from __future__ import annotations
from typing import TYPE_CHECKING
import ehrdata as ed
import missingno as msno
from ehrapy._compat import function_2D_only, use_ehrdata
if TYPE_CHECKING:
from anndata import AnnData
from ehrdata import EHRData
[docs]
@function_2D_only()
@use_ehrdata(deprecated_after="1.0.0")
def missing_values_matrix(
edata: EHRData | AnnData,
*,
filter: str | None = None,
max_cols: int = 0,
max_percentage: float = 0,
sort: str | None = None,
figsize: tuple = (25, 10),
width_ratios: tuple = (15, 1),
color: tuple = (0.25, 0.25, 0.25),
fontsize: float = 16,
labels: bool = True,
label_rotation: float = 45,
sparkline: bool = True,
categoricals: bool = False,
layer: str | None = None,
): # pragma: no cover
"""A matrix visualization of the nullity of the given data object.
Args:
edata: Central data object.
filter: The filter to apply to the matrix. Should be one of "top", "bottom", or None.
max_cols: The max number of columns from the data object to include.
max_percentage: The max percentage fill of the columns from the data object.
sort: The row sort order to apply. Can be "ascending", "descending", or None.
figsize: The size of the figure to display.
width_ratios: The ratio of the width of the matrix to the width of the sparkline.
color: The color of the filled columns.
fontsize: The figure's font size.
labels: Whether or not to display the column names.
label_rotation: What angle to rotate the text labels to.
sparkline: Whether or not to display the sparkline.
categoricals: Whether to include "ehrapycat" columns to the plot.
layer: The layer to use.
Returns:
The plot axis.
Examples:
>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pl.missing_values_matrix(edata, filter="bottom", max_cols=15, max_percentage=0.999)
Preview:
.. image:: /_static/docstring_previews/missingno_matrix.png
"""
df = ed.io.to_pandas(edata, layer=layer)
if not categoricals:
non_categorical_columns = [col for col in df if not col.startswith("ehrapycat")]
return msno.matrix(
df[non_categorical_columns],
filter,
max_cols,
max_percentage,
sort,
figsize,
width_ratios,
color,
fontsize,
labels,
label_rotation,
sparkline,
)
else:
return msno.matrix(
df,
filter,
max_cols,
max_percentage,
sort,
figsize,
width_ratios,
color,
fontsize,
labels,
label_rotation,
sparkline,
)
[docs]
@function_2D_only()
@use_ehrdata(deprecated_after="1.0.0")
def missing_values_barplot(
edata: EHRData | AnnData,
*,
log: bool = False,
filter: str | None = None,
max_cols: int = 0,
max_percentage: float = 0,
sort: str | None = None,
figsize: tuple | None = None,
color: str = "dimgray",
fontsize: float = 16,
labels: str | None = None,
label_rotation: float = 45,
orientation: str | None = None,
categoricals: bool = False,
layer: str | None = None,
): # pragma: no cover
"""A bar chart visualization of the nullity of the given data object.
Args:
edata: Central data object.
log: Whether to display a logarithmic plot.
filter: The filter to apply to the barplot. Should be one of "top", "bottom", or None.
max_cols: The max number of columns from the data object to include.
max_percentage: The max percentage fill of the columns from the data object.
sort: The row sort order to apply. Can be "ascending", "descending", or None.
figsize: The size of the figure to display.
color: The color of the filled columns.
fontsize: The figure's font size.
labels: Whether to display the column names.
label_rotation: What angle to rotate the text labels to.
orientation: The way the bar plot is oriented.
categoricals: Whether to include "ehrapycat" columns to the plot.
layer: The layer to use.
Returns:
The plot axis.
Examples:
>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pl.missing_values_barplot(edata, filter="bottom", max_cols=15, max_percentage=0.999)
Preview:
.. image:: /_static/docstring_previews/missingno_barplot.png
"""
df = ed.io.to_pandas(edata, layer=layer)
if not categoricals:
non_categorical_columns = [col for col in df if not col.startswith("ehrapycat")]
return msno.bar(
df[non_categorical_columns],
figsize,
fontsize,
labels,
label_rotation,
log,
color,
filter,
max_cols,
max_percentage,
sort,
orientation,
)
else:
return msno.bar(
df,
figsize,
fontsize,
labels,
label_rotation,
log,
color,
filter,
max_cols,
max_percentage,
sort,
orientation,
)
[docs]
@function_2D_only()
@use_ehrdata(deprecated_after="1.0.0")
def missing_values_heatmap(
edata: EHRData | AnnData,
*,
filter: str | None = None,
max_cols: int = 0,
max_percentage: float = 0,
sort: str | None = None,
figsize: tuple = (20, 12),
fontsize: float = 16,
labels: bool = True,
label_rotation: float = 45,
cmap: str = "RdBu",
vmin: int = -1,
vmax: int = 1,
cbar: bool = True,
categoricals: bool = False,
layer: str | None = None,
): # pragma: no cover
"""Presents a `seaborn` heatmap visualization of nullity correlation in the given data object.
Note that this visualization has no special support for large datasets. For those, try the dendrogram instead.
Args:
edata: Central data object.
filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None.
max_cols: The max number of columns from the data object to include.
max_percentage: The max percentage fill of the columns from the data object.
sort: The row sort order to apply. Can be "ascending", "descending", or None.
figsize: The size of the figure to display.
fontsize: The figure's font size.
labels: Whether or not to display the column names.
label_rotation: What angle to rotate the text labels to.
cmap: What `matplotlib` colormap to use.
vmin: The normalized colormap threshold.
vmax: The normalized colormap threshold.
cbar: Whether to draw a colorbar.
categoricals: Whether to include "ehrapycat" columns to the plot.
layer: The layer to use.
Returns:
The plot axis.
Examples:
>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pl.missing_values_heatmap(edata, filter="bottom", max_cols=15, max_percentage=0.999)
Preview:
.. image:: /_static/docstring_previews/missingno_heatmap.png
"""
df = ed.io.to_pandas(edata, layer=layer)
if not categoricals:
non_categorical_columns = [col for col in df if not col.startswith("ehrapycat")]
return msno.heatmap(
df[non_categorical_columns],
filter,
max_cols,
max_percentage,
sort,
figsize,
fontsize,
labels,
label_rotation,
cmap,
vmin,
vmax,
cbar,
)
else:
return msno.heatmap(
df,
filter,
max_cols,
max_percentage,
sort,
figsize,
fontsize,
labels,
label_rotation,
cmap,
vmin,
vmax,
cbar,
)
[docs]
@function_2D_only()
@use_ehrdata(deprecated_after="1.0.0")
def missing_values_dendrogram(
edata: EHRData | AnnData,
*,
method: str = "average",
filter: str | None = None,
max_cols: int = 0,
max_percentage: float = 0,
orientation: str | None = None,
figsize: tuple | None = None,
fontsize: float = 16,
label_rotation: float = 45,
categoricals: bool = False,
layer: str | None = None,
):
"""Fits a `scipy` hierarchical clustering algorithm and visualizes the results as a `scipy` dendrogram.
The default vertical display will fit up to 50 columns. If more than 50 columns are specified and orientation is
left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.
Args:
edata: Central data object.
method: The distance measure being used for clustering. This parameter is passed to `scipy.hierarchy`.
filter: The filter to apply to the dendrogram. Should be one of "top", "bottom", or None.
max_cols: The max number of columns from the data object to include.
max_percentage: The max percentage fill of the columns from the data object.
figsize: The size of the figure to display.
fontsize: The figure's font size.
orientation: The way the dendrogram is oriented.
label_rotation: What angle to rotate the text labels to. .
categoricals: Whether to include "ehrapycat" columns to the plot.
layer: The layer to use.
Returns:
The plot axis.
Example:
>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pl.missing_values_dendrogram(edata, filter="bottom", max_cols=15, max_percentage=0.999)
Preview:
.. image:: /_static/docstring_previews/missingno_dendrogram.png
"""
df = ed.io.to_pandas(edata, layer=layer)
if not categoricals:
non_categorical_columns = [col for col in df if not col.startswith("ehrapycat")]
return msno.dendrogram(
df[non_categorical_columns],
method,
filter,
max_cols,
max_percentage,
orientation,
figsize,
fontsize,
label_rotation,
)
else:
return msno.dendrogram(
df, method, filter, max_cols, max_percentage, orientation, figsize, fontsize, label_rotation
)
