Re: [Matplotlib-users] plot isnt plotting anymore, error in code?

[peter <com...@ya...>]

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: 
> https://dpaste.de/5ZrX     it got a nice python code formatter.
>

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
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19    0.031981054
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23    0.031981054
24    0.05114151
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29    0.099042646
30    0.060721738
31    -0.015920084
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34    0.26190652
35    0.37686926
36    0.12778333
37    -0.044660769
38    -0.26500601
39    -0.41828965
40    -0.38954897
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42    -0.14046305
43    -0.073401452
44    -0.12130259
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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
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102    0.089462421
103    0.2523263
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105    0.22358561
106    0.1852647
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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
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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