class TimeSpentvsLevel(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='linear', position='bottom')],
'yAxes': [Axes(type='linear', ticks={'stepSize': 1}, position='left')]
}
def get_datasets(self, *args, **kwargs):
data = []
level_data = GameSave.objects.all().order_by('time')
for item in level_data:
data.append({
'y': item.next_level,
'x': round(item.time.seconds / 3600, 2)
})
return [
DataSet(
type='line',
label='Time(Hours) Vs Level',
data=data,
showLine=False,
color=(56, 163, 235),
)
]
class SurveyVpChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
'yAxes': [Axes(ticks={
'min': 0,
'max': 4
})],
}
def get_datasets(self, *args, **kwargs):
now = timezone.now()
year_ago = now - datetime.timedelta(days=365)
events = BaseEvent.objects \
.filter(approved=True, datetime_start__gte=year_ago, datetime_end__lt=now) \
.filter(surveys__isnull=False) \
.distinct()
data_communication_responsiveness = []
for event in events:
data = event.surveys.aggregate(
Avg('communication_responsiveness'), )
data_communication_responsiveness.append({
'x':
event.datetime_start.isoformat(),
'y':
data['communication_responsiveness__avg']
})
options = {'type': 'line', 'fill': False, 'lineTension': 0}
return [
DataSet(label='Communication responsiveness',
data=data_communication_responsiveness,
color=(193, 37, 82),
**options),
]
class SomaticsChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, player_id, param):
user_somatics_factors = SomaticsFactors.objects.user_params(player_id)
if param == "weight":
data = [{
'x': sf.date,
'y': sf.weight
} for sf in user_somatics_factors]
elif param == "fat":
data = [{
'x': sf.date,
'y': sf.fat
} for sf in user_somatics_factors]
elif param == "bmi":
data = [{
'x': sf.date,
'y': sf.bmi
} for sf in user_somatics_factors]
else:
data = None
return [
DataSet(
type='line',
label='Time Series',
borderColor='black',
data=data,
)
]
class GPM_XPM(Chart):
chart_type = 'line'
scales = {
'xAxes': [
Axes(position='bottom',
scaleLabel={
'display': 'true',
'labelString': "Latest-->Most recent"
})
],
}
def get_datasets(self, **kwargs):
return [{
'label': "GPM",
'data': GPM_list,
'backgroundColor': [
'rgba(75, 192, 192, 0.2)',
],
'borderColor': [
'rgba(75, 192, 192, 1)',
],
'borderWidth': 1,
}, {
'label': "XPM",
'data': XPM_list,
'backgroundColor': ['rgba(255, 99, 132, 0.2)'],
'borderColor': ['rgba(255, 99, 132, 1)'],
'borderWidth': 1,
}]
def get_labels(self, **kwargs):
return list(range(1, matches_requested + 1))
class SurveyPricelistChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
'yAxes': [Axes(ticks={
'min': 0,
'max': 4
})],
}
def get_datasets(self, *args, **kwargs):
now = timezone.now()
year_ago = now - datetime.timedelta(days=365)
events = BaseEvent.objects \
.filter(approved=True, datetime_start__gte=year_ago, datetime_end__lt=now) \
.filter(surveys__isnull=False) \
.distinct()
data_pricelist_ux = []
data_quote_as_expected = []
data_price_appropriate = []
for event in events:
data = event.surveys.aggregate(
Avg('pricelist_ux'),
# Avg('quote_as_expected'),
Avg('price_appropriate'),
)
data_pricelist_ux.append({
'x': event.datetime_start.isoformat(),
'y': data['pricelist_ux__avg']
})
# data_quote_as_expected.append({'x': event.datetime_start.isoformat(), 'y': data['quote_as_expected__avg']})
data_price_appropriate.append({
'x': event.datetime_start.isoformat(),
'y': data['price_appropriate__avg']
})
options = {'type': 'line', 'fill': False, 'lineTension': 0}
return [
DataSet(label='Pricelist UX',
data=data_pricelist_ux,
color=(193, 37, 82),
**options),
# DataSet(label='Quote as expected', data=data_quote_as_expected, color=(245, 199, 0), **options),
DataSet(label='Price appropriate',
data=data_price_appropriate,
color=(0, 133, 53),
**options),
]
class SurveyLnlChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
'yAxes': [Axes(ticks={
'min': 0,
'max': 4
})],
}
def get_datasets(self, *args, **kwargs):
now = timezone.now()
year_ago = now - datetime.timedelta(days=365)
events = BaseEvent.objects \
.filter(approved=True, datetime_start__gte=year_ago, datetime_end__lt=now) \
.filter(surveys__isnull=False) \
.distinct()
data_services_quality = []
data_customer_would_return = []
for event in events:
data = event.surveys.aggregate(
Avg('services_quality'),
Avg('customer_would_return'),
)
data_services_quality.append({
'x': event.datetime_start.isoformat(),
'y': data['services_quality__avg']
})
data_customer_would_return.append({
'x':
event.datetime_start.isoformat(),
'y':
data['customer_would_return__avg']
})
options = {'type': 'line', 'fill': False, 'lineTension': 0}
return [
DataSet(label='Services quality',
data=data_services_quality,
color=(193, 37, 82),
**options),
DataSet(label='Customer would return',
data=data_customer_would_return,
color=(106, 150, 31),
**options),
]
def __init__(self, meter, *args, **kwargs):
super().__init__(*args, **kwargs)
self.readings = models.MeterReadings.objects.filter(meter=meter)
self.scales = {
'yAxes': [
Axes(scaleLabel=ScaleLabel(
display=True,
labelString=f'{meter.fuel.name} ({meter.fuel.unit})'))
]
}
class TimeSeriesChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom',)],
}
legend = {
'display': False,
}
title = {
'display': True,
'text': ''
}
end_date=datetime.date.today()
start_date=datetime.date.today() - datetime.timedelta(365)
def get_datasets(self, target='ZAR', base='USD', start_date=start_date.strftime("%Y-%m-%d"), end_date=end_date.strftime("%Y-%m-%d")):
# Set the title
self.title = {
'display': True,
'text': '{} to {} chart between {} and {}.'.format(base, target, start_date, end_date)
}
# Use a lambda for a switch statement
curRate = lambda x, y: {'AUD': y.curencyAUD,
'CAD': y.curencyCAD,
'CHF': y.curencyCHF,
'CNY': y.curencyCNY,
'EUR': y.curencyEUR,
'GBP': y.curencyGBP,
'NZD': y.curencyNZD,
'ZAR': y.curencyZAR,
'USD': 1}[x]
historic = rates.objects.filter(date__range=[start_date, end_date]).order_by('-date')
# Use list comprehension to get the data
data = [{'y': curRate(target, result) / curRate(base, result), 'x': result.date} for result in historic]
return [DataSet(
type='line',
label='Time Series',
data=data,
borderColor='#0000ff',
pointRadius=0,
borderWidth=1,
backgroundColor='rgba(0, 0, 255, 0.1)',
)]
def __init__(self, meter, *args, **kwargs):
super().__init__(*args, **kwargs)
self.readings = models.MeterReadings.objects.filter(meter=meter) \
.annotate(reading_day=Trunc('reading_date_time', 'day')) \
.values('reading_day') \
.annotate(consumption=Sum('consumption'))
self.scales = {
'yAxes': [
Axes(scaleLabel=ScaleLabel(
display=True,
labelString=f'{meter.fuel.name} ({meter.fuel.unit})'))
]
}
class LineChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
factures = Facture.objects.all().values('date').annotate(y=Sum(F("lignes__produit__prix")*F("lignes__qte"),output_field=FloatField()))
data=factures.annotate(x=F('date')).values('x','y')
return[DataSet(
type='line',
label='Evaluation du chiffre d\'affaire par jour',
data=list(data)
)]
class LineChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def getXdataYdata(self,querySet,labels):
if querySet.count() == 0:
self.myLabel = labels
self.ydataArr = [[0]]
self.xdata = [0]
return 0
self.myLabel = labels
n = len(querySet[0])
self.ydataArr = [[j["the_count" + str(1 + i)] for j in querySet] for i in range(n - 1)]
self.xdata = [i['eval_date'] for i in querySet]
return 0
def get_datasets(self, **kwargs):
colors = [
(255, 99, 132),
(54, 162, 235),
(255, 206, 86),
(75, 192, 192),
(153, 102, 255),
(255, 159, 64),
(142, 68, 173),
(23, 165, 137),
(241, 196, 15),
(51, 255, 66),
(255, 51, 249)
]
dataSetArr = []
def roud_None(y):
try:
return '%.1f' % round(y, 2)
except:
None
for ydata,label,color in zip(self.ydataArr,self.myLabel,colors[:len(self.myLabel)]):
data_i = []
for x,y in zip(self.xdata,ydata):
data_i.append({"y": roud_None(y), "x": str(x)})
DataSet_i = DataSet(type='line',
label = label,
data=data_i,
color=color,
fill=False)
dataSetArr.append(DataSet_i)
return dataSetArr
class LineChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, dates):
# passed in dict contains the dates and the number describing activity as the value
data = []
for date in dates:
data.append({'y': dates[date], 'x': date})
return [DataSet(
type='line',
label='Activity',
data=data,
)]
class graficaHumedad(Chart):
chart_type = 'line'
responsive = True
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
#Se leen todas los registros de Humedad desde BD
humedades = sensorHumedad.objects.all()
#Para cada registro de humedad, se obtiene el ID del muestreo y valor. Se presenta como lista
data = [{'y': hum.humedad, 'x': hum.id} for hum in humedades]
return [{
'label': "Humedad Registrada",
'data': data,
'borderColor': 'orange'
}]
class graficaTemperatura(Chart):
chart_type = 'line'
responsive = True
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
#Se leen todas las temperaturas desde BD
temperaturas = sensorTemperatura.objects.all()
#Para cada registro de temperatura, se obtiene el ID del muestreo y valor. Se presenta como lista
#data = [{'x': temp.idMuestreo_id, 'y': temp.temperatura} for temp in temperaturas]
data = [{'x': temp.id, 'y': temp.temperatura} for temp in temperaturas]
data_scatter = data
data_line = data
return [{
'type': 'line',
'label': "Temperatura Registrada",
'data': data,
'borderColor': 'brown'
}]
class KDA(Chart):
chart_type = 'line'
title = Title(text='KDA over the past 25 games')
scales = {
'xAxes': [
Axes(position='bottom',
scaleLabel={
'display': 'true',
'labelString': "Latest-->Most recent"
})
],
}
def get_datasets(self, **kwargs):
return [{
'label': "Kills",
'data': kills_list,
'backgroundColor': [
'rgba(75, 192, 192, 0.2)',
],
'borderColor': [
'rgba(75, 192, 192, 1)',
],
'borderWidth': 1,
}, {
'label': "Deaths",
'data': deaths_list,
'backgroundColor': ['rgba(255, 99, 132, 0.2)'],
'borderColor': ['rgba(255, 99, 132, 1)'],
'borderWidth': 1,
}, {
'label': "Assists",
'data': assists_list,
'backgroundColor': ['rgba(255, 206, 86, 0.2)'],
'borderColor': ['rgba(255, 206, 86, 1)'],
'borderWidth': 1,
}]
def get_labels(self, **kwargs):
return list(range(1, matches_requested + 1))
class LineChart(Chart):
chart_type = 'line'
resposive = True
rrlist = []
scales = {
'xAxes': [Axes(type='linear', position='bottom')],
}
def __init__(
self, records
): # there must be initialization list with multiple arguments
self.rrlist = records
super(LineChart, self).__init__()
def get_datasets(self, **kwargs):
data = [
DataSet(label="Expenses",
backgroundColor='rgba(230,71,89, 0.7)',
data=[{
'x': record[0],
'y': record[1]
} for record in self.rrlist[0]]),
DataSet(label="Income",
backgroundColor='rgba(27, 201, 142, 0.7)',
data=[{
'x': record[0],
'y': record[1]
} for record in self.rrlist[1]]),
DataSet(label="Balance",
backgroundColor='rgba(27, 201, 142, 0.7)',
data=[{
'x': record[0],
'y': record[1]
} for record in self.rrlist[1]])
]
return data
class StudentChart(Chart):
chart_type = 'horizontalBar'
height = 50
scales = {
'xAxes': [Axes(stacked=True, display=False)],
'yAxes': [Axes(stacked=True)],
}
responsive = True
options = {'maintainAspectRatio': False, 'height': 50}
def get_labels(self, **kwargs):
labels = []
for point in self.syllabus_areas:
labels.append(str(point))
return labels
def get_datasets(self, **kwargs):
""" For each point, generate a new point in the correct range. """
points_0_to_1 = []
for point in self.syllabus_areas:
# Need to include -1 here to catch the 0s.
total_points = point.syllabus_points().count()
count = point.cohort_rating_number(self.students, -1, 1)
points_0_to_1.append(round(count / total_points * 100, 0))
points_1_to_2 = []
for point in self.syllabus_areas:
total_points = point.syllabus_points().count()
count = point.cohort_rating_number(self.students, 1, 2)
points_1_to_2.append(round(count / total_points * 100, 0))
points_2_to_3 = []
for point in self.syllabus_areas:
total_points = point.syllabus_points().count()
count = point.cohort_rating_number(self.students, 2, 3)
points_2_to_3.append(round(count / total_points * 100, 0))
points_3_to_4 = []
for point in self.syllabus_areas:
total_points = point.syllabus_points().count()
count = point.cohort_rating_number(self.students, 3, 4)
points_3_to_4.append(round(count / total_points * 100, 0))
points_4_to_5 = []
for point in self.syllabus_areas:
total_points = point.syllabus_points().count()
count = point.cohort_rating_number(self.students, 4, 5)
points_4_to_5.append(round(count / total_points * 100, 0))
return_data = []
return_data.append(
DataSet(
label='0-1',
data=points_0_to_1,
borderWidth=2,
color=(171, 9, 0),
))
return_data.append(
DataSet(
label='1-2',
data=points_1_to_2,
borderWidth=2,
color=(166, 78, 46),
))
return_data.append(
DataSet(
label='2-3',
data=points_2_to_3,
borderWidth=2,
color=(255, 190, 67),
))
return_data.append(
DataSet(
label='3-4',
data=points_3_to_4,
borderWidth=2,
color=(122, 159, 191),
))
return_data.append(
DataSet(
label='4-5',
data=points_4_to_5,
borderWidth=2,
color=(163, 191, 63),
))
return return_data
class CohortGraph(Chart):
chart_type = 'horizontalBar'
scales = {
'xAxes': [Axes(stacked=True)],
'yAxes': [Axes(stacked=True)],
}
def get_labels(self, **kwargs):
labels = []
for point in self.syllabus_areas:
labels.append(str(point))
return labels
def get_datasets(self, **kwargs):
""" For each point, generate a new point in the correct range. """
total_students = self.students.count()
points_0_to_1 = []
for point in self.syllabus_areas:
# Need to include -1 here to catch the 0s.
count = point.cohort_rating_number(self.students, -1, 1)
points_0_to_1.append(count / total_students)
points_1_to_2 = []
for point in self.syllabus_areas:
count = point.cohort_rating_number(self.students, 1, 2)
points_1_to_2.append(count / total_students)
points_2_to_3 = []
for point in self.syllabus_areas:
count = point.cohort_rating_number(self.students, 2, 3)
points_2_to_3.append(count / total_students)
points_3_to_4 = []
for point in self.syllabus_areas:
count = point.cohort_rating_number(self.students, 3, 4)
points_3_to_4.append(count / total_students)
points_4_to_5 = []
for point in self.syllabus_areas:
count = point.cohort_rating_number(self.students, 4, 5)
points_4_to_5.append(count / total_students)
return_data = []
return_data.append(
DataSet(
label='0-1',
data=points_0_to_1,
borderWidth=2,
color=(171, 9, 0),
))
return_data.append(
DataSet(
label='1-2',
data=points_1_to_2,
borderWidth=2,
color=(166, 78, 46),
))
return_data.append(
DataSet(
label='2-3',
data=points_2_to_3,
borderWidth=2,
color=(255, 190, 67),
))
return_data.append(
DataSet(
label='3-4',
data=points_3_to_4,
borderWidth=2,
color=(163, 191, 63),
))
return_data.append(
DataSet(
label='4-5',
data=points_4_to_5,
borderWidth=2,
color=(122, 159, 191),
))
return return_data
class TimeSeriesChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
"""This is very likely way too slow!"""
results = Result.objects.all()
#
# lines follows this bnf:
#
# { <label> : { <timestamp>: [ < ( upload, download, ping )+ > ] }
#
# where:
#
# label is < client_id > ":" < server_id >
# timestamp is the timestamp a measurement was made
# upload is the number of bits per second in upload
# download is the number of bits per second in download
# ping is the number of ms taken between the client server ping
#
lines = {}
for result in results:
logger.debug("Handling {0}...".format(result))
dt = result.timestamp.isoformat(timespec='seconds')
key = "{0}:{1}".format(result.client_id, result.server_id)
entry = (result.upload, result.download, result.ping)
if key in lines:
if result.timestamp in lines[key]:
logger.debug(
"Wow! {0} already in {1}. Appending entry...".format(
dt, lines[key]))
lines[key][result.timestamp].append(entry)
else:
logger.debug("Adding {{ {0}: {1} }} to {2}.".format(
result.timestamp, entry, key))
lines[key][result.timestamp] = [entry]
else:
logger.debug("Creating {{ {0}: {1} }} in {2}.".format(
dt, entry, key)) # lines[key]))
lines[key] = {result.timestamp: [entry]}
if logger.isEnabledFor(logging.DEBUG):
import pprint
pp = pprint.PrettyPrinter(indent=4, width=150)
logger.debug(pp.pformat(lines))
# transform to chart
datasets = []
upload_combined = []
download_combined = []
for label in lines.keys():
# label is <client_id> ":" <server_id>
(client_id, separator, server_id) = label.partition(":")
client = Client.objects.get(id=client_id)
server = Server.objects.get(id=server_id)
fancy_label = "from {0} (in {1}) to {2} (in {3})".format(
client.ip, client.country.name, server.host,
server.country.name)
logger.debug("Converted label {0} to {1}.".format(
label, fancy_label))
upload_line = []
download_line = []
for key in sorted(lines[label].keys()):
upload = 0
download = 0
for entry in lines[label][key]:
upload += entry[0]
download += entry[1]
upload = upload / len(lines[label][key])
download = download / len(lines[label][key])
upload_line.append({'x': str(key), 'y': upload})
download_line.append({'x': str(key), 'y': download})
upload_combined.append({'x': str(key), 'y': upload})
download_combined.append({'x': str(key), 'y': download})
datasets.append(
DataSet(type='line',
label="Upload " + fancy_label,
borderColor="green",
data=upload_line))
datasets.append(
DataSet(type='line',
label="Download " + fancy_label,
borderColor="blue",
data=download_line))
datasets.append(
DataSet(type='line',
label='Upload combined',
borderColor='yellow',
data=sorted(upload_combined, key=lambda e: e['x'])))
datasets.append(
DataSet(type='line',
label='Download combined',
borderColor='red',
data=sorted(download_combined, key=lambda e: e['x'])))
if logger.isEnabledFor(logging.DEBUG):
import pprint
pp = pprint.PrettyPrinter(indent=4, width=150)
logger.debug(pp.pformat(datasets))
return datasets
class ScatterLineChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
time_set = [
'2018-11-14 21:00', '2018-11-14 21:20', '2018-11-14 21:40',
'2018-11-14 22:00', '2018-11-14 22:20', '2018-11-14 22:40',
'2018-11-14 23:00'
]
fosters = []
g_and_t = []
captain_morgan = []
pampero = []
roe_and_co = []
jaegerbomb = []
vodka_redbull = []
prices = [
PriceStatic(5.80, 9.20, 8.40, 15.80, 9.20, 16.00, 19.80, 0),
PriceStatic(2.50, 9.00, 8.40, 10.00, 6.00, 10.00, 19.80, 1),
PriceStatic(5.80, 6.00, 8.40, 10.00, 6.00, 16.00, 19.80, 2),
PriceStatic(2.50, 6.00, 8.40, 15.80, 9.20, 16.00, 10.00, 3),
PriceStatic(5.80, 9.20, 5.00, 10.00, 9.20, 10.00, 10.00, 4),
PriceStatic(2.50, 9.20, 5.00, 10.00, 6.00, 16.00, 19.80, 5),
PriceStatic(2.50, 6.00, 8.40, 15.80, 6.00, 10.00, 19.80, 6),
PriceStatic(5.80, 9.20, 8.40, 15.80, 9.20, 10.00, 10.00, 7),
PriceStatic(5.8, 6.00, 5.00, 10.00, 9.20, 16.00, 10.00, 8),
PriceStatic(2.50, 9.20, 8.40, 10.00, 6.00, 16.00, 10.00, 9)
]
hour = 21
minute = 00
interval = 0
max_interval = 9
time_set2 = [{
'hour': 21,
'minute': 00
}, {
'hour': 21,
'minute': 20
}, {
'hour': 21,
'minute': 40
}, {
'hour': 22,
'minute': 00
}, {
'hour': 22,
'minute': 20
}, {
'hour': 22,
'minute': 40
}, {
'hour': 23,
'minute': 00
}]
localtime = time.localtime(time.time())
local_hour = localtime.tm_hour
local_minute = localtime.tm_min
for curtime in time_set2:
if local_hour >= curtime['hour'] and local_minute >= curtime[
'minute']:
interval = time_set2.index(curtime)
#if (local_hour >= time_set2[0]['hour']):
# current_minute = time_set2[0]['minute']
# while(local_minute>current_minute):
# current_minute += 1
# if ()
current_time = time_set2[interval]
if interval != max_interval:
next_time = time_set2[interval + 1]
if local_hour == next_time['hour'] and local_minute == next_time[
'minute']:
interval = interval + 1
for price in prices:
if price.interval_index <= interval:
fosters.append({
'y': price.foster,
'x': time_set[int(price.interval_index)]
})
g_and_t.append({
'y': price.g_t,
'x': time_set[int(price.interval_index)]
})
captain_morgan.append({
'y': price.captain_morgan,
'x': time_set[int(price.interval_index)]
})
pampero.append({
'y': price.pampero,
'x': time_set[int(price.interval_index)]
})
roe_and_co.append({
'y': price.roe_and_co,
'x': time_set[int(price.interval_index)]
})
jaegerbomb.append({
'y': price.jaegerbomb,
'x': time_set[int(price.interval_index)]
})
vodka_redbull.append({
'y': price.vodka_redbull,
'x': time_set[int(price.interval_index)]
})
return [
DataSet(type='line',
label='Fosters',
borderColor='blue',
data=fosters),
DataSet(type='line',
label='Gin and Tonic',
borderColor='red',
data=g_and_t),
DataSet(type='line',
label='Captain Morgan Splash',
borderColor='orange',
data=captain_morgan),
DataSet(type='line',
label='Pampero Especial',
borderColor='green',
data=pampero),
DataSet(type='line',
label='Roe & Co and Ginger Ale',
borderColor='yellow',
data=roe_and_co),
DataSet(type='line',
label='Double Jaeger-Bomb',
borderColor='purple',
data=jaegerbomb),
DataSet(type='line',
label='Double Vodka Redbull',
borderColor='pink',
data=vodka_redbull)
]
class ScatterLineChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
data_scatter = [{
'y': 24,
'x': '2017-01-01T21:00:00'
}, {
'y': 1,
'x': '2017-01-02T03:00:00'
}, {
'y': 7,
'x': '2017-01-02T14:00:00'
}, {
'y': 7,
'x': '2017-01-03T08:00:00'
}, {
'y': 13,
'x': '2017-01-04T00:00:00'
}, {
'y': 7,
'x': '2017-01-04T07:00:00'
}, {
'y': 19,
'x': '2017-01-05T01:00:00'
}, {
'y': 18,
'x': '2017-01-05T15:00:00'
}, {
'y': 14,
'x': '2017-01-06T00:00:00'
}, {
'y': 2,
'x': '2017-01-06T07:00:00'
}, {
'y': 18,
'x': '2017-01-07T06:00:00'
}, {
'y': 4,
'x': '2017-01-07T07:00:00'
}, {
'y': 21,
'x': '2017-01-07T21:00:00'
}, {
'y': 5,
'x': '2017-01-08T00:00:00'
}, {
'y': 16,
'x': '2017-01-08T07:00:00'
}, {
'y': 14,
'x': '2017-01-08T11:00:00'
}, {
'y': 21,
'x': '2017-01-09T04:00:00'
}, {
'y': 25,
'x': '2017-01-09T20:00:00'
}, {
'y': 9,
'x': '2017-01-10T15:00:00'
}, {
'y': 25,
'x': '2017-01-11T10:00:00'
}, {
'y': 17,
'x': '2017-01-11T17:00:00'
}, {
'y': 10,
'x': '2017-01-12T11:00:00'
}, {
'y': 7,
'x': '2017-01-12T17:00:00'
}, {
'y': 11,
'x': '2017-01-12T22:00:00'
}, {
'y': 2,
'x': '2017-01-13T04:00:00'
}, {
'y': 13,
'x': '2017-01-13T12:00:00'
}, {
'y': 12,
'x': '2017-01-14T12:00:00'
}, {
'y': 16,
'x': '2017-01-15T10:00:00'
}, {
'y': 15,
'x': '2017-01-16T00:00:00'
}, {
'y': 23,
'x': '2017-01-16T17:00:00'
}, {
'y': 15,
'x': '2017-01-17T02:00:00'
}, {
'y': 22,
'x': '2017-01-17T12:00:00'
}, {
'y': 18,
'x': '2017-01-17T15:00:00'
}, {
'y': 16,
'x': '2017-01-18T14:00:00'
}, {
'y': 7,
'x': '2017-01-19T09:00:00'
}, {
'y': 10,
'x': '2017-01-20T02:00:00'
}, {
'y': 7,
'x': '2017-01-20T13:00:00'
}, {
'y': 5,
'x': '2017-01-20T17:00:00'
}, {
'y': 15,
'x': '2017-01-20T20:00:00'
}, {
'y': 5,
'x': '2017-01-21T06:00:00'
}, {
'y': 13,
'x': '2017-01-21T18:00:00'
}, {
'y': 20,
'x': '2017-01-22T13:00:00'
}, {
'y': 20,
'x': '2017-01-22T16:00:00'
}, {
'y': 23,
'x': '2017-01-23T15:00:00'
}, {
'y': 3,
'x': '2017-01-23T20:00:00'
}, {
'y': 20,
'x': '2017-01-24T15:00:00'
}, {
'y': 19,
'x': '2017-01-24T16:00:00'
}, {
'y': 1,
'x': '2017-01-25T00:00:00'
}, {
'y': 3,
'x': '2017-01-25T02:00:00'
}, {
'y': 22,
'x': '2017-01-25T23:00:00'
}, {
'y': 6,
'x': '2017-01-26T19:00:00'
}, {
'y': 17,
'x': '2017-01-27T10:00:00'
}, {
'y': 7,
'x': '2017-01-28T09:00:00'
}, {
'y': 23,
'x': '2017-01-29T05:00:00'
}, {
'y': 19,
'x': '2017-01-29T17:00:00'
}, {
'y': 16,
'x': '2017-01-30T08:00:00'
}, {
'y': 19,
'x': '2017-01-30T09:00:00'
}, {
'y': 23,
'x': '2017-01-31T06:00:00'
}, {
'y': 18,
'x': '2017-02-01T05:00:00'
}]
data_line = [{
'y': 20,
'x': '2017-01-02T00:00:00'
}, {
'y': 3,
'x': '2017-01-03T00:00:00'
}, {
'y': 2,
'x': '2017-01-04T00:00:00'
}, {
'y': 18,
'x': '2017-01-05T00:00:00'
}, {
'y': 19,
'x': '2017-01-06T00:00:00'
}, {
'y': 20,
'x': '2017-01-07T00:00:00'
}, {
'y': 5,
'x': '2017-01-08T00:00:00'
}, {
'y': 23,
'x': '2017-01-09T00:00:00'
}, {
'y': 18,
'x': '2017-01-10T00:00:00'
}, {
'y': 5,
'x': '2017-01-11T00:00:00'
}, {
'y': 6,
'x': '2017-01-12T00:00:00'
}, {
'y': 2,
'x': '2017-01-13T00:00:00'
}, {
'y': 23,
'x': '2017-01-14T00:00:00'
}, {
'y': 3,
'x': '2017-01-15T00:00:00'
}, {
'y': 24,
'x': '2017-01-16T00:00:00'
}, {
'y': 10,
'x': '2017-01-17T00:00:00'
}, {
'y': 9,
'x': '2017-01-18T00:00:00'
}, {
'y': 11,
'x': '2017-01-19T00:00:00'
}, {
'y': 10,
'x': '2017-01-20T00:00:00'
}, {
'y': 2,
'x': '2017-01-21T00:00:00'
}, {
'y': 16,
'x': '2017-01-22T00:00:00'
}, {
'y': 24,
'x': '2017-01-23T00:00:00'
}, {
'y': 3,
'x': '2017-01-24T00:00:00'
}, {
'y': 13,
'x': '2017-01-25T00:00:00'
}, {
'y': 7,
'x': '2017-01-26T00:00:00'
}, {
'y': 10,
'x': '2017-01-27T00:00:00'
}, {
'y': 7,
'x': '2017-01-28T00:00:00'
}, {
'y': 13,
'x': '2017-01-29T00:00:00'
}, {
'y': 1,
'x': '2017-01-30T00:00:00'
}, {
'y': 10,
'x': '2017-01-31T00:00:00'
}, {
'y': 7,
'x': '2017-02-01T00:00:00'
}]
return [
DataSet(type='line',
label='Scatter',
showLine=False,
data=data_scatter),
DataSet(type='line',
label='Line',
borderColor='red',
data=data_line)
]
class SurveyCrewChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
'yAxes': [Axes(ticks={
'min': 0,
'max': 4
})],
}
def get_datasets(self, *args, **kwargs):
now = timezone.now()
year_ago = now - datetime.timedelta(days=365)
events = BaseEvent.objects \
.filter(approved=True, datetime_start__gte=year_ago, datetime_end__lt=now) \
.filter(surveys__isnull=False) \
.distinct()
data_lighting_quality = []
data_sound_quality = []
data_setup_on_time = []
data_crew_respectfulness = []
data_crew_preparedness = []
data_crew_knowledgeability = []
for event in events:
data = event.surveys.aggregate(
Avg('lighting_quality'),
Avg('sound_quality'),
Avg('setup_on_time'),
Avg('crew_respectfulness'),
# Avg('crew_preparedness'),
# Avg('crew_knowledgeability'),
)
data_lighting_quality.append({
'x': event.datetime_start.isoformat(),
'y': data['lighting_quality__avg']
})
data_sound_quality.append({
'x': event.datetime_start.isoformat(),
'y': data['sound_quality__avg']
})
data_setup_on_time.append({
'x': event.datetime_start.isoformat(),
'y': data['setup_on_time__avg']
})
data_crew_respectfulness.append({
'x':
event.datetime_start.isoformat(),
'y':
data['crew_respectfulness__avg']
})
# data_crew_preparedness.append({'x': event.datetime_start.isoformat(), 'y': data['crew_preparedness__avg']})
# data_crew_knowledgeability.append({'x': event.datetime_start.isoformat(), 'y': data['crew_knowledgeability__avg']})
options = {'type': 'line', 'fill': False, 'lineTension': 0}
return [
DataSet(label='Lighting quality',
data=data_lighting_quality,
color=(193, 37, 82),
**options),
DataSet(label='Sound quality',
data=data_sound_quality,
color=(255, 102, 0),
**options),
DataSet(label='Setup on time',
data=data_setup_on_time,
color=(245, 199, 0),
**options),
DataSet(label='Crew was helpful',
data=data_crew_respectfulness,
color=(106, 150, 31),
**options),
# DataSet(label='Crew preparedness', data=data_crew_preparedness, color=(0, 133, 53), **options),
# DataSet(label='Crew knowledgeability', data=data_crew_knowledgeability, color=(110, 45, 214), **options),
]
class TimeSeriesChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [Axes(type='time', position='bottom')],
}
def get_datasets(self, **kwargs):
data = [{
'y': 0,
'x': '2017-01-02T00:00:00'
}, {
'y': 1,
'x': '2017-01-03T00:00:00'
}, {
'y': 4,
'x': '2017-01-04T00:00:00'
}, {
'y': 9,
'x': '2017-01-05T00:00:00'
}, {
'y': 16,
'x': '2017-01-06T00:00:00'
}, {
'y': 25,
'x': '2017-01-07T00:00:00'
}, {
'y': 36,
'x': '2017-01-08T00:00:00'
}, {
'y': 49,
'x': '2017-01-09T00:00:00'
}, {
'y': 64,
'x': '2017-01-10T00:00:00'
}, {
'y': 81,
'x': '2017-01-11T00:00:00'
}, {
'y': 100,
'x': '2017-01-12T00:00:00'
}, {
'y': 121,
'x': '2017-01-13T00:00:00'
}, {
'y': 144,
'x': '2017-01-14T00:00:00'
}, {
'y': 169,
'x': '2017-01-15T00:00:00'
}, {
'y': 196,
'x': '2017-01-16T00:00:00'
}, {
'y': 225,
'x': '2017-01-17T00:00:00'
}, {
'y': 256,
'x': '2017-01-18T00:00:00'
}, {
'y': 289,
'x': '2017-01-19T00:00:00'
}, {
'y': 324,
'x': '2017-01-20T00:00:00'
}, {
'y': 361,
'x': '2017-01-21T00:00:00'
}, {
'y': 400,
'x': '2017-01-22T00:00:00'
}, {
'y': 441,
'x': '2017-01-23T00:00:00'
}, {
'y': 484,
'x': '2017-01-24T00:00:00'
}, {
'y': 529,
'x': '2017-01-25T00:00:00'
}, {
'y': 576,
'x': '2017-01-26T00:00:00'
}, {
'y': 625,
'x': '2017-01-27T00:00:00'
}, {
'y': 676,
'x': '2017-01-28T00:00:00'
}, {
'y': 729,
'x': '2017-01-29T00:00:00'
}, {
'y': 784,
'x': '2017-01-30T00:00:00'
}, {
'y': 841,
'x': '2017-01-31T00:00:00'
}, {
'y': 900,
'x': '2017-02-01T00:00:00'
}]
return [DataSet(
type='line',
label='Time Series',
data=data,
)]
class ScoreMassChart(Chart):
chart_type = 'line'
scales = {
'xAxes': [
Axes(type='linear',
position='bottom',
scaleLabel=ScaleLabel(display=True,
labelString='quality score'))
],
'yAxes': [
Axes(
type='linear',
scaleLabel=ScaleLabel(
display=True,
labelString='% of sentences',
),
#ticks={'stepSize': 1},
)
],
}
def get_datasets(self, document_id):
total = MachineTranslationEvaluation.objects. \
filter(translation__source__document_id=document_id).count()
count_0 = MachineTranslationEvaluation.objects. \
filter(translation__source__document_id=document_id,
score=0).count()
count_1 = MachineTranslationEvaluation.objects. \
filter(translation__source__document_id=document_id,
score=1).count()
dotsdata = [{'x': 0, 'y': count_0 * 100.0 / total}]
#for score in sorted(list(set(scores))):
step = 0.1
for min_score in [round(x * step, 4) for x in range(0, 10)]:
max_score = min_score + step
evaluations_per_score = \
MachineTranslationEvaluation.objects. \
filter(translation__source__document_id=document_id,
score__gte=min_score,
score__lte=max_score).count()
score = (max_score - min_score) * .5 + min_score
dotsdata.append({
'x': score,
'y': 100.0 * evaluations_per_score / total
})
dotsdata.append({'x': 1, 'y': count_1 * 100.0 / total})
return [
DataSet(
type='line',
data=dotsdata,
label="mass per decimile",
pointRadius=1,
),
]