def make_tpf_figure_elements(tpf,
tpf_source,
pedestal=None,
fiducial_frame=None,
plot_width=370,
plot_height=340,
scale='log',
vmin=None,
vmax=None,
cmap='Viridis256',
tools='tap,box_select,wheel_zoom,reset'):
"""Returns the lightcurve figure elements.
Parameters
----------
tpf : TargetPixelFile
TPF to show.
tpf_source : bokeh.plotting.ColumnDataSource
TPF data source.
pedestal: float
A scalar value to be added to the TPF flux values, often to avoid
taking the log of a negative number in colorbars.
Defaults to `-min(tpf.flux) + 1`
fiducial_frame: int
The tpf slice to start with by default, it is assumed the WCS
is exact for this frame.
scale: str
Color scale for tpf figure. Default is 'log'
vmin: int [optional]
Minimum color scale for tpf figure
vmax: int [optional]
Maximum color scale for tpf figure
cmap: str
Colormap to use for tpf plot. Default is 'Viridis256'
tools: str
Bokeh tool list
Returns
-------
fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider
"""
if pedestal is None:
pedestal = -np.nanmin(tpf.flux.value) + 1
if scale == 'linear':
pedestal = 0
if tpf.mission in ['Kepler', 'K2']:
title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)
elif tpf.mission == 'TESS':
title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)
else:
title = "Pixel data"
# We subtract 0.5 from the range below because pixel coordinates refer to
# the middle of a pixel, e.g. (col, row) = (10.0, 20.0) is a pixel center.
fig = figure(plot_width=plot_width,
plot_height=plot_height,
x_range=(tpf.column - 0.5, tpf.column + tpf.shape[2] - 0.5),
y_range=(tpf.row - 0.5, tpf.row + tpf.shape[1] - 0.5),
title=title,
tools=tools,
toolbar_location="below",
border_fill_color="whitesmoke")
fig.yaxis.axis_label = 'Pixel Row Number'
fig.xaxis.axis_label = 'Pixel Column Number'
vlo, lo, hi, vhi = np.nanpercentile(tpf.flux.value + pedestal,
[0.2, 1, 95, 99.8])
if vmin is not None:
vlo, lo = vmin, vmin
if vmax is not None:
vhi, hi = vmax, vmax
if scale == 'log':
vstep = (np.log10(vhi) -
np.log10(vlo)) / 300.0 # assumes counts >> 1.0!
if scale == 'linear':
vstep = (vhi - vlo) / 300.0 # assumes counts >> 1.0!
if scale == 'log':
color_mapper = LogColorMapper(palette=cmap, low=lo, high=hi)
elif scale == 'linear':
color_mapper = LinearColorMapper(palette=cmap, low=lo, high=hi)
else:
raise ValueError(
'Please specify either `linear` or `log` scale for color.')
fig.image([tpf.flux.value[fiducial_frame, :, :] + pedestal],
x=tpf.column - 0.5,
y=tpf.row - 0.5,
dw=tpf.shape[2],
dh=tpf.shape[1],
dilate=True,
color_mapper=color_mapper,
name="tpfimg")
# The colorbar will update with the screen stretch slider
# The colorbar margin increases as the length of the tick labels grows.
# This colorbar share of the plot window grows, shrinking plot area.
# This effect is known, some workarounds might work to fix the plot area:
# https://github.com/bokeh/bokeh/issues/5186
if scale == 'log':
ticker = LogTicker(desired_num_ticks=8)
elif scale == 'linear':
ticker = BasicTicker(desired_num_ticks=8)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=ticker,
label_standoff=-10,
border_line_color=None,
location=(0, 0),
background_fill_color='whitesmoke',
major_label_text_align='left',
major_label_text_baseline='middle',
title='e/s',
margin=0)
fig.add_layout(color_bar, 'right')
color_bar.formatter = PrintfTickFormatter(format="%14i")
if tpf_source is not None:
fig.rect('xx',
'yy',
1,
1,
source=tpf_source,
fill_color='gray',
fill_alpha=0.4,
line_color='white')
# Configure the stretch slider and its callback function
if scale == 'log':
start, end = np.log10(vlo), np.log10(vhi)
values = (np.log10(lo), np.log10(hi))
elif scale == 'linear':
start, end = vlo, vhi
values = (lo, hi)
stretch_slider = RangeSlider(start=start,
end=end,
step=vstep,
title='Screen Stretch ({})'.format(scale),
value=values,
orientation='horizontal',
width=200,
direction='ltr',
show_value=True,
sizing_mode='fixed',
height=15,
name='tpfstretch')
def stretch_change_callback_log(attr, old, new):
"""TPF stretch slider callback."""
fig.select('tpfimg')[0].glyph.color_mapper.high = 10**new[1]
fig.select('tpfimg')[0].glyph.color_mapper.low = 10**new[0]
def stretch_change_callback_linear(attr, old, new):
"""TPF stretch slider callback."""
fig.select('tpfimg')[0].glyph.color_mapper.high = new[1]
fig.select('tpfimg')[0].glyph.color_mapper.low = new[0]
if scale == 'log':
stretch_slider.on_change('value', stretch_change_callback_log)
if scale == 'linear':
stretch_slider.on_change('value', stretch_change_callback_linear)
return fig, stretch_slider
def make_tpf_figure_elements(tpf,
tpf_source,
pedestal=None,
fiducial_frame=None,
plot_width=370,
plot_height=340):
"""Returns the lightcurve figure elements.
Parameters
----------
tpf : TargetPixelFile
TPF to show.
tpf_source : bokeh.plotting.ColumnDataSource
TPF data source.
pedestal: float
A scalar value to be added to the TPF flux values, often to avoid
taking the log of a negative number in colorbars.
Defaults to `-min(tpf.flux) + 1`
fiducial_frame: int
The tpf slice to start with by default, it is assumed the WCS
is exact for this frame.
Returns
-------
fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider
"""
if pedestal is None:
pedestal = -np.nanmin(tpf.flux) + 1
if tpf.mission in ['Kepler', 'K2']:
title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)
elif tpf.mission == 'TESS':
title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)
else:
title = "Pixel data"
fig = figure(plot_width=plot_width,
plot_height=plot_height,
x_range=(tpf.column, tpf.column + tpf.shape[2]),
y_range=(tpf.row, tpf.row + tpf.shape[1]),
title=title,
tools='tap,box_select,wheel_zoom,reset',
toolbar_location="below",
border_fill_color="whitesmoke")
fig.yaxis.axis_label = 'Pixel Row Number'
fig.xaxis.axis_label = 'Pixel Column Number'
vlo, lo, hi, vhi = np.nanpercentile(tpf.flux + pedestal,
[0.2, 1, 95, 99.8])
vstep = (np.log10(vhi) - np.log10(vlo)) / 300.0 # assumes counts >> 1.0!
color_mapper = LogColorMapper(palette="Viridis256", low=lo, high=hi)
fig.image([tpf.flux[fiducial_frame, :, :] + pedestal],
x=tpf.column,
y=tpf.row,
dw=tpf.shape[2],
dh=tpf.shape[1],
dilate=True,
color_mapper=color_mapper,
name="tpfimg")
# The colorbar will update with the screen stretch slider
# The colorbar margin increases as the length of the tick labels grows.
# This colorbar share of the plot window grows, shrinking plot area.
# This effect is known, some workarounds might work to fix the plot area:
# https://github.com/bokeh/bokeh/issues/5186
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(desired_num_ticks=8),
label_standoff=-10,
border_line_color=None,
location=(0, 0),
background_fill_color='whitesmoke',
major_label_text_align='left',
major_label_text_baseline='middle',
title='e/s',
margin=0)
fig.add_layout(color_bar, 'right')
color_bar.formatter = PrintfTickFormatter(format="%14u")
if tpf_source is not None:
fig.rect('xx',
'yy',
1,
1,
source=tpf_source,
fill_color='gray',
fill_alpha=0.4,
line_color='white')
# Configure the stretch slider and its callback function
stretch_slider = RangeSlider(start=np.log10(vlo),
end=np.log10(vhi),
step=vstep,
title='Screen Stretch (log)',
value=(np.log10(lo), np.log10(hi)),
orientation='horizontal',
width=200,
height=10,
direction='ltr',
show_value=True,
sizing_mode='fixed',
name='tpfstretch')
def stretch_change_callback(attr, old, new):
"""TPF stretch slider callback."""
fig.select('tpfimg')[0].glyph.color_mapper.high = 10**new[1]
fig.select('tpfimg')[0].glyph.color_mapper.low = 10**new[0]
stretch_slider.on_change('value', stretch_change_callback)
return fig, stretch_slider
def density_handler(doc: Document) -> None:
"""
Handler function for the density application.
"""
task_id = doc.session_context.request.arguments.get('task_id')
username = doc.session_context.request.arguments.get('username')
user_dir = os.path.join(settings.USER_DATA, username)
path_to_db = os.path.join(user_dir, task_id)
try:
conn = os.path.join(path_to_db, task_id + '.csv')
df = pd.read_csv(conn)
proteins = list(df.Protein.unique())
lipids = list(df.Lipids.unique())
except:
proteins, lipids = [], []
for o in os.listdir(path_to_db):
key = o.split('/')[-1]
if key.endswith('.npy'):
if key.startswith('prot_'):
proteins.append('Protein(s)')
else:
lipid = key.split('_')[0]
lipids.append(lipid)
all_mpl_cmaps = [
'viridis',
'plasma',
'inferno',
'cividis',
'Greys',
'Purples',
'Blues',
'Greens',
'Oranges',
'Reds',
'RdYlBu',
'RdYlGn',
'Spectral',
'Spectral_r',
'coolwarm',
'coolwarm_r',
'seismic',
'YlOrBr',
'YlOrRd',
'OrRd',
'PuRd',
'RdPu',
'BuPu',
'GnBu',
'PuBu',
'YlGnBu',
'PuBuGn',
'BuGn',
'YlGn',
'PiYG',
'PRGn',
'BrBG',
'PuOr',
'RdGy',
'RdBu',
]
#TODO: globals declaration should not be necessary anymore.
global prot_xyz
prot_xyz = np.load(os.path.join(path_to_db, "prot_" + task_id + '.npy'))
global l_xyz
global x_min, x_max, y_min, y_max, z_min, z_max
l_xyz = np.load(
os.path.join(path_to_db, lipids[0] + "_" + task_id + '.npy'))
x_min, x_max = l_xyz[:, 0].min(), l_xyz[:, 0].max()
y_min, y_max = l_xyz[:, 1].min(), l_xyz[:, 1].max()
z_min, z_max = l_xyz[:, 2].min(), l_xyz[:, 2].max()
# widgets
color_map = Select(title="Colormap",
value="viridis",
options=all_mpl_cmaps,
width=120)
lipid = Select(title="Lipids", value=lipids[0], options=lipids, width=100)
denstype = Select(title="Density Type",
value="All Proteins",
options=["All Proteins", "Average"],
width=120)
number = Slider(title="Number of Bins",
value=380,
start=80,
end=500,
step=10,
width=200)
protein = Select(title="Show Protein",
value="No",
options=["Yes", "No"],
width=90)
gpcr = Select(title="Protein",
value=proteins[0],
options=proteins,
width=130)
zrange = RangeSlider(start=z_min,
end=z_max,
value=(z_min, z_max),
step=0.2,
title="Z-Axis Range",
callback_policy='mouseup',
width=352)
cbar_range = Slider(value=256,
start=0,
end=256,
step=1,
height=600,
orientation='vertical',
callback_policy='mouseup',
margin=(15, 0),
tooltips=False,
show_value=False)
# colorschemes
colormap = cm.get_cmap(color_map.value)
bokehpalette = [
mpl.colors.rgb2hex(m) for m in colormap(np.arange(colormap.N))
]
def lipid_density(array, bins=160):
"""Given a 2D array, containing the x, y values of a lipid,
return its histogram with edges."""
x = array[:, 0]
y = array[:, 1]
lipid, e1, e2 = np.histogram2d(x, y, density=True, bins=bins)
return lipid, e1, e2
# Plot histogram image
H, xe, ye = lipid_density(l_xyz, number.value)
minx = np.abs(xe.min())
miny = np.abs(ye.min())
p1 = figure(plot_height=640, plot_width=640, tools='')
image_source = ColumnDataSource(data=dict(image=[]))
img = p1.image(image="image",
x=xe[0],
y=ye[0],
dw=xe[-1] + minx,
dh=ye[-1] + miny,
palette=bokehpalette,
source=image_source)
circle_prot_source = ColumnDataSource(
data=dict(x1=prot_xyz[:, 0], y1=prot_xyz[:, 1]))
p1.circle(x="x1",
y="y1",
source=circle_prot_source,
size=2,
fill_alpha=0.2)
cb_palette = LinearColorMapper(palette=bokehpalette,
low=H.min(),
high=H.max())
color_bar = ColorBar(color_mapper=cb_palette,
width=8,
location=(0, 0),
label_standoff=10)
color_bar.formatter = BasicTickFormatter(use_scientific=False)
p1.add_layout(color_bar, 'right')
# Make graph pretty
p1.xgrid.grid_line_color = None
p1.ygrid.grid_line_color = None
p1.xaxis.major_tick_line_color = None
p1.xaxis.minor_tick_line_color = None
p1.yaxis.major_tick_line_color = None
p1.yaxis.minor_tick_line_color = None
p1.xaxis.major_label_text_font_size = '0pt'
p1.yaxis.major_label_text_font_size = '0pt'
p1.grid.visible = False
p1.toolbar.logo = None
p1.toolbar_location = None
def update_all(cond=False, cmap=False):
"""
Update the image showing all proteins.
"""
if cond:
# For efficiency execute only if GPCR structure changes
global l_xyz
global x_min, x_max, y_min, y_max, z_min, z_max
l_xyz = np.load(
os.path.join(path_to_db,
str(lipid.value) + "_" + task_id + '.npy'))
x_min, x_max = l_xyz[:, 0].min(), l_xyz[:, 0].max()
y_min, y_max = l_xyz[:, 1].min(), l_xyz[:, 1].max()
z_min, z_max = l_xyz[:, 2].min(), l_xyz[:, 2].max()
zrange.start = z_min
zrange.end = z_max
index = np.where((l_xyz[:, 2] > zrange.value[0])
& (l_xyz[:, 2] < zrange.value[1]))
l_xyz_new = l_xyz[index]
else:
l_xyz_new = l_xyz
if cmap:
# For efficiency execute only if image colormap changes
cb_cut_value = 256 - cbar_range.value
cmap = color_map.value
colormap = cm.get_cmap(cmap)
bokehpalette = [
mpl.colors.rgb2hex(m) for m in colormap(np.arange(colormap.N))
]
bp_i = 0
while bp_i < len(bokehpalette[:cb_cut_value]):
bokehpalette[bp_i] = '#ffffff'
bp_i += 1
img.glyph.color_mapper.palette = bokehpalette
color_bar.color_mapper.palette = bokehpalette
# Update histogram image
H, xe, ye = lipid_density(l_xyz_new, number.value)
minx = np.abs(xe.min())
miny = np.abs(ye.min())
img.glyph.dw = xe[-1] + minx
img.glyph.dh = ye[-1] + miny
# update image source
image_source.data = dict(image=[H])
def update_protein(cond=False):
"""
Update the protein representation.
"""
if cond:
# For efficiency execute only if GPCR structure changes
global prot_xyz
if protein.value == "Yes":
circle_prot_source.data = dict(x1=prot_xyz[:, 0],
y1=prot_xyz[:, 1])
elif protein.value == "No":
circle_prot_source.data = dict(x1=[], y1=[])
def update_cbar():
cb_cut_value = 256 - cbar_range.value
cmap = color_map.value
colormap = cm.get_cmap(cmap)
bokehpalette = [
mpl.colors.rgb2hex(m) for m in colormap(np.arange(colormap.N))
]
bp_i = 0
while bp_i < len(bokehpalette[:cb_cut_value]):
bokehpalette[bp_i] = '#ffffff'
bp_i += 1
img.glyph.color_mapper.palette = bokehpalette
color_bar.color_mapper.palette = bokehpalette
# event listeners
controls = [lipid, zrange, gpcr]
for control in controls:
control.on_change('value',
lambda attr, old, new: update_denstype(cond=True))
number.on_change('value', lambda attr, old, new: update_denstype())
color_map.on_change('value',
lambda attr, old, new: update_denstype(cmap=True))
protein.on_change('value', lambda attr, old, new: update_protein())
denstype.on_change('value', lambda attr, old, new: update_denstype())
cbar_range.on_change('value', lambda attr, old, new: update_cbar())
# deal with what gets updated and what not.
def update_denstype(cond=False, cmap=False):
update_all(cond, cmap)
update_protein(cond)
update_denstype()
input1 = row([gpcr, lipid, color_map, protein])
input2 = row([p1, cbar_range])
input3 = row([number, zrange])
input3 = column([input1, input2, input3])
l = layout([input3])
doc.add_root(l)
doc.title = "Density App"
def make_map():
#Create figure object.
p = figure(title='Map of COVID-19 ' + plot_title[sel_var] + ' (' +
txt_src + ')',
plot_height=550,
plot_width=950,
x_range=(-180, 180),
y_range=(-65, 90),
toolbar_location='above',
tools='pan, wheel_zoom, box_zoom, reset, tap',
sizing_mode="scale_width")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# Choose linear or logarithmic color mapper
if tog_lin.active:
mapper = LinearColorMapper(palette=palette,
low=0,
high=plot_max[sel_var])
ticker = BasicTicker()
else:
mapper = LogColorMapper(palette=palette,
low=plot_min[sel_var],
high=plot_max[sel_var])
ticker = LogTicker()
color_bar = ColorBar(color_mapper=mapper,
label_standoff=8,
height=20,
ticker=ticker,
border_line_color=None,
location=(0, 0),
orientation='horizontal')
if not rb_abs_rel.active:
color_bar.formatter = NumeralTickFormatter(format='0[.]0a')
elif not tog_lin.active:
color_bar.formatter = NumeralTickFormatter(format='0.[00000]')
#Add patch renderer to figure.
ren_map = p.patches('xs',
'ys',
source=source_map,
line_color='black',
line_width=0.25,
fill_color={
'field': 'Selected',
'transform': mapper
},
fill_alpha=1)
#Specify figure layout.
p.add_layout(color_bar, 'below')
#Add hover tool
p.add_tools(
HoverTool(tooltips=[
('Country/region', '@Country'), ('Population', '@Population'),
('Tot Cases', '@Cases_Tot_Abs @Cases_Tot_Rel{custom}'),
('New Cases', '@Cases_New_Abs @Cases_New_Rel{custom}'),
('Avg Cases', '@Cases_Avg_Abs @Cases_Avg_Rel{custom}'),
('Tot Deaths', '@Deaths_Tot_Abs @Deaths_Tot_Rel{custom}'),
('New Deaths', '@Deaths_New_Abs @Deaths_New_Rel{custom}'),
('Avg Deaths', '@Deaths_Avg_Abs @Deaths_Avg_Rel{custom}')
],
formatters={
'@Cases_Tot_Rel': custom,
'@Cases_New_Rel': custom,
'@Cases_Avg_Rel': custom,
'@Deaths_Tot_Rel': custom,
'@Deaths_New_Rel': custom,
'@Deaths_Avg_Rel': custom
}))
return p
def densityApp(doc):
"""
Handler function for the density application.
"""
nonlocal t, lipids, resolution, prot_xyz, l_xyz
all_mpl_cmaps = [
'viridis',
'plasma',
'inferno',
'cividis',
'Greys',
'Purples',
'Blues',
'Greens',
'Oranges',
'Reds',
'RdYlBu',
'RdYlGn',
'Spectral',
'Spectral_r',
'coolwarm',
'coolwarm_r',
'seismic',
'YlOrBr',
'YlOrRd',
'OrRd',
'PuRd',
'RdPu',
'BuPu',
'GnBu',
'PuBu',
'YlGnBu',
'PuBuGn',
'BuGn',
'YlGn',
'PiYG',
'PRGn',
'BrBG',
'PuOr',
'RdGy',
'RdBu',
]
lipids = [x for x in lipids if not x.startswith('prot')]
x_min, x_max = l_xyz[:, 0].min(), l_xyz[:, 0].max()
y_min, y_max = l_xyz[:, 1].min(), l_xyz[:, 1].max()
z_min, z_max = l_xyz[:, 2].min(), l_xyz[:, 2].max()
# widgets
color_map = Select(title="Colormap",
value="viridis",
options=all_mpl_cmaps,
width=120)
lipid = Select(title="Lipids",
value=lipids[0],
options=lipids,
width=100)
number = Slider(title="Number of Bins",
value=150,
start=80,
end=500,
step=10,
width=200)
protein = Select(title="Show Protein",
value="No",
options=["Yes", "No"],
width=90)
zrange = RangeSlider(start=z_min,
end=z_max,
value=(z_min, z_max),
step=0.2,
title="Z-Axis Range",
callback_policy='mouseup',
width=352)
cbar_range = Slider(value=256,
start=0,
end=256,
step=1,
height=600,
orientation='vertical',
callback_policy='mouseup',
margin=(15, 0),
tooltips=False,
show_value=False)
# colorschemes
colormap = cm.get_cmap(color_map.value)
bokehpalette = [
mpl.colors.rgb2hex(m) for m in colormap(np.arange(colormap.N))
]
def lipid_density(array, bins=160):
"""Given a 2D array, containing the x, y values of a lipid,
return its histogram with edges."""
x = array[:, 0]
y = array[:, 1]
lipid, e1, e2 = np.histogram2d(x, y, density=True, bins=bins)
return lipid, e1, e2
# Plot histogram image
H, xe, ye = lipid_density(l_xyz, number.value)
minx = np.abs(xe.min())
miny = np.abs(ye.min())
p1 = figure(plot_height=640, plot_width=640, tools='')
image_source = ColumnDataSource(data=dict(image=[]))
img = p1.image(image="image",
x=xe[0],
y=ye[0],
dw=xe[-1] + minx,
dh=ye[-1] + miny,
palette=bokehpalette,
source=image_source)
circle_prot_source = ColumnDataSource(
data=dict(x1=prot_xyz[:, 0], y1=prot_xyz[:, 1]))
p1.circle(x="x1",
y="y1",
source=circle_prot_source,
size=2,
fill_alpha=0.2)
cb_palette = LinearColorMapper(palette=bokehpalette,
low=H.min(),
high=H.max())
color_bar = ColorBar(color_mapper=cb_palette,
width=8,
location=(0, 0),
label_standoff=10)
color_bar.formatter = BasicTickFormatter(use_scientific=False)
p1.add_layout(color_bar, 'right')
# Make graph pretty
p1.xgrid.grid_line_color = None
p1.ygrid.grid_line_color = None
p1.xaxis.major_tick_line_color = None
p1.xaxis.minor_tick_line_color = None
p1.yaxis.major_tick_line_color = None
p1.yaxis.minor_tick_line_color = None
p1.xaxis.major_label_text_font_size = '0pt'
p1.yaxis.major_label_text_font_size = '0pt'
p1.grid.visible = False
p1.toolbar.logo = None
p1.toolbar_location = None
def update_all(cond=False, cmap=False):
"""
Update the image showing all proteins.
"""
if cond:
# For efficiency execute only if GPCR structure changes
if filenames is not None:
update_l_xyz = np.load(lipid.value)
else:
update_l_xyz = flat_density(t, [lipid.value], t.n_frames)
x_min, x_max = update_l_xyz[:, 0].min(), update_l_xyz[:,
0].max()
y_min, y_max = update_l_xyz[:, 1].min(), update_l_xyz[:,
1].max()
z_min, z_max = update_l_xyz[:, 2].min(), update_l_xyz[:,
2].max()
zrange.start = z_min
zrange.end = z_max
index = np.where((update_l_xyz[:, 2] > zrange.value[0])
& (update_l_xyz[:, 2] < zrange.value[1]))
l_xyz_new = update_l_xyz[index]
else:
l_xyz_new = l_xyz
if cmap:
# For efficiency execute only if image colormap changes
cb_cut_value = 256 - cbar_range.value
cmap = color_map.value
colormap = cm.get_cmap(cmap)
bokehpalette = [
mpl.colors.rgb2hex(m)
for m in colormap(np.arange(colormap.N))
]
bp_i = 0
while bp_i < len(bokehpalette[:cb_cut_value]):
bokehpalette[bp_i] = '#ffffff'
bp_i += 1
img.glyph.color_mapper.palette = bokehpalette
color_bar.color_mapper.palette = bokehpalette
# Update histogram image
H, xe, ye = lipid_density(l_xyz_new, number.value)
minx = np.abs(xe.min())
miny = np.abs(ye.min())
img.glyph.dw = xe[-1] + minx
img.glyph.dh = ye[-1] + miny
# update image source
image_source.data = dict(image=[H])
def update_protein():
"""
Update the protein representation.
"""
if protein.value == "Yes":
circle_prot_source.data = dict(x1=prot_xyz[:, 0],
y1=prot_xyz[:, 1])
elif protein.value == "No":
circle_prot_source.data = dict(x1=[], y1=[])
def update_cbar():
cb_cut_value = 256 - cbar_range.value
cmap = color_map.value
colormap = cm.get_cmap(cmap)
bokehpalette = [
mpl.colors.rgb2hex(m) for m in colormap(np.arange(colormap.N))
]
bp_i = 0
while bp_i < len(bokehpalette[:cb_cut_value]):
bokehpalette[bp_i] = '#ffffff'
bp_i += 1
img.glyph.color_mapper.palette = bokehpalette
color_bar.color_mapper.palette = bokehpalette
# event listeners
controls = [lipid, zrange]
for control in controls:
control.on_change(
'value', lambda attr, old, new: update_denstype(cond=True))
number.on_change('value', lambda attr, old, new: update_denstype())
color_map.on_change('value',
lambda attr, old, new: update_denstype(cmap=True))
protein.on_change('value', lambda attr, old, new: update_protein())
cbar_range.on_change('value', lambda attr, old, new: update_cbar())
# deal with what gets updated and what not.
def update_denstype(cond=False, cmap=False):
update_all(cond, cmap)
update_protein()
update_denstype()
input1 = row([lipid, color_map, protein])
input2 = row([p1, cbar_range])
input3 = row([number, zrange])
input3 = column([input1, input2, input3])
l = layout([input3])
doc.add_root(l)
doc.title = "Density App"
# marking the Hover point
p2.circle('ra',
'dec',
source=source2,
radius=0.0186,
fill_color=None,
line_color=None,
hover_fill_color={
'field': 'integ',
'transform': mapper
},
line_width=3,
hover_line_color='red')
color_bar.formatter = formatter
p2.add_layout(color_bar, 'left')
#infos
info, nlines = write_info('integ', tests['integ'])
txt = PreText(text=info, height=nlines * 20, width=p2.plot_width)
p2txt = column(widgetbox(txt), p2)
#layout = gridplot([[p2txt]], responsive=False)
info_col = Div(text=write_description('integ'), width=p2.plot_width)
layout = column(info_col, p2)
# End of Bokeh Block
curdoc().add_root(layout)
curdoc().title = "INTEG"
def lidar2js(path, ncfile, jsfile):
fname_nc = join(path,ncfile)
if isfile(fname_nc):
if Debug:
print "Found file: ", ncfile
ds = Dataset(fname_nc,'r',format="NETCDF3_CLASSIC")
t_raw = ds.variables["time"]
z = ds.variables["alt1"][:]
bsc532 = ds.variables["bsc532"][:]
bsc1064 = ds.variables["bsc1064"][:]
zb = ds.variables["zb"][:]
zpbl = ds.variables["zpbl"][:]
t = num2date(t_raw[:], units = t_raw.units)
tm = t_raw[:]
ds.close()
else:
if Debug:
print "Not found file: ", ncfile
return "No Data"
DX = t[1]-t[0]
Dx_sec = DX.total_seconds()
Nx = len(t)
Nx0 = int(max_days*86400.0/Dx_sec)
if Nx0<Nx: Nx=Nx0
tmin = t[-Nx]
tmax = t[-1]+DX
twidth = 1000.0*(tmax-tmin).total_seconds()
zmin = 0
zmax = z[-1]
my_cmap,my_norm,my_rgb = build_palette4(levs1,levs2,levs3,levs4)
img532_raw = my_norm(bsc532[-Nx:,:])
img1064_raw = my_norm(bsc1064[-Nx:,:])
data1 = {'zbase': zb[-Nx:],
'zpbl': zpbl[-Nx:],
'tcenter': t[-Nx:] + DX/2,
}
data2 = { 'image532': [np.array(img532_raw.filled().T, dtype=np.int8)],
'image1064': [np.array(img1064_raw.filled().T, dtype=np.int8)],
}
data3 = { 'profile532': bsc532[-1,:],
'range': z
}
src = ColumnDataSource(data=data1)
src_img = ColumnDataSource(data=data2)
src_z = ColumnDataSource(data=data3)
color_mapper = LinearColorMapper(palette=[rgb2hex(item) for item in my_rgb],
low=0,
high=my_cmap.N
)
plot = figure(x_range=(tmin,tmax), y_range=(zmin,zmax),
title="Attenuated Backscatter coefficient [/sr /km]",
toolbar_location="above",
tools = "pan,wheel_zoom,box_zoom,reset,save",
active_scroll=None,
active_drag=None,
active_inspect=None,
# toolbar_sticky=False,
y_axis_label = 'Height [km]',
plot_width=900,
plot_height=350,
)
plot.toolbar.logo=None
plot2 = figure(title="Last Profile at 532 nm - {}".format(t[-1]),
tools = "pan,wheel_zoom,box_zoom,reset,save",
y_axis_label = 'Height [km]',
x_axis_label = 'Attenuated Backscatter coefficient [/sr /km]',
# y_axis_type="log",
active_inspect=None,
plot_width=900,
plot_height=350
)
plot2.toolbar.logo=None
plot2.line(x='profile532', y='range',
source=src_z,
line_color="black",
)
im = plot.image(image="image532",
source=src_img,
color_mapper=color_mapper,
dh=zmax, dw=twidth,
x=tmin, y=zmin
)
#l1 = plot.line( x='tcenter', y='zbase', source=source, line_width=2, alpha=0.8, color="black")
#l2 = plot.line( x='tcenter', y='zpbl' , source=source, line_width=2, alpha=0.8, color="red")
r1 = plot.circle( x='tcenter', y='zbase',
source=src,
fill_color="black",
line_color="black",
fill_alpha=0.3,
size=4,
name="r1"
)
r2 = plot.square( x='tcenter', y='zpbl',
source=src,
fill_color="red",
line_color="red",
fill_alpha=0.3,
size=4,
name="r2"
)
for item in [r1,r2]: item.visible = False
color_bar = ColorBar(color_mapper=color_mapper,
ticker=FixedTicker(ticks=[0,5,20,25]),
label_standoff=12,
border_line_color=None,
location=(0,0)
)
color_bar.bar_line_color = "black"
color_bar.major_tick_line_color = "black"
color_bar.formatter = FuncTickFormatter(code="return {}[tick].toExponential(2)".format(levs))
legend = Legend(items=[("Cloud Base",[r1]), ("PBL Height",[r2])],
location="top_left"
)
legend.click_policy = "hide"
legend.visible = False
hover = HoverTool(names=["r1","r2"])
hover.tooltips=[("Cloud base", "@zbase km"),
("PBL height", "@zpbl km"),
("Time", "@tcenter{%d-%b-%y %H:%M}")]
hover.formatters = { "tcenter": "datetime"}
hover2 = HoverTool(tooltips=[
("Signal", "@profile532"),
("Range", "@range"),
])
plot.add_layout(color_bar, 'right')
plot.add_layout(legend)
plot.add_tools(hover)
plot2.add_tools(hover2)
plot.xaxis.formatter = DatetimeTickFormatter(months = ['%Y-%b'],
years = ['%Y'],
days = ['%d-%b-%Y']
)
callback = CustomJS(args=dict(im=im), code="""
var f = cb_obj.active
if (f==0){
im.glyph.image.field = "image532"
} else {
im.glyph.image.field = "image1064"
}
im.glyph.change.emit();
""")
radio_button_group = RadioButtonGroup(labels=["532 nm", "1064 nm"], active=0)
radio_button_group.js_on_change('active',callback)
callback2 = CustomJS(args=dict(leg=legend), code="""
leg.visible = !leg.visible
console.log("ol")
""")
toggle = Toggle(label="Legend", button_type="default")
toggle.js_on_click(callback2)
layout = column(
children=[
row(widgetbox(radio_button_group, width=200), widgetbox(toggle, width=80) ),
plot,
Spacer(height=50),
# row(Spacer(width=50),plot2),
plot2,
],
)
#show(plot)
return create_js(layout,path,jsfile)
def make_plots_tab2(source_heat_map_temp, source_events_temp, source_boundaries, source_rolling_temp, source_data_aggregated_day_temp, dates_list, hour_list):
colors_heat_map = ['#fff7fb', '#ece7f2', '#d0d1e6', '#a6bddb', '#74a9cf', '#3690c0', '#0570b0', '#045a8d', '#023858']
mapper_heat_map = LogColorMapper(palette=colors_heat_map, low= 0, high=1000000)
TOOLS_heat_map = "save,pan ,reset, xwheel_zoom"
p_heat_map = figure(title="Water consumption in Log(Litres)",x_axis_type="datetime", x_range = dates_list, y_range = list(reversed(hour_list)),
tools=TOOLS_heat_map)
heat_map = p_heat_map.rect(x="date", y="hour", width=1, height=1, source = source_heat_map_temp, fill_color={'field': 'rate', 'transform': mapper_heat_map}, line_color=None)
p_events = p_heat_map.triangle(x = 'date', y = 'Hour', legend= "Events", source = source_events_temp, color = 'color', size= 12, line_color="black")
#boundaries
p_boundary_start = p_heat_map.line(x = 'start_date', y = 'hour', source =source_boundaries, line_dash = 'dashed', color = "firebrick", line_width = 4 )
p_boundary_end = p_heat_map.line(x = 'end_date', y = 'hour', source =source_boundaries, line_dash = 'dashed', color = "firebrick", line_width = 4 )
color_bar = ColorBar(color_mapper=mapper_heat_map, border_line_color=None,label_standoff=12, location=(0, 0))
color_bar.formatter = NumeralTickFormatter(format='0.0')
p_heat_map.add_layout(color_bar, 'right')
heat_map_hover = HoverTool(renderers=[heat_map],
tooltips=OrderedDict([('Water Consumption (Litres)', '@rate'),
('date hour', '@date'),
('hour', '@hour'),
]))
events_hover = HoverTool(renderers=[p_events],
tooltips=OrderedDict([('Event description',
'@{Hoofdtype Melding}'),
]))
p_heat_map.grid.grid_line_color = None
p_heat_map.axis.axis_line_color = None
p_heat_map.axis.major_tick_line_color = None
p_heat_map.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p_heat_map.yaxis.axis_label = 'Hour'
p_heat_map.xaxis.axis_label = None
p_heat_map.axis.major_label_standoff = 0
p_heat_map.legend.location = "top_left"
p_heat_map.legend.click_policy= "hide"
p_heat_map.add_tools(heat_map_hover)
p_heat_map.add_tools(events_hover)
p_outliers = figure(title="Daily water consumptions in Litres", x_axis_type="datetime", tools=TOOLS_heat_map, x_range = dates_list)
p_circle = p_outliers.circle(x = 'date', y = 'delta_total', size='s', color= 'c', alpha='a', legend= "Consumption in L",
source = source_data_aggregated_day_temp)
p_ub = p_outliers.line(x='date', y='ub', legend='upper_bound (2 sigma)', line_dash = 'dashed', line_width = 4, color = '#984ea3',
source = source_rolling_temp)
p_mean = p_outliers.line(x='date', y='y_mean', source = source_rolling_temp, line_dash = 'dashed', line_width = 3,
legend='moving_average', color = '#4daf4a')
#Dynamic boundaries
p_boundary_start = p_outliers.line(x = 'start_date', y = 'y', source =source_boundaries, line_dash = 'dashed', color = "firebrick", line_width = 4 )
p_boundary_end = p_outliers.line(x = 'end_date', y = 'y', source =source_boundaries, line_dash = 'dashed', color = "firebrick", line_width = 4 )
p_outliers.legend.location = "top_left"
p_outliers.legend.orientation = "horizontal"
p_outliers.legend.click_policy= "hide"
p_outliers.ygrid.band_fill_color = "olive"
p_outliers.ygrid.band_fill_alpha = 0.1
p_outliers.xaxis.axis_label = None
p_outliers.yaxis.axis_label = 'Litres'
p_outliers.xaxis.major_label_orientation = 3.1416 / 3
p_outliers.x_range = p_heat_map.x_range# Same axes as the heatMap
p_outliers.xaxis.formatter = FuncTickFormatter(code=""" var labels = %s; return labels[tick];""" % dates_list)
circle_hover = HoverTool(renderers=[p_circle],
tooltips=OrderedDict([('date', '@date'),
('Water Consumption (L)', '@delta_total'),
]))
p_ub_hover = HoverTool(renderers=[p_ub],
tooltips=OrderedDict([('date', '@date'),
('UpperBound water consumption (L)', '@ub'),
]))
p_mean_hover = HoverTool(renderers=[p_mean],
tooltips=OrderedDict([('date', '@date'),
('Mean water consumption (L)', '@y_mean'),
]))
p_outliers.add_tools(circle_hover)
p_outliers.add_tools(p_ub_hover)
p_outliers.add_tools(p_mean_hover)
return p_heat_map, p_outliers
