def update_data(attrname, old, new):
compounds_in_radar = []
for i in checkbox_group.active:
compounds_in_radar.append(Groups[i])
df_temp = df_means
norm_fac = 100 / np.max(df_means[compounds_in_radar].max())
df_temp[compounds_in_radar] = norm_fac * df_temp[compounds_in_radar]
theta_label = np.linspace(0,
2 * np.pi,
len(compounds_in_radar),
endpoint=False)
theta_label += np.pi / 2 # rotate theta such that the first axis is at the top
flist = []
for tt, tt_str in enumerate(time_vals):
if len(Treatments) == 4:
df_for_radar = df_temp.loc[
(df_temp[Time[0]] == tt_str)
& (df_temp[Treatments[0]] == select0.value) &
(df_temp[Treatments[1]] == select1.value) &
(df_temp[Treatments[2]] == select2.value) &
(df_temp[Treatments[3]] == select3.value)]
elif len(Treatments) == 3:
df_for_radar = df_temp.loc[
(df_temp[Time[0]] == tt_str)
& (df_temp[Treatments[0]] == select0.value) &
(df_temp[Treatments[1]] == select1.value) &
(df_temp[Treatments[2]] == select2.value)]
elif len(Treatments) == 2:
df_for_radar = df_temp.loc[
(df_temp[Time[0]] == tt_str)
& (df_temp[Treatments[0]] == select0.value) &
(df_temp[Treatments[1]] == select1.value)]
elif len(Treatments) == 1:
df_for_radar = df_temp.loc[(df_temp[Time[0]] == tt_str) & (
df_temp[Treatments[0]] == select0.value)]
df_radar_values = df_for_radar[compounds_in_radar]
radar_vals = list(df_radar_values.values.flatten())
if not radar_vals:
radar_vals = [0] * len(compounds_in_radar)
flist.append(radar_vals)
x_label, y_label = circ(100, theta_label)
radars_res = {}
x_t_str = []
y_t_str = []
for i in range(len(time_vals)):
x_t_str.append('x_' + time_vals[i])
y_t_str.append('y_' + time_vals[i])
xt, yt = circ(flist[i], theta_label)
radars_res.update({x_t_str[i]: xt})
radars_res.update({y_t_str[i]: yt})
radars_res.update({'thetas': theta_label})
radars_res.update({'x_label': x_label})
radars_res.update({'y_label': y_label})
radars_res.update({'text': compounds_in_radar})
source4.data = radars_res
def update_data(attrname, old, new):
compounds_in_ring=[]
for i in checkbox_group.active:
compounds_in_ring.append(cmpd_options[i])
ring_colors=colors[0:len(compounds_in_ring)]
starts=[]
ends=[]
mid=[]
per_labels=[]
results3 = {'color' : ring_colors}
labels_dicts={}
p_x_strs=[]
p_y_strs=[]
for tt, tt_str in enumerate(time_vals):
if len(Treatments) == 4:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) & (df_means[Treatments[1]]==select1.value) & (df_means[Treatments[2]]==select2.value) & (df_means[Treatments[3]]==select3.value)]
elif len(Treatments) == 3:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) & (df_means[Treatments[1]]==select1.value) & (df_means[Treatments[2]]==select2.value) ]
elif len(Treatments) == 2:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) & (df_means[Treatments[1]]==select1.value) ]
elif len(Treatments) == 1:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) ]
df_ring_values=df_for_ring[compounds_in_ring]
ring_vals=list(df_ring_values.values.flatten())
ring_sums=sum(ring_vals)
ring_fracs = [x / ring_sums for x in ring_vals]
percents=[0]
perper_labels=[]
for f in range(0,len(ring_fracs)):
ff=percents[f]+ring_fracs[f]
percents.append(ff)
if tt==(len(time_vals)-1):
perper_labels.append(compounds_in_ring[f]+': '+str("{0:0.1f}".format(100.0*ring_fracs[f])))
else:
perper_labels.append(str("{0:0.1f}".format(100.0*ring_fracs[f])))
per_labels.append(perper_labels)
starts=[per*2*pi for per in percents[:-1]]
ends=[per*2*pi for per in percents[1:]]
if len(ends)==0:
ends=[0] * len(compounds_in_ring)
starts=[0] * len(compounds_in_ring)
#per_labels=[0] * len(compounds_in_pie)
#per_labels=['{:.2f}'.format(x) for x in per_labels]
mid=[]
for pp in range(len(starts)):
mid.append(.5*starts[pp]+.5*ends[pp])
results3.update({start_keys[tt]:starts})
results3.update({end_keys[tt]:ends})
results3.update({mid_keys[tt]:mid})
labels_dicts.update({per_keys[tt]:list(per_labels[tt])})
x_label, y_label=circ(.5+tt,mid)
x_str='xlabel_'+str(tt)
p_x_strs.append(x_str)
y_str='ylabel_'+str(tt)
p_y_strs.append(y_str)
labels_dicts.update({x_str:x_label})
labels_dicts.update({y_str:y_label})
p_label_source.data=labels_dicts
source3.data=results3
def update_data(attrname, old, new):
if len(Treatments) == 4:
df_for_pie = df_means.loc[
(df_means[Time[0]] == sel_t.value)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value) &
(df_means[Treatments[2]] == select2.value) &
(df_means[Treatments[3]] == select3.value)]
elif len(Treatments) == 3:
df_for_pie = df_means.loc[
(df_means[Time[0]] == sel_t.value)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value) &
(df_means[Treatments[2]] == select2.value)]
elif len(Treatments) == 2:
df_for_pie = df_means.loc[
(df_means[Time[0]] == sel_t.value)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value)]
elif len(Treatments) == 1:
df_for_pie = df_means.loc[(df_means[Time[0]] == sel_t.value) & (
df_means[Treatments[0]] == select0.value)]
compounds_in_pie = []
for i in checkbox_group.active:
compounds_in_pie.append(cmpd_options[i])
df_pie_values = df_for_pie[compounds_in_pie]
pie_vals = list(df_pie_values.values.flatten())
pie_fracs = [x / sum(pie_vals) for x in pie_vals]
percents = [0]
per_labels = []
for f in range(0, len(pie_fracs)):
ff = percents[f] + pie_fracs[f]
percents.append(ff)
per_labels.append(compounds_in_pie[f] + ':' +
str("{0:0.1f}".format(100.0 * pie_fracs[f])))
starts = [per * 2 * pi for per in percents[:-1]]
ends = [per * 2 * pi for per in percents[1:]]
if len(ends) == 0:
ends = [0] * len(compounds_in_pie)
starts = [0] * len(compounds_in_pie)
#per_labels=[0] * len(compounds_in_pie)
#per_labels=['{:.2f}'.format(x) for x in per_labels]
mid = []
for pp in range(len(starts)):
mid.append(.5 * starts[pp] + .5 * ends[pp])
pie_colors = colors[0:len(compounds_in_pie)]
x_label, y_label = circ(.5, mid)
result2_label = {
'x_label': x_label,
'y_label': y_label,
'p_labels': per_labels
}
result2 = {'starts': starts, 'ends': ends, 'color': pie_colors}
source.data = result2
p_label_data.data = result2_label
def single_pie_tab(df_means, df_stdev, Time, Treatments, number_cmpds_run):
len_t = len(Treatments)
colors = [
"firebrick", "navy", 'green', 'orange', 'violet', 'lawngreen',
'lightgreen', 'yellow', 'olive', 'red', 'grey', 'skyblue', 'indigo',
'slategray', 'hotpink', 'peachpuff', 'powderblue'
]
df_means = df_means.reindex(index=order_by_index(
df_means.index, index_natsorted(df_means[Time[0]])))
cmpd_options = cmpd_options_func(df_means, len_t + 1, number_cmpds_run)
time_vals = df_means[Time[0]].drop_duplicates().tolist()
if len(Treatments) == 4:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
df_means[Treatments[2]] = df_means[Treatments[2]].astype('str')
df_means[Treatments[3]] = df_means[Treatments[3]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals = df_means[Treatments[1]].drop_duplicates().tolist()
tm2_vals = df_means[Treatments[2]].drop_duplicates().tolist()
tm3_vals = df_means[Treatments[3]].drop_duplicates().tolist()
df_for_pie = df_means.loc[(df_means[Time[0]] == time_vals[0])
& (df_means[Treatments[0]] == tm0_vals[0]) &
(df_means[Treatments[1]] == tm1_vals[0]) &
(df_means[Treatments[2]] == tm2_vals[0]) &
(df_means[Treatments[3]] == tm3_vals[0])]
elif len(Treatments) == 3:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
df_means[Treatments[2]] = df_means[Treatments[2]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals = df_means[Treatments[1]].drop_duplicates().tolist()
tm2_vals = df_means[Treatments[2]].drop_duplicates().tolist()
df_for_pie = df_means.loc[(df_means[Time[0]] == time_vals[0])
& (df_means[Treatments[0]] == tm0_vals[0]) &
(df_means[Treatments[1]] == tm1_vals[0]) &
(df_means[Treatments[2]] == tm2_vals[0])]
elif len(Treatments) == 2:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals = df_means[Treatments[1]].drop_duplicates().tolist()
df_for_pie = df_means.loc[(df_means[Time[0]] == time_vals[0])
& (df_means[Treatments[0]] == tm0_vals[0]) &
(df_means[Treatments[1]] == tm1_vals[0])]
elif len(Treatments) == 1:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
df_for_pie = df_means.loc[(df_means[Time[0]] == time_vals[0])
& (df_means[Treatments[0]] == tm0_vals[0])]
df_means = df_means.replace(float('nan'), 0)
def_cmpds = []
for i in range(5):
def_cmpds.append(df_means.columns[len_t + 1 + i])
len_cmpds = len(def_cmpds)
df_pie_values = df_for_pie[def_cmpds]
pie_vals = list(df_pie_values.values.flatten())
pie_fracs = [x / sum(pie_vals) for x in pie_vals]
percents = [0]
per_labels = []
for f in range(0, len(pie_fracs)):
ff = percents[f] + pie_fracs[f]
percents.append(ff)
per_labels.append(df_means.columns[len_t + 1 + f] + ':' +
str("{0:0.1f}".format(100.0 * pie_fracs[f])))
ss = [per * 2 * pi for per in percents[:-1]]
ee = [per * 2 * pi for per in percents[1:]]
mid = []
for pp in range(len(ss)):
mid.append(.5 * ss[pp] + .5 * ee[pp])
cc = colors[0:len_cmpds]
source = ColumnDataSource(data=dict(starts=ss, ends=ee, color=cc))
x_label, y_label = circ(.5, mid)
p_label_data = ColumnDataSource({
'x_label': x_label,
'y_label': y_label,
'p_labels': per_labels
})
p = figure(match_aspect=True, plot_height=1000, plot_width=1000)
x_vals, y_vals = circ(1)
p.line(x_vals, y_vals, line_width=5, color='black')
p.wedge(x=0,
y=0,
radius=1,
start_angle='starts',
end_angle='ends',
color='color',
source=source)
p_labels = LabelSet(x="x_label",
y="y_label",
text="p_labels",
source=p_label_data,
text_color='black')
p.add_layout(p_labels)
#widget for pie charts
sel_t = Select(title="Choose a time:",
value=time_vals[0],
options=time_vals)
if len(Treatments) == 4:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
select1 = Select(title=Treatments[1],
value=str(tm1_vals[0]),
options=tm1_vals)
select2 = Select(title=Treatments[2],
value=str(tm2_vals[0]),
options=tm2_vals)
select3 = Select(title=Treatments[3],
value=str(tm3_vals[0]),
options=tm3_vals)
elif len(Treatments) == 3:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
select1 = Select(title=Treatments[1],
value=str(tm1_vals[0]),
options=tm1_vals)
select2 = Select(title=Treatments[2],
value=str(tm2_vals[0]),
options=tm2_vals)
elif len(Treatments) == 2:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
select1 = Select(title=Treatments[1],
value=str(tm1_vals[0]),
options=tm1_vals)
elif len(Treatments) == 1:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
checkbox_group = CheckboxGroup(labels=cmpd_options, active=[0, 1, 2, 3, 4])
def update_data(attrname, old, new):
if len(Treatments) == 4:
df_for_pie = df_means.loc[
(df_means[Time[0]] == sel_t.value)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value) &
(df_means[Treatments[2]] == select2.value) &
(df_means[Treatments[3]] == select3.value)]
elif len(Treatments) == 3:
df_for_pie = df_means.loc[
(df_means[Time[0]] == sel_t.value)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value) &
(df_means[Treatments[2]] == select2.value)]
elif len(Treatments) == 2:
df_for_pie = df_means.loc[
(df_means[Time[0]] == sel_t.value)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value)]
elif len(Treatments) == 1:
df_for_pie = df_means.loc[(df_means[Time[0]] == sel_t.value) & (
df_means[Treatments[0]] == select0.value)]
compounds_in_pie = []
for i in checkbox_group.active:
compounds_in_pie.append(cmpd_options[i])
df_pie_values = df_for_pie[compounds_in_pie]
pie_vals = list(df_pie_values.values.flatten())
pie_fracs = [x / sum(pie_vals) for x in pie_vals]
percents = [0]
per_labels = []
for f in range(0, len(pie_fracs)):
ff = percents[f] + pie_fracs[f]
percents.append(ff)
per_labels.append(compounds_in_pie[f] + ':' +
str("{0:0.1f}".format(100.0 * pie_fracs[f])))
starts = [per * 2 * pi for per in percents[:-1]]
ends = [per * 2 * pi for per in percents[1:]]
if len(ends) == 0:
ends = [0] * len(compounds_in_pie)
starts = [0] * len(compounds_in_pie)
#per_labels=[0] * len(compounds_in_pie)
#per_labels=['{:.2f}'.format(x) for x in per_labels]
mid = []
for pp in range(len(starts)):
mid.append(.5 * starts[pp] + .5 * ends[pp])
pie_colors = colors[0:len(compounds_in_pie)]
x_label, y_label = circ(.5, mid)
result2_label = {
'x_label': x_label,
'y_label': y_label,
'p_labels': per_labels
}
result2 = {'starts': starts, 'ends': ends, 'color': pie_colors}
source.data = result2
p_label_data.data = result2_label
if len(Treatments) == 4:
for w in [sel_t, select0, select1, select2, select3]:
w.on_change('value', update_data)
elif len(Treatments) == 3:
for w in [sel_t, select0, select1, select2]:
w.on_change('value', update_data)
elif len(Treatments) == 2:
for w in [sel_t, select0, select1]:
w.on_change('value', update_data)
elif len(Treatments) == 1:
for w in [sel_t, select0]:
w.on_change('value', update_data)
checkbox_group.on_change('active', update_data)
## Set up the widgets for pie
if len(Treatments) == 4:
inputs1 = widgetbox(sel_t, select0, select1, select2, select3)
elif len(Treatments) == 3:
inputs1 = widgetbox(sel_t, select0, select1, select2)
elif len(Treatments) == 2:
inputs1 = widgetbox(sel_t, select0, select1)
elif len(Treatments) == 1:
inputs1 = widgetbox(sel_t, select0)
inputs2 = widgetbox(checkbox_group)
# Set up layouts and add to document
layout = row(column(inputs1), inputs2, p, width=1500)
tab = Panel(child=layout, title='Pie Charts')
return tab
def radar_tab(df_means, df_stdev, Time, Treatments, number_cmpds_run, Groups):
colors = [
"firebrick", "navy", 'green', 'orange', 'violet', 'skyblue', 'indigo',
'slategray', 'hotpink', 'peachpuff', 'powderblue'
]
df_means = df_means.reindex(index=order_by_index(
df_means.index, index_natsorted(df_means[Time[0]])))
time_vals = df_means[Time[0]].drop_duplicates().tolist()
time_vals = natsorted(time_vals)
def_grps = Groups
if len(Treatments) == 4:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
df_means[Treatments[2]] = df_means[Treatments[2]].astype('str')
df_means[Treatments[3]] = df_means[Treatments[3]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals = df_means[Treatments[1]].drop_duplicates().tolist()
tm2_vals = df_means[Treatments[2]].drop_duplicates().tolist()
tm3_vals = df_means[Treatments[3]].drop_duplicates().tolist()
elif len(Treatments) == 3:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
df_means[Treatments[2]] = df_means[Treatments[2]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals = df_means[Treatments[1]].drop_duplicates().tolist()
tm2_vals = df_means[Treatments[2]].drop_duplicates().tolist()
elif len(Treatments) == 2:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals = df_means[Treatments[1]].drop_duplicates().tolist()
elif len(Treatments) == 1:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
tm0_vals = df_means[Treatments[0]].drop_duplicates().tolist()
#norm_fac=100/np.max(df_means[Groups].max())
#def_grps2=[]
#for gr in Groups:
# new_str='norm_'+gr
# df_means[new_str]=norm_fac*df_means[gr]
#for gr in def_grps:
# new_str='norm_'+gr
# def_grps2.append(new_str)
num_vars = len(def_grps)
theta_label = np.linspace(0, 2 * np.pi, num_vars, endpoint=False)
theta_label += np.pi / 2 # rotate theta such that the first axis is at the top
flist = []
for tt, tt_str in enumerate(time_vals):
if len(Treatments) == 4:
df_for_radar = df_means.loc[
(df_means[Time[0]] == tt_str)
& (df_means[Treatments[0]] == tm0_vals[0]) &
(df_means[Treatments[1]] == tm1_vals[0]) &
(df_means[Treatments[2]] == tm2_vals[0]) &
(df_means[Treatments[3]] == tm3_vals[0])]
elif len(Treatments) == 3:
df_for_radar = df_means.loc[
(df_means[Time[0]] == tt_str)
& (df_means[Treatments[0]] == tm0_vals[0]) &
(df_means[Treatments[1]] == tm1_vals[0]) &
(df_means[Treatments[2]] == tm2_vals[0])]
elif len(Treatments) == 2:
df_for_radar = df_means.loc[
(df_means[Time[0]] == tt_str)
& (df_means[Treatments[0]] == tm0_vals[0]) &
(df_means[Treatments[1]] == tm1_vals[0])]
elif len(Treatments) == 1:
df_for_radar = df_means.loc[(df_means[Time[0]] == tt_str) & (
df_means[Treatments[0]] == tm0_vals[0])]
df_radar_values = df_for_radar[def_grps]
radar_vals = list(df_radar_values.values.flatten())
flist.append(radar_vals)
rad_of_circ = max(df_means[Groups].max())
x_label, y_label = circ(rad_of_circ, theta_label)
x_vals, y_vals = circ(rad_of_circ)
radars = {}
x_t_str = []
y_t_str = []
for i in range(len(time_vals)):
x_t_str.append('x_' + time_vals[i])
y_t_str.append('y_' + time_vals[i])
xt, yt = circ(flist[i], theta_label)
radars.update({x_t_str[i]: xt})
radars.update({y_t_str[i]: yt})
radars.update({'thetas': theta_label})
radars.update({'x_label': x_label})
radars.update({'y_label': y_label})
radars.update({'text': def_grps})
p = figure(title="Radar",
match_aspect=True,
plot_height=1000,
plot_width=1000)
source4 = ColumnDataSource(data=radars)
labels = LabelSet(x="x_label", y="y_label", text="text", source=source4)
p.add_layout(labels)
p.line(x_vals, y_vals, line_width=3, color='black')
for i in range(len(time_vals)):
p.patch(x=x_t_str[i],
y=y_t_str[i],
fill_alpha=0,
line_color=colors[i],
line_width=3,
source=source4)
#widget for radar charts
if len(Treatments) == 4:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
select1 = Select(title=Treatments[1],
value=str(tm1_vals[0]),
options=tm1_vals)
select2 = Select(title=Treatments[2],
value=str(tm2_vals[0]),
options=tm2_vals)
select3 = Select(title=Treatments[3],
value=str(tm3_vals[0]),
options=tm3_vals)
elif len(Treatments) == 3:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
select1 = Select(title=Treatments[1],
value=str(tm1_vals[0]),
options=tm1_vals)
select2 = Select(title=Treatments[2],
value=str(tm2_vals[0]),
options=tm2_vals)
elif len(Treatments) == 2:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
select1 = Select(title=Treatments[1],
value=str(tm1_vals[0]),
options=tm1_vals)
elif len(Treatments) == 1:
select0 = Select(title=Treatments[0],
value=str(tm0_vals[0]),
options=tm0_vals)
checkbox_group = CheckboxGroup(labels=Groups, active=[0, 1, 2, 3, 4, 5])
if len(Treatments) == 4:
def update_title(attrname, old, new):
p.title.text = select0.value + ' ' + select1.value + ' ' + select2.value + ' ' + select3.value
elif len(Treatments) == 3:
def update_title(attrname, old, new):
p.title.text = select0.value + ' ' + select1.value + ' ' + select2.value
elif len(Treatments) == 2:
def update_title(attrname, old, new):
p.title.text = select0.value + ' ' + select1.value
elif len(Treatments) == 1:
def update_title(attrname, old, new):
p.title.text = select0.value
def update_data(attrname, old, new):
compounds_in_radar = []
for i in checkbox_group.active:
compounds_in_radar.append(Groups[i])
theta_label = np.linspace(0,
2 * np.pi,
len(compounds_in_radar),
endpoint=False)
theta_label += np.pi / 2 # rotate theta such that the first axis is at the top
flist = []
for tt, tt_str in enumerate(time_vals):
if len(Treatments) == 4:
df_for_radar = df_means.loc[
(df_means[Time[0]] == tt_str)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value) &
(df_means[Treatments[2]] == select2.value) &
(df_means[Treatments[3]] == select3.value)]
elif len(Treatments) == 3:
df_for_radar = df_means.loc[
(df_means[Time[0]] == tt_str)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value) &
(df_means[Treatments[2]] == select2.value)]
elif len(Treatments) == 2:
df_for_radar = df_means.loc[
(df_means[Time[0]] == tt_str)
& (df_means[Treatments[0]] == select0.value) &
(df_means[Treatments[1]] == select1.value)]
elif len(Treatments) == 1:
df_for_radar = df_means.loc[(df_means[Time[0]] == tt_str) & (
df_means[Treatments[0]] == select0.value)]
df_radar_values = df_for_radar[compounds_in_radar]
radar_vals = list(df_radar_values.values.flatten())
if not radar_vals:
radar_vals = [0] * len(compounds_in_radar)
flist.append(radar_vals)
x_label, y_label = circ(rad_of_circ, theta_label)
radars_res = {}
x_t_str = []
y_t_str = []
for i in range(len(time_vals)):
x_t_str.append('x_' + time_vals[i])
y_t_str.append('y_' + time_vals[i])
xt, yt = circ(flist[i], theta_label)
radars_res.update({x_t_str[i]: xt})
radars_res.update({y_t_str[i]: yt})
radars_res.update({'thetas': theta_label})
radars_res.update({'x_label': x_label})
radars_res.update({'y_label': y_label})
radars_res.update({'text': compounds_in_radar})
source4.data = radars_res
if len(Treatments) == 4:
select0.on_change('value', update_title)
select1.on_change('value', update_title)
select2.on_change('value', update_title)
select3.on_change('value', update_title)
elif len(Treatments) == 3:
select0.on_change('value', update_title)
select1.on_change('value', update_title)
select2.on_change('value', update_title)
elif len(Treatments) == 2:
select0.on_change('value', update_title)
select1.on_change('value', update_title)
elif len(Treatments) == 1:
select0.on_change('value', update_title)
if len(Treatments) == 4:
for w in [select0, select1, select2, select3]:
w.on_change('value', update_data)
elif len(Treatments) == 3:
for w in [select0, select1, select2]:
w.on_change('value', update_data)
elif len(Treatments) == 2:
for w in [select0, select1]:
w.on_change('value', update_data)
elif len(Treatments) == 1:
for w in [select0]:
w.on_change('value', update_data)
checkbox_group.on_change('active', update_data)
## Set up the widgets for panel 2
if len(Treatments) == 4:
inputs1 = widgetbox(select0, select1, select2, select3)
elif len(Treatments) == 3:
inputs1 = widgetbox(select0, select1, select2)
elif len(Treatments) == 2:
inputs1 = widgetbox(select0, select1)
elif len(Treatments) == 1:
inputs1 = widgetbox(select0)
inputs2 = widgetbox(checkbox_group)
#### this completes the 4th tab with radar chart
layout = row(column(inputs1), inputs2, p, width=1500)
tab = Panel(child=layout, title='Radar Chart')
return tab
def rings_tab(df_means,df_stdev, Time, Treatments ,number_cmpds_run):
len_t=len(Treatments)
colors=["firebrick", "navy", 'green', 'orange', 'violet','lawngreen','lightgreen', 'yellow','olive','red', 'grey','skyblue','indigo','slategray','hotpink','peachpuff','powderblue']
df_means=df_means.reindex(index=order_by_index(df_means.index, index_natsorted(df_means[Time[0]])))
cmpd_options=cmpd_options_func(df_means,len_t+1,number_cmpds_run)
time_vals=df_means[Time[0]].drop_duplicates().tolist()
time_vals=natsorted(time_vals)
if len(Treatments) == 4:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
df_means[Treatments[2]] = df_means[Treatments[2]].astype('str')
df_means[Treatments[3]] = df_means[Treatments[3]].astype('str')
tm0_vals=df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals=df_means[Treatments[1]].drop_duplicates().tolist()
tm2_vals=df_means[Treatments[2]].drop_duplicates().tolist()
tm3_vals=df_means[Treatments[3]].drop_duplicates().tolist()
elif len(Treatments) == 3:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
df_means[Treatments[2]] = df_means[Treatments[2]].astype('str')
tm0_vals=df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals=df_means[Treatments[1]].drop_duplicates().tolist()
tm2_vals=df_means[Treatments[2]].drop_duplicates().tolist()
elif len(Treatments) == 2:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
tm0_vals=df_means[Treatments[0]].drop_duplicates().tolist()
tm1_vals=df_means[Treatments[1]].drop_duplicates().tolist()
elif len(Treatments) == 1:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
tm0_vals=df_means[Treatments[0]].drop_duplicates().tolist()
df_means = df_means.replace(float('nan'),0)
start_keys=[]
end_keys=[]
mid_keys=[]
per_keys=[]
for h in time_vals:
start_keys.append('start '+h)
end_keys.append('end '+h)
mid_keys.append('mid '+h)
per_keys.append('per '+h)
def_cmpds=[]
for i in range(5):
def_cmpds.append(df_means.columns[len_t+1+i])
len_cmpds=len(def_cmpds)
p=figure(match_aspect=True, plot_height=1000, plot_width=1000)
starts=[]
ends=[]
mid=[]
per_labels=[]
ring_colors=colors[0:len_cmpds]
results3 = {'color' : ring_colors}
labels_dicts={}
p_x_strs=[]
p_y_strs=[]
for tt, tt_str in enumerate(time_vals):
if len(Treatments) == 4:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==tm0_vals[0]) & (df_means[Treatments[1]]==tm1_vals[0]) & (df_means[Treatments[2]]==tm2_vals[0]) & (df_means[Treatments[3]]==tm3_vals[0])]
elif len(Treatments) == 3:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==tm0_vals[0]) & (df_means[Treatments[1]]==tm1_vals[0]) & (df_means[Treatments[2]]==tm2_vals[0]) ]
elif len(Treatments) == 2:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==tm0_vals[0]) & (df_means[Treatments[1]]==tm1_vals[0]) ]
elif len(Treatments) == 1:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==tm0_vals[0]) ]
df_ring_values=df_for_ring[def_cmpds]
ring_vals=list(df_ring_values.values.flatten())
ring_sums=sum(ring_vals)
ring_fracs = [x / ring_sums for x in ring_vals]
percents=[0]
perper_labels=[]
for f in range(0,len(ring_fracs)):
ff=percents[f]+ring_fracs[f]
percents.append(ff)
if tt==(len(time_vals)-1):
perper_labels.append(def_cmpds[f]+': '+str("{0:0.1f}".format(100.0*ring_fracs[f])))
else:
perper_labels.append(str("{0:0.1f}".format(100.0*ring_fracs[f])))
per_labels.append(perper_labels)
starts=[per*2*pi for per in percents[:-1]]
ends=[per*2*pi for per in percents[1:]]
mid=[]
for pp in range(len(starts)):
mid.append(.5*starts[pp]+.5*ends[pp])
results3.update({start_keys[tt]:starts})
results3.update({end_keys[tt]:ends})
results3.update({mid_keys[tt]:mid})
labels_dicts.update({per_keys[tt]:list(per_labels[tt])})
x_label, y_label=circ(.5+tt,mid)
x_str='xlabel_'+str(tt)
p_x_strs.append(x_str)
y_str='ylabel_'+str(tt)
p_y_strs.append(y_str)
labels_dicts.update({x_str:x_label})
labels_dicts.update({y_str:y_label})
source3 = ColumnDataSource(data=results3)
p_label_source=ColumnDataSource(data=labels_dicts)
for tt, tt_str in enumerate(time_vals):
p.annular_wedge(x=0, y=0, inner_radius=tt, outer_radius=tt+1, start_angle=start_keys[tt], end_angle=end_keys[tt], color='color',source=source3)
x_vals,y_vals=circ(tt+1)
p.line(x_vals, y_vals, line_width=3,color='black')
p_labels = LabelSet(x=p_x_strs[tt],y=p_y_strs[tt],text=per_keys[tt],source=p_label_source)
p.add_layout(p_labels)
#widget for ring charts
if len(Treatments) == 4:
select0 = Select(title=Treatments[0], value=str(tm0_vals[0]), options=tm0_vals)
select1 = Select(title=Treatments[1], value=str(tm1_vals[0]), options=tm1_vals)
select2 = Select(title=Treatments[2], value=str(tm2_vals[0]), options=tm2_vals)
select3 = Select(title=Treatments[3], value=str(tm3_vals[0]), options=tm3_vals)
elif len(Treatments) == 3:
select0 = Select(title=Treatments[0], value=str(tm0_vals[0]), options=tm0_vals)
select1 = Select(title=Treatments[1], value=str(tm1_vals[0]), options=tm1_vals)
select2 = Select(title=Treatments[2], value=str(tm2_vals[0]), options=tm2_vals)
elif len(Treatments) == 2:
select0 = Select(title=Treatments[0], value=str(tm0_vals[0]), options=tm0_vals)
select1 = Select(title=Treatments[1], value=str(tm1_vals[0]), options=tm1_vals)
elif len(Treatments) == 1:
select0 = Select(title=Treatments[0], value=str(tm0_vals[0]), options=tm0_vals)
checkbox_group = CheckboxGroup(
labels=cmpd_options, active=[0, 1, 2, 3, 4])
#widget for ring charts
if len(Treatments) == 4:
def update_title(attrname, old, new):
p.title.text = select0.value+' '+select1.value+' '+select2.value+' '+select3.value
elif len(Treatments) == 3:
def update_title(attrname, old, new):
p.title.text = select0.value+' '+select1.value+' '+select2.value
elif len(Treatments) == 2:
def update_title(attrname, old, new):
p.title.text = select0.value+' '+select1.value
elif len(Treatments) == 1:
def update_title(attrname, old, new):
p.title.text = select0.value
#widget for ring charts
if len(Treatments) == 4:
select0.on_change('value', update_title)
select1.on_change('value', update_title)
select2.on_change('value', update_title)
select3.on_change('value', update_title)
elif len(Treatments) == 3:
select0.on_change('value', update_title)
select1.on_change('value', update_title)
select2.on_change('value', update_title)
elif len(Treatments) == 2:
select0.on_change('value', update_title)
select1.on_change('value', update_title)
elif len(Treatments) == 1:
select0.on_change('value', update_title)
def update_data(attrname, old, new):
compounds_in_ring=[]
for i in checkbox_group.active:
compounds_in_ring.append(cmpd_options[i])
ring_colors=colors[0:len(compounds_in_ring)]
starts=[]
ends=[]
mid=[]
per_labels=[]
results3 = {'color' : ring_colors}
labels_dicts={}
p_x_strs=[]
p_y_strs=[]
for tt, tt_str in enumerate(time_vals):
if len(Treatments) == 4:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) & (df_means[Treatments[1]]==select1.value) & (df_means[Treatments[2]]==select2.value) & (df_means[Treatments[3]]==select3.value)]
elif len(Treatments) == 3:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) & (df_means[Treatments[1]]==select1.value) & (df_means[Treatments[2]]==select2.value) ]
elif len(Treatments) == 2:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) & (df_means[Treatments[1]]==select1.value) ]
elif len(Treatments) == 1:
df_for_ring=df_means.loc[(df_means[Time[0]] == tt_str) & (df_means[Treatments[0]]==select0.value) ]
df_ring_values=df_for_ring[compounds_in_ring]
ring_vals=list(df_ring_values.values.flatten())
ring_sums=sum(ring_vals)
ring_fracs = [x / ring_sums for x in ring_vals]
percents=[0]
perper_labels=[]
for f in range(0,len(ring_fracs)):
ff=percents[f]+ring_fracs[f]
percents.append(ff)
if tt==(len(time_vals)-1):
perper_labels.append(compounds_in_ring[f]+': '+str("{0:0.1f}".format(100.0*ring_fracs[f])))
else:
perper_labels.append(str("{0:0.1f}".format(100.0*ring_fracs[f])))
per_labels.append(perper_labels)
starts=[per*2*pi for per in percents[:-1]]
ends=[per*2*pi for per in percents[1:]]
if len(ends)==0:
ends=[0] * len(compounds_in_ring)
starts=[0] * len(compounds_in_ring)
#per_labels=[0] * len(compounds_in_pie)
#per_labels=['{:.2f}'.format(x) for x in per_labels]
mid=[]
for pp in range(len(starts)):
mid.append(.5*starts[pp]+.5*ends[pp])
results3.update({start_keys[tt]:starts})
results3.update({end_keys[tt]:ends})
results3.update({mid_keys[tt]:mid})
labels_dicts.update({per_keys[tt]:list(per_labels[tt])})
x_label, y_label=circ(.5+tt,mid)
x_str='xlabel_'+str(tt)
p_x_strs.append(x_str)
y_str='ylabel_'+str(tt)
p_y_strs.append(y_str)
labels_dicts.update({x_str:x_label})
labels_dicts.update({y_str:y_label})
p_label_source.data=labels_dicts
source3.data=results3
if len(Treatments) == 4:
for w in [select0,select1,select2,select3]:
w.on_change('value', update_data)
elif len(Treatments) == 3:
for w in [select0,select1,select2]:
w.on_change('value', update_data)
elif len(Treatments) == 2:
for w in [select0,select1]:
w.on_change('value', update_data)
elif len(Treatments) == 1:
for w in [select0]:
w.on_change('value', update_data)
checkbox_group.on_change('active', update_data)
## Set up the widgets for rings
if len(Treatments) == 4:
inputs1 = widgetbox(select0,select1,select2,select3)
elif len(Treatments) == 3:
inputs1 = widgetbox(select0,select1,select2)
elif len(Treatments) == 2:
inputs1 = widgetbox(select0,select1)
elif len(Treatments) == 1:
inputs1 = widgetbox(select0)
inputs2 = widgetbox(checkbox_group)
layout=row(column(inputs1),inputs2, p, width=1500)
tab = Panel(child=layout, title="Pie in Pie Chart")
return tab