plot isnt plotting anymore, error in code?

hi,

my code was working fine, but now i cant figure out what went wrong.
any ideas?

the code is supposed to plot a timeseries which it does and overlay

it with another that is partially defined
the input file is contructed like this:
the first line is just for information purposes.
after that:
the first row is a growing number (the x value), the second is the
timeseries and the third is the partially defined second timeseries

this is the code, after the code is a example input file.
the code is also accessible via this paste service:
 it got a nice python code formatter.
···

https://dpaste.de/5ZrX

  1. def plotTimeSeriesAndSAX(inputfile_tmp, verbose=False):
  2. if verbose:
  3. print “plotTimeSeriesAndSAX()”
  4. print “\tinputfile:”, inputfile_tmp
  5. print “\toutputfile: %s.png” % inputfile_tmp
  6. inputfile =
    open(inputfile_tmp, “r”);
  7. this holds my timeseries

  8. x =
    []
  9. y =
    []
  10. this holds my “pattern”

  11. pattern_x_values = []
  12. pattern_y_values = []
  13.         # these are for temporary use
    

only, hold the current pattern data

  1. tmp_x =
    []
  2. tmp_y =
    []
  3.         # remove pattern/sax string,
    

sax_string_with_Z from the datafile, only used as text in
the plot

  1. first_line = inputfile.readline()
  2. pattern,
    sax, sax_string_with_Z =
    first_line.split()
  3. for line
    in inputfile.readlines():
  4. data =
    line.split()
  5. x_data = data[0]
  6. y_data = data[1]
  7.         #if there is a third line
    

(pattern at this position)

  1. if len(data)
    == 3:
  2. y2_data = data[2]
  3. tmp_y.append(y2_data)
  4. tmp_x.append(x_data)
  5. else:
  6.         # if the pattern ends,
    

add it to pattern_x/y_value and clear the tmp list

  1. if len(tmp_x) != 0:
  2. pattern_x_values.append(tmp_x)
  3. pattern_y_values.append(tmp_y)
  4. tmp_x = []
  5. tmp_y = []
  6. x.append(x_data)
  7. y.append(y_data)
  8. #if pattern == “ccd”:
  9.         #    print "pattern x_values:",
    

pattern_x_values

  1.         #    print "pattern y_values:",
    

pattern_y_values

  1. if verbose:
  2. print “\t timeseries
    y value”, y
  3. print “pattern x_values:”,
    pattern_x_values
  4. print “pattern y_values:”,
    pattern_y_values
  5. colors =
    [“red”, “magenta”, “mediumblue”, “darkorchid”, “grey”]
  6. #linestyle = ["-", “–”]
  7.         # without this, the second plot
    

contains the first and the second

  1.         # the third plot contains: the
    

first, second and third

  1. plot.clf()
  2.         # plot all my patterns into the
    

plot

  1. for s
    in range(0,len(pattern_x_values)):
  2. #if verbose:
  3.         #    print "\tpattern x
    

value:", pattern_x_values[s]

  1.         #    print "\tpattern y
    

value:", pattern_y_values[s]

  1. plot.plot(pattern_x_values[s],
    pattern_y_values[s], colors[1])
  2. #plot.plot(x_all[0], y_all[0])
  3. import matplotlib.patches
    as mpatches
  4.         #red_patch =
    

mpatches.Patch(color=‘red’, label=‘The red data’)

  1. from time
    import gmtime, strftime
  2. current_date = strftime("%Y-%m-%d %H:%M:%S", gmtime())
  3. fig =
    plot.figure()
  4. fig.text(0, 0, ‘bottom-left
    corner’)
  5. fig.text(0, 1, current_date, ha=‘left’, va=‘top’)
  6. mytext =
    “pattern: %s sax: %s sax with Z: %s” % (pattern, sax, sax_string_with_Z)
  7. fig.text(1,1, mytext )
  8.         # add the original timeseries
    

to the plot

  1. plot.plot(x,y,
    “forestgreen”)
  2. #if pattern == “ccd”:
  3. plot.show()

  4. directory,
    filename = os.path.split(inputfile_tmp)
  5. plot.savefig(os.path.join(directory, “plots/%s.png” % filename))#, bbox_inches=‘tight’)
  6.         # remove the last figure from
    

memory

  1. #plot.close()
  2. #input:
  3. dee ccccccccccaacddeedcccccccdc
    ZZZZZZZZZZZZZZdeeZZZZZZZZZZ
  4. 1 -0.015920084
  5. 2 -0.044660769
  6. 3 -0.044660769
  7. 4 -0.092561907
  8. 5 0.012820599
  9. 6 -0.015920084
  10. 7 0.012820599
  11. 8 -0.054240996
  12. 9 0.031981054
  13. 10 0.031981054
  14. 11 -0.025500313
  15. 12 -0.044660769
  16. 13 0.012820599
  17. 14 -0.025500313
  18. 15 0.0032403709
  19. 16 -0.006339857
  20. 17 0.0032403709
  21. 18 -0.025500313
  22. 19 0.031981054
  23. 20 0.031981054
  24. 21 0.031981054
  25. 22 0.022400826
  26. 23 0.031981054
  27. 24 0.05114151
  28. 25 0.079882193
  29. 26 0.05114151
  30. 27 0.05114151
  31. 28 0.05114151
  32. 29 0.099042646
  33. 30 0.060721738
  34. 31 -0.015920084
  35. 32 -0.054240996
  36. 33 0.23316584
  37. 34 0.26190652
  38. 35 0.37686926
  39. 36 0.12778333
  40. 37 -0.044660769
  41. 38 -0.26500601
  42. 39 -0.41828965
  43. 40 -0.38954897
  44. 41 -0.26500601
  45. 42 -0.14046305
  46. 43 -0.073401452
  47. 44 -0.12130259
  48. 45 -0.082981679
  49. 46 -0.14046305
  50. 47 -0.054240996
  51. 48 -0.082981679
  52. 49 -0.015920084
  53. 50 -0.073401452
  54. 51 -0.015920084
  55. 52 0.10862288
  56. 53 1.1816084
  57. 54 -1.3379915
  58. 55 -4.6335899
  59. 56 -6.74124
  60. 57 -4.7772933
  61. 58 -3.4839626
  62. 59 -2.075669
  63. 60 -1.0984858
  64. 61 -0.37038851
  65. 62 -0.063821223
  66. 63 0.11820311
  67. 64 0.13736356
  68. 65 0.15652401
  69. 66 0.11820311
  70. 67 0.32896812
  71. 68 0.27148675
  72. 69 0.30022744
  73. 70 0.31938789
  74. 71 0.3577088
    0.5449999999999999
  75. 72 0.40560994
    0.5449999999999999
  76. 73 0.44393085
    0.5449999999999999
  77. 74 0.49183198
    0.5449999999999999
  78. 75 0.67385632
    0.5449999999999999
  79. 76 0.79839928
    0.84
  80. 77 0.9995841
    0.84
  81. 78 1.1528677
    0.84
  82. 79 1.4115338
    0.84
  83. 80 1.5552373
    0.84
  84. 81 1.7468418
    0.84
  85. 82 1.7755825
    0.84
  86. 83 1.7276813
    0.84
  87. 84 1.4115338
    0.84
  88. 85 1.0858061
    0.84
  89. 86 0.65469586
  90. 87 0.43435063
  91. 88 0.21400538
  92. 89 0.14694379
  93. 90 0.089462421
  94. 91 0.070301966
  95. 92 0.031981054
  96. 93 0.05114151
  97. 94 0.070301966
  98. 95 0.13736356
  99. 96 0.079882193
  100. 97 0.12778333
  101. 98 0.15652401
  102. 99 0.16610425
  103. 100 0.13736356
  104. 101 0.13736356
  105. 102 0.089462421
  106. 103 0.2523263
  107. 104 0.21400538
  108. 105 0.22358561
  109. 106 0.1852647
  110. 107 0.19484493
  111. 108 0.1852647
  112. 109 0.16610425
  113. 110 0.13736356
  114. 111 0.15652401
  115. 112 0.14694379
  116. 113 0.16610425
  117. 114 0.099042646
  118. 115 0.12778333
  119. 116 0.13736356
  120. 117 0.089462421
  121. 118 0.079882193
  122. 119 0.089462421
  123. 120 0.041561282
  124. 121 0.041561282
  125. 122 0.079882193
  126. 123 0.11820311
  127. 124 0.099042646
  128. 125 0.089462421
  129. 126 0.05114151
  130. 127 0.17568447
  131. 128 0.30022744
  132. 129 0.32896812
  133. 130 0.42477039
  134. 131 0.17568447
  135. 132 0.022400826
  136. 133 -0.20752464
  137. 134 -0.24584556
  138. 135 -0.24584556

ups, the last mail had a leading number from dpaste, this is the
code without:
def plotTimeSeriesAndSAX(inputfile_tmp, verbose=False):
��� ��� if verbose:
������� print “plotTimeSeriesAndSAX()”
������� print “\tinputfile:”, inputfile_tmp
������� print “\toutputfile: %s.png” % inputfile_tmp
������� ��� inputfile = open(inputfile_tmp, “r”);
��� ��� ��� # this holds my timeseries
��� x = []
��� y = []
��� ��� # this holds my “pattern”
��� pattern_x_values = []
��� pattern_y_values = []
��� ��� # these are for temporary use only, hold the current pattern
data
��� tmp_x = []
��� tmp_y = []
��� ��� ��� # remove pattern/sax string, sax_string_with_Z from the
datafile, only used as text in the plot
��� first_line = inputfile.readline()
��� pattern, sax, sax_string_with_Z = first_line.split()
� ��� ��� ��� ��� for line in inputfile.readlines():
������������� ������� data = line.split()
������� x_data = data[0]
������� y_data = data[1]
������� ������� #if there is a third line (pattern at this position)
������� if len(data) == 3:
����������� y2_data = data[2]
����������� tmp_y.append(y2_data)
����������� tmp_x.append(x_data)
������� else:
����������� # if the pattern ends, add it to pattern_x/y_value and
clear the tmp list
����������� if len(tmp_x) != 0:
��������������� pattern_x_values.append(tmp_x)
��������������� pattern_y_values.append(tmp_y)
��������������� tmp_x = []
��������������� tmp_y = []
������� ������� ������� x.append(x_data)
������� y.append(y_data)
������� ��� #if pattern == “ccd”:
��� #��� print “pattern x_values:”, pattern_x_values
��� #��� print “pattern y_values:”, pattern_y_values
��� if verbose:
������� print “\ttimeseries y value”, y
������� print “pattern x_values:”, pattern_x_values
������� print “pattern y_values:”, pattern_y_values
��� ��� ��� ��� colors = [“red”, “magenta”, “mediumblue”, “darkorchid”, “grey”]
��� #linestyle = ["-", “–”]
��� ��� # without this, the second plot contains the first and the
second
��� # the third plot contains: the first, second and third
��� plot.clf()
��� ��� # plot all my patterns into the plot
��� for s in range(0,len(pattern_x_values)):
������� #if verbose:
������� #��� print “\tpattern x value:”, pattern_x_values[s]
������� #��� print “\tpattern y value:”, pattern_y_values[s]
����������� ������� plot.plot(pattern_x_values[s], pattern_y_values[s],
colors[1])
������� ��� ��� #plot.plot(x_all[0], y_all[0])� ��� �� ��� import matplotlib.patches as mpatches
��� #red_patch = mpatches.Patch(color=‘red’, label=‘The red data’)
��� ��� from time import gmtime, strftime
��� current_date = strftime("%Y-%m-%d %H:%M:%S", gmtime())
��� �
��� fig = plot.figure()
��� ��� ��� fig.text(0, 0, ‘bottom-left corner’)
��� fig.text(0, 1,� current_date, ha=‘left’, va=‘top’)
��� mytext = “pattern: %s sax: %s sax with Z: %s” % (pattern, sax,
sax_string_with_Z)
��� fig.text(1,1, mytext )
��� ��� ��� # add the original timeseries to the plot
��� plot.plot(x,y, “forestgreen”)
��� #if pattern == “ccd”:
��� #������� plot.show()
��� ��� ��� directory, filename = os.path.split(inputfile_tmp)
��� ��� plot.savefig(os.path.join(directory, “plots/%s.png” %
filename))#, bbox_inches=‘tight’)
��� # remove the last figure from memory
��� #plot.close()
dee ccccccccccaacddeedcccccccdc ZZZZZZZZZZZZZZdeeZZZZZZZZZZ
1��� -0.015920084��� � ��� 2��� -0.044660769��� � ��� 3��� -0.044660769��� � ��� 4��� -0.092561907��� � ��� 5��� 0.012820599��� � ��� 6��� -0.015920084��� � ��� 7��� 0.012820599��� � ��� 8��� -0.054240996��� � ��� 9��� 0.031981054��� � ��� 10��� 0.031981054��� � ��� 11��� -0.025500313��� � ��� 12��� -0.044660769��� � ��� 13��� 0.012820599��� � ��� 14��� -0.025500313��� � ��� 15��� 0.0032403709��� � ��� 16��� -0.006339857��� � ��� 17��� 0.0032403709��� � ��� 18��� -0.025500313��� � ��� 19��� 0.031981054��� � ��� 20��� 0.031981054��� � ��� 21��� 0.031981054��� � ��� 22��� 0.022400826��� � ��� 23��� 0.031981054��� � ��� 24��� 0.05114151��� � ��� 25��� 0.079882193��� � ��� 26��� 0.05114151��� � ��� 27��� 0.05114151��� � ��� 28��� 0.05114151��� � ��� 29��� 0.099042646��� � ��� 30��� 0.060721738��� � ��� 31��� -0.015920084��� � ��� 32��� -0.054240996��� � ��� 33��� 0.23316584��� � ��� 34��� 0.26190652��� � ��� 35��� 0.37686926��� � ��� 36��� 0.12778333��� � ��� 37��� -0.044660769��� � ��� 38��� -0.26500601��� � ��� 39��� -0.41828965��� � ��� 40��� -0.38954897��� � ��� 41��� -0.26500601��� � ��� 42��� -0.14046305��� � ��� 43��� -0.073401452��� � ��� 44��� -0.12130259��� � ��� 45��� -0.082981679��� � ��� 46��� -0.14046305��� � ��� 47��� -0.054240996��� � ��� 48��� -0.082981679��� � ��� 49��� -0.015920084��� � ��� 50��� -0.073401452��� � ��� 51��� -0.015920084��� � ��� 52��� 0.10862288��� � ��� 53��� 1.1816084��� � ��� 54��� -1.3379915��� � ��� 55��� -4.6335899��� � ��� 56��� -6.74124��� � ��� 57��� -4.7772933��� � ��� 58��� -3.4839626��� � ��� 59��� -2.075669��� � ��� 60��� -1.0984858��� � ��� 61��� -0.37038851��� � ��� 62��� -0.063821223��� � ��� 63��� 0.11820311��� � ��� 64��� 0.13736356��� � ��� 65��� 0.15652401��� � ��� 66��� 0.11820311��� � ��� 67��� 0.32896812��� � ��� 68��� 0.27148675��� � ��� 69��� 0.30022744��� � ��� 70��� 0.31938789��� � ��� 71��� 0.3577088��� �0.5449999999999999��� 72��� 0.40560994��� �0.5449999999999999��� 73��� 0.44393085��� �0.5449999999999999��� 74��� 0.49183198��� �0.5449999999999999��� 75��� 0.67385632��� �0.5449999999999999��� 76��� 0.79839928��� �0.84��� 77��� 0.9995841��� �0.84��� 78��� 1.1528677��� �0.84��� 79��� 1.4115338��� �0.84��� 80��� 1.5552373��� �0.84��� 81��� 1.7468418��� �0.84��� 82��� 1.7755825��� �0.84��� 83��� 1.7276813��� �0.84��� 84��� 1.4115338��� �0.84��� 85��� 1.0858061��� �0.84��� 86��� 0.65469586��� � ��� 87��� 0.43435063��� � ��� 88��� 0.21400538��� � ��� 89��� 0.14694379��� � ��� 90��� 0.089462421��� � ��� 91��� 0.070301966��� � ��� 92��� 0.031981054��� � ��� 93��� 0.05114151��� � ��� 94��� 0.070301966��� � ��� 95��� 0.13736356��� � ��� 96��� 0.079882193��� � ��� 97��� 0.12778333��� � ��� 98��� 0.15652401��� � ��� 99��� 0.16610425��� � ��� 100��� 0.13736356��� � ��� 101��� 0.13736356��� � ��� 102��� 0.089462421��� � ��� 103��� 0.2523263��� � ��� 104��� 0.21400538��� � ��� 105��� 0.22358561��� � ��� 106��� 0.1852647��� � ��� 107��� 0.19484493��� � ��� 108��� 0.1852647��� � ��� 109��� 0.16610425��� � ��� 110��� 0.13736356��� � ��� 111��� 0.15652401��� � ��� 112��� 0.14694379��� � ��� 113��� 0.16610425��� � ��� 114��� 0.099042646��� � ��� 115��� 0.12778333��� � ��� 116��� 0.13736356��� � ��� 117��� 0.089462421��� � ��� 118��� 0.079882193��� � ��� 119��� 0.089462421��� � ��� 120��� 0.041561282��� � ��� 121��� 0.041561282��� � ��� 122��� 0.079882193��� � ��� 123��� 0.11820311��� � ��� 124��� 0.099042646��� � ��� 125��� 0.089462421��� � ��� 126��� 0.05114151��� � ��� 127��� 0.17568447��� � ��� 128��� 0.30022744��� � ��� 129��� 0.32896812��� � ��� 130��� 0.42477039��� � ��� 131��� 0.17568447��� � ��� 132��� 0.022400826��� � ��� 133��� -0.20752464��� � ��� 134��� -0.24584556��� � ��� 135��� -0.24584556��� � ���

···

On 07/09/2015 06:40 PM, peter wrote:

hi,

  my code was working fine, but now i cant figure out what went

wrong.

  any ideas?



  the code is supposed to plot a timeseries which it does and

overlay it with another that is partially defined

  the input file is contructed like this:

  the first line is just for information purposes.

  after that:

  the first row is a growing number (the x value), the second is the

timeseries and the third is the partially defined second
timeseries

  this is the code, after the code is a example input file.

  the code is also accessible via this paste service: ���� it

got a nice python code formatter.

https://dpaste.de/5ZrX

Can you be more specific about the problem you are having?
-Sterling

···

On Jul 9, 2015, at 9:40AM, peter <commercials24@...2079...> wrote:

hi,

my code was working fine, but now i cant figure out what went wrong.
any ideas?

the code is supposed to plot a timeseries which it does and overlay it with another that is partially defined
the input file is contructed like this:
the first line is just for information purposes.
after that:
the first row is a growing number (the x value), the second is the timeseries and the third is the partially defined second timeseries

this is the code, after the code is a example input file.
the code is also accessible via this paste service: https://dpaste.de/5ZrX it got a nice python code formatter.

  • def plotTimeSeriesAndSAX(inputfile_tmp, verbose=False):
  •
  • if verbose:
  • print "plotTimeSeriesAndSAX()"
  • print "\tinputfile:", inputfile_tmp
  • print "\toutputfile: %s.png" % inputfile_tmp
  •
  • inputfile = open(inputfile_tmp, "r");
  •
  •
  • # this holds my timeseries
  • x = []
  • y = []
  •
  • # this holds my "pattern"
  • pattern_x_values = []
  • pattern_y_values = []
  •
  • # these are for temporary use only, hold the current pattern data
  • tmp_x = []
  • tmp_y = []
  •
  •
  • # remove pattern/sax string, sax_string_with_Z from the datafile, only used as text in the plot
  • first_line = inputfile.readline()
  • pattern, sax, sax_string_with_Z = first_line.split()
  •
  •
  •
  •
  • for line in inputfile.readlines():
  •
  • data = line.split()
  • x_data = data[0]
  • y_data = data[1]
  •
  • #if there is a third line (pattern at this position)
  • if len(data) == 3:
  • y2_data = data[2]
  • tmp_y.append(y2_data)
  • tmp_x.append(x_data)
  • else:
  • # if the pattern ends, add it to pattern_x/y_value and clear the tmp list
  • if len(tmp_x) != 0:
  • pattern_x_values.append(tmp_x)
  • pattern_y_values.append(tmp_y)
  • tmp_x = []
  • tmp_y = []
  •
  •
  • x.append(x_data)
  • y.append(y_data)
  •
  • #if pattern == "ccd":
  • # print "pattern x_values:", pattern_x_values
  • # print "pattern y_values:", pattern_y_values
  • if verbose:
  • print "\ttimeseries y value", y
  • print "pattern x_values:", pattern_x_values
  • print "pattern y_values:", pattern_y_values
  •
  •
  •
  • colors = ["red", "magenta", "mediumblue", "darkorchid", "grey"]
  • #linestyle = ["-", "--"]
  •
  • # without this, the second plot contains the first and the second
  • # the third plot contains: the first, second and third
  • plot.clf()
  •
  • # plot all my patterns into the plot
  • for s in range(0,len(pattern_x_values)):
  • #if verbose:
  • # print "\tpattern x value:", pattern_x_values[s]
  • # print "\tpattern y value:", pattern_y_values[s]
  •
  • plot.plot(pattern_x_values[s], pattern_y_values[s], colors[1])
  •
  •
  • #plot.plot(x_all[0], y_all[0])
  •
  •
  • import matplotlib.patches as mpatches
  •
  •
  • #red_patch = mpatches.Patch(color='red', label='The red data')
  •
  • from time import gmtime, strftime
  • current_date = strftime("%Y-%m-%d %H:%M:%S", gmtime())
  •
  •
  • fig = plot.figure()
  •
  •
  • fig.text(0, 0, 'bottom-left corner')
  • fig.text(0, 1, current_date, ha='left', va='top')
  • mytext = "pattern: %s sax: %s sax with Z: %s" % (pattern, sax, sax_string_with_Z)
  • fig.text(1,1, mytext )
  •
  •
  • # add the original timeseries to the plot
  • plot.plot(x,y, "forestgreen")
  • #if pattern == "ccd":
  • # plot.show()
  •
  •
  • directory, filename = os.path.split(inputfile_tmp)
  •
  • plot.savefig(os.path.join(directory, "plots/%s.png" % filename))#, bbox_inches='tight')
  • # remove the last figure from memory
  • #plot.close()
  •
  •
  •
  •
  •
  •
  •
  •
  • #input:
  • dee ccccccccccaacddeedcccccccdc ZZZZZZZZZZZZZZdeeZZZZZZZZZZ
  • 1 -0.015920084
  • 2 -0.044660769
  • 3 -0.044660769
  • 4 -0.092561907
  • 5 0.012820599
  • 6 -0.015920084
  • 7 0.012820599
  • 8 -0.054240996
  • 9 0.031981054
  • 10 0.031981054
  • 11 -0.025500313
  • 12 -0.044660769
  • 13 0.012820599
  • 14 -0.025500313
  • 15 0.0032403709
  • 16 -0.006339857
  • 17 0.0032403709
  • 18 -0.025500313
  • 19 0.031981054
  • 20 0.031981054
  • 21 0.031981054
  • 22 0.022400826
  • 23 0.031981054
  • 24 0.05114151
  • 25 0.079882193
  • 26 0.05114151
  • 27 0.05114151
  • 28 0.05114151
  • 29 0.099042646
  • 30 0.060721738
  • 31 -0.015920084
  • 32 -0.054240996
  • 33 0.23316584
  • 34 0.26190652
  • 35 0.37686926
  • 36 0.12778333
  • 37 -0.044660769
  • 38 -0.26500601
  • 39 -0.41828965
  • 40 -0.38954897
  • 41 -0.26500601
  • 42 -0.14046305
  • 43 -0.073401452
  • 44 -0.12130259
  • 45 -0.082981679
  • 46 -0.14046305
  • 47 -0.054240996
  • 48 -0.082981679
  • 49 -0.015920084
  • 50 -0.073401452
  • 51 -0.015920084
  • 52 0.10862288
  • 53 1.1816084
  • 54 -1.3379915
  • 55 -4.6335899
  • 56 -6.74124
  • 57 -4.7772933
  • 58 -3.4839626
  • 59 -2.075669
  • 60 -1.0984858
  • 61 -0.37038851
  • 62 -0.063821223
  • 63 0.11820311
  • 64 0.13736356
  • 65 0.15652401
  • 66 0.11820311
  • 67 0.32896812
  • 68 0.27148675
  • 69 0.30022744
  • 70 0.31938789
  • 71 0.3577088 0.5449999999999999
  • 72 0.40560994 0.5449999999999999
  • 73 0.44393085 0.5449999999999999
  • 74 0.49183198 0.5449999999999999
  • 75 0.67385632 0.5449999999999999
  • 76 0.79839928 0.84
  • 77 0.9995841 0.84
  • 78 1.1528677 0.84
  • 79 1.4115338 0.84
  • 80 1.5552373 0.84
  • 81 1.7468418 0.84
  • 82 1.7755825 0.84
  • 83 1.7276813 0.84
  • 84 1.4115338 0.84
  • 85 1.0858061 0.84
  • 86 0.65469586
  • 87 0.43435063
  • 88 0.21400538
  • 89 0.14694379
  • 90 0.089462421
  • 91 0.070301966
  • 92 0.031981054
  • 93 0.05114151
  • 94 0.070301966
  • 95 0.13736356
  • 96 0.079882193
  • 97 0.12778333
  • 98 0.15652401
  • 99 0.16610425
  • 100 0.13736356
  • 101 0.13736356
  • 102 0.089462421
  • 103 0.2523263
  • 104 0.21400538
  • 105 0.22358561
  • 106 0.1852647
  • 107 0.19484493
  • 108 0.1852647
  • 109 0.16610425
  • 110 0.13736356
  • 111 0.15652401
  • 112 0.14694379
  • 113 0.16610425
  • 114 0.099042646
  • 115 0.12778333
  • 116 0.13736356
  • 117 0.089462421
  • 118 0.079882193
  • 119 0.089462421
  • 120 0.041561282
  • 121 0.041561282
  • 122 0.079882193
  • 123 0.11820311
  • 124 0.099042646
  • 125 0.089462421
  • 126 0.05114151
  • 127 0.17568447
  • 128 0.30022744
  • 129 0.32896812
  • 130 0.42477039
  • 131 0.17568447
  • 132 0.022400826
  • 133 -0.20752464
  • 134 -0.24584556
  • 135 -0.24584556

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