legend = Legend(items=[(colors[0], [wedge1]), (colors[1], [wedge2]),
(colors[2], [wedge3]), (colors[3], [wedge4]),
(colors[4], [wedge5])],
location="bottom_right",
name="the_legend")
p.add_layout(legend, 'right')
labels = LabelSet(x='x',
y='y',
text='colors',
level='glyph',
source=pie_label_data,
render_mode='canvas',
text_color='black',
name="the_pie_labels")
p.add_layout(labels)
p.xaxis.visible = False
p.yaxis.visible = False
p.grid[0].visible = False
p.grid[1].visible = False
p.outline_line_color = None
# Export to HTML, PNG, and get bbox data
data = export_png_and_data(p, "pie_glyphs_legend.png",
"pie_glyphs_legend.html")
with open("pie_glyphs_legend.json", "w") as f:
json.dump(data, f)
from bokeh.io import export_png_and_data # Custom function
from bokeh.plotting import figure
if __name__ == "__main__":
# Set up the plot
categories = ['cat_a','cat_b','cat_c']
p = figure(plot_width=400, plot_height=400, title="title_title", toolbar_location=None, x_range=categories)
p.vbar(x=categories, width=0.5, bottom=0,
top=[1.2, 2.5, 3.7], color="firebrick", name="the_bars")
# Set identifiers for the figure elements
p.xaxis.name = "the_xaxis"
p.xaxis.axis_label = "xaxis_label"
p.yaxis.name = "the_yaxis"
p.yaxis.axis_label = "yaxis_label"
p.title.name = "the_title"
p.xaxis.major_label_orientation = "vertical"
if p.grid[0].dimension == 0:
p.grid[0].name = "the_x_gridlines"
p.grid[1].name = "the_y_gridlines"
else:
p.grid[0].name = "the_y_gridlines"
p.grid[1].name = "the_x_gridlines"
# Export to HTML, PNG, and get bbox data
data = export_png_and_data(p, "vbar_categorical.png", "vbar_categorical.html")
with open("vbar_categorical.json", "w") as f:
json.dump(data, f)
background_fill_color=None,
border_line_color=None,
background_fill_alpha=0
)
# Control where the legend appears
# Inside
if LEGEND_INSIDE:
p.add_layout(legend)
# Outside
else:
p.add_layout(legend, 'right')
p.xaxis.name = "the_xaxis"
p.xaxis.axis_label = "xaxis_label"
p.yaxis.name = "the_yaxis"
p.yaxis.axis_label = "yaxis_label"
p.title.name = "the_title"
if p.grid[0].dimension == 0:
p.grid[0].name = "the_x_gridlines"
p.grid[1].name = "the_y_gridlines"
else:
p.grid[0].name = "the_y_gridlines"
p.grid[1].name = "the_x_gridlines"
# Export to HTML, PNG, and get bbox data
data = export_png_and_data(p, "scatter_plot_legend.png", "scatter_plot_legend.html")
with open("scatter_plot_legend.json", "w") as f:
json.dump(data, f)
def generate_figures(source_data_json,
destination_directory,
add_bboxes=False,
supplied_webdriver=None):
# Setup dest dirs
qa_json_dir = os.path.join(destination_directory, "json_qa")
annotations_json_dir = os.path.join(destination_directory,
"json_annotations")
html_dir = destination_directory
png_dir = os.path.join(destination_directory, "png")
dirs = [destination_directory, qa_json_dir, annotations_json_dir, png_dir]
if add_bboxes:
bbox_img_dir = os.path.join(destination_directory, "bbox_png")
dirs.append(bbox_img_dir)
for dirpath in dirs:
if not os.path.exists(dirpath):
os.mkdir(dirpath)
# Create web driver
if supplied_webdriver:
webdriver = supplied_webdriver
else:
webdriver = seldriver.PhantomJS()
# Read in the synthetic data
with open(source_data_json, 'r') as f:
source_data_json = json.load(f)
for fig_id, source in tqdm(iter(enumerate(source_data_json['data'])),
total=len(source_data_json['data']),
desc="Plotting figures"):
point_sets = source['data']
fig = None
fig_type = source['type']
if fig_type == 'vbar_categorical':
fig = VBarGraphCategorical(point_sets[0], source['visuals'])
elif fig_type == 'hbar_categorical':
fig = HBarGraphCategorical(point_sets[0], source['visuals'])
elif fig_type == 'line':
fig = LinePlot(point_sets, source['visuals'])
elif fig_type == 'dot_line':
fig = DotLinePlot(point_sets, source['visuals'])
elif fig_type == 'pie':
fig = Pie(point_sets[0], source['visuals'])
if not fig:
continue
html_file = os.path.join(html_dir, "%d_%s.html" % (fig_id, fig_type))
png_file = os.path.join(png_dir, "%d_%s.png" % (fig_id, fig_type))
# Export to HTML, PNG, and get rendered data
rendered_data = export_png_and_data(fig.figure, png_file, html_file,
webdriver)
all_plot_data = combine_source_and_rendered_data(source, rendered_data)
qa_json_file = os.path.join(qa_json_dir,
"%s_%s.json" % (fig_id, fig_type))
annotations_json_file = os.path.join(
annotations_json_dir,
"%d_%s_annotations.json" % (fig_id, fig_type))
for qa in source['qa_pairs']:
qa['image'] = os.path.basename(png_file)
qa['annotations'] = os.path.basename(annotations_json_file)
with open(qa_json_file, 'w') as f:
json.dump(
{
'qa_pairs':
source['qa_pairs'],
'total_distinct_questions':
source_data_json['total_distinct_questions'],
'total_distinct_colors':
source_data_json['total_distinct_colors']
}, f)
with open(annotations_json_file, 'w') as f:
json.dump(all_plot_data, f)
if add_bboxes:
all_plot_data['image_index'] = fig_id
generate_all_images_with_bboxes_for_plot(all_plot_data,
png_file,
bbox_img_dir,
'red',
load_image=True)
# Cleanup
os.remove(html_file)
# Kill the newly created webdriver
if not supplied_webdriver:
webdriver.service.process.send_signal(signal.SIGTERM)
try:
RemoteWebDriver.quit(webdriver)
except:
pass
toolbar_location=None)
p.add_glyph(
segment_data,
Segment(x0="x_start",
y0="y_start",
x1="x_end",
y1="y_end",
line_color="#f4a582",
line_width=3,
name="the_segments"))
p.xaxis.name = "the_xaxis"
p.xaxis.axis_label = "xaxis_label"
p.yaxis.name = "the_yaxis"
p.yaxis.axis_label = "yaxis_label"
p.title.name = "the_title"
if p.grid[0].dimension == 0:
p.grid[0].name = "the_x_gridlines"
p.grid[1].name = "the_y_gridlines"
else:
p.grid[0].name = "the_y_gridlines"
p.grid[1].name = "the_x_gridlines"
# Export to HTML, PNG, and get bbox data
data = export_png_and_data(p, "segments.png", "segments.html")
#print data
with open("segments.json", "w") as f:
json.dump(data, f)
