atoti package¶
Subpackages¶
- atoti.config package
- atoti.query package
- Submodules
- atoti.query.auth module
- atoti.query.basic_auth module
- atoti.query.cube module
- atoti.query.cubes module
- atoti.query.hierarchies module
- atoti.query.hierarchy module
- atoti.query.level module
- atoti.query.levels module
- atoti.query.measure module
- atoti.query.measures module
- atoti.query.session module
- Module contents
- atoti.scope package
Submodules¶
atoti.agg module¶
Aggregation functions.
-
atoti.agg.
count_distinct
(column, *, scope=None)¶ Return a measure equal to the distinct count aggregation of the passed measure.
It represents the number of distinct values among the aggregated elements.
- Parameters
- Return type
-
atoti.agg.
long
(measure, *, scope=None)¶ Return a measure equal to the sum of the positive values of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
max
(measure, *, scope=None)¶ Return a measure equal to the maximum of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
max_member
(measure, level)¶ Return a measure equal to the member maximizing the passed measure on the given level.
When multiple members maximize the passed measure, the first one (according to the comparator of the given level) is returned.
Example
Considering this dataset:
Continent
City
Price
Europe
Paris
200.0
Europe
Berlin
150.0
Europe
London
240.0
North America
New York
270.0
And this measure:
m["City with max price"] = atoti.agg.max_member(m["Price"], lvl["Country"])
Then, at the given level, the measure is equal to the current member of the City level:
cube.query(m["Price"], m["City with max price"], levels=lvl["Country"])
City
Price
City with max price
Paris
200.0
Paris
Berlin
150.0
Berlin
London
240.0
London
New York
270.0
New York
At a level above it, the measure is equal to the city of each continent with the maximum price:
cube.query(m["City with min price"], levels=lvl["Continent"])
Continent
City with max price
Europe
London
North America
New York
At the top level, the measure is equal to the city with the maxium price across all continents:
cube.query(m["City with max price"])
City with max Price
New York
-
atoti.agg.
mean
(measure, *, scope=None)¶ Return a measure equal to the mean of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
median
(measure, *, scope=None)¶ Return a measure equal to the median of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
min
(measure, *, scope=None)¶ Return a measure equal to the minimum of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
min_member
(measure, level)¶ Return a measure equal to the member minimizing the passed measure on the given level.
When multiple members maximize the passed measure, the first one (according to the comparator of the given level) is returned.
Example
Considering this dataset:
Continent
City
Price
Europe
Paris
200.0
Europe
Berlin
150.0
Europe
London
240.0
North America
New York
270.0
And this measure:
m["City with min price"] = atoti.agg.min_member(m["Price"], lvl["Country"])
Then, at the given level, the measure is equal to the current member of the City level:
cube.query(m["Price"], m["City with min price"], levels=lvl["Country"])
City
Price
City with min price
Paris
200.0
Paris
Berlin
150.0
Berlin
London
240.0
London
New York
270.0
New York
At a level above it, the measure is equal to the city of each continent with the minimum price:
cube.query(m["City with min price"], levels=lvl["Continent"])
Continent
City with min price
Europe
Berlin
North America
New York
At the top level, the measure is equal to the city with the minium price across all continents:
cube.query(m["City with min price"])
City with min Price
Berlin
-
atoti.agg.
prod
(measure, *, scope=None)¶ Return a measure equal to the product of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
quantile
(measure, q, *, mode='inc', interpolation='linear', scope=None)¶ Return a measure equal to the requested quantile of the passed measure across the specified scope.
Here is how to obtain the same behaviour as these standard quantile calculation methods:
R-1:
mode="center"
andinterpolation="lower"
R-2:
mode="center"
andinterpolation="midpoint"
R-3:
mode="simple"
andinterpolation="nearest"
R-4:
mode="simple"
andinterpolation="linear"
R-5:
mode="center"
andinterpolation="linear"
R-6 (similar to Excel’s
PERCENTILE.EXC
):mode="exc"
andinterpolation="linear"
R-7 (similar to Excel’s
PERCENTILE.INC
):mode="inc"
andinterpolation="linear"
R-8 and R-9 are not supported
The formulae given for the calculation of the quantile index assume a 1-based indexing system.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to get the quantile of.q (
Union
[float
,Measure
]) – The quantile to take. Must be between 0 and 1. For instance, 0.95 is the 95th percentile and 0.5 is the median.mode (
Literal
[‘simple’, ‘centered’, ‘inc’, ‘exc’]) –The method used to calculate the index of the quantile, available options are, when search for the
q
quantile of a vectorX
:simple
:len(X) * q
centered
:len(X) * q + 0.5
exc
:(len(X) + 1) * q
inc
:(len(X) - 1) * q + 1
interpolation (
Literal
[‘linear’, ‘higher’, ‘lower’, ‘nearest’, ‘midpoint’]) –If the quantile index is not an integer, the interpolation decides what value is returned. The different options are, considering a quantile index
k
withi < k < j
for a sorted vectorX
:linear
:v = X[i] + (X[j] - X[i]) * (k - i)
lowest
:v = X[i]
highest
:v = X[j]
nearest
:v = X[i]
orv = X[j]
depending on which ofi
orj
is closest tok
midpoint
:v = (X[i] + X[j]) / 2
scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
short
(measure, *, scope=None)¶ Return a measure equal to the sum of the negative values of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
single_value
(measure, *, scope=None)¶ Return a measure equal to the single value aggregation of the passed measure across the specified scope.
The new measure value is equal to the value of the aggregated elements if they are all equal, and to
None
otherwise.Example
Considering this dataset with, for each day and product, the sold amount and the product price:
Date
Product ID
Category
Price
Amount
2020-01-01
001
TV
300.0
5.0
2020-01-02
001
TV
300.0
1.0
2020-01-01
002
Computer
900.0
2.0
2020-01-02
002
Computer
900.0
3.0
2020-01-01
003
TV
500.0
2.0
As the price is constant for a product,
single_value
can be used to aggregate it:m["Product Price"] = atoti.agg.single_value(store["Price"])
At the Product ID level, there is a single price so the aggregation forwards it:
cube.query(m["Product Price"], levels=lvl["Product ID"])
Product ID
Product Price
001
300.0
002
900.0
003
500.0
However, at the Category level, it behaves differently:
cube.query(m["Product Price"], levels=lvl["Category"])
Category
Product Price
Computer
900.0
TV
Computer: single price so the measure still forwards it.
TV: two different prices so the measure equals
None
.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
square_sum
(measure, *, scope=None)¶ Return a measure equal to the sum of the square of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
std
(measure, *, mode='sample', scope=None)¶ Return a measure equal to the standard deviation of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to get the standard deviation of.mode (
Literal
[‘sample’, ‘population’]) –One of the supported modes:
The
sample
standard deviation, similar to Excel’sSTDEV.S
, issqrt( sum((Xi - m)²) / (n - 1) )
wherem
is the sample mean andn
the size of the sample. Use this mode if your data represents a sample of the population.The
population
standard deviation, similar to Excel’sSTDEV.P
issqrt( sum((Xi - m)²) / n )
wherem
is the mean of theXi
elements andn
the size of the population. Use this mode if your data represents the entire population.
scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
stop
(measure, *at)¶ Return a measure equal to the passed measure at and below the given levels and
None
above them.
-
atoti.agg.
sum
(measure, *, scope=None)¶ Return a measure equal to the sum of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to aggregate.scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
-
atoti.agg.
var
(measure, *, mode='sample', scope=None)¶ Return a measure equal to the variance of the passed measure across the specified scope.
- Parameters
measure (
Union
[Measure
,MeasureConvertible
]) – The measure to get the variance of.mode (
Literal
[‘sample’, ‘population’]) –One of the supported modes:
The
sample
variance, similar to Excel’sVAR.S
, issum((Xi - m)²) / (n - 1)
wherem
is the sample mean andn
the size of the sample. Use this mode if your data represents a sample of the population.The
population
variance, similar to Excel’sVAR.P
issum((Xi - m)²) / n
wherem
is the mean of theXi
elements andn
the size of the population. Use this mode if your data represents the entire population.
scope (
Optional
[Scope
]) – The scope of the aggregation. WhenNone
is specified, the natural aggregation scope is used: it contains all the data in the cube which coordinates match the ones of the currently evaluated member.
- Return type
atoti.aggregates_cache module¶
Aggregates cache.
-
class
atoti.aggregates_cache.
AggregatesCache
(_java_api, _cube)¶ Bases:
object
The aggregates cache associated with a cube.
-
property
capacity
¶ Capacity of the cache.
It is the number of
{location: measure}
pairs of all the aggregates that can be stored.A strictly negative value will disable caching.
A zero value will enable sharing but no caching. This means that queries will share their computations if they are executed at the same time, but the aggregated values will not be stored to be retrieved later.
- Return type
-
property
atoti.array module¶
Functions operating on array measures.
-
atoti.array.
len
(measure)¶ Return a measure equal to the number of elements of the passed array measure.
- Return type
-
atoti.array.
max
(measure)¶ Return a measure equal to the maximum element of the passed array measure.
- Return type
-
atoti.array.
mean
(measure)¶ Return a measure equal to the mean of all the elements of the passed array measure.
- Return type
-
atoti.array.
min
(measure)¶ Return a measure equal to the minimum element of the passed array measure.
- Return type
-
atoti.array.
n_greatest
(measure, n)¶ Return an array measure containing the
n
greatest elements of the passed array measure.- Return type
-
atoti.array.
n_lowest
(measure, n)¶ Return an array measure containing the
n
lowest elements of the passed array measure.- Return type
-
atoti.array.
negative_values
(measure)¶ Return a measure where all the elements > 0 of the passed array measure are replaced by 0.
- Return type
-
atoti.array.
nth_greatest
(measure, n)¶ Return a measure equal to the
n
-th greatest element of the passed array measure.- Return type
-
atoti.array.
nth_lowest
(measure, n)¶ Return a measure equal to the
n
-th lowest element of the passed array measure.- Return type
-
atoti.array.
positive_values
(measure)¶ Return a measure where all the elements < 0 of the passed array measure are replaced by 0.
- Return type
-
atoti.array.
prefix_sum
(measure)¶ Return a measure equal to the sum of the previous elements in the passed array measure.
Example
If an array has the following values:
[2.0, 1.0, 0.0, 3.0]
, the returned array will be:[2.0, 3.0, 3.0, 6.0]
.- Return type
-
atoti.array.
quantile
(measure, q, *, mode='inc', interpolation='linear')¶ Return a measure equal to the requested quantile of the elements of the passed array measure.
Here is how to obtain the same behaviour as these standard quantile calculation methods:
R-1:
mode="center"
andinterpolation="lower"
R-2:
mode="center"
andinterpolation="midpoint"
R-3:
mode="simple"
andinterpolation="nearest"
R-4:
mode="simple"
andinterpolation="linear"
R-5:
mode="center"
andinterpolation="linear"
R-6 (similar to Excel’s
PERCENTILE.EXC
):mode="exc"
andinterpolation="linear"
R-7 (similar to Excel’s
PERCENTILE.INC
):mode="inc"
andinterpolation="linear"
R-8 and R-9 are not supported
The formulae given for the calculation of the quantile index assume a 1-based indexing system.
- Parameters
measure (
Measure
) – The measure to get the quantile of.q (
Union
[float
,Measure
]) – The quantile to take. Must be between 0 and 1. For instance, 0.95 is the 95th percentile and 0.5 is the median.mode (
Literal
[‘simple’, ‘centered’, ‘inc’, ‘exc’]) –The method used to calculate the index of the quantile, available options are, when search for the
q
quantile of a vectorX
:simple
:len(X) * q
centered
:len(X) * q + 0.5
exc
:(len(X) + 1) * q
inc
:(len(X) - 1) * q + 1
interpolation (
Literal
[‘linear’, ‘higher’, ‘lower’, ‘nearest’, ‘midpoint’]) –If the quantile index is not an integer, the interpolation decides what value is returned. The different options are, considering a quantile index
k
withi < k < j
for a sorted vectorX
:linear
:v = X[i] + (X[j] - X[i]) * (k - i)
lowest
:v = X[i]
highest
:v = X[j]
nearest
:v = X[i]
orv = X[j]
depending on which ofi
orj
is closest tok
midpoint
:v = (X[i] + X[j]) / 2
- Return type
-
atoti.array.
sort
(measure)¶ Return an array measure with the elements of the passed array measure sorted.
- Return type
-
atoti.array.
std
(measure, *, mode='sample')¶ Return a measure equal to the standard deviation of the elements of the passed array measure.
- Parameters
measure (
Measure
) – The measure to get the standard deviation of.mode (
Literal
[‘sample’, ‘population’]) –One of the supported modes:
The
sample
standard deviation, similar to Excel’sSTDEV.S
, issqrt( sum((Xi - m)²) / (n - 1) )
wherem
is the sample mean andn
the size of the sample. Use this mode if your data represents a sample of the population.The
population
standard deviation, similar to Excel’sSTDEV.P
issqrt( sum((Xi - m)²) / n )
wherem
is the mean of theXi
elements andn
the size of the population. Use this mode if your data represents the entire population.
- Return type
-
atoti.array.
sum
(measure)¶ Return a measure equal to the sum of all the elements of the passed array measure.
- Return type
-
atoti.array.
var
(measure, *, mode='sample')¶ Return a measure equal to the variance of the elements of the passed array measure.
- Parameters
measure (
Measure
) – The measure to get the variance of.mode (
Literal
[‘sample’, ‘population’]) –One of the supported modes:
The
sample
variance, similar to Excel’sVAR.S
, issum((Xi - m)²) / (n - 1)
wherem
is the sample mean andn
the size of the sample. Use this mode if your data represents a sample of the population.The
population
variance, similar to Excel’sVAR.P
issum((Xi - m)²) / n
wherem
is the mean of theXi
elements andn
the size of the population. Use this mode if your data represents the entire population.
- Return type
atoti.column module¶
Column of a Store.
-
class
atoti.column.
Column
(name, data_type, _store)¶ Bases:
atoti.measure.MeasureConvertible
Column of a Store.
-
data_type
: AtotiType = None¶
-
name
: str = None¶
-
atoti.comparator module¶
Level comparators.
-
atoti.comparator.
first_members
(members)¶ Create a level comparator with the given first members.
- Return type
atoti.copy_tutorial module¶
Copy the tutorial files when executed as the __main__ module.
atoti.cube module¶
Cube of a Session.
-
class
atoti.cube.
Cube
(java_api, name, base_store, session)¶ Bases:
object
Cube of a Session.
-
property
aggregates_cache
¶ Aggregates cache of the cube.
- Return type
-
create_parameter_hierarchy
(name, members, *, data_type=None, index_measure=None, indices=None, store_name=None)¶ Create an arbitrary single-level static hierarchy with the given members.
It can be used as a parameter hierarchy in advanced analyses.
- Parameters
name (
str
) – The name of hierarchy and its single level.data_type (
Optional
[AtotiType
]) – The type with which the members will be stored. Automatically inferred by default.index_measure (
Optional
[str
]) – The name of the indexing measure to create for this hierarchy, if any.indices (
Optional
[Sequence
[int
]]) – The custom indices for each member in the new hierarchy. They are used when accessing a member through theindex_measure
. Defaults torange(len(members))
.store_name (
Optional
[str
]) – The name of the store backing the parameter hierarchy. Defaults to the passedname
argument.
-
explain_query
(*measures, levels=None, condition=None, scenario='Base', timeout=30)¶ Run the query but return an explanation of the query instead of the result.
The explanation contains a summary, global timings and the query plan with all the retrievals.
- Parameters
measures (
NamedMeasure
) – The measures to query. If none are specified, all the measures are returned.levels (
Union
[Level
,Sequence
[Level
],None
]) – The levels to split on. If none are specified, the value of the measures at the top level is returned.condition (
Union
[LevelCondition
,MultiCondition
,None
]) –The filtering condition. Only conditions on level equality with a string are supported. For instance:
lvl["Country"] == "France"
(lvl["Country"] == "USA") & (lvl["Currency"] == "USD")
scenario (
str
) – The scenario to query.timeout (
int
) – The query timeout in seconds.
- Return type
QueryAnalysis
- Returns
The query explanation.
-
property
hierarchies
¶ Hierarchies of the cube.
- Return type
-
query
(*measures, levels=None, condition=None, scenario='Base', timeout=30)¶ Query the cube to get the value of some measures.
The value of the measures is given on all the members of the given levels.
- Parameters
measures (
NamedMeasure
) – The measures to query. If none are specified, all the measures are returned.levels (
Union
[Level
,Sequence
[Level
],None
]) – The levels to split on. If none are specified, the value of the measures at the top level is returned.condition (
Union
[LevelCondition
,MultiCondition
,None
]) –The filtering condition. Only conditions on level equality with a string are supported. For instance:
lvl["Country"] == "France"
(lvl["Country"] == "USA") & (lvl["Currency"] == "USD")
scenario (
str
) – The scenario to query.timeout (
int
) – The query timeout in seconds.
- Return type
DataFrame
- Returns
The resulting DataFrame.
-
property
schema
¶ Return the schema of the cube’s stores as an SVG graph.
Note
Graphviz is required to display the graph. It can be installed with Conda:
conda install graphviz
or by following the download instructions.- Return type
- Returns
An SVG image in IPython and a Path to the SVG file otherwise.
-
setup_simulation
(name, *, base_scenario='Base', levels=None, multiply=None, replace=None, add=None)¶ Create a simulation store for the given measures.
Simulations can have as many scenarios as desired.
The same measure cannot be passed in several methods.
- Parameters
name (
str
) – The name of the simulation.base_scenario (
str
) – The name of the base scenario.levels (
Optional
[Sequence
[Level
]]) – The levels to simulate on.multiply (
Optional
[Collection
[Measure
]]) – Measures whose values will be multiplied.replace (
Optional
[Collection
[Measure
]]) – Measures whose values will be replaced.add (
Optional
[Collection
[Measure
]]) – Measures whose values will be added (incremented).
- Return type
- Returns
The simulation on which scenarios can be made.
-
property
simulations
¶ Simulations of the cube.
- Return type
-
visualize
(name=None)¶ Display an atoti widget to explore the cube interactively.
This is only supported in JupyterLab and requires the atoti extension to be installed and enabled.
The widget state will be stored in the cell metadata. This state should not have to be edited but, if desired, it can be found in JupyterLab by opening the “Notebook tools” sidebar and expanding the the “Advanced Tools” section.
-
property
atoti.cubes module¶
Cubes.
-
class
atoti.cubes.
Cubes
(_java_api, _cubes=<factory>)¶ Bases:
collections.abc.MutableMapping
,typing.Generic
Manage the cubes of the session.
atoti.exceptions module¶
Custom atoti exceptions.
The custom exceptions are here to disguise the “ugly” stack traces which occur when Py4J raises a Java error. If any other exception is raised by the code inside the custom hook, it is processed normally.
This module is public so the exceptions classes are public and can be documented.
-
exception
atoti.exceptions.
AtotiException
¶ Bases:
Exception
The generic atoti exception class.
All exceptions which inherit from this class will be treated differently when raised. However, this exception is still handled by the default excepthook.
-
exception
atoti.exceptions.
AtotiJavaException
(message, java_traceback, java_exception)¶ Bases:
atoti.exceptions.AtotiException
Exception thrown when Py4J throws a Java exception.
-
exception
atoti.exceptions.
AtotiNetworkException
¶ Bases:
atoti.exceptions.AtotiException
Exception thrown when Py4J throws a network exception.
-
exception
atoti.exceptions.
AtotiPy4JException
¶ Bases:
atoti.exceptions.AtotiException
Exception thrown when Py4J throws a Py4JError.
atoti.hierarchies module¶
Hierarchies.
atoti.hierarchy module¶
Hierarchy of a Cube.
-
class
atoti.hierarchy.
Hierarchy
(_name, _levels, _dimension, _slicing, _cube, _java_api)¶ Bases:
object
Hierarchy of a Cube.
-
isin
(*member_paths)¶ Return a condition to check that the hierarchy is on one of the given members.
Considering
hierarchy_1
containinglevel_1
andlevel_2
,hierarchy_1.isin((a, x), (b,))
is equivalent to((level_1 == a) & (level_2 == x)) | (level_1 == b)
.Example
Considering a “Geography” hierarchy containing two levels “Country” and “City”, and this measure:
measures["Price in USA/Paris and Germany"] = atoti.filter( measures["Price"], hierarchies["Geography"].isin(("USA", "Paris"), ("Germany", )) )
The behavior is the following one:
Country
City
Price
measures[“Price in USA/Paris and Germany”]
France
Paris
200.0
Germany
Berlin
150.0
150.0
UK
London
240.0
USA
New York
270.0
USA
Paris
500.0
500.0
- Parameters
members – One or more members expressed as tuples on which the hierarchy should be. Each element in a tuple corresponds to a level of the hierarchy, from the shallowest to the deepest.
- Return type
HierarchyIsInCondition
-
atoti.level module¶
Level of a Hierarchy.
-
class
atoti.level.
Level
(_name, _column_name, _data_type, _hierarchy=None, _comparator=None)¶ Bases:
atoti.measure.MeasureConvertible
Level of a Hierarchy.
-
property
comparator
¶ Comparator of the level.
- Return type
-
isin
(*members)¶ Return a condition to check that the level is on one of the given members.
levels["x"].isin("a", "b")
is equivalent tolevels["x"] == "a" OR levels["x"] == "b"
.Example
Considering this measure:
measures["Price in Paris and London"] = atoti.filter( measures["Price"], levels["City"].isin("Paris", "London") )
The behavior is the following one:
City
Price
measures[“Price in Paris and London”]
Paris
200.0
200.0
Berlin
150.0
London
240.0
240.0
New York
270.0
- Parameters
members (
Any
) – One or more members on which the level should be.- Return type
LevelIsInCondition
-
property
atoti.levels module¶
Levels.
-
class
atoti.levels.
Levels
(_hierarchies)¶ Bases:
atoti._base_levels.BaseLevels
Flat representation of all the levels in the cube.
atoti.logs module¶
Logs.
atoti.measure module¶
Measure of a cube.
atoti.measures module¶
Measures.
-
class
atoti.measures.
Measures
(_java_api, _cube)¶ Bases:
atoti._mappings.DelegateMutableMapping
Manage the measures.
atoti.sampling module¶
Sampling modes describe how data is loaded into stores.
atoti can handle very large volumes of data while still providing fast answers to queries. However, loading a large amount of data during the modeling phase of the application is rarely a good idea because creating stores, joins, cubes, hierarchies and measures are all operations that take more time when there is more data.
atoti speeds up the sampling process by incoporating an automated sampling mechanism.
For instance datasets can be automatically sampled on their first lines
while
working on the model and then switched to the full
dataset when the project
is ready to be shared with other users.
By reducing the amount of data, sampling is a way to have immediate feedback for each cell run in a notebook and keep the modeling phase as snappy as possible.
As a rule of thumb:
sampling is always recommended while building a project.
atoti.session.Session.load_all_data()
should be called as late as possible.
-
atoti.sampling.
FULL
¶ Load all the data in all the stores.
-
class
atoti.sampling.
SamplingMode
(name, parameters)¶ Bases:
atoti._serialization_utils.FromDict
Mode of source loading.
-
name
: str = None¶
-
parameters
: List[Any] = None¶
-
-
atoti.sampling.
first_files
(limit)¶ Mode to load only the first files of the source.
- Parameters
limit (
int
) – The maximum number of files to read.- Return type
atoti.session module¶
Session.
-
class
atoti.session.
Session
(name, *, config, **kwargs)¶ Bases:
object
Holds a connection to the Java gateway.
-
close
()¶ Close this session and free all the associated resources.
-
create_cube
(base_store, name=None, *, mode='auto')¶ Create a cube using based on the passed store.
- Parameters
base_store (
Store
) – The cube’s base store.name (
Optional
[str
]) – The name of the created cube. Defaults to the name of the base store.mode (
Literal
[‘auto’, ‘manual’, ‘no_measures’]) –The cube creation mode:
auto
: Creates hierarchies for every non-numeric column, and measures for every numeric column.manual
: Does not create any hierarchy or measure (except from the count).no_measures
: Creates the hierarchies likeauto
but does not create any measures.
- Return type
-
create_scenario
(name, *, origin='Base')¶ Create a new source scenario in the datastore.
-
create_store
(types, store_name, *, keys=None, partitioning=None, sampling_mode=None)¶ Create a store from a schema.
- Parameters
types (Mapping[str, AtotiType]) – Types for all columns of the store. This defines the columns which will be expected in any future data loaded into the store.
store_name (str) – The name of the store to create.
keys (Optional[Sequence[str]]) – The columns that will become keys of the store.
partitioning (Optional[str]) –
The description of how the data will be split across partitions of the store. For instance,
hash4(country)
split the data across 4 partitions based on thecountry
column’s hash value.Only key columns can be used in the partitioning description.
By default, the partitioning is done on the key columns of the store modulo the number of available processors, or on the non-numerical columns of the store if it does not have any key columns.
sampling_mode (Optional[SamplingMode]) – The sampling mode. Defaults to this session’s one.
- Return type
-
delete_scenario
(scenario)¶ Delete the source scenario with the provided name if it exists.
- Return type
None
-
property
excel_url
¶ URL of the Excel endpoint.
To connect to the session in Excel, create a new connection to an Analysis Services. Use this URL for the server field and choose to connect with “User Name and Password”:
Without authentication, leave these fields blank.
With Basic authentication, fill them with your username and password.
Other authentication types (such as Auth0) are not supported by Excel.
- Return type
-
explain_mdx_query
(mdx, *, timeout=30)¶ Explain an MDX query.
-
load_all_data
()¶ Trigger the
full
loading of the data.Calling this method will change the
sampling mode
toatoti.sampling.FULL
which triggers the loading of all the data. All subsequent loads, including new stores, will not be sampled.When building a project, this method should be called as late as possible.
-
property
port
¶ Port on which the session is exposed.
Can be set in the session’s
configuration
.- Return type
-
query_mdx
(mdx, *, timeout=30)¶ Execute an MDX query and return its result as a pandas DataFrame.
-
read_csv
(path, *, keys=None, store_name=None, in_all_scenarios=True, sep=None, encoding='utf-8', process_quotes=None, partitioning=None, types=None, watch=False, array_sep=None, sampling_mode=None)¶ Read a CSV file into a store.
- Parameters
path (Union[pathlib.Path, str]) –
The path to the CSV file or directory to load.
If a path pointing to a directory is provided, all of the files with the
.csv
extension in the directory and subdirectories will be loaded into the same store and, as such, they are all expected to share the same schemaThe path can contain glob parameters (e.g.
path/to/directory/**.*.csv
) and will be expanded correctly. Be carefull, when using glob expressions in paths, all files which match the expression will be loaded, regardless of their extension. When the provided path is a directory, the default glob parameter of**.csv
is used.keys (Optional[Sequence[str]]) – The columns that will become keys of the store.
store_name (Optional[str]) – The name of the store to create. Defaults to the final component of the given
path
.in_all_scenarios (bool) – Whether to load the data in all existing scenarios.
sep (Optional[str]) – The delimiter to use. If
None
, the separator will automatically be detected.encoding (str) – The encoding to use to read the CSV.
encoding – The encoding to use to read the CSV.
process_quotes (Optional[bool]) –
Whether double quotes should be processed to follow the official CSV specification:
True
:Each field may or may not be enclosed in double quotes (however some programs, such as Microsoft Excel, do not use double quotes at all). If fields are not enclosed with double quotes, then double quotes may not appear inside the fields.
A double quote appearing inside a field must be escaped by preceding it with another double quote.
Fields containing line breaks, double quotes, and commas should be enclosed in double-quotes.
False
: all double-quotes within a field will be treated as any regular character, following Excel’s behavior. In this mode, it is expected that fields are not enclosed in double quotes. It is also not possible to have a line break inside a field.None
: the behavior will be inferred from the first lines of the CSV.
partitioning (Optional[str]) –
The description of how the data will be split across partitions of the store. For instance,
hash4(country)
split the data across 4 partitions based on thecountry
column’s hash value.Only key columns can be used in the partitioning description.
By default, the partitioning is done on the key columns of the store modulo the number of available processors, or on the non-numerical columns of the store if it does not have any key columns.
types (Optional[Mapping[str, AtotiType]]) – Types for some or all columns of the store. Types for non specified columns will be inferred from the first 1,000 lines.
watch (bool) – Whether the source file or directory at the given
path
should be watched for changes. When set toTrue
, changes to the source will automatically be reflected in the store. If the source is a directory, new files will be loaded into the same store as the initial data and must therefore have the same schema as the initial data as well. Any non-CSV files added to the directory will be ignored.array_sep (Optional[str]) – The delimiter to use for arrays. Setting it to a non-
None
value will parse all the columns containing this separator as arrays.sampling_mode (Optional[SamplingMode]) – The sampling mode. Defaults to this session’s one.
- Return type
- Returns
The created store holding the content of the CSV file(s).
-
read_numpy
(array, columns, store_name, *, keys=None, in_all_scenarios=True, partitioning=None, **kwargs)¶ Read a NumPy 2D array into a new store.
- Parameters
array (np.ndarray) – The NumPy 2D ndarray to read the data from.
columns (Sequence[str]) – The names to use for the store’s columns. They must be in the same order as the values in the NumPy array.
keys (Optional[Sequence[str]]) – The columns that will become keys of the store.
store_name (str) – The name of the store to create.
in_all_scenarios (bool) – Whether to load the data in all existing scenarios.
partitioning (Optional[str]) –
The description of how the data will be split across partitions of the store. For instance,
hash4(country)
split the data across 4 partitions based on thecountry
column’s hash value.Only key columns can be used in the partitioning description.
By default, the partitioning is done on the key columns of the store modulo the number of available processors, or on the non-numerical columns of the store if it does not have any key columns.
- Return type
- Returns
The created store holding the content of the array.
-
read_pandas
(dataframe, store_name, *, keys=None, in_all_scenarios=True, partitioning=None, types=None, **kwargs)¶ Read a pandas DataFrame into a store.
All the named indices of the DataFrame are included into the store. Multilevel columns are flattened into a single string name.
- Parameters
dataframe (
DataFrame
) – The DataFrame to load.keys (
Optional
[Sequence
[str
]]) – The columns that will become keys of the store.store_name (
str
) – The name of the store to create.in_all_scenarios (
bool
) – Whether to load the data in all existing scenarios.partitioning (
Optional
[str
]) –The description of how the data will be split across partitions of the store. For instance,
hash4(country)
split the data across 4 partitions based on thecountry
column’s hash value.Only key columns can be used in the partitioning description.
By default, the partitioning is done on the key columns of the store modulo the number of available processors, or on the non-numerical columns of the store if it does not have any key columns.
types (
Optional
[Mapping
[str
,AtotiType
]]) – Types for some or all columns of the store. Types for non specified columns will be inferred.
- Return type
- Returns
The created store holding the content of the DataFrame.
-
read_parquet
(path, *, keys=None, store_name=None, in_all_scenarios=True, partitioning=None, sampling_mode=None, watch=False)¶ Read a Parquet file into a store.
- Parameters
path (Union[pathlib.Path, str]) – The path to the Parquet file or directory. If the path points to a directory, all the files in the directory and subdirectories will be loaded into the store and, as such, are expected to have the same schema as the store and to be Parquet files.
keys (Optional[Sequence[str]]) – The columns that will become keys of the store.
store_name (Optional[str]) – The name of the store to create. Defaults to the final component of the given
path
.in_all_scenarios (bool) – Whether to load the data in all existing scenarios.
partitioning (Optional[str]) –
The description of how the data will be split across partitions of the store. For instance,
hash4(country)
split the data across 4 partitions based on thecountry
column’s hash value.Only key columns can be used in the partitioning description.
By default, the partitioning is done on the key columns of the store modulo the number of available processors, or on the non-numerical columns of the store if it does not have any key columns.
sampling_mode (Optional[SamplingMode]) – The sampling mode. Defaults to this session’s one.
watch (bool) – Whether the source file or directory at the given
path
should be watched for changes. When set toTrue
, changes to the source will automatically be reflected in the store. If the source is a directory, new files will be loaded into the same store as the initial data and must therefore have the same schema as the initial data as well.
- Return type
- Returns
The created store holding the content of the Parquet file(s).
-
read_spark
(dataframe, store_name, *, keys=None, in_all_scenarios=True, partitioning=None)¶ Read a Spark DataFrame into a store.
- Parameters
dataframe (SparkDataFrame) – The DataFrame to load.
keys (Optional[Sequence[str]]) – The columns that will become keys of the store.
store_name (str) – The name of the store to create.
in_all_scenarios (bool) – Whether to load the data in all existing scenarios.
partitioning (Optional[str]) –
The description of how the data will be split across partitions of the store. For instance,
hash4(country)
split the data across 4 partitions based on thecountry
column’s hash value.Only key columns can be used in the partitioning description.
By default, the partitioning is done on the key columns of the store modulo the number of available processors, or on the non-numerical columns of the store if it does not have any key columns.
- Return type
- Returns
The created store holding the content of the DataFrame.
-
property
scenarios
¶ Collection of source scenarios of the session.
- Return type
-
property
url
¶ Public URL of the session.
Can be set in the session’s
configuration
.- Return type
-
wait
()¶ Wait for the underlying server subprocess to terminate.
This will prevent the Python process to exit.
- Return type
None
-
atoti.simulation module¶
Simulation and related classes.
-
class
atoti.simulation.
Scenario
(name, _simulation, _java_api)¶ Bases:
object
A scenario for a simulation.
-
append
(*rows)¶ Add one or multiple rows to the scenario.
If a row with the same keys already exist in the scenario, it will be overridden by the passed one.
-
property
columns_without_priority
¶ Columns of the scenario (Priority column excluded).
-
head
(n=5)¶ Return the first
n
rows of the scenario as a pandas DataFrame.- Return type
DataFrame
-
load_csv
(path, *, sep=', ')¶ Load a CSV into this scenario.
The expected columns are
columns
orcolumns_without_priority
.The name of the scenario is automatically added before the row is added to the simulation store.
If the value of a column is left empty (
None
), then it will be treated as a wildcard value. i.e. it will match all the values of the corresponding column when performing the simulation.If a value for a column on a given row is None, then it will be treated as a wildcard. i.e it will match all the values of the corresponding column when performing the simulation.
- Parameters
path (Union[pathlib.Path, str]) – The path to the CSV file.
sep (Optional[str]) – The CSV separator character. If
None
, it is inferred by pandas.
-
load_pandas
(dataframe, **kwargs)¶ Load a pandas DataFrame into this scenario.
The expected columns are
columns
orcolumns_without_priority
.The name of the scenario is automatically added before the row is added to the simulation store.
If the value of a column is left empty (
None
), then it will be treated as a wildcard value. i.e. it will match all the values of the corresponding column when performing the simulation.- Parameters
dataframe (
DataFrame
) – The DataFrame to load.
-
name
: str = None¶
-
-
class
atoti.simulation.
Simulation
(_name, _levels, _multiply, _replace, _add, _base_scenario, _cube, _java_api)¶ Bases:
object
Represents a simulation.
-
property
columns_without_priority
¶ Columns of the simulation (Priority column excluded).
-
head
(n=5)¶ Return the first
n
rows of the simulation as a pandas DataFrame.- Return type
DataFrame
-
load_csv
(path, *, sep=None, encoding='utf-8', process_quotes=True, watch=False, array_sep=None)¶ Load a CSV into this simulation.
The expected columns are
columns
orcolumns_without_priority
.The value provided for
simulation_name
is the name of the scenario the values will be loaded into.If the value of a column is left empty (
None
), then it will be treated as a wildcard value. i.e. it will match all the values of the corresponding column when performing the simulation.If a value for a specific field is left empty, then it wil be treated as a wildcard value. i.e. it will match all the values of the corresponding column when performing the simulation.
- Parameters
path (Union[pathlib.Path, str]) –
The path to the CSV file or directory to load.
If a path pointing to a directory is provided, all of the files with the
.csv
extension in the directory and subdirectories will be loaded into the same store and, as such, they are all expected to share the same schemaThe path can contain glob parameters (e.g.
path/to/directory/**.*.csv
) and will be expanded correctly. Be carefull, when using glob expressions in paths, all files which match the expression will be loaded, regardless of their extension. When the provided path is a directory, the default glob parameter of**.csv
is used.sep (Optional[str]) – The delimiter to use. If
None
, the separator will automatically be detected.encoding (str) – The encoding to use to read the CSV.
encoding – The encoding to use to read the CSV.
process_quotes (bool) –
Whether double quotes should be processed to follow the official CSV specification:
True
:Each field may or may not be enclosed in double quotes (however some programs, such as Microsoft Excel, do not use double quotes at all). If fields are not enclosed with double quotes, then double quotes may not appear inside the fields.
A double quote appearing inside a field must be escaped by preceding it with another double quote.
Fields containing line breaks, double quotes, and commas should be enclosed in double-quotes.
False
: all double-quotes within a field will be treated as any regular character, following Excel’s behavior. In this mode, it is expected that fields are not enclosed in double quotes. It is also not possible to have a line break inside a field.None
: the behavior will be inferred from the first lines of the CSV.
watch (bool) – Whether the source file or directory at the given
path
should be watched for changes. When set toTrue
, changes to the source will automatically be reflected in the store. If the source is a directory, new files will be loaded into the same store as the initial data and must therefore have the same schema as the initial data as well. Any non-CSV files added to the directory will be ignored.array_sep (Optional[str]) – The delimiter to use for arrays. Setting it to a non-
None
value will parse all the columns containing this separator as arrays.
-
load_pandas
(dataframe, **kwargs)¶ Load a pandas DataFrame into this simulation.
The expected columns are
columns
orcolumns_without_priority
.The value provided for
simulation_name
is the name of the scenario the values will be loaded into.If the value of a column is left empty (
None
), then it will be treated as a wildcard value. i.e. it will match all the values of the corresponding column when performing the simulation.If the value of a column is left empty (
None
), then it will be treated as a wildcard value. i.e. it will match all the values of the corresponding column when performing the simulation.- Parameters
dataframe (
DataFrame
) – The DataFrame to load.
-
property
scenarios
¶ Scenarios of the simulation.
- Return type
-
property
-
class
atoti.simulation.
SimulationScenarios
(_simulation)¶ Bases:
collections.abc.MutableMapping
,typing.Generic
Manage the scenarios of a simulation.
atoti.simulations module¶
Simulations.
-
class
atoti.simulations.
Simulations
(_java_api, _simulations=<factory>)¶ Bases:
collections.abc.MutableMapping
,typing.Generic
Manage the simulations.
atoti.store module¶
Store and related classes.
-
class
atoti.store.
Store
(_name, _java_api, _scenario='Base', _columns=<factory>)¶ Bases:
object
Represents a single store.
-
append
(*rows)¶ Add one or multiple rows to the store.
If a row with the same keys already exist in the store, it will be overridden by the passed one.
-
head
(n=5)¶ Return the first
n
rows of the store as a pandas DataFrame.- Return type
DataFrame
-
join
(other, *, mapping=None)¶ Define a reference between this store and another.
There are two different possible situations when creating references:
All the key columns of the destination store are mapped: this is a normal reference.
Only some of the key columns of the destination store are mapped: this is a partial reference:
The columns from the source store used in the mapping must be attached to hierarchies.
The un-mapped key columns of the destination store will be converted into hierarchies.
Depending on the cube creation mode, the join will also generate different hierarchies and measures:
manual
: The un-mapped keys of the destination store will become hierarchies.no_measures
:All of the non-numeric columns from the destination store, as well as those containing integers, will be converted into hierarchies. No measures will be created in this mode.auto
: The same hierarchies will be created as in theno_measures
mode. Additionaly, columns containing numeric values, or arrays, except for columns which contain only integers, will be converted into measures.
-
load_csv
(path, *, sep=None, encoding='utf-8', process_quotes=True, in_all_scenarios=False, truncate=False, watch=False, array_sep=None)¶ Load a CSV into this scenario.
- Parameters
path (Union[pathlib.Path, str]) –
The path to the CSV file or directory to load.
If a path pointing to a directory is provided, all of the files with the
.csv
extension in the directory and subdirectories will be loaded into the same store and, as such, they are all expected to share the same schemaThe path can contain glob parameters (e.g.
path/to/directory/**.*.csv
) and will be expanded correctly. Be carefull, when using glob expressions in paths, all files which match the expression will be loaded, regardless of their extension. When the provided path is a directory, the default glob parameter of**.csv
is used.sep (Optional[str]) – The delimiter to use. If
None
, the separator will automatically be detected.encoding (str) – The encoding to use to read the CSV.
encoding – The encoding to use to read the CSV.
process_quotes (bool) –
Whether double quotes should be processed to follow the official CSV specification:
True
:Each field may or may not be enclosed in double quotes (however some programs, such as Microsoft Excel, do not use double quotes at all). If fields are not enclosed with double quotes, then double quotes may not appear inside the fields.
A double quote appearing inside a field must be escaped by preceding it with another double quote.
Fields containing line breaks, double quotes, and commas should be enclosed in double-quotes.
False
: all double-quotes within a field will be treated as any regular character, following Excel’s behavior. In this mode, it is expected that fields are not enclosed in double quotes. It is also not possible to have a line break inside a field.None
: the behavior will be inferred from the first lines of the CSV.
in_all_scenarios (bool) – Whether to load the data in all existing scenarios.
truncate (bool) – Whether to clear the store before loading the new data into it.
watch (bool) – Whether the source file or directory at the given
path
should be watched for changes. When set toTrue
, changes to the source will automatically be reflected in the store. If the source is a directory, new files will be loaded into the same store as the initial data and must therefore have the same schema as the initial data as well. Any non-CSV files added to the directory will be ignored.array_sep (Optional[str]) – The delimiter to use for arrays. Setting it to a non-
None
value will parse all the columns containing this separator as arrays.
-
load_pandas
(dataframe, *, in_all_scenarios=False, truncate=False, **kwargs)¶ Load a pandas DataFrame into this scenario.
-
load_parquet
(path, *, in_all_scenarios=False, truncate=False, watch=False)¶ Load a Parquet file into this scenario.
- Parameters
path (Union[pathlib.Path, str]) – The path to the Parquet file or directory. If the path points to a directory, all the files in the directory and subdirectories will be loaded into the store and, as such, are expected to have the same schema as the store and to be Parquet files.
in_all_scenarios (bool) – Whether to load the data in all existing scenarios.
truncate (bool) – Whether to clear the store before loading the new data into it.
watch (bool) – Whether the source file or directory at the given
path
should be watched for changes. When set toTrue
, changes to the source will automatically be reflected in the store. If the source is a directory, new files will be loaded into the same store as the initial data and must therefore have the same schema as the initial data as well.
-
load_spark
(dataframe, *, in_all_scenarios=False, truncate=False)¶ Load a Spark DataFrame into this scenario.
-
property
scenarios
¶ All the scenarios the store can be on.
- Return type
-
-
class
atoti.store.
StoreScenarios
(_java_api, _store)¶ Bases:
object
Scenarios of a store.
-
load_csv
(scenario_directory_path, *, sep=None, encoding='utf-8', process_quotes=True, truncate=False, watch=False, array_sep=None, pattern=None, base_scenario_directory='Base')¶ Load multiple CSV files into the store while automatically generating scenarios.
Loads the data from a directory into multiple scenarios, creating them as necessary, based on the directory’s structure. The contents of each sub-directory of the provided path will be loaded into a scenario with the same name. Here is an example of a valid directory structure:
ScenarioStore ├── Base │ └── base_data.csv ├── Scenario1 │ └── scenario1_data.csv └── Scenario2 │ └── scenario2_data.csv
With this structure:
The contents of the
Base
directory are loaded into the base scenario.Two new scenarios are created:
Scenario1
andScenario2
, containing respectively the data fromscenario1_data.csv
andscenario2_data.csv
.
- Parameters
scenario_directory_path (Union[pathlib.Path, str]) – The path pointing to the directory containing all of the scenarios.
sep (Optional[str]) – The delimiter to use. If
None
, the separator will automatically be detected.encoding (str) – The encoding to use to read the CSV.
encoding – The encoding to use to read the CSV.
process_quotes (bool) –
Whether double quotes should be processed to follow the official CSV specification:
True
:Each field may or may not be enclosed in double quotes (however some programs, such as Microsoft Excel, do not use double quotes at all). If fields are not enclosed with double quotes, then double quotes may not appear inside the fields.
A double quote appearing inside a field must be escaped by preceding it with another double quote.
Fields containing line breaks, double quotes, and commas should be enclosed in double-quotes.
False
: all double-quotes within a field will be treated as any regular character, following Excel’s behavior. In this mode, it is expected that fields are not enclosed in double quotes. It is also not possible to have a line break inside a field.None
: the behavior will be inferred from the first lines of the CSV.
truncate (bool) – Whether to clear the store before loading the new data into it.
watch (bool) – Whether the source file or directory at the given
path
should be watched for changes. When set toTrue
, changes to the source will automatically be reflected in the store. If the source is a directory, new files will be loaded into the same store as the initial data and must therefore have the same schema as the initial data as well. Any non-CSV files added to the directory will be ignored.array_sep (Optional[str]) – The delimiter to use for arrays. Setting it to a non-
None
value will parse all the columns containing this separator as arrays.pattern (Optional[str]) – A glob pattern used to specify which files to load in each scenario directory. If no pattern is provided, all files with the
.csv
extension will be loaded by default.base_scenario_directory (str) – The data from a scenario directory with this name will be loaded into the base scenario and not a new scenario with the original name of the directory.
-
atoti.stores module¶
Stores.
-
class
atoti.stores.
Stores
(java_api, mapping)¶ Bases:
atoti._mappings.ImmutableMapping
Manage the stores.
-
property
schema
¶ Datastore schema as an SVG graph.
Note
Graphviz is required to display the graph. It can be installed with Conda:
conda install graphviz
or by following the download instructions.- Return type
- Returns
An SVG image in IPython and a Path to the SVG file otherwise.
-
property
atoti.types module¶
atoti Types.
-
class
atoti.types.
AtotiType
(java_type, nullable)¶ Bases:
object
atoti Type.
-
java_type
¶ The name of the associated Java literal type.
-
nullable
¶ Whether the objects of this type can be
None
. Elements within array types cannot beNone
and must share the same scalar type.
-
java_type
: str = None
-
nullable
: bool = None
-
-
atoti.types.
local_date
(java_format)¶ Create a date type with the given Java date format.
-
atoti.types.
local_date_time
(java_format)¶ Create a datetime type with the given Java datetime format.
Module contents¶
atoti’s entrypoint.
-
atoti.
create_session
(name='Unnamed', *, config=None, **kwargs)¶ Create a session.
-
atoti.
open_query_session
(url, name=None, *, auth=None)¶ Open an existing session to query it.
- Parameters
- Return type
- Returns
The query session.
-
atoti.
abs
(measure)¶ Return a measure equal to the absolute value of the passed measure.
- Return type
-
atoti.
at
(measure, coordinates)¶ Return a measure equal to the passed measure at some other coordinates of the cube.
- Parameters
measure (
Measure
) – The measure to take at other coordinates.coordinates (
Mapping
[Level
,Any
]) –A
{level_to shift_on: value_to_shift_to}
mapping. Values can either be:A literal matching an existing member of the key level:
# Return the value of Quantity for France on each member of the Country level. atoti.at(m["Quantity"], {lvl["Country"]: "France"})
Another level whose current member the key level will be shifted to:
# Return the value of Quantity for the current member # of the Target Country and Target City levels. atoti.at(m["Quantity"], { lvl["Country"]: lvl["Target Country"], lvl["City"]: lvl["Target City"], })
If this other level is not expressed, the shifting will not be done.
-
atoti.
ceil
(measure)¶ Return a measure equal to the smallest integer that is >= to the passed measure.
- Return type
-
atoti.
cos
(measure)¶ Return a measure equal to the cosine of the passed measure in radians.
- Return type
-
atoti.
date_diff
(from_date, to_date, *, unit='days')¶ Return a measure equal to the difference between two dates.
- Parameters
from_date (
Union
[Measure
,date
,datetime
]) – The first date measure or object.to_date (
Union
[Measure
,date
,datetime
]) – The second date measure or object.unit (
Literal
[‘seconds’, ‘minutes’, ‘hours’, ‘days’, ‘weeks’, ‘months’, ‘years’]) – The difference unit. Seconds, minutes and hours are only allowed if the dates contain time information.
- Return type
-
atoti.
date_shift
(measure, on, offset, *, method='exact')¶ Return a measure equal to the passed mesure shifted to another date.
- Parameters
measure (
Measure
) – The measure to shift.on (
Hierarchy
) – The hierarchy to shift on.offset (
str
) – The offset of the formxxDxxWxxMxxQxxY
to shift by. Only theD
,W
,M
,Q
, andY
offset aliases are supported. Offset aliases have the same meaning as Pandas’.method (
Literal
[‘exact’, ‘previous’, ‘following’, ‘interpolate’]) –Determine the value to use when there is no member at the shifted date:
exact
:None
.previous
: Value at the previous existing date.following
: Value at the following existing date.interpolate
: Linear interpolation of the values at the previous and following existing dates:Example:
m2 = atoti.date_shift("m1", on=h["date"], offset="1M", method="interpolate")
date
m1
m2
explanation
2000-01-05
15
10.79
linear interpolation of 2000-02-03’s 10 and 2000-03-03’s 21 for 2000-02-05
2000-02-03
10
21
exact match at 2000-03-03: no need to interpolate
2000-03-03
21
9.73
linear interpolation of 2000-03-03’s 21 and 2000-04-05’s 9 for 2000-04-03
2000-04-05
9
∅
no record after 2000-04-05: cannot interpolate
- Return type
-
atoti.
exp
(measure)¶ Return a measure equal to the exponential value of the passed measure.
- Return type
-
atoti.
filter
(measure, condition)¶ Return a filtered measure.
The new measure is equal to the passed one where the condition is
True
and toNone
elsewhere.Different types of conditions are supported:
Levels compared to levels:
lvl["source"] == lvl["destination"]
Levels compared to literals:
lvl["city"] == "Paris"
A conjunction of conditions using the
&
operator:(lvl["source"] == lvl["destination"]) & (lvl["city"] == "Paris")
-
atoti.
floor
(measure)¶ Return a measure equal to the largest integer <= to the passed measure.
- Return type
-
atoti.
log
(measure)¶ Return a measure equal to the natural logarithm (base e) of the passed measure.
- Return type
-
atoti.
log10
(measure)¶ Return a measure equal to the base 10 logarithm of the passed measure.
- Return type
-
atoti.
max
(*measures)¶ Return a measure equal to the maximum of the passed arguments.
- Return type
-
atoti.
min
(*measures)¶ Return a measure equal to the minimum of the passed arguments.
- Return type
-
atoti.
parent_value
(measure, on, *, apply_filters=False, degree=1, total_value=None)¶ Return a measure equal to the passed measure at the parent member on the given hierarchy.
Example
Measure definitions:
m1 = parent_value(Quantity.SUM, Date) m2 = parent_value(Quantity.SUM, Date, degree=3) m3 = parent_value(Quantity.SUM, Date, degree=3, total_value=Quantity.SUM)) m4 = parent_value(Quantity.SUM, Date, degree=3, total_value=Other.SUM))
Considering a non slicing hierarchy
Date
with three levelsYears
,Month
andDay
:Year
Month
Day
Quantity.SUM
Other.SUM
m1
m2
m3
m4
2019
75
1000
110
null
110
1500
7
35
750
75
null
110
1500
1
15
245
35
110
110
110
2
20
505
35
110
110
110
6
40
250
75
null
110
1500
1
25
115
40
110
110
110
2
15
135
40
110
110
110
2018
35
500
110
null
110
1500
7
15
200
35
null
110
1500
1
5
55
15
110
110
110
2
10
145
15
110
110
110
6
20
300
35
null
110
1500
1
15
145
20
110
110
110
2
5
155
20
110
110
110
Considering a slicing hierarchy
Date
with three levelsYears
,Month
andDay
:Year
Month
Day
Quantity.SUM
Other.SUM
m1
m2
m3
m4
2019
75
1000
75
null
75
1000
7
35
750
75
null
75
1000
1
15
245
35
75
75
75
2
20
505
35
75
75
75
6
40
250
75
null
75
1000
1
25
115
40
75
75
75
2
15
135
40
75
75
75
2018
35
500
35
null
35
500
7
15
200
35
null
35
500
1
5
55
15
35
35
35
2
10
145
15
35
35
35
6
20
300
35
null
35
500
1
15
145
20
35
35
35
2
5
155
20
35
35
35
- Parameters
measure (
Union
[Measure
,str
]) – The measure to take the parent value of.on (
Hierarchy
) – The hierarchy to drill up to take the parent value.apply_filters (
bool
) – Whether to apply the query filters when computing the value at the parent member.degree (
int
) – The number of levels to go up to take the value on. A value of1
as parent_degree will do a one step drill up in the hierarchy.total_value (
Union
[date
,datetime
,int
,float
,str
,Measure
,MeasureConvertible
,None
]) – The value to take when the drill up went above the top level of the hierarchy.
- Return type
-
atoti.
rank
(measure, hierarchy, ascending=True, apply_filters=True)¶ Return a measure equal to the rank of a hierarchy’s members according to a reference measure.
Members with equal values are further ranked using the level comparator.
Example:
m2 = atoti.rank(m1, hierarchy["date"])
Year
Month
Day
m1
m2
Comments
2000
90
1
01
25
2
01
15
1
02
10
2
02
50
1
01
30
1
same value as 2000/02/05 but this member comes first
03
20
3
05
30
2
same value as 2000/02/01 but this member comes last
04
15
3
05
5
2
05
10
1
- Parameters
measure (
Measure
) – The measure on which the ranking is done.hierarchy (
Hierarchy
) – The hierarchy containing the members to rank.ascending (
bool
) – When set toFalse
, the 1st place goes to the member with greatest value.apply_filters (
bool
) – WhenTrue
, query filters will be applied before ranking members. Whenquery filters will be applied after the ranking, resulting in "holes" in the (False,) –
ranks. –
-
atoti.
round
(measure)¶ Return a measure equal to the closest integer to the passed measure.
- Return type
-
atoti.
shift
(measure, on, *, offset=1)¶ Return a measure equal to the passed measure shifted to another member.
-
atoti.
sin
(measure)¶ Return a measure equal to the sine of the passed measure in radians.
- Return type
-
atoti.
sqrt
(measure)¶ Return a measure equal to the square root of the passed measure.
- Return type
-
atoti.
tan
(measure)¶ Return a measure equal to the tangent of the passed measure in radians.
- Return type
-
atoti.
total
(measure, on)¶ Return a measure equal to the measure on the top level member on each hierarchy member.
It ignores the filters on this hierarchy.
If the hierarchy is not slicing, total is equal to the value for all the members. If the hierarchy is slicing, total is equal to the value on the first level.
Example
Considering a hierarchy
Date
with three levelsYear, Month and Day
. In the first caseDate
is not slicing. In the second case Date is slicing.Year
Month
Day
Price
total(Price) NON SLICING
total(Price) SLICING
2019
75.0
110.0
75.0
7
35.0
110.0
75.0
1
15.0
110.0
75.0
2
20.0
110.0
75.0
6
40.0
110.0
75.0
1
25.0
110.0
75.0
2
15.0
110.0
75.0
2018
35.0
110.0
35.0
7
15.0
110.0
35.0
1
5.0
110.0
35.0
2
10.0
110.0
35.0
6
20.0
110.0
35.0
1
15.0
110.0
35.0
2
5.0
110.0
35.0
-
atoti.
where
(condition, true_measure, false_measure=None)¶ Return a conditional measure.
This function is like an if-then-else statement:
Where the condition is
True
, the new measure will be equal totrue_measure
.Where the condition is
False
, the new measure will be equal tofalse_measure
.
Different types of conditions are supported:
Measures compared to anything measure-like:
m["Test"] == 20
Levels compared to levels:
lvl["source"] == lvl["destination"]
Levels compared to literals:
lvl["city"] == "Paris"
A conjunction of conditions using the
&
operator:(m["Test"] == 20) & (lvl["city"] == "Paris")
- Parameters
condition (
Union
[BooleanMeasure
,LevelCondition
,MultiCondition
,HierarchyIsInCondition
,LevelIsInCondition
]) – The condition to evaluate.true_measure (
Union
[date
,datetime
,int
,float
,str
,Measure
,MeasureConvertible
]) – The measure to propagate where the condition isTrue
.false_measure (
Union
[date
,datetime
,int
,float
,str
,Measure
,MeasureConvertible
,None
]) – The measure to propagate where the condition isFalse
.
- Return type