# Plots¶

The plots module contains plotting functions for Curveball. Functions that plot growth data expect a pandas.DataFrame generated by the ioutils module. Functions that plot the results of growth model fitting often expect the result of the fitting - one or more lmfit.model.ModelResult objects.

## Members¶

curveball.plots.plot_model_residuals(model_fit, rv=<scipy.stats._continuous_distns.norm_gen object>, color='k')[source]

Plot of the residuals of a model fit.

The function will plot the residuals - the difference between data and model - for a given model fit. The left panel shows the residuals over time; the right panel shows the histogram of the residuals with a fitted distribution curve.

model_fitlmfit.ModelResult

the result of a model fitting procedure.

rvscipy.stats.rv_continuous, optional

scipy.stats.rv_continuous random variable whose probability density function (pdf) will be fitted to the histogram. Defaults to the normal distribution (scipy.stats.norm).

colorstr, optional

color string for the plot, defaults to k for black.

figmatplotlib.figure.Figure

figure object

axnumpy.ndarray

array of axis objects.

curveball.plots.plot_params_distribution(param_samples, color='k', cmap='viridis', alpha=None)[source]

Plots a distribution of model parameter samples generated with curveball.models.sample_params().

param_samplespandas.DataFrame

data frame of samples; each row is one sample, each column is one parameter.

alphafloat

transparency of plot markers, defaults to $$1/n^{1/4}$$ where n is number of rows in param_samples.

seaborn.Grid

figure object

curveball.plots.plot_plate(data, edge_color='#888888', output_filename=None)[source]

Plot of the plate color mapping.

The function will plot the color mapping in data: a grid with enough columns and rows for the Col and Row columns in data, where the color of each grid cell given by the Color column.

datapandas.DataFrame

growth curve data, see curveball.ioutils for a detailed definition.

edge_colorstr

color hex string for the grid edges.

figmatplotlib.figure.Figure

figure object

axnumpy.ndarray

array of axis objects.

curveball.plots.plot_residuals(df, time='Time', value='OD', resid_func=<function <lambda>>, rv=<scipy.stats._continuous_distns.norm_gen object>, color='k', ax=None)[source]

Plot of the residuals of in the data.

The function will plot the residuals - the difference between data and average at each time point. The left panel shows the residuals over time. The middle panel shows the histogram of the residuals with a fitted distribution (defaults to Gaussian). The right panel shows the regression between the standard deviation at time t+1 and t to identify autocorrelation.

dfpandas.DataFrame

a data frame with columns Time and OD.

timestr, optional

name of column over which to group and plot the residuals. Defaults to Time.

valuestr, optional

name of column in df of the value on which to compute the residuals. Defaults to OD.

resid_funcfunction, optional

function to calculate residuals. Defaults to x - x.mean().

rvscipy.stats.rv_continuous, optional

scipy.stats.rv_continuous random variable whose probability density function (pdf) will be fitted to the histogram. Defaults the normal distribution (scipy.stats.norm).

colorstr, optional

color string for the plot, defaults to k for black.

figmatplotlib.figure.Figure

figure object

axnumpy.ndarray

array of axis objects.

curveball.plots.plot_sample_fit(model_fit, param_samples, fit_kws=None, data_kws=None, sample_kws=None)[source]

Plot of sampled curve fits.

The function will plot the main model fit and the sampled curve fits based on a table of sample parameters.

model_fitlmfit.ModelResult

the result of a model fitting procedure.

param_samplespandas.DataFrame

data frame of samples; each row is one sample, each column is one parameter.

fit_kws, data_kws, sample_kwsdict

dictionaries of plot directives for the fit, data, and sampled fit curves.

figmatplotlib.figure.Figure

figure object

axnumpy.ndarray

array of axis objects.

curveball.plots.plot_strains(data, x='Time', y='OD', plot_func=<function plot>, by=None, agg_func=<function mean>, hue='Strain', color=None, output_filename=None, **kwargs)[source]

Aggregate by strain and plot the results on one figure with different color for each strain.

The grouping of the data is done by the Strain and either Cycle Nr. or Time columns of data; the aggregation is done by the agg_func, which defaults to numpy.mean(). The colors are given by the Color column, the labels of the colors are given by the Strain column of data.

datapandas.DataFrame

growth curve data, see curveball.ioutils for a detailed definition.

xstr, optional

name of column for x-axis, defaults to Time.

ystr, optional

name of column for y-axis, defaults to OD.

plot_funcfunc, optional

function to use for plotting, defaults to matplotlib.pyplot.plot()

bytuple of str, optional

used for grouping the data, defaults to ('Strain', 'Cycle Nr.') or ('Strain', 'Time'), whichever is available.

plot_funcfunc, optional

function to use for aggregating the data, defaults to numpy.mean().

colorseaborn color palette

a seaborn color palette to use if there is no Color column; if not given, using the default palette.

output_filenamestr, optional

filename to save the resulting figure; if not given, figure is not saved.

seaborn.FacetGrid

figure object.

ValueError

raised if by isn’t set and data doesn’t contain Strain and either Time or Cycle Nr..

curveball.plots.plot_wells(df, x='Time', y='OD', plot_func=<function plot>, output_filename=None)[source]

Plot a grid of plots, one for each well in the plate.

The facetting is done by the Row and Col columns of df. The colors are given by the Color column, the labels of the colors are given by the Strain column. If Strain is missing then the coloring is done by the Well column.

dfpandas.DataFrame

growth curve data, see curveball.ioutils for a detailed definition.

xstr, optional

name of column for x-axis, defaults to Time.

ystr, optional

name of column for y-axis, defaults to OD.

plot_funcfunc, optional

function to use for plotting, defaults to matplotlib.pyplot.plot()

output_filenamestr, optional

filename to save the resulting figure; if not given, figure is not saved.

seaborn.FacetGrid

figure object.

curveball.plots.tsplot(data, x='Time', y='OD', ci_level=95, ax=None, color=None, output_filename=None, **kwargs)[source]

Time series plot of the data by strain (if applicable) or well.

The grouping of the data is done by the value of x and Strain, if such a column exists in data; otherwise it is done by x and Well. The aggregation is done by seaborn.lineplot() which calculates the mean with a confidence interval. The colors are given by the Color column, the labels of the colors are given by the Strain column; if Strain and Color don’t exist in data then the function will use a default palette and color the lines by well.

datapandas.DataFrame

growth curve data, see curveball.ioutils for a detailed definition.

xstr, optional

name of column for x-axis, defaults to Time.

ystr, optional

name of column for y-axis, defaults to OD.

ci_levelint, optional

confidence interval width in precent (0-100), defaults to 95.

axmatplotlib.axes.Axes, optional

plot into this axes, if not given create a new figure.

colorseaborn color palette

a seaborn color palette to use if there is no Color column; if not given, using the default palette.

output_filenamestr, optional

filename to save the resulting figure; if not given, figure is not saved.

matplotlib.axes.Axes

axes object