def getTimeSeries_works(df_tws):
output_file("js_on_change.html")
x = [x * 0.005 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
plot = Figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=dict(source=source),
code="""
var data = source.data;
var f = cb_obj.value
var x = data['x']
var y = data['y']
for (var i = 0; i < x.length; i++) {
y[i] = Math.pow(x[i], f)
}
source.change.emit();
""")
slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
slider.js_on_change('value', callback)
layout = column(plot, slider)
return layout
def mg_slider(source, timeList, time, bar_source, status):
slider = Slider(start=1, end=len(time), value=1, step=1, title="Time")
with open(dir_path + '/slider.js', 'r') as slider_file:
slider_code = slider_file.read()
callback = CustomJS(args=dict(source=source,
slider=slider,
timeList=timeList,
bar_source=bar_source,
status=status),
code=slider_code)
slider.js_on_change('value', callback)
return slider
def init():
output_notebook()
display(Javascript(_init_js))
but = '<img src="resources/show.png" width="34" height="25" style="display: inline" alt="Slideshow button" title="Enter/Exit RISE Slideshow">'
txt = Div(text='<h2>You can now start the slideshow!</h3>' +
f'<h3 style="margin: 0.5em 0;">Just click the RISE slideshow button above - the one that looks like: {but}<br/>' +
'(or you can press alt+R on your keyboard instead if you prefer).</h3>')
clearbutton = Button(label="Clear")
clearbutton.js_on_click(CustomJS(code='primes_clear();'))
cleartext = Paragraph(text='Clear all plots and outputs (e.g. before restarting slideshow).')
increm = Toggle(label='Incremental', active=True)
increm.js_on_click(CustomJS(code='primes_incremental(cb_obj.active)'))
incremtext = Paragraph(text='Update timing plots incrementally (disable for static slide show).')
repeats = Slider(start=1, end=10, value=3)
repeats.js_on_change('value', CustomJS(code='primes_repeats(cb_obj.value)'))
repeatstext = Paragraph(text='Repeats for timing measurements (higher is more accurate, but slower).')
controls = layout([[clearbutton, cleartext],
[increm, incremtext],
[repeats, repeatstext]])
show(column(txt, controls, sizing_mode='stretch_width'))
def make_interactive_plot(qr: np.array, pvals, disp_data_full: list,
disp_inter_full: list, labels: dict) -> None:
""" Create the interactive Bokeh plot as a standalone html web page
:param qr: x-values for the points
:type qr: np.array
:param pvals: slider values
:type pvals: list
:param disp_data_full: list of arrays containing q vectors and eigenvalues for points
:type disp_data_full: list
:param disp_inter_full: list of arrays containing interpolated x and eigen values
:type disp_inter_full: list
:param labels: labels for the x-axis indicating special points
:type labels: dict
:return: None
.. todo:: add output file name option
"""
# Plotting
from bokeh.plotting import figure, output_file
from bokeh.io import show, output_notebook, push_notebook
from bokeh.models import ColumnDataSource, Panel, CustomJS, Circle, Line, Div, HoverTool
from bokeh.models.widgets import CheckboxGroup, Slider, RangeSlider
from bokeh.layouts import column, row
from bokeh.application import Application
nfiles = len(disp_data_full)
neigvals = disp_inter_full[0].shape[1] - 1 # first column is x-values
# Output file name (web page)
output_file("disp_app.html")
# Create the figure
p = figure(title="Dispersion curves for FCC",
y_range=(0, 2.25),
x_range=(0, qr[-1] + 0.001),
x_axis_label="q",
y_axis_label="ω (THz)",
plot_width=800)
# Glyphs for points and lines (intepolation)
raw_glyphs = []
inter_glyphs = []
# Make dictionaries for Bokeh ColumnDataSource
draw = {"xraw": qr}
dint = {"xint": disp_inter_full[0][:, 0]}
for ip in range(nfiles):
for ie in range(neigvals):
ei = "e" + str(ip) + "_" + str(ie)
draw[ei] = disp_data_full[ip][:, ie + 3]
dint[ei] = disp_inter_full[ip][:, ie + 1]
srcraw = ColumnDataSource(draw)
srcint = ColumnDataSource(dint)
# Build glyphs for each pressure point
for ip in range(nfiles):
lalpha = 0
for ie in range(neigvals):
ei = "e" + str(ip) + "_" + str(ie)
if ip == 0: lalpha = 1
raw_glyphs.append(
Circle(x="xraw", y=ei, line_alpha=lalpha, fill_alpha=lalpha))
inter_glyphs.append(Line(x="xint", y=ei, line_alpha=lalpha))
p.add_glyph(srcraw, raw_glyphs[-1])
p.add_glyph(srcint, inter_glyphs[-1])
# Styling
p.xaxis.ticker = list(labels.keys()) # SPqr
p.xaxis.major_label_overrides = labels
# Widgets
EIGEN_LBLS = ["Eigen " + str(i + 1) for i in range(neigvals)]
eigen_chkbx_grp = CheckboxGroup(labels=EIGEN_LBLS,
active=[i for i in range(neigvals)])
slider = Slider(start=1,
end=nfiles,
value=1,
step=1,
title="Pressure series")
#pvals = [''.join(re.findall(regex, f)) for f in onlylogfiles ]
div = Div(text="Pressure = " + pvals[0] + " bar", name="pval")
# Callbacks for widgets
## Slider Callback
slider.js_on_change(
"value",
CustomJS(args=dict(glp=raw_glyphs,
glq=inter_glyphs,
neig=neigvals,
lbl=div,
vals=pvals,
chkbx=eigen_chkbx_grp),
code="""
var i;
var eigs = chkbx.active
for (i=0; i<glp.length; i=i+1){
if (i>=(this.value-1)*neig && i < this.value*neig){
if( eigs.includes(i%neig) ){
glp[i]["fill_alpha"] = 1;
glp[i]["line_alpha"] = 1;
glq[i]["line_alpha"] = 1;
} else {
glp[i]["fill_alpha"] = 0;
glp[i]["line_alpha"] = 0;
glq[i]["line_alpha"] = 0;
}
console.log(i,'YES')
}else{
glp[i]["fill_alpha"] = 0;
glp[i]["line_alpha"] = 0;
glq[i]["line_alpha"] = 0;
console.log(i,'NO')
}
}
lbl["text"] = "Pressure = " + vals[this.value-1] + " bar"
"""))
## Checkbox callback
eigen_chkbx_grp.js_on_click(
CustomJS(args=dict(glp=raw_glyphs,
glq=inter_glyphs,
neig=neigvals,
sldr=slider),
code="""
var actv = this.active
var i
console.log("sldr ",sldr.value)
for (i=(sldr.value-1)*neig; i<sldr.value*neig; i=i+1){
console.log('i, i%neig res = ', i, i%neig, actv.includes(i%neig))
if (actv.includes(i%neig)){
glp[i]["fill_alpha"]=1;
glp[i]["line_alpha"]=1;
glq[i]["line_alpha"]=1;
} else {
glp[i]["fill_alpha"]=0;
glp[i]["line_alpha"]=0;
glq[i]["line_alpha"]=0;
}
}
"""))
## Add hover widget
hover = HoverTool(tooltips=[("ω (THz)", "$y")])
p.add_tools(hover)
## Make the layout
layout = column(eigen_chkbx_grp, p, row(slider, div))
## Make the webpage
show(layout)
def similarityGraph(si, sy, ey, db, abstractsim):
#calculate similarity and return necessary info
#ids = pmids of search, dates = dates of pmids, authors = authors of pmids, pmccites = number of cites, Y is x,y coordinate
#from dimensionality reduction, topwords = labels of clusters from kmeans/tfidf, kcenters = x,y centers of clusters from kmeans
#d is dictionary for all data storage
d = SimilarityCalc(si, sy, ey, db, abstractsim)
# for testing purposes
# with open('data.pk','wb') as f:
# pickle.dump(d,f)
#convert authors list of lists to list of strings for display
authors_str = []
for auths in d['authors']:
authors_str.append(", ".join(auths))
#calcualte a scaled pt size based on citation quantity
minw = 8
maxw = 30
# with open('citespkl.p','wb') as f:
# pickle.dump(list(map(int,pmccites)),f)
ptsizes = getScaledSizes(list(map(int, d['pmccites'])), minw, maxw)
#create colors based on years published
colors = getScaledColors(d['dates'])
#colors = ['blue']*len(ids)
alphas = [1] * len(d['ids'])
source = ColumnDataSource(data=dict(x=d['Y'][:, 0],
y=d['Y'][:, 1],
PMID=d['ids'],
titles=d['titles'],
authors=authors_str,
journals=d['journals'],
dates=d['dates'],
alphas=alphas,
pmccites=d['pmccites'],
ptsizes=ptsizes,
colors=colors,
colorsperm=colors))
########publication view table from selected on tsne plot
pubview_data = dict(titles=["Title"],
dates=["Date"],
journals=["Journal"],
authors=["Author"],
pmccites=["PMC Citations"],
PMID=["pmids"])
pubview_source = ColumnDataSource(pubview_data)
pubview_columns = [
TableColumn(field="titles", title="Article Title", width=400),
TableColumn(field="authors", title="Authors", width=50),
TableColumn(field="journals", title="Journal", width=50),
TableColumn(field="dates", title="Date", width=80),
TableColumn(field="pmccites", title="PMC Citations", width=80),
TableColumn(field="PMID", title="PMIDS", width=0),
]
pubview_table = DataTable(source=pubview_source,
columns=pubview_columns,
width=930,
height=400)
source.callback = CustomJS(args=dict(pubview_table=pubview_table),
code="""
var selecteddata = cb_obj.selected["1d"].indices
var count = 0
var s1 = cb_obj.get('data');
var d2 = pubview_table.get('source').get('data');
d2.index = []
d2.authors = []
d2.titles = []
d2.journals = []
d2.dates = []
d2.pmccites = []
d2.PMID = []
for(k = 0; k < selecteddata.length; k++){
tind = selecteddata[k]
d2.index.push(count)
d2.authors.push(s1.authors[tind])
d2.titles.push(s1.titles[tind])
d2.journals.push(s1.journals[tind])
d2.dates.push(s1.dates[tind])
d2.pmccites.push(parseInt(s1.pmccites[tind]))
d2.PMID.push(s1.PMID[tind])
count += 1
}
console.log(d2)
pubview_table.trigger('change');
""")
pubview_source.callback = CustomJS(code="""
var selecteddata = cb_obj.selected["1d"].indices
var s1 = cb_obj.get('data');
var url = "https://www.ncbi.nlm.nih.gov/pubmed/"+s1.PMID[selecteddata[0]]
window.open(url,'_blank');
""")
#######END TABLE DISPLAY CODE#####
#####max-width IS IMPORTANT FOR PROPER WRAPPING OF TEXT
hover = HoverTool(tooltips="""
<div>
<div style="max-width: 400px;">
<span style="font-size: 12px; font-weight: bold;">@titles</span>
</div>
<div style="max-width: 400px;">
<span style="font-size: 12px; color: #966;">@authors</span>
<div>
<div style="max-width: 400px;">
<span style="font-size: 12px; font-style: italic;">@journals, @dates</span>
<div style="max-width: 400px;">
<span style="font-size: 10px;">PMID</span>
<span style="font-size: 10px; color: #696;">@PMID</span>
</div>
<div style="max-width: 400px;">
<span style="font-size: 10px;">PMC Citations</span>
<span style="font-size: 10px; color: #696;">@pmccites</span>
</div>
</div>
""")
resetCallback = CustomJS(args=dict(source=source),
code="""
var data = source.get('data')
var titles = data['titles']
for (i=0; i < titles.length; i++) {
data.colors[i]=data.colorsperm[i]
data.alphas[i]= 1
}
source.trigger('change')
""")
#move function from reset callback to below so stuff updates automatically on textbox change
textCallback = CustomJS(args=dict(source=source),
code="""
var data = source.get('data')
var value = cb_obj.get('value')
var words = value.split(" ")
for (i=0; i < data.titles.length; i++) {
data.alphas[i]= 0.3
data.colors[i]=data.colorsperm[i]
}
for (i=0; i < data.titles.length; i++) {
for(j=0; j < words.length; j++){
if (data.titles[i].toLowerCase().indexOf(words[j].toLowerCase()) !== -1) {
if(j == words.length-1){
data.colors[i]='orange'
data.alphas[i]= 1
}
}else if(data.authors[i].toLowerCase().indexOf(words[j].toLowerCase()) !== -1){
if(j == words.length-1){
data.colors[i]='orange'
data.alphas[i]= 1
}
}else if(data.journals[i].toLowerCase().indexOf(words[j].toLowerCase()) !== -1){
if(j == words.length-1){
data.colors[i]='orange'
data.alphas[i]= 1
}
}else{
break
}
}
}
source.trigger('change')
""")
publistcallback = CustomJS(args=dict(pubview_table=pubview_table),
code="""
var pmids = pubview_table.get('source').get('data').PMID;
var pmidlist = pmids.join()
var url = "https://www.ncbi.nlm.nih.gov/pubmed/"+pmidlist
window.open(url,'_blank');
""")
TOOLS = 'pan,lasso_select,wheel_zoom,tap,reset'
p = figure(plot_width=900,
plot_height=600,
title="'" + si + "' tSNE similarity",
tools=[TOOLS, hover],
active_scroll='wheel_zoom',
active_drag="lasso_select")
p.circle('x',
'y',
fill_color='colors',
fill_alpha='alphas',
size='ptsizes',
line_color="#000000",
line_alpha=0.2,
source=source)
#word labeles for plots
wordsources = []
for idx in list(range(len(d['topwords']))):
wordsources.append(
ColumnDataSource(
dict(x=d['kcenters'][idx][:, 0],
y=d['kcenters'][idx][:, 1],
text=d['topwords'][idx])))
wordglyph = Text(x="x",
y="y",
text="text",
text_color="#000000",
text_font_style="bold",
text_font_size="14pt")
#5 is used for initial slider set below
initialclust = 5
wordholdsource = ColumnDataSource(
dict(x=d['kcenters'][initialclust][:, 0],
y=d['kcenters'][initialclust][:, 1],
text=d['topwords'][initialclust]))
p.add_glyph(wordholdsource, wordglyph)
# source = ColumnDataSource(data=dict(x=x, y=y))
#
# plot = Figure(plot_width=400, plot_height=400)
# plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
args = {}
args["wordholdsource"] = wordholdsource
for idx in list(range(len(d['topwords']))):
args["wordsource" + str(idx + d['minc'])] = wordsources[idx]
#had to use eval hack because of limitations on the type of objects that can be passed into the callback, limited by bokeh backend
slidercallback = CustomJS(args=args,
code="""
var f = cb_obj.value
var ndata = eval('wordsource' + f.toString()).data;
wordholdsource.data.x = ndata.x
wordholdsource.data.y = ndata.y
wordholdsource.data.text = ndata.text
wordholdsource.trigger('change');
""")
wslider = Slider(start=d['minc'],
end=d['maxc'],
value=d['minc'] + initialclust,
step=1,
title="# of labels")
# slider = Slider(start=0.1, end=4, value=1, step=.1, title="power", callback=callback)
wslider.js_on_change('value', slidercallback)
#formatting plot
p.xaxis.axis_label = "Hover to view publication info, Click to open Pubmed link"
p.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p.yaxis.minor_tick_line_color = None
p.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p.yaxis.major_label_text_font_size = '0pt'
left, right, bottom, top = np.amin(d['Y'][:, 0]) * 1.1, np.amax(
d['Y'][:, 0]) * 1.1, np.amin(d['Y'][:, 1]) * 1.1, np.amax(
d['Y'][:, 1]) * 1.1
p.x_range = Range1d(left, right)
p.y_range = Range1d(bottom, top)
#tap tool callback
url = "https://www.ncbi.nlm.nih.gov/pubmed/@PMID"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
#work input callback
word_input = TextInput(title="Search for term(s) in graph",
placeholder="Enter term to highlight",
callback=textCallback)
reset = Button(label="Clear Highlighting",
callback=resetCallback,
width=150)
spdiv = Div(text=" ", width=100, height=20)
#add addition message saying data was cutoff in case of vague search terms
if (len(d['rids']) == d['maxids']):
cutoff_message = "<br>(Search was truncated to " + str(
d['maxids']) + " newest articles due to memory constraints)"
else:
cutoff_message = ""
tit1 = Div(text="<h1>" + si +
" similarity plot</h1><br><h5>Displaying the " +
str(d['sdfc_len']) + "/" + str(len(d['rids'])) +
" articles that have data on pubmed" + cutoff_message + "</h5>",
width=930)
pubmed_list_button = Button(label="Export selected publications to pubmed",
callback=publistcallback)
lt = layout([[tit1], [word_input], [reset, spdiv, wslider], [p],
[pubmed_list_button], [pubview_table]])
return lt
def plot_cross_section_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, cross_data, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and cross-sections using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all is a list of velocity profiles
cross_lat_data_all is a list of cross-sections along latitude
lat_value_all is a list of corresponding latitudes for these cross-sections
cross_lon_data_all is a list of cross-sections along longitude
lon_value_all is a list of corresponding longitudes for these cross-sections
boundary_data is a list of boundaries
style_parameter contains parameters to customize the plots
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
palette_r = palette[::-1]
ncolor = len(palette_r)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min) * 1. / ncolor
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append(
'Depth: {0:8.0f} km'.format(map_depth))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[
style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(
data=dict(lat=[map_view_label_lat],
lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_2d = map_color.view('uint32').reshape(map_color.shape[:2])
map_color_all_slices.append(map_color_2d)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']],\
dh=[style_parameter['nlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
#
plot_depth = np.shape(cross_data)[0] * style_parameter['cross_ddepth']
plot_lon = great_arc_distance(style_parameter['cross_default_lat0'], style_parameter['cross_default_lon0'],\
style_parameter['cross_default_lat1'], style_parameter['cross_default_lon1'])
vs_min = style_parameter['cross_view_vs_min']
vs_max = style_parameter['cross_view_vs_max']
cross_color = val_to_rgb(cross_data, palette_r, vs_min, vs_max)
cross_color_2d = cross_color.view('uint32').reshape(cross_color.shape[:2])
cross_data_bokeh = ColumnDataSource(data=dict(x=[0],\
y=[plot_depth],dw=[plot_lon],\
dh=[plot_depth],cross_data=[cross_color_2d]))
map_line_bokeh = ColumnDataSource(data=dict(lat=[style_parameter['cross_default_lat0'], style_parameter['cross_default_lat1']],\
lon=[style_parameter['cross_default_lon0'], style_parameter['cross_default_lon1']]))
#
ncolor_cross = len(my_palette)
colorbar_top_cross = [0.1 for i in range(ncolor_cross)]
colorbar_bottom_cross = [0 for i in range(ncolor_cross)]
color_min_cross = style_parameter['cross_view_vs_min']
color_max_cross = style_parameter['cross_view_vs_max']
color_step_cross = (color_max_cross - color_min_cross) * 1. / ncolor_cross
colorbar_left_cross = np.linspace(color_min_cross,
color_max_cross - color_step_cross,
ncolor_cross)
colorbar_right_cross = np.linspace(color_min_cross + color_step_cross,
color_max_cross, ncolor_cross)
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50)
depth_slider.js_on_change('value', depth_slider_callback)
depth_slider_callback.args["depth_index"] = depth_slider
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.line('lon', 'lat', source=map_line_bokeh, line_dash=[8,2,8,2], line_color='#00ff00',\
nonselection_line_alpha=1.0, line_width=5.,\
nonselection_line_color='black')
map_view.text([style_parameter['cross_default_lon0']],[style_parameter['cross_default_lat0']], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
map_view.text([style_parameter['cross_default_lon1']],[style_parameter['cross_default_lat1']], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text="""<p style="font-size:16px">""", \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
# plot cross-section along latitude
cross_section_plot_width = int(style_parameter['cross_plot_height'] * 1.0 /
plot_depth * plot_lon / 10.)
cross_view = Figure(plot_width=cross_section_plot_width, plot_height=style_parameter['cross_plot_height'], \
tools=style_parameter['cross_view_tools'], title=style_parameter['cross_view_title'], \
y_range=[plot_depth, -30],\
x_range=[0, plot_lon])
cross_view.image_rgba('cross_data',x='x',\
y='y',dw='dw',dh='dh',\
source=cross_data_bokeh, level='image')
cross_view.text([plot_lon*0.1], [-10], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
cross_view.text([plot_lon*0.9], [-10], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
# ------------------------------
# change style
cross_view.title.text_font_size = style_parameter['title_font_size']
cross_view.title.align = 'center'
cross_view.title.text_font_style = 'normal'
cross_view.xaxis.axis_label = style_parameter['cross_view_xlabel']
cross_view.xaxis.axis_label_text_font_style = 'normal'
cross_view.xaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.xaxis.major_label_text_font_size = xlabel_fontsize
cross_view.yaxis.axis_label = style_parameter['cross_view_ylabel']
cross_view.yaxis.axis_label_text_font_style = 'normal'
cross_view.yaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.yaxis.major_label_text_font_size = xlabel_fontsize
cross_view.xgrid.grid_line_color = None
cross_view.ygrid.grid_line_color = None
cross_view.toolbar.logo = None
cross_view.toolbar_location = 'right'
cross_view.toolbar_sticky = False
# ==============================
colorbar_fig_right = Figure(tools=[], y_range=(0,0.1),plot_width=cross_section_plot_width, \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig_right.toolbar_location = None
colorbar_fig_right.quad(top=colorbar_top_cross,bottom=colorbar_bottom_cross,\
left=colorbar_left_cross,right=colorbar_right_cross,\
color=my_palette)
colorbar_fig_right.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig_right.xgrid.grid_line_color = None
colorbar_fig_right.ygrid.grid_line_color = None
colorbar_fig_right.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig_right.title.text_font_size = xlabel_fontsize
colorbar_fig_right.title.align = 'center'
colorbar_fig_right.title.text_font_style = 'normal'
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_column = Column(depth_slider,
map_view,
colorbar_fig,
annotating_fig01,
width=style_parameter['left_column_width'])
right_column = Column(annotating_fig02,
cross_view,
colorbar_fig_right,
width=cross_section_plot_width)
layout = Row(left_column, right_column, height=800)
save(layout)
# Done! Not too bad. Now let's define the arguments for the callback function
# Define arguments for JavaScript callback function
cb_args = {
'source': source,
'muAaSlider': mu_Aa,
'muaASlider': mu_aA,
'xoSlider': x_init
}
# Asign arguments to function
cb = CustomJS(args=cb_args, code=cb_script)
# Now we must assign this callback function to each of the sliders. What this means is that we must indicate that every time the slider value is changed, the `JavaScript` callback function must be executed.
# Assign callback function to widgets
x_init.js_on_change('value', cb)
mu_Aa.js_on_change('value', cb)
mu_aA.js_on_change('value', cb)
# Alright. Now everything is setup for our interactive plot! Now we just need to define the bokeh plot.
# Define bokeh axis
x_allele_ax = bokeh.plotting.figure(width=300,
height=275,
x_axis_label='time (a.u.)',
y_axis_label='allele frequency',
y_range=[-0.05, 1.05])
# Populate the plot with our line coming from the Data Source
x_allele_ax.line(x='time', y='x_allele', line_width=2, source=source)
def cluster_gui(doc):
global s2, s1, old_indices
old_indices = []
output_file("tile.html")
tile_provider = get_provider(CARTODBPOSITRON)
x = []
y = []
name = []
global fig03
fig03 = figure(
plot_width=400,
plot_height=400,
tools=["box_zoom", "wheel_zoom", "reset", "save"],
title="Waveforms from current selection",
)
for i, st in enumerate(stations):
xm, ym = merc(st.lat, st.lon)
x.append(xm)
y.append(ym)
name.append(st.nsl_string())
# create first subplot
plot_width = 400
plot_height = 400
d = num.ones_like(x)
s1 = ColumnDataSource(data=dict(x=x, y=y, ind=d, name=name))
# range bounds supplied in web mercator coordinates
fig01 = figure(
x_axis_type="mercator",
y_axis_type="mercator",
plot_width=plot_width,
plot_height=plot_height,
tools=[
"lasso_select", "box_select", "reset", "save", "box_zoom",
"wheel_zoom"
],
title="Select",
)
fig01.add_tile(tile_provider)
fig01.scatter("x", "y", source=s1, alpha=0.6, size=8)
# create second subplot
s2 = ColumnDataSource(data=dict(x=[], y=[], ind=[], name=[]))
color_mapper = LinearColorMapper(palette='Magma256', low=1, high=100)
fig02 = figure(
x_axis_type="mercator",
y_axis_type="mercator",
plot_width=plot_width,
plot_height=plot_height,
x_range=(num.min(x), num.max(x)),
y_range=(num.min(y), num.max(y)),
tools=["box_zoom", "wheel_zoom", "reset", "save"],
title="Stations selected for Array",
)
fig02.add_tile(tile_provider)
fig02.scatter("x",
"y",
source=s2,
alpha=1,
color={
'field': 'ind',
'transform': color_mapper
},
size=8)
x_event, y_event = merc(event.lat, event.lon)
fig01.scatter(x_event, y_event, size=8, color="red")
fig02.scatter(x_event, y_event, size=8, color="red")
columns = [
TableColumn(field="x", title="X axis"),
TableColumn(field="y", title="Y axis"),
TableColumn(field="ind", title="indices"),
TableColumn(field="name", title="name"),
]
table = DataTable(
source=s2,
columns=columns,
width=400,
height=600,
sortable=True,
selectable=True,
editable=True,
)
source_count = 0
callback_slider = CustomJS(code="""
source_count = slider.value;
""")
global slider
slider = Slider(start=1, end=100, value=1, step=1, title="Array number")
slider.js_on_change('value', callback_slider)
s1.selected.js_on_change(
"indices",
CustomJS(args=dict(s1=s1, s2=s2, s3=slider, table=table),
code="""
var inds = cb_obj.indices;
var d1 = s1.data;
var d2 = s2.data;
const A = s3.value;
for (var i = 0; i < inds.length; i++) {
d2['x'].push(d1['x'][inds[i]])
d2['y'].push(d1['y'][inds[i]])
d2['name'].push(d1['name'][inds[i]])
d2['ind'].push(A)
}
s2.change.emit();
table.change.emit();
s2.data = s2.data;
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "name, x, y, ind\\n";
for (i = 0; i < inds.length; i++) {
out += data['name'][inds[i]] + "," + data['x'][inds[i]] + "," + data['y'][inds[i]] + "," + data['ind'][inds[i]] + "\\n";
}
var file = new Blob([out], {type: 'text/plain'});
"""),
),
savebutton = Button(label="Save", button_type="success")
savebutton.callback = CustomJS(
args=dict(source_data=s1),
code="""
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "name, x, y, ind\\n";
for (i = 0; i < inds.length; i++) {
out += data['name'][inds[i]] + "," + data['x'][inds[i]] + "," + data['y'][inds[i]] + "," + data['ind'][inds[i]] + "\\n";
}
var file = new Blob([out], {type: 'text/plain'});
var elem = window.document.createElement('a');
elem.href = window.URL.createObjectURL(file);
elem.download = 'arrays.txt';
document.body.appendChild(elem);
elem.click();
document.body.removeChild(elem);
""",
)
tooltips = [
("X:", "@x"),
("Y:", "@y"),
("Array:", "@ind"),
("Station:", "@name"),
]
fig01.add_tools(HoverTool(tooltips=tooltips))
fig02.add_tools(HoverTool(tooltips=tooltips))
fig03.add_tools(HoverTool(tooltips=tooltips))
endbutton = Button(label="End and proceed", button_type="success")
endbutton.on_click(button_callback)
clearbutton = Button(label="Clear all", button_type="success")
clearbutton.on_click(clearbuttonlast_callback)
clearbuttonlast = Button(label="Clear last selection",
button_type="success")
clearbuttonlast.on_click(clearbuttonlast_callback)
clearbuttonone = Button(label="Remove one from list",
button_type="success")
clearbuttonone.on_click(clearbuttonone_callback)
b = Button(label="Reset all plots")
b.js_on_click(
CustomJS(code="""\
document.querySelectorAll('.bk-tool-icon-reset[title="Reset"]').forEach(d => d.click())
"""))
#layout = grid([fig01, fig02, table, fig03, slider, clearbuttonlast, clearbutton, savebutton, endbutton], ncols=3, nrows=4)
global text_input
text_input = TextInput(value="1", title="Array number:")
buttons = column(clearbuttonlast, clearbuttonone, clearbutton, savebutton,
endbutton)
inputs = column(text_input, slider)
layout_grid = layout([fig01, fig02, buttons], [fig03, inputs, table])
#curdoc().add_root(layout)
cluster_result = []
doc.add_root(layout_grid)
#global session
#session = push_session(curdoc())
#curdoc().add_periodic_callback(update, 100)
#session.show(layout)
doc.add_periodic_callback(update, 900)
curdoc().title = "Array selection"
'CDS': CDSimages['weighted'],
'NSA': NSA_weighted
},
code="""
FFidx = Math.round((cb_obj.value - cb_obj.start ) / cb_obj.step)
CDS[0].data.imageData = [NSA.map(PF => PF.map(f => f[FFidx][0])).flat()]
CDS[1].data.imageData = [NSA.map(PF => PF.map(f => f[FFidx][1])).flat()]
CDS[0].change.emit();
CDS[1].change.emit();
""")
sliderW = Slider(start=0,
end=1,
value=0,
step=1.0 / (nFFs - 1),
title="Fat fraction")
sliderW.js_on_change('value', sliderCallbackW)
sliderCallbackUW = CustomJS(args={
'CDS': CDSimages['unweighted'],
'NSA': NSA_equalWeights
},
code="""
FFidx = Math.round((cb_obj.value - cb_obj.start ) / cb_obj.step)
CDS[0].data.imageData = [NSA.map(PF => PF.map(f => f[FFidx][0])).flat()]
CDS[1].data.imageData = [NSA.map(PF => PF.map(f => f[FFidx][1])).flat()]
CDS[0].change.emit();
CDS[1].change.emit();
""")
sliderUW = Slider(start=0,
end=1,
value=0,
def plot_dispersion_bokeh(filename, period_array, curve_data_array,
boundary_data, style_parameter):
'''
Plot dispersion maps and curves using bokeh
Input:
filename is the filename of the resulting html file
period_array is a list of period
curve_data_array is a list of dispersion curves
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
# ==============================
# prepare data
map_data_all_slices_velocity = []
map_data_all_slices_period = []
map_data_all_slices_color = []
colorbar_data_all_left = []
colorbar_data_all_right = []
nperiod = len(period_array)
ncurve = len(curve_data_array)
ncolor = len(palette)
palette_r = palette[::-1]
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
for iperiod in range(nperiod):
one_slice_lat_list = []
one_slice_lon_list = []
one_slice_vel_list = []
map_period = period_array[iperiod]
for icurve in range(ncurve):
acurve = curve_data_array[icurve]
curve_lat = acurve['latitude']
curve_lon = acurve['longitude']
curve_vel = acurve['velocity']
curve_period = acurve['period']
one_slice_lat_list.append(curve_lat)
one_slice_lon_list.append(curve_lon)
if map_period in curve_period:
curve_period_index = curve_period.index(map_period)
one_slice_vel_list.append(curve_vel[curve_period_index])
else:
one_slice_vel_list.append(style_parameter['nan_value'])
# get color for dispersion values
one_slice_vel_mean = np.nanmean(one_slice_vel_list)
one_slice_vel_std = np.nanstd(one_slice_vel_list)
color_min = one_slice_vel_mean - one_slice_vel_std * style_parameter[
'spread_factor']
color_max = one_slice_vel_mean + one_slice_vel_std * style_parameter[
'spread_factor']
color_step = (color_max - color_min) * 1. / ncolor
one_slice_color_list = get_color_list(one_slice_vel_list,color_min,color_max,palette_r,\
style_parameter['nan_value'],style_parameter['nan_color'])
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
if one_slice_lat_list:
map_data_all_slices_velocity.append(one_slice_vel_list)
map_data_all_slices_period.append(
'Period: {0:6.1f} s'.format(map_period))
map_data_all_slices_color.append(one_slice_color_list)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
# get location for all points
map_lat_list, map_lon_list = [], []
map_lat_label_list, map_lon_label_list = [], []
for i in range(ncurve):
acurve = curve_data_array[i]
map_lat_list.append(acurve['latitude'])
map_lon_list.append(acurve['longitude'])
map_lat_label_list.append('Lat: {0:12.3f}'.format(acurve['latitude']))
map_lon_label_list.append('Lon: {0:12.3f}'.format(acurve['longitude']))
# data for the map view plot
map_view_label_lon = style_parameter['map_view_period_label_lon']
map_view_label_lat = style_parameter['map_view_period_label_lat']
map_data_one_slice = map_data_all_slices_color[
style_parameter['map_view_default_index']]
map_data_one_slice_period = map_data_all_slices_period[
style_parameter['map_view_default_index']]
map_data_one_slice_bokeh = ColumnDataSource(data=dict(map_lat_list=map_lat_list,\
map_lon_list=map_lon_list,\
map_data_one_slice=map_data_one_slice))
map_data_one_slice_period_bokeh = ColumnDataSource(
data=dict(lat=[map_view_label_lat],
lon=[map_view_label_lon],
map_period=[map_data_one_slice_period]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices_color=map_data_all_slices_color,\
map_data_all_slices_period=map_data_all_slices_period))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
# data for dispersion curves
curve_default_index = style_parameter['curve_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[map_lat_list[curve_default_index]],\
lon=[map_lon_list[curve_default_index]],\
color=[map_data_one_slice[curve_default_index]],\
index=[curve_default_index]))
selected_curve_data = curve_data_array[curve_default_index]
selected_curve_data_bokeh = ColumnDataSource(data=dict(curve_period=selected_curve_data['period'],\
curve_velocity=selected_curve_data['velocity']))
period_all = []
velocity_all = []
for acurve in curve_data_array:
period_all.append(acurve['period'])
velocity_all.append(acurve['velocity'])
curve_data_all_bokeh = ColumnDataSource(
data=dict(period_all=period_all, velocity_all=velocity_all))
selected_curve_lat_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lat_label_x']], \
y=[style_parameter['curve_lat_label_y']],\
lat_label=[map_lat_label_list[curve_default_index]]))
selected_curve_lon_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lon_label_x']], \
y=[style_parameter['curve_lon_label_y']],\
lon_label=[map_lon_label_list[curve_default_index]]))
all_curve_lat_label_bokeh = ColumnDataSource(data=dict(
map_lat_label_list=map_lat_label_list))
all_curve_lon_label_bokeh = ColumnDataSource(data=dict(
map_lon_label_list=map_lon_label_list))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add period label
map_view.rect(style_parameter['map_view_period_box_lon'], style_parameter['map_view_period_box_lat'], \
width=style_parameter['map_view_period_box_width'], height=style_parameter['map_view_period_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_period', source=map_data_one_slice_period_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
# plot dots
map_view.circle('map_lon_list', 'map_lat_list', color='map_data_one_slice', \
source=map_data_one_slice_bokeh, size=style_parameter['marker_size'],\
line_width=0.2, line_color='black', alpha=1.0,\
selection_color='map_data_one_slice', selection_line_color='black',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='map_data_one_slice',\
nonselection_line_color='black', nonselection_line_alpha=1.0)
map_view.circle('lon', 'lat', color='color', source=selected_dot_on_map_bokeh, \
line_color='#00ff00', line_width=4.0, alpha=1.0, \
size=style_parameter['selected_marker_size'])
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
fill_color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
curve_fig = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(style_parameter['curve_y_min'],style_parameter['curve_y_max']), \
x_range=(style_parameter['curve_x_min'],style_parameter['curve_x_max']),x_axis_type='log',\
tools=['save','box_zoom','wheel_zoom','reset','crosshair','pan'],
title=style_parameter['curve_title'])
# ------------------------------
curve_fig.rect([style_parameter['curve_label_box_x']], [style_parameter['curve_label_box_y']], \
width=style_parameter['curve_label_box_width'], height=style_parameter['curve_label_box_height'], \
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
curve_fig.text('x', 'y', \
'lat_label', source=selected_curve_lat_label_bokeh)
curve_fig.text('x', 'y', \
'lon_label', source=selected_curve_lon_label_bokeh)
# ------------------------------
curve_fig.line('curve_period',
'curve_velocity',
source=selected_curve_data_bokeh,
color='black')
curve_fig.circle('curve_period',
'curve_velocity',
source=selected_curve_data_bokeh,
size=5,
color='black')
# ------------------------------
curve_fig.title.text_font_size = style_parameter['title_font_size']
curve_fig.title.align = 'center'
curve_fig.title.text_font_style = 'normal'
curve_fig.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig.xaxis.axis_label_text_font_style = 'normal'
curve_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig.yaxis.axis_label_text_font_style = 'normal'
curve_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.xgrid.grid_line_dash = [4, 2]
curve_fig.ygrid.grid_line_dash = [4, 2]
curve_fig.xaxis[0].formatter = PrintfTickFormatter(format="%4.0f")
curve_fig.toolbar.logo = None
curve_fig.toolbar_location = 'above'
curve_fig.toolbar_sticky = False
# ==============================
map_data_one_slice_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh), code="""
var inds = cb_obj.indices
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_slice['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_slice['map_lon_list'][inds]]
selected_dot_on_map_bokeh.data['color'] = [new_slice['map_data_one_slice'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][inds]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][inds]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[inds]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[inds]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
map_data_one_slice_bokeh.selected.js_on_change('indices',
map_data_one_slice_js)
# ==============================
period_slider_callback = CustomJS(args=dict(map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh, \
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_period_bokeh=map_data_one_slice_period_bokeh),\
code="""
var p_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
var map_data_new_slice = map_data_all_slices['map_data_all_slices_color'][p_index]
map_data_one_slice_bokeh.data['map_data_one_slice'] = map_data_new_slice
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][p_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][p_index]
colorbar_data_one_slice_bokeh.change.emit()
var selected_index = selected_dot_on_map_bokeh.data['index']
selected_dot_on_map_bokeh.data['color'] = [map_data_new_slice[selected_index]]
selected_dot_on_map_bokeh.change.emit()
map_data_one_slice_period_bokeh.data['map_period'] = [map_data_all_slices['map_data_all_slices_period'][p_index]]
map_data_one_slice_period_bokeh.change.emit()
""")
period_slider = Slider(start=0, end=nperiod-1, value=style_parameter['map_view_default_index'], \
step=1, title=style_parameter['period_slider_title'], \
width=style_parameter['period_slider_plot_width'],\
height=50)
period_slider.js_on_change('value', period_slider_callback)
period_slider_callback.args['period_index'] = period_slider
# ==============================
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh),\
code="""
var c_index = Math.round(cb_obj.value)
var one_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['index'] = [c_index]
selected_dot_on_map_bokeh.data['lat'] = [one_slice['map_lat_list'][c_index]]
selected_dot_on_map_bokeh.data['lon'] = [one_slice['map_lon_list'][c_index]]
selected_dot_on_map_bokeh.data['color'] = [one_slice['map_data_one_slice'][c_index]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][c_index]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][c_index]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[c_index]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[c_index]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['curve_plot_width'],\
height=50)
curve_slider.js_on_change('value', curve_slider_callback)
curve_slider_callback.args['curve_index'] = curve_slider
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_fig = Column(period_slider, map_view, colorbar_fig, annotating_fig01,\
width=style_parameter['left_column_width'] )
right_fig = Column(curve_slider, curve_fig, annotating_fig02, \
width=style_parameter['right_column_width'] )
layout = Row(left_fig, right_fig)
save(layout)
def getPlotByYear(title,plotdict,classification,parameters,contextdict,maxval):
category=str(contextdict["name"][0]).replace("'","").encode('ascii','ignore').decode("utf-8")
classification=sorted(classification)
if (category == 'All'):
hover = HoverTool(tooltips="""
<div style=" opacity: .8; padding: 5px; background-color: @type_color;color: @font_color;>
<h1 style="margin: 0; font-size: 12px;"> All museums:</h1>
<h1 style="margin: 0; font-size: 24px;"><strong> @counts </strong></h1>
</div>
"""
)
else:
hover = HoverTool(tooltips="""
<div style=" opacity: .8; padding: 5px; background-color: @type_color;color: @font_color;>
<h1 style="margin: 0; font-size: 12px;"> @classification</h1>
<h1 style="margin: 0; font-size: 24px;"><strong> @counts </strong></h1>
</div>
"""
)
bucketlen=0
if ("Time" in classification):
bucketlen=len(plotdict["Time"])
classification.remove("Time")
classlen=len(classification)
for i, l in enumerate(classification):
classification[i]=bokutils.makeLegendKey(l)
shortkeydict={}
for key,val in plotdict.iteritems():
shortkeydict[bokutils.makeLegendKey(key)]=plotdict[key]
plotdict=shortkeydict
my_palette=bokutils.getColors(classlen,True,False)
colordict={}
for i, val in enumerate(my_palette):
colordict[i]=val
valuearr=[]
for year in range(0,bucketlen):
counts=[]
for c in classification:
if (c in plotdict):
counts.append(int(plotdict[c][year]+float(0.5)))
else:
counts.append(0)
valuearr.append(counts)
plotdict=None
labellist=[]
for l in counts:
labellist.append(str(l))
source = ColumnDataSource(data=dict(classification=classification,
counts=counts,
labellist=labellist,
type_color=[colordict[x] for x in colordict],
font_color=[bokutils.contrasting_text_color(colordict[x]) for x in colordict]))
allsource = ColumnDataSource(data=dict(plotlist=valuearr))
if (bokutils.ACCUMULATE_TRUE in parameters):
acc=True
if (bokutils.ACCUMULATE_FALSE in parameters):
acc=False
paramsource = ColumnDataSource(data=dict(params=[acc,False,"Time"]))
plotwidth=bokutils.PLOT_WIDTH
p = figure(x_range=classification, plot_height=bokutils.PLOT_HEIGHT,
plot_width=plotwidth,
tools=[hover],
toolbar_location=None,
title=title)
# set fixed padding for small numbers, but could also be percentage
if (len(classification) < 6):
p.x_range.range_padding = 0.7
p.vbar(x='classification', top='counts',width=0.9,line_width=5.9 , source=source, legend="classification",
line_color='white', fill_color=factor_cmap('classification', palette=my_palette, factors=classification))
p.xgrid.grid_line_color = None
p.y_range.start = 0
#p.x_range.factors = classification[::-1]
#p.y_range = Range1d(start=0,end=4000,bounds='auto')
p.y_range.end = maxval+int(0.05*float(maxval))
blabel = LabelSet(x='classification', y='counts', text='labellist', level='glyph',
x_offset=-11, y_offset=2, source=source, render_mode='canvas')
p.add_layout(blabel)
p.legend.orientation = "vertical"
p.xaxis.major_label_orientation = 1.2
new_legend = p.legend[0]
p.legend[0].plot = None
p.add_layout(new_legend, 'right')
slider = Slider(start=bokutils.FIRST_YEAR,
end=bokutils.LAST_YEAR,
value=bokutils.LAST_YEAR,
step=1,
title='Year',
bar_color="#b3cccc")
callback = CustomJS(args=dict(source=source,slider=slider,plot=p,window=None,source2=allsource,source3=paramsource),
code="""
var cb;
cb = function (source,
slider,
plot,
window,
source2,
source3)
{
var al, arr, data, end, i;
source = (source === undefined) ? source: source;
//console.log("slider "+slider);
slider = (slider === undefined) ? slider: slider;
plot = (plot === undefined) ? p: plot;
window = (window === undefined) ? null: window;
data = source.data;
var arr = source.data["counts"];
var labels = source.data["labellist"];
var allcounts=source2.data["plotlist"];
var al = arr.length;
//console.log(al);
var i =0;
while (i < al)
{
arr[i] = 0;
i = i + 1;
}
i =0;
var startidx=slider.value - 1960;
//console.log("START"+startidx);
// No accumulation
i=0;
while (i < al)
{
arr[i] = arr[i] = allcounts[startidx][i];
labels[i]=arr[i]+"";
i = i + 1;
}
source.change.emit();
return null;
};
cb(source, slider, plot,window,source2,source3);
""")
slider.js_on_change('value', callback)
wb=widgetbox(children=[slider], sizing_mode='scale_width')
thisrow=Column(children=[p, wb],sizing_mode='scale_both')
return thisrow
def main():
print('''Please select the CSV dataset you\'d like to use.
The dataset should contain these columns:
- metric to apply threshold to
- indicator of event to detect (e.g. malicious activity)
- Please label this as 1 or 0 (true or false);
This will not work otherwise!
''')
# Import the dataset
imported_data = None
while isinstance(imported_data, pd.DataFrame) == False:
file_path = input('Enter the path of your dataset: ')
imported_data = file_to_df(file_path)
time.sleep(1)
print(f'''\nGreat! Here is a preview of your data:
Imported fields:''')
# List headers by column index.
cols = list(imported_data.columns)
for index in range(len(cols)):
print(f'{index}: {cols[index]}')
print(f'Number of records: {len(imported_data.index)}\n')
# Preview the DataFrame
time.sleep(1)
print(imported_data.head(), '\n')
# Prompt for the metric and source of truth.
time.sleep(1)
metric_col, indicator_col = columns_picker(cols)
# User self-validation.
col_check = input('Can you confirm if this is correct? (y/n): ').lower()
# If it's wrong, let them try again
while col_check != 'y':
metric_col, indicator_col = columns_picker(cols)
col_check = input(
'Can you confirm if this is correct? (y/n): ').lower()
else:
print(
'''\nGreat! Thanks for your patience. Generating summary stats now..\n'''
)
# Generate summary stats.
time.sleep(1)
malicious, normal = classification_split(imported_data, metric_col,
indicator_col)
mal_mean = malicious.mean()
mal_stddev = malicious.std()
mal_count = malicious.size
mal_median = malicious.median()
norm_mean = normal.mean()
norm_stddev = normal.std()
norm_count = normal.size
norm_median = normal.median()
print(f'''Normal vs Malicious Summary (metric = {metric_col}):
Normal:
-----------------------------
Observations: {round(norm_count, 2)}
Average: {round(norm_mean, 2)}
Median: {round(norm_median, 2)}
Standard Deviation: {round(norm_stddev, 2)}
Malicious:
-----------------------------
Observations: {round(mal_count, 2)}
Average: {round(mal_mean, 2)}
Median: {round(mal_median, 2)}
Standard Deviation: {round(mal_stddev, 2)}
''')
# Insights and advisories
# Provide the accuracy metrics of a generic threshold at avg + 3 std deviations
generic_threshold = confusion_matrix(
malicious, normal, threshold_calc(norm_mean, norm_stddev, 3))
time.sleep(1)
print(
f'''A threshold at (average + 3x standard deviations) {metric_col} would result in:
- True Positives (correctly identified malicious events: {generic_threshold['TP']:,}
- False Positives (wrongly identified normal events: {generic_threshold['FP']:,}
- True Negatives (correctly identified normal events: {generic_threshold['TN']:,}
- False Negatives (wrongly identified malicious events: {generic_threshold['FN']:,}
Accuracy Metrics:
- Precision (what % of events above threshold are actually malicious): {round(generic_threshold['precision'] * 100, 1)}%
- Recall (what % of malicious events did we catch): {round(generic_threshold['recall'] * 100, 1)}%
- F1 Score (blends precision and recall): {round(generic_threshold['f1_score'] * 100, 1)}%'''
)
# Distribution skew check.
if norm_mean >= (norm_median * 1.1):
time.sleep(1)
print(
f'''\nYou may want to be cautious as your normal traffic\'s {metric_col}
has a long tail towards high values. The median is {round(norm_median, 2)}
compared to {round(norm_mean, 2)} for the average.''')
if mal_mean < threshold_calc(norm_mean, norm_stddev, 2):
time.sleep(1)
print(
f'''\nWarning: you may find it difficult to avoid false positives as the average
{metric_col} for malicious traffic is under the 95th percentile of the normal traffic.'''
)
# For fun/anticipation. Actually a nerd joke because of the method we'll be using.
if '-q' not in sys.argv[1:]:
time.sleep(1)
play_a_game.billy()
decision = input('yes/no: ').lower()
while decision != 'yes':
time.sleep(1)
print('...That\'s no fun...')
decision = input('Let\'s try that again: ').lower()
# Let's get to the simulations!
time.sleep(1)
print('''\nInstead of manually experimenting with threshold multipliers,
let\'s simulate a range of options and see what produces the best result.
This is similar to what is known as \"Monte Carlo simulation\".\n''')
# Initialize session name & create app folder if there isn't one.
time.sleep(1)
session_name = input('Please provide a name for this project/session: ')
session_folder = make_folder(session_name)
# Generate list of multipliers to iterate over.
time.sleep(1)
mult_start = float(
input(
'Please provide the minimum multiplier you want to start at. We recommend 2: '
))
# Set the max to how many std deviations away the sample max is.
mult_end = (imported_data[metric_col].max() - norm_mean) / norm_stddev
mult_interval = float(
input('Please provide the desired gap between multiplier options: '))
# range() only allows integers, let's manually populate a list
multipliers = []
mult_counter = mult_start
while mult_counter < mult_end:
multipliers.append(round(mult_counter, 2))
mult_counter += mult_interval
print('Generating simulations..\n')
# Run simulations using our multipliers.
simulations = monte_carlo(malicious, normal, norm_mean, norm_stddev,
multipliers)
print('Done!')
time.sleep(1)
# Save simulations as CSV for later use.
simulation_filepath = os.path.join(
session_folder, f'{session_name}_simulation_results.csv')
simulations.to_csv(simulation_filepath, index=False)
print(f'Saved results to: {simulation_filepath}')
# Find the first threshold with the highest F1 score.
# This provides a balanced approach between precision and recall.
f1_max = simulations[simulations.f1_score ==
simulations.f1_score.max()].head(1)
f1_max_mult = f1_max.squeeze()['multiplier']
time.sleep(1)
print(
f'''\nBased on the F1 score metric, setting a threshold at {round(f1_max_mult,1)} standard deviations
above the average magnitude might provide optimal results.\n''')
time.sleep(1)
print(f'''{f1_max}
We recommend that you skim the CSV and the following visualization outputs
to sanity check results and make your own judgement.
''')
# Now for the fun part..generating the visualizations via Bokeh.
# Header & internal CSS.
title_text = '''
<style>
@font-face {
font-family: RobotoBlack;
src: url(fonts/Roboto-Black.ttf);
font-weight: bold;
}
@font-face {
font-family: RobotoBold;
src: url(fonts/Roboto-Bold.ttf);
font-weight: bold;
}
@font-face {
font-family: RobotoRegular;
src: url(fonts/Roboto-Regular.ttf);
}
body {
background-color: #f2ebe6;
}
title_header {
font-size: 80px;
font-style: bold;
font-family: RobotoBlack, Helvetica;
font-weight: bold;
margin-bottom: -200px;
}
h1, h2, h3 {
font-family: RobotoBlack, Helvetica;
color: #313596;
}
p {
font-size: 12px;
font-family: RobotoRegular
}
b {
color: #58c491;
}
th, td {
text-align:left;
padding: 5px;
}
tr:nth-child(even) {
background-color: white;
opacity: .7;
}
.vertical {
border-left: 1px solid black;
height: 190px;
}
</style>
<title_header style="text-align:left; color: white;">
Cream.
</title_header>
<p style="font-family: RobotoBold, Helvetica;
font-size:18px;
margin-top: 0px;
margin-left: 5px;">
Because time is money, and <b style="font-size=18px;">"Cash Rules Everything Around Me"</b>.
</p>
</div>
'''
title_div = Div(text=title_text,
width=800,
height=160,
margin=(40, 0, 0, 70))
# Summary stats from earlier.
summary_text = f'''
<h1>Results Overview</h1>
<i>metric = magnitude</i>
<table style="width:100%">
<tr>
<th>Metric</th>
<th>Normal Events</th>
<th>Malicious Events</th>
</tr>
<tr>
<td>Observations</td>
<td>{norm_count:,}</td>
<td>{mal_count:,}</td>
</tr>
<tr>
<td>Average</td>
<td>{round(norm_mean, 2):,}</td>
<td>{round(mal_mean, 2):,}</td>
</tr>
<tr>
<td>Median</td>
<td>{round(norm_median, 2):,}</td>
<td>{round(mal_median, 2):,}</td>
</tr>
<tr>
<td>Standard Deviation</td>
<td>{round(norm_stddev, 2):,}</td>
<td>{round(mal_stddev, 2):,}</td>
</tr>
</table>
'''
summary_div = Div(text=summary_text,
width=470,
height=320,
margin=(3, 0, -70, 73))
# Results of the hypothetical threshold.
hypothetical = f'''
<h1>"Rule of thumb" Hypothetical Threshold</h1>
<p>A threshold at <i>(average + 3x standard deviations)</i> {metric_col} would result in:</p>
<ul>
<li>True Positives (correctly identified malicious events:
<b>{generic_threshold['TP']:,}</b></li>
<li>False Positives (wrongly identified normal events:
<b>{generic_threshold['FP']:,}</b></li>
<li>True Negatives (correctly identified normal events:
<b>{generic_threshold['TN']:,}</b></li>
<li>False Negatives (wrongly identified malicious events:
<b>{generic_threshold['FN']:,}</b></li>
</ul>
<h2>Accuracy Metrics</h2>
<ul>
<li>Precision (what % of events above threshold are actually malicious):
<b>{round(generic_threshold['precision'] * 100, 1)}%</b></li>
<li>Recall (what % of malicious events did we catch):
<b>{round(generic_threshold['recall'] * 100, 1)}%</b></li>
<li>F1 Score (blends precision and recall):
<b>{round(generic_threshold['f1_score'] * 100, 1)}%</b></li>
</ul>
'''
hypo_div = Div(text=hypothetical,
width=600,
height=320,
margin=(5, 0, -70, 95))
line = '''
<div class="vertical"></div>
'''
vertical_line = Div(text=line,
width=20,
height=320,
margin=(80, 0, -70, -10))
# Let's get the exploratory charts generated.
malicious_hist, malicious_edge = np.histogram(malicious, bins=100)
mal_hist_df = pd.DataFrame({
'metric': malicious_hist,
'left': malicious_edge[:-1],
'right': malicious_edge[1:]
})
normal_hist, normal_edge = np.histogram(normal, bins=100)
norm_hist_df = pd.DataFrame({
'metric': normal_hist,
'left': normal_edge[:-1],
'right': normal_edge[1:]
})
exploratory = figure(
plot_width=plot_width,
plot_height=plot_height,
sizing_mode='fixed',
title=f'{metric_col.capitalize()} Distribution (σ = std dev)',
x_axis_label=f'{metric_col.capitalize()}',
y_axis_label='Observations')
exploratory.title.text_font_size = title_font_size
exploratory.border_fill_color = cell_bg_color
exploratory.border_fill_alpha = cell_bg_alpha
exploratory.background_fill_color = cell_bg_color
exploratory.background_fill_alpha = plot_bg_alpha
exploratory.min_border_left = left_border
exploratory.min_border_right = right_border
exploratory.min_border_top = top_border
exploratory.min_border_bottom = bottom_border
exploratory.quad(bottom=0,
top=mal_hist_df.metric,
left=mal_hist_df.left,
right=mal_hist_df.right,
legend_label='malicious',
fill_color=malicious_color,
alpha=.85,
line_alpha=.35,
line_width=.5)
exploratory.quad(bottom=0,
top=norm_hist_df.metric,
left=norm_hist_df.left,
right=norm_hist_df.right,
legend_label='normal',
fill_color=normal_color,
alpha=.35,
line_alpha=.35,
line_width=.5)
exploratory.add_layout(
Arrow(end=NormalHead(fill_color=malicious_color, size=10,
line_alpha=0),
line_color=malicious_color,
x_start=mal_mean,
y_start=mal_count,
x_end=mal_mean,
y_end=0))
arrow_label = Label(x=mal_mean,
y=mal_count,
y_offset=5,
text='Malicious Events',
text_font_style='bold',
text_color=malicious_color,
text_font_size='10pt')
exploratory.add_layout(arrow_label)
exploratory.xaxis.formatter = NumeralTickFormatter(format='0,0')
exploratory.yaxis.formatter = NumeralTickFormatter(format='0,0')
# 3 sigma reference line
sigma_ref(exploratory, norm_mean, norm_stddev)
exploratory.legend.location = "top_right"
exploratory.legend.background_fill_alpha = .3
# Zoomed in version
overlap_view = figure(
plot_width=plot_width,
plot_height=plot_height,
sizing_mode='fixed',
title=f'Overlap Highlight',
x_axis_label=f'{metric_col.capitalize()}',
y_axis_label='Observations',
y_range=(0, mal_count * .33),
x_range=(norm_mean + (norm_stddev * 2.5), mal_mean + (mal_stddev * 3)),
)
overlap_view.title.text_font_size = title_font_size
overlap_view.border_fill_color = cell_bg_color
overlap_view.border_fill_alpha = cell_bg_alpha
overlap_view.background_fill_color = cell_bg_color
overlap_view.background_fill_alpha = plot_bg_alpha
overlap_view.min_border_left = left_border
overlap_view.min_border_right = right_border
overlap_view.min_border_top = top_border
overlap_view.min_border_bottom = bottom_border
overlap_view.quad(bottom=0,
top=mal_hist_df.metric,
left=mal_hist_df.left,
right=mal_hist_df.right,
legend_label='malicious',
fill_color=malicious_color,
alpha=.85,
line_alpha=.35,
line_width=.5)
overlap_view.quad(bottom=0,
top=norm_hist_df.metric,
left=norm_hist_df.left,
right=norm_hist_df.right,
legend_label='normal',
fill_color=normal_color,
alpha=.35,
line_alpha=.35,
line_width=.5)
overlap_view.xaxis.formatter = NumeralTickFormatter(format='0,0')
overlap_view.yaxis.formatter = NumeralTickFormatter(format='0,0')
sigma_ref(overlap_view, norm_mean, norm_stddev)
overlap_view.legend.location = "top_right"
overlap_view.legend.background_fill_alpha = .3
# Probability Density - bigger bins for sparser malicous observations
malicious_hist_dense, malicious_edge_dense = np.histogram(malicious,
density=True,
bins=50)
mal_hist_dense_df = pd.DataFrame({
'metric': malicious_hist_dense,
'left': malicious_edge_dense[:-1],
'right': malicious_edge_dense[1:]
})
normal_hist_dense, normal_edge_dense = np.histogram(normal,
density=True,
bins=100)
norm_hist_dense_df = pd.DataFrame({
'metric': normal_hist_dense,
'left': normal_edge_dense[:-1],
'right': normal_edge_dense[1:]
})
density = figure(plot_width=plot_width,
plot_height=plot_height,
sizing_mode='fixed',
title='Probability Density',
x_axis_label=f'{metric_col.capitalize()}',
y_axis_label='% of Group Total')
density.title.text_font_size = title_font_size
density.border_fill_color = cell_bg_color
density.border_fill_alpha = cell_bg_alpha
density.background_fill_color = cell_bg_color
density.background_fill_alpha = plot_bg_alpha
density.min_border_left = left_border
density.min_border_right = right_border
density.min_border_top = top_border
density.min_border_bottom = bottom_border
density.quad(bottom=0,
top=mal_hist_dense_df.metric,
left=mal_hist_dense_df.left,
right=mal_hist_dense_df.right,
legend_label='malicious',
fill_color=malicious_color,
alpha=.85,
line_alpha=.35,
line_width=.5)
density.quad(bottom=0,
top=norm_hist_dense_df.metric,
left=norm_hist_dense_df.left,
right=norm_hist_dense_df.right,
legend_label='normal',
fill_color=normal_color,
alpha=.35,
line_alpha=.35,
line_width=.5)
density.xaxis.formatter = NumeralTickFormatter(format='0,0')
density.yaxis.formatter = NumeralTickFormatter(format='0.000%')
sigma_ref(density, norm_mean, norm_stddev)
density.legend.location = "top_right"
density.legend.background_fill_alpha = .3
# Simulation Series to be used
false_positives = simulations.FP
false_negatives = simulations.FN
multiplier = simulations.multiplier
precision = simulations.precision
recall = simulations.recall
f1_score = simulations.f1_score
f1_max = simulations[simulations.f1_score == simulations.f1_score.max(
)].head(1).squeeze()['multiplier']
# False Positives vs False Negatives
errors = figure(plot_width=plot_width,
plot_height=plot_height,
sizing_mode='fixed',
x_range=(multiplier.min(), multiplier.max()),
y_range=(0, false_positives.max()),
title='False Positives vs False Negatives',
x_axis_label='Multiplier',
y_axis_label='Count')
errors.title.text_font_size = title_font_size
errors.border_fill_color = cell_bg_color
errors.border_fill_alpha = cell_bg_alpha
errors.background_fill_color = cell_bg_color
errors.background_fill_alpha = plot_bg_alpha
errors.min_border_left = left_border
errors.min_border_right = right_border
errors.min_border_top = top_border
errors.min_border_bottom = right_border
errors.line(multiplier,
false_positives,
legend_label='false positives',
line_width=2,
color=fp_color)
errors.line(multiplier,
false_negatives,
legend_label='false negatives',
line_width=2,
color=fn_color)
errors.yaxis.formatter = NumeralTickFormatter(format='0,0')
errors.extra_y_ranges = {"y2": Range1d(start=0, end=1.1)}
errors.add_layout(
LinearAxis(y_range_name="y2",
axis_label="Score",
formatter=NumeralTickFormatter(format='0.00%')), 'right')
errors.line(multiplier,
f1_score,
line_width=2,
color=f1_color,
legend_label='F1 Score',
y_range_name="y2")
# F1 Score Maximization point
f1_thresh = Span(location=f1_max,
dimension='height',
line_color=f1_color,
line_dash='dashed',
line_width=2)
f1_label = Label(x=f1_max + .05,
y=180,
y_units='screen',
text=f'F1 Max: {round(f1_max,2)}',
text_font_size='10pt',
text_font_style='bold',
text_align='left',
text_color=f1_color)
errors.add_layout(f1_thresh)
errors.add_layout(f1_label)
errors.legend.location = "top_right"
errors.legend.background_fill_alpha = .3
# False Negative Weighting.
# Intro.
weighting_intro = f'''
<h3>Error types differ in impact.</h3>
<p>In the case of security incidents, a false negative,
though possibly rarer than false positives, is likely more costly. For example, downtime suffered
from a DDoS attack (lost sales/customers) incurs more loss than time wasted chasing a false positive
(labor hours). </p>
<p>Try playing around with the slider to the right to see how your thresholding strategy might need to change
depending on the relative weight of false negatives to false positives. What does it look like at
1:1, 50:1, etc.?</p>
'''
weighting_div = Div(text=weighting_intro,
width=420,
height=180,
margin=(0, 75, 0, 0))
# Now for the weighted errors viz
default_weighting = 10
initial_fp_cost = 100
simulations['weighted_FN'] = simulations.FN * default_weighting
weighted_fn = simulations.weighted_FN
simulations[
'total_weighted_error'] = simulations.FP + simulations.weighted_FN
total_weighted_error = simulations.total_weighted_error
simulations['fp_cost'] = initial_fp_cost
fp_cost = simulations.fp_cost
simulations[
'total_estimated_cost'] = simulations.total_weighted_error * simulations.fp_cost
total_estimated_cost = simulations.total_estimated_cost
twe_min = simulations[simulations.total_weighted_error ==
simulations.total_weighted_error.min()].head(
1).squeeze()['multiplier']
twe_min_count = simulations[simulations.multiplier == twe_min].head(
1).squeeze()['total_weighted_error']
generic_twe = simulations[simulations.multiplier.apply(
lambda x: round(x, 2)) == 3.00].squeeze()['total_weighted_error']
comparison = f'''
<p>Based on your inputs, the optimal threshold is around <b>{twe_min}</b>.
This would result in an estimated <b>{int(twe_min_count):,}</b> total weighted errors and
<b>${int(twe_min_count * initial_fp_cost):,}</b> in losses.</p>
<p>The generic threshold of 3.0 standard deviations would result in <b>{int(generic_twe):,}</b>
total weighted errors and <b>${int(generic_twe * initial_fp_cost):,}</b> in losses.</p>
<p>Using the optimal threshold would save <b>${int((generic_twe - twe_min_count) * initial_fp_cost):,}</b>,
reducing costs by <b>{(generic_twe - twe_min_count) / generic_twe * 100:.1f}%</b>
(assuming near-future events are distributed similarly to those from the past).</p>
'''
comparison_div = Div(text=comparison,
width=420,
height=230,
margin=(0, 75, 0, 0))
loss_min = ColumnDataSource(data=dict(multiplier=multiplier,
fp=false_positives,
fn=false_negatives,
weighted_fn=weighted_fn,
twe=total_weighted_error,
fpc=fp_cost,
tec=total_estimated_cost,
precision=precision,
recall=recall,
f1=f1_score))
evaluation = Figure(plot_width=900,
plot_height=520,
sizing_mode='fixed',
x_range=(multiplier.min(), multiplier.max()),
title='Evaluation Metrics vs Total Estimated Cost',
x_axis_label='Multiplier',
y_axis_label='Cost')
evaluation.title.text_font_size = title_font_size
evaluation.border_fill_color = cell_bg_color
evaluation.border_fill_alpha = cell_bg_alpha
evaluation.background_fill_color = cell_bg_color
evaluation.background_fill_alpha = plot_bg_alpha
evaluation.min_border_left = left_border
evaluation.min_border_right = right_border
evaluation.min_border_top = top_border
evaluation.min_border_bottom = bottom_border
evaluation.line('multiplier',
'tec',
source=loss_min,
line_width=3,
line_alpha=0.6,
color=total_weighted_color,
legend_label='Total Estimated Cost')
evaluation.yaxis.formatter = NumeralTickFormatter(format='$0,0')
# Evaluation metrics on second right axis.
evaluation.extra_y_ranges = {"y2": Range1d(start=0, end=1.1)}
evaluation.add_layout(
LinearAxis(y_range_name="y2",
axis_label="Score",
formatter=NumeralTickFormatter(format='0.00%')), 'right')
evaluation.line('multiplier',
'precision',
source=loss_min,
line_width=3,
line_alpha=0.6,
color=precision_color,
legend_label='Precision',
y_range_name="y2")
evaluation.line('multiplier',
'recall',
source=loss_min,
line_width=3,
line_alpha=0.6,
color=recall_color,
legend_label='Recall',
y_range_name="y2")
evaluation.line('multiplier',
'f1',
source=loss_min,
line_width=3,
line_alpha=0.6,
color=f1_color,
legend_label='F1 score',
y_range_name="y2")
evaluation.legend.location = "bottom_right"
evaluation.legend.background_fill_alpha = .3
twe_thresh = Span(location=twe_min,
dimension='height',
line_color=total_weighted_color,
line_dash='dashed',
line_width=2)
twe_label = Label(x=twe_min - .05,
y=240,
y_units='screen',
text=f'Cost Min: {round(twe_min,2)}',
text_font_size='10pt',
text_font_style='bold',
text_align='right',
text_color=total_weighted_color)
evaluation.add_layout(twe_thresh)
evaluation.add_layout(twe_label)
# Add in same f1 thresh as previous viz
evaluation.add_layout(f1_thresh)
evaluation.add_layout(f1_label)
handler = CustomJS(args=dict(source=loss_min,
thresh=twe_thresh,
label=twe_label,
comparison=comparison_div),
code="""
var data = source.data
var ratio = cb_obj.value
var multiplier = data['multiplier']
var fp = data['fp']
var fn = data['fn']
var weighted_fn = data['weighted_fn']
var twe = data['twe']
var fpc = data['fpc']
var tec = data['tec']
var generic_twe = 0
function round(value, decimals) {
return Number(Math.round(value+'e'+decimals)+'e-'+decimals);
}
function comma_sep(x) {
return x.toString().replace(/\B(?<!\.\d*)(?=(\d{3})+(?!\d))/g, ",");
}
for (var i = 0; i < multiplier.length; i++) {
weighted_fn[i] = Math.round(fn[i] * ratio)
twe[i] = weighted_fn[i] + fp[i]
tec[i] = twe[i] * fpc[i]
if (round(multiplier[i],2) == 3.00) {
generic_twe = twe[i]
}
}
var min_loss = Math.min.apply(null,twe)
var new_thresh = 0
for (var i = 0; i < multiplier.length; i++) {
if (twe[i] == min_loss) {
new_thresh = multiplier[i]
thresh.location = new_thresh
thresh.change.emit()
label.x = new_thresh
label.text = `Cost Min: ${new_thresh}`
label.change.emit()
comparison.text = `
<p>Based on your inputs, the optimal threshold is around <b>${new_thresh}</b>.
This would result in an estimated <b>${comma_sep(round(min_loss,0))}</b> total weighted errors and
<b>$${comma_sep(round(min_loss * fpc[i],0))}</b> in losses.</p>
<p>The generic threshold of 3.0 standard deviations would result in <b>${comma_sep(round(generic_twe,0))}</b>
total weighted errors and <b>$${comma_sep(round(generic_twe * fpc[i],0))}</b> in losses.</p>
<p>Using the optimal threshold would save <b>$${comma_sep(round((generic_twe - min_loss) * fpc[i],0))}</b>,
reducing costs by <b>${comma_sep(round((generic_twe - min_loss) / generic_twe * 100,0))}%</b>
(assuming near-future events are distributed similarly to those from the past).</p>
`
comparison.change.emit()
}
}
source.change.emit();
""")
slider = Slider(start=1.0,
end=500,
value=default_weighting,
step=.25,
title="FN:FP Ratio",
bar_color='#FFD100',
height=50,
margin=(5, 0, 5, 0))
slider.js_on_change('value', handler)
cost_handler = CustomJS(args=dict(source=loss_min,
comparison=comparison_div),
code="""
var data = source.data
var new_cost = cb_obj.value
var multiplier = data['multiplier']
var fp = data['fp']
var fn = data['fn']
var weighted_fn = data['weighted_fn']
var twe = data['twe']
var fpc = data['fpc']
var tec = data['tec']
var generic_twe = 0
function round(value, decimals) {
return Number(Math.round(value+'e'+decimals)+'e-'+decimals);
}
function comma_sep(x) {
return x.toString().replace(/\B(?<!\.\d*)(?=(\d{3})+(?!\d))/g, ",");
}
for (var i = 0; i < multiplier.length; i++) {
fpc[i] = new_cost
tec[i] = twe[i] * fpc[i]
if (round(multiplier[i],2) == 3.00) {
generic_twe = twe[i]
}
}
var min_loss = Math.min.apply(null,twe)
var new_thresh = 0
for (var i = 0; i < multiplier.length; i++) {
if (twe[i] == min_loss) {
new_thresh = multiplier[i]
comparison.text = `
<p>Based on your inputs, the optimal threshold is around <b>${new_thresh}</b>.
This would result in an estimated <b>${comma_sep(round(min_loss,0))}</b> total weighted errors and
<b>$${comma_sep(round(min_loss * new_cost,0))}</b> in losses.</p>
<p>The generic threshold of 3.0 standard deviations would result in
<b>${comma_sep(round(generic_twe,0))}</b> total weighted errors and
<b>$${comma_sep(round(generic_twe * new_cost,0))}</b> in losses.</p>
<p>Using the optimal threshold would save
<b>$${comma_sep(round((generic_twe - min_loss) * new_cost,0))}</b>,
reducing costs by <b>${comma_sep(round((generic_twe - min_loss)/generic_twe * 100,0))}%</b>
(assuming near-future events are distributed similarly to those from the past).</p>
`
comparison.change.emit()
}
}
source.change.emit();
""")
cost_input = TextInput(value=f"{initial_fp_cost}",
title="How much a false positive costs:",
height=75,
margin=(20, 75, 20, 0))
cost_input.js_on_change('value', cost_handler)
# Include DataTable of simulation results
dt_columns = [
TableColumn(field="multiplier", title="Multiplier"),
TableColumn(field="fp",
title="False Positives",
formatter=NumberFormatter(format='0,0')),
TableColumn(field="fn",
title="False Negatives",
formatter=NumberFormatter(format='0,0')),
TableColumn(field="weighted_fn",
title="Weighted False Negatives",
formatter=NumberFormatter(format='0,0.00')),
TableColumn(field="twe",
title="Total Weighted Errors",
formatter=NumberFormatter(format='0,0.00')),
TableColumn(field="fpc",
title="Estimated FP Cost",
formatter=NumberFormatter(format='$0,0.00')),
TableColumn(field="tec",
title="Estimated Total Cost",
formatter=NumberFormatter(format='$0,0.00')),
TableColumn(field="precision",
title="Precision",
formatter=NumberFormatter(format='0.00%')),
TableColumn(field="recall",
title="Recall",
formatter=NumberFormatter(format='0.00%')),
TableColumn(field="f1",
title="F1 Score",
formatter=NumberFormatter(format='0.00%')),
]
data_table = DataTable(source=loss_min,
columns=dt_columns,
width=1400,
height=700,
sizing_mode='fixed',
fit_columns=True,
reorderable=True,
sortable=True,
margin=(30, 0, 20, 0))
# weighting_layout = column([weighting_div, evaluation, slider, data_table])
weighting_layout = column(
row(column(weighting_div, cost_input, comparison_div),
column(slider, evaluation), Div(text='', height=200, width=60)),
data_table)
# Initialize visualizations in browser
time.sleep(1.5)
layout = grid([
[title_div],
[row(summary_div, vertical_line, hypo_div)],
[
row(Div(text='', height=200, width=60), exploratory,
Div(text='', height=200, width=10), overlap_view,
Div(text='', height=200, width=40))
],
[Div(text='', height=10, width=200)],
[
row(Div(text='', height=200, width=60), density,
Div(text='', height=200, width=10), errors,
Div(text='', height=200, width=40))
],
[Div(text='', height=10, width=200)],
[
row(Div(text='', height=200, width=60), weighting_layout,
Div(text='', height=200, width=40))
],
])
# Generate html resources for dashboard
fonts = os.path.join(os.getcwd(), 'fonts')
if os.path.isdir(os.path.join(session_folder, 'fonts')):
shutil.rmtree(os.path.join(session_folder, 'fonts'))
shutil.copytree(fonts, os.path.join(session_folder, 'fonts'))
else:
shutil.copytree(fonts, os.path.join(session_folder, 'fonts'))
html = file_html(layout, INLINE, "Cream")
with open(os.path.join(session_folder, f'{session_name}.html'),
"w") as file:
file.write(html)
webbrowser.open("file://" +
os.path.join(session_folder, f'{session_name}.html'))
def bokeh_xy_sliders(outputMatrix):
# outputArray is a 2d array composed of
# a transposed array of unique group names, competitiveness scores,
# comp p-values, lottery scores, and lottery p-values.
# Scatterplot with sliders to filter significance
output_file("scatterplot.html")
TOOLS = ("hover,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,reset, save, tap")
TOOLTIPS = [ # Displays on hover
("group", "@groups"), ("Comp P-value", "@compP{0.0000}"),
("Lottery P-value", "@lotP{0.0000}"), ("(x,y)", "(@x, @y)")
]
# create a new plot with a title and axis labels
p = figure(tools=TOOLS,
title="Scatterplot",
x_axis_label='Lottery Score',
y_axis_label='Competitiveness',
tooltips=TOOLTIPS,
output_backend="webgl",
toolbar_location="above")
# Making sliders
slider = Slider(start=0.,
end=1.,
value=1.,
step=.01,
title="Comp P-value Filter")
slider2 = Slider(start=0.,
end=1.,
value=1.,
step=.01,
title="Lottery P-value Filter")
try:
colors = viridis(len(outputMatrix.uniqueGroups))
except ValueError:
print("Error: Bokeh can only display 256 colors. You requested ",
len(outputMatrix.uniqueGroups),
" Please try chart style 2 instead.")
for i in range(len(outputMatrix.uniqueGroups)):
# Assigning data to each point
dx = [outputMatrix.lotScores[i]]
dy = [outputMatrix.compScores[i]]
dcompP = [outputMatrix.compPValues[i]]
dlotP = [outputMatrix.lotPValues[i]]
dgroup = [outputMatrix.uniqueGroups[i]]
dcolor = colors[i]
source = ColumnDataSource(data=dict(x=dx,
y=dy,
groups=dgroup,
compP=dcompP,
lotP=dlotP,
size=[15],
color=[dcolor]))
# Callback for when the slider is changed
callback = CustomJS(args=dict(source=source),
code="""
source.change.emit();
""")
slider.js_on_change('value', callback)
slider2.js_on_change('value', callback)
# Custom Javascript boolean filters
js_filter = CustomJSFilter(args=dict(slider=slider, source=source),
code="""
bools = []
for (i = 0; i < source.data.x.length; i++) {
if (source.data.compP[i] < slider.value) {
bools.push(true);
}
else {
bools.push(false);
}
}
return bools;
""")
js_filter2 = CustomJSFilter(args=dict(slider=slider2, source=source),
code="""
bools = []
for (i = 0; i < source.data.x.length; i++) {
if (source.data.lotP[i] < slider.value) {
bools.push(true);
}
else {
bools.push(false);
}
}
return bools;
""")
# Using those filters to change what data is displayed
view = CDSView(source=source, filters=[js_filter, js_filter2])
p.circle(x="x",
y="y",
source=source,
size="size",
legend="groups",
color="color",
view=view)
p.legend.click_policy = "hide" # Click legend entry to hide that point
sliders = column(slider, slider2)
layout = row(p, sliders)
show(layout)
def plot_linked_heatmap_weights(new_df,
x_key=None,
y_key=None,
save_path=None,
add_t_slider=True,
RF_plot_title='Corresponding spatial RFs'):
# new_df = new_df.sort_index()
#sort the rows of interest
if x_key is None and y_key is None:
x_tick_labels = new_df.sort_values(
'x', ascending=False)['x'].unique().tolist()
y_tick_labels = new_df.sort_values(
'y', ascending=True)['y'].unique().tolist()
else:
x_tick_labels = new_df.sort_values(
x_key, ascending=True)['x'].unique().tolist()
y_tick_labels = new_df.sort_values(
y_key, ascending=False)['y'].unique().tolist()
#make the heatmap plot
s1 = ColumnDataSource(data=new_df)
p1 = figure(title="Heatmap",
tools="hover",
toolbar_location='above',
x_range=x_tick_labels,
y_range=y_tick_labels,
x_axis_label="Log_10 of L1 regularisation on weights",
y_axis_label='Number of hidden units',
width=600,
height=600) #, output_backend="webgl")
r1 = p1.rect('x', 'y', color='color', source=s1, width=1, height=1)
#add the colorbar and its label
colormap = cm.get_cmap("jet") #choose any matplotlib colormap here
bokehpalette = [
matplotlib.colors.rgb2hex(m) for m in colormap(np.arange(colormap.N))
]
color_mapper = LinearColorMapper(palette=bokehpalette,
low=np.min(new_df.z_value.values),
high=np.max(new_df.z_value.values))
color_bar = ColorBar(color_mapper=color_mapper, location=(1, 1)) #,
# label_standoff=12, border_line_color=None, location=(0,0))
c_bar_plot = figure(title="Final validation loss",
title_location="right",
height=611,
width=140,
toolbar_location=None,
min_border=0,
outline_line_color=None)
c_bar_plot.add_layout(color_bar, 'right')
c_bar_plot.title.align = "center"
s2 = ColumnDataSource(data=dict(url=[], x=[], y=[], dw=[], dh=[]))
s3 = ColumnDataSource(data=new_df)
p2 = figure(title=RF_plot_title,
tools=["zoom_in", "zoom_out", "pan", "reset"],
x_range=[str(x) for x in np.arange(0, 10)],
y_range=[str(y) for y in np.arange(0, 10)],
width=600,
height=600,
min_border_left=40)
# turn off x-axis major ticks
p2.xaxis.major_tick_line_color = None
# turn off y-axis major ticks
p2.yaxis.major_tick_line_color = None
r2 = p2.image_url(url='url',
x=0,
y=0,
w='dw',
h='dh',
source=s2,
anchor="bottom_left")
p1.title.text_font_size = '20pt'
p1.xaxis.axis_label_text_font_size = "20pt"
p1.xaxis.major_label_text_font_size = "12pt"
p1.yaxis.major_label_text_font_size = "12pt"
p1.yaxis.axis_label_text_font_size = "20pt"
p2.yaxis.major_label_text_font_size = "0pt"
p2.xaxis.major_label_text_font_size = "0pt"
p2.title.text_font_size = '20pt'
c_bar_plot.title.text_font_size = '16pt'
if add_t_slider:
# slider, slider_callback_code = get_t_slider(s2, s3)
slider = Slider(start=1,
end=7,
value=7,
step=1,
title="Time step",
callback_policy='continuous',
orientation='vertical',
height=200,
bar_color='grey')
slider_callback_code = """
//check if the slider value has changed.
//If it has, update the timestep of the image being displayed
var value = cb_obj.value
if (value === parseInt(value, 10)) {
//console.log(value)
//console.log(s2.data)
var old_url = s2.data.url
//console.log(old_url)
if (old_url != []) {
old_url=old_url[0]
//console.log(old_url)
var split_url = old_url.split("=");
split_url[1] = split_url[1].replace(split_url[1][0], value);
new_url = split_url[0]+"="+split_url[1];
var d2 = s2.data;
d2['url'] = [new_url]
s2.change.emit();
}
}
"""
def display_event(s2=s2, s3=s3):
return CustomJS(args=dict(s2=s2, s3=s3), code=slider_callback_code)
# slider_callback = CustomJS(args=dict(s2=s2, s3=s3), code=slider_callback_code)
slider.js_on_change('value', display_event())
else:
slider = None
slider_callback_code = ""
hover = p1.select_one(HoverTool)
hover.tooltips = None
hover_callback_code = """
//console.log(cb_data)
var indices = cb_data.index['1d'].indices;
// console.log(indices)
if (indices.length > 0) {
//console.log(indices[0])
//console.log('here!!!')
//var imgWidth = image.width || image.naturalWidth;
//var imgHeight = image.height || image.naturalHeight;
//var img = s3.data.image[indices[0]];
var url = s3.data.url[indices[0]];
//console.log(s3.data.image[0][0])
//console.log(img)
if (slider != null) {
var value = slider.value
var old_url = url
//console.log(old_url)
if (old_url != []) {
if (value === parseInt(value, 10)) {
//console.log(value)
//console.log(old_url)
var split_url = old_url.split("=");
split_url[1] = split_url[1].replace(split_url[1][0], value);
new_url = split_url[0]+"="+split_url[1];
url=new_url
}
}
}
var d2 = s2.data;
d2['url'] = [url]
d2['x'] = [0]
d2['y'] = [0]
d2['dw'] = [10]
d2['dh'] = [10]
s2.change.emit();
}
"""
hover.callback = CustomJS(args=dict(s2=s2, s3=s3, slider=slider),
code=hover_callback_code)
if slider is not None:
layout = row(p1, c_bar_plot, p2, slider)
else:
layout = row(p1, c_bar_plot, p2)
if save_path is not None:
output_file(save_path)
show(layout)
class ViewerWidgets(object):
"""
Encapsulates Bokeh widgets, and related callbacks, that are part of prospect's GUI.
Except for VI widgets
"""
def __init__(self, plots, nspec):
self.js_files = get_resources('js')
self.navigation_button_width = 30
self.z_button_width = 30
self.plot_widget_width = (plots.plot_width+(plots.plot_height//2))//2 - 40 # used for widgets scaling
#-----
#- Ifiberslider and smoothing widgets
# Ifiberslider's value controls which spectrum is displayed
# These two widgets call update_plot(), later defined
slider_end = nspec-1 if nspec > 1 else 0.5 # Slider cannot have start=end
self.ifiberslider = Slider(start=0, end=slider_end, value=0, step=1, title='Spectrum (of '+str(nspec)+')')
self.smootherslider = Slider(start=0, end=26, value=0, step=1.0, title='Gaussian Sigma Smooth')
self.coaddcam_buttons = None
self.model_select = None
def add_navigation(self, nspec):
#-----
#- Navigation buttons
self.prev_button = Button(label="<", width=self.navigation_button_width)
self.next_button = Button(label=">", width=self.navigation_button_width)
self.prev_callback = CustomJS(
args=dict(ifiberslider=self.ifiberslider),
code="""
if(ifiberslider.value>0 && ifiberslider.end>=1) {
ifiberslider.value--
}
""")
self.next_callback = CustomJS(
args=dict(ifiberslider=self.ifiberslider, nspec=nspec),
code="""
if(ifiberslider.value<nspec-1 && ifiberslider.end>=1) {
ifiberslider.value++
}
""")
self.prev_button.js_on_event('button_click', self.prev_callback)
self.next_button.js_on_event('button_click', self.next_callback)
def add_resetrange(self, viewer_cds, plots):
#-----
#- Axis reset button (superseeds the default bokeh "reset"
self.reset_plotrange_button = Button(label="Reset X-Y range", button_type="default")
reset_plotrange_code = self.js_files["adapt_plotrange.js"] + self.js_files["reset_plotrange.js"]
self.reset_plotrange_callback = CustomJS(args = dict(fig=plots.fig, xmin=plots.xmin, xmax=plots.xmax, spectra=viewer_cds.cds_spectra),
code = reset_plotrange_code)
self.reset_plotrange_button.js_on_event('button_click', self.reset_plotrange_callback)
def add_redshift_widgets(self, z, viewer_cds, plots):
## TODO handle "z" (same issue as viewerplots TBD)
#-----
#- Redshift / wavelength scale widgets
z1 = np.floor(z*100)/100
dz = z-z1
self.zslider = Slider(start=-0.1, end=5.0, value=z1, step=0.01, title='Redshift rough tuning')
self.dzslider = Slider(start=0.0, end=0.0099, value=dz, step=0.0001, title='Redshift fine-tuning')
self.dzslider.format = "0[.]0000"
self.z_input = TextInput(value="{:.4f}".format(z), title="Redshift value:")
#- Observer vs. Rest frame wavelengths
self.waveframe_buttons = RadioButtonGroup(
labels=["Obs", "Rest"], active=0)
self.zslider_callback = CustomJS(
args=dict(zslider=self.zslider, dzslider=self.dzslider, z_input=self.z_input),
code="""
// Protect against 1) recursive call with z_input callback;
// 2) out-of-range zslider values (should never happen in principle)
var z1 = Math.floor(parseFloat(z_input.value)*100) / 100
if ( (Math.abs(zslider.value-z1) >= 0.01) &&
(zslider.value >= -0.1) && (zslider.value <= 5.0) ){
var new_z = zslider.value + dzslider.value
z_input.value = new_z.toFixed(4)
}
""")
self.dzslider_callback = CustomJS(
args=dict(zslider=self.zslider, dzslider=self.dzslider, z_input=self.z_input),
code="""
var z = parseFloat(z_input.value)
var z1 = Math.floor(z) / 100
var z2 = z-z1
if ( (Math.abs(dzslider.value-z2) >= 0.0001) &&
(dzslider.value >= 0.0) && (dzslider.value <= 0.0099) ){
var new_z = zslider.value + dzslider.value
z_input.value = new_z.toFixed(4)
}
""")
self.zslider.js_on_change('value', self.zslider_callback)
self.dzslider.js_on_change('value', self.dzslider_callback)
self.z_minus_button = Button(label="<", width=self.z_button_width)
self.z_plus_button = Button(label=">", width=self.z_button_width)
self.z_minus_callback = CustomJS(
args=dict(z_input=self.z_input),
code="""
var z = parseFloat(z_input.value)
if(z >= -0.09) {
z -= 0.01
z_input.value = z.toFixed(4)
}
""")
self.z_plus_callback = CustomJS(
args=dict(z_input=self.z_input),
code="""
var z = parseFloat(z_input.value)
if(z <= 4.99) {
z += 0.01
z_input.value = z.toFixed(4)
}
""")
self.z_minus_button.js_on_event('button_click', self.z_minus_callback)
self.z_plus_button.js_on_event('button_click', self.z_plus_callback)
self.zreset_button = Button(label='Reset to z_pipe')
self.zreset_callback = CustomJS(
args=dict(z_input=self.z_input, metadata=viewer_cds.cds_metadata, ifiberslider=self.ifiberslider),
code="""
var ifiber = ifiberslider.value
var z = metadata.data['Z'][ifiber]
z_input.value = z.toFixed(4)
""")
self.zreset_button.js_on_event('button_click', self.zreset_callback)
self.z_input_callback = CustomJS(
args=dict(spectra = viewer_cds.cds_spectra,
coaddcam_spec = viewer_cds.cds_coaddcam_spec,
model = viewer_cds.cds_model,
othermodel = viewer_cds.cds_othermodel,
metadata = viewer_cds.cds_metadata,
ifiberslider = self.ifiberslider,
zslider = self.zslider,
dzslider = self.dzslider,
z_input = self.z_input,
waveframe_buttons = self.waveframe_buttons,
line_data = viewer_cds.cds_spectral_lines,
lines = plots.speclines,
line_labels = plots.specline_labels,
zlines = plots.zoom_speclines,
zline_labels = plots.zoom_specline_labels,
overlap_waves = plots.overlap_waves,
overlap_bands = plots.overlap_bands,
fig = plots.fig
),
code="""
var z = parseFloat(z_input.value)
if ( z >=-0.1 && z <= 5.0 ) {
// update zsliders only if needed (avoid recursive call)
z_input.value = parseFloat(z_input.value).toFixed(4)
var z1 = Math.floor(z*100) / 100
var z2 = z-z1
if ( Math.abs(z1-zslider.value) >= 0.01) zslider.value = parseFloat(parseFloat(z1).toFixed(2))
if ( Math.abs(z2-dzslider.value) >= 0.0001) dzslider.value = parseFloat(parseFloat(z2).toFixed(4))
} else {
if (z_input.value < -0.1) z_input.value = (-0.1).toFixed(4)
if (z_input.value > 5) z_input.value = (5.0).toFixed(4)
}
var line_restwave = line_data.data['restwave']
var ifiber = ifiberslider.value
var waveshift_lines = (waveframe_buttons.active == 0) ? 1+z : 1 ;
var waveshift_spec = (waveframe_buttons.active == 0) ? 1 : 1/(1+z) ;
for(var i=0; i<line_restwave.length; i++) {
lines[i].location = line_restwave[i] * waveshift_lines
line_labels[i].x = line_restwave[i] * waveshift_lines
zlines[i].location = line_restwave[i] * waveshift_lines
zline_labels[i].x = line_restwave[i] * waveshift_lines
}
if (overlap_bands.length>0) {
for (var i=0; i<overlap_bands.length; i++) {
overlap_bands[i].left = overlap_waves[i][0] * waveshift_spec
overlap_bands[i].right = overlap_waves[i][1] * waveshift_spec
}
}
function shift_plotwave(cds_spec, waveshift) {
var data = cds_spec.data
var origwave = data['origwave']
var plotwave = data['plotwave']
if ( plotwave[0] != origwave[0] * waveshift ) { // Avoid redo calculation if not needed
for (var j=0; j<plotwave.length; j++) {
plotwave[j] = origwave[j] * waveshift ;
}
cds_spec.change.emit()
}
}
for(var i=0; i<spectra.length; i++) {
shift_plotwave(spectra[i], waveshift_spec)
}
if (coaddcam_spec) shift_plotwave(coaddcam_spec, waveshift_spec)
// Update model wavelength array
// NEW : don't shift model if othermodel is there
if (othermodel) {
var zref = othermodel.data['zref'][0]
var waveshift_model = (waveframe_buttons.active == 0) ? (1+z)/(1+zref) : 1/(1+zref) ;
shift_plotwave(othermodel, waveshift_model)
} else if (model) {
var zfit = 0.0
if(metadata.data['Z'] !== undefined) {
zfit = metadata.data['Z'][ifiber]
}
var waveshift_model = (waveframe_buttons.active == 0) ? (1+z)/(1+zfit) : 1/(1+zfit) ;
shift_plotwave(model, waveshift_model)
}
""")
self.z_input.js_on_change('value', self.z_input_callback)
self.waveframe_buttons.js_on_click(self.z_input_callback)
self.plotrange_callback = CustomJS(
args = dict(
z_input=self.z_input,
waveframe_buttons=self.waveframe_buttons,
fig=plots.fig,
),
code="""
var z = parseFloat(z_input.value)
// Observer Frame
if(waveframe_buttons.active == 0) {
fig.x_range.start = fig.x_range.start * (1+z)
fig.x_range.end = fig.x_range.end * (1+z)
} else {
fig.x_range.start = fig.x_range.start / (1+z)
fig.x_range.end = fig.x_range.end / (1+z)
}
"""
)
self.waveframe_buttons.js_on_click(self.plotrange_callback) # TODO: for record: is this related to waveframe bug? : 2 callbakcs for same click...
def add_oii_widgets(self, plots):
#------
#- Zoom on the OII doublet TODO mv js code to other file
# TODO: is there another trick than using a cds to pass the "oii_saveinfo" ?
# TODO: optimize smoothing for autozoom (current value: 0)
cds_oii_saveinfo = ColumnDataSource(
{'xmin':[plots.fig.x_range.start], 'xmax':[plots.fig.x_range.end], 'nsmooth':[self.smootherslider.value]})
self.oii_zoom_button = Button(label="OII-zoom", button_type="default")
self.oii_zoom_callback = CustomJS(
args = dict(z_input=self.z_input, fig=plots.fig, smootherslider=self.smootherslider,
cds_oii_saveinfo=cds_oii_saveinfo),
code = """
// Save previous setting (for the "Undo" button)
cds_oii_saveinfo.data['xmin'] = [fig.x_range.start]
cds_oii_saveinfo.data['xmax'] = [fig.x_range.end]
cds_oii_saveinfo.data['nsmooth'] = [smootherslider.value]
// Center on the middle of the redshifted OII doublet (vaccum)
var z = parseFloat(z_input.value)
fig.x_range.start = 3728.48 * (1+z) - 100
fig.x_range.end = 3728.48 * (1+z) + 100
// No smoothing (this implies a call to update_plot)
smootherslider.value = 0
""")
self.oii_zoom_button.js_on_event('button_click', self.oii_zoom_callback)
self.oii_undo_button = Button(label="Undo OII-zoom", button_type="default")
self.oii_undo_callback = CustomJS(
args = dict(fig=plots.fig, smootherslider=self.smootherslider, cds_oii_saveinfo=cds_oii_saveinfo),
code = """
fig.x_range.start = cds_oii_saveinfo.data['xmin'][0]
fig.x_range.end = cds_oii_saveinfo.data['xmax'][0]
smootherslider.value = cds_oii_saveinfo.data['nsmooth'][0]
""")
self.oii_undo_button.js_on_event('button_click', self.oii_undo_callback)
def add_coaddcam(self, plots):
#-----
#- Highlight individual-arm or camera-coadded spectra
coaddcam_labels = ["Camera-coadded", "Single-arm"]
self.coaddcam_buttons = RadioButtonGroup(labels=coaddcam_labels, active=0)
self.coaddcam_callback = CustomJS(
args = dict(coaddcam_buttons = self.coaddcam_buttons,
list_lines=[plots.data_lines, plots.noise_lines,
plots.zoom_data_lines, plots.zoom_noise_lines],
alpha_discrete = plots.alpha_discrete,
overlap_bands = plots.overlap_bands,
alpha_overlapband = plots.alpha_overlapband),
code="""
var n_lines = list_lines[0].length
for (var i=0; i<n_lines; i++) {
var new_alpha = 1
if (coaddcam_buttons.active == 0 && i<n_lines-1) new_alpha = alpha_discrete
if (coaddcam_buttons.active == 1 && i==n_lines-1) new_alpha = alpha_discrete
for (var j=0; j<list_lines.length; j++) {
list_lines[j][i].glyph.line_alpha = new_alpha
}
}
var new_alpha = 0
if (coaddcam_buttons.active == 0) new_alpha = alpha_overlapband
for (var j=0; j<overlap_bands.length; j++) {
overlap_bands[j].fill_alpha = new_alpha
}
"""
)
self.coaddcam_buttons.js_on_click(self.coaddcam_callback)
def add_metadata_tables(self, viewer_cds, show_zcat=True, template_dicts=None,
top_metadata=['TARGETID', 'EXPID']):
""" Display object-related informations
top_metadata: metadata to be highlighted in table_a
Note: "short" CDS, with a single row, are used to fill these bokeh tables.
When changing object, js code modifies these short CDS so that tables are updated.
"""
#- Sorted list of potential metadata:
metadata_to_check = ['TARGETID', 'HPXPIXEL', 'TILEID', 'COADD_NUMEXP', 'COADD_EXPTIME',
'NIGHT', 'EXPID', 'FIBER', 'CAMERA', 'MORPHTYPE']
metadata_to_check += [ ('mag_'+x) for x in viewer_cds.phot_bands ]
table_keys = []
for key in metadata_to_check:
if key in viewer_cds.cds_metadata.data.keys():
table_keys.append(key)
if 'NUM_'+key in viewer_cds.cds_metadata.data.keys():
for prefix in ['FIRST','LAST','NUM']:
table_keys.append(prefix+'_'+key)
if key in top_metadata:
top_metadata.append(prefix+'_'+key)
#- Table a: "top metadata"
table_a_keys = [ x for x in table_keys if x in top_metadata ]
self.shortcds_table_a, self.table_a = _metadata_table(table_a_keys, viewer_cds, table_width=600,
shortcds_name='shortcds_table_a', selectable=True)
#- Table b: Targeting information
self.shortcds_table_b, self.table_b = _metadata_table(['Targeting masks'], viewer_cds, table_width=self.plot_widget_width,
shortcds_name='shortcds_table_b', selectable=True)
#- Table(s) c/d : Other information (imaging, etc.)
remaining_keys = [ x for x in table_keys if x not in top_metadata ]
if len(remaining_keys) > 7:
table_c_keys = remaining_keys[0:len(remaining_keys)//2]
table_d_keys = remaining_keys[len(remaining_keys)//2:]
else:
table_c_keys = remaining_keys
table_d_keys = None
self.shortcds_table_c, self.table_c = _metadata_table(table_c_keys, viewer_cds, table_width=self.plot_widget_width,
shortcds_name='shortcds_table_c', selectable=False)
if table_d_keys is None:
self.shortcds_table_d, self.table_d = None, None
else:
self.shortcds_table_d, self.table_d = _metadata_table(table_d_keys, viewer_cds, table_width=self.plot_widget_width,
shortcds_name='shortcds_table_d', selectable=False)
#- Table z: redshift fitting information
if show_zcat is not None :
if template_dicts is not None : # Add other best fits
fit_results = template_dicts[1]
# Case of DeltaChi2 : compute it from Chi2s
# The "DeltaChi2" in rr fits is between best fits for a given (spectype,subtype)
# Convention: DeltaChi2 = -1 for the last fit.
chi2s = fit_results['CHI2'][0]
full_deltachi2s = np.zeros(len(chi2s))-1
full_deltachi2s[:-1] = chi2s[1:]-chi2s[:-1]
cdsdata = dict(Nfit = np.arange(1,len(chi2s)+1),
SPECTYPE = fit_results['SPECTYPE'][0], # [0:num_best_fits] (if we want to restrict... TODO?)
SUBTYPE = fit_results['SUBTYPE'][0],
Z = [ "{:.4f}".format(x) for x in fit_results['Z'][0] ],
ZERR = [ "{:.4f}".format(x) for x in fit_results['ZERR'][0] ],
ZWARN = fit_results['ZWARN'][0],
CHI2 = [ "{:.1f}".format(x) for x in fit_results['CHI2'][0] ],
DELTACHI2 = [ "{:.1f}".format(x) for x in full_deltachi2s ])
self.shortcds_table_z = ColumnDataSource(cdsdata, name='shortcds_table_z')
columns_table_z = [ TableColumn(field=x, title=t, width=w) for x,t,w in [ ('Nfit','Nfit',5), ('SPECTYPE','SPECTYPE',70), ('SUBTYPE','SUBTYPE',60), ('Z','Z',50) , ('ZERR','ZERR',50), ('ZWARN','ZWARN',50), ('DELTACHI2','Δχ2(N+1/N)',70)] ]
self.table_z = DataTable(source=self.shortcds_table_z, columns=columns_table_z,
selectable=False, index_position=None, width=self.plot_widget_width)
self.table_z.height = 3 * self.table_z.row_height
else :
self.shortcds_table_z, self.table_z = _metadata_table(viewer_cds.zcat_keys, viewer_cds,
table_width=self.plot_widget_width, shortcds_name='shortcds_table_z', selectable=False)
else :
self.table_z = Div(text="Not available ")
self.shortcds_table_z = None
def add_specline_toggles(self, viewer_cds, plots):
#-----
#- Toggle lines
self.speclines_button_group = CheckboxButtonGroup(
labels=["Emission lines", "Absorption lines"], active=[])
self.majorline_checkbox = CheckboxGroup(
labels=['Show only major lines'], active=[])
self.speclines_callback = CustomJS(
args = dict(line_data = viewer_cds.cds_spectral_lines,
lines = plots.speclines,
line_labels = plots.specline_labels,
zlines = plots.zoom_speclines,
zline_labels = plots.zoom_specline_labels,
lines_button_group = self.speclines_button_group,
majorline_checkbox = self.majorline_checkbox),
code="""
var show_emission = false
var show_absorption = false
if (lines_button_group.active.indexOf(0) >= 0) { // index 0=Emission in active list
show_emission = true
}
if (lines_button_group.active.indexOf(1) >= 0) { // index 1=Absorption in active list
show_absorption = true
}
for(var i=0; i<lines.length; i++) {
if ( !(line_data.data['major'][i]) && (majorline_checkbox.active.indexOf(0)>=0) ) {
lines[i].visible = false
line_labels[i].visible = false
zlines[i].visible = false
zline_labels[i].visible = false
} else if (line_data.data['emission'][i]) {
lines[i].visible = show_emission
line_labels[i].visible = show_emission
zlines[i].visible = show_emission
zline_labels[i].visible = show_emission
} else {
lines[i].visible = show_absorption
line_labels[i].visible = show_absorption
zlines[i].visible = show_absorption
zline_labels[i].visible = show_absorption
}
}
"""
)
self.speclines_button_group.js_on_click(self.speclines_callback)
self.majorline_checkbox.js_on_click(self.speclines_callback)
def add_model_select(self, viewer_cds, template_dicts, num_approx_fits, with_full_2ndfit=True):
#------
#- Select secondary model to display
model_options = ['Best fit', '2nd best fit']
for i in range(1,1+num_approx_fits) :
ith = 'th'
if i==1 : ith='st'
if i==2 : ith='nd'
if i==3 : ith='rd'
model_options.append(str(i)+ith+' fit (approx)')
if with_full_2ndfit is False :
model_options.remove('2nd best fit')
for std_template in ['QSO', 'GALAXY', 'STAR'] :
model_options.append('STD '+std_template)
self.model_select = Select(value=model_options[0], title="Other model (dashed curve):", options=model_options)
model_select_code = self.js_files["interp_grid.js"] + self.js_files["smooth_data.js"] + self.js_files["select_model.js"]
self.model_select_callback = CustomJS(
args = dict(ifiberslider = self.ifiberslider,
model_select = self.model_select,
fit_templates=template_dicts[0],
cds_othermodel = viewer_cds.cds_othermodel,
cds_model_2ndfit = viewer_cds.cds_model_2ndfit,
cds_model = viewer_cds.cds_model,
fit_results=template_dicts[1],
std_templates=template_dicts[2],
median_spectra = viewer_cds.cds_median_spectra,
smootherslider = self.smootherslider,
z_input = self.z_input,
cds_metadata = viewer_cds.cds_metadata),
code = model_select_code)
self.model_select.js_on_change('value', self.model_select_callback)
def add_update_plot_callback(self, viewer_cds, plots, vi_widgets, template_dicts):
#-----
#- Main js code to update plots
update_plot_code = (self.js_files["adapt_plotrange.js"] + self.js_files["interp_grid.js"] +
self.js_files["smooth_data.js"] + self.js_files["coadd_brz_cameras.js"] +
self.js_files["update_plot.js"])
# TMP handling of template_dicts
the_fit_results = None if template_dicts is None else template_dicts[1] # dirty
self.update_plot_callback = CustomJS(
args = dict(
spectra = viewer_cds.cds_spectra,
coaddcam_spec = viewer_cds.cds_coaddcam_spec,
model = viewer_cds.cds_model,
othermodel = viewer_cds.cds_othermodel,
model_2ndfit = viewer_cds.cds_model_2ndfit,
metadata = viewer_cds.cds_metadata,
fit_results = the_fit_results,
shortcds_table_z = self.shortcds_table_z,
shortcds_table_a = self.shortcds_table_a,
shortcds_table_b = self.shortcds_table_b,
shortcds_table_c = self.shortcds_table_c,
shortcds_table_d = self.shortcds_table_d,
ifiberslider = self.ifiberslider,
smootherslider = self.smootherslider,
z_input = self.z_input,
fig = plots.fig,
imfig_source = plots.imfig_source,
imfig_urls = plots.imfig_urls,
model_select = self.model_select,
vi_comment_input = vi_widgets.vi_comment_input,
vi_std_comment_select = vi_widgets.vi_std_comment_select,
vi_name_input = vi_widgets.vi_name_input,
vi_quality_input = vi_widgets.vi_quality_input,
vi_quality_labels = vi_widgets.vi_quality_labels,
vi_issue_input = vi_widgets.vi_issue_input,
vi_z_input = vi_widgets.vi_z_input,
vi_category_select = vi_widgets.vi_category_select,
vi_issue_slabels = vi_widgets.vi_issue_slabels
),
code = update_plot_code
)
self.smootherslider.js_on_change('value', self.update_plot_callback)
self.ifiberslider.js_on_change('value', self.update_plot_callback)
def create_final_box_plot(method, region):
df, p1 = ([] for _ in range(2))
masks = [3,4,5,6]
for imask in masks:
with np.load('numpy_files/arrays_reg'+region+'_'+str(imask)+samples+'_'+method+'.npz') as f:
df.append(pd.DataFrame(f['response_eta']).transpose())
df[-1].columns = ['response', 'eta']
#data sources
data_limit = 64800
source1 = ColumnDataSource(data=dict(x_mask3=df[0].eta[:data_limit], x_mask4=df[1].eta[:data_limit], x_mask5=df[2].eta[:data_limit], x_mask6=df[3].eta[:data_limit],
y_mask3=df[0].response[:data_limit], y_mask4=df[1].response[:data_limit], y_mask5=df[2].response[:data_limit], y_mask6=df[3].response[:data_limit],
c_mask3=[colors[0]]*data_limit, c_mask4=[colors[1]]*data_limit, c_mask5=[colors[2]]*data_limit, c_mask6=[colors[3]]*data_limit))
source1_v2 = ColumnDataSource(data=dict(x_mask3=df[0].eta[:data_limit], x_mask4=df[1].eta[:data_limit], x_mask5=df[2].eta[:data_limit], x_mask6=df[3].eta[:data_limit],
y_mask3=df[0].response[:data_limit], y_mask4=df[1].response[:data_limit], y_mask5=df[2].response[:data_limit], y_mask6=df[3].response[:data_limit],
c_mask3=[colors[0]]*data_limit, c_mask4=[colors[1]]*data_limit, c_mask5=[colors[2]]*data_limit, c_mask6=[colors[3]]*data_limit))
source_vlines = ColumnDataSource(data=dict(etamin=[start], etamax=[end]))
source_outliers = ColumnDataSource(data=dict(x_mask3=[1]*data_limit, x_mask4=[2]*data_limit, x_mask5=[3]*data_limit, x_mask6=[4]*data_limit,
y_mask3=df[0].response[:data_limit]+100., y_mask4=df[1].response[:data_limit]+100., y_mask5=df[2].response[:data_limit]+100., y_mask6=df[3].response[:data_limit]+100.))
source2 = ColumnDataSource(data=dict(x_mask3=[1],x_mask4=[2],x_mask5=[3],x_mask6=[4],
y1_mask3=[0.],y1_mask4=[0.],y1_mask5=[0.],y1_mask6=[0.],
y2_mask3=[0.], y2_mask4=[0.], y2_mask5=[0.], y2_mask6=[0.],
height1_mask3=[.6],height1_mask4=[.6],height1_mask5=[.6],height1_mask6=[.6],
height2_mask3=[.6],height2_mask4=[.6],height2_mask5=[.6],height2_mask6=[.6],
q1_mask3=[-.25],q1_mask4=[-.25],q1_mask5=[-.25],q1_mask6=[-.25],
q3_mask3=[-.25],q3_mask4=[-.25],q3_mask5=[-.25],q3_mask6=[-.25],
upper_mask3=[-.6],upper_mask4=[-.6],upper_mask5=[-.6],upper_mask6=[-.6],
lower_mask3=[.6], lower_mask4=[.6], lower_mask5=[.6], lower_mask6=[.6]))
#1st figure
plot_options = dict(plot_height=500, plot_width=350, y_range=y_range, tools="wheel_zoom,box_zoom,box_select,pan,reset", output_backend="webgl")
for imask in masks:
p1.append(figure(title="Eta vs. Response Mask "+str(imask), **plot_options))
p1[-1].circle('x_mask'+str(imask), 'y_mask'+str(imask), color='c_mask'+str(imask), size=2, alpha=0.4, source=source1)
p1[-1].segment(x0='etamin', y0=y_range[0], x1='etamin', y1=y_range[1], source=source_vlines, line_color='black', line_width=2)
p1[-1].segment(x0='etamax', y0=y_range[0], x1='etamax', y1=y_range[1], source=source_vlines, line_color='black', line_width=2)
#2nd figure
plot2_options = dict(plot_height=300, plot_width=500, x_range=x_range, y_range=(y_range[0]-0.3,y_range[1]+0.3), tools="wheel_zoom,box_zoom,pan,reset", output_backend="webgl")
radius = dict({'1':'1.3cm', '2':'2.6cm', '3':'5.3cm'})
methoddict = dict({'nocorr':'No correction', 'corr_ed':'Shower leakage', 'corr_fineeta':'Brute force'})
p2 = figure(title="Signal integration radius: "+radius[region]+' Method: '+methoddict[method], **plot2_options)
p2.xaxis.visible = False
box_options = dict(line_color='black', line_width=2, source=source2)
for imask in masks:
#boxes
p2.rect(x='x_mask'+str(imask), y='y1_mask'+str(imask), width=0.9, height='height1_mask'+str(imask), color=colors[imask-masks[0]], legend='Mask '+str(imask), **box_options)
p2.rect(x='x_mask'+str(imask), y='y2_mask'+str(imask), width=0.9, height='height2_mask'+str(imask), color=colors[imask-masks[0]], **box_options)
#segments
p2.segment(x0='x_mask'+str(imask), y0='q3_mask'+str(imask), x1='x_mask'+str(imask), y1='upper_mask'+str(imask), **box_options)
p2.segment(x0='x_mask'+str(imask), y0='q1_mask'+str(imask), x1='x_mask'+str(imask), y1='lower_mask'+str(imask), **box_options)
#whiskers
p2.rect(x='x_mask'+str(imask), y='upper_mask'+str(imask), width=0.1, height=0.005, **box_options)
p2.rect(x='x_mask'+str(imask), y='lower_mask'+str(imask), width=0.1, height=0.005, **box_options)
#outliers
p2.diamond(x='x_mask'+str(imask), y='y_mask'+str(imask), line_color=colors_diamonds[imask-masks[0]], size=4, source=source_outliers)
#dashed line
p2.segment(x0=x_range[0]+0.5, y0=0., x1=x_range[1]-0.5, y1=0., line_color='black', line_dash='dashed')
#Dynamic behaviour (the model that triggers the callback is called 'cb_obj')
callback = CustomJS(args=dict(source1=source1, source1_v2=source1_v2, source2=source2,
source_vlines=source_vlines, source_outliers=source_outliers), code="""
var etamin = cb_obj.value;
var delta_eta = 0.03;
var data1 = source1.data;
var data1_v2 = source1_v2.data;
var data2 = source2.data;
var data_lines = source_vlines.data;
var data_out = source_outliers.data;
data_lines['etamin'][0] = etamin;
data_lines['etamax'][0] = etamin+delta_eta;
data1_v2['x_mask3'] = [];
data1_v2['x_mask4'] = [];
data1_v2['x_mask5'] = [];
data1_v2['x_mask6'] = [];
data1_v2['y_mask3'] = [];
data1_v2['y_mask4'] = [];
data1_v2['y_mask5'] = [];
data1_v2['y_mask6'] = [];
for (var i = 0; i < data1['x_mask3'].length; i++) {
if (data1['x_mask3'][i] > etamin && data1['x_mask3'][i] <= etamin+delta_eta) {
data1_v2['x_mask3'].push(data1['x_mask3'][i]);
data1_v2['y_mask3'].push(data1['y_mask3'][i]);
}
}
for (var i = 0; i < data1['x_mask4'].length; i++) {
if (data1['x_mask4'][i] > etamin && data1['x_mask4'][i] <= etamin+delta_eta) {
data1_v2['x_mask4'].push(data1['x_mask4'][i]);
data1_v2['y_mask4'].push(data1['y_mask4'][i]);
}
}
for (var i = 0; i < data1['x_mask5'].length; i++) {
if (data1['x_mask5'][i] > etamin && data1['x_mask5'][i] <= etamin+delta_eta) {
data1_v2['x_mask5'].push(data1['x_mask5'][i]);
data1_v2['y_mask5'].push(data1['y_mask5'][i]);
}
}
for (var i = 0; i < data1['x_mask6'].length; i++) {
if (data1['x_mask6'][i] > etamin && data1['x_mask6'][i] <= etamin+delta_eta) {
data1_v2['x_mask6'].push(data1['x_mask6'][i]);
data1_v2['y_mask6'].push(data1['y_mask6'][i]);
}
}
//define quantiles and related quantities
var data_tmp_mask3 = data1_v2['y_mask3'];
data_tmp_mask3.sort( function(a,b) {return a - b;} );
var l1_3 = Math.floor( (data_tmp_mask3.length) * 0.25);
var l2_3 = Math.floor( (data_tmp_mask3.length) * 0.50);
var l3_3 = Math.floor( (data_tmp_mask3.length) * 0.75);
var data_tmp_mask4 = data1_v2['y_mask4'];
data_tmp_mask4.sort( function(a,b) {return a - b;} );
var l1_4 = Math.floor( (data_tmp_mask4.length) * 0.25);
var l2_4 = Math.floor( (data_tmp_mask4.length) * 0.50);
var l3_4 = Math.floor( (data_tmp_mask4.length) * 0.75);
var data_tmp_mask5 = data1_v2['y_mask5'];
data_tmp_mask5.sort( function(a,b) {return a - b;} );
var l1_5 = Math.floor( (data_tmp_mask5.length) * 0.25);
var l2_5 = Math.floor( (data_tmp_mask5.length) * 0.50);
var l3_5 = Math.floor( (data_tmp_mask5.length) * 0.75);
var data_tmp_mask6 = data1_v2['y_mask6'];
data_tmp_mask6.sort( function(a,b) {return a - b;} );
var l1_6 = Math.floor( (data_tmp_mask6.length) * 0.25);
var l2_6 = Math.floor( (data_tmp_mask6.length) * 0.50);
var l3_6 = Math.floor( (data_tmp_mask6.length) * 0.75);
var upper_3;
var lower_3;
var upper_4;
var lower_4;
var upper_5;
var lower_5;
var upper_6;
var lower_6;
if(data_tmp_mask3.length % 2)
{
var q1 = data_tmp_mask3[l1_3];
var q2 = data_tmp_mask3[l2_3];
var q3 = data_tmp_mask3[l3_3];
data2['y1_mask3'][0] = (q2+q1)/2;
data2['y2_mask3'][0] = (q3+q2)/2;
data2['height1_mask3'][0] = q2-q1;
data2['height2_mask3'][0] = q3-q2;
data2['q1_mask3'][0] = q1;
data2['q3_mask3'][0] = q3;
upper_3 = q3+1.5*(q3-q1);
lower_3 = q1-1.5*(q3-q1);
data2['upper_mask3'][0] = upper_3;
data2['lower_mask3'][0] = lower_3;
}
else
{
var q1 = (data_tmp_mask3[l1_3-1]+data_tmp_mask3[l1_3])/2.0;
var q2 = (data_tmp_mask3[l2_3-1]+data_tmp_mask3[l2_3])/2.0;
var q3 = (data_tmp_mask3[l3_3-1]+data_tmp_mask3[l3_3])/2.0;
data2['y1_mask3'][0] = (q2+q1)/2;
data2['y2_mask3'][0] = (q3+q2)/2;
data2['height1_mask3'][0] = q2-q1;
data2['height2_mask3'][0] = q3-q2;
data2['q1_mask3'][0] = q1;
data2['q3_mask3'][0] = q3;
upper_3 = q3+1.5*(q3-q1);
lower_3 = q1-1.5*(q3-q1);
data2['upper_mask3'][0] = upper_3;
data2['lower_mask3'][0] = lower_3;
}
if(data_tmp_mask4.length % 2)
{
var q1 = data_tmp_mask4[l1_4];
var q2 = data_tmp_mask4[l2_4];
var q3 = data_tmp_mask4[l3_4];
data2['y1_mask4'][0] = (q2+q1)/2;
data2['y2_mask4'][0] = (q3+q2)/2;
data2['height1_mask4'][0] = q2-q1;
data2['height2_mask4'][0] = q3-q2;
data2['q1_mask4'][0] = q1;
data2['q3_mask4'][0] = q3;
upper_4 = q3+1.5*(q3-q1);
lower_4 = q1-1.5*(q3-q1);
data2['upper_mask4'][0] = upper_4;
data2['lower_mask4'][0] = lower_4;
}
else
{
var q1 = (data_tmp_mask4[l1_4-1]+data_tmp_mask4[l1_4])/2.0;
var q2 = (data_tmp_mask4[l2_4-1]+data_tmp_mask4[l2_4])/2.0;
var q3 = (data_tmp_mask4[l3_4-1]+data_tmp_mask4[l3_4])/2.0;
data2['y1_mask4'][0] = (q2+q1)/2;
data2['y2_mask4'][0] = (q3+q2)/2;
data2['height1_mask4'][0] = q2-q1;
data2['height2_mask4'][0] = q3-q2;
data2['q1_mask4'][0] = q1;
data2['q3_mask4'][0] = q3;
upper_4 = q3+1.5*(q3-q1);
lower_4 = q1-1.5*(q3-q1);
data2['upper_mask4'][0] = upper_4;
data2['lower_mask4'][0] = lower_4;
}
if(data_tmp_mask5.length % 2)
{
var q1 = data_tmp_mask5[l1_5];
var q2 = data_tmp_mask5[l2_5];
var q3 = data_tmp_mask5[l3_5];
data2['y1_mask5'][0] = (q2+q1)/2;
data2['y2_mask5'][0] = (q3+q2)/2;
data2['height1_mask5'][0] = q2-q1;
data2['height2_mask5'][0] = q3-q2;
data2['q1_mask5'][0] = q1;
data2['q3_mask5'][0] = q3;
upper_5 = q3+1.5*(q3-q1);
lower_5 = q1-1.5*(q3-q1);
data2['upper_mask5'][0] = upper_5;
data2['lower_mask5'][0] = lower_5;
}
else
{
q1 = (data_tmp_mask5[l1_5-1]+data_tmp_mask5[l1_5])/2.0;
q2 = (data_tmp_mask5[l2_5-1]+data_tmp_mask5[l2_5])/2.0;
q3 = (data_tmp_mask5[l3_5-1]+data_tmp_mask5[l3_5])/2.0;
data2['y1_mask5'][0] = (q2+q1)/2;
data2['y2_mask5'][0] = (q3+q2)/2;
data2['height1_mask5'][0] = q2-q1;
data2['height2_mask5'][0] = q3-q2;
data2['q1_mask5'][0] = q1;
data2['q3_mask5'][0] = q3;
upper_5 = q3+1.5*(q3-q1);
lower_5 = q1-1.5*(q3-q1);
data2['upper_mask5'][0] = upper_5;
data2['lower_mask5'][0] = lower_5;
}
if(data_tmp_mask6.length % 2)
{
var q1 = data_tmp_mask6[l1_6];
var q2 = data_tmp_mask6[l2_6];
var q3 = data_tmp_mask6[l3_6];
data2['y1_mask6'][0] = (q2+q1)/2;
data2['y2_mask6'][0] = (q3+q2)/2;
data2['height1_mask6'][0] = q2-q1;
data2['height2_mask6'][0] = q3-q2;
data2['q1_mask6'][0] = q1;
data2['q3_mask6'][0] = q3;
upper_6 = q3+1.5*(q3-q1);
lower_6 = q1-1.5*(q3-q1);
data2['upper_mask6'][0] = upper_6;
data2['lower_mask6'][0] = lower_6;
}
else
{
var q1 = (data_tmp_mask6[l1_6-1]+data_tmp_mask6[l1_6])/2.0;
var q2 = (data_tmp_mask6[l2_6-1]+data_tmp_mask6[l2_6])/2.0;
var q3 = (data_tmp_mask6[l3_6-1]+data_tmp_mask6[l3_6])/2.0;
data2['y1_mask6'][0] = (q2+q1)/2;
data2['y2_mask6'][0] = (q3+q2)/2;
data2['height1_mask6'][0] = q2-q1;
data2['height2_mask6'][0] = q3-q2;
data2['q1_mask6'][0] = q1;
data2['q3_mask6'][0] = q3;
upper_6 = q3+1.5*(q3-q1);
lower_6 = q1-1.5*(q3-q1);
data2['upper_mask6'][0] = upper_6;
data2['lower_mask6'][0] = lower_6;
}
//avoid putting the whiskers beyond the data points
var currMax_3 = Math.max.apply(Math, data1_v2['y_mask3']);
var currMin_3 = Math.min.apply(Math, data1_v2['y_mask3']);
var currMax_4 = Math.max.apply(Math, data1_v2['y_mask4']);
var currMin_4 = Math.min.apply(Math, data1_v2['y_mask4']);
var currMax_5 = Math.max.apply(Math, data1_v2['y_mask5']);
var currMin_5 = Math.min.apply(Math, data1_v2['y_mask5']);
var currMax_6 = Math.max.apply(Math, data1_v2['y_mask6']);
var currMin_6 = Math.min.apply(Math, data1_v2['y_mask6']);
if(currMax_3 > upper_3)
data2['upper_mask3'][0] = upper_3;
else
data2['upper_mask3'][0] = currMax_3;
if(currMin_3 < lower_3)
data2['lower_mask3'][0] = lower_3;
else
data2['lower_mask3'][0] = currMin_3;
if(currMax_4 > upper_4)
data2['upper_mask4'][0] = upper_4;
else
data2['upper_mask4'][0] = currMax_4;
if(currMin_4 < lower_4)
data2['lower_mask4'][0] = lower_4;
else
data2['lower_mask4'][0] = currMin_4;
if(currMax_5 > upper_5)
data2['upper_mask5'][0] = upper_5;
else
data2['upper_mask5'][0] = currMax_5;
if(currMin_5 < lower_5)
data2['lower_mask5'][0] = lower_5;
else
data2['lower_mask5'][0] = currMin_5;
if(currMax_6 > upper_6)
data2['upper_mask6'][0] = upper_6;
else
data2['upper_mask6'][0] = currMax_6;
if(currMin_6 < lower_6)
data2['lower_mask6'][0] = lower_6;
else
data2['lower_mask6'][0] = currMin_6;
//place outliers
data_out['y_mask3'] = [];
data_out['y_mask4'] = [];
data_out['y_mask5'] = [];
data_out['y_mask6'] = [];
for (var i = 0; i < data1_v2['x_mask3'].length; i++) {
if (data1_v2['y_mask3'][i] > upper_3 || data1_v2['y_mask3'][i] < lower_3)
data_out['y_mask3'].push(data1_v2['y_mask3'][i]);
}
for (var i = 0; i < data1_v2['x_mask4'].length; i++) {
if (data1_v2['y_mask4'][i] > upper_4 || data1_v2['y_mask4'][i] < lower_4)
data_out['y_mask4'].push(data1_v2['y_mask4'][i]);
}
for (var i = 0; i < data1_v2['x_mask5'].length; i++) {
if (data1_v2['y_mask5'][i] > upper_5 || data1_v2['y_mask5'][i] < lower_5)
data_out['y_mask5'].push(data1_v2['y_mask5'][i]);
}
for (var i = 0; i < data1_v2['x_mask6'].length; i++) {
if (data1_v2['y_mask6'][i] > upper_6 || data1_v2['y_mask6'][i] < lower_6)
data_out['y_mask6'].push(data1_v2['y_mask6'][i]);
}
//update sources
source1.change.emit();
source1_v2.change.emit();
source2.change.emit();
source_vlines.change.emit();
source_outliers.change.emit();
""")
slider = Slider(start=start, end=end, value=start, step=.0001, title="Pseudorapidity (left bin edge)")
slider.js_on_change('value', callback)
return slider, p2
def plot(numFrac, numPF, args):
B0 = 3 # Tesla
mapper = LinearColorMapper(palette='Spectral10', low=0, high=1)
colorBar = ColorBar(color_mapper=mapper, location=(0, 0))
acquistionTimes = np.arange(start=float(args.tmin),
stop=float(args.tmax),
step=float(args.dt))
numTa = len(acquistionTimes)
partialFourierFactors = np.linspace(start=0.5, stop=1, num=numPF)
dephasingTimes = np.empty(shape=(numPF, numFrac, numTa, 2),
dtype=np.float32)
firstEchoFractions = np.linspace(start=0, stop=1, num=numFrac)
NSA = np.empty(shape=(numPF, numFrac, numTa, 2),
dtype=np.float32) # NSA_ss [weighted, unweighted]
for nt, ta in enumerate(acquistionTimes):
for nPF, PF in enumerate(partialFourierFactors):
for nf, f in enumerate(firstEchoFractions):
dephasingTimes[nPF, nf,
nt, :] = getDephasingTimes(ta / 1.0e3, PF, f)
weights = weightsFromFraction(f)
NSA[nPF, nf, nt, 0] = np.reciprocal(
weightedCrbTwoEchoes(B0, dephasingTimes[nPF, nf, nt, :],
weights)[2]) # Weighted NSA_ss
NSA[nPF, nf, nt, 1] = np.reciprocal(
weightedCrbTwoEchoes(
B0, dephasingTimes[nPF, nf, nt, :],
weightsFromFraction(.5))[2]) # Unweighted NSA_ss
print(100. * (nt + 1) / numTa)
pWeighted = figure(height=350,
width=350,
toolbar_location=None,
title='Weighted NSA_ss (3T)')
pUnWeighted = figure(height=350,
width=350,
toolbar_location=None,
title='Unweighted NSA_ss (3T)')
pUnWeighted.add_layout(colorBar, 'right')
CDSimages = [
ColumnDataSource({'imageData': [NSA[:, :, -1, 0]]}),
ColumnDataSource({'imageData': [NSA[:, :, -1, 1]]})
]
pWeighted.image(image='imageData',
x=0,
y=0,
dw=numFrac,
dh=numPF,
color_mapper=mapper,
source=CDSimages[0])
pUnWeighted.image(image='imageData',
x=0,
y=0,
dw=numFrac,
dh=numPF,
color_mapper=mapper,
source=CDSimages[1])
for p in [pWeighted, pUnWeighted]:
p.xaxis.ticker = [
0, (numFrac - 1) / 4, (numFrac - 1) / 2, 3 * (numFrac - 1) / 4,
numFrac - 1
]
p.xaxis.major_label_overrides = {
0: '0',
(numFrac - 1) / 4: '.25',
(numFrac - 1) / 2: '.5',
3 * (numFrac - 1) / 4: '.75',
numFrac - 1: '1'
}
p.yaxis.ticker = [0, (numPF - 1) / 2, numPF - 1]
p.yaxis.major_label_overrides = {
0: '0.5',
(numPF - 1) / 2: '.75',
numPF - 1: '1.0'
}
pWeighted.x_range.range_padding = pWeighted.y_range.range_padding = 0
pUnWeighted.x_range.range_padding = pUnWeighted.y_range.range_padding = 0
spans = [
Span(location=-1,
dimension='height',
line_color='navy',
line_dash='dashed'),
Span(location=-1,
dimension='height',
line_color='chocolate',
line_dash='dashed')
]
pGrad = figure(height=350,
width=350,
toolbar_location=None,
title='Gradients')
pGrad.add_layout(spans[0])
pGrad.add_layout(spans[1])
CDSFirst = ColumnDataSource({'t': [0, 0, .5, .5], 'amp': [0, 1, 1, 0]})
CDSSecond = ColumnDataSource({'t': [.5, .5, 1, 1], 'amp': [0, -1, -1, 0]})
pGrad.line(x='t', y='amp', color='navy', line_width=2, source=CDSFirst)
pGrad.line(x='t',
y='amp',
color='chocolate',
line_width=2,
source=CDSSecond)
pCompass = figure(height=350,
width=350,
toolbar_location=None,
title='Fat vectors',
x_range=(-1.1, 1.1),
y_range=(-1.1, 1.1))
pCompass.circle(0, 0, radius=1, fill_color=None, line_color='black')
CDSArrow = [
ColumnDataSource({
'x': [0, 0],
'y': [0, 0]
}),
ColumnDataSource({
'x': [0, 0],
'y': [0, 0]
})
]
pCompass.line(x='x', y='y', color='navy', line_width=2, source=CDSArrow[0])
pCompass.line(x='x',
y='y',
color='chocolate',
line_width=2,
source=CDSArrow[1])
slider = Slider(start=np.min(acquistionTimes),
end=np.max(acquistionTimes),
value=np.max(acquistionTimes),
step=acquistionTimes[1] - acquistionTimes[0],
title="Available acquisition time [ms]")
hoverCallback = CustomJS(args={
'dephasingTimes': dephasingTimes,
'firstEchoFractions': firstEchoFractions,
'partialFourierFactors': partialFourierFactors,
'spans': spans,
'arrows': CDSArrow,
'first': CDSFirst,
'slider': slider,
'second': CDSSecond
},
code="""
if ( isFinite(cb_data['geometry']['x']) && isFinite(cb_data['geometry']['y']) ) {
let ta = slider.value / 1000.0
if (typeof window.taIdx == 'undefined') {
window.taIdx = dephasingTimes[0][0].length - 1;
}
let fIdx = Math.floor(cb_data["geometry"]['x']) % firstEchoFractions.length;
let pfIdx = Math.floor(cb_data["geometry"]['y']);
first.data.t[2] = first.data.t[3] = ta*firstEchoFractions[fIdx];
second.data.t[0] = second.data.t[1] = ta*firstEchoFractions[fIdx];
second.data.t[2] = second.data.t[3] = ta;
first.data.amp[1] = first.data.amp[2] = partialFourierFactors[pfIdx] / firstEchoFractions[fIdx];
second.data.amp[1] = second.data.amp[2] = - partialFourierFactors[pfIdx] / (1 - firstEchoFractions[fIdx]);
spans[0].location = ta/2.0 + dephasingTimes[pfIdx][fIdx][window.taIdx][0];
spans[1].location = ta/2.0 + dephasingTimes[pfIdx][fIdx][window.taIdx][1];
spans[0].change.emit();
spans[1].change.emit();
first.change.emit();
second.change.emit();
let omega = 2*Math.PI*42.58*3*3.4
for (let i = 0; i < 2; i++) {
arrows[i].data.x[1] = Math.cos(omega*dephasingTimes[pfIdx][fIdx][window.taIdx][i]);
arrows[i].data.y[1] = Math.sin(omega*dephasingTimes[pfIdx][fIdx][window.taIdx][i]);
arrows[i].change.emit();
}
window.fIdx = fIdx;
window.pfIdx = pfIdx;
}
""")
pWeighted.add_tools(
HoverTool(tooltips=[('index', '$index'), ('x', '$x'), ('y', '$y')],
callback=hoverCallback,
mode='mouse'))
pUnWeighted.add_tools(
HoverTool(tooltips=[('index', '$index'), ('x', '$x'), ('y', '$y')],
callback=hoverCallback,
mode='mouse'))
sliderCallback = CustomJS(args={
'acquistionTimes': acquistionTimes,
'images': CDSimages,
'NSA': NSA
},
code="""
window.taIdx = Math.floor((cb_obj.value - cb_obj.start ) / cb_obj.step)
console.log(window.taIdx)
images[0].data.imageData = [NSA.map(PF => PF.map(f => f[window.taIdx][0])).flat()]
images[1].data.imageData = [NSA.map(PF => PF.map(f => f[window.taIdx][1])).flat()]
images[0].change.emit();
images[1].change.emit();
""")
slider.js_on_change('value', sliderCallback)
if args.svg is True:
from bokeh.io import export_svgs
fname = 'pweighted.svg'
print('Saving ' + fname)
pWeighted.output_backend = "svg"
export_svgs(pWeighted, filename=fname)
print('Done')
else:
output_file(args.filename)
combinedPlot = column(row(pWeighted, pUnWeighted),
row(pGrad, pCompass), slider)
save(combinedPlot, filename=args.filename, title=args.title)
#---------------------------------------------------------------#
# Set Up Callbacks
callback = CustomJS(args=dict(s1=s1, s2=s2, s3=s3, s4=s4, paragraph=paragraph),
code="""
var inds = cb_obj.value;
var d1 = s1.data;
var d2 = s2.data;
var index = d2['image'];
d1['image'] = index[inds];
var months = s3.data['months'];
var years = s4.data['years'];
var month = months[inds % 12];
var year = years[Math.floor(inds / 12)]
title = `${month} ${year}`
paragraph.text = title
s1.change.emit();
""")
year.js_on_change('value', callback)
callback_opacity = CustomJS(args=dict(r=r),
code="""
choropleth_layer.glyph.global_alpha = cb_obj.value;
""")
opacity.js_on_change('value', callback_opacity)
#---------------------------------------------------------------#
# Create the Layout
layout = row(column(paragraph, widgetbox(year), opacity), p, width=800)
show(layout)
def plot_waveform_bokeh(filename,waveform_list,metadata_list,station_lat_list,\
station_lon_list, event_lat, event_lon, boundary_data, style_parameter):
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
map_station_location_bokeh = ColumnDataSource(data=dict(map_lat_list=station_lat_list,\
map_lon_list=station_lon_list))
dot_default_index = 0
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[station_lat_list[dot_default_index]],\
lon=[station_lon_list[dot_default_index]],\
index=[dot_default_index]))
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='gray',\
line_width=2, level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='gray',\
level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='gray',\
line_width=2, nonselection_line_alpha=1.0, level='underlay',
nonselection_line_color='gray')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='gray',\
level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
#
map_view.triangle('map_lon_list', 'map_lat_list', source=map_station_location_bokeh, \
line_color='gray', size=style_parameter['marker_size'], fill_color='black',\
selection_color='black', selection_line_color='gray',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='black',\
nonselection_line_color='gray', nonselection_line_alpha=1.0)
map_view.triangle('lon','lat', source=selected_dot_on_map_bokeh,\
size=style_parameter['selected_marker_size'], line_color='black',fill_color='red')
map_view.asterisk([event_lon], [event_lat], size=style_parameter['event_marker_size'], line_width=3, line_color='red', \
fill_color='red')
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# --------------------------------------------------------
max_waveform_length = 0
max_waveform_amp = 0
ncurve = len(waveform_list)
for a_sta in waveform_list:
for a_trace in a_sta:
if len(a_trace) > max_waveform_length:
max_waveform_length = len(a_trace)
if np.max(np.abs(a_trace)) > max_waveform_amp:
max_waveform_amp = np.max(np.abs(a_trace))
#
plotting_list = []
for a_sta in waveform_list:
temp = []
for a_trace in a_sta:
if len(a_trace) < max_waveform_length:
a_trace = np.append(
a_trace, np.zeros([(max_waveform_length - len(a_trace)),
1]))
temp.append(list(a_trace))
plotting_list.append(temp)
#
time_list = []
for ista in range(len(plotting_list)):
a_sta = plotting_list[ista]
temp = []
for itr in range(len(a_sta)):
a_trace = a_sta[itr]
delta = metadata_list[ista][itr]['delta']
time = list(np.arange(len(a_trace)) * delta)
temp.append(time)
#
time_list.append(temp)
#
reftime_label_list = []
channel_label_list = []
for ista in range(len(metadata_list)):
temp_ref = []
temp_channel = []
a_sta = metadata_list[ista]
for a_trace in a_sta:
temp_ref.append('Starting from ' + a_trace['starttime'])
temp_channel.append(a_trace['network'] + '_' + a_trace['station'] +
'_' + a_trace['channel'])
reftime_label_list.append(temp_ref)
channel_label_list.append(temp_channel)
# --------------------------------------------------------
curve_fig01 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 0
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh01 = ColumnDataSource(
data=dict(time=select_curve_time, amp=select_curve_data))
select_reftime_label = reftime_label_list[dot_default_index][curve_index]
selected_reftime_label_bokeh01 = ColumnDataSource(data=dict(x=[style_parameter['curve_reftime_label_x']],\
y=[style_parameter['curve_reftime_label_y']],\
label=[select_reftime_label]))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh01 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
all_curve_data_bokeh = ColumnDataSource(
data=dict(t=time_list, amp=plotting_list))
all_reftime_label_bokeh = ColumnDataSource(data=dict(
label=reftime_label_list))
all_channel_label_bokeh = ColumnDataSource(data=dict(
label=channel_label_list))
# plot waveform
curve_fig01.line('time','amp', source=selected_curve_data_bokeh01,\
line_color='black')
# add refference time as a label
curve_fig01.text('x', 'y', 'label', source=selected_reftime_label_bokeh01)
# add channel label
curve_fig01.text('x', 'y', 'label', source=selected_channel_label_bokeh01)
# change style
curve_fig01.title.text_font_size = style_parameter['title_font_size']
curve_fig01.title.align = 'center'
curve_fig01.title.text_font_style = 'normal'
curve_fig01.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig01.xaxis.axis_label_text_font_style = 'normal'
curve_fig01.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig01.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig01.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig01.yaxis.axis_label_text_font_style = 'normal'
curve_fig01.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig01.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig01.toolbar.logo = None
curve_fig01.toolbar_location = 'above'
curve_fig01.toolbar_sticky = False
# --------------------------------------------------------
curve_fig02 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 1
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh02 = ColumnDataSource(
data=dict(time=select_curve_time, amp=select_curve_data))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh02 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
# plot waveform
curve_fig02.line('time','amp', source=selected_curve_data_bokeh02,\
line_color='black')
# add channel label
curve_fig02.text('x', 'y', 'label', source=selected_channel_label_bokeh02)
# change style
curve_fig02.title.text_font_size = style_parameter['title_font_size']
curve_fig02.title.align = 'center'
curve_fig02.title.text_font_style = 'normal'
curve_fig02.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig02.xaxis.axis_label_text_font_style = 'normal'
curve_fig02.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig02.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig02.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig02.yaxis.axis_label_text_font_style = 'normal'
curve_fig02.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig02.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig02.toolbar.logo = None
curve_fig02.toolbar_location = 'above'
curve_fig02.toolbar_sticky = False
# --------------------------------------------------------
curve_fig03 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 2
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh03 = ColumnDataSource(
data=dict(time=select_curve_time, amp=select_curve_data))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh03 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
# plot waveform
curve_fig03.line('time','amp', source=selected_curve_data_bokeh03,\
line_color='black')
# add channel label
curve_fig03.text('x', 'y', 'label', source=selected_channel_label_bokeh03)
# change style
curve_fig03.title.text_font_size = style_parameter['title_font_size']
curve_fig03.title.align = 'center'
curve_fig03.title.text_font_style = 'normal'
curve_fig03.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig03.xaxis.axis_label_text_font_style = 'normal'
curve_fig03.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig03.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig03.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig03.yaxis.axis_label_text_font_style = 'normal'
curve_fig03.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig03.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig03.toolbar.logo = None
curve_fig03.toolbar_location = 'above'
curve_fig03.toolbar_sticky = False
# --------------------------------------------------------
map_station_location_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_station_location_bokeh=map_station_location_bokeh,\
selected_curve_data_bokeh01=selected_curve_data_bokeh01,\
selected_curve_data_bokeh02=selected_curve_data_bokeh02,\
selected_curve_data_bokeh03=selected_curve_data_bokeh03,\
selected_channel_label_bokeh01=selected_channel_label_bokeh01,\
selected_channel_label_bokeh02=selected_channel_label_bokeh02,\
selected_channel_label_bokeh03=selected_channel_label_bokeh03,\
selected_reftime_label_bokeh01=selected_reftime_label_bokeh01,\
all_reftime_label_bokeh=all_reftime_label_bokeh,\
all_channel_label_bokeh=all_channel_label_bokeh,\
all_curve_data_bokeh=all_curve_data_bokeh), code="""
var inds = cb_obj.indices
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_loc = map_station_location_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_loc['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_loc['map_lon_list'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh01.data['t'] = all_curve_data_bokeh.data['t'][inds][0]
selected_curve_data_bokeh01.data['amp'] = all_curve_data_bokeh.data['amp'][inds][0]
selected_curve_data_bokeh01.change.emit()
selected_curve_data_bokeh02.data['t'] = all_curve_data_bokeh.data['t'][inds][1]
selected_curve_data_bokeh02.data['amp'] = all_curve_data_bokeh.data['amp'][inds][1]
selected_curve_data_bokeh02.change.emit()
selected_curve_data_bokeh03.data['t'] = all_curve_data_bokeh.data['t'][inds][2]
selected_curve_data_bokeh03.data['amp'] = all_curve_data_bokeh.data['amp'][inds][2]
selected_curve_data_bokeh03.change.emit()
selected_reftime_label_bokeh01.data['label'] = [all_reftime_label_bokeh.data['label'][inds][0]]
selected_reftime_label_bokeh01.change.emit()
selected_channel_label_bokeh01.data['label'] = [all_channel_label_bokeh.data['label'][inds][0]]
selected_channel_label_bokeh01.change.emit()
selected_channel_label_bokeh02.data['label'] = [all_channel_label_bokeh.data['label'][inds][1]]
selected_channel_label_bokeh02.change.emit()
selected_channel_label_bokeh03.data['label'] = [all_channel_label_bokeh.data['label'][inds][2]]
selected_channel_label_bokeh03.change.emit()
""")
#
map_station_location_bokeh.selected.js_on_change('indices',
map_station_location_js)
#
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_station_location_bokeh=map_station_location_bokeh,\
selected_curve_data_bokeh01=selected_curve_data_bokeh01,\
selected_curve_data_bokeh02=selected_curve_data_bokeh02,\
selected_curve_data_bokeh03=selected_curve_data_bokeh03,\
selected_channel_label_bokeh01=selected_channel_label_bokeh01,\
selected_channel_label_bokeh02=selected_channel_label_bokeh02,\
selected_channel_label_bokeh03=selected_channel_label_bokeh03,\
selected_reftime_label_bokeh01=selected_reftime_label_bokeh01,\
all_reftime_label_bokeh=all_reftime_label_bokeh,\
all_channel_label_bokeh=all_channel_label_bokeh,\
all_curve_data_bokeh=all_curve_data_bokeh),code="""
var inds = Math.round(cb_obj.value)
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_loc = map_station_location_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_loc['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_loc['map_lon_list'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh01.data['t'] = all_curve_data_bokeh.data['t'][inds][0]
selected_curve_data_bokeh01.data['amp'] = all_curve_data_bokeh.data['amp'][inds][0]
selected_curve_data_bokeh01.change.emit()
selected_curve_data_bokeh02.data['t'] = all_curve_data_bokeh.data['t'][inds][1]
selected_curve_data_bokeh02.data['amp'] = all_curve_data_bokeh.data['amp'][inds][1]
selected_curve_data_bokeh02.change.emit()
selected_curve_data_bokeh03.data['t'] = all_curve_data_bokeh.data['t'][inds][2]
selected_curve_data_bokeh03.data['amp'] = all_curve_data_bokeh.data['amp'][inds][2]
selected_curve_data_bokeh03.change.emit()
selected_reftime_label_bokeh01.data['label'] = [all_reftime_label_bokeh.data['label'][inds][0]]
selected_reftime_label_bokeh01.change.emit()
selected_channel_label_bokeh01.data['label'] = [all_channel_label_bokeh.data['label'][inds][0]]
selected_channel_label_bokeh01.change.emit()
selected_channel_label_bokeh02.data['label'] = [all_channel_label_bokeh.data['label'][inds][1]]
selected_channel_label_bokeh02.change.emit()
selected_channel_label_bokeh03.data['label'] = [all_channel_label_bokeh.data['label'][inds][2]]
selected_channel_label_bokeh03.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['map_view_plot_width'],\
height=50)
curve_slider.js_on_change('value', curve_slider_callback)
curve_slider_callback.args['curve_index'] = curve_slider
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
#
left_fig = Column(curve_slider,
map_view,
annotating_fig01,
width=style_parameter['left_column_width'])
right_fig = Column(curve_fig01,
curve_fig02,
curve_fig03,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_fig, right_fig)
save(layout)
var n = cb_obj.value;
var data1 = S1.data;
for(var i = 0; i < data1.y.length; i++)
{
data1['noise'][i] = 'False';
if(Math.abs(data1.x[i] + data1.y[i] - data1.RRSec_n_minus_2[i]) <= n)
{
data1['noise'][i] = 'True';
}
}
S1.change.emit();
""")
slider = Slider(start=0.0, end=0.5,value=0.0, step=0.1, title="noise%",height=65)
slider.js_on_change('value', callback)
callback.args["slider"] = slider
# ------------------------------------------ FIGURE 1 ----------------------------------------------------------
# ------------------------------------------ FIGURE 2 ----------------------------------------------------------
p2 = figure(output_backend="webgl", plot_width=700, plot_height=700, x_range=p1.x_range, y_range=p1.y_range)
p2.xaxis.axis_label = "RRn-1 (seconds)"
p2.xaxis.axis_label_text_font_size = "25px"
# p2.xaxis.axis_label_text_color = "#22ACE2"
p2.xaxis.axis_label_text_font = font_name
p2.xaxis.major_label_text_font = font_name
p2.yaxis.axis_label = "RRn (seconds)"
def create_vp_plot(data):
ds = ColumnDataSource(data)
# tools_to_show = "box_zoom, pan,save, hover, reset, wheel_zoom"
var_label = '@{' + str(data.columns[0] + '}')
try:
var_tooltip_label = str(data.variable_metadata['long_name'])
except KeyError:
var_tooltip_label = str(data.variable_metadata['standard_name'])
try:
units = list({'unit', 'units'}.intersection(data.variable_metadata))[0]
x_axis_label = " ".join(
[var_tooltip_label, '[', data.variable_metadata[units], ']'])
except IndexError:
print('no units found')
x_axis_label = var_tooltip_label
p = figure(toolbar_location="above",
tools="crosshair,box_zoom, pan,save, reset, wheel_zoom",
x_axis_type="linear",
x_axis_label=x_axis_label)
p.sizing_mode = 'stretch_width'
if len(data.dataset_metadata['dimension']) == 2:
try:
vertical_level, time_level = data.dataset_metadata['dimension']
except KeyError:
vertical_level, time_level = ('obsdepth', 'time')
else:
vertical_level = data.dataset_metadata['dimension'][0]
try:
var_tooltip_label = str(data.variable_metadata['long_name'])
except KeyError:
var_tooltip_label = str(data.variable_metadata['standard_name'])
# if " " in var_label:
# var_label = '@{' + var_label + '}'
# else:
# var_label = var_label
# var_label = var_label
hover = HoverTool(tooltips=[("Depth", "@" + vertical_level),
(var_tooltip_label, var_label)])
p.add_tools(hover)
p.y_range.flipped = True
p.min_border_left = 80
p.min_border_right = 80
p.background_fill_color = "SeaShell"
p.background_fill_alpha = 0.5
line_renderer = p.line(data.columns[0],
vertical_level,
source=ds,
line_alpha=0.6, color='RoyalBlue',
)
point_renderer = p.circle(data.columns[0],
vertical_level,
source=ds,
color='RoyalBlue',
size=3,
fill_alpha=0.5,
fill_color='white',
legend_label=data.columns[0],
)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
if len(list(data.columns)) >= 2:
# Div
html_text = get_datetime_string(list(data.columns)[0])
par = Div(text=html_text)
# Slider Labels
end_label = Div(text=list(data.columns)[-1])
start_label = Div(text=list(data.columns)[0])
# Buttons
left_btn = Button(label='<', width=30)
right_btn = Button(label='>', width=30)
# Spacer
sp = Spacer(width=50)
# Slider Labels
end_label = Div(text=list(data.columns)[-1].split('T')[0] + \
'<br>' \
+ list(data.columns)[-1].split('T')[1],
style={'text-align': 'right'})
start_label = Div(text=list(data.columns)[0].split('T')[0] + \
'<br>' \
+ list(data.columns)[0].split('T')[1],
style={'text-align': 'left'})
select = Select(title="Profile-record:",
options=list(data.columns),
value=list(data.columns)[0])
slider = Slider(title="Profile #",
value=0,
start=0,
end=len(data.columns) - 1,
step=1,
show_value=True,
tooltips=False) #
select_handler = CustomJS(args=dict(line_renderer=line_renderer,
point_renderer=point_renderer,
slider=slider,
par=par),
code="""
line_renderer.glyph.x = {field: cb_obj.value};
point_renderer.glyph.x = {field: cb_obj.value};
slider.value = cb_obj.options.indexOf(cb_obj.value);
var date_time = cb_obj.value.split("T");
var date = date_time[0];
var time = date_time[1];
par.text = `<ul style="text-align:left;"><li>Date: <b>`+date+`</b></li><li>Time: <b>`+time+`</b></li></ul>`;
""")
select.js_on_change('value', select_handler)
slider_handler = CustomJS(args=dict(select=select),
code="""
select.value = select.options[cb_obj.value];
""")
slider.js_on_change('value', slider_handler)
# Left button cb
left_btn_args = dict(slider=slider)
left_btn_handler = """
if(slider.value > slider.start) {
slider.value = slider.value - 1;
slider.change.emit();
}
"""
left_btn_callback = CustomJS(args=left_btn_args, code=left_btn_handler)
left_btn.js_on_click(left_btn_callback)
# Right button cb
right_btn_args = dict(slider=slider)
right_btn_handler = """
if(slider.value <= slider.end - 1) {
slider.value = slider.value + 1;
slider.change.emit();
}
"""
right_btn_callback = CustomJS(args=right_btn_args, code=right_btn_handler)
right_btn.js_on_click(right_btn_callback)
# buttons = row(left_btn, right_btn)
# inputs = row(sp,slider,buttons, par)
# return column(select, slider, p, par, sizing_mode="stretch_width")
# return column(p, select, inputs, sizing_mode="stretch_width")
# Set up layouts and add to document
# slider_wrapper = layout([
# [slider],
# [start_label, Spacer(sizing_mode="stretch_width"), end_label]
# ])
slider_wrapper = layout([
[sp, sp, slider, left_btn, right_btn, par],
[sp, start_label, sp, sp, end_label, sp, sp],
])
# buttons = row(left_btn, right_btn)
# inputs = row(sp, start_label, left_btn, sp, slider, sp, right_btn, end_label, par)
return column(select, p, slider_wrapper, sizing_mode="stretch_width")
else:
return column(p, sizing_mode="stretch_width")
Sexpsq+=exp[i]*exp[i];
}
TSR=(95*Sytexp-Sexp*Syt)/Math.sqrt(95*Sytsq-Syt*Syt)/Math.sqrt(95*Sexpsq-Sexp*Sexp)
TSR=Math.round(TSR*100)/100
corlabTS.text='TS R='.concat(TSR.toString())
source.trigger('change');
""")
cbox.js_on_change('active', cb)
slider.js_on_change('value', cb_slider)
rb_group.js_on_change('active', cb_ff)
rb_group2.js_on_change('active', cb_struct)
div = Div(text="""<b>Figure: Comparison of H-SASA profiles calculated with different parameters with experimental cleavage frequencies. Top strand (TS) of <i>S. cerevisiae</i> centromeric nucleosome with 601TA DNA sequence.</b>
<br><br>
This interactive plot allows to explore dependence of H-SASA profiles on different paramters. Correlation coefficient is interactively displayed in the bottom left corner.
Use contols above to choose between following paramters:<br><br>
<b>Structure used for calculations</b>: X-H nuclear - orginal X-ray derived structure, hydrogen atoms added via REDUCE program with nuclear distances for X-H bond length;
X-H electron - original X-ray derived structure, hydrogen atoms added via REDUCE program with electron cloud distances for X-H bond length;
MD average - average profile for 50 frames spaced 1 ns apart from molecular dynamics simulations with CHARMM36 force field with NAMD.
<br><br>
<b>Radii of atoms used for SASA calculations</b>: FreeSASA Default - default radii in FreeSASA program; CHARMM36-rmin - van der Waals radii (Rmin paramter) of atoms as defined by CHARMM36 force field;
AMBER10-rmin - van der Waals radii (Rmin paramter) of atoms as defined by AMBER10 force field.
<br><br>
<b>Contributions of deoxyribose hydrogen atoms</b>: SASA of deoxyribose hydrogen atoms selected will be included in calculation of H-SASA profile.
<br><br>
mass_finder_mass = Slider(value=100, start=0.0, end=1000.0, step=10.0, title='Mass of Complex (kDa)',name='gau_sigma', width=250, height=30)
mass_finder_exact_mass_text = Div(text= "Enter exact Mass (Da)", width= 150, height=30 )
mass_finder_exact_mass_sele = TextInput(value=str(mass_finder_mass.value*1000), disabled=False, width=100, height=30)
mass_finder_line_text = Div(text= "Show mz prediction", width= 150, height=30 )
mass_finder_line_sele = Toggle(label='off', active=False, width=100, height=30, callback=toggle_cb)
mass_finder_cb =CustomJS(args=dict(mass_finder_line_sele=mass_finder_line_sele, raw_mz=raw_mz, mass_finder_data=mass_finder_data, mass_finder_exact_mass_sele=mass_finder_exact_mass_sele, mass_finder_mass=mass_finder_mass, mass_finder_range_slider=mass_finder_range_slider, mfl=mfl), code=open(os.path.join(os.getcwd(), 'JS_Functions', "mass_finder_cb.js")).read())
mass_finder_exact_cb =CustomJS(args=dict(mass_finder_line_sele=mass_finder_line_sele, mass_finder_exact_mass_sele=mass_finder_exact_mass_sele, mass_finder_mass=mass_finder_mass), code=open(os.path.join(os.getcwd(), 'JS_Functions', "mass_finder_exact_cb.js")).read())
mass_finder_exact_mass_sele.js_on_change('value', mass_finder_exact_cb)
mass_finder_column=Column(mass_finder_header,mass_finder_mass, mass_finder_range_slider, Row(mass_finder_exact_mass_text,mass_finder_exact_mass_sele), Row(mass_finder_line_text, mass_finder_line_sele), visible=False)
mass_finder.js_link('active', mass_finder_column, 'visible')
mass_finder_line_sele.js_link('active', mfl, 'visible')
mass_finder_mass.js_on_change('value', mass_finder_cb)
mass_finder_line_sele.js_on_change('active', mass_finder_cb)
mass_finder_range_slider.js_on_change('value',mass_finder_cb)
### DATA PROCESSING ###
cropping = Div(text= " Range mz:", width= 150, height=30 )
# crop_max = Div(text= " ", width= 150, height=30 )
gau_name = Div(text= " Gaussian Smoothing:", width= 150, height=30 )
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 )
'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
},
code="""
const select_value = mvmt_name_mapper[select.value]
p1_cds.data = cds_pr_cal[select_value][cb_obj.value].data
p1_cds.change.emit()
"""))
# Horozontal range slider for gitub calendar plot
date_slider_callback = CustomJS(args=dict(p=pcal),
code="""
var a = cb_obj.value;
var delta = p.x_range.end - p.x_range.start
p.x_range.start = a;
p.x_range.end = a+delta;
""")
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
def plotspectra(spectra,
zcatalog=None,
model=None,
notebook=False,
title=None):
'''
TODO: document
'''
if notebook:
bk.output_notebook()
#- If inputs are frames, convert to a spectra object
if isinstance(spectra, list) and isinstance(spectra[0],
desispec.frame.Frame):
spectra = frames2spectra(spectra)
frame_input = True
else:
frame_input = False
if frame_input and title is None:
meta = spectra.meta
title = 'Night {} ExpID {} Spectrograph {}'.format(
meta['NIGHT'],
meta['EXPID'],
meta['CAMERA'][1],
)
#- Gather spectra into ColumnDataSource objects for Bokeh
nspec = spectra.num_spectra()
cds_spectra = list()
for band in spectra.bands:
#- Set masked bins to NaN so that Bokeh won't plot them
bad = (spectra.ivar[band] == 0.0) | (spectra.mask[band] != 0)
spectra.flux[band][bad] = np.nan
cdsdata = dict(
origwave=spectra.wave[band].copy(),
plotwave=spectra.wave[band].copy(),
)
for i in range(nspec):
key = 'origflux' + str(i)
cdsdata[key] = spectra.flux[band][i]
cdsdata['plotflux'] = cdsdata['origflux0']
cds_spectra.append(bk.ColumnDataSource(cdsdata, name=band))
#- Reorder zcatalog to match input targets
#- TODO: allow more than one zcatalog entry with different ZNUM per targetid
targetids = spectra.target_ids()
if zcatalog is not None:
ii = np.argsort(np.argsort(targetids))
jj = np.argsort(zcatalog['TARGETID'])
kk = jj[ii]
zcatalog = zcatalog[kk]
#- That sequence of argsorts may feel like magic,
#- so make sure we got it right
assert np.all(zcatalog['TARGETID'] == targetids)
assert np.all(zcatalog['TARGETID'] == spectra.fibermap['TARGETID'])
#- Also need to re-order input model fluxes
if model is not None:
mwave, mflux = model
model = mwave, mflux[kk]
#- Gather models into ColumnDataSource objects, row matched to spectra
if model is not None:
mwave, mflux = model
model_obswave = mwave.copy()
model_restwave = mwave.copy()
cds_model_data = dict(
origwave=mwave.copy(),
plotwave=mwave.copy(),
plotflux=np.zeros(len(mwave)),
)
for i in range(nspec):
key = 'origflux' + str(i)
cds_model_data[key] = mflux[i]
cds_model_data['plotflux'] = cds_model_data['origflux0']
cds_model = bk.ColumnDataSource(cds_model_data)
else:
cds_model = None
#- Subset of zcatalog and fibermap columns into ColumnDataSource
target_info = list()
for i, row in enumerate(spectra.fibermap):
target_bit_names = ' '.join(desi_mask.names(row['DESI_TARGET']))
txt = 'Target {}: {}'.format(row['TARGETID'], target_bit_names)
if zcatalog is not None:
txt += '<BR/>{} z={:.4f} ± {:.4f} ZWARN={}'.format(
zcatalog['SPECTYPE'][i],
zcatalog['Z'][i],
zcatalog['ZERR'][i],
zcatalog['ZWARN'][i],
)
target_info.append(txt)
cds_targetinfo = bk.ColumnDataSource(dict(target_info=target_info),
name='targetinfo')
if zcatalog is not None:
cds_targetinfo.add(zcatalog['Z'], name='z')
plot_width = 800
plot_height = 400
# tools = 'pan,box_zoom,wheel_zoom,undo,redo,reset,save'
tools = 'pan,box_zoom,wheel_zoom,reset,save'
fig = bk.figure(height=plot_height,
width=plot_width,
title=title,
tools=tools,
toolbar_location='above',
y_range=(-10, 20))
fig.toolbar.active_drag = fig.tools[1] #- box zoom
fig.toolbar.active_scroll = fig.tools[2] #- wheel zoom
fig.xaxis.axis_label = 'Wavelength [Å]'
fig.yaxis.axis_label = 'Flux'
fig.xaxis.axis_label_text_font_style = 'normal'
fig.yaxis.axis_label_text_font_style = 'normal'
colors = dict(b='#1f77b4', r='#d62728', z='maroon')
data_lines = list()
for spec in cds_spectra:
lx = fig.line('plotwave',
'plotflux',
source=spec,
line_color=colors[spec.name])
data_lines.append(lx)
if cds_model is not None:
model_lines = list()
lx = fig.line('plotwave',
'plotflux',
source=cds_model,
line_color='black')
model_lines.append(lx)
legend = Legend(items=[
("data",
data_lines[-1::-1]), #- reversed to get blue as lengend entry
("model", model_lines),
])
else:
legend = Legend(items=[
("data",
data_lines[-1::-1]), #- reversed to get blue as lengend entry
])
fig.add_layout(legend, 'center')
fig.legend.click_policy = 'hide' #- or 'mute'
#- Zoom figure around mouse hover of main plot
zoomfig = bk.figure(
height=plot_height // 2,
width=plot_height // 2,
y_range=fig.y_range,
x_range=(5000, 5100),
# output_backend="webgl",
toolbar_location=None,
tools=[])
for spec in cds_spectra:
zoomfig.line('plotwave',
'plotflux',
source=spec,
line_color=colors[spec.name],
line_width=1,
line_alpha=1.0)
if cds_model is not None:
zoomfig.line('plotwave',
'plotflux',
source=cds_model,
line_color='black')
#- Callback to update zoom window x-range
zoom_callback = CustomJS(args=dict(zoomfig=zoomfig),
code="""
zoomfig.x_range.start = cb_obj.x - 100;
zoomfig.x_range.end = cb_obj.x + 100;
""")
fig.js_on_event(bokeh.events.MouseMove, zoom_callback)
#-----
#- Emission and absorption lines
z = zcatalog['Z'][0] if (zcatalog is not None) else 0.0
line_data, lines, line_labels = add_lines(fig, z=z)
#-----
#- Add widgets for controling plots
z1 = np.floor(z * 100) / 100
dz = z - z1
zslider = Slider(start=0.0, end=4.0, value=z1, step=0.01, title='Redshift')
dzslider = Slider(start=0.0,
end=0.01,
value=dz,
step=0.0001,
title='+ Delta redshift')
dzslider.format = "0[.]0000"
#- Observer vs. Rest frame wavelengths
waveframe_buttons = RadioButtonGroup(labels=["Obs", "Rest"], active=0)
ifiberslider = Slider(start=0, end=nspec - 1, value=0, step=1)
if frame_input:
ifiberslider.title = 'Fiber'
else:
ifiberslider.title = 'Target'
zslider_callback = CustomJS(
args=dict(
spectra=cds_spectra,
model=cds_model,
targetinfo=cds_targetinfo,
ifiberslider=ifiberslider,
zslider=zslider,
dzslider=dzslider,
waveframe_buttons=waveframe_buttons,
line_data=line_data,
lines=lines,
line_labels=line_labels,
fig=fig,
),
#- TODO: reorder to reduce duplicated code
code="""
var z = zslider.value + dzslider.value
var line_restwave = line_data.data['restwave']
var ifiber = ifiberslider.value
var zfit = 0.0
if(targetinfo.data['z'] != undefined) {
zfit = targetinfo.data['z'][ifiber]
}
// Observer Frame
if(waveframe_buttons.active == 0) {
var x = 0.0
for(var i=0; i<line_restwave.length; i++) {
x = line_restwave[i] * (1+z)
lines[i].location = x
line_labels[i].x = x
}
for(var i=0; i<spectra.length; i++) {
var data = spectra[i].data
var origwave = data['origwave']
var plotwave = data['plotwave']
for (var j=0; j<plotwave.length; j++) {
plotwave[j] = origwave[j]
}
spectra[i].change.emit()
}
// Update model wavelength array
if(model) {
var origwave = model.data['origwave']
var plotwave = model.data['plotwave']
for(var i=0; i<plotwave.length; i++) {
plotwave[i] = origwave[i] * (1+z) / (1+zfit)
}
model.change.emit()
}
// Rest Frame
} else {
for(i=0; i<line_restwave.length; i++) {
lines[i].location = line_restwave[i]
line_labels[i].x = line_restwave[i]
}
for (var i=0; i<spectra.length; i++) {
var data = spectra[i].data
var origwave = data['origwave']
var plotwave = data['plotwave']
for (var j=0; j<plotwave.length; j++) {
plotwave[j] = origwave[j] / (1+z)
}
spectra[i].change.emit()
}
// Update model wavelength array
if(model) {
var origwave = model.data['origwave']
var plotwave = model.data['plotwave']
for(var i=0; i<plotwave.length; i++) {
plotwave[i] = origwave[i] / (1+zfit)
}
model.change.emit()
}
}
""")
zslider.js_on_change('value', zslider_callback)
dzslider.js_on_change('value', zslider_callback)
waveframe_buttons.js_on_click(zslider_callback)
plotrange_callback = CustomJS(args=dict(
zslider=zslider,
dzslider=dzslider,
waveframe_buttons=waveframe_buttons,
fig=fig,
),
code="""
var z = zslider.value + dzslider.value
// Observer Frame
if(waveframe_buttons.active == 0) {
fig.x_range.start = fig.x_range.start * (1+z)
fig.x_range.end = fig.x_range.end * (1+z)
} else {
fig.x_range.start = fig.x_range.start / (1+z)
fig.x_range.end = fig.x_range.end / (1+z)
}
""")
waveframe_buttons.js_on_click(plotrange_callback)
smootherslider = Slider(start=0,
end=31,
value=0,
step=1.0,
title='Gaussian Sigma Smooth')
target_info_div = Div(text=target_info[0])
#-----
#- Toggle lines
lines_button_group = CheckboxButtonGroup(labels=["Emission", "Absorption"],
active=[])
lines_callback = CustomJS(args=dict(line_data=line_data,
lines=lines,
line_labels=line_labels),
code="""
var show_emission = false
var show_absorption = false
if (cb_obj.active.indexOf(0) >= 0) { // index 0=Emission in active list
show_emission = true
}
if (cb_obj.active.indexOf(1) >= 0) { // index 1=Absorption in active list
show_absorption = true
}
for(var i=0; i<lines.length; i++) {
if(line_data.data['emission'][i]) {
lines[i].visible = show_emission
line_labels[i].visible = show_emission
} else {
lines[i].visible = show_absorption
line_labels[i].visible = show_absorption
}
}
""")
lines_button_group.js_on_click(lines_callback)
# lines_button_group.js_on_change('value', lines_callback)
#-----
update_plot = CustomJS(args=dict(
spectra=cds_spectra,
model=cds_model,
targetinfo=cds_targetinfo,
target_info_div=target_info_div,
ifiberslider=ifiberslider,
smootherslider=smootherslider,
zslider=zslider,
dzslider=dzslider,
lines_button_group=lines_button_group,
fig=fig,
),
code="""
var ifiber = ifiberslider.value
var nsmooth = smootherslider.value
target_info_div.text = targetinfo.data['target_info'][ifiber]
if(targetinfo.data['z'] != undefined) {
var z = targetinfo.data['z'][ifiber]
var z1 = Math.floor(z*100) / 100
zslider.value = z1
dzslider.value = (z - z1)
}
function get_y_minmax(pmin, pmax, data) {
// copy before sorting to not impact original, and filter out NaN
var dx = data.slice().filter(Boolean)
dx.sort()
var imin = Math.floor(pmin * dx.length)
var imax = Math.floor(pmax * dx.length)
return [dx[imin], dx[imax]]
}
// Smoothing kernel
var kernel = [];
for(var i=-2*nsmooth; i<=2*nsmooth; i++) {
kernel.push(Math.exp(-(i**2)/(2*nsmooth)))
}
var kernel_offset = Math.floor(kernel.length/2)
// Smooth plot and recalculate ymin/ymax
// TODO: add smoother function to reduce duplicated code
var ymin = 0.0
var ymax = 0.0
for (var i=0; i<spectra.length; i++) {
var data = spectra[i].data
var plotflux = data['plotflux']
var origflux = data['origflux'+ifiber]
for (var j=0; j<plotflux.length; j++) {
if(nsmooth == 0) {
plotflux[j] = origflux[j]
} else {
plotflux[j] = 0.0
var weight = 0.0
// TODO: speed could be improved by moving `if` out of loop
for (var k=0; k<kernel.length; k++) {
var m = j+k-kernel_offset
if((m >= 0) && (m < plotflux.length)) {
var fx = origflux[m]
if(fx == fx) {
plotflux[j] = plotflux[j] + fx * kernel[k]
weight += kernel[k]
}
}
}
plotflux[j] = plotflux[j] / weight
}
}
spectra[i].change.emit()
tmp = get_y_minmax(0.01, 0.99, plotflux)
ymin = Math.min(ymin, tmp[0])
ymax = Math.max(ymax, tmp[1])
}
// update model
if(model) {
var plotflux = model.data['plotflux']
var origflux = model.data['origflux'+ifiber]
for (var j=0; j<plotflux.length; j++) {
if(nsmooth == 0) {
plotflux[j] = origflux[j]
} else {
plotflux[j] = 0.0
var weight = 0.0
// TODO: speed could be improved by moving `if` out of loop
for (var k=0; k<kernel.length; k++) {
var m = j+k-kernel_offset
if((m >= 0) && (m < plotflux.length)) {
var fx = origflux[m]
if(fx == fx) {
plotflux[j] = plotflux[j] + fx * kernel[k]
weight += kernel[k]
}
}
}
plotflux[j] = plotflux[j] / weight
}
}
model.change.emit()
}
// update y_range
if(ymin<0) {
fig.y_range.start = ymin * 1.4
} else {
fig.y_range.start = ymin * 0.6
}
fig.y_range.end = ymax * 1.4
""")
smootherslider.js_on_change('value', update_plot)
ifiberslider.js_on_change('value', update_plot)
#-----
#- Add navigation buttons
navigation_button_width = 30
prev_button = Button(label="<", width=navigation_button_width)
next_button = Button(label=">", width=navigation_button_width)
prev_callback = CustomJS(args=dict(ifiberslider=ifiberslider),
code="""
if(ifiberslider.value>0) {
ifiberslider.value--
}
""")
next_callback = CustomJS(args=dict(ifiberslider=ifiberslider, nspec=nspec),
code="""
if(ifiberslider.value<nspec+1) {
ifiberslider.value++
}
""")
prev_button.js_on_event('button_click', prev_callback)
next_button.js_on_event('button_click', next_callback)
#-----
slider_width = plot_width - 2 * navigation_button_width
navigator = bk.Row(
widgetbox(prev_button, width=navigation_button_width),
widgetbox(next_button, width=navigation_button_width + 20),
widgetbox(ifiberslider, width=slider_width - 20))
bk.show(
bk.Column(
bk.Row(fig, zoomfig),
widgetbox(target_info_div, width=plot_width),
navigator,
widgetbox(smootherslider, width=plot_width // 2),
bk.Row(
widgetbox(waveframe_buttons, width=120),
widgetbox(zslider, width=plot_width // 2 - 60),
widgetbox(dzslider, width=plot_width // 2 - 60),
),
widgetbox(lines_button_group),
))
p_crd.add_tools(draw_tool)
p_crd.toolbar.active_tap = draw_tool
slider_sx = Slider(start=0, end=5, value=5, step=.1, title="Persistency", sizing_mode='stretch_width')
slider_sx.js_on_change('value',
CustomJS(args=dict(box=box, source_sx=source_sx, source_sx_persistency=source_sx_persistency),
code="""
box.bottom= cb_obj.value;
var d1 = source_sx.data;
const d2 = {'index': [], 'sx_value': [],
'start_datetime_str': [], 'end_datetime_str' : [],
'start_datetime_dt': [], 'end_datetime_dt' : []};
for (var i = 0; i < d1['index'].length; i++) {
if (d1['sx_value'][i] > cb_obj.value) {
d2['index'].push( d1['index'][i]);
d2['sx_value'].push( d1['sx_value'][i]);
d2['start_datetime_str'].push(d1['start_datetime_str'][i]);
d2['end_datetime_str'].push( d1['end_datetime_str'][i]);
d2['start_datetime_dt'].push( d1['start_datetime_dt'][i]);
d2['end_datetime_dt'].push( d1['end_datetime_dt'][i]);
}
}
source_sx_persistency.data = d2;
console.log(d2);
source_sx_persistency.change.emit();
"""))
toggle_sx = Toggle(label="Persistence", button_type="success", active=True)
toggle1 = Toggle(label="Word", button_type="success", active=True)
def plot_3DModel_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, profile_data_all, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and velocity profiles using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all constains the velocity model parameters saved for profile plots
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
ncolor = len(palette)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min) * 1. / ncolor
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append(
'Depth: {0:8.1f} km'.format(map_depth))
#
palette_r = palette[::-1]
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[
style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth],
left=[style_parameter['profile_plot_xmin']], \
right=[style_parameter['profile_plot_xmax']]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
#
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_2d = map_color.view('uint32').reshape(map_color.shape[:2])
map_color_all_slices.append(map_color_2d)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']*style_parameter['dlon']],\
dh=[style_parameter['nlat']*style_parameter['dlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
# ------------------------------
nprofile = len(profile_data_all)
grid_lat_list = []
grid_lon_list = []
width_list = []
height_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
grid_lat_list.append(aprofile['lat'])
grid_lon_list.append(aprofile['lon'])
width_list.append(style_parameter['map_view_grid_width'])
height_list.append(style_parameter['map_view_grid_height'])
grid_data_bokeh = ColumnDataSource(data=dict(lon=grid_lon_list,lat=grid_lat_list,\
width=width_list, height=height_list))
profile_default_index = style_parameter['profile_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[grid_lat_list[profile_default_index]], \
lon=[grid_lon_list[profile_default_index]], \
width=[style_parameter['map_view_grid_width']],\
height=[style_parameter['map_view_grid_height']],\
index=[profile_default_index]))
# ------------------------------
profile_vs_all = []
profile_depth_all = []
profile_ndepth = style_parameter['profile_ndepth']
profile_lat_label_list = []
profile_lon_label_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
vs_raw = aprofile['vs']
top_raw = aprofile['top']
profile_lat_label_list.append('Lat: {0:12.1f}'.format(aprofile['lat']))
profile_lon_label_list.append('Lon: {0:12.1f}'.format(aprofile['lon']))
vs_plot = []
depth_plot = []
for idepth in range(profile_ndepth):
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth])
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth + 1])
profile_vs_all.append(vs_plot)
profile_depth_all.append(depth_plot)
profile_data_all_bokeh = ColumnDataSource(data=dict(profile_vs_all=profile_vs_all, \
profile_depth_all=profile_depth_all))
selected_profile_data_bokeh = ColumnDataSource(data=dict(vs=profile_vs_all[profile_default_index],\
depth=profile_depth_all[profile_default_index]))
selected_profile_lat_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lat_label_x']], y=[style_parameter['profile_lat_label_y']],\
lat_label=[profile_lat_label_list[profile_default_index]]))
selected_profile_lon_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lon_label_x']], y=[style_parameter['profile_lon_label_y']],\
lon_label=[profile_lon_label_list[profile_default_index]]))
all_profile_lat_label_bokeh = ColumnDataSource(data=dict(
profile_lat_label_list=profile_lat_label_list))
all_profile_lon_label_bokeh = ColumnDataSource(data=dict(
profile_lon_label_list=profile_lon_label_list))
#
button_ndepth = style_parameter['button_ndepth']
button_data_all_vs = []
button_data_all_vp = []
button_data_all_rho = []
button_data_all_top = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
button_data_all_vs.append(aprofile['vs'][:button_ndepth])
button_data_all_vp.append(aprofile['vp'][:button_ndepth])
button_data_all_rho.append(aprofile['rho'][:button_ndepth])
button_data_all_top.append(aprofile['top'][:button_ndepth])
button_data_all_bokeh = ColumnDataSource(data=dict(button_data_all_vs=button_data_all_vs,\
button_data_all_vp=button_data_all_vp,\
button_data_all_rho=button_data_all_rho,\
button_data_all_top=button_data_all_top))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50)
depth_slider.js_on_change('value', depth_slider_callback)
depth_slider_callback.args["depth_index"] = depth_slider
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.rect('lon', 'lat', width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='gray', line_alpha=0.5, \
selection_line_color='gray', selection_line_alpha=0.5, selection_fill_color=None,\
nonselection_line_color='gray',nonselection_line_alpha=0.5, nonselection_fill_color=None,\
source=grid_data_bokeh, color=None, line_width=1, level='glyph')
map_view.rect('lon', 'lat',width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='#00ff00', line_alpha=1.0, \
source=selected_dot_on_map_bokeh, fill_color=None, line_width=3.,level='glyph')
# ------------------------------
grid_data_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh, \
grid_data_bokeh=grid_data_bokeh,\
profile_data_all_bokeh=profile_data_all_bokeh,\
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh, \
), code="""
var inds = cb_obj.indices
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][inds]]
selected_dot_on_map_bokeh.data['index'] = [inds]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][inds]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][inds]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[inds]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[inds]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
grid_data_bokeh.selected.js_on_change('indices', grid_data_js)
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
profile_xrange = Range1d(start=style_parameter['profile_plot_xmin'],
end=style_parameter['profile_plot_xmax'])
profile_yrange = Range1d(start=style_parameter['profile_plot_ymax'],
end=style_parameter['profile_plot_ymin'])
profile_fig = Figure(plot_width=style_parameter['profile_plot_width'], plot_height=style_parameter['profile_plot_height'],\
x_range=profile_xrange, y_range=profile_yrange, tools=style_parameter['profile_tools'],\
title=style_parameter['profile_title'])
profile_fig.line('vs',
'depth',
source=selected_profile_data_bokeh,
line_width=2,
line_color='black')
# ------------------------------
# add lat, lon
profile_fig.rect([style_parameter['profile_label_box_x']], [style_parameter['profile_label_box_y']],\
width=style_parameter['profile_label_box_width'], height=style_parameter['profile_label_box_height'],\
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black',\
level='underlay')
profile_fig.text('x',
'y',
'lat_label',
source=selected_profile_lat_label_bokeh)
profile_fig.text('x',
'y',
'lon_label',
source=selected_profile_lon_label_bokeh)
# ------------------------------
# change style
profile_fig.xaxis.axis_label = style_parameter['profile_xlabel']
profile_fig.xaxis.axis_label_text_font_style = 'normal'
profile_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.xaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.axis_label = style_parameter['profile_ylabel']
profile_fig.yaxis.axis_label_text_font_style = 'normal'
profile_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.xgrid.grid_line_dash = [4, 2]
profile_fig.ygrid.grid_line_dash = [4, 2]
profile_fig.title.text_font_size = style_parameter['title_font_size']
profile_fig.title.align = 'center'
profile_fig.title.text_font_style = 'normal'
profile_fig.toolbar_location = 'above'
profile_fig.toolbar_sticky = False
profile_fig.toolbar.logo = None
# ==============================
profile_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
grid_data_bokeh=grid_data_bokeh, \
profile_data_all_bokeh=profile_data_all_bokeh, \
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh), code="""
var p_index = Math.round(cb_obj.value)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][p_index]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][p_index]]
selected_dot_on_map_bokeh.data['index'] = [p_index]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][p_index]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][p_index]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[p_index]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[p_index]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
profile_slider = Slider(start=0, end=nprofile-1, value=style_parameter['profile_default_index'], \
step=1, title=style_parameter['profile_slider_title'], \
width=style_parameter['profile_plot_width'], height=50)
profile_slider_callback.args['profile_index'] = profile_slider
profile_slider.js_on_change('value', profile_slider_callback)
# ==============================
simple_text_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "# Layer Top (km) Vs(km/s) Vp(km/s) Rho(g/cm^3) \\n"
while(button_data_vp[i]) {
temp+=button_data_top[i].toPrecision(6) + " " + button_data_vs[i].toPrecision(4) + " " + \
button_data_vp[i].toPrecision(4) + " " + button_data_rho[i].toPrecision(4) + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
simple_text_button = Button(
label=style_parameter['simple_text_button_label'],
button_type='default',
width=style_parameter['button_width'])
simple_text_button.js_on_click(simple_text_button_callback)
# ------------------------------
model96_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var lat = selected_dot_on_map_bokeh.data['lat']
var lon = selected_dot_on_map_bokeh.data['lon']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "MODEL." + index + " \\n"
temp += "ShearVelocityModel Lat: "+ lat +" Lon: " + lon + "\\n"
temp += "ISOTROPIC \\n"
temp += "KGS \\n"
temp += "SPHERICAL EARTH \\n"
temp += "1-D \\n"
temp += "CONSTANT VELOCITY \\n"
temp += "LINE08 \\n"
temp += "LINE09 \\n"
temp += "LINE10 \\n"
temp += "LINE11 \\n"
temp += " H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS \\n"
while(button_data_vp[i+1]) {
var thickness = button_data_top[i+1] - button_data_top[i]
temp+=" " +thickness.toPrecision(6) + " " + button_data_vp[i].toPrecision(4) + " " + button_data_vs[i].toPrecision(4) \
+ " " + button_data_rho[i].toPrecision(4) + " 0.00 0.00 0.00 0.00 1.00 1.00" + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model96.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
model96_button = Button(label=style_parameter['model96_button_label'],
button_type='default',
width=style_parameter['button_width'])
model96_button.js_on_click(model96_button_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_column = Column(depth_slider,
map_view,
colorbar_fig,
annotating_fig01,
width=style_parameter['left_column_width'])
button_pannel = Row(simple_text_button, model96_button)
right_column = Column(profile_slider,
profile_fig,
button_pannel,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_column, right_column)
save(layout)
def getHeatMap(results, parameters, bgcolor, contextdict):
Yname = parameters[2]
Xname = parameters[3]
Xdict = BokehHeatmap.getDataParts(Xname, results)
Ydict = BokehHeatmap.getDataParts(Yname, results)
#colours row wise
factorsx = sorted(list(Xdict))
factorsy = sorted(list(Ydict), reverse=True)
rowlen = len(factorsx)
collen = len(factorsy)
countmatrix, maxval, observations = BokehHeatmap.getCountMatrix(
Xname, Yname, factorsx, factorsy, results, bokutils.FIRST_YEAR,
bokutils.LAST_YEAR)
rate = []
LOWVALUE = 1
HIGHVALUE = int(float(maxval) + float(0.5)) - 1
COLOURBAR = True
hcolors = bokutils.generateColorGradientHex(
bokutils.hex_to_rgb(bgcolor), (255, 204, 204), 4)
hcolors = ["#0C7D03", "#489D42", "#85BE81", "#C2DEC0"]
textcolors = hcolors[::-1]
for i, items in enumerate(factorsx):
factorsx[i] = bokutils.makeLegendKey(items)
for i, items in enumerate(factorsy):
factorsy[i] = bokutils.makeLegendKey(items)
x = []
y = []
totcount = 0
# Initialise with last year as base
for col in range(collen):
for row in range(rowlen):
x.append(factorsx[row])
y.append(factorsy[col])
count = int(countmatrix[-1][col][row] + float(0.5))
rate.append(count)
totcount = totcount + count
if (totcount == 0):
COLOURBAR = False
HIGHVALUE = float(2)
LOWVALUE = float(0.8)
elif (HIGHVALUE == -1):
COLOURBAR = False
HIGHVALUE = 1
elif (HIGHVALUE == 0):
COLOURBAR = False
HIGHVALUE = float(0.9)
LOWVALUE = float(0.8)
elif (HIGHVALUE == 1):
HIGHVALUE = float(1.9)
LOWVALUE = float(0.8)
textcolors = ["#C2DEC0"]
hcolors = ["#0C7D03"]
elif (HIGHVALUE <= LOWVALUE):
HIGHVALUE = float(HIGHVALUE) - 0.1
LOWVALUE = float(HIGHVALUE - 0.1)
COLOURBAR = False
mapper = LinearColorMapper(palette=textcolors,
high=float(HIGHVALUE),
low=float(LOWVALUE),
low_color="white",
high_color="#45334C")
source = ColumnDataSource(data=dict(x=x, y=y, rate=rate))
allsource = ColumnDataSource(data=dict(ratebyyear=countmatrix))
textmapper = LinearColorMapper(palette=hcolors[:], high=maxval)
TOOLS = "hover,save"
if (str(type(Xname)) == "<type 'str'>"):
DXname = Xname
else:
DXname = Xname[0]
if (str(type(Yname)) == "<type 'str'>"):
DYname = Yname
else:
DYname = Yname[0]
if (DXname == bokutils.PLOT_GLOCATION_KEY
or DXname == bokutils.PLOT_GLOCATION_KEY):
location = parameters[5]
title = "Visualisations/Plot/X = " + location[
1:] + " Y = " + "/".join(Yname).replace("_", " ")
else:
location = ", location=" + str(
contextdict[bokutils.PLOT_LOCATION_KEY][0].replace("'", ""))
title = "Visualisations/Plot/X = " + "/".join(Xname).replace(
"_", " ") + " Y = " + "/".join(Yname).replace("_", " ")
#Visualisations/Plot/X = Governance/Independent Y = Classification 2018
p = figure(title=title,
x_range=factorsx,
y_range=factorsy,
x_axis_location="above",
plot_width=900,
plot_height=900,
tools=TOOLS,
toolbar_location='below')
# border_fill_color=hcolors[0])
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
labels = LabelSet(x='x',
y='y',
text='rate',
level='glyph',
text_color={
'field': 'rate',
'transform': textmapper
},
x_offset=-10,
y_offset=-10,
source=source,
render_mode='canvas')
p.rect(x="x",
y="y",
width=1,
height=1,
source=source,
fill_color={
'field': 'rate',
'transform': mapper
},
line_color="white")
if (COLOURBAR):
color_bar = ColorBar(
color_mapper=mapper,
major_label_text_font_size="10pt",
ticker=BasicTicker(desired_num_ticks=len(hcolors)),
formatter=PrintfTickFormatter(format="%d"),
label_standoff=6,
border_line_color=hcolors[0],
location=(0, 0))
p.add_layout(color_bar, 'right')
p.add_layout(Title(text="X", align="center"), "below")
p.add_layout(Title(text="Y", align="center"), "left")
p.add_layout(Title(text="Colour to count legend", align="center"),
"right")
p.add_layout(labels)
p.select_one(HoverTool).tooltips = [
('Point', '@y # @x'),
('Count', '@rate '),
]
slider = Slider(start=bokutils.FIRST_YEAR,
end=bokutils.LAST_YEAR,
value=bokutils.LAST_YEAR,
step=1,
title='Year',
bar_color=hcolors[0])
paramsource = ColumnDataSource(data=dict(params=[rowlen, collen]))
callback = CustomJS(args=dict(source=source,
slider=slider,
plot=p,
window=None,
source2=allsource,
source3=paramsource),
code="""
var cb;
cb = function (source,
slider,
plot,
window,
source2,
source3)
{
var al, arr, data, end, i;
source = (source === undefined) ? source: source;
//console.log("slider "+slider);
slider = (slider === undefined) ? slider: slider;
plot = (plot === undefined) ? p: plot;
window = (window === undefined) ? null: window;
data = source.data;
var arr = source.data["rate"];
//console.log("rate"+arr);
var allcounts=source2.data["ratebyyear"];
//console.log(allcounts);
var params=source3.data["params"];
var rowlen=params[0];
var collen=params[1];
//console.log("arrlen"+arr.length);
//console.log("rowlen"+rowlen);
//console.log("collen"+collen);
var startidx=slider.value - 1960;
//console.log("START"+startidx);
var i=0;
var j=0;
var tot=0;
while (j < collen)
{
while (i < rowlen)
{
arr[tot] = Math.round(allcounts[startidx][j][i]);
i = i + 1;
tot=tot+1;
}
j = j + 1;
i=0;
}
//console.log("TOT="+tot);
//console.log("rate"+arr);
source.change.emit();
return null;
};
cb(source, slider, plot,window,source2,source3);
""")
slider.js_on_change('value', callback)
wb = widgetbox(children=[slider], sizing_mode='scale_width')
thisrow = Column(children=[p, wb], sizing_mode='scale_both')
return thisrow
def generate_performance_plot(hds, hcols):
aux_cols = ['Model', "Number_of_Drives", "Percent_of_Drives", "Color"]
cols = ['Failure_Rate', 'Capacity', 'Interface', 'Cache', 'RPM', 'Price_GB']
data = {}
for c in cols:
#print('col: ', c)
data[c] = hds[c]
for c in aux_cols:
#print('col: ', c)
data[c] = hds[c]
max_scale = 1.0
min_scale = 0.0
max_cache = np.max(hds["Cache"])
min_cache = np.min(hds["Cache"])
cache = ( (max_scale - min_scale) /(max_cache - min_cache))*(hds["Cache"] - max_cache) + max_scale
max_rpm = np.max(hds["RPM"])
min_rpm = np.min(hds["RPM"])
rpm = ( (max_scale - min_scale) /(max_rpm - min_rpm))*(hds["RPM"] - max_rpm) + max_scale
max_interface = np.max(hds["Interface"])
min_interface = np.min(hds["Interface"])
interface = ( (max_scale - min_scale) /(max_interface - min_interface))*(hds["Interface"] - max_interface) + max_scale
performance = interface + rpm + cache
max_performance = np.max(performance)
min_performance = np.min(performance)
performance = ( (max_scale - min_scale) /(max_performance - min_performance))*(performance - max_performance) + max_scale
max_failure = np.max(hds["Failure_Rate"])
min_failure = np.min(hds["Failure_Rate"])
reliability = ( (max_scale - min_scale) /(max_failure - min_failure))*(hds["Failure_Rate"] - max_failure) + max_scale
reliability = 1.0 - reliability
max_cost = np.max(hds["Price_GB"])
min_cost = np.min(hds["Price_GB"])
cost = ( (max_scale - min_scale) /(max_cost-min_cost))*(hds["Price_GB"]-max_cost) + max_scale
cost = 1.0 - cost
slider_start = .5
data["x"] = np.arange(0,len(hds['Model']),1)
data["y"] = slider_start * cost + slider_start * performance + slider_start * reliability
data["Cost"] = slider_start * cost
data["Performance"] = slider_start * performance
data["Reliability"] = slider_start * reliability
sizes = list(range(6, 24, 4))
groups = pd.cut(hds["Capacity"].values, len(sizes))
sz = [sizes[i] for i in groups.codes]
data["Size"] = sz
static_data = {}
static_data["Cost"] = cost
static_data["Performance"] = performance
static_data["Reliability"] = reliability
_source = ColumnDataSource(data=data)
_static_source = ColumnDataSource(data=static_data)
title = "Relative Hard Drive Value"
plot = figure(title=title, x_axis_location='below', y_axis_location='left', tools=['hover','save'])
hover = plot.select(dict(type=HoverTool))
hover.tooltips = [
("Model ", "@Model"),
("Failure Rate ", "@Failure_Rate"),
(hcols["Capacity"], "@Capacity"),
(hcols["Interface"], "@Interface"),
(hcols["RPM"], "@RPM"),
(hcols["Cache"], "@Cache"),
(hcols["Price_GB"], "@Price_GB{1.11}")
]
p1 = plot.circle('x', 'y', source = _source, size='Size', color="Color")#, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
from bokeh.models import FuncTickFormatter#, FixedTickFormatter
label_dict = {}
for i, s in enumerate(hds["Model"]):
label_dict[i] = s
plot.y_range = Range1d(-.1, 3.1)
plot.toolbar.logo = None
plot.xaxis.visible = False
plot.yaxis.visible = False
from bokeh.models import SingleIntervalTicker
ticker = SingleIntervalTicker(interval=1, num_minor_ticks=0)
xaxis = LinearAxis(axis_label="Model", ticker=ticker)
#yaxis = LinearAxis()#axis_label="Relative Merit")
xaxis.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % label_dict)
xaxis.major_label_orientation = -np.pi/2.7
plot.add_layout(xaxis, 'below')
#plot.add_layout(yaxis, 'left')
callback1 = CustomJS(args=dict(source=_source, static_source=_static_source), code="""
var data = source.get("data");
var static_data = static_source.get("data");
var f = cb_obj.value
y = data['y']
reli = data['Reliability']
perf = data['Performance']
cost = data['Cost']
static_cost = static_data['Cost']
for (i = 0; i < y.length; i++) {
cost[i] = f * static_cost[i]
y[i] = reli[i] + cost[i] + perf[i]
}
source.trigger('change');
""")
callback2 = CustomJS(args=dict(source=_source, static_source=_static_source), code="""
var data = source.get("data");
var static_data = static_source.get("data");
var f = cb_obj.value
y = data['y']
reli = data['Reliability']
static_reli = static_data['Reliability']
perf = data['Performance']
cost = data['Cost']
for (i = 0; i < y.length; i++) {
reli[i] = f * static_reli[i]
y[i] = reli[i] + cost[i] + perf[i]
}
source.trigger('change');
""")
callback3 = CustomJS(args=dict(source=_source, static_source=_static_source), code="""
var data = source.get("data");
static_data = static_source.get("data");
var f = cb_obj.value
y = data['y']
reli = data['Reliability']
perf = data['Performance']
static_perf = static_data['Performance']
cost = data['Cost']
for (i = 0; i < y.length; i++) {
perf[i] = f*static_perf[i]
y[i] = reli[i] + cost[i] + perf[i]
}
source.trigger('change');
""")
plot.min_border_left = 0
plot.xaxis.axis_line_width = 2
plot.yaxis.axis_line_width = 2
plot.title.text_font_size = '16pt'
plot.xaxis.axis_label_text_font_size = "14pt"
plot.xaxis.major_label_text_font_size = "14pt"
plot.yaxis.axis_label_text_font_size = "14pt"
plot.yaxis.major_label_text_font_size = "14pt"
plot.ygrid.grid_line_color = None
plot.xgrid.grid_line_color = None
plot.toolbar.logo = None
plot.outline_line_width = 0
plot.outline_line_color = "white"
slider1 = Slider(start=0.0, end=1.0, value=slider_start, step=.05, title="Price")
slider2 = Slider(start=0.0, end=1.0, value=slider_start, step=.05, title="Reliability")
slider3 = Slider(start=0.0, end=1.0, value=slider_start, step=.05, title="Performance")
slider1.js_on_change('value', callback1)
slider2.js_on_change('value', callback2)
slider3.js_on_change('value', callback3)
controls = widgetbox([slider1, slider2,slider3], width=200)
layout = row(controls, plot)
return layout
