def test_js_on_change_executes(self,
bokeh_model_page: BokehModelPage) -> None:
group = CheckboxButtonGroup(labels=LABELS)
group.js_on_click(CustomJS(code=RECORD("active", "cb_obj.active")))
page = bokeh_model_page(group)
el = find_element_for(page.driver, group, ".bk-btn:nth-child(3)")
el.click()
results = page.results
assert results['active'] == [2]
el = find_element_for(page.driver, group, ".bk-btn:nth-child(1)")
el.click()
results = page.results
assert results['active'] == [0, 2]
el = find_element_for(page.driver, group, ".bk-btn:nth-child(3)")
el.click()
results = page.results
assert results['active'] == [0]
assert page.has_no_console_errors()
def test_js_on_change_executes(self, bokeh_model_page) -> None:
group = CheckboxButtonGroup(labels=LABELS, css_classes=["foo"])
group.js_on_click(CustomJS(code=RECORD("active", "cb_obj.active")))
page = bokeh_model_page(group)
el = page.driver.find_element_by_css_selector(
'.foo .bk-btn:nth-child(3)')
el.click()
results = page.results
assert results['active'] == [2]
el = page.driver.find_element_by_css_selector(
'.foo .bk-btn:nth-child(1)')
el.click()
results = page.results
assert results['active'] == [0, 2]
el = page.driver.find_element_by_css_selector(
'.foo .bk-btn:nth-child(3)')
el.click()
results = page.results
assert results['active'] == [0]
assert page.has_no_console_errors()
def test_js_on_change_executes(self, bokeh_model_page):
group = CheckboxButtonGroup(labels=LABELS, css_classes=["foo"])
group.js_on_click(CustomJS(code=RECORD("active", "cb_obj.active")))
page = bokeh_model_page(group)
el = page.driver.find_element_by_css_selector('div.foo div label input[value="2"]')
el = el.find_element_by_xpath('..')
el.click()
results = page.results
assert results['active'] == [2]
el = page.driver.find_element_by_css_selector('div.foo div label input[value="0"]')
el = el.find_element_by_xpath('..')
el.click()
results = page.results
assert results['active'] == [0, 2]
el = page.driver.find_element_by_css_selector('div.foo div label input[value="2"]')
el = el.find_element_by_xpath('..')
el.click()
results = page.results
assert results['active'] == [0]
assert page.has_no_console_errors()
def test_js_on_change_executes(self, bokeh_model_page):
group = CheckboxButtonGroup(labels=LABELS, css_classes=["foo"])
group.js_on_click(CustomJS(code=RECORD("active", "cb_obj.active")))
page = bokeh_model_page(group)
el = page.driver.find_element_by_css_selector('.foo .bk-btn:nth-child(3)')
el.click()
results = page.results
assert results['active'] == [2]
el = page.driver.find_element_by_css_selector('.foo .bk-btn:nth-child(1)')
el.click()
results = page.results
assert results['active'] == [0, 2]
el = page.driver.find_element_by_css_selector('.foo .bk-btn:nth-child(3)')
el.click()
results = page.results
assert results['active'] == [0]
assert page.has_no_console_errors()
//console.log(cb_obj.active);
for (var i in lines) {
lines[i].visible = false;
lines2[i].visible = false;
}
for (var i in cb_obj.active) {
//console.log(cb_obj.active[i]);
lines[cb_obj.active[i]].visible = true;
lines2[cb_obj.active[i]].visible = true;
}
""")
# Build the checkbox group
checkbox = CheckboxButtonGroup(active=list(range(len(lines))),
labels=names)
# Link it to the callback we built
checkbox.js_on_click(CB_callback)
# Add a text block for citing the data sources - Current Web Page cites in the html, so omitting this here.
citation_text = ""
"""
citation_text = "Data Sources: \n"
for index, name in enumerate(names):
citation_text = citation_text + '\'' + names[index] + "\': " + authors[index] + '. ' + links[index] + '\n'
"""
citation_text = citation_text + "\nPlots created using the \'Bokeh\' plotting library.\nCreated by C.J. Ogilvie "\
"under the supervision of Dr. Matthew Toohey at the University of Saskatchewan."
citation = PreText(text=citation_text)
# Combine the main plot, range select plot, and checkbox together on a single tab
tabs.append(
Panel(child=column(checkbox, plot, select), title="Global Mean SAOD"))
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
CustomJS(
code=
"console.log('checkbox_group: active=' + this.active, this.toString())"
))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.js_on_click(
CustomJS(
code=
"console.log('radio_group: active=' + this.active, this.toString())"))
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_button_group.js_on_click(
CustomJS(
code=
"console.log('checkbox_button_group: active=' + this.active, this.toString())"
))
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_button_group.js_on_click(
CustomJS(
code=
"console.log('radio_button_group: active=' + this.active, this.toString())"
))
widget_box = Column(children=[
button,
button_disabled,
toggle_inactive,
def bk_scatter(stat_one,
stat_one_name,
stat_two,
stat_two_name,
stat='goal_contributions',
stat_name='Goal Contributions'):
mapper = CategoricalColorMapper(palette=[
'#496cc3', '#00a499', '#4b3291', '#fcb50b', '#db0006', '#98c2e8'
],
factors=[
"Frank Lampard", "Steven Gerrard",
"Xabi Alonso", "Ngolo Kante",
"Paul Scholes", "Yaya Toure"
])
LABELS = [
"Frank Lampard", "Steven Gerrard", "Xabi Alonso", "Ngolo Kante",
"Paul Scholes", "Yaya Toure"
]
checkbox_button_group = CheckboxButtonGroup(labels=LABELS)
TOOLTIPS = [('Player', '@name'), ('Season', '@year'),
('Appearances', '@appearances'),
(stat_one_name, '@' + stat_one),
(stat_two_name, '@' + stat_two)]
if stat_name == 'Goal Contributions':
TOOLTIPS.append((stat_name, '@' + stat))
PLOT_OPTIONS = dict(plot_width=950, plot_height=450)
p = figure(title="Comparison of " + stat_name + " Recorded in a Season",
x_axis_label=stat_one_name,
y_axis_label=stat_two_name,
tools='pan,zoom_in,zoom_out,reset',
tooltips=TOOLTIPS,
**PLOT_OPTIONS)
p.add_layout(Legend(), 'right')
# for player in players:
if stat_one_name in ['Assists', 'Total Shots']:
df = combined('attack')
if stat_one_name == 'Total Passes':
df = combined('team_play')
if stat_one_name in ['Total Tackles', 'Duels Won']:
df = combined('defensive')
if stat_one_name == 'Total Shots':
df = df[df.year >= '2006/07']
df['size'] = [1.3 * exp(0.09 * val) for val in df['appearances']]
df['fill'] = 0.8
df['line'] = 1
if stat_one_name in ['Total Shots', 'Total Tackles']:
df[stat_two] = df[stat_two].str.replace('%', '').astype('int')
if stat_one_name == 'Total Passes':
df['total_pass'] = df['total_pass'].str.replace(',', '').astype('int')
source = ColumnDataSource(df)
p.circle(x=stat_one,
y=stat_two,
source=source,
legend_group='name',
size='size',
fill_alpha='fill',
line_color='#7c7e71',
line_width=0.5,
line_alpha='line',
color={
'field': 'name',
'transform': mapper
})
# p.legend.click_policy = 'mute'
p.grid.grid_line_dash = [6, 4]
checkbox_button_group.js_on_click(
CustomJS(args=dict(source=source),
code="""
var labels = ["Frank Lampard", "Steven Gerrard", "Xabi Alonso", "Ngolo Kante", "Paul Scholes", "Yaya Toure"];
var ind;
var act = [];
var t = this.active
console.log(t);
for (ind in t) {
console.log(t[ind])
act.push(labels[t[ind]]);
}
var data=source.data;
var name = data['name'];
var fill = data['fill'];
var line = data['line'];
for (var i = 0; i < name.length; i++) {
if (act.includes(name[i])) {
fill[i] = 0.1;
line[i] = 0.1;
} else {
fill[i] = 0.8;
line[i] = 1;
}
}
source.change.emit();
"""))
l = layout(column(p, checkbox_button_group))
return l
def bk_line(stat, stat_name):
mapper = CategoricalColorMapper(palette=[
'#496cc3', '#00a499', '#4b3291', '#fcb50b', '#db0006', '#98c2e8'
],
factors=[
"Frank Lampard", "Steven Gerrard",
"Xabi Alonso", "Ngolo Kante",
"Paul Scholes", "Yaya Toure"
])
LABELS = [
"Frank Lampard", "Steven Gerrard", "Xabi Alonso", "Ngolo Kante",
"Paul Scholes", "Yaya Toure"
]
checkbox_button_group = CheckboxButtonGroup(labels=LABELS)
year = [
'1994/95', '1995/96', '1996/97', '1997/98', '1998/99', '1999/00',
'2000/01', '2001/02', '2002/03', '2003/04', '2004/05', '2005/06',
'2006/07', '2007/08', '2008/09', '2009/10', '2010/11', '2011/12',
'2012/13', '2013/14', '2014/15', '2015/16', '2016/17', '2017/18',
'2018/19', '2019/20'
]
if stat_name == 'Through Balls' or stat_name == 'Recoveries':
year = year[13:]
elif stat_name == 'Long Balls' or stat_name == 'Interceptions':
year = year[12:]
TOOLTIPS = [('Player', '@name'), ('Season', '@year'),
('Appearances', '@appearances'), (stat_name, '@' + stat)]
PLOT_OPTIONS = dict(plot_width=950, plot_height=450)
p = figure(x_range=year,
title="Comparison of " + stat_name + " Recorded in a Season",
x_axis_label="Season",
y_axis_label=stat_name,
tools='pan,zoom_in,zoom_out,reset',
tooltips=TOOLTIPS,
**PLOT_OPTIONS)
p.add_layout(Legend(), 'right')
if stat in ['goals', 'goal_assist']:
df = combined('attack')
if stat in ['total_through_ball', 'accurate_long_balls']:
df = combined('team_play')
if stat in ['ball_recovery', 'interception']:
df = combined('defensive')
df['size'] = [1.3 * exp(0.09 * val) for val in df['appearances']]
df['fill'] = 0.8
df['line'] = 1
source = ColumnDataSource(df)
p.circle(x='year',
y=stat,
source=source,
legend_group='name',
size='size',
fill_alpha='fill',
line_color='#7c7e71',
line_width=0.5,
line_alpha='line',
color={
'field': 'name',
'transform': mapper
})
p.xaxis.major_label_orientation = 1.5
p.grid.grid_line_dash = [6, 4]
checkbox_button_group.js_on_click(
CustomJS(args=dict(source=source),
code="""
var labels = ["Frank Lampard", "Steven Gerrard", "Xabi Alonso", "Ngolo Kante", "Paul Scholes", "Yaya Toure"];
var ind;
var act = [];
var t = this.active
console.log(t);
for (ind in t) {
console.log(t[ind])
act.push(labels[t[ind]]);
}
var data=source.data;
var name = data['name'];
var fill = data['fill'];
var line = data['line'];
for (var i = 0; i < name.length; i++) {
if (act.includes(name[i])) {
fill[i] = 0.1;
line[i] = 0.1;
} else {
fill[i] = 0.8;
line[i] = 1;
}
}
source.change.emit();
"""))
l = layout(column(p, checkbox_button_group))
return l
def update_tiles(DF):
"""
Render tiles chart report
"""
df = DF.loc[
:,
[
"id",
"img_sd",
"img_hd",
"lat",
"lng",
"geometry",
"portals_id",
"portals_url",
"wikidata_id",
"omeka_url",
"rate",
],
]
# construct coordinates
coord = []
for y in range(0, 52):
for x in range(0, 100):
if len(coord) != len(df):
coord.append([(x, y)])
else:
break
df_coord = pd.DataFrame(coord, columns=["coordinate"])
df_coord[["x", "y"]] = pd.DataFrame(
df_coord["coordinate"].tolist(), index=df_coord.index
)
df_coord = df_coord.drop(columns="coordinate")
df_tiles = pd.merge(
df, df_coord, left_index=True, right_index=True, validate="one_to_one"
)
df_tiles_s = df_tiles.iloc[:, [0, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10]].sort_values(
by=["rate"], ascending=False, ignore_index=True
)
df_tiles_sort = df_tiles_s.join(df_tiles[["x", "y"]])
# setting colors
colors = ["#edf8e9", "#c7e9c0", "#a1d99b", "#74c476", "#31a354", "#006d2c"]
mapper = LinearColorMapper(palette=colors, low=df.rate.min(), high=df.rate.max())
# config tooltip
TOOLTIPS = """
<div style="margin: 5px; width: 400px" >
<h3
style='font-size: 12px; font-weight: bold;'>
@id
</h3>
<p
style='font-size: 10px; font-weight: bold;'>
Geolocated: (@lat,@lng)
</p>
<p
style='font-size: 10px; font-weight: bold;'>
Image: @img_sd @img_hd
</p>
<p
style='font-size: 10px; font-weight: bold;'>
Portals: @portals_url
</p>
<p
style='font-size: 10px; font-weight: bold;'>
Wikimedia: @wikidata_id
</p>
<p
style='font-size: 10px; font-weight: bold;'>
Omeka-S: @omeka_url
</p>
<img
src="@img_sd" alt="@img_sd" height=200
style="margin: 0px;"
border="2"
></img>
</div>
"""
# construct base chart
tiles = figure(
x_axis_type=None,
y_axis_type=None,
plot_width=1500,
plot_height=1000,
min_border_bottom=150,
min_border_top=150,
toolbar_location=None,
)
# create tiles
rect_sort = tiles.rect(
x="x",
y="y",
width=0.8,
height=0.8,
fill_color={"field": "rate", "transform": mapper},
line_color="black",
line_join="round",
line_width=1,
source=df_tiles_sort,
)
rect = tiles.rect(
x="x",
y="y",
width=0.8,
height=0.8,
fill_color={"field": "rate", "transform": mapper},
line_color="black",
line_join="round",
line_width=1,
source=df_tiles,
)
# add tooltip in hover
h1 = HoverTool(renderers=[rect], tooltips=TOOLTIPS, mode="mouse", show_arrow=False)
h2 = HoverTool(
renderers=[rect_sort], tooltips=TOOLTIPS, mode="mouse", show_arrow=False
)
callback = CustomJS(
args={"rect": rect, "rect_sort": rect_sort},
code="""
rect.visible = false;
rect_sort.visible = false;
if (cb_obj.active.includes(0)){rect_sort.visible = true;}
else{rect.visible = true;}
""",
)
button = CheckboxButtonGroup(labels=["Sort by rate"])
button.js_on_click(callback)
tiles.add_tools(h1, h2)
tiles.grid.grid_line_color = None
tiles.axis.axis_line_color = None
tiles.axis.major_tick_line_color = None
tiles.toolbar.active_drag = None
tiles.axis.major_label_standoff = 0
tiles.y_range.flipped = True
plot_tiles = Row(tiles, button)
return plot_tiles
def Mapa(dfBokeh, DatosBokeh):
"""
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(dfBokeh)
AltitudInicio, AltitudFin = dfBokeh.loc[dfBokeh.index.min() == dfBokeh.index, ['Altitud']].min()[0], dfBokeh.loc[dfBokeh.index.max() == dfBokeh.index, ['Altitud']].min()[0]
# Calculo de desniveles finales
DesnivelPositivo = dfBokeh['DesnivelPositivoAcumulado'].max()
DesnivelNegativo = dfBokeh['DesnivelNegativoAcumulado'].max()
DesnivelAcumulado = DesnivelPositivo + DesnivelNegativo
DesnivelPorKilometro = (DesnivelAcumulado/dfBokeh['Distancia'].max())*1000
"""
MAPA
CARTODBPOSITRON
CARTODBPOSITRON_RETINA
STAMEN_TERRAIN
STAMEN_TERRAIN_RETINA
STAMEN_TONER
STAMEN_TONER_BACKGROUND
STAMEN_TONER_LABELS
"""
# Creacion de un grafica
PLT_Mapa = figure(width=900, height=430, x_range=(DatosBokeh.data['LongitudMercator'].min()-100, DatosBokeh.data['LongitudMercator'].max()+100), y_range=(DatosBokeh.data['LatitudMercator'].min()-100, DatosBokeh.data['LatitudMercator'].max()+100), x_axis_type= 'mercator', y_axis_type= 'mercator', tools= 'wheel_zoom, reset, hover')
PLT_Mapa.add_tile(get_provider('STAMEN_TERRAIN'))
# Inclusion de datos
PLT_MP_Linea = PLT_Mapa.line(x= 'LongitudMercator', y= 'LatitudMercator', source= DatosBokeh, line_color= Grapefruit[2], line_width= 3, line_cap= 'round')
"""
#PLT_Mapa.circle(x= 'LongitudMercator', y= 'LatitudMercator', source= DatosBokeh, size= 5, line_color= None, fill_color= None, fill_alpha= 0, hover_fill_color= 'yellow', hover_line_color = 'black', hover_alpha= 1)
#PLT_Mapa.add_tools(HoverTool(tooltips=None, mode='mouse'))
"""
CoordenadasHitosKm, TiempoTotalKm, TiempoActividadKm, MinDistanciaKm = HitosKilometricos(dfBokeh)
CoordenadasPausas, TiempoTotalPausas, TiempoActividadPausas, DistanciasPausas = HitosPausas(dfBokeh)
# Ubicacion de puntos de inicio, fin y kilometros
LongitudKm =[]
LatitudKm =[]
PuntoKilometrico = []
for i, Km in enumerate(CoordenadasHitosKm):
if i == 0:
PLT_Mapa_Inicio = PLT_Mapa.circle(ConversorCoordenadasMercator(Km[1], Km[0])[0], ConversorCoordenadasMercator(Km[1], Km[0])[1], size= 8, line_color= 'black', fill_color= Spectral[2], visible= True)
elif i == len(CoordenadasHitosKm)-1:
PLT_Mapa_Fin = PLT_Mapa.circle(ConversorCoordenadasMercator(Km[1], Km[0])[0], ConversorCoordenadasMercator(Km[1], Km[0])[1], size= 8, line_color= 'black', fill_color= Spectral[7], visible= True)
else:
LongitudKm.append(ConversorCoordenadasMercator(Km[1], Km[0])[0])
LatitudKm.append(ConversorCoordenadasMercator(Km[1], Km[0])[1])
PuntoKilometrico.append(str(i))
CDS_PuntosKm = ColumnDataSource(data= dict(Longitud= LongitudKm, Latitud= LatitudKm, PuntoKilometrico= PuntoKilometrico))
PLT_Mapa_PuntoKm = PLT_Mapa.circle(x= 'Longitud', y= 'Latitud', source= CDS_PuntosKm, color= 'white', size= 8, line_color= 'black', fill_color= 'white', visible= True)
PLT_Mapa_PuntoKm_TXT = LabelSet(x= 'Longitud', y= 'Latitud', text='PuntoKilometrico', level='glyph', x_offset= 5, y_offset= 0, source= CDS_PuntosKm, render_mode='canvas', text_font_size= '10pt', text_color= 'black', text_align= 'left', text_baseline= 'middle', text_font_style= 'bold', visible= True)
PLT_Mapa.add_layout(PLT_Mapa_PuntoKm_TXT)
# Ubicacion de pausas
LongitudPausa =[]
LatitudPausa =[]
for i, Km in enumerate(CoordenadasPausas):
LongitudPausa.append(ConversorCoordenadasMercator(Km[1], Km[0])[0])
LatitudPausa.append(ConversorCoordenadasMercator(Km[1], Km[0])[1])
PLT_Mapa_Pausas = PLT_Mapa.x(LongitudPausa, LatitudPausa, line_color= 'black', line_width= 2, fill_color= None, visible= False)
# Identificacion de pico y valle en trails
for index, row in dfBokeh.iterrows():
LongitudMercator, LatitudMercator = ConversorCoordenadasMercator(row.Longitud, row.Latitud)
dfBokeh.at[index,'LongitudMercator'] = LongitudMercator
dfBokeh.at[index,'LatitudMercator'] = LatitudMercator
if (DesnivelPorKilometro > 40) and (MAX_Altitud[0] >= (AltitudInicio + 50) and MAX_Altitud[0] >= (AltitudFin + 50)):
PLT_Mapa_Cima = PLT_Mapa.triangle(dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LongitudMercator'].min(), dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LatitudMercator'].min(), size= 10, line_color= 'black', line_width= 2, fill_color= Spectral[4], visible= False)
PLT_Mapa_Cima_TXT = Label(x= dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LongitudMercator'].min(), y= dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LatitudMercator'].min(), text= str(round(MAX_Altitud[0])), x_offset= 5, y_offset= 0, text_font_size= '10pt', text_color= 'black', text_align= 'left', text_baseline= 'middle', text_font_style= 'bold', visible= False)
PLT_Mapa.add_layout(PLT_Mapa_Cima_TXT)
else:
PLT_Mapa_Cima = PLT_Mapa.triangle(0, 0, size= 0, line_alpha= 0, visible= False)
PLT_Mapa_Cima_TXT = Label(x= 0, y= 0, text= '', text_font_size= '0pt', text_alpha= 0, visible= False)
if (DesnivelPorKilometro > 40) and (MIN_Altitud[0] <= (AltitudInicio - 50) and MIN_Altitud[0] <= (AltitudFin - 50)):
PLT_Mapa_Valle = PLT_Mapa.inverted_triangle(dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LongitudMercator'].min(), dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LatitudMercator'].min(), size= 10, line_color= 'black', line_width= 2, fill_color= Spectral[0], visible= False)
PLT_Mapa_Valle_TXT = Label(x= dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LongitudMercator'].min(), y= dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LatitudMercator'].min(), text= str(round(MIN_Altitud[0])), x_offset= 5, y_offset= 0, text_font_size= '10pt', text_color= 'black', text_align= 'left', text_baseline= 'middle', text_font_style= 'bold', visible= False)
PLT_Mapa.add_layout(PLT_Mapa_Valle_TXT)
else:
PLT_Mapa_Valle = PLT_Mapa.inverted_triangle(0, 0, size= 0, line_alpha= 0, visible= False)
PLT_Mapa_Valle_TXT = Label(x= 0, y= 0, text= '', text_font_size= '0pt', text_alpha= 0, visible= False)
# Atributos
PLT_Mapa.sizing_mode = 'fixed'
PLT_Mapa.xaxis.major_tick_line_color = None
PLT_Mapa.xaxis.minor_tick_line_color = None
PLT_Mapa.yaxis.major_tick_line_color = None
PLT_Mapa.yaxis.minor_tick_line_color = None
PLT_Mapa.xaxis.major_label_text_font_size = '0pt'
PLT_Mapa.yaxis.major_label_text_font_size = '0pt'
PLT_Mapa.grid.visible = False
PLT_Mapa.toolbar.autohide = True
# Sin bordes
PLT_Mapa.min_border_left = 0
PLT_Mapa.min_border_right = 0
PLT_Mapa.min_border_top = 0
PLT_Mapa.min_border_bottom = 0
# Linea exterior
PLT_Mapa.outline_line_width = 3
PLT_Mapa.outline_line_alpha = 0.3
PLT_Mapa.outline_line_color = 'black'
"""
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,0)>=0;
l2.visible = indexOf.call(checkbox.active,1)>=0;
l3.visible = indexOf.call(checkbox.active,1)>=0;
l4.visible = indexOf.call(checkbox.active,2)>=0;
l5.visible = indexOf.call(checkbox.active,2)>=0;
l6.visible = indexOf.call(checkbox.active,2)>=0;
l7.visible = indexOf.call(checkbox.active,2)>=0;
l8.visible = indexOf.call(checkbox.active,3)>=0;
"""
BotonesMapa = CheckboxButtonGroup(labels=["INICIO/FIN", "PUNTOS KILOMETRICOS", "CIMA/VALLE", 'PAUSAS'], active=[0, 1], width=300, height=30)
CodigoJSMapa = CustomJS(code=CodigoJS, args=dict(l0=PLT_Mapa_Inicio, l1=PLT_Mapa_Fin, l2=PLT_Mapa_PuntoKm, l3= PLT_Mapa_PuntoKm_TXT, l4=PLT_Mapa_Cima, l5=PLT_Mapa_Cima_TXT, l6=PLT_Mapa_Valle, l7=PLT_Mapa_Valle_TXT, l8=PLT_Mapa_Pausas, checkbox= BotonesMapa))
BotonesMapa.js_on_click(CodigoJSMapa)
GridMapa = layout([PLT_Mapa, Column(BotonesMapa, width=300, height=35)], sizing_mode='stretch_width', width=900, height=470)
return GridMapa
