def add_axes(plot):
from bokeh.models import (
CategoricalAxis,
Grid,
)
x_axis = CategoricalAxis(major_label_orientation=0.785,
major_label_text_font=MONO_FONT)
y_axis = CategoricalAxis(major_label_text_font=MONO_FONT)
plot.add_layout(x_axis, 'below')
plot.add_layout(y_axis, 'left')
plot.add_layout(Grid(dimension=0, ticker=x_axis.ticker))
plot.add_layout(Grid(dimension=1, ticker=y_axis.ticker))
def pyramid():
xdr = DataRange1d()
ydr = FactorRange(factors=groups)
plot = Plot(x_range=xdr,
y_range=ydr,
plot_width=600,
plot_height=500,
toolbar_location=None)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
plot.add_layout(CategoricalAxis(), 'left')
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
m = HBar(left="value", right=0, y="group", height=1, fill_color="#3B8686")
mglyph = plot.add_glyph(source_pyramid_m, m)
f = HBar(left=0, right="value", y="group", height=1, fill_color="#CFF09E")
fglyph = plot.add_glyph(source_pyramid_f, f)
plot.add_layout(Legend(items=[("Male", [mglyph]), ("Female", [fglyph])]))
return plot
def population():
xdr = FactorRange(factors=years)
ydr = DataRange1d()
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(
location="bottom_right",
legends=[("known", [line_known_glyph]),
("predicted", [line_predicted_glyph])],
))
return plot
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 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 topicplot():
xdr = DataRange1d(sources=[topicsource.columns("width")])
ydr = FactorRange(factors=list(reversed(list(barDefault2.Term))))
#"Top-{R} Most Relevant Terms for Topic {topic} ({count}% of tokens)".format(R=R, ...)
title = "Top-{R} Most Salient Terms".format(R=R)
plot = Plot(title=title,
title_text_font_size="16px",
x_range=xdr,
y_range=ydr,
plot_width=mdswidth,
plot_height=mdsheight)
plot.add_glyph(
topicsource,
Rect(x="x",
y="y",
width="width",
height=1,
fill_color=base_color,
fill_alpha=0.2,
line_color=base_color))
plot.add_layout(LinearAxis(), "above")
plot.add_layout(CategoricalAxis(), "left")
return plot
def population():
xdr = FactorRange(factors=years)
ydr = DataRange1d()
plot = Plot(x_range=xdr,
y_range=ydr,
plot_width=600,
plot_height=150,
toolbar_location=None)
plot.add_layout(CategoricalAxis(major_label_orientation=pi / 4), 'below')
known = Line(x="x", y="y", line_color="violet", line_width=2)
known_glyph = plot.add_glyph(source_known, known)
predicted = Line(x="x",
y="y",
line_color="violet",
line_width=2,
line_dash="dashed")
predicted_glyph = plot.add_glyph(source_predicted, predicted)
legend = Legend(location="bottom_right",
items=[("known", [known_glyph]),
("predicted", [predicted_glyph])])
plot.add_layout(legend)
return plot
def jobtype_builder():
jtypes = ["Half Time", "Full Time", "Hourly", "Temporary"]
xdr = FactorRange(factors=jtypes)
ydr = DataRange1d(sources=[source_jobtype.columns("data_range")])
plot = Plot(title="Job Type",
data_sources=[source_jobtype],
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="jobtypes",
y="jobtype_half",
height="count",
width=0.9,
fill_color="#33A6A4")
bars = Glyph(data_source=source_jobtype,
xdata_range=xdr,
ydata_range=ydr,
glyph=quad)
plot.renderers.append(bars)
plot.background_fill = '#686975'
return plot
def make_axis(self, dim, location, scale, label):
"""Create linear, date or categorical axis depending on the location,
scale and with the proper labels.
Args:
location(str): the space localization of the axis. It can be
``left``, ``right``, ``above`` or ``below``.
scale (str): the scale on the axis. It can be ``linear``, ``datetime``
or ``categorical``.
label (str): the label on the axis.
Return:
Axis: Axis instance
"""
# ToDo: revisit how to handle multiple ranges
# set the last range to the chart's range
if len(self._ranges[dim]) == 0:
raise ValueError('Ranges must be added to derive axis type.')
data_range = self._ranges[dim][-1]
setattr(self, dim + '_range', data_range)
if scale == "auto":
if isinstance(data_range, FactorRange):
scale = 'categorical'
else:
scale = 'linear'
if scale == "linear":
axis = LinearAxis(axis_label=label)
elif scale == "datetime":
axis = DatetimeAxis(axis_label=label)
elif scale == "categorical":
axis = CategoricalAxis(
major_label_orientation=np.pi / 4, axis_label=label
)
else:
axis = LinearAxis(axis_label=label)
self.add_layout(axis, location)
return axis
def site_tld_bar(df, plot_width=325, plot_height=200):
bars = df[['tld','url']].groupby('tld').count().sort_values('url', ascending=False)
bars['y'] = bars.url / 2.
source = ColumnDataSource(bars)
plot = Plot(title="Most Common Top-level Domains",
plot_width=plot_width, plot_height=plot_height,
x_range=Range1d(0,bars.url.max()),
y_range=FactorRange(factors=bars.index.tolist()[::-1]),
**PLOT_FORMATS)
plot.add_glyph(
source,
Rect(x='y', y='tld', height=0.8, width='url', fill_color=BLUE,
fill_alpha=0.6, line_color=None)
)
plot.add_layout(LinearAxis(axis_label="Occurences", **AXIS_FORMATS), 'below')
plot.add_layout(CategoricalAxis(**AXIS_FORMATS), 'left')
return plot
def most_common_url_bar(df, plot_width=400, plot_height=250, top_n=None):
bars = df[['hostname','url']].groupby('hostname').count().sort_values('url', ascending=False)
bars['y'] = bars.url / 2.
if top_n:
bars = bars.nlargest(top_n, 'url')
source = ColumnDataSource(bars)
plot = Plot(title="Most Common Sites",
plot_width=plot_width, plot_height=plot_height,
x_range=Range1d(0,bars.url.max()),
y_range=FactorRange(factors=bars.index.tolist()[::-1]),
**PLOT_FORMATS)
plot.add_glyph(
source,
Rect(x='y', y='hostname', height=0.8, width='url', fill_color=BLUE,
fill_alpha=0.6, line_color=None)
)
plot.add_layout(LinearAxis(axis_label="Occurences", **AXIS_FORMATS), 'below')
plot.add_layout(CategoricalAxis(**AXIS_FORMATS), 'left')
return plot
x_range = FactorRange(factors=df["Tool"].drop_duplicates().tolist())
choose_color = 0
for c in df.sort_values(by="Category")["Category"].drop_duplicates().tolist():
df.loc[df.Category == c, "color"] = palette[choose_color]
choose_color += 1
source = ColumnDataSource(
data=dict(tool=df["Tool"].tolist(),
feature=df[["Category", "Features"]].values.tolist(),
color=df["color"].tolist(),
category=df["Category"].tolist(),
desc=df["Definition"].tolist()))
hover = HoverTool(tooltips=[("Tool", "@tool"), ("Feature Definition",
"@desc")])
p = figure(x_range=x_range,
y_range=y_range,
plot_height=7500,
plot_width=1500,
x_axis_location="above",
tools=[hover])
p.circle(x="tool", y="feature", color="black", source=source, size=28)
p.add_layout(CategoricalAxis(), 'below')
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
def make_calendar(year, month, firstweekday="Mon"):
firstweekday = list(day_abbrs).index(firstweekday)
calendar = Calendar(firstweekday=firstweekday)
month_days = [ None if not day else str(day) for day in calendar.itermonthdays(year, month) ]
month_weeks = len(month_days)//7
workday = "linen"
weekend = "lightsteelblue"
def weekday(date):
return (date.weekday() - firstweekday) % 7
def pick_weekdays(days):
return [ days[i % 7] for i in range(firstweekday, firstweekday+7) ]
day_names = pick_weekdays(day_abbrs)
week_days = pick_weekdays([workday]*5 + [weekend]*2)
source = ColumnDataSource(data=dict(
days = list(day_names)*month_weeks,
weeks = sum([ [str(week)]*7 for week in range(month_weeks) ], []),
month_days = month_days,
day_backgrounds = sum([week_days]*month_weeks, []),
))
holidays = [ (date, summary.replace("(US-OPM)", "").strip()) for (date, summary) in us_holidays
if date.year == year and date.month == month and "(US-OPM)" in summary ]
holidays_source = ColumnDataSource(data=dict(
holidays_days = [ day_names[weekday(date)] for date, _ in holidays ],
holidays_weeks = [ str((weekday(date.replace(day=1)) + date.day) // 7) for date, _ in holidays ],
month_holidays = [ summary for _, summary in holidays ],
))
xdr = FactorRange(factors=list(day_names))
ydr = FactorRange(factors=list(reversed([ str(week) for week in range(month_weeks) ])))
x_scale, y_scale = CategoricalScale(), CategoricalScale()
plot = Plot(x_range=xdr, y_range=ydr, x_scale=x_scale, y_scale=y_scale,
plot_width=300, plot_height=300, outline_line_color=None)
plot.title.text = month_names[month]
plot.title.text_font_size = "16px"
plot.title.text_color = "darkolivegreen"
plot.title.offset = 25
plot.min_border_left = 0
plot.min_border_bottom = 5
rect = Rect(x="days", y="weeks", width=0.9, height=0.9, fill_color="day_backgrounds", line_color="silver")
plot.add_glyph(source, rect)
rect = Rect(x="holidays_days", y="holidays_weeks", width=0.9, height=0.9, fill_color="pink", line_color="indianred")
rect_renderer = plot.add_glyph(holidays_source, rect)
text = Text(x="days", y="weeks", text="month_days", text_align="center", text_baseline="middle")
plot.add_glyph(source, text)
xaxis = CategoricalAxis()
xaxis.major_label_text_font_size = "11px"
xaxis.major_label_standoff = 0
xaxis.major_tick_line_color = None
xaxis.axis_line_color = None
plot.add_layout(xaxis, 'above')
hover_tool = HoverTool(renderers=[rect_renderer], tooltips=[("Holiday", "@month_holidays")])
plot.tools.append(hover_tool)
return plot
def generate_bar(dataset, plot_height, dimension_name, measure_name, measure_label_name=None, measure_label_function=None, category_field=None, fill_color=None, line_color=None,
dimension_type="continuous", max_dimension_range=None, dimension_interval=2, goals_dataset=None, goal_measure_type="absolute", goal_dimension_type="value", goal_label_function=None, tap_tool_callback=None):
# IMPORTANT: Assumes that data is ordered descending by dimension values when working out the axis range, use inverse for ascending
dimension_values = []
dimension_labels = []
measure_values = []
measure_labels = []
fill_colors = []
line_colors = []
if measure_label_name is None:
measure_label_name = measure_name
for row in dataset:
if dimension_type=="timedelta":
dimension_values.append(utils.seconds_to_datetime(getattr(row, dimension_name)))
dimension_labels.append(utils.convert_seconds_to_minutes_formatted(getattr(row, dimension_name)))
else:
dimension_values.append(getattr(row, dimension_name))
dimension_labels.append(getattr(row, dimension_name))
measure_values.append(getattr(row, measure_name))
if measure_label_function is None:
measure_labels.append(getattr(row, measure_label_name))
else:
measure_labels.append(measure_label_function(getattr(row, measure_label_name)))
if category_field is not None:
fill_colors.append(getattr(row, category_field).fill_color if getattr(row, category_field) is not None else "#292b2c")
line_colors.append(getattr(row, category_field).line_color if getattr(row, category_field) is not None else "#292b2c")
elif fill_color is not None:
fill_colors.append(fill_color),
line_colors.append(line_color)
else:
fill_colors.append("#292b2c")
line_colors.append("#292b2c")
source=ColumnDataSource(dict(dimension=dimension_values,
dimension_label=dimension_labels,
measure=measure_values,
measure_label=measure_labels,
fill_color=fill_colors,
line_color=line_colors))
# Set the dimension ranges without knowledge of goal targets for now
if dimension_type == "continuous":
if max_dimension_range is None:
dimension_range_min = dimension_values[-1]
dimension_range_max = dimension_values[0]
else:
dimension_range_min = dimension_values[-1] if dimension_values[-1] > max_dimension_range[0] else max_dimension_range[0]
dimension_range_max = dimension_values[0] if dimension_values[0] < max_dimension_range[1] else max_dimension_range[1]
elif dimension_type == "timedelta":
if max_dimension_range is None:
dimension_range_min = dimension_values[-1]
dimension_range_max = dimension_values[0]
else:
dimension_range_min = dimension_values[-1] if dimension_values[-1] > max_dimension_range[0] else max_dimension_range[0]
dimension_range_max = dimension_values[0] if dimension_values[0] < max_dimension_range[1] else max_dimension_range[1]
measure_range_max = max(measure_values)
# Prep the goals data if we have any
if goals_dataset is not None:
goals_source = prepare_goals_source(goals_dataset=goals_dataset, goal_dimension_type=goal_dimension_type, goal_measure_type=goal_measure_type,
measure_label_function=measure_label_function, goal_label_function=goal_label_function)
# Update the max ranges
if dimension_type == "continuous":
if min(goals_source.data["dimension"]) < dimension_range_min:
dimension_range_min = min(goals_source.data["dimension"])-1
if max(goals_source.data["dimension"]) > dimension_range_max:
dimension_range_max = max(goals_source.data["dimension"])+1
if max(goals_source.data["measure"]) > measure_range_max:
measure_range_max = max(goals_source.data["measure"])
if dimension_type == "continuous":
dimension_range = (dimension_range_min-1, dimension_range_max+1)
bar_height = 0.6 * dimension_interval
goal_bar_height = 0.9 * dimension_interval
elif dimension_type == "timedelta":
dimension_range = (dimension_range_max+timedelta(seconds=2.5), dimension_range_min-timedelta(seconds=2.5))
bar_height = 0.6 * dimension_interval
goal_bar_height = 0.9 * dimension_interval
elif dimension_type == "discrete":
dimension_range = dimension_values
bar_height = 0.6
goal_bar_height = 0.8
measure_range = (-1, float(measure_range_max)*1.3) # Ideally this would be more responsive for the viewport but need to look at options
if dimension_type=="timedelta":
plot = figure(plot_height=plot_height, y_axis_type="datetime", y_range=dimension_range, x_range=measure_range, tooltips="@dimension_label: @measure_label", toolbar_location=None, tools=["tap"])
plot.yaxis.formatter=DatetimeTickFormatter(minsec='%M:%S')
else:
plot = figure(plot_height=plot_height, y_range=dimension_range, x_range=measure_range, toolbar_location=None, tooltips="@dimension: @measure_label", y_axis_type=None, tools=["tap"])
plot.hbar(source=source, y="dimension", right="measure", height=bar_height, color="fill_color", line_color="line_color", fill_alpha=0.8, hover_color="fill_color", hover_alpha=1)
labels = LabelSet(source=source, x="measure", y="dimension", text="measure_label", level="glyph",
x_offset=5, y_offset=-5, render_mode="canvas", text_font = "sans-serif", text_font_size = "7pt", text_color="fill_color")
plot.add_layout(labels)
# Add dashed lines for any goal targets that are set
if goals_dataset is not None:
plot.hbar(source=goals_source, y="dimension", right="measure", height=goal_bar_height, fill_alpha=0, line_color="#666666")
goal_labels = LabelSet(source=goals_source, x="measure", y="dimension", text="measure_label", level="glyph",
x_offset=5, y_offset=-5, render_mode="canvas", text_font = "sans-serif", text_font_size = "7pt", text_color="#666666")
plot.add_layout(goal_labels)
else:
goals_source = None
if tap_tool_callback is not None:
tap_tool = plot.select(type=TapTool)
tap_tool.callback = CustomJS(args=dict(source=source, goals_source=goals_source), code=tap_tool_callback)
# TODO: This will need to be more flexible for data other than cadence
if dimension_type == "continuous":
y_ticker = SingleIntervalTicker(interval=2*dimension_interval, num_minor_ticks=2)
y_axis = LinearAxis(ticker=y_ticker)
plot.add_layout(y_axis, "left")
elif dimension_type == "timedelta":
pass
else:
y_axis = CategoricalAxis()
plot.add_layout(y_axis, "left")
plot.xaxis.visible = False
plot.sizing_mode = "scale_width"
plot.axis.minor_tick_line_color = None
plot.axis.axis_line_color = "#999999"
plot.axis.major_label_text_color = "#666666"
plot.axis.major_label_text_font_size = "7pt"
plot.axis.major_tick_line_color = "#999999"
plot.grid.grid_line_color = None
plot.outline_line_color = None
return plot
text_font_style='italic',
text=['net tax increase'],
text_font_size='8pt',
text_color='#666666'))
bars.add_glyph(bars_source,
Text(x='annotation_x',
y='annotation_y',
text='annotation',
text_font_style='italic',
text_font_size='8pt',
text_color=DARK_GRAY))
#bars.add_layout(LinearAxis(**AXIS_FORMATS), 'below')
#bars.add_layout(LinearAxis(**AXIS_FORMATS), 'left')
bars.add_layout(CategoricalAxis(axis_label="Annual Adjusted Gross Income", **AXIS_FORMATS), 'left')
# create text plots -------------------------------------
taxcut_source = ColumnDataSource(taxcut_sources['ds_000_taxcut'].data)
percentcut_text = Plot(plot_width=800,
plot_height=text_plot_height,
x_range=Range1d(0, 100),
y_range=Range1d(0, 100),
**PLOT_FORMATS)
textbottom_renderer = percentcut_text.add_glyph(taxcut_source,
Text(x=3.15,
y=10,
text_font_style='normal',
text='text',
def make_bokeh_cat_float_scat(self,
p,
df,
cat_order,
title='Needs Title',
xlabel='Needs xlabel',
ylabel='Needs ylabel',
cat_color='navy'):
'''Make a simple bokeh scatter plot with categorical on y axis. Has
simple controls for scaling, positioning, and reset.
Assumes a figure p pre-established
df is a dataframe with:
xvalues in column 0
float values in columns 1 to col_max-1
cat values in col_max
cat_order is an ordered list that aligns cat axis
and contains axis_labelunique categories
titles/labels are simple strings'''
from bokeh.palettes import brewer
from bokeh.models import ColumnDataSource,\
Legend, LegendItem, LinearAxis,FactorRange,\
CategoricalAxis
data_names = df.columns
colors = brewer['RdBu'][4]
legend_items = []
for y in range(1, len(data_names)):
color = colors[y % 4]
r = p.circle(x=df.iloc[:, 0],
y=df.iloc[:, y],
size=7.5,
color=color)
#items.append((data_names[y],[r]))
legend_items.append(LegendItem(label=data_names[y], renderers=[r]))
#Set second y_axis
print(type(cat_order), cat_order)
p.extra_y_ranges = {"cat_ax": FactorRange(factors=cat_order)}
#Add second y axis
p.add_layout(CategoricalAxis(y_range_name="cat_ax"), "right")
last_col = len(df.columns) - 1
r = p.circle(x=df.iloc[:, 0],
y=df.iloc[:, last_col],
y_range_name="cat_ax",
size=7.5,
color=cat_color)
legend_items.append(
LegendItem(label=data_names[last_col], renderers=[r]))
p.xaxis.axis_label = xlabel
p.yaxis.axis_label = ylabel
p.title.text = title
legend1 = Legend(items=legend_items, location='center')
p.add_layout(legend1, 'right')
p.legend.click_policy = "hide"
return p
xdr = FactorRange(factors=list(css3_colors.Group.unique()))
ydr = FactorRange(factors=list(reversed(css3_colors.Name)))
plot = Plot(x_range=xdr, y_range=ydr, plot_width=600, plot_height=2000)
plot.title.text = "CSS3 Color Names"
rect = Rect(x="groups",
y="names",
width=1,
height=1,
fill_color="colors",
line_color=None)
rect_renderer = 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')
url = "http://www.colors.commutercreative.com/@names/"
tooltips = """Click the color to go to:<br /><a href="{url}">{url}</a>""".format(
url=url)
tap = TapTool(plot=plot, renderers=[rect_renderer], callback=OpenURL(url=url))
hover = HoverTool(plot=plot, renderers=[rect_renderer], tooltips=tooltips)
plot.tools.extend([tap, hover])
def _newaxis(plot, _):
plot.state.extra_x_ranges = {
"track": FactorRange(*frame.track.values)
}
plot.state.add_layout(CategoricalAxis(x_range_name="track"),
'above')
def make_calendar(sp500_data_lst,
djia_data_lst,
nasdaq_data_lst,
twitter_data_lst,
holiday_lst,
nyt_data_lst,
approval_data_lst,
generic_dem_lst,
generic_rep_lst,
plot_wid,
plot_ht,
year,
month,
firstweekday="Sun"):
firstweekday = list(day_abbrs).index(firstweekday)
calendar = Calendar(firstweekday=firstweekday)
month_days = [
None if not day else str(day)
for day in calendar.itermonthdays(year, month)
]
month_weeks = len(month_days) // 7
workday = "linen"
weekend = "lightsteelblue"
def weekday(date):
return (date.weekday() - firstweekday) % 7
def pick_weekdays(days):
return [days[i % 7] for i in range(firstweekday, firstweekday + 7)]
day_names = pick_weekdays(day_abbrs)
week_days = pick_weekdays([workday] * 5 + [weekend] * 2)
source = ColumnDataSource(data=dict(
days=list(day_names) * month_weeks,
weeks=sum([[str(week)] * 7 for week in range(month_weeks)], []),
month_days=month_days,
day_backgrounds=['white'] * len(month_days),
))
djia_data = [(dj_date, DJIA) for (dj_date, DJIA) in djia_data_lst
if dj_date.year == year and dj_date.month == month]
nasdaq_data = [(nas_date, NASDAQCOM)
for (nas_date, NASDAQCOM) in nasdaq_data_lst
if nas_date.year == year and nas_date.month == month]
sp500_data = [(sp500_date, SP500) for (sp500_date, SP500) in sp500_data_lst
if sp500_date.year == year and sp500_date.month == month]
holidays = [(holiday_date, Holiday)
for (holiday_date, Holiday) in holiday_lst
if holiday_date.year == year and holiday_date.month == month]
twitter_data = [
(twitter_date, topics) for (twitter_date, topics) in twitter_data_lst
if twitter_date.year == year and twitter_date.month == month
]
nyt_data = [(nyt_date, headlines) for (nyt_date, headlines) in nyt_data_lst
if nyt_date.year == year and nyt_date.month == month]
approval_data = [
(approval_date, approve_estimate)
for (approval_date, approve_estimate) in approval_data_lst
if approval_date.year == year and approval_date.month == month
]
approval_data.sort()
generic_dem = [(generic_date, dem_estimate)
for (generic_date, dem_estimate) in generic_dem_lst
if generic_date.year == year and generic_date.month == month
]
generic_dem.sort()
generic_rep = [(generic_date, rep_estimate)
for (generic_date, rep_estimate) in generic_rep_lst
if generic_date.year == year and generic_date.month == month
]
generic_rep.sort()
colors_djia = [DJIA for _, DJIA in djia_data]
colors_sp500 = [SP500 for _, SP500 in sp500_data]
colors_nasdaq = [NASDAQCOM for _, NASDAQCOM in nasdaq_data]
for i in range(len(colors_djia) - 1):
avg = np.mean([colors_djia[i], colors_sp500[i], colors_nasdaq[i]])
if 0 < avg <= 11000:
colors_djia[i] = '#E52700'
elif 11000 < avg <= 11100:
colors_djia[i] = '#E33A00'
elif 11100 < avg <= 11200:
colors_djia[i] = '#E14C00'
elif 11200 < avg <= 11300:
colors_djia[i] = '#DF5E00'
elif 11300 < avg <= 11400:
colors_djia[i] = '#DD6F00'
elif 11400 < avg <= 11500:
colors_djia[i] = '#DB8000'
elif 11500 < avg <= 11600:
colors_djia[i] = '#D99100'
elif 11600 < avg <= 11700:
colors_djia[i] = '#D7A100'
elif 11700 < avg <= 11800:
colors_djia[i] = '#D5B100'
elif 11800 < avg <= 11900:
colors_djia[i] = '#D3C100'
elif 11900 < avg <= 12000:
colors_djia[i] = '#D1D000'
elif 12000 < avg <= 12100:
colors_djia[i] = '#BECF00'
elif 12200 < avg <= 12300:
colors_djia[i] = '#ABCD00'
elif 12300 < avg <= 12400:
colors_djia[i] = '#99CB00'
elif 12400 < avg <= 12500:
colors_djia[i] = '#87C900'
elif 12500 < avg <= 12600:
colors_djia[i] = '#75C700'
elif 12500 < avg <= 12600:
colors_djia[i] = '#64C500'
else:
colors_djia[i] = '#53C300'
holiday_source = ColumnDataSource(data=dict(
month_djia=[DJIA for _, DJIA in djia_data],
month_nasdaq=[NASDAQCOM for _, NASDAQCOM in nasdaq_data],
month_sp500=[SP500 for _, SP500 in sp500_data],
month_twitter=[topics for _, topics in twitter_data],
month_holidays=[Holiday for _, Holiday in holidays],
nyt_days=[day_names[weekday(nyt_date)] for nyt_date, _ in nyt_data],
nyt_weeks=['0'] + [
str((weekday(nyt_date.replace(day=1)) + nyt_date.day) // 7)
for nyt_date, _ in nyt_data
],
month_nyt=[headlines for _, headlines in nyt_data],
month_approval=[
approve_estimate for _, approve_estimate in approval_data
],
month_generic_dem=[dem_estimate for _, dem_estimate in generic_dem],
month_generic_rep=[rep_estimate for _, rep_estimate in generic_rep],
day_backgrounds=colors_djia,
))
xdr = FactorRange(factors=list(day_names))
ydr = FactorRange(
factors=list(reversed([str(week) for week in range(month_weeks)])))
x_scale, y_scale = CategoricalScale(), CategoricalScale()
plot = Plot(x_range=xdr,
y_range=ydr,
x_scale=x_scale,
y_scale=y_scale,
plot_width=plot_wid,
plot_height=plot_ht)
plot.title.text = month_names[month] + " " + str(year)
plot.title.text_font_size = "14pt"
plot.title.text_color = "black"
plot.title.offset = 25
plot.min_border_left = 5
plot.min_border_top = 5
plot.min_border_bottom = 190
plot.border_fill_color = "white"
plot.background_fill_alpha = 0.5
plot.border_fill_alpha = 0.3
rect = Rect(x="days",
y="weeks",
width=0.9,
height=0.9,
fill_color="day_backgrounds",
line_color="silver")
plot.add_glyph(source, rect)
rect = Rect(x="nyt_days",
y="nyt_weeks",
width=0.9,
fill_color="day_backgrounds",
height=0.9)
rect_renderer = plot.add_glyph(holiday_source, rect)
text = Text(x="days",
y="weeks",
text="month_days",
text_align="center",
text_baseline="middle")
plot.add_glyph(source, text)
xaxis = CategoricalAxis()
xaxis.major_label_text_font_size = "10pt"
xaxis.major_label_standoff = 0
xaxis.major_tick_line_color = None
xaxis.axis_line_color = None
plot.add_layout(xaxis, 'above')
TOOLTIPS = """
<div style="height:100%; max-width:300px; min-width:200px;background-color: aliceblue; position:relative;">
<div>
<span style="font-size: 17px; font-weight: bold;"> Holiday: @month_holidays</span><br>
<span style="font-size: 15px; font-weight: bold; color: darkgrey;"> Trump Approval Rating: @month_approval{0,0.0}%</span><br>
<span style="font-size: 15px; font-weight: bold; color: blue;"> Generic Democrat: @month_generic_dem{0,0.0}%</span><br>
<span style="font-size: 15px; font-weight: bold; color: red;"> Generic Republican: @month_generic_rep{0,0.0}%</span><br>
<span style="font-size: 17px; font-weight: bold;"> NASDAQ: @month_nasdaq{0,0.00}</span><br>
<span style="font-size: 17px; font-weight: bold;"> DJIA: @month_djia{0,0.00}</span><br>
<span style="font-size: 17px; font-weight: bold;">S&P500: @month_sp500{0,0.00}</span><br>
</div>
<div>
<img
src="/static/img/nyt_logo.png" height="15" width="15"
style="float: left;"></img>
</div>
<div>
<span style="font-size: 17px; font-weight: bold;">NYT Headlines:</span>
<span style="font-size: 15px;">@month_nyt</span>
</div>
<div>
<img
src="/static/img/twitter_logo.png" height="15" width="15"
style="float: left;"></img>
</div>
<div>
<span style="font-size: 17px; color:blue; font-weight: bold;">Trending Tweets:</span>
<span style="font-size: 15px; color:blue;">@month_twitter</span>
</div>
</div>
"""
hover_tool = HoverTool(renderers=[rect_renderer], tooltips=TOOLTIPS)
# hover_tool = HoverTool(renderers=[rect_renderer], tooltips=[("Holiday", "@month_holidays"),("DJIA", "@month_djia{0,0.00}"),
# ("NASDAQ", "@month_nasdaq{0,0.00}"),("S&P500", "@month_sp500{0,0.00}"),("NYT Headlines", "@month_nyt"),("Trending Tweets","@month_twitter")])
plot.tools.append(hover_tool)
return plot