--- a/trafficintelligence/utils.py	Fri Jun 15 11:19:10 2018 -0400
+++ b/trafficintelligence/utils.py	Mon Jun 18 22:50:59 2018 -0400
@@ -1,17 +1,21 @@
 #! /usr/bin/env python
-# -*- coding: utf-8 -*-
 ''' Generic utilities.'''
 
-import matplotlib.pyplot as plt
 from datetime import time, datetime
 from argparse import ArgumentTypeError
 from pathlib import Path
 from math import sqrt, ceil, floor
-from scipy.stats import rv_continuous, kruskal, shapiro, lognorm
+from copy import deepcopy, copy
+
+from scipy.stats import rv_continuous, kruskal, shapiro, lognorm, norm, t
 from scipy.spatial import distance
 from scipy.sparse import dok_matrix
-from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log
+from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log, polyfit
+from numpy.random import permutation as nppermutation
+from pandas import DataFrame, concat
+import matplotlib.pyplot as plt
 
+from trafficintelligence.storage import openCheck
 
 datetimeFormat = "%Y-%m-%d %H:%M:%S"
 
@@ -67,7 +71,6 @@
     return log(scale), shape
 
 def sampleSize(stdev, tolerance, percentConfidence, nRoundingDigits = None, printLatex = False):
-    from scipy.stats.distributions import norm
     if nRoundingDigits is None:
         k = round(norm.ppf(0.5+percentConfidence/200., 0, 1), 2) # 1.-(100-percentConfidence)/200.
     else:
@@ -84,7 +87,6 @@
     Use otherwise t.interval or norm.interval for the boundaries
     ex: norm.interval(0.95)
     t.interval(0.95, nSamples-1)'''
-    from scipy.stats.distributions import norm, t
     if trueStd:
         k = round(norm.ppf(0.5+percentConfidence/200., 0, 1), 2)
     else: # use Student
@@ -344,7 +346,6 @@
 def cat_mvgavg(cat_list, halfWidth):
     ''' Return a list of categories/values smoothed according to a window. 
         halfWidth is the search radius on either side'''
-    from copy import deepcopy
     smoothed = deepcopy(cat_list)
     for point in range(len(cat_list)):
         lower_bound_check = max(0,point-halfWidth)
@@ -366,8 +367,6 @@
 def linearRegression(x, y, deg = 1, plotData = False):
     '''returns the least square estimation of the linear regression of y = ax+b
     as well as the plot'''
-    from numpy.lib.polynomial import polyfit
-    from numpy.core.multiarray import arange
     coef = polyfit(x, y, deg)
     if plotData:
         def poly(x):
@@ -437,7 +436,6 @@
     independentVariableValues = sorted(tmp[independentVariable].unique().tolist())
     if len(independentVariableValues) >= 2:
         if saveLatex:
-            from storage import openCheck
             out = openCheck(filenamePrefix+'-{}-{}.tex'.format(dependentVariable, independentVariable), 'w')
         for x in independentVariableValues:
             print('Shapiro-Wilk normality test for {} when {}={}: {} obs'.format(dependentVariable,independentVariable, x, len(tmp.loc[tmp[independentVariable] == x, dependentVariable])))
@@ -476,8 +474,6 @@
     text is the template to display for the two types of printout (see default): 3 elements if no saving to latex file, 8 otherwise
 
     TODO: pass the dummies for nominal variables and remove if all dummies are correlated, or none is correlated with the dependentvariable'''    
-    from copy import copy
-    from pandas import DataFrame
     result = copy(independentVariables)
     table1 = ''
     table2 = {}
@@ -516,7 +512,6 @@
                 table2['Valeurs p'].append(p)
 
     if saveFiles:
-        from storage import openCheck
         out = openCheck(filenamePrefix+'-removed-variables.tex', 'w')
         out.write(latexHeader)
         out.write(table1)
@@ -598,7 +593,6 @@
 
 def generateExperiments(independentVariables):
     '''Generates all possible models for including or not each independent variable'''
-    from pandas import DataFrame
     experiments = {}
     nIndependentVariables = len(independentVariables)
     if nIndependentVariables != len(set(independentVariables)):
@@ -620,8 +614,6 @@
     '''Generates all possible model with the independentVariables
     and runs them, saving the results in experiments
     with multiprocess option'''
-    from pandas import concat
-    from multiprocessing import Pool
     experiments = generateExperiments(independentVariables)
     nModels = len(experiments)
     print("Running {} models with {} processes".format(nModels, nProcesses))
@@ -642,7 +634,6 @@
     The results are added to experiments if provided as argument
     Storing in experiment relies on the index being the number equal 
     to the binary code derived from the independent variables'''
-    from numpy.random import permutation as nppermutation
     if experiments is None:
         experiments = generateExperiments(independentVariables)
     nIndependentVariables = len(independentVariables)
@@ -712,7 +703,6 @@
             plt.ylabel(text['qqplot.ylabel'])
         plt.tight_layout()
         if filenamePrefix is not None:
-            from storage import openCheck
             out = openCheck(filenamePrefix+'-coefficients.html', 'w')
             out.write(results.summary().as_html())
             plt.savefig(filenamePrefix+'-model-results.'+figureFileType)
@@ -885,9 +875,8 @@
 
 def plotPolygon(poly, options = '', **kwargs):
     'Plots shapely polygon poly'
-    from matplotlib.pyplot import plot
     x,y = poly.exterior.xy
-    plot(x, y, options, **kwargs)
+    plt.plot(x, y, options, **kwargs)
 
 def stepPlot(X, firstX, lastX, initialCount = 0, increment = 1):
     '''for each value in X, increment by increment the initial count
@@ -927,7 +916,6 @@
     plt.rc('axes', prop_cycle=monochrome)
 
 def plotIndicatorMap(indicatorMap, squareSize, masked = True, defaultValue=-1):
-    from matplotlib.pyplot import pcolor
     coords = array(list(indicatorMap.keys()))
     minX = min(coords[:,0])
     minY = min(coords[:,1])
@@ -937,9 +925,9 @@
     for k,v in indicatorMap.items():
         C[k[1]-minY,k[0]-minX] = v
     if masked:
-        pcolor(X, Y, ma.masked_where(C==defaultValue,C))
+        plt.pcolor(X, Y, ma.masked_where(C==defaultValue,C))
     else:
-        pcolor(X, Y, C)
+        plt.pcolor(X, Y, C)
 
 #########################
 # Data download