def __init__(
self,
fig: Figure,
x_bounds: tuple,
y_bounds: tuple,
grid_size: float,
legend_label: str,
):
"""
Creates a HeatmapPlotter for the given figure, with the given x-axis and y-axis bounds and
grid cell size. This will show up in the Bokeh figure legend as legend_label.
:param fig: a Bokeh figure
:param x_bounds: the x-axis bounds of the heatmap
:param y_bounds: the y-axis bounds of the heatmap
:param grid_size: the size of a grid cell on the heatmap
:param legend_label: the legend label of the heatmap
"""
self.image_data_source = ColumnDataSource(dict(image=[]))
self.x_bounds = x_bounds
self.y_bounds = y_bounds
self.grid_size = grid_size
fig.image(
source=self.image_data_source,
image="image",
x=min(x_bounds),
y=min(y_bounds),
dh=max(y_bounds) - min(y_bounds),
dw=max(x_bounds) - min(x_bounds),
palette=palettes.viridis(100),
level="image",
legend_label=legend_label,
)
def jwst_2d_det(result_dict, plot=True, output_file='det2d.html'):
"""Plot 2d detector image
Parameters
----------
result_dict : dict
Dictionary from pandexo output. If parameter space was run in run_pandexo
make sure to restructure the input as a list of dictionaries without they key words
that run_pandexo assigns.
plot : bool
(Optional) True renders plot, Flase does not. Default=True
output_file : str
(Optional) Default = 'det2d.html'
Return
------
numpy array
2D array of out of transit detector simulation
See Also
--------
jwst_1d_spec, jwst_1d_bkg, jwst_1d_flux, jwst_1d_snr, jwst_noise, jwst_2d_sat
"""
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
out = result_dict['PandeiaOutTrans']
data = out['2d']['detector']
xr, yr = data.shape
plot_detector_2d = Figure(
tools="pan,wheel_zoom,box_zoom,resize,reset,hover,save",
x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Pixel',
y_axis_label='Spatial',
title="2D Detector Image",
plot_width=800,
plot_height=300)
plot_detector_2d.image(image=[data],
x=[0],
y=[0],
dh=[xr],
dw=[yr],
palette="Spectral11")
if plot:
outputfile(output_file)
show(plot_detector_2d)
return data
def create_fig(self, sources):
"""
Class implementation of :meth:`LivePlot.create_fig`.
"""
fig = Figure(logo=None,
x_range=[-self._sample_rate/2.0, self._sample_rate/2.0],
y_range=[0,self._height], **self._fig_args)
fig.image('image', source= sources['wfall'], x=-self._sample_rate/2.0, y = 0, dw=self._sample_rate,dh=self._height,palette=Defaults.WFALL_COLORMAP )
fig.xgrid.grid_line_color = None
fig.ygrid.grid_line_color = None
fig.axis.visible = False
return fig
def view_database_file(_id, file_type):
ds = load_file(_id, file_type)
d = {}
for data_var in ds.data_vars:
d[data_var] = [ds[data_var].values]
d["to_plot"] = [ds[list(ds.data_vars)[0]].values]
source = ColumnDataSource(d)
callback = CustomJS(
args=dict(source=source),
code="""
var data = source.data;
data['to_plot'] = data[cb_obj.value];
source.change.emit();
""",
)
select = Select(title="Variable:", options=list(ds.data_vars))
select.js_on_change("value", callback)
p = Figure(
x_range=(-180, 180),
y_range=(-90, 90),
aspect_ratio=2.5,
tools="pan,wheel_zoom,box_zoom,reset, hover",
)
p.sizing_mode = "scale_width"
p.image(
image="to_plot",
x=-180,
y=-90,
dw=360,
dh=180,
source=source,
palette="Viridis11",
)
script, div = components(
column(column(select), p, sizing_mode="stretch_width"))
return render_template(
"app/bokeh_plot.html",
script=script,
div=div,
data_file_id=_id,
title=file_type.capitalize(),
)
def jwst_2d_det(result_dict, plot=True, output_file='det2d.html'):
"""Plot 2d detector image
Parameters
----------
result_dict : dict
Dictionary from pandexo output. If parameter space was run in run_pandexo
make sure to restructure the input as a list of dictionaries without they key words
that run_pandexo assigns.
plot : bool
(Optional) True renders plot, Flase does not. Default=True
output_file : str
(Optional) Default = 'det2d.html'
Return
------
numpy array
2D array of out of transit detector simulation
See Also
--------
jwst_1d_spec, jwst_1d_bkg, jwst_1d_flux, jwst_1d_snr, jwst_noise, jwst_2d_sat
"""
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
out = result_dict['PandeiaOutTrans']
data = out['2d']['detector']
xr, yr = data.shape
plot_detector_2d = Figure(tools="pan,wheel_zoom,box_zoom,resize,reset,hover,save",
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="2D Detector Image",
plot_width=800, plot_height=300)
plot_detector_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
if plot:
outputfile(output_file)
show(plot_detector_2d)
return data
def create_component_jwst(result_dict):
"""Generate front end plots JWST
Function that is responsible for generating the front-end interactive plots for JWST.
Parameters
----------
result_dict : dict
the dictionary returned from a PandExo run
Returns
-------
tuple
A tuple containing `(script, div)`, where the `script` is the
front-end javascript required, and `div` is a dictionary of plot
objects.
"""
timing = result_dict['timing']
noccultations = result_dict['timing']['Number of Transits']
out = result_dict['PandeiaOutTrans']
# select the tools we want
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
#Define units for x and y axis
punit = result_dict['input']['Primary/Secondary']
p=1.0
if punit == 'fp/f*': p = -1.0
else: punit = punit
if result_dict['input']['Calculation Type'] =='phase_spec':
x_axis_label='Time (secs)'
frac = 1.0
else:
x_axis_label='Wavelength [microns]'
frac = result_dict['timing']['Num Integrations Out of Transit']/result_dict['timing']['Num Integrations In Transit']
electrons_out = result_dict['RawData']['electrons_out']
electrons_in = result_dict['RawData']['electrons_in']
var_in = result_dict['RawData']['var_in']
var_out = result_dict['RawData']['var_out']
x = result_dict['FinalSpectrum']['wave']
y = result_dict['FinalSpectrum']['spectrum_w_rand']
err = result_dict['FinalSpectrum']['error_w_floor']
y_err = []
x_err = []
for px, py, yerr in zip(x, y, err):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
source = ColumnDataSource(data=dict(x=x, y=y, y_err=y_err, x_err=x_err, err=err,
electrons_out=electrons_out, electrons_in=electrons_in, var_in=var_in, var_out=var_out,
p=var_in*0+p,nocc=var_in*0+noccultations, frac = var_in*0+frac))
original = ColumnDataSource(data=dict(x=x, y=y, y_err=y_err, x_err=x_err, err=err, electrons_out=electrons_out, electrons_in=electrons_in, var_in=var_in, var_out=var_out))
ylims = [min(result_dict['OriginalInput']['model_spec'])- 0.1*min(result_dict['OriginalInput']['model_spec']),
0.1*max(result_dict['OriginalInput']['model_spec'])+max(result_dict['OriginalInput']['model_spec'])]
xlims = [min(result_dict['FinalSpectrum']['wave']), max(result_dict['FinalSpectrum']['wave'])]
plot_spectrum = Figure(plot_width=800, plot_height=300, x_range=xlims,
y_range=ylims, tools=TOOLS,#responsive=True,
x_axis_label=x_axis_label,
y_axis_label=punit,
title="Original Model with Observation")
plot_spectrum.line(result_dict['OriginalInput']['model_wave'],result_dict['OriginalInput']['model_spec'], color= "black", alpha = 0.5, line_width = 4)
plot_spectrum.circle('x', 'y', source=source, line_width=3, line_alpha=0.6)
plot_spectrum.multi_line('x_err', 'y_err', source=source)
callback = CustomJS(args=dict(source=source, original=original), code="""
// Grab some references to the data
var sdata = source.get('data');
var odata = original.get('data');
// Create copies of the original data, store them as the source data
sdata['x'] = odata['x'].slice(0);
sdata['y'] = odata['y'].slice(0);
sdata['y_err'] = odata['y_err'].slice(0);
sdata['x_err'] = odata['x_err'].slice(0);
sdata['err'] = odata['err'].slice(0);
sdata['electrons_out'] = odata['electrons_out'].slice(0);
sdata['electrons_in'] = odata['electrons_in'].slice(0);
sdata['var_in'] = odata['var_in'].slice(0);
sdata['var_out'] = odata['var_out'].slice(0);
// Create some variables referencing the source data
var x = sdata['x'];
var y = sdata['y'];
var y_err = sdata['y_err'];
var x_err = sdata['x_err'];
var err = sdata['err'];
var p = sdata['p'];
var frac = sdata['frac'];
var og_ntran = sdata['nocc'];
var electrons_out = sdata['electrons_out'];
var electrons_in = sdata['electrons_in'];
var var_in = sdata['var_in'];
var var_out = sdata['var_out'];
var f = wbin.get('value');
var ntran = ntran.get('value');
var wlength = Math.pow(10.0,f);
var ind = [];
ind.push(0);
var start = 0;
for (i = 0; i < x.length-1; i++) {
if (x[i+1] - x[start] >= wlength) {
ind.push(i+1);
start = i;
}
}
if (ind[ind.length-1] != x.length) {
ind.push(x.length);
}
var xout = [];
var foutout = [];
var finout = [];
var varinout = [];
var varoutout = [];
var xslice = [];
var foutslice = [];
var finslice = [];
var varoutslice = [];
var varinslice = [];
function add(a, b) {
return a+b;
}
for (i = 0; i < ind.length-1; i++) {
xslice = x.slice(ind[i],ind[i+1]);
foutslice = electrons_out.slice(ind[i],ind[i+1]);
finslice = electrons_in.slice(ind[i],ind[i+1]);
varinslice = var_in.slice(ind[i],ind[i+1]);
varoutslice = var_out.slice(ind[i],ind[i+1]);
xout.push(xslice.reduce(add, 0)/xslice.length);
foutout.push(foutslice.reduce(add, 0));
finout.push(finslice.reduce(add, 0));
varinout.push(varinslice.reduce(add, 0));
varoutout.push(varoutslice.reduce(add, 0));
xslice = [];
foutslice = [];
finslice = [];
varinslice = [];
varoutslice = [];
}
var new_err = 1.0;
var rand = 1.0;
for (i = 0; i < x.length; i++) {
new_err = Math.pow((frac[i]/foutout[i]),2)*varinout[i] + Math.pow((finout[i]*frac[i]/Math.pow(foutout[i],2)),2)*varoutout[i];
new_err = Math.sqrt(new_err)*Math.sqrt(og_ntran[i]/ntran);
rand = new_err*(Math.random()-Math.random());
y[i] = p[i]*((1.0 - frac[i]*finout[i]/foutout[i]) + rand);
x[i] = xout[i];
x_err[i][0] = xout[i];
x_err[i][1] = xout[i];
y_err[i][0] = y[i] + new_err;
y_err[i][1] = y[i] - new_err;
}
source.trigger('change');
""")
#var_tot = (frac/electrons_out)**2.0 * var_in + (electrons_in*frac/electrons_out**2.0)**2.0 * var_out
sliderWbin = Slider(title="binning", value=np.log10(x[1]-x[0]), start=np.log10(x[1]-x[0]), end=np.log10(max(x)/2.0), step= .05, callback=callback)
callback.args["wbin"] = sliderWbin
sliderTrans = Slider(title="Num Trans", value=noccultations, start=1, end=50, step= 1, callback=callback)
callback.args["ntran"] = sliderTrans
layout = column(row(sliderWbin,sliderTrans), plot_spectrum)
#out of transit 2d output
raw = result_dict['RawData']
# Flux 1d
x, y = raw['wave'], raw['e_rate_out']*result_dict['timing']['Seconds per Frame']*(timing["APT: Num Groups per Integration"]-1)
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
plot_flux_1d1 = Figure(tools=TOOLS,
x_axis_label='Wavelength [microns]',
y_axis_label='e-/integration', title="Flux Per Integration",
plot_width=800, plot_height=300)
plot_flux_1d1.line(x, y, line_width = 4, alpha = .7)
tab1 = Panel(child=plot_flux_1d1, title="Flux per Int")
# BG 1d
#x, y = out['1d']['extracted_bg_only']
#y = y[~np.isnan(y)]
#x = x[~np.isnan(y)]
#plot_bg_1d1 = Figure(tools=TOOLS,
# x_axis_label='Wavelength [microns]',
# y_axis_label='Flux (e/s)', title="Background",
# plot_width=800, plot_height=300)
#plot_bg_1d1.line(x, y, line_width = 4, alpha = .7)
#tab2 = Panel(child=plot_bg_1d1, title="Background Flux")
# SNR
y = np.sqrt(y) #this is computing the SNR (sqrt of photons in a single integration)
plot_snr_1d1 = Figure(tools=TOOLS,
x_axis_label=x_axis_label,
y_axis_label='sqrt(e-)/integration', title="SNR per integration",
plot_width=800, plot_height=300)
plot_snr_1d1.line(x, y, line_width = 4, alpha = .7)
tab3 = Panel(child=plot_snr_1d1, title="SNR per Int")
# Error bars (ppm)
x = result_dict['FinalSpectrum']['wave']
y = result_dict['FinalSpectrum']['error_w_floor']*1e6
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
ymed = np.median(y)
plot_noise_1d1 = Figure(tools=TOOLS,#responsive=True,
x_axis_label=x_axis_label,
y_axis_label='Spectral Precision (ppm)', title="Spectral Precision",
plot_width=800, plot_height=300, y_range = [0,2.0*ymed])
ymed = np.median(y)
plot_noise_1d1.circle(x, y, line_width = 4, alpha = .7)
tab4 = Panel(child=plot_noise_1d1, title="Precision")
#Not happy? Need help picking a different mode?
plot_spectrum2 = Figure(plot_width=800, plot_height=300, x_range=xlims,y_range=ylims, tools=TOOLS,
x_axis_label=x_axis_label,
y_axis_label=punit, title="Original Model",y_axis_type="log")
plot_spectrum2.line(result_dict['OriginalInput']['model_wave'],result_dict['OriginalInput']['model_spec'],
line_width = 4,alpha = .7)
tab5 = Panel(child=plot_spectrum2, title="Original Model")
#create set of five tabs
tabs1d = Tabs(tabs=[ tab1,tab3, tab4, tab5])
# Detector 2d
data = out['2d']['detector']
xr, yr = data.shape
plot_detector_2d = Figure(tools="pan,wheel_zoom,box_zoom,resize,reset,hover,save",
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="2D Detector Image",
plot_width=800, plot_height=300)
plot_detector_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
#2d tabs
#2d snr
data = out['2d']['snr']
data[np.isinf(data)] = 0.0
xr, yr = data.shape
plot_snr_2d = Figure(tools=TOOLS,
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="Signal-to-Noise Ratio",
plot_width=800, plot_height=300)
plot_snr_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
tab1b = Panel(child=plot_snr_2d, title="SNR")
#saturation
data = out['2d']['saturation']
xr, yr = data.shape
plot_sat_2d = Figure(tools=TOOLS,
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="Saturation",
plot_width=800, plot_height=300)
plot_sat_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
tab2b = Panel(child=plot_sat_2d, title="Saturation")
tabs2d = Tabs(tabs=[ tab1b, tab2b])
result_comp = components({'plot_spectrum':layout,
'tabs1d': tabs1d, 'det_2d': plot_detector_2d,
'tabs2d': tabs2d})
return result_comp
def create_component_jwst(result_dict):
"""Generate front end plots JWST
Function that is responsible for generating the front-end interactive plots for JWST.
Parameters
----------
result_dict : dict
the dictionary returned from a PandExo run
Returns
-------
tuple
A tuple containing `(script, div)`, where the `script` is the
front-end javascript required, and `div` is a dictionary of plot
objects.
"""
noccultations = result_dict['timing']['Number of Transits']
# select the tools we want
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
#Define units for x and y axis
punit = result_dict['input']['Primary/Secondary']
p=1.0
if punit == 'fp/f*': p = -1.0
else: punit = '('+punit+')^2'
if result_dict['input']['Calculation Type'] =='phase_spec':
x_axis_label='Time (secs)'
frac = 1.0
else:
x_axis_label='Wavelength [microns]'
frac = result_dict['timing']['Num Integrations Out of Transit']/result_dict['timing']['Num Integrations In Transit']
electrons_out = result_dict['RawData']['electrons_out']
electrons_in = result_dict['RawData']['electrons_in']
var_in = result_dict['RawData']['var_in']
var_out = result_dict['RawData']['var_out']
x = result_dict['FinalSpectrum']['wave']
y = result_dict['FinalSpectrum']['spectrum_w_rand']
err = result_dict['FinalSpectrum']['error_w_floor']
y_err = []
x_err = []
for px, py, yerr in zip(x, y, err):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
source = ColumnDataSource(data=dict(x=x, y=y, y_err=y_err, x_err=x_err, err=err,
electrons_out=electrons_out, electrons_in=electrons_in, var_in=var_in, var_out=var_out,
p=var_in*0+p,nocc=var_in*0+noccultations, frac = var_in*0+frac))
original = ColumnDataSource(data=dict(x=x, y=y, y_err=y_err, x_err=x_err, err=err, electrons_out=electrons_out, electrons_in=electrons_in, var_in=var_in, var_out=var_out))
ylims = [min(result_dict['OriginalInput']['model_spec'])- 0.1*min(result_dict['OriginalInput']['model_spec']),
0.1*max(result_dict['OriginalInput']['model_spec'])+max(result_dict['OriginalInput']['model_spec'])]
xlims = [min(result_dict['FinalSpectrum']['wave']), max(result_dict['FinalSpectrum']['wave'])]
plot_spectrum = Figure(plot_width=800, plot_height=300, x_range=xlims,
y_range=ylims, tools=TOOLS,#responsive=True,
x_axis_label=x_axis_label,
y_axis_label=punit,
title="Original Model with Observation")
plot_spectrum.line(result_dict['OriginalInput']['model_wave'],result_dict['OriginalInput']['model_spec'], color= "black", alpha = 0.5, line_width = 4)
plot_spectrum.circle('x', 'y', source=source, line_width=3, line_alpha=0.6)
plot_spectrum.multi_line('x_err', 'y_err', source=source)
callback = CustomJS(args=dict(source=source, original=original), code="""
// Grab some references to the data
var sdata = source.get('data');
var odata = original.get('data');
// Create copies of the original data, store them as the source data
sdata['x'] = odata['x'].slice(0);
sdata['y'] = odata['y'].slice(0);
sdata['y_err'] = odata['y_err'].slice(0);
sdata['x_err'] = odata['x_err'].slice(0);
sdata['err'] = odata['err'].slice(0);
sdata['electrons_out'] = odata['electrons_out'].slice(0);
sdata['electrons_in'] = odata['electrons_in'].slice(0);
sdata['var_in'] = odata['var_in'].slice(0);
sdata['var_out'] = odata['var_out'].slice(0);
// Create some variables referencing the source data
var x = sdata['x'];
var y = sdata['y'];
var y_err = sdata['y_err'];
var x_err = sdata['x_err'];
var err = sdata['err'];
var p = sdata['p'];
var frac = sdata['frac'];
var og_ntran = sdata['nocc'];
var electrons_out = sdata['electrons_out'];
var electrons_in = sdata['electrons_in'];
var var_in = sdata['var_in'];
var var_out = sdata['var_out'];
var f = wbin.get('value');
var ntran = ntran.get('value');
var wlength = Math.pow(10.0,f);
var ind = [];
ind.push(0);
var start = 0;
for (i = 0; i < x.length-1; i++) {
if (x[i+1] - x[start] >= wlength) {
ind.push(i+1);
start = i;
}
}
if (ind[ind.length-1] != x.length) {
ind.push(x.length);
}
var xout = [];
var foutout = [];
var finout = [];
var varinout = [];
var varoutout = [];
var xslice = [];
var foutslice = [];
var finslice = [];
var varoutslice = [];
var varinslice = [];
function add(a, b) {
return a+b;
}
for (i = 0; i < ind.length-1; i++) {
xslice = x.slice(ind[i],ind[i+1]);
foutslice = electrons_out.slice(ind[i],ind[i+1]);
finslice = electrons_in.slice(ind[i],ind[i+1]);
varinslice = var_in.slice(ind[i],ind[i+1]);
varoutslice = var_out.slice(ind[i],ind[i+1]);
xout.push(xslice.reduce(add, 0)/xslice.length);
foutout.push(foutslice.reduce(add, 0));
finout.push(finslice.reduce(add, 0));
varinout.push(varinslice.reduce(add, 0));
varoutout.push(varoutslice.reduce(add, 0));
xslice = [];
foutslice = [];
finslice = [];
varinslice = [];
varoutslice = [];
}
var new_err = 1.0;
var rand = 1.0;
for (i = 0; i < x.length; i++) {
new_err = Math.pow((frac[i]/foutout[i]),2)*varinout[i] + Math.pow((finout[i]*frac[i]/Math.pow(foutout[i],2)),2)*varoutout[i];
new_err = Math.sqrt(new_err)*Math.sqrt(og_ntran[i]/ntran);
rand = new_err*(Math.random()-Math.random());
y[i] = p[i]*((1.0 - frac[i]*finout[i]/foutout[i]) + rand);
x[i] = xout[i];
x_err[i][0] = xout[i];
x_err[i][1] = xout[i];
y_err[i][0] = y[i] + new_err;
y_err[i][1] = y[i] - new_err;
}
source.trigger('change');
""")
#var_tot = (frac/electrons_out)**2.0 * var_in + (electrons_in*frac/electrons_out**2.0)**2.0 * var_out
sliderWbin = Slider(title="binning", value=np.log10(x[1]-x[0]), start=np.log10(x[1]-x[0]), end=np.log10(max(x)/2.0), step= .05, callback=callback)
callback.args["wbin"] = sliderWbin
sliderTrans = Slider(title="Num Trans", value=noccultations, start=1, end=50, step= 1, callback=callback)
callback.args["ntran"] = sliderTrans
layout = column(row(sliderWbin,sliderTrans), plot_spectrum)
#out of transit 2d output
out = result_dict['PandeiaOutTrans']
# Flux 1d
x, y = out['1d']['extracted_flux']
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
plot_flux_1d1 = Figure(tools=TOOLS,
x_axis_label='Wavelength [microns]',
y_axis_label='Flux (e/s)', title="Out of Transit Flux Rate",
plot_width=800, plot_height=300)
plot_flux_1d1.line(x, y, line_width = 4, alpha = .7)
tab1 = Panel(child=plot_flux_1d1, title="Total Flux")
# BG 1d
x, y = out['1d']['extracted_bg_only']
y = y[~np.isnan(y)]
x = x[~np.isnan(y)]
plot_bg_1d1 = Figure(tools=TOOLS,
x_axis_label='Wavelength [microns]',
y_axis_label='Flux (e/s)', title="Background",
plot_width=800, plot_height=300)
plot_bg_1d1.line(x, y, line_width = 4, alpha = .7)
tab2 = Panel(child=plot_bg_1d1, title="Background Flux")
# SNR 1d accounting for number of occultations
x= out['1d']['sn'][0]
y = out['1d']['sn'][1]
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
y = y*np.sqrt(noccultations)
plot_snr_1d1 = Figure(tools=TOOLS,
x_axis_label=x_axis_label,
y_axis_label='SNR', title="Pandeia SNR",
plot_width=800, plot_height=300)
plot_snr_1d1.line(x, y, line_width = 4, alpha = .7)
tab3 = Panel(child=plot_snr_1d1, title="SNR")
# Error bars (ppm)
x = result_dict['FinalSpectrum']['wave']
y = result_dict['FinalSpectrum']['error_w_floor']*1e6
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
ymed = np.median(y)
plot_noise_1d1 = Figure(tools=TOOLS,#responsive=True,
x_axis_label=x_axis_label,
y_axis_label='Error on Spectrum (PPM)', title="Error Curve",
plot_width=800, plot_height=300, y_range = [0,2.0*ymed])
ymed = np.median(y)
plot_noise_1d1.circle(x, y, line_width = 4, alpha = .7)
tab4 = Panel(child=plot_noise_1d1, title="Error")
#Not happy? Need help picking a different mode?
plot_spectrum2 = Figure(plot_width=800, plot_height=300, x_range=xlims,y_range=ylims, tools=TOOLS,
x_axis_label=x_axis_label,
y_axis_label=punit, title="Original Model",y_axis_type="log")
plot_spectrum2.line(result_dict['OriginalInput']['model_wave'],result_dict['OriginalInput']['model_spec'],
line_width = 4,alpha = .7)
tab5 = Panel(child=plot_spectrum2, title="Original Model")
#create set of five tabs
tabs1d = Tabs(tabs=[ tab1, tab2,tab3, tab4, tab5])
# Detector 2d
data = out['2d']['detector']
xr, yr = data.shape
plot_detector_2d = Figure(tools="pan,wheel_zoom,box_zoom,resize,reset,hover,save",
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="2D Detector Image",
plot_width=800, plot_height=300)
plot_detector_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
#2d tabs
#2d snr
data = out['2d']['snr']
data[np.isinf(data)] = 0.0
xr, yr = data.shape
plot_snr_2d = Figure(tools=TOOLS,
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="Signal-to-Noise Ratio",
plot_width=800, plot_height=300)
plot_snr_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
tab1b = Panel(child=plot_snr_2d, title="SNR")
#saturation
data = out['2d']['saturation']
xr, yr = data.shape
plot_sat_2d = Figure(tools=TOOLS,
x_range=[0, yr], y_range=[0, xr],
x_axis_label='Pixel', y_axis_label='Spatial',
title="Saturation",
plot_width=800, plot_height=300)
plot_sat_2d.image(image=[data], x=[0], y=[0], dh=[xr], dw=[yr],
palette="Spectral11")
tab2b = Panel(child=plot_sat_2d, title="Saturation")
tabs2d = Tabs(tabs=[ tab1b, tab2b])
result_comp = components({'plot_spectrum':layout,
'tabs1d': tabs1d, 'det_2d': plot_detector_2d,
'tabs2d': tabs2d})
return result_comp
from bokeh.plotting import Figure, show
from scipy import misc
from bokeh.io import curdoc
from bokeh.models import HBox, VBoxForm, VBox, ColumnDataSource, BoxSelectTool
import numpy
from bokeh.models.widgets import Slider
imageRaw = numpy.random.rand(256,256)
source = ColumnDataSource(data={'image': [imageRaw]})
p = Figure(x_range=[0, 10], y_range=[0, 10],plot_width=400,plot_height=400,
tools="crosshair, box_select, pan, reset, resize, save, wheel_zoom")
p.image(image="image", x=[0], y=[0], dw=[10], dh=[10],source=source)
p.select(BoxSelectTool).select_every_mousemove=False
imageFFT = numpy.fft.fft2(imageRaw)
imageFFT=numpy.fft.fftshift(imageFFT)
x,y=numpy.meshgrid(range(imageFFT.shape[1]),range(imageFFT.shape[0]),indexing='xy')
x = x - imageFFT.shape[1]/2
y = y - imageFFT.shape[0]/2
r = numpy.sqrt(x*x+y*y) # units are pixels
theta = numpy.arctan2(x,y)
fband=[16,16]
SF = numpy.exp(-((r-fband[0])**2/(2.0*fband[1]*fband[1])))
#SF=numpy.ones(r.shape)
#ori=45*(numpy.pi/180.0)
#oband=[ori*0.95, ori*1.05]
oband=[numpy.pi/4.0, numpy.pi/8.0]
t1 = numpy.angle(numpy.exp(complex(0,1)*(theta-oband[0]))) # center it
#colormapHSV=cm.get_cmap("hsv")
#bokehpaletteHSV=[plt.colors.rgb2hex(m) for m in colormapHSV(numpy.arange(colormapHSV.N))]
#myColorMapperHSV=LinearColorMapper(bokehpaletteHSV)
#colormapAu=cm.get_cmap("jet")
#bokehpaletteAu=[plt.colors.rgb2hex(m) for m in colormapAu(numpy.arange(colormapAu.N))]
#myColorMapperAu=LinearColorMapper(bokehpaletteAu)
#set up the plots
#need 4x4 grid for input image, FFT of input, filter itself (with sliders), and filtered image
#need size to be dynamic? need ranges to be right
inputPlot=Figure(title="input image",
tools="crosshair, pan, reset, resize, save, wheel_zoom",
plot_width=400,plot_height=400,
x_range=[0,10], y_range=[0,10])
#http://bokeh.pydata.org/en/0.10.0/docs/gallery/image.html
inputPlot.image(image=[imageRaw],x=[0],y=[0],dw=[10],dh=[10])#,palette=myColorMapperGrey.palette)
#would be awesome to have a custom palette that maps orientation to color like VV
inputPlot.axis.visible=None
#inputFFTPlot=Figure(title="FFT of input image",
# x_range=[0,10], y_range=[0,10])
#inputFFTPlot.image(image=[imageFFT],x=[0],y=[0],dw=[10],dh=[10],palette="Greys9")
#
filterPlot=Figure(title="current filter",
plot_width=400,plot_height=400,
x_range=[0,10], y_range=[0,10])
filterPlot.image(image=[customFilter],x=[0],y=[0],dw=[10],dh=[10],palette="Spectral11")#,palette=myColorMapperAu.palette)
filterPlot.axis.visible=None
outputPlot=Figure(title="filtered image",
plot_width=400,plot_height=400,
x_range=[0,10], y_range=[0,10])
def plot_cross_section_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, cross_data, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and cross-sections using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all is a list of velocity profiles
cross_lat_data_all is a list of cross-sections along latitude
lat_value_all is a list of corresponding latitudes for these cross-sections
cross_lon_data_all is a list of cross-sections along longitude
lon_value_all is a list of corresponding longitudes for these cross-sections
boundary_data is a list of boundaries
style_parameter contains parameters to customize the plots
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
palette_r = palette[::-1]
ncolor = len(palette_r)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min)*1./ncolor
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append('Depth: {0:8.0f} km'.format(map_depth))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
map_data_one_slice = map_data_all_slices[map_view_default_index]
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']],\
dh=[style_parameter['nlat']],map_data_one_slice=[map_data_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_data_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
#
plot_depth = np.shape(cross_data)[0] * style_parameter['cross_ddepth']
plot_lon = great_arc_distance(style_parameter['cross_default_lat0'], style_parameter['cross_default_lon0'],\
style_parameter['cross_default_lat1'], style_parameter['cross_default_lon1'])
cross_data_bokeh = ColumnDataSource(data=dict(x=[0],\
y=[plot_depth],dw=[plot_lon],\
dh=[plot_depth],cross_data=[cross_data]))
map_line_bokeh = ColumnDataSource(data=dict(lat=[style_parameter['cross_default_lat0'], style_parameter['cross_default_lat1']],\
lon=[style_parameter['cross_default_lon0'], style_parameter['cross_default_lon1']]))
#
ncolor_cross = len(my_palette)
colorbar_top_cross = [0.1 for i in range(ncolor_cross)]
colorbar_bottom_cross = [0 for i in range(ncolor_cross)]
color_min_cross = style_parameter['cross_view_vs_min']
color_max_cross = style_parameter['cross_view_vs_max']
color_step_cross = (color_max_cross - color_min_cross)*1./ncolor_cross
colorbar_left_cross = np.linspace(color_min_cross, color_max_cross-color_step_cross, ncolor_cross)
colorbar_right_cross = np.linspace(color_min_cross+color_step_cross, color_max_cross, ncolor_cross)
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image('map_data_one_slice',x='x',\
y='y',dw='dw',\
dh='dh',palette=palette_r,\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50, \
callback=depth_slider_callback)
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.line('lon', 'lat', source=map_line_bokeh, line_dash=[8,2,8,2], line_color='#00ff00',\
nonselection_line_alpha=1.0, line_width=5.,\
nonselection_line_color='black')
map_view.text([style_parameter['cross_default_lon0']],[style_parameter['cross_default_lat0']], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
map_view.text([style_parameter['cross_default_lon1']],[style_parameter['cross_default_lat1']], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text="""<p style="font-size:16px">""", \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
# plot cross-section along latitude
cross_section_plot_width = int(style_parameter['cross_plot_height']*1.0/plot_depth*plot_lon/10.)
cross_view = Figure(plot_width=cross_section_plot_width, plot_height=style_parameter['cross_plot_height'], \
tools=style_parameter['cross_view_tools'], title=style_parameter['cross_view_title'], \
y_range=[plot_depth, -30],\
x_range=[0, plot_lon])
cross_view.image('cross_data',x='x',\
y='y',dw='dw',\
dh='dh',palette=my_palette,\
source=cross_data_bokeh, level='image')
cross_view.text([plot_lon*0.1], [-10], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
cross_view.text([plot_lon*0.9], [-10], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
# ------------------------------
# change style
cross_view.title.text_font_size = style_parameter['title_font_size']
cross_view.title.align = 'center'
cross_view.title.text_font_style = 'normal'
cross_view.xaxis.axis_label = style_parameter['cross_view_xlabel']
cross_view.xaxis.axis_label_text_font_style = 'normal'
cross_view.xaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.xaxis.major_label_text_font_size = xlabel_fontsize
cross_view.yaxis.axis_label = style_parameter['cross_view_ylabel']
cross_view.yaxis.axis_label_text_font_style = 'normal'
cross_view.yaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.yaxis.major_label_text_font_size = xlabel_fontsize
cross_view.xgrid.grid_line_color = None
cross_view.ygrid.grid_line_color = None
cross_view.toolbar.logo = None
cross_view.toolbar_location = 'right'
cross_view.toolbar_sticky = False
# ==============================
colorbar_fig_right = Figure(tools=[], y_range=(0,0.1),plot_width=cross_section_plot_width, \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig_right.toolbar_location=None
colorbar_fig_right.quad(top=colorbar_top_cross,bottom=colorbar_bottom_cross,\
left=colorbar_left_cross,right=colorbar_right_cross,\
color=my_palette)
colorbar_fig_right.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig_right.xgrid.grid_line_color = None
colorbar_fig_right.ygrid.grid_line_color = None
colorbar_fig_right.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig_right.title.text_font_size = xlabel_fontsize
colorbar_fig_right.title.align = 'center'
colorbar_fig_right.title.text_font_style = 'normal'
# ==============================
output_file(filename,title=style_parameter['html_title'], mode=style_parameter['library_source'])
left_column = Column(depth_slider, map_view, colorbar_fig, annotating_fig01, width=style_parameter['left_column_width'])
right_column = Column(annotating_fig02, cross_view, colorbar_fig_right, width=cross_section_plot_width)
layout = Row(left_column, right_column, height=800)
save(layout)
def plot_3DModel_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, profile_data_all, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and velocity profiles using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all constains the velocity model parameters saved for profile plots
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
ncolor = len(palette)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min)*1./ncolor
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append('Depth: {0:8.0f} km'.format(map_depth))
#
palette_r = palette[::-1]
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
map_data_one_slice = map_data_all_slices[map_view_default_index]
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']*style_parameter['dlon']],\
dh=[style_parameter['nlat']*style_parameter['dlat']],map_data_one_slice=[map_data_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_data_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
# ------------------------------
nprofile = len(profile_data_all)
grid_lat_list = []
grid_lon_list = []
width_list = []
height_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
grid_lat_list.append(aprofile['lat'])
grid_lon_list.append(aprofile['lon'])
width_list.append(style_parameter['map_view_grid_width'])
height_list.append(style_parameter['map_view_grid_height'])
grid_data_bokeh = ColumnDataSource(data=dict(lon=grid_lon_list,lat=grid_lat_list,\
width=width_list, height=height_list))
profile_default_index = style_parameter['profile_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[grid_lat_list[profile_default_index]], \
lon=[grid_lon_list[profile_default_index]], \
width=[style_parameter['map_view_grid_width']],\
height=[style_parameter['map_view_grid_height']],\
index=[profile_default_index]))
# ------------------------------
profile_vs_all = []
profile_depth_all = []
profile_ndepth = style_parameter['profile_ndepth']
profile_lat_label_list = []
profile_lon_label_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
vs_raw = aprofile['vs']
top_raw = aprofile['top']
profile_lat_label_list.append('Lat: {0:12.1f}'.format(aprofile['lat']))
profile_lon_label_list.append('Lon: {0:12.1f}'.format(aprofile['lon']))
vs_plot = []
depth_plot = []
for idepth in range(profile_ndepth):
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth])
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth+1])
profile_vs_all.append(vs_plot)
profile_depth_all.append(depth_plot)
profile_data_all_bokeh = ColumnDataSource(data=dict(profile_vs_all=profile_vs_all, \
profile_depth_all=profile_depth_all))
selected_profile_data_bokeh = ColumnDataSource(data=dict(vs=profile_vs_all[profile_default_index],\
depth=profile_depth_all[profile_default_index]))
selected_profile_lat_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lat_label_x']], y=[style_parameter['profile_lat_label_y']],\
lat_label=[profile_lat_label_list[profile_default_index]]))
selected_profile_lon_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lon_label_x']], y=[style_parameter['profile_lon_label_y']],\
lon_label=[profile_lon_label_list[profile_default_index]]))
all_profile_lat_label_bokeh = ColumnDataSource(data=dict(profile_lat_label_list=profile_lat_label_list))
all_profile_lon_label_bokeh = ColumnDataSource(data=dict(profile_lon_label_list=profile_lon_label_list))
#
button_ndepth = style_parameter['button_ndepth']
button_data_all_vs = []
button_data_all_vp = []
button_data_all_rho = []
button_data_all_top = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
button_data_all_vs.append(aprofile['vs'][:button_ndepth])
button_data_all_vp.append(aprofile['vp'][:button_ndepth])
button_data_all_rho.append(aprofile['rho'][:button_ndepth])
button_data_all_top.append(aprofile['top'][:button_ndepth])
button_data_all_bokeh = ColumnDataSource(data=dict(button_data_all_vs=button_data_all_vs,\
button_data_all_vp=button_data_all_vp,\
button_data_all_rho=button_data_all_rho,\
button_data_all_top=button_data_all_top))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image('map_data_one_slice',x='x',\
y='y',dw='dw',\
dh='dh',palette=palette_r,\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50, \
callback=depth_slider_callback)
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.rect('lon', 'lat', width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='gray', line_alpha=0.5, \
selection_line_color='gray', selection_line_alpha=0.5, selection_fill_color=None,\
nonselection_line_color='gray',nonselection_line_alpha=0.5, nonselection_fill_color=None,\
source=grid_data_bokeh, color=None, line_width=1, level='glyph')
map_view.rect('lon', 'lat',width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='#00ff00', line_alpha=1.0, \
source=selected_dot_on_map_bokeh, fill_color=None, line_width=3.,level='glyph')
# ------------------------------
grid_data_bokeh.callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh, \
grid_data_bokeh=grid_data_bokeh,\
profile_data_all_bokeh=profile_data_all_bokeh,\
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh, \
), code="""
var inds = Math.round(cb_obj.selected['1d'].indices)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][inds]]
selected_dot_on_map_bokeh.data['index'] = [inds]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][inds]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][inds]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[inds]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[inds]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
profile_xrange = Range1d(start=style_parameter['profile_plot_xmin'], end=style_parameter['profile_plot_xmax'])
profile_yrange = Range1d(start=style_parameter['profile_plot_ymax'], end=style_parameter['profile_plot_ymin'])
profile_fig = Figure(plot_width=style_parameter['profile_plot_width'], plot_height=style_parameter['profile_plot_height'],\
x_range=profile_xrange, y_range=profile_yrange, tools=style_parameter['profile_tools'],\
title=style_parameter['profile_title'])
profile_fig.line('vs','depth',source=selected_profile_data_bokeh, line_width=2, line_color='black')
# ------------------------------
# add lat, lon
profile_fig.rect([style_parameter['profile_label_box_x']], [style_parameter['profile_label_box_y']],\
width=style_parameter['profile_label_box_width'], height=style_parameter['profile_label_box_height'],\
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black',\
level='underlay')
profile_fig.text('x','y','lat_label', source=selected_profile_lat_label_bokeh)
profile_fig.text('x','y','lon_label', source=selected_profile_lon_label_bokeh)
# ------------------------------
# change style
profile_fig.xaxis.axis_label = style_parameter['profile_xlabel']
profile_fig.xaxis.axis_label_text_font_style = 'normal'
profile_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.xaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.axis_label = style_parameter['profile_ylabel']
profile_fig.yaxis.axis_label_text_font_style = 'normal'
profile_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.xgrid.grid_line_dash = [4, 2]
profile_fig.ygrid.grid_line_dash = [4, 2]
profile_fig.title.text_font_size = style_parameter['title_font_size']
profile_fig.title.align = 'center'
profile_fig.title.text_font_style = 'normal'
profile_fig.toolbar_location = 'above'
profile_fig.toolbar_sticky = False
profile_fig.toolbar.logo = None
# ==============================
profile_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
grid_data_bokeh=grid_data_bokeh, \
profile_data_all_bokeh=profile_data_all_bokeh, \
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh), code="""
var p_index = Math.round(cb_obj.value)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][p_index]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][p_index]]
selected_dot_on_map_bokeh.data['index'] = [p_index]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][p_index]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][p_index]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[p_index]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[p_index]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
profile_slider = Slider(start=0, end=nprofile-1, value=style_parameter['profile_default_index'], \
step=1, title=style_parameter['profile_slider_title'], \
width=style_parameter['profile_plot_width'], height=50,\
callback=profile_slider_callback)
profile_slider_callback.args['profile_index'] = profile_slider
# ==============================
simple_text_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = "data:text;charset=utf-8,"
var i = 0
var temp = csvContent
temp += "# Layer Top (km) Vs(km/s) Vp(km/s) Rho(g/cm^3) \\n"
while(button_data_vp[i]) {
temp+=button_data_top[i].toPrecision(6) + " " + button_data_vs[i].toPrecision(4) + " " + \
button_data_vp[i].toPrecision(4) + " " + button_data_rho[i].toPrecision(4) + "\\n"
i = i + 1
}
var encodedUri = encodeURI(temp)
link = document.createElement('a');
link.setAttribute('href', encodedUri);
link.setAttribute('download', 'vel_model.txt');
link.click();
""")
simple_text_button = Button(label=style_parameter['simple_text_button_label'], button_type='default', width=style_parameter['button_width'],\
callback=simple_text_button_callback)
# ------------------------------
model96_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var lat = selected_dot_on_map_bokeh.data['lat']
var lon = selected_dot_on_map_bokeh.data['lon']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = "data:text;charset=utf-8,"
var i = 0
var temp = csvContent
temp += "MODEL." + index + " \\n"
temp += "ShearVelocityModel Lat: "+ lat +" Lon: " + lon + "\\n"
temp += "ISOTROPIC \\n"
temp += "KGS \\n"
temp += "SPHERICAL EARTH \\n"
temp += "1-D \\n"
temp += "CONSTANT VELOCITY \\n"
temp += "LINE08 \\n"
temp += "LINE09 \\n"
temp += "LINE10 \\n"
temp += "LINE11 \\n"
temp += " H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS \\n"
while(button_data_vp[i+1]) {
var thickness = button_data_top[i+1] - button_data_top[i]
temp+=" " +thickness.toPrecision(6) + " " + button_data_vp[i].toPrecision(4) + " " + button_data_vs[i].toPrecision(4) \
+ " " + button_data_rho[i].toPrecision(4) + " 0.00 0.00 0.00 0.00 1.00 1.00" + "\\n"
i = i + 1
}
var encodedUri = encodeURI(temp)
link = document.createElement('a');
link.setAttribute('href', encodedUri);
link.setAttribute('download', 'vel_model96.txt');
link.click();
""")
model96_button = Button(label=style_parameter['model96_button_label'], button_type='default', width=style_parameter['button_width'],\
callback=model96_button_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,title=style_parameter['html_title'], mode=style_parameter['library_source'])
left_column = Column(depth_slider, map_view, colorbar_fig, annotating_fig01, width=style_parameter['left_column_width'])
button_pannel = Row(simple_text_button, model96_button)
right_column = Column(profile_slider, profile_fig, button_pannel, annotating_fig02, width=style_parameter['right_column_width'])
layout = Row(left_column, right_column)
save(layout)
