def polynomial():
""" Very simple embedding of a polynomial chart
"""
# Grab the inputs arguments from the URL
args = flask.request.args
# Get all the form arguments in the url with defaults
color = getitem(args, 'color', 'Black')
_from = int(getitem(args, '_from', 0))
to = int(getitem(args, 'to', 10))
# Create a polynomial line graph with those arguments
x = list(range(_from, to + 1))
fig = figure(title="Polynomial")
fig.line(x, [i ** 2 for i in x], color=colors[color], line_width=2)
resources = INLINE.render()
script, div = components(fig)
html = flask.render_template(
'embed.html',
plot_script=script,
plot_div=div,
resources=resources,
color=color,
_from=_from,
to=to
)
return encode_utf8(html)
{{ script }}
<style>
.embed-wrapper {
display: flex;
justify-content: space-evenly;
}
</style>
</head>
<body>
<div class="embed-wrapper">
{% for key in div.keys() %}
{{ div[key] }}
{% endfor %}
</div>
</body>
</html>
''')
resources = INLINE.render()
filename = 'embed_multiple.html'
html = template.render(resources=resources,
script=script,
div=div)
with io.open(filename, mode='w', encoding='utf-8') as f:
f.write(html)
view(filename)
<title>Bokeh Scatter Plots</title>
{{ resources }}
{{ script }}
<style>
.embed-wrapper {
display: flex;
justify-content: space-evenly;
}
</style>
</head>
<body>
<div class="embed-wrapper">
{% for key in div.keys() %}
{{ div[key] }}
{% endfor %}
</div>
</body>
</html>
''')
resources = INLINE.render()
filename = 'embed_multiple.html'
html = template.render(resources=resources, script=script, div=div)
with io.open(filename, mode='w', encoding='utf-8') as f:
f.write(html)
view(filename)
def usptoPlot(uspto_final, orgName):
uspto_dict = dict()
uspto_dict['application'] = 0
uspto_dict['grant'] = 1
uspto_final = uspto_final.drop_duplicates([
'_version_', 'applicationDate', 'applicationNumber', 'applicationType',
'documentId', 'documentType', 'productionDate', 'publicationDate',
'title', 'year'
])
uspto_final['year'] = uspto_final['year'].astype('int')
uspto_final['type_ind'] = uspto_final['documentType'].apply(
lambda x: uspto_dict[x])
source_2 = ColumnDataSource(data=uspto_final)
tooltips_2 = [("Title", "<strong>@title</strong>"),
("Application Date", "@applicationDate"),
("Application Type", "@applicationType"),
("Document Type", "@documentType"),
("Patent Number", "@patentNumber"),
("Publication Date", "@publicationDate"), ("year", "@year")]
grant_types = ['application', 'grant']
p = figure(title=orgName + ' PATENTS',
y_axis_label='YEAR',
x_axis_label='PATENT',
y_range=(int(uspto_final.year.min()) - 1,
int(uspto_final.year.max()) + 1),
x_range=grant_types,
tooltips=tooltips_2,
tools=['hover', 'tap'],
plot_width=500,
plot_height=550)
url = "@pdfPath"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
color_mapper = CategoricalColorMapper(factors=list(
uspto_final['title'].unique()),
palette=all_palettes['Set1'][9])
p.scatter(x='documentType',
y=jitter('year', 0.5),
size=10,
source=source_2,
alpha=0.5,
color={
'field': 'title',
'transform': color_mapper
})
p.title.text_font_size = "18pt"
p.xaxis.axis_label_text_font_size = "16pt"
p.yaxis.axis_label_text_font_size = "16pt"
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
p.toolbar.logo = None
p.toolbar_location = None
source, div = components(p)
resources = INLINE.render()
return p
def plot_tweets(tweets, sentiment_scores):
"""
Create a histogram-style barplot of tweets and their sentiment.
Return a bokeh plot object, expressed as a tuple of (resources, script, div).
Where :
resources: some CSS, etc. that goes in the head of the webpage for styling the plot.
script: javascript for the plot to function. expressed as string.
div: html div container for the plot. expressed as string.
"""
# Sort tweets from negative to positive.
# This step is not strictly necessary, but makes it easier to see the overall shape of the data.
sorted_indices = np.argsort(sentiment_scores)
sentiment_scores = np.array(sentiment_scores)[sorted_indices]
tweets = np.array(tweets)[sorted_indices]
# Express the data as a bokeh data source object.
source = ColumnDataSource(data={
"text": tweets,
"sentiment": sentiment_scores,
"x": np.arange(len(tweets)),
})
"""
Create plot.
"""
# Create plot object.
width = 0.9
p = figure(x_axis_label="Tweet", y_axis_label="Sentiment (0 = Neutral)")
p.vbar(source=source, x="x", top="sentiment", width=width)
# Add hover tool, allowing mouseover to view text and sentiment.
hover = HoverTool(
tooltips=[
("text", "@text"),
("sentiment", "@sentiment")
],
formatters={
"text": "printf",
"sentiment": "printf"
},
mode="vline"
)
p.add_tools(hover)
"""
Format plot.
"""
# axis font size
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
# remove tick marks from axes
p.xaxis.major_tick_line_color = None
p.xaxis.minor_tick_line_color = None
p.yaxis.major_tick_line_color = None
p.yaxis.minor_tick_line_color = None
# adjust plot width, height
scale = 1.5
p.plot_height = int(250 * scale)
p.plot_width = int(450 * scale)
# remove toolbar (e.g. move, resize, etc) from right of plot.
p.toolbar.logo = None
p.toolbar_location = None
# remove gridlines
p.xgrid.visible = False
p.ygrid.visible = False
# remove x axis tick labels (done by setting label fontsize to 0 pt)
p.xaxis.major_label_text_font_size = '0pt'
"""
Export plot
"""
# Create resources string, which is CSS, etc. that goes in the head of
resources = INLINE.render()
# Get javascript (script) and HTML div (div) for the plot.
script, div = components(p)
return (resources, script, div)
def modify_doc(cls):
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
season = Season()
seasons_1990_on = season.seasons_1990_on
data_dict = seasons_1990_on.to_dict('list')
source = ColumnDataSource(data=data_dict)
slider = Slider(title="Year", start=1990, end=2017, step=1, value=2006)
menu_options_list = ["ALL"] + seasons_1990_on["Tm"].unique().tolist()
team_menu = Select(options=menu_options_list,
value="ALL",
title="Team")
columns = list(seasons_1990_on.columns)
x_axis_menu = Select(options=columns, value="3PA", title="X Axis")
y_axis_menu = Select(options=columns, value="3P", title="Y Axis")
palette = season.get_palette()
color_mapper = CategoricalColorMapper(
factors=seasons_1990_on["Tm"].unique().tolist(), palette=palette)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
p1 = figure(x_axis_label="3 Points Attempted",
y_axis_label="3 Points Made",
tools=TOOLS)
###################
p1.circle(
"X",
"Y",
source=source,
alpha=0.8,
nonselection_alpha=0.1,
)
p1.legend.location = "bottom_right"
####################
####################
#######################
cls.add_tooltips(p1)
column1 = column(widgetbox(team_menu), widgetbox(slider),
widgetbox(x_axis_menu), widgetbox(y_axis_menu))
layout = row(column1, p1)
args = {
'source': source,
'data_dict': data_dict,
'team_menu': team_menu,
'slider': slider,
'x_axis_menu': x_axis_menu,
'y_axis_menu': y_axis_menu
}
callback = CustomJS(args=args,
code="""
data_copy = JSON.parse(JSON.stringify(data_dict))
console.log(data_copy, "<-- Here is data copy");
if (team_menu.value === "ALL") {
console.log("What's up. I am ALL");
}
source.change.emit();
""")
x_axis_menu.js_on_change('value', callback)
resources = INLINE.render()
script, div = components({'p': layout})
return {'script': script, 'div': div, 'resources': resources}
def callback(attr, old, new):
if menu.value == "ALL":
new_df = seasons_1990_on[seasons_1990_on['Year'] ==
slider.value]
new_x = new_df[x_axis_menu.value]
new_y = new_df[y_axis_menu.value]
else:
new_df = seasons_1990_on[
(seasons_1990_on['Year'] == slider.value)
& (seasons_1990_on['Tm'] == menu.value)]
new_x = new_df[x_axis_menu.value]
new_y = new_df[y_axis_menu.value]
source.data['X'] = new_x
source.data['Y'] = new_y
new_df['X'] = new_x
new_df['Y'] = new_y
tooltips = [("Players", "@Player"),
(x_axis_menu.value, "@{}".format(x_axis_menu.value))(
y_axis_menu.value,
"@{}".format(y_axis_menu.value))]
source.data = new_df
cls.add_tooltips(plot=p1, tooltips=tooltips)
print("Here is x", new_x)
print("Here is y", new_y)
print("Here is y name", new_y.name)
p1.xaxis.axis_label = new_x.name
p1.yaxis.axis_label = new_y.name
slider.on_change("value", callback)
menu.on_change("value", callback)
x_axis_menu.on_change("value", callback)
y_axis_menu.on_change("value", callback)