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
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. Reach for the Cloud.
GigeNET's Cloud Solutions provide you with the tools and support that
you need to offload your IT needs and focus on growing your business.
Configured For All Businesses. Start Your Cloud Today.
https://www.gigenetcloud.com/
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
