def create_figure(s1, s3):
"""Return figure object of probability distribution."""
plot = Figure(title='Prior Distribution')
plot.xaxis.axis_label = 'Probability of Heads (-)'
plot.yaxis.axis_label = 'Probability Density (-)'
plot.line('x', 'p', source=s1, line_width=4)
plot.patch('x', 'y', source=s3, alpha=0.25, line_width=0)
return plot
def create_fig(self, sources):
"""
Implementation of :meth:`LivePlot.create_fig` to set up the bokeh figure
"""
fig = Figure(background_fill_color= None,
toolbar_location = None,
x_range=[-100, 100], y_range=[-100, 100], tools="", **self._fig_args)
# Remove existing axes and grid
fig.xgrid.grid_line_color = None
fig.ygrid.grid_line_color = None
fig.axis.visible = False
# Draw circular grid
th = np.linspace(0, 360, 100)
# Draw visibility field
self.vis_horizon = 10
#fig.patch(*pol2rect([90 - self.vis_horizon]*len(th), th), color='lightgray')
fig.patch(*azel2rect(th, [self.vis_horizon]*len(th)), color='lightgray')
for r in [0, 20, 40, 60, 80]:
fig.line(*azel2rect(th, [r]*len(th)), line_dash='dashed')
for th in np.arange(0,360,45):
fig.line(*azel2rect([th,th], [0,90]), line_dash='dashed')
# Add grid labels
lx,ly = azel2rect([0, 90, 180, 270], [0]*4)
lx[1] -= 30
lx[2] -= 20
ly[0] -= 10
lx[0] -= 10
src_labels = ColumnDataSource(dict(
labels= ['N (0)', 'E (90)', 'S (180)', 'W (270)'],
label_x= lx,
label_y =ly))
fig.add_layout(LabelSet(x='label_x', y='label_y', text='labels', source=src_labels))
fig.line(source=sources['dpass'], x='x', y = 'y', line_width=2)
fig.circle(source=sources['ant_cmd'], x='x', y='y', size=20, fill_color=None)
fig.circle(source=sources['ant_cur'], x='x', y='y', size=10, color='green')
fig.circle(source=sources['sat'], x= 'x', y ='y', size=10, color= 'yellow')
return fig
def add_geom(fig: Figure, geom: BaseGeometry, **kwargs):
"""Add Shapely geom into Bokeh plot.
Args:
fig (Figure):
geom (BaseGeometry):
"""
if isinstance(geom, Point):
fig.circle(*geom.xy, **kwargs)
elif isinstance(geom, LineString):
fig.line(*geom.xy, **kwargs)
elif isinstance(geom, Polygon):
fig.patch(*geom.exterior.xy, **kwargs)
elif isinstance(geom, BaseMultipartGeometry):
for item in geom:
add_geom(fig, item, **kwargs)
else:
raise TypeError('Object geom {geom} no instance of {types}.'.format(
geom=geom, types=BaseGeometry))
function1_input.on_change('value', input_change)
# text input for the second function to be convolved
function2_input = TextInput(value=convolution_settings.function1_input_init, title="my second function:")
function2_input.on_change('value', input_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="Convolution of two functions",
x_range=[convolution_settings.x_min_view, convolution_settings.x_max_view],
y_range=[convolution_settings.y_min_view, convolution_settings.y_max_view])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source_function1, line_width=3, line_alpha=0.6, color='red', legend='function 1')
plot.line('x', 'y', source=source_function2, color='green', line_width=3, line_alpha=0.6, legend='function 2')
plot.line('x', 'y', source=source_result, color='blue', line_width=3, line_alpha=0.6, legend='convolution')
plot.scatter('x', 'y', source=source_xmarker, color='black')
plot.line('x', 'y', source=source_xmarker, color='black', line_width=3)
plot.patch('x', 'y_pos', source=source_overlay, fill_color='blue', fill_alpha=.2)
plot.patch('x', 'y_neg', source=source_overlay, fill_color='red', fill_alpha=.2)
# calculate data
update_data()
# lists all the controls in our app
controls = widgetbox(x_value_input, function_type, function1_input, function2_input, width=400)
# make layout
curdoc().add_root(row(plot, controls, width=800))
p.line(x='x',y='z',source=latprim4,line_dash='dotted')
p.line(x='x',y='z',source=latprim5,line_dash='dotted')
left=np.linspace(start=scanel[0],stop=scanel[1],num=101)
top=np.linspace(start=scanaz[0],stop=scanaz[1],num=101)
scanpts=np.append(np.stack((left*0+scanaz[0],left),axis=-1),
np.stack((top,top*0+scanel[1]),axis=-1),axis=0)
scanpts=np.append(scanpts,np.stack((left*0+scanaz[1],np.flipud(left)),axis=-1),axis=0)
scanpts=np.append(scanpts,np.stack((np.flipud(top),top*0+scanel[0]),axis=-1),axis=0)*np.pi/180
scanu=np.sin(scanpts[:,0])*np.cos(scanpts[:,1])
scanv=np.sin(scanpts[:,1])*np.cos(tilt)-np.cos(scanpts[:,0])*np.cos(scanpts[:,1])*np.sin(tilt)
scansource = ColumnDataSource(dict(x=scanu, y=scanv))
p.patch(x='x',y='y',source=scansource,alpha=0.3,line_width=5)
p.line(x='x',y='y',source=scansource)
grate1=ColumnDataSource(dict(x=londf.values[:,0]-kgx*kmult,y=londf.values[:,2]+kgy*kmult))
grate2=ColumnDataSource(dict(x=londf.values[:,0]+kgx*kmult,y=londf.values[:,2]-kgy*kmult))
grate3=ColumnDataSource(dict(x=londf.values[:,0]+kgx*kmult,y=londf.values[:,2]+kgy*kmult))
grate4=ColumnDataSource(dict(x=londf.values[:,0]-kgx*kmult,y=londf.values[:,2]-kgy*kmult))
grate5=ColumnDataSource(dict(x=londf.values[:,0]-kgx,y=londf.values[:,2]))
grate6=ColumnDataSource(dict(x=londf.values[:,0]+kgx,y=londf.values[:,2]))
grate7=ColumnDataSource(dict(x=londf.values[:,0],y=londf.values[:,2]+kgy))
grate8=ColumnDataSource(dict(x=londf.values[:,0],y=londf.values[:,2]-kgy))
p.line(x='x',y='y',source=grate1, color="red",line_width=5)
p.line(x='x',y='y',source=grate2, color="red",line_width=5)
p.line(x='x',y='y',source=grate3, color="red",line_width=5)
p.line(x='x',y='y',source=grate4, color="red",line_width=5)
color='green',
line_width=3,
line_alpha=0.6,
legend_label='function 2')
plot.line('x',
'y',
source=source_result,
color='blue',
line_width=3,
line_alpha=0.6,
legend_label='convolution')
plot.scatter('x', 'y', source=source_xmarker, color='black')
plot.line('x', 'y', source=source_xmarker, color='black', line_width=3)
plot.patch('x',
'y_pos',
source=source_overlay,
fill_color='blue',
fill_alpha=.2)
plot.patch('x',
'y_neg',
source=source_overlay,
fill_color='red',
fill_alpha=.2)
# calculate data
update_data()
# lists all the controls in our app
controls = widgetbox(x_value_input,
function_type,
function1_input,
wavelength_slider.on_change('value',set_slider_has_changed)
interactor.on_click(on_click_change)
# create plots
#REMARK: color might not be used from internal vis.js and has no effect - colormap always according to z! see https://github.com/bokeh/bokeh/issues/7814
surface = diffraction_Surface3d(x="x", y="y", z="z", color="color", data_source=source_surf, width=500,height=100) # wave surface
# contour plots of wave
contour_zero = diffraction_Contour(plot, line_width=2,line_color='black', path_filter = 10) # zero level
contour_pos = diffraction_Contour(plot, line_width=1, line_color='red', path_filter = 10) # some other levels
contour_neg = diffraction_Contour(plot, line_width=1, line_color='blue', path_filter = 10) # some other levels
kvector = diffraction_Quiver(plot, fix_at_middle=False) # visualization of wave k vector
plot.line(x=[x_min,0], y=[0,0], line_dash='dashed')
plot.line(x=[x_max,0], y=[0,0], line_width=10) # the wall
plot.line(x='x', y='y', source=source_wavefront) # wavefront visualization
plot.patch(x='x', y='y', color='yellow', source=source_light, alpha=.1) # light area
plot.patch(x='x', y='y', color='red',source=source_reflection, alpha=.1) # reflection area
plot.patch(x='x', y='y', color='blue', source=source_shadow, alpha=.1) # shadow area
plot.scatter(x='x',y='y', source=source_value_plotter, size=10) # value probing
plot.toolbar.logo = None
initialize()
# add app description
description_filename = join(dirname(__file__), "description.html")
description = LatexDiv(text=open(description_filename).read(), render_as_text=False, width=1130)
# add area image
area_image = Div(text="""
<p>
menu=leibnitz_settings.sample_curve_names)
sample_curve_input.on_change('value', sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Leibnitz sector formula",
x_range=[leibnitz_settings.x_min_view, leibnitz_settings.x_max_view],
y_range=[leibnitz_settings.y_min_view, leibnitz_settings.y_max_view])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source_curve, line_width=3, line_alpha=1, color='black', legend_label='curve')
plot.scatter('x', 'y', source=source_point, color='blue', legend_label='point at t')
plot.scatter([0], [0], color='black', marker='x')
pat = plot.patch('x', 'y', source=source_sector, fill_color='blue', fill_alpha=.2, legend_label='area')
plot.line('x_start', 'y_start', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.line('x_end', 'y_end', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.text('x', 'y', text='text', text_color='text_color', source=source_text)
# calculate data
update_curve()
update_point()
# lists all the controls in our app
controls = widgetbox(t_value_input, sample_curve_input, x_component_input, y_component_input)
# make layout
curdoc().add_root(row(plot, controls))
curdoc().title = split(dirname(__file__))[-1].replace('_',' ').replace('-',' ') # get path of parent directory and only use the name of the Parent Directory for the tab name. Replace underscores '_' and minuses '-' with blanks ' '
r_mean = 658.0
r_start = 658.0-138.0
r_end = 658.0+138.0
r_dev = 138.0/2.335
r_x = np.linspace(r_start,r_end,1000)
r_y = bb_filter_my(r_mean,r_dev,r_x)
'''
p3 = Figure(plot_width=500, plot_height=500)
p3.title = "Plot at 5500K"
p3.yaxis.axis_label = "Flux"
p3.xaxis.axis_label = "Wavelength (nm)"
p3.patch(ux_pass,
uz_pass,
line_width=2,
line_alpha=0.1,
color=(62, 6, 148),
legend="Ultraviolet(U)")
p3.patch(bx_pass,
bz_pass,
line_width=2,
line_alpha=0.1,
color="blue",
legend="Blue(B)")
p3.patch(vx_pass,
vz_pass,
line_width=2,
line_alpha=0.1,
color="violet",
legend="Violet(V)")
p3.patch(rx_pass,
#standard_dev = 138/2.355
r_mean = 658.0
r_start = 658.0-138.0
r_end = 658.0+138.0
r_dev = 138.0/2.335
r_x = np.linspace(r_start,r_end,1000)
r_y = bb_filter_my(r_mean,r_dev,r_x)
'''
p3 = Figure(plot_width = 500,plot_height = 500)
p3.title = "Plot at 5500K"
p3.yaxis.axis_label = "Flux"
p3.xaxis.axis_label = "Wavelength (nm)"
p3.patch(ux_pass,uz_pass,line_width = 2, line_alpha = 0.1, color = (62,6,148), legend = "Ultraviolet(U)")
p3.patch(bx_pass,bz_pass,line_width = 2, line_alpha = 0.1, color = "blue", legend = "Blue(B)")
p3.patch(vx_pass,vz_pass,line_width = 2, line_alpha = 0.1, color = "violet", legend = "Violet(V)")
p3.patch(rx_pass,rz_pass,line_width = 2, line_alpha = 0.1, color = "red", legend = "Red(R)")
#.....................................FluxVsWavelength Implementation.........................................................
x = np.linspace(50,3000,500)
#print x
y = bb_flux(x,temp)
#print y
source = ColumnDataSource( data=dict( x=x,y=y,gv=gv))
#source1 = ColumnDataSource( data=dict( x=x,y=y,gv=gv,))
hover = HoverTool( tooltips = [ ("(w,B)", "($x, $y)"), ] )
import matplotlib.image as mpimg
from astropy.table import Table
from bokeh.plotting import Figure, gridplot, output_file, show
from bokeh.models import ColumnDataSource, HBox, VBoxForm, Paragraph, Range1d
from bokeh.io import curdoc
from bokeh.models.widgets import Select, CheckboxButtonGroup, CheckboxGroup
from bokeh.models import NumeralTickFormatter
from __future__ import print_function
mjup = 317.8
p1 = Figure(x_range=[0.003, 3000], y_range=[0.01, 30000], tools="pan,save,wheel_zoom,reset",toolbar_location='above', \
title=' ', y_axis_type="log", x_axis_type='log', plot_height=400, plot_width=800)
p1.background_fill_color = "white"
p1.patch([0.003, 1.0, 0.2, 0.003, 0.003], [1, 20, 30000., 30000., 1.],
alpha=0.5,
fill_color='pink',
line_width=0)
p1.patch([0.003, 0.04, 0.04, 0.003, 0.003], [100, 100, 30000., 30000., 10000.],
alpha=0.6,
fill_color='lightblue',
line_width=0)
p1.patch([3, 3000, 3000, 30, 3], [300, 300, 30000., 30000., 300.],
alpha=0.6,
fill_color='purple',
line_width=0)
p1.yaxis.axis_label = 'Planet Mass / Earth'
p1.xaxis.axis_label = 'Semi-major Axis / Snow Line'
p1.xaxis.axis_label_text_font_style = 'bold'
p1.yaxis.axis_label_text_font_style = 'bold'
p1.xaxis[0].formatter = NumeralTickFormatter(format="0.00")
p1.x_range = Range1d(0.003, 3000, bounds=(0.003, 3000))
def create_plot(Pi, a_prior, b_prior):
"""Plots the prior probability distribution of an unfair coin using Bayes'
theorem.
Parameters
----------
Pi : float
Probability of the coin to come up heads.
Returns
-------
Script and div components of the Bokeh document.
"""
# generate prior probability distribution
n = 1000
a = a_prior
b = b_prior
x = np.linspace(0, 1, n)
dist = beta(a, b)
p = dist.pdf(x)
s1 = ColumnDataSource(data=dict(x=x, p=p))
s2 = ColumnDataSource(data=dict(params=[Pi, a_prior, b_prior, a, b]))
# arrays for the area under the curve patch
xs = np.hstack((x, [1, 0]))
ys = np.hstack((p, [0, 0]))
s3 = ColumnDataSource(data=dict(x=xs, y=ys))
# plot probability distribution
plot = Figure(title='Posterior Distribution')
plot.xaxis.axis_label = 'Probability of Heads (-)'
plot.yaxis.axis_label = 'Probability Density (-)'
plot.line('x', 'p', source=s1, line_width=4)
plot.patch('x', 'y', source=s3, alpha=0.25, line_width=0)
# calculate mode of prior
if a == 1 and b == 1:
mode_str = "any value"
else:
mode_str = str(round((a - 1.0) / (a + b - 2.0), 7))
# add current stats of simulation
text = """<b>True Probability:</b> {:g}<br>
<b>Number of Heads:</b> {:d}<br>
<b>Number of Tails:</b> {:d}<br>
<b>Mode:</b> {:s}<br>
<b>Variance: </b> {:g}
""".format(Pi, a - a_prior, b - b_prior, mode_str, 1.0 / 12)
div = Div(text=text)
# create button widget and JS callback
with open(os.path.join('flask_app', 'static', 'callback.js'), 'r') as fp:
code = fp.read()
callback = CustomJS(args=dict(s1=s1, s2=s2, s3=s3, div=div), code=code)
button = Button(label='Flip Coin', callback=callback)
# combine button and plot into one object and return components
widgets = row(button, div)
layout = column(widgets, plot)
return components(layout)
sample_curve_input = Dropdown(label="choose a sample function pair or enter one below",
menu=leibnitz_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="Leibnitz sector formula",
x_range=[leibnitz_settings.x_min_view, leibnitz_settings.x_max_view],
y_range=[leibnitz_settings.y_min_view, leibnitz_settings.y_max_view])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source_curve, line_width=3, line_alpha=1, color='black', legend='curve')
plot.scatter('x', 'y', source=source_point, color='blue', legend='point at t')
plot.scatter([0], [0], color='black', marker='x')
pat = plot.patch('x', 'y', source=source_sector, fill_color='blue', fill_alpha=.2, legend='area')
plot.line('x_start', 'y_start', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.line('x_end', 'y_end', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.text('x', 'y', text='text', text_color='text_color', source=source_text)
# calculate data
update_curve()
update_point()
# lists all the controls in our app
controls = widgetbox(t_value_input, sample_curve_input, x_component_input, y_component_input)
# make layout
curdoc().add_root(row(plot, controls))
plot.line('t',
'x_orig',
source=source_orig,
line_width=3,
line_alpha=0.6,
color='red',
legend='original function')
plot.line('t',
'x_fourier',
source=source_fourier,
color='green',
line_width=3,
line_alpha=0.6,
legend='fourier series')
plot.patch('x_patch', 'y_patch', source=source_interval_patch, alpha=.2)
plot.line('x_min', 'y_minmax', source=source_interval_bound)
plot.line('x_max', 'y_minmax', source=source_interval_bound)
sample_controls = widgetbox(sample_function_type)
default_controls = column(default_function_input,
default_function_period_start,
default_function_period_end)
# Panels for sample functions or default functions
sample_funs = Panel(child=sample_controls, title='sample function')
default_funs = Panel(child=default_controls, title='default function')
# Add panels to tabs
fun_tabs = Tabs(tabs=[sample_funs, default_funs])
fun_tabs.on_change('active', type_input_change) # add callback for panel tabs
)
# Plot the line by the x,y values in the source property
plot.line('t', 'x_orig', source=source_orig,
line_width=3,
line_alpha=0.6,
color='red',
legend='original function'
)
plot.line('t', 'x_fourier', source=source_fourier,
color='green',
line_width=3,
line_alpha=0.6,
legend='fourier series'
)
plot.patch('x_patch', 'y_patch', source=source_interval_patch, alpha=.2)
plot.line('x_min', 'y_minmax', source=source_interval_bound)
plot.line('x_max', 'y_minmax', source=source_interval_bound)
sample_controls = widgetbox(sample_function_type)
default_controls = column(default_function_input,default_function_period_start,default_function_period_end)
# Panels for sample functions or default functions
sample_funs = Panel(child=sample_controls, title='sample function')
default_funs = Panel(child=default_controls, title='default function')
# Add panels to tabs
fun_tabs = Tabs(tabs=[sample_funs, default_funs])
fun_tabs.on_change('active', type_input_change) # add callback for panel tabs
# lists all the controls in our app
