import numpy as np
from collections import defaultdict
from scipy.stats import norm

from bokeh.plotting import show, figure
from bokeh.models import ColumnDataSource, HoverTool, TapTool
from bokeh.layouts import gridplot
from bokeh.palettes import Viridis6

mass_spec = defaultdict(list)

RT_x = np.linspace(118, 123, num=50)
norm_dist = norm(loc=120.4).pdf(RT_x)

# Generate several gaussian distributions and spectral lines
for scale, mz in [(1.0, 83), (0.9, 55), (0.6, 98), (0.4, 43), (0.2, 39), (0.12, 29)]:
    mass_spec["RT"].append(RT_x)
    mass_spec["RT_intensity"].append(norm_dist * scale)
    mass_spec["MZ"].append([mz, mz])
    mass_spec["MZ_intensity"].append([0, scale])
    mass_spec['MZ_tip'].append(mz)
    mass_spec['Intensity_tip'].append(scale)

mass_spec['color'] = Viridis6

source = ColumnDataSource(mass_spec)

figure_opts = dict(plot_width=450, plot_height=300)
hover_opts = dict(
    tooltips=[('MZ', '@MZ_tip'), ('Rel Intensity', '@Intensity_tip')],
    show_arrow=False,
    line_policy='next'
)
line_opts = dict(
    line_width=5, line_color='color', line_alpha=0.6,
    hover_line_color='color', hover_line_alpha=1.0,
    source=source
)

rt_plot = figure(tools=[HoverTool(**hover_opts), TapTool()], **figure_opts)
rt_plot.multi_line(xs='RT', ys='RT_intensity', legend="Intensity_tip", **line_opts)
rt_plot.xaxis.axis_label = "Retention Time (sec)"
rt_plot.yaxis.axis_label = "Intensity"

mz_plot = figure(tools=[HoverTool(**hover_opts), TapTool()], **figure_opts)
mz_plot.multi_line(xs='MZ', ys='MZ_intensity', legend="Intensity_tip", **line_opts)
mz_plot.xaxis.axis_label = "MZ"
mz_plot.yaxis.axis_label = "Intensity"

show(gridplot([[rt_plot, mz_plot]]))
