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#!/usr/bin/env python3 # -*- coding: utf-8 -*-
Visualizing the Output of LDA Models ************************************
Functions and classes of this module are for visualizing LDA models.
Contents ******** * """
ColumnDataSource, HoverTool, LinearColorMapper, BasicTicker, ColorBar )
"""Plots a wordcloud based on tokens and frequencies.
Args: weights (dict): A dictionary (or :module:``pandas`` Series) with tokens as keys and frequencies as values. enable_notebook (bool), optional: If True, enables :module:``matplotlib`` to show its figures within a Jupyter notebook. font_path (str), optional: Font path to the font that will be used (OTF or TTF). Defaults to DroidSansMono path on a Linux machine. If you are on another OS or don't have this font, you need to adjust this path. width (int), optional: Width of the canvas. Defaults to 400. height (int), optional: Height of the canvas. Defaults to 200. prefer_horizontal (float): The ratio of times to try horizontal fitting as opposed to vertical. If ``prefer_horizontal < 1``, the algorithm will try rotating the word if it doesn't fit. (There is currently no built-in way to get only vertical words. Defaults to 0.90. mask (nd-array), optional: If not None, gives a binary mask on where to draw words. If mask is not None, width and height will be ignored and the shape of mask will be used instead. All white (#FF or #FFFFFF) entries will be considerd 'masked out' while other entries will be free to draw on. Defaults to None. scale (float), optional: Scaling between computation and drawing. For large word-cloud images, using scale instead of larger canvas size is significantly faster, but might lead to a coarser fit for the words. Defaults to 1. min_font_size (int), optional: Smallest font size to use. Will stop when there is no more room in this size. Defaults to 4. font_step (int), optional: Step size for the font. ``font_step > 1`` might speed up computation but give a worse fit. Defaults to 1. max_words (int), optional: The maximum number of words. Defaults to 200. stopwords (set), optional: The words that will be eliminated. If None, the build-in stopwords list will be used. background_color (str), optional: Background color for the word cloud image. Defaults to ``black``. max_font_size (int), optional: Maximum font size for the largest word. If None, height of the image is used. mode (str), optional: Transparent background will be generated when mode is ``RGBA`` and background_color is None. Defaults to ``RGB``. relative_scaling (float), optional: Importance of relative word frequencies for font-size. With ``relative_scaling=0``, only word-ranks are considered. With ``relative_scaling=1``, a word that is twice as frequent will have twice the size. If you want to consider the word frequencies and not only their rank, ``relative_scaling`` around .5 often looks good. Defaults to 0.5. color_func (callable), optional: Callable with parameters ``word``, ``font_size``, ``position``, ``orientation``, ``font_path``, ``random_state`` that returns a PIL color for each word. Overwrites ``colormap``. See ``colormap`` for specifying a :module:``matplotlib`` colormap instead. collocations (bool), optional: Whether to include collocations (bigrams) of two words. Defaults to True. colormap (str), optional: :module:``matplotlib`` colormap to randomly draw colors from for each word. Ignored if ``color_func`` is specified. Defaults to ``viridis``. normalize_plurals (bool), optional: Whether to remove trailing 's' from words. If True and a word appears with and without a trailing 's', the one with trailing 's' is removed and its counts are added to the version without trailing 's' -- unless the word ends with 'ss'. Defaults to True.
Returns: WordCloud object.
Example: >>> weights = {'an': 2, 'example': 1} >>> plot_wordcloud(weights, enable_notebook=False) # doctest: +ELLIPSIS <wordcloud.wordcloud.WordCloud object at ...> """ wordcloud = WordCloud(**kwargs).fit_words(weights) if enable_notebook: from IPython import get_ipython get_ipython().run_line_magic('matplotlib', 'inline') try: fig, ax = plt.subplots(figsize=(kwargs['width'] / 96, kwargs['height'] / 96)) except KeyError: fig, ax = plt.subplots(figsize=(400 / 96, 200 / 96)) ax.axis('off') ax.imshow(wordcloud) return wordcloud
within_topic_color='#FF1727', document_term_matrix=None, model=None, vocabulary=None, topic_no=None, overall_color='#053967', figsize=(15, 7), dpi=None, overall_edgecolor=None, overall_linewidth=None, overall_alpha=0.9, within_topic_edgecolor=None, within_topic_linewidth=None, within_topic_alpha=0.9, label_fontsize=15, num_keys=None, tick_fontsize=14, legend_fontsize=15, legend=True, enable_notebook=True): """Plots key frequencies overall and from within topic.
Args: keys (list): A list of tokens. Defaults to None. overall_freqs (list): A list of frequencies. Defaults to None. within_topic_freqs (list): A list of frequencies. Defaults to None. within_topic_color (str), optional: Color for topic frequencies bar. Defaults to ``#FF1727``. document_term_matrix (pandas DataFrame), optional: A document-term matrix. Defaults to None. model, optional: A LDA model. Defaults to None. vocabulary (list), optional: Vocabulary of the corpus. Defaults to None. topic_no (int), optional: Number of topic. Defaults to None. overall_color (str), optional: Color for overall frequencies bar. Defaults to ``#053967``. figsize (tuple), optional: Size of the figure. Defaults to ``(15, 7)``. dpi (int), optional: Dots per inch. Defaults to None. overall_edgecolor (str), optional: Color for edgecolors of overall frequencies bar. Defaults to None. overall_linewidth (int), optional: Linewidth of overall frequencies bar. Defaults to None. overall_alpha (int), optional: Alpha for overall frequencies bar. Defaults to 0.9. within_topic_edgecolor (str), optional: Color for edgecolors of overall frequencies bar. Defaults to None. within_topic_linewidth (int), optional: Linewidth of overall frequencies bar. Defaults to None. within_topic_alpha (int), optional: Alpha for overall frequencies bar. Defaults to 0.9. label_fontsize (int), optional: Fontsize of x-axis and y-axis labels. Defaults to 15. num_keys (int), optional: Number of tokens for y-axis. Defaults to None. tick_fontsize (int), optional: Fontsize of x- and y-ticks. Defaults to 14. legend_fontsize (int), optional: Fontsize of the legend. Defaults to 15. legend (bool), optional: If True, legend will be displayed. Defaults to True. enable_notebook (bool), optional: If True, enables :module:``matplotlib`` to show its figures within a Jupyter notebook.
Returns: Figure object.
Example: >>> keys = ['one', 'example'] >>> overall_freqs = [20, 10] >>> within_topic_freqs = [10, 5] >>> plot_key_frequencies(keys=keys, ... overall_freqs=overall_freqs, ... within_topic_freqs=within_topic_freqs, ... enable_notebook=False) # doctest: +ELLIPSIS <matplotlib.figure.Figure object at ...> """ if enable_notebook: from IPython import get_ipython get_ipython().run_line_magic('matplotlib', 'inline') if model: within_topic_freqs = postprocessing.get_sorted_values_from_distribution(model.components_[topic_no], model.components_[topic_no], num_keys) within_topic_freqs = [dist * len(vocabulary) for dist in within_topic_freqs] total = [document_term_matrix[token].sum() for token in vocabulary] overall_freqs = postprocessing.get_sorted_values_from_distribution(total, model.components_[topic_no], num_keys) keys = postprocessing.get_sorted_values_from_distribution(vocabulary, model.components_[topic_no], num_keys) fig, ax = plt.subplots(figsize=figsize, dpi=dpi) y_axis = np.arange(len(keys)) overall = ax.barh(y_axis, overall_freqs, color=overall_color, edgecolor=overall_edgecolor, linewidth=overall_linewidth, alpha=overall_alpha) within = ax.barh(y_axis, within_topic_freqs, color=within_topic_color, edgecolor=within_topic_edgecolor, linewidth=within_topic_linewidth, alpha=within_topic_alpha) ax.set_xlabel('Frequency', fontsize=label_fontsize) ax.set_ylabel('Key', fontsize=label_fontsize) ax.set_yticks(y_axis) ax.set_yticklabels(keys, fontsize=tick_fontsize) ax.tick_params(axis='x', labelsize=tick_fontsize) if legend: ax.legend(handles=[overall, within], labels=['Overall', 'Within Topic'], loc='best', fontsize=legend_fontsize) return fig
""" Class to visualize document-topic matrix. """ self.document_topics = document_topics self.enable_notebook = enable_notebook if enable_notebook: self.show = self.notebook_handling()
def notebook_handling(): """Runs cell magic for Jupyter notebooks """ from IPython import get_ipython get_ipython().run_line_magic('matplotlib', 'inline') from bokeh.io import output_notebook, show output_notebook() return show
labels_fontsize=13, cmap='Blues', ticks_fontsize=12, xlabel='Document', ylabel='Topic', xticks_bottom=0.1, xticks_rotation=50, xticks_ha='right', colorbar=False): """Plots a static heatmap.
Args: figsize (tuple), optional: Size of the figure in inches. Defaults to ``(1000 / 96, 500 / 96)``. dpi (int), optional: Dots per inch. Defaults to None. labels_fontsize (int), optional: Fontsize of the figure labels. Defaults to 13. cmap (str), optional: Colormap for the figure. Defaults to ``Blues``. ticks_fontsize (int), optional: Fontsize of axis ticks. Defaults to 12. xlabel (str), optional: Label of x-axis. Defaults to ``Document``. ylabel (str), optional: Label of y-axis. Defaults to ``Topic``. xticks_bottom (str), optional: Distance to bottom of x-ticks. Defaults to 0.1. xticks_rotation (int), optional: Rotation degree of x-ticks. Defaults to 50. xticks_ha (str), optional: The horizontal alignment of the x-tick labels. Defaulst to ``right``. colorbar (bool), optional: If True, include colorbar. Defaults to True.
Returns: Figure object. """ fig, ax = plt.subplots(figsize=figsize, dpi=dpi) heatmap = ax.pcolor(self.document_topics, cmap=cmap) ax.set_xlabel(xlabel, fontsize=labels_fontsize) ax.set_ylabel(ylabel, fontsize=labels_fontsize) ax.set_xticks(np.arange(self.document_topics.shape[1]) + 0.5) ax.set_yticks(np.arange(self.document_topics.shape[0]) + 0.5) ax.set_xticklabels(list(self.document_topics.columns), fontsize=ticks_fontsize) ax.set_yticklabels(list(self.document_topics.index), fontsize=ticks_fontsize) fig.autofmt_xdate(bottom=xticks_bottom, rotation=xticks_rotation, ha=xticks_ha) if colorbar: cax = ax.imshow(self.document_topics, interpolation='nearest', cmap=cmap) cbar = fig.colorbar(cax, ticks=np.arange(0, 1, 0.1)) return fig
edgecolor=None, linewidth=None, alpha=None, labels_fontsize=15, ticks_fontsize=14, title=True, title_fontsize=17, dpi=None, transpose_data=False): """Plots a static barchart.
Args: index Union(int, str): Index of document-topics matrix column or name of column. describer (str): Describer of what the plot shows, e.g. either document or topic. title (bool), optional: If True, figure will have a title in the format ``describer: index``. title_fontsize (int), optional: Fontsize of figure title. transpose_data (bool): If True. document-topics matrix will be transposed. Defaults to False. color (str), optional: Color of the bins. Defaults to ``#053967``. edgecolor (str), optional: Color of the bin edges. Defaults to None. lindewidth (float), optional: Width of bin lines. Defaults to None. alpha (float): Alpha value used for blending. Defaults to None. figsize (tuple), optional: Size of the figure in inches. Defaults to ``(1000 / 96, 500 / 96)``. dpi (int), optional: Dots per inch. Defaults to None. labels_fontsize (int), optional: Fontsize of the figure labels. Defaults to 15. ticks_fontsize (int), optional: Fontsize of axis ticks. Defaults to 14.
Returns: Figure object. """ fig, ax = plt.subplots(figsize=figsize, dpi=dpi) if isinstance(index, int): if transpose_data: proportions = self.document_topics.T.iloc[index] else: proportions = self.document_topics.iloc[index] if title: plot_title = '{0}: {1}'.format(describer, proportions.name) ax.set_title(plot_title, fontsize=title_fontsize) elif isinstance(index, str): if transpose: proportions = self.document_topics.T.loc[index] else: proportions = self.document_topics.loc[index] if title: plot_title = '{}: {}'.format(describer, index) ax.set_title(plot_title, fontsize=title_fontsize) else: raise ValueError("{} must be int or str.".format(index))
y_axis = np.arange(len(proportions)) x_axis = proportions y_ticks_labels = proportions.index ax.barh(y_axis, x_axis, color=color, edgecolor=edgecolor, linewidth=linewidth, alpha=alpha) ax.set_xlabel('Proportion', fontsize=labels_fontsize) ax.set_ylabel(describer, fontsize=labels_fontsize) ax.set_yticks(y_axis) ax.set_yticklabels(y_ticks_labels, fontsize=ticks_fontsize) ax.tick_params(axis='x', labelsize=ticks_fontsize) return fig
"""Plots a static barchart per topic.
Args: index Union(int, str): Index of document-topics matrix column or name of column. describer (str): Describer of what the plot shows, e.g. either document or topic. title (bool), optional: If True, figure will have a title in the format ``describer: index``. title_fontsize (int), optional: Fontsize of figure title. transpose_data (bool): If True. document-topics matrix will be transposed. Defaults to False. color (str), optional: Color of the bins. Defaults to ``#053967``. edgecolor (str), optional: Color of the bin edges. Defaults to None. lindewidth (float), optional: Width of bin lines. Defaults to None. alpha (float): Alpha value used for blending. Defaults to None. figsize (tuple), optional: Size of the figure in inches. Defaults to ``(1000 / 96, 500 / 96)``. dpi (int), optional: Dots per inch. Defaults to None. labels_fontsize (int), optional: Fontsize of the figure labels. Defaults to 15. ticks_fontsize (int), optional: Fontsize of axis ticks. Defaults to 14.
Returns: Figure object. """ return self.__static_barchart(**kwargs)
"""Plots a static barchart per document.
Args: index Union(int, str): Index of document-topics matrix column or name of column. describer (str): Describer of what the plot shows, e.g. either document or topic. title (bool), optional: If True, figure will have a title in the format ``describer: index``. title_fontsize (int), optional: Fontsize of figure title. transpose_data (bool): If True. document-topics matrix will be transposed. Defaults to False. color (str), optional: Color of the bins. Defaults to ``#053967``. edgecolor (str), optional: Color of the bin edges. Defaults to None. lindewidth (float), optional: Width of bin lines. Defaults to None. alpha (float): Alpha value used for blending. Defaults to None. figsize (tuple), optional: Size of the figure in inches. Defaults to ``(1000 / 96, 500 / 96)``. dpi (int), optional: Dots per inch. Defaults to None. labels_fontsize (int), optional: Fontsize of the figure labels. Defaults to 15. ticks_fontsize (int), optional: Fontsize of axis ticks. Defaults to 14.
Returns: Figure object. """ return self.__static_barchart(**kwargs, transpose_data=True)
tools='hover, pan, reset, save, wheel_zoom, zoom_in, zoom_out', width=1000, height=550, x_axis_location='below', toolbar_location='above', sizing_mode='fixed', line_color=None, grid_line_color=None, axis_line_color=None, major_tick_line_color=None, major_label_text_font_size='9pt', major_label_standoff=0, major_label_orientation=3.14/3, colorbar=True): """Plots an interactive heatmap.
Args: palette (list), optional: A list of color values. Defaults to ``palettes.Blues[9]``. reverse_palette (bool), optional: If True, color values of ``palette`` will be reversed. Defaults to True. tools (str), optional: Tools, which will be includeded. Defaults to ``hover, pan, reset, save, wheel_zoom, zoom_in, zoom_out``. width (int), optional: Width of the figure. Defaults to 1000. height (int), optional: Height of the figure. Defaults to 550. x_axis_location (str), optional: Location of the x-axis. Defaults to ``below``. toolbar_location (str), optional: Location of the toolbar. Defaults to ``above``. sizing_mode (str), optional: Size fixed or width oriented. Defaults to ``fixed``. line_color (str): Color for lines. Defaults to None. grid_line_color (str): Color for grid lines. Defaults to None. axis_line_color (str): Color for axis lines. Defaults to None. major_tick_line_color (str): Color for major tick lines. Defaults to None. major_label_text_font_size (str): Font size for major label text. Defaults to ``9pt``. major_label_standoff (int): Standoff for major labels. Defaults to 0. major_label_orientation (float): Orientation for major labels. Defaults to ``3.14 / 3``. colorbar (bool): If True, colorbar will be included.
Returns: Figure object. """ if reverse_palette: palette = list(reversed(palette))
x_range = list(self.document_topics.columns) y_range = list(self.document_topics.index)
stacked_data = pd.DataFrame(self.document_topics.stack()).reset_index() stacked_data.columns = ['Topics', 'Documents', 'Distributions'] mapper = LinearColorMapper(palette=palette, low=stacked_data.Distributions.min(), high=stacked_data.Distributions.max()) source = ColumnDataSource(stacked_data)
fig = figure(x_range=x_range, y_range=y_range, x_axis_location=x_axis_location, plot_width=width, plot_height=height, tools=tools, toolbar_location=toolbar_location, sizing_mode=sizing_mode, logo=None) fig.rect(x='Documents', y='Topics', source=source, width=1, height=1, fill_color={'field': 'Distributions', 'transform': mapper}, line_color=line_color)
fig.grid.grid_line_color = grid_line_color fig.axis.axis_line_color = axis_line_color fig.axis.major_tick_line_color = major_tick_line_color fig.axis.major_label_text_font_size = major_label_text_font_size fig.axis.major_label_standoff = major_label_standoff fig.xaxis.major_label_orientation = major_label_orientation
if 'hover' in tools: fig.select_one(HoverTool).tooltips = [('Document', '@Documents'), ('Topic', '@Topics'), ('Score', '@Distributions')]
if colorbar: feature = ColorBar(color_mapper=mapper, major_label_text_font_size=major_label_text_font_size, ticker=BasicTicker(desired_num_ticks=len(palette)), label_standoff=6, border_line_color=None, location=(0, 0)) fig.add_layout(feature, 'right') if self.enable_notebook: self.show(fig, notebook_handle=True) return fig
width=1000, height=400, toolbar_location='above', sizing_mode='fixed', line_color=None, grid_line_color=None, axis_line_color=None, major_tick_line_color=None, major_label_text_font_size='9pt', major_label_standoff=0, title=True, bin_height=0.5, transpose_data=False, bar_color='#053967'): """Plots an interactive barchart.
Args: index Union(int, str): Index of document-topics matrix column or name of column. describer (str): Describer of what the plot shows, e.g. either document or topic. bar_color (str), optional: Color of bars. Defaults to ``#053967``. transpose_data (bool): If True. document-topics matrix will be transposed. Defaults to False. title (bool), optional: If True, figure will have a title in the format ``describer: index``. tools (str), optional: Tools, which will be includeded. Defaults to ``hover, pan, reset, save, wheel_zoom, zoom_in, zoom_out``. width (int), optional: Width of the figure. Defaults to 1000. height (int), optional: Height of the figure. Defaults to 400. x_axis_location (str), optional: Location of the x-axis. Defaults to ``below``. toolbar_location (str), optional: Location of the toolbar. Defaults to ``above``. sizing_mode (str), optional: Size fixed or width oriented. Defaults to ``fixed``. line_color (str): Color for lines. Defaults to None. grid_line_color (str): Color for grid lines. Defaults to None. axis_line_color (str): Color for axis lines. Defaults to None. major_tick_line_color (str): Color for major tick lines. Defaults to None. major_label_text_font_size (str): Font size for major label text. Defaults to ``9pt``. major_label_standoff (int): Standoff for major labels. Defaults to 0.
Returns: Figure object. """ if isinstance(index, int): if transpose_data: proportions = self.document_topics.T.iloc[index] else: proportions = self.document_topics.iloc[index] if title: plot_title = '{}: {}'.format(describer, proportions.name) elif isinstance(index, str): if transpose_data: proportions = self.document_topics.T.loc[index] else: proportions = self.document_topics.loc[index] if title: plot_title = '{}: {}'.format(describer, index) else: raise ValueError("{} must be int or str.".format(index))
x_axis = proportions y_range = list(proportions.index)
source = ColumnDataSource(dict(Describer=y_range, Proportion=x_axis))
fig = figure(y_range=y_range, title=plot_title, plot_width=width, plot_height=height, tools=tools, toolbar_location=toolbar_location, sizing_mode=sizing_mode, logo=None) fig.hbar(y='Describer', right='Proportion', height=bin_height, source=source, line_color=line_color, color=bar_color)
fig.xgrid.grid_line_color = None fig.x_range.start = 0 fig.grid.grid_line_color = grid_line_color fig.axis.axis_line_color = axis_line_color fig.axis.major_tick_line_color = major_tick_line_color fig.axis.major_label_text_font_size = major_label_text_font_size fig.axis.major_label_standoff = major_label_standoff
if 'hover' in tools: fig.select_one(HoverTool).tooltips = [('Proportion', '@Proportion')] if self.enable_notebook: self.show(fig, notebook_handle=True) return fig
"""Plots an interactive barchart per topic.
Args: index Union(int, str): Index of document-topics matrix column or name of column. describer (str): Describer of what the plot shows, e.g. either document or topic. bar_color (str), optional: Color of bars. Defaults to ``#053967``. transpose_data (bool): If True. document-topics matrix will be transposed. Defaults to False. title (bool), optional: If True, figure will have a title in the format ``describer: index``. tools (str), optional: Tools, which will be includeded. Defaults to ``hover, pan, reset, save, wheel_zoom, zoom_in, zoom_out``. width (int), optional: Width of the figure. Defaults to 1000. height (int), optional: Height of the figure. Defaults to 400. x_axis_location (str), optional: Location of the x-axis. Defaults to ``below``. toolbar_location (str), optional: Location of the toolbar. Defaults to ``above``. sizing_mode (str), optional: Size fixed or width oriented. Defaults to ``fixed``. line_color (str): Color for lines. Defaults to None. grid_line_color (str): Color for grid lines. Defaults to None. axis_line_color (str): Color for axis lines. Defaults to None. major_tick_line_color (str): Color for major tick lines. Defaults to None. major_label_text_font_size (str): Font size for major label text. Defaults to ``9pt``. major_label_standoff (int): Standoff for major labels. Defaults to 0.
Returns: Figure object. """ return self.__interactive_barchart(**kwargs)
"""Plots an interactive barchart per document.
Args: index Union(int, str): Index of document-topics matrix column or name of column. describer (str): Describer of what the plot shows, e.g. either document or topic. bar_color (str), optional: Color of bars. Defaults to ``#053967``. transpose_data (bool): If True. document-topics matrix will be transposed. Defaults to False. title (bool), optional: If True, figure will have a title in the format ``describer: index``. tools (str), optional: Tools, which will be includeded. Defaults to ``hover, pan, reset, save, wheel_zoom, zoom_in, zoom_out``. width (int), optional: Width of the figure. Defaults to 1000. height (int), optional: Height of the figure. Defaults to 400. x_axis_location (str), optional: Location of the x-axis. Defaults to ``below``. toolbar_location (str), optional: Location of the toolbar. Defaults to ``above``. sizing_mode (str), optional: Size fixed or width oriented. Defaults to ``fixed``. line_color (str): Color for lines. Defaults to None. grid_line_color (str): Color for grid lines. Defaults to None. axis_line_color (str): Color for axis lines. Defaults to None. major_tick_line_color (str): Color for major tick lines. Defaults to None. major_label_text_font_size (str): Font size for major label text. Defaults to ``9pt``. major_label_standoff (int): Standoff for major labels. Defaults to 0.
Returns: Figure object. """ return self.__interactive_barchart(**kwargs, transpose_data=True)
"""Creates a visualization that shows topics over time.
Description: With this function you can plot topics over time using metadata stored in the documents name. Only works with mallet output.
Args: labels(list): first three keys in a topic to select threshold(float): threshold set to define if a topic in a document is viable starttime(int): sets starting point for visualization endtime(int): sets ending point for visualization
Returns: matplotlib plot
Note: this function is created for a corpus with filenames that looks like: 1866_ArticleName.txt
ToDo: make it compatible with gensim output Doctest
""" years=list(range(starttime,endtime)) #doc_topicT = doc_topics.T topiclabels = [] reg = regex.compile(pattern) for topiclabel in self.document_topics.index.values: for topiclabel in topiclabels: topic_over_threshold_per_year = [] mask = doc_topics.loc[topiclabel] > threshold df = doc_topics.loc[topiclabel].loc[mask] #df = doc_topics.loc[doc_topics.loc[topiclabel] > threshold] #print (df) d = defaultdict(int) for item in df.index.values: year = reg.findall(item) d[year[0]]+=1 for year in years: topic_over_threshold_per_year.append(d[str(year)]) plt.plot(years, topic_over_threshold_per_year, label=topiclabel)
plt.xlabel('Year') plt.ylabel('count topics over threshold') plt.legend() fig = plt.gcf() fig.set_size_inches(18.5, 10.5) return fig
def to_file(fig, filename): """Saves a figure object to file.
Args: fig Union(bokeh.figure, matplotlib.figure): Figure produced by either bokeh or matplotlib. filename (str): Name of the file with an extension, e.g. ``plot.png``.
Returns: None. """ import matplotlib import bokeh if isinstance(fig, bokeh.plotting.figure.Figure): ext = os.path.splitext(filename)[1] if ext == '.png': export_png(fig, filename) elif ext == '.svg': fig.output_backend = 'svg' export_svgs(fig, filename) elif ext == '.html': output_file(filename) elif isinstance(fig, matplotlib.figure.Figure): fig.savefig(filename) return None
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