analyzer

flamme.analyzer

Contain DataFrame analyzers.

flamme.analyzer.BaseAnalyzer

Bases: ABC

Define the base class to analyze a DataFrame.

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import NullValueAnalyzer
>>> analyzer = NullValueAnalyzer()
>>> analyzer
NullValueAnalyzer(figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> analyzer.analyze(frame)

flamme.analyzer.BaseAnalyzer.analyze

analyze(frame: DataFrame) -> BaseSection

Analyze the data in a DataFrame.

Parameters:

Name	Type	Description	Default
frame	DataFrame	The DataFrame with the data to analyze.	required

Returns:

Type	Description
BaseSection	The section report.

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import NullValueAnalyzer
>>> analyzer = NullValueAnalyzer()
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> analyzer.analyze(frame)

flamme.analyzer.ChoiceAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to analyze multiple analyzers.

Parameters:

Name	Type	Description	Default
analyzers	Mapping[str, BaseAnalyzer | dict]	The mappings to analyze. The key of each analyzer is used to organize the metrics and report.	required
selection_fn	Callable[[DataFrame], str]	Specifies a callable with the selection logic. The callable returns the key of the analyzer to use based on the data in the input DataFrame.	required

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import (
...     ChoiceAnalyzer,
...     FilteredAnalyzer,
...     NullValueAnalyzer,
...     DuplicatedRowAnalyzer,
... )
>>> analyzer = ChoiceAnalyzer(
...     {"null": NullValueAnalyzer(), "duplicate": DuplicatedRowAnalyzer()},
...     selection_fn=lambda frame: "null" if frame.isna().values.any() else "duplicate",
... )
>>> analyzer
ChoiceAnalyzer(
  (null): NullValueAnalyzer(figsize=None)
  (duplicate): DuplicatedRowAnalyzer(columns=None, figsize=None)
)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)
>>> section.__class__.__qualname__
NullValueSection
>>> frame = pd.DataFrame({"col": np.arange(10)})
>>> section = analyzer.analyze(frame)
>>> section.__class__.__qualname__
DuplicatedRowSection

flamme.analyzer.ColumnContinuousAdvancedAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the temporal distribution of continuous values.

Parameters:

Name	Type	Description	Default
column	str	The column name.	required
nbins	int | None	The number of bins in the histogram.	None
yscale	str	The y-axis scale. If 'auto', the 'linear' or 'log'/'symlog' scale is chosen based on the distribution.	'auto'
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ColumnContinuousAdvancedAnalyzer
>>> analyzer = ColumnContinuousAdvancedAnalyzer(column="float")
>>> analyzer
ColumnContinuousAdvancedAnalyzer(column=float, nbins=None, yscale=auto, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ColumnContinuousAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the temporal distribution of continuous values.

Parameters:

Name	Type	Description	Default
column	str	The column name.	required
nbins	int | None	The number of bins in the histogram.	None
yscale	str	The y-axis scale. If 'auto', the 'linear' or 'log'/'symlog' scale is chosen based on the distribution.	'auto'
xmin	float | str | None	The minimum value of the range or its associated quantile. q0.1 means the 10% quantile. 0 is the minimum value and 1 is the maximum value.	'q0'
xmax	float | str | None	The maximum value of the range or its associated quantile. q0.9 means the 90% quantile. 0 is the minimum value and 1 is the maximum value.	'q1'
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ColumnContinuousAnalyzer
>>> analyzer = ColumnContinuousAnalyzer(column="float")
>>> analyzer
ColumnContinuousAnalyzer(column=float, nbins=None, yscale=auto, xmin=q0, xmax=q1, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ColumnDiscreteAnalyzer

Bases: BaseAnalyzer

Implement a discrete distribution analyzer.

Parameters:

Name	Type	Description	Default
column	str	The column to analyze.	required
dropna	bool	If True, the NaN values are not included in the analysis.	False
max_rows	int	The maximum number of rows to show in the table.	20
yscale	str	The y-axis scale. If 'auto', the 'linear' or 'log' scale is chosen based on the distribution.	'auto'
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ColumnDiscreteAnalyzer
>>> analyzer = ColumnDiscreteAnalyzer(column="str")
>>> analyzer
ColumnDiscreteAnalyzer(column=str, dropna=False, max_rows=20, yscale=auto, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ColumnSubsetAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to analyze only a subset of the columns.

Parameters:

Name	Type	Description	Default
columns	Sequence[str]	Soecifies the columns to select.	required
analyzer	BaseAnalyzer | dict	The analyzer or its configuration.	required

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ColumnSubsetAnalyzer, NullValueAnalyzer
>>> analyzer = ColumnSubsetAnalyzer(columns=["int", "float"], analyzer=NullValueAnalyzer())
>>> analyzer
ColumnSubsetAnalyzer(
  (columns): 2 ['int', 'float']
  (analyzer): NullValueAnalyzer(figsize=None)
)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ColumnTemporalContinuousAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the temporal distribution of continuous values.

Parameters:

Name	Type	Description	Default
column	str	The column to analyze.	required
dt_column	str	The datetime column used to analyze the temporal distribution.	required
period	str	The temporal period e.g. monthly or daily.	required
yscale	str	The y-axis scale. If 'auto', the 'linear' or 'log'/'symlog' scale is chosen based on the distribution.	'auto'
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import TemporalNullValueAnalyzer
>>> analyzer = ColumnTemporalContinuousAnalyzer(
...     column="float", dt_column="datetime", period="M"
... )
>>> analyzer
ColumnTemporalContinuousAnalyzer(column=float, dt_column=datetime, period=M, yscale=auto, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...         "datetime": pd.to_datetime(
...             ["2020-01-03", "2020-02-03", "2020-03-03", "2020-04-03"]
...         ),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ColumnTemporalDiscreteAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the temporal distribution of discrete values.

Parameters:

Name	Type	Description	Default
column	str	The column to analyze.	required
dt_column	str	The datetime column used to analyze the temporal distribution.	required
period	str	The temporal period e.g. monthly or daily.	required
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ColumnTemporalDiscreteAnalyzer
>>> analyzer = ColumnTemporalDiscreteAnalyzer(
...     column="str", dt_column="datetime", period="M"
... )
>>> analyzer
ColumnTemporalDiscreteAnalyzer(column=str, dt_column=datetime, period=M, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...         "datetime": pd.to_datetime(
...             ["2020-01-03", "2020-02-03", "2020-03-03", "2020-04-03"]
...         ),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ColumnTemporalNullValueAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the temporal distribution of null values for all columns.

A plot is generated for each column.

Parameters:

Name	Type	Description	Default
dt_column	str	The datetime column used to analyze the temporal distribution.	required
period	str	The temporal period e.g. monthly or daily.	required
columns	Sequence[str] | None	The list of columns to analyze. A plot is generated for each column. None means all the columns.	None
ncols	int	The number of columns.	2
figsize	tuple[float, float]	The figure size in inches. The first dimension is the width and the second is the height.	(7, 5)

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ColumnTemporalNullValueAnalyzer
>>> analyzer = ColumnTemporalNullValueAnalyzer("datetime", period="M")
>>> analyzer
ColumnTemporalNullValueAnalyzer(
  (columns): None
  (dt_column): datetime
  (period): M
  (ncols): 2
  (figsize): (7, 5)
)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...         "datetime": pd.to_datetime(
...             ["2020-01-03", "2020-02-03", "2020-03-03", "2020-04-03"]
...         ),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.ContentAnalyzer

Bases: BaseAnalyzer

Implement an analyzer that generates the given custom content.

Parameters:

Name	Type	Description	Default
content	str	The content to use in the HTML code.	required

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import ContentAnalyzer
>>> analyzer = ContentAnalyzer(content="meow")
>>> analyzer
ContentAnalyzer()
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.DataFrameSummaryAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show a summary of the DataFrame.

Parameters:

Name	Type	Description	Default
top	int	The number of most frequent values to show.	5
sort	bool	If True, sort the columns by alphabetical order.	False

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import DataFrameSummaryAnalyzer
>>> analyzer = DataFrameSummaryAnalyzer()
>>> analyzer
DataFrameSummaryAnalyzer(top=5, sort=False)
>>> frame = pd.DataFrame(
...     {
...         "col1": np.array([0, 1, 0, 1]),
...         "col2": np.array([1, 0, 1, 0]),
...         "col3": np.array([1, 1, 1, 1]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.DataTypeAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to find all the value types in each column.

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import DataTypeAnalyzer
>>> analyzer = DataTypeAnalyzer()
>>> analyzer
DataTypeAnalyzer()
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.DuplicatedRowAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the number of duplicated rows.

Parameters:

Name	Type	Description	Default
columns	Sequence[str] | None	The columns used to compute the duplicated rows. None means all the columns.	None
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import DuplicatedRowAnalyzer
>>> analyzer = DuplicatedRowAnalyzer()
>>> analyzer
DuplicatedRowAnalyzer(columns=None, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "col1": np.array([0, 1, 0, 1]),
...         "col2": np.array([1, 0, 1, 0]),
...         "col3": np.array([1, 1, 1, 1]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.FilteredAnalyzer

Bases: BaseAnalyzer

Implement an analyzer that filters the data before to analyze the data.

Parameters:

Name	Type	Description	Default
query	str	Soecifies the query.	required
analyzer	BaseAnalyzer | dict	The analyzer or its configuration.	required

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import FilteredAnalyzer, NullValueAnalyzer
>>> analyzer = FilteredAnalyzer(query="float >= 2.0", analyzer=NullValueAnalyzer())
>>> analyzer
FilteredAnalyzer(
  (query): float >= 2.0
  (analyzer): NullValueAnalyzer(figsize=None)
)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.MappingAnalyzer

Bases: BaseAnalyzer

Implement an analyzer that combine multiple analyzers.

Parameters:

Name	Type	Description	Default
analyzers	Mapping[str, BaseAnalyzer | dict]	The mappings to analyze. The key of each analyzer is used to organize the metrics and report.	required
max_toc_depth	int	The maximum level to show in the table of content. Set this value to 0 to not show the table of content at the beginning of the section.	0

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import (
...     FilteredAnalyzer,
...     NullValueAnalyzer,
...     DuplicatedRowAnalyzer,
...     MappingAnalyzer,
... )
>>> analyzer = MappingAnalyzer(
...     {"null": NullValueAnalyzer(), "duplicate": DuplicatedRowAnalyzer()}
... )
>>> analyzer
MappingAnalyzer(
  (null): NullValueAnalyzer(figsize=None)
  (duplicate): DuplicatedRowAnalyzer(columns=None, figsize=None)
)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.MappingAnalyzer.add_analyzer

add_analyzer(
    key: str,
    analyzer: BaseAnalyzer,
    replace_ok: bool = False,
) -> None

Add an analyzer to the current analyzer.

Parameters:

Name	Type	Description	Default
key	str	The key of the analyzer.	required
analyzer	BaseAnalyzer	The analyzer to add.	required
replace_ok	bool	If False, KeyError is raised if an analyzer with the same key exists. If True, the new analyzer will replace the existing analyzer.	False

Raises:

Type	Description
KeyError	if an analyzer with the same key exists.

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import MappingAnalyzer, NullValueAnalyzer, DuplicatedRowAnalyzer
>>> analyzer = MappingAnalyzer({"null": NullValueAnalyzer()})
>>> analyzer.add_analyzer("duplicate", DuplicatedRowAnalyzer())
>>> analyzer
MappingAnalyzer(
  (null): NullValueAnalyzer(figsize=None)
  (duplicate): DuplicatedRowAnalyzer(columns=None, figsize=None)
)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.MarkdownAnalyzer

Bases: BaseAnalyzer

Implement an analyzer that adds a mardown string to the report.

Parameters:

Name	Type	Description	Default
desc	str	The markdown description.	required

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import MarkdownAnalyzer
>>> analyzer = MarkdownAnalyzer(desc="hello cats!")
>>> analyzer
MarkdownAnalyzer()
>>> frame = pd.DataFrame({})
>>> section = analyzer.analyze(frame)

flamme.analyzer.MostFrequentValuesAnalyzer

Bases: BaseAnalyzer

Implement a most frequent values analyzer for a given column.

Parameters:

Name	Type	Description	Default
column	str	The column to analyze.	required
dropna	bool	If True, the NaN values are not included in the analysis.	False
top	int	The maximum number of values to show.	100

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import MostFrequentValuesAnalyzer
>>> analyzer = MostFrequentValuesAnalyzer(column="str")
>>> analyzer
MostFrequentValuesAnalyzer(column=str, dropna=False, top=100)
>>> frame = pd.DataFrame({"col": np.array([np.nan, 1, 0, 1])})
>>> section = analyzer.analyze(frame)

flamme.analyzer.NullValueAnalyzer

Bases: BaseAnalyzer

Implement a null value analyzer.

Parameters:

Name	Type	Description	Default
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import NullValueAnalyzer
>>> analyzer = NullValueAnalyzer()
>>> analyzer
NullValueAnalyzer(figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "int": np.array([np.nan, 1, 0, 1]),
...         "float": np.array([1.2, 4.2, np.nan, 2.2]),
...         "str": np.array(["A", "B", None, np.nan]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.TableOfContentAnalyzer

Bases: BaseAnalyzer

Implement a wrapper around an analyzer to add a table of content to the generated section report.

Parameters:

Name	Type	Description	Default
analyzer	BaseAnalyzer | dict	The analyzer or its configuration.	required
max_toc_depth	int	The maximum level to show in the table of content.	1

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import TableOfContentAnalyzer, DuplicatedRowAnalyzer
>>> analyzer = TableOfContentAnalyzer(DuplicatedRowAnalyzer())
>>> analyzer
TableOfContentAnalyzer(
  (analyzer): DuplicatedRowAnalyzer(columns=None, figsize=None)
  (max_toc_depth): 1
)
>>> frame = pd.DataFrame(
...     {
...         "col1": np.array([0, 1, 0, 1]),
...         "col2": np.array([1, 0, 1, 0]),
...         "col3": np.array([1, 1, 1, 1]),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.TemporalNullValueAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the temporal distribution of null values for all columns.

Parameters:

Name	Type	Description	Default
dt_column	str	The datetime column used to analyze the temporal distribution.	required
period	str	The temporal period e.g. monthly or daily.	required
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import TemporalNullValueAnalyzer
>>> analyzer = TemporalNullValueAnalyzer(dt_column="datetime", period="M")
>>> analyzer
TemporalNullValueAnalyzer(
  (columns): None
  (dt_column): datetime
  (period): M
  (figsize): None
)
>>> frame = pd.DataFrame(
...     {
...         "col": np.array([np.nan, 1, 0, 1]),
...         "datetime": pd.to_datetime(
...             ["2020-01-03", "2020-02-03", "2020-03-03", "2020-04-03"]
...         ),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.TemporalRowCountAnalyzer

Bases: BaseAnalyzer

Implement an analyzer to show the number of rows per temporal window.

Parameters:

Name	Type	Description	Default
dt_column	str	The datetime column used to analyze the temporal distribution.	required
period	str	The temporal period e.g. monthly or daily.	required
figsize	tuple[float, float] | None	The figure size in inches. The first dimension is the width and the second is the height.	None

Example usage:

>>> import numpy as np
>>> import pandas as pd
>>> from flamme.analyzer import TemporalRowCountAnalyzer
>>> analyzer = TemporalRowCountAnalyzer(dt_column="datetime", period="M")
>>> analyzer
TemporalRowCountAnalyzer(dt_column=datetime, period=M, figsize=None)
>>> frame = pd.DataFrame(
...     {
...         "datetime": pd.to_datetime(
...             ["2020-01-03", "2020-02-03", "2020-03-03", "2020-04-03"]
...         ),
...     }
... )
>>> section = analyzer.analyze(frame)

flamme.analyzer.is_analyzer_config

is_analyzer_config(config: dict) -> bool

Indicate if the input configuration is a configuration for a BaseAnalyzer.

This function only checks if the value of the key _target_ is valid. It does not check the other values. If _target_ indicates a function, the returned type hint is used to check the class.

Parameters:

Name	Type	Description	Default
config	dict	The configuration to check.	required

Returns:

Type	Description
bool	True if the input configuration is a configuration for a BaseAnalyzer object.

Example usage:

>>> from flamme.analyzer import is_analyzer_config
>>> is_analyzer_config({"_target_": "flamme.analyzer.NullValueAnalyzer"})
True

flamme.analyzer.setup_analyzer

setup_analyzer(
    analyzer: BaseAnalyzer | dict,
) -> BaseAnalyzer

Set up an analyzer.

The analyzer is instantiated from its configuration by using the BaseAnalyzer factory function.

Parameters:

Name	Type	Description	Default
analyzer	BaseAnalyzer | dict	Specifies an analyzer or its configuration.	required

Returns:

Type	Description
BaseAnalyzer	An instantiated analyzer.

Example usage:

>>> from flamme.analyzer import setup_analyzer
>>> analyzer = setup_analyzer({"_target_": "flamme.analyzer.NullValueAnalyzer"})
>>> analyzer
NullValueAnalyzer(figsize=None)