Example #1
0
def calculate_act_acd(total_calls, total_duration):
    """Calculate aggregation on some metrics:

        - ACH: Average Call per Hour
        - ACD: Average Call Duration

    >>> calculate_act_acd(5, 100)
    {'ACD': '00:20', 'ACH': 0.0}
    """
    ACH = math.floor(total_calls / 24)
    if total_calls == 0:
        ACD = 0
    else:
        ACD = int_convert_to_minute(math.floor(total_duration / total_calls))

    return {'ACH': ACH, 'ACD': ACD}
Example #2
0
def calculate_act_acd(total_calls, total_duration):
    """Calculate aggregation on some metrics:

        - ACH: Average Call per Hour
        - ACD: Average Call Duration

    >>> calculate_act_acd(5, 100)
    {'ACD': '00:20', 'ACH': 0.0}
    """
    ACH = math.floor(total_calls / 24)
    if total_calls == 0:
        ACD = 0
    else:
        ACD = int_convert_to_minute(math.floor(total_duration / total_calls))

    return {'ACH': ACH, 'ACD': ACD}
Example #3
0
def cdr_dashboard(request):
    """CDR dashboard on the last 24 hours

    **Attributes**:

        * ``template`` - cdr/dashboard.html
        * ``form`` - SwitchForm

    **Logic Description**:

        Display calls aggregated information for the last 24hours, several report will be
        created and displayed such as hourly call report and hangup cause/country analytics.
    """
    logging.debug('CDR dashboard view start')
    form = SwitchForm(request.POST or None)

    if form.is_valid():
        logging.debug('CDR dashboard view with search option')
        switch_id = int(getvar(request, 'switch_id'))
    else:
        switch_id = 0

    # Get list of calls/duration for each of the last 24 hours
    (calls_hour_aggr, total_calls, total_duration, total_billsec, total_buy_cost, total_sell_cost) = custom_sql_matv_voip_cdr_aggr_last24hours(request.user, switch_id)

    # Build chart data for last 24h calls
    (xdata, ydata, ydata2, ydata3, ydata4, ydata5) = ([], [], [], [], [], [])
    for i in calls_hour_aggr:
        start_time = (time.mktime(calls_hour_aggr[i]['calltime'].timetuple()) * 1000)
        xdata.append(start_time)
        ydata.append(int(calls_hour_aggr[i]['nbcalls']))
        ydata2.append(int(calls_hour_aggr[i]['duration']/60))
        ydata3.append(int(calls_hour_aggr[i]['billsec']/60))
        ydata4.append(int(calls_hour_aggr[i]['buy_cost']))
        ydata5.append(int(calls_hour_aggr[i]['sell_cost']))

    tooltip_date = "%d %b %y %H:%M %p"
    extra_serie1 = {"tooltip": {"y_start": "", "y_end": " calls"}, "date_format": tooltip_date}
    extra_serie2 = {"tooltip": {"y_start": "", "y_end": " min"}, "date_format": tooltip_date}
    extra_serie3 = {"tooltip": {"y_start": "", "y_end": " min"}, "date_format": tooltip_date}
    extra_serie4 = {"tooltip": {"y_start": "", "y_end": ""}, "date_format": tooltip_date}
    extra_serie5 = {"tooltip": {"y_start": "", "y_end": ""}, "date_format": tooltip_date}

    kwargs1 = {}
    kwargs1['bar'] = True

    final_chartdata = {
        'x': xdata,
        'name1': 'Calls', 'y1': ydata, 'extra1': extra_serie1, 'kwargs1': kwargs1,
        'name2': 'Duration', 'y2': ydata2, 'extra2': extra_serie2,
        'name3': 'Billsec', 'y3': ydata3, 'extra3': extra_serie3,
        'name4': 'Buy cost', 'y4': ydata4, 'extra4': extra_serie4,
        'name5': 'Sell cost', 'y5': ydata5, 'extra5': extra_serie5,
    }
    final_charttype = "linePlusBarChart"

    # Get top 5 of country calls for last 24 hours
    country_data = custom_sql_aggr_top_country_last24hours(request.user, switch_id, limit=5)

    # Build pie chart data for last 24h calls per country
    (xdata, ydata) = ([], [])
    for country in country_data:
        xdata.append(get_country_name(country["country_id"]))
        ydata.append(percentage(country["nbcalls"], total_calls))

    color_list = ['#FFC36C', '#FFFF9D', '#BEEB9F', '#79BD8F', '#FFB391']
    extra_serie = {"tooltip": {"y_start": "", "y_end": " %"}, "color_list": color_list}
    country_analytic_chartdata = {'x': xdata, 'y1': ydata, 'extra1': extra_serie}
    country_analytic_charttype = "pieChart"

    country_extra = {
        'x_is_date': False,
        'x_axis_format': '',
        'tag_script_js': True,
        'jquery_on_ready': True,
    }

    # Get top 10 of hangup cause calls for last 24 hours
    hangup_cause_data = custom_sql_aggr_top_hangup_last24hours(request.user, switch_id)

    # hangup analytic pie chart data
    (xdata, ydata) = ([], [])
    for hangup_cause in hangup_cause_data:
        xdata.append(str(get_hangupcause_name(hangup_cause["hangup_cause_id"])))
        ydata.append(str(percentage(hangup_cause["nbcalls"], total_calls)))

    color_list = ['#2A343F', '#7E8282', '#EA9664', '#30998F', '#449935']
    extra_serie = {"tooltip": {"y_start": "", "y_end": " %"}, "color_list": color_list}
    hangup_analytic_chartdata = {'x': xdata, 'y1': ydata, 'extra1': extra_serie}
    hangup_analytic_charttype = "pieChart"

    hangup_extra = country_extra

    logging.debug("Result calls_hour_aggr %d" % len(calls_hour_aggr))
    logging.debug("Result hangup_cause_data %d" % len(hangup_cause_data))
    logging.debug("Result country_data %d" % len(country_data))

    # Calculate the Average Time of Call
    metric_aggr = calculate_act_acd(total_calls, total_duration)

    final_extra = {
        'x_is_date': True,
        'x_axis_format': '%H:%M',
        # 'x_axis_format': '%d %b %Y',
        'tag_script_js': True,
        'jquery_on_ready': True,
        'focus_enable': True,
    }

    logging.debug('CDR dashboard view end')
    variables = {
        'total_calls': total_calls,
        'total_duration': int_convert_to_minute(total_duration),
        'total_buy_cost': total_buy_cost,
        'total_sell_cost': total_sell_cost,
        'metric_aggr': metric_aggr,
        'country_data': country_data,
        'hangup_analytic': hangup_cause_data,
        'form': form,
        'final_chartdata': final_chartdata,
        'final_charttype': final_charttype,
        'final_chartcontainer': 'final_container',
        'final_extra': final_extra,
        'hangup_analytic_charttype': hangup_analytic_charttype,
        'hangup_analytic_chartdata': hangup_analytic_chartdata,
        'hangup_chartcontainer': 'hangup_piechart_container',
        'hangup_extra': hangup_extra,
        'country_analytic_charttype': country_analytic_charttype,
        'country_analytic_chartdata': country_analytic_chartdata,
        'country_chartcontainer': 'country_piechart_container',
        'country_extra': country_extra,
    }
    return render_to_response('cdr/dashboard.html', variables, context_instance=RequestContext(request))
Example #4
0
def cdr_dashboard(request):
    """CDR dashboard on the last 24 hours

    **Attributes**:

        * ``template`` - cdr/dashboard.html
        * ``form`` - SwitchForm

    **Logic Description**:

        Display calls aggregated information for the last 24hours, several report will be
        created and displayed such as hourly call report and hangup cause/country analytics.
    """
    logging.debug('CDR dashboard view start')
    form = SwitchForm(request.POST or None)

    if form.is_valid():
        logging.debug('CDR dashboard view with search option')
        switch_id = int(getvar(request, 'switch_id'))
    else:
        switch_id = 0

    # Get list of calls/duration for each of the last 24 hours
    (calls_hour_aggr, total_calls, total_duration, total_billsec, total_buy_cost, total_sell_cost) = custom_sql_matv_voip_cdr_aggr_last24hours(request.user, switch_id)

    # Build chart data for last 24h calls
    (xdata, ydata, ydata2, ydata3, ydata4, ydata5) = ([], [], [], [], [], [])
    for i in calls_hour_aggr:
        start_time = (time.mktime(calls_hour_aggr[i]['calltime'].timetuple()) * 1000)
        xdata.append(start_time)
        ydata.append(int(calls_hour_aggr[i]['nbcalls']))
        ydata2.append(int(calls_hour_aggr[i]['duration']/60))
        ydata3.append(int(calls_hour_aggr[i]['billsec']/60))
        ydata4.append(int(calls_hour_aggr[i]['buy_cost']))
        ydata5.append(int(calls_hour_aggr[i]['sell_cost']))

    tooltip_date = "%d %b %y %H:%M %p"
    extra_serie1 = {"tooltip": {"y_start": "", "y_end": " calls"}, "date_format": tooltip_date}
    extra_serie2 = {"tooltip": {"y_start": "", "y_end": " min"}, "date_format": tooltip_date}
    extra_serie3 = {"tooltip": {"y_start": "", "y_end": " min"}, "date_format": tooltip_date}
    extra_serie4 = {"tooltip": {"y_start": "", "y_end": ""}, "date_format": tooltip_date}
    extra_serie5 = {"tooltip": {"y_start": "", "y_end": ""}, "date_format": tooltip_date}

    kwargs1 = {}
    kwargs1['bar'] = True

    final_chartdata = {
        'x': xdata,
        'name1': 'Calls', 'y1': ydata, 'extra1': extra_serie1, 'kwargs1': kwargs1,
        'name2': 'Duration', 'y2': ydata2, 'extra2': extra_serie2,
        'name3': 'Billsec', 'y3': ydata3, 'extra3': extra_serie3,
        'name4': 'Buy cost', 'y4': ydata4, 'extra4': extra_serie4,
        'name5': 'Sell cost', 'y5': ydata5, 'extra5': extra_serie5,
    }
    final_charttype = "linePlusBarChart"

    # Get top 5 of country calls for last 24 hours
    country_data = custom_sql_aggr_top_country_last24hours(request.user, switch_id, limit=5)

    # Build pie chart data for last 24h calls per country
    (xdata, ydata) = ([], [])
    for country in country_data:
        xdata.append(get_country_name(country["country_id"]))
        ydata.append(percentage(country["nbcalls"], total_calls))

    color_list = ['#FFC36C', '#FFFF9D', '#BEEB9F', '#79BD8F', '#FFB391']
    extra_serie = {"tooltip": {"y_start": "", "y_end": " %"}, "color_list": color_list}
    country_analytic_chartdata = {'x': xdata, 'y1': ydata, 'extra1': extra_serie}
    country_analytic_charttype = "pieChart"

    country_extra = {
        'x_is_date': False,
        'x_axis_format': '',
        'tag_script_js': True,
        'jquery_on_ready': True,
    }

    # Get top 10 of hangup cause calls for last 24 hours
    hangup_cause_data = custom_sql_aggr_top_hangup_last24hours(request.user, switch_id)

    # hangup analytic pie chart data
    (xdata, ydata) = ([], [])
    for hangup_cause in hangup_cause_data:
        xdata.append(str(get_hangupcause_name(hangup_cause["hangup_cause_id"])))
        ydata.append(str(percentage(hangup_cause["nbcalls"], total_calls)))

    color_list = ['#2A343F', '#7E8282', '#EA9664', '#30998F', '#449935']
    extra_serie = {"tooltip": {"y_start": "", "y_end": " %"}, "color_list": color_list}
    hangup_analytic_chartdata = {'x': xdata, 'y1': ydata, 'extra1': extra_serie}
    hangup_analytic_charttype = "pieChart"

    hangup_extra = country_extra

    logging.debug("Result calls_hour_aggr %d" % len(calls_hour_aggr))
    logging.debug("Result hangup_cause_data %d" % len(hangup_cause_data))
    logging.debug("Result country_data %d" % len(country_data))

    # Calculate the Average Time of Call
    metric_aggr = calculate_act_acd(total_calls, total_duration)

    final_extra = {
        'x_is_date': True,
        'x_axis_format': '%H:%M',
        # 'x_axis_format': '%d %b %Y',
        'tag_script_js': True,
        'jquery_on_ready': True,
        'focus_enable': True,
    }

    logging.debug('CDR dashboard view end')
    variables = {
        'total_calls': total_calls,
        'total_duration': int_convert_to_minute(total_duration),
        'total_buy_cost': total_buy_cost,
        'total_sell_cost': total_sell_cost,
        'metric_aggr': metric_aggr,
        'country_data': country_data,
        'hangup_analytic': hangup_cause_data,
        'form': form,
        'final_chartdata': final_chartdata,
        'final_charttype': final_charttype,
        'final_chartcontainer': 'final_container',
        'final_extra': final_extra,
        'hangup_analytic_charttype': hangup_analytic_charttype,
        'hangup_analytic_chartdata': hangup_analytic_chartdata,
        'hangup_chartcontainer': 'hangup_piechart_container',
        'hangup_extra': hangup_extra,
        'country_analytic_charttype': country_analytic_charttype,
        'country_analytic_chartdata': country_analytic_chartdata,
        'country_chartcontainer': 'country_piechart_container',
        'country_extra': country_extra,
    }
    return render_to_response('cdr/dashboard.html', variables, context_instance=RequestContext(request))