 0.90196078, 0.03431373],
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[0., 0., 0., 0.19444444, 0.80555556]],
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[[0.84693878, 0.15306122, 0., 0., 0.],
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[0.08133971, 0.78947368, 0.1291866, 0., 0.],
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[0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135],
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[0., 0., 0.09411765, 0.87058824, 0.03529412],
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[0., 0., 0., 0.10204082, 0.89795918]],
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[[0.8852459, 0.09836066, 0., 0.01639344, 0.],
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[0.03875969, 0.81395349, 0.13953488, 0., 0.00775194],
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[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
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[0., 0.02339181, 0.12865497, 0.75438596, 0.09356725],
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[0., 0., 0., 0.09661836, 0.90338164]],
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[[0.33333333, 0.66666667, 0., 0., 0.],
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[0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.],
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[0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.],
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[0., 0.01036269, 0.06217617, 0.89637306, 0.03108808],
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[0., 0., 0., 0.02352941, 0.97647059]]]
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)
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def test_spatial_markov(self):
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"""Test Spatial Markov."""
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data = [ { 'id': d['id'],
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'attr1': d['y1995'],
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'attr2': d['y1996'],
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'attr3': d['y1997'],
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'attr4': d['y1998'],
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'attr5': d['y1999'],
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'attr6': d['y2000'],
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'attr7': d['y2001'],
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'attr8': d['y2002'],
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'attr9': d['y2003'],
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'attr10': d['y2004'],
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'attr11': d['y2005'],
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'attr12': d['y2006'],
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'attr13': d['y2007'],
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'attr14': d['y2008'],
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'attr15': d['y2009'],
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'neighbors': d['neighbors'] } for d in self.neighbors_data]
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print(str(data[0]))
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plpy._define_result('select', data)
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data = [{'id': d['id'],
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'attr1': d['y1995'],
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'attr2': d['y1996'],
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'attr3': d['y1997'],
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'attr4': d['y1998'],
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'attr5': d['y1999'],
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'attr6': d['y2000'],
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'attr7': d['y2001'],
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'attr8': d['y2002'],
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'attr9': d['y2003'],
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'attr10': d['y2004'],
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'attr11': d['y2005'],
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'attr12': d['y2006'],
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'attr13': d['y2007'],
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'attr14': d['y2008'],
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'attr15': d['y2009'],
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'neighbors': d['neighbors']} for d in self.neighbors_data]
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# print(str(data[0]))
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markov = Markov(FakeQueryRunner(data))
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random_seeds.set_random_seeds(1234)
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|
||||
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
|
||||
result = markov.spatial_trend('subquery',
|
||||
['y1995', 'y1996', 'y1997', 'y1998',
|
||||
'y1999', 'y2000', 'y2001', 'y2002',
|
||||
'y2003', 'y2004', 'y2005', 'y2006',
|
||||
'y2007', 'y2008', 'y2009'],
|
||||
5, 'knn', 5, 0, 'the_geom',
|
||||
'cartodb_id')
|
||||
|
||||
self.assertTrue(result != None)
|
||||
self.assertTrue(result is not None)
|
||||
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
|
||||
print result[0]
|
||||
expected = self.markov_data
|
||||
@ -94,173 +113,178 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
|
||||
def test_get_time_data(self):
|
||||
"""Test get_time_data"""
|
||||
data = [ { 'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'] } for d in self.neighbors_data]
|
||||
data = [{'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009']} for d in self.neighbors_data]
|
||||
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998',
|
||||
'y1999', 'y2000', 'y2001', 'y2002',
|
||||
'y2003', 'y2004', 'y2005', 'y2006',
|
||||
'y2007', 'y2008', 'y2009'])
|
||||
|
||||
## expected was prepared from PySAL example:
|
||||
### f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
|
||||
### rpci = pci / (pci.mean(axis = 0))
|
||||
# expected was prepared from PySAL example:
|
||||
# f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
# pci = np.array([f.by_col[str(y)]
|
||||
# for y in range(1995, 2010)]).transpose()
|
||||
# rpci = pci / (pci.mean(axis = 0))
|
||||
|
||||
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
|
||||
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
|
||||
, 0.86148267, 0.86252252, 0.86746356],
|
||||
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
|
||||
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
|
||||
, 0.90139505, 0.87897455, 0.86216858],
|
||||
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
|
||||
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
|
||||
, 0.83675961, 0.83574199, 0.84647177],
|
||||
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
|
||||
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
|
||||
, 1.12958508, 1.11371818, 1.09936775],
|
||||
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
|
||||
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
|
||||
, 1.10863242, 1.09642516, 1.07762337],
|
||||
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
|
||||
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
|
||||
, 1.45241216, 1.43306098, 1.4174431 ],
|
||||
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
|
||||
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
|
||||
, 1.08401709, 1.06959206, 1.07875225],
|
||||
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
|
||||
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
|
||||
, 1.01749645, 0.9936394, 0.98279746],
|
||||
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
|
||||
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
|
||||
, 0.91284091, 0.89689833, 0.88928858],
|
||||
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
|
||||
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
|
||||
, 0.86633032, 0.84946077, 0.83287146],
|
||||
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
|
||||
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
|
||||
, 1.11059842, 1.10509795, 1.10020097],
|
||||
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
|
||||
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
|
||||
, 0.89280392, 0.89298243, 0.89165385],
|
||||
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
|
||||
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
|
||||
, 0.94281559, 0.96520315, 0.96715441],
|
||||
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
|
||||
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
|
||||
, 0.98423613, 0.99823861, 0.99545704],
|
||||
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
|
||||
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
|
||||
, 0.8198736, 0.82265395, 0.8399404 ],
|
||||
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
|
||||
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
|
||||
, 0.9422231, 0.93902708, 0.94479184],
|
||||
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
|
||||
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
|
||||
, 0.90543476, 0.91737138, 0.94793657],
|
||||
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
|
||||
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
|
||||
, 1.23418676, 1.23626739, 1.26754398],
|
||||
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
|
||||
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
|
||||
, 1.32936244, 1.33001241, 1.33288462],
|
||||
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
|
||||
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
|
||||
, 0.92754547, 0.92549227, 0.92138102],
|
||||
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
|
||||
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
|
||||
, 1.10577769, 1.11166825, 1.09985155],
|
||||
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
|
||||
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
|
||||
, 0.77487118, 0.77768683, 0.78801192],
|
||||
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
|
||||
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
|
||||
, 0.93069645, 0.94020973, 0.94358232],
|
||||
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
|
||||
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
|
||||
, 0.87863799, 0.87497981, 0.87888675],
|
||||
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
|
||||
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
|
||||
, 0.99323123, 1.00204939, 0.99602148],
|
||||
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
|
||||
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
|
||||
, 1.08603695, 1.05997314, 1.02160943],
|
||||
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
|
||||
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
|
||||
, 1.12357937, 1.1159258, 1.12570828],
|
||||
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
|
||||
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
|
||||
, 1.29734097, 1.29390205, 1.29345446],
|
||||
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
|
||||
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
|
||||
, 0.84349247, 0.85637272, 0.86539395],
|
||||
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
|
||||
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
|
||||
, 1.27962009, 1.27968392, 1.25907738],
|
||||
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
|
||||
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
|
||||
, 0.91475304, 0.90249622, 0.9021363 ],
|
||||
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
|
||||
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
|
||||
, 0.96635366, 1.02520312, 1.0394296 ],
|
||||
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
|
||||
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
|
||||
, 0.93759089, 0.93353431, 0.94121705],
|
||||
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
|
||||
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
|
||||
, 0.88283929, 0.90349491, 0.90600675],
|
||||
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
|
||||
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
|
||||
, 0.95011401, 0.9434686, 0.94637765],
|
||||
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
|
||||
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
|
||||
, 1.02347523, 1.02517684, 1.04359571],
|
||||
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
|
||||
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
|
||||
, 1.08859544, 1.09177338, 1.1096083 ],
|
||||
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
|
||||
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
|
||||
, 0.83374329, 0.82812044, 0.82878599],
|
||||
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
|
||||
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
|
||||
, 0.96947771, 1.00362626, 0.98102351],
|
||||
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
|
||||
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
|
||||
, 0.90430364, 0.89994694, 0.90073864],
|
||||
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
|
||||
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
|
||||
, 0.9700702, 0.97158948, 0.95884908],
|
||||
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
|
||||
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
|
||||
, 0.83511679, 0.82136617, 0.80921874],
|
||||
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
|
||||
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
|
||||
, 0.97001634, 0.97082733, 0.98782366],
|
||||
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
|
||||
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
|
||||
, 1.14474747, 1.14084037, 1.16142926],
|
||||
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
|
||||
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
|
||||
, 1.09831768, 1.09298232, 1.09176125],
|
||||
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
|
||||
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
|
||||
, 0.80172182, 0.82168155, 0.85587911],
|
||||
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
|
||||
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
|
||||
, 0.96519561, 0.96173403, 0.95890284],
|
||||
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
|
||||
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
|
||||
, 1.17025154, 1.18730553, 1.14242645]])
|
||||
expected = np.array(
|
||||
[[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,
|
||||
0.83271652, 0.83786314, 0.85012593, 0.85509656, 0.86416612,
|
||||
0.87119375, 0.86302631, 0.86148267, 0.86252252, 0.86746356],
|
||||
[0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388,
|
||||
0.90746978, 0.89830489, 0.89431991, 0.88924794, 0.89815176,
|
||||
0.91832091, 0.91706054, 0.90139505, 0.87897455, 0.86216858],
|
||||
[0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522,
|
||||
0.78964559, 0.80584442, 0.8084998, 0.82258551, 0.82668196,
|
||||
0.82373724, 0.81814804, 0.83675961, 0.83574199, 0.84647177],
|
||||
[1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841,
|
||||
1.14506948, 1.12151133, 1.11160697, 1.10888621, 1.11399806,
|
||||
1.12168029, 1.13164797, 1.12958508, 1.11371818, 1.09936775],
|
||||
[1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025,
|
||||
1.16898201, 1.17212488, 1.14752303, 1.11843284, 1.11024964,
|
||||
1.11943471, 1.11736468, 1.10863242, 1.09642516, 1.07762337],
|
||||
[1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684,
|
||||
1.44184737, 1.44782832, 1.41978227, 1.39092208, 1.4059372,
|
||||
1.40788646, 1.44052766, 1.45241216, 1.43306098, 1.4174431],
|
||||
[1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149,
|
||||
1.10888138, 1.11856629, 1.13062931, 1.11944984, 1.12446239,
|
||||
1.11671008, 1.10880034, 1.08401709, 1.06959206, 1.07875225],
|
||||
[1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545,
|
||||
0.99854316, 0.9880258, 0.99669587, 0.99327676, 1.01400905,
|
||||
1.03176742, 1.040511, 1.01749645, 0.9936394, 0.98279746],
|
||||
[0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845,
|
||||
0.99127006, 0.97925917, 0.9683482, 0.95335147, 0.93694787,
|
||||
0.94308213, 0.92232874, 0.91284091, 0.89689833, 0.88928858],
|
||||
[0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044,
|
||||
0.8578708, 0.86036185, 0.86107306, 0.8500772, 0.86981998,
|
||||
0.86837929, 0.87204141, 0.86633032, 0.84946077, 0.83287146],
|
||||
[1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624,
|
||||
1.14450183, 1.12349752, 1.12596664, 1.12213996, 1.1119989,
|
||||
1.10257792, 1.10491258, 1.11059842, 1.10509795, 1.10020097],
|
||||
[0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687,
|
||||
0.95831051, 0.94480909, 0.94804195, 0.95430286, 0.94103989,
|
||||
0.92122519, 0.91010201, 0.89280392, 0.89298243, 0.89165385],
|
||||
[0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647,
|
||||
0.9480927, 0.93539182, 0.95388718, 0.94597005, 0.96918424,
|
||||
0.94781281, 0.93466815, 0.94281559, 0.96520315, 0.96715441],
|
||||
[0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897,
|
||||
0.98687073, 0.99237486, 0.98209969, 0.9877653, 0.97399471,
|
||||
0.96910087, 0.98416665, 0.98423613, 0.99823861, 0.99545704],
|
||||
[0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,
|
||||
0.86191535, 0.84981451, 0.85472102, 0.84564835, 0.83998883,
|
||||
0.83478547, 0.82803648, 0.8198736, 0.82265395, 0.8399404],
|
||||
[0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136,
|
||||
0.82785597, 0.86008789, 0.86776298, 0.86720209, 0.8676334,
|
||||
0.89179317, 0.94202108, 0.9422231, 0.93902708, 0.94479184],
|
||||
[0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238,
|
||||
0.90906632, 0.92693339, 0.93695966, 0.94242697, 0.94338265,
|
||||
0.91981796, 0.91108804, 0.90543476, 0.91737138, 0.94793657],
|
||||
[1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723,
|
||||
1.20172869, 1.21328691, 1.22624778, 1.22397075, 1.23857042,
|
||||
1.24419893, 1.23929384, 1.23418676, 1.23626739, 1.26754398],
|
||||
[1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667,
|
||||
1.34790023, 1.34399863, 1.32575181, 1.30795492, 1.30544841,
|
||||
1.30303302, 1.32107766, 1.32936244, 1.33001241, 1.33288462],
|
||||
[1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093,
|
||||
1.05059016, 1.03405057, 1.02747623, 1.03162734, 0.9961416,
|
||||
0.97356208, 0.94241549, 0.92754547, 0.92549227, 0.92138102],
|
||||
[1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264,
|
||||
1.13889622, 1.12442212, 1.13367018, 1.13982256, 1.14029944,
|
||||
1.11979401, 1.10905389, 1.10577769, 1.11166825, 1.09985155],
|
||||
[0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284,
|
||||
0.74480073, 0.76098396, 0.76156903, 0.76651952, 0.76533288,
|
||||
0.78205934, 0.76842416, 0.77487118, 0.77768683, 0.78801192],
|
||||
[0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803,
|
||||
0.97370819, 0.96419154, 0.97209861, 0.97441313, 0.96356162,
|
||||
0.94745352, 0.93965462, 0.93069645, 0.94020973, 0.94358232],
|
||||
[0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801,
|
||||
0.80071489, 0.83358256, 0.83451613, 0.85175032, 0.85954307,
|
||||
0.86790024, 0.87170334, 0.87863799, 0.87497981, 0.87888675],
|
||||
[0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619,
|
||||
0.98733195, 0.99644997, 0.99669587, 1.02559097, 1.01116651,
|
||||
0.99988024, 0.97906749, 0.99323123, 1.00204939, 0.99602148],
|
||||
[1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683,
|
||||
1.08312397, 1.05192626, 1.04230892, 1.05577278, 1.08569751,
|
||||
1.12443486, 1.08891079, 1.08603695, 1.05997314, 1.02160943],
|
||||
[1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272,
|
||||
1.18257029, 1.16226243, 1.16009196, 1.14467789, 1.14820235,
|
||||
1.12386598, 1.12680236, 1.12357937, 1.1159258, 1.12570828],
|
||||
[1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667,
|
||||
1.31210239, 1.29989156, 1.29203193, 1.27183516, 1.26830786,
|
||||
1.2617743, 1.28656675, 1.29734097, 1.29390205, 1.29345446],
|
||||
[0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864,
|
||||
0.78772975, 0.82848011, 0.8259679, 0.82435705, 0.83108634,
|
||||
0.84373784, 0.83891093, 0.84349247, 0.85637272, 0.86539395],
|
||||
[1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626,
|
||||
1.2256767, 1.21126648, 1.19377804, 1.18355337, 1.19674434,
|
||||
1.21536573, 1.23653297, 1.27962009, 1.27968392, 1.25907738],
|
||||
[0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282,
|
||||
0.96480308, 0.94686376, 0.93679073, 0.92540049, 0.92988835,
|
||||
0.93442917, 0.92100464, 0.91475304, 0.90249622, 0.9021363],
|
||||
[0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368,
|
||||
0.88937573, 0.894401, 0.90448993, 0.95495898, 0.92698333,
|
||||
0.94745352, 0.92562488, 0.96635366, 1.02520312, 1.0394296],
|
||||
[1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073,
|
||||
1.00759019, 0.99192968, 0.99747298, 0.99550759, 0.97583768,
|
||||
0.9610168, 0.94779638, 0.93759089, 0.93353431, 0.94121705],
|
||||
[0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613,
|
||||
0.83434854, 0.85813595, 0.84667961, 0.84374558, 0.85951183,
|
||||
0.87194227, 0.89455097, 0.88283929, 0.90349491, 0.90600675],
|
||||
[1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086,
|
||||
1.00581626, 0.98850522, 0.99291168, 0.98983209, 0.97511924,
|
||||
0.96134615, 0.96382634, 0.95011401, 0.9434686, 0.94637765],
|
||||
[1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857,
|
||||
1.04800023, 1.03024941, 1.04200483, 1.0402554, 1.03296979,
|
||||
1.02191682, 1.02476275, 1.02347523, 1.02517684, 1.04359571],
|
||||
[1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,
|
||||
1.0531801, 1.07452771, 1.09383478, 1.1052447, 1.10322136,
|
||||
1.09167939, 1.08772756, 1.08859544, 1.09177338, 1.1096083],
|
||||
[0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809,
|
||||
0.86287327, 0.85169796, 0.85411285, 0.84886336, 0.84517414,
|
||||
0.84843858, 0.84488343, 0.83374329, 0.82812044, 0.82878599],
|
||||
[0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286,
|
||||
0.92652175, 0.94278865, 0.93682452, 0.98655146, 0.992237,
|
||||
0.9798497, 0.93869677, 0.96947771, 1.00362626, 0.98102351],
|
||||
[0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,
|
||||
0.93092109, 0.92662519, 0.93412152, 0.93501274, 0.92879506,
|
||||
0.92110542, 0.91035556, 0.90430364, 0.89994694, 0.90073864],
|
||||
[0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824,
|
||||
0.98882205, 0.97662234, 0.95601578, 0.94905385, 0.94934888,
|
||||
0.97152609, 0.97163004, 0.9700702, 0.97158948, 0.95884908],
|
||||
[0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751,
|
||||
0.84818516, 0.85265681, 0.84502402, 0.82645665, 0.81743586,
|
||||
0.83550406, 0.83338919, 0.83511679, 0.82136617, 0.80921874],
|
||||
[0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441,
|
||||
0.95440787, 0.96364363, 0.96804412, 0.97136214, 0.97583768,
|
||||
0.95571724, 0.96895368, 0.97001634, 0.97082733, 0.98782366],
|
||||
[1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249,
|
||||
1.10558188, 1.1214086, 1.12292577, 1.13021031, 1.13342735,
|
||||
1.14686068, 1.14502975, 1.14474747, 1.14084037, 1.16142926],
|
||||
[1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863,
|
||||
1.11856702, 1.09764283, 1.08815849, 1.08044313, 1.09278827,
|
||||
1.07003204, 1.08398066, 1.09831768, 1.09298232, 1.09176125],
|
||||
[0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744,
|
||||
0.77751194, 0.79902974, 0.81437881, 0.80788828, 0.79603865,
|
||||
0.78966436, 0.79949807, 0.80172182, 0.82168155, 0.85587911],
|
||||
[1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561,
|
||||
1.00162979, 0.99860739, 1.00814981, 1.00574316, 0.99030032,
|
||||
0.97682565, 0.97292596, 0.96519561, 0.96173403, 0.95890284],
|
||||
[0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289,
|
||||
0.96608031, 0.99727185, 1.00781194, 1.03484236, 1.05333619,
|
||||
1.0983263, 1.1704974, 1.17025154, 1.18730553, 1.14242645]])
|
||||
|
||||
self.assertTrue(np.allclose(result, expected))
|
||||
self.assertTrue(type(result) == type(expected))
|
||||
@ -268,32 +292,35 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
|
||||
def test_rebin_data(self):
|
||||
"""Test rebin_data"""
|
||||
## sample in double the time (even case since 10 % 2 = 0):
|
||||
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
## = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
# sample in double the time (even case since 10 % 2 = 0):
|
||||
# (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
# = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
|
||||
for i in range(0, 10, 2)]).T
|
||||
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
self.assertTrue(
|
||||
np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
|
||||
## sample in triple the time (uneven since 10 % 3 = 1):
|
||||
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
## = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
# sample in triple the time (uneven since 10 % 3 = 1):
|
||||
# (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
# = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(
|
||||
np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
|
||||
def test_get_prob_dist(self):
|
||||
"""Test get_prob_dist"""
|
||||
lag_indices = np.array([1, 2, 3, 4])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
|
||||
[0., 0., 0.09411765, 0.87058824, 0.03529412],
|
||||
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
|
||||
[0., 0., 0., 0.02352941, 0.97647059]
|
||||
])
|
||||
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
|
||||
result = std.get_prob_dist(self.transition_matrix,
|
||||
lag_indices, unit_indices)
|
||||
|
||||
self.assertTrue(np.array_equal(result, answer))
|
||||
|
||||
@ -301,16 +328,20 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
"""Test get_prob_stats"""
|
||||
|
||||
probs = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
|
||||
[0., 0., 0.09411765, 0.87058824, 0.03529412],
|
||||
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
|
||||
[0., 0., 0., 0.02352941, 0.97647059]
|
||||
])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
|
||||
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
|
||||
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
|
||||
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
|
||||
answer_trend = np.array([-0.03301887 / 0.88207547,
|
||||
-0.05882353 / 0.87058824,
|
||||
0.02475248 / 0.77722772,
|
||||
-0.02352941 / 0.97647059])
|
||||
answer_volatility = np.array([0.34221495, 0.33705421,
|
||||
0.29226542, 0.38834223])
|
||||
|
||||
result = std.get_prob_stats(probs, unit_indices)
|
||||
result_up = result[0]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user