def _create_statdata(chart_type, stats, terms=None, term_names=None):
if chart_type == 'bar':
statdata = DataPool(series=[{
'options': {
'source': stats.filter(feature=term)
},
'legend_by':
'feature',
'terms': [{
'time_%s' % term: 'time'
}, {
term_names[term]: 'value',
'name': 'feature'
}]
} for term in terms])
elif chart_type == 'pie':
statdata = DataPool(series=[{
'options': {
'source': stats
},
'legend_by': 'feature',
'terms': ['feature', 'value'],
}])
else:
statdata = None
return statdata
def get_pool(auctions):
len_sold_auction = len(auctions)
if len_sold_auction == 0:
return
ds = DataPool(series=[{
'options': {
'source': auctions
},
'terms': ['expire_date', 'current_price']
}])
chrt = Chart(datasource=ds,
series_options=[{
'options': {
'type': 'line',
'stacking': False
},
'terms': {
'expire_date': ['current_price']
}
}],
chart_options={
'title': {
'text': ''
},
'xAxis': {
'title': {
'text': 'Data'
}
}
})
return chrt
def graphPrediction(request, company_id):
# Step 1: Create a DataPool with the data we want to retrieve.
Stockdata = \
DataPool(
series=
[{'options': {
'source': PredictionList.objects.filter(predictionFK=company_id)},
'terms': [
'dateStock',
'estimate']}
])
cht = Chart(datasource=Stockdata,
series_options=[{
'options': {
'type': 'line',
'stacking': False
},
'terms': {
'dateStock': ['estimate']
}
}],
chart_options={
'title': {
'text': 'Predicted Values'
},
'xAxis': {
'title': {
'text': 'Month number'
}
}
})
return render_to_response('roboadvisor/graph.html', {'weatherchart': cht})
def sales(request):
salle = 'Nordique'
Newsalle = request.POST.get('Room')
if Newsalle != None:
salle = Newsalle
drink = 'Champagne'
Newdrink = request.POST.get('Drinks')
if Newdrink != None:
drink = Newdrink
sales= \
DataPool(
series=[{'options':{'source': VenteSum.objects.filter(drink = dk.objects.filter(name = drink)[0].id, room = rooms.objects.filter(name = salle)[0].id)},
'terms':['date','quantitySum']}])
salle_drink = drink + ' ' + salle
cht = Chart(datasource=sales,
series_options=[{
'options': {
'type': 'line'
},
'terms': {
'date': ['quantitySum']
}
}],
chart_options={
'title': {
'text': salle_drink
},
'xAxis': {
'title': {
'text': drink
}
}
})
return render(request, 'main/sales.html', {'chart_list': [cht]})
def chart(request):
ridershipdata = \
DataPool(
series=
[{'options': {
'source': PeopleCount.objects.all()}, # PeopleCount.objects.all()},
'terms': [
'Count',
'StopID',
'LocID']}
])
riderchart = Chart(datasource=ridershipdata,
series_options=[{
'options': {
'type': 'pie',
'stacking': False
},
'terms': {
'StopID': ['Count']
}
}],
chart_options={
'title': {
'text': 'Ridership Data'
},
'xAxis': {
'title': {
'text': 'Rider Count'
}
}
},
x_sortf_mapf_mts=(None, stopNames, False))
return render_to_response('chart.html', {'ridershipChart': riderchart})
def weather_chart_view(request):
#Step 1: Create a DataPool with the data we want to retrieve.
weatherdata = \
DataPool(
series=
[{'options': {
'source': Product.objects.all()},
'terms': [
'MonthofEntry',
'OEM': ['123456'],
'TotalPrice']}
])
#Step 2: Create the Chart object
cht = Chart(
datasource = weatherdata,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'MonthofEntry': [
'OEM': ['123456'],
'TotalPrice']
}}],
chart_options =
{'title': {
'text': 'Weather Data of Boston and Houston'},
'xAxis': {
'title': {
'text': 'Month number'}}})
#Step 3: Send the chart object to the template.
return render_to_response({'weatherchart': cht})
def simple(request):
#Step 1: Create a DataPool with the data we want to retrieve.
weatherdata = \
DataPool(
series=
[{'options': {
'source': Survey.objects.all()},
'terms': [
'date',
'total_percent_wasted',
]}
])
#Step 2: Create the Chart object
cht = Chart(
datasource = weatherdata,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'date': [
'total_percent_wasted',
]
}}],
chart_options =
{'title': {
'text': 'TotalPercentWasted'},
'xAxis': {
'title': {
'text': 'Date '}}})
#Step 3: Send the chart object to the template.
return render(request, '../templates/survey_chart.html',{'weatherchart': cht})
def get_all_puzzle_user_day():
data = DataPool(series=[{
'options': {
'source': AllPuzzleUserByDay.objects.filter().order_by('day')
},
'terms': ['daystr', 'answer_num', 'phone_num']
}])
cht = Chart(datasource=data,
series_options=[{
'options': {
'type': 'line',
'stacking': False
},
'terms': {
'daystr': ['answer_num', 'phone_num']
}
}],
chart_options={
'title': {
'text': u'使用过趣味答题的用户'
},
'xAxis': {
'title': {
'text': u'日期'
}
},
'colors': ['#2f7ed8', '#AA4643']
})
return cht
def inandout(request):
member = Member.objects.get(user_id=request.user.id)
family_id = member.fam_id
all_family_members = Member.objects.filter(fam_id=family_id)
Earnings = Earning.objects.filter(Member_id__in=all_family_members)
Spendings = Spending.objects.filter(Member_id__in=all_family_members)
debt = DataPool( # what data is being retrieved and where it is being retrieved from
series=[{
'options': {
'source': Earnings
},
'terms': [{
'wage': 'wage',
'year': 'year'
}]
}, {
'options': {
'source': Spendings
},
'terms': [{
'neg_amount': 'neg_amount',
'year2': 'year'
}]
}])
cht = Chart(
datasource=debt,
series_options=[{
'options': {
'type': 'column',
'stacking': False
},
'terms': {
'year': ['wage'],
'year2': ['neg_amount']
}
}],
chart_options={
'title': {
'text': 'Income vs Spending over the Last 10 Years'
},
'xAxis': {
'title': {
'text': 'Income and Spending'
}
},
'yAxis': {
'title': {
'text': 'Amount'
}
},
'legend': {
'enabled': True
},
'credits': {
'enabled': True
}
},
)
return render(request, 'inandout.html', {'chart_list': [cht]})
def errorsdetPie(request):
ds = DataPool(
series=[
{
'options': {
'source': datewisedetailknownerrcounts.objects.values('err_name').annotate(
sumerrcnts=Sum('err_counts'))},
'terms': [
'err_name', 'sumerrcnts'
]
}
]
)
cht = Chart(
datasource=ds,
series_options=[
{
'options': {
'type': 'pie', 'stacking': False,
'options3d': {'enabled': True, 'alpha': 45, 'beta': 0}
}, 'terms': {'err_name': ['sumerrcnts']}
}
],
chart_options={
'title': {'text': 'DetailErrors Report - Pie Chart'}
}
)
return render(request, 'ErrDetailDatapiechart.html',{
'chart_list': cht,
'code': "newcode",
'title': "Detailed Error Pie Chart",
'doc': "This Chart Pie Chart for Detailed Errror Messages sum by Error Counts",
'sidebar_items': "SIdeBarItem"})
def multi_table_same_x(request, title, code, doc, sidebar_items):
"""
Data from multiple models on same chart
----------------------------------------
This example demonstrates data from two different models
``MonthlyWeatherByCity`` and ``MonthlyWeatherSeattle`` on the same chart
and on the same x-axis.
Points to note:
- The `month` in ``terms`` for seattle data is written as
``{'month_seattle': 'month'}`` instead of as just ``'month'`` because
in the latter case it would overwrite the ``'month'`` term from the
other model.
- Notice that the Seattle weather data in the database does not have any
data for August (8) and September (9). Chartit gracefully skips them
and plots the rest of the data points aligned correctly.
"""
#start_code
ds = DataPool(
series=
[{'options': {
'source': MonthlyWeatherByCity.objects.all()},
'terms': [
'month',
'houston_temp',
'boston_temp']},
{'options': {
'source': MonthlyWeatherSeattle.objects.all()},
'terms': [
{'month_seattle': 'month'},
'seattle_temp']}
])
cht = Chart(
datasource = ds,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'month': [
'boston_temp',
'houston_temp'],
'month_seattle': [
'seattle_temp']
}}],
chart_options =
{'title': {
'text': 'Weather by Month (from 2 different tables)'},
'xAxis': {
'title': {
'text': 'Month number'}}})
#end_code
return render_to_response('chart_code.html', {'chart_list': cht,
'code': code,
'title': title,
'doc': doc,
'sidebar_items': sidebar_items})
def test_load_charts_with_single_chart(self):
chart_data = DataPool(series=[{
'options': {
'source': SalesHistory.objects.all()
},
'terms': ['price', 'sale_date']
}])
chart = Chart(datasource=chart_data,
series_options=[{
'options': {
'type': 'column',
'stacking': False
},
'terms': {
'sale_date': ['price']
}
}])
html = chartit.load_charts(chart, 'my_chart')
self.assertIn('<script type="text/javascript">', html)
self.assertIn('"stacking": false', html)
self.assertIn('"data": []', html)
self.assertIn('"type": "column"', html)
self.assertIn('"name": "price"', html)
self.assertIn('{"renderTo": "my_chart"}', html)
self.assertIn('"title": {"text": "Price vs. Sale Date"}', html)
self.assertIn(
'<script src="/static/chartit/js/chartloader.js" type="text/javascript">',
html)
def checkbox_bar_chart(request, question):
"""
@return Barchart for checkbox results
"""
ds = DataPool(series=[{
'options': {
'source': CheckboxResults.objects.filter(
question__exact=question)
},
'terms': ['answer', 'answer_count']
}])
chart = Chart(datasource=ds,
series_options=[{
'options': {
'type': 'column',
'stacking': True
},
'terms': {
'answer': ['answer_count']
}
}],
chart_options={
'title': {
'text': question.text
},
'xAxis': {
'title': {
'text': 'Answers'
}
}
})
return chart
def select_pie_chart(request, question):
"""
@return Piechart object for select results
"""
ds = DataPool(series=[{
'options': {
'source': SelectResults.objects.filter(
question__exact=question)
},
'terms': ['answer', 'answer_count']
}])
chart = Chart(datasource=ds,
series_options=[{
'options': {
'type': 'pie',
'stacking': False
},
'terms': {
'answer': ['answer_count']
}
}],
chart_options={'title': {
'text': question.text
}})
return chart
def get_scatter_chart(pts, x_axis, symbol):
data = \
DataPool(
series=
[{'options': {
'source': pts},
'terms': [
x_axis,
'percent_correct']}
])
#Step 2: Create the Chart object
cht = Chart(datasource=data,
series_options=[{
'options': {
'type': 'scatter'
},
'terms': {
x_axis: ['percent_correct']
}
}],
chart_options={
'title': {
'text':
'{} percent correct by {}'.format(symbol, x_axis)
},
'xAxis': {
'title': {
'text': x_axis
}
}
})
return cht
def citySales(request):
ds = DataPool(series=[{
'options': {
'source':
SalesHistory.objects.only('bookstore__city', 'sale_qty')[:10],
},
'terms': ['bookstore__city__region', 'sale_qty']
}])
cht = Chart(datasource=ds,
series_options=[
{
'options': {
'type': 'bar',
'stacking': True,
'stack': 0,
},
'terms': {
'bookstore__city__region': ['sale_qty']
}
},
],
chart_options={
'title': {
'text': 'Sales reports'
},
'xAxis': {
'title': {
'text': 'City'
},
'legend': {
"reversed": True,
}
}
})
return render_to_response('citySales.html', {'citySales': cht})
def get_puzzle_user_puzzle():
data = DataPool(series=[{
'options': {
'source': PuzzleUserByPuzzle.objects.filter().order_by('puzzle')
},
'terms': ['puzzle', 'new_user', 'old_user']
}])
cht = Chart(datasource=data,
series_options=[{
'options': {
'type': 'column',
'stacking': True
},
'terms': {
'puzzle': ['new_user', 'old_user']
}
}],
chart_options={
'title': {
'text': u'答题人数 by 题目'
},
'xAxis': {
'title': {
'text': u'题目'
}
},
'colors': ['#2f7ed8', '#AA4643']
})
return cht
def setUp(self):
ds = DataPool(series=[{
'options': {
'source':
SalesHistory.objects.filter(
bookstore=BookStore.objects.first())[:10]
},
'terms': ['sale_date', 'sale_qty']
}])
self.chart = Chart(datasource=ds,
series_options=[{
'options': {
'type': 'line',
'stacking': False
},
'terms': {
'sale_date': ['sale_qty']
}
}],
chart_options={
'title': {
'text': 'Sales QTY per day'
},
'xAxis': {
'title': {
'text': 'Sale date'
}
}
})
def get_delta_puzzle_user_day():
data = DataPool(series=[{
'options': {
'source': DeltaPuzzleUserByDay.objects.filter().order_by('day')
},
'terms': ['daystr', 'new_user', 'old_user']
}])
cht = Chart(datasource=data,
series_options=[{
'options': {
'type': 'column',
'stacking': True
},
'terms': {
'daystr': ['new_user', 'old_user']
}
}],
chart_options={
'title': {
'text': u'答题人数 by 日期'
},
'xAxis': {
'title': {
'text': u'日期'
}
},
'colors': ['#2f7ed8', '#AA4643']
})
return cht
def ping_status_view(request):
ds = DataPool(
series =
[{'options':{
'source': PingStatusValue.objects.filter(ip=IP.objects.get(ip='8.8.8.8'))},
'terms': [
('check_time', lambda d: check_time.mktime(d.timetuple())),'ping_value']
}]
)
cht = Chart(
datasource = ds,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'check_time':[
'ping_value']}
}],
chart_options =
{'title':{
'text':'ping_value status'},
'xAxis': {
'title':{
'text':'check date'}}}
)
return render_to_response('highchart2.html',{'pingChart':cht})
def data_chart_cicle(self):
ds = DataPool(
series=[{
'options': {
'source': DataSession.objects.values('summary_status',).annotate(count=Count('summary_status')),
},
'terms': [
'summary_status',
'count',
]
}]
)
cht = Chart(
datasource=ds,
series_options=[{
'options': {
'type': 'pie',
'stacking': False
},
'terms': {
'summary_status': ['count']
}
}],
chart_options={
'title': {
'text': 'Summary status all days'
}
},
)
return cht
def sample(request):
weatherdata = \
customerMarital_List = []
with open(
"/Users/Rick/Desktop/projectFinalFolder/segments/customerMaritalStatusMoreOverTime.yaml"
) as w:
customerMaritalStatusMoreOverTime = yaml.load(w)
w.close()
for row in customerMaritalStatusMoreOverTime.items():
for value in row:
customerMarital_List.append(value)
DataPool(series=[{
'options': {
'source': customerMarital_List
},
'terms': ['Marital status', 'Household', 'Income']
}])
cht = Chart(datasource=weatherdata,
chart_options={'title': {
'text': 'Customer Marital Status'
}})
return render_to_response('none.html', {'weatherchart': cht})
def weatherByCity(request):
ds = DataPool(series=[{
'options': {
'source': Desktop.objects.all()
},
'terms': ['Model_USBheadset', 'Secreen1_Model', 'houston_temp']
}])
cht = Chart(datasource=ds,
series_options=[{
'options': {
'type': 'line'
},
'terms': {
'month': ['boston_temp']
}
}, {
'options': {
'type': 'pie',
'center': [150, 100],
'size': '50%'
},
'terms': {
'month': ['houston_temp']
}
}],
chart_options={
'title': {
'text':
'Weather Data of Boston (line) and Houston (pie)'
}
},
x_sortf_mapf_mts=[(None, False), (None, False)])
return render_to_response('weatherByCity.html', {'weatherByCity': cht})
def chart(request):
hdata = Humidity.objects.filter(log_date__second__lt=1)
humiditydata = \
DataPool(
series =
[{'options' : {
'source': hdata},
'terms': [
'humidity',
'temp',
'log_date']}
])
cht = Chart(
datasource = humiditydata,
series_options =
[{'options':{
'type' : 'line',
'stacking' : False},
'terms':{
'log_date':[
'humidity',
'temp']
}}],
chart_options =
{'title':{
'text':"Humidity and Temp data for my apartment"},
'xAxis':{
'title' :{
'text': "Time"}}})
#return render(request, 'humidity_display/chart.html', cht)
return render_to_response('humidity_display/chart.html',{'humiditychart':cht})
def graficos(request, param):
if len(Marca.objects.filter(nome=param)) != 0:
veiculos = VeiculoConsulta.objects.filter(marca=Marca.objects.get(nome=param)).exclude(preco_min__isnull=True)
if len(Veiculo.objects.filter(categoria=param)) != 0:
veiculos = VeiculoConsulta.objects.filter(categoria=param).exclude(preco_min__isnull=True)
if len(VeiculoConsulta.objects.filter(modelo=param)) != 0:
veiculos = VeiculoConsulta.objects.filter(modelo=param).exclude(preco_min__isnull=True)
ds = DataPool(
series=
[{'options': {
'source': veiculos},
'terms': [
'ano_modelo',
'preco_min',
'preco_max']}
])
cht = Chart(
datasource = ds,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'ano_modelo': [
'preco_min',
'preco_max']
}}],
chart_options =
{'title': {
'text': param},
'xAxis': {
'title': {
'text': 'Ano/Modelo'}}})
return render_to_response('shopcar/veiculos/graficos.html', {'cht': cht})
def graph(request):
graph = \
DataPool(
series=
[{'options':{
'source':GraphData.objects.all()},
'terms':[
'block_name',
'yf',
'awc',
'cc',]}
])
#create the chart object
cht = Chart(datasource=graph,
series_options=[{
'options': {
'type': 'column',
'stacking': False
},
'terms': {
'block_name': ['block_name', 'yf', 'awc', 'cc']
}
}],
chart_options={
'title': {
'text': 'Total complete work'
},
'xAxis': {
'title': {
'text': 'work done'
}
}
})
return render(request, 'Report.html', {'cht': cht})
def generalGraph(request, userId):
# Step 1: Create a DataPool with the data we want to retrieve.
Stockdata = \
DataPool(
series=
[{'options': {
'source': UserProjection.objects.filter(userid=userId)
},
'terms': [
'date',
'investment']}
])
cht = Chart(datasource=Stockdata,
series_options=[{
'options': {
'type': 'area',
'stacking': False
},
'terms': {
'date': ['investment']
}
}],
chart_options={
'title': {
'text': 'Estimate return '
},
'xAxis': {
'title': {
'text': 'Day'
}
}
})
return cht
def weather_chart_view(request):
#Step 1: Create a DataPool with the data we want to retrieve.
weatherdata = \
DataPool(
series=
[{'options': {
'source': MonthlyWeatherByCity.objects.all()},
'terms': [
'month',
'houston_temp',
'boston_temp']}
])
#Step 2: Create the Chart object
cht = Chart(
datasource = weatherdata,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'month': [
'boston_temp',
'houston_temp']
}}],
chart_options =
{'title': {
'text': 'Weather Data of Boston and Houston'},
'xAxis': {
'title': {
'text': 'Month number'}}})
#Step 3: Send the chart object to the template.
return render_to_response({'weatherchart': cht})
def graphHistory(userId):
Stockdata = \
DataPool(
series=
[{'options': {
'source': Performance.objects.filter(userid=userId)
},
'terms': [
'date',
'amount']}
])
cht = Chart(datasource=Stockdata,
series_options=[{
'options': {
'type': 'area',
'stacking': False
},
'terms': {
'date': ['amount']
}
}],
chart_options={
'title': {
'text': 'Historical performance '
},
'xAxis': {
'title': {
'text': 'Day'
}
}
})
return cht
def weather_view(request):
#datapool with the data we want to retrieve
weatherdata = DataPool(series=[{
'options': {
'source': MonthlyWeatherByCity.objects.all()
},
'terms': ['month', 'boston_temp']
}])
#Create chart object
cht = Chart(
datasource=weatherdata,
series_options=[{
'options': {
'type': 'pie',
'stacking': False
},
'terms': {
'month': ['boston_temp']
}
}],
chart_options={'title': {
'text': 'Monthly Temperature of Boston'
}},
x_sortf_mapf_mts=(None, monthname, False))
#Send the chart object to the template
return render_to_response('home/weather.html', {'weatherchart': cht})
