teilab.plot.matplotlib module

A group of plot functions useful for analysis using matplotlib.

If you would like to

• make changes to the plot or draw other plots, please refer to the official documentation.

• see the analysis examples, please refer to the notebook/Local/Main-Lecture-Material-matplotlib.ipynb

※ Compared to teilab.plot.plotly module, you can see the difference between the two libraries. (matplotlib, and plotly)

teilab.plot.matplotlib.density_plot(data: nptyping.types._ndarray.NDArray[Any, Any, nptyping.types._object.Object], labels: List[str] = [], colors: List[Any] = [], cmap: Optional[Union[str, matplotlib.colors.Colormap]] = None, bins: Union[int, List[numbers.Number], str] = 100, range: Optional[Tuple[float, float]] = None, title: str = 'Density Distribution', ax: Optional[matplotlib.axes._axes.Axes] = None, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → matplotlib.axes._axes.Axes

Plot density dirstibutions.

Parameters

• data (NDArray[(Any,Any),Number]) – Input data. Shape = ( n_samples, n_features )

• labels (List[str], optional) – Labels for each sample. Defaults to [].

• colors (List[Any], optional) – Colors for each sample. Defaults to [].

• cmap (Optional[Union[str,Colormap]], optional) – A Colormap object or a color map name. Defaults to None.

• bins (Union[int,List[Number],str], optional) – The number of equal-width bins in the given range. Defaults to 100.

• range (Optional[Tuple[float,float]], optional) – The lower and upper range of the bins. If not provided, range is simply (data[i].min(), data[i].max()). Defaults to None.

• title (str, optional) – Figure Title. Defaults to "Density Distribution".

• ax (Optional[Axes], optional) – An instance of Axes. Defaults to None.

• plotkwargs (Dict[str,Any]) – Keyword arguments for ax.plot. Defaults to {}.

• layoutkwargs (Dict[str,Any]) – Keyword arguments for update_layout. Defaults to {}.

Returns

An instance of Axes with density distributions.

Return type

Axes

>>> import numpy as np
>>> from teilab.utils import dict2str, subplots_create
>>> from teilab.plot.matplotlib import density_plot
>>> n_samples, n_features = (4, 1000)
>>> data = np.random.RandomState(0).normal(loc=np.expand_dims(np.arange(n_samples), axis=1), size=(n_samples,n_features))
>>> kwargses = [{"bins":100},{"bins":10},{"bins":"auto"}]
>>> nfigs = len(kwargses)
>>> fig, axes = subplots_create(ncols=nfigs, figsize=(int(6*nfigs),4), style="matplotlib")
>>> for ax,kwargs in zip(axes,kwargses):
...     _ = density_plot(data, ax=ax, title=dict2str(kwargs), **kwargs)
>>> fig.show()

teilab.plot.matplotlib.cumulative_density_plot(data: Union[nptyping.types._ndarray.NDArray[Any, Any, nptyping.types._object.Object], pandas.core.series.Series], labels: List[str] = [], colors: List[Any] = [], cmap: Optional[Union[str, matplotlib.colors.Colormap]] = None, title: str = 'Cumulative Density Distribution', ylabel='Frequency', ax: Optional[matplotlib.axes._axes.Axes] = None, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → matplotlib.axes._axes.Axes

Plot cumulative density dirstibutions.

Parameters

• data (Union[NDArray[(Any,Any),Number],Series]) – Input data. Shape = ( n_samples, n_features )

• labels (List[str], optional) – Labels for each sample. Defaults to [].

• colors (List[Any], optional) – Colors for each sample. Defaults to [].

• cmap (Optional[Union[str,Colormap]], optional) – A Colormap object or a color map name. Defaults to None.

• title (str, optional) – Subplot Title. Defaults to "Cumulative Density Distribution".

• ylabel (str, optional) – Subplot y-axis label. Defaults to "Frequency"

• ax (Optional[Axes], optional) – An instance of Axes. Defaults to None.

• plotkwargs (Dict[str,Any]) – Keyword arguments for ax.plot. Defaults to {}.

• layoutkwargs (Dict[str,Any]) – Keyword arguments for update_layout. Defaults to {}.

Returns

An instance of Axes with cumulative density distributions.

Return type

Axes

>>> import numpy as np
>>> from teilab.utils import dict2str, subplots_create
>>> from teilab.plot.matplotlib import cumulative_density_plot
>>> n_samples, n_features = (4, 1000)
>>> data = np.random.RandomState(0).normal(loc=np.expand_dims(np.arange(n_samples), axis=1), size=(n_samples,n_features))
>>> fig, ax = subplots_create(figsize=(6,4), style="matplotlib")
>>> ax = cumulative_density_plot(data, ax=ax, xlabel="value")
>>> fig.show()

teilab.plot.matplotlib.boxplot(data: nptyping.types._ndarray.NDArray[Any, Any, nptyping.types._object.Object], labels: List[str] = [], colors: List[Any] = [], cmap: Optional[Union[str, matplotlib.colors.Colormap]] = None, vert: bool = True, title: str = 'Box Plot', ax: Optional[matplotlib.axes._axes.Axes] = None, plotkwargs: Dict[str, Any] = {'flierprops': {'marker': 'o', 'markeredgecolor': 'black', 'markerfacecolor': 'red', 'markersize': 4}, 'medianprops': {'color': 'black'}}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → matplotlib.axes._axes.Axes

Plot box plots.

Parameters

• data (NDArray[(Any,Any),Number]) – Input data. Shape = ( n_samples, n_features )

• labels (List[str], optional) – Labels for each sample. Defaults to [].

• colors (List[Any], optional) – Colors for each sample. Defaults to [].

• cmap (Optional[Union[str,Colormap]], optional) – A Colormap object or a color map name. Defaults to None.

• vert (bool, optional) – Whether to draw vertical boxes or horizontal boxes. Defaults to True .

• title (str, optional) – Figure Title. Defaults to "Box Plot".

• ax (Optional[Axes], optional) – An instance of Axes. Defaults to None.

• plotkwargs (Dict[str,Any]) – Keyword arguments for ax.plot`. Defaults to ``dict(medianprops={"color": "black"}, flierprops={"marker":'o',"markersize":4,"markerfacecolor":"red","markeredgecolor":"black"}).

• layoutkwargs (Dict[str,Any]) – Keyword arguments for update_layout. Defaults to {}.

Returns

An instance of Axes with box plots.

Return type

Axes

>>> import numpy as np
>>> from teilab.utils import dict2str, subplots_create
>>> from teilab.plot.matplotlib import boxplot
>>> n_samples, n_features = (4, 1000)
>>> data = np.random.RandomState(0).normal(loc=np.expand_dims(np.arange(n_samples), axis=1), size=(n_samples,n_features))
>>> kwargses = [{"vert":True},{"vert":False}]
>>> nfigs = len(kwargses)
>>> fig, axes = subplots_create(ncols=nfigs, figsize=(int(6*nfigs),4), style="matplotlib")
>>> for ax,kwargs in zip(axes,kwargses):
...     _ = boxplot(data, title=dict2str(kwargs), ax=ax, **kwargs)
>>> fig.show()

teilab.plot.matplotlib.XYplot(df: pandas.core.frame.DataFrame, x: str, y: str, logarithmic: bool = True, color: Optional[str] = None, size: Optional[int] = None, ax: Optional[matplotlib.axes._axes.Axes] = None, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → matplotlib.axes._axes.Axes

XY plot.

• x-axis : \(\log_2{(\text{gProcessedSignal})}\) for each gene in sample X

• y-axis : \(\log_2{(\text{gProcessedSignal})}\) for each gene in sample Y

Parameters

• df (pd.DataFrame) – DataFrame

• x (str) – The column name for sample X.

• y (str) – The column name for sample Y.

• logarithmic (bool) – Whether to log the values of df[x] and df[y]

• color (Optional[str], optional) – The column name in df to assign color to marks. Defaults to None.

• size (Optional[str], optional) – The column name in df to assign mark sizes. Defaults to None.

• ax (Optional[Axes], optional) – An instance of Axes. Defaults to None.

• plotkwargs (Dict[str,Any]) – Keyword arguments for ax.plot`. Defaults to ``dict(medianprops={"color": "black"}, flierprops={"marker":'o',"markersize":4,"markerfacecolor":"red","markeredgecolor":"black"}).

• layoutkwargs (Dict[str,Any]) – Keyword arguments for update_layout. Defaults to {}.

Returns

An instance of Axes with XY plot.

Return type

Axes

>>> import pandas as pd
>>> from teilab.datasets import TeiLabDataSets
>>> from teilab.plot.matplotlib import XYplot
>>> from teilab.utils import subplots_create
>>> datasets = TeiLabDataSets(verbose=False)
>>> df_anno = datasets.read_data(no=0, usecols=datasets.ANNO_COLNAMES)
>>> reliable_index = set(df_anno.index)
>>> df_combined = df_anno.copy(deep=True)
>>> for no in range(2):
...     df_data = datasets.read_data(no=no)
...     reliable_index = reliable_index & set(datasets.reliable_filter(df=df_data))
...     df_combined = pd.concat([
...         df_combined,
...         df_data[[datasets.TARGET_COLNAME]].rename(columns={datasets.TARGET_COLNAME: datasets.samples.Condition[no]})
...     ], axis=1)
>>> df_combined = df_combined.loc[reliable_index, :].reset_index(drop=True)
>>> fig, ax = subplots_create(figsize=(6,4), style="matplotlib")
>>> ax = XYplot(df=df_combined, x=datasets.samples.Condition[0], y=datasets.samples.Condition[1], ax=ax)
>>> fig.show()

teilab.plot.matplotlib.MAplot(df: pandas.core.frame.DataFrame, x: str, y: str, color: Optional[str] = None, size: Optional[str] = None, hlines: Union[Dict[numbers.Number, Dict[str, Any]], List[numbers.Number]] = [], ax: Optional[matplotlib.axes._axes.Axes] = None, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → matplotlib.axes._axes.Axes

MA plot.

• x-axis (Average): \(\log_2{(\text{gProcessedSignal})}\) for each gene in sample X

• y-axis (Minus) : \(\log_2{(\text{gProcessedSignal})}\) for each gene in sample Y

Parameters

• df (pd.DataFrame) – DataFrame

• x (str) – The column name for sample X.

• y (str) – The column name for sample Y.

• color (Optional[str], optional) – The column name in df to assign color to marks. Defaults to None.

• size (Optional[str], optional) – The column name in df to assign mark sizes. Defaults to None.

• hlines (Union[Dict[Number,Dict[str,Any]],List[Number]]) – Height (y) to draw a horizon. If given a dictionary, values means kwargs of ax.hlines

• ax (Optional[Axes], optional) – An instance of Axes. Defaults to None.

• plotkwargs (Dict[str,Any]) – Keyword arguments for ax.plot`. Defaults to ``dict(medianprops={"color": "black"}, flierprops={"marker":'o',"markersize":4,"markerfacecolor":"red","markeredgecolor":"black"}).

• layoutkwargs (Dict[str,Any]) – Keyword arguments for update_layout. Defaults to {}.

Returns

An instance of Axes with MA plot.

Return type

Axes

>>> import pandas as pd
>>> from teilab.datasets import TeiLabDataSets
>>> from teilab.plot.matplotlib import MAplot
>>> from teilab.utils import subplots_create
>>> datasets = TeiLabDataSets(verbose=False)
>>> df_anno = datasets.read_data(no=0, usecols=datasets.ANNO_COLNAMES)
>>> reliable_index = set(df_anno.index)
>>> df_combined = df_anno.copy(deep=True)
>>> for no in range(2):
...     df_data = datasets.read_data(no=no)
...     reliable_index = reliable_index & set(datasets.reliable_filter(df=df_data))
...     df_combined = pd.concat([
...         df_combined,
...         df_data[[datasets.TARGET_COLNAME]].rename(columns={datasets.TARGET_COLNAME: datasets.samples.Condition[no]})
...     ], axis=1)
>>> df_combined = df_combined.loc[reliable_index, :].reset_index(drop=True)
>>> fig, ax = subplots_create(figsize=(6,4), style="matplotlib")
>>> ax = MAplot(
...     df=df_combined,
...     x=datasets.samples.Condition[0], y=datasets.samples.Condition[1], ax=ax,
...     hlines={
...         -1 : dict(colors='r', linewidths=1),
...          0 : dict(colors='r', linewidths=2),
...          1 : dict(colors='r', linewidths=1),
...     }
>>> )
>>> fig.show()

teilab.plot.matplotlib.update_layout(ax: matplotlib.axes._axes.Axes, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None, xlim: List[float] = [], ylim: List[float] = [], legend: bool = True) → matplotlib.axes._axes.Axes

Update the layout of matplotlib.axes.Axes object. See Documentation for details.

Parameters

• ax (Axes) – An instance of Axes.

• title (Optional[str], optional) – Figure title. Defaults to None.

• xlabel (Optional[str], optional) – X axis label. Defaults to None.

• ylabel (Optional[str], optional) – Y axis label. Defaults to None.

• xlim (List[float]) – X axis range. Defaults to [].

• ylim (List[float]) – Y axis range. Defaults to [].

• legend (bool, optional) – Whether to show legend. Defaults to True.

Returns

A figure element with layout updated.

Return type

Axes

>>> from teilab.utils import subplots_create
>>> from teilab.plot.matplotlib import update_layout
>>> fig, axes = subplots_create(ncols=2, style="matplotlib", figsize=(8,4))
>>> for ax in axes: ax.scatter(1,1,label="center")
>>> _ = update_layout(ax=axes[1], xlim=(0,2), ylim=(0,2), legend=True)
>>> fig.show()