def main(options, args):
logger = log.get_logger("ginga", options=options)
# create a new plot with default tools, using figure
fig = figure(x_range=[0,600], y_range=[0,600], plot_width=600, plot_height=600,
toolbar_location=None)
viewer = ib.CanvasView(logger)
viewer.set_figure(fig)
def load_file(path):
image = AstroImage(logger)
image.load_file(path)
viewer.set_image(image)
def load_file_cb(attr_name, old_val, new_val):
#print(attr_name, old_val, new_val)
load_file(new_val)
# add a entry widget and configure with the call back
dstdir = options.indir
path_w = TextInput(value=dstdir, title="File:")
path_w.on_change('value', load_file_cb)
if len(args) > 0:
load_file(args[0])
# put the path widget and viewer in a layout and add to the document
curdoc().add_root(vplot(fig, path_w))
def make_inputs(self):
self.ticker1_select = Select(
name='ticker1',
title='Portfolio:',
value='MSFT',
options = ['INTC', 'Tech Basket', 'IBB', 'IGOV']
)
self.ticker2_select = Select(
name='ticker2',
title='Risk/Performance Metric:',
value='Price',
options=['Daily Prices', 'Daily Returns', 'Daily Cum Returns', 'Max DD Percentage', 'Percentage Up Days', 'Rolling 95% VaR', 'Rolling Ann. Volatility', 'Rolling Worst Dly. Loss', 'Ann. Sharpe Ratio']
)
self.ticker3_select = TextInput(
name='ticker3',
title='Window Size:',
value='63'
)
self.ticker4_select = TextInput(
name='ticker4',
title='Start Date:',
value='2010-01-01'
)
self.ticker5_select = TextInput(
name='ticker5',
title='End Date:',
value='2015-08-01'
)
def make_inputs(self):
self.ticker1_select = TextInput(
name='ticker1',
title='Drift Function:',
value='1.2*(1.1-x)',
)
self.ticker1p_select = TextInput(
name='ticker1p',
title='Drift Derivative:',
value='-1.2',
)
self.ticker2_select = TextInput(
name='ticker2',
title='Volatility Function:',
value='4.0',
)
self.ticker2p_select = TextInput(
name='ticker2p',
title='Volatility Derivative:',
value='0.0',
)
self.ticker3_select = TextInput(
name='ticker3',
title='Number of Paths:',
value='500'
)
self.ticker3_1_select = TextInput(
name='ticker3_1',
title='Number of Points:',
value='252'
)
self.ticker3_2_select = TextInput(
name='ticker3_2',
title='Time Step:',
value='0.01'
)
self.ticker4_select = TextInput(
name='ticker4',
title='Histogram Line:',
value='100'
)
self.ticker4_1_select = TextInput(
name='ticker4_1',
title='Initial Value:',
value='1.01'
)
self.ticker4_2_select = Select(
name='ticker4_2',
title='MC Scheme:',
value='Milstein',
options=['Euler','Milstein', 'Pred/Corr']
)
self.button_select = TextInput(
name='button',
title='Type any word containing "run" to run Simulation ',
value = ''
)
def plot():
# Set up data
N = 200
x = np.linspace(0, 4*np.pi, N)
y = np.sin(x)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plots
plot = Figure(plot_height=400, plot_width=400, title="my sine wave",
tools="crosshair,pan,reset,resize,save,wheel_zoom",
x_range=[0, 4*np.pi], y_range=[-2.5, 2.5])
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0)
phase = Slider(title="phase", value=0.0, start=0.0, end=2*np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
a = amplitude.value
b = offset.value
w = phase.value
k = freq.value
# Generate the new curve
x = np.linspace(0, 4*np.pi, N)
y = a*np.sin(k*x + w) + b
source.data = dict(x=x, y=y)
for w in [offset, amplitude, phase, freq]:
w.on_change('value', update_data)
# Set up layouts and add to document
inputs = VBoxForm(children=[text, offset, amplitude, phase, freq])
fullformat = HBox(children=[inputs, plot], width=800)
return fullformat, []
def main(options, args):
logger = log.get_logger("ginga", options=options)
TOOLS = "pan,wheel_zoom,box_select,tap"
# create a new plot with default tools, using figure
fig = figure(x_range=[0,600], y_range=[0,600], plot_width=600, plot_height=600,
tools=TOOLS)
viewer = ib.CanvasView(logger)
viewer.set_figure(fig)
## box_select_tool = fig.select(dict(type=BoxSelectTool))
## box_select_tool.select_every_mousemove = True
#tap_tool = fig.select_one(TapTool).renderers = [cr]
# open a session to keep our local document in sync with server
session = push_session(curdoc())
#curdoc().add_periodic_callback(update, 50)
def load_file(path):
image = AstroImage(logger)
image.load_file(path)
viewer.set_image(image)
def load_file_cb(attr_name, old_val, new_val):
#print(attr_name, old_val, new_val)
load_file(new_val)
# add a entry widget and configure with the call back
dstdir = options.indir
path_w = TextInput(value=dstdir, title="File:")
path_w.on_change('value', load_file_cb)
curdoc().add_root(vplot(fig, path_w))
if len(args) > 0:
load_file(args[0])
# open the document in a browser
session.show()
# run forever
session.loop_until_closed()
def plot():
# FIGURES AND X-AXIS
fig1 = Figure(title = 'Energy', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS)
timeticks = DatetimeTickFormatter(formats=dict(seconds =["%b%d %H:%M:%S"],
minutes =["%b%d %H:%M"],
hours =["%b%d %H:%M"],
days =["%b%d %H:%M"],
months=["%b%d %H:%M"],
years =["%b%d %H:%M %Y"]))
fig1.xaxis.formatter = timeticks
# INPUT WIDGETS
collection_list = CONN[DB].collection_names(include_system_collections=False)
gliders = sorted([platformID for platformID in collection_list if len(platformID)>2])
gliders = Select(title = 'PlatformID', value = gliders[0], options = gliders)
prev_glider = Button(label = '<')
next_glider = Button(label = '>')
glider_controlbox = HBox(children = [gliders, prev_glider, next_glider])
max_amphr = TextInput(title='Max AmpHrs', value='1040')
deadby_date = TextInput(title='Deadby Date', value='')
data_controlbox = HBox(max_amphr, deadby_date, width = 300)
control_box = HBox(glider_controlbox,
data_controlbox)
# DATA VARS
coulombs_raw = ColumnDataSource(dict(x=[],y=[]))
coulombs_ext = ColumnDataSource(dict(x=[],y=[]))
coulombs_per = ColumnDataSource(dict(x=[],y=[]))
# AXIS setup
fig1.yaxis.axis_label = 'Coulombs (AmpHr)'
fig1.extra_y_ranges = {'usage': Range1d(start=0, end=1200)}
# PLOT OBJECTS
fig1.line( 'x', 'y', source = coulombs_raw, legend = 'm_coulombs_amphr_total', color = 'blue')
fig1.circle('x', 'y', source = coulombs_raw, legend = 'm_coulombs_amphr_total', color = 'blue')
fig1.line( 'x', 'y', source = coulombs_ext, legend = 'projected', color = 'red')
#fig1.cross('x', 'y', source = coulombs_ext, legend = 'projected', size=10, color = 'red')
fig1.renderers.append(Span(name = 'maxamp_span', location = int(max_amphr.value), dimension = 'width', line_color= 'green', line_dash='dashed', line_width=2))
fig1.renderers.append(Span(name = 'maxamp_intersect', location = 1000*time.time(), dimension = 'height', line_color= 'green', line_dash='dashed', line_width=2))
fig1.legend[0].location = 'top_left'
fig1.legend[0].legend_padding = 30
# CALLBACK FUNCS
def update_coulombs(attrib,old,new):
g = gliders.value
coulombs_raw.data = load_sensor(g, 'm_coulomb_amphr_total')
#coulombs_per.data = moving_usage(coulombs_raw.data)
update_projection(None,None,None)
def update_projection(attrib,old,new):
g = gliders.value
try:
fig1.select('maxamp_span')[0].location = int(max_amphr.value)
coulombs_ext.data, deadby_date.value = calc_deadby_date(g, int(max_amphr.value))
fig1.select('maxamp_intersect')[0].location = coulombs_ext.data['x'][-1]
except Exception as e:
print('update_projection error',type(e),e)
#GLIDER SELECTS
def glider_buttons(increment):
ops = gliders.options
new_index = ops.index(gliders.value) + increment
if new_index >= len(ops):
new_index = 0
elif new_index < 0:
new_index = len(ops)-1
gliders.value = ops[new_index]
def next_glider_func():
glider_buttons(1)
def prev_glider_func():
glider_buttons(-1)
gliders.on_change('value', update_coulombs)
next_glider.on_click(next_glider_func)
prev_glider.on_click(prev_glider_func)
max_amphr.on_change('value', update_projection)
update_coulombs(None,None,None)
return vplot(control_box, fig1)
def __init__(self, sources, categories, dvhs, rad_bio, roi_viewer, time_series,
correlation, regression, mlc_analyzer, custom_title, data_tables):
self.sources = sources
self.selector_categories = categories.selector
self.range_categories = categories.range
self.correlation_variables = categories.correlation_variables
self.dvhs = dvhs
self.rad_bio = rad_bio
self.roi_viewer = roi_viewer
self.time_series = time_series
self.correlation = correlation
self.regression = regression
self.mlc_analyzer = mlc_analyzer
self.uids = {n: [] for n in GROUP_LABELS}
self.allow_source_update = True
self.current_dvh = []
self.anon_id_map = []
self.colors = itertools.cycle(palette)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Selection Filter UI objects
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
category_options = list(self.selector_categories)
category_options.sort()
# Add Current row to source
self.add_selector_row_button = Button(label="Add Selection Filter", button_type="primary", width=200)
self.add_selector_row_button.on_click(self.add_selector_row)
# Row
self.selector_row = Select(value='1', options=['1'], width=50, title="Row")
self.selector_row.on_change('value', self.selector_row_ticker)
# Category 1
self.select_category1 = Select(value="ROI Institutional Category", options=category_options, width=300,
title="Category 1")
self.select_category1.on_change('value', self.select_category1_ticker)
# Category 2
cat_2_sql_table = self.selector_categories[self.select_category1.value]['table']
cat_2_var_name = self.selector_categories[self.select_category1.value]['var_name']
self.category2_values = DVH_SQL().get_unique_values(cat_2_sql_table, cat_2_var_name)
self.select_category2 = Select(value=self.category2_values[0], options=self.category2_values, width=300,
title="Category 2")
self.select_category2.on_change('value', self.select_category2_ticker)
# Misc
self.delete_selector_row_button = Button(label="Delete", button_type="warning", width=100)
self.delete_selector_row_button.on_click(self.delete_selector_row)
self.group_selector = CheckboxButtonGroup(labels=["Group 1", "Group 2"], active=[0], width=180)
self.group_selector.on_change('active', self.ensure_selector_group_is_assigned)
self.selector_not_operator_checkbox = CheckboxGroup(labels=['Not'], active=[])
self.selector_not_operator_checkbox.on_change('active', self.selector_not_operator_ticker)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Range Filter UI objects
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
category_options = list(self.range_categories)
category_options.sort()
# Add Current row to source
self.add_range_row_button = Button(label="Add Range Filter", button_type="primary", width=200)
self.add_range_row_button.on_click(self.add_range_row)
# Row
self.range_row = Select(value='', options=[''], width=50, title="Row")
self.range_row.on_change('value', self.range_row_ticker)
# Category
self.select_category = Select(value=options.SELECT_CATEGORY_DEFAULT, options=category_options, width=240, title="Category")
self.select_category.on_change('value', self.select_category_ticker)
# Min and max
self.text_min = TextInput(value='', title='Min: ', width=150)
self.text_min.on_change('value', self.min_text_ticker)
self.text_max = TextInput(value='', title='Max: ', width=150)
self.text_max.on_change('value', self.max_text_ticker)
# Misc
self.delete_range_row_button = Button(label="Delete", button_type="warning", width=100)
self.delete_range_row_button.on_click(self.delete_range_row)
self.group_range = CheckboxButtonGroup(labels=["Group 1", "Group 2"], active=[0], width=180)
self.group_range.on_change('active', self.ensure_range_group_is_assigned)
self.range_not_operator_checkbox = CheckboxGroup(labels=['Not'], active=[])
self.range_not_operator_checkbox.on_change('active', self.range_not_operator_ticker)
self.query_button = Button(label="Query", button_type="success", width=100)
self.query_button.on_click(self.update_data)
# define Download button and call download.js on click
menu = [("All Data", "all"), ("Lite", "lite"), ("Only DVHs", "dvhs"), ("Anonymized DVHs", "anon_dvhs")]
self.download_dropdown = Dropdown(label="Download", button_type="default", menu=menu, width=100)
self.download_dropdown.callback = CustomJS(args=dict(source=sources.dvhs,
source_rxs=sources.rxs,
source_plans=sources.plans,
source_beams=sources.beams),
code=open(join(dirname(dirname(__file__)), "download.js")).read())
self.layout = column(Div(text="<b>DVH Analytics v%s</b>" % options.VERSION),
row(custom_title['1']['query'], Spacer(width=50), custom_title['2']['query'],
Spacer(width=50), self.query_button, Spacer(width=50), self.download_dropdown),
Div(text="<b>Query by Categorical Data</b>", width=1000),
self.add_selector_row_button,
row(self.selector_row, Spacer(width=10), self.select_category1, self.select_category2,
self.group_selector, self.delete_selector_row_button, Spacer(width=10),
self.selector_not_operator_checkbox),
data_tables.selection_filter,
Div(text="<hr>", width=1050),
Div(text="<b>Query by Numerical Data</b>", width=1000),
self.add_range_row_button,
row(self.range_row, Spacer(width=10), self.select_category, self.text_min,
Spacer(width=30),
self.text_max, Spacer(width=30), self.group_range,
self.delete_range_row_button, Spacer(width=10), self.range_not_operator_checkbox),
data_tables.range_filter)
exponent_slider = Slider(title="Weighted Sum Exponent",
value=0.1,
start=0,
end=1,
step=0.1,
width=200)
samples_slider = Slider(title="Number of Aspects",
value=15,
start=1,
end=30,
step=1,
width=200)
ratings_box = TextInput(value="1,2,3,4,5", title="Star Rating:", width=185)
def update_dataset(attrname, old, new):
dataset = dataset_select.value
n_expon = exponent_slider.value
n_samples = int(samples_slider.value)
n_stars = [int(val) for val in ratings_box.value.split(',')]
newdata = get_dataset(dataset, n_expon, n_stars)
source_all.data = dict(newdata.to_dict('list'))
newdata = get_ends(n_samples)
source_cut.data = dict(newdata.to_dict('list'))
plot.x_range.factors = newdata['aspects'].tolist() # this was missing
order = int(new)
update_data()
def on_text_value_change(attr, old, new):
try:
global expr
expr = sy.sympify(new, dict(x=xs))
except (sy.SympifyError, TypeError, ValueError) as exception:
dialog.content = str(exception)
dialog.visible = True
else:
update_data()
dialog = Dialog(title="Invalid expression")
slider = Slider(start=1, end=20, value=order, step=1, title="Order:")
slider.on_change('value', on_slider_value_change)
text = TextInput(value=str(expr), title="Expression:")
text.on_change('value', on_text_value_change)
inputs = HBox(children=[slider, text])
layout = VBox(children=[inputs, plot, dialog])
update_data()
document.add_root(layout)
session.show(layout)
if __name__ == "__main__":
print("\npress ctrl-C to exit")
session.loop_until_closed()
source_point_arc = ColumnDataSource(data=dict(x=[], y=[]))
# initialize controls
# choose between original and arc length parametrization
parametrization_input = CheckboxGroup(labels=['show original parametrization',
'show arc length parametrization'],
active=[0, 1])
parametrization_input.on_click(parametrization_change)
# slider controlling the current parameter t
t_value_input = Slider(title="parameter t", name='parameter t', value=arc_settings.t_value_init,
start=arc_settings.t_value_min, end=arc_settings.t_value_max,
step=arc_settings.t_value_step)
t_value_input.on_change('value', t_value_change)
# text input for the x component of the curve
x_component_input = TextInput(value=arc_settings.x_component_input_msg, title="curve x")
x_component_input.on_change('value', curve_change)
# text input for the y component of the curve
y_component_input = TextInput(value=arc_settings.y_component_input_msg, title="curve y")
y_component_input.on_change('value', curve_change)
# dropdown menu for selecting one of the sample curves
sample_curve_input = Dropdown(label="choose a sample function pair or enter one below",
menu=arc_settings.sample_curve_names)
sample_curve_input.on_click(sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Arc length parametrization",
P2_source = ColumnDataSource(data=dict(x=t, y=0*t ))
P0_experiment_source = ColumnDataSource(data=dict(x=t, y=0*t ))
P1_experiment_source = ColumnDataSource(data=dict(x=t, y=0*t ))
P2_experiment_source = ColumnDataSource(data=dict(x=t, y=0*t ))
#plot.multi_line('x', 'y', source=source, line_width=3, line_alpha=0.6)
plot.line('x', 'y', source=P0_source, line_width=3, line_alpha=0.6,color='red')
plot.line('x', 'y', source=P1_source, line_width=3, line_alpha=0.6,color='green')
plot.line('x', 'y', source=P2_source, line_width=3, line_alpha=0.6,color='blue')
plot.circle( 'x', 'y', source=P0_experiment_source,color='red', size=6 , legend='SS' ) #,'green','blue']
plot.circle( 'x', 'y', source=P1_experiment_source,color='green', size=6, legend='SD+DS' ) #,'green','blue']
plot.circle( 'x', 'y', source=P2_experiment_source,color='blue', size=6, legend='DD ' ) #,'green','blue']
# Set up widgets
path = TextInput(title="file path", value='my sine wave')
file_name = TextInput(title="file name", value='1203_24')
etta = Slider(title="etta", value=0.08637, start=0.0, end=0.15, step=0.01)
#delta = Slider(title="delta [KHz]", value=0.08637, start=0.0, end=0.15, step=0.01)
Pi_time = Slider(title="pi_time [us]", value=9.6, start=5, end=15, step=0.05)
n_bar = Slider(title="n_bar", value=0.0, start=0.0, end=10, step=0.1)
chi = TextInput(title="chi", value='7.0')
# Set up callbacks
def update_exp_data(attrname, old, new):
# set up the data
try:
#csv_reader = csv.reader(open('00001 - MS Gate 2017Sep13_1203_24.csv'))
def plotting(self):
#Tools = [hover, TapTool(), BoxZoomTool(), BoxSelectTool(), PreviewSaveTool(), ResetTool()]
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave"
tab_plots = []
#output_file("test.html")
self.all_elements = []
self.elements_comparison = []
for attr_id, i in zip(self.attribute_ids, range(len(self.attribute_ids))):
"""
create plots for each datafile and put them in a tab.
"""
list_of_datasets = getattr(self, attr_id)
y_axis_units = [x["y_unit"] for x in list_of_datasets]
x_axis_units = [x["x_unit"] for x in list_of_datasets]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log",
title = attr_id, tools = TOOLS)
#figure_obj.axes.major_label_text_font_size("12pt")
#figure_obj.major_label_text_font_size("12pt")
setattr(self, attr_id+"_"+"figure_obj",figure_obj)
figure_obj.yaxis.axis_label = y_axis_units[0]
figure_obj.xaxis.axis_label = x_axis_units[0]
if not all(x == y_axis_units[0] for x in y_axis_units):
for unit, data in zip(y_axis_units, list_of_datasets):
if not unit == y_axis_units[0]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["data"]["y"]),
end = np.amax(data["data"]["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[0] for x in x_axis_units):
for unit, data in zip(x_axis_units, list_of_datasets):
if not unit == x_axis_units[0]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["data"]["x"]),
end = np.amax(data["data"]["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
figure_obj.xaxis.axis_label = list_of_datasets[0]["x_unit"]
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
list_of_elements = []
for dataset, color_index in zip(list_of_datasets, colour_indices):
self.all_elements.append(dataset["sample element"]) #strip isotope number
color = colour_list[color_index]
source = ColumnDataSource(data = dataset["data"]) #Datastructure for source of plotting
setattr(self, attr_id+"_"+dataset["sample element"]+"_source", source) #Source element generalized for all plotting
list_of_elements.append(dataset["sample element"])
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]
+"_source"), line_width = 2, line_color = color,
legend = dataset["sample element"], name = dataset["sample element"],
)
hover = figure_obj.select_one(HoverTool).tooltips = [("element", "@element"), ("(x,y)", "($x, $y)")]
radio_group = RadioGroup(labels = list_of_elements, active=0)
"""
Need to fetch default variables from input file and replace DEFAULT
Block of code produces the layout of buttons and callbacks
"""
#Calculations on the dataset
text_input_rsf = TextInput(value = "default", title = "RSF (at/cm^3): ")
do_integral_button = Button(label = "Calibration Integral")
smoothing_button = Button(label = "Smoothing on selected curve")
text_input_sputter = TextInput(value = "default", title = "Sputter speed: float unit")
text_input_crater_depth = TextInput(value = "default", title = "Depth of crater in: float")
radio_group.on_change("active", lambda attr, old, new: None)
text_input_xval_integral = TextInput(value = "0", title = "x-value for calibration integral ")
text_input_yval_integral = TextInput(value = "0", title = "y-value for calibration integral ")
#Save files for later use
save_flexDPE_button = Button(label = "Save element for FlexPDE")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
#Pointers to methods on click / change handlers
do_integral_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group,
x_box = text_input_xval_integral, y_box = text_input_yval_integral:
self.integrate(identity, radio, x_box, y_box))
smoothing_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group:
self.smoothing(identity, radio) )
save_flexDPE_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group:
self.write_to_flexPDE(identity, radio))
save_all_flexDPE_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group:
self.write_all_to_flexPDE(identity, radio))
text_input_rsf.on_change("value", lambda attr, old, new, radio = radio_group,
identity = self.attribute_ids[i], text_input = text_input_rsf, which = "rsf":
self.update_data(identity, radio, text_input, new, which))
text_input_sputter.on_change("value", lambda attr, old, new, radio = radio_group,
identity = self.attribute_ids[i], text_input = text_input_sputter, which = "sputter":
self.update_data(identity, radio, text_input, new, which))
text_input_crater_depth.on_change("value", lambda attr, old, new, radio = radio_group,
identity = self.attribute_ids[i], text_input = text_input_crater_depth, which = "crater_depth":
self.update_data(identity, radio, text_input, new, which))
#Initialization of actual plotting.
tab_plots.append(Panel(child = hplot(figure_obj,
vform(radio_group, save_flexDPE_button, save_all_flexDPE_button),
vform(text_input_rsf, smoothing_button, text_input_sputter, text_input_crater_depth),
vform(text_input_xval_integral, text_input_yval_integral, do_integral_button)),
title = attr_id))
"""
Check to see if one or more element exists in the samples and creat a comparison plot for each
of those elements.
"""
for element in self.all_elements:
checkers = list(self.all_elements)
checkers.remove(element)
if element in checkers and not element in self.elements_comparison:
self.elements_comparison.append(element)
"""create plots for each element that is to be compared """
for comparison_element in self.elements_comparison:
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log", title = comparison_element, tools = TOOLS)
#figure_obj.xaxis.major_label_text_font_size("12pt")
#figure_obj.yaxis.major_label_text_font_size("12pt")
y_axis_units = []
x_axis_units = []
comparison_datasets = []
for attr_id, color_index in zip(self.attribute_ids, colour_indices):
list_of_datasets = getattr(self, attr_id)
for dataset in list_of_datasets:
if dataset["sample element"] == comparison_element:
comparison_datasets.append(dataset)
y_axis_units.append(dataset["y_unit"])
x_axis_units.append(dataset["x_unit"])
figure_obj.xaxis.axis_label = comparison_datasets[-1]["x_unit"]
figure_obj.yaxis.axis_label = comparison_datasets[-1]["y_unit"]
if not all(x == y_axis_units[-1] for x in y_axis_units):
for unit, data in zip(y_axis_units, comparison_datasets):
if not unit == y_axis_units[-1]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["data"]["y"]),
end = np.amax(data["data"]["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[-1] for x in x_axis_units):
for unit, data in zip(x_axis_units, comparison_datasets):
if not unit == x_axis_units[-1]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["data"]["x"]),
end = np.amax(data["data"]["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
for attr_id, color_index in zip(self.attribute_ids, colour_indices):
list_of_datasets = getattr(self, attr_id)
for dataset in list_of_datasets:
if dataset["sample element"] == comparison_element:
color = colour_list[color_index]
"""
Logic that ensures that plots get put with correspoinding axes.
"""
if dataset["x_unit"] != x_axis_units[-1] or dataset["y_unit"] != y_axis_units[-1]:
if dataset["x_unit"] != x_axis_units[-1] and dataset["y_unit"] != y_axis_units[-1]:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id, x_range_name = "bar", y_range_name = "foo")
elif dataset["x_unit"] != x_axis_units[-1]:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id, x_range_name = "bar")
else:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id, y_range_name = "foo")
else:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id)
tab_plots.append(Panel(child = figure_obj, title = comparison_element))
tabs = Tabs(tabs = tab_plots)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
return df
# Column order for displaying the details of a specific review
col_order = ["price", "points", "variety", "province", "description"]
all_provinces = [
"All", "South Australia", "Victoria", "Western Australia",
"Australia Other", "New South Wales", "Tasmania"
]
# Setup the display portions including widgets as well as text and HTML
desc = Div(text="All Provinces", width=800)
province = Select(title="Province", options=all_provinces, value="All")
price_max = Slider(start=0, end=900, step=5, value=200, title="Maximum Price")
title = TextInput(title="Title Contains")
details = Div(text="Selection Details:", width=800)
# Populate the data with the dataframe
source = ColumnDataSource(data=load_data())
# Build out the hover tools
hover = HoverTool(tooltips=[
("title", "@title"),
("variety", "@variety"),
])
# Define the tool list as a list of the objects so it is easier to customize
# each object
TOOLS = [
hover,
input_file = 'input.txt'
bert = sys.argv[1]
layers = '0'
for i in range(int(sys.argv[2]) - 1):
layers += ',' + str(i + 1)
else:
input_file = sys.argv[1]
bert = sys.argv[2]
layers = '0'
for i in range(int(sys.argv[3]) - 1):
layers += ',' + str(i + 1)
layer_idxs = [int(x) for x in layers.split(',')]
text_count = 1
text_input = TextInput()
button = Button(label='Обработать', button_type='success')
div = Div(text='')
def calculate():
with open(input_file, 'w') as f:
f.write(text_input.value)
args = ['python', '../bert/extract_features.py']
args.append('--input_file=' + input_file)
args.append('--output_file=')
args.append('--vocab_file=' + bert + 'vocab.txt')
args.append('--bert_config_file=' + bert + 'bert_config.json')
args.append('--init_checkpoint=' + bert + 'bert_model.ckpt')
args.append('--layers=' + layers)
source=source,
line_width=3,
line_alpha=0.6,
color="purple")
plot.line('x', 'yB', source=source, line_width=3, line_alpha=0.6, color="blue")
plot.circle('x', 'y', source=source2, size=5, color="black")
plot.circle('x', 'yG', source=source2, size=5, color="green")
plot.circle('x', 'yB', source=source2, size=5, color="blue")
plot.circle('x', 'yR', source=source2, size=5, color="red")
plot.circle('x', 'yi', source=source2, size=5, color="purple")
#plot.line('x', 'yV', source=source, line_width=3, line_alpha=0.6, color="turquoise")
#plot.line('x', 'yU', source=source, line_width=3, line_alpha=0.6, color="purple")
arrayoftimes = np.array(photometry_time)
text = TextInput(title="title", value='my parabola', callback=callback2)
lumdist_input = TextInput(title="title", value=str(lumdist))
redshift_input = TextInput(title="title", value=str(redshift))
M_slider = Slider(start=0.1,
end=10,
value=1,
step=.1,
title="Ejecta Mass",
callback=callback)
v_slider = Slider(start=5000,
end=20000,
value=10000,
step=1000,
title="Ejecta Velocity",
callback=callback)
def callback_subtract_noise_from_signal_button():
global signal_data_averaged_dict
global noise_data_averaged_dict
waveform_data_source.data = CCD_utils.subtract_dict_data(
signal_data_averaged_dict, noise_data_averaged_dict)
doc.add_next_tick_callback(callback_update_raman_spec)
### -------------- define callbacks --------------- ###
### -------------- make the document -------------- ###
# --- add widgets
# Select Serial Port Text Input
select_serial_port_text_input = TextInput(
title=raman_languages.TEXT__SERIAL_PORT,
value=raman_configs.DEFAULT_SERIAL_PORT)
select_serial_port_text_input.on_change(
'value', callback_select_serial_port_text_input)
# Select Baud Rate
select_baud_rate_text_input = TextInput(title=raman_languages.TEXT__BAUD_RATE,
value=str(
raman_configs.DEFAULT_BAUD_RATE))
select_baud_rate_text_input.on_change('value',
callback_select_baud_rate_text_input)
# Open Serial Port Button
open_serial_port_button = Button(label=raman_languages.TEXT__OPEN_SERIAL_PORT)
open_serial_port_button.on_click(callback_open_serial_port)
# Slice direction dropdown
def sliceDropdown():
pass
sliceDropdownMenu = Select(title="For fMRI Connectivity Only: Choose Direction:", value="X", options=["X", "Y", "Z"], width=int(total_width/6), height=50)
# Add ROI button
def addroiButton():
pass
roiButton = Button(label="Add ROI", width=int(total_width/6), height=30)
roiButton.on_click(addroiButton)
# ROI name textbox
def roiNameTextbox():
pass
roiName = TextInput(value="default", title="ROI Name", width=int(total_width/6), height=50)
# Choose ROI dropdown
def roiDropdown():
pass
roiDropdownMenu = Select(title="Choose ROI:", value="X", options=["X", "Y", "Z"], width=int(total_width/6), height=50)
#orgenize figures
curdoc().add_root(layout([
[fileButton, mapName, ConnectivityButton, AtlasButton, atlasName,MetadataButton],
[connectivityMatrix, [sliceDropdownMenu, imageView]],
[connectivityDropdown, connectivityMeasure, roiButton, roiName, roiDropdownMenu],
dsTable.datasets_table,
dsTable.vars_table,
btn_plot_lonXlat,
plotLayout,
status_bar,
name='mainLayout')
doc.remove_root(loadLayout)
doc.add_root(mainLayout)
def log(msg, ex=None):
status_bar.text = msg
if ex is None:
logger.debug(msg)
else:
logger.exception(msg, ex)
doc = curdoc()
log("Load an index file to get started.")
btnLoad = Button(label="Load")
btnLoad.on_click(lambda: load_file(txt_file.value))
txt_file = TextInput(value="index_201x.json",
title="Specify index file to load")
loadLayout = column(Div(height=50, style={"height": "50px"}), txt_file,
btnLoad, status_bar)
doc.add_root(loadLayout) # [plot.init_layout()]
style={
"font-family": "Arial",
"font-size": "15px"
},
width=300)
rectangular_pretext = Div(text='Rectangular Reflectors/Sources',
style={
"font-family": "Arial",
"font-size": "15px"
},
width=300)
#rotation_pretext = PreText(text='Rotation (Degrees)', width=300)
#simulation_params_pretext = PreText(text='Rotation (Degrees)', width=300)
# Text Input
mesh_width_input = TextInput(value='50', title='Mesh Width', width=100)
mesh_height_input = TextInput(value='50', title='Mesh Height', width=100)
quality_factor_input = TextInput(value='100', title='Plasmon Q', width=100)
plasmon_wavelength_input = TextInput(value='4',
title='Plasmon ' + u"\u03BB",
width=100)
mesh_density_input = TextInput(value='100', title='Mesh Density', width=200)
lambda_input = TextInput(value='100',
title='Excitation ' + u"\u03BB",
width=100)
phi_input = TextInput(value='90', title='Excitation ' + u"\u03B8", width=100)
# Sliders
slider_end_value = min(int(mesh_width_input.value),
int(mesh_height_input.value))
circular_radius = Slider(title='Radius',
plot.vbar(x='x', top='y', width=0.5, source=source, color="red")
plot2 = figure(plot_height=400, plot_width=1000, title="Coincidence counts",
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=coinc, y_range=[0, 4000])
plot2.background_fill_color = "black"
plot2.border_fill_color = "black"
plot2.vbar(x='x', top='y', width=0.5, source=source2, color="yellow")
# Set up widgets to control scale of plots
# TODO change these to actual range sliders
command = TextInput(title="Command Entry:", value='raw counts')
scalemin = Slider(title="Singles Scale minimum", value=0.0, start=0.0, end=1000.0, step=100)
scalemax = Slider(title="Singles Scale maximum", value=70000.0, start=1000.0, end=500000.0, step=100)
scalemin2 = Slider(title="Coinc. Scale minimum", value=0.0, start=0.0, end=5000.0, step=100)
scalemax2 = Slider(title="Coinc. Scale maximum", value=4000.0, start=1000.0, end=100000.0, step=100)
# other widgets (not all are used yet)
phase = Slider(title="phase", value=0.0, start=0.0, end=5.0, step=0.1)
points = Slider(title="data points", value=20, start=0, end=500, step=1)
statsA = Paragraph(text="100", width=400, height=40)
statsB = Paragraph(text="100", width=400, height=40)
g2 = Paragraph(text="100", width=400, height=80)
g2_2d = Paragraph(text="100", width=400, height=40)
# Set up callbacks
import logging
logging.basicConfig(level=logging.DEBUG)
import numpy as np
from bokeh.plotting import Figure
from bokeh.models import Plot, ColumnDataSource
from bokeh.models.widgets import HBox, Slider, TextInput, VBoxForm
from bokeh.io import curdoc
source = ColumnDataSource(data=dict(x=[], y=[]))
text = TextInput(
title="title", name='title', value='my sine wave'
)
offset = Slider(
title="offset", name='offset',
value=0.0, start=-5.0, end=5.0, step=0.1
)
amplitude = Slider(
title="amplitude", name='amplitude',
value=1.0, start=-5.0, end=5.0
)
phase = Slider(
title="phase", name='phase',
value=0.0, start=0.0, end=2*np.pi
)
freq = Slider(
class Dashboard:
def __init__(self, default_dir, port):
# path to source directory
self.src_dir = os.path.dirname(os.path.abspath(__file__))
# MD directory and files selection
self.md_dir = TextInput(
title="Path to MD directory containing mdin and mdout files",
value="",
width=750)
self.anim_button = Toggle(label="▶ Load",
button_type="warning",
width=80,
height=50,
active=False)
self.port = port
# container for the buttons that are created while the user types in the textinput
self.autocomp_results = column(children=[])
# file used to display temperature, pressure...etc. plots
self.mdout_sel = Select(
title="MDout file",
width=230,
value=None,
options=[],
)
# button to load content
self.mdout_button = Button(width=80,
height=50,
label="Plot",
button_type="primary")
self.mdout_files = [None]
self.md_mdout_files = []
# mdinfo figures
progressbar_tooltip = """
<span style="color:#428df5">@completed{0,0}</span> out of <span style="color:#428df5">@total{0,0}</span> steps (<span style="color:#428df5">@remaining{0,0}</span> remaining)
"""
self.progressbar = figure(title="Current progress",
x_range=Range1d(0, 10),
tooltips=progressbar_tooltip,
height=70,
width=350,
tools="hover",
toolbar_location=None)
self.progressbar.xgrid.grid_line_color = None
self.progressbar.ygrid.grid_line_color = None
self.progressbar.axis.visible = False
self.progressbar.outline_line_color = "#444444"
self.steps_CDS = ColumnDataSource({
"total": [np.nan],
"completed": [np.nan],
"remaining": [np.nan],
"color": ['#428df5'],
})
self.progressbar.hbar(
y=0,
left=0,
right="completed",
source=self.steps_CDS,
height=0.5,
color="color",
)
self.progressbar.hover[0].mode = "hline"
self.calc_speed = Div(width=150,
height=50,
text="Calculation speed:",
style={
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
})
self.eta = Div(width=280,
height=50,
text="Estimated time remaining:",
style={
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
})
self.last_update = Div(width=280,
height=50,
text="Last update:",
style={
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
})
# number of mdout files displayed on the dashboard at max
self.slider = Slider(start=1,
end=12,
value=2,
step=1,
callback_policy="mouseup",
title="Number of simulations displayed")
self.dashboard_CDS = ColumnDataSource({
"y_coords": [0, 1],
"mdout": ["heat.out", "prod.out"],
"time": [42, 200],
"angle": [1.09, 5.193],
"color": [sim_palette[0], sim_palette[1]],
})
dashboard_tooltip = """
<span style="color:@color">@mdout</span>: @time{0,0.00} ns
"""
# pie plot
self.pie = figure(plot_height=300,
width=500,
title="Simulations length",
toolbar_location=None,
tools="hover",
tooltips=dashboard_tooltip,
x_range=Range1d(-0.5, 1.0))
self.rpie = self.pie.wedge(x=0,
y=1,
radius=0.4,
source=self.dashboard_CDS,
start_angle=cumsum('angle',
include_zero=True),
end_angle=cumsum('angle'),
line_color="white",
fill_color='color',
legend="mdout")
self.pie.axis.axis_label = None
self.pie.axis.visible = False
self.pie.grid.grid_line_color = None
self.pie.legend.label_text_font_size = '9pt'
self.pie.legend.border_line_width = 0
self.pie.legend.border_line_alpha = 0
self.pie.legend.spacing = 0
self.pie.legend.margin = 0
# hbar plot
self.bar = figure(width=850,
plot_height=300,
toolbar_location=None,
tools="hover",
tooltips=dashboard_tooltip)
self.rbar = self.bar.hbar(y="y_coords",
left=0,
right="time",
source=self.dashboard_CDS,
height=0.8,
color="color")
self.bar.x_range.set_from_json("start", 0)
self.bar.xaxis.axis_label = "Time (ns)"
self.bar.yaxis.axis_label = None
self.bar.yaxis.visible = False
self.bar.hover[0].mode = "hline"
## Mdout figures
self.mdinfo_CDS = ColumnDataSource(copy.deepcopy(empty_mddata_dic))
self.moving_avg_trans = CustomJSTransform(v_func=moving_avg_func)
ticker = PrintfTickFormatter(format="%4.0e")
# Temperature
self.temperature_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.temperature_fig.toolbar.autohide = True
self.temperature_fig.xaxis.axis_label = "Number of steps"
self.temperature_fig.yaxis.axis_label = "Temperature (K)"
self.temperature_fig.xaxis.formatter = ticker
r = self.temperature_fig.line("Nsteps",
"Temperature",
color=palette[0],
source=self.mdinfo_CDS,
_width=1,
alpha=0.15)
self.temperature_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Temperature",
self.moving_avg_trans),
color=colorscale(palette[0], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.temperature_fig.add_tools(make_hover([r]))
# Pressure
self.pressure_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.pressure_fig.toolbar.autohide = True
self.pressure_fig.xaxis.axis_label = "Number of steps"
self.pressure_fig.yaxis.axis_label = "Pressure"
self.pressure_fig.xaxis.formatter = ticker
r = self.pressure_fig.line("Nsteps",
"Pressure",
color=palette[1],
source=self.mdinfo_CDS,
line_width=1,
alpha=0.15)
self.pressure_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Pressure", self.moving_avg_trans),
color=colorscale(palette[1], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.pressure_fig.add_tools(make_hover([r]))
# Energy
self.energy_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
etot = self.energy_fig.line("Nsteps",
"Etot",
color=palette[2],
source=self.mdinfo_CDS,
line_width=1)
ektot = self.energy_fig.line("Nsteps",
"EKtot",
color=palette[3],
source=self.mdinfo_CDS,
line_width=1)
eptot = self.energy_fig.line("Nsteps",
"EPtot",
color=palette[4],
source=self.mdinfo_CDS,
line_width=1)
legend = Legend(items=[
("Total", [etot]),
("Kinetic", [ektot]),
("Potential", [eptot]),
],
location="top_right")
self.energy_fig.add_layout(legend, 'right')
self.energy_fig.add_tools(make_hover([etot]))
self.energy_fig.legend.location = "top_left"
self.energy_fig.legend.click_policy = "hide"
self.energy_fig.toolbar.autohide = True
self.energy_fig.xaxis.axis_label = "Number of steps"
self.energy_fig.yaxis.axis_label = "Energy"
self.energy_fig.xaxis.formatter = ticker
# Volume
self.vol_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.vol_fig.toolbar.autohide = True
self.vol_fig.xaxis.axis_label = "Number of steps"
self.vol_fig.yaxis.axis_label = "Volume"
self.vol_fig.xaxis.formatter = ticker
r = self.vol_fig.line("Nsteps",
"Volume",
color=palette[6],
source=self.mdinfo_CDS,
line_width=1,
alpha=0.15)
self.vol_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Volume", self.moving_avg_trans),
color=colorscale(palette[6], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.vol_fig.add_tools(make_hover([r]))
# Density
self.density_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.density_fig.toolbar.autohide = True
self.density_fig.xaxis.axis_label = "Number of steps"
self.density_fig.yaxis.axis_label = "Density"
self.density_fig.xaxis.formatter = ticker
r = self.density_fig.line("Nsteps",
"Density",
color=palette[7],
source=self.mdinfo_CDS,
line_width=1,
alpha=0.15)
self.density_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Density", self.moving_avg_trans),
color=colorscale(palette[7], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.density_fig.add_tools(make_hover([r]))
## RMSD figure
self.empty_rmsd_dic = {k: [] for k in ["Time", "RMSD"]}
self.rmsd_CDS = ColumnDataSource(self.empty_rmsd_dic)
self.rmsd_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.rmsd_fig.toolbar.autohide = True
self.rmsd_fig.xaxis.axis_label = "Time (ps)"
self.rmsd_fig.yaxis.axis_label = "RMSD (Å)"
self.rmsd_fig.xaxis.formatter = ticker
r = self.rmsd_fig.line("Time",
"RMSD",
color=palette[8],
source=self.rmsd_CDS,
line_width=2)
self.rmsd_fig.add_tools(
make_hover([r],
tooltips=[("Time (ps)", "@Time{0,0}"),
("RMSD (Å)", "@RMSD")]))
self.rmsd_button = Button(width=100,
label="Calculate RMSD",
button_type="primary")
self.trajectory = MultiSelect(
title="Trajectory file(s)",
width=400,
value=None,
options=[],
)
self.rst_traj = Select(
title="Restart file",
width=400,
value=None,
options=[],
)
self.topology = Select(
title="Topology file",
width=200,
value=None,
options=[],
)
self.mask = TextInput(title="Mask",
value="protein@CA,C,O,N",
width=200)
# NGLview
self.last_rst_update = 0
self.view_button = Button(width=80,
label="Visualize",
button_type="primary")
self.view_canvas = Div(width=size[0],
height=size[1] + 60,
css_classes=["ngldiv"],
text="")
self.ngl_help_div = Div(width=0, height=0, text="")
self.ngl_help_button = Toggle(width=80, label="Help", active=False)
self.ngl_lig = TextInput(title="Ligand name", value="LIG", width=80)
self.ngl_representations = CheckboxButtonGroup(
labels=["Protein", "Ligand", "Water", "Lipids", "Ions"],
active=[0, 1, 2, 3, 4],
)
# info about simulation files (min, dt, rst and mdcrd files)
self.mdout_info = {}
# add callbacks
self.add_callbacks()
self.md_dir.value = default_dir
def ngl_help(self, new_value):
"""Show help on NGL controls"""
if self.ngl_help_button.active:
self.ngl_help_div.width = 300
self.ngl_help_div.height = size[1]
self.ngl_help_button.label = "Hide"
self.ngl_help_div.text = NGL_HELP_TEXT
else:
self.ngl_help_div.width = 300
self.ngl_help_div.height = size[1]
self.ngl_help_button.label = "Help"
self.ngl_help_div.text = ""
def autocomp_callback(self, attr, old, new):
"""List all directories for the current typed path, output as buttons to click"""
path = os.path.join(new + "*", "")
opts = [] if new == "" else glob.glob(path)
opts = sorted(opts, key=lambda x: x.split("/")[-2].lower())
buttons = [Button(width=750, label=opt) for opt in opts]
for b in buttons:
cb = CustomJS(args=dict(md_dir=self.md_dir, button=b),
code="""
md_dir.value_input = button.label;
md_dir.value = button.label;
""")
b.js_on_click(cb)
self.autocomp_results.children = buttons
mdinfo_file = os.path.join(new, "mdinfo")
if os.path.exists(mdinfo_file):
self.anim_button.button_type = "success"
else:
self.anim_button.button_type = "warning"
def traj_top_callback(self, attr, old, new):
log.debug(f"Updating list of trajectory and topology files")
try:
# search netcdf files
traj = [
f for f in os.listdir(self.md_dir.value)
if re.search(r'.+\.n(et)?c(df)?$', f)
]
traj.sort(key=lambda f: os.path.getmtime(
os.path.join(self.md_dir.value, f)),
reverse=True)
self.trajectory.options = traj
# search restart files
restart = [
f for f in os.listdir(self.md_dir.value)
if re.search(r'.+\.rst7?$', f)
]
restart.sort(key=lambda f: os.path.getmtime(
os.path.join(self.md_dir.value, f)),
reverse=True)
self.rst_traj.options = restart
if self.rst_traj.options:
self.rst_traj.value = self.rst_traj.options[0]
# search for .top, .prmtop, .parm7 or .prm
top = [
f for f in os.listdir(self.md_dir.value)
if re.search(r'.+\.(prm)?top$', f)
or re.search(r'.+\.pa?rm7?$', f)
]
self.topology.options = top
if self.topology.options:
self.topology.value = self.topology.options[0]
except FileNotFoundError:
pass
def compute_rmsd(self):
"""Compute RMSD during a trajectory"""
self.rmsd_button.button_type = "default"
mask = self.mask.value.replace(
"protein",
":ALA,ARG,ASH,ASN,ASP,CYM,CYS,CYX,GLH,GLN,GLU,GLY,HID,HIE,HIP,HYP,HIS,ILE,LEU,LYN,LYS,MET,PHE,PRO,SER,THR,TRP,TYR,VAL"
)
topology = os.path.join(self.md_dir.value, self.topology.value)
trajectories = [
os.path.join(self.md_dir.value, f) for f in self.trajectory.value
]
trajectories.sort(key=lambda f: os.path.getmtime(f), reverse=False)
traj = pt.iterload(trajectories, topology)
stepsize = get_stepsize(traj)
frames = list(
traj.iterframe(step=stepsize,
autoimage=True,
rmsfit=False,
mask=mask))
log.debug(
f"Computing RMSD for top {topology} and traj {trajectories} with a step of {stepsize}, using mask {mask}"
)
ref = frames[0]
results = {"Time": [], "RMSD": []}
with ProcessPoolExecutor(max_workers=max_workers) as ex:
for rmsd, frame in zip(
ex.map(partial(compute_rmsd, ref=ref), frames), frames):
results["Time"].append(frame.time)
results["RMSD"].append(rmsd)
del traj
self.rmsd_CDS.data = results
self.rmsd_button.button_type = "primary"
def autoview_structure(self):
"""Load structure automatically if it has been modified recently"""
# check if rst7 file was rewritten recently
update_time = os.path.getmtime(
os.path.join(self.md_dir.value, self.rst_traj.value))
# and viewing the latest rst7 file
if (update_time > self.last_rst_update) and (
self.rst_traj.value == self.rst_traj.options[0]):
log.debug(
f"Updating {self.rst_traj.value} with more recent version: {update_time}"
)
self.last_rst_update = update_time
self.view_structure()
else:
log.debug(f"No recent update of restart {self.rst_traj.value}")
def view_structure(self):
"""Visualize a restart file with NGL"""
log.debug(
f"Visualizing top {self.topology.value} and restart {self.rst_traj.value}"
)
# load rst7 with pytraj (NGL cannot read it directly)
traj = pt.load(os.path.join(self.md_dir.value, self.rst_traj.value),
os.path.join(self.md_dir.value, self.topology.value))
traj = pt.autoimage(traj)
## pass to parmed to write the pdb data in a StringIO
# struct = pmd.load_file(os.path.join(self.md_dir.value, self.topology.value), xyz=traj.xyz)
# f = StringIO()
# struct.write_pdb(f)
# pdb_data = f.getvalue().encode("ascii")
# f.close()
# write as pdb to temporary file (much faster than parmed + StringIO)
with NamedTemporaryFile() as f:
pt.write_traj(f.name, traj, format="pdb", overwrite=True)
pdb_data = f.read()
# create javascript code
with open(os.path.join(self.src_dir, "static", "js",
"nglviewer.js")) as f:
JS_TEMPLATE = f.read()
self.js_view_structure.code = JS_TEMPLATE % (pdb_data)
# trigger javascript callback by adding an invisible character to the button label
self.view_button.label += " "
def clear_canvas(self):
"""Clear the canvas"""
log.debug("Clearing canvas")
self.mdinfo_CDS.data = copy.deepcopy(empty_mddata_dic)
def read_mdout_header(self, mdout):
"""Read the header of mdout file to search for info on minimization, dt, and output files"""
log.debug(f"Reading header of {mdout} mdout file")
mdout_path = os.path.join(self.md_dir.value, mdout)
found_min, found_dt, found_rst, found_mdcrd = (False, False, False,
False)
with open(mdout_path, 'r') as f:
for i, line in enumerate(f):
re1 = re.search(r"imin\s*=\s*([01])", line)
if re1:
self.mdout_info[mdout]["min"] = bool(int(re1.group(1)))
found_min = True
re2 = re.search(r"dt\s*=\s*([\.0-9]+)", line)
if re2:
self.mdout_info[mdout]["dt"] = float(re2.group(1))
found_dt = True
re3 = re.search(r"^\| RESTRT: ([^\s]+)\s*$", line)
if re3:
self.mdout_info[mdout]["rst"] = re3.group(1)
found_rst = True
re4 = re.search(r"^\| MDCRD: ([^\s]+)\s*$", line)
if re4:
self.mdout_info[mdout]["mdcrd"] = re4.group(1)
found_mdcrd = True
if found_min and found_rst and found_mdcrd:
if self.mdout_info[mdout][
"min"]: # if min, there's no dt to find
log.debug(
f"Finished reading header of {mdout}. Closing minimization file."
)
break
else:
if found_dt:
log.debug(
f"Finished reading header of {mdout}. Closing MD file."
)
break
elif i > 150:
log.debug(
f"Could not find all the information within the first 150 lines of {mdout}. Closing file."
)
break
def is_min(self, mdout):
"""Returns True if minimization, False if MD, None if the 'imin' keyword was not found"""
try:
t = self.mdout_info[mdout]["min"]
except KeyError:
log.debug(
f"Parsing {mdout} mdout file to see if it's a minimization")
self.read_mdout_header(mdout)
t = self.mdout_info[mdout].get("min", None)
log.debug(f"{mdout} is a minimization: {t}")
return t
def stream_mdout(self):
"""Parse and stream data from mdout files (minimization or MD simulation)"""
self.mdout_button.button_type = "default"
self.clear_canvas()
mdout = self.mdout_sel.value
mdout_path = os.path.join(self.md_dir.value, mdout)
mdout_data = copy.deepcopy(empty_mddata_dic)
# open file
with open(mdout_path, 'r') as f:
log.debug(f"Parsing data from {mdout} mdout file")
lines = []
# stop reading when reaching the following lines
for line in f:
if ("A V E R A G E S O V E R" in line) or (
"Maximum number of minimization cycles reached"
in line):
break
lines.append(line)
# check if min or md:
parse_func = parse_min_data if self.is_min(mdout) else parse_md_data
# parse in parallel
with ProcessPoolExecutor(max_workers=max_workers) as ex:
for res in ex.map(parse_func, lines):
for k, v in res.items():
mdout_data[k].extend(v)
# convert to numpy
for key, lst in mdout_data.items():
mdout_data[key] = np.array(lst)
# stream to CDS
log.debug(f"Done. Streaming the data from {mdout}")
self.mdinfo_CDS.stream(mdout_data)
mdout_data = copy.deepcopy(empty_mddata_dic)
self.mdout_button.button_type = "primary"
def latest_mdout_files(self):
"""List all mdout files present in the MD directory, sorted by modification time"""
mdout_files = [
f for f in os.listdir(self.md_dir.value)
if re.search(r'.+\.(md)?out$', f) and ("nohup.out" not in f)
]
mdout_files.sort(
key=lambda f: os.path.getmtime(os.path.join(self.md_dir.value, f)),
reverse=True)
return mdout_files
def get_mdout_files(self):
"""Update the list of mdout files and automatically select the latest one"""
log.debug("Updating the list of mdout files")
self.mdout_files = [None]
# set mdout file to read
self.mdout_files = self.latest_mdout_files()
for mdout in self.mdout_files:
if not mdout in self.mdout_info:
self.mdout_info[mdout] = {}
mdout_options = self.mdout_sel.options
self.mdout_sel.options = self.mdout_files
# if new mdout is created
if len(self.mdout_files) > len(mdout_options):
self.mdout_sel.value = self.mdout_files[0]
def parse_mdinfo(self):
"""Parse and stream data read from the mdinfo file"""
log.debug("Parsing mdinfo file")
mdinfo_path = os.path.join(self.md_dir.value, "mdinfo")
try:
with open(mdinfo_path, 'r') as f:
lines = f.readlines()
except FileNotFoundError:
log.error("No mdinfo file in the current directory")
return
mdinfo_data = copy.deepcopy(empty_mddata_dic)
# min or md
latest_mdout_file = self.latest_mdout_files()[0]
parse_func = parse_min_data if self.is_min(
latest_mdout_file) else parse_md_data
for i, line in enumerate(lines):
# data
res = parse_func(line)
for k, v in res.items():
mdinfo_data[k].extend(v)
# number of steps
re_steps = re.search(
r"Total steps :\s*(\d+) \| Completed :\s*(\d+) \| Remaining :\s*(\d+)",
line)
if re_steps:
total = int(re_steps.group(1))
completed = int(re_steps.group(2))
remaining = int(re_steps.group(3))
steps_patch = {
"total": [(0, total)],
"completed": [(0, completed)],
"remaining": [(0, remaining)],
}
self.steps_CDS.patch(steps_patch)
progress = 100 * completed / total
self.progressbar.title.text = f"Progress: {progress:6.2f}%"
self.progressbar.x_range.set_from_json("end", total)
# calculation speed (ns/day)
re_speed = re.search(r'Average timings for last', line)
if re_speed:
re_speed = re.search(r'ns/day =\s*([\.0-9]+)', lines[i + 2])
speed = float(re_speed.group(1))
self.calc_speed.text = f"Calculation speed:<br/>{speed} ns/day"
# time remaining
re_time = re.search(r'Estimated time remaining:\s*(.+).$', line)
if re_time:
time_left = re_time.group(1)
time_left = pretty_time(time_left)
self.eta.text = f"Estimated time remaining:<br/>{time_left}"
break
# last update
self.last_update.text = f"Last update:<br/>{time_passed(os.path.getmtime(mdinfo_path))}"
update_time = os.path.getmtime(mdinfo_path)
if time.time() - update_time > 5 * 60: # not updated recently
self.last_update.style = {
"font-weight": "bold",
"color": "#d62727",
"margin-top": "5px"
}
else:
self.last_update.style = {
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
}
# only update plots if monitoring the latest mdout file
if self.mdout_sel.value == latest_mdout_file:
log.debug(
f"Currently watching the latest mdout '{self.mdout_sel.value}'"
)
# fetch previous stream data as dict
last_mdinfo_stream = self.mdinfo_CDS.to_df().tail(1).reset_index(
drop=True).T.to_dict().get(0)
if last_mdinfo_stream:
# format the dict
for key, value in last_mdinfo_stream.items():
last_mdinfo_stream[key] = [value]
# update if mdinfo is different from the previous stream
if mdinfo_data != last_mdinfo_stream:
log.debug("Streaming new data from mdinfo")
for key, value in mdinfo_data.items():
mdinfo_data[key] = np.array(value)
self.mdinfo_CDS.stream(mdinfo_data)
else:
log.debug(
f"No previous mdinfo data could be retrieved. Streaming new data"
)
for key, value in mdinfo_data.items():
mdinfo_data[key] = np.array(value)
self.mdinfo_CDS.stream(mdinfo_data)
else:
log.debug(
f"Currently watching mdout '{self.mdout_sel.value}' != '{latest_mdout_file}'"
)
def display_simulations_length(self):
"""Displays simulation length"""
log.debug("Computing total time of simulation(s) displayed")
current_time = OrderedDict()
# discard min files and limit to XX most recent MD files
self.md_mdout_files = [
f for f in self.mdout_sel.options if not self.is_min(f)
][:self.slider.value]
for mdout in self.md_mdout_files:
mdout_path = os.path.join(self.md_dir.value, mdout)
i = 0
for line in readlines_reverse(mdout_path):
i += 1
re1 = re.search(r"NSTEP =\s*(\d+)", line)
if re1:
current_time[mdout] = int(
re1.group(1)) * self.mdout_info[mdout].get(
"dt", 0.002) * 1e-3 # in ns
break
if i > 150:
break
data = pd.DataFrame.from_dict(
current_time, orient="index",
columns=["time"]).reset_index().rename(columns={"index": "mdout"})
# compute properties for the pie plot
data['angle'] = data['time'] / data['time'].sum() * 2 * pi
# color palette
data['color'] = sim_palette[:len(current_time)]
# reverse index order for the barplot
data = data.reindex(index=data.index[::-1]).reset_index(drop=True)
data = data.reset_index().rename(columns={"index": "y_coords"})
# update
self.dashboard_CDS.data = {k: data[k].tolist() for k in data.columns}
total_time = data["time"].sum()
self.pie.title.text = f"Simulations length: {total_time:.2f} ns"
def update_dashboard(self):
log.debug("Starting update of the dashboard")
self.get_mdout_files()
self.parse_mdinfo()
self.display_simulations_length()
self.autoview_structure()
log.debug("Finished updating the dashboard")
def callback_slider(self, attr, old, new):
log.debug(f"Slider update detected: from {old} to {new}")
self.display_simulations_length()
def add_callbacks(self):
log.debug("Adding callbacks to the widgets")
# User input
self.md_dir.on_change("value_input", self.autocomp_callback)
self.md_dir.on_change("value", self.traj_top_callback)
# RMSD
self.rmsd_button.on_click(self.compute_rmsd)
# NGLView
self.js_view_structure = CustomJS(code="",
args={
"ligand_mask": self.ngl_lig,
"repr": self.ngl_representations
})
self.ngl_help_button.on_click(self.ngl_help)
# hack to execute both python and JS code on button click
self.view_button.js_on_change("label", self.js_view_structure)
self.view_button.on_click(self.view_structure)
# MDout parsing
self.mdout_button.on_click(self.stream_mdout)
self.slider.on_change("value_throttled", self.callback_slider)
def __init__(self, default_dir, port):
# path to source directory
self.src_dir = os.path.dirname(os.path.abspath(__file__))
# MD directory and files selection
self.md_dir = TextInput(
title="Path to MD directory containing mdin and mdout files",
value="",
width=750)
self.anim_button = Toggle(label="▶ Load",
button_type="warning",
width=80,
height=50,
active=False)
self.port = port
# container for the buttons that are created while the user types in the textinput
self.autocomp_results = column(children=[])
# file used to display temperature, pressure...etc. plots
self.mdout_sel = Select(
title="MDout file",
width=230,
value=None,
options=[],
)
# button to load content
self.mdout_button = Button(width=80,
height=50,
label="Plot",
button_type="primary")
self.mdout_files = [None]
self.md_mdout_files = []
# mdinfo figures
progressbar_tooltip = """
<span style="color:#428df5">@completed{0,0}</span> out of <span style="color:#428df5">@total{0,0}</span> steps (<span style="color:#428df5">@remaining{0,0}</span> remaining)
"""
self.progressbar = figure(title="Current progress",
x_range=Range1d(0, 10),
tooltips=progressbar_tooltip,
height=70,
width=350,
tools="hover",
toolbar_location=None)
self.progressbar.xgrid.grid_line_color = None
self.progressbar.ygrid.grid_line_color = None
self.progressbar.axis.visible = False
self.progressbar.outline_line_color = "#444444"
self.steps_CDS = ColumnDataSource({
"total": [np.nan],
"completed": [np.nan],
"remaining": [np.nan],
"color": ['#428df5'],
})
self.progressbar.hbar(
y=0,
left=0,
right="completed",
source=self.steps_CDS,
height=0.5,
color="color",
)
self.progressbar.hover[0].mode = "hline"
self.calc_speed = Div(width=150,
height=50,
text="Calculation speed:",
style={
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
})
self.eta = Div(width=280,
height=50,
text="Estimated time remaining:",
style={
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
})
self.last_update = Div(width=280,
height=50,
text="Last update:",
style={
"font-weight": "bold",
"color": "#444444",
"margin-top": "5px"
})
# number of mdout files displayed on the dashboard at max
self.slider = Slider(start=1,
end=12,
value=2,
step=1,
callback_policy="mouseup",
title="Number of simulations displayed")
self.dashboard_CDS = ColumnDataSource({
"y_coords": [0, 1],
"mdout": ["heat.out", "prod.out"],
"time": [42, 200],
"angle": [1.09, 5.193],
"color": [sim_palette[0], sim_palette[1]],
})
dashboard_tooltip = """
<span style="color:@color">@mdout</span>: @time{0,0.00} ns
"""
# pie plot
self.pie = figure(plot_height=300,
width=500,
title="Simulations length",
toolbar_location=None,
tools="hover",
tooltips=dashboard_tooltip,
x_range=Range1d(-0.5, 1.0))
self.rpie = self.pie.wedge(x=0,
y=1,
radius=0.4,
source=self.dashboard_CDS,
start_angle=cumsum('angle',
include_zero=True),
end_angle=cumsum('angle'),
line_color="white",
fill_color='color',
legend="mdout")
self.pie.axis.axis_label = None
self.pie.axis.visible = False
self.pie.grid.grid_line_color = None
self.pie.legend.label_text_font_size = '9pt'
self.pie.legend.border_line_width = 0
self.pie.legend.border_line_alpha = 0
self.pie.legend.spacing = 0
self.pie.legend.margin = 0
# hbar plot
self.bar = figure(width=850,
plot_height=300,
toolbar_location=None,
tools="hover",
tooltips=dashboard_tooltip)
self.rbar = self.bar.hbar(y="y_coords",
left=0,
right="time",
source=self.dashboard_CDS,
height=0.8,
color="color")
self.bar.x_range.set_from_json("start", 0)
self.bar.xaxis.axis_label = "Time (ns)"
self.bar.yaxis.axis_label = None
self.bar.yaxis.visible = False
self.bar.hover[0].mode = "hline"
## Mdout figures
self.mdinfo_CDS = ColumnDataSource(copy.deepcopy(empty_mddata_dic))
self.moving_avg_trans = CustomJSTransform(v_func=moving_avg_func)
ticker = PrintfTickFormatter(format="%4.0e")
# Temperature
self.temperature_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.temperature_fig.toolbar.autohide = True
self.temperature_fig.xaxis.axis_label = "Number of steps"
self.temperature_fig.yaxis.axis_label = "Temperature (K)"
self.temperature_fig.xaxis.formatter = ticker
r = self.temperature_fig.line("Nsteps",
"Temperature",
color=palette[0],
source=self.mdinfo_CDS,
_width=1,
alpha=0.15)
self.temperature_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Temperature",
self.moving_avg_trans),
color=colorscale(palette[0], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.temperature_fig.add_tools(make_hover([r]))
# Pressure
self.pressure_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.pressure_fig.toolbar.autohide = True
self.pressure_fig.xaxis.axis_label = "Number of steps"
self.pressure_fig.yaxis.axis_label = "Pressure"
self.pressure_fig.xaxis.formatter = ticker
r = self.pressure_fig.line("Nsteps",
"Pressure",
color=palette[1],
source=self.mdinfo_CDS,
line_width=1,
alpha=0.15)
self.pressure_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Pressure", self.moving_avg_trans),
color=colorscale(palette[1], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.pressure_fig.add_tools(make_hover([r]))
# Energy
self.energy_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
etot = self.energy_fig.line("Nsteps",
"Etot",
color=palette[2],
source=self.mdinfo_CDS,
line_width=1)
ektot = self.energy_fig.line("Nsteps",
"EKtot",
color=palette[3],
source=self.mdinfo_CDS,
line_width=1)
eptot = self.energy_fig.line("Nsteps",
"EPtot",
color=palette[4],
source=self.mdinfo_CDS,
line_width=1)
legend = Legend(items=[
("Total", [etot]),
("Kinetic", [ektot]),
("Potential", [eptot]),
],
location="top_right")
self.energy_fig.add_layout(legend, 'right')
self.energy_fig.add_tools(make_hover([etot]))
self.energy_fig.legend.location = "top_left"
self.energy_fig.legend.click_policy = "hide"
self.energy_fig.toolbar.autohide = True
self.energy_fig.xaxis.axis_label = "Number of steps"
self.energy_fig.yaxis.axis_label = "Energy"
self.energy_fig.xaxis.formatter = ticker
# Volume
self.vol_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.vol_fig.toolbar.autohide = True
self.vol_fig.xaxis.axis_label = "Number of steps"
self.vol_fig.yaxis.axis_label = "Volume"
self.vol_fig.xaxis.formatter = ticker
r = self.vol_fig.line("Nsteps",
"Volume",
color=palette[6],
source=self.mdinfo_CDS,
line_width=1,
alpha=0.15)
self.vol_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Volume", self.moving_avg_trans),
color=colorscale(palette[6], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.vol_fig.add_tools(make_hover([r]))
# Density
self.density_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.density_fig.toolbar.autohide = True
self.density_fig.xaxis.axis_label = "Number of steps"
self.density_fig.yaxis.axis_label = "Density"
self.density_fig.xaxis.formatter = ticker
r = self.density_fig.line("Nsteps",
"Density",
color=palette[7],
source=self.mdinfo_CDS,
line_width=1,
alpha=0.15)
self.density_fig.line(transform("Nsteps", self.moving_avg_trans),
transform("Density", self.moving_avg_trans),
color=colorscale(palette[7], 0.85),
source=self.mdinfo_CDS,
line_width=3)
self.density_fig.add_tools(make_hover([r]))
## RMSD figure
self.empty_rmsd_dic = {k: [] for k in ["Time", "RMSD"]}
self.rmsd_CDS = ColumnDataSource(self.empty_rmsd_dic)
self.rmsd_fig = figure(
plot_height=size[1],
plot_width=size[0],
active_scroll="wheel_zoom",
)
self.rmsd_fig.toolbar.autohide = True
self.rmsd_fig.xaxis.axis_label = "Time (ps)"
self.rmsd_fig.yaxis.axis_label = "RMSD (Å)"
self.rmsd_fig.xaxis.formatter = ticker
r = self.rmsd_fig.line("Time",
"RMSD",
color=palette[8],
source=self.rmsd_CDS,
line_width=2)
self.rmsd_fig.add_tools(
make_hover([r],
tooltips=[("Time (ps)", "@Time{0,0}"),
("RMSD (Å)", "@RMSD")]))
self.rmsd_button = Button(width=100,
label="Calculate RMSD",
button_type="primary")
self.trajectory = MultiSelect(
title="Trajectory file(s)",
width=400,
value=None,
options=[],
)
self.rst_traj = Select(
title="Restart file",
width=400,
value=None,
options=[],
)
self.topology = Select(
title="Topology file",
width=200,
value=None,
options=[],
)
self.mask = TextInput(title="Mask",
value="protein@CA,C,O,N",
width=200)
# NGLview
self.last_rst_update = 0
self.view_button = Button(width=80,
label="Visualize",
button_type="primary")
self.view_canvas = Div(width=size[0],
height=size[1] + 60,
css_classes=["ngldiv"],
text="")
self.ngl_help_div = Div(width=0, height=0, text="")
self.ngl_help_button = Toggle(width=80, label="Help", active=False)
self.ngl_lig = TextInput(title="Ligand name", value="LIG", width=80)
self.ngl_representations = CheckboxButtonGroup(
labels=["Protein", "Ligand", "Water", "Lipids", "Ions"],
active=[0, 1, 2, 3, 4],
)
# info about simulation files (min, dt, rst and mdcrd files)
self.mdout_info = {}
# add callbacks
self.add_callbacks()
self.md_dir.value = default_dir
curdoc().add_root(layout_child)
#iterator = iter(file_name_list)
button = Button(label=' Play', width=60)
button.on_click(animate)
button_next = Button(label='Next', width=60)
button_next.on_click(partial(next_image, files=file_name_list, action='next'))
button_prev = Button(label='Prev', width=60)
button_prev.on_click(partial(next_image, files=file_name_list, action='prev'))
# https://bokeh.pydata.org/en/latest/docs/reference/models/widgets.inputs.html
# TODO(ahundt) switch to AutocompleteInput with list of files
file_textbox = TextInput(value=file_name_list[index].split('\\')[-1],
width=width)
# TODO(ahundt) load another file when it changes
# def textbox_update(attrname, old, new):
# plot.update(slider.value)
# file_textbox.on_change(textbox_update)
# Combine the bokeh plot on plot.state with the widgets
plot_list = [[plot.state], [gripper_plot.state], [action_plot.state]]
widget_list = [[slider, button, button_prev, button_next], [file_textbox]]
# "gripper_action" plot, labels based on the gripper opening and closing
plot_list.append([gripper_action_plot.state])
class StockApp(VBox):
extra_generated_classes = [["StockApp", "StockApp", "VBox"]]
jsmodel = "VBox"
Y = Instance(ColumnDataSource)
# plots
hist1 = Instance(Plot)
hist2 = Instance(Plot)
hist3 = Instance(Plot)
# data source
source = Instance(ColumnDataSource)
risk_source = Instance(ColumnDataSource)
# layout boxes
mainrow = Instance(HBox)
ticker1_box = Instance(HBox)
ticker2_box = Instance(HBox)
ticker3_box = Instance(HBox)
ticker4_box = Instance(HBox)
ticker5_box = Instance(HBox)
second_row = Instance(HBox)
histrow = Instance(HBox)
# inputs
ticker1 = String(default="1.2*(1.1-x)")
ticker1p = String(default="-1.2")
ticker2 = String(default="4.0")
ticker2p = String(default="0.0")
ticker3 = String(default="500")
ticker3_1 = String(default="252")
ticker3_2 = String(default="0.01")
ticker4 = String(default="100")
ticker4_1 = String(default="1.01")
ticker4_2 = String(default="Milstein")
button = String(default="")
ticker1_select = Instance(TextInput)
ticker1p_select = Instance(TextInput)
ticker2_select = Instance(TextInput)
ticker2p_select = Instance(TextInput)
ticker3_select = Instance(TextInput)
ticker3_1_select = Instance(TextInput)
ticker3_2_select = Instance(TextInput)
ticker4_select = Instance(TextInput)
ticker4_1_select = Instance(TextInput)
ticker4_2_select = Instance(Select)
button_select = Instance(TextInput)
input_box = Instance(VBoxForm)
def __init__(self, *args, **kwargs):
super(StockApp, self).__init__(*args, **kwargs)
self._dfs = {}
@classmethod
def create(cls):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
# create layout widgets
obj = cls()
obj.mainrow = HBox()
obj.ticker1_box = HBox(width=500)
obj.ticker2_box = HBox(width=500)
obj.ticker3_box = HBox(width=467)
obj.ticker4_box = HBox(width=500)
obj.ticker5_box = HBox(width=500)
obj.second_row = HBox()
obj.histrow = HBox()
obj.input_box = VBoxForm(width=600)
# create input widgets
obj.make_inputs()
# outputs
#obj.make_source()
obj.main_mc(252,500,0.01,'Milstein',1.01)
obj.make_plots()
# layout
obj.set_children()
return obj
def make_inputs(self):
self.ticker1_select = TextInput(
name='ticker1',
title='Drift Function:',
value='1.2*(1.1-x)',
)
self.ticker1p_select = TextInput(
name='ticker1p',
title='Drift Derivative:',
value='-1.2',
)
self.ticker2_select = TextInput(
name='ticker2',
title='Volatility Function:',
value='4.0',
)
self.ticker2p_select = TextInput(
name='ticker2p',
title='Volatility Derivative:',
value='0.0',
)
self.ticker3_select = TextInput(
name='ticker3',
title='Number of Paths:',
value='500'
)
self.ticker3_1_select = TextInput(
name='ticker3_1',
title='Number of Points:',
value='252'
)
self.ticker3_2_select = TextInput(
name='ticker3_2',
title='Time Step:',
value='0.01'
)
self.ticker4_select = TextInput(
name='ticker4',
title='Histogram Line:',
value='100'
)
self.ticker4_1_select = TextInput(
name='ticker4_1',
title='Initial Value:',
value='1.01'
)
self.ticker4_2_select = Select(
name='ticker4_2',
title='MC Scheme:',
value='Milstein',
options=['Euler','Milstein', 'Pred/Corr']
)
self.button_select = TextInput(
name='button',
title='Type any word containing "run" to run Simulation ',
value = ''
)
def make_source(self):
self.source = ColumnDataSource(data=self.Y)
def main_mc(self,num_pts,num_paths, delta_t, method, Y0):
def a(x):
return eval(self.ticker1)
def ap(x):
return eval(self.ticker1p)
def b(x):
return eval(self.ticker2)
def bp(x):
return eval(self.ticker2p)
rpaths = np.random.normal(0, delta_t, size=(num_pts,num_paths))
Y = np.array([[Y0]*num_paths])
dt_vec = np.array([delta_t]*num_paths)
if method == 'Milstein':
for i in xrange(0,num_pts):
tY = Y[-1,:]
dW = rpaths[i,:]
Y = np.vstack([Y, tY + a(tY)*dt_vec + b(tY)*dW + 0.5*b(tY)*bp(tY)*(dW*dW-dt_vec)])
elif method == 'Pred/Corr':
# Predictor corrector method is taken from equation 2.6 in this paper:
# http://www.qfrc.uts.edu.au/research/research_papers/rp222.pdf
rpaths2 = np.random.normal(0, delta_t, size=(num_pts,num_paths))
for i in xrange(0,num_pts):
tY = Y[-1,:]
Ybar = tY + a(tY)*dt_vec + b(tY)*rpaths[i,:]
abar_before = a(tY) - 0.5*b(tY)*bp(tY)
abar_after = a(Ybar) - 0.5*b(Ybar)*bp(Ybar)
Y = np.vstack([Y, tY + 0.5*(abar_before + abar_after)*dt_vec + 0.5*(b(tY)+b(Ybar))*rpaths2[i,:]])
else: # default to Euler Scheme
for i in xrange(0,num_pts):
tY = Y[-1,:]
Y = np.vstack([Y, tY + a(tY)*dt_vec + b(tY)*rpaths[i,:]])
return Y # return simulated paths
def path_plot(self):
num_paths_plot = min(50,int(self.ticker3))
hist_point = int(self.ticker4)
#print 'Hist Point ', hist_point
Y = self.Y.as_matrix()
pltdat = Y[:,0:num_paths_plot]
mY, MY = min(Y[hist_point,:]), max(Y[hist_point,:])
plt.plot(pltdat, alpha=0.1, linewidth=1.8)
sns.tsplot(pltdat.T,err_style='ci_band', ci=[68,95,99], alpha=1, \
linewidth = 2.5, color='indianred')
#sns.tsplot(pltdat.T,err_style='ci_band', ci=[68,95,99,99.99999], alpha=1, \
# linewidth = 2.5, condition='Mean Path', color='indianred')
plt.plot([hist_point, hist_point], [0.99*mY, 1.01*MY], 'k-',label='Time Series Histogram')
plt.xlabel('Time Step')
plt.ylabel('Price')
#plt.legend()
p = mpl.to_bokeh()
p.title = 'Mean Path (Red), MC Paths (Background) and Density Line (Black)'
p.title_text_font_size= str(TITLE_SIZE)+'pt'
return p
def hist_den_plot(self):
Y = self.Y.as_matrix()
hist_point = int(self.ticker4)
delta_t = float(self.ticker3_2)
data = Y[hist_point,:]
sns.distplot(data, color='k', hist_kws={"color":"b"}, norm_hist=True)
#sns.distplot(data, color='k', hist_kws={"color":"b"})
plt.hist(data)
plt.title('Distribution at T = ' + str(np.round(delta_t*hist_point,4)) + ' with Mean: ' +str(np.round(np.mean(data),4)) + ' and Std Dev: ' + str(np.round(np.std(data),4)))
plt.xlabel('Price Bins')
plt.ylabel('Bin Count')
p = mpl.to_bokeh()
p.title_text_font_size= str(TITLE_SIZE)+'pt'
return p
def mc_results(self):
# Compute Monte Carlo results
Y = self.Y.as_matrix()
Y0 = float(self.ticker4_1)
hist_point = int(self.ticker4)
num_paths = int(self.ticker3)
center_point = np.mean(Y[hist_point,:])
stkgrid = np.linspace(0.5*center_point,1.5*center_point,100)
meanlst = np.array([])
stdlst = np.array([])
paylst = np.array([])
for stk in stkgrid:
meanlst = np.append(meanlst, np.mean(payoff(Y[hist_point,:],stk)))
stdlst = np.append(stdlst,np.std(payoff(Y[hist_point,:],stk))/np.sqrt(num_paths))
plt.plot(stkgrid,meanlst+2*stdlst, 'g-')
plt.plot(stkgrid,meanlst-2*stdlst,'g-',label='2-Sig Error')
plt.plot(stkgrid,meanlst+stdlst,'r-')
plt.plot(stkgrid,meanlst-stdlst,'r-',label='1-Sig Error')
plt.plot(stkgrid,meanlst,'b',label='Mean')
plt.title('MC Option Price (Blue) with 1-Sig (Red) and 2-Sig (Green) Errors')
plt.xlabel('Strike')
plt.ylabel('Value')
p = mpl.to_bokeh()
p.title_text_font_size= str(TITLE_SIZE)+'pt'
return p
def hist_plot(self):
histdf = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))
#pltdf = self.source.to_df().set_index('date').dropna()
#qlow, qhigh = mquantiles(pltdf[ticker],prob=[0.01,0.99])
#tdf = pltdf[ticker]
#histdf = tdf[((tdf > qlow) & (tdf < qhigh))]
hist, bins = np.histogram(histdf, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
start = bins.min()
end = bins.max()
top = hist.max()
p = figure(
title=' Histogram',
plot_width=600, plot_height=400,
tools="",
title_text_font_size="16pt",
x_range=[start, end],
y_range=[0, top],
x_axis_label = ' Bins',
y_axis_label = 'Bin Count'
)
p.rect(center, hist / 2.0, width, hist)
return p
def make_plots(self):
self.hist_plots()
def hist_plots(self):
self.hist1 = self.path_plot()
self.hist2 = self.hist_den_plot()
self.hist3 = self.mc_results()
def set_children(self):
self.children = [self.mainrow, self.second_row]
self.mainrow.children = [self.input_box, self.hist1]
self.second_row.children = [self.hist2, self.hist3]
self.input_box.children = [self.ticker1_box, self.ticker2_box, self.ticker3_box,self.ticker4_box,self.ticker5_box]
self.ticker1_box.children =[self.ticker1_select, self.ticker1p_select]
self.ticker2_box.children =[self.ticker2_select, self.ticker2p_select]
self.ticker3_box.children =[self.ticker3_select, self.ticker3_1_select, self.ticker3_2_select]
self.ticker4_box.children =[self.ticker4_select, self.ticker4_1_select, self.ticker4_2_select]
self.ticker5_box.children =[self.button_select]
def input_change(self, obj, attrname, old, new):
if obj == self.ticker4_2_select:
self.ticker4_2 = new
if obj == self.ticker4_1_select:
self.ticker4_1 = new
if obj == self.ticker4_select:
self.ticker4 = new
if obj == self.ticker3_2_select:
self.ticker3_2 = new
if obj == self.ticker3_1_select:
self.ticker3_1 = new
if obj == self.ticker3_select:
self.ticker3 = new
if obj == self.ticker2p_select:
self.ticker2p = new
if obj == self.ticker2_select:
self.ticker2 = new
if obj == self.ticker1p_select:
self.ticker1p = new
if obj == self.ticker1_select:
self.ticker1 = new
if obj == self.button_select:
self.button = new
if 'run' in self.button:
self.make_source()
self.make_plots()
self.set_children()
curdoc().add(self)
#self.make_source()
#self.make_plots()
#self.set_children()
#curdoc().add(self)
def setup_events(self):
super(StockApp, self).setup_events()
if self.ticker1_select:
self.ticker1_select.on_change('value', self, 'input_change')
if self.ticker1p_select:
self.ticker1p_select.on_change('value', self, 'input_change')
if self.ticker2_select:
self.ticker2_select.on_change('value', self, 'input_change')
if self.ticker2p_select:
self.ticker2p_select.on_change('value', self, 'input_change')
if self.ticker3_select:
self.ticker3_select.on_change('value', self, 'input_change')
if self.ticker3_1_select:
self.ticker3_1_select.on_change('value', self, 'input_change')
if self.ticker3_2_select:
self.ticker3_2_select.on_change('value', self, 'input_change')
if self.ticker4_select:
self.ticker4_select.on_change('value', self, 'input_change')
if self.ticker4_1_select:
self.ticker4_1_select.on_change('value', self, 'input_change')
if self.ticker4_2_select:
self.ticker4_2_select.on_change('value', self, 'input_change')
if self.button_select:
self.button_select.on_change('value',self, 'input_change')
@property
def Y(self):
tmpdf = pd.DataFrame(self.main_mc(int(self.ticker3_1),int(self.ticker3),float(self.ticker3_2),self.ticker4_2,float(self.ticker4_1)))
tmpdf.columns = ['Col_' + str(i) for i in xrange(len(tmpdf.columns))]
#print tmpdf
return tmpdf
#==================
#create plot
#==================
# Set up data
#global:
param_choice = data['resolution2']
#
#source = ColumnDataSource(data=dict(x=data['scene_lat'] , y=data['scene_lon'], c=param_choice ))
plot.circle('scene_lat', 'scene_lon', source=all_data, line_width=3, alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='Daily Resolution')
print("You've made it this far, traveller!")
year_slider = Slider(title="Year", value=all_data['year'].min(), start=all_data['year'].min(), end=all_data['year'].max(), step=1)
month_slider = Slider(title="Month", value=all_data['year'].min(), start=all_data['year'].min(), end=all_data['year'].max(), step=1)
day_slider = DateSlider(title="Day", start=all_data['date'].min(), end=all_data['date'].max(), value=all_data['date'].min(), step=1)
#year_slider = Slider(title="Year", value=data['year'].min(), start=data['year'].min(), end=data['year'].max(), step=1)
#month_slider = Slider(title="Month", value=data['year'].min(), start=data['year'].min(), end=data['year'].max(), step=1)
#day_slider = DateSlider(title="Day", start=data['date'].min(), end=data['date'].max(), value=data['date'].min(), step=1)
#date = DateSlider(title="Day", start=data['date'].min(), end=data['date'].max(), value=data['date'].min(), step=1)
#param_button_group = RadioButtonGroup(labels=["Resolution", "Potential Water", "Cloud Coverage", "Gelbstoff"], active=0)
time_button_group = RadioButtonGroup(labels=["Yearly", "Monthly", "Daily"], active=0)
# Set up callbacks
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plot
plot = figure(
plot_height=400,
plot_width=400,
title="my sine wave",
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, 4 * np.pi],
y_range=[-2.5, 2.5],
)
plot.line("x", "y", source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value="my sine wave")
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0, step=0.1)
phase = Slider(title="phase", value=0.0, start=0.0, end=2 * np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1, step=0.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
text.on_change("value", update_title)
def update_data(attrname, old, new):
def __init__(self):
self.options = load_options()
self.option_types = ['AUTH_USER_REQ', 'DISABLE_BACKUP_TAB', 'OPTIONAL_TABS', 'LITE_VIEW', 'COLORS', 'SIZE',
'LINE_WIDTH', 'LINE_DASH', 'ALPHA', 'ENDPOINT_COUNT', 'RESAMPLED_DVH_BIN_COUNT']
div = {key: Div(text=key) for key in self.option_types}
self.input = {}
self.input['AUTH_USER_REQ'] = RadioButtonGroup(labels=["True", "False"], active=1-int(self.options.AUTH_USER_REQ))
self.input['AUTH_USER_REQ'].on_change('active', self.update_auth_user_req)
self.input['DISABLE_BACKUP_TAB'] = RadioButtonGroup(labels=["True", "False"], active=1-int(self.options.DISABLE_BACKUP_TAB))
self.input['DISABLE_BACKUP_TAB'].on_change('active', self.update_disable_backup_tab)
labels = ['ROI Viewer', 'Planning Data', 'Time-Series', 'Correlation', 'Regression', 'MLC Analyzer']
active = [l for l in range(0, len(labels)) if self.options.OPTIONAL_TABS[labels[l]]]
self.input['OPTIONAL_TABS'] = CheckboxGroup(labels=labels, active=active)
self.input['OPTIONAL_TABS'].on_change('active', self.update_options_tabs)
self.input['LITE_VIEW'] = RadioButtonGroup(labels=["True", "False"], active=1 - int(self.options.LITE_VIEW))
self.input['LITE_VIEW'].on_change('active', self.update_lite_view)
color_variables = [c for c in self.options.__dict__ if c.find('COLOR') > -1]
color_variables.sort()
colors = plot_colors.cnames.keys()
colors.sort()
self.input['COLORS_var'] = Select(value=color_variables[0], options=color_variables)
self.input['COLORS_var'].on_change('value', self.update_input_colors_var)
self.input['COLORS_val'] = Select(value=getattr(self.options, color_variables[0]), options=colors)
self.input['COLORS_val'].on_change('value', self.update_input_colors_val)
size_variables = [s for s in self.options.__dict__ if s.find('SIZE') > -1]
size_variables.sort()
self.input['SIZE_var'] = Select(value=size_variables[0], options=size_variables)
self.input['SIZE_var'].on_change('value', self.update_size_var)
self.input['SIZE_val'] = TextInput(value=str(getattr(self.options, size_variables[0])).replace('pt', ''))
self.input['SIZE_val'].on_change('value', self.update_size_val)
width_variables = [l for l in self.options.__dict__ if l.find('LINE_WIDTH') > -1]
width_variables.sort()
self.input['LINE_WIDTH_var'] = Select(value=width_variables[0], options=width_variables)
self.input['LINE_WIDTH_var'].on_change('value', self.update_line_width_var)
self.input['LINE_WIDTH_val'] = TextInput(value=str(getattr(self.options, width_variables[0])))
self.input['LINE_WIDTH_val'].on_change('value', self.update_line_width_val)
line_dash_variables = [d for d in self.options.__dict__ if d.find('LINE_DASH') > -1]
line_dash_variables.sort()
self.input['LINE_DASH_var'] = Select(value=line_dash_variables[0], options=line_dash_variables)
self.input['LINE_DASH_var'].on_change('value', self.update_line_dash_var)
line_dash_options = ['solid', 'dashed', 'dotted', 'dotdash', 'dashdot']
self.input['LINE_DASH_val'] = Select(value=getattr(self.options, line_dash_variables[0]),
options=line_dash_options)
self.input['LINE_DASH_val'].on_change('value', self.update_line_dash_val)
alpha_variables = [a for a in self.options.__dict__ if a.find('ALPHA') > -1]
alpha_variables.sort()
self.input['ALPHA_var'] = Select(value=alpha_variables[0], options=alpha_variables)
self.input['ALPHA_var'].on_change('value', self.update_alpha_var)
self.input['ALPHA_val'] = TextInput(value=str(getattr(self.options, alpha_variables[0])))
self.input['ALPHA_val'].on_change('value', self.update_alpha_val)
self.input['ENDPOINT_COUNT'] = TextInput(value=str(self.options.ENDPOINT_COUNT))
self.input['ENDPOINT_COUNT'].on_change('value', self.update_endpoint_count)
self.input['RESAMPLED_DVH_BIN_COUNT'] = TextInput(value=str(self.options.RESAMPLED_DVH_BIN_COUNT))
self.input['RESAMPLED_DVH_BIN_COUNT'].on_change('value', self.update_resampled_dvh_bin_count)
self.default_options_button = Button(label="Restore Default Options", button_type='warning')
self.default_options_button.on_click(self.restore_default_options)
self.layout = column(Div(text="<hr>", width=900),
Div(text="<b>Options</b>"),
row(div['AUTH_USER_REQ'], self.input['AUTH_USER_REQ']),
row(div['DISABLE_BACKUP_TAB'], self.input['DISABLE_BACKUP_TAB']),
row(div['OPTIONAL_TABS'], self.input['OPTIONAL_TABS']),
row(div['LITE_VIEW'], self.input['LITE_VIEW']),
row(div['COLORS'], self.input['COLORS_var'], self.input['COLORS_val']),
row(div['SIZE'], self.input['SIZE_var'], self.input['SIZE_val']),
row(div['LINE_WIDTH'], self.input['LINE_WIDTH_var'], self.input['LINE_WIDTH_val']),
row(div['LINE_DASH'], self.input['LINE_DASH_var'], self.input['LINE_DASH_val']),
row(div['ALPHA'], self.input['ALPHA_var'], self.input['ALPHA_val']),
row(div['ENDPOINT_COUNT'], self.input['ENDPOINT_COUNT']),
row(div['RESAMPLED_DVH_BIN_COUNT'], self.input['RESAMPLED_DVH_BIN_COUNT']),
row(self.default_options_button,
Div(text="Must restart Settings server to take effect after click")))
def plot():
# FIGURES AND X-AXIS
fig1 = Figure(title = 'Dive Profile', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS)
fig2 = Figure(title = 'Dive Controls', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS, x_range=fig1.x_range)
fig3 = Figure(title = 'Attitude', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS, x_range=fig1.x_range)
figs = gridplot([[fig1],[fig2],[fig3]])
# Formatting x-axis
timeticks = DatetimeTickFormatter(formats=dict(seconds =["%b%d %H:%M:%S"],
minutes =["%b%d %H:%M"],
hourmin =["%b%d %H:%M"],
hours =["%b%d %H:%M"],
days =["%b%d %H:%M"],
months=["%b%d %H:%M"],
years =["%b%d %H:%M %Y"]))
fig1.xaxis.formatter = timeticks
fig2.xaxis.formatter = timeticks
fig3.xaxis.formatter = timeticks
# removing gridlines
fig1.xgrid.grid_line_color = None
fig1.ygrid.grid_line_color = None
fig2.xgrid.grid_line_color = None
fig2.ygrid.grid_line_color = None
fig3.xgrid.grid_line_color = None
fig3.ygrid.grid_line_color = None
# INPUT WIDGETS
collection_list = CONN[DB].collection_names(include_system_collections=False)
gliders = sorted([platformID for platformID in collection_list if len(platformID)>2])
gliders = Select(title = 'PlatformID', value = gliders[0], options = gliders)
prev_glider = Button(label = '<')
next_glider = Button(label = '>')
glider_controlbox = HBox(children = [gliders, prev_glider, next_glider], height=80)
chunkations = Select(title = 'Chunkation', value = 'segment', options = ['segment', '24hr', '30days', '-ALL-'])
chunk_indicator = TextInput(title = 'index', value = '0')
prev_chunk = Button(label = '<')
next_chunk = Button(label = '>')
chunk_ID = PreText(height=80)
chunk_controlbox = HBox(chunkations,
HBox(chunk_indicator, width=25),
prev_chunk, next_chunk,
chunk_ID,
height = 80)
control_box = HBox(glider_controlbox,
chunk_controlbox)
# DATA VARS
deadby_date = ''
depth = ColumnDataSource(dict(x=[],y=[]))
vert_vel = ColumnDataSource(dict(x=[],y=[]))
mbpump = ColumnDataSource(dict(x=[],y=[]))
battpos = ColumnDataSource(dict(x=[],y=[]))
pitch = ColumnDataSource(dict(x=[],y=[]))
mfin = ColumnDataSource(dict(x=[],y=[]))
cfin = ColumnDataSource(dict(x=[],y=[]))
mroll = ColumnDataSource(dict(x=[],y=[]))
mheading = ColumnDataSource(dict(x=[],y=[]))
cheading = ColumnDataSource(dict(x=[],y=[]))
# AXIS setup
colors = COLORS[:]
fig1.y_range.flipped = True
fig1.yaxis.axis_label = 'm_depth (m)'
fig1.extra_y_ranges = {'vert_vel': Range1d(start=-50, end=50),
'dummy': Range1d(start=0, end=100)}
fig1.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'vert_vel',
axis_label = 'vertical velocity (cm/s)'))
fig1.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'dummy',
axis_label = ' '))
fig1.yaxis[1].visible = False
fig1.yaxis[1].axis_line_alpha = 0
fig1.yaxis[1].major_label_text_alpha = 0
fig1.yaxis[1].major_tick_line_alpha = 0
fig1.yaxis[1].minor_tick_line_alpha = 0
fig2.yaxis.axis_label = 'pitch (deg)'
fig2.y_range.start, fig2.y_range.end = -40,40
fig2.extra_y_ranges = {'battpos': Range1d(start=-1, end = 1),
'bpump': Range1d(start=-275, end=275)}
fig2.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'battpos',
axis_label = 'battpos (in)'))
fig2.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'bpump',
axis_label = 'bpump (cc)'))
fig2.yaxis[1].visible = False # necessary for spacing. later gets set to true
fig3.yaxis.axis_label = 'fin/roll (deg)'
fig3.y_range.start, fig3.y_range.end = -30, 30
fig3.extra_y_ranges = {'heading': Range1d(start=0, end=360), #TODO dynamic avg centering
'dummy': Range1d(start=0, end=100)}
fig3.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'heading',
axis_label = 'headings (deg)'))
fig3.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'dummy',
axis_label = ' '))
fig3.yaxis[1].visible = False
fig3.yaxis[1].axis_line_alpha = 0
fig3.yaxis[1].major_label_text_alpha = 0
fig3.yaxis[1].major_tick_line_alpha = 0
fig3.yaxis[1].minor_tick_line_alpha = 0
# PLOT OBJECTS
fig1.line( 'x', 'y', source = depth, legend = 'm_depth', color = 'red')
fig1.circle('x', 'y', source = depth, legend = 'm_depth', color = 'red')
fig1.line( 'x', 'y', source = vert_vel, legend = 'vert_vel', color = 'green', y_range_name = 'vert_vel')
fig1.circle('x', 'y', source = vert_vel, legend = 'vert_vel', color = 'green', y_range_name = 'vert_vel')
fig1.renderers.append(Span(location = 0, dimension = 'width', y_range_name = 'vert_vel',
line_color= 'green', line_dash='dashed', line_width=1))
fig2.line( 'x', 'y', source = pitch, legend = "m_pitch", color = 'indigo')
fig2.circle('x', 'y', source = pitch, legend = "m_pitch", color = 'indigo')
fig2.line( 'x', 'y', source = battpos, legend = 'm_battpos', color = 'magenta', y_range_name = 'battpos')
fig2.circle('x', 'y', source = battpos, legend = 'm_battpos', color = 'magenta', y_range_name = 'battpos')
fig2.line( 'x', 'y', source = mbpump, legend = "m_'bpump'", color = 'blue', y_range_name = 'bpump')
fig2.circle('x', 'y', source = mbpump, legend = "m_'bpump'", color = 'blue', y_range_name = 'bpump')
fig2.renderers.append(Span(location = 0, dimension = 'width',
line_color= 'black', line_dash='dashed', line_width=1))
fig3.line( 'x', 'y', source = mfin, legend = 'm_fin', color = 'cyan')
fig3.circle('x', 'y', source = mfin, legend = 'm_fin', color = 'cyan')
fig3.line( 'x', 'y', source = cfin, legend = 'c_fin', color = 'orange')
fig3.circle('x', 'y', source = cfin, legend = 'c_fin', color = 'orange')
fig3.line( 'x', 'y', source = mroll, legend = 'm_roll', color = 'magenta')
fig3.circle('x', 'y', source = mroll, legend = 'm_roll', color = 'magenta')
fig3.line( 'x', 'y', source = mheading, legend = 'm_heading', color = 'blue', y_range_name = 'heading')
fig3.circle('x', 'y', source = mheading, legend = 'm_heading', color = 'blue', y_range_name = 'heading')
fig3.line( 'x', 'y', source = cheading, legend = 'c_heading', color = 'indigo', y_range_name = 'heading')
fig3.circle('x', 'y', source = cheading, legend = 'c_heading', color = 'indigo', y_range_name = 'heading')
fig3.renderers.append(Span(location = 0, dimension = 'width', y_range_name = 'default',
line_color= 'black', line_dash='dashed', line_width=1))
# CALLBACK FUNCS
def update_data(attrib,old,new):
g = gliders.value
chnk = chunkations.value
chindex = abs(int(chunk_indicator.value))
depth.data = dict(x=[],y=[])
vert_vel.data = dict(x=[],y=[])
mbpump.data = dict(x=[],y=[])
battpos.data = dict(x=[],y=[])
pitch.data = dict(x=[],y=[])
mfin.data = dict(x=[],y=[])
cfin.data = dict(x=[],y=[])
mroll.data = dict(x=[],y=[])
mheading.data = dict(x=[],y=[])
cheading.data = dict(x=[],y=[])
depth.data,startend = load_sensor(g, 'm_depth', chnk, chindex)
if chnk == 'segment':
xbd = startend[2]
chunk_ID.text = '{} {} \n{} ({}) \nSTART: {} \nEND: {}'.format(g, xbd['mission'],
xbd['onboard_filename'], xbd['the8x3_filename'],
e2ts(xbd['start']), e2ts(xbd['end']))
if len(set(depth.data['x']))<=1 and attrib == 'chunk':
if old > new:
next_chunk.clicks += 1
else:
prev_chunk.clicks += 1
return
elif len(set(depth.data['x']))<=1 and chunk_indicator.value == 0:
chunk_indicator.value = 1
elif chnk in ['24hr', '30days']:
chunk_ID.text = '{} \nSTART: {} \nEND: {}'.format(g, e2ts(startend[0]), e2ts(startend[1]))
elif chnk == '-ALL-':
chunk_ID.text = '{} \nSTART: {} \nEND: {}'.format(g,e2ts(depth.data['x'][0] /1000),
e2ts(depth.data['x'][-1]/1000))
vert_vel.data = calc_vert_vel(depth.data)
mbpump.data,_ = load_sensor(g, 'm_de_oil_vol', chnk, chindex)
if len(mbpump.data['x']) > 1:
#for yax in fig2.select('mbpump'):
# yax.legend = 'm_de_oil_vol'
pass
else:
mbpump.data,_ = load_sensor(g, 'm_ballast_pumped', chnk, chindex)
#for yax in fig2.select('mbpump'):
# yax.legend = 'm_ballast_pumped'
battpos.data,_ = load_sensor(g, 'm_battpos', chnk, chindex)
pitch.data,_ = load_sensor(g, 'm_pitch', chnk, chindex)
pitch.data['y'] = [math.degrees(y) for y in pitch.data['y']]
mfin.data,_ = load_sensor(g, 'm_fin', chnk, chindex)
cfin.data,_ = load_sensor(g, 'c_fin', chnk, chindex)
mroll.data,_ = load_sensor(g, 'm_roll', chnk, chindex)
mheading.data,_ = load_sensor(g, 'm_heading', chnk, chindex)
cheading.data,_ = load_sensor(g, 'c_heading', chnk, chindex)
mfin.data['y'] = [math.degrees(y) for y in mfin.data['y']]
cfin.data['y'] = [math.degrees(y) for y in cfin.data['y']]
mheading.data['y'] = [math.degrees(y) for y in mheading.data['y']]
cheading.data['y'] = [math.degrees(y) for y in cheading.data['y']]
mroll.data['y'] = [math.degrees(y) for y in mroll.data['y']]
fig1.yaxis[1].visible = True
fig2.yaxis[1].visible = True
fig3.yaxis[1].visible = True
#GLIDER SELECTS
def glider_buttons(increment):
ops = gliders.options
new_index = ops.index(gliders.value) + increment
if new_index >= len(ops):
new_index = 0
elif new_index < 0:
new_index = len(ops)-1
gliders.value = ops[new_index]
chunkation_update(None, None, None) #reset chunk indicator and clicks
def next_glider_func():
glider_buttons(1)
def prev_glider_func():
glider_buttons(-1)
def update_glider(attrib,old,new):
chunk_indicator.value = '0'
#update_data(None,None,None)
gliders.on_change('value', update_glider)
next_glider.on_click(next_glider_func)
prev_glider.on_click(prev_glider_func)
#CHUNK SELECTS
def chunkation_update(attrib,old,new):
chunk_indicator.value = '0'
prev_chunk.clicks = 0
next_chunk.clicks = 0
update_data(None,None,None)
if new == '-ALL-':
chunk_indicator.value = '-'
def chunk_func():
chunkdiff = prev_chunk.clicks - next_chunk.clicks
if chunkdiff < 0:
prev_chunk.clicks = 0
next_chunk.clicks = 0
chunkdiff = 0
print (chunkdiff)
chunk_indicator.value = str(chunkdiff)
def chunk_indicator_update(attrib,old,new):
try:
if abs(int(old)-int(new))>1: #manual update, triggers new non-manual indicator update, ie else clause below
prev_chunk.clicks = int(new)
next_chunk.clicks = 0
else:
update_data('chunk',int(old),int(new))
print("UPDATE", old, new)
except Exception as e:
print(type(e),e, old, new)
chunkations.on_change('value', chunkation_update)
chunk_indicator.on_change('value', chunk_indicator_update)
next_chunk.on_click(chunk_func)
prev_chunk.on_click(chunk_func)
update_data(None,None,None)
return vplot(control_box, figs)
data.stream(
dict(time=new_price["delayedPriceTime"],
display_time=new_price["processedTime"],
price=new_price["delayedPrice"]), 10000)
return
hover = HoverTool(tooltips=[("Time",
"@display_time"), ("IEX Real-Time Price",
"@price")])
price_plot = figure(plot_width=800,
plot_height=400,
x_axis_type='datetime',
tools=[hover],
title="Real-Time Price Plot")
price_plot.line(source=data, x='time', y='price')
price_plot.xaxis.axis_label = "Time"
price_plot.yaxis.axis_label = "IEX Real-Time Price"
price_plot.title.text = "IEX Real Time Price: " + TICKER
ticker_textbox = TextInput(placeholder="Ticker")
update = Button(label="Update")
update.on_click(update_ticker)
inputs = widgetbox([ticker_textbox, update], width=200)
curdoc().add_root(row(inputs, price_plot, width=1600))
curdoc().title = "Real-Time Price Plot from IEX"
curdoc().add_periodic_callback(update_price, 1000)
class Query:
def __init__(self, sources, categories, dvhs, rad_bio, roi_viewer, time_series,
correlation, regression, mlc_analyzer, custom_title, data_tables):
self.sources = sources
self.selector_categories = categories.selector
self.range_categories = categories.range
self.correlation_variables = categories.correlation_variables
self.dvhs = dvhs
self.rad_bio = rad_bio
self.roi_viewer = roi_viewer
self.time_series = time_series
self.correlation = correlation
self.regression = regression
self.mlc_analyzer = mlc_analyzer
self.uids = {n: [] for n in GROUP_LABELS}
self.allow_source_update = True
self.current_dvh = []
self.anon_id_map = []
self.colors = itertools.cycle(palette)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Selection Filter UI objects
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
category_options = list(self.selector_categories)
category_options.sort()
# Add Current row to source
self.add_selector_row_button = Button(label="Add Selection Filter", button_type="primary", width=200)
self.add_selector_row_button.on_click(self.add_selector_row)
# Row
self.selector_row = Select(value='1', options=['1'], width=50, title="Row")
self.selector_row.on_change('value', self.selector_row_ticker)
# Category 1
self.select_category1 = Select(value="ROI Institutional Category", options=category_options, width=300,
title="Category 1")
self.select_category1.on_change('value', self.select_category1_ticker)
# Category 2
cat_2_sql_table = self.selector_categories[self.select_category1.value]['table']
cat_2_var_name = self.selector_categories[self.select_category1.value]['var_name']
self.category2_values = DVH_SQL().get_unique_values(cat_2_sql_table, cat_2_var_name)
self.select_category2 = Select(value=self.category2_values[0], options=self.category2_values, width=300,
title="Category 2")
self.select_category2.on_change('value', self.select_category2_ticker)
# Misc
self.delete_selector_row_button = Button(label="Delete", button_type="warning", width=100)
self.delete_selector_row_button.on_click(self.delete_selector_row)
self.group_selector = CheckboxButtonGroup(labels=["Group 1", "Group 2"], active=[0], width=180)
self.group_selector.on_change('active', self.ensure_selector_group_is_assigned)
self.selector_not_operator_checkbox = CheckboxGroup(labels=['Not'], active=[])
self.selector_not_operator_checkbox.on_change('active', self.selector_not_operator_ticker)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Range Filter UI objects
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
category_options = list(self.range_categories)
category_options.sort()
# Add Current row to source
self.add_range_row_button = Button(label="Add Range Filter", button_type="primary", width=200)
self.add_range_row_button.on_click(self.add_range_row)
# Row
self.range_row = Select(value='', options=[''], width=50, title="Row")
self.range_row.on_change('value', self.range_row_ticker)
# Category
self.select_category = Select(value=options.SELECT_CATEGORY_DEFAULT, options=category_options, width=240, title="Category")
self.select_category.on_change('value', self.select_category_ticker)
# Min and max
self.text_min = TextInput(value='', title='Min: ', width=150)
self.text_min.on_change('value', self.min_text_ticker)
self.text_max = TextInput(value='', title='Max: ', width=150)
self.text_max.on_change('value', self.max_text_ticker)
# Misc
self.delete_range_row_button = Button(label="Delete", button_type="warning", width=100)
self.delete_range_row_button.on_click(self.delete_range_row)
self.group_range = CheckboxButtonGroup(labels=["Group 1", "Group 2"], active=[0], width=180)
self.group_range.on_change('active', self.ensure_range_group_is_assigned)
self.range_not_operator_checkbox = CheckboxGroup(labels=['Not'], active=[])
self.range_not_operator_checkbox.on_change('active', self.range_not_operator_ticker)
self.query_button = Button(label="Query", button_type="success", width=100)
self.query_button.on_click(self.update_data)
# define Download button and call download.js on click
menu = [("All Data", "all"), ("Lite", "lite"), ("Only DVHs", "dvhs"), ("Anonymized DVHs", "anon_dvhs")]
self.download_dropdown = Dropdown(label="Download", button_type="default", menu=menu, width=100)
self.download_dropdown.callback = CustomJS(args=dict(source=sources.dvhs,
source_rxs=sources.rxs,
source_plans=sources.plans,
source_beams=sources.beams),
code=open(join(dirname(dirname(__file__)), "download.js")).read())
self.layout = column(Div(text="<b>DVH Analytics v%s</b>" % options.VERSION),
row(custom_title['1']['query'], Spacer(width=50), custom_title['2']['query'],
Spacer(width=50), self.query_button, Spacer(width=50), self.download_dropdown),
Div(text="<b>Query by Categorical Data</b>", width=1000),
self.add_selector_row_button,
row(self.selector_row, Spacer(width=10), self.select_category1, self.select_category2,
self.group_selector, self.delete_selector_row_button, Spacer(width=10),
self.selector_not_operator_checkbox),
data_tables.selection_filter,
Div(text="<hr>", width=1050),
Div(text="<b>Query by Numerical Data</b>", width=1000),
self.add_range_row_button,
row(self.range_row, Spacer(width=10), self.select_category, self.text_min,
Spacer(width=30),
self.text_max, Spacer(width=30), self.group_range,
self.delete_range_row_button, Spacer(width=10), self.range_not_operator_checkbox),
data_tables.range_filter)
def get_query(self, group=None):
if group:
if group == 1:
active_groups = [1]
elif group == 2:
active_groups = [2]
else:
active_groups = [1, 2]
# Used to accumulate lists of query strings for each table
# Will assume each item in list is complete query for that SQL column
queries = {'Plans': [], 'Rxs': [], 'Beams': [], 'DVHs': []}
# Used to group queries by variable, will combine all queries of same variable with an OR operator
# e.g., queries_by_sql_column['Plans'][key] = list of strings, where key is sql column
queries_by_sql_column = {'Plans': {}, 'Rxs': {}, 'Beams': {}, 'DVHs': {}}
for active_group in active_groups:
# Accumulate categorical query strings
data = self.sources.selectors.data
for r in data['row']:
r = int(r)
if data['group'][r - 1] in {active_group, 3}:
var_name = self.selector_categories[data['category1'][r - 1]]['var_name']
table = self.selector_categories[data['category1'][r - 1]]['table']
value = data['category2'][r - 1]
if data['not_status'][r - 1]:
operator = "!="
else:
operator = "="
query_str = "%s %s '%s'" % (var_name, operator, value)
# Append query_str in query_by_sql_column
if var_name not in queries_by_sql_column[table].keys():
queries_by_sql_column[table][var_name] = []
queries_by_sql_column[table][var_name].append(query_str)
# Accumulate numerical query strings
data = self.sources.ranges.data
for r in data['row']:
r = int(r)
if data['group'][r - 1] in {active_group, 3}:
var_name = self.range_categories[data['category'][r - 1]]['var_name']
table = self.range_categories[data['category'][r - 1]]['table']
value_low, value_high = data['min'][r - 1], data['max'][r - 1]
if data['category'][r - 1] != 'Simulation Date':
value_low, value_high = float(value_low), float(value_high)
# Modify value_low and value_high so SQL interprets values as dates, if applicable
if var_name in {'sim_study_date', 'birth_date'}:
value_low = "'%s'" % value_low
value_high = "'%s'" % value_high
if data['not_status'][r - 1]:
query_str = var_name + " NOT BETWEEN " + str(value_low) + " AND " + str(value_high)
else:
query_str = var_name + " BETWEEN " + str(value_low) + " AND " + str(value_high)
# Append query_str in query_by_sql_column
if var_name not in queries_by_sql_column[table]:
queries_by_sql_column[table][var_name] = []
queries_by_sql_column[table][var_name].append(query_str)
for table in queries:
temp_str = []
for v in queries_by_sql_column[table].keys():
# collect all constraints for a given sql column into one list
q_by_sql_col = [q for q in queries_by_sql_column[table][v]]
# combine all constraints for a given sql column with 'or' operators
temp_str.append("(%s)" % ' OR '.join(q_by_sql_col))
queries[table] = ' AND '.join(temp_str)
print(str(datetime.now()), '%s = %s' % (table, queries[table]), sep=' ')
# Get a list of UIDs that fit the plan, rx, and beam query criteria. DVH query criteria will not alter the
# list of UIDs, therefore dvh_query is not needed to get the UID list
print(str(datetime.now()), 'getting uids', sep=' ')
uids = get_study_instance_uids(plans=queries['Plans'], rxs=queries['Rxs'], beams=queries['Beams'])['union']
# uids: a unique list of all uids that satisfy the criteria
# queries['DVHs']: the dvh query string for SQL
return uids, queries['DVHs']
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Functions for Querying by categorical data
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
def update_select_category2_values(self):
new = self.select_category1.value
table_new = self.selector_categories[new]['table']
var_name_new = self.selector_categories[new]['var_name']
new_options = DVH_SQL().get_unique_values(table_new, var_name_new)
self.select_category2.options = new_options
self.select_category2.value = new_options[0]
def ensure_selector_group_is_assigned(self, attr, old, new):
if not self.group_selector.active:
self.group_selector.active = [-old[0] + 1]
self.update_selector_source()
def update_selector_source(self):
if self.selector_row.value:
r = int(self.selector_row.value) - 1
group = sum([i + 1 for i in self.group_selector.active])
group_labels = ['1', '2', '1 & 2']
group_label = group_labels[group - 1]
not_status = ['', 'Not'][len(self.selector_not_operator_checkbox.active)]
patch = {'category1': [(r, self.select_category1.value)], 'category2': [(r, self.select_category2.value)],
'group': [(r, group)], 'group_label': [(r, group_label)], 'not_status': [(r, not_status)]}
self.sources.selectors.patch(patch)
def add_selector_row(self):
if self.sources.selectors.data['row']:
temp = self.sources.selectors.data
for key in list(temp):
temp[key].append('')
temp['row'][-1] = len(temp['row'])
self.sources.selectors.data = temp
new_options = [str(x + 1) for x in range(len(temp['row']))]
self.selector_row.options = new_options
self.selector_row.value = new_options[-1]
self.select_category1.value = options.SELECT_CATEGORY1_DEFAULT
self.select_category2.value = self.select_category2.options[0]
self.selector_not_operator_checkbox.active = []
else:
self.selector_row.options = ['1']
self.selector_row.value = '1'
self.sources.selectors.data = dict(row=[1], category1=[''], category2=[''],
group=[], group_label=[''], not_status=[''])
self.update_selector_source()
clear_source_selection(self.sources, 'selectors')
def select_category1_ticker(self, attr, old, new):
self.update_select_category2_values()
self.update_selector_source()
def select_category2_ticker(self, attr, old, new):
self.update_selector_source()
def selector_not_operator_ticker(self, attr, old, new):
self.update_selector_source()
def selector_row_ticker(self, attr, old, new):
if self.sources.selectors.data['category1'] and self.sources.selectors.data['category1'][-1]:
r = int(self.selector_row.value) - 1
category1 = self.sources.selectors.data['category1'][r]
category2 = self.sources.selectors.data['category2'][r]
group = self.sources.selectors.data['group'][r]
not_status = self.sources.selectors.data['not_status'][r]
self.select_category1.value = category1
self.select_category2.value = category2
self.group_selector.active = [[0], [1], [0, 1]][group - 1]
if not_status:
self.selector_not_operator_checkbox.active = [0]
else:
self.selector_not_operator_checkbox.active = []
def update_selector_row_on_selection(self, attr, old, new):
if new:
self.selector_row.value = self.selector_row.options[min(new)]
def delete_selector_row(self):
if self.selector_row.value:
new_selectors_source = self.sources.selectors.data
index_to_delete = int(self.selector_row.value) - 1
new_source_length = len(self.sources.selectors.data['category1']) - 1
if new_source_length == 0:
clear_source_data(self.sources, 'selector')
self.selector_row.options = ['']
self.selector_row.value = ''
self.group_selector.active = [0]
self.selector_not_operator_checkbox.active = []
self.select_category1.value = options.SELECT_CATEGORY1_DEFAULT
self.select_category2.value = self.select_category2.options[0]
else:
for key in list(new_selectors_source):
new_selectors_source[key].pop(index_to_delete)
for i in range(index_to_delete, new_source_length):
new_selectors_source['row'][i] -= 1
self.selector_row.options = [str(x + 1) for x in range(new_source_length)]
if self.selector_row.value not in self.selector_row.options:
self.selector_row.value = self.selector_row.options[-1]
self.sources.selectors.data = new_selectors_source
clear_source_selection(self.sources, 'selectors')
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Functions for Querying by numerical data
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
def add_range_row(self):
if self.sources.ranges.data['row']:
temp = self.sources.ranges.data
for key in list(temp):
temp[key].append('')
temp['row'][-1] = len(temp['row'])
self.sources.ranges.data = temp
new_options = [str(x + 1) for x in range(len(temp['row']))]
self.range_row.options = new_options
self.range_row.value = new_options[-1]
self.select_category.value = options.SELECT_CATEGORY_DEFAULT
self.group_range.active = [0]
self.range_not_operator_checkbox.active = []
else:
self.range_row.options = ['1']
self.range_row.value = '1'
self.sources.ranges.data = dict(row=['1'], category=[''], min=[''], max=[''], min_display=[''],
max_display=[''], group=[''], group_label=[''], not_status=[''])
self.update_range_titles(reset_values=True)
self.update_range_source()
clear_source_selection(self.sources, 'ranges')
def update_range_source(self):
if self.range_row.value:
table = self.range_categories[self.select_category.value]['table']
var_name = self.range_categories[self.select_category.value]['var_name']
r = int(self.range_row.value) - 1
group = sum([i + 1 for i in self.group_range.active]) # a result of 3 means group 1 & 2
group_labels = ['1', '2', '1 & 2']
group_label = group_labels[group - 1]
not_status = ['', 'Not'][len(self.range_not_operator_checkbox.active)]
if self.select_category.value == 'Simulation Date':
min_value = str(parse(self.text_min.value).date())
min_display = min_value
max_value = str(parse(self.text_max.value).date())
max_display = max_value
else:
try:
min_value = float(self.text_min.value)
except ValueError:
try:
min_value = float(DVH_SQL().get_min_value(table, var_name))
except TypeError:
min_value = ''
try:
max_value = float(self.text_max.value)
except ValueError:
try:
max_value = float(DVH_SQL().get_max_value(table, var_name))
except TypeError:
max_value = ''
if min_value or min_value == 0.:
min_display = "%s %s" % (str(min_value), self.range_categories[self.select_category.value]['units'])
else:
min_display = 'None'
if max_value or max_value == 0.:
max_display = "%s %s" % (str(max_value), self.range_categories[self.select_category.value]['units'])
else:
max_display = 'None'
patch = {'category': [(r, self.select_category.value)], 'min': [(r, min_value)], 'max': [(r, max_value)],
'min_display': [(r, min_display)], 'max_display': [(r, max_display)],
'group': [(r, group)], 'group_label': [(r, group_label)], 'not_status': [(r, not_status)]}
self.sources.ranges.patch(patch)
self.group_range.active = [[0], [1], [0, 1]][group - 1]
self.text_min.value = str(min_value)
self.text_max.value = str(max_value)
def update_range_titles(self, reset_values=False):
table = self.range_categories[self.select_category.value]['table']
var_name = self.range_categories[self.select_category.value]['var_name']
min_value = DVH_SQL().get_min_value(table, var_name)
self.text_min.title = 'Min: ' + str(min_value) + ' ' + self.range_categories[self.select_category.value]['units']
max_value = DVH_SQL().get_max_value(table, var_name)
self.text_max.title = 'Max: ' + str(max_value) + ' ' + self.range_categories[self.select_category.value]['units']
if reset_values:
self.text_min.value = str(min_value)
self.text_max.value = str(max_value)
def range_row_ticker(self, attr, old, new):
if self.sources.ranges.data['category'] and self.sources.ranges.data['category'][-1]:
r = int(new) - 1
category = self.sources.ranges.data['category'][r]
min_new = self.sources.ranges.data['min'][r]
max_new = self.sources.ranges.data['max'][r]
group = self.sources.ranges.data['group'][r]
not_status = self.sources.ranges.data['not_status'][r]
self.allow_source_update = False
self.select_category.value = category
self.text_min.value = str(min_new)
self.text_max.value = str(max_new)
self.update_range_titles()
self.group_range.active = [[0], [1], [0, 1]][group - 1]
self.allow_source_update = True
if not_status:
self.range_not_operator_checkbox.active = [0]
else:
self.range_not_operator_checkbox.active = []
def select_category_ticker(self, attr, old, new):
if self.allow_source_update:
self.update_range_titles(reset_values=True)
self.update_range_source()
def min_text_ticker(self, attr, old, new):
if self.allow_source_update:
self.update_range_source()
def max_text_ticker(self, attr, old, new):
if self.allow_source_update:
self.update_range_source()
def range_not_operator_ticker(self, attr, old, new):
if self.allow_source_update:
self.update_range_source()
def delete_range_row(self):
if self.range_row.value:
new_range_source = self.sources.ranges.data
index_to_delete = int(self.range_row.value) - 1
new_source_length = len(self.sources.ranges.data['category']) - 1
if new_source_length == 0:
clear_source_data(self.sources, 'ranges')
self.range_row.options = ['']
self.range_row.value = ''
self.group_range.active = [0]
self.range_not_operator_checkbox.active = []
self.select_category.value = options.SELECT_CATEGORY_DEFAULT
self.text_min.value = ''
self.text_max.value = ''
else:
for key in list(new_range_source):
new_range_source[key].pop(index_to_delete)
for i in range(index_to_delete, new_source_length):
new_range_source['row'][i] -= 1
self.range_row.options = [str(x + 1) for x in range(new_source_length)]
if self.range_row.value not in self.range_row.options:
self.range_row.value = self.range_row.options[-1]
self.sources.ranges.data = new_range_source
clear_source_selection(self.sources, 'ranges')
def ensure_range_group_is_assigned(self, attr, old, new):
if not self.group_range.active:
self.group_range.active = [-old[0] + 1]
self.update_range_source()
def update_range_row_on_selection(self, attr, old, new):
if new:
self.range_row.value = self.range_row.options[min(new)]
# main update function
def update_data(self):
global BAD_UID
BAD_UID = {n: [] for n in GROUP_LABELS}
old_update_button_label = self.query_button.label
old_update_button_type = self.query_button.button_type
self.query_button.label = 'Updating...'
self.query_button.button_type = 'warning'
print(str(datetime.now()), 'Constructing query for complete dataset', sep=' ')
uids, dvh_query_str = self.get_query()
print(str(datetime.now()), 'getting dvh data', sep=' ')
self.current_dvh = DVH(uid=uids, dvh_condition=dvh_query_str)
if self.current_dvh.count:
print(str(datetime.now()), 'initializing source data ', self.current_dvh.query, sep=' ')
self.time_series.update_current_dvh_group(self.update_dvh_data(self.current_dvh))
if not options.LITE_VIEW:
print(str(datetime.now()), 'updating correlation data')
self.correlation.update_data(self.correlation_variables)
print(str(datetime.now()), 'correlation data updated')
self.dvhs.update_source_endpoint_calcs()
if not options.LITE_VIEW:
self.dvhs.calculate_review_dvh()
self.rad_bio.initialize()
self.time_series.y_axis.value = ''
self.roi_viewer.update_mrn()
try:
self.mlc_analyzer.update_mrn()
except:
pass
else:
print(str(datetime.now()), 'empty dataset returned', sep=' ')
self.query_button.label = 'No Data'
self.query_button.button_type = 'danger'
time.sleep(2.5)
self.query_button.label = old_update_button_label
self.query_button.button_type = old_update_button_type
# updates beam ColumnSourceData for a given list of uids
def update_beam_data(self, uids):
cond_str = "study_instance_uid in ('" + "', '".join(uids) + "')"
beam_data = QuerySQL('Beams', cond_str)
groups = self.get_group_list(beam_data.study_instance_uid)
anon_id = [self.anon_id_map[beam_data.mrn[i]] for i in range(len(beam_data.mrn))]
attributes = ['mrn', 'beam_dose', 'beam_energy_min', 'beam_energy_max', 'beam_mu', 'beam_mu_per_deg',
'beam_mu_per_cp', 'beam_name', 'beam_number', 'beam_type', 'scan_mode', 'scan_spot_count',
'control_point_count', 'fx_count', 'fx_grp_beam_count', 'fx_grp_number', 'gantry_start',
'gantry_end', 'gantry_rot_dir', 'gantry_range', 'gantry_min', 'gantry_max', 'collimator_start',
'collimator_end', 'collimator_rot_dir', 'collimator_range', 'collimator_min', 'collimator_max',
'couch_start', 'couch_end', 'couch_rot_dir', 'couch_range', 'couch_min', 'couch_max',
'radiation_type', 'ssd', 'treatment_machine']
for i in ['min', 'mean', 'median', 'max']:
for j in ['area', 'complexity', 'cp_mu', 'x_perim', 'y_perim']:
attributes.append('%s_%s' % (j, i))
data = {attr: getattr(beam_data, attr) for attr in attributes}
data['anon_id'] = anon_id
data['group'] = groups
data['uid'] = beam_data.study_instance_uid
self.sources.beams.data = data
# updates plan ColumnSourceData for a given list of uids
def update_plan_data(self, uids):
cond_str = "study_instance_uid in ('" + "', '".join(uids) + "')"
plan_data = QuerySQL('Plans', cond_str)
# Determine Groups
groups = self.get_group_list(plan_data.study_instance_uid)
anon_id = [self.anon_id_map[plan_data.mrn[i]] for i in range(len(plan_data.mrn))]
attributes = ['mrn', 'age', 'birth_date', 'dose_grid_res', 'fxs', 'patient_orientation', 'patient_sex',
'physician', 'rx_dose', 'sim_study_date', 'total_mu', 'tx_modality', 'tx_site',
'heterogeneity_correction', 'complexity']
data = {attr: getattr(plan_data, attr) for attr in attributes}
data['anon_id'] = anon_id
data['group'] = groups
data['uid'] = plan_data.study_instance_uid
self.sources.plans.data = data
# updates rx ColumnSourceData for a given list of uids
def update_rx_data(self, uids):
cond_str = "study_instance_uid in ('" + "', '".join(uids) + "')"
rx_data = QuerySQL('Rxs', cond_str)
groups = self.get_group_list(rx_data.study_instance_uid)
anon_id = [self.anon_id_map[rx_data.mrn[i]] for i in range(len(rx_data.mrn))]
attributes = ['mrn', 'plan_name', 'fx_dose', 'rx_percent', 'fxs', 'rx_dose', 'fx_grp_count', 'fx_grp_name',
'fx_grp_number', 'normalization_method', 'normalization_object']
data = {attr: getattr(rx_data, attr) for attr in attributes}
data['anon_id'] = anon_id
data['group'] = groups
data['uid'] = rx_data.study_instance_uid
self.sources.rxs.data = data
def get_group_list(self, uids):
groups = []
for r in range(len(uids)):
if uids[r] in self.uids['1']:
if uids[r] in self.uids['2']:
groups.append('Group 1 & 2')
else:
groups.append('Group 1')
else:
groups.append('Group 2')
return groups
def update_dvh_data(self, dvh):
dvh_group_1, dvh_group_2 = [], []
group_1_constraint_count, group_2_constraint_count = group_constraint_count(self.sources)
if group_1_constraint_count and group_2_constraint_count:
extra_rows = 12
elif group_1_constraint_count or group_2_constraint_count:
extra_rows = 6
else:
extra_rows = 0
print(str(datetime.now()), 'updating dvh data', sep=' ')
line_colors = [color for j, color in itertools.izip(range(dvh.count + extra_rows), self.colors)]
x_axis = np.round(np.add(np.linspace(0, dvh.bin_count, dvh.bin_count) / 100., 0.005), 3)
print(str(datetime.now()), 'beginning stat calcs', sep=' ')
if self.dvhs.radio_group_dose.active == 1:
stat_dose_scale = 'relative'
x_axis_stat = dvh.get_resampled_x_axis()
else:
stat_dose_scale = 'absolute'
x_axis_stat = x_axis
if self.dvhs.radio_group_volume.active == 0:
stat_volume_scale = 'absolute'
else:
stat_volume_scale = 'relative'
print(str(datetime.now()), 'calculating patches', sep=' ')
if group_1_constraint_count == 0:
self.uids['1'] = []
clear_source_data(self.sources, 'patch_1')
clear_source_data(self.sources, 'stats_1')
else:
print(str(datetime.now()), 'Constructing Group 1 query', sep=' ')
self.uids['1'], dvh_query_str = self.get_query(group=1)
dvh_group_1 = DVH(uid=self.uids['1'], dvh_condition=dvh_query_str)
self.uids['1'] = dvh_group_1.study_instance_uid
stat_dvhs_1 = dvh_group_1.get_standard_stat_dvh(dose_scale=stat_dose_scale,
volume_scale=stat_volume_scale)
if self.dvhs.radio_group_dose.active == 1:
x_axis_1 = dvh_group_1.get_resampled_x_axis()
else:
x_axis_1 = np.add(np.linspace(0, dvh_group_1.bin_count, dvh_group_1.bin_count) / 100., 0.005)
self.sources.patch_1.data = {'x_patch': np.append(x_axis_1, x_axis_1[::-1]).tolist(),
'y_patch': np.append(stat_dvhs_1['q3'], stat_dvhs_1['q1'][::-1]).tolist()}
self.sources.stats_1.data = {'x': x_axis_1.tolist(),
'min': stat_dvhs_1['min'].tolist(),
'q1': stat_dvhs_1['q1'].tolist(),
'mean': stat_dvhs_1['mean'].tolist(),
'median': stat_dvhs_1['median'].tolist(),
'q3': stat_dvhs_1['q3'].tolist(),
'max': stat_dvhs_1['max'].tolist()}
if group_2_constraint_count == 0:
self.uids['2'] = []
clear_source_data(self.sources, 'patch_2')
clear_source_data(self.sources, 'stats_2')
else:
print(str(datetime.now()), 'Constructing Group 2 query', sep=' ')
self.uids['2'], dvh_query_str = self.get_query(group=2)
dvh_group_2 = DVH(uid=self.uids['2'], dvh_condition=dvh_query_str)
self.uids['2'] = dvh_group_2.study_instance_uid
stat_dvhs_2 = dvh_group_2.get_standard_stat_dvh(dose_scale=stat_dose_scale,
volume_scale=stat_volume_scale)
if self.dvhs.radio_group_dose.active == 1:
x_axis_2 = dvh_group_2.get_resampled_x_axis()
else:
x_axis_2 = np.add(np.linspace(0, dvh_group_2.bin_count, dvh_group_2.bin_count) / 100., 0.005)
self.sources.patch_2.data = {'x_patch': np.append(x_axis_2, x_axis_2[::-1]).tolist(),
'y_patch': np.append(stat_dvhs_2['q3'], stat_dvhs_2['q1'][::-1]).tolist()}
self.sources.stats_2.data = {'x': x_axis_2.tolist(),
'min': stat_dvhs_2['min'].tolist(),
'q1': stat_dvhs_2['q1'].tolist(),
'mean': stat_dvhs_2['mean'].tolist(),
'median': stat_dvhs_2['median'].tolist(),
'q3': stat_dvhs_2['q3'].tolist(),
'max': stat_dvhs_2['max'].tolist()}
print(str(datetime.now()), 'patches calculated', sep=' ')
if self.dvhs.radio_group_dose.active == 0:
x_scale = ['Gy'] * (dvh.count + extra_rows + 1)
self.dvhs.plot.xaxis.axis_label = "Dose (Gy)"
else:
x_scale = ['%RxDose'] * (dvh.count + extra_rows + 1)
self.dvhs.plot.xaxis.axis_label = "Relative Dose (to Rx)"
if self.dvhs.radio_group_volume.active == 0:
y_scale = ['cm^3'] * (dvh.count + extra_rows + 1)
self.dvhs.plot.yaxis.axis_label = "Absolute Volume (cc)"
else:
y_scale = ['%Vol'] * (dvh.count + extra_rows + 1)
self.dvhs.plot.yaxis.axis_label = "Relative Volume"
# new_endpoint_columns = [''] * (dvh.count + extra_rows + 1)
x_data, y_data = [], []
for n in range(dvh.count):
if self.dvhs.radio_group_dose.active == 0:
x_data.append(x_axis.tolist())
else:
x_data.append(np.divide(x_axis, dvh.rx_dose[n]).tolist())
if self.dvhs.radio_group_volume.active == 0:
y_data.append(np.multiply(dvh.dvh[:, n], dvh.volume[n]).tolist())
else:
y_data.append(dvh.dvh[:, n].tolist())
y_names = ['Max', 'Q3', 'Median', 'Mean', 'Q1', 'Min']
# Determine Population group (blue (1) or red (2))
dvh_groups = []
for r in range(len(dvh.study_instance_uid)):
current_uid = dvh.study_instance_uid[r]
current_roi = dvh.roi_name[r]
if dvh_group_1:
for r1 in range(len(dvh_group_1.study_instance_uid)):
if dvh_group_1.study_instance_uid[r1] == current_uid and dvh_group_1.roi_name[r1] == current_roi:
dvh_groups.append('Group 1')
if dvh_group_2:
for r2 in range(len(dvh_group_2.study_instance_uid)):
if dvh_group_2.study_instance_uid[r2] == current_uid and dvh_group_2.roi_name[r2] == current_roi:
if len(dvh_groups) == r + 1:
dvh_groups[r] = 'Group 1 & 2'
else:
dvh_groups.append('Group 2')
if len(dvh_groups) < r + 1:
dvh_groups.append('error')
dvh_groups.insert(0, 'Review')
for n in range(6):
if group_1_constraint_count > 0:
dvh.mrn.append(y_names[n])
dvh.roi_name.append('N/A')
x_data.append(x_axis_stat.tolist())
current = stat_dvhs_1[y_names[n].lower()].tolist()
y_data.append(current)
dvh_groups.append('Group 1')
if group_2_constraint_count > 0:
dvh.mrn.append(y_names[n])
dvh.roi_name.append('N/A')
x_data.append(x_axis_stat.tolist())
current = stat_dvhs_2[y_names[n].lower()].tolist()
y_data.append(current)
dvh_groups.append('Group 2')
# Adjust dvh object to include stats data
attributes = ['rx_dose', 'volume', 'surface_area', 'min_dose', 'mean_dose', 'max_dose', 'dist_to_ptv_min',
'dist_to_ptv_median', 'dist_to_ptv_mean', 'dist_to_ptv_max', 'dist_to_ptv_centroids',
'ptv_overlap', 'cross_section_max', 'cross_section_median', 'spread_x', 'spread_y',
'spread_z', 'toxicity_grade']
if extra_rows > 0:
dvh.study_instance_uid.extend(['N/A'] * extra_rows)
dvh.institutional_roi.extend(['N/A'] * extra_rows)
dvh.physician_roi.extend(['N/A'] * extra_rows)
dvh.roi_type.extend(['Stat'] * extra_rows)
if group_1_constraint_count > 0:
for attr in attributes:
getattr(dvh, attr).extend(calc_stats(getattr(dvh_group_1, attr)))
if group_2_constraint_count > 0:
for attr in attributes:
getattr(dvh, attr).extend(calc_stats(getattr(dvh_group_2, attr)))
# Adjust dvh object for review dvh
dvh.dvh = np.insert(dvh.dvh, 0, 0, 1)
dvh.count += 1
dvh.mrn.insert(0, self.dvhs.select_reviewed_mrn.value)
dvh.study_instance_uid.insert(0, '')
dvh.institutional_roi.insert(0, '')
dvh.physician_roi.insert(0, '')
dvh.roi_name.insert(0, self.dvhs.select_reviewed_dvh.value)
dvh.roi_type.insert(0, 'Review')
dvh.rx_dose.insert(0, 0)
dvh.volume.insert(0, 0)
dvh.surface_area.insert(0, '')
dvh.min_dose.insert(0, '')
dvh.mean_dose.insert(0, '')
dvh.max_dose.insert(0, '')
dvh.dist_to_ptv_min.insert(0, 'N/A')
dvh.dist_to_ptv_mean.insert(0, 'N/A')
dvh.dist_to_ptv_median.insert(0, 'N/A')
dvh.dist_to_ptv_max.insert(0, 'N/A')
dvh.dist_to_ptv_centroids.insert(0, 'N/A')
dvh.ptv_overlap.insert(0, 'N/A')
dvh.cross_section_max.insert(0, 'N/A')
dvh.cross_section_median.insert(0, 'N/A')
dvh.spread_x.insert(0, 'N/A')
dvh.spread_y.insert(0, 'N/A')
dvh.spread_z.insert(0, 'N/A')
dvh.toxicity_grade.insert(0, -1)
line_colors.insert(0, options.REVIEW_DVH_COLOR)
x_data.insert(0, [0])
y_data.insert(0, [0])
# anonymize ids
self.anon_id_map = {mrn: i for i, mrn in enumerate(list(set(dvh.mrn)))}
anon_id = [self.anon_id_map[dvh.mrn[i]] for i in range(len(dvh.mrn))]
print(str(datetime.now()), "writing sources.dvhs.data", sep=' ')
self.sources.dvhs.data = {'mrn': dvh.mrn,
'anon_id': anon_id,
'group': dvh_groups,
'uid': dvh.study_instance_uid,
'roi_institutional': dvh.institutional_roi,
'roi_physician': dvh.physician_roi,
'roi_name': dvh.roi_name,
'roi_type': dvh.roi_type,
'rx_dose': dvh.rx_dose,
'volume': dvh.volume,
'surface_area': dvh.surface_area,
'min_dose': dvh.min_dose,
'mean_dose': dvh.mean_dose,
'max_dose': dvh.max_dose,
'dist_to_ptv_min': dvh.dist_to_ptv_min,
'dist_to_ptv_mean': dvh.dist_to_ptv_mean,
'dist_to_ptv_median': dvh.dist_to_ptv_median,
'dist_to_ptv_max': dvh.dist_to_ptv_max,
'dist_to_ptv_centroids': dvh.dist_to_ptv_centroids,
'ptv_overlap': dvh.ptv_overlap,
'cross_section_max': dvh.cross_section_max,
'cross_section_median': dvh.cross_section_median,
'spread_x': dvh.spread_x,
'spread_y': dvh.spread_y,
'spread_z': dvh.spread_z,
'x': x_data,
'y': y_data,
'color': line_colors,
'x_scale': x_scale,
'y_scale': y_scale,
'toxicity_grade': dvh.toxicity_grade}
print(str(datetime.now()), 'begin updating beam, plan, rx data sources', sep=' ')
self.update_beam_data(dvh.study_instance_uid)
self.update_plan_data(dvh.study_instance_uid)
self.update_rx_data(dvh.study_instance_uid)
print(str(datetime.now()), 'all sources set', sep=' ')
return {'1': dvh_group_1, '2': dvh_group_2}
def volcano(data,
folder='',
tohighlight=None,
tooltips=[('gene', '@gene_id')],
title="volcano plot",
xlabel='log-fold change',
ylabel='-log(Q)',
maxvalue=100,
searchbox=False,
logfoldtohighlight=0.15,
pvaltohighlight=0.1,
showlabels=False):
"""
Make an interactive volcano plot from Differential Expression analysis tools outputs
Args:
-----
data: a df with rows genes and cols [log2FoldChange, pvalue, gene_id]
folder: str of location where to save the plot, won't save if empty
tohighlight: list[str] of genes to highlight in the plot
tooltips: list[tuples(str,str)] if user wants tot specify another bokeh tooltip
title: str plot title
xlabel: str if user wants to specify the title of the x axis
ylabel: str if user wants tot specify the title of the y axis
maxvalue: float the max -log2(pvalue authorized usefull when managing inf vals)
searchbox: bool whether or not to add a searchBox to interactively highlight genes
logfoldtohighlight: float min logfoldchange when to diplay points
pvaltohighlight: float min pvalue when to diplay points
showlabels: bool whether or not to show a text above each datapoint with its label information
Returns:
--------
The bokeh object
"""
# pdb.set_trace()
to_plot_not, to_plot_yes = selector(
data, tohighlight if tohighlight is not None else [],
logfoldtohighlight, pvaltohighlight)
hover = bokeh.models.HoverTool(tooltips=tooltips, names=['circles'])
# Create figure
p = bokeh.plotting.figure(title=title, plot_width=650, plot_height=450)
p.xgrid.grid_line_color = 'white'
p.ygrid.grid_line_color = 'white'
p.xaxis.axis_label = xlabel
p.yaxis.axis_label = ylabel
# Add the hover tool
p.add_tools(hover)
p, source1 = add_points(p,
to_plot_not,
'log2FoldChange',
'pvalue',
color='#1a9641',
maxvalue=maxvalue)
p, source2 = add_points(p,
to_plot_yes,
'log2FoldChange',
'pvalue',
color='#fc8d59',
alpha=0.6,
outline=True,
maxvalue=maxvalue)
if showlabels:
labels = LabelSet(x='log2FoldChange',
y='transformed_q',
text_font_size='7pt',
text="gene_id",
level="glyph",
x_offset=5,
y_offset=5,
source=source2,
render_mode='canvas')
p.add_layout(labels)
if searchbox:
text = TextInput(title="text", value="gene")
text.js_on_change(
'value',
CustomJS(args=dict(source=source1),
code="""
var data = source.data
var value = cb_obj.value
var gene_id = data.gene_id
var a = -1
for (let i=0; i < gene_id.length; i++) {
if ( gene_id[i]===value ) { a=i; console.log(i); data.size[i]=7; data.alpha[i]=1; data.color[i]='#fc8d59' }
}
source.data = data
console.log(source)
console.log(cb_obj)
source.change.emit()
console.log(source)
"""))
p = column(text, p)
p.output_backend = "svg"
if folder:
save(p, folder + title.replace(' ', "_") + "_volcano.html")
export_svg(p,
filename=folder + title.replace(' ', "_") + "_volcano.svg")
try:
show(p)
except:
show(p)
return p
PLOT_WIDTH=500
sourceQuad = ColumnDataSource(dict(left=[min(source_small.data['x'])],top=[Max_Y],right=[max(source_small.data['x'])],bottom=[Min_Y]))
toolset = "box_zoom,resize,pan,reset,save,xwheel_zoom"
plot_zoomed = Figure(plot_width=1000, plot_height=500, x_axis_type="datetime",tools=toolset, lod_factor=100,lod_interval=1000)
plot_zoomed.line('x', 'y',source=source_zoomed, color='navy', alpha=0.5)
plot = Figure(plot_width=PLOT_WIDTH, plot_height=250, x_axis_type="datetime",toolbar_location=None,lod_factor=100,lod_interval=1000)
plot.line('x', 'y',source=source_small, color='navy', alpha=0.5)
plot.y_range.start=Min_Y
plot.y_range.end=Max_Y
glyph = Quad(left="left", right="right", top="top", bottom="bottom", fill_color="#b3de69", fill_alpha=0.1)
plot.add_glyph(sourceQuad, glyph)
RangeStartX=0
RangeEndX=1
text_start_x = TextInput(title="X range start", name='x_range_start', value="0")
text_end_x = TextInput(title="X range end", name='x_range_end', value="1")
text_start_x.on_change('value', update_data)
text_end_x.on_change('value', update_data)
toolset = "box_zoom,resize,pan,reset,save,x_wheel_zoom"
plot_zoomed.x_range.callback = CustomJS(args=dict(xrange=plot_zoomed.x_range,start_x=text_start_x,end_x=text_end_x),code="""
var start = xrange.get("start");
var end = xrange.get("end");
start_x.set("value",start.toString());
end_x.set("value",end.toString());
start_x.trigger('change');
end_x.trigger('change');
""")
# Authorize access to discogs database
d = discogs_client.Client(user_agent, user_token=user_token)
df = pd.read_csv(join(dirname(__file__), 'decks_genre_filtered.csv'))
# df.release_date.apply(lambda x: datetime.strptime(x, "%Y-%m-%d"))
source = ColumnDataSource(data=dict())
# Input Buttons
genre_filter = [
'All', 'Acid', 'Ambient', 'Breaks', 'Chicago', 'Deep House', 'Detroit',
'Dub Techno', 'House', 'Minimal', 'Techhouse', 'Techno'
]
genre = Select(title="genre", value="All", options=genre_filter)
slider_min = Slider(title="Minimum Price", start=0, end=25, value=0, step=1)
slider_max = Slider(title="Maximum Price", start=0, end=150, value=20, step=1)
search_text = TextInput(title="Search")
columns = [
TableColumn(field="release_date", title="Release Date"),
TableColumn(field="release", title="Release Name"),
TableColumn(field="artist", title="Artist"),
TableColumn(field="label", title="Label"),
TableColumn(field="genre", title="Genre"),
TableColumn(field="catalog_num", title="Catalog Number"),
TableColumn(field="price",
title="Price (in USD)",
formatter=NumberFormatter(format="$0.00")),
TableColumn(field="in_stock",
title="Percent Available",
formatter=NumberFormatter(format="‘0.00%"))
]
tools = [PanTool(), BoxZoomTool(), WheelZoomTool(), ResetTool(), hover, PreviewSaveTool()]
p = Figure(title="NicheLife Map", plot_width=800, plot_height=700, tools=tools)
# tools="pan,wheel_zoom,reset,box_zoom,save") # toolbar_location="top", #box_select,
p.grid.grid_line_alpha = 0
p.patches("ct_x", "ct_y", source=source, fill_color="QI_colmap", fill_alpha=0.7, line_color="white", line_width=0.5)
# image1 = ImageURL(url=source.data["image"], x=-74.04, y=40.85)
# p.add_glyph(source, image1)
p.image_url(url=imageurl, x="-74.04", y="40.85")
# Set up widgets
text = TextInput(title="Map Name", value="NicheLife Map")
feature1 = Slider(title="Subway Accessibility", value=0.5, start=0, end=1, step=0.1)
feature2 = Slider(title="Safety", value=0.5, start=0, end=1, step=0.1)
feature3 = Slider(title="Public Satisfaction", value=0.5, start=0, end=1, step=0.1)
feature4 = Slider(title="Restaurants", value=0.5, start=0, end=1, step=0.1)
feature5 = Slider(title="Grocery Stores", value=0.5, start=0, end=1, step=0.1)
feature6 = Slider(title="Nightlife", value=0.5, start=0, end=1, step=0.1)
price = Select(title="Show Affordability", options=["Yes", "No"])
# Set up callbacks
def update_title(attrname, old, new):
p.title = text.value
text.on_change("value", update_title)
metric.value,
size=10,
color=color_set[i % len(color_set)],
source=main_source[i])
i += 1
return p
def update(attr, old, new):
layout.children[1] = create_figure()
#Creates the widgets and updates values when they are changed
cat = Select(title="Category:", value="ReagentLot", options=cat_list)
cat.on_change('value', update)
metric = Select(title="Metric:", value="Q30", options=metric_list)
metric.on_change('value', update)
text = TextInput(title='N', value='300')
text.on_change('value', update)
cat_wig = widgetbox(cat)
metric_wig = widgetbox(metric)
text_wig = widgetbox(text)
layout = column(row(metric_wig, cat_wig, text_wig), create_figure())
curdoc().add_root(layout)
# p_logo = figure(toolbar_location=None, tools = "lasso_select", x_range=(0,1), y_range=(0,1), plot_width=520, plot_height=200)
# img = mpimg.imread("cylance_logo.png")
# p_logo.image_url(url=["cylance_logo.png"], x=0, y=0)
# p_logo.background_fill_color = "black"
# p_logo.border_fill_color = "black"
# p_logo.grid.grid_line_color = "black"
# p_logo.outline_line_color = "black"
# radio button group for uploading file
radio_group = RadioButtonGroup(labels=["NO-SPLIT", "SPLIT", "START"], button_type="success")
radio_group.width = 500
radio_group.on_click(_fit_util)
# text for console output
text_input = TextInput(value="", title="CONSOLE")
text_input.width = 500
cluster_stats = Div(
render_as_text=False,
text=generate_display_string("", name="Cluster Statistics", height=100)
)
cluster_stats.width = 500
cluster_stats.height = 100
cluster_commonality = Div(
render_as_text=False,
text=generate_display_string("")
)
cluster_commonality.width = 500
cluster_commonality.height = 300
def initialize(curr_doc: CurrentDoc, max_indep_elements=20, catch_radius=0.15):
"""
Creates and configures all bokeh objects and starts the web app.
:param max_indep_elements: maximum number of node independent elements accepted in the input plot (int)
:param catch_radius: radius in which to select an element in input plot (double)
:return: none
"""
curr_doc.catch_radius = catch_radius
'''
###############################
# TEXT BOXES
###############################
'''
# Div object showing x and y position of the cursor in the input plot
div_xy = Div(width=75, height=25)
# Div object showing hints for the graphical input into the input plot through the element buttons
div_input_width = curr_doc.plot_input.plot_width - div_xy.width - 10
curr_doc.div_input = Div(width=div_input_width, height=div_xy.height)
# Div object showing general text messages to the user
curr_doc.div_msg = Div(css_classes=["MSG_BOX"],
text=" ",
width=curr_doc.plot_input.plot_width,
height=100)
# doc.div_msg = Div(css_classes=["MSG_BOX"], text=" ", width=doc.plot_input.plot_width, height=100)
'''
###############################
# INPUT PLOT
###############################
'''
# style and configuration of the input plot
# this plot is used to allow the user to connect mechanical elements
tooltips = [("name", "@name_user"), ("(x, y)", "(@x{0.0}, @y{0.0})")]
curr_doc.plot_input = figure(
# plot_width=600, plot_height=300,
tools="pan,wheel_zoom,reset,lasso_select,hover,save",
toolbar_location="above",
# x_axis_label='x', y_axis_label='y',
x_minor_ticks=10,
y_minor_ticks=10,
x_range=Range1d(start=0, end=5),
y_range=Range1d(start=0, end=5),
match_aspect=True,
aspect_scale=1.0,
tooltips=tooltips,
)
curr_doc.plot_input.toolbar.logo = None
configure_input_plot(curr_doc, curr_doc.plot_input, div_xy,
max_indep_elements)
'''
###############################
# OUTPUT PLOTS
###############################
'''
# initialize plots for the output after calculations
plot_output_width = 800
plot_output_height = 250
curr_doc.plot_normal_f = figure(plot_width=plot_output_width,
plot_height=plot_output_height,
active_scroll="wheel_zoom")
curr_doc.plot_normal_f.toolbar.logo = None
curr_doc.plot_normal_f.title.text = 'Normal force'
curr_doc.plot_normal_disp = figure(plot_width=plot_output_width,
plot_height=plot_output_height,
active_scroll="wheel_zoom")
curr_doc.plot_normal_disp.toolbar.logo = None
curr_doc.plot_normal_disp.title.text = 'Normal displacement'
curr_doc.plot_shear_f = figure(plot_width=plot_output_width,
plot_height=plot_output_height,
active_scroll="wheel_zoom")
curr_doc.plot_shear_f.toolbar.logo = None
curr_doc.plot_shear_f.title.text = 'Shear force'
curr_doc.plot_moment = figure(plot_width=plot_output_width,
plot_height=plot_output_height,
active_scroll="wheel_zoom")
curr_doc.plot_moment.toolbar.logo = None
curr_doc.plot_moment.title.text = 'Bending moment'
curr_doc.plot_shear_disp = figure(plot_width=plot_output_width,
plot_height=plot_output_height,
active_scroll="wheel_zoom")
curr_doc.plot_shear_disp.toolbar.logo = None
curr_doc.plot_shear_disp.title.text = 'Shear displacement'
curr_doc.plot_shear_angle = figure(plot_width=plot_output_width,
plot_height=plot_output_height,
active_scroll="wheel_zoom")
curr_doc.plot_shear_angle.toolbar.logo = None
curr_doc.plot_shear_angle.title.text = 'Rotation angle'
curr_doc.plot_list = [
curr_doc.plot_normal_f, curr_doc.plot_normal_disp,
curr_doc.plot_shear_f, curr_doc.plot_moment, curr_doc.plot_shear_angle,
curr_doc.plot_shear_disp
]
# add plot renderer
ds = ColumnDataSource(data=dict(x=[], y=[]))
curr_doc.plot_normal_f.circle('x', 'y', source=ds)
curr_doc.plot_normal_disp.circle('x', 'y', source=ds)
curr_doc.plot_shear_f.circle('x', 'y', source=ds)
curr_doc.plot_moment.circle('x', 'y', source=ds)
curr_doc.plot_shear_disp.circle('x', 'y', source=ds)
curr_doc.plot_normal_f.circle('x', 'y', source=ds)
curr_doc.plot_shear_angle.circle('x', 'y', source=ds)
'''
###############################
# TEST CASES
###############################
'''
menu_tc = [("Single beam load", "single_beam_load"),
("Two beam lineload", "two_beam_lineload"),
("Angled structure", "fin_struc_soft_lab")]
dropdown_tc = Dropdown(label="Show test case", menu=menu_tc, width=150)
dropdown_tc.on_change('value',
partial(vis_cbs.cb_plot_testcase, curr_doc=curr_doc))
# partial(vis_cbs.cb_get_textinput, key=key_elinfo_n)
'''
###############################
# CALCULATE AND DELETE BUTTONS
###############################
'''
# add and configure a button to start calculations
button_calc = Button(label="Calculate", width=240)
button_calc.on_click(
partial(vis_cbs.cb_button_calculation, curr_doc, button_calc))
# add and configure a button to delete selected elements of the input graph
b_del_w = int((button_calc.width - 10) / 2)
button_del_selected = Button(label="Delete selected", width=b_del_w)
button_del_selected.on_click(
partial(vis_cbs.cb_button_delete,
curr_doc=curr_doc,
all_selected=False))
# add and configure a button to delete selected elements of the input graph
button_del_all = Button(label="Delete all", width=b_del_w)
button_del_all.on_click(
partial(vis_cbs.cb_button_delete, curr_doc=curr_doc,
all_selected=True))
'''
############################################
# BOX OF ELEMENTS TO SELECT FOR INPUT PLOT
############################################
'''
# titles for groups of mechanical elements
text_supports = Div(text="Supports:", width=100, height=20)
text_springs = Div(text="Springs:", width=100, height=20)
text_node = Div(text="Node:", width=100, height=20)
text_joints = Div(text="Joints:", width=100, height=20)
text_elements = Div(text="Line elements:", width=100, height=20)
text_loads = Div(text="Loads:", width=100, height=20)
b_height = 50
b_line_width = 72
# configure buttons for mechanical supports
button_support_clamped = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_CLAMPED.name],
width=b_height,
height=b_height)
curr_doc.buttons[
eLnum.ElSupEnum.SUPPORT_CLAMPED.value] = button_support_clamped
button_support_clamped.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_CLAMPED))
button_support_normal = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_NORMAL_FORCE.name],
width=b_height,
height=b_height)
curr_doc.buttons[
eLnum.ElSupEnum.SUPPORT_NORMAL_FORCE.value] = button_support_normal
button_support_normal.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_NORMAL_FORCE))
button_support_transverse = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_TRANSVERSE_FORCE.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.SUPPORT_TRANSVERSE_FORCE.
value] = button_support_transverse
button_support_transverse.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_TRANSVERSE_FORCE))
button_support_fixed_conti = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_FIXED_CONTINUOUS.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.SUPPORT_FIXED_CONTINUOUS.
value] = button_support_fixed_conti
button_support_fixed_conti.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_FIXED_CONTINUOUS))
button_support_fixed_joint = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_FIXED_JOINT.name],
width=b_height,
height=b_height)
curr_doc.buttons[
eLnum.ElSupEnum.SUPPORT_FIXED_JOINT.value] = button_support_fixed_joint
button_support_fixed_joint.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_FIXED_JOINT))
button_support_roller_conti = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_ROLLER_CONTINUOUS.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.SUPPORT_ROLLER_CONTINUOUS.
value] = button_support_roller_conti
button_support_roller_conti.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_ROLLER_CONTINUOUS))
button_support_roller_joint = Button(
label="",
css_classes=[eLnum.ElSupEnum.SUPPORT_ROLLER_JOINT.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.SUPPORT_ROLLER_JOINT.
value] = button_support_roller_joint
button_support_roller_joint.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SUPPORT_ROLLER_JOINT))
button_spring_support = Button(
label="",
css_classes=[eLnum.ElSupEnum.SPRING_SUPPORT.name],
width=b_height,
height=b_height)
curr_doc.buttons[
eLnum.ElSupEnum.SPRING_SUPPORT.value] = button_spring_support
button_spring_support.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SPRING_SUPPORT))
button_spring_moment_support = Button(
label="",
css_classes=[eLnum.ElSupEnum.SPRING_MOMENT_SUPPORT.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.SPRING_MOMENT_SUPPORT.
value] = button_spring_moment_support
button_spring_moment_support.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SPRING_MOMENT_SUPPORT))
# configure curr_doc.buttons for connectors
button_node = Button(label="",
css_classes=[eLnum.ElSupEnum.NODE.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.NODE.value] = button_node
button_node.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.NODE))
button_joint = Button(label="",
css_classes=[eLnum.ElSupEnum.JOINT.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.JOINT.value] = button_joint
button_joint.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.JOINT))
button_joint_normal = Button(
label="",
css_classes=[eLnum.ElSupEnum.JOINT_NORMAL_FORCE.name],
width=b_height,
height=b_height)
curr_doc.buttons[
eLnum.ElSupEnum.JOINT_NORMAL_FORCE.value] = button_joint_normal
button_joint_normal.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.JOINT_NORMAL_FORCE))
button_joint_transverse = Button(
label="",
css_classes=[eLnum.ElSupEnum.JOINT_TRANSVERSE_FORCE.name],
width=b_height,
height=b_height)
curr_doc.buttons[
eLnum.ElSupEnum.JOINT_TRANSVERSE_FORCE.value] = button_joint_transverse
button_joint_transverse.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.JOINT_TRANSVERSE_FORCE))
button_spring = Button(label="",
css_classes=[eLnum.ElSupEnum.SPRING.name],
width=b_line_width,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.SPRING.value] = button_spring
button_spring.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.SPRING))
# configure buttons for mechanical 1D or 2D elements
# button_rod = Button(label="", css_classes=[eLnum.ElSupEnum.ROD.name], width=b_line_width, height=b_height)
# curr_doc.buttons[eLnum.ElSupEnum.ROD.value] = button_rod
# button_rod.on_click(partial(vis_cbs.cb_button_element_click, button_enum=eLnum.ElSupEnum.ROD))
button_beam = Button(label="",
css_classes=[eLnum.ElSupEnum.BEAM.name],
width=b_line_width,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.BEAM.value] = button_beam
button_beam.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.BEAM))
# configure buttons for mechanical loads
button_load_point = Button(label="",
css_classes=[eLnum.ElSupEnum.LOAD_POINT.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.LOAD_POINT.value] = button_load_point
button_load_point.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.LOAD_POINT))
button_load_moment = Button(label="",
css_classes=[eLnum.ElSupEnum.LOAD_MOMENT.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.LOAD_MOMENT.value] = button_load_moment
button_load_moment.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.LOAD_MOMENT))
button_load_random = Button(label="",
css_classes=[eLnum.ElSupEnum.LOAD_LINE.name],
width=b_line_width,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.LOAD_LINE.value] = button_load_random
button_load_random.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.LOAD_LINE))
button_load_temp = Button(label="",
css_classes=[eLnum.ElSupEnum.LOAD_TEMP.name],
width=b_height,
height=b_height)
curr_doc.buttons[eLnum.ElSupEnum.LOAD_TEMP.value] = button_load_temp
button_load_temp.on_click(
partial(vis_cbs.cb_button_element_click,
curr_doc=curr_doc,
button_enum=eLnum.ElSupEnum.LOAD_TEMP))
'''
###############################
# ELEMENT INFO BOX
###############################
'''
elinfo_object_height = 26
elinfo_label_width1 = 60
elinfo_label_width2 = 40
elinfo_input_width = 60
# labels for values of an element of the input plot
text_elinfo_name = Div(text="name:",
width=elinfo_label_width1,
height=elinfo_object_height)
text_elinfo_x = Div(text="x:",
width=elinfo_label_width1,
height=elinfo_object_height)
curr_doc.div_element_info["x"] = text_elinfo_x
text_elinfo_y = Div(text="y:",
width=elinfo_label_width1,
height=elinfo_object_height)
curr_doc.div_element_info["y"] = text_elinfo_y
text_elinfo_angle1 = Div(text="angle:",
width=elinfo_label_width1,
height=elinfo_object_height)
text_elinfo_angle2 = Div(text="°",
width=elinfo_label_width2 - 20,
height=elinfo_object_height)
spacer_y_a = Div(text="",
width=text_elinfo_angle2.width,
height=elinfo_object_height)
text_elinfo_k1 = Div(text="spring:",
width=elinfo_label_width1,
height=elinfo_object_height)
text_elinfo_k2 = Div(text="* k",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_length1 = Div(text="length:",
width=elinfo_label_width1,
height=elinfo_object_height)
text_elinfo_length2 = Div(text="* l",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_force1 = Div(text="force:",
width=elinfo_label_width1,
height=elinfo_object_height)
text_elinfo_force2 = Div(text="* F",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_moment1 = Div(text="moment:",
width=elinfo_label_width1,
height=elinfo_object_height)
text_elinfo_moment2 = Div(text="* M",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_beam = Div(text="BEAM",
width=elinfo_object_height,
height=elinfo_label_width1,
css_classes=["ELINFO_VERTICAL_TEXT"])
text_elinfo_h = Div(text="* h",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_ei = Div(text="* EI",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_ea = Div(text="* EA",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_lineload = Div(text="LINE LOAD",
width=elinfo_object_height,
height=elinfo_label_width1,
css_classes=["ELINFO_VERTICAL_TEXT"])
text_elinfo_xn = Div(text="* n",
width=elinfo_label_width2 - 10,
height=elinfo_object_height)
curr_doc.div_element_info["xn"] = text_elinfo_xn
text_elinfo_yq = Div(text="* q",
width=elinfo_label_width2 - 10,
height=elinfo_object_height)
curr_doc.div_element_info["yq"] = text_elinfo_yq
text_elinfo_temp = Div(text="TEMP.",
width=elinfo_object_height,
height=elinfo_label_width1,
css_classes=["ELINFO_VERTICAL_TEXT"])
text_elinfo_dt = Div(text="* dT",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_tt = Div(text="* T",
width=elinfo_label_width2,
height=elinfo_object_height)
text_elinfo_at = Div(text="* αT",
width=elinfo_label_width2,
height=elinfo_object_height)
# text inputs showing the current value of an input plot element and taking input for a value change
# name
input_elinfo_name = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_n = "name"
curr_doc.input_element_info[key_elinfo_n] = input_elinfo_name
input_elinfo_name.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_n))
# xy
input_elinfo_x = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_x = "x"
curr_doc.input_element_info[key_elinfo_x] = input_elinfo_x
input_elinfo_x.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_x))
input_elinfo_y = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_y = "y"
curr_doc.input_element_info[key_elinfo_y] = input_elinfo_y
input_elinfo_y.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_y))
# angle
input_elinfo_angle = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_angle = "angle"
curr_doc.input_element_info[key_elinfo_angle] = input_elinfo_angle
input_elinfo_angle.on_change(
'value',
partial(vis_cbs.cb_get_textinput,
curr_doc=curr_doc,
key=key_elinfo_angle))
# spring constant
input_elinfo_k = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_k = "k"
curr_doc.input_element_info[key_elinfo_k] = input_elinfo_k
input_elinfo_k.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_k))
# length
input_elinfo_length = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_length = "length"
curr_doc.input_element_info[key_elinfo_length] = input_elinfo_length
input_elinfo_k.on_change(
'value',
partial(vis_cbs.cb_get_textinput,
curr_doc=curr_doc,
key=key_elinfo_length))
# point load
input_elinfo_force = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_f = "force"
curr_doc.input_element_info[key_elinfo_f] = input_elinfo_force
input_elinfo_force.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_f))
# moment
input_elinfo_moment = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_m = "moment"
curr_doc.input_element_info[key_elinfo_m] = input_elinfo_moment
input_elinfo_moment.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_m))
# beam
input_elinfo_h = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_h = "h"
curr_doc.input_element_info[key_elinfo_h] = input_elinfo_h
input_elinfo_h.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc, key=key_elinfo_h))
check_elinfo_beam = CheckboxGroup(labels=["EA -> inf.", "EI -> inf."],
active=[],
disabled=True,
width=elinfo_input_width +
elinfo_label_width1)
curr_doc.group_element_info["beam"] = check_elinfo_beam
check_elinfo_beam.on_change(
'active', partial(vis_cbs.cb_elinfo_beam, curr_doc=curr_doc))
input_elinfo_ea = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_ea = "ea"
curr_doc.input_element_info[key_elinfo_ea] = input_elinfo_ea
input_elinfo_ea.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc,
key=key_elinfo_ea))
input_elinfo_ei = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_ei = "ei"
curr_doc.input_element_info[key_elinfo_ei] = input_elinfo_ei
input_elinfo_ei.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc,
key=key_elinfo_ei))
# line load
radio_elinfo_ll = RadioGroup(
labels=["local", "global"],
active=0,
disabled=True, # "angle"
width=elinfo_input_width + elinfo_label_width1)
curr_doc.group_element_info["ll"] = radio_elinfo_ll
radio_elinfo_ll.on_change(
'active', partial(vis_cbs.cb_elinfo_lineload, curr_doc=curr_doc))
input_elinfo_xns = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height + 20,
disabled=True,
title="start")
key_elinfo_xns = "xn_start"
curr_doc.input_element_info[key_elinfo_xns] = input_elinfo_xns
input_elinfo_xns.on_change(
'value',
partial(vis_cbs.cb_get_textinput,
curr_doc=curr_doc,
key=key_elinfo_xns))
input_elinfo_xne = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height + 20,
disabled=True,
title="end")
key_elinfo_xne = "xn_end"
curr_doc.input_element_info[key_elinfo_xne] = input_elinfo_xne
input_elinfo_xne.on_change(
'value',
partial(vis_cbs.cb_get_textinput,
curr_doc=curr_doc,
key=key_elinfo_xne))
input_elinfo_yqs = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_yqs = "yq_start"
curr_doc.input_element_info[key_elinfo_yqs] = input_elinfo_yqs
input_elinfo_yqs.on_change(
'value',
partial(vis_cbs.cb_get_textinput,
curr_doc=curr_doc,
key=key_elinfo_yqs))
input_elinfo_yqe = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_yqe = "yq_end"
curr_doc.input_element_info[key_elinfo_yqe] = input_elinfo_yqe
input_elinfo_yqe.on_change(
'value',
partial(vis_cbs.cb_get_textinput,
curr_doc=curr_doc,
key=key_elinfo_yqe))
# temperature load
input_elinfo_dt = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_dt = "dT"
curr_doc.input_element_info[key_elinfo_dt] = input_elinfo_dt
input_elinfo_dt.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc,
key=key_elinfo_dt))
input_elinfo_tt = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_tt = "T"
curr_doc.input_element_info[key_elinfo_tt] = input_elinfo_tt
input_elinfo_tt.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc,
key=key_elinfo_tt))
input_elinfo_at = TextInput(value="-",
width=elinfo_input_width,
height=elinfo_object_height,
disabled=True)
key_elinfo_at = "aT"
curr_doc.input_element_info[key_elinfo_at] = input_elinfo_at
input_elinfo_at.on_change(
'value',
partial(vis_cbs.cb_get_textinput, curr_doc=curr_doc,
key=key_elinfo_at))
# button for deleting an input plot element
button_elinfo_del = Button(label="Delete element",
width=elinfo_label_width1 + elinfo_input_width +
elinfo_label_width2,
height=elinfo_object_height + 10,
disabled=False)
button_elinfo_del.on_click(
partial(vis_cbs.cb_button_delete, curr_doc=curr_doc, single=True))
'''
###############################
# CHECKBOXES FOR OUTPUT PLOTS
###############################
'''
check_labels = ["Min/ max values", "Start/ end values", "Zero points"]
check_init_active = [1]
check_plot_nf = CheckboxGroup(labels=check_labels,
active=check_init_active) # inline=False
text_plot_nf = Div(text="", width=100, height=10)
check_plot_nf.on_change(
'active',
partial(vis_cbs.cb_toggle_characteristic_values,
curr_doc=curr_doc,
output_plot=curr_doc.plot_normal_f,
div=text_plot_nf))
check_plot_nd = CheckboxGroup(labels=check_labels,
active=check_init_active)
text_plot_nd = Div(text="", width=100, height=10)
check_plot_nd.on_change(
'active',
partial(vis_cbs.cb_toggle_characteristic_values,
curr_doc=curr_doc,
output_plot=curr_doc.plot_normal_disp,
div=text_plot_nd))
check_plot_sf = CheckboxGroup(labels=check_labels,
active=check_init_active)
text_plot_sf = Div(text="", width=100, height=10)
check_plot_sf.on_change(
'active',
partial(vis_cbs.cb_toggle_characteristic_values,
curr_doc=curr_doc,
output_plot=curr_doc.plot_shear_f,
div=text_plot_sf))
check_plot_mo = CheckboxGroup(labels=check_labels,
active=check_init_active)
text_plot_mo = Div(text="", width=100, height=10)
check_plot_mo.on_change(
'active',
partial(vis_cbs.cb_toggle_characteristic_values,
curr_doc=curr_doc,
output_plot=curr_doc.plot_moment,
div=text_plot_mo))
check_plot_sa = CheckboxGroup(labels=check_labels,
active=check_init_active)
text_plot_sa = Div(text="", width=100, height=10)
check_plot_sa.on_change(
'active',
partial(vis_cbs.cb_toggle_characteristic_values,
curr_doc=curr_doc,
output_plot=curr_doc.plot_shear_angle,
div=text_plot_sa))
check_plot_sd = CheckboxGroup(labels=check_labels,
active=check_init_active)
text_plot_sd = Div(text="", width=100, height=10)
check_plot_sd.on_change(
'active',
partial(vis_cbs.cb_toggle_characteristic_values,
curr_doc=curr_doc,
output_plot=curr_doc.plot_shear_disp,
div=text_plot_sd))
'''
###############################
# IMAGES: SIGN CONVENTION
###############################
'''
sign_convention_N = Div(
text=
"<img src='/System_of_Beams/static/images/sign_convention_N_u.png' width=100 height=60>",
width=100,
height=60)
sign_convention_u = Div(
text=
"<img src='/System_of_Beams/static/images/sign_convention_N_u.png' width=100 height=60>",
width=100,
height=60)
sign_convention_SF = Div(
text=
"<img src='/System_of_Beams/static/images/sign_convention_SF_w.png' width=100 height=60>",
width=100,
height=60)
sign_convention_M = Div(
text=
"<img src='/System_of_Beams/static/images/sign_convention_M.png' width=100 height=60>",
width=100,
height=60)
sign_convention_phi = Div(
text=
"<img src='/System_of_Beams/static/images/sign_convention_phi.png' width=100 height=60>",
width=100,
height=60)
sign_convention_w = Div(
text=
"<img src='/System_of_Beams/static/images/sign_convention_SF_w.png' width=100 height=60>",
width=100,
height=60)
'''
###############################
# HTML LATEX DESCRIPTION
###############################
'''
# latex packages not working with updated bokeh!
# description_filename = join(dirname(__file__), "description.html")
# description = LatexDiv(text=open(description_filename).read(), render_as_text=False, width=910)
'''
####################################
# CREATE LAYOUT AND START DOCUMENT
####################################
'''
spacer = Div(text="", width=20, height=20)
minispacer = Div(text="", width=0, height=0)
# element buttons
layout_supports_1 = row(button_support_clamped, button_support_normal,
button_support_transverse)
layout_supports_2 = row(button_support_fixed_joint,
button_support_fixed_conti,
button_support_roller_joint,
button_support_roller_conti)
layout_springs = row(button_spring_support, button_spring_moment_support)
layout_node = row(button_node)
layout_joints = row(button_joint, button_joint_normal,
button_joint_transverse, button_spring)
layout_elements = row(button_beam) # button_rod,
layout_loads = row(button_load_point, button_load_moment,
button_load_random, button_load_temp)
layout_input_elements = column(
spacer, text_supports, layout_supports_1, layout_supports_2,
row(column(text_springs, layout_springs), spacer, minispacer,
column(text_node, layout_node)), text_joints, layout_joints,
text_elements, layout_elements, text_loads, layout_loads)
# element info box
elinfo_name = row(text_elinfo_name, input_elinfo_name)
curr_doc.children_element_info[key_elinfo_n] = elinfo_name
elinfo_xy = row(text_elinfo_x, input_elinfo_x, spacer_y_a, text_elinfo_y,
input_elinfo_y)
curr_doc.children_element_info[key_elinfo_x] = elinfo_xy
elinfo_angle_length = row(text_elinfo_angle1, input_elinfo_angle,
text_elinfo_angle2, text_elinfo_length1,
input_elinfo_length, text_elinfo_length2)
curr_doc.children_element_info[key_elinfo_angle] = elinfo_angle_length
elinfo_k = row(text_elinfo_k1, input_elinfo_k, text_elinfo_k2)
curr_doc.children_element_info[key_elinfo_k] = elinfo_k
elinfo_force = row(text_elinfo_force1, input_elinfo_force,
text_elinfo_force2)
curr_doc.children_element_info[key_elinfo_f] = elinfo_force
elinfo_moment = row(text_elinfo_moment1, input_elinfo_moment,
text_elinfo_moment2)
curr_doc.children_element_info[key_elinfo_m] = elinfo_moment
elinfo_beam = row(
minispacer, column(minispacer, text_elinfo_beam),
column(check_elinfo_beam, row(input_elinfo_h, text_elinfo_h)),
column(row(input_elinfo_ea, text_elinfo_ea),
row(input_elinfo_ei, text_elinfo_ei)))
curr_doc.children_element_info[key_elinfo_h] = elinfo_beam
elinfo_lineload = row(
minispacer, column(spacer, text_elinfo_lineload),
column(spacer, radio_elinfo_ll),
column(row(input_elinfo_xns, input_elinfo_xne),
row(input_elinfo_yqs, input_elinfo_yqe)),
column(spacer, text_elinfo_xn, text_elinfo_yq))
curr_doc.children_element_info[key_elinfo_ei] = elinfo_lineload
elinfo_dt = row(
minispacer, column(minispacer, text_elinfo_temp),
column(
row(input_elinfo_dt, text_elinfo_dt, minispacer, input_elinfo_at,
text_elinfo_at), row(input_elinfo_tt, text_elinfo_tt)))
curr_doc.children_element_info[key_elinfo_dt] = elinfo_dt
curr_doc.layout_element_info = row(
minispacer,
column(minispacer, elinfo_name, elinfo_xy, elinfo_angle_length, spacer,
elinfo_k, elinfo_force, elinfo_moment, spacer, elinfo_beam,
spacer, elinfo_lineload, spacer, elinfo_dt, spacer,
button_elinfo_del, minispacer),
minispacer,
css_classes=["ELEMENT_INFO_BOX"],
margin=(20, 0, 0, 20),
visible=True)
# input plot with element buttons, delete and calculation buttons and divs
user_input = row(curr_doc.plot_input, spacer, layout_input_elements)
user_input_info = row(curr_doc.div_input, div_xy, spacer,
button_del_selected, button_del_all)
user_msg = row(curr_doc.div_msg, spacer, button_calc)
# output plots and check boxes for characteristic values
user_output = column(
row(curr_doc.plot_normal_f, spacer,
column(spacer, check_plot_nf, sign_convention_N)),
row(curr_doc.plot_normal_disp, spacer,
column(spacer, check_plot_nd, sign_convention_u)),
row(curr_doc.plot_shear_f, spacer,
column(spacer, check_plot_sf, sign_convention_SF)),
row(curr_doc.plot_moment, spacer,
column(spacer, check_plot_mo, sign_convention_M)),
row(curr_doc.plot_shear_angle, spacer,
column(spacer, check_plot_sa, sign_convention_phi)),
row(curr_doc.plot_shear_disp, spacer,
column(spacer, check_plot_sd, sign_convention_w)))
# assemble complete layout
doc_layout = column(
spacer, row(spacer, spacer, dropdown_tc),
row(column(user_input, user_input_info, user_msg), minispacer,
curr_doc.layout_element_info), spacer, user_output)
# add layout
curr_doc.curr_doc.add_root(doc_layout)
# set title of browser tab
curr_doc.curr_doc.title = split(dirname(__file__))[-1].replace(
'_', ' ').replace('-', ' ')
doc_title = split(dirname(__file__))[-1].replace('_',
' ').replace('-', ' ')
# initialize data source
source_fourier = ColumnDataSource(data=dict(t=[], x_fourier=[]))
source_orig = ColumnDataSource(data=dict(t=[], x_orig=[]))
source_interval_patch = ColumnDataSource(data=dict(x_patch=[], y_patch=[]))
source_interval_bound = ColumnDataSource(data=dict(x_min=[], x_max=[], y_minmax=[]))
source_coeff = ColumnDataSource(data=dict(a=[], b=[]))
source_f = ColumnDataSource(data=dict(f=[None]))
source_periodicity = ColumnDataSource(data=dict(t_start=[None], t_end=[None]))
source_view = ColumnDataSource(data=dict(x_start=[None], x_end=[None], y_start=[None], y_end=[None]))
# initialize controls
# buttons for choosing a sample function
sample_function_type = RadioButtonGroup(labels=fs.function_names, active=fs.function_init)
# here one can choose arbitrary input function
default_function_input = TextInput(value=fs.function_input_init)
default_function_period_start = TextInput(title='period start', value=fs.timeinterval_start_init)
default_function_period_end = TextInput(title='period end', value=fs.timeinterval_end_init)
# slider controlling degree of the fourier series
degree = Slider(title="degree", name='degree', value=fs.degree_init, start=fs.degree_min,
end=fs.degree_max, step=fs.degree_step)
# initialize callback behaviour
degree.on_change('value', degree_change)
default_function_input.on_change('value',
type_input_change) # todo write default functions for any callback, like above
default_function_period_start.on_change('value', type_input_change)
default_function_period_end.on_change('value', type_input_change)
sample_function_type.on_change('active', type_input_change)
Stream.twitterStream.disconnect()
global straming
straming = False
rdf['flash'] = 0
# def update_count():
# start_button_click
p.x_range.on_change('end', update_lod)
start_button = Button(label="Start", button_type="primary", width=80)
start_button.on_click(start_button_click)
stop_button = Button(label="Stop", button_type="danger", width=80)
stop_button.on_click(stop_button_click)
text_input = TextInput(placeholder="Enter query here", width=400)
spacer_row = Spacer(width=450)
spacer_pad_col = Spacer(height=20)
spacer_pad_row = Spacer(width=100)
spacer_col = Spacer(height=20)
layout = column(row(
spacer_row, text_input, start_button, stop_button, spacer_row), spacer_col, p)
main_layout = row(spacer_pad_row, column(
spacer_pad_col, layout, spacer_pad_col), spacer_pad_row)
def update_counts():
if straming:
def plotting():
forcs = ptsgdf.drop('geometry', axis=1).copy()
ptscs = ColumnDataSource(forcs)
# uscs = usmap.drop('geometry', axis=1).copy()
# usmcs = ColumnDataSource(uscs)
plotp = figure(title="Airport Rankings", plot_width=1200, plot_height=1000)
text_input1 = TextInput(value="", title="Enter Departing City")
text_input2 = TextInput(value="", title="Enter Destination City")
text_input3 = TextInput(
value="", title="Number of Flights from Departing City to Destination")
def update1(attr, old, new):
global liness
if liness is not None:
liness.visible = False
xlist = list()
ylist = list()
loccom = text_input1.value
if loccom:
for i in range(nc):
ind = list(dadfra.index).index(loccom)
if dadfra.loc[loccom, :][i] > 0:
xlist.append([ptsgdf.loc[i, "x"], ptsgdf.loc[ind, "x"]])
ylist.append([ptsgdf.loc[i, "y"], ptsgdf.loc[ind, "y"]])
# hover2 = HoverTool(names = ["airl"])
# hover2.tooltips = [("Number of Flights")]
# plotp.add_tools(hover1)
liness = plotp.multi_line(xlist,
ylist,
line_width=1,
line_color="green")
liness.visible = True
if loccom:
if text_input2.value:
text_input3.value = str(
int(dadfra.loc[text_input1.value, text_input2.value]))
#inst = inst +1
text_input1.on_change("value", update1)
def update2(attr, old, new):
if text_input1.value:
if text_input2.value:
text_input3.value = str(
int(dadfra.loc[text_input1.value, text_input2.value]))
text_input2.on_change("value", update2)
hover1 = HoverTool(names=["airp"])
plotp.patches(xarr,
yarr,
color="cyan",
alpha=1.0,
line_color="black",
line_width=2)
plotp.circle('x', 'y', source=ptscs, name="airp", color='red', size=5)
hover1.tooltips = [("Airport Name", "@City"), ("Airport Rank", "@Rank")]
plotp.add_tools(hover1)
curdoc().add_root(row(plotp, column(text_input1, text_input2,
text_input3)))
# CONTROLS
dataset = Select(title="Dataset",
options=data_manager.dataset_ids,
value=selected_dataset)
measure = Select(title="Measure",
options=[
('cosine_similarity', 'Cosine Similarity'),
('cosine', 'Cosine Distance'),
('euclidean', 'Euclidean'),
('dot_product', 'Dot product'),
('correlation', 'Correlation'),
],
value='cosine_similarity')
axes_preset = '; '.join(['usa', 'europe', 'china', 'japan', 'brazil'])
axes = TextInput(title='Axes Formulae (separated by ";")',
placeholder=axes_preset)
items_preset = '; '.join(['food', 'movie'])
items = TextInput(title='Items Formulae (separated by ";")',
placeholder=items_preset)
visualization_title = Div(text="<strong>Visualization</strong>")
opacity = Slider(title="Opacity", value=0.2, start=0, end=1, step=0.1)
# BIND CONTROL EVENTS
dataset.on_change('value', update)
axes.on_change('value', update)
items.on_change('value', update)
measure.on_change('value', update)
opacity.on_change('value', update)
self.fasta = fasta
self.title = title
self.notes = notes
self.highsupport = highsupport
self.full = full
self.partial = partial
self.annotations = annotations
GTF = TextInput(title="Enter the name of annotation file", value="gencode.vM8.annotation.gtf")
Format = TextInput(title="Enter the format of annotation file, standard is gtf", value="standard")
Matches = TextInput(
title="Enter the name of pickle files from MatchAnnot, e.g. of multiple files: match1.pickle,match2.pickle",
value="matches.pickle",
)
Gene = TextInput(title="Select gene to visualize")
Alpha = Slider(title="Alpha value of exons", value=1.0, start=0, end=1.0, step=0.1)
Full = Slider(title="Full support threshold", value=0, start=0, end=30, step=1.0)
Partial = Slider(title="Partial support threshold", value=0, start=0, end=50, step=1.0)
Group = Select(title="Group isoform or not", value="on", options=["on", "off"])
Cluster = Slider(title="The number of groups", value=3, start=1, end=5, step=1.0)
Width = Slider(title="The width of transcripts", value=20, start=5, end=30, step=1)
Save = TextInput(title="Enter the folder name to data in Fasta", value=None)
blockSource = ColumnDataSource(
data=dict(top=[], bottom=[], left=[], right=[], exon=[], start=[], end=[], chromosome=[], xs=[], ys=[])
)
source = ColumnDataSource(data=dict(x=[], y=[], color=[], line_alpha=[], width=[], QScore=[], start=[], end=[]))
geneSource = ColumnDataSource(data=dict(Gene=[], Cluster=[]))
df = pd.DataFrame()
'lws':[2]
}
))
circobs = p.circle(x='xs', y='ys', color='colors',
source=ColumnDataSource(data=
{
'xs':[[]],
'ys':[[]],
'colors':['white'],
}
))
# add a button widget and configure with the call back
button = Button(label="Fetch Data")
namefield = TextInput(value="", title="Name(s):")
bandfield = TextInput(value="", title="Band(s):")
# add a text renderer to out plot (no data yet)
with open('/root/astrocats/astrocats/supernovae/output/names.min.json') as f:
names = json.loads(f.read())
names = list(names.keys())
unames = [x.upper() for x in names]
bands = plotting.bandcodes
ubands = [x.upper() for x in bands]
nds = []
def callback():
value="10")
top_n_clusters_dropdown = Dropdown(label="Show Top 100 Clusters",
button_type="warning",
menu=n_clusters_menu,
value="100")
tfidf_slider = Slider(start=0,
end=1,
value=0,
step=.05,
title="Informativeness")
cval_slider = Slider(start=0, end=1, value=0, step=.05, title="Completeness")
freq_slider = Slider(start=0, end=1, value=0, step=.05, title="Linguistical")
filter_topics_tab = Panel(child=filter_topics_table, title="Filter Topics")
filter_custom_tab = Panel(child=filter_custom_table, title="Custom Trends")
filter_tabs = Tabs(tabs=[filter_topics_tab, filter_custom_tab])
search_input_box = TextInput(title="Search:", value="", width=300)
search_button = Button(label="Go",
button_type="success",
width=50,
css_classes=['search_button'])
clear_button = Button(label="Clear",
button_type="success",
width=50,
css_classes=['clear_button'])
buttons_layout = column(Div(height=0), Row(search_button, clear_button))
analyse_button = Button(label="re-analyze", button_type="success")
filter_label = Div(text="",
style={
'color': 'red',
'padding-bottom': '0px',
'font-size': '15px'
# Plot constraint function contour g(x,y)=0
contour_g = my_bokeh_utils.Contour(plot, line_color='red', line_width=2, legend='g(x,y) = 0')
# Plot corresponding tangent vector
quiver_isolevel = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='black')
# Plot corresponding tangent vector
quiver_constraint = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='red')
# Plot mark at position on constraint function
plot.cross(x='x', y='y', color='red', size=10, line_width=2, source=source_mark)
# object that detects, if a position in the plot is clicked on
interactor = my_bokeh_utils.Interactor(plot)
# adds callback function to interactor, if position in plot is clicked, call on_selection_change
interactor.on_click(on_selection_change)
# text input window for objective function f(x,y) to be optimized
f_input = TextInput(value=lagrange_settings.f_init, title="f(x,y):")
f_input.on_change('value', f_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_f = Dropdown(label="choose a sample function f(x,y) or enter one below",
menu=lagrange_settings.sample_f_names)
sample_fun_input_f.on_click(sample_fun_input_f_changed)
# text input window for side condition g(x,y)=0
g_input = TextInput(value=lagrange_settings.g_init, title="g(x,y):")
g_input.on_change('value', g_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_g = Dropdown(label="choose a sample function g(x,y) or enter one below",
menu=lagrange_settings.sample_g_names)
sample_fun_input_g.on_click(sample_fun_input_g_changed)
def on_text_value_change(attr, old, new):
try:
global expr
expr = sy.sympify(new, dict(x=xs))
except (sy.SympifyError, TypeError, ValueError) as exception:
dialog.content = str(exception)
dialog.visible = True
else:
update_data()
dialog = Dialog(title="Invalid expression")
slider = Slider(start=1, end=20, value=order, step=1, title="Order", callback_policy="mouseup")
slider.on_change("value", on_slider_value_change)
text = TextInput(value=str(expr), title="Expression:")
text.on_change("value", on_text_value_change)
inputs = WidgetBox(children=[slider, text], width=400)
layout = Column(children=[inputs, plot, dialog])
update_data()
document.add_root(layout)
session.show(layout)
if __name__ == "__main__":
print("\npress ctrl-C to exit")
session.loop_until_closed()
def compare():
# if proj among arguments, show this tree first.
try:
proj_a = int(request.args.get('proj_a', None))
proj_b = int(request.args.get('proj_b', None))
except (TypeError, ValueError):
proj_a = proj_b = None
include = request.args.get('include', None)
# list of projects (and proj_names) used to create dropdown project selector
upload_list = UserFile.query.filter_by(user_id=current_user.id).\
filter_by(run_complete=True).order_by(UserFile.file_id).all()
if len(upload_list) > 1:
# Use specified project from args or highest file_id as CURRENT PROJECT
current_proj = upload_list[-1] # override if valid proj specified
if proj_a and proj_b:
current_temp_a = [u for u in upload_list if u.file_id == proj_a]
current_temp_b = [u for u in upload_list if u.file_id == proj_b]
# if not among user's finished projects, use highest file_id
if len(current_temp_a) == 1 and len(current_temp_b) == 1:
current_proj_a = current_temp_a[0]
current_proj_b = current_temp_b[0]
else:
current_proj_a = upload_list[-2]
current_proj_b = upload_list[-1]
else:
current_proj_a = upload_list[-2]
current_proj_b = upload_list[-1]
detail_path1 = naming_rules.get_detailed_path(current_proj_a)
detail_path2 = naming_rules.get_detailed_path(current_proj_b)
js_name1 = naming_rules.get_js_name(current_proj_a)
js_name2 = naming_rules.get_js_name(current_proj_b)
xlabel = u"Effect size ({})".format(current_proj_a.get_fancy_filename())
ylabel = u"Effect size ({})".format(current_proj_b.get_fancy_filename())
# load pathways with 1+ mutation in 1+ patients,
# ignoring ones with 'cancer' etc in name
all_paths1 = load_pathway_list_from_file(detail_path1)
all_paths2 = load_pathway_list_from_file(detail_path2)
# IDs with p<0.05 and +ve effect
sig_p = OrderedDict(
[(i.path_id, i.nice_name) for i in all_paths1 if i.gene_set])
sig_pids1 = [i for i in sig_p]
sig_p2 = OrderedDict(
[(i.path_id, i.nice_name) for i in all_paths2 if i.gene_set])
sig_pids2 = [i for i in sig_p2]
sig_p.update(sig_p2) # ORDERED by proj1 effect size
# BUILD DATAFRAME WITH ALL sig PATHWAYS, proj1 object order.
pway_names = sig_p.values() # order important
columns = ['path_id', 'pname', 'ind1', 'ind2', 'e1', 'e2', 'e1_only',
'e2_only', 'q1', 'q2']
df = pd.DataFrame(index=sig_p.keys(), data={'pname': pway_names},
columns=columns)
for path_group, evar, qvar, ind, sigs in \
[(all_paths1, 'e1', 'q1', 'ind1', sig_pids1),
(all_paths2, 'e2', 'q2', 'ind2', sig_pids2)]:
for path in path_group:
path_id = path.path_id
if path_id not in sig_p:
continue
df.loc[path_id, evar] = get_effect(path)
df.loc[path_id, qvar] = get_q(path)
temp_ind = sigs.index(path_id) if path_id in sigs else -1
df.loc[path_id, ind] = temp_ind
df.ind1.fillna(-1, inplace=True)
df.ind2.fillna(-1, inplace=True)
df.e1_only = df.where(df.e2.isnull())['e1']
df.e2_only = df.where(df.e1.isnull())['e2']
inds1 = list(df.ind1)
inds2 = list(df.ind2)
source = ColumnDataSource(data=df)
source_full = ColumnDataSource(data=df)
source.name, source_full.name = 'data_visible', 'data_full'
# SET UP FIGURE
minx = df.e1.min()
minx *= 1 - minx / abs(minx) * 0.2
miny = df.e2.min()
miny *= 1 - miny/abs(miny) * 0.2
maxx = df.e1.max() * 1.2
maxy = df.e2.max() * 1.2
TOOLS = "lasso_select,box_select,hover,crosshair,pan,wheel_zoom,"\
"box_zoom,reset,tap,help" # poly_select,lasso_select, previewsave
# SUPLOTS
p = figure(plot_width=DIM_COMP_W, plot_height=DIM_COMP_H, tools=TOOLS,
title=None, logo=None, toolbar_location="above",
x_range=Range1d(minx, maxx), y_range=Range1d(miny, maxy),
x_axis_type="log", y_axis_type="log"
)
pb = figure(plot_width=DIM_COMP_SM, plot_height=DIM_COMP_H, tools=TOOLS,
y_range=p.y_range, x_axis_type="log", y_axis_type="log")
pa = figure(plot_width=DIM_COMP_W, plot_height=DIM_COMP_SM, tools=TOOLS,
x_range=p.x_range, x_axis_type="log", y_axis_type="log")
pp = figure(plot_width=DIM_COMP_SM, plot_height=DIM_COMP_SM,
tools=TOOLS, outline_line_color=None)
# SPANS
p.add_layout(plot_fns.get_span(1, 'height'))
p.add_layout(plot_fns.get_span(1, 'width'))
pa.add_layout(plot_fns.get_span(1, 'height'))
pb.add_layout(plot_fns.get_span(1, 'width'))
# STYLE
for ax in [p, pa, pb]:
ax.grid.visible = False
ax.outline_line_width = 2
ax.background_fill_color = 'whitesmoke'
for ax in [pa, pb]:
ax.xaxis.visible = False
ax.yaxis.visible = False
pa.title.text = xlabel
pb.title.text = ylabel
pa.title_location, pa.title.align = 'below', 'center'
pb.title_location, pb.title.align = 'left', 'center'
# WIDGETS
q_input = TextInput(value='', title="P* cutoff",
placeholder='e.g. 0.05')
gene_input = TextInput(value='', title="Gene list",
placeholder='e.g. TP53,BRAF')
radio_include = RadioGroup(labels=["Include", "Exclude"], active=0)
widgets = widgetbox(q_input, gene_input, radio_include, width=200,
css_classes=['widgets_sg'])
grid = gridplot([[pb, p, widgets],
[Spacer(width=DIM_COMP_SM), pa, Spacer()]],
sizing_mode='fixed')
cb_inclusion = CustomJS(args=dict(genes=gene_input), code="""
var gene_str = genes.value
if (!gene_str)
return;
var include = cb_obj.active == 0 ? true : false
selectPathwaysByGenes(gene_str, include);
""")
cb_genes = CustomJS(args=dict(radio=radio_include), code="""
var gene_str = cb_obj.value
if (!gene_str)
return;
var include = radio.active == 0 ? true : false
selectPathwaysByGenes(gene_str, include);
""")
radio_include.js_on_change('active', cb_inclusion)
gene_input.js_on_change('value', cb_genes)
# SCATTER
p.circle("e1", "e2", source=source, **SCATTER_KW)
pa.circle('e1_only', 1, source=source, **SCATTER_KW)
pb.circle(1, 'e2_only', source=source, **SCATTER_KW)
# HOVER
for hover in grid.select(dict(type=HoverTool)):
hover.tooltips = OrderedDict([
("name", "@pname"),
("effects", "(@e1, @e2)"),
("P*", ("(@q1, @q2)"))
])
# ADD Q FILTERING CALLBACK
callback = CustomJS(args=dict(source=source, full=source_full), code="""
// get old selection indices, if any
var prv_selected = source.selected['1d'].indices;
var prv_select_full = []
for(var i=0; i<prv_selected.length; i++){
prv_select_full.push(scatter_array[prv_selected[i]])
}
var new_selected = []
var q_val = cb_obj.value;
if(q_val == '')
q_val = 1
var fullset = full.data;
var n_total = fullset['e1'].length;
// Convert float64arrays to array
var col_names = %s ;
col_names.forEach(function(col_name){
source.data[col_name] = [].slice.call(source.data[col_name])
source.data[col_name].length = 0
})
scatter_array.length = 0;
var j = -1; // new glyph indices
for (i = 0; i < n_total; i++) {
this_q1 = fullset['q1'][i];
this_q2 = fullset['q2'][i];
if(this_q1 <= q_val || this_q2 <= q_val){
j++; // preserve previous selection if still visible
col_names.forEach(function(col){
source.data[col].push(fullset[col][i]);
})
scatter_array.push(i)
if($.inArray(i, prv_select_full) > -1){
new_selected.push(j);
}
}
}
source.selected['1d'].indices = new_selected;
source.trigger('change');
updateIfSelectionChange_afterWait();
""" % columns)
q_input.js_on_change('value', callback)
script, div = plot_fns.get_bokeh_components(grid)
proj_dir_a = naming_rules.get_project_folder(current_proj_a)
proj_dir_b = naming_rules.get_project_folder(current_proj_b)
if os.path.exists(os.path.join(proj_dir_a, 'matrix_svg_cnv')) and \
os.path.exists(os.path.join(proj_dir_b, 'matrix_svg_cnv')):
has_cnv = True
else:
has_cnv = False
else: # not enough projects yet!
flash("Two completed projects are required for a comparison.",
"warning")
return redirect(url_for('.index'))
return render_template('pway/compare.html',
current_projs=[current_proj_a, current_proj_b],
inds_use=[inds1, inds2],
has_cnv=has_cnv,
js_name_a=js_name1,
js_name_b=js_name2,
projects=upload_list,
bokeh_script=script,
bokeh_div=div, include_genes=include,
resources=plot_fns.resources)
x = np.arange(0, max_days_to_compute, 1) # times
y = get_util_vs_time_data(days=x)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plot
plot = figure(plot_height=plot_height, plot_width=plot_width, title=plot_title,
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, max_days_to_display],
x_axis_label="Days Since Attack",
y_axis_label="Relative Profit of Attacking (Millions of USD)")
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="Title", value='mighty bitcoin')
mining_power = Slider(title="Mining Power", start=min_mining_power, end=max_mining_power, value=default_mining_power, step=mining_power_step)
discount_rate = Slider(title="Discount Rate", start=min_discount, end=max_discount, value=default_discount, step=discount_step)
btc_stolen = Slider(title="Amount of BTC Stolen", start=min_btc_stolen, end=max_btc_stolen, value=default_btc_stolen, step=btc_stolen_step)
btc_owned_0 = Slider(title="BTC Owned At Time 0", start=min_btc_owned_0, end=max_btc_owned_0, value=default_btc_owned_0, step=btc_owned_0_step)
exchange_rate = Slider(title="BTC-USD Exchange Rate", start=min_rate, end=max_rate, value=default_rate, step=rate_step)
btc_f_stolen = Slider(title="Fraction Amount of BTC Stolen", start=min_btc_f_stolen, end=max_btc_f_stolen, value=default_btc_f_stolen, step=btc_f_stolen_step)
btc_f_owned_0 = Slider(title="Fraction BTC Owned At Time 0", start=min_btc_f_owned_0, end=max_btc_f_owned_0, value=default_btc_f_owned_0, step=btc_f_owned_0_step)
hline = Span(location=0, dimension='width', line_color='green', line_width=1.5)
plot.renderers.extend([hline])
# Set up callbacks
def update_title(attrname, old, new):
def explore_tab(df):
def get_dataset(src, name, words, start, end):
df = src[src.user == name].copy()
mask = (df['timestamp'] > start) & (df['timestamp'] <= end)
df = df[mask]
words = [str(i) for i in words.split()]
safe_words = []
for word in words:
word = re.escape(word)
word = "(?=.*{})".format(word)
safe_words.append(word)
df = df[df['texts'].str.contains(''.join(safe_words))]
source = ColumnDataSource(data=dict())
cols = ['texts', 'displaySource', 'source']
df[cols] = df[cols].replace({',': '', ',,': '', ';': ''}, regex=True)
source.data = {
# 'index': df.index,
'impressionTime': df.impressionTime,
'impressionOrder': df.impressionOrder,
'source': df.source,
'fblinktype': df.fblinktype,
'texts': df.texts,
'textsize': df.textsize,
'publicationTime': df.publicationTime,
'permaLink': df.permaLink,
'nature': df.nature,
'ANGRY': df.ANGRY,
'HAHA': df.HAHA,
'LIKE': df.LIKE,
'LOVE': df.LOVE,
'SAD': df.SAD,
'WOW': df.WOW,
'displaySource': df.displaySource,
'id': df.id,
'timestamp': df.timestamp,
# 'images': df.images,
# 'opengraph': df.opengraph,
'postId': df.postId,
# 'semanticCount': df.semanticCount,
# 'semanticId': df.semanticId,
'sourceLink': df.sourceLink,
'timeline': df.timeline,
'user': df.user,
# 'videoautoplay': df.videoautoplay
}
return source
def make_table(source):
# Columns of tablem
table_columns = [
TableColumn(field='impressionTime', title='Time'),
TableColumn(field='impressionOrder', title='Order'),
TableColumn(field='source', title='Source'),
TableColumn(field='fblinktype', title='Type'),
TableColumn(field='texts', title='Text'),
TableColumn(field='textsize', title='Text Size'),
TableColumn(field='publicationTime', title='Publication Time'),
TableColumn(field='permaLink', title='Link'),
TableColumn(field='nature', title='Nature'),
TableColumn(field='ANGRY', title='Angry'),
TableColumn(field='HAHA', title='Haha'),
TableColumn(field='LIKE', title='Like'),
TableColumn(field='LOVE', title='Love'),
TableColumn(field='SAD', title='Sad'),
TableColumn(field='WOW', title='Wow')
]
user_table = DataTable(source=source,
columns=table_columns,
width=1400)
return user_table
def update(attrname, old, new):
name = name_select.value
text_filter = text_input.value
start = date_slider.value[0]
end = date_slider.value[1]
src = get_dataset(df, name, text_filter, start, end)
source.data.update(src.data)
name = df.user.iloc[0]
words = ''
names = df.user.unique()
start = df.timestamp.min()
end = df.timestamp.max()
name_select = Select(value=name, title='User', options=sorted(names))
text_input = TextInput(value="", title="Filter text:")
date_slider = DateRangeSlider(title="Date Range: ",
start=df.timestamp.min(),
end=date.today(),
value=(df.timestamp.min(), date.today()),
step=1,
callback_policy='mouseup')
button = Button(label="Download", button_type="success")
source = get_dataset(df, name, words, start, end)
table = make_table(source)
name_select.on_change('value', update)
text_input.on_change('value', update)
date_slider.on_change('value', update)
button.js_on_click(
CustomJS(args=dict(source=source),
code=open(join(dirname(__file__), "download.js")).read()))
controls = column(name_select, date_slider, text_input, button)
tab = Panel(child=row(table, controls), title='Explore')
return tab
def plotting(self):
if self.debug:
self.debug_file = open("debug_output.txt", "w")
self.debug_file.write("Initialized plotting subroutine \n")
TOOLS="pan,wheel_zoom,box_zoom,reset,hover,previewsave"
tab_plots = []
self.all_elements = []
self.elements_comparison = []
for filename in self.filenames:
if "ITO" in filename:
tab_plots.append(self.mass_plotting(filename))
continue
data_dict = self.data_generation(filename)
self.data_test(data_dict)
name_check = data_dict["gen_info"]["DATA FILES"]
attr_id = name_check[1][4][:-3] + "_" + name_check[2][2]
self.attribute_ids.append(attr_id)
attr_extra_y_ranges = False
attr_extra_x_ranges = False
local_source_line = []
"""
create plots for each datafile and put them in a tab.
"""
y_axis_units = [x["y_unit"] for x in data_dict["data"]]
x_axis_units = [x["x_unit"] for x in data_dict["data"]]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log",
title = attr_id, tools = TOOLS)
#figure_obj.axes.major_label_text_font_size("12pt")
#figure_obj.major_label_text_font_size("12pt")
hover = figure_obj.select(dict(type = HoverTool))
hover.tooltips = [
("Element:", "@element"),
("(x, y):", "($x, $y)")]
self.figure_data.append((figure_obj, data_dict))
figure_obj.yaxis.axis_label = y_axis_units[0]
figure_obj.xaxis.axis_label = x_axis_units[0]
if not all(x == y_axis_units[0] for x in y_axis_units):
for unit, dataset in zip(y_axis_units, data_dict["data"]):
if not unit == y_axis_units[0]:
extra_y_ranges_exists = attr_extra_y_ranges
extra_y_ranges_exists = True
if self.debug:
self.debug_file.write("Added extra y-axis for file_id: %s, element: %s | New length %g \n"
%(attr_id, dataset["sample_element"], len(figure_obj.yaxis)))
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(dataset["y"]),
end = np.amax(dataset["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[0] for x in x_axis_units):
for unit, dataset in zip(x_axis_units, data_dict["data"]):
if not unit == x_axis_units[0]:
extra_x_ranges_exists = attr_extra_x_ranges
extra_x_ranges_exists = True
if self.debug:
self.debug_file.write("Added extra x-axis for file_id: %s, element: %s. | New length %g \n"
%(attr_id, dataset["sample_element"], len(figure_obj.yaxis)))
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(dataset["x"]),
end = np.amax(dataset["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
figure_obj.xaxis.axis_label = x_axis_units[0]
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
list_of_elements = []
source_list = []
line_list = []
for dataset, color_index in zip(data_dict["data"], colour_indices):
self.all_elements.append(dataset["sample_element"]) #strip isotope number
color = colour_list[color_index]
source = ColumnDataSource(data = dataset) #Datastructure for source of plotting
self.source_test(source)
list_of_elements.append(dataset["sample_element"])
line_glyph = figure_obj.line("x", "y", source = source,
line_width = 2,
line_color = color,
legend = dataset["sample_element"])
if self.debug:
self.debug_file.write("Create line object on figure %s at %s \n" %(id(figure_obj), id(line_glyph)))
line_list.append(line_glyph)
source_list.append(source)
local_source_line.append([[source, line] for source, line in zip(source_list, line_list)])
self.source_line.append(local_source_line)
#Calculations on the dataset
text_input_rsf = TextInput(value = "default", title = "RSF or SF (at/cm^3): ")
do_integral_button = Button(label = "Calibration integral")
smoothing_button = Button(label = "smth selct elem")
matplot_button = Button(label = "Create matplotlib fig")
text_input_sputter = TextInput(value = "default", title = "Sputter speed: number unit")
text_input_crater_depth = TextInput(value = "default", title = "Depth of crater in: number unit")
radio_group = RadioGroup(labels = list_of_elements, active=0)
text_input_xval_integral = TextInput(value = "0", title = "x-delimiter ")
text_input_dose = TextInput(value = "0", title = "Dose[cm^-2] ")
#Save files for later use
save_flexDPE_button = Button(label = "Save element for FlexPDE")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
save_textfile_button = Button(label = "Sava Data in textfile")
#Pointers to methods on click / change handlers
radio_group.on_change("active", lambda attr, old, new: None)
matplot_button.on_click(lambda source_list = source_list:
self.matplotlib_export(source_list))
do_integral_button.on_click(lambda
source_list = source_list,
line_list = line_list,
source_line = self.source_line,
figure_data = self.figure_data,
data_dict = data_dict,
radio = radio_group,
x_box = text_input_xval_integral,
dose = text_input_dose,
extra_y_ranges = attr_extra_y_ranges:
self.integrate(data_dict, source_list, line_list, source_line, figure_data, radio, x_box, dose, extra_y_ranges))
smoothing_button.on_click(lambda
source_list = source_list,
radio = radio_group,
data_dict = data_dict,
x_box = text_input_xval_integral:
self.smoothing(source_list, data_dict, radio, x_box) )
save_flexDPE_button.on_click(lambda
source_list = source_list,
attrname = attr_id,
radio = radio_group:
self.write_to_flexPDE(source_list, attrname, radio))
save_all_flexDPE_button.on_click(lambda
source_list = source_list,
attrname = attr_id:
self.write_all_to_flexPDE(source_list, attrname))
save_textfile_button.on_click(lambda
data_dict = data_dict,
source_list = source_list,
attrname = attr_id,
radio = radio_group:
self.write_new_datafile(data_dict, source_list, attrname,radio))
text_input_rsf.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
figure = figure_obj,
source_list = source_list,
text_input = text_input_rsf,
line_list = line_list,
which = "rsf":
self.update_data(line_list, data_dict, source_list, figure, radio, text_input, new, which))
text_input_sputter.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
figure = figure_obj,
source_list = source_list,
text_input = text_input_sputter,
which = "sputter":
self.update_data(data_dict, source_list, figure, radio, text_input, new, which))
text_input_crater_depth.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
source_list = source_list,
figure = figure_obj,
text_input = text_input_crater_depth,
which = "crater_depth":
self.update_data(data_dict, source_list, figure, radio, text_input, new, which))
#Initialization of actual plotting.
tab_plots.append(Panel(child = hplot(figure_obj,
vform(
vform(radio_group, save_flexDPE_button, save_all_flexDPE_button, save_textfile_button, matplot_button),
vform(text_input_rsf, smoothing_button, text_input_sputter, text_input_crater_depth)
),
vform(text_input_xval_integral, text_input_dose, do_integral_button)),
title = attr_id))
"""
Check to see if one or more element exists in the samples and creat a comparison plot for each
of those elements.
"""
for element in self.all_elements:
checkers = list(self.all_elements)
checkers.remove(element)
if element in checkers and not element in self.elements_comparison:
self.elements_comparison.append(element)
"""create plots for each element that is to be compared """
for comparison_element in self.elements_comparison:
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log", title = comparison_element, tools = TOOLS)
#figure_obj.xaxis.major_label_text_font_size("12pt")
#figure_obj.yaxis.major_label_text_font_size("12pt")
y_axis_units = []
x_axis_units = []
comparison_datasets = []
for data_dict_iter in self.column(self.figure_data, 1):
for dataset in data_dict_iter["data"]:
if dataset["sample_element"] == comparison_element:
comparison_datasets.append(dataset)
y_axis_units.append(dataset["y_unit"])
x_axis_units.append(dataset["x_unit"])
figure_obj.xaxis.axis_label = comparison_datasets[-1]["x_unit"]
figure_obj.yaxis.axis_label = comparison_datasets[-1]["y_unit"]
if not all(x == y_axis_units[-1] for x in y_axis_units):
for unit, data in zip(y_axis_units, comparison_datasets):
if not unit == y_axis_units[-1]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["y"]),
end = np.amax(data["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[-1] for x in x_axis_units):
for unit, data in zip(x_axis_units, comparison_datasets):
if not unit == x_axis_units[-1]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["x"]),
end = np.amax(data["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
active_sources = []
for data_dict, source_line_nested, attr_id, color_index in zip(self.column(self.figure_data, 1), self.source_line, self.attribute_ids, colour_indices):
for dataset, source_lis_coup, in zip(data_dict["data"], source_line_nested[0]):
source_local = source_lis_coup[0]
active_sources.append(source_local)
self.source_test(source_local)
self.source_dataset_test(source_local, dataset)
if dataset["sample_element"] == comparison_element:
color = colour_list[color_index]
"""
Logic that ensures that plots get put with correspoinding axes.
"""
if dataset["x_unit"] != x_axis_units[-1] or dataset["y_unit"] != y_axis_units[-1]:
if dataset["x_unit"] != x_axis_units[-1] and dataset["y_unit"] != y_axis_units[-1]:
name_check = data_dict["gen_info"]["DATA FILES"]
attr_id = name_check[1][4][:-3] + "_" + name_check[2][2]
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
x_range_name = "bar",
y_range_name = "foo")
elif dataset["x_unit"] != x_axis_units[-1]:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
x_range_name = "bar")
else:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
y_range_name = "foo")
else:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id)
matplot_button = Button(label = "Create matplotlib fig")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
matplot_button.on_click(lambda source_list = active_sources:
self.matplotlib_export(source_list))
save_all_flexDPE_button.on_click(lambda
source_list = active_sources,
attrname = comparison_element:
self.write_all_to_flexPDE(source_list, attrname))
tab_plots.append(Panel(child = hplot(figure_obj, vform(save_all_flexDPE_button, matplot_button)),
title = comparison_element))
tabs = Tabs(tabs = tab_plots)
#curdoc().add_root(tabs)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
acq_period_select = Select(title="Acquisition Period (s):", value="100",
options=[".5","1","10","100","300","1000"])
num_avgs_select = Select(title="Number of Points to Average:", value="100",
options=["100","1000","10000","100000","1000000"])
channel_name_select1 = Select(title='Channel Select:', value="Channel 1",
options =["Channel 1", "Channel 2", "Channel 3", "Channel 4"])
channel_name_select2 = Select(title='Channel Names:', value="Channel 1",
options =["Channel 1", "Channel 2", "Channel 3", "Channel 4"])
number_points_select = Select(title="Number of Points", value="100",
options =["100", "200", "500", "1000", "5000", "10000", "100000", "1000000"])
load_plot_type_select = Select(title="Plot Type:", value="Multi-plot",
options =["Multi-plot", "Pulse Capture Plot"])
# Text inputs
channel_name_input = TextInput(title="Name:")
trigger_level_input = TextInput(value="1", title="Trigger Level (V)")
time_range_input = TextInput(value="1000", title="Time Range (us)")
load_file_input = TextInput(value=home_dir, title="Load file:")
save_filepath_input = TextInput(value=home_dir, title="Save to directory:")
force_save_filename_input = TextInput(value=str(dt.now(PT).year)+"_"+str(dt.now(PT).month)+"_"+str(dt.now(PT).day)+".h5",
title="Save as filename:")
save_filepath_PC_input = TextInput(value=home_dir, title="Save to directory:")
save_filename_PC_input = TextInput(value="PC_"+str(dt.now(PT).year)+"_"+str(dt.now(PT).month)+"_"+str(dt.now(PT).day)+".h5",
title="Save as filename:")
#setup event handlers
all_off_button.on_click(lambda x: allOff_ButtonClick())
all_on_button.on_click(lambda x: allOn_ButtonClick())
reset_button.on_click(lambda x: reset_ButtonClick())
height_map={
"Current Location":y_local_height,
"Current Industry":y_indus_height,
"Current Company":y_company_height
}
new_axis_map={
"Current Location":'location',
"Current Industry":'industry',
"Current Company":'company'
}
source = ColumnDataSource(data=dict(x=[],y=[],height=[]))
cities=TextInput(title='location name')
companies=TextInput(title='company name')
industries=TextInput(title='industry name')
x_axis=Select(title='X axis',options=sorted(axis_map.keys()),value='Current Industry')
plot = figure(plot_height=600, plot_width=800, title="", toolbar_location=None, tools=[hover],x_range=industry[:10],y_range=[0,2000])
plot.rect(x='x',y='y',width=.8,height='height',source=source)
plot.xaxis.major_label_orientation = np.pi/3
controls=[x_axis,cities,companies,industries]
inputs=HBox(VBoxForm(*controls))
def select_types():
city_val=cities.value.strip()
company_val=companies.value.strip()
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.models.widgets import TextInput, Button, Paragraph
# create some widgets
button = Button(label="Say HI")
input = TextInput(value="Bokeh")
output = Paragraph()
# add a callback to a widget
def update():
output.text = "Hello, " + input.value
button.on_click(update)
# create a layout for everything
layout = column(button, input, output)
# add the layout to curdoc
curdoc().add_root(layout)
# create a grid of samples
xx, hx = np.linspace(source_view.data['x_start'][0], source_view.data['x_end'][0], curveintegral_settings.n_sample,
retstep=True)
yy, hy = np.linspace(source_view.data['y_start'][0], source_view.data['y_end'][0], curveintegral_settings.n_sample,
retstep=True)
x_val, y_val = np.meshgrid(xx, yy)
# evaluate function at sample points
u_val = u_fun(x_val, y_val)
v_val = v_fun(x_val, y_val)
return x_val, y_val, u_val, v_val, hx
# initialize controls
# text input for input of the vector function [fx(x,y),fy(x,y)]
u_input = TextInput(value=curveintegral_settings.sample_functions[curveintegral_settings.init_fun_key][0],
title="fx(x,y):")
v_input = TextInput(value=curveintegral_settings.sample_functions[curveintegral_settings.init_fun_key][1],
title="fy(x,y):")
u_input.on_change('value', function_change)
v_input.on_change('value', function_change)
# text input for input of the parametrized curve [cx(t),cy(t)]
cx_input = TextInput(value=curveintegral_settings.sample_curves[curveintegral_settings.init_curve_key][0],
title="cx(t):")
cy_input = TextInput(value=curveintegral_settings.sample_curves[curveintegral_settings.init_curve_key][1],
title="cy(t):")
# slider controlling the parameter t
parameter_input = Slider(title="t",
value=curveintegral_settings.parameter_input_init,
start=curveintegral_settings.parameter_min,
x = np.linspace(0, 4 * np.pi, N)
y = np.sin(x)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plot
plot = figure(plot_height=800,
plot_width=800,
title="my sine wave",
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, 4 * np.pi],
y_range=[-2.5, 2.5])
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0, step=0.1)
phase = Slider(title="phase", value=0.0, start=0.0, end=2 * np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1, step=0.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
N = 200
x = np.linspace(0, 4*np.pi, N)
y = np.sin(x)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plot
plot = Figure(plot_height=400, plot_width=400, title="my sine wave",
tools="crosshair,pan,reset,resize,save,wheel_zoom",
x_range=[0, 4*np.pi], y_range=[-2.5, 2.5])
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0)
phase = Slider(title="phase", value=0.0, start=0.0, end=2*np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
width=200,
options=bond_type_selections)
scale_select = Select(value=scale_selections[0],
title=u'基金资产净值',
width=200,
options=scale_selections)
time_select = Select(value=u'1年',
title=u'业绩时间窗口',
width=200,
options=time_selections)
time_select.on_change('value', lambda attr, old, new: update_data())
fund_button = Button(label=u"筛选基金", button_type="success", width=200)
fund_button.on_click(select_fund)
update_button = Button(label=u'更新数据', button_type='success', width=200)
update_button.on_click(update_new_data)
fund_text = TextInput(value='000088.OF', title=u'基金Wind代码', width=200)
fund_text.on_change('value', lambda attr, old, new: update_data())
columns = [
TableColumn(field='sec_name', title=u'基金简称'),
TableColumn(field='wind_code', title=u'万得代码'),
TableColumn(field='fundmanager', title=u'基金经理'),
TableColumn(field='netasset',
title=u'基金资产净值',
formatter=NumberFormatter(format='$0,0.00')),
TableColumn(field='current return',
title=u'当前业绩',
formatter=NumberFormatter(format='0.00%')),
TableColumn(field='volatility',
title=u'波动率',
formatter=NumberFormatter(format='0.00%')),
from bokeh.models.widgets import Button, TextInput
label_saccade_button = Button(label='saccade')
label_saccade_button.on_click(label_saccade_cb)
label_pursuit_button = Button(label='pursuit')
label_pursuit_button.on_click(label_pursuit_cb)
label_fixation_button = Button(label='fixation')
label_fixation_button.on_click(label_fixation_cb)
remove_button = Button(label='remove')
remove_button.on_click(remove_cb)
trial_text = TextInput(value=str(trialNum))
trial_text.on_change('value', trial_text_cb)
nextTrial_button = Button(label='+trial')
nextTrial_button .on_click(next_trial_cb)
prevTrial_button= Button(label='-trial')
prevTrial_button.on_click(prev_trial_cb)
from bokeh.layouts import row
buttonBox = row(label_saccade_button, label_pursuit_button, label_fixation_button, remove_button)
trialSelectBox = row(prevTrial_button, trial_text, nextTrial_button)
#, sizing_mode='scale_width'
###########################################################################
# Get the HTML description header
update_volume_table()
update_eyby()
update_industry_corr()
# update_min_liquidity()
# update_wei_index_table()
# update_wei_index()
update_record_high_table()
asset_select = Select(value=u"万得全A指数",
title="资产",
width=300,
options=asset_selections)
asset_select.on_change('value', lambda attr, old, new: update_data())
time_text = TextInput(value="2005-01-01",
title="开始时间（例如：20050101或2005-01-01）",
width=300)
time_text.on_change('value', lambda attr, old, new: update_all())
today = (datetime.datetime.today() - datetime.timedelta(1))
time_end_text = TextInput(value=today.strftime("%Y-%m-%d"),
title="终止时间",
width=300)
time_end_text.on_change('value', lambda attr, old, new: update_all())
asset_text_1 = TextInput(value="881001.WI", title=u"资产一（万得代码）", width=300)
asset_text_2 = TextInput(value="HSI.HI", title=u"资产二（万得代码）", width=300)
department_select = Select(value=u"万得全A",
title=u"选择板块",
width=300,
options=index_selections)
department_select.on_change('value', lambda attr, old, new: update_cost())
industry_select = Select(value=u"石油石化",