234 lines
12 KiB
Python
234 lines
12 KiB
Python
from influxdb import InfluxDB, InfluxDataGetter
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import time
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import logging
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from colors import assign_colors_to_curves
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import json
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import io
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class InfluxGraph:
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"""
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Class implementing the logic of the different routes that are called by the client to retrieve graph data with InfluxDB.
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Global constants :
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HISTORICAL (int) : value that represents the "historical" visualisation mode, meaning that the most recent point is not in the visualisation window.
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ACTUAL (int) : value that represents the "actual" visualisation mode, meaning that the curves stay in place, but new data is being added to the right (expansion in the future).
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LIVE (int) : value that represents the "live" visualisation mode, meaning that the curves move with the most recent point always at the same place.
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Attributes :
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db (InfluxDB) : the InfluxDB instance that holds the connection with InfluxDB.
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influx_data_getter (InfluxDataGetter) : the InfluxDataGetter instance that allows to get data out of InfluxDB.
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livemode (int) : the type of visualisation the user is currently in. Can be HISTORICAL, ACTUAL or LIVE.
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time ([(int)]) : an array of unix timestamp in seconds, where the first value is the last most recent requested point in time,
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and the second value is the current most recent requested point in time.
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lastvalues ({(str):((int), (float))}) : a dictionnary where the keys are the variable names, and the values are tuples, where the first
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value is the unix timestamp of the most recent value known for this variable, and the second value its corresponding value
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"""
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HISTORICAL = 0
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ACTUAL = 1
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LIVE = 2
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def __init__(self, influx_data_getter):
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self.influx_data_getter = influx_data_getter
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self.livemode = self.HISTORICAL
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self.end_query = 0
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self.lastvalues = {}
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self.variables = {} # name:label
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def milliseconds_to_nano(self, milliseconds):
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"""
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Converts milliseconds to nanoseconds
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Parameters:
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milliseconds (int)
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Returns :
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int
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"""
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return milliseconds*1000000
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def get_abs_time(self, times):
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"""
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Gets the absolute times for the given pontential relative times. If the given timestamps are less than one year, then the value is relative
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and converted into an asbolute timestamps
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Parameters :
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times([(float)]) : an array of unix timestamps or relative duration (< 1 year) as floats
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Returns :
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[(float)] : an array of absolute unix timestamps as floats
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"""
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now = int(time.time() + 0.999)
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oneyear = 365 * 24 * 3600
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return [t + now if t < oneyear else t for t in times]
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def complete_to_end(self, result, endtime):
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"""
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Completes the data until the last requested point in time by adding the last known y-value at the end point.
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Parameters :
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result ({(str):[[(int),(float)]]}) : a dictionnary with the variable names as key, and an array of points,
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which are an array containing the timestamp as their first value, and the y-value in float as their second one.
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endtime (int) : the unix timestamp in seconds of the time we want to have data until
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"""
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for var, c in result.items():
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if c:
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lastt, lastx = c[-1]
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if lastt < endtime:
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c.append((endtime, lastx))
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self.lastvalues[var] = (lastt, lastx)
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def w_graph(self, variables, time="-1800,0", interval=None):
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"""
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Gets the curves given by variables in the time range "time"
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Called when the route /graph is reached.
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Parameters :
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variables (str) : a comma separataed value string of variable names (influx names) to retrieve
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time (str) : a commma separated value string (range) of seconds. They are treated as relative from now if they are lesser than one year.
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interval (str) : the interval (resolution) of the values to get (string in seconds)
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Returns :
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{"type":"graph-draw", "reduced":(bool), "graph":{(str):[[(int),(float)]]}} : a dictionnary with its "graph-draw" type (so it can be processed by the client), a "reduced" value
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indicating if the data is reduced or not (meaning the data is sampled to be lighter for data viewing), and a "graph" dictionnary with the variable names as key, and an array of points,
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which are an array containing the timestamp as their first value, and the y-value in float as their second one.
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"""
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time = [float(t) for t in time.split(',')]
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start, end, now = self.get_abs_time(time + [0])
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start, end, now = int(start), int(end), int(now)
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queried_time_range = [start, end]
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queried_variables = variables.split(',')
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self.livemode = self.ACTUAL if end+10 >= now else self.HISTORICAL
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logging.info('LIVE %g %g %d %d', end, now, end >= now, self.livemode)
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if interval : interval = self.milliseconds_to_nano(int(interval))
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result = self.influx_data_getter.get_curves_in_timerange(queried_variables, queried_time_range, interval)
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self.complete_to_end(result, min(end, now))
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self.end_query = end
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# reduction not yet implemented
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return dict(type='graph-draw', reduced=False, graph=result)
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def w_gettime(self, time):
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"""
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Gets the server time for the give time.
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Called when the route /gettime is reached.
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Parameters :
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time (str="-1800,0") : the given point in time represented by a string, which is a comma separated unix timestamp values list (in seconds). They are treated as relative from now if they are lesser than one year.
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Returns :
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{"type":"time", "time":(int)} : a dictionnary with its "time" type (so the data can be processed by the client) and the server unix timestamp in seconds corresponding
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to the time asked by the client
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"""
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time = [float(t) for t in time.split(',')]
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return dict(type='time', time= self.get_abs_time(time))
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def w_getvars(self, time):
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"""
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Gets the curve names available at a given point in time.
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Called when the route /getvars is reached.
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Parameters :
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time (str) : the given point in time represented by a string, which is a comma separated unix timestamp values list (in seconds). They are treated as relative from now if they are lesser than one year.
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Returns :
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{"type":"var_list", "blocks":[{"tag":(str),"unit":(str), "curves":[{"name":(str), "label":(str), "color":(str), "original_color":(str)}]}]} :
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a dictionnary with its "var_list" type (so the data can be processed by the client) and the available curves with the name of the internal variable,
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the color to display for this curve, its original color in SEA, grouped by their tag (which is a unit augmented with an index) and their unit ("in blocks")
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"""
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time = [float(t) for t in time.split(',')]
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start_time = int(self.get_abs_time(time)[0])
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end_time = int(self.get_abs_time(time)[-1])
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blocks = self.influx_data_getter.get_available_variables_at_time([start_time, end_time])
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device_name = self.influx_data_getter.get_device_name(end_time)
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# updates the self.variables attribute to keep track of the available variables
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self.variables = {variable["name"]:variable["label"] for block in blocks for variable in block["curves"]}
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assign_colors_to_curves(blocks)
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result = dict(type='var_list')
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result['blocks'] = blocks
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result['device'] = device_name
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return result
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def w_updategraph(self):
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"""
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Sets the current visualisation mode to LIVE if not in HISTORICAL mode.
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Called when the route /updategraph is reached.
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Returns :
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{"type":"accept-graph", "live": bool} : a dict with its "accept-graph" type and a "live" value telling if the server could change its visualization mode to live
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"""
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logging.info("UPD GRAPH %d", self.livemode)
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if self.livemode == self.HISTORICAL:
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return dict(type='accept-graph', live=False)
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else:
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self.livemode = self.LIVE
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return dict(type='accept-graph', live=True)
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def w_export(self, variables, time, nan, interval):
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"""
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Returns the bytes of a dataframe with the curves given by variables in the time range "time"
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Called when the route /export is reached.
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Parameters :
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variables (str) : a comma separataed value string of variable names (influx names) to retrieve
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time (str) : a commma separated value string (range) of seconds.
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nan (str) : the representation for NaN values in the TSV
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interval (str) : the interval (resolution) of the values to get (string in seconds)
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Returns :
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io.BytesIO : an BytesIO object containing the dataframe to retrieve
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"""
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time = [float(t) for t in time.split(',')]
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start, end = self.get_abs_time(time)
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start, end = int(start), int(end)
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queried_variables = variables.split(',')
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if interval != "None" : interval = int(interval)
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df = self.influx_data_getter.get_curves_data_frame(queried_variables, [start, end], interval, self.variables)
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mem = io.BytesIO()
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df.to_csv(mem, sep="\t", index=False, float_format="%.15g", na_rep=nan)
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mem.seek(0)
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return mem
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def graphpoll(self):
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"""
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Polls the last known values for all the available variables, and returns only those whose polled value is more recent than the nost recent displayed one.
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Every plain minute, all the variables are returned with a point having their last known value at the current timestamp to synchronize all the curves on the GUI.
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Returns :
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{"type":"graph-update", "reduced":(bool), "time":(int), "graph":{(str):[[(int),(float)]]}} | None : a dictionnary with its "graph-update" type
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(so it can be processed by the client), a "reduced" value indicating if the data is reduced or not (meaning the data is sampled to be lighter
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for data viewing), and a "graph" dictionnary with the variable names as key, and an array of points, which are an array containing the timestamp
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as their first value, and the y-value in float as their second one
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"""
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if self.livemode != self.LIVE:
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return None
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now, = self.get_abs_time([0])
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result = self.influx_data_getter.poll_last_values(list(self.variables.keys()), self.lastvalues, now)
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for variable in self.lastvalues.keys():
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if variable in result.keys():
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# removes points older than the last known point (queries are in seconds and might return points already displayed)
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while len(result[variable]) > 0:
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if result[variable][0][0] <= self.lastvalues[variable][0]:
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result[variable].pop(0)
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else:
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break
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if len(result[variable]) > 0 and result[variable][-1][0] > self.lastvalues[variable][0]:
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self.lastvalues[variable] = (result[variable][-1][0], result[variable][-1][1])
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else:
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del result[variable]
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if int(now / 60) != int(self.end_query / 60):
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# Update unchanged values every plain minute
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for var, (_, lastx) in self.lastvalues.items():
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if var not in result:
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result[var] = [(now, lastx)]
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self.end_query = now
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if len(result) > 0:
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return dict(type='graph-update', reduced=False, time=now, graph=result)
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return None |