Can you be more specific about the problem you are having? -Sterling On Jul 9, 2015, at 9:40AM, peter <commercial...@yahoo.de> 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 > > > > ------------------------------------------------------------------------------ > Don't Limit Your Business. 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