def modify_doc():
def make_dataset(range_start=-60, range_end=120, bin_width=2):
by_code_pos = pd.DataFrame(
columns=['mortality_change', 'state', 'cause'])
by_code_neg = pd.DataFrame(
columns=['mortality_change', 'state', 'cause'])
data_gender = full_data[full_data['sex'] == "Both"]
for state in state_select_down:
pos_perc_change = []
neg_perc_change = []
cause_pos_name = []
cause_neg_name = []
len_pos = 0
len_neg = 0
for cause in cause_select_down:
data_state = data_gender[data_gender['location_name'] == state]
data_cause = data_state[data_state['cause_name'] == cause]
subset = data_cause[data_cause['year_id'] == 1980]
mortality_1980 = subset['mx'].astype(float)
mortality_1980 = mortality_1980.values[0]
subset = data_cause[data_cause['year_id'] == 2014]
mortality_2014 = subset['mx'].astype(float)
mortality_2014 = mortality_2014.values[0]
perc_change = (
(mortality_2014 - mortality_1980) / mortality_1980) * 100
if perc_change <= 0:
arr_df_neg = pd.DataFrame(
{'mortality_change': subset['mx']})
arr_df_neg['mortality_change'] = perc_change * (-1)
arr_df_neg['state'] = state
arr_df_neg['cause'] = cause
by_code_neg = by_code_neg.append(arr_df_neg)
else:
arr_df_pos = pd.DataFrame(
{'mortality_change': subset['mx']})
arr_df_pos['mortality_change'] = perc_change
arr_df_pos['state'] = state
arr_df_pos['cause'] = cause
by_code_pos = by_code_pos.append(arr_df_pos)
return by_code_pos, ColumnDataSource(
by_code_pos), by_code_neg, ColumnDataSource(by_code_neg)
def make_plot(df, src):
mapper = LinearColorMapper(palette=colors_list,
low=df.mortality_change.min(),
high=df.mortality_change.max())
heat_map = figure(plot_height=500,
plot_width=1000,
x_range=state_select_down,
y_range=cause_select_down)
heat_map.rect(x="state",
y="cause",
source=src,
width=1,
height=1,
line_color=None,
fill_color=transform('mortality_change', mapper))
color_bar = ColorBar(color_mapper=mapper, location=(0, 0))
heat_map.add_layout(color_bar, 'right')
heat_map.axis.axis_line_color = None
heat_map.axis.major_tick_line_color = None
heat_map.axis.major_label_text_font_size = "5pt"
heat_map.axis.major_label_standoff = 0
heat_map.xaxis.major_label_orientation = 1.0
heat_map.xaxis.major_label_orientation = math.pi / 2
heat_map.xaxis.axis_label = "State"
heat_map.yaxis.axis_label = "Cause"
hover_bar = HoverTool(tooltips=[('State', '@state'), (
'Cause', '@cause'), ('Mortality Rate Change',
'@mortality_change')])
heat_map.add_tools(hover_bar)
return heat_map
df_pos, src_pos, df_neg, src_neg = make_dataset()
heat_map_pos = make_plot(df_pos, src_pos)
title_pos = Title()
title_pos.align = "center"
title_pos.text = "Figure 2(a). Causes having positive change in Mortality Rates between 1980 and 2014"
title_pos.vertical_align = 'bottom'
heat_map_pos.title = title_pos
heat_map_neg = make_plot(df_neg, src_neg)
title_neg = Title()
title_neg.align = "center"
title_neg.text = "Figure 2(b). Causes having negative change in Mortality Rates between 1980 and 2014"
title_neg.vertical_align = 'bottom'
heat_map_neg.title = title_neg
return column(heat_map_pos, heat_map_neg)
def plot_violin(
ax,
plotters,
figsize,
rows,
cols,
sharex,
sharey,
shade_kwargs,
shade,
rug,
rug_kwargs,
bw,
textsize,
circular,
hdi_prob,
quartiles,
backend_kwargs,
show,
):
"""Bokeh violin plot."""
if backend_kwargs is None:
backend_kwargs = {}
backend_kwargs = {
**backend_kwarg_defaults(("dpi", "plot.bokeh.figure.dpi"), ),
**backend_kwargs,
}
(figsize, *_, linewidth, _) = _scale_fig_size(figsize, textsize, rows,
cols)
shade_kwargs = {} if shade_kwargs is None else shade_kwargs
rug_kwargs = {} if rug_kwargs is None else rug_kwargs
rug_kwargs.setdefault("fill_alpha", 0.1)
rug_kwargs.setdefault("line_alpha", 0.1)
if ax is None:
ax = create_axes_grid(
len(plotters),
rows,
cols,
sharex=sharex,
sharey=sharey,
figsize=figsize,
backend_kwargs=backend_kwargs,
)
else:
ax = np.atleast_2d(ax)
current_col = 0
for (var_name, selection,
x), ax_ in zip(plotters,
(item for item in ax.flatten() if item is not None)):
val = x.flatten()
if val[0].dtype.kind == "i":
dens = cat_hist(val, rug, shade, ax_, **shade_kwargs)
else:
dens = _violinplot(val, rug, shade, bw, circular, ax_,
**shade_kwargs)
if rug:
rug_x = -np.abs(
np.random.normal(scale=max(dens) / 3.5, size=len(val)))
ax_.scatter(rug_x, val, **rug_kwargs)
per = np.nanpercentile(val, [25, 75, 50])
hdi_probs = hdi(val, hdi_prob, multimodal=False, skipna=True)
if quartiles:
ax_.line([0, 0],
per[:2],
line_width=linewidth * 3,
line_color="black",
line_cap="round")
ax_.line([0, 0],
hdi_probs,
line_width=linewidth,
line_color="black",
line_cap="round")
ax_.circle(
0,
per[-1],
line_color="white",
fill_color="white",
size=linewidth * 1.5,
line_width=linewidth,
)
_title = Title()
_title.align = "center"
_title.text = make_label(var_name, selection)
ax_.title = _title
ax_.xaxis.major_tick_line_color = None
ax_.xaxis.minor_tick_line_color = None
ax_.xaxis.major_label_text_font_size = "0pt"
if current_col != 0:
ax_.xaxis.major_label_text_font_size = "0pt"
ax_.yaxis.major_label_text_font_size = "0pt"
ax_.yaxis.major_tick_line_color = None
ax_.yaxis.minor_tick_line_color = None
ax_.yaxis.axis_line_color = None
current_col += 1
if current_col == cols:
current_col = 0
show_layout(ax, show)
return ax
df = data.DataReader(
name='amzn', data_source='yahoo', start=start,
end=end) #The analyzed entity is Amazon,in the last 6 months
p = figure(x_axis_type='datetime',
width=1000,
height=300,
sizing_mode="scale_width")
t = Title()
t.text = "Candlestick Chart"
p.title = t
p.grid.grid_line_alpha = 0.6
t.text_font_size = "24pt" # defining the css of the title
t.align = "center"
date_increase = df.index[df.Close > df.Open]
date_decrease = df.index[df.Close < df.Open]
p.segment(df.index, df.High, df.index, df.Low, color="black")
p.rect(date_increase, (df.Close + df.Open) / 2,
hours_12,
abs(df.Open - df.Close),
fill_color="green",
line_color="black")
p.rect(date_decrease, (df.Close + df.Open) / 2,
hours_12,
abs(df.Open - df.Close),
fill_color="red",
line_color="black")
output_file("CS.html")
show(p)