Prediction path

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>
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< # | Copyright Mathias Kettner 2014             mk@mathias-kettner.de |
---
> # | Copyright Mathias Kettner 2013             mk@mathias-kettner.de |
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> def get_influx_data(hostname, service_description, varname, cf, fromtime, untiltime, influx_client):
>     service_desc_influx = service_description.replace(" ", "")
>     hostname_influx = hostname.replace(".", "_")
>     data_raw = list(influx_client.query("select max(value) from %s where time > %is and time < %is and host='%s' and check='%s' group by time(2m);" % (service_desc_influx, fromtime, untiltime, hostname_influx, varname))[service_desc_influx])
>     data_list = []
>     for d in data_raw:
>         data_list.append(d["max"])
>     return 120, data_list
>
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< # Check wether a certain time stamp lies with in daylight safing time (DST)
< def is_dst(timestamp):
<     return time.localtime(timestamp).tm_isdst
<
< # Returns the timezone *including* DST shift at a certain point of time
< def timezone_at(timestamp):
<     if is_dst(timestamp):
<         return time.altzone
<     else:
<         return time.timezone
<
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<     day_of_epoch, rel_time = divmod(t - timezone_at(t), 86400)
---
>     day_of_epoch, rel_time = divmod(t - time.timezone, 86400)
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<     return "everyday", (t - timezone_at(t)) % 86400
---
>     return "everyday", (t - time.timezone) % 86400
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<     return str(mday), (t - timezone_at(t)) % 86400
---
>     return str(mday), (t - time.timezone) % 86400
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<     return "everyhour", (t - timezone_at(t)) % 3600
<
---
>     return "everyhour", (t - time.timezone) % 3600
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<         "slice"     : 86400, # 7 slices
<         "groupby"   : group_by_wday,
<         "valid"     : 7,
---
>         "slice" : 86400, # 7 slices
>         "groupby" : group_by_wday,
>         "valid" : 7,
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<         "slice"     : 86400, # 31 slices
<         "groupby"   : group_by_day_of_month,
<         "valid"     : 28,
---
>         "slice" : 86400, # 31 slices
>         "groupby" : group_by_day_of_month,
>         "valid" : 28,
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<         "slice"     : 86400, # 1 slice
<         "groupby"   : group_by_day,
<         "valid"     : 1,
---
>         "slice" : 86400, # 1 slice
>         "groupby" : group_by_day,
>         "valid" : 1,
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<         "slice"     : 3600, # 1 slice
<         "groupby"   : group_by_everyhour,
<         "valid"     : 24,
---
>         "slice" : 3600, # 1 slice
>         "groupby" : group_by_everyhour,
>         "valid" : 24,
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> def compute_prediction_influxdb(pred_file, timegroup, params, period_info, from_time, dsname, cf, ic):
>     import math, json
>
>     begin = from_time
>     slices = []
>     absolute_begin = from_time - params["horizon"] * 86400
>
>     smallest_step=120
>     while begin >= absolute_begin:
>         tg, fr, un, rel = get_prediction_timegroup(begin, period_info)
>         if tg == timegroup:
>             step, data = get_influx_data(g_hostname, g_service_description,
>                                       dsname, cf, fr, un-1, ic)
>             slices.append((fr, data))
>         begin -= period_info["slice"]
>
>
>     num_points=len(slices[0][1])
>     consolidated = []
>     for i in xrange(num_points):
>         point_line=[]
>         for from_time, data in slices:
>             if i < len(data):
>                 d = data[i]
>             if d != None:
>                 point_line.append(d)
>         if point_line:
>             average = sum(point_line) / len(point_line)
>             consolidated.append([
>                  average,
>                  min(point_line),
>                  max(point_line),
>                  stdev(point_line, average),
>             ])
>         else:
>             consolidated.append([None, None, None, None])
>
>     result = {
>         "num_points" : num_points,
>         "step"       : smallest_step,
>         "columns"    : [ "average", "min", "max", "stdev" ],
>         "points"     : consolidated,
>     }
>     return result
>
>
>
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<
<     # The resolutions of the different time ranges differ. We interpolate
---
>     # The resolution of the different time ranges differs. We interpolate
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<     # finest resolution. We also assume, that each step is always dividable
---
>     # finest resolution. We also assume, that step step is always dividable
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<
<     # Note: due to the f**king DST, we can have several shifts between
<     # DST and non-DST during are computation. We need to compensate for
<     # those. DST swaps within slices are being ignored. The DST flag
<     # is checked against the beginning of the slice.
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<     num_points = len(slices[0][2])
---
>     num_points = len(slices[0][2]) # The number of data points from the latest day
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>     from influxdb import InfluxDBClient
>     influx_settings = read_influx_config('/etc/check_mk/influxdb.config')
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<         last_info = None
---
>             last_info = None
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<         prediction = compute_prediction(pred_file, timegroup, params, period_info, from_time, dsname, cf)
---
>         if influx_settings['use_influx']:
>             client = InfluxDBClient(influx_settings['host'], influx_settings['port'], influx_settings['user'], influx_settings['password'], influx_settings['database'])
>             prediction = compute_prediction_influxdb(pred_file, timegroup, params, period_info, from_time, dsname, cf, client)
>         else:
>             prediction = compute_prediction(pred_file, timegroup, params, period_info, from_time, dsname, cf)
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>
> def read_influx_config(config_path):
>     import ConfigParser as cp
>     config = cp.SafeConfigParser({'host' : 'localhost', 'port' : '8086', 'user' : 'root', 'password' : 'root', 'database' : 'checkmk', 'use_influx' : 'False'})
>     config.read(config_path)
>     section = 'InfluxSettings'
>     options = config.options(section)
>     settings = {}
>     for option in options:
>         if option == 'use_influx':
>             settings[option] = config.getboolean(section, option)
>         elif option == 'port':
>             settings[option] = config.getint(section, option)
>         else:
>             settings[option] = config.get(section, option)
>     return settings