"""Plot data distribution based on pandas `DataFrames` or `Series`."""
import logging
from typing import Iterable, Optional, Tuple, Union
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib.axes import Axes
logger = logging.getLogger(__name__)
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def min_max(s: pd.Series) -> Tuple[int]:
"""Get the min and max as integer from a pandas.Series.
Parameters
----------
s : pd.Series
Series of intensities.
Returns
-------
Tuple[int]
_description_
"""
min_bin, max_bin = (int(s.min()), (int(s.max()) + 1))
return min_bin, max_bin
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def get_min_max_iterable(series: Iterable[pd.Series]) -> Tuple[int]:
"""Get the min and max as integer from an iterable of pandas.Series."""
min_bin = int(min((s.min() for s in series)))
max_bin = int(max(s.max() for s in series))
return min_bin, max_bin
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def plot_histogram_intensities(
s: pd.Series, interval_bins=1, min_max: Tuple[int] = None, ax=None, **kwargs
) -> Tuple[Axes, range]:
"""Plot intensities in Series in a certain range and equally spaced intervals."""
if min_max is None:
min_max = get_min_max_iterable([s])
min_bin, max_bin = min_max
bins = range(min_bin, max_bin, interval_bins)
ax = s.plot.hist(bins=bins, xticks=list(bins), ax=ax, **kwargs)
ax.yaxis.set_major_formatter("{x:,.0f}")
return ax, bins
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def plot_observations(
df: pd.DataFrame,
ax: Axes = None,
title: str = "",
axis: int = 1,
size: int = 1,
ylabel: str = "Frequency",
xlabel: Optional[str] = None,
) -> Axes:
"""Plot non missing observations by row (axis=1) or column (axis=0) in
order of number of available observations.
No binning is applied, only counts of non-missing values are plotted.
Parameters
----------
df : pd.DataFrame
DataFrame on which `notna` is applied
ax : Axes, optional
Axes to plot on, by default None
title : str, optional
Axes title, by default ''
axis : int, optional
dimension to sum over, by default 1
ylabel : str, optional
y-Axis label, by default 'number of features'
xlabel : str, optional
x-Axis label, by default 'Samples ordered by number of features'
Returns
-------
Axes
Axes on which plot was plotted
"""
if xlabel is None:
if df.columns.name:
xlabel = f"Samples ordered by identified {df.columns.name}"
else:
xlabel = "Samples ordered by identified features"
ax = (
df.notna()
.sum(axis=axis)
.sort_values()
.reset_index(drop=True)
.plot(ax=ax, style=".", ms=size, title=title, ylabel=ylabel, xlabel=xlabel)
)
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
return ax
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def plot_missing_dist_highdim(
data: pd.DataFrame,
min_feat_per_sample: int = None,
min_samples_per_feat: int = None,
) -> matplotlib.figure.Figure:
"""Plot missing distribution (cdf) in high dimensional data.
Parameters
----------
data : pd.DataFrame
Intensity table with samples in rows and features in columns.
min_feat_per_sample : int, optional
Show the minimum required features a sample has to have, by default None
min_samples_per_feat : int, optional
Show the minimum required number of samples a feature has to be found in, by default None
Returns
-------
matplotlib.figure.Figure
Figure with two plots (Axes).
"""
fig, axes = plt.subplots(1, 2, figsize=(4, 2))
not_na = data.notna()
name = "features per sample"
ax = (
not_na.sum(axis=1)
.to_frame(name)
.groupby(name)
.size()
.sort_index()
.plot.line(style=".", ax=axes[0])
)
ax.set_ylabel("observations (samples)")
if min_feat_per_sample is not None:
ax.vlines(min_feat_per_sample, *ax.get_ylim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
name = "samples per feature"
ax = (
not_na.sum(axis=0)
.to_frame(name)
.groupby(name)
.size()
.sort_index()
.plot.line(style=".", ax=axes[1])
)
if min_samples_per_feat is not None:
ax.vlines(min_samples_per_feat, *ax.get_ylim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
ax.set_ylabel("observations (features)")
fig.tight_layout()
return fig
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def plot_missing_dist_boxplots(
data: pd.DataFrame, min_feat_per_sample=None, min_samples_per_feat=None
) -> matplotlib.figure.Figure:
fig, axes = plt.subplots(1, 2, figsize=(4, 2))
not_na = data.notna()
idx_label, col_label = "feature", "sample"
if data.index.name:
idx_label = data.index.name
if data.columns.name:
col_label = data.columns.name
ax = (
not_na.sum(axis=1)
.rename(f"observation ({idx_label}) per {col_label}")
.plot.box(ax=axes[0])
)
if min_feat_per_sample is not None:
ax.hlines(min_feat_per_sample, *ax.get_xlim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
ax = (
not_na.sum(axis=0)
.rename(f"observation ({idx_label}) per {col_label}")
.plot.box(ax=axes[1])
)
if min_samples_per_feat is not None:
ax.hlines(min_samples_per_feat, *ax.get_xlim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
return fig
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def plot_missing_pattern_violinplot(
data: pd.DataFrame, min_feat_per_sample=None, min_samples_per_feat=None
) -> matplotlib.figure.Figure:
fig, axes = plt.subplots(1, 2, figsize=(4, 2))
not_na = data.notna()
name = "features per sample"
ax = sns.violinplot(
data=not_na.sum(axis=1).to_frame(name),
ax=axes[0],
)
if min_feat_per_sample is not None:
ax.hlines(min_feat_per_sample, *ax.get_xlim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
ax.set_ylabel("observations (features)")
name = "samples per feature"
ax = sns.violinplot(
data=not_na.sum(axis=0).to_frame(name),
ax=axes[1],
)
if min_samples_per_feat is not None:
ax.hlines(min_samples_per_feat, *ax.get_xlim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
ax.set_ylabel("observations (samples)")
fig.tight_layout()
return fig
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def plot_missing_pattern_histogram(
data: pd.DataFrame,
bins: int = 20,
min_feat_per_sample=None,
min_samples_per_feat=None,
) -> matplotlib.figure.Figure:
fig, axes = plt.subplots(1, 2, figsize=(4, 2))
not_na = data.notna()
idx_label, col_label = "sample", "feature"
if data.index.name:
idx_label = data.index.name
if data.columns.name:
col_label = data.columns.name
name = f"observations per {idx_label}"
ax = (
not_na.sum(axis=1)
.to_frame(name)
.plot.hist(
ax=axes[0],
bins=bins,
legend=False,
)
)
if min_feat_per_sample is not None:
ax.vlines(min_feat_per_sample, *ax.get_ylim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
ax.set_xlabel(name)
ax.set_ylabel("observations in bin")
# second
name = f"observations per {col_label}"
ax = data = (
not_na.sum(axis=0)
.to_frame(name)
.plot.hist(
ax=axes[1],
bins=bins,
legend=False,
)
)
if min_samples_per_feat is not None:
ax.vlines(min_samples_per_feat, *ax.get_ylim(), color="red")
ax.locator_params(axis="y", integer=True)
ax.yaxis.set_major_formatter("{x:,.0f}")
ax.set_xlabel(name)
ax.set_ylabel(None)
fig.tight_layout()
return fig
