def _make_plot(num_objects=0):
source = ColumnDataSource(dict(xs=[], ys=[]))
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 3), y_range=Range1d(0, 3), min_border=0)
renderer = plot.add_glyph(source, MultiLine(xs='xs', ys='ys'))
tool = FreehandDrawTool(num_objects=num_objects, renderers=[renderer])
plot.add_tools(tool)
plot.toolbar.active_multi = tool
code = RECORD("xs", "source.data.xs") + RECORD("ys", "source.data.ys")
plot.add_tools(CustomAction(callback=CustomJS(args=dict(source=source), code=code)))
plot.toolbar_sticky = False
return plot
def initialize(self, plot_id=None):
plot = self.plot
stream = self.streams[0]
if stream.styles:
self._create_style_callback(plot.handles['cds'], plot.handles['glyph'], 'xs')
poly_tool = FreehandDrawTool(
empty_value=stream.empty_value,
num_objects=stream.num_objects,
renderers=[plot.handles['glyph_renderer']],
)
plot.state.tools.append(poly_tool)
self._update_cds_vdims()
CDSCallback.initialize(self, plot_id)
def initialize(self, plot_id=None):
try:
from bokeh.models import FreehandDrawTool
except:
param.main.warning('FreehandDraw requires bokeh >= 0.13.0')
return
plot = self.plot
stream = self.streams[0]
poly_tool = FreehandDrawTool(
empty_value=stream.empty_value,
num_objects=stream.num_objects,
renderers=[plot.handles['glyph_renderer']],
)
plot.state.tools.append(poly_tool)
super(FreehandDrawCallback, self).initialize(plot_id)
def modify_doc(doc):
source = ColumnDataSource(dict(xs=[], ys=[]))
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 3), y_range=Range1d(0, 3), min_border=0)
renderer = plot.add_glyph(source, MultiLine(xs='xs', ys='ys'))
tool = FreehandDrawTool(num_objects=num_objects, renderers=[renderer])
plot.add_tools(tool)
plot.toolbar.active_multi = tool
div = Div(text='False')
def cb(attr, old, new):
if cds_data_almost_equal(new, expected):
div.text = 'True'
source.on_change('data', cb)
code = RECORD("matches", "div.text")
plot.add_tools(CustomAction(callback=CustomJS(args=dict(div=div), code=code)))
doc.add_root(column(plot, div))
def modify_doc(doc):
source = ColumnDataSource(dict(xs=[], ys=[]))
renderer = plot.add_glyph(source, MultiLine(xs='xs', ys='ys'))
tool = FreehandDrawTool(num_objects=num_objects, renderers=[renderer])
plot.add_tools(tool)
plot.toolbar.active_multi = tool
div = Div(text='False')
def cb(attr, old, new):
if cds_data_almost_equal(new, expected):
div.text = 'True'
source.on_change('data', cb)
code = RECORD("matches", "div.text")
plot.tags.append(
CustomJS(name="custom-action", args=dict(div=div), code=code))
doc.add_root(column(plot, div))
def initialize(self, plot_id=None):
plot = self.plot
cds = plot.handles['cds']
glyph = plot.handles['glyph']
stream = self.streams[0]
if stream.styles:
self._create_style_callback(cds, glyph)
kwargs = {}
if stream.tooltip:
kwargs[CUSTOM_TOOLTIP] = stream.tooltip
if stream.empty_value is not None:
kwargs['empty_value'] = stream.empty_value
poly_tool = FreehandDrawTool(
num_objects=stream.num_objects,
renderers=[plot.handles['glyph_renderer']],
**kwargs)
plot.state.tools.append(poly_tool)
self._update_cds_vdims(cds.data)
CDSCallback.initialize(self, plot_id)
from bokeh.plotting import figure, output_file, show
from bokeh.models import FreehandDrawTool
output_file("tools_box_edit.html")
p = figure(x_range=(0, 10),
y_range=(0, 10),
width=400,
height=400,
title='Freehand Draw Tool')
renderer = p.multi_line([[1, 9]], [[5, 5]],
line_width=5,
alpha=0.4,
color='red')
draw_tool = FreehandDrawTool(renderers=[renderer], num_objects=3)
p.add_tools(draw_tool)
p.toolbar.active_drag = draw_tool
show(p)
def create_interact_ui(doc):
source = ColumnDataSource(
data={
"image": [z],
"x": x,
"y": y,
"dw": [x.max() - x.min()],
"dh": [y.max() - y.min()],
}
)
plot = figure(
x_range=(x.min(), x.max()),
y_range=(y.min(), y.max()),
plot_width=550,
plot_height=600
)
palette = get_bokeh_palette(cmap)
full_plot = plot.image(
image="image",
x="x",
y="y",
dw="dw",
dh=y.max() - y.min(),
source=source,
palette=palette,
)
plot.xaxis.axis_label = u"Frequency mod \u0394\u03BD"
plot.yaxis.axis_label = "Frequency"
slider = b_Slider(
start=dnu_min,
end=dnu_max,
value=(dnu_min + dnu_max) / 2,
step=step,
title=u"\u0394\u03BD",
format="0.000",
)
# Slider callback
def update_upon_dnu_change(attr, old, new):
x, y, z = echelle(
freq,
power,
new,
sampling=sampling,
)
if scale is not None:
if scale is "sqrt":
z = np.sqrt(z)
elif scale is "log":
z = np.log10(z)
full_plot.data_source.data["image"] = [z]
full_plot.data_source.data["dw"] = [x.max() - x.min()]
plot.x_range.start = x.min()
plot.x_range.end = x.max()
slider.on_change("value", update_upon_dnu_change)
# Adjust some toolbar options
r = plot.multi_line(line_width=15, alpha=0.2, color="red")
plot.add_tools(FreehandDrawTool(renderers=[r]))
plot.toolbar.logo = None
plot.toolbar.active_drag = None
# Layout all of the plots
widgets_and_figures = column(slider, plot)
doc.add_root(widgets_and_figures)
def JSandDivDoublePlotWithToggle(x_axis,
y_axisRaw,
y_axisBin,
change_points=[],
title="",
TOOLTIPS=[],
transList=[],
alarmList=[],
abnormalList=[]):
if TOOLTIPS:
plot = figure(tooltips=TOOLTIPS,
title=title,
x_axis_label='Timestamp',
y_axis_label='Flux',
sizing_mode="stretch_both")
else:
TOOLTIPS = [
("index", "$index"),
("(x,y)", "(@x{0,0}, @y{0,0.000}"),
]
plot = figure(tooltips=TOOLTIPS,
title=title,
x_axis_label='Timestamp',
y_axis_label='Flux',
sizing_mode="stretch_both")
rawSource = ColumnDataSource(data=dict(x=x_axis, y=y_axisRaw))
rawLine = plot.line('x',
'y',
source=rawSource,
line_alpha=0.4,
color='black',
line_width=2,
legend_label="Raw")
rawCircle = plot.circle(x_axis,
y_axisRaw,
color="black",
fill_alpha=0.4,
legend_label="Raw",
size=2)
toggle1 = Toggle(label="Raw Data", button_type="success", active=True)
toggle1.js_link('active', rawLine, 'visible')
toggle1.js_link('active', rawCircle, 'visible')
binSource = ColumnDataSource(data=dict(x=x_axis, y=y_axisBin))
binLine = plot.line('x',
'y',
source=binSource,
color='red',
line_width=4,
legend_label="DyBin")
toggle2 = Toggle(label="Dynamic bin", button_type="success", active=True)
toggle2.js_link('active', binLine, 'visible')
if change_points:
for change_point in change_points:
change_point_span = Span(location=float(change_point),
dimension='height',
line_color='#009E73',
line_dash='dashed',
line_width=1)
plot.add_layout(change_point_span)
toggle3 = Toggle(label="AnswerTran", button_type="success", active=True)
if transList:
for trans in transList:
start_point = int(trans[0])
end_point = int(trans[1])
tran_box = BoxAnnotation(left=x_axis[start_point],
right=x_axis[end_point],
fill_alpha=0.2,
fill_color="#F0E442")
L_span = Span(location=x_axis[start_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
R_span = Span(location=x_axis[end_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
plot.add_layout(tran_box)
plot.add_layout(L_span)
plot.add_layout(R_span)
toggle3.js_link('active', tran_box, 'visible')
toggle3.js_link('active', L_span, 'visible')
toggle3.js_link('active', R_span, 'visible')
toggle4 = Toggle(label="Alarm", button_type="success", active=True)
if alarmList:
for alarm in alarmList:
# alarm : start,end,alarm
start_point = int(alarm['start'])
end_point = int(alarm['end'])
alarm_point = int(alarm['alarm'])
alarm_box = BoxAnnotation(left=x_axis[start_point],
right=x_axis[end_point],
fill_alpha=0.2,
fill_color="#F0E442")
alarm_span = Span(location=x_axis[alarm_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
plot.add_layout(alarm_box)
plot.add_layout(alarm_span)
toggle4.js_link('active', alarm_box, 'visible')
toggle4.js_link('active', alarm_span, 'visible')
toggle5 = Toggle(label="F-test", button_type="success", active=True)
if abnormalList:
for abnormal in abnormalList:
start_point = int(abnormal[0])
end_point = int(abnormal[1])
abnormal_box = BoxAnnotation(left=x_axis[start_point],
right=x_axis[end_point],
fill_alpha=0.2,
fill_color="#F0E442")
L_abnormal_span = Span(location=x_axis[start_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
R_abnormal_span = Span(location=x_axis[end_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
plot.add_layout(abnormal_box)
plot.add_layout(L_abnormal_span)
plot.add_layout(R_abnormal_span)
toggle5.js_link('active', abnormal_box, 'visible')
toggle5.js_link('active', L_abnormal_span, 'visible')
toggle5.js_link('active', R_abnormal_span, 'visible')
plot.legend.border_line_width = 3
plot.legend.click_policy = "hide"
plot.legend.border_line_color = "navy"
plot.legend.border_line_alpha = 0.
plot.xaxis.axis_label_text_font_size = "16pt"
plot.xaxis.major_label_text_font_size = "13pt"
plot.yaxis.axis_label_text_font_size = "16pt"
plot.yaxis.major_label_text_font_size = "13pt"
drawSource = ColumnDataSource(data=dict(x=[], y=[]))
r = plot.multi_line('xs', 'ys', source=drawSource)
tool = FreehandDrawTool(renderers=[r])
plot.add_tools(tool)
js, div = components(
layout([plot],
row(toggle1,
toggle2,
toggle3,
toggle4,
toggle5,
sizing_mode='scale_width'),
sizing_mode="stretch_both"))
return js, div
def plot_image(filename, save=False):
hdu = fits.open(filename)
dheader = dict(hdu[0].header)
for k in dheader:
if len(k) >= 2:
print(f"{k}: {dheader[k]}")
print(hdu.info())
data = hdu[0].data
# quick hot pixel/ cosmic ray mask
mask, cdata = detect_cosmics(
data, psfmodel='gauss',
psffwhm=4, psfsize=2*round(4)+1, # just a guess
sepmed=False, sigclip = 4.25,
niter=3, objlim=10, cleantype='idw', verbose=False
)
# show how many pixels are saturated
SATURATION = 2**(hdu[0].header['bitpix'])
mmask = cdata >= SATURATION*0.9
labels, ngroups = label(mmask)
print('Saturated Areas:',ngroups)
labeli, counts = np.unique(labels, return_counts=True)
bad_pix = {'x':[], 'y':[], 'value':[]}
# loop through each group to find position
for i in range(1,labeli[-1]+1):
imask = labels == i
yc,xc = np.argwhere(imask).mean(0)
bad_pix['x'].append(xc)
bad_pix['y'].append(yc)
bad_pix['value'].append(cdata[imask].mean())
pprint(bad_pix)
# create a figure with text on mouse hover\
print("Saturated pixels are marked with red. These are pixels which have exceeded the maximum value for brightness, and are thus not suitable for use as comparison stars.")
fig = figure(tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")], plot_width=800, plot_height=800,
tools=[PanTool(),BoxZoomTool(),WheelZoomTool(),ResetTool(),HoverTool()])
fig.x_range.range_padding = fig.y_range.range_padding = 0
r = fig.multi_line('x', 'y', source={'x':[],'y':[]},color='white',line_width=3)
fig.add_tools(FreehandDrawTool(renderers=[r]))
##TODO: add colorbar
# set up a colobar + data range
color_mapper = LogColorMapper(palette="Cividis256", low=np.percentile(data, 55), high=np.percentile(data, 99))
# must give a vector of image data for image parameter
fig.image(
image=[cdata],
x=0, y=0, dw=hdu[0].data.shape[1], dh=hdu[0].data.shape[0],
level="image", color_mapper=color_mapper
)
# plot saturated stars
fig.x(bad_pix['x'], bad_pix['y'], size=25, color='red', line_width=3)
fig.x(bad_pix['x'], bad_pix['y'], size=25, color='white', line_width=1)
# TODO figure out hover value
fig.grid.grid_line_width = 0.5
color_bar = ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
fig.add_layout(color_bar, 'right')
if save:
output_file("interactivefits.html")
else:
show(fig)