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pylinting changes

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This commit is contained in:
Andy Eschbacher 2016-03-25 22:49:29 -04:00
parent e32bab3f88
commit 369d1d2f41
1 changed files with 29 additions and 18 deletions
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47
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
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@ -8,7 +8,8 @@ import pysal as ps
import plpy import plpy
from crankshaft.clustering import get_query, get_weight from crankshaft.clustering import get_query, get_weight
def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, geom_col, id_col, w_type, num_ngbrs): def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
permutations, geom_col, id_col, w_type, num_ngbrs):
""" """
Predict the trends of a unit based on: Predict the trends of a unit based on:
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile) 1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
@ -33,6 +34,9 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
@param @param
""" """
if num_time_per_bin < 1:
plpy.error('Error: number of time bins must be >= 1')
qvals = {"id_col": id_col, qvals = {"id_col": id_col,
"time_cols": time_cols, "time_cols": time_cols,
"geom_col": geom_col, "geom_col": geom_col,
@ -58,13 +62,14 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
## rebin ## rebin
t_data = rebin_data(t_data, int(num_time_per_bin)) t_data = rebin_data(t_data, int(num_time_per_bin))
sp_markov_result = ps.Spatial_Markov(t_data, weights, k=7, fixed=False) sp_markov_result = ps.Spatial_Markov(t_data,
weights,
## get lags of last time slice k=7,
lags = ps.lag_spatial(weights, t_data[:, -1]) fixed=False,
permutations=permutations)
## get lag classes ## get lag classes
lag_classes = ps.Quantiles(lags, k=7).yb lag_classes = ps.Quantiles(ps.lag_spatial(weights, t_data[:, -1]), k=7).yb
## look up probablity distribution for each unit according to class and lag class ## look up probablity distribution for each unit according to class and lag class
prob_dist = get_prob_dist(sp_markov_result.P, lag_classes, sp_markov_result.classes[:, -1]) prob_dist = get_prob_dist(sp_markov_result.P, lag_classes, sp_markov_result.classes[:, -1])
@ -86,7 +91,8 @@ def get_time_data(markov_data, time_cols):
def rebin_data(time_data, num_time_per_bin): def rebin_data(time_data, num_time_per_bin):
""" """
convert an n x l matrix into an (n/m) x l matrix where the values are reduced (averaged) for the intervening states: convert an n x l matrix into an (n/m) x l matrix where the values are
reduced (averaged) for the intervening states:
1 2 3 4 1.5 3.5 1 2 3 4 1.5 3.5
5 6 7 8 -> 5.5 7.5 5 6 7 8 -> 5.5 7.5
9 8 7 6 8.5 6.5 9 8 7 6 8.5 6.5
@ -94,12 +100,17 @@ def rebin_data(time_data, num_time_per_bin):
if m = 2 if m = 2
This process effectively resamples the data at a longer time span n units longer than the input data. This process effectively resamples the data at a longer time span n
For cases when there is a remainder (remainder(5/3) = 2), the remaining two columns are binned together as the last time period, while the first three are binned together. units longer than the input data.
For cases when there is a remainder (remainder(5/3) = 2), the remaining
two columns are binned together as the last time period, while the
first three are binned together.
Input: Input:
@param time_data n x l ndarray: measurements of an attribute at different time intervals @param time_data n x l ndarray: measurements of an attribute at
@param num_time_per_bin int: number of columns to average into a new column different time intervals
@param num_time_per_bin int: number of columns to average into a new
column
Output: Output:
ceil(n / m) x l ndarray of resampled time series ceil(n / m) x l ndarray of resampled time series
""" """
@ -111,13 +122,12 @@ def rebin_data(time_data, num_time_per_bin):
## fit remainders into an additional column ## fit remainders into an additional column
n_max = time_data.shape[1] / num_time_per_bin + 1 n_max = time_data.shape[1] / num_time_per_bin + 1
return np.array([ return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
time_data[:,
num_time_per_bin*i:num_time_per_bin*(i+1)].mean(axis=1)
for i in range(n_max)]).T for i in range(n_max)]).T
def get_prob_dist(transition_matrix, lag_indices, unit_indices): def get_prob_dist(transition_matrix, lag_indices, unit_indices):
""" """
given an array of transition matrices, look up the probability associated with the arrangements passed given an array of transition matrices, look up the probability
associated with the arrangements passed
Input: Input:
@param transition_matrix ndarray[k,k,k]: @param transition_matrix ndarray[k,k,k]:
@ -128,7 +138,8 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
Array of probability distributions Array of probability distributions
""" """
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] for i in range(len(lag_indices))]) return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
for i in range(len(lag_indices))])
def get_prob_stats(prob_dist, unit_indices): def get_prob_stats(prob_dist, unit_indices):
""" """
@ -144,9 +155,9 @@ def get_prob_stats(prob_dist, unit_indices):
""" """
num_elements = len(unit_indices) num_elements = len(unit_indices)
trend_up = np.empty(num_elements, dtype=float) trend_up = np.empty(num_elements, dtype=float)
trend_down = np.empty(num_elements, dtype=float) trend_down = np.empty(num_elements, dtype=float)
trend = np.empty(num_elements, dtype=float) trend = np.empty(num_elements, dtype=float)
for i in range(num_elements): for i in range(num_elements):
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum() trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()