def addtodoc(self, *_) -> List[Widget]:
"creates the widget"
cols = [
TableColumn(
field = i[0],
title = i[1],
formatter = (
StringFormatter() if i[2] == '' else
DpxNumberFormatter(format = i[2], text_align = 'right')
),
width = i[3]
)
for i in self.__theme.columns
]
self.__widget = DataTable(
source = ColumnDataSource(self.__data()),
columns = cols,
editable = False,
index_position = None,
width = self.__theme.width,
height = self.__theme.height,
name = "Messages:List"
)
return [self.__widget]
def __columns(self):
return [
TableColumn(
field=i[0],
title=i[1],
width=i[2],
formatter=DpxNumberFormatter(format=i[3], text_align='right')
if i[3] else StringFormatter()) for i in self.__theme.columns
]
def get_formatter(data):
if data.dtype.name == 'datetime64[ns]':
return DateFormatter(format='%Y-%m-%d %H:%M:%S')
if data.dtype.name in ['float64', 'int64']:
return NumberFormatter(format='0.0[000]')
if data.dtype.name == 'bool':
return BooleanFormatter()
return StringFormatter()
def __init__(self, datatable_height=300, datatable_width=700, check_shape=None):
"""Initialize a metadata handler.
Args:
datatable_height (int, optional): Height of datatable in screen pixels. Defaults to 300.
datatable_width (int, optional): Width of plot area in screen pixels. Defaults to 700.
check_shape (tuple, optional): A tuple to be used for checking shape of received images.
If None, then no shape checks are conducted. Defaults to None.
"""
# If we should verify image shape
self.check_shape = check_shape
# Metadata datatable
datatable_source = ColumnDataSource(dict(metadata=["", "", ""], value=["", "", ""]))
datatable = DataTable(
source=datatable_source,
columns=[
TableColumn(field="metadata", title="Metadata Name"),
TableColumn(field="value", title="Value"),
],
width=datatable_width,
height=datatable_height,
index_position=None,
selectable=False,
)
self._datatable_source = datatable_source
self.datatable = datatable
# Metadata issues datatable
self._issues_menu = []
issues_datatable_formatter = StringFormatter(font_style="bold", text_color="red")
issues_datatable_source = ColumnDataSource(dict(issues=[]))
issues_datatable = DataTable(
source=issues_datatable_source,
columns=[
TableColumn(
field="issues", title="Metadata Issues", formatter=issues_datatable_formatter
)
],
width=datatable_width,
height=datatable_height,
index_position=None,
selectable=False,
)
self._issues_datatable_source = issues_datatable_source
self.issues_datatable = issues_datatable
# Show all toggle
show_all_toggle = CheckboxGroup(labels=["Show All Metadata"], default_size=145)
self.show_all_toggle = show_all_toggle
def _get_formatter(self, ctype: ColummDataType):
if ctype == ColummDataType.FLOAT:
return NumberFormatter(format=self._scheme.number_format)
elif ctype == ColummDataType.INT:
return NumberFormatter()
elif ctype == ColummDataType.DATETIME:
return DateFormatter(format="%c")
elif ctype == ColummDataType.STRING:
return StringFormatter()
elif ctype == ColummDataType.PERCENTAGE:
return NumberFormatter(format="0.000 %")
else:
raise Exception(f"Unsupported ColumnDataType: '{ctype}'")
def create_table(self) -> None:
"""
Create the table and populate prelim data.
"""
self.table_data = dict(x=self.names, y=self.output_values)
self.source = ColumnDataSource(self.table_data)
columns = [
TableColumn(field="x",
title="Outputs",
formatter=StringFormatter(font_style="bold")),
TableColumn(field="y", title="Current Value"),
]
self.table = DataTable(source=self.source,
columns=columns,
width=400,
height=280)
def addtodoc(ctrl, theme, data) -> Tuple[Any, ColumnDataSource, DataTable]:
"creates the widget"
theme = ctrl.theme.add(theme, False)
src = ColumnDataSource(data)
cols = [
TableColumn(
field = i[0], title = i[1], width = i[2],
formatter = (
StringFormatter() if i[3] == "" else
DpxNumberFormatter(format = i[3], text_align = 'right')
)
)
for i in theme.columns
]
widget = DataTable(source = src,
columns = cols,
editable = False,
index_position = None,
width = theme.width,
height = theme.tableheight,
header_row = theme.headers)
return theme, src, widget
def create_table(self) -> None:
"""
Creates the bokeh table and populates variable data.
"""
x_vals = [self._labels[var] for var in self._output_values.keys()]
y_vals = list(self._output_values.values())
table_data = dict(x=x_vals, y=y_vals)
self._source = ColumnDataSource(table_data)
columns = [
TableColumn(
field="x",
title="Variable",
formatter=StringFormatter(font_style="bold"),
),
TableColumn(field="y", title="Current Value"),
]
self.table = DataTable(
source=self._source,
columns=columns,
sizing_mode="stretch_both",
index_position=None,
)
"SSSCC", "Bottle", "CTDPRS", "CTDSAL", "SALNTY", "Residual", "Flag",
"Comments"
]
widths = [40, 20, 75, 65, 65, 65, 15, 135]
for (field, title, width) in zip(fields, titles, widths):
if field == "flag":
strfmt_in = {"text_align": "center", "font_style": "bold"}
elif field == "Comments":
strfmt_in = {}
else:
strfmt_in = {"text_align": "right"}
columns.append(
TableColumn(field=field,
title=title,
width=width,
formatter=StringFormatter(**strfmt_in)))
columns_changed = []
fields = ["SSSCC", "SAMPNO", "diff", "flag_old", "flag_new", "Comments"]
titles = ["SSSCC", "Bottle", "Residual", "Old", "New", "Comments"]
widths = [40, 20, 40, 20, 20, 200]
for (field, title, width) in zip(fields, titles, widths):
if field == "flag_old":
strfmt_in = {"text_align": "center", "font_style": "bold"}
elif field == "flag_new":
strfmt_in = {
"text_align": "center",
"font_style": "bold",
"text_color": "red"
}
elif field == "Comments":
from bokeh.embed import file_html
from bokeh.models import (ColumnDataSource, DataCube, GroupingInfo,
StringFormatter, SumAggregator, TableColumn)
from bokeh.resources import INLINE
from bokeh.util.browser import view
source = ColumnDataSource(data=dict(
d0=['A', 'E', 'E', 'E', 'J', 'L', 'M'],
d1=['B', 'D', 'D', 'H', 'K', 'L', 'N'],
d2=['C', 'F', 'G', 'H', 'K', 'L', 'O'],
px=[10, 20, 30, 40, 50, 60, 70],
))
target = ColumnDataSource(data=dict(row_indices=[], labels=[]))
formatter = StringFormatter(font_style='bold')
columns = [
TableColumn(field='d2', title='Name', width=80, sortable=False, formatter=formatter),
TableColumn(field='px', title='Price', width=40, sortable=False),
]
grouping = [
GroupingInfo(getter='d0', aggregators=[SumAggregator(field_='px')]),
GroupingInfo(getter='d1', aggregators=[SumAggregator(field_='px')])
]
cube = DataCube(source=source, columns=columns, grouping=grouping, target=target)
doc = Document()
doc.add_root(cube)
def upload_table_data(attr, old, new):
curdoc().get_model_by_name('informUser').text = 'uploading fusions'
decoded = b64decode(new)
f = io.BytesIO(decoded)
totalData = []
count = 0
c = 0
for line in f:
line = line.decode('utf-8').strip().split('\t')
if c == 0:
line.insert(1, "subgroup")
else:
line.insert(1, " ")
c += 1
genes = line[0].split('--')
totalData.append(line)
if len(genes) > 2:
count += 1
for i in range(len(genes)-1):
temp = [" "]*len(line)
temp[10] = line[-(((len(genes)-1)*2)-(2*i))]
temp[11] = line[-(((len(genes)-1)*2)-(2*i)-1)]
temp[1] = "--".join(genes[i:i+2])
totalData.append(temp)
if count == 0:
curdoc().get_model_by_name('tableSource').columns = [curdoc().get_model_by_name('tableSource').columns[0]] + curdoc().get_model_by_name('tableSource').columns[2:]
else:
if len(curdoc().get_model_by_name('tableSource').columns) < 7:
curdoc().get_model_by_name('tableSource').columns.insert(1, TableColumn(field = " ", title = "Subgroup Name", formatter=StringFormatter(), width=450))
headers = totalData.pop(0)
new_df = pd.DataFrame(totalData, columns=headers)
tableSource.data.update(ColumnDataSource(new_df).data)
data_table = data_table_formatter(0, tableSource)
currLayout = curdoc().get_model_by_name('subColumn').children
currLayout.remove(curdoc().get_model_by_name("tableSource"))
currLayout.append(data_table)
curdoc().get_model_by_name('informUser').text = 'fusions uploaded'
def _fmt(i, j):
return (StringFormatter(**self.__model.text) if i == '' else
((NumberFormatter if j else DpxNumberFormatter)(
format=i, **self.__model.number)))
def _fmt(line):
return (StringFormatter(text_align='center') if line == '' else
DpxNumberFormatter(format=line, text_align='right'))
NumberEditor, NumberFormatter, Plot, SelectEditor,
StringEditor, StringFormatter, TableColumn)
from bokeh.resources import INLINE
from bokeh.sampledata.autompg2 import autompg2 as mpg
from bokeh.util.browser import view
source = ColumnDataSource(mpg)
manufacturers = sorted(mpg["manufacturer"].unique())
models = sorted(mpg["model"].unique())
transmissions = sorted(mpg["trans"].unique())
drives = sorted(mpg["drv"].unique())
classes = sorted(mpg["class"].unique())
columns = [
TableColumn(field="manufacturer", title="Manufacturer", editor=SelectEditor(options=manufacturers), formatter=StringFormatter(font_style="bold")),
TableColumn(field="model", title="Model", editor=StringEditor(completions=models)),
TableColumn(field="displ", title="Displacement", editor=NumberEditor(step=0.1), formatter=NumberFormatter(format="0.0")),
TableColumn(field="year", title="Year", editor=IntEditor()),
TableColumn(field="cyl", title="Cylinders", editor=IntEditor()),
TableColumn(field="trans", title="Transmission", editor=SelectEditor(options=transmissions)),
TableColumn(field="drv", title="Drive", editor=SelectEditor(options=drives)),
TableColumn(field="class", title="Class", editor=SelectEditor(options=classes)),
TableColumn(field="cty", title="City MPG", editor=IntEditor()),
TableColumn(field="hwy", title="Highway MPG", editor=IntEditor()),
]
data_table = DataTable(source=source, columns=columns, editable=True, width=1000,
index_position=-1, index_header="row index", index_width=60)
plot = Plot(title=None, width=1000, height=300)
def save_data():
print("Save button clicked...")
# df_out =
button.on_click(save_data)
columns = [
TableColumn(
field="SSSCC",
title="SSSCC",
width=40,
formatter=StringFormatter(text_align="right"),
),
TableColumn(
field="CTDPRS",
title="CTDPRS",
width=80,
formatter=StringFormatter(text_align="right"),
),
TableColumn(
field="REFOXY",
title="REFOXY",
width=80,
formatter=StringFormatter(text_align="right"),
),
TableColumn(
field="CTDRINKO",
def TablaKmResumen(df):
"""
DATOS EN FORMA DE TABLA POR PARCIALES KILOMETRICOS
"""
dfTramosKm = TablaParcialesKilometricos(df)
dfTramosKm['Ritmo_STR'] = dfTramosKm.Ritmo.apply(
lambda x: FormateoTiempos(x, 'R'))
OrigenTramosKm = ColumnDataSource(dfTramosKm)
TablaKmResumen = [
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_TablaKmResumen = DataTable(source=OrigenTramosKm,
columns=TablaKmResumen,
width=360,
height=550,
fit_columns=False,
sortable=False,
reorderable=False,
selectable=True,
editable=False,
index_position=None,
header_row=True,
row_height=25)
return PLT_TablaKmResumen
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
pre_text = PreText(text="some text")
def mk_tab(color):
plot = figure(width=300, height=300)
plot.scatter(flowers["petal_length"], flowers["petal_width"], color=color, fill_alpha=0.2, size=12)
return Panel(title="Tab 1: %s" % color.capitalize(), child=plot)
tabs = Tabs(tabs=[mk_tab("red"), mk_tab("green"), mk_tab("blue")])
source = ColumnDataSource(data=mpg)
columns = [
TableColumn(field="manufacturer",
title="Manufacturer",
editor=SelectEditor(options=sorted(mpg["manufacturer"].unique())),
formatter=StringFormatter(font_style="bold")),
TableColumn(field="model",
title="Model",
editor=StringEditor(completions=sorted(mpg["model"].unique()))),
TableColumn(field="displ",
title="Displacement",
editor=NumberEditor(step=0.1),
formatter=NumberFormatter(format="0.0")),
TableColumn(field="year",
title="Year",
editor=IntEditor()),
TableColumn(field="cyl",
title="Cylinders",
editor=IntEditor()),
TableColumn(field="trans",
title="Transmission",
elements['name_lower'] = elements['name'].str.lower()
source = ColumnDataSource(elements)
html_font_template = '<font color="<%= CPK %>"><%= value %></font>'
html_image_template = """
<a href="http://images-of-elements.com/<%= value %>.php" target="_blank">
<img src="http://images-of-elements.com/<%= value %>.jpg" style="width:40px;height:40px;border:0">
</a>
"""
columns = [
TableColumn(field='atomic number',
title='Atomic Number',
formatter=NumberFormatter(text_align="right")),
TableColumn(field='symbol',
title='Symbol',
formatter=StringFormatter(text_align="center")),
TableColumn(field='name',
title='Name',
formatter=HTMLTemplateFormatter(template=html_font_template)),
TableColumn(field='name_lower',
title='Image',
formatter=HTMLTemplateFormatter(template=html_image_template))
]
data_table = DataTable(source=source,
columns=columns,
editable=False,
row_height=45)
save(data_table)
###########################
table_dict = {}
table_dict["tok"] = []
table_dict["rate"] = []
table_dict["liq"] = []
for tok in tokens:
table_dict["tok"].append(tok.upper())
table_dict["rate"].append(round(_rate[tok][-1],2))
table_dict["liq"].append(int(_liq[tok][-1]))
pdsource = ColumnDataSource(data=pd.DataFrame(table_dict))
columns = [
TableColumn(field="tok", title="Token", formatter=StringFormatter(text_align="center")),
TableColumn(field="rate", title="Price [ZIL]", formatter=StringFormatter(text_align="center")),
TableColumn(field="liq", title="Liquitidy [ZIL]", formatter=NumberFormatter(text_align="center")),
]
table = DataTable(source=pdsource, columns=columns, height=193, width=330, name="table", sizing_mode="scale_both")
#layout = row(region, table)
curdoc().add_root(region)
curdoc().add_root(table)
###########################
### Setup ####
###########################
