# Bokeh Test

## What This Is

This is a re-do of the final plot done for data-science with python course 2 week 4. The original was done with matplotlib and this was done with bokeh to get some interaction working. When I tried to create it the first time bokeh raised some errors saying that height had been defined more than once. I don't know what really caused it - possibly a namespace clash where I was re-using something I didn't intend to re-use - but when I created a new notebook that only created the one plot it worked. Since this uses javascript I used Jupyter and the web-inteface to test it out (emacs ipython doesn't seem to be able to render javascript (unless I'm doing it wrong)).

## How It Got Exported

I won't go over the creating of the data (since I just copied it from an earlier notebook) but this is how the bokeh plot was created.

### Imports

These were the bokeh parts I needed.

from bokeh.models import (
BoxAnnotation,
CustomJS,
Span,
Toggle,
)

from bokeh.io import (
output_file,
output_notebook,
show,
)

from bokeh.plotting import (
figure,
ColumnDataSource,
)

from bokeh.models import (
CrosshairTool,
HoverTool,
PanTool,
ResetTool,
ResizeTool,
SaveTool,
UndoTool,
WheelZoomTool,
)

from bokeh.layouts import column
from bokeh.resources import CDN


### Some Constants

NATIONAL_COLOR = "slategrey"
NATIONAL_LABEL = "National"
PORTLAND_COLOR = "cornflowerblue"
PORTLAND_LABEL = "Portland-Hillsboro-Vancouver"
S_AND_P_COLOR = "#90151B"
S_AND_P_LABEL = "S & P 500 Index"
HOUSING_COLOR = "#D89159"
HOUSING_LABEL = "House Price Index"


### The Data

bokeh doesn't work with pandas DataFrame's (or at least I couldn't get it to work). Instead you create a DataFrame-like object using the ColumnDataSource.

portland_source = ColumnDataSource(
data=dict(
month_data=portland.datetime,
unemployment=portland.unemployment_rate,
month_label=portland.date,
)
)

national_source = ColumnDataSource(
data=dict(
month_data=national.datetime,
unemployment=national.unemployment_rate,
month_label=national.date,
)
)

housing_source = ColumnDataSource(
data=dict(
month_data=house_price_index.datetime,
price=house_price_index.price,
month_label=s_and_p_index.date,
)
)

s_and_p_source = ColumnDataSource(
data=dict(
month_data=s_and_p_index.datetime,
value=s_and_p_index.VALUE,
month_label=s_and_p_index.date,
)
)


### The Tools

These are the things that add interactivity to the plot. You have to create new ones for each figure so I made a function to get them.

def make_tools():
"""makes the tools for the figures

Returns:
list: tool objects
"""
hover = HoverTool(tooltips=[
("month", "@month_label"),
("unemployment", "@unemployment"),
])

tools = [
hover,
CrosshairTool(),
PanTool(),
ResetTool(),
ResizeTool(),
SaveTool(),
UndoTool(),
WheelZoomTool(),
]


The HoverTool tooltips argument is a list of tuples - one tuple for each dimension of the data. The first argument of the tuple (e.g. "month") is the label that will appear when the user hover's over the data point, while the second (e.g. "@month_label") tells bokeh which column to use for the data (so it has to match the key you used in the ColumnDataSource creation).

### Helper Functions

The sub-figures needed some common elements so I created functions for them.

#### Scaling The Timestamps

The timestamps by default are unreadable (because there are so many). This re-scales them so they are more readable.

def scale_timestamp(index):
"""gets the scaled timestamp for element location

Args:
index: index in the portland.datetime series
Returns:
epoch timestamp used to locate place in plot
"""
return portland.datetime[index].timestamp() * TIME_SCALE


#### Drawing the Recession

The recession is indicated as a blue box on each plot.

def make_recession():
"""Makes the box for the recession

Returns:
BoxAnnotation to color the recession
"""
return BoxAnnotation(
left=scale_timestamp(recession_start),
right=scale_timestamp(recession_end),
fill_color="blue",
fill_alpha=0.1)


#### Vertical Lines

Things like the unemployment lows and highs are indicated by a vertical line.

def make_vertical(location, color="darkorange"):
"""makes a vertical line

Args:
location: place on the x-axis for the line
color (str): line-color for the line
Returns:
Span at index
"""
return Span(
location=location,
line_color=color,
dimension="height",
)


#### Make Verticals

Since there's more than one line, this function adds all the lines.

def make_verticals(fig):
"""makes the verticals and adds them to the figures"""
location=scale_timestamp(unemployment_peaks[0]),
color="darkorange",
))
color="crimson"))
color="limegreen"))
location=scale_timestamp(national_peak[0][0]),
color="grey"))
return


### The Figures

This plot has three sub-figures, each of which is created separately then added to the Column.

#### Unemployment

tools = make_tools()
unemployment_figure = figure(
plot_width=FIGURE_WIDTH,
plot_height=FIGURE_HEIGHT,
x_axis_type="datetime",
tools=tools,
title="Portland Unemployment (2007-2017)"
)


Next the lines for the time-series data are added.

unemployment_figure.line(
"month_data", "unemployment",
source=portland_source,
line_color=PORTLAND_COLOR,
legend=PORTLAND_LABEL,
)

line = unemployment_figure.line(
"month_data", "unemployment",
source=national_source,
line_color=NATIONAL_COLOR,
legend=NATIONAL_LABEL,
)


Now the recession-box and high and low points for each plot is added.

unemployment_figure.add_layout(make_recession())
make_verticals(unemployment_figure)


Now some labels are added and the grid is turned off.

unemployment_figure.yaxis.axis_label = "% Unemployment"
unemployment_figure.xaxis.axis_label = "Month"
unemployment_figure.xgrid.visible = False
unemployment_figure.ygrid.visible = False


#### S & P 500

The S & P 500 had didn't have unemployment as the dependent variable so I made a different set of tools to change the label for the hover.

hover = HoverTool(tooltips=[
("Month", "@month_label"),
("Value", "@value"),
])
tools = [
hover,
CrosshairTool(),
PanTool(),
ResetTool(),
ResizeTool(),
SaveTool(),
UndoTool(),
WheelZoomTool(),
]
s_and_p_figure = figure(
plot_width=FIGURE_WIDTH,
plot_height=FIGURE_HEIGHT,
x_range=unemployment_figure.x_range,
x_axis_type="datetime",
tools=tools,
title="S & P 500 Index",
)
line = s_and_p_figure.line("month_data", "value",
source=s_and_p_source,
line_color=S_AND_P_COLOR)
make_verticals(s_and_p_figure)
s_and_p_figure.yaxis.axis_label = "S & P 500 Valuation"
s_and_p_figure.xaxis.axis_label = "Month"
s_and_p_figure.xgrid.visible = False
s_and_p_figure.ygrid.visible = False
s_and_p_figure.legend.location = "bottom_right"


#### Housing

hover = HoverTool(tooltips=[
("Month", "@month_label"),
("Price", "@price"),
])
tools = [
hover,
CrosshairTool(),
PanTool(),
ResetTool(),
ResizeTool(),
SaveTool(),
UndoTool(),
WheelZoomTool(),
]
housing_figure = figure(
plot_width=FIGURE_WIDTH,
plot_height=FIGURE_HEIGHT,
x_range=unemployment_figure.x_range,
x_axis_type="datetime",
tools=tools,
title="House Price Index",
)
line = housing_figure.line("month_data", "price",
source=housing_source,
line_color=HOUSING_COLOR)
make_verticals(housing_figure)
housing_figure.yaxis.axis_label = "Sale Price (\$1,000)"
housing_figure.xaxis.axis_label = "Month"
housing_figure.xgrid.visible = False
housing_figure.ygrid.visible = False
housing_figure.legend.location = "bottom_right"


### Combining

Once the figures were created I combined them into a column, since I wanted them stacked verticallly.

combined = column(unemployment_figure, s_and_p_figure, housing_figure)


### Outputting The Code

In order to be able to embed the code, you need to have bokeh export it. There are multiple ways to do this, but I chose the autoload_static method.

OUTPUT_JAVASCRIPT = "portland_unemployment.js"
js, tag = autoload_static(combined, CDN, OUTPUT_JAVASCRIPT)


The third argument (OUTPUT_JAVASCRIPT) is the path you want to refer to in the tag. The returned js variable contains the javascript you need to save (using the filename you gave autoload_static) and the tag contains the HTML tag that you embed to let the server know you want to use the javascript that was saved.

Since both values are just strings, and nothing was saved to disk, I saved it for later.

with open(OUTPUT_JAVASCRIPT, "w") as writer:
writer.write(js)

with open("portland_tag.html", 'w') as writer:
writer.write(tag)


### Getting It Into Nikola

The first thing was to create this file using nikola new_post (it's called bokeh-test.rst). Next I created a directory in the files folder that had the same name as this file (without the ".rst" extension) to put the javascript in so nikola would find it when I built the HTML.

mkdir files/posts/bokeh-test


Once I copied the portland_unemployment.js file to the bokeh-test directory I opened the portland_tag.html file and embedded it directly into the post sing the raw restructureText directive.

.. raw:: html

<script
src="portland_unemployment.js"
id="686c5dd6-168a-4f7d-acbc-524875d93b59"
data-bokeh-model-id="c473232a-dc2c-4b75-988c-f9bc6517b4b9"
data-bokeh-doc-id="402d8e3c-1595-4d65-9f76-e11068c629ab"
></script>