def layout_padding(plots, renderer):
"""
Pads Nones in a list of lists of plots with empty plots.
"""
widths, heights = defaultdict(int), defaultdict(int)
for r, row in enumerate(plots):
for c, p in enumerate(row):
if p is not None:
width, height = renderer.get_size(p)
widths[c] = max(widths[c], width)
heights[r] = max(heights[r], height)
expanded_plots = []
for r, row in enumerate(plots):
expanded_plots.append([])
for c, p in enumerate(row):
if p is None:
x_range = Range1d(start=0, end=1)
y_range = Range1d(start=0, end=1)
p = Figure(plot_width=widths[c],
plot_height=heights[r],
x_range=x_range,
y_range=y_range)
p.xaxis.visible = False
p.yaxis.visible = False
p.outline_line_alpha = 0
p.grid.grid_line_alpha = 0
expanded_plots[r].append(p)
return expanded_plots
def empty_plot(width, height):
"""
Creates an empty and invisible plot of the specified size.
"""
x_range = Range1d(start=0, end=1)
y_range = Range1d(start=0, end=1)
p = Figure(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
p.xaxis.visible = False
p.yaxis.visible = False
p.outline_line_alpha = 0
p.grid.grid_line_alpha = 0
return p
def empty_plot(width, height):
"""
Creates an empty and invisible plot of the specified size.
"""
x_range = Range1d(start=0, end=1)
y_range = Range1d(start=0, end=1)
p = Figure(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
p.xaxis.visible = False
p.yaxis.visible = False
p.outline_line_alpha = 0
p.grid.grid_line_alpha = 0
return p
def style_snapshot(figure: Figure) -> Figure:
"""Style a bokeh figure as a configuration snapshot.
This is collection of style changes to make the output of a snapshot consistent and
nice. Primarily it removes all the extra stuff which isn't helpful in defining the
configuration like the axes, and the interactive tools.
"""
figure.axis.visible = False
figure.xgrid.visible = False
figure.ygrid.visible = False
figure.toolbar_location = None
figure.toolbar.logo = None
figure.outline_line_width = 0
figure.outline_line_alpha = 0
return figure
def make_axis(axis,
size,
factors,
dim,
flip=False,
rotation=0,
label_size=None,
tick_size=None,
axis_height=35):
factors = list(map(dim.pprint_value, factors))
nchars = np.max([len(f) for f in factors])
ranges = FactorRange(factors=factors)
ranges2 = Range1d(start=0, end=1)
axis_label = dim_axis_label(dim)
reset = "range.setv({start: 0, end: range.factors.length})"
ranges.callback = CustomJS(args=dict(range=ranges), code=reset)
axis_props = {}
if label_size:
axis_props['axis_label_text_font_size'] = value(label_size)
if tick_size:
axis_props['major_label_text_font_size'] = value(tick_size)
tick_px = font_size_to_pixels(tick_size)
if tick_px is None:
tick_px = 8
label_px = font_size_to_pixels(label_size)
if label_px is None:
label_px = 10
rotation = np.radians(rotation)
if axis == 'x':
align = 'center'
# Adjust height to compensate for label rotation
height = int(axis_height + np.abs(np.sin(rotation)) *
((nchars * tick_px) * 0.82)) + tick_px + label_px
opts = dict(x_axis_type='auto',
x_axis_label=axis_label,
x_range=ranges,
y_range=ranges2,
plot_height=height,
plot_width=size)
else:
# Adjust width to compensate for label rotation
align = 'left' if flip else 'right'
width = int(axis_height + np.abs(np.cos(rotation)) *
((nchars * tick_px) * 0.82)) + tick_px + label_px
opts = dict(y_axis_label=axis_label,
x_range=ranges2,
y_range=ranges,
plot_width=width,
plot_height=size)
p = Figure(toolbar_location=None, **opts)
p.outline_line_alpha = 0
p.grid.grid_line_alpha = 0
if axis == 'x':
p.yaxis.visible = False
axis = p.xaxis[0]
if flip:
p.above = p.below
p.below = []
p.xaxis[:] = p.above
else:
p.xaxis.visible = False
axis = p.yaxis[0]
if flip:
p.right = p.left
p.left = []
p.yaxis[:] = p.right
axis.major_label_orientation = rotation
axis.major_label_text_align = align
axis.major_label_text_baseline = 'middle'
axis.update(**axis_props)
return p
def importance_plot(dates, ratings, importance, reviews, bad_words):
"""This plot generates the figure seen on the YelpHelp website. It plots a review history, in which potential
tipping point reviews are plotted with inflated circles. It also defines a hover-tool tip allowing one to
visualize the full text of the review by mousing over a point, as well as the keywords the model found important
contained within the review."""
min_size = 8
max_growth = 32
importance = np.round((max_growth * importance) + min_size)
source = ColumnDataSource(
data=dict(
desc=reviews,
bad=bad_words,
ratings_source=np.zeros(len(ratings))
)
)
# Create the figure!
title_string = 'Review History'
review_plot = Figure(plot_width=600, plot_height=600, x_axis_type="datetime", tools=[], responsive=True,
toolbar_location=None,webgl=True,title=title_string)
cr = review_plot.circle(dates, ratings, size=importance,
color="navy", hover_fill_color="firebrick",
fill_alpha=0.9, hover_alpha=0.9,
line_color=None, hover_line_color="white", source=source)
hover = HoverTool(
tooltips="""
<link href="../static/css/bootstrap.min.css" rel="stylesheet">
<div>
<div style="font-family: verdana; width : 550px; position: fixed; left: 650px; top: 180px; padding: 10px">
<span style="font-size: 17px;"> <b>Review: </b> @desc</span>
</div>
<div style="font-family: verdana; width : 550px; position: fixed; left: 650px; top: 120px; padding: 10px">
<span style="font-size: 14px; font-weight: bold;"> Keywords: @bad</span>
</div>
</div>""",
renderers=[cr]
)
review_plot.add_tools(hover)
newdate = pd.date_range(dates.max(), periods=5, freq='120D')
projected_mean = 0 # estimated_switch[-1]
projection_uncertainty = 20 # np.round(estimated_sigma * 2 * 10)
review_plot.xaxis.axis_label = "Date"
review_plot.xaxis.axis_line_width = 3
review_plot.xaxis.axis_label_text_font_style = "bold"
review_plot.xaxis.axis_label_text_font_size = '20pt'
review_plot.xaxis.major_label_text_font_size = '16pt'
review_plot.yaxis.axis_line_width = 3
review_plot.yaxis.axis_label_text_font_style = "bold"
review_plot.yaxis.axis_label_text_font_size = '20pt'
review_plot.yaxis.ticker = FixedTicker(ticks=[-3, -2, -1, 0, 1, 2, 3])
review_plot.yaxis.major_label_text_font_size = '16pt'
review_plot.xgrid.visible = False
review_plot.ygrid.visible = False
review_plot.outline_line_alpha = 0
review_plot.xaxis.axis_label = 'Date Posted'
review_plot.yaxis.axis_label = 'Rating (Normed to User Average)'
review_plot.title.text_font_size = '16pt'
review_plot.title.align='center'
#rolling_ratings, edited_dates, interp_ratings, samplerange = rolling_average_rating(business_reviews, 100)
# show the results
return review_plot
button.on_click(button_fun)
rbutton.on_click(init)
init()
create_orig(orig)
ps = 0.3
plot = Figure(tools = "",title="Maxwell",title_location = "above", x_range=(0.1-ps,0.8+ps), y_range=(0.0,1.0))
plot.line(x='x', y='y', source=orig.pts, color='Black',line_width=3)
plot.line(x='x', y='y', source=f1.pts, color="#808080",line_width=5)
plot.line(x='x', y='y', source=f2.pts, color="#E37222",line_width=5)
plot.line(x='x', y='y', source=t_line, color="Black",line_width=5)
plot.triangle(x='x', y='y', size = 'size', source= f1.tri,color="#808080", line_width=2)
plot.triangle(x='x', y='y', size = 'size', source= f2.tri,color="#E37222", line_width=2)
plot.axis.visible = False
plot.outline_line_width = 7
plot.outline_line_alpha = 0.3
plot.outline_line_color = "Black"
plot.title.text_color = "black"
plot.title.text_font_style = "bold"
plot.title.align = "center"
labels1 = LabelSet(x='x', y='y', text='name', level='glyph',
x_offset=0, y_offset=0, source=f1.label, render_mode='canvas')
labels2 = LabelSet(x='x', y='y', text='name', level='glyph',
x_offset=0, y_offset=0, source=f2.label, render_mode='canvas')
#P arrow:
p1_arrow_glyph = Arrow(end=NormalHead(line_color="#0065BD",line_width= 4, size=10),
x_start='xS', y_start='yS', x_end='xE', y_end='yE',line_width= "lW", source=f1.arrow_source,line_color="#0065BD")
p2_arrow_glyph = Arrow(end=NormalHead(line_color="#E37222",line_width= 4, size=10),
def make_axis(axis, size, factors, dim, flip=False, rotation=0,
label_size=None, tick_size=None, axis_height=35):
factors = list(map(dim.pprint_value, factors))
nchars = np.max([len(f) for f in factors])
ranges = FactorRange(factors=factors)
ranges2 = Range1d(start=0, end=1)
axis_label = dim_axis_label(dim)
reset = "range.setv({start: 0, end: range.factors.length})"
ranges.callback = CustomJS(args=dict(range=ranges), code=reset)
axis_props = {}
if label_size:
axis_props['axis_label_text_font_size'] = value(label_size)
if tick_size:
axis_props['major_label_text_font_size'] = value(tick_size)
tick_px = font_size_to_pixels(tick_size)
if tick_px is None:
tick_px = 8
label_px = font_size_to_pixels(label_size)
if label_px is None:
label_px = 10
rotation = np.radians(rotation)
if axis == 'x':
align = 'center'
# Adjust height to compensate for label rotation
height = int(axis_height + np.abs(np.sin(rotation)) *
((nchars*tick_px)*0.82)) + tick_px + label_px
opts = dict(x_axis_type='auto', x_axis_label=axis_label,
x_range=ranges, y_range=ranges2, plot_height=height,
plot_width=size)
else:
# Adjust width to compensate for label rotation
align = 'left' if flip else 'right'
width = int(axis_height + np.abs(np.cos(rotation)) *
((nchars*tick_px)*0.82)) + tick_px + label_px
opts = dict(y_axis_label=axis_label, x_range=ranges2,
y_range=ranges, plot_width=width, plot_height=size)
p = Figure(toolbar_location=None, tools=[], **opts)
p.outline_line_alpha = 0
p.grid.grid_line_alpha = 0
if axis == 'x':
p.yaxis.visible = False
axis = p.xaxis[0]
if flip:
p.above = p.below
p.below = []
p.xaxis[:] = p.above
else:
p.xaxis.visible = False
axis = p.yaxis[0]
if flip:
p.right = p.left
p.left = []
p.yaxis[:] = p.right
axis.major_label_orientation = rotation
axis.major_label_text_align = align
axis.major_label_text_baseline = 'middle'
axis.update(**axis_props)
return p