def plot_dendrogram(data, labels=None, ax=None):
"""Plot a dendrogram of the given data.
Parameters
----------
data : 2d array
Data to plot in a dendrogram.
labels : list of str, optional
Labels for the dendrogram.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
Notes
-----
This function is a wrapper of the scipy `dendrogram` plot function.
Examples
--------
See the example for the :meth:`~.compute_score` method of the :class:`~.Counts` class.
"""
linkage_data = hier.linkage(data, method='complete', metric='cosine')
hier.dendrogram(linkage_data,
orientation='left',
color_threshold=0.25,
leaf_font_size=12,
ax=check_ax(ax),
**check_args(['labels'], labels))
plt.tight_layout()
def plot_matrix(data,
x_labels=None,
y_labels=None,
attribute='score',
transpose=False,
cmap='purple',
square=False,
ax=None,
**kwargs):
"""Plot a matrix as a heatmap.
Parameters
----------
data : Counts or 2d array
Data to plot in matrix format.
x_labels : list of str, optional
Labels for the x-axis.
y_labels : list of str, optional
Labels for the y-axis.
attribute : {'score', 'counts'}, optional
Which data attribute from the counts object to plot the data for.
Only used if the `data` input is a Counts object.
transpose : bool, optional, default: False
Whether to transpose the data before plotting.
cmap : {'purple', 'blue'} or matplotlib.cmap
Colormap to use for the plot.
If string, uses a sequential palette of the specified color.
square : bool
Whether to plot all the cells as equally sized squares.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
**kwargs
Additional keyword arguments to pass through to seaborn.heatmap.
Notes
-----
This function is a wrapper of the seaborn `heatmap` plot function.
Examples
--------
See the example for the :meth:`~.compute_score` method of the :class:`~.Counts` class.
"""
if isinstance(cmap, str):
cmap = get_cmap(cmap)
data, x_labels, y_labels = counts_data_helper(data, x_labels, y_labels,
attribute, transpose)
with sns.plotting_context("notebook",
font_scale=kwargs.pop('font_scale', 1.0)):
sns.heatmap(data,
square=square,
ax=check_ax(ax, kwargs.pop('figsize', None)),
cmap=cmap,
**check_args(['xticklabels', 'yticklabels'], x_labels,
y_labels),
**kwargs)
plt.tight_layout()
def plot_dendrogram(data,
labels=None,
attribute='score',
transpose=False,
method='complete',
metric='cosine',
ax=None,
**kwargs):
"""Plot a dendrogram of the given data based on hierarchical clustering.
Parameters
----------
data : Counts or 2d array
Data to plot in a dendrogram.
labels : list of str, optional
Labels for the dendrogram.
attribute : {'score', 'counts'}, optional
Which data attribute from the counts object to plot the data for.
Only used if the `data` input is a Counts object.
transpose : bool, optional, default: False
Whether to transpose the data before plotting.
method : str, optional, default: 'complete'
The linkage algorithm to use. See `scipy.cluster.hierarchy.linkage` for options.
metric : str or function, optional, default: 'cosine'
The distance metric to use. See `scipy.spatial.distance.pdist` for options.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
**kwargs
Additional keyword arguments to pass through to scipy.cluster.hierarchy.dendrogram.
Notes
-----
This function is a wrapper of the `scipy.cluster.hierarchy.dendrogram' plot function.
Examples
--------
See the example for the :meth:`~.compute_score` method of the :class:`~.Counts` class.
"""
if isinstance(data, Counts):
labels = data.terms['A' if not transpose else 'B'].labels
data = getattr(data, attribute).T if transpose else getattr(
data, attribute)
linkage_data = hier.linkage(data, method=method, metric=metric)
with sns.plotting_context("notebook",
font_scale=kwargs.pop('font_scale', 1.0)):
hier.dendrogram(linkage_data,
orientation=kwargs.pop('orientation', 'left'),
color_threshold=kwargs.pop('color_threshold', 0.25),
leaf_font_size=kwargs.pop('leaf_font_size', 12),
ax=check_ax(ax, kwargs.pop('figsize', None)),
**check_args(['labels'], labels),
**kwargs)
plt.tight_layout()
def plot_clustermap(data, x_labels=None, y_labels=None, cmap='purple'):
"""Plot a clustermap of the given data.
Parameters
----------
data : 2d array
Data to plot, as a clustermap.
x_labels : list of str
Labels for the x-axis.
y_labels : list of str
Labels for the y-axis.
cmap : {'purple', 'blue'} or matplotlib.cmap
Colormap to use for the plot.
If string, uses a sequential palette of the specified color.
Notes
-----
This function is a wrapper of the seaborn `clustermap` plot function.
Examples
--------
See the example for the :meth:`~.compute_score` method of the :class:`~.Counts` class.
"""
sns.set()
sns.set_context("paper", font_scale=1.5)
if isinstance(cmap, str):
cmap = get_cmap(cmap)
cg = sns.clustermap(
data,
cmap=cmap,
method='complete',
metric='cosine', # figsize=(8, 8),
**check_args(['xticklabels', 'yticklabels'], x_labels, y_labels))
_ = plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(),
rotation=60,
ha='right')
_ = plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
cg.fig.subplots_adjust(bottom=0.25)
cg.fig.subplots_adjust(right=0.75)
def plot_matrix(data,
x_labels=None,
y_labels=None,
cmap='purple',
square=False,
ax=None):
"""Plot a matrix representation of given data.
Parameters
----------
data : 2d array
Data to plot in matrix format.
x_labels : list of str
Labels for the x-axis.
y_labels : list of str
Labels for the y-axis.
cmap : {'purple', 'blue'} or matplotlib.cmap
Colormap to use for the plot.
If string, uses a sequential palette of the specified color.
square : bool
Whether to plot all the cells as equally sized squares.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
Notes
-----
This function is a wrapper of the seaborn `heatmap` plot function.
Examples
--------
See the example for the :meth:`~.compute_score` method of the :class:`~.Counts` class.
"""
if isinstance(cmap, str):
cmap = get_cmap(cmap)
sns.heatmap(data,
square=square,
ax=check_ax(ax),
cmap=cmap,
**check_args(['xticklabels', 'yticklabels'], x_labels,
y_labels))
plt.tight_layout()
def plot_clustermap(data,
x_labels=None,
y_labels=None,
attribute='score',
transpose=False,
method='complete',
metric='cosine',
cmap='purple',
**kwargs):
"""Plot a clustermap of the given data.
Parameters
----------
data : Counts or 2d array
Data to plot, as a clustermap.
x_labels : list of str, optional
Labels for the x-axis.
y_labels : list of str, optional
Labels for the y-axis.
attribute : {'score', 'counts'}, optional
Which data attribute from the counts object to plot the data for.
Only used if the `data` input is a Counts object.
transpose : bool, optional, default: False
Whether to transpose the data before plotting.
method : str, optional, default: 'complete'
The linkage algorithm to use. See `scipy.cluster.hierarchy.linkage` for options.
metric : str or function, optional, default: 'cosine'
The distance metric to use. See `scipy.spatial.distance.pdist` for options.
cmap : {'purple', 'blue'} or matplotlib.cmap
Colormap to use for the plot.
If string, uses a sequential palette of the specified color.
**kwargs
Additional keyword arguments to pass through to seaborn.clustermap.
Notes
-----
This function is a wrapper of the `seaborn.clustermap` plot function.
Examples
--------
See the example for the :meth:`~.compute_score` method of the :class:`~.Counts` class.
"""
if isinstance(cmap, str):
cmap = get_cmap(cmap)
data, x_labels, y_labels = counts_data_helper(data, x_labels, y_labels,
attribute, transpose)
with sns.plotting_context("notebook",
font_scale=kwargs.pop('font_scale', 1.0)):
cg = sns.clustermap(data,
method=method,
metric=metric,
cmap=cmap,
figsize=kwargs.pop('figsize', None),
**check_args(['xticklabels', 'yticklabels'],
x_labels, y_labels),
**kwargs)
_ = plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(),
rotation=60,
ha='right')
_ = plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)