def plot(
metrics,
x="step",
y="mean_episode_return",
palette="Set1",
kdims=["lr"],
kdims_facet=["lstm"],
subsample=1000,
cols=3,
):
hmap = {}
model2color = {}
colors = hv.Palette(palette).values
colors = list(set(colors))
for _facet, models in metrics.items():
for model, _ in models.items():
if model not in model2color:
model2color[model] = colors[len(model2color) - 1]
for facet, models in metrics.items():
p = plot_facet(
models, x, y, palette, model2color, kdims=kdims_facet, subsample=subsample
)
if facet not in hmap:
hmap[facet] = {}
hmap[facet] = p
p = hv.HoloMap(hmap, kdims=kdims)
p = hv.NdLayout(p).cols(cols)
return p
def hv_plot_param(df_tidy, species='H', param='N'):
hv_fig = hv.Curve(
df_tidy,
kdims=['time', species],
vdims=[param],
).groupby(param).overlay().opts(frame_height=250, frame_width=250 * 3 // 2)
hv_fig.opts(opts.Curve(color=hv.Palette('Viridis'), width=600))
# Take out the Bokeh object
p = hv.render(hv_fig)
return p
def get_ccaa(complete_df):
# preprocessing
df = complete_df[['Comunidad Autónoma', 'Fecha', 'Casos']]
df = df.sort_values(by='Casos', ascending=False)
df.reset_index(drop=True, inplace=True)
# plot
# for better date representation
df.Fecha = df.loc[:, 'Fecha'].dt.strftime('%d-%m')
color_cycle = hv.Palette('Inferno')
opts_dict = dict(color=color_cycle,
width=width,
height=450,
xrotation=90,
invert_xaxis=True,
shared_axes=False,
tools=['hover'],
stacked=True,
show_legend=False,
colorbar=True,
line_color='white',
show_grid=True,
show_title=False)
bars = hv.Bars(df,
kdims=['Fecha', 'Comunidad Autónoma'],
vdims='Casos',
label='Escala lineal')
bars.opts(**opts_dict)
log_bars = hv.Bars(df,
kdims=['Fecha', 'Comunidad Autónoma'],
vdims='Casos',
label='Escala logarítmica')
opts_dict['logy'] = True
opts_dict['title'] = 'Escala logarítmica'
log_bars.opts(**opts_dict)
layout = (bars * log_bars)
layout.opts(tabs=True, title='Casos por comunidad')
return layout
def get_split(complete_df):
# preprocessing
df = complete_df.copy()
df['Leves'] = abs(df['Casos'] - (df['Hospitalizados'] + df['UCI']))
df = df[[
'Comunidad Autónoma', 'Fecha', 'Leves', 'Hospitalizados', 'UCI',
'Fallecidos'
]]
df = pd.melt(df,
id_vars=df.columns[:2],
var_name='Estado',
value_name='Número')
# plot
key_dimensions = ['Fecha', 'Estado']
value_dimensions = ['Número']
# for better date representation
df.Fecha = df.loc[:, 'Fecha'].dt.strftime('%d-%m')
macro = hv.Table(df, key_dimensions, value_dimensions)
bars = macro.to.bars(key_dimensions, value_dimensions,
'Comunidad Autónoma')
# color_cycle = hv.Cycle(['#FEE5AD', '#F7A541', '#F45D4C', '#2E2633'])
color_cycle = hv.Palette('Inferno', reverse=True)
bars.opts(
opts.Bars(color=color_cycle,
show_legend=True,
stacked=True,
width=900,
height=450,
shared_axes=False,
ylabel='',
xlabel='Fecha',
responsive=True,
tools=['hover'],
title='Desglose por estado',
legend_position='top_left',
xrotation=90,
active_tools=['pan', 'wheel_zoom']), )
return bars
def plot_facet(
models,
x="step",
y="mean_episode_return",
palette="Set1",
model2color=None,
**plot_model_args,
):
if model2color is None:
colors = hv.Palette(palette).values
colors = list(set(colors))
model2color = {}
hmap = {}
for model, runs in models.items():
if model not in model2color:
model2color[model] = colors[(len(model2color) - 1) % len(colors)]
color = model2color[model]
hmap[model] = plot_model(runs, x, y, model, color, **plot_model_args)
p = hv.HoloMap(hmap, kdims=get_kdims(next(iter(models))))
p = p.overlay()
return p
def plot_facet(
models,
x="step",
y="mean_episode_return",
palette="Set1",
model2color=None,
kdims=["lstm"],
subsample=1000,
):
if model2color is None:
colors = hv.Palette(palette).values
colors = list(set(colors))
model2color = {}
hmap = {}
for model, runs in models.items():
if model not in model2color:
model2color[model] = colors[len(model2color) - 1]
color = model2color[model]
hmap[model] = plot_model(runs, x, y, model, color, subsample)
p = hv.HoloMap(hmap, kdims=kdims)
p = p.overlay()
return p
def plot(
metrics,
x="step",
y="mean_episode_return",
palette="Set1",
subsample=1000,
cols=3,
y_min=None,
y_max=None,
):
hmap = {}
model2color = {}
colors = hv.Palette(palette).values
colors = list(set(colors))
for _facet, models in metrics.items():
for model, _ in models.items():
if model not in model2color:
model2color[model] = colors[(len(model2color) - 1) %
len(colors)]
for facet, models in metrics.items():
p = plot_facet(
models,
x,
y,
palette,
model2color,
subsample=subsample,
y_min=y_min,
y_max=y_max,
)
if facet not in hmap:
hmap[facet] = {}
hmap[facet] = p
kdims = get_kdims(next(iter(metrics)))
p = hv.HoloMap(hmap, kdims=kdims)
p = hv.NdLayout(p).cols(cols)
return p
def plot_scatter(df,
x,
y,
x_round_val=1,
y_round_val=1,
x_tooltip='',
y_tooltip=''):
'''
Returns a HoloViews plot layout.
Arguments:
df - Dataframe to process, must have the column 'Country' adn the columns x and y within.
x - Column in Dataframe where values are evaluated for the x-axis
y - Column in Dataframe where values are evaluated for the y-axis
x_round_val (optional) - single numeric value to set the x axis limits on max found within x
y_round_val (optional) - single numeric value to set the y axis limits on max found within y
x_tooltip (optional) - tooltip to be set in the plots for the x values
y_tooltip (optional) - tooltip to be set in the plots for the y values
'''
max_y = np.ceil(np.nanmax(df[y].values))
max_y = max_y - max_y % y_round_val + 2 * y_round_val
max_x = np.ceil(np.nanmax(df[x].values))
max_x = max_x - max_x % x_round_val + 2 * x_round_val
'''
if max_x > max_y:
max_y = max_x
else:
max_x=max_y
'''
if x_tooltip == '':
x_tooltip = '@{' + x + '}{0,0.0}'
if y_tooltip == '':
y_tooltip = '@{' + y + '}{0,0.0}'
# Plot settings and parameters
hover = HoverTool(tooltips=[('Country',
'@Country'), (x, x_tooltip), (y, y_tooltip)])
padding = dict(x=(-1.2, 1.2), y=(-1.2, 1.2))
options_shared = dict(width=700,
height=700,
xlim=(0, max_x),
ylim=(0, max_y),
hooks=[axis_not_scientific],
active_tools=['wheel_zoom'],
padding=(0.1, 0.1),
show_grid=True,
show_legend=True,
legend_position='bottom',
legend_cols=3)
options = [
opts.Scatter(marker='o',
size=10,
fill_alpha=0.6,
tools=[hover],
color=hv.Palette('Set2'),
**options_shared),
opts.Points(color='Country',
cmap=cc.cm.fire,
size=8,
tools=[hover],
**options_shared),
opts.Labels(text_font_size='8pt', yoffset=y_round_val / 5),
opts.Overlay(**options_shared)
]
ds = hv.Table(df)
# Create the plot
layout = ( #hv.Scatter(df, kdims=[x], vdims=[y, 'Country'])*
ds.to(hv.Scatter, x, y, 'Country').overlay() *
hv.Labels(ds, kdims=[x, y], vdims=['Country'])).opts(options)
return layout