def volcanoPlot(df,
vertical_line_r=None,
vertical_line_l=None,
horizontal_line=None,
plotWidth=1000,
plotHeight=1000,
x_label="x-label",
y_label="y-label",
title="Volcano plot of Proteins",
outputFile=None):
plot_width = plotWidth
plot_height = plotHeight
title = title
source = bpl.ColumnDataSource.from_df(df)
hover = bmo.HoverTool(tooltips=[
("(log2fc,logFDR)", "(@log2fc, @logFDR)"),
('protein', '@protein'),
])
boxSelect = bmo.BoxSelectTool()
p = bpl.figure(plot_width=plot_width,
plot_height=plot_height,
tools=[hover, boxSelect],
title=title,
x_axis_label=x_label,
y_axis_label=y_label)
# Vertical line
if vertical_line_r != None:
vline_r = bmo.Span(location=vertical_line_r,
dimension='height',
line_color='red',
line_dash="dashed",
line_width=1,
line_alpha=0.8)
p.renderers.extend([vline_r])
if vertical_line_l != None:
vline_l = bmo.Span(location=vertical_line_l,
dimension='height',
line_color='red',
line_dash="dashed",
line_width=1,
line_alpha=0.8)
p.renderers.extend([vline_l])
# Horizontal line
if horizontal_line != None:
hline = bmo.Span(location=horizontal_line,
dimension='width',
line_color='red',
line_dash="dashed",
line_width=1,
line_alpha=0.8)
p.renderers.extend([hline])
p.scatter('log2fc', 'logFDR', source=source, color='color')
if outputFile != None:
bpl.output_file(outputFile)
#bpl.show(p)
bpl.save(p)
def plot_generic_hist(datafile):
hdf = h5py.File(datafile, 'r')
figures = {}
if not 'PARAMETERS' in hdf:
return figures
data = hdf['PARAMETERS']
for i, (name, dataset) in enumerate(data.items()):
if 'DISTRIBUTION' in dataset:
err = dataset.attrs.get('err', 0.0)
emin = dataset.attrs.get('emin', err)
emax = dataset.attrs.get('emax', err)
value = dataset.attrs.get('value', 0.0)
title = "{} = {:.2f} + {:.2f} - {:.2f}".format(name, value, emax, emin)
fig = bpl.figure(plot_width=280, plot_height=280, tools=[], title=title)
xpar = get_attr(dataset, 'xpar', 'x')
ypar = get_attr(dataset, 'ypar', 'y')
x = dataset['DISTRIBUTION'][xpar]
y = dataset['DISTRIBUTION'][ypar]
width = np.average(x[1:] - x[0:-1])
fig.vbar(x=x, width=width, bottom=0, top=y, color="black", fill_alpha=0)
best = mpl.Span(location=value, dimension='height', line_color='red', line_width=2, line_dash='solid')
minv = mpl.Span(location=value-emin, dimension='height', line_color='red', line_width=2, line_dash='dashed')
maxv = mpl.Span(location=value+emin, dimension='height', line_color='red', line_width=2, line_dash='dashed')
fig.renderers.extend([best, minv, maxv])
fig.min_border = 10
fig.min_border_top = 1
fig.min_border_bottom = 40
fig.toolbar.logo = None
fig.toolbar_location = None
fig.title.align = 'center'
figures[name] = fig
return figures
def init_phase_plot(**figure_kwargs):
p = figure(**figure_kwargs)
p.xaxis.axis_label = 'ϕ'
p.yaxis.axis_label = 'dϕ / dt'
p.xaxis.axis_label_text_font = 'helvetica'
p.yaxis.axis_label_text_font = 'helvetica'
# Scale is arbitrary, so no need for y axis ticks.
y_ticker = models.FixedTicker(ticks=[])
p.yaxis.ticker = y_ticker
p.ygrid.ticker = y_ticker
p.axis.axis_label_text_font_size = AXIS_TITLE_FONT_SIZE
p.axis.major_label_text_font_size = AXIS_TICK_FONT_SIZE
# Draw line at y=0.
p.add_layout(
models.Span(
location=0,
dimension='width',
line_color='black',
))
return p
def hlines(y, color='gray', style='dashed', thickness=1, hold=False):
"""Draw horizontal lines on a plot.
:param y: y location of lines
:param color: line color (see `Bokeh colors`_)
:param style: line style ('solid', 'dashed', 'dotted', 'dotdash', 'dashdot')
:param thickness: line width in pixels
:param hold: if set to True, output is not plotted immediately, but combined with the next plot
>>> import arlpy.plot
>>> arlpy.plot.plot([0, 20], [0, 10], hold=True)
>>> arlpy.plot.hlines(3, color='red', style='dotted')
"""
global _figure
if _figure is None:
return
y = _np.array(y, ndmin=1, dtype=_np.float, copy=False)
for j in range(y.size):
_figure.add_layout(
_bmodels.Span(location=y[j],
dimension='width',
line_color=color,
line_dash=style,
line_width=thickness))
if not hold and not _hold:
_show(_figure)
_figure = None
def plot_generic_OC(datafile):
hdf = h5py.File(datafile, 'r')
TOOLS = "pan, box_zoom, wheel_zoom, reset"
xscale = get_attr(hdf['O-C'], 'xscale', default='linear') if 'O-C' in hdf else 'linear'
yscale = get_attr(hdf['O-C'], 'yscale', default='linear') if 'O-C' in hdf else 'linear'
fig = bpl.figure(plot_width=800, plot_height=200, toolbar_location='right',
tools=TOOLS, x_axis_type=xscale, y_axis_type=yscale)
colors = ['red', 'blue', 'green']
#-- plot the O-C
if 'O-C' in hdf:
models = hdf['O-C']
for i, (name, dataset) in enumerate(models.items()):
xpar = get_attr(dataset, 'xpar', 'x')
ypar = get_attr(dataset, 'ypar', 'y')
if get_attr(dataset, 'datatype', None) == 'continuous':
fig.line(dataset[xpar], dataset[ypar], color=colors[i], legend=name)
elif get_attr(dataset, 'datatype', None) == 'discrete':
fig.circle(dataset[xpar], dataset[ypar], color=colors[i], legend=name, size=7)
plot_errorbars(fig, dataset[xpar], dataset[ypar], dataset[ypar+'_err'],
line_width=1, color=colors[i])
hline = mpl.Span(location=0, dimension='width', line_color='black', line_width=2, line_dash='dashed')
fig.add_layout(hline)
fig.yaxis.axis_label = get_attr(models, 'ylabel', 'y')
fig.xaxis.axis_label = get_attr(models, 'xlabel', 'x')
else:
error_text = mpl.Label(x=400, y=100, x_units='screen', y_units='screen',
text='No O-C data available.',
text_color='red', text_align='center')
fig.add_layout(error_text)
fig.toolbar.logo=None
fig.yaxis.axis_label_text_font_size = '10pt'
fig.xaxis.axis_label_text_font_size = '10pt'
fig.min_border = 5
hdf.close()
return fig
def create_panel(k):
# Create plot
p[k] = bpl.figure(tools=[TOOLS,hover],toolbar_location="right")
p[k].circle(x=inp_xaxis.value, y=inp_yaxis.value,
source = pdf_ts[k],
size=12)
p[k].xaxis.axis_label = "climate change signal {}".format(inp_xaxis.value)
p[k].yaxis.axis_label = "climate change signal {}".format(inp_yaxis.value)
# Horizontal line
hline = bmo.Span(location=0, dimension='width', line_color='black', line_width=3)
vline = bmo.Span(location=0, dimension='height', line_color='black', line_width=3)
p[k].renderers.extend([vline, hline])
# Create table
columns = [
bmo.widgets.TableColumn(field="index", title="model"),
bmo.widgets.TableColumn(field="{}".format(inp_xaxis.value), title="{}".format(inp_xaxis.value.title()), width=65,formatter=bmo.NumberFormatter(format="0.000")),
bmo.widgets.TableColumn(field="{}_percentiles".format(inp_xaxis.value), title="{} Perc.".format(inp_xaxis.value.title()), width=70,formatter=bmo.NumberFormatter(format="0.000")),
bmo.widgets.TableColumn(field="{}".format(inp_yaxis.value), title="{}".format(inp_yaxis.value.title()), width=65,formatter=bmo.NumberFormatter(format="0.000")),
bmo.widgets.TableColumn(field="{}_percentiles".format(inp_yaxis.value), title="{} Perc.".format(inp_yaxis.value.title()), width=70,formatter=bmo.NumberFormatter(format="0.000")),
]
data_table = bmo.widgets.DataTable(source=pdf_ts[k], columns=columns, fit_columns=False,
selectable='checkbox', height=p[k].plot_height-100, index_position=None)
down_button = bmo.widgets.Button(label="Download CSV", button_type="primary")
down_button.callback = bmo.CustomJS(args=dict(source=pdf_ts[k], filename="{}_{}_{}.csv".format(k, inp_time_mean.value, inp_exp.value)),
code=open(join(dirname(__file__), "download.js")).read())
dct_buttons[k] = down_button
l_panel = bo.layouts.row([
bo.layouts.column([p[k]]),
bo.layouts.column([down_button, data_table]),
])
panel = bmo.widgets.Panel(child=l_panel, title=time_description[k])
return panel
def make_scaled_feature_plot(data, parameters, scalers):
plot_width, plot_height = 300, 300
org_plots = []
for xpar in parameters:
# make a histogram
p = bpl.figure(plot_width=plot_width, plot_height=plot_height, title='original')
bokeh_ext.histogram(p, data[xpar])
p.xaxis.axis_label = xpar
p.title.align = 'center'
org_plots.append(p)
scl_plots = []
for xpar in parameters:
# make a histogram
if xpar in scalers and scalers[xpar] is not None:
p = bpl.figure(plot_width=plot_width, plot_height=plot_height,
title='scaled: {}'.format(scalers[xpar].__class__.__name__))
scaler = scalers[xpar]
scaled_data = scaler.transform(data[[xpar]])
bokeh_ext.histogram(p, scaled_data, fill_color="green")
# add indicators for -1, 0 and 1 to easily judge is distribution is well scaled.
for spanval in [-1, 0, +1]:
span_ = mpl.Span(location=spanval, dimension='height', line_color='red', line_dash='dashed',
line_width=1.5)
p.add_layout(span_)
p.xaxis.axis_label = xpar
p.title.align = 'center'
else:
p = bpl.figure(plot_width=plot_width, plot_height=plot_height,
title='No Scaler')
p.title.align = 'center'
scl_plots.append(p)
# check if there is at least 1 scaled plot, otherwise only return original distributions
if all([xpar not in scalers or scalers[xpar] is None for xpar in parameters]):
return gridplot([org_plots])
return gridplot([org_plots, scl_plots])
def configure(self, doc, samplerate, framesize):
self.samplerate = samplerate
self.framesize = framesize
self.doc = doc
self.elapsed_samples = 0
self.buffer_size = samplerate
self.buffer = CircularBuffer(self.buffer_size)
self.trigger_on = False
self.triggered_at = None
self.raise_or_fall_value = 0
self.trigger_points = np.ones([self.point_count])
self.display_points = np.ones([self.point_count])
self.display_ticks = np.zeros([self.point_count])
self.source = ColumnDataSource(data=dict(
x=self.display_ticks,
y=self.display_points,
trigger=self.trigger_points,
))
self.plot = figure(plot_height=400,
plot_width=400,
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, 10],
y_range=[-2.5, 2.5])
self.vline = md.Span(location=5,
dimension='height',
line_color='red',
line_width=1,
line_dash='dotted',
line_alpha=0)
self.set_time_width(10)
self.set_trigger_value(2)
def __init__(self,
start_position,
goal_position,
obstacles,
speed=1,
param_values=None):
self.position = start_position
self.goal_position = goal_position
self.obstacles = obstacles
self.obstacle_symbols = [
sp.symbols(f'o{i}x, o{i}y') for i, _ in enumerate(obstacles)
]
self.speed = speed
self.params = self.default_params
self.params.update(param_values or {})
xs = [start_position[0], goal_position[0], *(o[0] for o in obstacles)]
ys = [start_position[1], goal_position[1], *(o[1] for o in obstacles)]
self.birdseye_plot = figure(
width=FIGURE_WIDTH,
match_aspect=True,
x_range=models.Range1d(min(xs) - PADDING,
max(xs) + PADDING),
y_range=models.Range1d(min(ys) - PADDING,
max(ys) + PADDING),
title='Top-down view',
)
super().__init__()
self.t_slider.value = 20 # Simulation length.
self.numeric_eqn = None
self.numeric_nullcline_eqn = None
self.compiled_eqn = None
self.compile()
self.objects_source = models.ColumnDataSource(
data=dict(x=[], y=[], color=[], name=[]))
self.objects_source.on_change('data', self.positions_changed)
self.trajectory_source = models.ColumnDataSource(data=dict(x=[], y=[]))
self.angle_indicator_source = models.ColumnDataSource(
data=dict(x=[], y=[], color=[]))
self.nullcline_source = models.ColumnDataSource(data=dict(x=[], y=[]))
self.plot.width = FIGURE_WIDTH
self.plot.width = FIGURE_WIDTH
self.plot.xaxis.axis_label = 'φ'
self.plot.yaxis.axis_label = 'dφ/dt'
self.plot.line(
x='x',
y='y',
source=self.nullcline_source,
color='black',
line_width=NULLCLINE_WIDTH,
)
self.plot.cross(
x='x',
y='y',
color='color',
source=self.angle_indicator_source,
size=INDICATOR_SIZE,
line_width=INDICATOR_LINE_WIDTH,
alpha=0.7,
)
for dim in ['width', 'height']:
self.plot.add_layout(
models.Span(
location=0,
dimension=dim,
line_color='black',
line_width=1,
line_dash='dashed',
))
self.birdseye_plot.axis.visible = False
self.birdseye_plot.grid.visible = False
self.birdseye_plot.on_event(events.DoubleTap, self.on_doubleclick)
objects = self.birdseye_plot.circle(
x='x',
y='y',
color='color',
source=self.objects_source,
size=CIRCLE_SIZE,
)
self.birdseye_plot.line(
x='x',
y='y',
source=self.trajectory_source,
color=AGENT_COLOR,
)
drag_tool = models.PointDrawTool(renderers=[objects])
hover_tool = models.HoverTool(
renderers=[objects],
tooltips=[('Object', '@name')],
)
self.birdseye_plot.tools = [drag_tool, hover_tool]
self.birdseye_plot.toolbar.active_drag = drag_tool
self.sim_button = models.Button(label='Sim')
self.sim_button.on_click(self.sim_button_clicked)
self.clear_button = models.Button(label='Clear')
self.clear_button.on_click(self.clear_button_clicked)
self.update_birdseye_plot()
np.percentile(cputs_full, 50, axis=0),
np.percentile(Fs[aidx, :, :], 50, axis=0) /
np.percentile(Fs[2, :, :], 50),
line_color=clr,
line_width=8,
legend=anm)
rndlbl = bkm.Label(x=1.0,
x_offset=-10,
y=700,
y_units='screen',
text='Full Dataset MCMC',
angle=90,
angle_units='deg',
text_font_size='30pt')
rndspan = bkm.Span(location=1.0,
dimension='height',
line_width=8,
line_color='black',
line_dash='40 40')
fig_cput.add_layout(rndspan)
fig_cput.add_layout(rndlbl)
fig_cput.legend.label_text_font_size = legend_font_size
fig_cput.legend.glyph_width = 40
fig_cput.legend.glyph_height = 80
fig_cput.legend.spacing = 20
fig_cput.legend.orientation = 'horizontal'
bkp.show(fig_cput)
q_lower = np.percentile(data, 2.5, axis=1)
q_upper = np.percentile(data, 97.5, axis=1)
return mean, std, lower, upper, sem, sem_lower, sem_upper, q_lower, q_upper
fin_point_rr = (base_data.growth_times_rr[1] -
base_data.growth_times_sem_rr[1],
base_data.growth_mass_rr[1] + base_data.growth_mass_sem_rr[1])
fin_point_ss = (base_data.growth_times_ss[1] -
base_data.growth_times_sem_ss[1],
base_data.growth_mass_ss[1] + base_data.growth_mass_sem_ss[1])
fin_time_homo_r = mdl.Span(location=fin_point_rr[0],
dimension='height',
line_color=colors[0],
line_width=line_width,
line_dash='dotted')
fin_mass_homo_r = mdl.Span(location=fin_point_rr[1],
dimension='width',
line_color=colors[0],
line_width=line_width,
line_dash='dotted')
fin_time_homo_s = mdl.Span(location=fin_point_ss[0],
dimension='height',
line_color=colors[1],
line_width=line_width,
line_dash='dotted')
fin_mass_homo_s = mdl.Span(location=fin_point_ss[1],
dimension='width',
# layout = lay.column(egg_plot, larva_plot, hist_plot)
# plt.show(layout)
fin_point_rr = (
base.growth_times_rr[1] - base.growth_times_sem_rr[1],
base.growth_mass_rr[ 1] + base.growth_mass_sem_rr[ 1]
)
fin_point_ss = (
base.growth_times_ss[1] - base.growth_times_sem_ss[1],
base.growth_mass_ss[ 1] + base.growth_mass_sem_ss[ 1]
)
fin_time_homo_r = mdl.Span(location=fin_point_rr[0],
dimension='height', line_alpha=0.5,
line_color=colors[0], line_width=line_width)
fin_time_homo_s = mdl.Span(location=fin_point_ss[0],
dimension='height', line_alpha=0.5,
line_color=colors[1], line_width=line_width)
print('{} Running Development No Offset Larva simulations'.
format(datetime.datetime.now()))
larva_larva_off = []
larva_pupa_off = []
pupa_mass_off_homo_r = []
pupa_mass_off_hetero = []
pupa_mass_off_homo_s = []
for num in range(trials):
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
rate.append(rho_rate)
rate_lower.append(rho_rate_lower)
rate_upper.append(rho_rate_upper)
return prop, prop_lower, prop_upper, rate, rate_lower, rate_upper
raffa_point_high = 38.6 / 100
raffa_point_low = 17.7 / 100
raffa_point_high_sur = 1 - raffa_point_high
raffa_point_low_sur = 1 - raffa_point_low
raffa_span_high = mdl.Span(location=raffa_point_high,
dimension='width',
line_color=colors[2],
line_width=line_width,
line_dash='dotted')
raffa_span_low = mdl.Span(location=raffa_point_low,
dimension='width',
line_color=colors[2],
line_width=line_width,
line_dash='dotted')
t = list(range(num_steps))
# encounters = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
encounters = np.geomspace(0.01, 2, 50)
print('{} Running Cannibalism simulations for RR'.format(
datetime.datetime.now()))
initial_pops = ((0, 0, 0), (num_larvae, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0))
prop_rr, prop_lower_rr, prop_upper_rr,\
def volcanoPlot_spectronaut_triqler(df_spec,
df_triq,
vertical_line_r=None,
vertical_line_l=None,
horizontal_line=None,
plot_width=1000,
plot_height=1000,
title="plot",
x_label="xlabel",
y_label="ylabel",
outputFile=None):
plot_width = plot_width
plot_height = plot_height
title = title
hover = bmo.HoverTool(tooltips=[
("(log2fc,logFDR)", "(@log2fc, @logFDR)"),
('protein', '@protein'),
])
boxSelect = bmo.BoxSelectTool()
p = bpl.figure(plot_width=plot_width,
plot_height=plot_height,
tools=[hover, boxSelect],
title=title,
x_axis_label=x_label,
y_axis_label=y_label)
# Vertical line
if vertical_line_r != None:
vline_r = bmo.Span(location=vertical_line_r,
dimension='height',
line_color='red',
line_dash="dashed",
line_width=1,
line_alpha=0.8)
p.renderers.extend([vline_r])
if vertical_line_l != None:
vline_l = bmo.Span(location=vertical_line_l,
dimension='height',
line_color='red',
line_dash="dashed",
line_width=1,
line_alpha=0.8)
p.renderers.extend([vline_l])
# Horizontal line
if horizontal_line != None:
hline = bmo.Span(location=horizontal_line,
dimension='width',
line_color='red',
line_dash="dashed",
line_width=1,
line_alpha=0.8)
p.renderers.extend([hline])
for data, name in zip([df_spec, df_triq], ["Spectronaut", "Triqler"]):
df = pd.DataFrame(data)
source = bpl.ColumnDataSource.from_df(df)
p.scatter('log2fc',
'logFDR',
source=source,
color='color',
alpha=0.8,
muted_color='color',
muted_alpha=0.05,
legend=name)
p.legend.location = "top_left"
p.legend.click_policy = "mute"
if outputFile != None:
bpl.output_file(outputFile)
#bpl.show(p)
bpl.save(p)
def x_zeroline(f):
f.add_layout(bom.Span(location=0, dimension='height'))
sr_x_rr[keyword.hetero][1], rr_x_rr[keyword.hetero][1]
]
rr_bar_start = [
ss_x_ss[keyword.homo_r][0], ss_x_sr[keyword.homo_r][0],
ss_x_rr[keyword.homo_r][0], sr_x_sr[keyword.homo_r][0],
sr_x_rr[keyword.homo_r][0], rr_x_rr[keyword.homo_r][0]
]
rr_bar_end = [
ss_x_ss[keyword.homo_r][1], ss_x_sr[keyword.homo_r][1],
ss_x_rr[keyword.homo_r][1], sr_x_sr[keyword.homo_r][1],
sr_x_rr[keyword.homo_r][1], rr_x_rr[keyword.homo_r][1]
]
line_25 = mdl.Span(location=0.25,
dimension='width',
line_color='black',
line_width=line_width,
line_dash='dotted')
line_50 = mdl.Span(location=0.50,
dimension='width',
line_color='black',
line_width=line_width,
line_dash='dotted')
line_75 = mdl.Span(location=0.75,
dimension='width',
line_color='black',
line_width=line_width,
line_dash='dotted')
genotype_plot = plt.figure(plot_width=plot_width,
plot_height=plot_height,
