def create_alpha_diversity_tab():
img_shannon = '<img src="plot_shannon.png" />'
img_shannon_diff = '<img src="plot_shannon_diff.png" />'
div_plot_shannon = Div(text=img_shannon)
source = ColumnDataSource(data=dict(
active=[img_shannon, img_shannon_diff]))
checkbox_callback = CustomJS(args=dict(source=source),
code="""
if(this.active.length > 0) {
div_plot_shannon.text = source.data['active'][1];
} else {
div_plot_shannon.text = source.data['active'][0];
}
""")
checkbox_group = CheckboxGroup(
labels=["Absolute differences to gold standard"],
active=[],
width=300,
css_classes=['bk-checkbox-shannon'])
checkbox_group.js_on_click(checkbox_callback)
checkbox_callback.args["div_plot_shannon"] = div_plot_shannon
shannon_column = column(checkbox_group,
div_plot_shannon,
responsive=True,
css_classes=[
'bk-shannon-plots', 'bk-width-auto',
'bk-width-auto-main'
])
shannon_panel = Panel(child=shannon_column, title='Shannon')
tabs_plots = Tabs(tabs=[shannon_panel],
css_classes=['bk-tabs-margin', 'bk-tabs-margin-lr'])
return tabs_plots
dropdown_disabled.on_click(
lambda value: print('dropdown_disabled: %s' % value))
dropdown_disabled.js_on_click(
CustomJS(
code="console.log('dropdown_disabled: ' + this.value, this.toString())"
))
#dropdown_split = Dropdown(label="Split button", button_type="danger", menu=menu, default_value="default")
#dropdown_split.on_click(lambda value: print('dropdown_split: %s' % value))
#dropdown_split.js_on_click(CustomJS(code="console.log('dropdown_split: ' + this.value, this.toString())"))
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
checkbox_group.on_click(lambda value: print('checkbox_group: %s' % value))
checkbox_group.js_on_click(
CustomJS(
code="console.log('checkbox_group: ' + this.active, this.toString())"))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.on_click(lambda value: print('radio_group: %s' % value))
radio_group.js_on_click(
CustomJS(
code="console.log('radio_group: ' + this.active, this.toString())"))
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_button_group.on_click(
lambda value: print('checkbox_button_group: %s' % value))
checkbox_button_group.js_on_click(
CustomJS(
code=
args={
'aline': aline,
'bline': bline
})
callback2 = CustomJS(code="""cline.visible = false; // aline and etc.. are
dline.visible = false; // passed in from args
// cb_obj is injected in thanks to the callback
if (cb_obj.active.includes(0)){cline.visible = true;}
// 0 index box is aline
if (cb_obj.active.includes(1)){dline.visible = true;}
// 1 index box is bline
""",
args={
'cline': cline,
'dline': dline
})
checkboxes1.js_on_click(callback1)
checkboxes2.js_on_click(callback2)
layout1 = row(p1, checkboxes1)
layout2 = row(p2, checkboxes2)
layout = column(layout1, layout2)
# output_file('BV2_DUT_sensors_134_sections.html')
# show(column(p1, p2, checkboxes))
curdoc().add_root(layout)
curdoc().title = "Temperatures_BV12"
show(layout)
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 html_postages(coord=None, idx=None, veto=None, info=None, layer_list=None, BoxSize=40):
plots = {}
sources = []
layers = []
RA, DEC = coord[0], coord[1]
scale_unit='pixscale'
scale=0.262
boxsize = BoxSize #2*m*radius*3600
radius = BoxSize/(2 * 3600)
size = int(round(boxsize/scale))
figsize = int(128)
#print('BoxSize', boxsize)
#print('Size', size)
idx_list = idx #random.sample(list(idx), rows*cols)
if info is None:
info = {'RA':RA, 'DEC':DEC}
if veto is None:
veto = {'all':np.ones_like(RA, dtype=bool)}
if layer_list is None:
layer_list = ['dr9f-south', 'dr9f-south-model', 'dr9f-south-resid', 'dr9g-south', 'dr9g-south-model', 'dr9g-south-resid',
'dr9f-north', 'dr9f-north-model', 'dr9f-north-resid', 'dr9g-north', 'dr9g-north-model', 'dr9g-north-resid']
for idx in idx_list:
RAidx = RA[idx]
DECidx = DEC[idx]
ramin, ramax = RAidx-radius, RAidx+radius
decmin, decmax = DECidx-radius, DECidx+radius
dra = (ramax - ramin)/40
ddec = (decmax - decmin)/40
mask = (RA > ramin + dra) & (RA < ramax - dra) & (DEC > decmin + ddec) & (DEC < decmax - ddec)
TOOLTIPS = []
for key in info.keys():
TOOLTIPS.append((key, '@'+key))
p = figure(plot_width=2*figsize, plot_height=2*figsize, tooltips=TOOLTIPS, tools="tap, save, zoom_in, zoom_out, crosshair")
p.axis.visible = False
p.toolbar.logo = None
p.toolbar_location = None
#p.min_border = 0
layers2 = []
for layer in layer_list:
source='https://www.legacysurvey.org/viewer-dev/jpeg-cutout/?ra=%.12f&dec=%.12f&%s=%g&layer=%s&size=%g' % (RAidx, DECidx, scale_unit, scale, layer, size)
url='https://www.legacysurvey.org/viewer-dev?ra=%.12f&dec=%.12f&layer=%s&zoom=15' %(RAidx, DECidx, layer)
imfig_source = ColumnDataSource(data=dict(url=[source], txt=[source]))
image1 = ImageURL(url="url", x=0, y=1, w=size, h=size, anchor='bottom_left')
img_source = p.add_glyph(imfig_source, image1)
layers2.append(img_source)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
colors = ['green', 'red', 'blue', 'cyan', 'yellow']
circle_i = []
for color, key, val in zip(colors, veto.keys(), veto.values()):
ravpix, decvpix = coordtopix(center=[RAidx, DECidx], coord=[RA[(mask) & (val)], DEC[(mask) & (val)]], size=size, scale=scale)
data = {}
data['x'] = ravpix
data['y'] = decvpix
for info_key, info_val in zip(info.keys(), info.values()):
if isinstance(info_val[0], (bytes, bytearray)):
data[info_key] = np.array(info_val)[(mask) & (val)].astype('U13')
else:
data[info_key] = np.array(info_val)[(mask) & (val)]
sourceCirc = ColumnDataSource(data=data)
circle = p.circle('x', 'y', source=sourceCirc, size=15, fill_color=None, line_color=color, line_width=3)
circle_i.append(circle)
lineh = Span(location=size/2, dimension='height', line_color='white', line_dash='solid', line_width=1)
linew = Span(location=size/2, dimension='width', line_color='white', line_dash='solid', line_width=1)
p.add_layout(lineh)
p.add_layout(linew)
p.background_fill_color = "black"
#p.outline_line_width = 15
#p.outline_line_alpha = 0.7
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
plots[str(idx)] = p
sources.append(circle_i)
layers.append(layers2)
checkbox = CheckboxGroup(labels=list(veto.keys()), active=list(np.arange(len(veto))))
iterable = [elem for part in [[('_'.join(['line',str(figid),str(lineid)]),line) for lineid,line in enumerate(elem)] for figid,elem in enumerate(sources)] for elem in part]
checkbox_code = ''.join([elem[0]+'.visible=checkbox.active.includes('+elem[0].split('_')[-1]+');' for elem in iterable])
callback = CustomJS(args={key:value for key,value in iterable+[('checkbox',checkbox)]}, code=checkbox_code)
# print('===== plots ======')
# print(checkbox_code)
# print('====')
# print({key:value for key,value in iterable+[('checkbox',checkbox)]})
checkbox.js_on_click(callback)
#script, div = components(plots)
#print(div)
radio = RadioGroup(labels=layer_list, active=len(layer_list)-1)
iterable2 = [elem for part in [[('_'.join(['line',str(figid),str(lineid)]),line) for lineid,line in enumerate(elem)] for figid,elem in enumerate(layers)] for elem in part]
#
N = len(layer_list)
text = []
for elem in iterable2[::N]:
for n in range(N):
text.append('%s%s.visible=false;' %(elem[0][:-1], str(n)))
for n in range(N):
if n == 0: text.append('if (cb_obj.active == 0) {%s%s.visible = true;}' %(elem[0][:-1], str(0)))
if n != 0: text.append('else if (cb_obj.active == %s) {%s%s.visible = true;}' %(str(n), elem[0][:-1], str(n)))
radiogroup_code = ''.join(text)
callback2 = CustomJS(args={key:value for key,value in iterable2+[('radio',radio)]}, code=radiogroup_code)
radio.js_on_change('active', callback2)
# for key,val in zip(plots.keys(), plots.values()):
# print(key, '\t', val)
#script, div = components(plots)
#controls = WidgetBox(radio, checkbox)
#plots['controls'] = components(radio)
#script_2, div_2 = components(controls)
#print('===== plots ======')
plots['checkbox'] = checkbox
plots['radio'] = radio
return components(plots)
def dash_line(x: str,
y: str,
data: pd.DataFrame,
figsize: tuple = (1000, 400),
title: str = None,
x_axis_label: str = None,
y_axis_label: str = None):
"""Create dashboard line plot
Args:
x (str): Column in x axis
y (str): Column in y axis
data (pd.DataFrame): Data frame contaning the above. It also must contain
columns for the max, min and mean of y axis
figsize (tuple, optional): Width and height of figure. Defaults to (1000, 400).
title (str, optional): Figure title. Defaults to None.
x_axis_label (str, optional): X-axis label. Defaults to None.
y_axis_label (str, optional): Y-axis label. Defaults to None.
Returns:
bokeh object: Plotted figure
References:
[1] https://www.reddit.com/r/learnpython/comments/8ythxo/how_to_use_checkbox_in_bokeh_with_pandas/
Example: TODO:
>>> show(dash_line())
"""
# 1. Define variables
y_max = f"{y}_max"
y_min = f"{y}_min"
y_mean = f"{y}_mean"
title = title or f"{y} vs {x}"
tools = "xwheel_pan, ywheel_pan, pan,wheel_zoom,xwheel_zoom,ywheel_zoom,box_zoom,reset,save"
# 2. Instantiate plot object
if np.issubdtype(data[x].dtype, np.datetime64):
line = figure(x_axis_type="datetime",
plot_width=figsize[0],
plot_height=figsize[1],
title=title,
toolbar_location="above",
tools=tools)
else:
line = figure(plot_width=figsize[0],
plot_height=figsize[1],
title=title,
toolbar_location="above",
tools=tools)
## 2.1. Add axes labels
line.xaxis.axis_label = x or x_axis_label
line.yaxis.axis_label = y or y_axis_label
# 3. Add circles
line.circle(x=x, y=y, source=data)
# 4. Add hover tooltip
## 4.1. Format the tooltip
tooltips = [(y, f"@{y}"), (x, f"@{x}")]
hover = HoverTool(tooltips=tooltips)
## 4.2. Add the HoverTool to the figure
line.add_tools(HoverTool(tooltips=tooltips))
# 5. Format figure
## 5.1. Remove grey frame
line.outline_line_color = None
## 5.2. Remove axis line
line.xaxis.axis_line_width = 0
line.yaxis.axis_line_width = 0
## 5.3. Remove grid
line.xgrid.grid_line_color = None
line.ygrid.grid_line_color = None
## 5.4. Remove tick markers
# line.xaxis.major_tick_line_color = None # turn off x-axis major ticks
line.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
# line.yaxis.major_tick_line_color = None # turn off y-axis major ticks
line.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
## 5.5. Add text
# mytext = Label(x=data[x].max(), y=data[y].iloc[-1], text=y)
# line.add_layout(mytext)
# 6. Create rendering objects for callback
line_renderer = line.line(x=x, y=y, line_width=2, source=data)
line_max = line.line(x=x,
y=y_max,
line_width=1,
source=data,
line_dash="dashed",
line_alpha=0.25,
line_color="black")
line_mean = line.line(x=x,
y=y_mean,
line_width=1,
source=data,
line_dash="dotted",
line_alpha=0.25,
line_color="black")
line_min = line.line(x=x,
y=y_min,
line_width=1,
source=data,
line_dash="dashed",
line_alpha=0.25,
line_color="black")
# 7. Create checkbox callback. See [1]
checkboxes = CheckboxGroup(labels=["max", "mean", "min"], active=[0, 1, 2])
callback = CustomJS(
code="""line_max.visible = false; // same xline passed in from args
line_mean.visible = false;
line_min.visible = false;
// cb_obj injected in by the callback
if (cb_obj.active.includes(0)){line_max.visible = true;}
if (cb_obj.active.includes(1)){line_mean.visible = true;}
if (cb_obj.active.includes(2)){line_min.visible = true;}
""",
args={
'line_max': line_max,
'line_mean': line_mean,
'line_min': line_min
})
checkboxes.js_on_click(callback)
return row(line, checkboxes)
toggle_active = Toggle(label="Toggle button (initially active)", button_type="success", active=True)
toggle_active.js_on_click(CustomJS(code="console.log('toggle_active: ' + this.active, this.toString())"))
menu = [("Item 1", "item_1_value"), ("Item 2", "item_2_value"), None, ("Item 3", "item_3_value")]
dropdown = Dropdown(label="Dropdown button", button_type="warning", menu=menu)
dropdown.js_on_click(CustomJS(code="console.log('dropdown: ' + this.value, this.toString())"))
dropdown_disabled = Dropdown(label="Dropdown button (disabled)", button_type="warning", disabled=True, menu=menu)
dropdown_disabled.js_on_click(CustomJS(code="console.log('dropdown_disabled: ' + this.value, this.toString())"))
#dropdown_split = Dropdown(label="Split button", button_type="danger", menu=menu, default_value="default")
#dropdown_split.js_on_click(CustomJS(code="console.log('dropdown_split: ' + this.value, this.toString())"))
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_group.js_on_click(CustomJS(code="console.log('checkbox_group: ' + 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: ' + 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: ' + 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: ' + this.active, this.toString())"))
widget_box = WidgetBox(children=[
button, button_disabled,
toggle_inactive, toggle_active,
dropdown, dropdown_disabled, #dropdown_split,
checkbox_group, radio_group,
if (cb_obj.active.includes(12)){jline.visible = true;}
// 1 index box is bline
if (cb_obj.active.includes(13)){kline.visible = true;}
if (cb_obj.active.includes(14)){kkline.visible = true;}
// 1 index box is bline
""",
args={
'aline': aline,
'bline': bline,
'cline': cline,
'ccline': ccline,
'dline': dline,
'ddline': ddline,
'eline': eline,
'fline': fline,
'gline': gline,
'ggline': ggline,
'hline': hline,
'iline': iline,
'jline': jline,
'kline': kline,
'kkline': kkline
})
checkboxes.js_on_click(callback)
layout = row(p1, checkboxes)
# output_file('BV2_DUT_sensors_134_sections.html')
# show(column(p1, p2, checkboxes))
curdoc().add_root(layout)
curdoc().title = "BOKEH_TEMPLATE"
show(layout)
class ViewerVIWidgets(object):
"""
Encapsulates Bokeh widgets, and related callbacks, used for VI
"""
def __init__(self, title, viewer_cds):
self.vi_quality_labels = [ x["label"] for x in vi_flags if x["type"]=="quality" ]
self.vi_issue_labels = [ x["label"] for x in vi_flags if x["type"]=="issue" ]
self.vi_issue_slabels = [ x["shortlabel"] for x in vi_flags if x["type"]=="issue" ]
self.js_files = get_resources('js')
self.title = title
self.vi_countdown_toggle = None
#- List of fields to be recorded in output csv file, contains for each field:
# [ field name (in VI file header), associated variable in viewer_cds.cds_metadata ]
self.output_file_fields = []
for file_field in vi_file_fields:
if file_field[1] in viewer_cds.cds_metadata.data.keys() :
self.output_file_fields.append([file_field[0], file_field[1]])
def add_filename(self, username='******'):
#- VI file name
default_vi_filename = "desi-vi_"+self.title
default_vi_filename += ("_"+username)
default_vi_filename += ".csv"
self.vi_filename_input = TextInput(value=default_vi_filename, title="VI file name:")
def add_vi_issues(self, viewer_cds, widgets):
#- Optional VI flags (issues)
self.vi_issue_input = CheckboxGroup(labels=self.vi_issue_labels, active=[])
vi_issue_code = self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
vi_issue_code += """
var issues = []
for (var i=0; i<vi_issue_labels.length; i++) {
if (vi_issue_input.active.indexOf(i) >= 0) issues.push(vi_issue_slabels[i])
}
if (issues.length > 0) {
cds_metadata.data['VI_issue_flag'][ifiberslider.value] = ( issues.join('') )
} else {
cds_metadata.data['VI_issue_flag'][ifiberslider.value] = " "
}
autosave_vi_localStorage(output_file_fields, cds_metadata.data, title)
cds_metadata.change.emit()
"""
self.vi_issue_callback = CustomJS(
args=dict(cds_metadata = viewer_cds.cds_metadata,
ifiberslider = widgets.ifiberslider,
vi_issue_input = self.vi_issue_input,
vi_issue_labels = self.vi_issue_labels,
vi_issue_slabels = self.vi_issue_slabels,
title = self.title, output_file_fields = self.output_file_fields),
code = vi_issue_code )
self.vi_issue_input.js_on_click(self.vi_issue_callback)
def add_vi_z(self, viewer_cds, widgets):
## TODO: z_tovi behaviour if with_vi_widget=False ..?
#- Optional VI information on redshift
self.vi_z_input = TextInput(value='', title="VI redshift:")
vi_z_code = self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
vi_z_code += """
cds_metadata.data['VI_z'][ifiberslider.value]=vi_z_input.value
autosave_vi_localStorage(output_file_fields, cds_metadata.data, title)
cds_metadata.change.emit()
"""
self.vi_z_callback = CustomJS(
args=dict(cds_metadata = viewer_cds.cds_metadata,
ifiberslider = widgets.ifiberslider,
vi_z_input = self.vi_z_input,
title = self.title, output_file_fields=self.output_file_fields),
code = vi_z_code )
self.vi_z_input.js_on_change('value', self.vi_z_callback)
# Copy z value from redshift slider to VI
self.z_tovi_button = Button(label='Copy z to VI')
self.z_tovi_callback = CustomJS(
args=dict(z_input=widgets.z_input, vi_z_input=self.vi_z_input),
code="""
vi_z_input.value = z_input.value
""")
self.z_tovi_button.js_on_event('button_click', self.z_tovi_callback)
def add_vi_spectype(self, viewer_cds, widgets):
#- Optional VI information on spectral type
self.vi_category_select = Select(value=' ', title="VI spectype:", options=([' '] + vi_spectypes))
# The default value set to ' ' as setting value='' does not seem to work well with Select.
vi_category_code = self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
vi_category_code += """
if (vi_category_select.value == ' ') {
cds_metadata.data['VI_spectype'][ifiberslider.value]=''
} else {
cds_metadata.data['VI_spectype'][ifiberslider.value]=vi_category_select.value
}
autosave_vi_localStorage(output_file_fields, cds_metadata.data, title)
cds_metadata.change.emit()
"""
self.vi_category_callback = CustomJS(
args=dict(cds_metadata=viewer_cds.cds_metadata,
ifiberslider = widgets.ifiberslider,
vi_category_select=self.vi_category_select,
title=self.title, output_file_fields=self.output_file_fields),
code=vi_category_code )
self.vi_category_select.js_on_change('value', self.vi_category_callback)
def add_vi_comment(self, viewer_cds, widgets):
#- Optional VI comment
self.vi_comment_input = TextInput(value='', title="VI comment (see guidelines):")
vi_comment_code = self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
vi_comment_code += """
var stored_comment = (vi_comment_input.value).replace(/./g, function(char){
if ( char==',' ) {
return ';'
} else if ( char.charCodeAt(0)<=127 ) {
return char
} else {
var char_list = ['Å','α','β','γ','δ','λ']
var char_replace = ['Angstrom','alpha','beta','gamma','delta','lambda']
for (var i=0; i<char_list.length; i++) {
if ( char==char_list[i] ) return char_replace[i]
}
return '?'
}
})
cds_metadata.data['VI_comment'][ifiberslider.value] = stored_comment
autosave_vi_localStorage(output_file_fields, cds_metadata.data, title)
cds_metadata.change.emit()
"""
self.vi_comment_callback = CustomJS(
args=dict(cds_metadata = viewer_cds.cds_metadata,
ifiberslider = widgets.ifiberslider,
vi_comment_input = self.vi_comment_input,
title=self.title, output_file_fields=self.output_file_fields),
code=vi_comment_code )
self.vi_comment_input.js_on_change('value',self.vi_comment_callback)
#- List of "standard" VI comment
self.vi_std_comment_select = Select(value=" ", title="Standard comment:", options=([' '] + vi_std_comments))
vi_std_comment_code = """
if (vi_std_comment_select.value != ' ') {
if (vi_comment_input.value != '') {
vi_comment_input.value = vi_comment_input.value + " " + vi_std_comment_select.value
} else {
vi_comment_input.value = vi_std_comment_select.value
}
}
"""
self.vi_std_comment_callback = CustomJS(
args = dict(vi_std_comment_select = self.vi_std_comment_select,
vi_comment_input = self.vi_comment_input),
code = vi_std_comment_code )
self.vi_std_comment_select.js_on_change('value', self.vi_std_comment_callback)
def add_vi_quality(self, viewer_cds, widgets):
#- Main VI quality widget
self.vi_quality_input = RadioButtonGroup(labels=self.vi_quality_labels)
vi_quality_code = self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
vi_quality_code += """
if ( vi_quality_input.active >= 0 ) {
cds_metadata.data['VI_quality_flag'][ifiberslider.value] = vi_quality_labels[vi_quality_input.active]
} else {
cds_metadata.data['VI_quality_flag'][ifiberslider.value] = "-1"
}
autosave_vi_localStorage(output_file_fields, cds_metadata.data, title)
cds_metadata.change.emit()
"""
self.vi_quality_callback = CustomJS(
args = dict(cds_metadata = viewer_cds.cds_metadata,
vi_quality_input = self.vi_quality_input,
vi_quality_labels = self.vi_quality_labels,
ifiberslider = widgets.ifiberslider,
title=self.title, output_file_fields = self.output_file_fields),
code=vi_quality_code )
self.vi_quality_input.js_on_click(self.vi_quality_callback)
def add_vi_scanner(self, viewer_cds):
#- VI scanner name
self.vi_name_input = TextInput(value=(viewer_cds.cds_metadata.data['VI_scanner'][0]).strip(), title="Your name (3-letter acronym):")
vi_name_code = self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
vi_name_code += """
for (var i=0; i<(cds_metadata.data['VI_scanner']).length; i++) {
cds_metadata.data['VI_scanner'][i]=vi_name_input.value
}
var newname = vi_filename_input.value
var name_chunks = newname.split("_")
newname = ( name_chunks.slice(0,name_chunks.length-1).join("_") ) + ("_"+vi_name_input.value+".csv")
vi_filename_input.value = newname
autosave_vi_localStorage(output_file_fields, cds_metadata.data, title)
"""
self.vi_name_callback = CustomJS(
args = dict(cds_metadata = viewer_cds.cds_metadata,
vi_name_input = self.vi_name_input,
vi_filename_input = self.vi_filename_input, title=self.title,
output_file_fields = self.output_file_fields),
code=vi_name_code )
self.vi_name_input.js_on_change('value', self.vi_name_callback)
def add_guidelines(self):
#- Guidelines for VI flags
vi_guideline_txt = "<B> VI guidelines </B>"
vi_guideline_txt += "<BR /> <B> Classification flags: </B>"
for flag in vi_flags :
if flag['type'] == 'quality' : vi_guideline_txt += ("<BR />  [ "+flag['label']+" ] "+flag['description'])
vi_guideline_txt += "<BR /> <B> Optional indications: </B>"
for flag in vi_flags :
if flag['type'] == 'issue' :
vi_guideline_txt += ( "<BR />  [ " + flag['label'] +
" (" + flag['shortlabel'] + ") ] " + flag['description'] )
vi_guideline_txt += "<BR /> <B> Comments: </B> <BR /> 100 characters max, avoid commas (automatically replaced by semi-columns), ASCII only."
self.vi_guideline_div = Div(text=vi_guideline_txt)
def add_vi_storage(self, viewer_cds, widgets):
#- Save VI info to CSV file
self.save_vi_button = Button(label="Download VI", button_type="success")
save_vi_code = self.js_files["FileSaver.js"] + self.js_files["CSVtoArray.js"] + self.js_files["save_vi.js"]
save_vi_code += """
download_vi_file(output_file_fields, cds_metadata.data, vi_filename_input.value)
"""
self.save_vi_callback = CustomJS(
args = dict(cds_metadata = viewer_cds.cds_metadata,
output_file_fields = self.output_file_fields,
vi_filename_input = self.vi_filename_input),
code=save_vi_code )
self.save_vi_button.js_on_event('button_click', self.save_vi_callback)
#- Recover auto-saved VI data in browser
self.recover_vi_button = Button(label="Recover auto-saved VI", button_type="default")
recover_vi_code = self.js_files["CSVtoArray.js"] + self.js_files["recover_autosave_vi.js"]
self.recover_vi_callback = CustomJS(
args = dict(title=self.title, output_file_fields=self.output_file_fields,
cds_metadata = viewer_cds.cds_metadata,
ifiber = widgets.ifiberslider.value, vi_comment_input = self.vi_comment_input,
vi_name_input=self.vi_name_input, vi_quality_input=self.vi_quality_input,
vi_issue_input=self.vi_issue_input,
vi_issue_slabels=self.vi_issue_slabels, vi_quality_labels=self.vi_quality_labels),
code = recover_vi_code )
self.recover_vi_button.js_on_event('button_click', self.recover_vi_callback)
#- Clear all auto-saved VI
self.clear_vi_button = Button(label="Clear all auto-saved VI", button_type="default")
self.clear_vi_callback = CustomJS( args = dict(), code = """
localStorage.clear()
""" )
self.clear_vi_button.js_on_event('button_click', self.clear_vi_callback)
def add_vi_table(self, viewer_cds):
#- Show VI in a table
vi_table_columns = [
TableColumn(field="VI_quality_flag", title="Flag", width=40),
TableColumn(field="VI_issue_flag", title="Opt.", width=50),
TableColumn(field="VI_z", title="VI z", width=50),
TableColumn(field="VI_spectype", title="VI spectype", width=150),
TableColumn(field="VI_comment", title="VI comment", width=200)
]
self.vi_table = DataTable(source=viewer_cds.cds_metadata, columns=vi_table_columns, width=500)
self.vi_table.height = 10 * self.vi_table.row_height
def add_countdown(self, vi_countdown):
#- VI countdown
assert vi_countdown > 0
self.vi_countdown_callback = CustomJS(args=dict(vi_countdown=vi_countdown), code="""
if ( (cb_obj.label).includes('Start') ) { // Callback doesn't do anything after countdown started
var countDownDate = new Date().getTime() + (1000 * 60 * vi_countdown);
var countDownLoop = setInterval(function(){
var now = new Date().getTime();
var distance = countDownDate - now;
if (distance<0) {
cb_obj.label = "Time's up !";
clearInterval(countDownLoop);
} else {
var days = Math.floor(distance / (1000 * 60 * 60 * 24));
var hours = Math.floor((distance % (1000 * 60 * 60 * 24)) / (1000 * 60 * 60));
var minutes = Math.floor((distance % (1000 * 60 * 60)) / (1000 * 60));
var seconds = Math.floor((distance % (1000 * 60)) / 1000);
//var stuff = days + "d " + hours + "h " + minutes + "m " + seconds + "s ";
var stuff = minutes + "m " + seconds + "s ";
cb_obj.label = "Countdown: " + stuff;
}
}, 1000);
}
""")
self.vi_countdown_toggle = Toggle(label='Start countdown ('+str(vi_countdown)+' min)', active=False, button_type="success")
self.vi_countdown_toggle.js_on_change('active', self.vi_countdown_callback)
def ajax_getplot(x, y, y_bottom, ref_value): #fi,ff,y_bottom,ref_value,x,y):
# En la ventana del plot
graf = figure(tools="save",
x_axis_label="Frequency [Hz]",
y_axis_label='Power [dBm]',
width=700,
height=400,
sizing_mode='stretch_width',
x_range=(x[0], x[-1]),
y_range=(float(y_bottom), float(ref_value)))
graf.toolbar.logo = None
graf.toolbar.active_drag = None
graf.toolbar.active_scroll = None
graf.yaxis.major_label_text_font_style = 'bold'
graf.xaxis.major_label_text_font_style = 'bold'
graf.xaxis.axis_label_text_font_style = 'bold'
graf.yaxis.axis_label_text_font_style = 'bold'
graf.xaxis[0].formatter.use_scientific = True
graf.xaxis[0].ticker.desired_num_ticks = 15
graf.yaxis[0].ticker.desired_num_ticks = 10
graf.ygrid.grid_line_alpha = 0.4
graf.ygrid.grid_line_dash = [6, 4]
graf.xgrid.grid_line_alpha = 0.4
graf.xgrid.grid_line_dash = [6, 4]
# graf.ygrid.minor_grid_line_alpha = 0.1
# graf.ygrid.minor_grid_line_color = 'navy'
# graf.xgrid.minor_grid_line_alpha = 0.1
graf.background_fill_color = "black"
graf.border_fill_color = "black"
graf.border_fill_alpha = 0
graf.xaxis.axis_line_color = "white"
graf.yaxis.axis_line_color = "white"
props = dict(line_width=4, line_alpha=0.7)
# source for (marker_abs, markers [CANT_MARKERS])
source_markers = ColumnDataSource(data={})
# source for table of graf's markers
source_table = ColumnDataSource()
# strings for table of graf's markers
source_table_share = ColumnDataSource(
) # only for share strings of table's rows between graf_adapter.js, checkbox_callback.js
#################################################################################################################################################################
# # --------------------------------------------------------------------------------------
# button_height = 30
# button_width = 70
#SLIDER_WIDTH = 500 # px
CHECKBOX_WIDTH = 100
# --------------------------------------------------------------------------------------
if x != 0 and y != 0:
max_abs_y = max(y)
max_abs_x = argmax(y)
#x_npa=asarray(x)
y_npa = asarray(y)
x_axis = linspace(x[0], x[-1], len(x))
else:
y_bottom = -100
ref_value = -60
y_npa = asarray([0, 0])
x_axis = [0, 0]
max_abs_y = 0
max_abs_x = 0
#################################################################################################################################################################
# Sliders de marcadores
# solicitar frecuencias al analizador
sliders = []
if x[0] != x[-1]:
step = (x[-1] - x[0]) / len(x)
if step <= 0:
step = 1
init = (max_abs_x * step) + x[0]
for i in range(0, CANT_MARKERS):
sliders.append(
Slider(start=x[0],
end=x[-1],
value=init,
step=step,
title="MARKER " + str(i + 1)))
else:
for i in range(0, CANT_MARKERS):
sliders.append(
Slider(start=0,
end=1,
value=0,
step=1,
title="MARKER " + str(i + 1)))
#################################################################################################################################################################
# Tablas de configuracion (las de solo lectura)
source_set_an = ColumnDataSource()
source_set_gen = ColumnDataSource()
source_set_powm = ColumnDataSource()
#################################################################################################################################################################
# preparo el marcador movil
# ve posibilidad de agregar PointDrawTool de hovertools
# http://docs.bokeh.org/en/1.0.0/docs/user_guide/tools.html#click-tap-tools
graf.add_tools(
HoverTool(
tooltips=[
('frec', '@x{0,0} Hz'),
('amp', '@y dBm'), # use @{ } for field names with spaces
],
point_policy='follow_mouse',
mode='vline'))
with open("static/js/graf_adapter.js", "r") as f:
code = f.read()
adapter = CustomJS(args=dict(graf=graf,
sliders=sliders,
source_markers=source_markers,
source_table=source_table,
source_table_share=source_table_share,
source_set_an=source_set_an,
source_set_gen=source_set_gen,
source_set_powm=source_set_powm),
code=code)
source = AjaxDataSource(data_url=request.url_root + 'data/',
polling_interval=1000,
mode='replace',
adapter=adapter)
source.data = dict(x=[],
y=[],
y_bottom=[],
ref_value=[],
start_freq=[],
stop_freq=[]) #, power = [] )
# span = [], center_freq = [], demod_time = [], ref_level = [], # ANALIZADOR
# lf_freq = [], lf_level = [], rf_freq = [], rf_level = [], fm_mod_freq = [], am_mod_freq = [],am_mod_index= [],fm_mod_index= [],pm_mod_index= [], power = []) # GENERADOR
graf.line('x', 'y', source=source, line_color="cyan")
###############################################################################
# maximos relativos
max_rel_x = argrelextrema(y_npa, greater) #dato pasado a numpy array
###############################################################################
# datos para la tabla de datos
info = []
value = []
# tabla de datos
source_table.data = dict(x_data=info, y_data=value)
source_table_share.data = dict(x_data=info, y_data=value)
#source_table = source
columns = [
TableColumn(field="x_data", title="Information"),
TableColumn(field="y_data", title="Value")
]
data_table = DataTable(source=source_table,
columns=columns,
height=200,
width=400)
###############################################################################
set_div = Div(text="<b>Current Settings</b>")
# tabla de valores seteados actualmente del analizador
source_set_an.data = dict(
configuration=[
"X scale", "Y scale", "Start freq", "Stop freq", "Span",
"Center freq", "Ref level", "Demod time", "Demod type",
"Input att", "Resolution BW", "Scale div", "Avg state", "Avg times"
],
value=[
"LIN", "LOG", "0.0", "1.0", "1.0", "1.0", "0.0", "0.0", "FM", "10",
"100000", "10.0", "WRITe", "1"
],
)
columns_set_an = [
TableColumn(field="configuration", title="Analizer"),
TableColumn(field="value", title="value"),
]
info_table_an = DataTable(source=source_set_an,
columns=columns_set_an,
width=220,
height=180)
# tabla de valuees seteados actualmente en el generador
source_set_gen.data = dict(
configuration=[
"LF state", "LF freq", "LF level", "LF waveform", "RF state",
"RF freq", "RF level", "AM state", "AM source", "AM mod freq",
"AM mod index", "AM waveform", "Modulation type",
"Modulation state", "FM mod freq", "FM mod index", "FM waveform",
"PM mod freq", "PM mod index", "PM waveform"
],
value=[
"OFF", "0.0", "0.0", "SINE", "0.0", "0.0", "0.0", "OFF", "INT",
"0.0", "0.0", "SINE", "FM", "OFF", "0.0", "0.0", "SINE", "0.0",
"0.0", "0.0"
],
)
columns_set_gen = [
TableColumn(field="configuration", title="Generator"),
TableColumn(field="value", title="value"),
]
info_table_gen = DataTable(source=source_set_gen,
columns=columns_set_gen,
width=220,
height=180)
# tabla de valuees seteados actualmente en el generador
source_set_powm.data = dict(
configuration=["Duty cycle", "Average"],
value=["50", "1"],
)
columns_set_powm = [
TableColumn(field="configuration", title="Power meter"),
TableColumn(field="value", title="value"),
]
info_table_powm = DataTable(source=source_set_powm,
columns=columns_set_powm,
width=200,
height=180)
#source_table = source
###############################################################################
## cosas a graficar
# source5 = ColumnDataSource(data=dict(x5=[x_axis[max_abs_x]], y5=[max_abs_y]))
# source4 = ColumnDataSource(data=dict(x4=[x_axis[max_abs_x]], y4=[max_abs_y]))
# source3 = ColumnDataSource(data=dict(x3=[x_axis[max_abs_x]], y3=[max_abs_y]))
# source2 = ColumnDataSource(data=dict(x2=[x_axis[max_abs_x]], y2=[max_abs_y]))
# source5 = ColumnDataSource(data=dict(x5=[], y5=[]))
# source4 = ColumnDataSource(data=dict(x4=[], y4=[]))
# source3 = ColumnDataSource(data=dict(x3=[], y3=[]))
# source2 = ColumnDataSource(data=dict(x2=[], y2=[]))
# marcadores moviles que arrancan en el absolute maximum
# l5=graf.circle('x5', 'y5', source=source5, color="lawngreen", line_width=8, line_alpha=0.7 )
# l4=graf.circle('x4', 'y4', source=source4, color="lime", line_width=8, line_alpha=0.7)
# l3=graf.circle('x3', 'y3', source=source3, color="yellow", line_width=8, line_alpha=0.7)
# l2=graf.circle('x2', 'y2', source=source2, color="blue", line_width=8, line_alpha=0.7)
# custom markers
#markers_rel_dict = {}
markers = []
colors = ["yellow", "red", "pink", "lime"]
for i in range(0, CANT_MARKERS):
x_label = 'x_mark_' + str(i + 1)
y_label = 'y_mark_' + str(i + 1)
source_markers.data[x_label] = [x_axis[max_abs_x]]
source_markers.data[y_label] = [max_abs_y]
markers.append(
graf.circle(x_label,
y_label,
source=source_markers,
color=colors[i],
line_width=8,
line_alpha=0.7))
#l1=graf.circle(x_axis[max_rel_x[0]] , y_npa[max_rel_x[0]], color="yellowgreen", **props)
# max abs marker
source_markers.data['x_abs'] = [x_axis[max_abs_x]]
source_markers.data['y_abs'] = [max_abs_y]
marker_abs = graf.circle(x='x_abs',
y='y_abs',
source=source_markers,
color="red",
line_width=8,
line_alpha=0.7)
#marker_abs=graf.circle(x_axis[max_abs_x],max_abs_y, color="green", **props)
###############################################################################
# presentacion del maximo
#maximo=str('%.2f' % max_abs_y)+"V @ "+str('%.2f' % x_axis[max_abs_x]+" rad")
# presentacion de maximos relativos
max_rel = ["a" for i in range(len(max_rel_x[0]))]
for i in range(len((max_rel_x[0]))):
max_rel[i] = (str('%.2f' % y_npa[max_rel_x[0][i]]) + "V @ " +
str('%.2f' % x_axis[max_rel_x[0][i]] + " rad"))
###############################################################################
# Sliders de marcadores
#callback unico para todos los sliders
with open("static/js/callback_sm.js", "r") as f:
callback_sm_code = f.read()
callback_sm = CustomJS(args=dict(source_table=source_table,
source=source,
source_markers=source_markers,
sliders=sliders,
markers=markers,
graf=graf,
props=props),
code=callback_sm_code)
for i in range(0, CANT_MARKERS):
sliders[i].js_on_change('value', callback_sm)
###############################################################################
# Acciones relativas a los checkbox
checkbox_labels = ["Max. Abs"]
# checkbox_labels = ["Max. Abs", "Max. Rel"]
checkbox_preset = CheckboxGroup(labels=checkbox_labels,
active=[],
width=CHECKBOX_WIDTH)
checkbox_mark = CheckboxGroup(
labels=["Mark 1", "Mark 2", "Mark 3", "Mark 4"],
active=[],
width=CHECKBOX_WIDTH)
#checkbox.active=[] <- indica los índices de los elementos "activados" (para activar el 1: [1], para activar el 0 y el 3: [0 3])
#checkbox.active=[]
#checkbox.active[0]=False
marker_abs.visible = False
#checkbox.active[1]=False
for i in range(0, CANT_MARKERS):
markers[i].visible = False
pass
#checkbox_mark.active=[]
#checkbox_mark.active[0]=False
# marker[i].visible=False
#checkbox_mark.active[1]=False
# marker[i].visible=False
#checkbox_mark.active[2]=False
# marker[i].visible=False
#checkbox_mark.active[3]=False
# marker[i].visible=False
##---------------------------------------------------------------------------##
with open("static/js/checkbox_callback.js", "r") as f:
checkbox_code = f.read()
cjs = CustomJS(args=dict(marker_abs=marker_abs,
markers=markers,
checkbox_preset=checkbox_preset,
checkbox_mark=checkbox_mark,
source_table=source_table,
source=source,
source_markers=source_markers,
source_table_share=source_table_share,
sliders=sliders),
code=checkbox_code)
checkbox_preset.js_on_click(cjs)
checkbox_mark.js_on_click(cjs)
##---------------------------------------------------------------------------##
sliders_col = column(sliders)
# Ploteos
layout_graf = gridplot([[graf]],
sizing_mode='scale_width',
toolbar_location="right")
layout_widgets = widgetbox(row(checkbox_preset, checkbox_mark, sliders_col,
data_table),
sizing_mode='scale_width')
layout_seteos = widgetbox(row(info_table_an, info_table_gen,
info_table_powm),
sizing_mode='scale_width')
analyzer_layout = layout(
[layout_graf, layout_widgets, set_div, layout_seteos],
sizing_mode='scale_width')
return components(analyzer_layout)
class ViewerWidgets(object):
"""
Encapsulates Bokeh widgets, and related callbacks, that are part of prospect's GUI.
Except for VI widgets
"""
def __init__(self, plots, nspec):
self.js_files = get_resources('js')
self.navigation_button_width = 30
self.z_button_width = 30
self.plot_widget_width = (plots.plot_width+(plots.plot_height//2))//2 - 40 # used for widgets scaling
#-----
#- Ifiberslider and smoothing widgets
# Ifiberslider's value controls which spectrum is displayed
# These two widgets call update_plot(), later defined
slider_end = nspec-1 if nspec > 1 else 0.5 # Slider cannot have start=end
self.ifiberslider = Slider(start=0, end=slider_end, value=0, step=1, title='Spectrum (of '+str(nspec)+')')
self.smootherslider = Slider(start=0, end=26, value=0, step=1.0, title='Gaussian Sigma Smooth')
self.coaddcam_buttons = None
self.model_select = None
def add_navigation(self, nspec):
#-----
#- Navigation buttons
self.prev_button = Button(label="<", width=self.navigation_button_width)
self.next_button = Button(label=">", width=self.navigation_button_width)
self.prev_callback = CustomJS(
args=dict(ifiberslider=self.ifiberslider),
code="""
if(ifiberslider.value>0 && ifiberslider.end>=1) {
ifiberslider.value--
}
""")
self.next_callback = CustomJS(
args=dict(ifiberslider=self.ifiberslider, nspec=nspec),
code="""
if(ifiberslider.value<nspec-1 && ifiberslider.end>=1) {
ifiberslider.value++
}
""")
self.prev_button.js_on_event('button_click', self.prev_callback)
self.next_button.js_on_event('button_click', self.next_callback)
def add_resetrange(self, viewer_cds, plots):
#-----
#- Axis reset button (superseeds the default bokeh "reset"
self.reset_plotrange_button = Button(label="Reset X-Y range", button_type="default")
reset_plotrange_code = self.js_files["adapt_plotrange.js"] + self.js_files["reset_plotrange.js"]
self.reset_plotrange_callback = CustomJS(args = dict(fig=plots.fig, xmin=plots.xmin, xmax=plots.xmax, spectra=viewer_cds.cds_spectra),
code = reset_plotrange_code)
self.reset_plotrange_button.js_on_event('button_click', self.reset_plotrange_callback)
def add_redshift_widgets(self, z, viewer_cds, plots):
## TODO handle "z" (same issue as viewerplots TBD)
#-----
#- Redshift / wavelength scale widgets
z1 = np.floor(z*100)/100
dz = z-z1
self.zslider = Slider(start=-0.1, end=5.0, value=z1, step=0.01, title='Redshift rough tuning')
self.dzslider = Slider(start=0.0, end=0.0099, value=dz, step=0.0001, title='Redshift fine-tuning')
self.dzslider.format = "0[.]0000"
self.z_input = TextInput(value="{:.4f}".format(z), title="Redshift value:")
#- Observer vs. Rest frame wavelengths
self.waveframe_buttons = RadioButtonGroup(
labels=["Obs", "Rest"], active=0)
self.zslider_callback = CustomJS(
args=dict(zslider=self.zslider, dzslider=self.dzslider, z_input=self.z_input),
code="""
// Protect against 1) recursive call with z_input callback;
// 2) out-of-range zslider values (should never happen in principle)
var z1 = Math.floor(parseFloat(z_input.value)*100) / 100
if ( (Math.abs(zslider.value-z1) >= 0.01) &&
(zslider.value >= -0.1) && (zslider.value <= 5.0) ){
var new_z = zslider.value + dzslider.value
z_input.value = new_z.toFixed(4)
}
""")
self.dzslider_callback = CustomJS(
args=dict(zslider=self.zslider, dzslider=self.dzslider, z_input=self.z_input),
code="""
var z = parseFloat(z_input.value)
var z1 = Math.floor(z) / 100
var z2 = z-z1
if ( (Math.abs(dzslider.value-z2) >= 0.0001) &&
(dzslider.value >= 0.0) && (dzslider.value <= 0.0099) ){
var new_z = zslider.value + dzslider.value
z_input.value = new_z.toFixed(4)
}
""")
self.zslider.js_on_change('value', self.zslider_callback)
self.dzslider.js_on_change('value', self.dzslider_callback)
self.z_minus_button = Button(label="<", width=self.z_button_width)
self.z_plus_button = Button(label=">", width=self.z_button_width)
self.z_minus_callback = CustomJS(
args=dict(z_input=self.z_input),
code="""
var z = parseFloat(z_input.value)
if(z >= -0.09) {
z -= 0.01
z_input.value = z.toFixed(4)
}
""")
self.z_plus_callback = CustomJS(
args=dict(z_input=self.z_input),
code="""
var z = parseFloat(z_input.value)
if(z <= 4.99) {
z += 0.01
z_input.value = z.toFixed(4)
}
""")
self.z_minus_button.js_on_event('button_click', self.z_minus_callback)
self.z_plus_button.js_on_event('button_click', self.z_plus_callback)
self.zreset_button = Button(label='Reset to z_pipe')
self.zreset_callback = CustomJS(
args=dict(z_input=self.z_input, metadata=viewer_cds.cds_metadata, ifiberslider=self.ifiberslider),
code="""
var ifiber = ifiberslider.value
var z = metadata.data['Z'][ifiber]
z_input.value = z.toFixed(4)
""")
self.zreset_button.js_on_event('button_click', self.zreset_callback)
self.z_input_callback = CustomJS(
args=dict(spectra = viewer_cds.cds_spectra,
coaddcam_spec = viewer_cds.cds_coaddcam_spec,
model = viewer_cds.cds_model,
othermodel = viewer_cds.cds_othermodel,
metadata = viewer_cds.cds_metadata,
ifiberslider = self.ifiberslider,
zslider = self.zslider,
dzslider = self.dzslider,
z_input = self.z_input,
waveframe_buttons = self.waveframe_buttons,
line_data = viewer_cds.cds_spectral_lines,
lines = plots.speclines,
line_labels = plots.specline_labels,
zlines = plots.zoom_speclines,
zline_labels = plots.zoom_specline_labels,
overlap_waves = plots.overlap_waves,
overlap_bands = plots.overlap_bands,
fig = plots.fig
),
code="""
var z = parseFloat(z_input.value)
if ( z >=-0.1 && z <= 5.0 ) {
// update zsliders only if needed (avoid recursive call)
z_input.value = parseFloat(z_input.value).toFixed(4)
var z1 = Math.floor(z*100) / 100
var z2 = z-z1
if ( Math.abs(z1-zslider.value) >= 0.01) zslider.value = parseFloat(parseFloat(z1).toFixed(2))
if ( Math.abs(z2-dzslider.value) >= 0.0001) dzslider.value = parseFloat(parseFloat(z2).toFixed(4))
} else {
if (z_input.value < -0.1) z_input.value = (-0.1).toFixed(4)
if (z_input.value > 5) z_input.value = (5.0).toFixed(4)
}
var line_restwave = line_data.data['restwave']
var ifiber = ifiberslider.value
var waveshift_lines = (waveframe_buttons.active == 0) ? 1+z : 1 ;
var waveshift_spec = (waveframe_buttons.active == 0) ? 1 : 1/(1+z) ;
for(var i=0; i<line_restwave.length; i++) {
lines[i].location = line_restwave[i] * waveshift_lines
line_labels[i].x = line_restwave[i] * waveshift_lines
zlines[i].location = line_restwave[i] * waveshift_lines
zline_labels[i].x = line_restwave[i] * waveshift_lines
}
if (overlap_bands.length>0) {
for (var i=0; i<overlap_bands.length; i++) {
overlap_bands[i].left = overlap_waves[i][0] * waveshift_spec
overlap_bands[i].right = overlap_waves[i][1] * waveshift_spec
}
}
function shift_plotwave(cds_spec, waveshift) {
var data = cds_spec.data
var origwave = data['origwave']
var plotwave = data['plotwave']
if ( plotwave[0] != origwave[0] * waveshift ) { // Avoid redo calculation if not needed
for (var j=0; j<plotwave.length; j++) {
plotwave[j] = origwave[j] * waveshift ;
}
cds_spec.change.emit()
}
}
for(var i=0; i<spectra.length; i++) {
shift_plotwave(spectra[i], waveshift_spec)
}
if (coaddcam_spec) shift_plotwave(coaddcam_spec, waveshift_spec)
// Update model wavelength array
// NEW : don't shift model if othermodel is there
if (othermodel) {
var zref = othermodel.data['zref'][0]
var waveshift_model = (waveframe_buttons.active == 0) ? (1+z)/(1+zref) : 1/(1+zref) ;
shift_plotwave(othermodel, waveshift_model)
} else if (model) {
var zfit = 0.0
if(metadata.data['Z'] !== undefined) {
zfit = metadata.data['Z'][ifiber]
}
var waveshift_model = (waveframe_buttons.active == 0) ? (1+z)/(1+zfit) : 1/(1+zfit) ;
shift_plotwave(model, waveshift_model)
}
""")
self.z_input.js_on_change('value', self.z_input_callback)
self.waveframe_buttons.js_on_click(self.z_input_callback)
self.plotrange_callback = CustomJS(
args = dict(
z_input=self.z_input,
waveframe_buttons=self.waveframe_buttons,
fig=plots.fig,
),
code="""
var z = parseFloat(z_input.value)
// Observer Frame
if(waveframe_buttons.active == 0) {
fig.x_range.start = fig.x_range.start * (1+z)
fig.x_range.end = fig.x_range.end * (1+z)
} else {
fig.x_range.start = fig.x_range.start / (1+z)
fig.x_range.end = fig.x_range.end / (1+z)
}
"""
)
self.waveframe_buttons.js_on_click(self.plotrange_callback) # TODO: for record: is this related to waveframe bug? : 2 callbakcs for same click...
def add_oii_widgets(self, plots):
#------
#- Zoom on the OII doublet TODO mv js code to other file
# TODO: is there another trick than using a cds to pass the "oii_saveinfo" ?
# TODO: optimize smoothing for autozoom (current value: 0)
cds_oii_saveinfo = ColumnDataSource(
{'xmin':[plots.fig.x_range.start], 'xmax':[plots.fig.x_range.end], 'nsmooth':[self.smootherslider.value]})
self.oii_zoom_button = Button(label="OII-zoom", button_type="default")
self.oii_zoom_callback = CustomJS(
args = dict(z_input=self.z_input, fig=plots.fig, smootherslider=self.smootherslider,
cds_oii_saveinfo=cds_oii_saveinfo),
code = """
// Save previous setting (for the "Undo" button)
cds_oii_saveinfo.data['xmin'] = [fig.x_range.start]
cds_oii_saveinfo.data['xmax'] = [fig.x_range.end]
cds_oii_saveinfo.data['nsmooth'] = [smootherslider.value]
// Center on the middle of the redshifted OII doublet (vaccum)
var z = parseFloat(z_input.value)
fig.x_range.start = 3728.48 * (1+z) - 100
fig.x_range.end = 3728.48 * (1+z) + 100
// No smoothing (this implies a call to update_plot)
smootherslider.value = 0
""")
self.oii_zoom_button.js_on_event('button_click', self.oii_zoom_callback)
self.oii_undo_button = Button(label="Undo OII-zoom", button_type="default")
self.oii_undo_callback = CustomJS(
args = dict(fig=plots.fig, smootherslider=self.smootherslider, cds_oii_saveinfo=cds_oii_saveinfo),
code = """
fig.x_range.start = cds_oii_saveinfo.data['xmin'][0]
fig.x_range.end = cds_oii_saveinfo.data['xmax'][0]
smootherslider.value = cds_oii_saveinfo.data['nsmooth'][0]
""")
self.oii_undo_button.js_on_event('button_click', self.oii_undo_callback)
def add_coaddcam(self, plots):
#-----
#- Highlight individual-arm or camera-coadded spectra
coaddcam_labels = ["Camera-coadded", "Single-arm"]
self.coaddcam_buttons = RadioButtonGroup(labels=coaddcam_labels, active=0)
self.coaddcam_callback = CustomJS(
args = dict(coaddcam_buttons = self.coaddcam_buttons,
list_lines=[plots.data_lines, plots.noise_lines,
plots.zoom_data_lines, plots.zoom_noise_lines],
alpha_discrete = plots.alpha_discrete,
overlap_bands = plots.overlap_bands,
alpha_overlapband = plots.alpha_overlapband),
code="""
var n_lines = list_lines[0].length
for (var i=0; i<n_lines; i++) {
var new_alpha = 1
if (coaddcam_buttons.active == 0 && i<n_lines-1) new_alpha = alpha_discrete
if (coaddcam_buttons.active == 1 && i==n_lines-1) new_alpha = alpha_discrete
for (var j=0; j<list_lines.length; j++) {
list_lines[j][i].glyph.line_alpha = new_alpha
}
}
var new_alpha = 0
if (coaddcam_buttons.active == 0) new_alpha = alpha_overlapband
for (var j=0; j<overlap_bands.length; j++) {
overlap_bands[j].fill_alpha = new_alpha
}
"""
)
self.coaddcam_buttons.js_on_click(self.coaddcam_callback)
def add_metadata_tables(self, viewer_cds, show_zcat=True, template_dicts=None,
top_metadata=['TARGETID', 'EXPID']):
""" Display object-related informations
top_metadata: metadata to be highlighted in table_a
Note: "short" CDS, with a single row, are used to fill these bokeh tables.
When changing object, js code modifies these short CDS so that tables are updated.
"""
#- Sorted list of potential metadata:
metadata_to_check = ['TARGETID', 'HPXPIXEL', 'TILEID', 'COADD_NUMEXP', 'COADD_EXPTIME',
'NIGHT', 'EXPID', 'FIBER', 'CAMERA', 'MORPHTYPE']
metadata_to_check += [ ('mag_'+x) for x in viewer_cds.phot_bands ]
table_keys = []
for key in metadata_to_check:
if key in viewer_cds.cds_metadata.data.keys():
table_keys.append(key)
if 'NUM_'+key in viewer_cds.cds_metadata.data.keys():
for prefix in ['FIRST','LAST','NUM']:
table_keys.append(prefix+'_'+key)
if key in top_metadata:
top_metadata.append(prefix+'_'+key)
#- Table a: "top metadata"
table_a_keys = [ x for x in table_keys if x in top_metadata ]
self.shortcds_table_a, self.table_a = _metadata_table(table_a_keys, viewer_cds, table_width=600,
shortcds_name='shortcds_table_a', selectable=True)
#- Table b: Targeting information
self.shortcds_table_b, self.table_b = _metadata_table(['Targeting masks'], viewer_cds, table_width=self.plot_widget_width,
shortcds_name='shortcds_table_b', selectable=True)
#- Table(s) c/d : Other information (imaging, etc.)
remaining_keys = [ x for x in table_keys if x not in top_metadata ]
if len(remaining_keys) > 7:
table_c_keys = remaining_keys[0:len(remaining_keys)//2]
table_d_keys = remaining_keys[len(remaining_keys)//2:]
else:
table_c_keys = remaining_keys
table_d_keys = None
self.shortcds_table_c, self.table_c = _metadata_table(table_c_keys, viewer_cds, table_width=self.plot_widget_width,
shortcds_name='shortcds_table_c', selectable=False)
if table_d_keys is None:
self.shortcds_table_d, self.table_d = None, None
else:
self.shortcds_table_d, self.table_d = _metadata_table(table_d_keys, viewer_cds, table_width=self.plot_widget_width,
shortcds_name='shortcds_table_d', selectable=False)
#- Table z: redshift fitting information
if show_zcat is not None :
if template_dicts is not None : # Add other best fits
fit_results = template_dicts[1]
# Case of DeltaChi2 : compute it from Chi2s
# The "DeltaChi2" in rr fits is between best fits for a given (spectype,subtype)
# Convention: DeltaChi2 = -1 for the last fit.
chi2s = fit_results['CHI2'][0]
full_deltachi2s = np.zeros(len(chi2s))-1
full_deltachi2s[:-1] = chi2s[1:]-chi2s[:-1]
cdsdata = dict(Nfit = np.arange(1,len(chi2s)+1),
SPECTYPE = fit_results['SPECTYPE'][0], # [0:num_best_fits] (if we want to restrict... TODO?)
SUBTYPE = fit_results['SUBTYPE'][0],
Z = [ "{:.4f}".format(x) for x in fit_results['Z'][0] ],
ZERR = [ "{:.4f}".format(x) for x in fit_results['ZERR'][0] ],
ZWARN = fit_results['ZWARN'][0],
CHI2 = [ "{:.1f}".format(x) for x in fit_results['CHI2'][0] ],
DELTACHI2 = [ "{:.1f}".format(x) for x in full_deltachi2s ])
self.shortcds_table_z = ColumnDataSource(cdsdata, name='shortcds_table_z')
columns_table_z = [ TableColumn(field=x, title=t, width=w) for x,t,w in [ ('Nfit','Nfit',5), ('SPECTYPE','SPECTYPE',70), ('SUBTYPE','SUBTYPE',60), ('Z','Z',50) , ('ZERR','ZERR',50), ('ZWARN','ZWARN',50), ('DELTACHI2','Δχ2(N+1/N)',70)] ]
self.table_z = DataTable(source=self.shortcds_table_z, columns=columns_table_z,
selectable=False, index_position=None, width=self.plot_widget_width)
self.table_z.height = 3 * self.table_z.row_height
else :
self.shortcds_table_z, self.table_z = _metadata_table(viewer_cds.zcat_keys, viewer_cds,
table_width=self.plot_widget_width, shortcds_name='shortcds_table_z', selectable=False)
else :
self.table_z = Div(text="Not available ")
self.shortcds_table_z = None
def add_specline_toggles(self, viewer_cds, plots):
#-----
#- Toggle lines
self.speclines_button_group = CheckboxButtonGroup(
labels=["Emission lines", "Absorption lines"], active=[])
self.majorline_checkbox = CheckboxGroup(
labels=['Show only major lines'], active=[])
self.speclines_callback = CustomJS(
args = dict(line_data = viewer_cds.cds_spectral_lines,
lines = plots.speclines,
line_labels = plots.specline_labels,
zlines = plots.zoom_speclines,
zline_labels = plots.zoom_specline_labels,
lines_button_group = self.speclines_button_group,
majorline_checkbox = self.majorline_checkbox),
code="""
var show_emission = false
var show_absorption = false
if (lines_button_group.active.indexOf(0) >= 0) { // index 0=Emission in active list
show_emission = true
}
if (lines_button_group.active.indexOf(1) >= 0) { // index 1=Absorption in active list
show_absorption = true
}
for(var i=0; i<lines.length; i++) {
if ( !(line_data.data['major'][i]) && (majorline_checkbox.active.indexOf(0)>=0) ) {
lines[i].visible = false
line_labels[i].visible = false
zlines[i].visible = false
zline_labels[i].visible = false
} else if (line_data.data['emission'][i]) {
lines[i].visible = show_emission
line_labels[i].visible = show_emission
zlines[i].visible = show_emission
zline_labels[i].visible = show_emission
} else {
lines[i].visible = show_absorption
line_labels[i].visible = show_absorption
zlines[i].visible = show_absorption
zline_labels[i].visible = show_absorption
}
}
"""
)
self.speclines_button_group.js_on_click(self.speclines_callback)
self.majorline_checkbox.js_on_click(self.speclines_callback)
def add_model_select(self, viewer_cds, template_dicts, num_approx_fits, with_full_2ndfit=True):
#------
#- Select secondary model to display
model_options = ['Best fit', '2nd best fit']
for i in range(1,1+num_approx_fits) :
ith = 'th'
if i==1 : ith='st'
if i==2 : ith='nd'
if i==3 : ith='rd'
model_options.append(str(i)+ith+' fit (approx)')
if with_full_2ndfit is False :
model_options.remove('2nd best fit')
for std_template in ['QSO', 'GALAXY', 'STAR'] :
model_options.append('STD '+std_template)
self.model_select = Select(value=model_options[0], title="Other model (dashed curve):", options=model_options)
model_select_code = self.js_files["interp_grid.js"] + self.js_files["smooth_data.js"] + self.js_files["select_model.js"]
self.model_select_callback = CustomJS(
args = dict(ifiberslider = self.ifiberslider,
model_select = self.model_select,
fit_templates=template_dicts[0],
cds_othermodel = viewer_cds.cds_othermodel,
cds_model_2ndfit = viewer_cds.cds_model_2ndfit,
cds_model = viewer_cds.cds_model,
fit_results=template_dicts[1],
std_templates=template_dicts[2],
median_spectra = viewer_cds.cds_median_spectra,
smootherslider = self.smootherslider,
z_input = self.z_input,
cds_metadata = viewer_cds.cds_metadata),
code = model_select_code)
self.model_select.js_on_change('value', self.model_select_callback)
def add_update_plot_callback(self, viewer_cds, plots, vi_widgets, template_dicts):
#-----
#- Main js code to update plots
update_plot_code = (self.js_files["adapt_plotrange.js"] + self.js_files["interp_grid.js"] +
self.js_files["smooth_data.js"] + self.js_files["coadd_brz_cameras.js"] +
self.js_files["update_plot.js"])
# TMP handling of template_dicts
the_fit_results = None if template_dicts is None else template_dicts[1] # dirty
self.update_plot_callback = CustomJS(
args = dict(
spectra = viewer_cds.cds_spectra,
coaddcam_spec = viewer_cds.cds_coaddcam_spec,
model = viewer_cds.cds_model,
othermodel = viewer_cds.cds_othermodel,
model_2ndfit = viewer_cds.cds_model_2ndfit,
metadata = viewer_cds.cds_metadata,
fit_results = the_fit_results,
shortcds_table_z = self.shortcds_table_z,
shortcds_table_a = self.shortcds_table_a,
shortcds_table_b = self.shortcds_table_b,
shortcds_table_c = self.shortcds_table_c,
shortcds_table_d = self.shortcds_table_d,
ifiberslider = self.ifiberslider,
smootherslider = self.smootherslider,
z_input = self.z_input,
fig = plots.fig,
imfig_source = plots.imfig_source,
imfig_urls = plots.imfig_urls,
model_select = self.model_select,
vi_comment_input = vi_widgets.vi_comment_input,
vi_std_comment_select = vi_widgets.vi_std_comment_select,
vi_name_input = vi_widgets.vi_name_input,
vi_quality_input = vi_widgets.vi_quality_input,
vi_quality_labels = vi_widgets.vi_quality_labels,
vi_issue_input = vi_widgets.vi_issue_input,
vi_z_input = vi_widgets.vi_z_input,
vi_category_select = vi_widgets.vi_category_select,
vi_issue_slabels = vi_widgets.vi_issue_slabels
),
code = update_plot_code
)
self.smootherslider.js_on_change('value', self.update_plot_callback)
self.ifiberslider.js_on_change('value', self.update_plot_callback)
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
def make_interactive_plot(qr: np.array, pvals, disp_data_full: list,
disp_inter_full: list, labels: dict) -> None:
""" Create the interactive Bokeh plot as a standalone html web page
:param qr: x-values for the points
:type qr: np.array
:param pvals: slider values
:type pvals: list
:param disp_data_full: list of arrays containing q vectors and eigenvalues for points
:type disp_data_full: list
:param disp_inter_full: list of arrays containing interpolated x and eigen values
:type disp_inter_full: list
:param labels: labels for the x-axis indicating special points
:type labels: dict
:return: None
.. todo:: add output file name option
"""
# Plotting
from bokeh.plotting import figure, output_file
from bokeh.io import show, output_notebook, push_notebook
from bokeh.models import ColumnDataSource, Panel, CustomJS, Circle, Line, Div, HoverTool
from bokeh.models.widgets import CheckboxGroup, Slider, RangeSlider
from bokeh.layouts import column, row
from bokeh.application import Application
nfiles = len(disp_data_full)
neigvals = disp_inter_full[0].shape[1] - 1 # first column is x-values
# Output file name (web page)
output_file("disp_app.html")
# Create the figure
p = figure(title="Dispersion curves for FCC",
y_range=(0, 2.25),
x_range=(0, qr[-1] + 0.001),
x_axis_label="q",
y_axis_label="ω (THz)",
plot_width=800)
# Glyphs for points and lines (intepolation)
raw_glyphs = []
inter_glyphs = []
# Make dictionaries for Bokeh ColumnDataSource
draw = {"xraw": qr}
dint = {"xint": disp_inter_full[0][:, 0]}
for ip in range(nfiles):
for ie in range(neigvals):
ei = "e" + str(ip) + "_" + str(ie)
draw[ei] = disp_data_full[ip][:, ie + 3]
dint[ei] = disp_inter_full[ip][:, ie + 1]
srcraw = ColumnDataSource(draw)
srcint = ColumnDataSource(dint)
# Build glyphs for each pressure point
for ip in range(nfiles):
lalpha = 0
for ie in range(neigvals):
ei = "e" + str(ip) + "_" + str(ie)
if ip == 0: lalpha = 1
raw_glyphs.append(
Circle(x="xraw", y=ei, line_alpha=lalpha, fill_alpha=lalpha))
inter_glyphs.append(Line(x="xint", y=ei, line_alpha=lalpha))
p.add_glyph(srcraw, raw_glyphs[-1])
p.add_glyph(srcint, inter_glyphs[-1])
# Styling
p.xaxis.ticker = list(labels.keys()) # SPqr
p.xaxis.major_label_overrides = labels
# Widgets
EIGEN_LBLS = ["Eigen " + str(i + 1) for i in range(neigvals)]
eigen_chkbx_grp = CheckboxGroup(labels=EIGEN_LBLS,
active=[i for i in range(neigvals)])
slider = Slider(start=1,
end=nfiles,
value=1,
step=1,
title="Pressure series")
#pvals = [''.join(re.findall(regex, f)) for f in onlylogfiles ]
div = Div(text="Pressure = " + pvals[0] + " bar", name="pval")
# Callbacks for widgets
## Slider Callback
slider.js_on_change(
"value",
CustomJS(args=dict(glp=raw_glyphs,
glq=inter_glyphs,
neig=neigvals,
lbl=div,
vals=pvals,
chkbx=eigen_chkbx_grp),
code="""
var i;
var eigs = chkbx.active
for (i=0; i<glp.length; i=i+1){
if (i>=(this.value-1)*neig && i < this.value*neig){
if( eigs.includes(i%neig) ){
glp[i]["fill_alpha"] = 1;
glp[i]["line_alpha"] = 1;
glq[i]["line_alpha"] = 1;
} else {
glp[i]["fill_alpha"] = 0;
glp[i]["line_alpha"] = 0;
glq[i]["line_alpha"] = 0;
}
console.log(i,'YES')
}else{
glp[i]["fill_alpha"] = 0;
glp[i]["line_alpha"] = 0;
glq[i]["line_alpha"] = 0;
console.log(i,'NO')
}
}
lbl["text"] = "Pressure = " + vals[this.value-1] + " bar"
"""))
## Checkbox callback
eigen_chkbx_grp.js_on_click(
CustomJS(args=dict(glp=raw_glyphs,
glq=inter_glyphs,
neig=neigvals,
sldr=slider),
code="""
var actv = this.active
var i
console.log("sldr ",sldr.value)
for (i=(sldr.value-1)*neig; i<sldr.value*neig; i=i+1){
console.log('i, i%neig res = ', i, i%neig, actv.includes(i%neig))
if (actv.includes(i%neig)){
glp[i]["fill_alpha"]=1;
glp[i]["line_alpha"]=1;
glq[i]["line_alpha"]=1;
} else {
glp[i]["fill_alpha"]=0;
glp[i]["line_alpha"]=0;
glq[i]["line_alpha"]=0;
}
}
"""))
## Add hover widget
hover = HoverTool(tooltips=[("ω (THz)", "$y")])
p.add_tools(hover)
## Make the layout
layout = column(eigen_chkbx_grp, p, row(slider, div))
## Make the webpage
show(layout)
def plot_linechart_areas_short_term_migration():
wb = openpyxl.load_workbook(
'data/Short term migration.xlsx') # Import datasets
ws = wb.get_sheet_by_name('Data')
x = []
areas = []
y1,y2,y3,y4,y5,y6,y7,y8,y9,y10,y11,y12,y13,y14,y15,y16,y17,y18,y19,y20,y21,y22,y23,y24,y25,y26,y27,y28,y29,y30,y31,y32,y33,y34 = [],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
#Rearrange data
for column in range(2, 12):
areas.append(ws.cell(row=row, column=1).value)
x.append(ws.cell(row=5, column=column).value)
y1.append(ws.cell(row=6, column=column).value)
y2.append(ws.cell(row=7, column=column).value)
y3.append(ws.cell(row=8, column=column).value)
y4.append(ws.cell(row=9, column=column).value)
y5.append(ws.cell(row=10, column=column).value)
y6.append(ws.cell(row=11, column=column).value)
y7.append(ws.cell(row=12, column=column).value)
y8.append(ws.cell(row=13, column=column).value)
y9.append(ws.cell(row=14, column=column).value)
y10.append(ws.cell(row=15, column=column).value)
y11.append(ws.cell(row=16, column=column).value)
y12.append(ws.cell(row=17, column=column).value)
y13.append(ws.cell(row=18, column=column).value)
y14.append(ws.cell(row=19, column=column).value)
y15.append(ws.cell(row=20, column=column).value)
y16.append(ws.cell(row=21, column=column).value)
y17.append(ws.cell(row=22, column=column).value)
y18.append(ws.cell(row=23, column=column).value)
y19.append(ws.cell(row=24, column=column).value)
y20.append(ws.cell(row=25, column=column).value)
y21.append(ws.cell(row=26, column=column).value)
y22.append(ws.cell(row=27, column=column).value)
y23.append(ws.cell(row=28, column=column).value)
y24.append(ws.cell(row=29, column=column).value)
y25.append(ws.cell(row=30, column=column).value)
y26.append(ws.cell(row=31, column=column).value)
y27.append(ws.cell(row=32, column=column).value)
y28.append(ws.cell(row=33, column=column).value)
y29.append(ws.cell(row=34, column=column).value)
y30.append(ws.cell(row=35, column=column).value)
y31.append(ws.cell(row=36, column=column).value)
y32.append(ws.cell(row=37, column=column).value)
y33.append(ws.cell(row=38, column=column).value)
y34.append(ws.cell(row=39, column=column).value)
p_areas = figure(
plot_width=1000,
plot_height=500,
x_axis_label="dates",
y_axis_label="Migration population",
y_range=(0, 7000),
tools="hover,pan,box_zoom,save,reset,undo,zoom_in,zoom_out,wheel_zoom",
title="Areas of London short-term migration") #Create a new figure
p2 = p_areas.line(x=x,
y=y2,
line_width=1,
color="#000003",
legend="City of London") #draw a line chart
p3 = p_areas.line(x=x,
y=y3,
line_width=1,
color="#140D35",
legend="Barking and Dagenham") #draw a line chart
p4 = p_areas.line(x=x,
y=y4,
line_width=1,
color="#3B0F6F",
legend="Barnet") #draw a line chart
p5 = p_areas.line(x=x,
y=y5,
line_width=1,
color="#63197F",
legend="Bexley") #draw a line chart
p6 = p_areas.line(x=x, y=y6, line_width=1, color="#8C2980",
legend="Brent") #draw a line chart
p7 = p_areas.line(x=x,
y=y7,
line_width=1,
color="#B53679",
legend="Bromley") #draw a line chart
p8 = p_areas.line(x=x,
y=y8,
line_width=1,
color="#DD4968",
legend="Camden") #draw a line chart
p9 = p_areas.line(x=x,
y=y9,
line_width=1,
color="#F66E5B",
legend="Croydon") #draw a line chart
p10 = p_areas.line(x=x,
y=y10,
line_width=1,
color="#FD9F6C",
legend="Ealing") #draw a line chart
p11 = p_areas.line(x=x,
y=y11,
line_width=1,
color="#FDCD90",
legend="Enfield") #draw a line chart
p12 = p_areas.line(x=x,
y=y12,
line_width=1,
color="#FBFCBF",
legend="Greenwich") #draw a line chart
p13 = p_areas.line(x=x,
y=y13,
line_width=1,
color="#a6cee3",
legend="Hackney") #draw a line chart
p14 = p_areas.line(x=x,
y=y14,
line_width=1,
color="#1f78b4",
legend="Hammersmith and Fulham") #draw a line chart
p15 = p_areas.line(x=x,
y=y15,
line_width=1,
color="#b2df8a",
legend="Haringey") #draw a line chart
p16 = p_areas.line(x=x,
y=y16,
line_width=1,
color="#33a02c",
legend="Harrow") #draw a line chart
p17 = p_areas.line(x=x,
y=y17,
line_width=1,
color="#fb9a99",
legend="Havering") #draw a line chart
p18 = p_areas.line(x=x,
y=y18,
line_width=1,
color="#e31a1c",
legend="Hillingdon") #draw a line chart
p19 = p_areas.line(x=x,
y=y19,
line_width=1,
color="#fdbf6f",
legend="Hounslow") #draw a line chart
p20 = p_areas.line(x=x,
y=y20,
line_width=1,
color="#ff7f00",
legend="Islington") #draw a line chart
p21 = p_areas.line(x=x,
y=y21,
line_width=1,
color="#cab2d6",
legend="Kensington and Chelsea") #draw a line chart
p22 = p_areas.line(x=x,
y=y22,
line_width=1,
color="#6a3d9a",
legend="Kingston upon Thames") #draw a line chart
p23 = p_areas.line(x=x,
y=y23,
line_width=1,
color="#ffff99",
legend="Lambeth") #draw a line chart
p24 = p_areas.line(x=x,
y=y24,
line_width=1,
color="#b15928",
legend="Lewisham") #draw a line chart
p25 = p_areas.line(x=x,
y=y25,
line_width=1,
color="#e41a1c",
legend="Merton") #draw a line chart
p26 = p_areas.line(x=x,
y=y26,
line_width=1,
color="#377eb8",
legend="Newham") #draw a line chart
p27 = p_areas.line(x=x,
y=y27,
line_width=1,
color="#4daf4a",
legend="Redbridge") #draw a line chart
p28 = p_areas.line(x=x,
y=y28,
line_width=1,
color="#984ea3",
legend="Richmond upon Thames") #draw a line chart
p29 = p_areas.line(x=x,
y=y29,
line_width=1,
color="#ff7f00",
legend="Southwark") #draw a line chart
p30 = p_areas.line(x=x,
y=y30,
line_width=1,
color="#ffff33",
legend="Sutton") #draw a line chart
p31 = p_areas.line(x=x,
y=y31,
line_width=1,
color="#a65628",
legend="Tower Hamlets") #draw a line chart
p32 = p_areas.line(x=x,
y=y32,
line_width=1,
color="#f781bf",
legend="Waltham Forest") #draw a line chart
p33 = p_areas.line(x=x,
y=y33,
line_width=1,
color="#410967",
legend="Wandsworth") #draw a line chart
p34 = p_areas.line(x=x,
y=y34,
line_width=1,
color="#6A176E",
legend="Westminster") #draw a line chart
#Set legend location adn legemd orientation
p_areas.legend.location = "top_left"
p_areas.legend.orientation = "horizontal"
#Set callback function after click checkboxes
display_event = CustomJS(code="""
p2.visible = false;
p3.visible = false;
p4.visible = false;
p5.visible = false;
p6.visible = false;
p7.visible = false;
p8.visible = false;
p9.visible = false;
p10.visible = false;
p11.visible = false;
p12.visible = false;
p13.visible = false;
p14.visible = false;
p15.visible = false;
p16.visible = false;
p17.visible = false;
p18.visible = false;
p19.visible = false;
p20.visible = false;
p21.visible = false;
p22.visible = false;
p23.visible = false;
p24.visible = false;
p25.visible = false;
p26.visible = false;
p27.visible = false;
p28.visible = false;
p29.visible = false;
p30.visible = false;
p31.visible = false;
p32.visible = false;
p33.visible = false;
p34.visible = false;
if(cb_obj.active.includes(0)){
p2.visible = true;
}
if (cb_obj.active.includes(1)){
p3.visible = true;
}
if (cb_obj.active.includes(2)){
p4.visible = true;
}
if (cb_obj.active.includes(3)){
p5.visible = true;
}
if (cb_obj.active.includes(4)){
p6.visible = true;
}
if (cb_obj.active.includes(5)){
p7.visible = true;
}
if (cb_obj.active.includes(6)){
p8.visible = true;
}
if (cb_obj.active.includes(7)){
p9.visible = true;
}
if (cb_obj.active.includes(8)){
p10.visible = true;
}
if (cb_obj.active.includes(9)){
p11.visible = true;
}
if (cb_obj.active.includes(10)){
p12.visible = true;
}
if (cb_obj.active.includes(11)){
p13.visible = true;
}
if (cb_obj.active.includes(12)){
p14.visible = true;
}
if (cb_obj.active.includes(13)){
p15.visible = true;
}
if (cb_obj.active.includes(14)){
p16.visible = true;
}
if (cb_obj.active.includes(15)){
p17.visible = true;
}
if (cb_obj.active.includes(16)){
p18.visible = true;
}
if (cb_obj.active.includes(17)){
p19.visible = true;
}
if (cb_obj.active.includes(18)){
p20.visible = true;
}
if (cb_obj.active.includes(19)){
p21.visible = true;
}
if (cb_obj.active.includes(20)){
p22.visible = true;
}
if (cb_obj.active.includes(21)){
p23.visible = true;
}
if (cb_obj.active.includes(22)){
p24.visible = true;
}
if (cb_obj.active.includes(23)){
p25.visible = true;
}
if (cb_obj.active.includes(24)){
p26.visible = true;
}
if (cb_obj.active.includes(25)){
p27.visible = true;
}
if (cb_obj.active.includes(26)){
p28.visible = true;
}
if (cb_obj.active.includes(27)){
p29.visible = true;
}
if (cb_obj.active.includes(28)){
p30.visible = true;
}
if (cb_obj.active.includes(29)){
p31.visible = true;
}
if (cb_obj.active.includes(30)){
p32.visible = true;
}
if (cb_obj.active.includes(31)){
p33.visible = true;
}
if (cb_obj.active.includes(32)){
p34.visible = true;
}
""",
args={
'p2': p2,
'p3': p3,
'p4': p4,
'p5': p5,
'p6': p6,
'p7': p7,
'p8': p8,
'p9': p9,
'p10': p10,
'p11': p11,
'p12': p12,
'p13': p13,
'p14': p14,
'p15': p15,
'p16': p16,
'p17': p17,
'p18': p18,
'p19': p19,
'p20': p20,
'p21': p21,
'p22': p22,
'p23': p23,
'p24': p24,
'p25': p25,
'p26': p26,
'p27': p27,
'p28': p28,
'p29': p29,
'p30': p30,
'p31': p31,
'p32': p32,
'p33': p33,
'p34': p34
})
#Set widgets checkboxes
selection_box = CheckboxGroup(labels=[
"City of London", "Barking and Dagenham", "Barnet", "Bexley", "Brent",
"Bromley", "Camden", "Croydon", "Ealing", "Enfield", "Greenwich",
"Hackney", "Hammersmith and Fulham", "Haringey", "Harrow", "Havering",
"Hillingdon", "Hounslow", "Islington", "Kensington and Chelsea",
"Kingston upon Thames", "Lambeth", "Lewisham", "Merton", "Newham",
"Redbridge", "Richmond upon Thames", "Southwark", "Sutton",
"Tower Hamlets", "Waltham Forest", "Wandsworth", "Westminster"
],
active=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33
])
selection_box.js_on_click(display_event)
row_1 = [p_areas,
selection_box] #Make selection boxes located besides line chart
return row_1
