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 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 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 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 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 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 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 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 stats_chart(stats_config_dict, request, stats, entity, protocols=None): terms = stats_config_dict['statistics'][entity]['terms'] title = stats_config_dict['statistics'][entity]['title'] x_title = stats_config_dict['statistics'][entity]['x_title'] y_title = stats_config_dict['statistics'][entity]['y_title'] chart_type = 'column' stacking = True term_names = stats_config_dict['feature_names'] time_format = stats_config_dict['time_format'] statdata = _create_statdata('bar', stats, terms, term_names) series_options = [] for stack in range(len(protocols) if protocols else 1): for term in terms: if not protocols or term.endswith(protocols[stack]): series_options += \ [{'options':{ 'type': chart_type, 'stacking': stacking, 'stack': stack, }, 'terms':{ 'time_%s' %term: [{term_names[term]: {'stack': stack, }}], }}] chart_options = _get_chart_options('bar', title, x_title, y_title) return Chart(datasource=statdata, series_options=series_options, chart_options=chart_options, x_sortf_mapf_mts=(None, lambda i: datetime.fromtimestamp( time.mktime( i.replace(tzinfo=tz.gettz('UTC')).astimezone( tz.tzlocal()).timetuple())).strftime(time_format), False))
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})