def heatmap_tab(df_means, df_stdev, Time, Treatments, number_cmpds_run):
Cmpd0 = df_means.columns[len(Treatments) + len(Time)]
cmpd_options = cmpd_options_func(df_means,
len(Treatments) + len(Time),
number_cmpds_run)
df_means = df_means.reindex(index=order_by_index(
df_means.index, index_natsorted(df_means[Time[0]])))
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')
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')
elif len(Treatments) == 2:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
elif len(Treatments) == 1:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means['Treatment'] = df_means[Time[0]].str.cat(df_means[Treatments],
sep=' - ')
df_stdev['Treatment'] = df_means[Time[0]].str.cat(df_means[Treatments],
sep=' - ')
treatments = list(df_means['Treatment'])
df_m = df_means[['Treatment', Cmpd0]].copy()
df_m2 = df_m.set_index(df_m[df_m.columns[0]].astype(str))
df_m2.drop(df_m.columns[0], axis=1, inplace=True)
df_s = df_stdev[['Treatment', Cmpd0]].copy()
df_s2 = df_s.set_index(df_s[df_s.columns[0]].astype(str))
df_s2.drop(df_s2.columns[0], axis=1, inplace=True)
df_HM = pd.DataFrame(index=df_means['Treatment'],
columns=df_means['Treatment'])
df_HM.index.name = 'Treatment1'
df_HM.columns.name = 'Treatment2'
for i_t in treatments:
for j_t in treatments:
m1 = df_m2.loc[i_t, Cmpd0]
m2 = df_m2.loc[j_t, Cmpd0]
sd1 = df_s2.loc[i_t, Cmpd0]
sd2 = df_s2.loc[j_t, Cmpd0]
a1 = [m1 - sd1, m1 + sd1]
b1 = [m2 - sd2, m2 + sd2]
a2 = [m1 - 2 * sd1, m1 + 2 * sd1]
b2 = [m2 - 2 * sd2, m2 + 2 * sd2]
a3 = [m1 - 3 * sd1, m1 + 3 * sd1]
b3 = [m2 - 3 * sd2, m2 + 3 * sd2]
if getOverlap(a1, b1) > 0:
marker = 'green'
elif getOverlap(a2, b2) > 0:
marker = 'yellow'
elif getOverlap(a3, b3) > 0:
marker = 'orange'
else:
marker = 'red'
df_HM.loc[i_t, j_t] = marker
hm_colors = df_HM.values.reshape(-1).tolist()
t2 = treatments * len(treatments)
t1 = []
for tt in treatments:
for i in range(len(treatments)):
t1.append(tt)
source = ColumnDataSource({
'treat1': t1,
'treat2': t2,
'colors': hm_colors
})
p = figure(title="Categorical Heatmap",
x_range=treatments,
y_range=treatments,
plot_height=1000,
plot_width=1000)
p.rect(x='treat1',
y='treat2',
color='colors',
width=1,
height=1,
line_color='black',
line_width=2,
source=source)
p.xaxis.major_label_orientation = np.pi / 2
select = Select(title='Select your compound:',
value=Cmpd0,
options=cmpd_options)
def update_data(attrname, old, new):
cmpd = select.value
df_m = df_means[['Treatment', cmpd]].copy()
df_m2 = df_m.set_index(df_m[df_m.columns[0]].astype(str))
df_m2.drop(df_m.columns[0], axis=1, inplace=True)
df_s = df_stdev[['Treatment', cmpd]].copy()
df_s2 = df_s.set_index(df_stdev[df_s.columns[0]].astype(str))
df_s2.drop(df_s2.columns[0], axis=1, inplace=True)
df_HM = pd.DataFrame(index=df_means['Treatment'],
columns=df_means['Treatment'])
df_HM.index.name = 'Treatment1'
df_HM.columns.name = 'Treatment2'
for i_t in treatments:
for j_t in treatments:
m1 = df_m2.loc[i_t, cmpd]
m2 = df_m2.loc[j_t, cmpd]
sd1 = df_s2.loc[i_t, cmpd]
sd2 = df_s2.loc[j_t, cmpd]
a1 = [m1 - sd1, m1 + sd1]
b1 = [m2 - sd2, m2 + sd2]
a2 = [m1 - 2 * sd1, m1 + 2 * sd1]
b2 = [m2 - 2 * sd2, m2 + 2 * sd2]
a3 = [m1 - 3 * sd1, m1 + 3 * sd1]
b3 = [m2 - 3 * sd2, m2 + 3 * sd2]
if getOverlap(a1, b1) > 0:
marker = 'green'
elif getOverlap(a2, b2) > 0:
marker = 'yellow'
elif getOverlap(a3, b3) > 0:
marker = 'orange'
else:
marker = 'red'
df_HM.loc[i_t, j_t] = marker
hm_colors = df_HM.values.reshape(-1).tolist()
t2 = treatments * len(treatments)
t1 = []
for tt in treatments:
for i in range(len(treatments)):
t1.append(tt)
results1 = {'treat1': t1, 'treat2': t2, 'colors': hm_colors}
source.data = results1
for w in [select]:
w.on_change('value', update_data)
# Create a row layout
inputs = widgetbox(select)
layout = row(inputs, p, width=1500)
tab = Panel(child=layout, title='Heatmap')
return tab
def bar_tab(df_means, df_stdev, Time, Treatments, number_cmpds_run):
colors = [
"firebrick", "navy", 'green', 'orange', 'violet', 'lawngreen',
'powderblue', 'lightgreen', 'yellow', 'olive', 'red', 'grey',
'skyblue', 'indigo', 'slategray', 'hotpink', 'peachpuff', 'powderblue'
]
Cmpd0 = df_means.columns[len(Treatments) + len(Time)]
cmpd_options = cmpd_options_func(df_means,
len(Treatments) + len(Time),
number_cmpds_run)
time_vals = df_means[Time[0]].drop_duplicates().tolist()
time_vals = natsorted(time_vals)
df_means = df_means.reindex(index=order_by_index(
df_means.index, index_natsorted(df_means[Time[0]])))
MEANs = df_means.groupby(Treatments)[Cmpd0].apply(list).to_dict()
STDs = df_stdev.groupby(Treatments)[Cmpd0].apply(list).to_dict()
keys = []
u_keys = []
l_keys = []
results = {'time_vals': time_vals}
for h in range(len(MEANs)):
kk = list(MEANs.keys())[h][0]
for tot in range(1, len(Treatments)):
sk = list(MEANs.keys())[h][tot]
if type(sk).__name__ != 'str':
sk = str(sk)
kk += '_' + sk
keys.append(kk)
u_keys.append('upper ' + kk)
l_keys.append('lower ' + kk)
mu = list(MEANs.values())[h]
sd = list(STDs.values())[h]
upper = [x + e for x, e in zip(mu, sd)]
lower = [x - e for x, e in zip(mu, sd)]
results.update({keys[h]: mu})
results.update({u_keys[h]: upper})
results.update({l_keys[h]: lower})
source = ColumnDataSource(data=results)
p = figure(x_range=time_vals,
plot_height=1000,
plot_width=1000,
title=Cmpd0,
toolbar_location="right")
legend_it = []
for hh in range(len(MEANs)):
c = p.vbar(x=dodge('time_vals',
-0.4 + (.8 * hh / len(MEANs)),
range=p.x_range),
top=keys[hh],
width=(0.8 / len(MEANs)),
source=source,
color=colors[hh])
p.add_layout(
Whisker(source=source,
base=dodge('time_vals',
-0.4 + (.8 * hh / len(MEANs)),
range=p.x_range),
upper=u_keys[hh],
lower=l_keys[hh],
level="overlay"))
legend_it.append((keys[hh], [c]))
legend = Legend(items=legend_it, location=(0, 0))
legend.click_policy = "mute"
p.add_layout(legend, 'right')
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.orientation = "vertical"
#This is where the widget is setup
select = Select(title='Select your compound:',
value=Cmpd0,
options=cmpd_options)
select_sd = Select(title="Standard Deviation:",
value='1',
options=['0', '1', '2', '3'])
def update_title(attrname, old, new):
p.title.text = select.value
select.on_change('value', update_title)
def update_data(attrname, old, new):
cmpd = select.value
std = int(select_sd.value)
MEANs = df_means.groupby(Treatments)[cmpd].apply(list).to_dict()
STDs = df_stdev.groupby(Treatments)[cmpd].apply(list).to_dict()
results1 = {'time_vals': time_vals}
for y in range(len(MEANs)):
mu = list(MEANs.values())[y]
sd = list(STDs.values())[y]
upper = [x + std * e for x, e in zip(mu, sd)]
lower = [x - std * e for x, e in zip(mu, sd)]
results1.update({keys[y]: mu})
results1.update({u_keys[y]: upper})
results1.update({l_keys[y]: lower})
source.data = results1
for w in [select, select_sd]:
w.on_change('value', update_data)
# Set up layouts and add to document
inputs = widgetbox(select, select_sd)
layout = row(column(inputs), p, width=1000)
tab = Panel(child=layout, title='Bar Charts')
return tab
def intervals_tab(df_means,df_stdev, Time, Treatments ,number_cmpds_run):
len_t=len(Treatments)
time_vals=df_means[Time[0]].drop_duplicates().tolist()
time_vals=natsorted(time_vals)
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)
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')
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')
elif len(Treatments) == 2:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
df_means[Treatments[1]] = df_means[Treatments[1]].astype('str')
elif len(Treatments) == 1:
df_means[Treatments[0]] = df_means[Treatments[0]].astype('str')
def_cmpds=[]
for i in range(5):
def_cmpds.append(df_means.columns[len_t+1+i])
df_means['Treatment'] = df_means[Time[0]].str.cat(df_means[Treatments], sep=' - ')
treats=df_means['Treatment'].tolist()
xs=[]
ys=[]
delta=0
err_xs_t = []
err_ys_t = []
results={}
for cc, cmpd in enumerate(def_cmpds):
xs.append(np.linspace(0+delta,(len(treats)-1)+delta,len(treats)))
delta=delta+.2
ys.append(df_means[cmpd])
yerrs=df_stdev[cmpd].tolist()
err_xs = []
err_ys = []
for x, y, yerr in zip(xs[cc], ys[cc], yerrs):
err_xs.append((x, x))
err_ys.append((y - yerr, y + yerr))
err_xs_t.append(err_xs)
err_ys_t.append(err_ys)
results.update({'x_'+str(cc) : xs[cc]})
results.update({'y_'+str(cc) : ys[cc]})
results.update({'xe_'+str(cc) : err_xs_t[cc]})
results.update({'ye_'+str(cc) : err_ys_t[cc]})
source = ColumnDataSource(data=results)
pt_colors=['red','blue','green','black','orange']
ln_colors=['lightcoral','lightblue','lightgreen','gray','peachpuff']
p = figure(title='Simple bar chart', plot_height=1000, plot_width=1000)
for cc, cmpd in enumerate(def_cmpds):
l_str='Opt'+str(int(cc+1))
p.multi_line(xs='xe_'+str(cc), ys='ye_'+str(cc), color=ln_colors[cc],line_width=6, line_alpha=.8,source=source)
p.circle('x_'+str(cc), 'y_'+str(cc), color=pt_colors[cc], size=10, line_alpha=0,legend=l_str,source=source)
x_dict={}
for x,tr in zip(xs[1],treats):
x_dict.update({int(x):tr})
p.xaxis.ticker = xs[0]
p.xaxis.major_label_overrides = x_dict
p.xaxis.major_label_orientation = np.pi/2
p.xgrid[0].ticker=FixedTicker(ticks=np.arange(-.5,len(treats)-.5,len(time_vals)))
#widget for pie charts
select = Select(title="Standard Deviation:", value='1', options=['0','1','2','3','4'])
select_c1 = Select(title='Opt1: Select your 1st compound:', value=def_cmpds[0], options=cmpd_options)
select_c2 = Select(title='Opt2: Select your 2nd compound:', value=def_cmpds[1], options=cmpd_options)
select_c3 = Select(title='Opt3: Select your 3rd compound:', value=def_cmpds[2], options=cmpd_options)
select_c4 = Select(title='Opt4: Select your 4th compound:', value=def_cmpds[3], options=cmpd_options)
select_c5 = Select(title='Opt5: Select your 5th compound:', value=def_cmpds[4], options=cmpd_options)
def update_title(attrname, old, new):
p.title.text = select.value+'std deviation for' + select_c1.value + ' and ' + select_c2.value+ ' and ' + select_c3.value+ ' and ' + select_c4.value+ ' and ' + select_c5.value
def update_data(attrname, old, new):
sd=int(select.value)
xs=[]
ys=[]
delta=0
err_xs_t = []
err_ys_t = []
results1={}
def_cmpds=[select_c1.value,select_c2.value,select_c3.value,select_c4.value,select_c5.value]
for cc, cmpd in enumerate(def_cmpds):
xs.append(np.linspace(0+delta,(len(treats)-1)+delta,len(treats)))
delta=delta+.2
ys.append(df_means[cmpd])
yerrs=df_stdev[cmpd].tolist()
err_xs = []
err_ys = []
for x, y, yerr in zip(xs[cc], ys[cc], yerrs):
err_xs.append((x, x))
err_ys.append((y - sd*yerr, y + sd*yerr))
err_xs_t.append(err_xs)
err_ys_t.append(err_ys)
results1.update({'x_'+str(cc) : xs[cc]})
results1.update({'y_'+str(cc) : ys[cc]})
results1.update({'xe_'+str(cc) : err_xs_t[cc]})
results1.update({'ye_'+str(cc) : err_ys_t[cc]})
source.data = results1
select_c1.on_change('value', update_title)
select_c2.on_change('value', update_title)
select_c3.on_change('value', update_title)
select_c4.on_change('value', update_title)
select_c5.on_change('value', update_title)
select.on_change('value', update_title)
for w in [select_c1,select_c2,select_c3,select_c4,select_c5,select]:
w.on_change('value', update_data)
## Set up the widgets for panel 2
inputs1 = widgetbox(select)
inputs2 = widgetbox(select_c1,select_c2,select_c3,select_c4,select_c5)
layout=row(column(inputs1),column(inputs2), p, width=1500)
tab = Panel(child=layout,title='Intervals')
return tab
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 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