def get_stats_volume(cls):
data = []
data1 = {'key': [], 'values': []} # , 'color': '#2ca02c'
data2 = {'key': [], 'values': [], 'bar': 'true'}
values_duration = []
values_total_calls = []
values_success_calls = []
qs_d = DimCustomerDestination.objects.all()
#qs_h = DimCustomerHangupcause.objects.all()
today = datetime.date.today() - datetime.timedelta(days=0)
firstday = today - datetime.timedelta(days=90)
ts_total_calls = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_calls'))
ts_success_calls = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('success_calls'))
stats_duration = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_duration'))
for i in range(len(stats_duration)):
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
int(round_value(ts_total_calls[i][1]))]
values_total_calls.append(temp_data)
data1['key'].append("Total calls")
data1['values'] = values_total_calls
data.append(data1)
for i in range(len(stats_duration)):
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
int(round_value(ts_success_calls[i][1]))]
values_success_calls.append(temp_data)
data2['values'] = values_success_calls
# data2['bar'].append('true')
data2['key'].append("Success calls")
data.append(data2)
return data
def get_stats_volume(cls):
data = []
data1 = {"key": [], "values": []} # , 'color': '#2ca02c'
data2 = {"key": [], "values": [], "bar": "true"}
values_duration = []
values_total_calls = []
values_success_calls = []
qs_d = DimCustomerDestination.objects.all()
# qs_h = DimCustomerHangupcause.objects.all()
today = datetime.date.today() - datetime.timedelta(days=0)
firstday = today - datetime.timedelta(days=90)
ts_total_calls = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_calls"))
ts_success_calls = time_series(qs_d, "date__date", [firstday, today], func=Sum("success_calls"))
stats_duration = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_duration"))
for i in range(len(stats_duration)):
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
int(round_value(ts_total_calls[i][1])),
]
values_total_calls.append(temp_data)
data1["key"].append("Total calls")
data1["values"] = values_total_calls
data.append(data1)
for i in range(len(stats_duration)):
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
int(round_value(ts_success_calls[i][1])),
]
values_success_calls.append(temp_data)
data2["values"] = values_success_calls
# data2['bar'].append('true')
data2["key"].append("Success calls")
data.append(data2)
return data
def get_stats_minute(cls):
data = []
data1 = {'key': [], 'values': []}
data2 = {'key': [], 'bar': 'true', 'values': []}
values_duration = []
values_acd = []
acd = []
qs_d = DimCustomerDestination.objects.all()
# qs_h = DimCustomerHangupcause.objects.all()
today = datetime.date.today() - datetime.timedelta(days=0)
firstday = today - datetime.timedelta(days=90)
ts_success_calls = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('success_calls'))
stats_duration = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_duration'))
for i in range(len(stats_duration)):
if stats_duration[i][1]:
acd.append(stats_duration[i][1] / ts_success_calls[i][1])
else:
acd.append(0)
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
acd[i]]
values_acd.append(temp_data)
data1['key'].append("ACD in seconds")
data1['values'] = values_acd
data.append(data1)
for i in range(len(stats_duration)):
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
int(round_value(stats_duration[i][1]) / 60)]
values_duration.append(temp_data)
data2['values'] = values_duration
# data2['bar'].append('true')
data2['key'].append("Volume in minutes")
data.append(data2)
return data
def get_stats_minute(cls):
data = []
data1 = {"key": [], "values": []}
data2 = {"key": [], "bar": "true", "values": []}
values_duration = []
values_acd = []
acd = []
qs_d = DimCustomerDestination.objects.all()
# qs_h = DimCustomerHangupcause.objects.all()
today = datetime.date.today() - datetime.timedelta(days=0)
firstday = today - datetime.timedelta(days=90)
ts_success_calls = time_series(qs_d, "date__date", [firstday, today], func=Sum("success_calls"))
stats_duration = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_duration"))
for i in range(len(stats_duration)):
if stats_duration[i][1]:
acd.append(stats_duration[i][1] / ts_success_calls[i][1])
else:
acd.append(0)
temp_data = [int(time.mktime(stats_duration[i][0].timetuple()) * 1000), acd[i]]
values_acd.append(temp_data)
data1["key"].append("ACD in seconds")
data1["values"] = values_acd
data.append(data1)
for i in range(len(stats_duration)):
temp_data = [
int(time.mktime(stats_duration[i][0].timetuple()) * 1000),
int(round_value(stats_duration[i][1]) / 60),
]
values_duration.append(temp_data)
data2["values"] = values_duration
# data2['bar'].append('true')
data2["key"].append("Volume in minutes")
data.append(data2)
return data
def get_stats_revenue(cls):
data = []
data1 = {'key': [], 'values': [], 'color': '#2ca02c'}
data2 = {'key': [], 'values': []}
data3 = {'key': [], 'area': 'true', 'values': [], 'color': '#ff7f0e'}
data4 = {'key': [], 'area': 'true', 'values': [], 'color': '#7777ff'}
values_sell = []
values_cost = []
values_duration = []
margin = []
values_margin = []
qs = CDR.objects.filter(effective_duration__gt="0")
qs_d = DimCustomerDestination.objects.all()
# qs_h = DimCustomerHangupcause.objects.all()
qss_sell = qsstats.QuerySetStats(qs, 'start_stamp',
aggregate=Sum('total_sell'))
qss_cost = qsstats.QuerySetStats(qs, 'start_stamp',
aggregate=Sum('total_cost'))
qss_sum_duration = qsstats.QuerySetStats(qs, 'start_stamp',
aggregate=Sum('effective_duration'))
today = datetime.date.today() - datetime.timedelta(days=0)
firstday = today - datetime.timedelta(days=90)
# stats_sell = qss_sell.time_series(seven_days_ago, today)
# stats_cost = qss_sell.time_series(seven_days_ago, today)
# stats_duration = qss_sum_duration.time_series(seven_days_ago, today)
ts_total_calls = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_calls'))
ts_success_calls = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('success_calls'))
stats_duration = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_duration'))
stats_sell = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_sell'))
stats_cost = time_series(
qs_d, 'date__date', [firstday, today], func=Sum('total_cost'))
for i in range(len(stats_sell)):
values_sell.append(
[int(time.mktime(stats_sell[i][0].timetuple()) * 1000),
round_value(stats_sell[i][1])])
data1['key'].append("Revenue")
data1['values'] = values_sell
data.append(data1)
for i in range(len(stats_sell)):
temp_data = [
int(time.mktime(stats_sell[i][0].timetuple()) * 1000),
# round_value(stats_sell[i][1])
# round_value(stats_cost[i][1]),
int(round_value(stats_duration[i][1]))
]
values_duration.append(temp_data)
data2['values'] = values_duration
# data2['bar'].append('true')
data2['key'].append("Duration")
# data.append(data2)
for i in range(len(stats_sell)):
values_cost.append(
[int(time.mktime(stats_cost[i][0].timetuple()) * 1000),
round_value(stats_cost[i][1])])
data3['key'].append("Cost")
data3['values'] = values_cost
data.append(data3)
for i in range(len(stats_sell)):
if stats_sell[i][1]:
if stats_cost[i][1]:
margin.append(stats_sell[i][1] - stats_cost[i][1])
else:
margin.append(stats_sell[i][1])
else:
if stats_cost[i][1]:
margin.append(0 - stats_cost[i][1])
else:
margin.append(0)
values_margin.append(
[int(time.mktime(stats_cost[i][0].timetuple()) * 1000),
round_value(margin[i])])
data4['key'].append("Margin")
data4['values'] = values_margin
data.append(data4)
#data = [{"values": [[1400281200000, 3.36], [1400367600000, 0.03], [1400454000000, 30.15], [1400540400000, 34.57], [1400626800000, 30.73], [1400713200000, 32.12], [1400799600000, 60.69], [1400886000000, 3.61], [1400972400000, 0.05], [1401058800000, 68.54], [1401145200000, 339.0], [1401231600000, 130.58], [1401318000000, 17.12], [1401404400000, 133.52], [1401490800000, 111.67], [1401577200000, 0.02], [1401663600000, 640.63], [1401750000000, 565.65], [1401836400000, 646.74], [1401922800000, 639.96], [1402009200000, 798.42], [1402095600000, 493.09], [1402182000000, 65.13], [1402268400000, 380.07], [1402354800000, 17.01], [1402441200000, 388.32], [1402527600000, 0], [1402614000000, 0], [1402700400000, 0], [1402786800000, 0], [1402873200000, 0]], "bar": ["true"], "key": ["Revenue"]}, {"values": [[1400281200000, 25562], [1400367600000, 65], [1400454000000, 232339], [1400540400000, 225068], [1400626800000, 225401], [1400713200000, 198695], [1400799600000, 257652], [1400886000000, 14543], [1400972400000, 92], [1401058800000, 295177], [1401145200000, 980922], [1401231600000, 467542], [1401318000000, 70453], [1401404400000, 369460], [1401490800000, 307402], [1401577200000, 84], [1401663600000, 1814630], [1401750000000, 1578658], [1401836400000, 1799965], [1401922800000, 2344407], [1402009200000, 2540328], [1402095600000, 1345970], [1402182000000, 21832], [1402268400000, 1010094], [1402354800000, 66511], [1402441200000, 1078292], [1402527600000, 0], [1402614000000, 0], [1402700400000, 0], [1402786800000, 0], [1402873200000, 0]], "key": ["Duration"]}]
return data
def get_stats_revenue(cls):
data = []
data1 = {"key": [], "values": [], "color": "#2ca02c"}
data2 = {"key": [], "values": []}
data3 = {"key": [], "area": "true", "values": [], "color": "#ff7f0e"}
data4 = {"key": [], "area": "true", "values": [], "color": "#7777ff"}
values_sell = []
values_cost = []
values_duration = []
margin = []
values_margin = []
qs = CDR.objects.filter(effective_duration__gt="0")
qs_d = DimCustomerDestination.objects.all()
# qs_h = DimCustomerHangupcause.objects.all()
qss_sell = qsstats.QuerySetStats(qs, "start_stamp", aggregate=Sum("total_sell"))
qss_cost = qsstats.QuerySetStats(qs, "start_stamp", aggregate=Sum("total_cost"))
qss_sum_duration = qsstats.QuerySetStats(qs, "start_stamp", aggregate=Sum("effective_duration"))
today = datetime.date.today() - datetime.timedelta(days=0)
firstday = today - datetime.timedelta(days=90)
# stats_sell = qss_sell.time_series(seven_days_ago, today)
# stats_cost = qss_sell.time_series(seven_days_ago, today)
# stats_duration = qss_sum_duration.time_series(seven_days_ago, today)
ts_total_calls = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_calls"))
ts_success_calls = time_series(qs_d, "date__date", [firstday, today], func=Sum("success_calls"))
stats_duration = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_duration"))
stats_sell = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_sell"))
stats_cost = time_series(qs_d, "date__date", [firstday, today], func=Sum("total_cost"))
for i in range(len(stats_sell)):
values_sell.append([int(time.mktime(stats_sell[i][0].timetuple()) * 1000), round_value(stats_sell[i][1])])
data1["key"].append("Revenue")
data1["values"] = values_sell
data.append(data1)
for i in range(len(stats_sell)):
temp_data = [
int(time.mktime(stats_sell[i][0].timetuple()) * 1000),
# round_value(stats_sell[i][1])
# round_value(stats_cost[i][1]),
int(round_value(stats_duration[i][1])),
]
values_duration.append(temp_data)
data2["values"] = values_duration
# data2['bar'].append('true')
data2["key"].append("Duration")
# data.append(data2)
for i in range(len(stats_sell)):
values_cost.append([int(time.mktime(stats_cost[i][0].timetuple()) * 1000), round_value(stats_cost[i][1])])
data3["key"].append("Cost")
data3["values"] = values_cost
data.append(data3)
for i in range(len(stats_sell)):
if stats_sell[i][1]:
if stats_cost[i][1]:
margin.append(stats_sell[i][1] - stats_cost[i][1])
else:
margin.append(stats_sell[i][1])
else:
if stats_cost[i][1]:
margin.append(0 - stats_cost[i][1])
else:
margin.append(0)
values_margin.append([int(time.mktime(stats_cost[i][0].timetuple()) * 1000), round_value(margin[i])])
data4["key"].append("Margin")
data4["values"] = values_margin
data.append(data4)
# data = [{"values": [[1400281200000, 3.36], [1400367600000, 0.03], [1400454000000, 30.15], [1400540400000, 34.57], [1400626800000, 30.73], [1400713200000, 32.12], [1400799600000, 60.69], [1400886000000, 3.61], [1400972400000, 0.05], [1401058800000, 68.54], [1401145200000, 339.0], [1401231600000, 130.58], [1401318000000, 17.12], [1401404400000, 133.52], [1401490800000, 111.67], [1401577200000, 0.02], [1401663600000, 640.63], [1401750000000, 565.65], [1401836400000, 646.74], [1401922800000, 639.96], [1402009200000, 798.42], [1402095600000, 493.09], [1402182000000, 65.13], [1402268400000, 380.07], [1402354800000, 17.01], [1402441200000, 388.32], [1402527600000, 0], [1402614000000, 0], [1402700400000, 0], [1402786800000, 0], [1402873200000, 0]], "bar": ["true"], "key": ["Revenue"]}, {"values": [[1400281200000, 25562], [1400367600000, 65], [1400454000000, 232339], [1400540400000, 225068], [1400626800000, 225401], [1400713200000, 198695], [1400799600000, 257652], [1400886000000, 14543], [1400972400000, 92], [1401058800000, 295177], [1401145200000, 980922], [1401231600000, 467542], [1401318000000, 70453], [1401404400000, 369460], [1401490800000, 307402], [1401577200000, 84], [1401663600000, 1814630], [1401750000000, 1578658], [1401836400000, 1799965], [1401922800000, 2344407], [1402009200000, 2540328], [1402095600000, 1345970], [1402182000000, 21832], [1402268400000, 1010094], [1402354800000, 66511], [1402441200000, 1078292], [1402527600000, 0], [1402614000000, 0], [1402700400000, 0], [1402786800000, 0], [1402873200000, 0]], "key": ["Duration"]}]
return data
