def update_output(records):
pkd = utils.dt2pkdata(records)
if not pkd.empty:
subjects = pkd.subject_index.unique()
else:
subjects = []
fig_data = []
results = {}
for subject in subjects:
df = pkd.loc[pkd.subject_index == subject, ["time", "conc"]]
fig_data.append(
go.Scatter(
x=df["time"],
y=df["conc"],
name="Subj{}".format(subject + 1),
mode="lines+markers",
))
results[subject] = utils.calc_pk(df["time"], df["conc"])
figure = go.Figure(
data=fig_data,
layout=go.Layout(
xaxis=dict(zeroline=False),
yaxis=dict(
title=dict(
text="Conc (uM)",
font=dict(
family='"Open Sans", "HelveticaNeue", "Helvetica Neue",'
" Helvetica, Arial, sans-serif",
size=12,
),
),
type="log",
rangemode="tozero",
zeroline=False,
showticklabels=False,
),
margin=dict(l=40, r=30, b=50, t=50),
showlegend=False,
height=294,
paper_bgcolor="rgb(245, 247, 249)",
plot_bgcolor="rgb(245, 247, 249)",
),
)
columns = ([{
"name": "Parameter",
"id": "param"
}] + [{
"name": "Subj{}".format(subject + 1),
"id": subject,
"type": "numeric"
} for subject in subjects] + [{
"name": "Mean",
"id": "mean"
}, {
"name": "StDev",
"id": "stdev"
}])
result_names = OrderedDict(
t_half="T½ (hr)",
auc0_t="AUC_0-t (uM*hr)",
auc0_inf="AUC_0-inf (uM*hr)",
percent_extrap="%Extrap",
c_max="Cmax (uM)",
t_max="Tmax (hr)",
)
data = []
for key, name in result_names.items():
d = dict(param=name)
for subject in subjects:
try:
d[int(subject)] = round(getattr(results[subject], key), 1)
except (AttributeError, TypeError):
d[int(subject)] = None
try:
d["mean"] = round(
statistics.mean([getattr(results[s], key) for s in subjects]),
1)
d["stdev"] = round(
statistics.stdev([getattr(results[s], key) for s in subjects]),
2)
except (statistics.StatisticsError, AttributeError, TypeError):
d["mean"] = None
d["stdev"] = None
data.append(d)
return figure, columns, data
def update_output(records):
pkd = utils.dt2pkdata(records)
if not pkd.empty:
subjects = pkd.subject_index.unique()
else:
subjects = []
fig_data = []
results = {}
for subject in subjects:
df = pkd.loc[pkd.subject_index == subject, ['time', 'conc']]
fig_data.append(
go.Scatter(
x=df['time'],
y=df['conc'],
name='Subj{}'.format(subject + 1),
mode='lines+markers'
)
)
results[subject] = utils.calc_pk(df['time'], df['conc'])
figure = go.Figure(
data=fig_data,
layout=go.Layout(
xaxis=dict(zeroline=False),
yaxis=dict(title=dict(text='Conc (uM)',
font=dict(
family='"Open Sans", "HelveticaNeue", "Helvetica Neue",'
' Helvetica, Arial, sans-serif',
size=12)
),
type='log',
rangemode='tozero',
zeroline=False,
showticklabels=False
),
margin=dict(
l=40,
r=30,
b=50,
t=50,
),
showlegend=False,
height=294,
paper_bgcolor='rgb(245, 247, 249)',
plot_bgcolor='rgb(245, 247, 249)',
)
)
columns = [{"name": "Parameter", "id": 'param'}] + \
[{"name": 'Subj{}'.format(subject + 1), 'id': subject, 'type': 'numeric'}
for subject in subjects] + \
[{'name': 'Mean', 'id': 'mean'}, {'name': 'StDev', 'id': 'stdev'}]
result_names = OrderedDict(t_half='T½ (hr)', auc0_t='AUC_0-t (uM*hr)', auc0_inf='AUC_0-inf (uM*hr)',
percent_extrap='%Extrap', c_max='Cmax (uM)', t_max='Tmax (hr)')
data = []
for key, name in result_names.items():
d = dict(param=name)
for subject in subjects:
try:
d[int(subject)] = round(getattr(results[subject], key), 1)
except (AttributeError, TypeError):
d[int(subject)] = None
try:
d['mean'] = round(statistics.mean([getattr(results[s], key) for s in subjects]), 1)
d['stdev'] = round(statistics.stdev([getattr(results[s], key) for s in subjects]), 2)
except (statistics.StatisticsError, AttributeError, TypeError):
d['mean'] = None
d['stdev'] = None
data.append(d)
return figure, columns, data