Stefan Urbanek Stiivi

## crosstable_example.py
import sqlalchemy
import cubes
import cubes.tutorial.sql as tutorial

DATA = "../examples/hello_world/data.csv"
MODEL = "../examples/hello_world/model.json"

engine = sqlalchemy.create_engine('sqlite:///:memory:')
tutorial.create_table_from_csv(engine,
                      DATA,

## sqlalchemy.py
"""Aliases for SQL/SQLAlchemy objects that are assured to be correctly
type-checked."""

import sqlalchemy


# Engine
# ======
Engine = sqlalchemy.engine.base.Engine
Connection = sqlalchemy.engine.base.Connection

## filestosql.zsh
#!/bin/zsh
#
# Create/update a SQLite3 database file with all files in a given path.
#
# Usage:
#
#    filestosql SQLITE_DATABASE [PATH]
#

DB_FILE=${1}

## apriori_toy.py
from collections import Counter
from itertools import combinations

def distinct_items(transactions, support=None):
    """Returns counted set of distinct items in transactions"""
    counter = Counter()
    for trans in transactions:
        counter.update(trans)

    if support is not None:

## polymerase.sepro
DEF TAG polymerase
DEF SLOT complement
# Template site
DEF SLOT t_site
# Complementary site
DEF SLOT c_site
DEF TAG nucleotide
DEF TAG promoter
DEF TAG free
DEF TAG taken

## brewery_example-aggregate.py
"""
Data Brewery Example

Aggregate a remote CSV file.
"""
import brewery

main = brewery.create_builder()

main.csv_source("https://raw.github.com/Stiivi/cubes/master/examples/hello_world/data.csv")

## dallas_data_brewery-answers.markdown

      
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                Stiivi
                / dallas_data_brewery-answers.markdown
            
            
              Last active
              June 23, 2018 04:15
            
              
                Dallas Data Brewery meetup group answers
              
          
    What tools do you use?


Propreitary Software, R, Python, SQL, Gephi
Tableau; Excel; Access
SPSS in the application of psych statistics and research methods
Tableau, SQL, SPSS, R and other statistical tools.
SSMS, R, SSAS
Python, Matplotlib, Disco...
Proprietary
R, SPSS, SAS, Relational DB


## bubbles_pipeline_join.py
import bubbles

stores = { "target": bubbles.open_store("sql", "sqlite:///") }

p = bubbles.Pipeline(stores=stores)
p.source_object("csv_source", resource="data.csv", infer_fields=True)

# Uncomment this and see the difference in logs - SQL will be used
# p.create("target", "data")

## cubes2.0-goals.md

      
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                Stiivi
                / cubes2.0-goals.md
            
            
              Last active
              August 5, 2017 00:51
            
              
                Cubes 2.0 Goals
              
          
    Cubes 2.0

Hi there. After almost two years of none or very sparse activity due to life and career situation, I’m committing myself back to the Cubes project. It will take some time to ramp-up, but we will eventually get there. I apologize for not meeting expectations lately and  for letting the framework, mailing list and discussions go stale.
I got quite a lot of useful feedback and recommendations from users and people in the domain and that revived my motivation to spend more of my spare time to make Cubes better and modern OLAP toolkit.
Now, let’s move forward. To do any improvements or changes, Cubes needs quite a lot of housekeeping. The whole 2.0 release addresses that. Only when we have consistent, well-defined interface, when we have goals and equally importantly non-goals set, we can start growing Cubes again.
Links:

  
## interfaces.pyi
from typing import Any, Iterator, List, Mapping, Optional

# Note: The value type `Any` should be a DB API 2 value type once defined
# TODO: See #1037

class RowProxy(Mapping[str, Any]): ...

class ResultProxy(Iterator[RowProxy]):
    def keys(self) -> List[str]: ...
    def close(self) -> None: ...
	import sqlalchemy
	import cubes
	import cubes.tutorial.sql as tutorial

	DATA = "../examples/hello_world/data.csv"
	MODEL = "../examples/hello_world/model.json"

	engine = sqlalchemy.create_engine('sqlite:///:memory:')
	tutorial.create_table_from_csv(engine,
	DATA,
	"""Aliases for SQL/SQLAlchemy objects that are assured to be correctly
	type-checked."""

	import sqlalchemy


	# Engine
	# ======
	Engine = sqlalchemy.engine.base.Engine
	Connection = sqlalchemy.engine.base.Connection
	#!/bin/zsh
	#
	# Create/update a SQLite3 database file with all files in a given path.
	#
	# Usage:
	#
	# filestosql SQLITE_DATABASE [PATH]
	#

	DB_FILE=${1}
	from collections import Counter
	from itertools import combinations

	def distinct_items(transactions, support=None):
	"""Returns counted set of distinct items in transactions"""
	counter = Counter()
	for trans in transactions:
	counter.update(trans)

	if support is not None:
	DEF TAG polymerase
	DEF SLOT complement
	# Template site
	DEF SLOT t_site
	# Complementary site
	DEF SLOT c_site
	DEF TAG nucleotide
	DEF TAG promoter
	DEF TAG free
	DEF TAG taken
	"""
	Data Brewery Example

	Aggregate a remote CSV file.
	"""
	import brewery

	main = brewery.create_builder()

	main.csv_source("https://raw.github.com/Stiivi/cubes/master/examples/hello_world/data.csv")
	import bubbles

	stores = { "target": bubbles.open_store("sql", "sqlite:///") }

	p = bubbles.Pipeline(stores=stores)
	p.source_object("csv_source", resource="data.csv", infer_fields=True)

	# Uncomment this and see the difference in logs - SQL will be used
	# p.create("target", "data")
	from typing import Any, Iterator, List, Mapping, Optional

	# Note: The value type `Any` should be a DB API 2 value type once defined
	# TODO: See #1037

	class RowProxy(Mapping[str, Any]): ...

	class ResultProxy(Iterator[RowProxy]):
	def keys(self) -> List[str]: ...
	def close(self) -> None: ...