def create_clusters_mean_plot(source: bm.ColumnDataSource,
filter_source: dict = None) -> bm.Box:
figure_opts = dict(plot_width=600,
plot_height=620,
title="Cluster mean trends (pchip spline)")
hover_opts = dict(tooltips=[('Cluster', '@legend')],
show_arrow=False,
line_policy='next')
line_opts = dict(
legend_field='legend',
line_color='color',
line_width=5,
line_alpha=0.4,
hover_line_color='color',
hover_line_alpha=1.0,
)
p: bp.Figure = bp.figure(tools=[bm.HoverTool(**hover_opts),
bm.TapTool()],
**figure_opts)
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.axis.minor_tick_line_color = None
# p.xaxis.ticker = list(range(1945, 1990))
p.y_range.start = 0
_ = p.multi_line(source=source, xs='xs', ys='ys', **line_opts)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
if filter_source is not None:
callback: bm.CustomJS = _create_multiline_multiselect_callback(source)
multi_select: bm.MultiSelect = bm.MultiSelect(
title='Show/hide',
options=filter_source['options'],
value=filter_source['values'],
size=min(len(filter_source['options']), 30),
)
multi_select.js_on_change('value', callback)
p: bm.Box = bokeh.layouts.row(p, multi_select)
return p
def plot_clusters_count(source: bm.ColumnDataSource):
figure_opts = dict(plot_width=500,
plot_height=600,
title="Cluster token count")
hover_opts = dict(tooltips='@legend: @count words',
show_arrow=False,
line_policy='next')
bar_opts = dict(
legend_field='legend',
fill_color='color',
fill_alpha=0.4,
hover_fill_alpha=1.0,
hover_fill_color='color',
line_color='color',
hover_line_color='color',
line_alpha=1.0,
hover_line_alpha=1.0,
height=0.75,
)
p = bp.figure(tools=[bm.HoverTool(**hover_opts),
bm.TapTool()],
**figure_opts)
# y_range=source.data['clusters'],
p.yaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.axis.minor_tick_line_color = None
p.x_range.start = 0
_ = p.hbar(source=source, y='cluster', right='count', **bar_opts)
return p
elif v['type'] == 'list':
if 'labels' not in v.keys():
v['labels'] = None
filters_dict[k] = get_select(desc, v['values'], v['default'],
v['labels'])
# plot button, output, graph
btn_plot = Button(label='Plot', button_type='primary')
info_block = PreText(text='', width=500, height=100)
plot_info = PreText(text='', width=300, height=100)
load_preset(None, None, get_preset_label_from_url())
source = bmd.ColumnDataSource(data=data_empty)
hover = bmd.HoverTool(tooltips=[])
tap = bmd.TapTool()
colorscale = [
'#CCFFCC', '#CCFFCC', '#CCFFCC', '#CCFFCC', '#CCFFCC', '#CCFFCC',
'#CCFFCC', '#CCFFCC', '#CCFFCC', '#CCFFCC', '#CCFFCC', '#CCFFCC',
'#CCFFCC', '#CCFFCC', '#CCFFCC', '#80FF66', '#80FF66', '#80FF66',
'#80FF66', '#80FF66', '#80FF66', '#80FF66', '#80FF66', '#80FF66',
'#80FF66', '#80FF66', '#80FF66', '#80FF66', '#80FF66', '#80FF66',
'#80FF66', '#80FF66', '#80FF66', '#80FF66', '#80FF66', '#80FF66',
'#80FF66', '#80FF66', '#80FF66', '#80FF66', '#19FF19', '#19FF19',
'#19FF19', '#19FF19', '#19FF19', '#19FF19', '#19FF19', '#19FF19',
'#19FF19', '#19FF19', '#19FF19', '#19FF19', '#19FF19', '#009900',
'#009900', '#009900', '#009900', '#009900', '#009900', '#009900',
'#009900', '#009900', '#009900', '#009900', '#009900', '#009900',
'#009900', '#009900', '#009900', '#009900', '#009900', '#009900',
'#009900', '#009900', '#009900', '#009900', '#009900', '#009900',
def get_plot(inp_x, inp_y, inp_clr):
"""Returns a Bokeh plot of the input values, and a message with the number of COFs found."""
q_list = [quantities[label] for label in [inp_x, inp_y, inp_clr]]
db_nodes_dict = get_db_nodes_dict(
) # TODO: try to move outside, to load it once!
results = get_figure_values(
db_nodes_dict,
q_list) #returns [inp_x_value, inp_y_value, inp_clr_value, cif.label]
# prepare data for plotting
nresults = len(results)
msg = "{} MOFs found.<br> <b>Click on any point for details!</b>".format(
nresults)
label, x, y, clrs = zip(*results)
x = list(map(float, x))
y = list(map(float, y))
clrs = list(map(float, clrs))
mat_id = label
data = {'x': x, 'y': y, 'color': clrs, 'mat_id': mat_id}
# create bokeh plot
source = bmd.ColumnDataSource(data=data)
hover = bmd.HoverTool(tooltips=[])
tap = bmd.TapTool()
p_new = bpl.figure(
plot_height=600,
plot_width=600,
toolbar_location='above',
tools=[
'pan',
'wheel_zoom',
'box_zoom',
'save',
'reset',
hover,
tap,
],
active_drag='box_zoom',
output_backend='webgl',
title='', # trick: title is used as the colorbar label
title_location='right',
x_axis_type=q_list[0]['scale'],
y_axis_type=q_list[1]['scale'],
)
p_new.title.align = 'center'
p_new.title.text_font_size = '10pt'
p_new.title.text_font_style = 'italic'
update_legends(p_new, q_list, hover)
tap.callback = bmd.OpenURL(url="detail_pyrenemofs?mat_id=@mat_id")
cmap = bmd.LinearColorMapper(palette=Plasma256,
low=min(clrs),
high=max(clrs))
fill_color = {'field': 'color', 'transform': cmap}
p_new.circle('x', 'y', size=10, source=source, fill_color=fill_color)
cbar = bmd.ColorBar(color_mapper=cmap, location=(0, 0))
p_new.add_layout(cbar, 'right')
return p_new, msg
def get_plot(appl): #pylint: disable=too-many-locals
"""Make the bokeh plot for a given application.
1. read important metrics from applications.yml
2. read metadata for thes metricy in quantities.yml
3. query the AiiDA database for values
4. rank materials according to their performance
5. generate the plot and return a message stating the number of found materials
"""
# Query for results
q_list = tuple([quantities[label] for label in [appl['x'], appl['y']]])
results_wnone = get_data_aiida(
q_list) #[[id_material, name_material, class_material, xval, yval]]
# Clean the query from None values projections
results = []
for l in results_wnone:
if None not in l:
results.append(l)
# Prepare data for plotting
nresults = len(results)
if not results:
results = [['none', 'none', 'none', 0, 0]]
msg = "No materials found"
else:
msg = "{} materials shown".format(nresults)
mat_id, name, class_mat, x, y = zip(*results)
x = list(map(float, x))
y = list(map(float, y))
rank = rank_materials(x, y, appl['wx'], appl['wy'])
# Setup plot
hover = bmd.HoverTool()
hover.tooltips = [
("name", "@name"),
("MAT_ID", "@mat_id"),
(q_list[0]["label"], "@x {}".format(q_list[0]["unit"])),
(q_list[1]["label"], "@y {}".format(q_list[1]["unit"])),
('ranking', '@color'),
]
tap = bmd.TapTool()
p = bpl.figure(
plot_height=600,
plot_width=600,
toolbar_location="right", # choose: above, below, right, left
tools=[
'pan',
'wheel_zoom',
'box_zoom',
'save',
'reset',
hover,
tap,
],
active_drag='box_zoom',
output_backend='webgl',
x_axis_type=q_list[0]['scale'],
y_axis_type=q_list[1]['scale'],
x_axis_label="{} [{}]".format(q_list[0]["label"], q_list[0]["unit"]),
y_axis_label="{} [{}]".format(q_list[1]["label"], q_list[1]["unit"]),
)
tap.callback = bmd.OpenURL(url="details?mat_id=@mat_id")
cmap = bmd.LinearColorMapper(palette=myRdYlGn)
fill_color = {'field': 'color', 'transform': cmap}
source = bmd.ColumnDataSource(
data={
'mat_id': mat_id,
'name': name,
'class_mat': class_mat,
'x': x,
'y': y,
'color': rank,
})
p.circle(x='x',
y='y',
size=10,
source=source,
fill_color=fill_color,
muted_alpha=0.2)
#cbar = bmd.ColorBar(color_mapper=cmap, location=(0, 0))
#p.add_layout(cbar, 'right') #show colorbar
return p, msg
def plot_isotherms(mat_id): #pylint: disable=too-many-locals
"""Plot figure with all isotherms."""
nodes_dict = get_isotherm_nodes(mat_id)
tooltips = [
("Molecule", "@legend_label"),
("Uptake (mol/kg)", "@q_avg"),
]
hover = bmd.HoverTool(tooltips=tooltips)
tap = bmd.TapTool()
tap.callback = bmd.OpenURL(url=get_provenance_url(uuid="@uuid"))
TOOLS = ["pan", "wheel_zoom", "box_zoom", "reset", "save", hover, tap]
p1 = figure(tools=TOOLS, height=350, width=450)
p1.xaxis.axis_label = 'Pressure (bar)'
p1.yaxis.axis_label = 'Uptake (mol/kg)'
for gas, gas_dict in gasses.items():
if gas not in nodes_dict:
continue
for node in nodes_dict[gas]:
try: # avoid to fail if there are problems with one dict
isot_out = node.get_dict()
if isot_out['is_porous']:
p = isot_out['isotherm']["pressure"] #(bar)
q_avg = isot_out['isotherm']["loading_absolute_average"] #(mol/kg)
q_dev = isot_out['isotherm']["loading_absolute_dev"] #(mol/kg)
q_upper = np.array(q_avg) + np.array(q_dev)
q_lower = np.array(q_avg) - np.array(q_dev)
h_avg = isot_out['isotherm']["enthalpy_of_adsorption_average"] #(kJ/mol)
h_dev = isot_out['isotherm']["enthalpy_of_adsorption_dev"] #(kJ/mol)
# TRICK: use the enthalpy from widom (energy-RT) which is more accurate that the one at 0.001 bar
# (and which also is NaN for weakly interacting systems)
h_avg[0] = isot_out['adsorption_energy_widom_average'] - isot_out['temperature'] / 120.027
h_dev[0] = isot_out['adsorption_energy_widom_dev']
h_upper = np.array(h_avg) + np.array(h_dev)
h_lower = np.array(h_avg) - np.array(h_dev)
else:
p = [0, 100]
q_avg = q_upper = q_lower = h_avg = h_upper = h_lower = [0, 0]
legend_label = "{} ({}K)".format(gas_dict['legend'], int(isot_out['temperature']))
data = dict(p=p,
q_avg=q_avg,
q_upper=q_upper,
q_lower=q_lower,
h_avg=h_avg,
h_upper=h_upper,
h_lower=h_lower,
uuid=[str(node.uuid) for _ in q_avg],
legend_label=[legend_label] * len(p))
source = bmd.ColumnDataSource(data=data)
p1.line(x='p',
y='q_avg',
source=source,
line_color=gas_dict['color'],
line_width=2,
legend_label=legend_label)
p1.circle(x='p', y='q_avg', source=source, color=gas_dict['color'], size=5, legend_label=legend_label)
p1.add_layout(bmd.Whisker(source=source, base="p", upper="q_upper", lower="q_lower"))
except (KeyError, TypeError):
continue
p1.legend.location = "bottom_right"
fig = p1
return fig
def get_plot(inp_x, inp_y, inp_clr):
"""Returns a Bokeh plot of the input values, and a message with the number of COFs found."""
q_list = [config.quantities[label] for label in [inp_x, inp_y, inp_clr]]
results_wnone = get_data_aiida(q_list) #returns ***
# dump None lists that make bokeh crash
results = []
for l in results_wnone:
if None not in l:
results.append(l)
# prepare data for plotting
nresults = len(results)
if not results:
results = [['x', 'x', 'x', 0, 0, 0]]
msg = "No matching COFs found."
else:
msg = "{} COFs found.<br> <b>Click on any point for details!</b>".format(nresults)
mat_id, mat_name, mat_class, x, y, clrs = zip(*results) # returned ***
x = list(map(float, x))
y = list(map(float, y))
clrs = list(map(float, clrs))
data = {'x': x, 'y': y, 'color': clrs, 'mat_id': mat_id, 'mat_name': mat_name, 'mat_class': mat_class}
# create bokeh plot
source = bmd.ColumnDataSource(data=data)
hover = bmd.HoverTool(tooltips=[])
tap = bmd.TapTool()
p_new = bpl.figure(
plot_height=600,
plot_width=600,
toolbar_location='above',
tools=[
'pan',
'wheel_zoom',
'box_zoom',
'save',
'reset',
hover,
tap,
],
active_drag='box_zoom',
output_backend='webgl',
title='', # trick: title is used as the colorbar label
title_location='right',
x_axis_type=q_list[0]['scale'],
y_axis_type=q_list[1]['scale'],
)
p_new.title.align = 'center'
p_new.title.text_font_size = '10pt'
p_new.title.text_font_style = 'italic'
update_legends(p_new, q_list, hover)
tap.callback = bmd.OpenURL(url="detail?mat_id=@mat_id")
# Plot vertical line for comparison with amine-based technology (PE=1MJ/kg)
if inp_y == 'CO2 parasitic energy (coal)':
hline = bmd.Span(location=1, dimension='width', line_dash='dashed', line_color='grey', line_width=3)
p_new.add_layout(hline)
hline_descr = bmd.Label(x=30, y=1, x_units='screen', text_color='grey', text='amine-based sep.')
p_new.add_layout(hline_descr)
cmap = bmd.LinearColorMapper(palette=Plasma256, low=min(clrs), high=max(clrs))
fill_color = {'field': 'color', 'transform': cmap}
p_new.circle('x', 'y', size=10, source=source, fill_color=fill_color)
cbar = bmd.ColorBar(color_mapper=cmap, location=(0, 0))
p_new.add_layout(cbar, 'right')
return p_new, msg
def get_plot(inp_x, inp_y, inp_clr):
"""Creates holoviews plot"""
data, msg = prepare_data(inp_x, inp_y, inp_clr)
source = bmd.ColumnDataSource(data=data)
# hovering
hover = get_hover(inp_x, inp_y, inp_clr)
# tap
tap = bmd.TapTool()
tap.callback = bmd.OpenURL(url=explore_url + "/details/@uuid")
# plot
points = hv.Points(
source.data,
kdims=['x', 'y'],
vdims=['color', 'name', 'uuid'],
)
filtered = points.apply(filter_points,
streams=[hv.streams.RangeXY(source=points)])
p_shaded = datashade(
filtered,
width=plot_px,
height=plot_px,
cmap=Plasma256,
aggregator=ds.mean('color') # we want color of mean value under pixel
)
p_hover = filtered.apply(hover_points)
update_fn = functools.partial(update_legends,
inp_x=inp_x,
inp_y=inp_y,
inp_clr=inp_clr)
hv_plot = (dynspread(p_shaded) * p_hover).opts(
hv.opts.Points(
tools=[
tap,
'pan',
'box_zoom',
'save',
'reset',
hover,
],
active_tools=['wheel_zoom'],
#active_scroll='box_zoom',
#active_drag='box_zoom',
alpha=0.2,
hover_alpha=0.5,
size=10,
width=plot_px,
height=plot_px,
color='color',
cmap=Plasma256,
colorbar=True,
show_grid=True,
),
hv.opts(toolbar='above', finalize_hooks=[update_fn]),
)
# output_backend='webgl',
p_new = hv_renderer.get_plot(hv_plot).state
return p_new, msg