def weekday_builder():
dow = [
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday"
]
xdr = FactorRange(factors=dow)
ydr = DataRange1d(sources=[source_dow.columns("data_range")])
plot = Plot(title="Weekday of Job Posting",
data_sources=[source_dow],
x_range=xdr,
y_range=ydr,
plot_width=760,
plot_height=500)
xaxis = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4.0)
yaxis = LinearAxis(plot=plot, dimension=1)
yaxis.major_tick_in = 0
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
quad = Rect(x="weekday",
y="weekday_half",
height="count",
width=0.9,
fill_color="#D9301A")
bars = Glyph(data_source=source_dow,
xdata_range=xdr,
ydata_range=ydr,
glyph=quad)
plot.renderers.append(bars)
plot.background_fill = '#686975'
return plot
def population():
xdr = FactorRange(factors=years)
ydr = DataRange1d(
sources=[source_known.columns("y"),
source_predicted.columns("y")])
plot = Plot(title=None,
x_range=xdr,
y_range=ydr,
plot_width=800,
plot_height=200)
plot.add_layout(CategoricalAxis(major_label_orientation=pi / 4), 'below')
line_known = Line(x="x", y="y", line_color="violet", line_width=2)
line_known_glyph = plot.add_glyph(source_known, line_known)
line_predicted = Line(x="x",
y="y",
line_color="violet",
line_width=2,
line_dash="dashed")
line_predicted_glyph = plot.add_glyph(source_predicted, line_predicted)
plot.add_layout(
Legend(orientation="bottom_right",
legends=dict(known=[line_known_glyph],
predicted=[line_predicted_glyph])))
return plot
def job_loc_plot_builder():
xdr = FactorRange(factors=countries)
ydr = DataRange1d(sources=[source_country.columns("data_range")])
plot = Plot(title="Postings by Job Location (Country)",
data_sources=[source_country],
x_range=xdr,
y_range=ydr,
plot_width=760,
plot_height=500)
xaxis = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4.0)
yaxis = LinearAxis(plot=plot, dimension=1)
yaxis.major_tick_in = 0
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
quad = Rect(x="country",
y="count_half",
height="count",
width=0.9,
fill_color="#483D8B")
bars = Glyph(data_source=source_country,
xdata_range=xdr,
ydata_range=ydr,
glyph=quad)
plot.renderers.append(bars)
plot.background_fill = '#333333'
return plot
def make_box_violin_plot(data, maxwidth=0.9):
"""
data: dict[Str -> List[Number]]
maxwidth: float
Maximum width of tornado plot within each factor/facet
Returns the plot object
"""
print("Plotting box violin graph")
plot_width = 500
plot_height = 350
df = pd.DataFrame(columns=["group", "width", "height", "texts", "cats"])
bar_height = 50
bins = [0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e10]
# Compute histograms, while keeping track of max Y values and max counts
for i, (group, vals) in enumerate(data.iteritems()):
hist, edges = np.histogram(vals, bins)
df = df.append(pd.DataFrame(dict(
group = group,
width = np.log2(hist[1:]),
height = np.ones(len(hist) - 1),
texts = ["%d Nodes" % i for i in hist[1:-1]] + ["%d" % hist[-1]],
cats = [">10^%d" % np.log10(bin) for bin in bins[1:-1]],
)))
df.replace(-np.inf, 0)
# Normalize the widths
df["width"] *= (maxwidth / df["width"].max())
ds = ColumnDataSource(df)
xdr = FactorRange(factors=sorted(df["group"].unique()))
ydr = FactorRange(factors=list(df["cats"]))
plot = Plot(data_sources=[ds], x_range=xdr, y_range=ydr,
title="Degree Distribution (log scale)",
plot_width=plot_width, plot_height=plot_height,
tools=[])
yaxis = CategoricalAxis(plot=plot, location="left", axis_label="degree")
plot.left.append(yaxis)
glyph = Rect(x="group", y="cats", width="width", height="height",
fill_color="#3366ff")
text_glyph = Text(x="group", y="cats", text="texts", text_baseline="middle",
text_align="center", angle=0)
plot.renderers.append(Glyph(data_source=ds, xdata_range=xdr, ydata_range=ydr,
glyph=glyph))
plot.renderers.append(Glyph(data_source=ds, xdata_range=xdr, ydata_range=ydr,
glyph=text_glyph))
return plot
def population():
xdr = FactorRange(factors=years)
ydr = DataRange1d(sources=[source_known.columns("y"), source_predicted.columns("y")])
plot = Plot(title=None, data_sources=[source_known, source_predicted], x_range=xdr, y_range=ydr, plot_width=800, plot_height=200)
xaxis = CategoricalAxis(plot=plot, dimension=0, major_label_orientation=pi/4)
# yaxis = LinearAxis(plot=plot, dimension=1, ...)
line_known = Line(x="x", y="y", line_color="violet", line_width=2)
line_known_glyph = Glyph(data_source=source_known, xdata_range=xdr, ydata_range=ydr, glyph=line_known)
plot.renderers.append(line_known_glyph)
line_predicted = Line(x="x", y="y", line_color="violet", line_width=2, line_dash="dashed")
line_predicted_glyph = Glyph(data_source=source_predicted, xdata_range=xdr, ydata_range=ydr, glyph=line_predicted)
plot.renderers.append(line_predicted_glyph)
legend = Legend(plot=plot, orientation="bottom_right", legends=dict(known=[line_known_glyph], predicted=[line_predicted_glyph]))
plot.renderers.append(legend)
return plot
source = ColumnDataSource(dict(
names = list(css3_colors.Name),
groups = list(css3_colors.Group),
colors = list(css3_colors.Color),
))
xdr = FactorRange(factors=list(css3_colors.Group.unique()))
ydr = FactorRange(factors=list(reversed(css3_colors.Name)))
plot = Plot(title="CSS3 Color Names", x_range=xdr, y_range=ydr, plot_width=600, plot_height=2000)
rect = Rect(x="groups", y="names", width=1, height=1, fill_color="colors", line_color=None)
plot.add_glyph(source, rect)
xaxis_above = CategoricalAxis(major_label_orientation=pi/4)
plot.add_layout(xaxis_above, 'above')
xaxis_below = CategoricalAxis(major_label_orientation=pi/4)
plot.add_layout(xaxis_below, 'below')
plot.add_layout(CategoricalAxis(), 'left')
doc = Document()
doc.add(plot)
if __name__ == "__main__":
filename = "colors.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "CSS3 Color Names"))
print("Wrote %s" % filename)
groups=list(css3_colors.Group),
colors=list(css3_colors.Color),
))
xdr = FactorRange(factors=list(css3_colors.Group.unique()))
ydr = FactorRange(factors=list(reversed(css3_colors.Name)))
plot = Plot(title="CSS3 Color Names",
data_sources=[source],
x_range=xdr,
y_range=ydr,
plot_width=600,
plot_height=2000)
xaxis_top = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4,
location="top")
xaxis_bottom = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4,
location="bottom")
yaxis = CategoricalAxis(plot=plot, dimension=1)
# XXX: Wrong radius. Doesn't respect 'radius'. 'line_color' on 'rect' affects 'circle'.
# circle = Circle(x="groups", y="names", radius=1, fill_color="colors")
# plot.renderers.append(Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=circle))
rect = Rect(x="groups",
y="names",
width=1,
height=1,
def make_box_violin_plot(data, num_bins, maxwidth=0.9):
"""
data: dict[Str -> List[Number]]
maxwidth: float
Maximum width of tornado plot within each factor/facet
Returns the plot object
"""
df = pd.DataFrame(columns=["group", "centers", "width", "height", "texts"])
bar_height = 50
bins = [0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6]
# Compute histograms, while keeping track of max Y values and max counts
for i, (group, vals) in enumerate(data.iteritems()):
hist, edges = np.histogram(vals, bins)
df = df.append(
pd.DataFrame(
dict(
group=group,
centers=np.arange(len(hist)) * bar_height,
width=np.log10(hist),
height=np.ones(hist.shape) * bar_height,
texts=map(str, hist),
)))
df.replace(-np.inf, 0)
# Normalize the widths
df["width"] *= (maxwidth / df["width"].max())
ds = ColumnDataSource(df)
xdr = FactorRange(factors=sorted(df["group"].unique()))
ydr = DataRange1d(sources=[ds.columns("centers")])
plot = Plot(data_sources=[ds],
x_range=xdr,
y_range=ydr,
title="Degree Distribution",
plot_width=750,
plot_height=600,
tools=[])
xaxis = CategoricalAxis(plot=plot,
location="bottom",
axis_label="number of nodes")
#yaxis = LogAxis(plot=plot, location="left", axis_label="degree")
plot.below.append(xaxis)
#plot.above.append(yaxis)
#xgrid = Grid(plot=plot, dimension=0, axis=xaxis)
#ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
glyph = Rect(x="group", y="centers", width="width", height="height")
text_glyph = Text(x="group",
y="centers",
text="texts",
text_baseline="middle",
text_align="center")
plot.renderers.append(
Glyph(data_source=ds, xdata_range=xdr, ydata_range=ydr, glyph=glyph))
plot.renderers.append(
Glyph(data_source=ds,
xdata_range=xdr,
ydata_range=ydr,
glyph=text_glyph))
return plot