Command reference¶

ititer.
inverse_logit
(x, a, b, c, d)¶ Inverse logit function.
 Parameters
x (Union[Iterable[numbers.Real], numbers.Real]) –
a (numbers.Real) – Location of inflection point on xaxis.
b (numbers.Real) – Gradient of slope.
c (numbers.Real) – Bottom yasymptote.
d (numbers.Real) – Distance to top yasymptote from bottom.
 Return type
Union[Iterable, numbers.Real]

ititer.
index_to_titer
(index, start, fold)¶ Transform a position in a dilution series to a titer.
Titers are referred to by their reciprocal, so a titer/dilution of ‘1/10’ is referred to as simply ‘10’.
A starting dilution of 10 and a fold change of 2 will generate a dilution series of: (10, 20, 40, 80, 160, …). This function takes a position in this dilution series and returns the dilution.
 Parameters
index (Union[Iterable[numbers.Real], numbers.Real]) – Indices(s) to transform.
start (numbers.Real) – Starting dilution of the series.
fold (numbers.Real) – Fold change of the series.
 Return type
Union[Iterable[numbers.Real], numbers.Real]

ititer.
load_example_data
()¶ Load an example pandas DataFrame to illustrate fitting sigmoid curves.
 Return type

class
ititer.
Sigmoid
(a='partial', b='full', c='full', d='full')¶ Sigmoid model for a dose response curve. The model fits a response, \(y\), as a function of log dilution, \(x\):
\(y = c + d / (1 + e^{b(x  a)})\)
Posterior distributions of each parameter can be inferred by either fully or partially pooling inference across samples (partial vs full). Parameters can also be fixed a priori, by providing a float.
Response values are standardised to have a range of 01 prior to inference. Log dilution values are standardised to have a mean of 0 and standard deviation of 1 prior to inference.
 Parameters
a –
'partial'
,'full'
or float.b –
'partial'
,'full'
or float.c –
'partial'
,'full'
or float.d –
'partial'
,'full'
or float.

fit
(log_dilution, response, sample_labels, data=None, draws=10000, prior_predictive=False, **kwds)¶ Fit parameters of the sigmoid curve to data.
 Parameters
log_dilution (Union[Iterable[numbers.Real], Hashable]) – Log dilution values.
response (Union[Iterable[numbers.Real], Hashable]) – Response values.
sample_labels (Union[Iterable[Hashable], Hashable]) – Sample labels.
data (Optional[pandas.core.frame.DataFrame]) – Optional DataFrame. If supplied then log_dilutions, response, and sample_labels should be columns in the DataFrame.
draws (int) – Number of samples to draw from the posterior distribution.
kwds – Passed to
pymc3.sample()
.prior_predictive (bool) – Sample from the prior predictive distribution. The returned Sigmoid object has a prior_predictive attribute.
 Returns
Sigmoid object with posterior attribute.

plot_sample
(sample, points=True, mean=False, scatter_kwds=None, line_kwds=None, step=1000, match_point_color_to_line_color=False, xmin=None, xmax=None)¶ Plot sigmoid curves from the posterior distribution of a sample.
 Parameters
sample (Hashable) – The sample to plot.
points (bool) – Whether to plot the data as well.
mean (bool) – Show the mean of the posterior distribution, rather than samples from the posterior.
scatter_kwds (Optional[dict]) – Passed to
matplotlib.pyplot.scatter()
. Used to control the appearance of the data points.line_kwds (Optional[dict]) – Passed to
matplotlib.pyplot.plot()
. Used to control the appearance of the lines.step (int) – Show every step’th sample from the posterior. Only has an effect if mean is False.
match_point_color_to_line_color (bool) – Match the color of the data points to the lines. This is useful when you want different samples on a single plot to have distinct colors, but for the lines and points of one sample to match.
xmin (Optional[numbers.Real]) – Lowest value on the xaxis to plot. Log dilution units. If None, the lowest log dilution in the data is used.
xmax (Optional[numbers.Real]) – Highest value on the xaxis to plot. Log dilution units. If None, the highest log dilution in the data is used.
 Return type
None

plot_samples
(samples, **kwds)¶ Plot sigmoid curves of samples using the mean value of the posterior distribution.
 Parameters
samples (Iterable[Hashable]) – List of samples to plot.
kwds – Passed to
Sigmoid.plot_sample()
.
 Return type
None

plot_all_samples
(samples_per_ax=9, n_cols=4, ax_width=7, ax_height=4)¶ Plot sigmoid curves for all samples using the mean posterior value of each parameter.
 Parameters
samples_per_ax (int) – Number of samples to put on a single ax.
n_cols (int) – Number of columns of axes. The number of rows is computed based on this and samples_per_ax.
ax_width (numbers.Real) – Width of a single ax.
ax_height (numbers.Real) – Height of a single ax.
 Return type
None

inflections
(hdi_prob=0.95)¶ Summarise the posterior distribution of inflection points for each sample.
The returned DataFrame has these columns:
mean: Mean value.
median: Median value.
hdi low: Lower boundary of the highest density interval (HDI).
hdi high: Upper boundary of the HDI.
 Parameters
hdi_prob (float) – The width of the HDI to calculate.
 Return type

endpoints
(cutoff_proportion=None, cutoff_absolute=None, hdi_prob=0.95)¶ Compute endpoints for each sample, given some response. An endpoint is the dilution at which a particular value of the response is obtained, known as the cutoff. The cutoff is either in absolute units, or given as a proportion of d.
Must supply exactly one of either cutoff_proportion or cutoff_absolute.
The returned DataFrame contains endpoints on the logtransformed scale.
 Parameters
cutoff_proportion (Optional[numbers.Real]) – Proportion of d. Must be in interval (0, 1).
cutoff_absolute (Optional[numbers.Real]) – Absolute value of d.
hdi_prob (numbers.Real) –
 Return type

scale
(x)¶ Log dilutions are scaled to have mean of 0 and standard deviation of 1 for efficient inference. Apply the same scaling to x, i.e. from the log dilution scale to the standardised log dilution scale.
 Parameters
x (Union[numbers.Real, numpy.ndarray]) – Value(s) to scale.
 Return type
Union[numbers.Real, numpy.ndarray]

inverse_scale
(x)¶ Log dilutions are scaled to have mean of 0 and standard deviation of 1 for efficient inference. Apply the inverse scaling to x, i.e. from the standardised log dilution scale back to the log dilution scale.
 Parameters
x (Union[numbers.Real, numpy.ndarray]) – Value(s) to scale.
 Return type
Union[numbers.Real, numpy.ndarray]

property
log_dilutions
¶ Sorted array of unique log dilutions in the data.

property
log_dilutions_std
¶ Sorted array of unique standardised log dilutions in the data.

ititer.
titer_to_index
(titer, start, fold)¶ Log transform a titer / dilution to its position in a dilution series.
Titers are referred to by their reciprocal, so a titer/dilution of ‘1/10’ is referred to as simply ‘10’.
A starting dilution of 10 and a fold change of 2 will generate a dilution series of: (10, 20, 40, 80, 160, …). This function takes a dilution, and returns its index in the dilution series.
 Parameters
titer (Union[Iterable[numbers.Real], numbers.Real]) – Titer(s) to transform.
start (numbers.Real) – Starting dilution of the series.
fold (numbers.Real) – Fold change of the series.
 Return type
Union[Iterable[numbers.Real], numbers.Real]