def createPlot(df, boundaryDF):
p = Figure(plot_height=900, plot_width=PLOT_WIDTH, title="", y_range=[], title_text_font_size=TITLE_FONT_SIZE)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
quad = p.quad(
top="top",
bottom="bottom",
left="left",
right="right",
source=blockSource,
fill_color="grey",
hover_fill_color="firebrick",
fill_alpha=0.05,
hover_alpha=0.3,
line_color=None,
hover_line_color="white",
)
p.multi_line(xs="xs", ys="ys", source=blockSource, color="black", line_width=2, line_alpha=0.4, line_dash="dotted")
p.multi_line(xs="xs", ys="ys", source=source, color="color", line_width="width", line_alpha="line_alpha")
p.add_tools(
HoverTool(
tooltips=[("chromosome", "@chromosome"), ("exon", "@exon"), ("start", "@start"), ("end", "@end")],
renderers=[quad],
)
)
return p
def _add_labels(G: nx.Graph, plot: Figure):
"""Add labels from G to the plot.
Args:
G (nx.Graph): Networkx graph.
plot (Figure): Plot to add the labels to.
"""
text = [round(w, 2) for w in nx.get_edge_attributes(G, 'weight').values()]
xs = np.array(list(nx.get_edge_attributes(G, 'xs').values()))
ys = np.array(list(nx.get_edge_attributes(G, 'ys').values()))
x_range = (plot.x_range.end - plot.x_range.start)
y_range = (plot.y_range.end - plot.y_range.start)
margin_shift_x = (x_range / (2 * PLOT_MARGIN + 1)) * PLOT_MARGIN
margin_shift_y = (y_range / (2 * PLOT_MARGIN + 1)) * PLOT_MARGIN
x_scale = plot.plot_width / x_range
y_scale = plot.plot_height / y_range
xs = (xs + margin_shift_x - plot.x_range.start) * x_scale
ys = (ys + margin_shift_y - plot.y_range.start) * y_scale
x = np.mean(xs, axis=1)
y = np.mean(ys, axis=1)
centers = np.vstack((x, y)).T
v = np.vstack((xs[:, 1] - xs[:, 0], ys[:, 1] - ys[:, 0])).T
n = np.divide(v.T, np.linalg.norm(v, axis=1)).T
lbl_size = np.array([len(str(w)) for w in text]) * 3
tmp = np.multiply(n, lbl_size[:, np.newaxis])
blank_start = centers - (n * 3 + tmp)
blank_end = centers + (n * 3 + tmp)
blank_xs = np.vstack((blank_start[:, 0], blank_end[:, 0])).T
blank_ys = np.vstack((blank_start[:, 1], blank_end[:, 1])).T
x = (x / x_scale) - margin_shift_x + plot.x_range.start
y = (y / y_scale) - margin_shift_y + plot.y_range.start
blank_xs = (blank_xs / x_scale) - margin_shift_x + plot.x_range.start
blank_ys = (blank_ys / y_scale) - margin_shift_y + plot.y_range.start
plot.multi_line(xs=blank_xs.tolist(),
ys=blank_ys.tolist(),
line_color='white',
line_width=LINE_WIDTH + 1,
nonselection_line_alpha=1,
level=LABEL_BACKGROUND_LEVEL)
labels_src = ColumnDataSource(data={'x': x, 'y': y, 'text': text})
labels = LabelSet(x='x',
y='y',
text='text',
text_align='center',
text_baseline='middle',
text_font_size='13px',
text_color='black',
level=LABEL_LEVEL,
source=labels_src)
plot.add_layout(labels)
def render_dendrogram(dend: Dict["str", Any], plot_width: int,
plot_height: int) -> Figure:
"""
Render a missing dendrogram.
"""
# list of lists of dcoords and icoords from scipy.dendrogram
xs, ys, cols = dend["icoord"], dend["dcoord"], dend["ivl"]
# if the number of columns is greater than 20, make the plot wider
if len(cols) > 20:
plot_width = 28 * len(cols)
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
tools="",
title=" ",
)
# round the coordinates to integers, and plot the dendrogram
xs = [[round(coord) for coord in coords] for coords in xs]
ys = [[round(coord, 2) for coord in coords] for coords in ys]
fig.multi_line(xs=xs, ys=ys, line_color="#8073ac")
# extract the horizontal lines for the hover tooltip
h_lns_x = [coords[1:3] for coords in xs]
h_lns_y = [coords[1:3] for coords in ys]
null_mismatch_vals = [coord[0] for coord in h_lns_y]
source = ColumnDataSource(dict(x=h_lns_x, y=h_lns_y, n=null_mismatch_vals))
h_lns = fig.multi_line(xs="x", ys="y", source=source, line_color="#8073ac")
hover_pts = HoverTool(
renderers=[h_lns],
tooltips=[("Average distance", "@n{0.1f}")],
line_policy="interp",
)
fig.add_tools(hover_pts)
# shorten column labels if necessary, and override coordinates with column names
cols = [f"{col[:16]}..." if len(col) > 18 else col for col in cols]
axis_coords = list(range(5, 10 * len(cols) + 1, 10))
axis_overrides = dict(zip(axis_coords, cols))
fig.xaxis.ticker = axis_coords
fig.xaxis.major_label_overrides = axis_overrides
fig.xaxis.major_label_orientation = np.pi / 3
fig.yaxis.axis_label = "Average Distance Between Clusters"
fig.grid.visible = False
fig.frame_width = plot_width
return fig
def gen_plot(ticker, features, end_date_year, end_date_month, end_date_day,
days_track):
quandl.ApiConfig.api_key = os.environ['QUANDL_KEY']
stock_indices = {
'Open': ['Opening Price', '#440154'],
'Close': ['Closing Price', '#30678D'],
'High': ['Daily High', '#35B778'],
'Low': ['Daily Low', '#0A333D']
}
end_date_list = [end_date_year, end_date_month, end_date_day]
end_date = '-'.join(end_date_list)
start_date = (pd.to_datetime(end_date) -
pd.DateOffset(days=int(days_track))).strftime('%Y-%m-%d')
mydata = pd.DataFrame(
quandl.get('WIKI/' + ticker, start_date=start_date,
end_date=end_date)).reset_index()
mydata['Date'] = pd.to_datetime(mydata.get('Date', None))
mydata = mydata.get(['Date', *features], None)
data = {
'xs': [mydata.get('Date', None) for col in features],
'ys': [mydata.get(col, None) for col in features],
'labels': [stock_indices.get(col, None)[0] for col in features],
'colors': [stock_indices.get(col, None)[1] for col in features]
}
source = ColumnDataSource(data)
p = Figure(plot_width=800,
plot_height=500,
x_axis_type="datetime",
x_axis_label='Initial',
title='Initial')
p.multi_line(xs='xs',
ys='ys',
legend='labels',
color='colors',
source=source)
p.yaxis.axis_label = ticker
p.xaxis.axis_label = 'Time'
p.title.text = 'Different indices of ' + ticker + ': '\
+ start_date + ' to ' + end_date
return p
def make_plot(source, title):
plot = Figure(plot_width=800,
plot_height=600,
tools="",
toolbar_location=None)
plot.title.text = title
colors = Blues4[0:3]
plot.scatter(x=x, y=y, source=source)
plot.multi_line('ci_x', 'ci', source=source)
# fixed attributes
plot.xaxis.axis_label = xlabel
plot.yaxis.axis_label = ylabel
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
return plot
def _add_edges(
G: nx.Graph,
plot: Figure,
show_labels: bool = True,
hover_line_color: str = TERTIARY_DARK_COLOR
) -> Union[ColumnDataSource, GlyphRenderer]:
"""Add edges from G to the plot.
Args:
G (nx.Graph): Networkx graph.
plot (figure): Plot to add the edges to.
show_labels (bool): True iff each edge should be labeled.
hover_line_color (str): Color of the edges when hovering over them.
Returns:
Union[ColumnDataSource, GlyphRenderer]: edge source and glyphs.
"""
edges_df = pd.DataFrame([G[u][v] for u, v in G.edges()])
u, v = zip(*[(u, v) for u, v in G.edges])
edges_df['u'] = u
edges_df['v'] = v
edges_src = ColumnDataSource(data=edges_df.to_dict(orient='list'))
edges_glyph = plot.multi_line(xs='xs',
ys='ys',
line_color='line_color',
line_cap='round',
hover_line_color=hover_line_color,
line_width=LINE_WIDTH,
nonselection_line_alpha=1,
level=EDGE_LEVEL,
source=edges_src)
if show_labels:
_add_labels(G, plot)
return edges_src, edges_glyph
def createPlot(height=600, width=1200):
"""
Create and return a plot for visualizing transcripts.
"""
TOOLS = "pan, wheel_zoom, save, reset, tap"
p = Figure(title="",
y_range=[],
webgl=True,
tools=TOOLS,
toolbar_location="above",
plot_height=height,
plot_width=width)
# This causes title to overlap plot substantially:
#p.title.text_font_size = TITLE_FONT_SIZE
p.xgrid.grid_line_color = None # get rid of the grid in bokeh
p.ygrid.grid_line_color = None
# the block of exons, there's mouse hover effect on that
quad = p.quad(top="top",
bottom="bottom",
left="left",
right="right",
source=blockSource,
fill_alpha=0,
line_dash="dotted",
line_alpha=0.4,
line_color='black',
hover_fill_color="red",
hover_alpha=0.3,
hover_line_color="white",
nonselection_fill_alpha=0,
nonselection_line_alpha=0.4,
nonselection_line_color='black')
# the block of each vertical transcript, each one can be selected
p.quad(top="top",
bottom="bottom",
right="right",
left="left",
source=tranSource,
fill_alpha=0,
line_alpha=0,
nonselection_fill_alpha=0,
nonselection_line_alpha=0)
# what exons really is
# Cannot use line_width="height" because it is broken.
p.multi_line(xs="xs",
ys="ys",
line_width=opt.height,
color="color",
line_alpha="line_alpha",
source=source)
# the start/stop codon
p.inverted_triangle(x="x",
y="y",
color="color",
source=codonSource,
size='size',
alpha=0.5)
# mouse hover on the block
p.add_tools(
HoverTool(tooltips=[("chromosome", "@chromosome"), ("exon", "@exon"),
("start", "@start"), ("end", "@end")],
renderers=[quad]))
return p
def plotHistogram(fileName,
initData,
stations,
dateRange,
bokehPlaceholderId='bokehContent'):
data = {
'xs': [initData['bins']],
'ys': [initData['values']],
'ss': [1, 2],
'es': [3, 4]
} #ss and es are for test purposes we'll add other values of the controlles e.g. age, usertype, Gender coming fetshed from initdata
source = ColumnDataSource(data=data)
stations.insert(0, "All")
selectSS = Select(title="Start Station:", value="All", options=stations)
selectES = Select(title="End Station:", value="All", options=stations)
selectUT = Select(title="User Type:",
value="All",
options=["All", "Subscriber", "Customer"])
selectGender = Select(title="Gender:",
value="All",
options=["All", "Male", "Female"])
sliderAge = Slider(start=8, end=100, value=30, step=5, title="Age")
startDP = DatePicker(title="Start Date:",
min_date=dateRange[0],
max_date=dateRange[1],
value=dateRange[0])
endDP = DatePicker(title="End Date:",
min_date=dateRange[0],
max_date=dateRange[1],
value=dateRange[1])
binSize = TextInput(value="15", title="Bin Size (Days):")
AddButton = Toggle(label="Add", type="success")
DeleteButton = Toggle(label="delete", type="success")
columns = [
TableColumn(field="ss", title="Start Station"),
TableColumn(field="es", title="End Station")
] # add other columns contains values of other controllers
data_table = DataTable(source=source,
columns=columns,
width=650,
height=300)
model = dict(source=source,
selectSS=selectSS,
selectES=selectES,
startDP=startDP,
endDP=endDP,
binSize=binSize,
selectUT=selectUT,
selectGender=selectGender,
sliderAge=sliderAge)
plot = Figure(plot_width=650, plot_height=400, x_axis_type="datetime")
plot.multi_line('xs',
'ys',
source=source,
line_width='width',
line_alpha=0.6,
line_color='color')
callback = CustomJS(args=model,
code="""
//alert("callback");
var startStation = selectSS.get('value');
var endStation = selectES.get('value');
var startDate = startDP.get('value');
if ( typeof(startDate) !== "number")
startDate = startDate.getTime();
var endDate = endDP.get('value');
if ( typeof(endDate) !== "number")
endDate = endDate.getTime();
var binSize = binSize.get('value');
//alert(startStation + " " + endStation + " " + startDate + " " + endDate + " " + binSize);
var xmlhttp;
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function() {
if (xmlhttp.readyState == XMLHttpRequest.DONE ) {
if(xmlhttp.status == 200){
var data = source.get('data');
var result = JSON.parse(xmlhttp.responseText);
var temp=[];
for(var date in result.x) {
temp.push(new Date(result.x[date]));
}
data['xs'].push(temp);
data['ys'].push(result.y);
source.trigger('change');
}
else if(xmlhttp.status == 400) {
alert(400);
}
else {
alert(xmlhttp.status);
}
}
};
var params = {ss:startStation, es:endStation, sd:startDate, ed:endDate, bs: binSize};
url = "/histogram?" + jQuery.param( params );
xmlhttp.open("GET", url, true);
xmlhttp.send();
""")
AddButton.callback = callback
#DeleteButton.on_click(callback1)
layout1 = vform(startDP, endDP, binSize)
layout2 = vform(plot, DeleteButton, data_table)
layout3 = vform(selectSS, selectES, selectUT, selectGender, sliderAge,
AddButton)
layout = hplot(layout1, layout2, layout3)
script, div = components(layout)
html = readHtmlFile(fileName)
html = insertScriptIntoHeader(html, script)
html = appendElementContent(html, div, "div", "bokehContent")
return html
def jwst_1d_spec(result_dict, model=True, title='Model + Data + Error Bars', output_file = 'data.html',legend = False,
R=False, num_tran = False, plot_width=800, plot_height=400,x_range=[1,10]):
"""Plots 1d simulated spectrum and rebin or rescale for more transits
Plots 1d data points with model in the background (if wanted). Designed to read in exact
output of run_pandexo.
Parameters
----------
result_dict : dict or list of 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.
model : bool
(Optional) True is default. True plots model, False does not plot model
title : str
(Optional) Title of plot. Default is "Model + Data + Error Bars".
output_file : str
(Optional) name of html file for you bokeh plot. After bokeh plot is rendered you will
have the option to save as png.
legend : bool
(Optional) Default is False. True, plots legend.
R : float
(Optional) Rebin data from native instrument resolution to specified resolution. Dafult is False,
no binning.
num_tran : float
(Optional) Scales data by number of transits to improve error by sqrt(`num_trans`)
plot_width : int
(Optional) Sets the width of the plot. Default = 800
plot_height : int
(Optional) Sets the height of the plot. Default = 400
x_range : list of int
(Optional) Sets x range of plot. Default = [1,10]
Returns
-------
x,y,e : list of arrays
Returns wave axis, spectrum and associated error in list format. x[0] will be correspond
to the first dictionary input, x[1] to the second, etc.
Examples
--------
>>> jwst_1d_spec(result_dict, num_tran = 3, R = 35) #for a single plot
If you wanted to save each of the axis that were being plotted:
>>> x,y,e = jwst_1d_data([result_dict1, result_dict2], model=False, num_tran = 5, R = 100) #for multiple
See Also
--------
jwst_noise, jwst_1d_bkg, jwst_1d_flux, jwst_1d_snr, jwst_2d_det, jwst_2d_sat
"""
outx=[]
outy=[]
oute=[]
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
outputfile(output_file)
colors = ['black','blue','red','orange','yellow','purple','pink','cyan','grey','brown']
#make sure its iterable
if type(result_dict) != list:
result_dict = [result_dict]
if type(legend)!=bool:
legend_keys = legend
legend = True
if type(legend_keys) != list:
legend_keys = [legend_keys]
i = 0
for dict in result_dict:
ntran_old = dict['timing']['Number of Transits']
to = dict['timing']["Num Integrations Out of Transit"]
ti = dict['timing']["Num Integrations In Transit"]
#remove any nans
y = dict['FinalSpectrum']['spectrum_w_rand']
x = dict['FinalSpectrum']['wave'][~np.isnan(y)]
err = dict['FinalSpectrum']['error_w_floor'][~np.isnan(y)]
y = y[~np.isnan(y)]
if (R == False) & (num_tran == False):
x=x
y=y
elif (R != False) & (num_tran != False):
new_wave = bin_wave_to_R(x, R)
out = uniform_tophat_sum(new_wave,x, dict['RawData']['electrons_out']*num_tran/ntran_old)
inn = uniform_tophat_sum(new_wave,x, dict['RawData']['electrons_in']*num_tran/ntran_old)
vout = uniform_tophat_sum(new_wave,x, dict['RawData']['var_out']*num_tran/ntran_old)
vin = uniform_tophat_sum(new_wave,x, dict['RawData']['var_in']*num_tran/ntran_old)
var_tot = (to/ti/out)**2.0 * vin + (inn*to/ti/out**2.0)**2.0 * vout
if dict['input']['Primary/Secondary']=='fp/f*':
fac = -1.0
else:
fac = 1.0
rand_noise = np.sqrt((var_tot))*(np.random.randn(len(new_wave)))
raw_spec = (out/to-inn/ti)/(out/to)
sim_spec = fac*(raw_spec + rand_noise )
x = new_wave
y = sim_spec
err = np.sqrt(var_tot)
elif (R == False) & (num_tran != False):
out = dict['RawData']['electrons_out']*num_tran/ntran_old
inn = dict['RawData']['electrons_in']*num_tran/ntran_old
vout = dict['RawData']['var_out']*num_tran/ntran_old
vin = dict['RawData']['var_in']*num_tran/ntran_old
var_tot = (to/ti/out)**2.0 * vin + (inn*to/ti/out**2.0)**2.0 * vout
if dict['input']['Primary/Secondary']=='fp/f*':
fac = -1.0
else:
fac = 1.0
rand_noise = np.sqrt((var_tot))*(np.random.randn(len(x)))
raw_spec = (out/to-inn/ti)/(out/to)
sim_spec = fac*(raw_spec + rand_noise )
x = x
y = sim_spec
err = np.sqrt(var_tot)
elif (R != False) & (num_tran == False):
new_wave = bin_wave_to_R(x, R)
out = uniform_tophat_sum(new_wave,x, dict['RawData']['electrons_out'])
inn = uniform_tophat_sum(new_wave,x, dict['RawData']['electrons_in'])
vout = uniform_tophat_sum(new_wave,x, dict['RawData']['var_out'])
vin = uniform_tophat_sum(new_wave,x, dict['RawData']['var_in'])
var_tot = (to/ti/out)**2.0 * vin + (inn*to/ti/out**2.0)**2.0 * vout
if dict['input']['Primary/Secondary']=='fp/f*':
fac = -1.0
else:
fac = 1.0
rand_noise = np.sqrt((var_tot))*(np.random.randn(len(new_wave)))
raw_spec = (out/to-inn/ti)/(out/to)
sim_spec = fac*(raw_spec + rand_noise )
x = new_wave
y = sim_spec
err = np.sqrt(var_tot)
else:
print("Something went wrong. Cannot enter both resolution and ask to bin to new wave")
return
#create error bars for Bokeh's multi_line
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)))
#initialize Figure
if i == 0:
#Define units for x and y axis
y_axis_label = dict['input']['Primary/Secondary']
if y_axis_label == 'fp/f*': p = -1.0
else: y_axis_label = '('+y_axis_label+')^2'
if dict['input']['Calculation Type'] =='phase_spec':
x_axis_label='Time (secs)'
x_range = [min(x), max(x)]
else:
x_axis_label='Wavelength [microns]'
ylims = [min(dict['OriginalInput']['model_spec'])- 0.1*min(dict['OriginalInput']['model_spec']),
0.1*max(dict['OriginalInput']['model_spec'])+max(dict['OriginalInput']['model_spec'])]
xlims = [min(x), max(x)]
fig1d = Figure(x_range=x_range, y_range = ylims,
plot_width = plot_width, plot_height =plot_height,title=title,x_axis_label=x_axis_label,
y_axis_label = y_axis_label, tools=TOOLS, background_fill_color = 'white')
#plot model, data, and errors
if model:
mxx = dict['OriginalInput']['model_wave']
myy = dict['OriginalInput']['model_spec']
my = uniform_tophat_mean(x, mxx,myy)
fig1d.line(x,my, color='black',alpha=0.2, line_width = 4)
if legend:
fig1d.circle(x, y, color=colors[i], legend = legend_keys[i])
else:
fig1d.circle(x, y, color=colors[i])
outx += [x]
outy += [y]
oute += [err]
fig1d.multi_line(x_err, y_err,color=colors[i])
i += 1
show(fig1d)
return outx,outy,oute
legend='(x0,y0)')
# Plot streamline
plot.line('x',
'y',
source=source_streamline,
color='black',
legend='streamline')
# Plot critical points and lines
plot.scatter('x',
'y',
source=source_critical_pts,
color='red',
legend='critical pts')
plot.multi_line('x_ls',
'y_ls',
source=source_critical_lines,
color='red',
legend='critical lines')
# initialize controls
# text input for input of the ode system [u,v] = [x',y']
u_input = TextInput(value=odesystem_settings.sample_system_functions[
odesystem_settings.init_fun_key][0],
title="u(x,y):")
v_input = TextInput(value=odesystem_settings.sample_system_functions[
odesystem_settings.init_fun_key][1],
title="v(x,y):")
# dropdown menu for selecting one of the sample functions
sample_fun_input = Dropdown(
label="choose a sample function pair or enter one below",
def hst_spec(result_dict, plot=True, output_file='hstspec.html', model=True):
"""Plot 1d spec with error bars for hst
Parameters
----------
result_dict : dict
Dictionary from pandexo output.
plot : bool
(Optional) True renders plot, False does not. Default=True
model : bool
(Optional) Plot model under data. Default=True
output_file : str
(Optional) Default = 'hstspec.html'
Return
------
x : numpy array
micron
y : numpy array
1D spec fp/f* or rp^2/r*^2
e : numpy array
1D rms noise
modelx : numpy array
micron
modely : numpy array
1D spec fp/f* or rp^2/r*^2
See Also
--------
hst_time
"""
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
#plot planet spectrum
mwave = result_dict['planet_spec']['model_wave']
mspec = result_dict['planet_spec']['model_spec']
binwave = result_dict['planet_spec']['binwave']
binspec = result_dict['planet_spec']['binspec']
error = result_dict['planet_spec']['error']
error = np.zeros(len(binspec)) + error
xlims = [
result_dict['planet_spec']['wmin'], result_dict['planet_spec']['wmax']
]
ylims = [
np.min(binspec) - 2.0 * error[0],
np.max(binspec) + 2.0 * error[0]
]
plot_spectrum = Figure(
plot_width=800,
plot_height=300,
x_range=xlims,
y_range=ylims,
tools=TOOLS, #responsive=True,
x_axis_label='Wavelength [microns]',
y_axis_label='Ratio',
title="Original Model with Observation")
y_err = []
x_err = []
for px, py, yerr in zip(binwave, binspec, error):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
if model:
plot_spectrum.line(mwave,
mspec,
color="black",
alpha=0.5,
line_width=4)
plot_spectrum.circle(binwave, binspec, line_width=3, line_alpha=0.6)
plot_spectrum.multi_line(x_err, y_err)
if plot:
outputfile(output_file)
show(plot_spectrum)
return binwave, binspec, error, mwave, mspec
def hst_simulated_lightcurve(result_dict,
plot=True,
output_file='hsttime.html',
model=True):
"""Plot simulated HST light curves (in fluece) for earliest and latest start times
Parameters
----------
result_dict : dict
Dictionary from pandexo output.
plot : bool
(Optional) True renders plot, False does not. Default=True
model : bool
(Optional) Plot model under data. Default=True
output_file : str
(Optional) Default = 'hsttime.html'
Return
------
obsphase1 : numpy array
earliest start time
counts1 : numpy array
white light curve in fluence (e/pixel)
obsphase2 : numpy array
latest start time
counts2 : numpy array
white light curve in fluence (e/pixel)
rms : numpy array
1D rms noise
See Also
--------
hst_spec
"""
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
# earliest and latest start times
obsphase1 = result_dict['light_curve']['obsphase1']
rms = result_dict['light_curve']['light_curve_rms']
obsphase2 = result_dict['light_curve']['obsphase2']
phase1 = result_dict['light_curve']['phase1']
phase2 = result_dict['light_curve']['phase2']
counts1 = result_dict['light_curve']['counts1']
counts2 = result_dict['light_curve']['counts2']
count_noise = result_dict['light_curve']['count_noise']
ramp_included = result_dict['light_curve']['ramp_included']
model_counts1 = result_dict['light_curve']['model_counts1']
model_counts2 = result_dict['light_curve']['model_counts2']
if isinstance(count_noise, float):
rms = np.zeros(len(counts1)) + count_noise
y_err1 = []
x_err1 = []
for px, py, yerr in zip(obsphase1, counts1, rms):
np.array(x_err1.append((px, px)))
np.array(y_err1.append((py - yerr, py + yerr)))
y_err2 = []
x_err2 = []
for px, py, yerr in zip(obsphase2, counts2, rms):
np.array(x_err2.append((px, px)))
np.array(y_err2.append((py - yerr, py + yerr)))
if ramp_included:
title_description = " (Ramp Included)"
else:
title_description = " (Ramp Removed)"
early = Figure(
plot_width=400,
plot_height=300,
tools=TOOLS, #responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux [electrons/pixel]',
title="Earliest Start Time" + title_description)
if model:
early.line(phase1,
model_counts1,
color='black',
alpha=0.5,
line_width=4)
early.circle(obsphase1, counts1, line_width=3, line_alpha=0.6)
early.multi_line(x_err1, y_err1)
late = Figure(
plot_width=400,
plot_height=300,
tools=TOOLS, # responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux [electrons/pixel]',
title="Latest Start Time" + title_description)
if model:
late.line(phase2,
model_counts2,
color='black',
alpha=0.5,
line_width=3)
late.circle(obsphase2, counts2, line_width=3, line_alpha=0.6)
late.multi_line(x_err2, y_err2)
start_time = row(early, late)
if plot:
outputfile(output_file)
show(start_time)
return obsphase1, counts1, obsphase2, counts2, rms
def plotHistogram(fileName, initData, stations, dateRange, bokehPlaceholderId='bokehContent'):
data = {'xs':[initData['bins']], 'ys':[initData['values']],'ss':[1,2], 'es':[3,4] }#ss and es are for test purposes we'll add other values of the controlles e.g. age, usertype, Gender coming fetshed from initdata
source = ColumnDataSource(data=data)
stations.insert(0, "All")
selectSS = Select(title="Start Station:", value="All", options=stations)
selectES = Select(title="End Station:", value="All", options=stations)
selectUT = Select(title="User Type:", value="All", options=["All", "Subscriber", "Customer"])
selectGender = Select(title="Gender:", value="All", options=["All", "Male", "Female"])
sliderAge = Slider(start=8, end=100, value=30, step=5, title="Age")
startDP = DatePicker(title="Start Date:", min_date=dateRange[0] ,max_date=dateRange[1], value=dateRange[0])
endDP = DatePicker(title="End Date:", min_date=dateRange[0] ,max_date=dateRange[1], value=dateRange[1])
binSize = TextInput(value="15", title="Bin Size (Days):")
AddButton = Toggle(label="Add", type="success")
DeleteButton = Toggle(label="delete", type="success")
columns = [TableColumn(field="ss", title="Start Station"),TableColumn(field="es", title="End Station")]# add other columns contains values of other controllers
data_table = DataTable(source=source, columns=columns, width=650, height=300)
model = dict(source=source, selectSS = selectSS, selectES = selectES, startDP = startDP, endDP = endDP, binSize = binSize,selectUT=selectUT,selectGender=selectGender,sliderAge=sliderAge)
plot = Figure(plot_width=650, plot_height=400, x_axis_type="datetime")
plot.multi_line('xs', 'ys', source=source, line_width='width', line_alpha=0.6, line_color='color')
callback = CustomJS(args=model, code="""
//alert("callback");
var startStation = selectSS.get('value');
var endStation = selectES.get('value');
var startDate = startDP.get('value');
if ( typeof(startDate) !== "number")
startDate = startDate.getTime();
var endDate = endDP.get('value');
if ( typeof(endDate) !== "number")
endDate = endDate.getTime();
var binSize = binSize.get('value');
//alert(startStation + " " + endStation + " " + startDate + " " + endDate + " " + binSize);
var xmlhttp;
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function() {
if (xmlhttp.readyState == XMLHttpRequest.DONE ) {
if(xmlhttp.status == 200){
var data = source.get('data');
var result = JSON.parse(xmlhttp.responseText);
var temp=[];
for(var date in result.x) {
temp.push(new Date(result.x[date]));
}
data['xs'].push(temp);
data['ys'].push(result.y);
source.trigger('change');
}
else if(xmlhttp.status == 400) {
alert(400);
}
else {
alert(xmlhttp.status);
}
}
};
var params = {ss:startStation, es:endStation, sd:startDate, ed:endDate, bs: binSize};
url = "/histogram?" + jQuery.param( params );
xmlhttp.open("GET", url, true);
xmlhttp.send();
""")
AddButton.callback = callback
#DeleteButton.on_click(callback1)
layout1 = vform (startDP,endDP,binSize)
layout2 = vform(plot,DeleteButton,data_table)
layout3 = vform(selectSS, selectES,selectUT,selectGender,sliderAge,AddButton)
layout = hplot(layout1,layout2,layout3)
script, div = components(layout)
html = readHtmlFile(fileName)
html = insertScriptIntoHeader(html, script)
html = appendElementContent(html, div, "div", "bokehContent")
return html
def plot_dispersion_bokeh(filename, period_array, curve_data_array, boundary_data, style_parameter):
'''
Plot dispersion maps and curves using bokeh
Input:
filename is the filename of the resulting html file
period_array is a list of period
curve_data_array is a list of dispersion curves
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
# ==============================
# prepare data
map_data_all_slices_velocity = []
map_data_all_slices_period = []
map_data_all_slices_color = []
colorbar_data_all_left = []
colorbar_data_all_right = []
nperiod = len(period_array)
ncurve = len(curve_data_array)
ncolor = len(palette)
palette_r = palette[::-1]
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
for iperiod in range(nperiod):
one_slice_lat_list = []
one_slice_lon_list = []
one_slice_vel_list = []
map_period = period_array[iperiod]
for icurve in range(ncurve):
acurve = curve_data_array[icurve]
curve_lat = acurve['latitude']
curve_lon = acurve['longitude']
curve_vel = acurve['velocity']
curve_period = acurve['period']
one_slice_lat_list.append(curve_lat)
one_slice_lon_list.append(curve_lon)
if map_period in curve_period:
curve_period_index = curve_period.index(map_period)
one_slice_vel_list.append(curve_vel[curve_period_index])
else:
one_slice_vel_list.append(style_parameter['nan_value'])
# get color for dispersion values
one_slice_vel_mean = np.nanmean(one_slice_vel_list)
one_slice_vel_std = np.nanstd(one_slice_vel_list)
color_min = one_slice_vel_mean - one_slice_vel_std * style_parameter['spread_factor']
color_max = one_slice_vel_mean + one_slice_vel_std * style_parameter['spread_factor']
color_step = (color_max - color_min)*1./ncolor
one_slice_color_list = get_color_list(one_slice_vel_list,color_min,color_max,palette_r,\
style_parameter['nan_value'],style_parameter['nan_color'])
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
if one_slice_lat_list:
map_data_all_slices_velocity.append(one_slice_vel_list)
map_data_all_slices_period.append('Period: {0:6.1f} s'.format(map_period))
map_data_all_slices_color.append(one_slice_color_list)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
# get location for all points
map_lat_list, map_lon_list = [], []
map_lat_label_list, map_lon_label_list = [], []
for i in range(ncurve):
acurve = curve_data_array[i]
map_lat_list.append(acurve['latitude'])
map_lon_list.append(acurve['longitude'])
map_lat_label_list.append('Lat: {0:12.3f}'.format(acurve['latitude']))
map_lon_label_list.append('Lon: {0:12.3f}'.format(acurve['longitude']))
# data for the map view plot
map_view_label_lon = style_parameter['map_view_period_label_lon']
map_view_label_lat = style_parameter['map_view_period_label_lat']
map_data_one_slice = map_data_all_slices_color[style_parameter['map_view_default_index']]
map_data_one_slice_period = map_data_all_slices_period[style_parameter['map_view_default_index']]
map_data_one_slice_bokeh = ColumnDataSource(data=dict(map_lat_list=map_lat_list,\
map_lon_list=map_lon_list,\
map_data_one_slice=map_data_one_slice))
map_data_one_slice_period_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_period=[map_data_one_slice_period]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices_color=map_data_all_slices_color,\
map_data_all_slices_period=map_data_all_slices_period))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
# data for dispersion curves
curve_default_index = style_parameter['curve_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[map_lat_list[curve_default_index]],\
lon=[map_lon_list[curve_default_index]],\
color=[map_data_one_slice[curve_default_index]],\
index=[curve_default_index]))
selected_curve_data = curve_data_array[curve_default_index]
selected_curve_data_bokeh = ColumnDataSource(data=dict(curve_period=selected_curve_data['period'],\
curve_velocity=selected_curve_data['velocity']))
period_all = []
velocity_all = []
for acurve in curve_data_array:
period_all.append(acurve['period'])
velocity_all.append(acurve['velocity'])
curve_data_all_bokeh = ColumnDataSource(data=dict(period_all=period_all, velocity_all=velocity_all))
selected_curve_lat_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lat_label_x']], \
y=[style_parameter['curve_lat_label_y']],\
lat_label=[map_lat_label_list[curve_default_index]]))
selected_curve_lon_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lon_label_x']], \
y=[style_parameter['curve_lon_label_y']],\
lon_label=[map_lon_label_list[curve_default_index]]))
all_curve_lat_label_bokeh = ColumnDataSource(data=dict(map_lat_label_list=map_lat_label_list))
all_curve_lon_label_bokeh = ColumnDataSource(data=dict(map_lon_label_list=map_lon_label_list))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add period label
map_view.rect(style_parameter['map_view_period_box_lon'], style_parameter['map_view_period_box_lat'], \
width=style_parameter['map_view_period_box_width'], height=style_parameter['map_view_period_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_period', source=map_data_one_slice_period_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
# plot dots
map_view.circle('map_lon_list', 'map_lat_list', color='map_data_one_slice', \
source=map_data_one_slice_bokeh, size=style_parameter['marker_size'],\
line_width=0.2, line_color='black', alpha=1.0,\
selection_color='map_data_one_slice', selection_line_color='black',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='map_data_one_slice',\
nonselection_line_color='black', nonselection_line_alpha=1.0)
map_view.circle('lon', 'lat', color='color', source=selected_dot_on_map_bokeh, \
line_color='#00ff00', line_width=4.0, alpha=1.0, \
size=style_parameter['selected_marker_size'])
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
fill_color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
curve_fig = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(style_parameter['curve_y_min'],style_parameter['curve_y_max']), \
x_range=(style_parameter['curve_x_min'],style_parameter['curve_x_max']),x_axis_type='log',\
tools=['save','box_zoom','wheel_zoom','reset','crosshair','pan'],
title=style_parameter['curve_title'])
# ------------------------------
curve_fig.rect([style_parameter['curve_label_box_x']], [style_parameter['curve_label_box_y']], \
width=style_parameter['curve_label_box_width'], height=style_parameter['curve_label_box_height'], \
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
curve_fig.text('x', 'y', \
'lat_label', source=selected_curve_lat_label_bokeh)
curve_fig.text('x', 'y', \
'lon_label', source=selected_curve_lon_label_bokeh)
# ------------------------------
curve_fig.line('curve_period', 'curve_velocity', source=selected_curve_data_bokeh, color='black')
curve_fig.circle('curve_period', 'curve_velocity', source=selected_curve_data_bokeh, size=5, color='black')
# ------------------------------
curve_fig.title.text_font_size = style_parameter['title_font_size']
curve_fig.title.align = 'center'
curve_fig.title.text_font_style = 'normal'
curve_fig.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig.xaxis.axis_label_text_font_style = 'normal'
curve_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig.yaxis.axis_label_text_font_style = 'normal'
curve_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.xgrid.grid_line_dash = [4, 2]
curve_fig.ygrid.grid_line_dash = [4, 2]
curve_fig.xaxis[0].formatter = PrintfTickFormatter(format="%4.0f")
curve_fig.toolbar.logo = None
curve_fig.toolbar_location = 'above'
curve_fig.toolbar_sticky = False
# ==============================
map_data_one_slice_bokeh.callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh), code="""
var inds = Math.round(cb_obj.selected['1d'].indices)
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_slice['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_slice['map_lon_list'][inds]]
selected_dot_on_map_bokeh.data['color'] = [new_slice['map_data_one_slice'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][inds]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][inds]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[inds]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[inds]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
# ==============================
period_slider_callback = CustomJS(args=dict(map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh, \
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_period_bokeh=map_data_one_slice_period_bokeh),\
code="""
var p_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
var map_data_new_slice = map_data_all_slices['map_data_all_slices_color'][p_index]
map_data_one_slice_bokeh.data['map_data_one_slice'] = map_data_new_slice
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][p_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][p_index]
colorbar_data_one_slice_bokeh.change.emit()
var selected_index = selected_dot_on_map_bokeh.data['index']
selected_dot_on_map_bokeh.data['color'] = [map_data_new_slice[selected_index]]
selected_dot_on_map_bokeh.change.emit()
map_data_one_slice_period_bokeh.data['map_period'] = [map_data_all_slices['map_data_all_slices_period'][p_index]]
map_data_one_slice_period_bokeh.change.emit()
""")
period_slider = Slider(start=0, end=nperiod-1, value=style_parameter['map_view_default_index'], \
step=1, title=style_parameter['period_slider_title'], \
width=style_parameter['period_slider_plot_width'],\
height=50, callback=period_slider_callback)
# ==============================
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh),\
code="""
var c_index = Math.round(cb_obj.value)
var one_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['index'] = [c_index]
selected_dot_on_map_bokeh.data['lat'] = [one_slice['map_lat_list'][c_index]]
selected_dot_on_map_bokeh.data['lon'] = [one_slice['map_lon_list'][c_index]]
selected_dot_on_map_bokeh.data['color'] = [one_slice['map_data_one_slice'][c_index]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][c_index]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][c_index]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[c_index]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[c_index]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['curve_plot_width'],\
height=50, callback=curve_slider_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_fig = Column(period_slider, map_view, colorbar_fig, annotating_fig01,\
width=style_parameter['left_column_width'] )
right_fig = Column(curve_slider, curve_fig, annotating_fig02, \
width=style_parameter['right_column_width'] )
layout = Row(left_fig, right_fig)
save(layout)
def plot_3DModel_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, profile_data_all, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and velocity profiles using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all constains the velocity model parameters saved for profile plots
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
ncolor = len(palette)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min) * 1. / ncolor
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append(
'Depth: {0:8.1f} km'.format(map_depth))
#
palette_r = palette[::-1]
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[
style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth],
left=[style_parameter['profile_plot_xmin']], \
right=[style_parameter['profile_plot_xmax']]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
#
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_2d = map_color.view('uint32').reshape(map_color.shape[:2])
map_color_all_slices.append(map_color_2d)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']*style_parameter['dlon']],\
dh=[style_parameter['nlat']*style_parameter['dlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
# ------------------------------
nprofile = len(profile_data_all)
grid_lat_list = []
grid_lon_list = []
width_list = []
height_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
grid_lat_list.append(aprofile['lat'])
grid_lon_list.append(aprofile['lon'])
width_list.append(style_parameter['map_view_grid_width'])
height_list.append(style_parameter['map_view_grid_height'])
grid_data_bokeh = ColumnDataSource(data=dict(lon=grid_lon_list,lat=grid_lat_list,\
width=width_list, height=height_list))
profile_default_index = style_parameter['profile_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[grid_lat_list[profile_default_index]], \
lon=[grid_lon_list[profile_default_index]], \
width=[style_parameter['map_view_grid_width']],\
height=[style_parameter['map_view_grid_height']],\
index=[profile_default_index]))
# ------------------------------
profile_vs_all = []
profile_depth_all = []
profile_ndepth = style_parameter['profile_ndepth']
profile_lat_label_list = []
profile_lon_label_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
vs_raw = aprofile['vs']
top_raw = aprofile['top']
profile_lat_label_list.append('Lat: {0:12.1f}'.format(aprofile['lat']))
profile_lon_label_list.append('Lon: {0:12.1f}'.format(aprofile['lon']))
vs_plot = []
depth_plot = []
for idepth in range(profile_ndepth):
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth])
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth + 1])
profile_vs_all.append(vs_plot)
profile_depth_all.append(depth_plot)
profile_data_all_bokeh = ColumnDataSource(data=dict(profile_vs_all=profile_vs_all, \
profile_depth_all=profile_depth_all))
selected_profile_data_bokeh = ColumnDataSource(data=dict(vs=profile_vs_all[profile_default_index],\
depth=profile_depth_all[profile_default_index]))
selected_profile_lat_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lat_label_x']], y=[style_parameter['profile_lat_label_y']],\
lat_label=[profile_lat_label_list[profile_default_index]]))
selected_profile_lon_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lon_label_x']], y=[style_parameter['profile_lon_label_y']],\
lon_label=[profile_lon_label_list[profile_default_index]]))
all_profile_lat_label_bokeh = ColumnDataSource(data=dict(
profile_lat_label_list=profile_lat_label_list))
all_profile_lon_label_bokeh = ColumnDataSource(data=dict(
profile_lon_label_list=profile_lon_label_list))
#
button_ndepth = style_parameter['button_ndepth']
button_data_all_vs = []
button_data_all_vp = []
button_data_all_rho = []
button_data_all_top = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
button_data_all_vs.append(aprofile['vs'][:button_ndepth])
button_data_all_vp.append(aprofile['vp'][:button_ndepth])
button_data_all_rho.append(aprofile['rho'][:button_ndepth])
button_data_all_top.append(aprofile['top'][:button_ndepth])
button_data_all_bokeh = ColumnDataSource(data=dict(button_data_all_vs=button_data_all_vs,\
button_data_all_vp=button_data_all_vp,\
button_data_all_rho=button_data_all_rho,\
button_data_all_top=button_data_all_top))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50)
depth_slider.js_on_change('value', depth_slider_callback)
depth_slider_callback.args["depth_index"] = depth_slider
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.rect('lon', 'lat', width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='gray', line_alpha=0.5, \
selection_line_color='gray', selection_line_alpha=0.5, selection_fill_color=None,\
nonselection_line_color='gray',nonselection_line_alpha=0.5, nonselection_fill_color=None,\
source=grid_data_bokeh, color=None, line_width=1, level='glyph')
map_view.rect('lon', 'lat',width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='#00ff00', line_alpha=1.0, \
source=selected_dot_on_map_bokeh, fill_color=None, line_width=3.,level='glyph')
# ------------------------------
grid_data_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh, \
grid_data_bokeh=grid_data_bokeh,\
profile_data_all_bokeh=profile_data_all_bokeh,\
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh, \
), code="""
var inds = cb_obj.indices
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][inds]]
selected_dot_on_map_bokeh.data['index'] = [inds]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][inds]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][inds]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[inds]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[inds]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
grid_data_bokeh.selected.js_on_change('indices', grid_data_js)
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
profile_xrange = Range1d(start=style_parameter['profile_plot_xmin'],
end=style_parameter['profile_plot_xmax'])
profile_yrange = Range1d(start=style_parameter['profile_plot_ymax'],
end=style_parameter['profile_plot_ymin'])
profile_fig = Figure(plot_width=style_parameter['profile_plot_width'], plot_height=style_parameter['profile_plot_height'],\
x_range=profile_xrange, y_range=profile_yrange, tools=style_parameter['profile_tools'],\
title=style_parameter['profile_title'])
profile_fig.line('vs',
'depth',
source=selected_profile_data_bokeh,
line_width=2,
line_color='black')
# ------------------------------
# add lat, lon
profile_fig.rect([style_parameter['profile_label_box_x']], [style_parameter['profile_label_box_y']],\
width=style_parameter['profile_label_box_width'], height=style_parameter['profile_label_box_height'],\
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black',\
level='underlay')
profile_fig.text('x',
'y',
'lat_label',
source=selected_profile_lat_label_bokeh)
profile_fig.text('x',
'y',
'lon_label',
source=selected_profile_lon_label_bokeh)
# ------------------------------
# change style
profile_fig.xaxis.axis_label = style_parameter['profile_xlabel']
profile_fig.xaxis.axis_label_text_font_style = 'normal'
profile_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.xaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.axis_label = style_parameter['profile_ylabel']
profile_fig.yaxis.axis_label_text_font_style = 'normal'
profile_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.xgrid.grid_line_dash = [4, 2]
profile_fig.ygrid.grid_line_dash = [4, 2]
profile_fig.title.text_font_size = style_parameter['title_font_size']
profile_fig.title.align = 'center'
profile_fig.title.text_font_style = 'normal'
profile_fig.toolbar_location = 'above'
profile_fig.toolbar_sticky = False
profile_fig.toolbar.logo = None
# ==============================
profile_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
grid_data_bokeh=grid_data_bokeh, \
profile_data_all_bokeh=profile_data_all_bokeh, \
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh), code="""
var p_index = Math.round(cb_obj.value)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][p_index]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][p_index]]
selected_dot_on_map_bokeh.data['index'] = [p_index]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][p_index]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][p_index]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[p_index]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[p_index]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
profile_slider = Slider(start=0, end=nprofile-1, value=style_parameter['profile_default_index'], \
step=1, title=style_parameter['profile_slider_title'], \
width=style_parameter['profile_plot_width'], height=50)
profile_slider_callback.args['profile_index'] = profile_slider
profile_slider.js_on_change('value', profile_slider_callback)
# ==============================
simple_text_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "# Layer Top (km) Vs(km/s) Vp(km/s) Rho(g/cm^3) \\n"
while(button_data_vp[i]) {
temp+=button_data_top[i].toPrecision(6) + " " + button_data_vs[i].toPrecision(4) + " " + \
button_data_vp[i].toPrecision(4) + " " + button_data_rho[i].toPrecision(4) + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
simple_text_button = Button(
label=style_parameter['simple_text_button_label'],
button_type='default',
width=style_parameter['button_width'])
simple_text_button.js_on_click(simple_text_button_callback)
# ------------------------------
model96_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var lat = selected_dot_on_map_bokeh.data['lat']
var lon = selected_dot_on_map_bokeh.data['lon']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "MODEL." + index + " \\n"
temp += "ShearVelocityModel Lat: "+ lat +" Lon: " + lon + "\\n"
temp += "ISOTROPIC \\n"
temp += "KGS \\n"
temp += "SPHERICAL EARTH \\n"
temp += "1-D \\n"
temp += "CONSTANT VELOCITY \\n"
temp += "LINE08 \\n"
temp += "LINE09 \\n"
temp += "LINE10 \\n"
temp += "LINE11 \\n"
temp += " H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS \\n"
while(button_data_vp[i+1]) {
var thickness = button_data_top[i+1] - button_data_top[i]
temp+=" " +thickness.toPrecision(6) + " " + button_data_vp[i].toPrecision(4) + " " + button_data_vs[i].toPrecision(4) \
+ " " + button_data_rho[i].toPrecision(4) + " 0.00 0.00 0.00 0.00 1.00 1.00" + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model96.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
model96_button = Button(label=style_parameter['model96_button_label'],
button_type='default',
width=style_parameter['button_width'])
model96_button.js_on_click(model96_button_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_column = Column(depth_slider,
map_view,
colorbar_fig,
annotating_fig01,
width=style_parameter['left_column_width'])
button_pannel = Row(simple_text_button, model96_button)
right_column = Column(profile_slider,
profile_fig,
button_pannel,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_column, right_column)
save(layout)
for cluster in clusters:
plot_data.append(np.where(predictions == cluster)[0])
plot_data = spike_waveforms[plot_data]
# Set up data
N = 0
x = np.arange(len(plot_data[N])/10)
y = spike_waveforms[0, ::10]
source = ColumnDataSource(data=dict(xs=[x for i in range(50)], ys = [plot_data[N + i, ::10] for i in range(50)]))
# Set up plot
plot = Figure(plot_height=400, plot_width=400, title="Unit waveforms",
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, 45], y_range=[-200, 200])
plot.multi_line('xs', 'ys', source=source, line_width=1, line_alpha=1.0)
# Set up widgets
# text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0, start=0, end=50000, step= 100) # put the end of the slider at a large enough value so that almost all cluster sizes will fit in
electrode = TextInput(title = 'Electrode Number', value = '0')
clusters = TextInput(title = 'Number of clusters', value = '2')
cluster_num = TextInput(title = 'Cluster Number', value = '0')
#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)
def update_data(attrname, old, new):
os.chdir(dir_name)
line_width=5) #Column 1
plot.line(x='x', y='y', source=col2.pts, color='#0065BD',
line_width=5) #Column 2
plot.line(x='x', y='y', source=col3.pts, color='#0065BD',
line_width=5) #Column 3
plot.line(x='x', y='y', source=col4.pts, color='#0065BD',
line_width=5) #Column 4
#Create the floors for each column:
plot.line(x='x', y='y', source=col1.floor, color='black', line_width=6)
plot.line(x='x', y='y', source=col2.floor, color='black', line_width=6)
plot.line(x='x', y='y', source=col3.floor, color='black', line_width=6)
plot.line(x='x', y='y', source=col4.floor, color='black', line_width=6)
#Create walls for columns that require a wall:
plot.line(x='x', y='y', source=col2.wall, color='black', line_width=6)
plot.line(x='x', y='y', source=col3.wall, color='black', line_width=6)
plot.multi_line(xs='x', ys='y', source=col4.wall, color='black', line_width=6)
#Create circles for columns that have pins:
plot.circle(x='x', y='y', source=col2.cir1, color='#0065BD', size=10)
plot.circle(x='x', y='y', source=col2.cir2, color='#0065BD', size=10)
plot.circle(x='x', y='y', source=col3.cir2, color='#0065BD', size=10)
#Create the shapes of the ends of the columns:
plot.triangle(x='x',
y='y',
source=col2.tri1,
color='black',
angle=0.0,
fill_alpha=0,
size=20)
plot.triangle(x='x',
y='y',
source=col2.tri2,
def hst_time(result_dict, plot=True, output_file ='hsttime.html', model = True):
"""Plot earliest and latest start times for hst observation
Parameters
----------
result_dict : dict
Dictionary from pandexo output.
plot : bool
(Optional) True renders plot, False does not. Default=True
model : bool
(Optional) Plot model under data. Default=True
output_file : str
(Optional) Default = 'hsttime.html'
Return
------
obsphase1 : numpy array
earliest start time
obstr1 : numpy array
white light curve
obsphase2 : numpy array
latest start time
obstr2 : numpy array
white light curve
rms : numpy array
1D rms noise
See Also
--------
hst_spec
"""
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
#earliest and latest start times
obsphase1 = result_dict['calc_start_window']['obsphase1']
obstr1 = result_dict['calc_start_window']['obstr1']
rms = result_dict['calc_start_window']['light_curve_rms']
obsphase2 = result_dict['calc_start_window']['obsphase2']
obstr2 = result_dict['calc_start_window']['obstr2']
phase1 = result_dict['calc_start_window']['phase1']
phase2 = result_dict['calc_start_window']['phase2']
trmodel1 = result_dict['calc_start_window']['trmodel1']
trmodel2 = result_dict['calc_start_window']['trmodel2']
if isinstance(rms, float):
rms = np.zeros(len(obsphase1))+rms
y_err1 = []
x_err1 = []
for px, py, yerr in zip(obsphase1, obstr1, rms):
np.array(x_err1.append((px, px)))
np.array(y_err1.append((py - yerr, py + yerr)))
y_err2 = []
x_err2 = []
for px, py, yerr in zip(obsphase2, obstr2, rms):
np.array(x_err2.append((px, px)))
np.array(y_err2.append((py - yerr, py + yerr)))
early = Figure(plot_width=400, plot_height=300,
tools=TOOLS,#responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Earliest Start Time")
if model: early.line(phase1, trmodel1, color='black',alpha=0.5, line_width = 4)
early.circle(obsphase1, obstr1, line_width=3, line_alpha=0.6)
early.multi_line(x_err1, y_err1)
late = Figure(plot_width=400, plot_height=300,
tools=TOOLS,#responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Latest Start Time")
if model: late.line(phase2, trmodel2, color='black',alpha=0.5, line_width = 3)
late.circle(obsphase2, obstr2, line_width=3, line_alpha=0.6)
late.multi_line(x_err2, y_err2)
start_time = row(early, late)
if plot:
outputfile(output_file)
show(start_time)
return obsphase1, obstr1, obsphase2, obstr2,rms
def hst_spec(result_dict, plot=True, output_file ='hstspec.html', model = True):
"""Plot 1d spec with error bars for hst
Parameters
----------
result_dict : dict
Dictionary from pandexo output.
plot : bool
(Optional) True renders plot, False does not. Default=True
model : bool
(Optional) Plot model under data. Default=True
output_file : str
(Optional) Default = 'hstspec.html'
Return
------
x : numpy array
micron
y : numpy array
1D spec fp/f* or rp^2/r*^2
e : numpy array
1D rms noise
modelx : numpy array
micron
modely : numpy array
1D spec fp/f* or rp^2/r*^2
See Also
--------
hst_time
"""
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
#plot planet spectrum
mwave = result_dict['planet_spec']['model_wave']
mspec = result_dict['planet_spec']['model_spec']
binwave = result_dict['planet_spec']['binwave']
binspec = result_dict['planet_spec']['binspec']
error = result_dict['planet_spec']['error']
error = np.zeros(len(binspec))+ error
xlims = [result_dict['planet_spec']['wmin'], result_dict['planet_spec']['wmax']]
ylims = [np.min(binspec)-2.0*error[0], np.max(binspec)+2.0*error[0]]
plot_spectrum = Figure(plot_width=800, plot_height=300, x_range=xlims,
y_range=ylims, tools=TOOLS,#responsive=True,
x_axis_label='Wavelength [microns]',
y_axis_label='Ratio',
title="Original Model with Observation")
y_err = []
x_err = []
for px, py, yerr in zip(binwave, binspec, error):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
if model:
plot_spectrum.line(mwave,mspec, color= "black", alpha = 0.5, line_width = 4)
plot_spectrum.circle(binwave,binspec, line_width=3, line_alpha=0.6)
plot_spectrum.multi_line(x_err, y_err)
if plot:
outputfile(output_file)
show(plot_spectrum)
return binwave, binspec, error, mwave, mspec
def create_component_hst(result_dict):
"""Generate front end plots HST
Function that is responsible for generating the front-end spectra plots for HST.
Parameters
----------
result_dict : dict
The dictionary returned from a PandExo (HST) 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.
"""
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
#plot planet spectrum
mwave = result_dict['planet_spec']['model_wave']
mspec = result_dict['planet_spec']['model_spec']
binwave = result_dict['planet_spec']['binwave']
binspec = result_dict['planet_spec']['binspec']
error = result_dict['planet_spec']['error']
error = np.zeros(len(binspec))+ error
xlims = [result_dict['planet_spec']['wmin'], result_dict['planet_spec']['wmax']]
ylims = [np.min(binspec)-2.0*error[0], np.max(binspec)+2.0*error[0]]
plot_spectrum = Figure(plot_width=800, plot_height=300, x_range=xlims,
y_range=ylims, tools=TOOLS,#responsive=True,
x_axis_label='Wavelength [microns]',
y_axis_label='(Rp/R*)^2',
title="Original Model with Observation")
y_err = []
x_err = []
for px, py, yerr in zip(binwave, binspec, error):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
plot_spectrum.line(mwave,mspec, color= "black", alpha = 0.5, line_width = 4)
plot_spectrum.circle(binwave,binspec, line_width=3, line_alpha=0.6)
plot_spectrum.multi_line(x_err, y_err)
#earliest and latest start times
obsphase1 = result_dict['calc_start_window']['obsphase1']
obstr1 = result_dict['calc_start_window']['obstr1']
rms = result_dict['calc_start_window']['light_curve_rms']
obsphase2 = result_dict['calc_start_window']['obsphase2']
obstr2 = result_dict['calc_start_window']['obstr2']
phase1 = result_dict['calc_start_window']['phase1']
phase2 = result_dict['calc_start_window']['phase2']
trmodel1 = result_dict['calc_start_window']['trmodel1']
trmodel2 = result_dict['calc_start_window']['trmodel2']
if isinstance(rms, float):
rms = np.zeros(len(obsphase1))+rms
y_err1 = []
x_err1 = []
for px, py, yerr in zip(obsphase1, obstr1, rms):
np.array(x_err1.append((px, px)))
np.array(y_err1.append((py - yerr, py + yerr)))
y_err2 = []
x_err2 = []
for px, py, yerr in zip(obsphase2, obstr2, rms):
np.array(x_err2.append((px, px)))
np.array(y_err2.append((py - yerr, py + yerr)))
early = Figure(plot_width=400, plot_height=300,
tools=TOOLS,#responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Earliest Start Time")
early.line(phase1, trmodel1, color='black',alpha=0.5, line_width = 4)
early.circle(obsphase1, obstr1, line_width=3, line_alpha=0.6)
early.multi_line(x_err1, y_err1)
late = Figure(plot_width=400, plot_height=300,
tools=TOOLS,#responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Latest Start Time")
late.line(phase2, trmodel2, color='black',alpha=0.5, line_width = 3)
late.circle(obsphase2, obstr2, line_width=3, line_alpha=0.6)
late.multi_line(x_err2, y_err2)
start_time = row(early, late)
result_comp = components({'plot_spectrum':plot_spectrum,
'start_time':start_time})
return result_comp
def create_component_hst(result_dict):
"""Generate front end plots HST
Function that is responsible for generating the front-end spectra plots for HST.
Parameters
----------
result_dict : dict
The dictionary returned from a PandExo (HST) 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.
"""
TOOLS = "pan,wheel_zoom,box_zoom,resize,reset,save"
#plot planet spectrum
mwave = result_dict['planet_spec']['model_wave']
mspec = result_dict['planet_spec']['model_spec']
binwave = result_dict['planet_spec']['binwave']
binspec = result_dict['planet_spec']['binspec']
error = result_dict['planet_spec']['error']
error = np.zeros(len(binspec))+ error
xlims = [result_dict['planet_spec']['wmin'], result_dict['planet_spec']['wmax']]
ylims = [np.min(binspec)-2.0*error[0], np.max(binspec)+2.0*error[0]]
eventType = result_dict['calc_start_window']['eventType']
if eventType=='tranist':
y_axis = '(Rp/R*)^2'
elif eventType =='eclipse':
y_axis='Fp/F*'
plot_spectrum = Figure(plot_width=800, plot_height=300, x_range=xlims,
y_range=ylims, tools=TOOLS,#responsive=True,
x_axis_label='Wavelength [microns]',
y_axis_label=y_axis,
title="Original Model with Observation")
y_err = []
x_err = []
for px, py, yerr in zip(binwave, binspec, error):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
plot_spectrum.line(mwave,mspec, color= "black", alpha = 0.5, line_width = 4)
plot_spectrum.circle(binwave,binspec, line_width=3, line_alpha=0.6)
plot_spectrum.multi_line(x_err, y_err)
#earliest and latest start times
obsphase1 = result_dict['calc_start_window']['obsphase1']
obstr1 = result_dict['calc_start_window']['obstr1']
rms = result_dict['calc_start_window']['light_curve_rms']
obsphase2 = result_dict['calc_start_window']['obsphase2']
obstr2 = result_dict['calc_start_window']['obstr2']
phase1 = result_dict['calc_start_window']['phase1']
phase2 = result_dict['calc_start_window']['phase2']
trmodel1 = result_dict['calc_start_window']['trmodel1']
trmodel2 = result_dict['calc_start_window']['trmodel2']
if isinstance(rms, float):
rms = np.zeros(len(obsphase1))+rms
y_err1 = []
x_err1 = []
for px, py, yerr in zip(obsphase1, obstr1, rms):
np.array(x_err1.append((px, px)))
np.array(y_err1.append((py - yerr, py + yerr)))
y_err2 = []
x_err2 = []
for px, py, yerr in zip(obsphase2, obstr2, rms):
np.array(x_err2.append((px, px)))
np.array(y_err2.append((py - yerr, py + yerr)))
early = Figure(plot_width=400, plot_height=300,
tools=TOOLS,#responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Earliest Start Time")
early.line(phase1, trmodel1, color='black',alpha=0.5, line_width = 4)
early.circle(obsphase1, obstr1, line_width=3, line_alpha=0.6)
early.multi_line(x_err1, y_err1)
late = Figure(plot_width=400, plot_height=300,
tools=TOOLS,#responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Latest Start Time")
late.line(phase2, trmodel2, color='black',alpha=0.5, line_width = 3)
late.circle(obsphase2, obstr2, line_width=3, line_alpha=0.6)
late.multi_line(x_err2, y_err2)
start_time = row(early, late)
result_comp = components({'plot_spectrum':plot_spectrum,
'start_time':start_time})
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
def plot_cross_section_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, cross_data, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and cross-sections using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all is a list of velocity profiles
cross_lat_data_all is a list of cross-sections along latitude
lat_value_all is a list of corresponding latitudes for these cross-sections
cross_lon_data_all is a list of cross-sections along longitude
lon_value_all is a list of corresponding longitudes for these cross-sections
boundary_data is a list of boundaries
style_parameter contains parameters to customize the plots
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
palette_r = palette[::-1]
ncolor = len(palette_r)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min)*1./ncolor
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append('Depth: {0:8.0f} km'.format(map_depth))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_all_slices.append(map_color)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']],\
dh=[style_parameter['nlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
#
plot_depth = np.shape(cross_data)[0] * style_parameter['cross_ddepth']
plot_lon = great_arc_distance(style_parameter['cross_default_lat0'], style_parameter['cross_default_lon0'],\
style_parameter['cross_default_lat1'], style_parameter['cross_default_lon1'])
vs_min = style_parameter['cross_view_vs_min']
vs_max = style_parameter['cross_view_vs_max']
cross_color = val_to_rgb(cross_data, palette_r, vmin, vmax)
cross_data_bokeh = ColumnDataSource(data=dict(x=[0],\
y=[plot_depth],dw=[plot_lon],\
dh=[plot_depth],cross_data=[cross_color]))
map_line_bokeh = ColumnDataSource(data=dict(lat=[style_parameter['cross_default_lat0'], style_parameter['cross_default_lat1']],\
lon=[style_parameter['cross_default_lon0'], style_parameter['cross_default_lon1']]))
#
ncolor_cross = len(my_palette)
colorbar_top_cross = [0.1 for i in range(ncolor_cross)]
colorbar_bottom_cross = [0 for i in range(ncolor_cross)]
color_min_cross = style_parameter['cross_view_vs_min']
color_max_cross = style_parameter['cross_view_vs_max']
color_step_cross = (color_max_cross - color_min_cross)*1./ncolor_cross
colorbar_left_cross = np.linspace(color_min_cross, color_max_cross-color_step_cross, ncolor_cross)
colorbar_right_cross = np.linspace(color_min_cross+color_step_cross, color_max_cross, ncolor_cross)
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50, \
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_rgba('cross_data',x='x',\
y='y',dw='dw',dh='dh',\
source=cross_data_bokeh, level='image')
cross_view.text([plot_lon*0.1], [-10], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
cross_view.text([plot_lon*0.9], [-10], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
# ------------------------------
# change style
cross_view.title.text_font_size = style_parameter['title_font_size']
cross_view.title.align = 'center'
cross_view.title.text_font_style = 'normal'
cross_view.xaxis.axis_label = style_parameter['cross_view_xlabel']
cross_view.xaxis.axis_label_text_font_style = 'normal'
cross_view.xaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.xaxis.major_label_text_font_size = xlabel_fontsize
cross_view.yaxis.axis_label = style_parameter['cross_view_ylabel']
cross_view.yaxis.axis_label_text_font_style = 'normal'
cross_view.yaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.yaxis.major_label_text_font_size = xlabel_fontsize
cross_view.xgrid.grid_line_color = None
cross_view.ygrid.grid_line_color = None
cross_view.toolbar.logo = None
cross_view.toolbar_location = 'right'
cross_view.toolbar_sticky = False
# ==============================
colorbar_fig_right = Figure(tools=[], y_range=(0,0.1),plot_width=cross_section_plot_width, \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig_right.toolbar_location=None
colorbar_fig_right.quad(top=colorbar_top_cross,bottom=colorbar_bottom_cross,\
left=colorbar_left_cross,right=colorbar_right_cross,\
color=my_palette)
colorbar_fig_right.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig_right.xgrid.grid_line_color = None
colorbar_fig_right.ygrid.grid_line_color = None
colorbar_fig_right.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig_right.title.text_font_size = xlabel_fontsize
colorbar_fig_right.title.align = 'center'
colorbar_fig_right.title.text_font_style = 'normal'
# ==============================
output_file(filename,title=style_parameter['html_title'], mode=style_parameter['library_source'])
left_column = Column(depth_slider, map_view, colorbar_fig, annotating_fig01, width=style_parameter['left_column_width'])
right_column = Column(annotating_fig02, cross_view, colorbar_fig_right, width=cross_section_plot_width)
layout = Row(left_column, right_column, height=800)
save(layout)
return {'year': tf.year, 'area': tf.area, 'region': tf.region}
#
source = ColumnDataSource(data=get_data('감자'))
# figure 생성부
p1 = Figure(plot_height=300,
plot_width=1000,
x_axis_label='Year',
y_axis_label='Area',
toolbar_location='above',
title='작물별 재배면적 시계열')
p1.multi_line('year',
'area',
alpha=1,
color='color',
legend='region',
source=source)
p2 = Figure(plot_height=300,
plot_width=1000,
x_axis_label='Year',
y_axis_label='Production',
toolbar_location='above',
title='작물별 생산량 시계열')
p2.multi_line('year',
'product',
alpha=1,
color='color',
legend='region',
source=source)
p3 = Figure(plot_height=300,
# Set up data
N = 0
x = np.arange(len(plot_data[N]) / 10)
y = spike_waveforms[0, ::10]
source = ColumnDataSource(data=dict(
xs=[x for i in range(50)], ys=[plot_data[N + i, ::10] for i in range(50)]))
# Set up plot
plot = Figure(plot_height=400,
plot_width=400,
title="Unit waveforms",
tools="crosshair,pan,reset,resize,save,wheel_zoom",
x_range=[0, 45],
y_range=[-200, 200])
plot.multi_line('xs', 'ys', source=source, line_width=1, line_alpha=1.0)
# Set up widgets
# text = TextInput(title="title", value='my sine wave')
offset = Slider(
title="offset", value=0, start=0, end=50000, step=100
) # put the end of the slider at a large enough value so that almost all cluster sizes will fit in
electrode = TextInput(title='Electrode Number', value='0')
clusters = TextInput(title='Number of clusters', value='2')
cluster_num = TextInput(title='Cluster Number', value='0')
#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)
def update_data(attrname, old, new):
def hst_time(result_dict, plot=True, output_file='hsttime.html', model=True):
"""Plot earliest and latest start times for hst observation
Parameters
----------
result_dict : dict
Dictionary from pandexo output.
plot : bool
(Optional) True renders plot, False does not. Default=True
model : bool
(Optional) Plot model under data. Default=True
output_file : str
(Optional) Default = 'hsttime.html'
Return
------
obsphase1 : numpy array
earliest start time
obstr1 : numpy array
white light curve
obsphase2 : numpy array
latest start time
obstr2 : numpy array
white light curve
rms : numpy array
1D rms noise
See Also
--------
hst_spec
"""
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
#earliest and latest start times
obsphase1 = result_dict['calc_start_window']['obsphase1']
obstr1 = result_dict['calc_start_window']['obstr1']
rms = result_dict['calc_start_window']['light_curve_rms']
obsphase2 = result_dict['calc_start_window']['obsphase2']
obstr2 = result_dict['calc_start_window']['obstr2']
phase1 = result_dict['calc_start_window']['phase1']
phase2 = result_dict['calc_start_window']['phase2']
trmodel1 = result_dict['calc_start_window']['trmodel1']
trmodel2 = result_dict['calc_start_window']['trmodel2']
if isinstance(rms, float):
rms = np.zeros(len(obsphase1)) + rms
y_err1 = []
x_err1 = []
for px, py, yerr in zip(obsphase1, obstr1, rms):
np.array(x_err1.append((px, px)))
np.array(y_err1.append((py - yerr, py + yerr)))
y_err2 = []
x_err2 = []
for px, py, yerr in zip(obsphase2, obstr2, rms):
np.array(x_err2.append((px, px)))
np.array(y_err2.append((py - yerr, py + yerr)))
early = Figure(
plot_width=400,
plot_height=300,
tools=TOOLS, #responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Earliest Start Time")
if model:
early.line(phase1, trmodel1, color='black', alpha=0.5, line_width=4)
early.circle(obsphase1, obstr1, line_width=3, line_alpha=0.6)
early.multi_line(x_err1, y_err1)
late = Figure(
plot_width=400,
plot_height=300,
tools=TOOLS, #responsive=True,
x_axis_label='Orbital Phase',
y_axis_label='Flux',
title="Latest Start Time")
if model:
late.line(phase2, trmodel2, color='black', alpha=0.5, line_width=3)
late.circle(obsphase2, obstr2, line_width=3, line_alpha=0.6)
late.multi_line(x_err2, y_err2)
start_time = row(early, late)
if plot:
outputfile(output_file)
show(start_time)
return obsphase1, obstr1, obsphase2, obstr2, rms
line_width=2,
line_dash='dashed',
line_alpha=0.3)
plot.line(x='x2',
y='y2',
source=MBD.f2.wdline21,
color=MBD.f2color,
line_width=2,
line_dash='dashed',
line_alpha=0.3)
#EDIT End
#creation of the a and b scale reference things:
plot.multi_line(
[[orig.x0, orig.xf], [orig.x0, orig.x0], [orig.xf, orig.xf]],
[[0, 0], [0 - abshift, 0 + abshift], [0 - abshift, 0 + abshift]],
color=["black", "black", "black"],
line_width=1)
plot.multi_line(
[[xb, xb], [xb - abshift, xb + abshift], [xb - abshift, xb + abshift]],
[[orig.y0, orig.yf], [orig.y0, orig.y0], [orig.yf, orig.yf]],
color=["black", "black", "black"],
line_width=1)
#Frame bases
plot.triangle(x='x',
y='y',
size='size',
source=default,
color="grey",
line_width=2)
def jwst_1d_spec(result_dict,
model=True,
title='Model + Data + Error Bars',
output_file='data.html',
legend=False,
R=False,
num_tran=False,
plot_width=800,
plot_height=400,
x_range=[1, 10],
y_range=None,
plot=True,
output_notebook=False):
"""Plots 1d simulated spectrum and rebin or rescale for more transits
Plots 1d data points with model in the background (if wanted). Designed to read in exact
output of run_pandexo.
Parameters
----------
result_dict : dict or list of 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.
model : bool
(Optional) True is default. True plots model, False does not plot model
title : str
(Optional) Title of plot. Default is "Model + Data + Error Bars".
output_file : str
(Optional) name of html file for you bokeh plot. After bokeh plot is rendered you will
have the option to save as png.
legend : bool
(Optional) Default is False. True, plots legend.
R : float
(Optional) Rebin data from native instrument resolution to specified resolution. Dafult is False,
no binning. Here I adopt R as w[1]/(w[2] - w[0]) to maintain consistency with `pandeia.engine`
num_tran : float
(Optional) Scales data by number of transits to improve error by sqrt(`num_trans`)
plot_width : int
(Optional) Sets the width of the plot. Default = 800
plot_height : int
(Optional) Sets the height of the plot. Default = 400
y_range : list of int
(Optional) sets y range of plot. Defaut is +- 10% of max and min
x_range : list of int
(Optional) Sets x range of plot. Default = [1,10]
plot : bool
(Optional) Supresses the plot if not wanted (Default = True)
out_notebook : bool
(Optional) Output notebook. Default is false, if true, outputs in the notebook
Returns
-------
x,y,e : list of arrays
Returns wave axis, spectrum and associated error in list format. x[0] will be correspond
to the first dictionary input, x[1] to the second, etc.
Examples
--------
>>> jwst_1d_spec(result_dict, num_tran = 3, R = 35) #for a single plot
If you wanted to save each of the axis that were being plotted:
>>> x,y,e = jwst_1d_data([result_dict1, result_dict2], model=False, num_tran = 5, R = 100) #for multiple
See Also
--------
jwst_noise, jwst_1d_bkg, jwst_1d_flux, jwst_1d_snr, jwst_2d_det, jwst_2d_sat
"""
outx = []
outy = []
oute = []
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
if output_notebook:
outnotebook()
else:
outputfile(output_file)
colors = [
'black', 'blue', 'red', 'orange', 'yellow', 'purple', 'pink', 'cyan',
'grey', 'brown'
]
#make sure its iterable
if type(result_dict) != list:
result_dict = [result_dict]
if type(legend) != bool:
legend_keys = legend
legend = True
if type(legend_keys) != list:
legend_keys = [legend_keys]
i = 0
for dictt in result_dict:
ntran_old = dictt['timing']['Number of Transits']
to = dictt['timing']["Num Integrations Out of Transit"]
ti = dictt['timing']["Num Integrations In Transit"]
#remove any nans
y = dictt['FinalSpectrum']['spectrum_w_rand']
x = dictt['FinalSpectrum']['wave'][~np.isnan(y)]
err = dictt['FinalSpectrum']['error_w_floor'][~np.isnan(y)]
y = y[~np.isnan(y)]
if (R == False) & (num_tran == False):
x = x
y = y
elif (R != False) & (num_tran != False):
new_wave = bin_wave_to_R(x, R)
out = uniform_tophat_sum(
new_wave, x,
dictt['RawData']['electrons_out'] * num_tran / ntran_old)
inn = uniform_tophat_sum(
new_wave, x,
dictt['RawData']['electrons_in'] * num_tran / ntran_old)
vout = uniform_tophat_sum(
new_wave, x,
dictt['RawData']['var_out'] * num_tran / ntran_old)
vin = uniform_tophat_sum(
new_wave, x, dictt['RawData']['var_in'] * num_tran / ntran_old)
var_tot = (to / ti / out)**2.0 * vin + (inn * to / ti /
out**2.0)**2.0 * vout
if dictt['input']['Primary/Secondary'] == 'fp/f*':
fac = -1.0
else:
fac = 1.0
rand_noise = np.sqrt((var_tot)) * (np.random.randn(len(new_wave)))
raw_spec = (out / to - inn / ti) / (out / to)
sim_spec = fac * (raw_spec + rand_noise)
x = new_wave
y = sim_spec
err = np.sqrt(var_tot)
elif (R == False) & (num_tran != False):
out = dictt['RawData']['electrons_out'] * num_tran / ntran_old
inn = dictt['RawData']['electrons_in'] * num_tran / ntran_old
vout = dictt['RawData']['var_out'] * num_tran / ntran_old
vin = dictt['RawData']['var_in'] * num_tran / ntran_old
var_tot = (to / ti / out)**2.0 * vin + (inn * to / ti /
out**2.0)**2.0 * vout
if dictt['input']['Primary/Secondary'] == 'fp/f*':
fac = -1.0
else:
fac = 1.0
rand_noise = np.sqrt((var_tot)) * (np.random.randn(len(x)))
raw_spec = (out / to - inn / ti) / (out / to)
sim_spec = fac * (raw_spec + rand_noise)
x = x
y = sim_spec
err = np.sqrt(var_tot)
elif (R != False) & (num_tran == False):
new_wave = bin_wave_to_R(x, R)
out = uniform_tophat_sum(new_wave, x,
dictt['RawData']['electrons_out'])
inn = uniform_tophat_sum(new_wave, x,
dictt['RawData']['electrons_in'])
vout = uniform_tophat_sum(new_wave, x, dictt['RawData']['var_out'])
vin = uniform_tophat_sum(new_wave, x, dictt['RawData']['var_in'])
var_tot = (to / ti / out)**2.0 * vin + (inn * to / ti /
out**2.0)**2.0 * vout
if dictt['input']['Primary/Secondary'] == 'fp/f*':
fac = -1.0
else:
fac = 1.0
rand_noise = np.sqrt((var_tot)) * (np.random.randn(len(new_wave)))
raw_spec = (out / to - inn / ti) / (out / to)
sim_spec = fac * (raw_spec + rand_noise)
x = new_wave
y = sim_spec
err = np.sqrt(var_tot)
else:
print(
"Something went wrong. Cannot enter both resolution and ask to bin to new wave"
)
return
#create error bars for Bokeh's multi_line and drop nans
data = pd.DataFrame({'x': x, 'y': y, 'err': err}).dropna()
y_err = []
x_err = []
for px, py, yerr in zip(data['x'], data['y'], data['err']):
np.array(x_err.append((px, px)))
np.array(y_err.append((py - yerr, py + yerr)))
#initialize Figure
if i == 0:
#Define units for x and y axis
y_axis_label = dictt['input']['Primary/Secondary']
if y_axis_label == 'fp/f*': p = -1.0
else: y_axis_label = y_axis_label
if dictt['input']['Calculation Type'] == 'phase_spec':
x_axis_label = 'Time (secs)'
x_range = [min(x), max(x)]
else:
x_axis_label = 'Wavelength [microns]'
if y_range != None:
ylims = y_range
else:
ylims = [
min(dictt['OriginalInput']['model_spec']) -
0.1 * min(dictt['OriginalInput']['model_spec']),
0.1 * max(dictt['OriginalInput']['model_spec']) +
max(dictt['OriginalInput']['model_spec'])
]
fig1d = Figure(x_range=x_range,
y_range=ylims,
plot_width=plot_width,
plot_height=plot_height,
title=title,
x_axis_label=x_axis_label,
y_axis_label=y_axis_label,
tools=TOOLS,
background_fill_color='white')
#plot model, data, and errors
if model:
mxx = dictt['OriginalInput']['model_wave']
myy = dictt['OriginalInput']['model_spec']
my = uniform_tophat_mean(x, mxx, myy)
model_line = pd.DataFrame({'x': x, 'my': my}).dropna()
fig1d.line(model_line['x'],
model_line['my'],
color=colors[i],
alpha=0.2,
line_width=4)
if legend:
fig1d.circle(data['x'],
data['y'],
color=colors[i],
legend=legend_keys[i])
else:
fig1d.circle(data['x'], data['y'], color=colors[i])
outx += [data['x'].values]
outy += [data['y'].values]
oute += [data['err'].values]
fig1d.multi_line(x_err, y_err, color=colors[i])
i += 1
if plot:
show(fig1d)
return outx, outy, oute
def tab_learning():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
select_data = Select(title="Data:", value="", options=[])
select_model = Select(title="Model script:", value="", options=[])
data_descriptor = Paragraph(text=""" Data descriptor """, width=250, height=250)
model_descriptor = Paragraph(text=""" Model descriptor """, width=250, height=250)
select_grid = gridplot([[select_data, select_model], [data_descriptor, model_descriptor]])
problem_type = RadioGroup(labels=["Classification", "Regression"], active=0)
def problem_handler(new):
if(new == 0):
select_data.options = glob.glob('./np/Classification/*.npy')
select_model.options = list(filter(lambda x: 'model_' in x, dir(classification)))
elif(new == 1):
select_data.options = glob.glob('./np/Regression/*.npy')
select_model.options = list(filter(lambda x: 'model_' in x, dir(regression)))
problem_type.on_click(problem_handler)
learning_rate = TextInput(value="0.01", title="Learning rate")
epoch_size = Slider(start=2, end=200, value=5, step=1, title="Epoch")
batch_size = Slider(start=16, end=256, value=64, step=1, title="Batch")
model_insert = TextInput(value="model", title="Model name")
opeimizer = Select(title="Optimizer:", value="", options=["SGD", "ADAM", "RMS"])
hyper_param = gridplot([[learning_rate], [epoch_size], [batch_size], [opeimizer], [model_insert]])
xs = [[1], [1]]
ys = [[1], [1]]
label = [['Train loss'], ['Validation loss']]
color = [['blue'], ['green']]
total_loss_src = ColumnDataSource(data=dict(xs=xs, ys=ys, label=label, color=color))
plot2 = Figure(plot_width=500, plot_height=300)
plot2.multi_line('xs', 'ys', color='color', source=total_loss_src, line_width=3, line_alpha=0.6)
TOOLTIPS = [("loss type", "@label"), ("loss value", "$y")]
plot2.add_tools(HoverTool(tooltips=TOOLTIPS))
t = Title()
t.text = 'Loss'
plot2.title = t
acc_src = ColumnDataSource(data=dict(x=[1], y=[1], label=['R^2 score']))
plot_acc = Figure(plot_width=500, plot_height=300, title="Accuracy")
plot_acc.line('x', 'y', source=acc_src, line_width=3, line_alpha=0.7, color='red')
TOOLTIPS = [("Type ", "@label"), ("Accuracy value", "$y")]
plot_acc.add_tools(HoverTool(tooltips=TOOLTIPS))
acc_list = []
notifier = Paragraph(text=""" Notification """, width=200, height=100)
def learning_handler():
print("Start learning")
del acc_list[:]
tf.reset_default_graph()
K.clear_session()
data = np.load(select_data.value)
data = data.item()
print("data load complete")
time_window = data.get('x').shape[-2]
model_name = model_insert.value
model_name = '(' + str(time_window) + ')' + model_name
if (problem_type.active == 0):
sub_path = 'Classification/'
elif (problem_type.active == 1):
sub_path = 'Regression/'
model_save_dir = './model/' + sub_path + model_name + '/'
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
x_shape = list(data.get('x').shape)
print("Optimizer: " + str(opeimizer.value))
print(select_model.value)
if (problem_type.active == 0):
target_model = getattr(classification, select_model.value)
model = target_model(x_shape[-3], x_shape[-2], float(learning_rate.value), str(opeimizer.value),
data.get('y').shape[-1])
elif (problem_type.active == 1):
target_model = getattr(regression, select_model.value)
model = target_model(x_shape[-3], x_shape[-2], float(learning_rate.value), str(opeimizer.value),
data.get('y').shape[-1])
notifier.text = """ get model """
training_epochs = int(epoch_size.value)
batch = int(batch_size.value)
loss_train = []
loss_val = []
train_ratio = 0.8
train_x = data.get('x')
train_y = data.get('y')
length = train_x.shape[0]
print(train_x.shape)
data_descriptor.text = "Data shape: " + str(train_x.shape)
# model_descriptor.text = "Model layer: " + str(model.model.summary())
val_x = train_x[int(length * train_ratio):]
if(val_x.shape[-1] == 1 and not 'cnn' in select_model.value):
val_x = np.squeeze(val_x, -1)
val_y = train_y[int(length * train_ratio):]
train_x = train_x[:int(length * train_ratio)]
if (train_x.shape[-1] == 1 and not 'cnn' in select_model.value):
train_x = np.squeeze(train_x, -1)
train_y = train_y[:int(length * train_ratio)]
print(train_x.shape)
if('model_dl' in select_model.value):
for epoch in range(training_epochs):
notifier.text = """ learning -- epoch: """ + str(epoch)
hist = model.fit(train_x,
train_y,
epochs=1,
batch_size=batch,
validation_data=(val_x, val_y),
verbose=1)
print("%d epoch's cost: %f" % (epoch, hist.history['loss'][0]))
loss_train.append(hist.history['loss'][0])
loss_val.append(hist.history['val_loss'][0])
xs_temp = []
xs_temp.append([i for i in range(epoch + 1)])
xs_temp.append([i for i in range(epoch + 1)])
ys_temp = []
ys_temp.append(loss_train)
ys_temp.append(loss_val)
total_loss_src.data['xs'] = xs_temp
total_loss_src.data['ys'] = ys_temp
if (problem_type.active == 0):
r2 = hist.history['val_acc'][0]
label_str = 'Class accuracy'
elif (problem_type.active == 1):
pred_y = model.predict(val_x)
r2 = r2_score(val_y, pred_y)
label_str = 'R^2 score'
print("%d epoch's acc: %f" % (epoch, r2))
acc_list.append(np.max([r2, 0]))
acc_src.data['x'] = [i for i in range(epoch+1)]
acc_src.data['y'] = acc_list
acc_src.data['label'] = [label_str for _ in range(epoch + 1)]
print(acc_src.data)
notifier.text = """ learning complete """
model.save(model_save_dir + model_name + '.h5')
notifier.text = """ model save complete """
K.clear_session()
elif('model_ml' in select_model.value):
notifier.text = """ Machine learning model """
if(train_x.shape[-2] != 1):
notifier.text = """ Data include more then one time-frame. \n\n Data will automatically be flatten"""
train_x = train_x.reshape([train_x.shape[0], -1])
val_x = val_x.reshape([val_x.shape[0], -1])
##### shit
if (problem_type.active == 0):
train_y = np.argmax(train_y, axis=-1).astype(float)
print(train_x.shape)
print(train_y.shape)
model.fit(train_x, train_y)
notifier.text = """ Training done """
pred_y = model.predict(val_x)
print(pred_y)
pickle.dump(model, open(model_save_dir + model_name + '.sav', 'wb'))
notifier.text = """ Machine learning model saved """
button_learning = Button(label="Run model")
button_learning.on_click(learning_handler)
learning_grid = gridplot(
[[problem_type],
[select_grid, hyper_param, button_learning, notifier],
[plot2, plot_acc]])
tab = Panel(child=learning_grid, title='Learning')
return tab
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
x_range=[odesystem_settings.x_min, odesystem_settings.x_max],
y_range=[odesystem_settings.y_min, odesystem_settings.y_max]
)
# remove grid from plot
plot.grid[0].grid_line_alpha = 0.0
plot.grid[1].grid_line_alpha = 0.0
# Plot the direction field
quiver = my_bokeh_utils.Quiver(plot)
# Plot initial values
plot.scatter('x0', 'y0', source=source_initialvalue, color='black', legend='(x0,y0)')
# Plot streamline
plot.line('x', 'y', source=source_streamline, color='black', legend='streamline')
# Plot critical points and lines
plot.scatter('x', 'y', source=source_critical_pts, color='red', legend='critical pts')
plot.multi_line('x_ls', 'y_ls', source=source_critical_lines, color='red', legend='critical lines')
# initialize controls
# text input for input of the ode system [u,v] = [x',y']
u_input = TextInput(value=odesystem_settings.sample_system_functions[odesystem_settings.init_fun_key][0], title="u(x,y):")
v_input = TextInput(value=odesystem_settings.sample_system_functions[odesystem_settings.init_fun_key][1], title="v(x,y):")
# dropdown menu for selecting one of the sample functions
sample_fun_input = Dropdown(label="choose a sample function pair or enter one below",
menu=odesystem_settings.sample_system_names)
# Interactor for entering starting point of initial condition
interactor = my_bokeh_utils.Interactor(plot)
# initialize callback behaviour
sample_fun_input.on_click(sample_fun_change)
dellconv = dell["StatTime"]
dell_time = pandas.to_datetime(dellconv, unit='s')
dell_2 = result[(result.DeviceName == 'Dell_2') & (result.TypeId == 2758)]
dell_2_up = dell_2["UpBytes"]
dell_2_down = dell_2["DownBytes"]
dell_2_con = dell_2["StatTime"]
dell_2_time = pandas.to_datetime(dell_2_con, unit='s')
juniper = result[(result.DeviceName == 'juniper') & (result.TypeId == 1234)]
juniper_up = juniper["UpBytes"]
juniper_down = juniper["DownBytes"]
juniperconv = juniper["StatTime"]
juniper_time = pandas.to_datetime(juniperconv, unit='s')
p.multi_line(xs = [juniper_time, juniper_time], ys = [juniper_up, juniper_down] color=['red', 'blue'] line_width=1, legend='juniper')
p.multi_line(xs = [cisco_time, cisco_time], ys = [cisco_up, cisco_down], color=['#EE0091','#2828B0'], line_width=1, legend='cisco')
p.multi_line(xs = [hp_time, hp_time], ys = [hp_up, hp_down], color=['yellow','green'], line_width=1, legend='cisco')
p.multi_line(xs = [cisco_time, cisco_time], ys = [cisco_up, cisco_down], color=['pink','black'], line_width=1, legend='cisco')
p.multi_line(xs = [cisco_time, cisco_time], ys = [cisco_up, cisco_down], color=['#498ABF','#2F00E1'], line_width=1, legend='cisco')
hover = HoverTool(tooltips = [('Time', '@x{int}'),
('Value', '@y{1.11} GB'),
('Device', '@DeviceName')])
hover.formatters = {"Date": "datetime"}
p.legend.label_text_font = "times"
p.legend.label_text_font_style = "italic"
p.grid.grid_line_alpha=0.5
p.xaxis.major_tick_line_width = 3
def plot_waveform_bokeh(filename,waveform_list,metadata_list,station_lat_list,\
station_lon_list, event_lat, event_lon, boundary_data, style_parameter):
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
map_station_location_bokeh = ColumnDataSource(data=dict(map_lat_list=station_lat_list,\
map_lon_list=station_lon_list))
dot_default_index = 0
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[station_lat_list[dot_default_index]],\
lon=[station_lon_list[dot_default_index]],\
index=[dot_default_index]))
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='gray',\
line_width=2, level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='gray',\
level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='gray',\
line_width=2, nonselection_line_alpha=1.0, level='underlay',
nonselection_line_color='gray')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='gray',\
level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
#
map_view.triangle('map_lon_list', 'map_lat_list', source=map_station_location_bokeh, \
line_color='gray', size=style_parameter['marker_size'], fill_color='black',\
selection_color='black', selection_line_color='gray',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='black',\
nonselection_line_color='gray', nonselection_line_alpha=1.0)
map_view.triangle('lon','lat', source=selected_dot_on_map_bokeh,\
size=style_parameter['selected_marker_size'], line_color='black',fill_color='red')
map_view.asterisk([event_lon], [event_lat], size=style_parameter['event_marker_size'], line_width=3, line_color='red', \
fill_color='red')
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# --------------------------------------------------------
max_waveform_length = 0
max_waveform_amp = 0
ncurve = len(waveform_list)
for a_sta in waveform_list:
for a_trace in a_sta:
if len(a_trace) > max_waveform_length:
max_waveform_length = len(a_trace)
if np.max(np.abs(a_trace)) > max_waveform_amp:
max_waveform_amp = np.max(np.abs(a_trace))
#
plotting_list = []
for a_sta in waveform_list:
temp = []
for a_trace in a_sta:
if len(a_trace) < max_waveform_length:
a_trace = np.append(a_trace,np.zeros([(max_waveform_length-len(a_trace)),1]))
temp.append(list(a_trace))
plotting_list.append(temp)
#
time_list = []
for ista in range(len(plotting_list)):
a_sta = plotting_list[ista]
temp = []
for itr in range(len(a_sta)):
a_trace = a_sta[itr]
delta = metadata_list[ista][itr]['delta']
time = list(np.arange(len(a_trace))*delta)
temp.append(time)
#
time_list.append(temp)
#
reftime_label_list = []
channel_label_list = []
for ista in range(len(metadata_list)):
temp_ref = []
temp_channel = []
a_sta = metadata_list[ista]
for a_trace in a_sta:
temp_ref.append('Starting from '+a_trace['starttime'])
temp_channel.append(a_trace['network']+'_'+a_trace['station']+'_'+a_trace['channel'])
reftime_label_list.append(temp_ref)
channel_label_list.append(temp_channel)
# --------------------------------------------------------
curve_fig01 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 0
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh01 = ColumnDataSource(data=dict(time=select_curve_time,amp=select_curve_data))
select_reftime_label = reftime_label_list[dot_default_index][curve_index]
selected_reftime_label_bokeh01 = ColumnDataSource(data=dict(x=[style_parameter['curve_reftime_label_x']],\
y=[style_parameter['curve_reftime_label_y']],\
label=[select_reftime_label]))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh01 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
all_curve_data_bokeh = ColumnDataSource(data=dict(t=time_list, amp=plotting_list))
all_reftime_label_bokeh = ColumnDataSource(data=dict(label=reftime_label_list))
all_channel_label_bokeh = ColumnDataSource(data=dict(label=channel_label_list))
# plot waveform
curve_fig01.line('time','amp', source=selected_curve_data_bokeh01,\
line_color='black')
# add refference time as a label
curve_fig01.text('x', 'y', 'label', source=selected_reftime_label_bokeh01)
# add channel label
curve_fig01.text('x', 'y', 'label', source=selected_channel_label_bokeh01)
# change style
curve_fig01.title.text_font_size = style_parameter['title_font_size']
curve_fig01.title.align = 'center'
curve_fig01.title.text_font_style = 'normal'
curve_fig01.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig01.xaxis.axis_label_text_font_style = 'normal'
curve_fig01.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig01.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig01.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig01.yaxis.axis_label_text_font_style = 'normal'
curve_fig01.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig01.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig01.toolbar.logo = None
curve_fig01.toolbar_location = 'above'
curve_fig01.toolbar_sticky = False
# --------------------------------------------------------
curve_fig02 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 1
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh02 = ColumnDataSource(data=dict(time=select_curve_time,amp=select_curve_data))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh02 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
# plot waveform
curve_fig02.line('time','amp', source=selected_curve_data_bokeh02,\
line_color='black')
# add channel label
curve_fig02.text('x', 'y', 'label', source=selected_channel_label_bokeh02)
# change style
curve_fig02.title.text_font_size = style_parameter['title_font_size']
curve_fig02.title.align = 'center'
curve_fig02.title.text_font_style = 'normal'
curve_fig02.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig02.xaxis.axis_label_text_font_style = 'normal'
curve_fig02.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig02.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig02.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig02.yaxis.axis_label_text_font_style = 'normal'
curve_fig02.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig02.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig02.toolbar.logo = None
curve_fig02.toolbar_location = 'above'
curve_fig02.toolbar_sticky = False
# --------------------------------------------------------
curve_fig03 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 2
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh03 = ColumnDataSource(data=dict(time=select_curve_time,amp=select_curve_data))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh03 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
# plot waveform
curve_fig03.line('time','amp', source=selected_curve_data_bokeh03,\
line_color='black')
# add channel label
curve_fig03.text('x', 'y', 'label', source=selected_channel_label_bokeh03)
# change style
curve_fig03.title.text_font_size = style_parameter['title_font_size']
curve_fig03.title.align = 'center'
curve_fig03.title.text_font_style = 'normal'
curve_fig03.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig03.xaxis.axis_label_text_font_style = 'normal'
curve_fig03.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig03.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig03.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig03.yaxis.axis_label_text_font_style = 'normal'
curve_fig03.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig03.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig03.toolbar.logo = None
curve_fig03.toolbar_location = 'above'
curve_fig03.toolbar_sticky = False
# --------------------------------------------------------
map_station_location_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_station_location_bokeh=map_station_location_bokeh,\
selected_curve_data_bokeh01=selected_curve_data_bokeh01,\
selected_curve_data_bokeh02=selected_curve_data_bokeh02,\
selected_curve_data_bokeh03=selected_curve_data_bokeh03,\
selected_channel_label_bokeh01=selected_channel_label_bokeh01,\
selected_channel_label_bokeh02=selected_channel_label_bokeh02,\
selected_channel_label_bokeh03=selected_channel_label_bokeh03,\
selected_reftime_label_bokeh01=selected_reftime_label_bokeh01,\
all_reftime_label_bokeh=all_reftime_label_bokeh,\
all_channel_label_bokeh=all_channel_label_bokeh,\
all_curve_data_bokeh=all_curve_data_bokeh), code="""
var inds = cb_obj.indices
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_loc = map_station_location_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_loc['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_loc['map_lon_list'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh01.data['t'] = all_curve_data_bokeh.data['t'][inds][0]
selected_curve_data_bokeh01.data['amp'] = all_curve_data_bokeh.data['amp'][inds][0]
selected_curve_data_bokeh01.change.emit()
selected_curve_data_bokeh02.data['t'] = all_curve_data_bokeh.data['t'][inds][1]
selected_curve_data_bokeh02.data['amp'] = all_curve_data_bokeh.data['amp'][inds][1]
selected_curve_data_bokeh02.change.emit()
selected_curve_data_bokeh03.data['t'] = all_curve_data_bokeh.data['t'][inds][2]
selected_curve_data_bokeh03.data['amp'] = all_curve_data_bokeh.data['amp'][inds][2]
selected_curve_data_bokeh03.change.emit()
selected_reftime_label_bokeh01.data['label'] = [all_reftime_label_bokeh.data['label'][inds][0]]
selected_reftime_label_bokeh01.change.emit()
selected_channel_label_bokeh01.data['label'] = [all_channel_label_bokeh.data['label'][inds][0]]
selected_channel_label_bokeh01.change.emit()
selected_channel_label_bokeh02.data['label'] = [all_channel_label_bokeh.data['label'][inds][1]]
selected_channel_label_bokeh02.change.emit()
selected_channel_label_bokeh03.data['label'] = [all_channel_label_bokeh.data['label'][inds][2]]
selected_channel_label_bokeh03.change.emit()
""")
#
map_station_location_bokeh.selected.js_on_change('indices', map_station_location_js)
#
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_station_location_bokeh=map_station_location_bokeh,\
selected_curve_data_bokeh01=selected_curve_data_bokeh01,\
selected_curve_data_bokeh02=selected_curve_data_bokeh02,\
selected_curve_data_bokeh03=selected_curve_data_bokeh03,\
selected_channel_label_bokeh01=selected_channel_label_bokeh01,\
selected_channel_label_bokeh02=selected_channel_label_bokeh02,\
selected_channel_label_bokeh03=selected_channel_label_bokeh03,\
selected_reftime_label_bokeh01=selected_reftime_label_bokeh01,\
all_reftime_label_bokeh=all_reftime_label_bokeh,\
all_channel_label_bokeh=all_channel_label_bokeh,\
all_curve_data_bokeh=all_curve_data_bokeh),code="""
var inds = Math.round(cb_obj.value)
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_loc = map_station_location_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_loc['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_loc['map_lon_list'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh01.data['t'] = all_curve_data_bokeh.data['t'][inds][0]
selected_curve_data_bokeh01.data['amp'] = all_curve_data_bokeh.data['amp'][inds][0]
selected_curve_data_bokeh01.change.emit()
selected_curve_data_bokeh02.data['t'] = all_curve_data_bokeh.data['t'][inds][1]
selected_curve_data_bokeh02.data['amp'] = all_curve_data_bokeh.data['amp'][inds][1]
selected_curve_data_bokeh02.change.emit()
selected_curve_data_bokeh03.data['t'] = all_curve_data_bokeh.data['t'][inds][2]
selected_curve_data_bokeh03.data['amp'] = all_curve_data_bokeh.data['amp'][inds][2]
selected_curve_data_bokeh03.change.emit()
selected_reftime_label_bokeh01.data['label'] = [all_reftime_label_bokeh.data['label'][inds][0]]
selected_reftime_label_bokeh01.change.emit()
selected_channel_label_bokeh01.data['label'] = [all_channel_label_bokeh.data['label'][inds][0]]
selected_channel_label_bokeh01.change.emit()
selected_channel_label_bokeh02.data['label'] = [all_channel_label_bokeh.data['label'][inds][1]]
selected_channel_label_bokeh02.change.emit()
selected_channel_label_bokeh03.data['label'] = [all_channel_label_bokeh.data['label'][inds][2]]
selected_channel_label_bokeh03.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['map_view_plot_width'],\
height=50, callback=curve_slider_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_fig = Column(curve_slider, map_view, annotating_fig01, width=style_parameter['left_column_width'] )
right_fig = Column(curve_fig01, curve_fig02, curve_fig03, annotating_fig02, width=style_parameter['right_column_width'])
layout = Row(left_fig, right_fig)
save(layout)
for rg in ragrid:
raxs.append([2.0**1.5 * cos(x) * sin(rg / 2.0) /
sqrt(1.0 + cos(x) * cos(rg / 2.0)) for x in decrange])
rays.append([sqrt(2.0) * sin(x) / sqrt(1.0 + cos(x) * cos(rg / 2.0))
for x in decrange])
decxs = []
decys = []
for dg in decgrid:
decxs.append([2.0**1.5 * cos(dg) * sin(x / 2.0) /
sqrt(1.0 + cos(dg) * cos(x / 2.0)) for x in rarange])
decys.append([sqrt(2.0) * sin(dg) / sqrt(1.0 + cos(dg) * cos(x / 2.0))
for x in rarange])
p1.add_tools(hover)
p1.multi_line(raxs, rays, color='#bbbbbb')
p1.multi_line(decxs, decys, color='#bbbbbb')
claimedtypes = sorted(list(set(sntypes)))
for ci, ct in enumerate(claimedtypes):
ind = [i for i, t in enumerate(sntypes) if t == ct]
source = ColumnDataSource(
data=dict(
x=[snhxs[i] for i in ind],
y=[snhys[i] for i in ind],
ra=[snras[i] for i in ind],
dec=[sndecs[i] for i in ind],
event=[snnames[i] for i in ind],
claimedtype=[sntypes[i] for i in ind]
end=25,
value=10,
step=1)
station_IDs_str = TextInput(title="Station IDs", value='6720 6551')
# Create Figure and Plot
hover = HoverTool(tooltips=[("Station ID", "@station_id"), ("Year", "@year")])
p = Figure(plot_height=600,
plot_width=800,
title="",
tools=[hover],
x_axis_type="datetime")
p.multi_line(
xs='x',
ys='y',
line_color='color',
source=source,
line_width=2,
)
p.x_range = DataRange1d(range_padding=0.0, bounds=None)
#p.xaxis[0].formatter = DatetimeTickFormatter(formats=dict(days=["%B %d"], months=["%B"], years=["%Y"]))
p.yaxis.axis_label = 'Cumulative GDD'
p.xaxis.axis_label = 'Month'
# Select Data based on input info
def select_data():
#p.title = p.title + str(' (Wait..)')
global station_IDs
# Make Stations ID's as a list
station_IDs = []
for x in decrange
])
decxs = []
decys = []
for dg in decgrid:
decxs.append([
2.0**1.5 * cos(dg) * sin(x / 2.0) / sqrt(1.0 + cos(dg) * cos(x / 2.0))
for x in rarange
])
decys.append([
sqrt(2.0) * sin(dg) / sqrt(1.0 + cos(dg) * cos(x / 2.0))
for x in rarange
])
p1.multi_line(raxs, rays, color='#bbbbbb')
p1.multi_line(decxs, decys, color='#bbbbbb')
claimedtypes = sorted(list(set(evtypes)))
glyphs = []
glsize = max(2.5, 7.0 - np.log10(len(evtypes)))
for ci, ct in enumerate(claimedtypes):
ind = [i for i, t in enumerate(evtypes) if t == ct]
source = ColumnDataSource(data=dict(x=[evhxs[i] for i in ind],
y=[evhys[i] for i in ind],
ra=[evras[i] for i in ind],
dec=[evdecs[i] for i in ind],
event=[evnames[i] for i in ind],
claimedtype=[evtypes[i] for i in ind]))
slider_degrees = Slider(start=1, end=10, step=1, value=5, title="Degrees")
slider_lines = Slider(start=1, end=50, step=1, value=10, title="Lines")
slider_points = Slider(start=1, end=100, step=1, value=20, title="Points")
# The datapoints
source_points = ColumnDataSource(data=dict(x=x, y=func(x)+err))
p.scatter(x='x', y='y', source=source_points, color="blue", line_width=3)
# The function where the datapoints come from
source_function = ColumnDataSource(data=dict(x=x, y=func(x)))
p.line(x='x', y='y', source=source_function, color="blue", line_width=1)
# The bootstrap lines
source_lines = ColumnDataSource(data=dict(xs=[ [], [] ], ys=[ [], [] ]))
p.multi_line(xs='xs', ys='ys', source=source_lines, color="pink", line_width=0)
# Their average
source_avg = ColumnDataSource(data=dict(x=[], y=[]))
p.line(x='x', y='y', source=source_avg, color="red", line_width=2)
# Basic instructions
div_instr = Div(text="<font color=black>\
<br> <font color=blue>The blue line </font>is the “true” curve we are trying to approximate.\
<br> <font color=blue><b>The blue dots </b></font>are points drawn from the blue curve with an added random error.\
<br> <font color=pink>Each pink line</font> is the polynomial regression fit over a randomly drawn (with replacement) subset of points.\
<br> <font color=red><b>The thick red line </b></font>is the average of the pink lines.</font>", width=800, height=100)
def update(attrname, old, new):
D=slider_degrees.value # number of degrees for the polynomial
L=slider_lines.value # number of bootstrap lines
src = [photosource[i] for i in indne]
)
)
p1.circle('x', 'y', source = source, color=bandcolorf(band), fill_color="white", legend=noerrorlegend, size=4)
source = ColumnDataSource(
data = dict(
x = [phototime[i] for i in indye],
y = [photoAB[i] for i in indye],
err = [photoerrs[i] for i in indye],
desc = [photoband[i] for i in indye],
instr = [photoinstru[i] for i in indye],
src = [photosource[i] for i in indye]
)
)
p1.multi_line([err_xs[x] for x in indye], [err_ys[x] for x in indye], color=bandcolorf(band))
p1.circle('x', 'y', source = source, color=bandcolorf(band), legend=bandname, size=4)
upplimlegend = bandname if len(indye) == 0 and len(indne) == 0 else ''
indt = [i for i, j in enumerate(phototype) if j]
ind = set(indb).intersection(indt)
p1.inverted_triangle([phototime[x] for x in ind], [photoAB[x] for x in ind],
color=bandcolorf(band), legend=upplimlegend, size=7)
if spectraavail and dohtml and args.writehtml:
spectrumwave = []
spectrumflux = []
spectrumerrs = []
for spectrum in catalog[entry]['spectra']:
spectrumdata = deepcopy(spectrum['data'])
def plotHistogram(fileName, initData, stations, dateRange, bokehPlaceholderId='bokehContent'):
MAX_AGE = 150
#The initial data
data = {
'xs':[initData['bins']],
'ys':[initData['values']],
'ss':["All"],
'es':["All"],
'ut':["All"],
'g':["All"],
'a':[MAX_AGE],
'color':["blue"],
'line_width':[2]
}
source = ColumnDataSource(data=data)
stations.insert(0, "All")
selectSS = Select(title="Start Station:", value="All", options=stations)
selectES = Select(title="End Station:", value="All", options=stations)
selectUT = Select(title="User Type:", value="All", options=["All", "Subscriber", "Customer"])
selectGender = Select(title="Gender:", value="All", options=["All", "Male", "Female", "Unknown"])
sliderAge = Slider(start=0, end=MAX_AGE, value=0, step=1, title="Age LEQ")
startDP = DatePicker(title="Start Date:", min_date=dateRange[0] ,max_date=dateRange[1], value=dateRange[0])
endDP = DatePicker(title="End Date:", min_date=dateRange[0] ,max_date=dateRange[1], value=dateRange[1])
binSize = TextInput(value="15", title="Bin Size (Days):")
AddButton = Toggle(label="Add", type="success")
DeleteButton = Toggle(label="Delete Selected", type="success")
columns = [
TableColumn(field="ss", title="SS"),
TableColumn(field="es", title="ES"),
TableColumn(field="ut", title="User Type"),
TableColumn(field="a", title="Age LEQ"),
TableColumn(field="g", title="Sex")
]
data_table = DataTable(source=source, columns=columns, width=400, row_headers=False, selectable='checkbox')
model = dict(source=source, selectSS = selectSS, selectES = selectES, startDP = startDP, endDP = endDP, binSize = binSize,selectUT=selectUT,selectGender=selectGender,sliderAge=sliderAge, dt = data_table)
addCallback = CustomJS(args=model, code="""
//alert("callback");
var startStation = selectSS.get('value');
var endStation = selectES.get('value');
var startDate = startDP.get('value');
if ( typeof(startDate) !== "number")
startDate = startDate.getTime();
var endDate = endDP.get('value');
if ( typeof(endDate) !== "number")
endDate = endDate.getTime();
var binSize = binSize.get('value');
var gender = selectGender.get('value');
var userType = selectUT.get('value');
var age = sliderAge.get('value');
//alert(age);
//alert(startStation + " " + endStation + " " + startDate + " " + endDate + " " + binSize);
var xmlhttp;
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function() {
if (xmlhttp.readyState == XMLHttpRequest.DONE ) {
if(xmlhttp.status == 200){
var data = source.get('data');
var result = JSON.parse(xmlhttp.responseText);
var temp=[];
for(var date in result.x) {
temp.push(new Date(result.x[date]));
}
data['xs'].push(temp);
data['ys'].push(result.y);
data['ss'].push(startStation);
data['es'].push(endStation);
data['ut'].push(userType);
data['g'].push(gender);
data['a'].push(age);
data['color'].push('blue');
data['line_width'].push(2);
source.trigger('change');
dt.trigger('change');
}
else if(xmlhttp.status == 400) {
alert(400);
}
else {
alert(xmlhttp.status);
}
}
};
var params = {ss:startStation, es:endStation, sd:startDate, ed:endDate, bs: binSize, g:gender, ut:userType, age:age};
url = "/histogram?" + jQuery.param( params );
xmlhttp.open("GET", url, true);
xmlhttp.send();
""")
deleteCallBack = CustomJS(args=dict(source=source, dt= data_table), code="""
var indices = source.get('selected')['1d'].indices;
if(indices.length != 0){
indices.sort();
var data = source.get('data');
var counter = 0;
var i = 0;
var key = 0;
var index = 0;
for(i in indices)
{
index = indices[i];
index -= counter;
for(key in data) {
data[key].splice(index, 1);
}
counter += 1;
}
source.trigger('change');
dt.trigger('change');
}
""")
AddButton.callback = addCallback
DeleteButton.callback = deleteCallBack;
plot = Figure(title="Number Of Trips Over Time", x_axis_label='Time', y_axis_label='Number of trips', plot_width=750, plot_height=400, x_axis_type="datetime")
plot.multi_line('xs', 'ys', source=source, line_width='line_width', line_alpha=0.9, line_color='color')
l2 = vform(plot, hplot(startDP, endDP), binSize)
l3 = vform(selectSS, selectES,selectUT,selectGender,sliderAge, hplot(AddButton, DeleteButton), data_table)
layout = hplot(l2, l3)
script, div = components(layout)
html = readHtmlFile(fileName)
html = insertScriptIntoHeader(html, script)
html = appendElementContent(html, div, "div", "bokehContent")
return html