def __init__(self):
print("Creating Workspace and model")
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("movie_ratings_model.json")
browser = workspace.browser("ratings")
self.browser = browser
def create_browser():
#workspace = Workspace(config="slicer.ini")
print("Creating Workspace and model")
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("movie_ratings_model.json")
browser = workspace.browser("ratings")
return browser
class SlicerTestCase(unittest.TestCase):
def setUp(self):
self.w = Workspace()
self.w.add_slicer("myslicer",
"http://localhost:5010",
username=os.environ.get("SLICER_USERNAME"),
password=os.environ.get("SLICER_PASSWORD"))
self.cube_list = self.w.list_cubes()
def first_date_dim(self, cube):
for d in cube.dimensions:
if (d.info.get('is_date')):
return d
raise BrowserError("No date dimension in cube %s" % cube.name)
def test_basic(self):
for c in self.cube_list:
if c.get('category') is not None and 'Mix' in c.get(
'category', ''):
continue
print("Doing %s..." % c.get('name')),
cube = self.w.cube(c.get('name'))
date_dim = self.first_date_dim(cube)
cut = cubes.browser.RangeCut(date_dim, [2013, 9, 25], None)
cell = cubes.browser.Cell(cube, [cut])
drill_levels = [
l for l in date_dim.hierarchy().levels
if l.name in ('day', 'date')
]
if not drill_levels:
print "Skipping cube %s with no day/date drilldown available." % c.get(
'name')
continue
drill = cubes.browser.Drilldown(
[(date_dim, None, date_dim.level(drill_levels[0]))], cell)
b = self.w.browser(cube)
try:
attr_dim = cube.dimension("attr")
split = cubes.browser.PointCut(attr_dim, ['paid', 'pnb'])
except:
split = None
try:
kw = {}
if cube.aggregates:
kw['aggregates'] = [cube.aggregates[0]]
elif cube.measures:
kw['measures'] = [cube.measures[0]]
else:
raise ValueError(
"Cube has neither aggregates nor measures")
result = b.aggregate(cell, drilldown=drill, split=split, **kw)
print result.cells
except:
traceback.print_exc()
def main():
settings = ConfigParser()
settings.read("slicer.ini")
# Creating workspace
workspace = Workspace(config=settings)
# Creating browser so that we can do actual aggregations and other data queries for the cube
browser = workspace.browser('death_fact')
cube = browser.cube
# Pass browser in data_aggregate - this function will do all aggregations and queries
compare_kill_distances(browser, cube)
count_match_deaths(browser, cube)
def analiza_temperatura(request):
if request.GET.get("czy_analiza", None):
print("Super dokonaj analizy!")
# Stwórz Workspace z pliku konfiguracyjnego:
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///db.sqlite3")
# Ładuj model:
workspace.import_model("model.json")
# Twórz obiekt browser:
browser = workspace.browser("analiza_temperatura")
# Twórz wyniki agregacji:
if request.GET.get("wiek_pacjenta", None):
res = browser.aggregate(drilldown=["wiek_pacjenta"])
po_czym = "wiek_pacjenta"
elif request.GET.get("data_pomiaru", None):
res = browser.aggregate(drilldown=["data_pomiaru"])
po_czym = "data_pomiaru"
elif request.GET.get("kontynent", None):
res = browser.aggregate(drilldown=["kontynent"])
po_czym = "kontynent"
elif request.GET.get("kraj", None):
res = browser.aggregate(drilldown=["kraj"])
po_czym = "kraj"
elif request.GET.get("obszar", None):
res = browser.aggregate(drilldown=["obszar"])
po_czym = "obszar"
# Wyświetl podsumowanie całkowite i dla grupy:
lista = []
print(res.summary)
for r in res:
print(type(r))
print(r)
lista.append(r)
# Twórz kontekst:
print(type(res))
cont = {"agre_list": lista, "czy_analiza": True, "po_czym": po_czym}
else:
cont = {}
print("Lipa")
return render(request, "aplikacja/analiza/temperatura.html", cont)
class SlicerTestCase(unittest.TestCase):
def setUp(self):
self.w = Workspace()
self.w.add_slicer("myslicer", "http://localhost:5010",
username=os.environ.get("SLICER_USERNAME"),
password=os.environ.get("SLICER_PASSWORD"))
self.cube_list = self.w.list_cubes()
def first_date_dim(self, cube):
for d in cube.dimensions:
if ( d.info.get('is_date') ):
return d
raise BrowserError("No date dimension in cube %s" % cube.name)
def test_basic(self):
for c in self.cube_list:
if c.get('category') is not None and 'Mix' in c.get('category', ''):
continue
cube = self.w.cube(c.get('name'))
date_dim = self.first_date_dim(cube)
cut = cubes.browser.RangeCut(date_dim, [ 2013, 9, 25 ], None)
cell = cubes.browser.Cell(cube, [ cut ])
drill_levels = [ l for l in date_dim.hierarchy().levels if l.name in ('day', 'date') ]
if not drill_levels:
continue
drill = cubes.browser.Drilldown([(date_dim, None, date_dim.level(drill_levels[0]))], cell)
b = self.w.browser(cube)
try:
attr_dim = cube.dimension("attr")
split = cubes.browser.PointCut(attr_dim, ['paid', 'pnb'])
except:
split = None
try:
kw = {}
if cube.aggregates:
kw['aggregates'] = [cube.aggregates[0]]
elif cube.measures:
kw['measures'] = [ cube.measures[0] ]
else:
raise ValueError("Cube has neither aggregates nor measures")
result = b.aggregate(cell, drilldown=drill, split=split, **kw)
except:
traceback.print_exc()
class SlicerTestCase(unittest.TestCase):
def setUp(self):
self.w = Workspace()
self.w.add_slicer("myslicer", "http://localhost:5010")
self.cube_list = self.w.list_cubes()
def first_date_dim(self, cube):
for d in cube.dimensions:
if ( d.info.get('is_date') ):
return d
raise BrowserError("No date dimension in cube %s" % cube.name)
def test_basic(self):
for c in self.cube_list:
if c.get('category') is not None and 'Mix' in c.get('category', ''):
continue
print ("Doing %s..." % c.get('name')),
cube = self.w.cube(c.get('name'))
date_dim = self.first_date_dim(cube)
cut = cubes.browser.RangeCut(date_dim, [ 2013, 9, 25 ], None)
cell = cubes.browser.Cell(cube, [ cut ])
drill = cubes.browser.Drilldown([(date_dim, None, date_dim.level('day'))], cell)
b = self.w.browser(cube)
try:
attr_dim = cube.dimension("attr")
split = cubes.browser.PointCut(attr_dim, ['paid', 'pnb'])
except:
split = None
try:
kw = {}
if cube.aggregates:
kw['aggregates'] = [cube.aggregates[0]]
elif cube.measures:
kw['measures'] = [ cube.measures[0] ]
else:
raise ValueError("Cube has neither aggregates nor measures")
result = b.aggregate(cell, drilldown=drill, split=split, **kw)
print result.cells
except:
import sys
print sys.exc_info()
from __future__ import print_function
from cubes import Workspace, Cell, PointCut
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data_sqlite/f1.sqlite")
workspace.import_model("models/model.json")
browser = workspace.browser("qualifying")
cut1 = PointCut("drivers", [])
cut2 = PointCut("races", [])
cell = Cell(browser.cube, cuts=[cut1, cut2])
result = browser.aggregate(cell, drilldown=["drivers", "races"])
list_res = [row for row in result]
def filter_racer(data, name, year):
temp = list(
filter(
lambda x: x['drivers.surname'] == name and x['races.year'] == year,
data))
return sorted(temp, key=lambda x: x['position_min'])
for line in filter_racer(list_res, 'Hamilton', 2009):
print(line)
class TweetCube:
def __init__(self, concept):
self.createCube()
self.concept = concept
def createCube(self):
self.workspace = Workspace()
self.workspace.register_default_store("sql",
url="mysql://root:@localhost/datawarehouse")
model = cubes.read_model_metadata_bundle("../CubeModelisation/model/")
self.workspace.import_model(model)
self.browserTweet = self.workspace.browser("tweet")
def getPieChartSource(self):
cube = self.workspace.cube("tweet")
cube.browser = self.browserTweet
cut = [PointCut("concept", [self.concept])]
cell = Cell(cube, cut)
result = self.browserTweet.aggregate(cell, drilldown=["location","source"],aggregates=["numberOfTweets_sum"])
output = defaultdict(lambda: defaultdict())
for row in result.table_rows("location"):
continent = row.record['location.continentName']
source = row.record['source.sourceName']
output[continent][source] = row.record['numberOfTweets_sum']
temp = {'continentName': '',
'sources': [{'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''},
{'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''}]}
print("output ",output)
i = 0
data = []
continentsList = ['Asia','Africa','Australia','Europe','North America','South America']
for continent in continentsList:
temp['continentName'] = continent
if output[continent]:
temp['sources'][i]['source'] = "iPhone"
temp['sources'][i]['numberOfTweets'] = output[continent].get('iPhone', 0)
i += 1
temp['sources'][i]['source'] = "Android"
temp['sources'][i]['numberOfTweets'] = output[continent].get('Android', 0)
i += 1
temp['sources'][i]['source'] = "Web"
temp['sources'][i]['numberOfTweets'] = output[continent].get('Web', 0)
i += 1
temp['sources'][i]['source'] = "Unknown"
temp['sources'][i]['numberOfTweets'] = output[continent].get('Unknown', 0)
else:
temp['sources'][i]['source'] = "iPhone"
temp['sources'][i]['numberOfTweets'] = 0
i += 1
temp['sources'][i]['source'] = "Android"
temp['sources'][i]['numberOfTweets'] = 0
i += 1
temp['sources'][i]['source'] = "Web"
temp['sources'][i]['numberOfTweets'] = 0
i += 1
temp['sources'][i]['source'] = "Unknown"
temp['sources'][i]['numberOfTweets'] = 0
i = 0
data.append(temp)
temp = {'continentName': '',
'sources': [{'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''},
{'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''}]}
return data
def getBarChartRaceByLanguageAndDate(self):
cube = self.workspace.cube("tweet")
cube.browser = self.browserTweet
cut = [PointCut("concept", [self.concept])]
cell = Cell(cube, cut)
result = self.browserTweet.aggregate(cell, drilldown=["time:day", "language"],
aggregates=["numberOfTweets_sum"])
output = []
for row in result.table_rows("time"):
output.append(row.record)
data = defaultdict(lambda: defaultdict(lambda: defaultdict()))
languagesList = []
for row in output:
date = row['time.day'] + "/" + row['time.month'] + "/" + row['time.year']
language = row['language.languageName']
languagesList.append(language)
# creating data structure containing all languages
data[date][language]['numberOfTweets'] = row['numberOfTweets_sum']
#GET LIST OF LANGUAGES FROM FILE
import pickle
with open('../Docs/languagesStructure.pickle', 'rb') as file:
languagesList = pickle.load(file)
print(len(languagesList))
element = {'date': '', 'languagesList': []}
dataList = []
for date in data:
element['date'] = date
element['languagesList'] = []
print(len(languagesList))
for language in languagesList:
if language in data[date]:
element['languagesList'].append({'language':language,'numberOfTweets':data[date][language]['numberOfTweets']})
else:
element['languagesList'].append({'language':language,'numberOfTweets':0})
dataList.append(element)
return dataList
def getBarChartRaceBySentimentAndDate(self):
cube = self.workspace.cube("tweet")
cube.browser = self.browserTweet
cut = [PointCut("concept", [self.concept])]
cell = Cell(cube, cut)
result = self.browserTweet.aggregate(cell, drilldown=["time:day", "sentiment"],
aggregates=["numberOfTweets_sum"])
output = []
for row in result.table_rows("time"):
output.append(row.record)
data = defaultdict(lambda: defaultdict(lambda: defaultdict()))
for row in output:
date = row['time.day'] + "/" + row['time.month'] + "/" + row['time.year']
sentiment = row['sentiment.sentimentLabel']
data[date][sentiment]['numberOfTweets'] = row['numberOfTweets_sum']
dataList = []
element = {'date': '', 'sentimentsList': []}
for date in data:
element['date'] = date
sentimentElement = {'sentiment': '', 'numberOfTweets': 0}
mySentimentsList = []
for sentiment in data[date]:
sentimentElement['sentiment'] = sentiment
sentimentElement['numberOfTweets'] = data[date][sentiment]['numberOfTweets']
mySentimentsList.append(sentimentElement)
sentimentElement = {'sentiment': '', 'numberOfTweets': 0}
element['sentimentsList'] = mySentimentsList
dataList.append(element)
element = {'date': '', 'sentimentsList': []}
return dataList
from cubes import Workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///webshop.sqlite")
workspace.import_model("model.json")
browser = workspace.browser("sales")
result = browser.aggregate()
print("sum = ", result.summary["quantity_sum"])
result = browser.aggregate(drilldown=["date_sale:week", "product"])
for record in result:
print(record)
from __future__ import print_function
from cubes import Workspace, Cell, PointCut
# 1. Create a workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("model.json")
# 2. Get a browser
browser = workspace.browser("airquality")
# 3. Play with aggregates
result = browser.aggregate()
print("Total\n" "----------------------")
print("Record count : %8d" % result.summary["record_count"])
print("Total amount : %8d" % result.summary["average_mean"])
#
# 4. Drill-down through a dimension
#
"""
print("\n"
"Drill Down by Category (top-level Item hierarchy)\n"
"==================================================")
#
result = browser.aggregate(drilldown=["location"])
#
print(("%-20s%10s%10s%10s\n"+"-"*50) % ("Category", "Count", "Total", "Double"))
#
class ValidateDjangoOrmBrowser(TransactionTestCase):
fixtures = ['irbdbalance.json']
maxDiff = None
def setUp(self):
super(ValidateDjangoOrmBrowser, self).setUp()
self.workspace = Workspace(
cubes_root=settings.SLICER_MODELS_DIR,
config=path.join(settings.SLICER_MODELS_DIR, 'slicer-django_backend.ini'),
)
self.browser = self.workspace.browser("irbd_balance")
def test_simple_aggregation(self):
result = self.browser.aggregate()
self.assertEquals(result.summary, {
'record_count': 62,
'amount_sum': 1116860,
})
def test_simple_drilldown(self):
result = self.browser.aggregate(drilldown=["item"])
values = [
(row.label, row.record["record_count"], row.record["amount_sum"])
for row in result.table_rows("item")
]
six.assertCountEqual(self, values, [
(u'Assets', 32, 558430), (u'Equity', 8, 77592), (u'Liabilities', 22, 480838)
])
def test_simple_slice(self):
cut = PointCut("item", ["e"])
cell = Cell(self.browser.cube, cuts=[cut])
result = self.browser.aggregate(cell, drilldown=["item"])
values = [
(row.label, row.record["record_count"], row.record["amount_sum"])
for row in result.table_rows("item")
]
six.assertCountEqual(self, values, [
(u'Retained Earnings', 2, 58663),
(u'Deferred Amounts', 2, 672),
(u'Capital Stock', 2, 22983),
(u'Other', 2, -4726)
])
def test_facts_list(self):
facts = self.browser.facts(page=1, page_size=10, order=['item.line_item', 'amount'])
six.assertCountEqual(self, facts, [
{
'amount': 2707,
'item.category': u'l',
'item.subcategory': u'ol',
'item.subcategory_label': u'Other Liabilities',
'item.category_label': u'Liabilities',
'id': 54,
'year': 2009,
'item.line_item': u'Accounts payable and misc liabilities'
}, {
'amount': 2793,
'item.category': u'l',
'item.subcategory': u'ol',
'item.subcategory_label': u'Other Liabilities',
'item.category_label': u'Liabilities',
'id': 53,
'year': 2010,
'item.line_item': u'Accounts payable and misc liabilities'
}, {
'amount': 1190,
'item.category': u'l',
'item.subcategory': u'ol',
'item.subcategory_label': u'Other Liabilities',
'item.category_label': u'Liabilities',
'id': 49,
'year': 2010,
'item.line_item': u'Accrued charges on borrowings'
}, {
'amount': 1495,
'item.category': u'l',
'item.subcategory': u'ol',
'item.subcategory_label': u'Other Liabilities',
'item.category_label': u'Liabilities',
'id': 50,
'year': 2009,
'item.line_item': u'Accrued charges on borrowings'
}, {
'amount': 764,
'item.category': u'a',
'item.subcategory': u'orcv',
'item.subcategory_label': u'Other Receivables',
'item.category_label': u'Assets',
'id': 23,
'year': 2010,
'item.line_item': u'Accrued income on loans'
}, {
'amount': 889,
'item.category': u'a',
'item.subcategory': u'orcv',
'item.subcategory_label': u'Other Receivables',
'item.category_label': u'Assets',
'id': 24,
'year': 2009,
'item.line_item': u'Accrued income on loans'
}, {
'amount': -3043,
'item.category': u'e',
'item.subcategory': u'oe',
'item.subcategory_label': u'Other',
'item.category_label': u'Equity',
'id': 61,
'year': 2010,
'item.line_item': u'Accumulated Other Comorehensive Loss'
}, {
'amount': -1683,
'item.category': u'e',
'item.subcategory': u'oe',
'item.subcategory_label': u'Other',
'item.category_label': u'Equity',
'id': 62,
'year': 2009,
'item.line_item': u'Accumulated Other Comorehensive Loss'
}, {
'amount': 110040,
'item.category': u'l',
'item.subcategory': u'b',
'item.subcategory_label': u'Borrowings',
'item.category_label': u'Liabilities',
'id': 34,
'year': 2009,
'item.line_item': u'All'
}, {
'amount': 128577,
'item.category': u'l',
'item.subcategory': u'b',
'item.subcategory_label': u'Borrowings',
'item.category_label': u'Liabilities',
'id': 33,
'year': 2010,
'item.line_item': u'All'
},
])
def test_multiple_drilldowns(self):
# "?drilldown=year&drilldown=item&aggregates=amount_sum"
result = self.browser.aggregate(drilldown=["year", "item"], aggregates=["amount_sum"])
values = [
(row.label, row.record)
for row in result.table_rows("item")
]
six.assertCountEqual(self, values, [
('Assets', {'amount_sum': 275420, 'item.category': 'a', 'item.category_label': 'Assets', 'year': 2009}),
('Equity', {'amount_sum': 40037, 'item.category': 'e', 'item.category_label': 'Equity', 'year': 2009}),
('Liabilities', {'amount_sum': 235383, 'item.category': 'l', 'item.category_label': 'Liabilities', 'year': 2009}),
('Assets', {'amount_sum': 283010, 'item.category': 'a', 'item.category_label': 'Assets', 'year': 2010}),
('Equity', {'amount_sum': 37555, 'item.category': 'e', 'item.category_label': 'Equity', 'year': 2010}),
('Liabilities', {'amount_sum': 245455, 'item.category': 'l', 'item.category_label': 'Liabilities', 'year': 2010})
])
@skip('SetCuts are not yet supported by this backend')
def test_multiple_drilldowns_with_set_cuts(self):
# "?drilldown=year&drilldown=item&aggregates=amount_sum&cut=item.category:a;e;l|year:2009;2010"
cuts = [
SetCut("item", [['a'], ['e'], ['l']]),
SetCut("year", [[2009], [2010]]),
]
cell = Cell(self.browser.cube, cuts=cuts)
result = self.browser.aggregate(cell, drilldown=["year", "item"], aggregates=["amount_sum"])
values = [
(row.label, row.record)
for row in result.table_rows("item")
]
six.assertCountEqual(self, values, [
('Assets', {'amount_sum': 275420, 'item.category': 'a', 'item.category_label': 'Assets', 'year': 2009}),
('Equity', {'amount_sum': 40037, 'item.category': 'e', 'item.category_label': 'Equity', 'year': 2009}),
('Liabilities', {'amount_sum': 235383, 'item.category': 'l', 'item.category_label': 'Liabilities', 'year': 2009}),
('Assets', {'amount_sum': 283010, 'item.category': 'a', 'item.category_label': 'Assets', 'year': 2010}),
('Equity', {'amount_sum': 37555, 'item.category': 'e', 'item.category_label': 'Equity', 'year': 2010}),
('Liabilities', {'amount_sum': 245455, 'item.category': 'l', 'item.category_label': 'Liabilities', 'year': 2010})
])
from cubes import Workspace, PointCut, Cell, Cube, cuts_from_string
CUBE_NAME = "ft_billing"
#analytical workspace
workspace = Workspace()
workspace.register_default_store(
"sql",
url="postgresql://venusbailey@localhost:5432/notify_reports",
schema="public")
#Model
workspace.import_model("./model.json")
# Aggregations
print('\n\n...test aggregation...')
browser = workspace.browser(CUBE_NAME)
cube = browser.cube
result = browser.aggregate()
print('Sum of record counts = ' + str(result.summary["record_count"]))
print('Amount = ' + str(result.summary["amount_sum"]))
# Drill down on year
print('\n\n....drilling down on year...')
result = browser.aggregate(drilldown=["dm_datetime:year"])
for record in result:
print(record)
# Drill down on service
print('\n\n...drilling down on services...')
result = browser.aggregate(drilldown=["dm_service:service_name"])
for record in result:
from cubes import Workspace, Cell, PointCut
# 1. Create a workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.add_model("model.json")
# 2. Get a browser
browser = workspace.browser("irbd_balance")
# 3. Play with aggregates
result = browser.aggregate()
print "Total\n" \
"----------------------"
print "Record count: %8d" % result.summary["record_count"]
print "Total amount: %8d" % result.summary["amount_sum"]
#
# 4. Drill-down through a dimension
#
print "\n" \
"Drill Down by Category (top-level Item hierarchy)\n" \
"================================================="
#
result = browser.aggregate(drilldown=["item"])
#
print ("%-20s%10s%10s\n"+"-"*40) % ("Category", "Count", "Total")
#
from cubes import Workspace, Cell
# 1. Create a workspace
workspace = Workspace()
workspace.register_default_store("sql",
url="sqlite:///vvo_data.sqlite",
dimension_prefix="dm_",
fact_prefix="ft_")
workspace.import_model("procurements.cubesmodel")
# 2. Get a browser
browser = workspace.browser("contracts")
cube = browser.cube
# workspace = cubes.create_workspace("sql", model, url="postgres://localhost/ep2012",
# schema="vvo",
# dimension_prefix="dm_",
# fact_prefix="ft_",
# denormalized_view_schema="views",
# use_denormalization=False,
# denormalized_view_prefix="mft_")
def drilldown(cell, dimension):
"""Drill-down and aggregate recursively through als levels of `dimension`.
This function is like recursively traversing directories on a file system
and aggregating the file sizes, for example.
* `cell` - cube cell to drill-down
* `dimension` - dimension to be traversed through all levels
"""
"label": "Nombre curso",
}, {
"name": "creditos",
"label": "Numero de creditos",
}]
}, {
"name":
"clase",
"label":
"Clase",
"attributes": [{
"name": "curso_materia",
"label": "Materia"
}, {
"name": "seccion_id",
"label": "Seccion",
}, {
"name": "salon_senhalizacion",
"label": "Salon",
}, {
"name": "franja_id",
"label": "Franja",
}]
}]
}
workspace.import_model(dicc)
browser = workspace.browser("clase")
result = browser.aggregate(drilldown=['franja'])
print(result)
from __future__ import print_function
from cubes import Workspace, Cell, PointCut
# 1. Create a workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("model.json")
# 2. Get a browser
browser = workspace.browser("quake_events")
# 3. Play with aggregates
result = browser.aggregate()
print("Total\n"
"----------------------")
print("Record count : %8d" % result.summary["record_count"])
print("Total amount : %8d" % result.summary["average_mean"])
#
# 4. Drill-down through a dimension
#
"""
print("\n"
"Drill Down by Category (top-level Item hierarchy)\n"
"==================================================")
#
result = browser.aggregate(drilldown=["location"])
#
print(("%-20s%10s%10s%10s\n"+"-"*50) % ("Category", "Count", "Total", "Double"))
("hours_fri_open", "string"),
("hours_fri_close", "string"),
("hours_sat_open", "string"),
("hours_sat_close", "string")],
create_id=False)
from cubes import Workspace
from cubes import PointCut
from cubes import Cell
from prettytable import PrettyTable
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("model.json")
browser = workspace.browser("restaurants")
cube = workspace.cube("restaurants")
'''
# aggregation by zipcode
print("ZipCode, # Restaurants")
result = browser.aggregate(drilldown=["location:zipcode"])
for record in result:
print(record['location.zipcode'], ",", record['num_restaurants'])
print()
'''
# aggregation by restaurant price
result = browser.aggregate(drilldown=["price"])
t = PrettyTable([
'Price', '# Restaurants', 'Avg. Rating', '# Reviews/Restaurant',
'Weekend Open Hours', 'Weekend Close Hours', 'Weekday Open Hours',
from __future__ import print_function
from cubes import Workspace, Cell, PointCut
# 1. Create a workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("model.json")
# 2. Get a browser
browser = workspace.browser("quake_events")
# 3. Play with aggregates
result = browser.aggregate()
print("Total\n" "----------------------")
print("Record count : %8d" % result.summary["record_count"])
print("Total amount : %8d" % result.summary["average_mean"])
#
# 4. Drill-down through a dimension
#
"""
print("\n"
"Drill Down by Category (top-level Item hierarchy)\n"
"==================================================")
#
result = browser.aggregate(drilldown=["location"])
#
print(("%-20s%10s%10s%10s\n"+"-"*50) % ("Category", "Count", "Total", "Double"))
#
# ("subcategory", "string"),
# ("subcategory_label", "string"),
# ("line_item", "string"),
# ("year", "integer"),
# ("amount", "integer")],
# create_id=True
# )
from cubes import Workspace, PointCut, Cell
workspace = Workspace()
workspace.register_default_store(
"sql", url="postgresql://*****:*****@localhost/willowood")
workspace.import_model("SalesTable.json")
browser = workspace.browser("salestable")
result = browser.aggregate()
print(result.summary["record_count"])
print(result.summary["Qty"])
print(result.summary["Value"])
cube = browser.cube
# result = browser.aggregate(drilldown=["billing_date"])
#
# for record in result:
# print(' record: ', record)
# TryingOut Yesterday, MTD , YTD, LYTD
cuts = [
from __future__ import print_function
from cubes import Workspace, Cell, PointCut
# 1. Create a workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("model.json")
# 2. Get a browser
browser = workspace.browser("irbd_balance")
# 3. Play with aggregates
result = browser.aggregate()
print("Total\n"
"----------------------")
print("Record count : %8d" % result.summary["record_count"])
print("Total amount : %8d" % result.summary["amount_sum"])
print("Double amount: %8d" % result.summary["double_amount_sum"])
#
# 4. Drill-down through a dimension
#
print("\n"
"Drill Down by Category (top-level Item hierarchy)\n"
"==================================================")
#
result = browser.aggregate(drilldown=["item"])
#
count += 1
if (count >= level):
count = 1
break
new_cell = cell.drilldown(dimension, row.key)
drilldown(new_cell, dimension, level)
# 1. Creating a workspace
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///restaurant.sqlite")
workspace.import_model("model.json")
# 2. Getting a browser
cube = workspace.cube("restaurant_details")
browser = workspace.browser(cube)
dimension = cube.dimension("location")
# Rolling up to State
print("\n"
"Roll up to state\n"
"================")
cell = Cell(browser.cube)
rollup(cell, "location")
# Drilling down into the cities of each state
print("\n"
"Drill down by state\n"
from flask import Flask, request
from cubes import Workspace
app = Flask(__name__)
app.debug = True
workspace = Workspace("slicer.ini")
rbrowser = workspace.browser("rating")
tbrowser = workspace.browser("thesis")
def determine_title(url):
if (url == "/rating"):
return "Рейтинг. Сводная таблица"
if (url == "/rating/group"):
return "Рейтинг по группам"
if (url == "/rating/group/student"):
return "Рейтинг по студентам"
if (url == "/rating/course"):
return "Рейтинг по дисциплинам"
if (url == "/rating/course/module"):
return "Рейтинг по модулям"
if (url == "/thesis"):
return "Дипломы. Сводная таблица"
if (url == "/thesis/group"):
return "Дипломы по группам"
if (url == "/thesis/group/student"):
return "Дипломы по студентам"
if (url == "/thesis/protected_at"):
return "Дипломы по дате защиты"
if (url == "/thesis/teacher"):
return "Дипломы по преподавателям"
from cubes import Workspace
print("Python Cubes - Test3")
# Stwórz Workspace z pliku konfiguracyjnego:
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///baza.sqlite")
# Ładuj model:
workspace.import_model("model.json")
# Twórz obiekt browser:
browser = workspace.browser("WIDOK")
# Twórz wyniki agregacji, agreguj po GRUPA:
res = browser.aggregate(drilldown=["GRUPA"])
res2 = browser.aggregate(drilldown=["ROK"])
res3 = browser.aggregate(drilldown=["PLEC"])
# Wyświetl podsumowanie całkowite i dla grupy:
print(res.summary)
for r in res:
print(r)
print("Ala ma kota!")
print(res2.summary)
for r in res2:
print(r)
print("Ala ma kota!")
from cubes import Workspace
print("Python Cubes - Test2")
# Stwórz Workspace z pliku konfiguracyjnego:
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///baza.sqlite")
# Ładuj model:
workspace.import_model("model.json")
# Twórz obiekt browser:
browser = workspace.browser("POMIAR")
# Twórz wyniki agregacji, agreguj po GRUPA:
res = browser.aggregate(drilldown=["GRUPA"])
res2 = browser.aggregate(drilldown=["ROK"])
# Wyświetl podsumowanie całkowite i dla grupy:
print(res.summary)
for r in res:
print(r)
print("Ala ma kota!")
print(res2.summary)
for r in res2:
print(r)
import os.path
BASE = os.path.dirname(os.path.abspath(__file__))
from cubes import Workspace, Cell, PointCut
from datetime import datetime, timedelta
import sys
import json
from django.http import JsonResponse
#-------------------------------------------------------------#
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///"+os.path.join(BASE,"myData.sqlite"))
workspace.import_model(os.path.join(BASE,"modal.json"))
browser = workspace.browser("FB_POSTS_DATA")
#-------------------------------------------------------------#
d = datetime.now() - timedelta(days=1)
cut = PointCut("pub_date", [d.year, d.month, d.day-6], None)
cell = Cell(browser.cube, cuts = [cut])
#-------------------------------------------------------------#
def get_post_by_shares():
result = browser.aggregate(cell, drilldown=["name"])
shares = []
from cubes import Workspace
print("Python Cubes Test!")
# Stwórz Workspace z pliku konfiguracyjnego:
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///db.sqlite3")
# Ładuj model:
workspace.import_model("model.json")
# Twórz obiekt browser:
browser = workspace.browser("analiza_temperatura")
# Twórz wyniki agregacji, agreguj po GRUPA:
res = browser.aggregate(drilldown=["kontynent"])
# Wyświetl podsumowanie całkowite i dla grupy:
print(res.summary)
for r in res:
print(r)
table_name="ibrd_balance",
fields=[("category", "string"),
("category_label", "string"),
("subcategory", "string"),
("subcategory_label", "string"),
("line_item", "string"), ("year", "integer"),
("amount", "integer")],
create_id=True)
#workspace = Workspace(config="slicer.ini")
print("Creating Workspace and model")
workspace = Workspace()
workspace.register_default_store("sql", url="sqlite:///data.sqlite")
workspace.import_model("tutorial_model.json")
browser = workspace.browser("ibrd_balance")
print()
result = browser.aggregate()
print("General aggregations")
print("Record count: %s" % result.summary["record_count"])
print("Amount sum: %s" % result.summary["amount_sum"])
print()
print("Drilldown by year")
result = browser.aggregate(drilldown=["year"])
for record in result:
print(record)
print("Drilldown by item")
result = browser.aggregate(drilldown=["item"])
for record in result:
