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

[Sterling S. <sm...@fu...>]

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

On Jul 9, 2015, at 9:40AM, peter <com...@ya...> 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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