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comparison python/utils.py @ 840:15a82ebc62c4

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utils for sparse matrix
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 08 Jul 2016 11:41:29 -0400
parents e01cabca4c55
children 90b7d6e19c55
comparison
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839:2c7b4e6a32dd 840:15a82ebc62c4
5 import matplotlib.pyplot as plt 5 import matplotlib.pyplot as plt
6 from datetime import time, datetime 6 from datetime import time, datetime
7 from math import sqrt, ceil, floor 7 from math import sqrt, ceil, floor
8 from scipy.stats import kruskal, shapiro 8 from scipy.stats import kruskal, shapiro
9 from scipy.spatial import distance 9 from scipy.spatial import distance
10 from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, median, isnan, ones, convolve, dtype, isnan, NaN, mean, ma, isinf 10 from scipy.sparse import dok_matrix
11 from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, median, isnan, ones, convolve, dtype, isnan, NaN, mean, ma, isinf, savez, load as npload
11 12
12 13
13 datetimeFormat = "%Y-%m-%d %H:%M:%S" 14 datetimeFormat = "%Y-%m-%d %H:%M:%S"
14 15
15 ######################### 16 #########################
481 table2['Variables'] = [var for var in result if data.dtypes[var] != dtype('O')] 482 table2['Variables'] = [var for var in result if data.dtypes[var] != dtype('O')]
482 out.write(DataFrame(table2)[['Variables', 'Correlations', 'Valeurs p']].to_html(formatters = {'Correlations': lambda x: '{:.2f}'.format(x), 'Valeurs p': lambda x: '{:.3f}'.format(x)}, index = False)) 483 out.write(DataFrame(table2)[['Variables', 'Correlations', 'Valeurs p']].to_html(formatters = {'Correlations': lambda x: '{:.2f}'.format(x), 'Valeurs p': lambda x: '{:.3f}'.format(x)}, index = False))
483 out.close() 484 out.close()
484 return result 485 return result
485 486
487 def saveDokMatrix(filename, m):
488 'Saves a dok_matrix using savez'
489 savez(filename, shape = m.shape, keys = m.keys(), values = m.values())
490
491 def loadDokMatrix(filename):
492 'Loads a dok_matrix saved using the above saveDokMatrix'
493 data = npload(filename)
494 m = dok_matrix(tuple(data['shape']))
495 for k, v in zip(data['keys'], data['values']):
496 m[tuple(k)] = v
497 return m
486 498
487 ######################### 499 #########################
488 # regression analysis using statsmodels (and pandas) 500 # regression analysis using statsmodels (and pandas)
489 ######################### 501 #########################
490 502