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1 import rrdtool |
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2 import os |
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3 import os.path |
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4 import sys |
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5 import glob |
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6 from colorsys import hls_to_rgb |
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7 |
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8 import config |
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9 |
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10 def sensor_rrd_path(s): |
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11 return '%s/sensor_%s.rrd' % (config.DATA_PATH, s) |
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12 |
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13 # returns (path, sensor_name) tuples |
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14 def all_sensors(): |
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15 return [(r, os.path.basename(r[:-4])) |
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16 for r in glob.glob('%s/*.rrd' % config.DATA_PATH)] |
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17 |
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18 def create_rrd(sensor_id): |
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19 rrdtool.create(sensor_rrd_path(sensor_id), '-s', '300', |
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20 'DS:temp:GAUGE:600:-10:100', |
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21 'RRA:AVERAGE:0.5:1:1051200') |
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22 |
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23 |
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24 # stolen from viewmtn, stolen from monotone-viz |
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25 def colour_from_string(str): |
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26 def f(off): |
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27 return ord(hashval[off]) / 256.0 |
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28 hashval = sha.new(str).digest() |
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29 hue = f(5) |
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30 li = f(1) * 0.15 + 0.55 |
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31 sat = f(2) * 0.5 + .5 |
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32 return ''.join(["%.2x" % int(x * 256) for x in hls_to_rgb(hue, li, sat)]) |
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33 |
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34 def graph_png(start, length): |
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35 rrds = all_sensors() |
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36 |
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37 graph_args = [] |
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38 for n, (rrdfile, sensor) in enumerate(rrds): |
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39 vname = 'temp%d' % n |
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40 graph_args.append('DEF:%(vname)s=%(rrdfile)s:temp:AVERAGE' % locals()) |
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41 width = config.LINE_WIDTH |
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42 legend = config.SENSOR_NAMES.get(sensor, sensor) |
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43 colour = config.SENSOR_COLOURS.get(legend, colour_from_string(r)) |
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44 graph_args.append('LINE%(width)f:%(vname)s#%(colour)s:%(legend)s' % locals()) |
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45 |
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46 tempf = tempfile.NamedTemporaryFile() |
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47 args = [tempf.name, '-s', str(start), |
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48 '-e', str(start+length), |
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49 '-w', config.GRAPH_WIDTH, |
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50 '--slope-mode', |
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51 '--imgformat', 'PNG'] |
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52 + graph_args |
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53 rrdtool.graph(*args) |
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54 return tempf.read() |
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55 |
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56 def sensor_update(sensor_id, measurements, first_real_time, time_step): |
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57 try: |
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58 open(sensor_rrd_path(sensor_id)) |
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59 except IOError, e: |
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60 create_rrd(sensor_id) |
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61 |
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62 value_text = ' '.join('%f:%f' % p for p in |
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63 zip(measurements, |
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64 (first_real_time + time_step*t for t in xrange(len(measurements))))) |
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65 |
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66 rrdtool.update(sensor_rrd_path(sensor_id), value_text) |
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67 |
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68 def parse(lines): |
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69 entries = dict(l.split('=', 1) for l in lines) |
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70 if len(entries) != len(lines); |
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71 raise Exception("Keys are not unique") |
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72 |
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73 num_sensors = int(entries['sensors']) |
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74 num_measurements = int(entries['sensors']) |
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75 |
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76 sensor_ids = [entries['sensor_id%d' % n] for n in xrange(num_sensors)] |
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77 |
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78 meas = [] |
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79 for s in sensors: |
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80 meas.append([]) |
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81 |
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82 def val_scale(v): |
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83 # convert decidegrees to degrees |
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84 return 0.1 * v |
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85 |
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86 for n in xrange(num_measurements): |
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87 vals = [val_scale(int(entries["meas%d" % n].strip().split()))] |
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88 if len(vals) != num_sensors: |
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89 raise Exception("Wrong number of sensors for measurement %d" % n) |
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90 # we make an array of values for each sensor |
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91 for s in xrange(num_sensors): |
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92 meas[s].append(vals[s]) |
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93 |
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94 avr_now = float(entries['now') |
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95 avr_first_time = float(entries['first_time']) |
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96 time_step = float(entries['time_step']) |
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97 |
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98 first_real_time = time.time() - (avr_now - avr_first_time) |
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99 |
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100 for sensor_id, measurements in zip(sensors, meas): |
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101 sensor_update(sensor_id, measurements, first_real_time, time_step) |
