def age_hist(dataset, title, color=palette[1]):
hist, edges = np.histogram(dataset['Age'].fillna(df['Age'].mean()),
bins=18,
range=(0, 90))
source = ColumnDataSource({
'hist': hist,
'edges_left': edges[:-1],
'edges_right': edges[1:]
})
hover_tool = HoverTool(tooltips=[('From', '@edges_left'),
('Thru', '@edges_right'),
('Count', '@hist')],
mode='vline')
p = figure(plot_height=400, title=title, tools=[hover_tool])
p.quad(top='hist',
bottom=0,
left='edges_left',
right='edges_right',
source=source,
fill_color=color,
line_color='black')
plot_styler(p)
p.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=2)
p.sizing_mode = 'scale_width'
return p
def pi_ticks(fig, interval, axis='x'):
ticker = SingleIntervalTicker(interval=interval)
if axis == 'x' or axis == 'both':
fig.xaxis.ticker = ticker
fig.xaxis.formatter = pi_formatter()
if axis == 'y' or axis == 'both':
fig.yaxis.ticker = ticker
fig.yaxis.formatter = pi_formatter()
def doPlot11(data, nrDataSource):
p = Line(data,
y_mapper_type="log",
x=data.columns[0],
xlabel=data.columns[0],
ylabel=data.columns[1],
title="Line graph: " + nrDataSource['name'],
responsive=True)
p._xaxis.ticker = SingleIntervalTicker(interval=5, num_minor_ticks=10)
c = components(p, resources=None, wrap_script=False, wrap_plot_info=True)
return c
def FFR_chart(data, low, up, name):
xdata, ydata, xrng, yrng = set_up(data['Month'], data['Last'])
now = dt.now().strftime('%B, %d, %Y')
p = figure(width=1400,
height=int(700 * 2 / 3),
title="CME 30-Day Federal Fund Futures: {}".format(now),
x_axis_label='Date',
x_axis_type='datetime',
y_axis_label='Implied Federal Funds Rate',
y_range=yrng,
x_range=xrng)
p.line(xdata, ydata, color='blue')
p.line(xrng, [low, low], color='Black', line_dash='dashed')
p.line(xrng, [up, up], color='Black', line_dash='dashed')
xpos = len(xdata) // 2
ypos = (low + up) / 2
lbl = Label(x=xdata[xpos], y=ypos - .0005, text='Current FFR Target Range')
p.add_layout(lbl)
for date in FOMC_MEETINGS:
p.line([date, date], yrng, color='black', alpha=0.3)
for y in range(0, int(yrng[-1] * 400)):
p.line(xrng, y / 400, color='grey', alpha=0.3)
ytik = SingleIntervalTicker()
ytik.interval = .0025
p.yaxis[0].ticker = ytik
p.xaxis[0].ticker.desired_num_ticks = len(xdata)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.yaxis.formatter = NumeralTickFormatter(format="0.00%")
export_png(p, filename=name + '.png')
return p
def create_bokeh_plot(file_path, title, clip_counts, old_bird_clip_counts):
output_file(file_path)
tools = 'save'
# tools = 'hover,save,pan,box_zoom,reset,wheel_zoom'
p = figure(plot_width=700, plot_height=700, tools=tools)
# Create plot lines.
for line_name, (detector_names, line_color) in PLOT_LINE_DATA.items():
create_bokeh_plot_line(p, line_name, detector_names, line_color,
clip_counts)
# Create Old Bird markers.
for marker_name, (detector_name, marker_color) in \
OLD_BIRD_PLOT_DATA.items():
create_bokeh_plot_marker(p, marker_name, detector_name, marker_color,
old_bird_clip_counts)
p.title.text = title
p.title.text_font_size = '12pt'
p.axis.major_tick_line_color = None
p.axis.minor_tick_line_color = None
p.xaxis.axis_label = 'Calls'
p.y_range = Range1d(0, 100)
ticker = SingleIntervalTicker(interval=20, num_minor_ticks=4)
p.yaxis.axis_label = 'Precision (%)'
p.yaxis.ticker = ticker
grid_line_color = 'black'
grid_line_alpha = .3
p.xgrid.grid_line_color = grid_line_color
p.xgrid.grid_line_alpha = grid_line_alpha
p.ygrid.ticker = ticker
p.ygrid.grid_line_color = grid_line_color
p.ygrid.grid_line_alpha = grid_line_alpha
p.ygrid.minor_grid_line_color = grid_line_color
p.ygrid.minor_grid_line_alpha = .1
p.legend.location = 'top_right'
p.legend.margin = 0
p.legend.label_text_font_size = '8pt'
show(p)
def plot(self):
self.fig = self.show_montage_centered(
self.signals,
self.loc_sec,
page_width_sec=self.page_width_secs,
chstart=0,
chstop=self.current_montage_instance.shape[0],
fs=self.fs,
ylabels=self.current_montage_instance.montage_labels,
yscale=self.yscale,
montage=self.current_montage_instance)
self.fig.xaxis.axis_label = 'seconds'
# make the xgrid mark every second
self.fig.xgrid.ticker = SingleIntervalTicker(
interval=1.0) # bokeh.models.tickers.SingleIntervalTicker
def get_card_y_responses(study):
rows = [str(i) for i in range(study.number_of_rows)]
cards = []
cards_y_data = {}
for card in study.card_set_y.cards:
cards.append(card.name)
cards_y_data[card.name] = [0 for x in range(study.number_of_rows)]
for response in study.responses:
for card_position in response.card_positions:
if card_position.card.card_set == study.card_set_y:
cards_y_data[card_position.card.name][
card_position.position] += 1
x = [(row, card) for row in rows for card in cards]
lists = list(cards_y_data.values())
counts = sum(zip(*lists), ())
title = "Count of each card in card set {} in each row".format(
study.card_set_y.name)
source = ColumnDataSource(data=dict(x=x, counts=counts))
p = figure(
x_range=FactorRange(*x),
height=400,
sizing_mode="stretch_width",
title=title,
toolbar_location=None,
tools="",
)
p.vbar(x="x", top="counts", width=0.9, source=source)
p.yaxis.ticker = SingleIntervalTicker(interval=1)
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
script, div = components(p)
return script, div
def gen_scatter(yvar_str='deficit_gdp',
xvar_str='dem_senateseats',
start_year='min',
main_df=main_df,
note_text_list=[],
fig_title_str='',
fig_path=''):
"""
Generates one of six different plot types of U.S. deficit/GDP by year, by
Democrat held Senate seats or House seats, and by three different measures
of political control
Args:
deficit_component (string): either "deficit", "spending", or "revenues"
seat_type (string): either "house" or "senate"
df (DataFrame): input data
show (boolean): =True shows figure by opening browser page
Returns:
Y (array_like): aggregate output
"""
# Create Variables for min and max values and plot buffer amounts
if start_year == 'min':
min_year = main_df['year'].min()
else:
min_year = int(start_year)
main_df = main_df[main_df['year'] >= min_year]
max_year = main_df['year'].max()
min_yvar = main_df[yvar_str].min()
max_yvar = main_df[yvar_str].max()
buffer_pct = 0.075
y_buffer = (max_yvar - min_yvar) * buffer_pct
max_seats = main_df[xvar_str].max()
min_seats = main_df[xvar_str].min()
x_buffer = (max_seats - min_seats) * buffer_pct
# Create x and y interval tick marks and gridlines for each plot
if xvar_str == 'dem_senateseats':
x_tick_interval = 2
x_num_minor_ticks = 2
x_grid_interval = x_tick_interval
mid_line = 50
if yvar_str == 'deficit_gdp':
y_tick_interval = 5
y_num_minor_ticks = 5
y_grid_interval = y_tick_interval
elif yvar_str == 'receipts_gdp':
y_tick_interval = 2
y_num_minor_ticks = 2
y_grid_interval = y_tick_interval
elif yvar_str == 'spend_nonint_gdp':
y_tick_interval = 5
y_num_minor_ticks = 5
y_grid_interval = y_tick_interval
elif xvar_str == 'dem_houseseats':
x_tick_interval = 10
x_num_minor_ticks = 2
x_grid_interval = x_tick_interval
mid_line = 217.5
if yvar_str == 'deficit_gdp':
y_tick_interval = 5
y_num_minor_ticks = 5
y_grid_interval = y_tick_interval
elif yvar_str == 'receipts_gdp':
y_tick_interval = 2
y_num_minor_ticks = 2
y_grid_interval = y_tick_interval
elif yvar_str == 'spend_nonint_gdp':
y_tick_interval = 5
y_num_minor_ticks = 5
y_grid_interval = y_tick_interval
# Create entire time series column data source for main and recession df's
main_cds = ColumnDataSource(main_df)
# Create entire time series column data source for main and recession df's
main_cds = ColumnDataSource(main_df)
# Create Full control (WH + Sen + HouseRep) Republican control elements
cntrl_all_rep_df = \
main_df[(main_df['president_party'] == 'Republican') &
(main_df['dem_senate_maj'] == 0) &
(main_df['dem_house_maj'] == 0)]
cntrl_all_rep_cds = ColumnDataSource(cntrl_all_rep_df)
# Create Full control (WH + Sen + HouseRep) Democrat control elements
cntrl_all_dem_df = \
main_df[(main_df['president_party'] == 'Democrat') &
(main_df['dem_senate_maj'] == 1) &
(main_df['dem_house_maj'] == 1)]
cntrl_all_dem_cds = ColumnDataSource(cntrl_all_dem_df)
# Create Full control (WH + Sen + HouseRep) split control elements
cntrl_all_split_df = \
main_df[((main_df['president_party'] == 'Republican') &
((main_df['dem_senate_maj'] == 1) |
(main_df['dem_house_maj'] == 1))) |
((main_df['president_party'] == 'Democrat') &
((main_df['dem_senate_maj'] == 0) |
(main_df['dem_house_maj'] == 0)))]
cntrl_all_split_cds = ColumnDataSource(cntrl_all_split_df)
# Create Senate control (WH + Sen) Republican control elements
cntrl_whsen_rep_df = \
main_df[(main_df['president_party'] == 'Republican') &
(main_df['dem_senate_maj'] == 0)]
cntrl_whsen_rep_cds = ColumnDataSource(cntrl_whsen_rep_df)
# Create Senate control (WH + Sen) Democrat control elements
cntrl_whsen_dem_df = \
main_df[(main_df['president_party'] == 'Democrat') &
(main_df['dem_senate_maj'] == 1)]
cntrl_whsen_dem_cds = ColumnDataSource(cntrl_whsen_dem_df)
# Create Senate control (WH + Sen) split control elements
cntrl_whsen_split_df = \
main_df[((main_df['president_party'] == 'Republican') &
(main_df['dem_senate_maj'] == 1)) |
((main_df['president_party'] == 'Democrat') &
(main_df['dem_senate_maj'] == 0))]
cntrl_whsen_split_cds = ColumnDataSource(cntrl_whsen_split_df)
# Create House control (WH + HouseRep) Republican control elements
cntrl_whhou_rep_df = \
main_df[(main_df['president_party'] == 'Republican') &
(main_df['dem_house_maj'] == 0)]
cntrl_whhou_rep_cds = ColumnDataSource(cntrl_whhou_rep_df)
# Create House control (WH + HouseRep) Democrat control elements
cntrl_whhou_dem_df = \
main_df[(main_df['president_party'] == 'Democrat') &
(main_df['dem_house_maj'] == 1)]
cntrl_whhou_dem_cds = ColumnDataSource(cntrl_whhou_dem_df)
# Create House control (WH + HouseRep) split control elements
cntrl_whhou_split_df = \
main_df[((main_df['president_party'] == 'Republican') &
(main_df['dem_house_maj'] == 1)) |
((main_df['president_party'] == 'Democrat') &
(main_df['dem_house_maj'] == 0))]
cntrl_whhou_split_cds = ColumnDataSource(cntrl_whhou_split_df)
cntrl_cds_list = \
[[cntrl_all_rep_cds, cntrl_all_dem_cds, cntrl_all_split_cds],
[cntrl_whsen_rep_cds, cntrl_whsen_dem_cds, cntrl_whsen_split_cds],
[cntrl_whhou_rep_cds, cntrl_whhou_dem_cds, cntrl_whhou_split_cds]]
# Output to HTML file
fig_title = fig_title_str
fig_path = fig_path
output_file(fig_path, title=fig_title)
# Set legend locations and label variables
if yvar_str == 'deficit_gdp':
legend_location = 'bottom_right'
elif yvar_str == 'spend_nonint_gdp':
legend_location = 'top_right'
elif yvar_str == 'receipts_gdp':
legend_location = 'top_right'
if xvar_str == 'dem_senateseats':
seat_type = 'Senate'
elif xvar_str == 'dem_houseseats':
seat_type = 'House'
'''
---------------------------------------------------------------------------
Create figure for each of the three party control definitions
---------------------------------------------------------------------------
'''
cntrl_str_list = ['all', 'whsen', 'whhou']
panel_title_list = \
['Full control: (White House + Senate + House of Reps.)',
'Senate control: (White House + Senate)',
'House control: (White House + House of Reps.)']
panel_list = []
for k, v in enumerate(cntrl_str_list):
# Create a figure with '% of GDP' as y-axis and num seats as x-axis
fig = figure(title=fig_title,
plot_height=650,
plot_width=1100,
x_axis_label='Democrat ' + seat_type + ' seats',
x_range=(min_seats - x_buffer, max_seats + x_buffer),
y_axis_label='Percent of Gross Domestic Product',
y_range=(min_yvar - y_buffer, max_yvar + y_buffer),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '15.5pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.yaxis.ticker = \
SingleIntervalTicker(interval=y_tick_interval,
num_minor_ticks=y_num_minor_ticks)
fig.ygrid.ticker = SingleIntervalTicker(interval=y_grid_interval)
fig.xaxis.ticker = \
SingleIntervalTicker(interval=x_tick_interval,
num_minor_ticks=x_num_minor_ticks)
fig.xgrid.ticker = SingleIntervalTicker(interval=x_grid_interval)
# Plotting the scatter point circles
fig.circle(x=xvar_str,
y=yvar_str,
source=cntrl_cds_list[k][0],
size=10,
line_width=1,
line_color='black',
fill_color='red',
alpha=0.7,
muted_alpha=0.2,
legend_label='Republican control')
fig.circle(x=xvar_str,
y=yvar_str,
source=cntrl_cds_list[k][1],
size=10,
line_width=1,
line_color='black',
fill_color='blue',
alpha=0.7,
muted_alpha=0.2,
legend_label='Democrat control')
fig.circle(x=xvar_str,
y=yvar_str,
source=cntrl_cds_list[k][2],
size=10,
line_width=1,
line_color='black',
fill_color='green',
alpha=0.7,
muted_alpha=0.2,
legend_label='Split control')
fig.segment(x0=mid_line,
y0=-40,
x1=mid_line,
y1=40,
color='black',
line_dash='6 2',
line_width=2)
# Add information on hover
if yvar_str == 'deficit_gdp':
tool_str = 'Deficit / GDP'
elif yvar_str == 'receipts_gdp':
tool_str = 'Receipts / GDP'
elif yvar_str == 'spend_nonint_gdp':
tool_str = 'NonInt Spend / GDP'
tooltips = [('Year', '@year'),
(tool_str, '@' + yvar_str + '{0.0}' + '%'),
('President', '@president'),
('White House', '@president_party'),
('Rep. House Seats', '@rep_houseseats'),
('Dem. House Seats', '@dem_houseseats'),
('Rep. Senate Seats', '@rep_senateseats'),
('Dem. Senate Seats', '@dem_senateseats')]
hover_glyph = fig.circle(x='year',
y=yvar_str,
source=main_cds,
size=10,
alpha=0,
hover_fill_color='gray',
hover_alpha=0.5)
fig.add_tools(HoverTool(tooltips=tooltips))
# Turn off scrolling
fig.toolbar.active_drag = None
# Add legend
fig.legend.location = legend_location
fig.legend.border_line_width = 2
fig.legend.border_line_color = 'black'
fig.legend.border_line_alpha = 1
fig.legend.label_text_font_size = '4mm'
# Set legend muting click policy
fig.legend.click_policy = 'mute'
# Add notes below image
for note_text in note_text_list[k]:
caption = Title(text=note_text,
align='left',
text_font_size='4mm',
text_font_style='italic')
fig.add_layout(caption, 'below')
panel = Panel(child=fig, title=panel_title_list[k])
panel_list.append(panel)
# Assign the panels to Tabs
tabs = Tabs(tabs=panel_list)
# Display the generated figure
# show(tabs)
return tabs
y=barclays_y,
size=10,
line_color="black",
fill_color="silver",
line_width=0.5)
# making scroll active by default
poverty.toolbar.active_scroll = poverty.select_one(WheelZoomTool)
fsrate.toolbar.active_scroll = fsrate.select_one(WheelZoomTool)
unemprate.toolbar.active_scroll = unemprate.select_one(WheelZoomTool)
# making a legend
pov_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=pov_palette,
low=0,
high=11),
ticker=SingleIntervalTicker(interval=1),
title="Population in poverty (%)",
label_standoff=12,
width=220,
orientation="horizontal",
border_line_color=None,
location=(0, 0))
unemp_color_bar = ColorBar(color_mapper=LinearColorMapper(
palette=unemp_palette, low=0, high=7),
ticker=SingleIntervalTicker(interval=.5),
title="Unemployment rate (%)",
label_standoff=12,
width=220,
orientation="horizontal",
border_line_color=None,
def gen_tseries(yvar_str='deficit_gdp',
start_year='min',
main_df=main_df,
recession_df=recession_df,
note_text_list=[],
fig_title_str='',
fig_path=''):
"""
This function creates a three-panel time-series plot--one panel for each
definition of party control--for a particular variable as a percent of GDP.
The particular variable is either deficits, receipts, or non-interest
spending.
Inputs:
yvar_str (string): either ''deficit_gdp', 'receipts_gdp', or
'spend_nonint_gdp'
"""
# Create Variables for min and max values
recession_data_length = len(recession_df['Peak'])
if start_year == 'min':
min_year = main_df['year'].min()
else:
min_year = int(start_year)
main_df = main_df[main_df['year'] >= min_year]
max_year = main_df['year'].max()
min_yvar = main_df[yvar_str].min()
max_yvar = main_df[yvar_str].max()
# Create entire time series column data source for main and recession df's
main_cds = ColumnDataSource(main_df)
# Create Full control (WH + Sen + HouseRep) Republican control elements
cntrl_all_rep_df = \
main_df[(main_df['president_party'] == 'Republican') &
(main_df['dem_senate_maj'] == 0) &
(main_df['dem_house_maj'] == 0)]
cntrl_all_rep_cds = ColumnDataSource(cntrl_all_rep_df)
# Create Full control (WH + Sen + HouseRep) Democrat control elements
cntrl_all_dem_df = \
main_df[(main_df['president_party'] == 'Democrat') &
(main_df['dem_senate_maj'] == 1) &
(main_df['dem_house_maj'] == 1)]
cntrl_all_dem_cds = ColumnDataSource(cntrl_all_dem_df)
# Create Full control (WH + Sen + HouseRep) split control elements
cntrl_all_split_df = \
main_df[((main_df['president_party'] == 'Republican') &
((main_df['dem_senate_maj'] == 1) |
(main_df['dem_house_maj'] == 1))) |
((main_df['president_party'] == 'Democrat') &
((main_df['dem_senate_maj'] == 0) |
(main_df['dem_house_maj'] == 0)))]
cntrl_all_split_cds = ColumnDataSource(cntrl_all_split_df)
# Create Senate control (WH + Sen) Republican control elements
cntrl_whsen_rep_df = \
main_df[(main_df['president_party'] == 'Republican') &
(main_df['dem_senate_maj'] == 0)]
cntrl_whsen_rep_cds = ColumnDataSource(cntrl_whsen_rep_df)
# Create Senate control (WH + Sen) Democrat control elements
cntrl_whsen_dem_df = \
main_df[(main_df['president_party'] == 'Democrat') &
(main_df['dem_senate_maj'] == 1)]
cntrl_whsen_dem_cds = ColumnDataSource(cntrl_whsen_dem_df)
# Create Senate control (WH + Sen) split control elements
cntrl_whsen_split_df = \
main_df[((main_df['president_party'] == 'Republican') &
(main_df['dem_senate_maj'] == 1)) |
((main_df['president_party'] == 'Democrat') &
(main_df['dem_senate_maj'] == 0))]
cntrl_whsen_split_cds = ColumnDataSource(cntrl_whsen_split_df)
# Create House control (WH + HouseRep) Republican control elements
cntrl_whhou_rep_df = \
main_df[(main_df['president_party'] == 'Republican') &
(main_df['dem_house_maj'] == 0)]
cntrl_whhou_rep_cds = ColumnDataSource(cntrl_whhou_rep_df)
# Create House control (WH + HouseRep) Democrat control elements
cntrl_whhou_dem_df = \
main_df[(main_df['president_party'] == 'Democrat') &
(main_df['dem_house_maj'] == 1)]
cntrl_whhou_dem_cds = ColumnDataSource(cntrl_whhou_dem_df)
# Create House control (WH + HouseRep) split control elements
cntrl_whhou_split_df = \
main_df[((main_df['president_party'] == 'Republican') &
(main_df['dem_house_maj'] == 1)) |
((main_df['president_party'] == 'Democrat') &
(main_df['dem_house_maj'] == 0))]
cntrl_whhou_split_cds = ColumnDataSource(cntrl_whhou_split_df)
cntrl_cds_list = \
[[cntrl_all_rep_cds, cntrl_all_dem_cds, cntrl_all_split_cds],
[cntrl_whsen_rep_cds, cntrl_whsen_dem_cds, cntrl_whsen_split_cds],
[cntrl_whhou_rep_cds, cntrl_whhou_dem_cds, cntrl_whhou_split_cds]]
# Output to HTML file
fig_title = fig_title_str
fig_path = fig_path
output_file(fig_path, title=fig_title)
'''
---------------------------------------------------------------------------
Create figure for each of the three party control definitions
---------------------------------------------------------------------------
'''
cntrl_str_list = ['all', 'whsen', 'whhou']
panel_title_list = \
['Full control: (White House + Senate + House of Reps.)',
'Senate control: (White House + Senate)',
'House control: (White House + House of Reps.)']
panel_list = []
for k, v in enumerate(cntrl_str_list):
# Create a figure with '% of GDP' as y-axis and year as x-axis
fig = figure(title=fig_title,
plot_height=650,
plot_width=1100,
x_axis_label='Year',
x_range=(min_year - 1, max_year + 1),
y_axis_label='Percent of Gross Domestic Product',
y_range=(min_yvar - 3, max_yvar + 3),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '15.5pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=2)
fig.xgrid.ticker = SingleIntervalTicker(interval=10)
fig.yaxis.ticker = SingleIntervalTicker(interval=5, num_minor_ticks=5)
fig.ygrid.ticker = SingleIntervalTicker(interval=5)
# Create recession bars
for x in range(0, recession_data_length):
peak_year = recession_df['Peak'][x].year
trough_year = recession_df['Trough'][x].year
if (peak_year >= min_year and trough_year >= min_year):
fig.patch(x=[peak_year, trough_year, trough_year, peak_year],
y=[-100, -100, max_yvar + 10, max_yvar + 10],
fill_color='gray',
fill_alpha=0.4,
line_width=0,
legend_label='Recession')
if (peak_year == trough_year and peak_year >= min_year
and trough_year >= min_year):
fig.patch(
x=[peak_year, trough_year + 1, trough_year + 1, peak_year],
y=[-100, -100, max_yvar + 10, max_yvar + 10],
fill_color='gray',
fill_alpha=0.4,
line_width=0,
legend_label='Recession')
# Plotting the line and scatter point circles
fig.line(x='year',
y=yvar_str,
source=main_cds,
color='#423D3C',
line_width=2)
fig.circle(x='year',
y=yvar_str,
source=cntrl_cds_list[k][0],
size=10,
line_width=1,
line_color='black',
fill_color='red',
alpha=0.7,
muted_alpha=0.2,
legend_label='Republican control')
fig.circle(x='year',
y=yvar_str,
source=cntrl_cds_list[k][1],
size=10,
line_width=1,
line_color='black',
fill_color='blue',
alpha=0.7,
muted_alpha=0.2,
legend_label='Democrat control')
fig.circle(x='year',
y=yvar_str,
source=cntrl_cds_list[k][2],
size=10,
line_width=1,
line_color='black',
fill_color='green',
alpha=0.7,
muted_alpha=0.2,
legend_label='Split control')
# Add information on hover
if yvar_str == 'deficit_gdp':
tool_str = 'Deficit / GDP'
elif yvar_str == 'receipts_gdp':
tool_str = 'Receipts / GDP'
elif yvar_str == 'spend_nonint_gdp':
tool_str = 'NonInt Spend / GDP'
tooltips = [('Year', '@year'),
(tool_str, '@' + yvar_str + '{0.0}' + '%'),
('President', '@president'),
('White House', '@president_party'),
('Rep. House Seats', '@rep_houseseats'),
('Dem. House Seats', '@dem_houseseats'),
('Rep. Senate Seats', '@rep_senateseats'),
('Dem. Senate Seats', '@dem_senateseats')]
hover_glyph = fig.circle(x='year',
y=yvar_str,
source=main_cds,
size=10,
alpha=0,
hover_fill_color='gray',
hover_alpha=0.5)
fig.add_tools(HoverTool(tooltips=tooltips))
# Turn off scrolling
fig.toolbar.active_drag = None
# Add legend
fig.legend.location = 'bottom_center'
fig.legend.border_line_width = 2
fig.legend.border_line_color = 'black'
fig.legend.border_line_alpha = 1
fig.legend.label_text_font_size = '4mm'
# Set legend muting click policy
fig.legend.click_policy = 'mute'
# Add notes below image
for note_text in note_text_list[k]:
caption = Title(text=note_text,
align='left',
text_font_size='4mm',
text_font_style='italic')
fig.add_layout(caption, 'below')
panel = Panel(child=fig, title=panel_title_list[k])
panel_list.append(panel)
# Assign the panels to Tabs
tabs = Tabs(tabs=panel_list)
# Display the generated figure
# show(tabs)
return tabs
def plot_bokeh(self, filename=None, show=True, savepng=False):
curdoc().theme = Theme(BOKEH_THEME_FILE)
if filename is not None:
if not filename.endswith('.html'):
filename += '.html'
else:
pass
bkp.output_file(filename)
else:
pass
# Format the date correction
self.data['utcstr'] = self.data['utc'].apply(lambda x: x.isoformat()[0:19])
# Put the dataframe in a useable form
self.data['lower'] = self.data.radiance - self.data.sigma
self.data['upper'] = self.data.radiance + self.data.sigma
self.data['lattandeg'] = self.data.lattan * 180 / np.pi
mask = self.data.alttan != self.data.alttan.max()
if np.any(mask):
m = self.data[self.data.alttan != self.data.alttan.max()].alttan.max()
else:
m = 1e10
col = np.interp(self.data.alttan, np.linspace(0, m, 256),
np.arange(256)).astype(int)
self.data['color'] = [Turbo256[c] for c in col]
source = bkp.ColumnDataSource(self.data)
# Tools
tools = [
'pan', 'box_zoom', 'wheel_zoom', 'xbox_select', 'hover', 'reset',
'save'
]
# tool tips
tips = [('index', '$index'), ('UTC', '@utcstr'),
('Radiance', '@radiance{0.2f} kR'),
('LTtan', '@loctimetan{2.1f} hr'),
('Lattan', '@lattandeg{3.1f} deg'), ('Alttan', '@alttan{0.f} km')]
# Make the figure
width, height = 1200, 600
fig0 = bkp.figure(plot_width=width,
plot_height=height,
x_axis_type='datetime',
title=f'{self.species}, {self.query}',
x_axis_label='UTC',
y_axis_label='Radiance (kR)',
y_range=[0, self.data.radiance.max() * 1.5],
tools=tools,
active_drag="xbox_select")
# plot the data
dplot = fig0.circle(x='utc',
y='radiance',
size=7,
color='black',
legend_label='Data',
hover_color='yellow',
source=source,
selection_color='orange')
fig0.line(x='utc',
y='radiance',
color='black',
legend_label='Data',
source=source)
fig0.xaxis.ticker = DatetimeTicker(num_minor_ticks=5)
# Add error bars
fig0.add_layout(
Whisker(source=source, base='utc', upper='upper', lower='lower'))
renderers = [dplot]
# Plot the model
col = color_generator()
modplots, maskedplots = [], []
for modkey, result in self.model_result.items():
c = next(col)
# fig0.line(x='utc', y=modkey, source=source,
# legend_label=result.label, color=c)
maskkey = modkey.replace('model', 'mask')
mask = (self.data[maskkey]).to_list()
view = CDSView(source=source, filters=[BooleanFilter(mask)])
modplots.append(
fig0.circle(x='utc',
y=modkey,
size=9,
color=c,
source=source,
legend_label=result.label,
view=view))
maskkey = modkey.replace('model', 'mask')
mask = np.logical_not(self.data[maskkey]).to_list()
view = CDSView(source=source, filters=[BooleanFilter(mask)])
maskedplots.append(
fig0.circle(x='utc',
y=modkey,
size=9,
source=source,
line_color=c,
fill_color='yellow',
view=view,
legend_label=result.label + ' (Data Point Not Used)'))
renderers.extend(modplots)
renderers.extend(maskedplots)
datahover = HoverTool(tooltips=tips, renderers=renderers)
fig0.add_tools(datahover)
##############
# Plot tangent point
color_mapper = LinearColorMapper(palette="Turbo256", low=0, high=m)
width, height = 1200, 600
tools = [
'pan', 'box_zoom', 'wheel_zoom', 'box_select', 'hover', 'reset', 'save'
]
fig1 = bkp.figure(plot_width=width,
plot_height=height,
title=f'Tangent Point Location',
x_axis_label='Local Time (hr)',
y_axis_label='Latitude (deg)',
x_range=[0, 24],
y_range=[-90, 90],
tools=tools,
active_drag="box_select")
tanplot = fig1.circle(x='loctimetan',
y='lattandeg',
size=5,
selection_color='orange',
hover_color='purple',
source=source,
color='color')
fig1.xaxis.ticker = SingleIntervalTicker(interval=6, num_minor_ticks=6)
fig1.yaxis.ticker = SingleIntervalTicker(interval=45, num_minor_ticks=3)
color_bar = ColorBar(color_mapper=color_mapper,
title='Altitude (km)',
label_standoff=12,
border_line_color=None,
location=(0, 0))
fig1.add_layout(color_bar, 'right')
datahover = HoverTool(tooltips=tips, renderers=[tanplot])
fig1.add_tools(datahover)
grid = column(fig0, fig1)
if filename is not None:
bkp.output_file(filename)
if savepng:
export_png(grid, filename=filename.replace('.html', '.png'))
else:
pass
bkp.save(grid)
else:
pass
if show:
bkp.show(grid)
return fig0, fig1
def gen_tseries_dy(var_list, legend_label_list, df, color_list, marker_list,
start_year='min', end_year='max', note_text_list=[],
fig_title_str='', fig_path=''):
"""
This function creates a plot of multiple time series of forecasts of
U.S. publicly held national debt.
"""
# Create Variables for min and max values
if start_year == 'min':
min_year = df['year'].min()
else:
min_year = int(start_year)
if end_year == 'max':
max_year = df['year'].max()
else:
max_year = int(end_year)
df_main = df[(df['year'] >= min_year) & (df['year'] <= max_year)]
# Find the min and max yvar values across the list of yvars and create
# separate ColumnDataSource objects for each forecast series (this helps
# with the hovertools)
min_yvar = 100
max_yvar = 0
cds_list = []
for k, yvar in enumerate(var_list):
min_yvar = np.minimum(min_yvar, df_main[yvar].min())
max_yvar = np.maximum(max_yvar, df_main[yvar].max())
frcst_df = df_main[['year', yvar]].dropna()
frcst_df.rename(columns={yvar: 'debt_gdp'}, inplace=True)
frcst_df = frcst_df[['year', 'debt_gdp']]
cds_list.append(ColumnDataSource(frcst_df))
# Output to HTML file
fig_title = fig_title_str
fig_path = fig_path
output_file(fig_path, title=fig_title)
fig = figure(title=fig_title,
plot_height=600,
plot_width=1100,
x_axis_label='Year',
x_range=(min_year - 1, max_year + 1),
y_axis_label='Percent of Gross Domestic Product',
y_range=(min_yvar - 5, max_yvar + 5),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '15pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=5, num_minor_ticks=5)
fig.xgrid.ticker = SingleIntervalTicker(interval=5)
fig.yaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=2)
fig.ygrid.ticker = SingleIntervalTicker(interval=10)
# Create lines and markers for time series
for k, yvar in enumerate(var_list):
fig.line(x='year', y='debt_gdp', source=cds_list[k], color=color_list[k],
line_width=3, alpha=0.7)
fig.scatter(x='year', y='debt_gdp', source=cds_list[k], size=8,
line_width=1, line_color='black', fill_color=color_list[k],
marker=marker_list[k], line_alpha=0.7, fill_alpha=0.7,
legend_label=legend_label_list[k])
# Add vertical dashed line at 2050
fig.segment(x0=2050, y0=90, x1=2050, y1=215, color='black',
line_dash='6 4', line_width=2)
# Add information on hover
tooltips = [('Year', '@year'),
('Debt/GDP','@debt_gdp{0.0}' + '%')]
fig.add_tools(HoverTool(tooltips=tooltips, toggleable=False))
# Add legend
fig.legend.location = 'top_center'
fig.legend.border_line_width = 1
fig.legend.border_line_color = 'black'
fig.legend.border_line_alpha = 1
fig.legend.label_text_font_size = '4mm'
# Add notes below image
for note_text in note_text_list:
caption = Title(text=note_text, align='left', text_font_size='4mm',
text_font_style='italic')
fig.add_layout(caption, 'below')
return fig
plot_width=1200,
x_axis_label='Year',
x_range=(min_year - 0.5, max_year + 0.5),
y_axis_label='Percent of Gross Domestic Product',
y_range=(min_deficit - 2, max_deficit + 5),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '18pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=5, num_minor_ticks=5)
fig.xgrid.ticker = SingleIntervalTicker(interval=5)
fig.yaxis.ticker = SingleIntervalTicker(interval=3, num_minor_ticks=3)
fig.ygrid.ticker = SingleIntervalTicker(interval=3)
# Plotting data
fig.segment(x0=-3000, y0=0, x1=3000, y1=0, color='gray', line_width=4)
bar_width = 0.5
# Bar graph for Net Interest
fig.vbar(x='year',
top='Primary Deficit',
bottom='Total Deficit',
source=deficit_cds,
width=bar_width,
fill_color='#6C9CB2',
legend_label='Net Interest')
un_hover = HoverTool(tooltips=[
("Name", "@name"), ("Population", "@totpop"), ("Unemployment Rate", "@unemprate{1.11%}"), ("(Long, Lat)", "($x, $y)")
], renderers=[un_patches])
unemprate.add_tools(un_hover)
unemprate.grid.grid_line_color = None
unemprate.hover.point_policy = "follow_mouse"
unemprate.circle(x=barclays_x, y=barclays_y, size=10, line_color="black", fill_color="silver", line_width=0.5)
# making scroll active by default
poverty.toolbar.active_scroll = poverty.select_one(WheelZoomTool)
fsrate.toolbar.active_scroll = fsrate.select_one(WheelZoomTool)
unemprate.toolbar.active_scroll = unemprate.select_one(WheelZoomTool)
# making a legend
pov_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=pov_palette, low=0, high=11), ticker=SingleIntervalTicker(interval=1), title="Population in poverty (%)",
label_standoff=12, width=220, orientation="horizontal", border_line_color=None, location=(0,0))
unemp_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=unemp_palette, low=0, high=7), ticker=SingleIntervalTicker(interval=.5), title="Unemployment rate (%)",
label_standoff=12, width=220, orientation="horizontal", border_line_color=None, location=(0,0))
fs_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=fs_palette, low=0, high=13), ticker=SingleIntervalTicker(interval=1), title="Food Stamps/SNAP use (%)",
label_standoff=12, width=220, orientation="horizontal", border_line_color=None, location=(0,0))
# adding the label on barclays and legend below each map
poverty.add_layout(pov_color_bar, 'below')
unemprate.add_layout(unemp_color_bar, 'below')
fsrate.add_layout(fs_color_bar, 'below')
poverty.add_layout(barclays_label1)
fsrate.add_layout(barclays_label2)
unemprate.add_layout(barclays_label3)
def gen_hhdist_2bars(x_cat_var, bar_hgt_vars1, bar_hgt_vars2, df, color_list,
legend_label_list, note_text_list=[], fig_title_str='',
fig_path=''):
"""
This function creates a bar chart of the effect of a reform on households
in different quantiles of the lifetime income distribution
"""
# Create a list of lifetime income groups
x_cat_list = df[x_cat_var].tolist()
x = [(inc_cat, var_label) for inc_cat in x_cat_list
for var_label in legend_label_list]
data1 = []
for obs, inc_cat in enumerate(x_cat_list):
data1.append(df[bar_hgt_vars1[0]].iloc[obs]),
data1.append(df[bar_hgt_vars1[1]].iloc[obs])
data1.append(df[bar_hgt_vars1[2]].iloc[obs])
data1.append(df[bar_hgt_vars1[3]].iloc[obs])
# data_dict1 = {x_cat_var: df[x_cat_var].tolist(),
# legend_label_list[0]: df[bar_hgt_vars1[0]].tolist(),
# legend_label_list[1]: df[bar_hgt_vars1[1]].tolist(),
# legend_label_list[2]: df[bar_hgt_vars1[2]].tolist(),
# legend_label_list[3]: df[bar_hgt_vars1[3]].tolist()}
# data1 = zip(data_dict1[legend_label_list[0]],
# data_dict1[legend_label_list[1]],
# data_dict1[legend_label_list[2]],
# data_dict1[legend_label_list[3]])
print('x =', x)
# print('data1 =', data1)
# yvar_list0 = df[bar_hgt_vars1[0]].tolist()
# Solve for ymin and ymax across variables and set buffer amount
bar_hgt_vars_all = [*bar_hgt_vars1, *bar_hgt_vars2]
min_yvar = 100
max_yvar = -100
cds_list = []
for yvar in bar_hgt_vars_all:
min_yvar = np.minimum(min_yvar, df[yvar].min())
max_yvar = np.maximum(max_yvar, df[yvar].max())
df_var = df[['lfinc_qntl', yvar]]
df_var.rename(columns={yvar: 'var_pct_chg'}, inplace=True)
cds_list.append(ColumnDataSource(df_var))
y_buffer_pct = 0.1
y_range = max_yvar - min_yvar
y_buffer_amt = y_buffer_pct * y_range
# Output to HTML file
fig_title = fig_title_str
fig_path = fig_path
output_file(fig_path, title=fig_title)
fig = figure(title=fig_title,
height=600,
x_axis_label='Lifetime income quantiles',
x_range=x_cat_list,
y_axis_label='Percent change',
y_range=(min_yvar - y_buffer_amt, max_yvar + y_buffer_amt),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '15pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for Y-axis
fig.yaxis.ticker = SingleIntervalTicker(interval=0.1, num_minor_ticks=5)
fig.ygrid.ticker = SingleIntervalTicker(interval=0.1)
fig.vbar(x=x, top=data1, width=0.8,
color=color_list[0], legend_label=legend_label_list[0])
# # Add information on hover
# tooltips = [('Percent change', '@var_pct_chg{0.0}' + '%')]
# fig.add_tools(HoverTool(tooltips=tooltips, toggleable=False))
# # Add legend
# fig.legend.location = 'top_center'
# fig.legend.border_line_width = 1
# fig.legend.border_line_color = 'black'
# fig.legend.border_line_alpha = 1
# fig.legend.label_text_font_size = '4mm'
# # Add notes below image
# for note_text in note_text_list:
# caption = Title(text=note_text, align='left', text_font_size='4mm',
# text_font_style='italic')
# fig.add_layout(caption, 'below')
return fig
def TabParcialesKilometricos(df):
"""
PREPARACION DE DATOS
"""
# Calculo de los valores agregados
AVG_Altitud, MAX_Altitud, MIN_Altitud, \
AVG_Velocidad, MAX_Velocidad, MIN_Velocidad, \
AVG_Ritmo, MAX_Ritmo, MIN_Ritmo, \
AVG_FrecuenciaCardiaca, MAX_FrecuenciaCardiaca, MIN_FrecuenciaCardiaca, \
AVG_Cadencia, MAX_Cadencia, MIN_Cadencia, \
AVG_Temperatura, MAX_Temperatura, MIN_Temperatura, \
AVG_LongitudZancada, MAX_LongitudZancada, MIN_LongitudZancada, \
AVG_Pendiente, MAX_Pendiente , MIN_Pendiente = CalculosVectoresAgregados(df)
# Calculo de desniveles finales
DesnivelPositivo = df['DesnivelPositivoAcumulado'].max()
DesnivelNegativo = df['DesnivelNegativoAcumulado'].max()
DesnivelAcumulado = DesnivelPositivo + DesnivelNegativo
DesnivelPorKilometro = (DesnivelAcumulado / df['Distancia'].max()) * 1000
# Factor de achatamiento de la altitud
if DesnivelPorKilometro > 40:
OffsetSuperiorAltitud = 0.1
OffsetInferiorAltitud = 0.03
else:
OffsetSuperiorAltitud = 2.5
OffsetInferiorAltitud = 0.5
dfTramosKm = TablaParcialesKilometricos(df)
dfTramosKm['Ritmo_STR'] = dfTramosKm.Ritmo.apply(
lambda x: FormateoTiempos(x, 'R'))
# Seleccion de un subconjunto de datos para visualizar
dfBokehParcialesKM = df[[
'Bloque', 'Distancia', 'Altitud', 'FrecuenciaCardiaca', 'Cadencia'
]].copy()
dfBokehParcialesKM['AltitudEscalada'] = Reescalado(
dfBokehParcialesKM['Altitud'], [
MIN_Altitud[0] -
(MAX_Altitud[0] - MIN_Altitud[0]) * OffsetInferiorAltitud,
MAX_Altitud[0] +
(MAX_Altitud[0] - MIN_Altitud[0]) * OffsetSuperiorAltitud
], [0, dfTramosKm.Velocidad.max()])
dfBokehParcialesKM['FrecuenciaCardiacaEscalada'] = Reescalado(
dfBokehParcialesKM['FrecuenciaCardiaca'],
[MIN_FrecuenciaCardiaca[0], MAX_FrecuenciaCardiaca[0]],
[0, dfTramosKm.Velocidad.max()])
dfBokehParcialesKM['CadenciaEscalada'] = Reescalado(
dfBokehParcialesKM['Cadencia'], [MIN_Cadencia[0], MAX_Cadencia[0]],
[0, dfTramosKm.Velocidad.max()])
# Reducion de la frecuencia de muestreo
dfBokehParcialesKM_Agg = dfBokehParcialesKM.groupby('Bloque').resample(
'10S').agg({
'Distancia': np.max,
'Cadencia': np.mean
})
dfBokehParcialesKM_Agg['Cadencia'] = dfBokehParcialesKM_Agg[
'Cadencia'].round()
dfBokehParcialesKM_Agg['CadenciaEscalada'] = Reescalado(
dfBokehParcialesKM_Agg['Cadencia'], [MIN_Cadencia[0], MAX_Cadencia[0]],
[0, dfTramosKm.Velocidad.max()])
# Creacion de los ColumnDataSource de origen de Bokeh
OrigenParcialesKM = ColumnDataSource(dfBokehParcialesKM)
OrigenParcialesKM_Agg = ColumnDataSource(dfBokehParcialesKM_Agg)
OrigenTramosKm = ColumnDataSource(dfTramosKm)
# Asignacion de tamaño segun el total de puntos
if df['Distancia'].max() < 5:
SizeCircle = 10
elif df['Distancia'].max() < 10:
SizeCircle = 8
else:
SizeCircle = 5
# Definicion de la paleta de colores por cadencia
MapaColorCadencia = LinearColorMapper(palette=paleta_cadencia,
low=110,
high=190)
"""
TRAMOS KILOMETRICOS | STRAVA
"""
PLT_TramosKm = figure(plot_width=900,
plot_height=500,
x_range=(0, df['Distancia'].max()),
y_range=(0, dfTramosKm.Velocidad.max() * 1.1),
tools='',
toolbar_location=None)
PLT_TramosKm.add_layout(
Span(location=AVG_Velocidad[0],
dimension='width',
line_color=BlueJeans[2],
line_dash='dashed',
line_width=0.1,
line_alpha=0.3))
PLT_BarrasKM = PLT_TramosKm.rect(x='x',
y='y',
width='Distancia',
height='Velocidad',
source=OrigenTramosKm,
line_width=1,
line_color=BlueJeans[2],
fill_color=BlueJeans[1])
PLT_TramosKm.add_tools(
HoverTool(tooltips=[('', '@Ritmo_STR')],
renderers=[PLT_BarrasKM],
mode='mouse'))
PropiedadesLineas = dict(line_width=2,
line_alpha=0.7,
line_cap='round',
visible=False)
PLT_TramosKM_Altitud = PLT_TramosKm.line('Distancia',
'AltitudEscalada',
source=OrigenParcialesKM,
color=paleta_verde[6],
**PropiedadesLineas)
PLT_TramosKM_FC = PLT_TramosKm.line('Distancia',
'FrecuenciaCardiacaEscalada',
source=OrigenParcialesKM,
color=paleta_rojo[6],
**PropiedadesLineas)
PLT_TramosKM_Cadencia = PLT_TramosKm.circle(
'Distancia',
'CadenciaEscalada',
source=OrigenParcialesKM_Agg,
size=SizeCircle,
line_color=transform('Cadencia', MapaColorCadencia),
color=transform('Cadencia', MapaColorCadencia),
fill_alpha=1,
visible=False)
# Atributos
PLT_TramosKm.title.text = 'RITMO MEDIO KILOMETRICO'
PLT_TramosKm.sizing_mode = 'fixed'
PLT_TramosKm.xaxis.axis_label = 'Distancia'
PLT_TramosKm.xaxis.formatter = NumeralTickFormatter(format='0 a')
PLT_TramosKm.yaxis.axis_label = 'Ritmo [min/km]'
PLT_TramosKm.grid.visible = False
PLT_TramosKm.yaxis.major_label_overrides = {
1: '16:40',
1.5: '16:06',
2: '8:20',
2.5: '6:40',
3: '5:33',
3.5: '4:45',
4: '4:10',
4.5: '3:42',
5: '3:20',
5.5: '3:01',
6: '2:46',
6.5: '2:34',
7: '2:22'
}
PLT_TramosKm.xaxis.ticker = SingleIntervalTicker(interval=1000)
PLT_TramosKm.xaxis.major_label_overrides = FormateoEjes(
OrigenParcialesKM.data['Distancia'], 1000, 1000, 0, 0)
"""
DATOS EN FORMA DE TABLA POR PARCIALES KILOMETRICOS
"""
TablaKm = [
TableColumn(field='TramoKm',
title='Km',
width=40,
default_sort='ascending',
sortable=False,
formatter=StringFormatter(font_style='normal',
text_align='center',
text_color='black')),
TableColumn(field='Ritmo_STR',
title='Ritmo[min/Km]',
width=80,
default_sort='ascending',
sortable=False,
formatter=StringFormatter(font_style='normal',
text_align='center',
text_color='black')),
TableColumn(field='FrecuenciaCardiaca',
title='FC[ppm]',
width=80,
default_sort='ascending',
sortable=False,
formatter=NumberFormatter(format='0,0',
language='es',
rounding='round',
font_style='normal',
text_align='center',
text_color='black')),
TableColumn(field='Cadencia',
title='Cadencia[ppm]',
width=80,
default_sort='ascending',
sortable=False,
formatter=NumberFormatter(format='0,0',
language='es',
rounding='round',
font_style='normal',
text_align='center',
text_color='black')),
TableColumn(field='DesnivelAcumulado',
title='Desnivel',
width=80,
default_sort='ascending',
sortable=False,
formatter=NumberFormatter(format='0,0',
language='es',
rounding='round',
font_style='normal',
text_align='center',
text_color='black'))
]
PLT_TablaKm = DataTable(source=OrigenTramosKm,
columns=TablaKm,
width=360,
height=550,
fit_columns=False,
sortable=False,
reorderable=False,
selectable=True,
editable=False,
index_position=None,
header_row=True,
row_height=25)
"""
BOTONES
"""
CodigoJS = """
var indexOf = [].indexOf || function(item) { for (var i = 0, l = this.length; i < l; i++) { if (i in this && this[i] === item) return i; } return -1; };
l0.visible = indexOf.call(checkbox.active,0)>=0;
l1.visible = indexOf.call(checkbox.active,1)>=0;
l2.visible = indexOf.call(checkbox.active,2)>=0;
"""
BotonesTramosKm = CheckboxButtonGroup(
labels=["Altitud", "Frecuencia Cardiaca", "Cadencia"],
active=[],
width=300,
height=30)
CodigoJSTramosKm = CustomJS(code=CodigoJS,
args=dict(l0=PLT_TramosKM_Altitud,
l1=PLT_TramosKM_FC,
l2=PLT_TramosKM_Cadencia,
checkbox=BotonesTramosKm))
BotonesTramosKm.js_on_click(CodigoJSTramosKm)
"""
LAYOUT
"""
GridGraficaTramosKm = layout([
Column(PLT_TramosKm, width=900, height=500),
[
Spacer(width=300, height=30),
Column(BotonesTramosKm, width=300, height=30),
Spacer(width=300, height=30)
]
],
sizing_mode='stretch_width',
width=900,
height=570)
GridTablaTramosKm = layout([
Spacer(width=360, height=25),
Column(PLT_TablaKm, width=360, height=550)
],
sizing_mode='stretch_width',
width=360,
height=570)
GridAnalisisKm = gridplot([GridGraficaTramosKm, GridTablaTramosKm],
ncols=2,
sizing_mode='stretch_width',
toolbar_location=None,
plot_width=1000,
plot_height=570)
return GridAnalisisKm
def DesnivelPositivoAcumulado(DatosBokeh, EjeX, MetricasAuxiliares):
"""
GRAFICO AREA | DESNIVEL POSITIVO ACUMULADO
"""
# Creacion del diccionario de metricas auxiliares
DiccionarioVariables = ParametrosVariables(DatosBokeh)
# Generacion del codigo JavaScript que habilita la visualizacion de metricas auxiliares
CodigoJS = GeneracionCodigoJS(MetricasAuxiliares)
# Asignacion de tamaño del punto en graficas discretas
SizeCircle = FunctionSizeCircle(DatosBokeh)
# Creacion de un grafica
PLT = figure(width=1000,
height=400,
x_range=(DatosBokeh.data[EjeX].min(),
DatosBokeh.data[EjeX].max()),
y_range=(LimiteEjeY(DatosBokeh, 'DESNIVEL POSITIVO',
'Inferior'),
LimiteEjeY(DatosBokeh, 'DESNIVEL POSITIVO',
'Superior')),
tools='',
toolbar_location=None)
# Creacion del area bajo la linea de la metrica a partir del CDS
Area = DatosBokeh.to_df().copy().reset_index()[[
EjeX, 'DesnivelPositivo[m]'
]].set_index(EjeX)
Area.rename(columns={'DesnivelPositivo[m]': 'Area'}, inplace=True)
AreaBottom = Area[::-1]
AreaBottom['Area'] = 0
PLT.patch(x=hstack((AreaBottom.index, Area.index)),
y=hstack((AreaBottom['Area'], Area['Area'])),
color=Lavender[0],
alpha=1,
line_color=None)
# Inclusion de datos
PLT_Linea = PLT.line(EjeX,
'DesnivelPositivo[m]',
source=DatosBokeh,
color=Lavender[1],
line_width=2,
line_cap='round')
PLT.add_tools(
HoverTool(tooltips=[('', '@{DesnivelPositivo[m]}{int} m')],
renderers=[PLT_Linea],
mode='vline'))
# Inclusion de lineas auxiliares
ListadoMetricasAuxiliares = {}
for Metrica in MetricasAuxiliares:
if DiccionarioVariables[Metrica]['Tipo'] == 'circle':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.circle(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['DESNIVEL POSITIVO']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
size=SizeCircle,
line_color=DiccionarioVariables[Metrica]['Color'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
elif DiccionarioVariables[Metrica]['Tipo'] == 'step':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.step(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['DESNIVEL POSITIVO']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
else:
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.line(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['DESNIVEL POSITIVO']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
# Atributos
PLT.title.text = 'DESNIVEL POSITIVO ACUMULADO'
PLT.sizing_mode = 'fixed'
PLT.yaxis.axis_label = 'Desnivel positivo acumulado [m]'
PLT.yaxis.formatter = NumeralTickFormatter(format='0')
PLT.grid.visible = False
PLT.yaxis.minor_tick_line_color = None
PLT.yaxis.major_label_overrides = FormateoEjes(
DatosBokeh.data['DesnivelPositivo[m]'], 100, 1)
# Asignacion de opciones de visualizacion del eje X
if EjeX == 'Distancia[m]':
PLT.xaxis.axis_label = 'Distancia'
PLT.xaxis.formatter = NumeralTickFormatter(format='0')
if DatosBokeh.data['Distancia[m]'].max() >= 4000:
PLT.xaxis.ticker = SingleIntervalTicker(interval=1000)
PLT.xaxis.major_label_overrides = FormateoEjes(
DatosBokeh.data['Distancia[m]'], 1000, 1000, 0, 0)
elif EjeX == 'TiempoActividad':
PLT.xaxis.axis_label = 'Tiempo actividad'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
elif EjeX == 'TiempoTotal':
PLT.xaxis.axis_label = 'Tiempo total'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
#Botones
Botones = CheckboxGroup(labels=MetricasAuxiliares,
active=[],
width=100,
height=380)
ListadoMetricasAuxiliares['checkbox'] = Botones
CodigoJSFrecuenciaCardiaca = CustomJS(code=CodigoJS,
args=ListadoMetricasAuxiliares)
Botones.js_on_click(CodigoJSFrecuenciaCardiaca)
#Layout
GridBotones = layout(
[Spacer(width=100, height=25),
Column(Botones, width=100, height=375)],
sizing_mode='fixed',
width=100,
height=400)
GridGrafica = gridplot([PLT, GridBotones],
ncols=2,
sizing_mode='stretch_width',
toolbar_location=None,
plot_width=1100,
plot_height=400)
return GridGrafica
def gen_tseries_frcst(frcst_var_list, legend_label_list, df=main_df,
main_start_year='min', main_end_year='max',
full_start_year='min', full_end_year='max',
note_text_list=[], fig_title_str='', fig_path=''):
"""
This function creates a plot of multiple time series of CBO forecasts of
U.S. publicly held national debt.
"""
# Create Variables for min and max values
if main_start_year == 'min':
main_min_year = df['year'].min()
else:
main_min_year = int(main_start_year)
if full_start_year == 'min':
full_min_year = df['year'].min()
else:
full_min_year = int(full_start_year)
if main_end_year == 'max':
main_max_year = df['year'].max()
else:
main_max_year = int(main_end_year)
if full_end_year == 'max':
full_max_year = df['year'].max()
else:
full_max_year = int(full_end_year)
df_full = df[(df['year'] >= full_min_year) & (df['year'] <= full_max_year)]
df_main = df[(df['year'] >= main_min_year) & (df['year'] <= main_max_year)]
# Find the min and max yvar values across the list of yvars and create
# separate ColumnDataSource objects for each forecast series (this helps
# with the hovertools)
main_min_yvar = 100
main_max_yvar = 0
cds_list = []
for k, yvar in enumerate(frcst_var_list):
main_min_yvar = np.minimum(main_min_yvar, df_main[yvar].min())
main_max_yvar = np.maximum(main_max_yvar, df_main[yvar].max())
frcst_df = df_full[['year', yvar, yvar + '_frcst']].dropna()
frcst_df['frcst'] = frcst_df[yvar + '_frcst'] = 1
frcst_df['frcst_label'] = legend_label_list[k]
frcst_df.rename(columns={yvar: 'debt_gdp'}, inplace=True)
frcst_df = frcst_df[['year', 'debt_gdp', 'frcst', 'frcst_label']]
cds_list.append(ColumnDataSource(frcst_df))
# Output to HTML file
fig_title = fig_title_str
fig_path = fig_path
output_file(fig_path, title=fig_title)
fig = figure(title=fig_title,
plot_height=600,
plot_width=1100,
x_axis_label='Year',
x_range=(main_min_year - 1, main_max_year + 1),
y_axis_label='Percent of Gross Domestic Product',
y_range=(main_min_yvar - 5, main_max_yvar + 5),
tools=['save', 'zoom_in', 'zoom_out', 'box_zoom',
'pan', 'undo', 'redo', 'reset', 'help'],
toolbar_location='left')
fig.toolbar.logo = None
# Set title font size and axes font sizes
fig.title.text_font_size = '15pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=2)
fig.xgrid.ticker = SingleIntervalTicker(interval=10)
fig.yaxis.ticker = SingleIntervalTicker(interval=20, num_minor_ticks=2)
fig.ygrid.ticker = SingleIntervalTicker(interval=20)
min_256_color_ind = 0
max_256_color_ind = 200
intercept = max_256_color_ind
slope = (min_256_color_ind - intercept) / (len(frcst_var_list) - 1)
legend_item_list = []
for k, v in enumerate(frcst_var_list):
color_ind = int(np.round(slope * k + intercept))
line = fig.line(x='year', y='debt_gdp', source=cds_list[k],
color=Reds[256][color_ind], line_width=3, alpha=0.7,
muted_alpha=0.15)
legend_item_list.append((legend_label_list[k], [line]))
# Add information on hover
tooltips = [('Year', '@year'),
('Debt/GDP','@debt_gdp'),
('Forecast', '@frcst'),
('Forecast date', '@frcst_label')]
fig.add_tools(HoverTool(tooltips=tooltips, toggleable=False))
# Add legend
legend = Legend(items=legend_item_list, location='center')
fig.add_layout(legend, 'right')
fig.legend.border_line_width = 1
fig.legend.border_line_color = 'black'
fig.legend.border_line_alpha = 1
fig.legend.label_text_font_size = '4mm'
# Set legend muting click policy
fig.legend.click_policy = 'mute'
# Add notes below image
for note_text in note_text_list:
caption = Title(text=note_text, align='left', text_font_size='4mm',
text_font_style='italic')
fig.add_layout(caption, 'below')
return fig
plot_width=1200,
x_axis_label='Year',
x_range=(min_year, max_year),
y_axis_label='Percent of Gross Domestic Product',
y_range=(0, max_debt + 20),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '18pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=0)
fig.xgrid.ticker = SingleIntervalTicker(interval=20)
fig.yaxis.ticker = SingleIntervalTicker(interval=25, num_minor_ticks=0)
fig.ygrid.ticker = SingleIntervalTicker(interval=50)
# Plotting the data line
fig.varea(x='year', y1='debt', y2=0, source=debt_cds, color='#C584DB')
# Vertical line showing start of forecast data
fig.segment(x0=2021,
y0=0,
x1=2021,
y1=300,
color='gray',
line_dash='6 2',
line_width=2)
def Zancada(DatosBokeh, EjeX, MetricasAuxiliares):
"""
GRAFICO DISCRETO | LONGITUD DE ZANCADA
"""
# Creacion del diccionario de metricas auxiliares
DiccionarioVariables = ParametrosVariables(DatosBokeh)
# Generacion del codigo JavaScript que habilita la visualizacion de metricas auxiliares
CodigoJS = GeneracionCodigoJS(MetricasAuxiliares)
# Asignacion de tamaño del punto en graficas discretas
SizeCircle = FunctionSizeCircle(DatosBokeh)
# Creacion de un grafica
PLT = figure(width=1000,
height=400,
x_range=(DatosBokeh.data[EjeX].min(),
DatosBokeh.data[EjeX].max()),
y_range=(LimiteEjeY(DatosBokeh, 'LONGITUD ZANCADA',
'Inferior'),
LimiteEjeY(DatosBokeh, 'LONGITUD ZANCADA',
'Superior')),
tools='',
toolbar_location=None)
# Inclusion de datos
PLT.circle(EjeX,
'LongitudZancada[m]',
source=DatosBokeh,
size=SizeCircle,
line_color=transform(
'LongitudZancada[m]',
LinearColorMapper(palette=PaletaColoresLinea,
low=0.8,
high=2)),
color=transform(
'LongitudZancada[m]',
LinearColorMapper(palette=PaletaColores, low=0.8, high=2)),
fill_alpha=1)
PLT_Linea = PLT.line(EjeX,
'LongitudZancada[m]',
source=DatosBokeh,
color='white',
line_width=0,
line_alpha=0)
PLT.add_tools(
HoverTool(tooltips=[('', '@{LongitudZancada[m]}{0,0.00} m')],
renderers=[PLT_Linea],
mode='vline'))
# Inclusion de lineas auxiliares
ListadoMetricasAuxiliares = {}
for Metrica in MetricasAuxiliares:
if DiccionarioVariables[Metrica]['Tipo'] == 'circle':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.circle(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['LONGITUD ZANCADA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
size=SizeCircle,
line_color=DiccionarioVariables[Metrica]['Color'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
elif DiccionarioVariables[Metrica]['Tipo'] == 'step':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.step(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['LONGITUD ZANCADA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
else:
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.line(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['LONGITUD ZANCADA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
# Atributos
PLT.title.text = 'LONGITUD DE ZANCADA'
PLT.sizing_mode = 'fixed'
PLT.yaxis.axis_label = 'Longitud de zancada [m]'
PLT.yaxis.formatter = NumeralTickFormatter(format='0.0')
PLT.grid.visible = False
PLT.yaxis.minor_tick_line_color = None
# Asignacion de opciones de visualizacion del eje X
if EjeX == 'Distancia[m]':
PLT.xaxis.axis_label = 'Distancia'
PLT.xaxis.formatter = NumeralTickFormatter(format='0')
if DatosBokeh.data['Distancia[m]'].max() >= 4000:
PLT.xaxis.ticker = SingleIntervalTicker(interval=1000)
PLT.xaxis.major_label_overrides = FormateoEjes(
DatosBokeh.data['Distancia[m]'], 1000, 1000, 0, 0)
elif EjeX == 'TiempoActividad':
PLT.xaxis.axis_label = 'Tiempo actividad'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
elif EjeX == 'TiempoTotal':
PLT.xaxis.axis_label = 'Tiempo total'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
#Botones
Botones = CheckboxGroup(labels=MetricasAuxiliares,
active=[],
width=100,
height=380)
ListadoMetricasAuxiliares['checkbox'] = Botones
CodigoJSFrecuenciaCardiaca = CustomJS(code=CodigoJS,
args=ListadoMetricasAuxiliares)
Botones.js_on_click(CodigoJSFrecuenciaCardiaca)
#Layout
GridBotones = layout(
[Spacer(width=100, height=25),
Column(Botones, width=100, height=375)],
sizing_mode='fixed',
width=100,
height=400)
GridGrafica = gridplot([PLT, GridBotones],
ncols=2,
sizing_mode='stretch_width',
toolbar_location=None,
plot_width=1100,
plot_height=400)
return GridGrafica
y=barclays_y,
size=10,
line_color="black",
fill_color="silver",
line_width=0.5)
# making scroll active by default
poverty.toolbar.active_scroll = poverty.select_one(WheelZoomTool)
fsrate.toolbar.active_scroll = fsrate.select_one(WheelZoomTool)
unemprate.toolbar.active_scroll = unemprate.select_one(WheelZoomTool)
# making a legend
pov_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=pov_palette,
low=-1.5,
high=2),
ticker=SingleIntervalTicker(interval=0.5),
title="Change in Poverty (%) from 2011 to 2016",
label_standoff=12,
width=220,
orientation="horizontal",
border_line_color=None,
location=(0, 0))
unemp_color_bar = ColorBar(
color_mapper=LinearColorMapper(palette=unemp_palette, low=-1.5, high=3),
ticker=SingleIntervalTicker(interval=0.5),
title="Change in Unemployment rate (%) from 2011 to 2016",
label_standoff=12,
width=220,
orientation="horizontal",
border_line_color=None,
def gen_one_tseries(tseries_var, hover_descr='yvar', df=main_df,
start_year='min', end_year='max', note_text_list=[],
fig_title_str='', fig_path='', line_color='blue'):
"""
This function creates a plot of a single time series from the set of
variables from the outlays.csv dataset.
"""
# Create Variables for min and max values
if start_year == 'min':
min_year = df['year'].min()
else:
min_year = int(start_year)
if end_year == 'max':
max_year = df['year'].max()
else:
max_year = int(end_year)
df_main = df[(df['year'] >= min_year) & (df['year'] <= max_year)]
min_yvar = df_main[tseries_var].min()
max_yvar = df_main[tseries_var].max()
buffer_pct_yvar = 0.1
buffer_yvar = buffer_pct_yvar * (max_yvar - min_yvar)
# Create column data source for visualization
cds = ColumnDataSource(df_main)
# Output to HTML file
fig_title = fig_title_str
fig_path = fig_path
output_file(fig_path, title=fig_title)
fig = figure(title=fig_title,
plot_height=600,
plot_width=1100,
x_axis_label='Year',
x_range=(min_year - 1, max_year + 1),
y_axis_label='Percent of Gross Domestic Product',
y_range=(min_yvar - buffer_yvar, max_yvar + buffer_yvar),
toolbar_location=None)
# Set title font size and axes font sizes
fig.title.text_font_size = '15pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=5, num_minor_ticks=5)
fig.xgrid.ticker = SingleIntervalTicker(interval=5)
if tseries_var == 'tot_nonint_outlays_gdp':
fig.yaxis.ticker = SingleIntervalTicker(interval=2, num_minor_ticks=2)
fig.ygrid.ticker = SingleIntervalTicker(interval=2)
elif tseries_var == 'mand_outlays_pct_tot_nonint':
fig.yaxis.ticker = SingleIntervalTicker(interval=5, num_minor_ticks=1)
fig.ygrid.ticker = SingleIntervalTicker(interval=5)
fig.line(x='year', y=tseries_var, source=cds, color=line_color,
line_width=4, alpha=0.7)
fig.circle(x='year', y=tseries_var, source=cds, color=line_color, size=7,
line_width=1, line_color='black', fill_color=line_color,
alpha=0.7)
# Add information on hover
tooltips = [('Year', '@year'),
(hover_descr, '@' + tseries_var + '{0.0}'+'%')]
hover_glyph = fig.circle(x='year', y=tseries_var, source=cds, size=10,
alpha=0, hover_fill_color='gray', hover_alpha=0.5)
fig.add_tools(HoverTool(tooltips=tooltips))
# Turn off scrolling
fig.toolbar.active_drag = None
# Add notes below image
for note_text in note_text_list:
caption = Title(text=note_text, align='left', text_font_size='4mm',
text_font_style='italic')
fig.add_layout(caption, 'below')
return fig
def bokeh_pi_ticks(fig, interval, axis='x'):
if axis == 'x':
fig.xaxis.ticker = SingleIntervalTicker(interval=interval)
fig.xaxis.formatter = pi_formatter()
y_axis_label='pdf f(t|df)',
y_range=(y_min, y_max),
toolbar_location=None)
# Output to HTML file
output_file(fig_path, title=fig_title)
# Set title font size and axes font sizes
fig.title.text_font_size = '15.5pt'
fig.xaxis.axis_label_text_font_size = '12pt'
fig.xaxis.major_label_text_font_size = '12pt'
fig.yaxis.axis_label_text_font_size = '12pt'
fig.yaxis.major_label_text_font_size = '12pt'
# Modify tick intervals for X-axis and Y-axis
fig.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=1)
# fig.xgrid.ticker = SingleIntervalTicker(interval=10)
# fig.xaxis.major_label_overrides = dict(zip([0, t_i], ['0', '|t_i|']))
fig.yaxis.ticker = SingleIntervalTicker(interval=0.1, num_minor_ticks=2)
fig.ygrid.ticker = SingleIntervalTicker(interval=10)
# Plotting the line
fig.line(x='t_stat_vals',
y='t_dist_vals',
source=t_cds,
color='black',
line_width=3)
# Create vertical dotted line at 0
fig.segment(x0=0,
y0=0,
