def create_beta_diversity_tab(labels, plots_list):
rank_to_img = {rank: [''] for rank in c.ALL_RANKS}
for rank in c.ALL_RANKS:
for label in labels:
file = os.path.join("by_tool", label, 'beta_diversity_bc_' + rank)
if file in plots_list:
rank_to_img[rank][0] = rank_to_img[rank][
0] + '<img src=' + '"' + file + '.png' + '"' + '/>'
div_plots = Div(text=rank_to_img[c.SPECIES][0],
css_classes=['bk-width-auto'])
source = ColumnDataSource(data=rank_to_img)
select2_rank_sample_callback = CustomJS(args=dict(source=source),
code="""
div_plots.text = source.data[select2_rank.value][0];
""")
select2_rank = Select(title="Taxonomic rank:",
value=c.SPECIES,
options=c.ALL_RANKS,
css_classes=['bk-fit-content'])
select2_rank.js_on_change('value', select2_rank_sample_callback)
select2_rank_sample_callback.args["select2_rank"] = select2_rank
select2_rank_sample_callback.args["div_plots"] = div_plots
beta_div_column = column(
select2_rank,
div_plots,
responsive=True,
css_classes=['bk-width-auto', 'bk-width-auto-main'])
return beta_div_column
def comnt_samp_selection(data_source=None, comnt_obj=None):
"""Return a select widget."""
code = """
//console.log('select: value=' + this.value, this.toString())
comnt_obj.value = this.value;
var data = data_source.data;
var selected_indices = [];
var comnt_value;
for (var i = 0; i < data.COMNT_SAMP.length; i++) {
comnt_value = data.COMNT_SAMP[i];
if (comnt_value == this.value) {
selected_indices.push(i);
}
}
data_source.selected.indices = selected_indices;
"""
callback = CustomJS(code=code,
args={
'data_source': data_source,
'comnt_obj': comnt_obj
})
select = Select(
title="Select comnt incl. index",
value='None',
options=[],
)
select.js_on_change("value", callback)
return select
def getTimeSeries_newish(df_tws):
df_tws['date'] = pd.to_datetime(df_tws['date'], format='%m/%d/%Y')
grouped = df_tws.groupby(
[pd.Grouper(key='date', freq='M'),
'keyword'])['sentiment'].count().to_frame(name='count').reset_index()
print(grouped)
source = ColumnDataSource(grouped)
p = figure(plot_height=400, x_axis_type='datetime')
p.line(x='date', y='count', line_width=2, source=source, legend='Music')
p.line(x='date', y='count', line_width=2, source=source, legend='Art')
p.yaxis.axis_label = 'Number of Tweets'
s = Select(title="test", value="music", options=['art', 'music'])
w2 = CheckboxGroup(labels=["One", "Two", "Three"], active=[1, 1, 1])
callback = CustomJS(args=dict(src=source),
code='''
src.change.emit();
''')
js_filter = CustomJSFilter(code='''
var indices = [];
for (var i = 0; i < src.get_length(); i++){
if (src.data['keyword'][i] == select.value){
indices.push(true);
} else {
indices.push(false);
}
}
return indices;
''')
s.js_on_change('value', callback)
return p, s, w2
def create():
columns = [
TableColumn(field="name", title="File name"),
TableColumn(field="datetime", title="Datetime"),
TableColumn(field="driver", title="Driver"),
TableColumn(field="track", title="Track"),
TableColumn(field="car", title="Car"),
TableColumn(field="lap", title="Lap"),
TableColumn(field="time", title="Lap time"),
]
source = ColumnDataSource()
filter_source = ColumnDataSource()
data_table = DataTable(source=filter_source, columns=columns, width=800)
### https://gist.github.com/dennisobrien/450d7da20daaba6d39d0
# callback code to be used by all the filter widgets
combined_callback_code = """
var data = {};
var original_data = source.data;
var track = track_select_obj.value;
var car = car_select_obj.value;
for (var key in original_data) {
data[key] = [];
for (var i = 0; i < original_data['track'].length; ++i) {
if ((track === "ALL" || original_data['track'][i] === track) &&
(car === "ALL" || original_data['car'][i] === car)) {
data[key].push(original_data[key][i]);
}
}
}
filter_source.data = data;
filter_source.change.emit();
target_obj.change.emit();
"""
# define the filter widgets
track_select = Select(title="Track:", value='ALL', options=['ALL'])
car_select = Select(title="Car:", value='ALL', options=['ALL'])
# define the callback object
generic_callback = CustomJS(args=dict(source=source,
filter_source=filter_source,
track_select_obj=track_select,
car_select_obj=car_select,
target_obj=data_table),
code=combined_callback_code)
# connect the callbacks to the filter widgets
track_select.js_on_change('value', generic_callback)
car_select.js_on_change('value', generic_callback)
filters = row(track_select, car_select)
######
return filters, data_table, source, filter_source, track_select, car_select
def mg_select(csvList, bar_source, status):
select = Select(title="csv File:",
value=csvList[0],
options=[*enumerate(csvList)])
# Select Code
with open(dir_path + '/select.js', 'r') as select_file:
select_code = select_file.read()
select_callback = CustomJS(args=dict(select=select,
bar_source=bar_source,
status=status),
code=select_code)
select.js_on_change('value', select_callback)
return select
from bokeh.models.widgets import Select
from bokeh.core.enums import SizingMode
N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [
"#%02x%02x%02x" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)
]
TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select"
sizing_mode = "fixed"
select = Select(title="Sizing mode", value=sizing_mode, options=list(SizingMode))
plot = figure(tools=TOOLS)
plot.scatter(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
layout = column(select, plot, sizing_mode=sizing_mode)
select.js_on_change('value', CustomJS(args=dict(layout=layout, plot=plot), code="""
var sizing_mode = this.value;
layout.sizing_mode = sizing_mode;
plot.sizing_mode = sizing_mode;
"""))
output_file("sizing_mode.html", title="sizing_mode.py example")
show(layout)
def bokeh_spacetimepop(
frm,
geometry,
title = '',
preamble = '',
varNames = None,
varNotes = dict(),
pw = 700,
ph = 700,
xZones = dict(),
):
import numpy as np
import pandas as pd
df = pd.DataFrame
idx = pd.IndexSlice
import geopandas as gpd
gdf = gpd.GeoDataFrame
from bokeh.models import ColumnDataSource, HoverTool, Legend, LegendItem, CDSView, IndexFilter
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
# frm = frm.reset_index().pivot(index = frm.index.names[0], columns = frm.index.names[1])
frm = frm.copy()
frm = frm.sort_index()
# geometry = geometry.copy()
from bokeh.models import Div
title = f'<h1>{title}</h1>'
title = Div(
text = title,
width = pw,
)
preamble = Div(
text = preamble,
width = pw,
)
if varNames is None:
varNames = frm.columns.sort_values()
varMetaName = varNames.name
else:
varMetaName = 'variable'
varNames = list(varNames)
seriesNames = frm.index.levels[1].sort_values()
seriesMetaName = seriesNames.name
seriesNames = list(seriesNames)
dates = [str(int(round(i.to_numpy().astype(int) / 1e6))) for i in frm.index.levels[0]]
frm.index = frm.index.set_levels(dates, level = 0)
defaultVar = varNames[0]
defaultDate = dates[-1]
pivotFrm = frm.reset_index() \
.pivot(index = frm.index.names[0], columns = frm.index.names[1]) \
.sort_index()
defaultVar = varNames[0]
defaultDate = dates[-1]
for key in varNames:
if not key in varNotes:
varNotes[key] = ''
else:
varNotes[key] = f'<i>{varNotes[key]}</i>'
varNote = Div(
text = varNotes[defaultVar],
width = pw - 120,
)
lineSources = {
key: ColumnDataSource(pivotFrm[key])
for key in pivotFrm.columns.levels[0]
}
lineSource = ColumnDataSource(pivotFrm[defaultVar])
lineSource.name = defaultVar
barSources = dict()
for varName in varNames:
for index, date in zip(sorted(pivotFrm.index), dates):
series = pivotFrm.loc[index, varName]
subFrm = df(dict(
name = series.index,
value = series.values,
height = abs(series.values),
offset = series.values / 2.
))
barSources[varName + '_' + date] = ColumnDataSource(subFrm)
barSource = ColumnDataSource(barSources[defaultVar + '_' + defaultDate].data)
barSource.name = ', '.join([str(defaultVar), str(defaultDate)])
bounds = geometry.bounds
minx = np.min(bounds['minx'])
maxx = np.max(bounds['maxx'])
miny = np.min(bounds['miny'])
maxy = np.max(bounds['maxy'])
aspect = (maxx - minx) / (maxy - miny)
from shapely.geometry import Polygon
import itertools
corners = list(itertools.product(geometry.total_bounds[::2], geometry.total_bounds[1::2]))
allPoly = Polygon([corners[0], corners[1], corners[3], corners[2]])
allPoly = allPoly.centroid.buffer(np.sqrt(allPoly.area) / 1e6)
for name in frm.index.levels[1]:
if not name in geometry.index:
geometry[name] = allPoly
geometry = geometry.simplify(np.sqrt(geometry.area).min() * 10. ** 3.5)
geoFrm = frm.reset_index().pivot(index = frm.index.names[1], columns = frm.index.names[0])
geoFrm.columns = geoFrm.columns.map('_'.join).str.strip('_')
geoFrm['geometry'] = geometry
geoFrm = gdf(geoFrm)
from bokeh.models import GeoJSONDataSource
geoJSON = geoFrm.reset_index().to_json()
geoSource = GeoJSONDataSource(geojson = geoJSON)
mins = {n: frm[n].min() for n in varNames}
maxs = {n: frm[n].max() for n in varNames}
xName = frm.index.names[0]
lineFig = figure(
x_axis_type = 'datetime',
y_range = (mins[defaultVar], maxs[defaultVar]),
plot_height = int((ph - 100) * 1. / 3.),
plot_width = pw,
toolbar_location = 'left',
tools = 'save, xpan, box_zoom, reset, xwheel_zoom',
active_scroll = 'auto',
# title = title,
)
barFig = figure(
x_range = seriesNames,
plot_height = int((ph - 100) * 1. / 2.),
plot_width = pw,
# title = "Scores on my birthday",
toolbar_location = None,
tools = ""
)
barFig.xgrid.grid_line_color = None
barFig.xaxis.major_label_orientation = 'vertical'
mapFig = figure(
plot_width = pw - 20,
plot_height = int(round((pw - 20) / aspect)),
toolbar_location = 'right',
tools = 'pan, wheel_zoom, reset',
background_fill_color = "lightgrey"
)
mapFig.xgrid.grid_line_color = None
mapFig.ygrid.grid_line_color = None
from matplotlib.pyplot import get_cmap
from matplotlib.colors import rgb2hex
cmap = get_cmap('nipy_spectral')
cs = [rgb2hex(cmap(i / len(seriesNames), alpha = 0.5)) for i in range(len(seriesNames))]
lines = []
for seriesName, colour in zip(seriesNames, cs):
line = lineFig.line(
xName,
seriesName,
source = lineSource,
color = colour,
alpha = 0.8,
muted_color = 'gray',
muted_alpha = 0.3,
muted = True,
line_width = 2,
# legend_label = seriesName,
)
from bokeh.models import HoverTool
lineFig.add_tools(HoverTool(
renderers = [
line,
],
tooltips = [
(seriesMetaName.capitalize(), seriesName),
(xName.capitalize(), f'@{xName}' + '{%Y-%m-%d}'),
('Value', f'@{{{seriesName}}}'),
],
formatters = {
f'@{xName}': 'datetime',
seriesName: 'numeral',
},
toggleable = False
))
lines.append(line)
bars = []
for i, (seriesName, colour) in enumerate(zip(seriesNames, cs)):
view = CDSView(source = barSource, filters = [IndexFilter([i,]),])
bar = barFig.rect(
source = barSource,
view = view,
x = 'name',
y = 'offset',
height = 'height',
width = 0.9,
color = colour,
muted_color = 'gray',
muted_alpha = 0.3,
muted = True,
)
bars.append(bar)
from bokeh.palettes import Viridis256
from bokeh.models import LinearColorMapper, ColorBar
palette = Viridis256
mapColourMapper = LinearColorMapper(
palette = palette,
low = frm.loc[idx[defaultDate, :], defaultVar].min(),
high = frm.loc[idx[defaultDate, :], defaultVar].max(),
)
mapColourBar = ColorBar(
color_mapper = mapColourMapper,
label_standoff = 8,
width = 30,
height = int(round(mapFig.plot_height * 0.9)),
border_line_color = None,
location = (0, 0),
orientation = 'vertical',
)
mapFig.add_layout(mapColourBar, 'left')
patches = []
for i, seriesName in enumerate(seriesNames):
view = CDSView(source = geoSource, filters = [IndexFilter([i,]),])
patch = mapFig.patches(
'xs',
'ys',
source = geoSource,
view = view,
fill_color = dict(
field = '_'.join([defaultVar, defaultDate]),
transform = mapColourMapper,
),
line_color = 'grey',
line_width = 0.25,
fill_alpha = 0.,
name = '_'.join([defaultVar, defaultDate])
)
patches.append(patch)
from bokeh.models import HoverTool
mapHover = HoverTool(
renderers = patches,
tooltips = [
(seriesMetaName.capitalize(), f'@{seriesMetaName}'),
('Value', '@$name'),
]
)
mapFig.add_tools(mapHover)
from bokeh.models import BoxAnnotation
from bokeh.models import Label
for name, zone in xZones.items():
convD = lambda x: int(round(pd.Timestamp(x).to_numpy().astype(int) / 1e6))
left, right = [None if val is None else convD(val) for val in zone]
zone = BoxAnnotation(
left = left,
right = right,
fill_alpha = 0.1,
fill_color = 'gray',
)
zoneLabel = Label(
text = name + ' (end)' if left is None else name,
text_font_size = '8pt',
x = right if left is None else left,
y = 10,
x_units = 'data',
y_units = 'screen',
angle = -90 if left is None else 90,
angle_units = 'deg',
x_offset = -10 if left is None else 10,
y_offset = 5 * (len(name) + 6) if left is None else 0
)
lineFig.add_layout(zone)
lineFig.add_layout(zoneLabel)
from bokeh.models import Span
span = Span(
location = int(defaultDate),
dimension = 'height',
line_color = 'red',
# line_dash = 'dashed',
line_width = 1
)
lineFig.add_layout(span)
from bokeh.models.widgets import DateSlider
slider = DateSlider(
title = 'Date',
start = int(dates[0]),
end = int(dates[-1]),
step = int(8.64 * 1e7), # days
value = int(defaultDate),
width = pw - 60,
align = 'end'
)
from bokeh.models.widgets import Select
select = Select(
title = "Choose data:",
options = varNames,
value = defaultVar,
width = 100,
)
from bokeh.models import CheckboxGroup
checkboxes = CheckboxGroup(
labels = seriesNames,
active = [],
)
checkboxAll = CheckboxGroup(
labels = ['All',],
active = [],
)
from bokeh.models import CustomJS
callback = CustomJS(
args = dict(
y_range = lineFig.y_range,
lineSources = lineSources,
lineSource = lineSource,
barSources = barSources,
barSource = barSource,
bars = bars,
lines = lines,
patches = patches,
select = select,
slider = slider,
span = span,
checkboxes = checkboxes,
varNote = varNote,
varNotes = varNotes,
geoSource = geoSource,
mapColourMapper = mapColourMapper,
mins = mins,
maxs = maxs,
),
code = """
lineSource.data = lineSources[select.value].data
lineSource.name = select.value
lineSource.change.emit()
span.location = slider.value
span.change.emit()
y_range.setv({'start': mins[select.value], 'end': maxs[select.value]})
varNote.text = varNotes[select.value]
varNote.change.emit()
const barChoice = select.value + '_' + slider.value
barSource.data = barSources[barChoice].data
barSource.name = select.value.toString() + ', ' + slider.value.toString()
barSource.change.emit()
for (let i = 0; i < lines.length; i++){
let checked = checkboxes.active.includes(i)
lines[i].muted = !(checked)
bars[i].muted = !(checked)
var alpha = checked ? 1 : 0;
patches[i].glyph.fill_alpha = alpha
}
const newCol = select.value + '_' + slider.value
for (let i = 0; i < lines.length; i++){
patches[i].glyph.fill_color['field'] = newCol
patches[i].name = newCol
}
mapColourMapper.low = mins[select.value]
mapColourMapper.high = maxs[select.value]
geoSource.change.emit()
""",
)
allCheckCallback = CustomJS(
args = dict(
lines = lines,
checkboxes = checkboxes,
checkboxAll = checkboxAll,
callback = callback
),
code = """
checkboxes.active.length = 0
if (checkboxAll.active.length > 0) {
let arr = []
for (let i = 0; i < lines.length; i++){
arr.push(i)
}
checkboxes.active.push(...arr)
}
checkboxes.change.emit()
callback.execute()
"""
)
slider.js_on_change('value', callback)
select.js_on_change('value', callback)
checkboxes.js_on_change('active', callback)
checkboxAll.js_on_change('active', allCheckCallback)
from bokeh.layouts import column, row
layout = column(
title,
preamble,
row(select, varNote),
row(column(lineFig, slider, barFig), column(checkboxes, checkboxAll)),
mapFig
)
return layout
def client1(request):
if request.user.is_authenticated():
username = request.user.get_username()
if request.method == 'GET' and request.user.is_authenticated():
user_profile = UserProfile.objects.get(user=request.user)
is_supervisor = user_profile.supervisor
#x= [1,2,3,4,5,6,7]
x = [
"20-Oct-17", "27-Oct-17", "02-Nov-17", "09-Nov-17", "16-Nov-17",
"23-Nov-17", "30-Nov-17"
]
y = [2500, 2250, 2300, 2150, 2050, 2100, 2200]
title = 'Weight and Calories over Time'
extra_y = [85, 83, 80, 78, 77, 76, 75]
hover = HoverTool(tooltips=[("calories", "@y kcal"), ("date", "@x")],
names=["calories"])
plot = figure(title=title,
x_range=x,
y_range=Range1d(2000, 2500),
x_axis_label='Time (Weekly)',
y_axis_label='Daily Average Calores (kcal)',
plot_width=600,
plot_height=400,
tools=[hover])
plot.line(x,
y,
legend='Calories Over Time',
line_width=2,
name="calories")
plot.circle(x,
y,
fill_color="blue",
size=8,
legend='Calories Over Time')
plot.extra_y_ranges['weight'] = Range1d(70, 90)
hoverweight = HoverTool(tooltips=[("weight", "@y kg"), ("date", "@x")],
names=["weight"])
#plot.multi_line([x, y], [x, extra_y], color=["firebrick", "navy"], line_width=4)
plot.add_layout(
LinearAxis(y_range_name="weight", axis_label="Weight (kg)"),
'right')
source = ColumnDataSource(data=dict(x=x, y=extra_y))
plot.circle('x',
'y',
source=source,
fill_color="firebrick",
line_color="firebrick",
size=8,
y_range_name="weight",
name="weight",
legend="Weight Over Time")
plot.line(x,
extra_y,
legend='Weight Over Time',
line_width=2,
y_range_name="weight",
name="weight",
color="firebrick")
zoom = WheelZoomTool()
plot.add_tools(hoverweight, PanTool(), zoom)
plot.legend.location = "bottom_left"
plot.legend.click_policy = "hide"
plot.toolbar.active_scroll = zoom
plot.toolbar.logo = None
plot.toolbar_location = None
#Store components
script, div = components(plot)
#The code which changes kilos to pounds. This needs to be integrated using ajax I think.
callback = CustomJS(args=dict(source=source),
code="""
var data = source.data;
var f = cb_obj.value
y = data['y']
if (f === "Kilos") {
for (i = 0; i < y.length; i++) {
y[i] = y[i] / 2.2
}
}
if (f === "Pounds") {
for (i = 0; i < y.length; i++) {
y[i] = y[i] * 2.2
}
}
source.change.emit();
""")
s = Select(title='Units', value="Kilos", options=['Kilos', 'Pounds'])
s.js_on_change('value', callback)
select_script, select_div = components(s)
#pie chart#
# a color for each pie piece
colors = ["red", "blue"]
p = figure(x_range=(-1, 1),
y_range=(-1, 1),
plot_width=200,
plot_height=200,
tools=[])
p.wedge(x=0,
y=0,
radius=1,
start_angle=0,
end_angle=0.12,
color="red",
legend="weight loss",
name="change")
p.wedge(x=0,
y=0,
radius=1,
start_angle=0.12,
end_angle=0,
color="blue",
legend="current weight",
name="current")
hoverchange = HoverTool(tooltips=[
("Total Weight Loss", "10kg (12%)"),
],
names=["change"])
hovercurrent = HoverTool(tooltips=[
("Current Weight", "75kg (88%)"),
],
names=["current"])
p.add_tools(hoverchange, hovercurrent, PanTool(), zoom)
# display/save everythin
script1, div1 = components(p)
#Feed them to the Django template.
return render(
request, 'visualisation/client1.html', {
'username': str(username),
'is_supervisor': is_supervisor,
'script': script,
'div': div,
'script1': script1,
'div1': div1,
'select_script': select_script,
'select_div': select_div
})
plot_height = 600
dw = 10
dh = 10
N = 651
select = Select(title='Select Year/Date', options=path, value=path[0])
callback = CustomJS(args=dict(source=source),
code="""
var data = source.data;
var s = cb_obj.value
x=data['infared_'+s]
data['infared'] = x
source.change.emit()
""")
select.js_on_change('value', callback)
p = figure(title='Infared outliers',
plot_width=plot_width,
plot_height=plot_height,
x_range=(0, dw),
y_range=(0, dh))
pg = p.image(image='infared',
x=0,
y=0,
dw=dw,
dh=dh,
source=source,
palette='Spectral11')
show(row(p, select))
n_smooth_name = Div(text= " Repeats of Smoothing:", width= 150, height=30 )
# bin_name = Div(text= " Bin Every:", width= 150, height=30 )
int_name = Div(text= " Intensity Threshold (%)", width= 150, height=30 )
sub_name = Select(options=['Substract Minimum', 'Substract Line', 'Substract Curved'], name='sub_mode', value='Substract Minimum', width= 150, height=30 )
# add_name = Div(text= " Adduct Mass (Da)", width= 150, height=30 )
# dat_name = Div(text= " Data Reduction (%)", width= 150, height=30 )
#pro_name = Div(text= " bla", width= 150, height=30 )
dt_name = Div(text= " <h2>Data Processing</h2>", height=45 )
dtp_update=CustomJS(args=dict(DataProcessingParameters=DataProcessingParameters), code=open(os.path.join(os.getcwd(), 'JS_Functions', "DataProcessingParameters_update_cb.js")).read())
cropping_slider.js_on_change('value', dtp_update)
n_smooth.js_on_change('value', dtp_update)
gaussian_smooth.js_on_change('value', dtp_update)
intensity_threshold.js_on_change('value', dtp_update)
substract.js_on_change('value', dtp_update)
sub_name.js_on_change('value', dtp_update)
processing_cb=CustomJS(args=dict(peak_mz=peak_mz, bg_mz=bg_mz, raw_mz=raw_mz, proc_mz=proc_mz, DataProcessingParameters=DataProcessingParameters, series_data=series_data, series_sele=series_sele, series_names=series_names, series_mz=series_mz), code=open(os.path.join(os.getcwd(), 'JS_Functions', "navia_functions.js")).read() + open(os.path.join(os.getcwd(), 'JS_Functions', "data_processing_cb.js")).read())
process_data=Button(label='Process Data', width=100, height=30, button_type='success', callback=processing_cb)
reset_cb=CustomJS(args=dict(process_data=process_data, peak_mz=peak_mz, bg_mz=bg_mz, raw_mz=raw_mz, proc_mz=proc_mz, series_data=series_data, series_sele=series_sele, series_names=series_names, series_mz=series_mz), code=open(os.path.join(os.getcwd(), 'JS_Functions', "navia_functions.js")).read() + open(os.path.join(os.getcwd(), 'JS_Functions', "reset_cb.js")).read())
reset_data=Button(label='Reset Processing', width=80, height=30, button_type='danger', callback=reset_cb)
dt_names = Column(cropping, gau_name, n_smooth_name ,int_name, sub_name, reset_data)#, pro_name)
dt_inp=Column(cropping_slider, gaussian_smooth, n_smooth, intensity_threshold, substract, process_data)
grid_text = Div(text= " Show Grid", width= 150, height=30 )
grid_sele = Toggle(label='on', active=True, width=100, height=30, callback=toggle_cb)
for x_grid_line in plot_canvas.xgrid:
grid_sele.js_link('active', x_grid_line, 'visible')
for y_grid_line in plot_canvas.ygrid:
def bokeh_plot(import_df):
import pandas as pd
import numpy as np
from bokeh.plotting import figure, show
from bokeh.layouts import layout, widgetbox, row, column, gridplot
from bokeh.models import ColumnDataSource, HoverTool, BoxZoomTool, ResetTool, PanTool, CustomJS, PrintfTickFormatter, WheelZoomTool, SaveTool, LassoSelectTool, NumeralTickFormatter
from bokeh.models.widgets import Slider, Select, TextInput, Div, Tabs, Panel, DataTable, DateFormatter, TableColumn, PreText, NumberFormatter, RangeSlider
from bokeh.io import curdoc
from functools import lru_cache
from bokeh.transform import dodge
from os.path import dirname, join
from bokeh.core.properties import value
#load plotting data here
@lru_cache()
def load_data():
df = import_df
df.dropna(how='all', axis=0)
#Northest=['3229','3277','3276','3230','3259','All_Stores_NE']
df.location_reference_id = df.location_reference_id.astype(str)
#df['region'] = ['Northeast' if x in Northest else 'Midwest' for x in df['location_reference_id']]
df['date'] = pd.to_datetime(df['date'])
df[[
'BOH_gt_Shelf_Capacity', 'OTL_gt_Shelf_Capacity',
'Ideal_BOH_gt_Shelf_Capacity', 'BOH_lt_Ideal', 'BOH_eq_Ideal',
'BOH_gt_Ideal', 'Demand_Fulfilled', 'Fill_Rate', 'Backroom_OH',
'Total_OH', 'Prop_OH_in_Backroom', 'Never_Q98_gt_POG',
'Never_Ideal_BOH_gt_POG', 'Sometimes_OTL_Casepack_1_gt_POG',
'Always_OTL_Casepack_1_le_POG', 'Non_POG'
]] = df[[
'BOH > Shelf Capacity', 'OTL > Shelf Capacity',
'Ideal BOH > Shelf Capacity', 'BOH < Ideal', 'BOH = Ideal',
'BOH > Ideal', 'Demand Fulfilled', 'Fill Rate', 'Backroom_OH',
'Total OH', 'Prop OH in Backroom', 'Never: Q98 > POG',
'Never: Ideal BOH > POG', 'Sometimes: OTL+Casepack-1 > POG',
'Always: OTL+Casepack-1 <= POG', 'Non-POG'
]]
df['date_bar'] = df['date']
df['date_bar'] = df['date_bar'].astype(str)
return df
#Filter data source for "All" stores OR data agrregation on DC level
df_agg = load_data().groupby(['location_reference_id'],
as_index=False).sum()
source1 = ColumnDataSource(data=df_agg)
sdate = min(load_data()['date'])
edate = max(load_data()['date'])
nodes = len(list(load_data().location_reference_id.unique()))
days = len(list(load_data().date.unique()))
policy = "Prod"
#list of dates for vbar charts
x_range_list = list(load_data().date_bar.unique())
#direct access to number of location_reference_idand region
all_locations1 = list(load_data().location_reference_id.unique())
#agg_value=['All']
#all location_reference_idfrom csv file along with an option for agg data "All"
#all_locations=all_locations1+agg_value
#all_regions = ['Northeast', 'Midwest']
all_regions = list(load_data().region.unique())
desc = Div(text="All locations", width=230)
pre = Div(text="_", width=230)
location = Select(title="Location",
options=all_locations1,
value="All_Stores_NE")
region = Select(title="Region", options=all_regions, value="NE")
text_input = TextInput(value="default", title="Search Location:")
#full data set from load_data(df=df_import)
source = ColumnDataSource(data=load_data())
original_source = ColumnDataSource(data=load_data())
#plotting starts........... here are total 8 graphs for each Metric.
#Back room on hand
hover = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), ("Backroom_OH", "@Backroom_OH{0,0.00}")])
TOOLS = [
hover,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
p = figure(x_range=x_range_list,
plot_width=1000,
plot_height=525,
title="Backroom On hand by store",
tools=TOOLS,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Backroom OH")
p.background_fill_color = "#e6e9ed"
p.background_fill_alpha = 0.5
p.vbar(x=dodge('date_bar', -0.25, range=p.x_range),
top='Backroom_OH',
hover_alpha=0.5,
hover_line_color='black',
width=0.8,
source=source,
color="#718dbf")
p.xaxis.major_label_orientation = 1
p.legend.border_line_width = 3
p.legend.border_line_color = None
p.legend.border_line_alpha = 0.5
p.title.text_color = "olive"
#inbound outbound
hover_m = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), (
"Inbound",
"@Inbound{0,0.00}"), ("Outbound", "@Outbound{0,0.00}")])
TOOLS_m = [
hover_m,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
m = figure(plot_height=525,
plot_width=1000,
x_range=x_range_list,
title="Inbound/Outbound by store",
tools=TOOLS_m,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Units")
m.background_fill_color = "#e6e9ed"
m.background_fill_alpha = 0.5
m.vbar(x=dodge('date_bar', -0.25, range=m.x_range),
top='Inbound',
hover_alpha=0.5,
hover_line_color='black',
width=0.4,
source=source,
color="#718dbf",
legend=value("Inbound"))
m.vbar(x=dodge('date_bar', 0.25, range=m.x_range),
top='Outbound',
hover_alpha=0.5,
hover_line_color='black',
width=0.4,
source=source,
color="#e84d60",
legend=value("Outbound"))
m.xaxis.major_label_orientation = 1
m.legend.border_line_width = 3
m.legend.border_line_color = None
m.legend.border_line_alpha = 0.5
m.title.text_color = "olive"
#Stockout
hover_s = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), (
"BOH_OOS",
"@BOH_OOS{0,0.000}"), ("EOH_OOS", "@EOH_OOS{0,0.000}")])
TOOLS_s = [
hover_s,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
s = figure(plot_height=525,
plot_width=1000,
title="Stockouts by store",
x_axis_type="datetime",
toolbar_location='above',
tools=TOOLS_s,
x_axis_label="Date",
y_axis_label="Prop Stockout")
s.background_fill_color = "#e6e9ed"
s.background_fill_alpha = 0.5
s.circle(x='date',
y='EOH_OOS',
source=source,
fill_color=None,
line_color="#4375c6")
s.line(x='date',
y='EOH_OOS',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='navy',
legend=value("EOH OOS"))
s.circle(x='date',
y='BOH_OOS',
source=source,
fill_color=None,
line_color="#4375c6")
s.line(x='date',
y='BOH_OOS',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='red',
legend=value("BOH OOS"))
s.legend.border_line_width = 3
s.legend.border_line_color = None
s.legend.border_line_alpha = 0.5
s.title.text_color = "olive"
#Fill rate
hover_t = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), ("Fill Rate", "@Fill_Rate{0,0.00}")])
TOOLS_t = [
hover_t,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
t = figure(plot_height=525,
x_range=x_range_list,
plot_width=1000,
title="Fill rates by store",
tools=TOOLS_t,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Fill rate")
t.background_fill_color = "#e6e9ed"
t.background_fill_alpha = 0.5
t.vbar(x=dodge('date_bar', -0.25, range=t.x_range),
top='Fill Rate',
hover_alpha=0.5,
hover_line_color='black',
width=0.8,
source=source,
color="#718dbf")
t.xaxis.major_label_orientation = 1
t.legend.border_line_width = 3
t.legend.border_line_color = None
t.legend.border_line_alpha = 0.5
t.title.text_color = "olive"
# % Backroom spillover
hover_w = HoverTool(
tooltips=[("Location", "@location_reference_id"), ("Date",
"@date_bar"),
("Prop OH in Backroom", "@Prop_OH_in_Backroom{0,0.00}")])
TOOLS_w = [
hover_w,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
w = figure(plot_height=525,
plot_width=1000,
title="Prop OH in Backroom by store",
x_axis_type="datetime",
tools=TOOLS_w,
toolbar_location='above',
x_axis_label="Date",
y_axis_label=" % Backroom spillover")
w.background_fill_color = "#e6e9ed"
w.background_fill_alpha = 0.5
w.circle(x='date',
y='Prop OH in Backroom',
source=source,
fill_color=None,
line_color="#4375c6")
w.line(x='date',
y='Prop OH in Backroom',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='navy')
w.title.text_font_style = "bold"
w.title.text_color = "olive"
w.legend.click_policy = "hide"
w.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
#BOH vs Ideal
hover_f = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date",
"@date_bar"), ('BOH < Ideal', "@BOH_lt_Ideal{0,0.00}"
), ('BOH > Ideal', "@BOH_gt_Ideal{0,0.00}"
), ('BOH = Ideal', "@BOH_eq_Ideal{0,0.00}")])
TOOLS_f = [
hover_f,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
colors = ["#c9d9d3", "#718dbf", "#e84d60"]
BOH_vs_ideal = ['BOH < Ideal', 'BOH > Ideal', 'BOH = Ideal']
f = figure(x_range=x_range_list,
plot_height=525,
plot_width=1000,
title="BOH vs Ideal by store",
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Prop",
tools=TOOLS_f)
f.vbar_stack(BOH_vs_ideal,
x='date_bar',
width=0.9,
color=colors,
source=source,
legend=[value(x) for x in BOH_vs_ideal],
name=BOH_vs_ideal)
f.xaxis.major_label_orientation = 1
f.legend.border_line_width = 3
f.legend.border_line_color = None
f.legend.border_line_alpha = 0.5
f.title.text_color = "olive"
#Pog Fit
hover_g = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date",
"@date_bar"), ('Never: Q98 > POG', "@Never_Q98_gt_POG{0,0.00}"),
("Never: Ideal BOH > POG",
"@Never_Ideal_BOH_gt_POG{0,0.00}"),
("Sometimes: OTL+Casepack-1 > POG",
"@Sometimes_OTL_Casepack_1_gt_POG{0,0.00}"),
("Always: OTL+Casepack-1 <= POG",
"@Always_OTL_Casepack_1_le_POG{0,0.00}"
), ("Non-POG'", "@Non_POG{0,0.00}")])
TOOLS_g = [
hover_g,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
colors2 = ['#79D151', "#718dbf", '#29788E', '#fc8d59', '#d53e4f']
pog_fit = [
'Never: Q98 > POG', 'Never: Ideal BOH > POG',
'Sometimes: OTL+Casepack-1 > POG', 'Always: OTL+Casepack-1 <= POG',
'Non-POG'
]
g = figure(x_range=x_range_list,
plot_height=525,
plot_width=1200,
title="Pog Fit by store",
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Counts",
tools=TOOLS_g)
g.vbar_stack(pog_fit,
x='date_bar',
width=0.9,
color=colors2,
source=source,
legend=[value(x) for x in pog_fit],
name=pog_fit)
g.xaxis.major_label_orientation = 1
g.legend.border_line_width = 3
g.legend.border_line_color = None
g.legend.border_line_alpha = 0.5
g.title.text_color = "olive"
g.legend.location = "top_right"
# BOH vs Pog
colors3 = ["#c9d9d3", "#718dbf", "#e84d60"]
shelf = [
'BOH > Shelf Capacity', 'OTL > Shelf Capacity',
'Ideal BOH > Shelf Capacity'
]
hover_h = HoverTool(
tooltips=[("Location", "@location_reference_id"), ("Date",
"@date_bar"),
("OTL > Shelf Capacity", "@OTL_gt_Shelf_Capacity{0,0.00}"
), ("BOH > Shelf Capacity",
"@BOH_gt_Shelf_Capacity{0,0.00}"),
("Ideal BOH > Shelf Capacity",
"@Ideal_BOH_gt_Shelf_Capacity{0,0.00}")])
TOOLS_h = [
hover_h,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
h = figure(plot_height=525,
plot_width=1000,
title="BOH vs Pog by store",
x_axis_type="datetime",
toolbar_location='above',
tools=TOOLS_h,
x_axis_label="Date",
y_axis_label="Prop")
h.background_fill_color = "#e6e9ed"
h.background_fill_alpha = 0.5
h.circle(x='date',
y='BOH > Shelf Capacity',
source=source,
fill_color=None,
line_color="#4375c6")
h.line(x='date',
y='BOH > Shelf Capacity',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='navy',
legend=value("BOH > Shelf Capacity"))
h.circle(x='date',
y='OTL > Shelf Capacity',
source=source,
fill_color=None,
line_color="#4375c6")
h.line(x='date',
y='OTL > Shelf Capacity',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color="green",
legend=value("OTL > Shelf Capacity"))
h.circle(x='date',
y='Ideal BOH > Shelf Capacity',
source=source,
fill_color=None,
line_color="#4375c6")
h.line(x='date',
y='Ideal BOH > Shelf Capacity',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color="#e84d60",
legend=value("Ideal BOH > Shelf Capacity"))
h.legend.border_line_width = 3
h.legend.border_line_color = None
h.legend.border_line_alpha = 0.5
h.title.text_color = "olive"
h.legend.click_policy = "mute"
# Inventory
hover_j = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), ("DFE_Q98", "@DFE_Q98{0,0.00}"),
("OTL",
"@OTL{0,0.00}"), ("EOH",
"@EOH{0,0.00}"), ("BOH", "@BOH{0,0.00}")])
TOOLS_j = [
hover_j,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
j = figure(plot_height=525,
plot_width=1200,
x_range=x_range_list,
title="Inbound/Outbound by store",
tools=TOOLS_j,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Units")
j.background_fill_color = "#e6e9ed"
j.background_fill_alpha = 0.5
j.vbar(x=dodge('date_bar', -0.40, range=j.x_range),
top='DFE_Q98',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color="#FBA40A",
legend=value("DFE_Q98"))
j.vbar(x=dodge('date_bar', -0.20, range=j.x_range),
top='OTL',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color="#4292c6",
legend=value("OTL"))
j.vbar(x=dodge('date_bar', 0.00, range=j.x_range),
top='EOH',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color='#a1dab4',
legend=value("EOH"))
j.vbar(x=dodge('date_bar', 0.20, range=j.x_range),
top='BOH',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color="#DC5039",
legend=value("BOH"))
j.xaxis.major_label_orientation = 1
j.legend.border_line_width = 3
j.legend.border_line_color = None
j.legend.border_line_alpha = 0.5
j.title.text_color = "olive"
j.legend.location = "top_left"
j.legend.click_policy = "mute"
#desc.text = " <br > <b> Region:</b> <i> </i> <br /> "
pre.text = " <b>Start date:</b> <i>{}</i> <br /> <b>End date:</b> <i>{}</i> <br /> <b>Time period:</b> <i>{}</i> days <br /> <b> Total Number of Nodes:</b> <i>{}</i> <br /> <b>Policy</b> = <i>{}</i><br /> ".format(
sdate, edate, days, nodes, policy)
#fuction to update data on selection
callback = CustomJS(args=dict(source=source,
original_source=original_source,
location_select_obj=location,
region_select_obj=region,
div=desc,
text_input=text_input),
code="""
var data = source.get('data');
var original_data = original_source.get('data');
var loc = location_select_obj.get('value');
var reg = region_select_obj.get('value');
var line = " <br /> <b> Region:</b>"+ reg + "<br /> <b>Location:</b> " + loc;
var text_input =text_input.get('value');
div.text=line;
for (var key in original_data) {
data[key] = [];
for (var i = 0; i < original_data['location_reference_id'].length; ++i) {
if ((original_data['location_reference_id'][i] === loc) && (original_data['region'][i] === reg) ) {
data[key].push(original_data[key][i]);
} } }
source.trigger('change');
""")
#controls = [location, region]
#for control in controls:
#control.js_on_change("value", callback)
#source.js_on_change("value", callback)
desc.js_on_event('event', callback)
location.js_on_change('value', callback)
region.js_on_change('value', callback)
text_input.js_on_change('value', callback)
#inputs = widgetbox(*controls, sizing_mode="fixed")
#inputs = widgetbox(*controls,width=220,height=500)
inputs = widgetbox(location, region, desc, pre, width=220, height=500)
# controls number of tabs
tab1 = Panel(child=p, title='Backroom OH')
tab2 = Panel(child=s, title='Stockouts')
tab3 = Panel(child=f, title='BOH vs Ideal')
tab4 = Panel(child=g, title='Pog Fit')
tab5 = Panel(child=m, title='Inbound/Outbound')
tab6 = Panel(child=h, title='BOH vs POG')
tab7 = Panel(child=t, title='Fill Rate')
tab8 = Panel(child=j, title='Inventory')
tab9 = Panel(child=w, title='Prop OH in Backroom')
#data table columns to summarize data
columns = [
TableColumn(field="location_reference_id", title="Location"),
TableColumn(field="Backroom_OH",
title="Backroom_OH",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="Outbound",
title="Outbound",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="Inbound",
title="Inbound",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="OTL",
title="OTL",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="DFE_Q98",
title="DFE_Q98",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="BOH",
title="BOH",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="EOH",
title="EOH",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="BOH_OOS",
title="BOH_OOS",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="EOH_OOS",
title="EOH_OOS",
formatter=NumberFormatter(format="0,0"))
]
data_table = DataTable(source=source1, columns=columns, width=1250)
tab10 = Panel(child=data_table, title='Summary Table')
view = Tabs(
tabs=[tab1, tab2, tab5, tab8, tab6, tab3, tab7, tab4, tab9, tab10])
layout_text = column(inputs)
layout1 = row(layout_text, view)
#laying out plot
layout2 = layout(children=[[layout_text, view]],
sizing_mode='scale_height')
#update plots
return layout2
def create_metrics_table(pd_metrics, labels, sample_ids_list):
rank_to_sample_pd = get_rank_to_sample_pd(pd_metrics)
all_sample_ids = sample_ids_list[:]
all_sample_ids.insert(0, '(average over samples)')
presence_metrics = [
c.RECALL, c.PRECISION, c.F1_SCORE, c.TP, c.FP, c.FN, c.JACCARD
]
estimates_metrics = [c.UNIFRAC, c.UNW_UNIFRAC, c.L1NORM, c.BRAY_CURTIS]
alpha_diversity_metics = [c.OTUS, c.SHANNON_DIVERSITY, c.SHANNON_EQUIT]
rank_independent_metrics = [c.UNIFRAC, c.UNW_UNIFRAC]
all_metrics = [presence_metrics, estimates_metrics, alpha_diversity_metics]
presence_metrics_label = 'Presence/absence of taxa'
estimates_metrics_label = 'Abundance estimates'
alpha_diversity_metics = 'Alpha diversity'
all_metrics_labels = [
presence_metrics_label, estimates_metrics_label, alpha_diversity_metics
]
styles = [{
'selector': 'td',
'props': [('width', '77pt')]
}, {
'selector': 'th',
'props': [('width', '77pt'), ('text-align', 'left')]
}, {
'selector': 'th:nth-child(1)',
'props': [('width', '120pt'), ('font-weight', 'normal')]
}, {
'selector':
'',
'props': [('width', 'max-content'), ('width', '-moz-max-content'),
('border-top', '1px solid lightgray'),
('border-spacing', '0px')]
}, {
'selector': 'expand-toggle:checked ~ * .data',
'props': [('background-color', 'white !important')]
}]
styles_hidden_thead = styles + [{
'selector': 'thead',
'props': [('display', 'none')]
}]
d = {
c.RECALL:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.RECALL, c.RECALL),
c.PRECISION:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.PRECISION, c.PRECISION),
c.F1_SCORE:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.F1_SCORE, c.F1_SCORE),
c.TP:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.TP, c.TP),
c.FP:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.FP, c.FP),
c.FN:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.FN, c.FN),
c.JACCARD:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.JACCARD, c.JACCARD),
c.UNIFRAC:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.UNIFRAC, c.UNIFRAC),
c.UNW_UNIFRAC:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.UNW_UNIFRAC, c.UNW_UNIFRAC),
c.L1NORM:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.L1NORM, c.L1NORM),
c.BRAY_CURTIS:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.BRAY_CURTIS, c.BRAY_CURTIS),
c.OTUS:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.OTUS, c.OTUS),
c.SHANNON_DIVERSITY:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.SHANNON_DIVERSITY, c.SHANNON_DIVERSITY),
c.SHANNON_EQUIT:
'<div class="tooltip">{}<span class="tooltiptext">{}</span></div>'.
format(c.SHANNON_EQUIT, c.SHANNON_EQUIT)
}
pattern = re.compile('|'.join(map(re.escape, d)))
def translate(match):
return d[match.group(0)]
rank_to_sample_to_html = defaultdict(list)
for rank in rank_to_sample_pd:
for sample_id in all_sample_ids:
if sample_id in rank_to_sample_pd[rank]:
mydf = rank_to_sample_pd[rank][sample_id]
mydf.index.name = None
html = ''
first_metrics = True
for metrics, metrics_label in zip(all_metrics,
all_metrics_labels):
if rank == 'rank independent':
metrics = rank_independent_metrics
metrics_label = estimates_metrics_label
html += '<p style="margin-bottom: auto"><b>{}</b></p>'.format(
metrics_label)
mydf_metrics = mydf.loc[metrics]
sorted_columns = get_columns(labels,
mydf_metrics.columns.tolist())
mydf_metrics = mydf_metrics.loc[:, sorted_columns]
if first_metrics:
this_style = styles
else:
this_style = styles_hidden_thead
if metrics_label == presence_metrics_label or metrics_label == estimates_metrics_label:
html += mydf_metrics.style.apply(
get_heatmap_colors,
df_metrics=mydf_metrics,
axis=1).set_precision(3).set_table_styles(
this_style).render()
else:
html += mydf_metrics.style.set_precision(
3).set_table_styles(this_style).render()
if rank == 'rank independent':
break
first_metrics = False
html = pattern.sub(translate, html)
rank_to_sample_to_html[rank].append(html)
else:
rank_to_sample_to_html[rank].append("")
mytable1 = Div(text="""<div>{}</div>""".format(
rank_to_sample_to_html[c.ALL_RANKS[0]][0]),
css_classes=['bk-width-auto'])
select_rank = Select(title="Taxonomic rank:",
value=c.ALL_RANKS[0],
options=c.ALL_RANKS + ['rank independent'],
css_classes=['bk-fit-content'])
select_sample = Select(title="Sample:",
value='0',
options=list(
zip(map(str, range(len(all_sample_ids))),
all_sample_ids)),
css_classes=['bk-fit-content'])
source = ColumnDataSource(data=rank_to_sample_to_html)
select_rank_sample_callback = CustomJS(args=dict(source=source),
code="""
mytable1.text = source.data[select_rank.value][select_sample.value];
""")
select_rank.js_on_change('value', select_rank_sample_callback)
select_sample.js_on_change('value', select_rank_sample_callback)
select_rank_sample_callback.args["select_rank"] = select_rank
select_rank_sample_callback.args["select_sample"] = select_sample
select_rank_sample_callback.args["mytable1"] = mytable1
heatmap_legend = '<img src="heatmap_bar.png" /><div style="text-align:left;font-size: 11px;">Worst<span style="float:right;">Best</span><span style="margin-right: 36px;float:right;">Median</span></div>'
heatmap_legend_div = Div(text=heatmap_legend,
style={
"width": "155px",
"margin-bottom": "-10px"
})
# <input type="checkbox" id="expand-toggle" /><label for="expand-toggle" id="expand-btn">Toggle</label>
return select_sample, select_rank, heatmap_legend_div, mytable1
def generate_summary_plot():
#x = [x*0.005 for x in range(0, 200)]
#y = x
x = np.arange(0, 10)
#x = [x*0.005 for x in range(0, 200)]
y = [i*i for i in x]
z = [i*3.0 for i in x]
source = ColumnDataSource(data=dict(x=x, y=y))
source2 = ColumnDataSource(data=dict(x=x, z=z))
print(source)
print(dict(source=source))
summary_fig = figure(x_axis_location='above', plot_width=400, plot_height=400)
line_one = summary_fig.line('x', 'y', source=source, line_width=3, line_alpha=0.6)#, xaxis.axis_label = 'initalized')#,axis.axis_label='title')
line_two = summary_fig.line('x', 'z', source=source2, line_width=3, line_alpha=0.6)#, xaxis.axis_label = 'initalized')#,axis.axis_label='title')
#selector_options=['alpha','beta','gamma']
#selector1=Select(title='X-Axis',value=selector_options[0],options=selector_options, callback = callback)
#summary_fig.xaxis.visible = None
xaxis = LinearAxis(axis_label="Initial x-axis label")
summary_fig.add_layout(xaxis, 'below')
#summary_fig.xaxis.axis_label = 'Time (Years)'
#selector1.on_change('value',callback) #plot.yaxis.axis_label = y_title
#controls = widgetbox([selector1], width=200)
code1 = '''\
object.visible = toggle.active
'''
code2 = '''\
object.visible = radio_button.active
'''
#var f = cb_obj.value
callback2 = CustomJS(args=dict(source=source2), code="""
var data = source.data;
var f = cb_obj.value
x = data['x']
z = data['z']
if (f == "alpha") {
for (i = 0; i < x.length; i++) {
z[i] = Math.pow(x[i], 3.)}
}
else if (f = "beta") {
for (i = 0; i < x.length; i++) {
z[i] = 20}
}
source.trigger('change');
""")
callback3 = CustomJS(args=dict(xaxis=xaxis), code="""
var f = cb_obj.value
xaxis.attributes.axis_label = f
xaxis.trigger('change');
""")
callback4 = CustomJS(args=dict(source=source2), code="""
var data = source.data;
var f = cb_obj.value
x = data['x']
z = data['z']
if (f == "A alpha") {
for (i = 0; i < x.length; i++) {
z[i] = Math.pow(x[i], 3.)}
}
else if (f = "B alpha") {
for (i = 0; i < x.length; i++) {
z[i] = 20}
}
source.trigger('change');
""")
callback1 = CustomJS.from_coffeescript(code=code1, args={})
toggle1 = Toggle(label="Green Box", button_type="success", callback=callback1)
callback1.args = {'toggle': toggle1, 'object': line_one}
#callback2 = CustomJS.from_coffeescript(code=code2, args={})
radio_button_group = RadioButtonGroup(labels=['alpha','beta','gamma'], active=0)# callback=callback2)
radio_button_group.js_on_change('value', callback2)
#print([method for method in dir(radio_button_group)])
#callback2.args = {'radio_button': radio_button_group, 'object': line_two}
#output_file("styling_visible_annotation_with_interaction.html")
selector_options = ['A alpha', 'B beta', 'G gamma']
selector1 = Select(title='X-Axis', value=selector_options[0], options=selector_options)#, callback=callback4)
selector1.js_on_change('value', callback4)
selector2_options = ['Aa alpha', 'Ba beta', 'Ga gamma']
selector2 = Select(title='X-Axis', value=selector_options[0], options=selector2_options)#, callback=callback4)
selector2.js_on_change('value', callback3)
#slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
#slider.js_on_change('value', callback)
controls = widgetbox([toggle1,radio_button_group, selector1, selector2], width=200)
layout = row(controls, summary_fig)
return 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 make_plot(emperor, df, column=None, value=None):
df = df[df['Emperor'] == emperor]
source = ColumnDataSource(df)
select_den = Select(title="Denomination",
value="All",
options=["All", "Aureus", "Denarius"])
select_grd = Select(title="Grade",
value="All",
options=[
'All', 'Fine', 'Good Fine', 'Near VF', 'VF',
'Good VF', 'Near EF', 'EF', 'Superb EF',
'Choice EF', 'FDC'
])
check_attractive = CheckboxGroup(labels=["Attractively Toned"], active=[])
check_cabinet = CheckboxGroup(labels=["Cabinet Toning"], active=[])
check_lusterous = CheckboxGroup(labels=["Lusterous"], active=[])
check_centered = CheckboxGroup(labels=["Well Centered"], active=[])
check_portrait = CheckboxGroup(labels=["Artistic Portrait"], active=[])
# This callback is crucial, otherwise the filter will
# not be triggered when the select changes
callback = CustomJS(args=dict(source=source),
code="""
source.change.emit();
""")
select_den.js_on_change('value', callback)
select_grd.js_on_change('value', callback)
check_attractive.js_on_change('active', callback)
check_cabinet.js_on_change('active', callback)
check_lusterous.js_on_change('active', callback)
check_centered.js_on_change('active', callback)
check_portrait.js_on_change('active', callback)
custom_filter_grd = CustomJSFilter(args=dict(select=select_grd,
source=source),
code='''
var indices = [];
console.log(select.value);
// no select cuts applied
if (select.value == 'All') {
for (var i = 0; i < source.get_length(); i++){
indices.push(true);
}
return indices;
}
// iterate through rows of data source and see if each satisfies some constraint
for (var i = 0; i < source.get_length(); i++){
if (source.data['Grade'][i] == select.value){
indices.push(true);
} else {
indices.push(false)
}
}
return indices;
''')
custom_filter_den = CustomJSFilter(args=dict(select=select_den,
source=source),
code='''
var indices = [];
console.log(select.value);
// no select cuts applied
if (select.value == 'All') {
for (var i = 0; i < source.get_length(); i++){
indices.push(true);
}
return indices;
}
// iterate through rows of data source and see if each satisfies some constraint
for (var i = 0; i < source.get_length(); i++){
if (source.data['Denomination'][i] == select.value){
indices.push(true);
} else {
indices.push(false)
}
}
//console.log(indices)
return indices;
''')
custom_filter_attractive = CustomJSFilter(args=dict(
checkbox=check_attractive, source=source),
code='''
var indices = [];
//console.log(checkbox.active);
if (checkbox.active.includes(0)) {
//console.log('0 on')
//console.log(checkbox.active.includes(0));
for (var i = 0; i < source.get_length(); i++) {
if (source.data['Attractively Toned'][i] == 1) {
indices.push(true);
} else {
indices.push(false)
}
}
} else {
//console.log('0 off')
for (var i = 0; i < source.get_length(); i++) {
indices.push(true);
}
}
return indices;
''')
custom_filter_cabinet = CustomJSFilter(args=dict(checkbox=check_cabinet,
source=source),
code='''
var indices = [];
//console.log(checkbox.active);
if (checkbox.active.includes(0)) {
//console.log('0 on')
//console.log(checkbox.active.includes(0));
for (var i = 0; i < source.get_length(); i++) {
if (source.data['Cabinet Toning'][i] == 1) {
indices.push(true);
} else {
indices.push(false)
}
}
} else {
//console.log('0 off')
for (var i = 0; i < source.get_length(); i++) {
indices.push(true);
}
}
return indices;
''')
custom_filter_lusterous = CustomJSFilter(args=dict(
checkbox=check_lusterous, source=source),
code='''
var indices = [];
//console.log(checkbox.active);
if (checkbox.active.includes(0)) {
//console.log('0 on')
//console.log(checkbox.active.includes(0));
for (var i = 0; i < source.get_length(); i++) {
if (source.data['Lusterous'][i] == 1) {
indices.push(true);
} else {
indices.push(false)
}
}
} else {
//console.log('0 off')
for (var i = 0; i < source.get_length(); i++) {
indices.push(true);
}
}
return indices;
''')
custom_filter_centered = CustomJSFilter(args=dict(checkbox=check_centered,
source=source),
code='''
var indices = [];
//console.log(checkbox.active);
if (checkbox.active.includes(0)) {
//console.log('0 on')
//console.log(checkbox.active.includes(0));
for (var i = 0; i < source.get_length(); i++) {
if (source.data['Cabinet Toning'][i] == 1) {
indices.push(true);
} else {
indices.push(false)
}
}
} else {
//console.log('0 off')
for (var i = 0; i < source.get_length(); i++) {
indices.push(true);
}
}
return indices;
''')
custom_filter_portrait = CustomJSFilter(args=dict(checkbox=check_portrait,
source=source),
code='''
var indices = [];
//console.log(checkbox.active);
if (checkbox.active.includes(0)) {
//console.log('0 on')
//console.log(checkbox.active.includes(0));
for (var i = 0; i < source.get_length(); i++) {
if (source.data['Quality Portrait'][i] == 1) {
indices.push(true);
} else {
indices.push(false)
}
}
} else {
//console.log('0 off')
for (var i = 0; i < source.get_length(); i++) {
indices.push(true);
}
}
return indices;
''')
view = CDSView(source=source,
filters=[
custom_filter_grd, custom_filter_den,
custom_filter_attractive, custom_filter_cabinet,
custom_filter_lusterous, custom_filter_centered,
custom_filter_portrait
])
TOOLS = ["pan, wheel_zoom, box_zoom, reset, save"]
TOOLTIPS = [("Auction", "@AuctionID"), ("Lot", "@LotNumber"),
("Emperor", "@Emperor"), ("RIC Number", "@RIC"),
("Estimate [USD]", "@Estimate"), ("Sale [USD]", "@Sale")]
plot = figure(
title='CNG Auctions through 2019 for Coins of Emperor ' + emperor,
plot_width=500,
plot_height=300,
tools=TOOLS,
tooltips=TOOLTIPS,
x_range=Range1d(start=20, end=80000, bounds=(None, None)),
y_range=[
'Fine', 'Good Fine', 'Near VF', 'VF', 'Good VF', 'Near EF', 'EF',
'Superb EF', 'Choice EF', 'FDC'
],
x_axis_type='log')
color_mapper = CategoricalColorMapper(factors=['Aureus', 'Denarius'],
palette=['#FFD700', '#C0C0C0'])
plot.circle(x='Sale',
y=jitter('Grade', 0.4, range=plot.y_range),
source=source,
view=view,
fill_alpha=0.8,
size=5,
legend='data',
line_color=None,
color={
'field': 'Denomination',
'transform': color_mapper
},
hover_color='red')
plot.xaxis.axis_label = "Sale Price (USD)"
plot.yaxis.axis_label = "Grade"
plot.xaxis.formatter = BasicTickFormatter(use_scientific=False)
plot.xaxis.formatter = NumeralTickFormatter(format='$0,0')
plot.legend.visible = False
return row(
plot,
widgetbox([
select_den, select_grd, check_attractive, check_cabinet,
check_lusterous, check_centered, check_portrait
]))
new_val = predict_data['proba']
console.log('Success:', predict_data)
data['new_y'][0] = new_val.toFixed(1)
data['y_perc'][0] = new_val.toFixed(1)+"%"
source.change.emit()
})
.catch((err) => {
console.log('Err', err)
});
""")
# Initiate callback with any value changes
age_radio.js_on_change('active', callback)
sex_radio.js_on_change('active', callback)
country_dropdown.js_on_change('value', callback)
edu_dropdown.js_on_change('value', callback)
n_slider.js_on_change('value', callback)
e_slider.js_on_change('value', callback)
o_slider.js_on_change('value', callback)
c_slider.js_on_change('value', callback)
a_slider.js_on_change('value', callback)
s_slider.js_on_change('value', callback)
i_slider.js_on_change('value', callback)
# List of all sliders
sliders = [
n_slider,
e_slider,
o_slider,
c_slider,
p.xaxis.major_label_text_font_size = '15pt'
p.legend.location = "bottom_right"
p.legend.orientation = "vertical"
p.legend.border_line_color = "black"
### add the select category option ###
callback = CustomJS(args={
'source': source,
'data': data
},
code="""source.data = data[cb_obj.value]""")
color_select = Select(title="Select Category",
value="Style",
options=["Style", "Category", "Face or body"])
color_select.js_on_change('value', callback)
### add hover and tap tools ###
hover = HoverTool(
tooltips=[('Art Title', '@title'), ('Category',
'@info'), ('Positive',
'@pos'), ('Negative',
'@neg')])
taptool = TapTool()
tap_code = """
var selected= source.selected['1d'].indices;
// console.log('tap, you selected:', data_source['Style'][selected])
var data = source.data,
selected = source.selected.indices,
def _plot(self, runs):
"""Create table and plot that reacts to selection of cells by updating the plotted data to visualize correlation.
Parameters
----------
runs: List[ConfiguratorRun]
list with runs (budgets) to be compared
"""
df = self._get_table(runs)
# Create CDS from pandas dataframe
budget_names = list(df.columns.values)
data = dict(df[budget_names])
data["Budget"] = df.index.tolist()
table_source = ColumnDataSource(data)
# Create bokeh-datatable
columns = [
TableColumn(
field='Budget', title="Budget", sortable=False, width=20)
] + [
TableColumn(field=header,
title=header,
default_sort='descending',
width=10) for header in budget_names
]
bokeh_table = DataTable(source=table_source,
columns=columns,
index_position=None,
sortable=False,
height=20 + 30 * len(data["Budget"]))
# Create CDS for scatter-plot
all_configs = set([
a for b in
[run.original_runhistory.get_all_configs() for run in runs]
for a in b
])
data = {
os.path.basename(run.folder).replace('_', ' '): [
run.original_runhistory.get_cost(c) if c in # TODO
run.original_runhistory.get_all_configs() else None
for c in all_configs
]
for run in runs
}
data['x'] = []
data['y'] = []
# Default scatter should be lowest vs highest:
for x, y in zip(data[budget_names[0]], data[budget_names[-1]]):
if x is not None and y is not None:
data['x'].append(x)
data['y'].append(y)
with warnings.catch_warnings(record=True) as list_of_warnings:
# Catch unmatching column lengths warning
warnings.simplefilter('always')
scatter_source = ColumnDataSource(data=data)
for w in list_of_warnings:
self.logger.debug(
"During budget correlation a %s was raised: %s",
str(w.category), w.message)
# Create figure and dynamically updating plot (linked with table)
min_val = min([
min([v for v in val if v]) for val in data.values() if len(val) > 0
])
max_val = max([
max([v for v in val if v]) for val in data.values() if len(val) > 0
])
padding = (
max_val - min_val
) / 10 # Small padding to border (fraction of total intervall)
min_val -= padding
max_val += padding
p = figure(plot_width=400,
plot_height=400,
match_aspect=True,
y_range=Range1d(start=min_val,
end=max_val,
bounds=(min_val, max_val)),
x_range=Range1d(start=min_val,
end=max_val,
bounds=(min_val, max_val)),
x_axis_label=budget_names[0],
y_axis_label=budget_names[-1])
p.circle(
x='x',
y='y',
#x=jitter('x', 0.1), y=jitter('y', 0.1),
source=scatter_source,
size=5,
color="navy",
alpha=0.5)
code_budgets = 'var budgets = ' + str(
budget_names) + '; console.log(budgets);'
code_try = 'try {'
code_get_selected_cell = """
// This first part only extracts selected row and column!
var grid = document.getElementsByClassName('grid-canvas')[0].children;
var row = '';
var col = '';
for (var i=0,max=grid.length;i<max;i++){
if (grid[i].outerHTML.includes('active')){
row=i;
for (var j=0,jmax=grid[i].children.length;j<jmax;j++){
if(grid[i].children[j].outerHTML.includes('active')){col=j}
}
}
}
col = col - 1;
console.log('row', row, budgets[row]);
console.log('col', col, budgets[col]);
table_source.selected.indices = []; // Reset, so gets triggered again when clicked again
"""
code_selected = """
row = budgets.indexOf(select_x.value);
col = budgets.indexOf(select_y.value);
"""
code_update_selection_values = """
select_x.value = budgets[row];
select_y.value = budgets[col];
"""
code_update_plot = """
// This is the actual updating of the plot
if (row => 0 && col > 0) {
// Copy relevant arrays
var new_x = scatter_source.data[budgets[row]].slice();
var new_y = scatter_source.data[budgets[col]].slice();
// Remove all pairs where one value is null
while ((next_null = new_x.indexOf(null)) > -1) {
new_x.splice(next_null, 1);
new_y.splice(next_null, 1);
}
while ((next_null = new_y.indexOf(null)) > -1) {
new_x.splice(next_null, 1);
new_y.splice(next_null, 1);
}
// Assign new data to the plotted columns
scatter_source.data['x'] = new_x;
scatter_source.data['y'] = new_y;
scatter_source.change.emit();
// Update axis-labels
xaxis.attributes.axis_label = budgets[row];
yaxis.attributes.axis_label = budgets[col];
// Update ranges
var min = Math.min(...[Math.min(...new_x), Math.min(...new_y)])
max = Math.max(...[Math.max(...new_x), Math.max(...new_y)]);
var padding = (max - min) / 10;
console.log(min, max, padding);
xr.start = min - padding;
yr.start = min - padding;
xr.end = max + padding;
yr.end = max + padding;
}
"""
code_catch = """
} catch(err) {
console.log(err.message);
}
"""
code_selected = code_budgets + code_try + code_selected + code_update_plot + code_catch
select_x = Select(title="X-axis:",
value=budget_names[0],
options=budget_names)
select_y = Select(title="Y-axis:",
value=budget_names[-1],
options=budget_names)
callback_select = CustomJS(args=dict(
scatter_source=scatter_source,
select_x=select_x,
select_y=select_y,
xaxis=p.xaxis[0],
yaxis=p.yaxis[0],
xr=p.x_range,
yr=p.y_range,
),
code=code_selected)
select_x.js_on_change('value', callback_select)
select_y.js_on_change('value', callback_select)
code_table_cell = code_budgets + code_try + code_get_selected_cell + code_update_selection_values + code_update_plot + code_catch
callback_table_cell = CustomJS(args=dict(
table_source=table_source,
scatter_source=scatter_source,
select_x=select_x,
select_y=select_y,
xaxis=p.xaxis[0],
yaxis=p.yaxis[0],
xr=p.x_range,
yr=p.y_range,
),
code=code_table_cell)
table_source.selected.js_on_change('indices', callback_table_cell)
layout = column(bokeh_table, row(p, column(select_x, select_y)))
return layout
'condition_filter': condition_filter,
'cog_class_filter': cog_class_filter,
'condition_view': condition_view,
'cog_desc_view': cog_desc_view,
'selection': selection,
'cog_class_treemap': class_treemap
}
selection_callback = CustomJS(args=args,
code=selection_cb + class_click_cb +
desc_click_cb)
class_click_callback = CustomJS(args=args,
code=class_click_cb + desc_click_cb +
selection_cb)
desc_click_callback = CustomJS(args=args,
code=desc_click_cb + class_click_cb +
selection_cb)
# Assign the callbacks to the interactions
class_click_event = class_treemap.select(type=TapTool)
desc_click_event = desc_treemap.select(type=TapTool)
class_click_event.callback = class_click_callback
desc_click_event.callback = desc_click_callback
selection.js_on_change('value', selection_callback)
row = bokeh.layouts.row(class_treemap, desc_treemap, gene_treemap)
col = bokeh.layouts.column(selection, row)
lay = bokeh.layouts.row(col)
bokeh.io.save(lay)
# %%
def plot_chart_bokeh(processed, dataCollection, keys, outputfilename):
stock = processed['data']
# Define constants
W_PLOT = 1000
H_PLOT = 360
TOOLS = 'pan,wheel_zoom,reset'
VBAR_WIDTH = 1 * 12 * 60 * 60 * 1000 # one day in ms
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
# ==========================================================================
# =================== PLOT CANDLE STICK GRAPH ====================
# ==========================================================================
p1 = figure(plot_width=W_PLOT,
plot_height=H_PLOT,
tools=TOOLS,
toolbar_location='right')
inc = stock.data['Close'] >= stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
# limit = stock.data['ZC_d2/dt2'] > 10
# limit = stock.data['ZC_d/dt'] > 0
# view_inc = CDSView(source=stock, filters=[BooleanFilter(inc), BooleanFilter(limit)])
# view_dec = CDSView(source=stock, filters=[BooleanFilter(dec), BooleanFilter(limit)])
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# # map dataframe indices to date strings and use as label overrides
p1.y_range.start = 0.9 * min(stock.data['Low'])
p1.y_range.end = 1.1 * max(stock.data['High'])
p1.segment(x0='Date',
x1='Date',
y0='Low',
y1='High',
color=GREEN,
source=stock,
view=view_inc)
p1.segment(x0='Date',
x1='Date',
y0='Low',
y1='High',
color=RED,
source=stock,
view=view_dec)
vb1 = p1.vbar(x='Date',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color='forestgreen',
fill_alpha=1,
line_color='forestgreen',
source=stock,
view=view_inc,
name="price")
vb2 = p1.vbar(x='Date',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color='orangered',
fill_alpha=1,
line_color='orangered',
source=stock,
view=view_dec,
name="price")
# Bollinger band plot
patch1 = p1.varea(x='Date',
y1='lowerband',
y2='upperband',
source=stock,
fill_alpha=0.05,
fill_color='dodgerblue')
patch_line1 = p1.line(x='Date',
y='lowerband',
source=stock,
line_color='blue',
line_alpha=0.4)
patch_line2 = p1.line(x='Date',
y='middleband',
source=stock,
line_color='grey',
line_alpha=0.8,
line_dash='dotdash')
patch_line3 = p1.line(x='Date',
y='upperband',
source=stock,
line_color='blue',
line_alpha=0.4)
# ZC Line plot
zc_7 = p1.line(x='Date',
y='ma7',
source=stock,
line_color='crimson',
line_alpha=0.4)
zc_26 = p1.line(x='Date',
y='ma26',
source=stock,
line_color='darkslateblue',
line_alpha=0.4)
# # Resistance plots
# r1 = p1.line(x='Date', y='r1', source=stock, line_color='forestgreen', line_dash='dotdash', line_alpha=0.6)
# r2 = p1.line(x='Date', y='r2', source=stock, line_color='forestgreen', line_dash='dotdash', line_alpha=0.8)
# r3 = p1.line(x='Date', y='r3', source=stock, line_color='forestgreen', line_dash='dotdash', line_alpha=1.0)
# # Support plots
# s1 = p1.line(x='Date', y='s1', source=stock, line_color='crimson', line_dash='dotdash', line_alpha=0.6)
# s2 = p1.line(x='Date', y='s2', source=stock, line_color='crimson', line_dash='dotdash', line_alpha=0.8)
# s3 = p1.line(x='Date', y='s3', source=stock, line_color='crimson', line_dash='dotdash', line_alpha=1.0)
# Extrema plots
# minima = p1.inverted_triangle(x='Date', y='minima', source=stock, size=5, color="goldenrod", alpha=0.5)
# maxima = p1.triangle(x='Date', y='maxima', source=stock, size=5, color="teal", alpha=0.5)
# minima = p1.circle(
# x='Date', y='minima', source=stock, size=10,
# fill_color="grey", hover_fill_color="firebrick",
# fill_alpha=0.2, hover_alpha=0.8, hover_line_color="white")
# maxima = p1.triangle(
# x='Date', y='maxima', source=stock,
# size=10, fill_color="grey", fill_alpha=0.2,
# hover_fill_color="firebrick", hover_alpha=0.8, hover_line_color="white")
# Volume plot
# Setting the second y axis range name and range
p1.extra_y_ranges = {
"vol_axis": Range1d(start=0, end=max(stock.data['Volume']) * 4)
}
# Adding the second axis to the plot.
p1.add_layout(LinearAxis(y_range_name="vol_axis", visible=False), 'right')
vol_inc = p1.vbar(x="Date",
top="Volume",
bottom=0,
width=int(VBAR_WIDTH * 2),
fill_color=GREEN,
fill_alpha=0.1,
line_color=GREEN,
line_alpha=0.2,
source=stock,
view=view_inc,
y_range_name="vol_axis")
vol_dec = p1.vbar(x="Date",
top="Volume",
bottom=0,
width=int(VBAR_WIDTH * 2),
fill_color=RED,
fill_alpha=0.1,
line_color=RED,
line_alpha=0.2,
source=stock,
view=view_dec,
y_range_name="vol_axis")
legend = Legend(items=[
LegendItem(
label="All",
renderers=[
patch1,
patch_line1,
patch_line2,
patch_line3,
vol_inc,
vol_dec,
zc_7,
zc_26,
# s1, s2, s3,r1, r2, r3,
# minima, maxima
],
index=0),
LegendItem(label="BB",
renderers=[patch1, patch_line1, patch_line2, patch_line3],
index=1),
LegendItem(label="Volume", renderers=[vol_inc, vol_dec], index=2),
LegendItem(label="ZC", renderers=[zc_7, zc_26], index=3),
LegendItem(label="MA7", renderers=[zc_7], index=4),
LegendItem(label="MA26", renderers=[zc_26], index=5),
# LegendItem(label="Support", renderers=[s1, s2, s3], index=6),
# LegendItem(label="Resistance", renderers=[r1, r2, r3], index=7),
# LegendItem(label="Extrema", renderers=[minima, maxima], index=8)
])
p1.add_layout(legend)
p1.legend.location = "top_left"
p1.legend.border_line_alpha = 0
p1.legend.background_fill_alpha = 0
p1.legend.click_policy = "hide"
p1.legend.orientation = "horizontal"
# p1.add_layout(Title(text="Stock price", align="left"), "left")
p1.yaxis.axis_label = 'Stock price'
p1.yaxis.formatter = NumeralTickFormatter(format='0.00')
p1.x_range.range_padding = 0.05
p1.xaxis.ticker.desired_num_ticks = 40
p1.xaxis.major_label_orientation = 3.14 / 4
p1.xaxis.visible = False
p1.xgrid.grid_line_color = None
p1.ygrid.grid_line_color = None
# Select specific tool for the plot
p1.add_tools(
HoverTool(
tooltips=[("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{0,0.00}"), ("Close", "@Close{0,0.00}"),
("Volume", "@Volume{(0.00 a)}")],
formatters={"@Date": 'datetime'},
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline',
renderers=[vb1, vb2]))
# ==========================================================================
# =================== PLOT STOCH / RSI GRAPH =================
# ==========================================================================
p2 = figure(plot_width=W_PLOT,
plot_height=int(H_PLOT / 4),
tools=TOOLS,
toolbar_location='above',
x_range=p1.x_range,
x_axis_type='datetime') # , y_range=(-20, 120)
stoch_k = p2.line(x='Date',
y='slowk',
source=stock,
line_color='royalblue',
alpha=0.8,
muted_alpha=0.2)
stoch_d = p2.line(x='Date',
y='slowd',
source=stock,
line_color='tomato',
alpha=0.8,
muted_alpha=0.2)
rsi = p2.line(x='Date',
y='rsi',
source=stock,
line_color='gray',
alpha=0.8,
muted_alpha=0.2)
mid_box = BoxAnnotation(bottom=20,
top=80,
fill_alpha=0.2,
fill_color='palegreen',
line_color='lightcoral',
line_alpha=0.4,
line_dash='dashed')
# candle = p2.line(x='Date', y='candle', source=stock, line_color='royalblue', alpha=0.8, muted_alpha=0.2)
# mid_box = BoxAnnotation(bottom=-300, top=300, fill_alpha=0.2, fill_color='palegreen', line_color='lightcoral', line_alpha=0.4, line_dash='dashed')
legend = Legend(items=[
LegendItem(label="Stoch", renderers=[stoch_k, stoch_d], index=0),
LegendItem(label="RSI", renderers=[rsi], index=1)
# LegendItem(label="Candle", renderers=[candle], index=1)
])
p2.add_layout(legend)
p2.add_layout(mid_box)
# p2.add_layout(lower)
zero = Span(location=0,
dimension='width',
line_color='seagreen',
line_dash='solid',
line_width=0.8)
p2.add_layout(zero)
p2.yaxis.axis_label = 'Stochastic / RSI'
p2.x_range.range_padding = 0.05
# p2.toolbar.autohide = True
p2.xaxis.visible = False
p2.legend.location = "top_left"
p2.legend.border_line_alpha = 0
p2.legend.background_fill_alpha = 0
p2.legend.click_policy = "mute"
p2.xgrid.grid_line_color = None
p2.ygrid.grid_line_color = None
# ==========================================================================
# =================== Plot MACD ====================
# ==========================================================================
y_limit = abs(max(stock.data['macd_hist'], key=abs))
y2_limit = abs(max(stock.data['macd_d/dt'], key=abs))
p3 = figure(plot_width=W_PLOT,
plot_height=int(H_PLOT / 2.5),
tools=TOOLS,
toolbar_location='above',
x_range=p1.x_range,
x_axis_type='datetime',
y_range=(-y_limit, y_limit))
mapper = LinearColorMapper(palette=Viridis256)
macd_line = p3.line(x='Date',
y='macd_hist',
source=stock,
line_color='darkgreen',
alpha=0.8,
muted_alpha=0.2)
# macd_hist = p3.vbar_stack(['macd'], x='Date', source=stock, width=int(VBAR_WIDTH * 2), fill_color={'field':'macd', 'transform': mapper})
mid_box = BoxAnnotation(bottom=-0.5,
top=0.5,
fill_alpha=0.2,
fill_color='blanchedalmond',
line_color='grey',
line_alpha=0.4,
line_dash='dashed')
zero = Span(location=0,
dimension='width',
line_color='seagreen',
line_dash='solid',
line_width=0.8)
p3.add_layout(zero)
p3.add_layout(mid_box)
# Setting the second y axis range name and range
p3.extra_y_ranges = {
"extra_y_axis": Range1d(start=-y2_limit, end=y2_limit)
}
# Adding the second axis to the plot.
p3.add_layout(LinearAxis(y_range_name="extra_y_axis"), 'right')
macd_v = p3.line(x='Date',
y='macd_d/dt',
source=stock,
line_color='dodgerblue',
line_dash='solid',
alpha=0.8,
muted_alpha=0.2,
y_range_name="extra_y_axis")
macd_acc = p3.line(x='Date',
y='macd_d2/dt2',
source=stock,
line_color='tomato',
line_dash='dotdash',
alpha=0.8,
muted_alpha=0.2,
y_range_name="extra_y_axis")
legend = Legend(items=[
LegendItem(label="MACD", renderers=[macd_line], index=0),
LegendItem(label="MACD-v", renderers=[macd_v], index=1),
LegendItem(label="MACD-a", renderers=[macd_acc], index=2)
])
p3.add_layout(legend)
p3.legend.location = "top_left"
p3.legend.border_line_alpha = 0
p3.legend.background_fill_alpha = 0
p3.legend.click_policy = "mute"
p3.legend.orientation = "horizontal"
# p3.add_layout(Title(text="MACD", align="center"), "left")
p3.yaxis.axis_label = 'MACD'
p3.x_range.range_padding = 0.05
p3.xaxis.visible = False
p3.xaxis.ticker.desired_num_ticks = 40
p3.xaxis.major_label_orientation = 3.14 / 4
p3.toolbar.autohide = True
p3.xgrid.grid_line_color = None
p3.ygrid.grid_line_color = None
# ==========================================================================
# =================== Plot ZC ====================
# ==========================================================================
y_limit = abs(max(stock.data['ZC'], key=abs))
y2_limit = abs(max(stock.data['ZC_d/dt'], key=abs))
# y_limit = abs(max(stock.data['slowk'], key=abs))
# y2_limit = abs(max(stock.data['slowk_d/dt'], key=abs))
p4 = figure(plot_width=W_PLOT,
plot_height=int(H_PLOT / 3),
tools=TOOLS,
toolbar_location='above',
x_range=p1.x_range,
x_axis_type='datetime',
y_range=(-y_limit, y_limit))
p4.xaxis.formatter = DatetimeTickFormatter(
hours=["%d.%m.%y"],
days=["%d.%m.%y"],
months=["%d.%m.%y"],
years=["%d.%m.%y"],
)
# macd_v = p4.line(x='Date', y='macd_d/dt', source=stock, line_color='royalblue', alpha=0.8, muted_alpha=0.2)
# macd_acc = p4.line(x='Date', y='macd_d2/dt2', source=stock, line_color='tomato', alpha=0.8, muted_alpha=0.2)
# ad = p4.line(x='Date', y='ck_AD', source=stock, line_color='royalblue', alpha=0.8, muted_alpha=0.2)
# adosc = p4.line(x='Date', y='ck_ADOSC', source=stock, line_color='tomato', alpha=0.8, muted_alpha=0.2)
# obv = p4.line(x='Date', y='OBV', source=stock, line_color='darkgreen', alpha=0.8, muted_alpha=0.2)
# Setting the second y axis range name and range
p4.extra_y_ranges = {
"extra_y_axis": Range1d(start=-y2_limit, end=y2_limit)
}
# Adding the second axis to the plot.
p4.add_layout(LinearAxis(y_range_name="extra_y_axis"), 'right')
zc = p4.line(x='Date',
y='ZC',
source=stock,
line_color='darkgreen',
alpha=0.8,
muted_alpha=0.2)
zc_v = p4.line(x='Date',
y='ZC_d/dt',
source=stock,
line_color='dodgerblue',
line_dash='dotdash',
alpha=0.8,
muted_alpha=0.2,
y_range_name="extra_y_axis")
zc_a = p4.line(x='Date',
y='ZC_d2/dt2',
source=stock,
line_color='tomato',
line_dash='dotdash',
alpha=0.8,
muted_alpha=0.2,
y_range_name="extra_y_axis")
# slowk = p4.line(x='Date', y='slowk', source=stock, line_color='darkgreen', alpha=0.8, muted_alpha=0.2)
# slowk_v = p4.line(x='Date', y='slowk_d/dt', source=stock, line_color='dodgerblue', line_dash='dotdash', alpha=0.8, muted_alpha=0.2, y_range_name="extra_y_axis")
# slowk_a = p4.line(x='Date', y='slowk_d2/dt2', source=stock, line_color='tomato', line_dash='dotdash', alpha=0.8, muted_alpha=0.2, y_range_name="extra_y_axis")
mid_box = BoxAnnotation(bottom=-0.5,
top=0.5,
fill_alpha=0.2,
fill_color='blanchedalmond',
line_color='grey',
line_alpha=0.4,
line_dash='dashed')
zero = Span(location=0,
dimension='width',
line_color='seagreen',
line_dash='solid',
line_width=0.8)
p4.add_layout(zero)
p4.add_layout(mid_box)
# p4.yaxis.axis_label = 'MACD v/acc'
legend = Legend(items=[
LegendItem(label="ZC", renderers=[zc], index=0),
LegendItem(label="ZC-v", renderers=[zc_v], index=1),
LegendItem(label="ZC-a", renderers=[zc_a], index=2),
# LegendItem(label="slowk", renderers=[slowk], index=0),
# LegendItem(label="slowk-v", renderers=[slowk_v], index=1),
# LegendItem(label="slowk-a", renderers=[slowk_a], index=2)
])
p4.add_layout(legend)
p4.legend.location = "top_left"
p4.legend.border_line_alpha = 0
p4.legend.background_fill_alpha = 0
p4.legend.click_policy = "mute"
p4.legend.orientation = "horizontal"
p4.x_range.range_padding = 0.05
p4.xaxis.ticker.desired_num_ticks = 40
p4.xaxis.major_label_orientation = 3.14 / 4
p4.toolbar.autohide = True
p4.xgrid.grid_line_color = None
p4.ygrid.grid_line_color = None
addSpans([p1, p2, p3, p4])
columns = [
TableColumn(field="Date",
title="Date",
formatter=DateFormatter(format='%d.%b')),
# TableColumn(field="Open", title="Open", formatter=NumberFormatter(format='0.00')),
# TableColumn(field="Close", title="Close", formatter=NumberFormatter(format='0.00')),
TableColumn(field="ZC",
title="ZC",
formatter=NumberFormatter(format='0.000',
text_align='right')),
TableColumn(field="ZC_d/dt",
title="ZC-v",
formatter=NumberFormatter(format='0.000',
text_align='right')),
TableColumn(field="macd_hist",
title="MACD",
formatter=NumberFormatter(format='0.000',
text_align='right')),
TableColumn(field="macd_d/dt",
title="MACD-v",
formatter=NumberFormatter(format='0.000',
text_align='right')),
# TableColumn(field="macd_d2/dt2", title="MACD-a", formatter=NumberFormatter(format='0.000')),
TableColumn(field="stoch",
title="STOCH",
formatter=NumberFormatter(format='0.0',
text_align='right')),
TableColumn(field="stoch-v",
title="STOCH-v",
formatter=NumberFormatter(format='0.0',
text_align='right')),
# TableColumn(field="slowk_d/dt", title="slowk-v", formatter=NumberFormatter(format='0.000')),
# TableColumn(field="slowk_d2/dt2", title="slowk-a", formatter=NumberFormatter(format='0.000')),
]
data_table = DataTable(source=stock,
columns=columns,
width=int(W_PLOT / 3),
height=int(H_PLOT * 2.2),
index_position=None,
width_policy='min')
# ==========================================================================
# =================== SELECT WIDGET ====================
# ==========================================================================
callback_select_main = """
var d0 = s0.data;
var symbol = cb_obj.value.split(" ")[0]
var data_all = dataCollection[symbol]
var data = data_all.data.data;
/// Iterate over keys in new data and reassign old data with new data
for (const key of Object.keys(data)) {
d0[key] = []
d0[key] = data[key]
}
s0.change.emit()
/// Update y-axes range
plot.y_range.have_updated_interactively = true
plot.y_range.start = 0.9 * Math.min(...data['Low'])
plot.y_range.end = 1.1 * Math.max(...data['High'])
plot.extra_y_ranges['vol_axis'].have_updated_interactively = true
plot.extra_y_ranges['vol_axis'].start = 0
plot.extra_y_ranges['vol_axis'].end = Math.max(...data['Volume']) * 4
"""
callback_select_va = """
var symbol = cb_obj.value.split(" ")[0]
var data_all = dataCollection[symbol]
var data = data_all.data.data;
var y_limit = Math.max.apply(null, data[param_main].map(Math.abs));
var y_extra_limit = Math.max.apply(null, data[param_extra].map(Math.abs));
/// Update y-axes range
plot.y_range.have_updated_interactively = true
plot.y_range.start = -y_limit
plot.y_range.end = y_limit
plot.extra_y_ranges['extra_y_axis'].have_updated_interactively = true
plot.extra_y_ranges['extra_y_axis'].start = -y_extra_limit
plot.extra_y_ranges['extra_y_axis'].end = y_extra_limit
"""
selecthandler_main = CustomJS(args={
's0': stock,
'dataCollection': dataCollection,
'plot': p1
},
code=callback_select_main)
selecthandler_p3 = CustomJS(args={
'dataCollection': dataCollection,
'plot': p3,
'param_main': 'macd_hist',
'param_extra': 'macd_d/dt'
},
code=callback_select_va)
selecthandler_p4 = CustomJS(args={
'dataCollection': dataCollection,
'plot': p4,
'param_main': 'ZC',
'param_extra': 'ZC_d/dt'
},
code=callback_select_va)
# selecthandler_p4 = CustomJS(args={'dataCollection':dataCollection, 'plot':p4, 'param_main': 'slowk', 'param_extra': 'slowk_d/dt'}, code = callback_select_va)
select = Select(title="Select:", value=keys[0], options=keys)
select.js_on_change('value', selecthandler_main)
select.js_on_change('value', selecthandler_p3)
select.js_on_change('value', selecthandler_p4)
# [cleanDate(x, stock.data) for x in [p1, p2, p3, p4]]
# show the results
gp1 = gridplot([select, data_table],
ncols=1,
plot_width=150,
toolbar_options=dict(autohide=True))
gp2 = gridplot([p1, p2, p3, p4],
ncols=1,
sizing_mode='scale_width',
toolbar_location='right')
output_file(outputfilename + '.html')
show(row(gp1, gp2))
# show(gp2)
return True
'Deadlift': 'deadlift',
}
select = Select(title="Movement",
value="Deadlift",
options=list(mvmt_name_mapper.keys()),
width=200)
slider = Slider(start=1, end=10, value=1, step=1, title="Reps", width=200)
select.js_on_change(
"value",
CustomJS(args={
'p1_cds': cds_pr_cal['Y'][1],
'cds_pr_cal': cds_pr_cal,
'slider': slider,
'mvmt_name_mapper': mvmt_name_mapper
},
code="""
const select_value = mvmt_name_mapper[cb_obj.value]
p1_cds.data = cds_pr_cal[select_value][slider.value].data
p1_cds.change.emit()
"""))
slider.js_on_change(
'value',
CustomJS(args={
'p1_cds': cds_pr_cal['Y'][1],
'cds_pr_cal': cds_pr_cal,
'select': select,
'mvmt_name_mapper': mvmt_name_mapper
},
//Toggle glyph visibility based on checkbox status
p0.visible = cb_obj.active.includes(0);
p1.visible = cb_obj.active.includes(1);
p2.visible = cb_obj.active.includes(2);
""")
click_reset_tool = CustomJS(args=dict(p=plot),
code="""
p.toolbar.tools[""" + util.reset_tool_index(g.TOOLS3) +
"""].trigger('do');
""")
device_slc.on_change('value', device_slc_callback)
device_slc.js_on_change(
'value',
CustomJS(args=dict(btn=clear_everything_btn),
code="""
document.getElementById('modelid_' + btn.id).click();
"""))
device_conn_btn.on_click(device_conn_btn_callback)
conn_status.js_on_change(
'value',
CustomJS(args=dict(btn=device_conn_btn),
code="""
console.log('This application is connected to ESP device: ' + cb_obj.value);
if(cb_obj.value) {
btn.label = "Disconnect from device";
btn.button_type = "warning";
} else {
stock_code_input, stock_name_input, stock_selector
]
btngroups = [cp_btnGroup, mb_btnGroup]
ssgroups = [stock_code_input, stock_name_input]
slidergroups = [mb_slider, cp_slider, date_range_slider]
for control in slidergroups:
control.callback_policy = 'mouseup'
control.callback = query_loading_spinning
control.on_change('value', lambda attr, old, new: slider_latency())
for control in ssgroups:
control.on_change('value', lambda attr, old, new: update())
control.js_on_change('value', query_loading_spinning)
for control in btngroups:
control.on_change('active', lambda attr, old, new: update())
control.js_on_change('active', query_loading_spinning)
stock_selector.on_change('value', lambda attr, old, new: select_stocks())
stock_selector.js_on_change('value', plot_loading_spinning)
update()
inputs = Column(*controls, width=320, height=600)
inputs.sizing_mode = "fixed"
l = layout([[desc], [inputs, p]], sizing_mode="stretch_both")
curdoc().add_root(l)
curdoc().title = "An Interactive Explorer for Stock Data"
def get_x_eval_selectors_list(result, active_list, x_eval):
# Returns a list of selectors. The selectors are sliders for numerical values and dropdown menus
# ("Select" object) for categorical values. The selectors are interactive with callbacks everytime
# a changed by the user
global x_eval_selectors_values
bounds = result.space.bounds # Used for defining what values can be selected
x_eval_selectors = []
n = 0 # Index of the plots. Example: If only parameter 3 and 5 is being plotted
# the selectors for these parameters still have index n = 0 and n= 1.
for i in active_list: # Only get selecters that is going to be shown in GUI
if isinstance(result.space.dimensions[i], Categorical): # Categorical
cats = list(result.space.dimensions[i].categories) # Categories
# Create a "Select" object which is a type of dropdown menu
# This object gets a title equal to the parameter number, and the value is set to
# x_eval
select = Select(title='X' + str(i),
value=x_eval[i],
options=cats,
width=200,
height=15)
# Here we define a callback that updates the appropiate red markers by changing
# with the current value of the selector by changing the global "source" variable
# The callback function is written in javascript
select.js_on_change(
'value',
CustomJS(args=dict(source=source, n=n, cats=cats),
code="""
// Convert categorical to index
var ind = cats.indexOf(cb_obj.value);
// Change red line in diagonal plots
source['reds'][n][n]['location'] = ind + 0.5;
// Change red markers in all contour plots
// First we change the plots in a vertical direction
for (i = n+1; i < source.reds.length; i++) {
source.reds[i][n].data.x = [ind + 0.5] ;
source.reds[i][n].change.emit()
}
// Then in a horizontal direction
for (j = 0; j < n; j++) {
source.reds[n][j].data.y = [ind + 0.5] ;
source.reds[n][j].change.emit();
}
"""))
x_eval_selectors.append(select)
# We update the global selector values
x_eval_selectors_values[i] = x_eval[i]
else: # Numerical
# For numerical values we create a slider
# Minimum and maximum values for slider
start = bounds[i][0]
end = bounds[i][1]
# We change the stepsize according to the range of the slider
step = get_step_size(start, end)
slider = Slider(start=start,
end=end,
value=x_eval[i],
step=step,
title='X' + str(i),
width=200,
height=30)
# javascript callback function that gets called everytime a user changes the slider value
slider.js_on_change(
'value',
CustomJS(args=dict(source=source, n=n),
code="""
source.reds[n][n].location = cb_obj.value;
source.reds[n][n].change.emit()
for (i = n+1; i < source.reds.length; i++) {
source.reds[i][n].data.x = [cb_obj.value] ;
source.reds[i][n].change.emit();
}
for (j = 0; j < n; j++) {
source.reds[n][j].data.y = [cb_obj.value] ;
source.reds[n][j].change.emit();
}
"""))
x_eval_selectors.append(slider)
x_eval_selectors_values[i] = x_eval[i]
n += 1
return x_eval_selectors
p1.legend.location = 'top_left'
#Legend(p0)
hover1 = p1.select_one(HoverTool)
hover1.point_policy = "follow_mouse"
hover1.tooltips = [("Category", "@cat"), ("Zone", "@zone")]
callback1 = CustomJS(args=dict(source=precip_source),
code="""
var data = source.data;
var f = cb_obj.value;
source.data.cat = data[f];
source.change.emit();
""")
select1 = Select(title='Month', value=time_index[-1], options=time_index)
select1.js_on_change('value', callback1)
#slider = Slider(start=0, end=len(time_index)-1, value=0, step=1)
#slider.js_on_change('value', callback)
layout1 = column(p1, select1)
tab1 = Panel(child=layout1, title='Precip')
## Figure 2 - flow
p2 = figure(title='Surface Water Flow Index',
tools=TOOLS,
logo=None,
active_scroll='wheel_zoom',
plot_height=h,
plot_width=w)
p2.patches('x',
'y',
growth_source.change.emit();
"""
click_code = """
var condition_ind = growth_source.selected['1d'].indices[0];
console.log(condition_ind);
var condition = growth_source.data['condition'][condition_ind]
console.log(condition);
process_filter.group = selection.value;
condition_filter.group = condition;
treemap_view.filters = [process_filter, condition_filter];
treemap_source.data.view = treemap_view;
treemap_source.change.emit();
"""
# Define the callbacks
selection_cb = CustomJS(args=args, code=selection_code)
click_cb = CustomJS(args=args, code=click_code)
# Assign callbacks as necessary
selection.js_on_change('value', selection_cb)
click_event = growth_plot.select(type=TapTool)
click_event.callback = click_cb
# Set up the layout
row = bokeh.layouts.row(growth_plot, treemap)
col = bokeh.layouts.column(selection, row)
lay = col
bokeh.io.save(lay)
# %%
class QCWorkTool:
"""The almighty QC-tool.
Used for quality control of high resolution CTD-data (oceanography).
"""
# TODO: Well, this is great.. However, we need to simplify and
# divide this class into widgets instead.. to be continued..
# - delete color fields? use flag fields instead?
def __init__(
self,
dataframe,
datasets=None,
settings=None,
tabs=None,
ctdpy_session=None,
export_folder=False,
output_filename="CTD_QC_VIZ.html",
output_as_notebook=False,
output_as_standalone=False,
):
"""Initiate."""
self.seconds = ColumnDataSource(
data=dict(tap_time=[None], reset_time=[None]))
self.ctd_session = ctdpy_session
self.multi_sensors = settings.multi_sensors
self.combo_plots = settings.combo_plots
self.map = None
self.plot_keys = settings.plot_keys
self.datasets = datasets
self.key_ds_mapper = self.get_mapper_key_to_ds(
settings.file_name_elements)
self.parameters = settings.data_parameters_with_units
self.plot_parameters_mapping = settings.plot_parameters_mapping
self.color_fields = settings.q_colors
self.size_fields = settings.scatter_size
self.qflag_fields = settings.q_parameters
self.auto_qflag_fields = settings.q0_plot_keys
self.tabs = tabs
self.output_as_notebook = output_as_notebook
self.as_standalone = output_as_standalone
if self.output_as_notebook:
raise NotImplementedError(
'Not yet applicable to work with notebooks!')
elif self.as_standalone:
output_file(output_filename)
self.tile_provider = get_provider(Vendors.CARTODBPOSITRON_RETINA)
self.figures = {}
xrange_callbacks = {}
y_range_setting = None
for p in self.plot_parameters_mapping:
if p == 'y' or 'q' in p or p[0].isupper() or \
p.startswith('color') or p.startswith('size'):
continue
param = self.plot_parameters_mapping.get(p)
self.figures[p] = figure(
tools="pan,reset,wheel_zoom,lasso_select,save",
active_drag="lasso_select",
title="",
height=400,
width=400,
y_range=y_range_setting,
tooltips=[(param, "@{}".format(p)), ("Pressure [dbar]", "@y"),
("Auto-QC", "@{}_q0".format(p))])
self.figures[p].title.align = 'center'
self.figures[p].xaxis.axis_label = param
self.figures[p].xaxis.axis_label_text_font_style = 'bold'
self.figures[p].ygrid.band_fill_alpha = 0.05
self.figures[p].ygrid.band_fill_color = "black"
self.figures[p].toolbar.active_scroll = self.figures[p].select_one(
WheelZoomTool)
if not y_range_setting or (self.multi_sensors and p == 'x4'):
self.figures[p].yaxis.axis_label = 'Pressure (dbar)'
self.figures[p].yaxis.axis_label_text_font_style = 'bold'
y_range_setting = y_range_setting or self.figures[p].y_range
xrange_callbacks[p] = cbs.x_range_callback(
x_range_obj=self.figures[p].x_range, seconds=self.seconds)
self.figures[p].x_range.js_on_change('start', xrange_callbacks[p])
if self.combo_plots and self.multi_sensors:
# TODO check the p2 (_) variable.. Should it not be used?
for name, p1, _ in zip(('COMBO_TEMP', 'COMBO_SALT', 'COMBO_DOXY'),
('x1', 'x2', 'x3'), ('x4', 'x5', 'x6')):
param = self.plot_parameters_mapping.get(p1)
self.figures[name] = figure(
tools="pan,reset,wheel_zoom,lasso_select,save",
active_drag="lasso_select",
title="",
height=400,
width=400,
y_range=y_range_setting,
)
self.figures[name].title.align = 'center'
self.figures[name].xaxis.axis_label = param
self.figures[name].xaxis.axis_label_text_font_style = 'bold'
self.figures[name].ygrid.band_fill_alpha = 0.05
self.figures[name].ygrid.band_fill_color = "black"
self.figures[name].toolbar.active_scroll = \
self.figures[name].select_one(WheelZoomTool)
if p1 == 'x1':
self.figures[name].yaxis.axis_label = 'Pressure (dbar)'
self.figures[
name].yaxis.axis_label_text_font_style = 'bold'
cbs.add_hlinked_crosshairs(*(fig_obj
for i, fig_obj in self.figures.items()))
self.ts = figure(
title="CTD TS Diagram",
tools=[PanTool(),
WheelZoomTool(),
ResetTool(),
SaveTool()],
tooltips=[("Serie", "@key")],
height=400,
width=400,
x_range=(2, 36),
y_range=(-2, 20))
self.ts.title.align = 'center'
self._setup_position_source(dataframe)
self.data_source = setup_data_source(
dataframe,
pmap=self.plot_parameters_mapping,
key_list=np.unique(self.position_source.data['KEY']),
parameter_list=self.color_fields + self.size_fields +
self.plot_keys + self.auto_qflag_fields +
['COMNT_SAMP'] # noqa: E501
)
self.ts_source = TS_Source()
self.ts_source.setup_source(dataframe, self.plot_parameters_mapping)
self.ts_plot_source = ColumnDataSource(
data=dict(x=[], y=[], color=[], key=[]))
self._setup_month_selector()
self._setup_comnt_inputs()
self._setup_selection_widgets()
self._setup_multiflag_widget()
self._setup_flag_widgets()
self._setup_reset_callback(**xrange_callbacks)
self._setup_datasource_callbacks()
self._setup_download_button(settings.user_download_directory,
export_folder=export_folder,
icons=settings.icons)
self._setup_get_file_button()
self._setup_serie_table()
self._setup_info_block(export_folder)
self._setup_map()
self.ts_axis_ranges = {
't_min': 0,
't_max': 25,
's_min': 2,
's_max': 36
}
def get_mapper_key_to_ds(self, file_name_elements):
"""Return mapper between dataset filename and key.
Key = serie key (eg. '20191009_77SE_0005')
"""
selected_keys = ('SDATE', 'SHIPC', 'SERNO')
mapper = {}
for ds_name in self.datasets:
name_map = {
k: v
for k, v in zip(file_name_elements,
ds_name.replace('.txt', '').split('_'))
}
mapper['_'.join(
(name_map.get(k) for k in selected_keys))] = ds_name
return mapper
@staticmethod
def _get_monthly_keys(position_df):
"""Return mapper of monthly keys."""
# FIXME Is this really necessary ?
dictionary = {'All': position_df['KEY'].to_list()}
for month in [str(m).zfill(2) for m in range(1, 13)]:
boolean = position_df['MONTH'] == month
dictionary[month] = position_df.loc[boolean, 'KEY'].to_list()
return dictionary
def _setup_position_source(self, df):
"""Set position data sources.
self.position_source: Contains all position information.
self.position_plot_source: Contains only the selected information.
"""
position_df = df[[
'STATION', 'LATITUDE_DD', 'LONGITUDE_DD', 'KEY', 'MONTH'
]].drop_duplicates(keep='first').reset_index(drop=True)
for pos_row in position_df.itertuples():
try:
float(pos_row.LATITUDE_DD)
except Exception:
print('not valid', pos_row.KEY)
xs, ys = convert_projection(
position_df['LATITUDE_DD'].astype(float).values,
position_df['LONGITUDE_DD'].astype(float).values)
position_df['LONGI'] = xs
position_df['LATIT'] = ys
comnts = []
for key in position_df['KEY']:
ds_meta = self.datasets[self.key_ds_mapper.get(key)]['metadata']
cv_boolean = ds_meta.str.startswith('//METADATA;COMNT_VISIT;')
value = ds_meta[cv_boolean].values[0].replace(
'//METADATA;COMNT_VISIT;', '')
comnts.append(value)
position_df['COMNT_VISIT'] = comnts
self.monthly_keys = self._get_monthly_keys(position_df)
self.position_source = ColumnDataSource(data=position_df)
self.position_plot_source = ColumnDataSource(data=position_df)
def _setup_month_selector(self):
"""Set month selection widget."""
callback = cbs.month_selection_callback(
position_source=self.position_source,
position_plot_source=self.position_plot_source)
self.month_selector = Select(
title="Select month",
value='All',
options=['All'] + pd.date_range(
start='2020-01', freq='M', periods=12).month_name().to_list())
self.month_selector.js_on_change('value', callback)
# self.month_selector.title.text_align = 'center'
callback.args["month"] = self.month_selector
def _setup_download_button(self, savepath, export_folder=None, icons=None):
"""Set download widget."""
self.download_button = cbs.get_download_widget(
self.datasets,
self.position_plot_source,
self.ctd_session,
self.key_ds_mapper,
savepath,
export_folder=export_folder,
icons=icons)
def _setup_get_file_button(self):
"""Set file button widget."""
self.file_button = cbs.get_file_widget()
def _setup_serie_table(self):
"""Create data table associated to the position_plot_source object."""
columns = [
TableColumn(field="STATION", title="Station"),
TableColumn(field="KEY", title="Key"),
]
self.selected_series = DataTable(source=self.position_plot_source,
columns=columns,
width=300,
height=322)
def _setup_flag_widgets(self):
"""Set flag widgets."""
self.flag_widgets = {}
for fig_key in self.figures.keys():
if fig_key.startswith('COMBO'):
continue
parameter = self.plot_parameters_mapping.get(fig_key).split()[0]
self.flag_widgets[fig_key] = cbs.get_flag_buttons_widget(
self.position_plot_source,
self.data_source['main_source'],
self.datasets,
key_mapper=self.key_ds_mapper,
figure_objs=self.figures,
flag_keys=self.plot_parameters_mapping[parameter].get(
'q_flags'),
color_keys=self.plot_parameters_mapping[parameter].get(
'color_keys'),
size_keys=self.plot_parameters_mapping[parameter].get(
'size_keys'),
select_button=self.select_all_button)
def _setup_comnt_inputs(self):
"""Set comment input widgets."""
self.comnt_visit = TextInput(value="", title="COMNT_VISIT:")
self.comnt_visit_button = cbs.comnt_visit_change_button(
datasets=self.datasets,
position_source=self.position_plot_source,
key_mapper=self.key_ds_mapper,
comnt_obj=self.comnt_visit,
)
self.comnt_samp = TextInput(value="", title="COMNT_SAMP:")
self.comnt_samp_button = cbs.comnt_samp_change_button(
datasets=self.datasets,
position_source=self.position_plot_source,
key_mapper=self.key_ds_mapper,
data_source=self.data_source['main_source'],
comnt_obj=self.comnt_samp,
)
self.comnt_samp_selector = cbs.comnt_samp_selection(
data_source=self.data_source['main_source'],
comnt_obj=self.comnt_samp,
)
def _setup_info_block(self, export_folder):
"""Set text block objects."""
self.info_block = get_info_block()
self.text_export = get_export_info_block(export_folder)
self.text_index_selection = standard_block_header(
text='Profile index selection', height=30)
self.text_multi_serie_flagging = standard_block_header(
text='Multi serie parameter flagging', height=30)
self.text_meta = standard_block_header(text='Change comments',
height=30)
self.text_import = standard_block_header(text='Not yet applicable',
height=30)
def _setup_selection_widgets(self):
"""Set selection widgets."""
self.select_all_button = cbs.select_button(
data_source=self.data_source['main_source'])
self.deselect_all_button = cbs.deselect_button(
data_source=self.data_source['main_source'])
self.pressure_slider = RangeSlider(start=0,
end=100,
value=(0, 100),
step=0.5,
title="Select with pressure range",
width=300)
callback = cbs.range_selection_callback(
data_source=self.data_source['main_source'])
self.pressure_slider.js_on_change('value', callback)
def _setup_multiflag_widget(self):
"""Set multiflag widgets."""
def sorted_params(plist):
para_list = []
i = 0
while i < len(plist):
if '2' in plist[i]:
para_list.extend([plist[i + 1], plist[i]])
i += 2
else:
para_list.append(plist[i])
i += 1
return para_list
parameter_list = []
for p in self.figures.keys():
if not p.startswith('COMBO'):
parameter_list.append(
self.plot_parameters_mapping.get(p).split()[0])
parameter_list = sorted_params(sorted(parameter_list))
self.parameter_selector = Select(title="Select parameter",
value=parameter_list[0],
options=parameter_list)
self.multi_flag_widget = cbs.get_multi_serie_flag_widget(
self.position_plot_source,
self.data_source,
self.datasets,
key_mapper=self.key_ds_mapper,
parameter_selector=self.parameter_selector,
parameter_mapping=self.plot_parameters_mapping,
figure_objs=None)
def _setup_reset_callback(self, **kwargs):
"""Set linked figure reset functionality.
Autoreset all figures.
"""
for p in self.figures.keys():
xr_cbs = (xr_cb for xr_cb in kwargs.values())
self.figures[p].js_on_event('reset',
cbs.reset_callback(self.seconds),
*xr_cbs)
def _setup_datasource_callbacks(self):
"""Set multiflag widgets."""
set_button_type_callback = cbs.change_button_type_callback(
button=self.select_all_button, btype='default')
self.data_source['main_source'].selected.js_on_change(
'indices', set_button_type_callback)
def _setup_map(self):
"""Initiate the map object including all the connected interactivity."""
pan = PanTool()
save = SaveTool()
tap = TapTool()
lasso = LassoSelectTool()
reset = ResetTool()
wheel = WheelZoomTool()
tooltips = HoverTool(tooltips=[("Station",
"@STATION"), ("Serie", "@KEY")])
# range bounds supplied in web mercator coordinates
self.map = figure(
x_range=(0, 4000000),
y_range=(7100000, 9850000),
x_axis_type="mercator",
y_axis_type="mercator",
plot_height=420,
plot_width=1000,
tools=[pan, wheel, tap, lasso, tooltips, reset, save])
self.map.yaxis.axis_label = ' ' # in order to aline y-axis with figure window below
self.map.xgrid.grid_line_color = None
self.map.ygrid.grid_line_color = None
self.map.toolbar.active_scroll = self.map.select_one(WheelZoomTool)
self.map.add_tile(self.tile_provider)
station_data_callback = cbs.station_callback(
position_source=self.position_plot_source,
data_source=self.data_source,
figures=self.figures,
seconds=self.seconds,
pmap=self.plot_parameters_mapping)
tap.callback = station_data_callback
# When we mark stations on the map using lasso selection, we activate the TS-diagram.
lasso_callback = cbs.lasso_callback(monthly_keys=self.monthly_keys,
in_data=self.ts_source,
plot_data=self.ts_plot_source,
x_range=self.ts.x_range,
y_range=self.ts.y_range)
station_data_callback_2 = cbs.station_callback(
position_source=self.position_plot_source,
data_source=self.data_source,
figures=self.figures,
seconds=self.seconds,
pmap=self.plot_parameters_mapping)
comnt_callback = cbs.comnt_callback(
position_source=self.position_plot_source,
comnt_obj=self.comnt_visit,
single_select=1)
comnt_samp_callback = cbs.comnt_samp_callback(
position_source=self.position_plot_source,
data_source=self.data_source['main_source'],
comnt_obj=self.comnt_samp,
comnt_selector=self.comnt_samp_selector,
single_select=1,
)
update_slider_callback = cbs.range_slider_update_callback(
slider=self.pressure_slider,
data_source=self.data_source['main_source'],
)
select_button_type_callback = cbs.change_button_type_callback(
button=self.select_all_button, btype='default')
lasso_callback.args["month"] = self.month_selector
self.position_plot_source.selected.js_on_change(
'indices',
lasso_callback,
station_data_callback_2,
comnt_callback,
update_slider_callback,
select_button_type_callback,
comnt_samp_callback,
)
def plot_stations(self):
"""Plot loaded stations on the map."""
renderer = self.map.circle('LONGI',
'LATIT',
source=self.position_plot_source,
color="#5BC798",
line_color="aquamarine",
size=10,
alpha=0.7)
selected_circle = Circle(fill_alpha=0.5,
fill_color="#FF0202",
line_color="aquamarine")
nonselected_circle = Circle(fill_alpha=0.3,
fill_color="#5BC798",
line_color="aquamarine")
renderer.selection_glyph = selected_circle
renderer.nonselection_glyph = nonselected_circle
def plot_data(self):
"""Plot default data in figures.
Default data: x=[1] and y=[1].
"""
combo_mapping = {
'COMBO_TEMP': ('x1', 'x4'),
'COMBO_SALT': ('x2', 'x5'),
'COMBO_DOXY': ('x3', 'x6')
}
nonselected_circle = Circle(fill_alpha=0.1,
fill_color="#898989",
line_color="lightgrey")
for p, item in self.figures.items():
if p.startswith('COMBO'):
p1, p2 = combo_mapping.get(p)
item.line(p1,
'y',
color=f"color_{p1}",
line_color="navy",
line_width=1,
alpha=0.3,
source=self.data_source['main_source'])
item.circle(p1,
'y',
color=f"color_{p1}",
line_color="white",
size=f"size_{p1}",
alpha=0.5,
source=self.data_source['main_source'])
item.line(p2,
'y',
color=f"color_{p2}",
line_color="navy",
line_width=1,
alpha=0.3,
source=self.data_source['main_source'])
item.cross(p2,
'y',
color=f"color_{p2}",
size=f"size_{p2}",
alpha=0.5,
source=self.data_source['main_source'])
item.y_range.flipped = True
else:
item.line(p,
'y',
color=f"color_{p}",
line_color="navy",
line_width=1,
alpha=0.3,
source=self.data_source['main_source'])
renderer = item.circle(p,
'y',
color=f"color_{p}",
line_color="white",
size=f"size_{p}",
alpha=0.5,
source=self.data_source['main_source'])
renderer.nonselection_glyph = nonselected_circle
item.y_range.flipped = True
# T/S - diagram
self.ts.circle('x',
'y',
color='color',
size=3,
alpha=0.8,
source=self.ts_plot_source,
legend_label='Sensor 1')
self.ts.toolbar.active_scroll = self.ts.select_one(WheelZoomTool)
self.ts.legend.location = "top_left"
number_of_colors = int(self.ts_source.data[
self.plot_parameters_mapping.get('y')].max()) * 2
number_of_colors += 1
cm_map = cm.get_cmap('cool', number_of_colors)
color_array = [
colors.to_hex(cm_map(c)) for c in range(number_of_colors)
]
color_bar = ColorBar(
color_mapper=LinearColorMapper(
palette=color_array,
low=0,
high=self.ts_source.data[self.plot_parameters_mapping.get(
'y')].max()),
location=(0, 0),
)
self.ts.add_layout(color_bar, 'right')
x_min, x_max, y_min, y_max = 0, 40, -10, 30
contour_data = get_contour_data(x_min, x_max, y_min, y_max)
for key in contour_data.keys():
self.ts.line(contour_data[key]['salt'],
contour_data[key]['temp'],
line_color="grey",
line_alpha=0.8,
line_width=1.5)
def append_qc_comment(self, meta_series):
"""Append commnet at the end of the metadata serie.
Will be placed on the row just above the data table in each data file.
"""
time_stamp = get_time_as_format(now=True, fmt='%Y%m%d%H%M')
meta_series[len(meta_series) + 1] = '//QC_COMNT; MANUAL QC PERFORMED BY {}; TIMESTAMP {}' \
''.format(self.ctd_session.settings.user, time_stamp)
def get_tab_layout(self):
"""Return bokeh tab widget."""
fig_tabs = [
Panel(child=column([Spacer(height=30, width=20), self.ts]),
title="TS")
]
for p, item in self.figures.items():
if (self.multi_sensors and p not in ['x1', 'x2', 'x3', 'x4', 'x5', 'x6',
'COMBO_TEMP', 'COMBO_SALT', 'COMBO_DOXY']) \
or (not self.multi_sensors and p not in ['x1', 'x2', 'x3']):
tab_layout = column([self.flag_widgets[p], item])
tab_name = self.plot_parameters_mapping.get(
p).split()[0].replace('_CTD', '')
pan = Panel(child=tab_layout, title=tab_name)
fig_tabs.append(pan)
return Tabs(tabs=fig_tabs)
def get_tabs(self, **kwargs):
"""Return bokeh tab widget."""
tabs = []
for name, item in kwargs.items():
tab = self.get_column(item)
pan = Panel(child=tab, title=name)
tabs.append(pan)
return Tabs(tabs=tabs)
def get_column(self, item):
"""Return bokeh column layout."""
c_list = []
for attr in item:
if type(attr) == str:
c_list.append(self.__getattribute__(attr))
elif type(attr) == tuple:
r = row([self.__getattribute__(a) for a in attr],
sizing_mode="stretch_width")
c_list.append(r)
return column(c_list)
def get_std_parameter_tab_layout(self):
"""Return list of bokeh column layouts."""
def pan_title(string):
return string.split()[0].replace('_CTD', '')
columns = []
if self.multi_sensors:
for params in zip(('x1', 'x2', 'x3'), ('x4', 'x5', 'x6'),
('COMBO_TEMP', 'COMBO_SALT', 'COMBO_DOXY')):
pans = []
for p in params:
if p in self.figures:
tab_cols = []
if p in self.flag_widgets:
tab_cols.append(self.flag_widgets[p])
else:
tab_cols.append(Spacer(width=20, height=41))
tab_cols.append(self.figures[p])
tab = column(tab_cols)
pan = Panel(child=tab,
title=pan_title(
self.plot_parameters_mapping.get(p)
or p))
pans.append(pan)
columns.append(column([Tabs(tabs=pans)]))
else:
for p1 in ('x1', 'x2', 'x3'):
columns.append(
column([self.flag_widgets[p1], self.figures[p1]]))
return columns
def get_layout(self):
"""Return the complete bokeh layout."""
tabs = self.get_tab_layout()
tab_kwargs = {
'Data': [
'text_index_selection',
('select_all_button', 'deselect_all_button'),
'pressure_slider', 'text_multi_serie_flagging',
'parameter_selector', 'multi_flag_widget'
],
'Metadata': [
'text_meta', 'comnt_visit', 'comnt_visit_button', 'comnt_samp',
'comnt_samp_selector', 'comnt_samp_button'
],
'Info': ['info_block']
}
if not self.as_standalone:
tab_kwargs['Import'] = ['text_import', 'file_button']
tab_kwargs['Export'] = ['text_export', 'download_button']
meta_tabs = self.get_tabs(**tab_kwargs)
std_parameter_tabs = self.get_std_parameter_tab_layout()
widgets_1 = column(
[self.month_selector, self.spacer, self.selected_series],
sizing_mode="fixed",
height=400,
width=200)
widgets_2 = column([Spacer(height=10, width=125)],
sizing_mode="fixed",
height=10,
width=125)
widgets_3 = column([meta_tabs],
sizing_mode="stretch_both",
height=100,
width=100)
return grid([
row([self.map, widgets_1, widgets_2, widgets_3]),
row([*std_parameter_tabs, column([tabs])])
])
@property
def spacer(self):
"""Return bokeh spacer."""
return Spacer(height=10, width=20)
def return_layout(self):
"""Return the layout.
Can be displayed in an embedded bokeh server.
"""
return self.get_layout()
def show_plot(self):
"""Show layout in a html-file or in jupyter notebook."""
show(self.get_layout())
TOOLS = "hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select"
sizing_mode = "fixed"
select = Select(title="Sizing mode",
value=sizing_mode,
options=list(SizingMode))
plot = figure(tools=TOOLS)
plot.scatter(x,
y,
radius=radii,
fill_color=colors,
fill_alpha=0.6,
line_color=None)
layout = column(select, plot, sizing_mode=sizing_mode)
select.js_on_change(
'value',
CustomJS(args=dict(layout=layout, plot=plot),
code="""
var sizing_mode = this.value;
layout.sizing_mode = sizing_mode;
plot.sizing_mode = sizing_mode;
"""))
output_file("sizing_mode.html", title="sizing_mode.py example")
show(layout)
p3.axis.axis_label = None
p3.axis.visible = False
p3.grid.grid_line_color = None
p3.title.text_font_size = '18pt'
#######pie
ind_hold = 3
hover_callback = CustomJS(args=dict(x=x,
source=source,
new_data_dict=new_data_dict,
inds=inds,
title=p3.title,
select=select,
new_savvy_dict=new_savvy_dict,
ind_hold=ind_hold),
code=code)
select.js_on_change('value', hover_callback)
from bokeh.models import FixedTicker, FuncTickFormatter
##hover_callback = CustomJS(args=dict(x=x,new_data_dict=new_data_dict,inds=inds), code = code)
##print('ahsdflasfd')
#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors. Input nan_color.
##color_mapper = LinearColorMapper(palette = palette, low = 0.5, high = 5.5, nan_color = '#d9d9d9')
color_mapper = LinearColorMapper(palette=palette,
low=0,
high=20,
nan_color='#d9d9d9')
##color_mapper = LinearColorMapper(palette = [palette[0],palette[-1]], low = 0, high = 25, nan_color = '#d9d9d9')
##LinearColorMapper(palette=Viridis256, low=0, high=100)
#Define custom tick labels for color bar.
colors = [
"#%02x%02x%02x" % (int(r), int(g), 150)
for r, g in zip(50 + 2 * x, 30 + 2 * y)
]
TOOLS = "hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select"
sizing_mode = "fixed"
select = Select(title="Sizing mode",
value=sizing_mode,
options=list(SizingMode))
plot = figure(tools=TOOLS, sizing_mode=sizing_mode)
plot.scatter(x,
y,
radius=radii,
fill_color=colors,
fill_alpha=0.6,
line_color=None)
layout = column(select, plot)
layout.sizing_mode = "stretch_both"
select.js_on_change(
'value',
CustomJS(args=dict(plot=plot), code="plot.sizing_mode = this.value;"))
output_file("sizing_mode.html", title="sizing_mode.py example")
show(layout)
def bokeh_summary_plot(df, savepath=None):
"""Summary plot"""
from bokeh.plotting import figure
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, Range1d, HoverTool, TapTool, CustomJS, OpenURL
TOOLS = "pan,wheel_zoom,hover,tap,reset,save"
colors = get_bokeh_colors()
df = df.rename(columns={'level_0': 'predictor'})
df['color'] = [colors[x] for x in df['predictor']]
p = figure(title="Summary", tools=TOOLS, width=500, height=500)
p.xaxis.axis_label = 'binder_density'
p.yaxis.axis_label = 'binders'
#make metric for point sizes
#df['point_size'] = df.binder_density
source = ColumnDataSource(data=df)
p.circle(x='binder_density',
y='binders',
line_color='black',
fill_color='color',
fill_alpha=0.4,
size=10,
source=source,
legend_group='predictor')
hover = p.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("name", "@name"),
("length", "@length"),
("binders", "@binders"),
("binder_density", "@binder_density"),
("top_peptide", "@top_peptide"),
("max_score", "@max_score"),
])
p.toolbar.logo = None
if savepath != None:
url = "http://*****:*****@name" % savepath
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
callback = CustomJS(args=dict(source=source),
code="""
var data = source.data;
var f = cb_obj.value
data['x'] = f
source.trigger('change');
source.change.emit();
""")
from bokeh.layouts import widgetbox
from bokeh.models.widgets import Select
menu = [(i, i) for i in df.columns]
select = Select(title='X', value='A', options=list(df.columns), width=8)
select.js_on_change('value', callback)
#layout = column(p, select, sizing_mode='scale_width')
return p
