def new_upload_button(self,
save_path,
callback,
name="Eigenfaces/static/tmp.jpg",
label="Upload your photo! (.jpg, 92x112)"):
def file_callback(attr, old, new):
raw_contents = source.data['contents'][0]
# remove the prefix that JS adds
prefix, b64_contents = raw_contents.split(",", 1)
file_contents = base64.b64decode(b64_contents)
fname = join(save_path, name)
with open(fname, "wb") as f:
print(fname)
f.write(file_contents)
logging.info("Success: file uploaded " + fname)
callback(fname)
source = ColumnDataSource({'contents': [], 'name': []})
source.on_change('data', file_callback)
button = Button(label=label, button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=self._upload_js)
return button
def recommendopposite(R, title, plot):
'''
recommendoppostie- Take vector of distances from current point and vector of titles and append 'recommend alternative' hyperlink button to the ploting canavas.
Returns
-------
grid: bokeh.layouts.grid_plot
Spatial arrangement of glyphs on canvas.
'''
# Select title of the most distant datapoint
maxR_id = np.argmax(R)
title_opp = title[maxR_id]
recomm = "Try reading something allternative:\n {}".format(title_opp[0])
# Create Search URL
base_url = "http://www.google.com/?#q="
final_url = base_url + quote(title_opp[0])
button = Button(label=recomm, button_type="primary")
# available button_types = default, primary, success, warning, danger, link
# Define event on callback
code = """
url = source.data['url']
window.open(url, "_self")
"""
source = ColumnDataSource(data=dict(url=[final_url]))
callback = CustomJS(args=dict(source=source), code=code)
button.callback = callback
wdgtbox = widgetbox(button)
# grid = gridplot([[plot],[wdgtbox]],toolbar_location='above', responsive = True, lnt = 1)
grid = gridplot([[plot], [wdgtbox]])
return grid
def update_annotation_exact(attr, old, new):
# This function probablby needs a rewrite if too slow. if not new is an empty search box, else has input text
if not new:
# Display table header as PreText
information_1 = "Showing {} of {} annotated genes total".format(maxindeces-1, totalrows)
annotation_information_1 = PreText(text=information_1)
funclayout.children[1] = annotation_information_1
# Reset table to original
annotation = ColumnDataSource(data=df.head(n=maxindeces))
csv_download = ColumnDataSource(data=df)
columns = [TableColumn(field=str(key), title=str(key)) for key in list(df)]
annotation_table = DataTable(source=annotation, columns=columns, width=1800)
funclayout.children[2] = annotation_table
# Redo buttons to original
funcbutton1 = Button(label="Download annotation (tsv)", button_type="primary")
funcbutton1.callback = CustomJS(args=dict(source=csv_download.data),code=open(join(dirname(__file__), "download_tsv.js")).read())
funcbutton2 = Button(label="Download sequences (Nuc, fasta)", button_type="success", disabled=True)
funcbutton3 = Button(label="Download sequences (Prot, fasta)", button_type="success")
funcbutton3.callback = CustomJS(args=dict(source=csv_download.data, seqs=seqs),code=open(join(dirname(__file__), "download_protseq.js")).read())
funcbuttons = row(funcbutton1, funcbutton2, funcbutton3)
funclayout.children[3] = funcbuttons
else:
# Display "Searching while searching"
annotation_information_1 = PreText(text="Searching...(expect atleast 5 second search time pr. 10000 annotated genes)")
funclayout.children[1] = annotation_information_1
# Use template df pandas to search and create new pandas with search results
current = df.where(df.apply(lambda row: row.astype(str).str.contains(new, case=False).any(), axis=1, result_type='broadcast')).dropna(how='all')
hitcount= len(current)
annotation = ColumnDataSource(data=current.head(n=maxindeces))
csv_download = ColumnDataSource(data=current)
columns = [TableColumn(field=str(key), title=str(key)) for key in list(current)]
annotation_table = DataTable(source=annotation, columns=columns, width=1800)
funclayout.children[2] = annotation_table
# Display hit information
tablecount = len(current.head(n=maxindeces))
information_1 = "Showing {} of {} hits of {} annotated genes total".format(tablecount, hitcount,totalrows)
annotation_information_1 = PreText(text=information_1)
funclayout.children[1] = annotation_information_1
# Change buttons
funcbutton1 = Button(label="Download annotation of current hits (tsv)", button_type="primary")
funcbutton1.callback = CustomJS(args=dict(source=csv_download.data),code=open(join(dirname(__file__), "download_tsv.js")).read())
funcbutton2 = Button(label="Download sequences of current hits (Nuc, fasta)", button_type="success", disabled=True)
funcbutton3 = Button(label="Download sequences of current hits (Prot, fasta)", button_type="success")
funcbutton3.callback = CustomJS(args=dict(source=csv_download.data, seqs=seqs),code=open(join(dirname(__file__), "download_protseq.js")).read())
funcbuttons = row(funcbutton1, funcbutton2, funcbutton3)
funclayout.children[3] = funcbuttons
def set_button(source, path):
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(
os.path.join(os.path.abspath('../../'),
path)).read())
return button
def set_button(source, path):
root = os.path.abspath(os.sep)
download_path = os.path.join(root, 'app/templates/download.js')
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(download_path).read())
return button
def button_print_page():
"""Button to print currently displayed webpage to paper or pdf.
Notes
-----
* Available styles: 'default', 'primary', 'success', 'warning', 'danger'
"""
button = Button(label="Print this page", button_type="success")
button.callback = CustomJS(code="""print()""")
return widgetbox(button)
def button_print_page():
"""Button to print currently displayed webpage to paper or pdf.
Notes
-----
* Available styles: 'default', 'primary', 'success', 'warning', 'danger'
"""
button = Button(label="Print this page", button_type="success")
button.callback = CustomJS(code="""print()""")
return widgetbox(button)
def button_save_table(table):
"""Button to save selected data table as csv.
Notes
-----
* Does not work for column values containing tuples (like 'neighbors')
* Currently columns being saved are hard coded in the javascript callback
* Available styles: 'default', 'primary', 'success', 'warning', 'danger'
"""
button = Button(label="Download selected data", button_type="success")
button.callback = CustomJS(args=dict(source=table.source),
code=open(join(dirname(__file__),
"js/download_data.js")).read())
return widgetbox(button)
def new_upload_button(save_path, callback, label="Upload Data File"):
def file_callback(attr, old, new):
raw_contents = source.data['contents'][0]
file_name = source.data['name'][0]
# remove the prefix that JS adds
prefix, b64_contents = raw_contents.split(",", 1)
file_contents = base64.b64decode(b64_contents)
callback(file_name, file_contents)
source = ColumnDataSource({'contents': [], 'name': []})
source.on_change('data', file_callback)
button = Button(label=label, button_type="success")
button.callback = CustomJS(args=dict(source=source), code=_upload_js)
return button, source
def button_save_table(table):
"""Button to save selected data table as csv.
Notes
-----
* Does not work for column values containing tuples (like 'neighbors')
* Currently columns being saved are hard coded in the javascript callback
* Available styles: 'default', 'primary', 'success', 'warning', 'danger'
"""
button = Button(label="Download selected data", button_type="success")
button.callback = CustomJS(args=dict(source=table.source),
code=open(
join(dirname(__file__),
"js/download_data.js")).read())
return widgetbox(button)
def make_table():
if __name__ == '__main__':
df = pd.read_csv('salary_data.csv')
else:
# df = pd.read_csv('./bokeh_pages/salary_data.csv')
df = pd.read_csv('salary_data.csv')
source = ColumnDataSource(data=dict())
def update():
current = df[df['salary'] <= slider.value].dropna()
source.data = {
'name': current.name,
'salary': current.salary,
'years_experience': current.years_experience,
}
slider = Slider(title="Max Salary",
start=10000,
end=250000,
value=150000,
step=1000)
slider.on_change('value', lambda attr, old, new: update())
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(join(dirname(__file__),
"download.js")).read())
columns = [
TableColumn(field="name", title="Employee Name"),
TableColumn(field="salary",
title="Income",
formatter=NumberFormatter(format="$0,0.00")),
TableColumn(field="years_experience", title="Experience (years)")
]
data_table = DataTable(source=source, columns=columns, width=800)
controls = widgetbox(slider, button)
table = widgetbox(data_table)
curdoc().add_root(row(controls, table))
curdoc().title = "Export CSV"
update()
def create_button(self):
s_java_script = """
function table_to_csv(source) {
const columns = Object.keys(source.data)
const nrows = source.get_length()
const lines = [columns.join(',')]
for (let i = 0; i < nrows; i++) {
let row = [];
for (let j = 0; j < columns.length; j++) {
const column = columns[j]
row.push(source.data[column][i].toString())
}
lines.push(row.join(','))
}
return lines.join('\\n').concat('\\n')
}
const filename = 'XXXXXX'
filetext = table_to_csv(source)
const blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' })
//addresses IE
if (navigator.msSaveBlob) {
navigator.msSaveBlob(blob, filename)
} else {
const link = document.createElement('a')
link.href = URL.createObjectURL(blob)
link.download = filename
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
}
"""
s_java_script = s_java_script.replace('XXXXXX', self.s_file_name)
source = ColumnDataSource(self.df_data)
o_btn = Button(label=self.s_btn_label, button_type="success")
o_btn.callback = CustomJS(args=dict(source=source), code=s_java_script)
# o_btn.js_on_click(CustomJS(args = dict(source = source), code = s_java_script))
return o_btn
def interactive_CMD(specs,cid1='g_SDSS',cid2='i_SDSS'):
'''
Simplistic tool to create an interactive
bokeh plot where outliers can be marked and saved in
'/home/ekaterina/Documents/appaloosa/stars_shortlist/share/temp'
'''
# Create some random data and put it into a ColumnDataSource
s = specs.set_index('EPIC')
s = s[[cid1, cid2, 'e_'+cid1,'e_'+cid2]].dropna(how='any')
x = list(s[cid1]-s[cid2])
y = list(s[cid2])
size = list(np.sqrt(s['e_'+cid1]**2+s['e_'+cid2]**2)*100.)
z = list(s.index.values)
source_data = ColumnDataSource(data=dict(x=x, y=y,desc=z))
# Create a button that saves the coordinates of selected data points to a file
savebutton = Button(label="Save", button_type="success")
savebutton.callback = CustomJS(args=dict(source_data=source_data), code="""
var inds = source_data.selected['1d'].indices;
var data = source_data.data;
var out = "";
for (i = 0; i < inds.length; i++) {
out += data['desc'][inds[i]] + " ";
}
var file = new Blob([out], {type: 'text/plain'});
var elem = window.document.createElement('a');
elem.href = window.URL.createObjectURL(file);
elem.download = 'selected-data.txt';
document.body.appendChild(elem);
elem.click();
document.body.removeChild(elem);
""")
# Plot the data and save the html file
p = figure(plot_width=800, plot_height=400,
#y_range=(20,7),
tools="lasso_select, reset, hover",)
p.circle(x='x', y='y', source=source_data, fill_alpha=0.8)#add size='desc' to visualize uncertainties on mag
p.xaxis.axis_label = '{}-{}'.format(cid1,cid2)
p.yaxis.axis_label = cid1
plot = Column(p, savebutton)
output_file("test.html")
show(plot)
return
def displayResultTable(sortedKeys, dictionnary, name):
#display a table in
source = ColumnDataSource(dictionnary)
columns = [
TableColumn(field=key, title=key)
for key in sortedKeys
]
area = DataTable(source=source, columns=columns, width=600, height=800)
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(join(dirname(__file__), "download.js")).read())
controls=widgetbox(button)
output_file(name+".html", title=name)
save(row(area,controls))
def button2csv():
button = Button(label="Download", button_type="success")
javaScript = """
function table_to_csv(source) {
const columns = Object.keys(source.data)
const nrows = source.get_length()
const lines = [columns.join(',')]
for (let i = 0; i < nrows; i++) {
let row = [];
for (let j = 0; j < columns.length; j++) {
const column = columns[j]
row.push(source.data[column][i].toString())
}
lines.push(row.join(','))
}
return lines.join('\\n').concat('\\n')
}
const filename = 'data_result.csv'
filetext = table_to_csv(source)
const blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' })
//addresses IE
if (navigator.msSaveBlob) {
navigator.msSaveBlob(blob, filename)
} else {
const link = document.createElement('a')
link.href = URL.createObjectURL(blob)
link.download = filename
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
}
"""
button.callback = CustomJS(args=dict(source=source), code=javaScript)
show(button)
def createUploadFileButton(self):
self.file_source.on_change('data', self.file_callback)
button = Button(label="Upload", button_type="success", name=str(uuid.uuid1()))
button.on_click(partial(self.disableButton, button=button))
#button.on_click(lambda: self.uploadFile(file_source=self.file_source, button=button))
button.callback = CustomJS(args=dict(file_source=self.file_source), code="""
function read_file(filename) {
var reader = new FileReader();
reader.onload = load_handler;
reader.onerror = error_handler;
// readAsDataURL represents the file's data as a base64 encoded string
reader.readAsDataURL(filename);
}
function load_handler(event) {
var b64string = event.target.result;
file_source.data = {'file_contents' : [b64string], 'file_name':[input.files[0].name]};
file_source.trigger("change");
}
function error_handler(evt) {
if(evt.target.error.name == "NotReadableError") {
alert("Can't read file!");
}
}
var input = document.createElement('input');
input.setAttribute('type', 'file');
input.onchange = function(){
if (window.FileReader) {
read_file(input.files[0]);
} else {
alert('FileReader is not supported in this browser');
}
}
input.click();
""")
return row(button)
def _set_button(self, specs):
button = Button()
# button = figure()
# button = figure(plot_width=400, plot_height=400,
# tools="tap", title="Click the Dots")
#
# source = ColumnDataSource(data=dict(
# x=[1, 2, 3, 4, 5],
# y=[2, 5, 8, 2, 7],
# color=["navy", "orange", "olive", "firebrick", "gold"]
# ))
#
# button.circle('x', 'y', color='color', size=20, source=source)
#
# url = _convert_url_to_bokeh("mast_folder/mast.html")
# taptool = button.select(type=TapTool)
# taptool.callback = OpenURL(url=url)
button.label = specs.text
button.width = specs.width
button.height = specs.height
button.name = _convert_url_to_bokeh("mast_folder/mast.html")
button.callback = self.widget_callback()
return button
source = ColumnDataSource(data=dict())
def update():
current = df[(df['salary'] >= slider.value[0]) & (df['salary'] <= slider.value[1])].dropna()
source.data = {
'name' : current.name,
'salary' : current.salary,
'years_experience' : current.years_experience,
}
slider = RangeSlider(title="Max Salary", start=10000, end=110000, value=(10000, 50000), step=1000, format="0,0")
slider.on_change('value', lambda attr, old, new: update())
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(join(dirname(__file__), "download.js")).read())
columns = [
TableColumn(field="name", title="Employee Name"),
TableColumn(field="salary", title="Income", formatter=NumberFormatter(format="$0,0.00")),
TableColumn(field="years_experience", title="Experience (years)")
]
data_table = DataTable(source=source, columns=columns, width=800)
controls = widgetbox(slider, button)
table = widgetbox(data_table)
curdoc().add_root(row(controls, table))
curdoc().title = "Export CSV"
step=10,
callback=callback)
callback.args["mej"] = M_slider
callback.args["vej"] = v_slider
callback.args["k"] = k_slider
callback.args["nindex"] = n_slider
callback.args["rhoo"] = rho_slider
callback.args["r0o"] = r0_slider
callback.args["t00"] = T_slider
button.callback = CustomJS(args=dict(source=source,
vej=v_slider,
name_v=text,
mej=M_slider,
myk=k_slider,
myt0=T_slider,
myn=n_slider,
myrho=rho_slider,
myr0=r0_slider),
code=javaScript)
callback2.args["SNName"] = text
text.on_change('value', update_title)
# Set up layouts and add to document
inputs = widgetbox(text, M_slider, v_slider, k_slider, n_slider, r0_slider,
rho_slider, T_slider)
layout = row(plot, column(inputs, button))
output_file("CSM.html")
from bokeh.layouts import column, row
from bokeh.plotting import show, output_file
from bokeh.models.callbacks import CustomJS
USER = "******"
PASSWD = "Bok3h"
text = PreText(text="LOGIN TO KNOW\nTHE SECRET:")
user = TextInput(placeholder="username", title="(UserName: "******")")
pwd = PasswordInput(placeholder="password", title="(Password: "******")")
btn = Button(label="GO!", width=150)
secret = PreText() # Secret information displayed if correct password entered
## Verify if the password typed is bokeh using a JS script
verify_pwd = CustomJS(args=dict(user=user, pwd=pwd, secret=secret),
code="""
secret.text = 'Wrong Password.';
if (user.value == %r && pwd.value == %r) {
secret.text = 'Right Password. The Secret is 42.';
}
""" % (USER, PASSWD))
#user.callback = verify_pwd # Check password pressing enter.
pwd.callback = verify_pwd # Check password pressing enter.
btn.callback = verify_pwd # Check password clicking on the Button.
output_file("using_password_input.html", title="Password Field")
page = row(column(text, user, pwd, btn), secret)
show(page)
p.x_range.end = x_minmax[1] + (x_minmax[1] - x_minmax[0] + 1) / 20
p.y_range.start = y_minmax[0] - (y_minmax[1] - y_minmax[0] + 1) / 20
p.y_range.end = y_minmax[1] + (y_minmax[1] - y_minmax[0] + 1) / 20
# Maintain current axes ranges upon reset
p.on_event('reset', readjust_axes)
# Add callbacks to the 3 widgets manually created back at the start
x_field.on_change('value', lambda a, r, g: update())
y_field.on_change('value', lambda a, r, g: update())
hide_null.on_click(lambda arg: update())
download_button = Button(label="Download", button_type="success")
download_button.callback = CustomJS(args=dict(source=source),
code=open(
join(dirname(__file__),
"js/csv_download_all.js")).read())
download_selected_button = Button(label="Download Selected",
button_type="success")
download_selected_button.callback = CustomJS(
args=dict(source=source),
code=open(join(dirname(__file__), "js/csv_download_selected.js")).read())
widgets.extend((download_button, download_selected_button))
# Add all models and widgets to the document
box = widgetbox(*widgets, sizing_mode='stretch_both')
fig = column(row(box, p), table, sizing_mode='scale_width')
update() # initial update
curdoc().add_root(fig)
def selection_plot(response):
# Let's move these into a settings file somewhere?
# height/width could potentially be driven by the request?
# Include color data in the ColumnDataSource to allow for changing the color on
# the client side.
urls = [x[0] for x in response["pages"]]
xdata = [x[1] for x in response["pages"]]
ydata = [x[2] for x in response["pages"]]
tags = [x[3] for x in response["pages"]]
color = [colormap(x[0]) if x else colormap(None) for x in tags]
source = ColumnDataSource(
data=dict(
x=xdata,
y=ydata,
urls=urls,
tags=tags,
color=color,
)
)
# Callback code for CDS.
source.callback = CustomJS(code="""
var inds = cb_obj.get('selected')["1d"].indices;
var data = cb_obj.get('data');
BokehPlots.showPages(inds);
""")
# Create the figure with FIGURE_WIDTH and FIGURE_HEIGHT
p = figure(tools="hover", width=FIGURE_WIDTH,
toolbar_location=None, responsive=True, tags=["clusterPlot"])
# Ensure that the lasso only selects with mouseup, not mousemove.
p.add_tools(LassoSelectTool(select_every_mousemove=False))
# These turn off the x/y axis ticks
p.axis.visible = None
# These turn the major grid off
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# Plot non-selected circles with a particular style using CIRCLE_SIZE and
# 'color' list
p.circle("x", "y", size=13, line_width=2, line_alpha=1,
line_color=None, fill_alpha=1, color='color', source=source,
name="urls")
nonselected_circle = Circle(fill_alpha=0.1, line_alpha=0.1, fill_color='color',
line_color='color')
renderer = p.select(name="urls")
renderer.nonselection_glyph = nonselected_circle
# Create buttons and their callbacks, use button_code string for callbacks.
button_code = """
event.preventDefault();
var inds = source.get('selected')["1d"].indices;
var data = source.get('data');
var selected = [];
tag = "%s";
for(var i = 0; i < inds.length; i++){
selected.push({
x: data.x[inds[i]],
y: data.y[inds[i]],
url: data.urls[inds[i]],
tags: data.tags[inds[i]],
selected: true,
possible: false,
});
data["color"][inds[i]] = "%s";
}
BokehPlots.updateTags(selected, tag, inds);
source.trigger("change");
"""
# Supply color with print formatting.
button1 = Button(label="Relevant", type="success")
button1.callback = CustomJS(args=dict(source=source),
code=button_code % ("Relevant", POSITIVE_COLOR))
button2 = Button(label="Irrelevant", type="success")
button2.callback = CustomJS(args=dict(source=source),
code=button_code % ("Irrelevant", NEGATIVE_COLOR))
button3 = Button(label="Neutral", type="success")
button3.callback = CustomJS(args=dict(source=source),
code=button_code % ("Neutral", NEUTRAL_COLOR))
# Adjust what attributes are displayed by the HoverTool
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
("urls", "@urls"),
]
layout = vform(p, button1, button2, button3)
# Combine script and div into a single string.
plot_code = components(layout)
return plot_code[0] + plot_code[1]
def load_page(experiment_df, experiment_db):
########## bokeh plots ##########
# general plot tools
plot_tools = 'wheel_zoom, pan, reset, save, hover'
hover = HoverTool(tooltips=[("(x,y)", "($x, $y)")])
# progress curve plots
global raw_source
raw_source = ColumnDataSource(data=dict(x=[], y=[], yr=[], yfit=[]))
global raw
raw = figure(title="Initial Rate Fit",
x_axis_label="Time",
y_axis_label="Signal",
plot_width=350,
plot_height=300,
tools=plot_tools)
raw.circle('x',
'y',
size=2,
source=raw_source,
color='gray',
selection_color="black",
alpha=0.6,
nonselection_alpha=0.2,
selection_alpha=0.6)
raw.line('x', 'yfit', source=raw_source, color='red')
global warning_source
warning_source = ColumnDataSource(data=dict(
x=[0],
y=[0],
t=[
'Please enter transform equation! \nMust convert signal to [substrate] \nin Schnell-Mendoza mode (e.g. via \nx/6.22/0.45/0.001 for sample data). \nNote: this transform may need \nto be inverted through multiplying \nby -1 when analyzing experiments \nthat measure increasing product \nconcentration over time)'
]))
global warning
warning = raw.text(x='x',
y='y',
text='t',
text_font_size='12pt',
angle=0,
source=warning_source)
warning.visible = False
global circles_source
circles_source = ColumnDataSource(
data=dict(x=[-.05, -.05, 1.6, 1.6], y=[0, 0.6, 0, 0.6]))
global circles
circles = raw.circle(x='x', y='y', alpha=0., source=circles_source)
circles.visible = False
global resi
resi = figure(title="Initial Rate Fit Residuals",
x_axis_label="Time",
y_axis_label="Residual",
plot_width=700,
plot_height=200,
tools='wheel_zoom,pan,reset')
resi.yaxis.formatter = BasicTickFormatter(precision=2, use_scientific=True)
resi.circle('x', 'yr', size=5, source=raw_source, color='grey', alpha=0.6)
# model plot for titration experiments
global model_data_source
model_data_source = ColumnDataSource(
data=dict(xt=[], yt=[], n=[], ct=[], et=[]))
global model_plot_source
model_plot_source = ColumnDataSource(
data=dict(xp=[], yp=[], l=[], u=[], cp=[], ep=[]))
global model_fit_source
model_fit_source = ColumnDataSource(data=dict(x=[], y=[]))
global varea_source
varea_source = ColumnDataSource(data=dict(x=[], r1=[], r2=[]))
global model
model = figure(title='Model Fit',
x_axis_label='Concentration',
y_axis_label='Rate',
plot_width=350,
plot_height=300,
tools=plot_tools)
model.circle('xp',
'yp',
size=8,
source=model_plot_source,
color='cp',
alpha=0.6)
model.add_layout(
Whisker(source=model_plot_source, base='xp', upper='u', lower='l'))
model.line('x',
'y',
source=model_fit_source,
line_width=3,
color='black',
alpha=0.8)
model.varea('x', 'r1', 'r2', source=varea_source, color='grey', alpha=0.3)
########## bokeh widgets ##########
# button for selecting progress curve fitting routine
global fit_button
fit_button = RadioButtonGroup(labels=[
'Maximize Slope Magnitude', 'Linear Fit', 'Logarithmic Fit',
'Schnell-Mendoza'
],
active=0,
width=375)
fit_button.on_change('active', widget_callback)
# button for selecting progress curve fitting routine
global scalex_box
scalex_box = CheckboxButtonGroup(
labels=["transform x-axis to Log10 scale"], active=[])
scalex_box.on_change('active', widget_callback)
# dropdown menu for selecting titration experiment model
global model_select
model_select = Select(
title='Choose Model',
value='Michaelis-Menten',
options=['Michaelis-Menten', 'pEC50/pIC50', 'High-Throughput Screen'],
width=350)
model_select.on_change('value', widget_callback)
# dropdown menu for selecting blank sample to subtract from remaining titration samples
global subtract_select
subtract_select = Select(title='Select Blank Sample for Subtraction',
value='',
options=list(experiment_df)[1:] + [''],
width=350)
subtract_select.on_change('value', widget_callback)
# dropdown menu for selecting titration sample to plot in current view
global sample_select
sample_select = Select(title='Y Axis Sample',
value=list(experiment_df)[-1],
options=list(experiment_df)[1:],
width=350)
sample_select.on_change('value', sample_callback)
# text input box for transforming slopes to rates
global transform_input
transform_input = TextInput(value='',
title="Enter Transform Equation",
width=350)
transform_input.on_change('value', widget_callback)
# text input box for setting delay time in logarithmic progress curve fitting
global offset_input
offset_input = TextInput(value='',
title="Enter Time Between Mixing and First Read",
width=350)
offset_input.on_change('value', widget_callback)
# text input boxes for fixing EC/IC50 parameters
global bottom_fix
bottom_fix = TextInput(value='', title="Fix pIC50/pEC50 Bottom")
bottom_fix.on_change('value', widget_callback)
global top_fix
top_fix = TextInput(value='', title="Fix pIC50/pEC50 Top")
top_fix.on_change('value', widget_callback)
global slope_fix
slope_fix = TextInput(value='', title="Fix pIC50/pEC50 Hill Slope")
slope_fix.on_change('value', widget_callback)
# text input boxes for progress curve xrange selection
global start_time
start_time = TextInput(value=str(
experiment_df[list(experiment_df)[0]].values[0]),
title="Enter Start Time")
global end_time
end_time = TextInput(value=str(
experiment_df[list(experiment_df)[0]].values[-1]),
title='Enter End Time')
start_time.on_change('value', xbox_callback)
end_time.on_change('value', xbox_callback)
# range slider to select threshold for hit detection in HTS mode
global threshold_slider
threshold_slider = Slider(start=0,
end=5,
value=2,
step=0.1,
title='HTS Hit Threshold (Standard Deviation)',
width=350)
threshold_slider.on_change('value', threshold_callback)
# range slider to update plots according to progress cuve xrange selection
xmin = experiment_df[experiment_df.columns[0]].values[0]
xmax = experiment_df[experiment_df.columns[0]].values[-1]
global range_slider
range_slider = RangeSlider(
start=xmin,
end=xmax,
value=(xmin, xmax),
step=experiment_df[experiment_df.columns[0]].values[1] - xmin,
title='Fine Tune X-Axis Range',
width=650)
range_slider.on_change('value', slider_callback)
# button to upload local data file
global file_source
file_source = ColumnDataSource(data=dict(file_contents=[], file_name=[]))
file_source.on_change('data', file_callback)
try:
output_filename = file_source.data['file_name'] + '-out.csv'
except:
output_filename = 'output.csv'
global upload_button
upload_button = Button(label="Upload Local File",
button_type="success",
width=350)
upload_button.callback = CustomJS(args=dict(file_source=file_source),
code=open(
join(dirname(__file__),
"upload.js")).read())
# table containing rate fits and errors
template = """
<div style="background:<%=ct%>"; color="white";>
<%= value %></div>
"""
formatter = HTMLTemplateFormatter(template=template)
columns = [
TableColumn(field='n', title='Sample'),
TableColumn(field='yt',
title='Slope (Initial Rate)',
formatter=formatter),
TableColumn(field='et', title='Std. Error')
]
global rate_table
rate_table = DataTable(source=model_data_source,
columns=columns,
width=350,
height=250,
selectable=True,
editable=True)
# tables containing model fits and errors
global mm_source
mm_source = ColumnDataSource(dict(label=[], Km=[], Vmax=[]))
columns = [
TableColumn(field='label', title=''),
TableColumn(field='Vmax', title='Vmax'),
TableColumn(field='Km', title='Km')
]
global mm_table
mm_table = DataTable(source=mm_source,
columns=columns,
width=350,
height=75,
selectable=True,
editable=True)
global ic_source
ic_source = ColumnDataSource(
dict(label=[], Bottom=[], Top=[], Slope=[], p50=[]))
columns = [
TableColumn(field='label', title=''),
TableColumn(field='Bottom', title='Bottom'),
TableColumn(field='Top', title='Top'),
TableColumn(field='Slope', title='Slope'),
TableColumn(field='p50', title='pEC/IC50')
]
global ic_table
ic_table = DataTable(source=ic_source,
columns=columns,
width=350,
height=75,
selectable=True,
editable=True)
# button for copying rate data table to clipboard
global copy_button
copy_button = Button(label="Copy Table to Clipboard",
button_type="primary",
width=350)
copy_button.callback = CustomJS(args=dict(source=model_data_source),
code=open(
join(dirname(__file__),
"copy.js")).read())
# button for downloading rate data table to local csv file
global download_button
download_button = Button(label="Download Table to CSV",
button_type="primary",
width=350)
download_button.callback = CustomJS(args=dict(source=model_data_source,
file_name=output_filename),
code=open(
join(dirname(__file__),
"download.js")).read())
########## document formatting #########
desc = Div(text=open(join(dirname(__file__), "description.html")).read(),
width=1400)
advanced = Div(
text="""<strong>Advanced Settings for \npEC/IC50 Analysis</strong>""")
widgets = widgetbox(model_select, sample_select, subtract_select,
transform_input, offset_input, advanced, scalex_box,
bottom_fix, top_fix, slope_fix)
table = widgetbox(rate_table)
main_row = row(
column(upload_button, widgets),
column(fit_button, row(raw, model), resi, row(start_time, end_time),
range_slider),
column(download_button, copy_button, table, mm_table, ic_table,
threshold_slider))
sizing_mode = 'scale_width'
l = layout([[desc], [main_row]], sizing_mode=sizing_mode)
update()
curdoc().clear()
curdoc().add_root(l)
curdoc().title = "ICEKAT"
source=source)
formatter = HTMLTemplateFormatter(template="<div><%= value %></div>")
table_source = ColumnDataSource(data=table_data)
columns = [TableColumn(field='IMGS', title='Structure', width=300, formatter=formatter),
TableColumn(field='SMILES', title='SMILES', width=300, editor=StringEditor())] + \
[TableColumn(field=prop, title=prop, width=150) for prop in properties.keys()]
table = HighTable(source=table_source,
columns=columns,
editable=True,
width=1200,
height=1200)
button = Button(label="Download", button_type="warning")
button.callback = CustomJS(args=dict(source=table_source),
code=open(
os.path.join(os.path.dirname(__file__),
"download.js")).read())
def colors_from_data(data):
min_data = min(data)
max_data = max(data)
if not max_data == min_data:
data_0_to_256 = [
int(255 * (data_point - min_data) / (max_data - min_data))
for data_point in data
]
colors = [bokeh.palettes.Plasma256[i] for i in data_0_to_256]
else:
colors = [bokeh.palettes.Plasma256[200] for _ in data]
return colors
def modify_doc(doc):
df = pd.read_csv(join(dirname(__file__), 'charity_input.csv'))
source = ColumnDataSource(data=dict())
table_title_div = Div(text="")
m_dict = dict({'True': True, 'False': False})
# calculate min and max of latest_income, to nearest £100,000
inc_min = math.floor(min(df['latest_income'])/100000)*100000
inc_max = math.ceil(max(df['latest_income'])/100000)*100000
# Create lists of all regions and districts present in dataset to use in dropdown filter
regions = sorted(list(set(df['region'][df.region.notnull()].values)))
regions.insert(0,'All')
districts = sorted(list(set(df['district'][df.district.notnull()].values)))
districts.insert(0,'All')
# function to quickly return sorted list of values from current selection,
# used to update dropdown filter below
def col_list(table, col):
l1 = ['All']
l2 = sorted(list(set(table[col][table[col].notnull()].values)))
l1.extend(l2)
return l1
def update():
# conditions of filters to create current selection
current = df[(df['latest_income'] >= inc_slider.value[0]) &
(df['latest_income'] <= inc_slider.value[1])]
if act_match.value != "All":
current = current[current['activity_keyword_match'] == m_dict[act_match.value]]
if gr_match.value != "All":
current = current[current['grant_keyword_match'] == m_dict[gr_match.value]]
if district_input.value != "All":
current = current[current['district'] == district_input.value]
if name_input.value != "":
current = current[current.name.str.contains(name_input.value)]
if id_input.value != "":
current = current[current.charity_id == int(id_input.value)]
if region_input.value != "All":
current = current[current['region'] == region_input.value]
district_input.options = col_list(current, 'district')
else: district_input.options = districts
# define source data as dictionary of current selection
source.data = current.to_dict('list')
# update selection counter div based on length of current
table_title = "<b>%s</b> charities selected" % "{:,}".format(len(current))
table_title_div.update(text=table_title)
match_options = ["All", "True", "False"]
inc_slider = RangeSlider(title="Latest Income", start=inc_min, end=inc_max, value=(inc_min, inc_max), step=100000, format="0,0")
act_match = Select(title="Activity Keyword Match", options=match_options, value="All")
gr_match = Select(title="Grant Keyword Match", options=match_options, value="All")
region_input = Select(title="Region", options=regions, value="All")
district_input = Select(title="District", options=districts, value="All")
name_input = TextInput(value="", title="Charity Name Search")
id_input = TextInput(value="", title="Charity ID Search")
bok_cols = [#TableColumn(field='charity_id', title='Charity ID', width=100),
TableColumn(field='charity_id', title='Charity ID', width=100, #),
formatter=HTMLTemplateFormatter(template='<a href="http://beta.charitycommission.gov.uk/charity-details/?regid=<%= value %>&subid=0"target="_blank"><%= value %></a>')),
TableColumn(field='name', title='Name', width=200),
TableColumn(field='activities', title='Activities', width=300),
TableColumn(field='website', title='Website', width=200,
formatter=HTMLTemplateFormatter(template='<a href="http://<%= value %>"target="_blank"><%= value %></a>')),
TableColumn(field='latest_income', title='Latest Income', width=100,
formatter=NumberFormatter(format='£ 0,0'))]
data_table = DataTable(source=source, columns=bok_cols, width=1000, height=600)
controls = [inc_slider, act_match, gr_match, region_input, district_input,name_input, id_input] #
for control in controls:
control.on_change('value', lambda attr, old, new: update())
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(join(dirname(__file__), "download.js")).read())
table = widgetbox(data_table)
table_section = column(table_title_div, data_table)
filters = widgetbox(*controls, button, sizing_mode='scale_width')
desc = Div(text=open(join(dirname(__file__), 'templates/index.html')).read(), width=1000)
l1 = layout([
[desc],
[filters, table_section]
# [Div()],
# [filters, table_section],
], sizing_mode='fixed')
update()
doc.add_root(l1)
})
#Text for tab3
texto1_tab3 = "The table of best distributions for your classification is: "
texto_tab3 = PreText(text=texto1_tab3)
#Table
texto2_tab3 = PreText(text="Select a filter to see results here")
#Buttom
downloadButton = Button(label="Export in csv")
#downloadButton.js_on_event(ButtonClick, CustomJS(args=dict(source=dataExport),
# code=open(join(dirname(__file__), "static/download.js")).read()))
downloadButton.callback = CustomJS(
code=open(join(dirname(__file__), "static/JS/download.js")).read())
##########################################################################
##########################################################################
#Actions and updatings
targetVar.on_change('value', updateHisto) #Updating target variable
filterSelect.on_change('value', check_box_populate, compute_summary,
clean_filter) #Updating filter variable
radioButtom_group.on_change('active', filterHisto) #Enabling checkbox
###########################################################################
#Layout and WebScreen
from bokeh.models.widgets import PasswordInput, TextInput, PreText, Button
from bokeh.layouts import column, row
from bokeh.plotting import show, output_file
from bokeh.models.callbacks import CustomJS
USER = "******"
PASSWD = "Bok3h"
text = PreText(text="LOGIN TO KNOW\nTHE SECRET:")
user = TextInput(placeholder="username", title="(UserName: "******")")
pwd = PasswordInput(placeholder="password", title="(Password: "******")")
btn = Button(label="GO!",width=150)
secret = PreText() # Secret information displayed if correct password entered
## Verify if the password typed is bokeh using a JS script
verify_pwd = CustomJS(args=dict(user=user, pwd=pwd, secret=secret), code="""
secret.text = 'Wrong Password.';
if ( user.value == %r && pwd.value == %r) {
secret.text = 'Right Password. The Secret is 42.';
}
""" % (USER, PASSWD))
#user.callback = verify_pwd # Check password pressing enter.
pwd.callback = verify_pwd # Check password pressing enter.
btn.callback = verify_pwd # Check password clicking on the Button.
output_file("using_password_input.html", title="Password Field")
page = row(column(text,user, pwd,btn),secret)
show(page)
def selection_plot(response):
# Let's move these into a settings file somewhere?
# height/width could potentially be driven by the request?
# Include color data in the ColumnDataSource to allow for changing the color on
# the client side.
urls = [x[0] for x in response["pages"]]
xdata = [x[1] for x in response["pages"]]
ydata = [x[2] for x in response["pages"]]
tags = [x[3] for x in response["pages"]]
color = []
custom_tags = ["Custom tags"]
for tag in tags:
custom = False
if tag:
for t in tag:
if t not in ["Relevant", "Irrelevant", ""]:
custom = True
if t not in custom_tags:
custom_tags.append(t)
if not custom:
color.append(colormap(tag[0]))
else:
color.append(colormap("Custom"))
else:
color.append(colormap(None))
source = ColumnDataSource(
data=dict(
x=xdata,
y=ydata,
urls=urls,
tags=tags,
color=color,
)
)
# Callback code for CDS.
source.callback = CustomJS(code="""
var inds = cb_obj.get('selected')["1d"].indices;
var data = cb_obj.get('data');
BokehPlots.showPages(inds);
""")
# Create the figure with FIGURE_WIDTH and FIGURE_HEIGHT
p = figure(
tools="hover,wheel_zoom,reset",
width=FIGURE_WIDTH,
responsive=True,
tags=["clusterPlot"],
)
# Ensure that the lasso only selects with mouseup, not mousemove.
p.add_tools(
LassoSelectTool(select_every_mousemove=False),
)
# These turn off the x/y axis ticks
p.axis.visible = None
# These turn the major grid off
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# Plot non-selected circles with a particular style using CIRCLE_SIZE and
# 'color' list
p.circle("x", "y", size=13, line_width=2, line_alpha=1,
line_color=None, fill_alpha=1, color='color', source=source,
name="urls")
nonselected_circle = Circle(fill_alpha=0.1, line_alpha=0.1, fill_color='color',
line_color='color')
renderer = p.select(name="urls")
renderer.nonselection_glyph = nonselected_circle
# Create buttons and their callbacks, use button_code string for callbacks.
button_code = """
event.preventDefault();
var inds = source.get('selected')["1d"].indices;
var data = source.get('data');
var selected = [];
tag = "%s";
for(var i = 0; i < inds.length; i++){
selected.push({
x: data.x[inds[i]],
y: data.y[inds[i]],
url: data.urls[inds[i]],
tags: data.tags[inds[i]],
selected: true,
possible: false,
});
data["color"][inds[i]] = "%s";
}
BokehPlots.updateTags(selected, tag, "Apply");
source.trigger("change");
"""
textinput_code = """
event.preventDefault();
var inds = source.get('selected')["1d"].indices;
var data = source.get('data');
var selected = [];
var tag = cb_obj.get("value");
// Reinitialise to the default value
cb_obj.set("value", "Add custom tag...")
if(tag.indexOf("Add custom tag...") < 0) {
//Update the custom tags selection list
var options = custom_tags_select.get("options");
if(options.indexOf(tag) < 0){
options.push(tag);
custom_tags_select.set("options", options);
}
for(var i = 0; i < inds.length; i++){
selected.push({
x: data.x[inds[i]],
y: data.y[inds[i]],
url: data.urls[inds[i]],
tags: data.tags[inds[i]],
selected: true,
possible: false,
});
data["color"][inds[i]] = "%s";
}
BokehPlots.updateTags(selected, tag, "Apply");
source.trigger("change");
}
"""
selectinput_code = """
event.preventDefault();
var inds = source.get('selected')["1d"].indices;
var data = source.get('data');
var selected = [];
var tag = cb_obj.get("value");
cb_obj.set("value", "Enter tags...")
if(tag.indexOf("Add custom tag...") < 0) {
for(var i = 0; i < inds.length; i++){
selected.push({
x: data.x[inds[i]],
y: data.y[inds[i]],
url: data.urls[inds[i]],
tags: data.tags[inds[i]],
selected: true,
possible: false,
});
data["color"][inds[i]] = "%s";
}
BokehPlots.updateTags(selected, tag, "Apply");
source.trigger("change");
}
"""
# Create buttons and their callbacks, use button_code string for callbacks.
crawl_code = """
event.preventDefault();
var inds = source.get('selected')["1d"].indices;
var data = source.get('data');
var selected = [];
var crawl = '%s';
for(var i = 0; i < inds.length; i++){
selected.push(data.urls[inds[i]]);
}
BokehPlots.crawlPages(selected, crawl);
source.trigger("change");
"""
# Supply color with print formatting.
but_relevant = Button(label="Relevant", type="success")
but_relevant.callback = CustomJS(args=dict(source=source),
code=button_code % ("Relevant", POSITIVE_COLOR))
but_irrelevant = Button(label="Irrelevant", type="success")
but_irrelevant.callback = CustomJS(args=dict(source=source),
code=button_code % ("Irrelevant", NEGATIVE_COLOR))
but_neutral = Button(label="Neutral", type="success")
but_neutral.callback = CustomJS(args=dict(source=source),
code=button_code % ("Neutral", NEUTRAL_COLOR))
custom_tag_input = TextInput(value="Add custom tag...")
custom_tag_input.callback = CustomJS(args=dict(source=source),
code=textinput_code % (CUSTOM_COLOR))
custom_tag_select = Select(value="Custom tags", options=custom_tags)
custom_tag_select.callback = CustomJS(args=dict(source=source),
code=selectinput_code % (CUSTOM_COLOR))
custom_tag_input.callback.args["custom_tags_select"] = custom_tag_select
but_backward_crawl = Button(label="Backlinks", type="success")
but_backward_crawl.callback = CustomJS(args=dict(source=source),
code=crawl_code % ("backward"))
but_forward_crawl = Button(label="Forwardlinks", type="success")
but_forward_crawl.callback = CustomJS(args=dict(source=source),
code=crawl_code % ("forward"))
# Adjust what attributes are displayed by the HoverTool
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
("urls", "@urls"),
]
tags = hplot(custom_tag_input, custom_tag_select, but_neutral, but_relevant, but_irrelevant)
tags_crawl = hplot(but_backward_crawl, but_forward_crawl)
layout = vplot(p, tags, tags_crawl)
# Combine script and div into a single string.
plot_code = components(layout)
return plot_code[0] + plot_code[1]
esiid_textbox = TextInput(title='ESIID', value='None')
#day_slider = Slider(title='day', value=1, start=1, end=31, step=1)
month_slider = Slider(title='month', value=finalMonth, start=1, end=12, step=1)
year_slider = Slider(title='year', value=finalYear, start=firstYear, end=finalYear, step=1)
tdsp_group = CheckboxGroup(labels=tdsps,
active=list(range(len(tdsps))))
exclude_approval_textbox = TextInput(title='Exclude' value='None')
savebutton = Button(label="Download", button_type="success")
savebutton.callback = CustomJS(args=dict(source=source_dl), code=open(os.path.join(os.path.dirname(__file__), "dl_820transactions.js")).read())
#updatebutton = Button(label="Approve", button_type="")
#updatebutton.callback =
columns = [
TableColumn(field="DocumentTrackingNumber", title="Document Tracking Number", width=150),
TableColumn(field="OriginalDocumentID", title="Original Document ID", width=100),
TableColumn(field="MarketerDUNS", title="Marketer DUNS", width=100),
TableColumn(field="TDSP", title="TDSP", width=75),
TableColumn(field="PaymentDueDate", title="Payment Due Date", width=100, formatter=DateFormatter(format="%m/%d/%Y")),
TableColumn(field="ESIID", title="ESIID", width=100),
TableColumn(field="InvoiceTotalAmount", title="Invoice Total Amount", width=100, formatter=NumberFormatter(format='$0,0.00')),
TableColumn(field="Processed", title="Processed", width=75),
]
# j2sPath="https://www.materialscloud.org/discover/scripts/external/jsmol/j2s",
serverURL="detail/static/jsmol/php/jsmol.php",
j2sPath="detail/static/jsmol/j2s",
script="""set antialiasDisplay ON;
load data "cifstring"
{}
end "cifstring"
""".format(cif_str)
## Note: Need PHP server for approach below to work
# script="""set antialiasDisplay ON;
# load cif::{};
# """.format(get_cif_url(entry.filename))
)
btn_download_cif.callback = bmd.CustomJS(args=dict(
string=cif_str, filename=entry.filename),
code=download_js)
script_source = bmd.ColumnDataSource()
applet = JSMol(
width=600,
height=600,
script_source=script_source,
info=info,
js_url="detail/static/jsmol/JSmol.min.js",
)
df_tailcorrection, df_no_tailcorrection = get_results_df()
if cof_name in used_block:
plot_info_ = plot_info_blocked_pockets
} else {
btn.label = "Connect to device";
btn.button_type = "success";
}
"""))
read_point_btn.on_click(read_point_btn_callback)
read_point_btn.callback = CustomJS(args=dict(
p=plot,
dt=data_table,
conn_status=conn_status,
),
code="""
if(!conn_status.value) {
console.log('This application must be connected to ESP device to read data from it.');
return;
}
p.toolbar.tools[""" + util.reset_tool_index(g.TOOLS3) +
"""].trigger('do');
setTimeout(function(){
dt.trigger('change');
},""" + str(g.DEBOUNCE_VAL) + """);
""")
clear_points_btn.on_click(clear_points_btn_callback)
clear_points_btn.callback = click_reset_tool
clear_everything_btn.on_click(clear_everything_btn_callback)
clear_everything_btn.callback = click_reset_tool
upload_button.callback = CustomJS(args=dict(file_source=file_source),
code="""
function read_file(filename) {
var reader = new FileReader();
reader.onload = load_handler;
reader.onerror = error_handler;
// readAsDataURL represents the file's data as a base64 encoded string
reader.readAsDataURL(filename);
}
function load_handler(event) {
var b64string = event.target.result;
file_source.data = {'file_contents' : [b64string], 'file_name':[input.files[0].name]};
file_source.trigger("change");
}
function error_handler(evt) {
if(evt.target.error.name == "NotReadableError") {
alert("Can't read file!");
}
}
var input = document.createElement('input');
input.setAttribute('type', 'file');
input.onchange = function(){
if (window.FileReader) {
read_file(input.files[0]);
} else {
alert('FileReader is not supported in this browser');
}
}
input.click();
""")
low=[low],
close=[close],
color=[color],
rsi=[rsi[-1]],
ema1=[EMA1[-1]],
ema2=[EMA2[-1]],
date=[date],
strdate=[strdate],
position=[strat_1.position])
#maj du titre
p.title.text = """{0} | prix: {1:.3f} | rsi : {2:.0f}| position : {3} |
stock : {4:.3f} | valeur : {5:.3f} | vars : {6}/{7} >>> {8}""".format(
strdate, close, rsi[-1], strat_1.position, strat_1.stock,
strat_1.stock * close, strat_1.varema, strat_1.varrsi,
strat_1.strategy)
close = source.data['close'] + [close]
new_data['emaD'] = [EMA1[-1] - EMA2[-1]]
source.stream(new_data, None)
curdoc().add_root(
row(
column(
gridplot([[p], [p2], [p3]],
toolbar_location="left",
plot_width=1000)),
column(DLbutton, data_table1, data_table2)))
curdoc().add_periodic_callback(update, 5000)
curdoc().title = "XRPUSDT"
DLbutton.callback = CustomJS(args=dict(source=source), code=javaScript)
for ci in source_df.columns
]
model_table.source.data = source_df.to_dict(orient='list')
print(source_df.to_dict(orient='list'))
# charge_model()
pay_button.on_event(ButtonClick, pay_and_get_model)
# Define the download button to download data from the table
download_button = Button(label="Download the Model",
width=360,
button_type="success")
download_button.callback = CustomJS(args=dict(source=model_table.source),
code=open(
join(dirname(__file__),
"download.js")).read())
model_widget_right = widgetbox(model_table,
modelhere_div,
download_button,
width=360)
#def show_model_param(event):
## source_table.data = (data_to_fill)
# model_table.source.data = model_param_to_fill
#
#pay_button.on_event(ButtonClick, show_model_param)
# Adding all widgets into the main port
curdoc().add_root(row(param_txt, width=800))
data['salary'][i].toString(),
data['years_experience'][i].toString().concat('\\n')];
var joined = currRow.join();
filetext = filetext.concat(joined);
}
var filename = 'data_result.csv';
var blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' });
//addresses IE
if (navigator.msSaveBlob) {
navigator.msSaveBlob(blob, filename);
}
else {
var link = document.createElement("a");
link = document.createElement('a')
link.href = URL.createObjectURL(blob);
link.download = filename
link.target = "_blank";
link.style.visibility = 'hidden';
link.dispatchEvent(new MouseEvent('click'))
}"""
button.callback = CustomJS(args=dict(source=source), code=js_callback)
controls = [salary_range, button]
inputs = HBox(VBoxForm(*controls), width=400)
update(None, None, None)
curdoc().add_root(HBox(inputs, data_table, width=800))
years_selections = [str(year) for year in range(2016, 2018)]
year_select = Select(value="2016",
title="开始年份",
width=200,
options=years_selections)
year_select.on_change("value", lambda attr, old, new: update_data())
slider = TextInput(title="阈值", value="0.5")
# slider = Slider(title="阈值", start=0.0, end=1.0, value=0.3, step=0.1)
slider.on_change('value', lambda attr, old, new: update_data())
text = TextInput(title="关键词（例如：MPA、房地产、通胀）", value=u'楼市')
text.on_change('value', lambda attr, old, new: update_data())
button = Button(label=u"下载数据", button_type="success", width=300)
button.callback = CustomJS(args=dict(source=source_download),
code=open(join(dirname(__file__),
"download.js")).read())
absolute_corr_columns = [TableColumn(field=x, title=x) for x in assets]
absolute_corr_data_table = DataTable(source=source_absolute_corr,
columns=absolute_corr_columns,
width=1000)
asset_text = TextInput(title=u'资产', value='AU9999.SGE')
asset_text.on_change('value', lambda attr, old, new: update_corr())
word_text = TextInput(title=u'关键词', value=u'加息')
word_text.on_change('value', lambda attr, old, new: update_corr())
update_data()
update_correlation()
update_corr()
update_percentile()
def showGUI(self, pth_to_img, y_form, pred):
'''
Method builds the bokeh GUI
Parameters
----------
pth_to_img: path to ultrasound image
y_form: true form of the lesion
pred: predicted form the lesion
'''
##############
#Set up a figure
##############
p = figure(x_range=(0, self.DIMS[0]),
y_range=(0, self.DIMS[1]),
tools=self._tools_to_show,
plot_width=self.DIMS[0],
plot_height=self.DIMS[1],
toolbar_location="above")
#Add image as background
p.image_url(url=[self.root_pth + pth_to_img],
x=431,
y=302,
w=862,
h=604,
anchor="center")
#Nicier plot
self._makeShiny(plot=p)
##############
#Add lines and plot them
##############
src_true, src_pred = self._getData()
self._plotLines(plot=p, src_true=src_true, src_pred=src_pred)
##############
#Add table
##############
table = self._addTable(src_pred=src_pred)
##############
#Add polygons
##############
true_pol, c_t = self._addLesionForm(form=y_form, color='red', plot=p)
pred_pol, c_p = self._addLesionForm(form=pred, color='blue', plot=p)
#Add toggles for polygons
toggle_true = Toggle(label="Show true form",
button_type="primary",
active=True)
toggle_true.js_link('active', true_pol, 'visible')
toggle_true.js_link('active', c_t, 'visible')
toggle_pred = Toggle(label="Show predicted form",
button_type="primary",
active=True)
toggle_pred.js_link('active', pred_pol, 'visible')
toggle_true.js_link('active', c_p, 'visible')
##############
#Add download button
##############
button_csv = Button(label="Download", button_type="primary")
button_csv.callback = CustomJS(args=dict(source=src_pred),
code=open(self.root_pth +
"download.js").read())
##############
#Add title div
##############
div_title = Div(text="""<div> <b>LESION ADJUSTER</b> </div>""",
align='center',
style={
'font-size': '150%',
'color': '#1f77b4'
})
##############
#Add description to the buttons
##############
div_desc = Div(text="""<div> <b>CONTROLS</b> </div>""",
align='center',
style={
'font-size': '110%',
'color': '#1f77b4'
})
##############
#Add Div to show euclidean distance and button to recalculate it
##############
div_euclid = Div(text="""
<b>Diameter of predicted form is:</b> 334.80 <br>
<b>Diameter of true form is:</b> 368.64 <br>
<b>RMSE is:</b> 34.13
""",
align='center',
style={'font-size': '100%'})
p.js_on_event(
events.MouseMove,
CustomJS(args=dict(div=div_euclid,
source_data_pred=src_pred,
source_data_true=src_true),
code="""
var data_p = source_data_pred.data;
var data_t = source_data_true.data;
var x_p = data_p['x']
var y_p = data_p['y']
var x_t = data_t['x']
var y_t = data_t['y']
var diam_p = 0
var diam_t = 0
var rmse = 0
//Diameter of pred form
diam_p = Math.sqrt(Math.pow((x_p[0]-x_p[1]),2) + Math.pow((y_p[0]-y_p[1]),2))
//Diameter of true form
diam_t = Math.sqrt(Math.pow((x_t[0]-x_t[1]),2) + Math.pow((y_t[0]-y_t[1]),2))
//RMSE
rmse = Math.sqrt(Math.pow(diam_p - diam_t,2)/1)
//Result
div.text = "<b>Diameter of predicted form is: </b>" + diam_p.toFixed(2) + "<br> <b>Diameter of true form is: </b>" + diam_t.toFixed(2) + " <br> <b>RMSE is: </b>" + rmse.toFixed(2);
"""))
##############
#Show
##############
show(
Column(
div_title,
Row(
Column(p, table),
Column(div_desc, toggle_true, toggle_pred, button_csv,
div_euclid))))
