teilab.plot.plotly module

A group of plot functions useful for analysis using plotly.

If you would like to

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

teilab.plot.plotly.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: int = 100, range: Optional[Tuple[float, float]] = None, title: str = 'Density Distribution', fig: Optional[plotly.graph_objs._figure.Figure] = None, row: int = 1, col: int = 1, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → plotly.graph_objs._figure.Figure[source]

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 (int, 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".

  • fig (Optional[Figure], optional) – An instance of Figure.

  • row (int, optional) – Row of subplots. Defaults to 1.

  • col (int, optional) – Column of subplots. Defaults to 1.

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

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

Returns

An instance of Figure with density distributions.

Return type

Figure

>>> import numpy as np
>>> from teilab.utils import dict2str, subplots_create
>>> from teilab.plot.plotly 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"}]
>>> title = ", ".join([dict2str(kwargs) for kwargs in kwargses])
>>> nfigs = len(kwargses)
>>> fig = subplots_create(ncols=nfigs, style="plotly")
>>> for i,(kwargs) in enumerate(kwargses, start=1):
...     _ = density_plot(data, fig=fig, title=title, col=i, legend=False, width=1000, height=400, **kwargs)
>>> fig.show()

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teilab.plot.plotly.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', fig: Optional[plotly.graph_objs._figure.Figure] = None, row: int = 1, col: int = 1, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → plotly.graph_objs._figure.Figure[source]

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".

  • fig (Optional[Figure], optional) – An instance of Figure.

  • row (int, optional) – Row of subplots. Defaults to 1.

  • col (int, optional) – Column of subplots. Defaults to 1.

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

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

Returns

An instance of Figure with box plot

Return type

Figure

>>> import numpy as np
>>> from teilab.utils import dict2str, subplots_create
>>> from teilab.plot.plotly 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}]
>>> title = ", ".join([dict2str(kwargs) for kwargs in kwargses])
>>> nfigs = len(kwargses)
>>> fig = subplots_create(ncols=nfigs, style="plotly")
>>> for col,kwargs in enumerate(kwargses, start=1):
...     _ = boxplot(data, title=title, fig=fig, col=col, width=1000, height=400, **kwargs)
>>> fig.show()

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teilab.plot.plotly.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', fig: Optional[plotly.graph_objs._figure.Figure] = None, row: int = 1, col: int = 1, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → plotly.graph_objs._figure.Figure[source]

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) – Figure Title. Defaults to "Cumulative Density Distribution".

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

  • fig (Optional[Figure], optional) – An instance of Figure.

  • row (int, optional) – Row of subplots. Defaults to 1.

  • col (int, optional) – Column of subplots. Defaults to 1.

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

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

Returns

An instance of Figure with cumulative density distributions.

Return type

Figure

>>> import numpy as np
>>> from teilab.plot.plotly 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 = cumulative_density_plot(data, fig=None, xlabel="value", width=800, height=400)
>>> fig.show()

Open html

teilab.plot.plotly.XYplot(df: pandas.core.frame.DataFrame, x: str, y: str, logarithmic: bool = True, color: Optional[str] = None, symbol: Optional[str] = None, size: Optional[str] = None, hover_data: Optional[List[str]] = None, hover_name: Optional[str] = None, fig: Optional[plotly.graph_objs._figure.Figure] = None, row: int = 1, col: int = 1, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → plotly.graph_objs._figure.Figure[source]

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.

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

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

  • hover_data (Optional[List[str]], optional) – Values in this column appear in bold in the hover tooltip. Defaults to None.

  • hover_name (Optional[str], optional) – Values in this column appear in the hover tooltip. Defaults to None.

  • fig (Optional[Figure], optional) – An instance of Figure.

  • row (int, optional) – Row of subplots. Defaults to 1.

  • col (int, optional) – Column of subplots. Defaults to 1.

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

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

Returns

An instance of Figure with XY plot.

Return type

Figure

>>> import pandas as pd
>>> from teilab.datasets import TeiLabDataSets
>>> from teilab.plot.plotly import XYplot
>>> 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 = XYplot(df=df_combined, x=datasets.samples.Condition[0], y=datasets.samples.Condition[1], hover_name="SystematicName", height=600, width=600)
>>> fig.show()

Open html

teilab.plot.plotly.MAplot(df: pandas.core.frame.DataFrame, x: str, y: str, color: Optional[str] = None, symbol: Optional[str] = None, size: Optional[str] = None, hover_data: Optional[List[str]] = None, hover_name: Optional[str] = None, hlines: Union[Dict[numbers.Number, Dict[str, Any]], List[numbers.Number]] = [], fig: Optional[plotly.graph_objs._figure.Figure] = None, row: int = 1, col: int = 1, plotkwargs: Dict[str, Any] = {}, layoutkwargs: Dict[str, Any] = {}, **kwargs) → plotly.graph_objs._figure.Figure[source]

MA plot.

  • x-axis (Average) : \(\log_{10}{\left(\text{gProcessedSignal}_X\times\text{gProcessedSignal}_Y\right)}\)

  • y-axis (Minus) : \(\log_{2}{\left(\text{gProcessedSignal}_Y / \text{gProcessedSignal}_X\right)}\)

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.

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

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

  • hover_data (Optional[List[str]], optional) – Values in this column appear in bold in the hover tooltip. Defaults to None.

  • hover_name (Optional[str], optional) – Values in this column appear in the hover tooltip. 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

  • fig (Optional[Figure], optional) – An instance of Figure.

  • row (int, optional) – Row of subplots. Defaults to 1.

  • col (int, optional) – Column of subplots. Defaults to 1.

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

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

Returns

An instance of Figure with MA plot.

Return type

Figure

>>> import pandas as pd
>>> from teilab.datasets import TeiLabDataSets
>>> from teilab.plot.plotly import MAplot
>>> 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 = MAplot(
...     df=df_combined,
...     x=datasets.samples.Condition[0], y=datasets.samples.Condition[1], hover_name="SystematicName",
...     hlines={
...         -1 : dict(fillcolor="red", marker={"color":"red"}, line={"width":1}, showlegend=False),
...         0  : dict(fillcolor="red", marker={"color":"red"}, line={"width":3}, showlegend=False),
...         1  : dict(fillcolor="red", marker={"color":"red"}, line={"width":1}, showlegend=False),
...     },
...     height=600, width=600,
>>> )
>>> fig.show()

Open html

teilab.plot.plotly.update_layout(fig: plotly.graph_objs._figure.Figure, row: int = 1, col: int = 1, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None, xlim: Optional[Tuple[Any, Any]] = None, ylim: Optional[Tuple[Any, Any]] = None, xrangeslider: Dict[str, Any] = {'visible': False}, font: Dict[str, Any] = {'family': 'verdana, arial, sans-serif', 'size': 14}, legend: bool = True, xaxis_type: str = 'linear', yaxis_type: str = 'linear', legend_tracegroupgap: int = 100, width: int = 600, height: int = 600, template: str = 'presentation') → plotly.graph_objs._figure.Figure[source]

Update the layout of plotly.graph_objs._figure.Figure object. See Documentation for details.

Parameters

  • fig (Figure) – A Figure object.

  • row (int, optional) – Row of subplots. Defaults to 1.

  • col (int, optional) – Column of subplots. Defaults to 1.

  • 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 (Optional[Tuple[Any,Any]], optional) – X axis range. Defaults to None.

  • ylim (Optional[Tuple[Any,Any]], optional) – Y axis range. Defaults to None.

  • xrangeslider (Dict[str,Any], optional) – Keyword arguments for Range Slider. Defaults to {"visible": False}.

  • font (Dict[str,Any], optional) – Keyword arguments for Fonts. Defaults to {"family": "Meiryo", "size": 20}.

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

  • xaxis_type (str, optional) – X axis type. Defaults to "linear".

  • yaxis_type (str, optional) – Y axis type. Defaults to "linear".

  • legend_tracegroupgap (int, optional) – Gap between legend groups. Defaults to 100.

  • width (int, optional) – Figure width. Defaults to 600.

  • height (int, optional) – Figure height. Defaults to 600.

  • template (str, optional) – Plotly themes. You can check the all template by python -c "import plotly.io as pio; print(pio.templates). Defaults to "plotly_white".

Returns

Figure object with layout updated.

Return type

Figure

>>> import plotly.graph_objects as go
>>> from teilab.utils import subplots_create
>>> from teilab.plot.plotly import update_layout
>>> fig = subplots_create(nrows=1, ncols=2, style="plotly")
>>> for c in range(1,3):
...     fig.add_trace(go.Scatter(x=[1,2,3],y=[4,5,6]),row=1,col=c)
>>> fig = update_layout(fig=fig, title="Sample", ylim=(4.5,5.5), col=2, height=400)
>>> fig.show()

Open html