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
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 get_cubes_workspace(self):
workspace = Workspace()
workspace.register_default_store(
"sql",
url=Connector.get_database_url(),
schema=settings.NIAMOTO_FACT_TABLES_SCHEMA,
dimension_schema=settings.NIAMOTO_DIMENSIONS_SCHEMA,
)
workspace.import_model(self.generate_cubes_model())
return workspace
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)
def setUp(self):
super(SlicerModelTestCase, self).setUp()
ws = Workspace()
ws.register_default_store("sql", url=TEST_DB_URL)
self.ws = ws
self.slicer.cubes_workspace = ws
# Satisfy browser with empty tables
# TODO: replace this once we have data
store = ws.get_store("default")
table = Table("sales", store.metadata)
table.append_column(Column("id", Integer))
table.create()
ws.import_model(self.model_path("model.json"))
ws.import_model(self.model_path("sales_no_date.json"))
def setUp(self):
super(SlicerModelTestCase, self).setUp()
ws = Workspace()
ws.register_default_store("sql", url=TEST_DB_URL)
self.ws = ws
self.slicer.cubes_workspace = ws
# Satisfy browser with empty tables
# TODO: replace this once we have data
store = ws.get_store("default")
table = Table("sales", store.metadata)
table.append_column(Column("id", Integer))
table.create()
ws.import_model(self.model_path("model.json"))
ws.import_model(self.model_path("sales_no_date.json"))
def create_workspace(self, store=None, model=None):
"""Create shared workspace. Add default store specified in `store` as
a dictionary and `model` which can be a filename relative to
``tests/models`` or a moel dictionary. If no store is provided but
class has an engine or `sql_engine` set, then the existing engine will
be used as the default SQL store."""
workspace = Workspace()
if store:
store = dict(store)
store_type = store.pop("type", "sql")
workspace.register_default_store(store_type, **store)
elif self.engine:
workspace.register_default_store("sql", engine=self.engine)
if model:
if isinstance(model, compat.string_type):
model = self.model_path(model)
workspace.import_model(model)
return workspace
def create_workspace(self, store=None, model=None):
"""Create shared workspace. Add default store specified in `store` as
a dictionary and `model` which can be a filename relative to
``tests/models`` or a moel dictionary. If no store is provided but
class has an engine or `sql_engine` set, then the existing engine will
be used as the default SQL store."""
workspace = Workspace()
if store:
store = dict(store)
store_type = store.pop("type", "sql")
workspace.register_default_store(store_type, **store)
elif self.engine:
workspace.register_default_store("sql", engine=self.engine)
if model:
if isinstance(model, compat.string_type):
model = self.model_path(model)
workspace.import_model(model)
return workspace
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)
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"))
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
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 = []
"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)
# ("category", "string"),
# ("category_label", "string"),
# ("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)
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
"""
"IBRD_Balance_Sheet__FY2010.csv",
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"])