Plot

There are multiple stages involved before one can start to plot the obtained data.

plot overview

  • For model description, see Model.

  • Trace data can be converted with covid19_npis.data.convert_trace_to_pandas_list().

  • Plotting WIP

Data converter

covid19_npis.data.convert_trace_to_dataframe_list(trace, sample_state)[source]

Converts the pymc4 arviz trace to multiple pandas dataframes. Also sets the right labels for the dimensions i.e splits data by country and age group.

Do not look too much into this function if you want to keep your sanity!

Parameters

  • trace (arivz InferenceData) –

  • sample_state (pymc4 sample state) –

Returns

Multiindex dataframe containing all samples by chain and other dimensions defined in config.py

Return type

list of pd.DataFrame

covid19_npis.data.convert_trace_to_dataframe(trace, sample_state, key, data_type=None)[source]

Converts the pymc4 arviz trace for a single key to a pandas dataframes. Also sets the right labels for the dimensions i.e splits data by country and age group.

Do not look too much into this function if you want to keep your sanity!

Parameters

  • trace (arivz InferenceData) –

  • sample_state (pymc4 sample state) –

  • key (str) – Name of variable in modelParams

  • data_type (str) – Type of trace, gets detected automatically normally. Possible values are: “posterior”, “prior_predictive”, “posterior_predictive”. Overwrites automatic behaviour! default: None

Returns

Multiindex dataframe containing all samples by chain and other dimensions defined in modelParams.py

Return type

pd.DataFrame

Distribution

covid19_npis.plot.distributions.distribution(trace, sample_state, key, dir_save=None, plot_age_groups_together=True, force_matrix=False)[source]

High level plotting function for distributions, plots prior and posterior if they are given in the trace.

Parameters

  • trace (arivz.InferenceData) – Raw data from pymc4 sampling, can contain both posterior data and prior data. Or only one of both!

  • sample_state (pymc4 sample state) – Used mainly for shape labels

  • key (str) – Name of the variable to plot

  • dir_save (str, optional) – where to save the the figures (expecting a folder). Does not save if None
    Default: None

  • force_matrix (bool, optional) – Forces matrix plotting behaviour for last two dimensions
    Default: False

Returns

one figure for each country

Return type

array of mpl figures

covid19_npis.plot.distributions.distribution_matrix(trace, sample_state, key, dir_save=None)[source]

High level function to create a distribution plot for matrix like variables e.g. ‘C’. Uses last two dimensions for matrix like plot.

Parameters

  • trace (arivz.InferenceData) – Raw data from pymc4 sampling, can contain both posterior data and prior data. Or only one of both!

  • sample_state (pymc4 sample state) – Used mainly for shape labels

  • key (str) – Name of the variable to plot

  • dir_save (str, optional) – where to save the the figures (expecting a folder). Does not save if None
    Default: None

Returns

Return type

axes

covid19_npis.plot.distributions._distribution(array_posterior, array_prior, dist_name, dist_math, suffix='', ax=None)[source]

Low level function to plots posterior and prior from arrays.

Parameters

  • array_prior (array_posterior,) – Sampling data as array, should be filtered beforehand. If none it does not get plotted!

  • dist_name (str) – name of distribution for plotting

  • dist_math (str) – math of distribution for plotting

  • suffix (str,optional) – Suffix for the plot title e.g. “age_group_1”
    Default: “”

  • ax (mpl axes element, optional) – Plot into an existing axes element
    Default: None

covid19_npis.plot.distributions._plot_prior(x, ax=None, **kwargs)[source]

Low level plotting function, plots the prior as line for sampling data by using kernel density estimation. For more references see scipy documentation.

It is highly recommended to pass an axis otherwise the xlim may be a bit wonky.

Parameters

  • x – Input values, from sampling

  • ax (mpl axes element, optional) – Plot into an existing axes element
    Default: None

  • kwargs (dict, optional) – Directly passed to plotting mpl.

covid19_npis.plot.distributions._plot_posterior(x, bins=50, ax=None, **kwargs)[source]

Low level plotting function to plot an sampling data as histogram.

Parameters

  • x – Input values, from sampling

  • bins (int, optional) – Defines the number of equal-width bins in the range.
    Default: 50

  • ax (mpl axes element, optional) – Plot into an existing axes element
    Default: None

  • kwargs (dict, optional) – Directly passed to plotting mpl.

covid19_npis.plot.distributions._get_mpl_text_coordinates(text, ax)[source]

helper to get coordinates of a text object in the coordinates of the axes element [0,1]. used for the rectangle backdrop.

Returns: x_min, x_max, y_min, y_max

covid19_npis.plot.distributions._add_mpl_rect_around_text(text_list, ax, x_padding=0.05, y_padding=0.05, **kwargs)[source]

add a rectangle to the axes (behind the text)

provide a list of text elements and possible options passed to mpl.patches.Rectangle e.g. facecolor=”grey”, alpha=0.2, zorder=99,

Time series

covid19_npis.plot.time_series.timeseries(trace, sample_state, key, sampling_type='posterior', plot_chain_separated=False, plot_age_groups_together=True, dir_save=None, observed=None)[source]

High level plotting fucntion to create time series for a a give variable, i.e. plot for every additional dimension. Can only be done for variables with a time or date in shape_labels!

Does NOT plot observed cases, these have to be added manually for now.

Parameters

  • trace_prior (trace_posterior,) – Raw data from pymc4 sampling

  • sample_state (pymc4 sample stae) –

  • key (str) – Name of the timeseries variable to plot. Same name as in the model definitions.

  • sampling_type (str, optional) – Name of the type (group) in the arviz inference data.
    Default: posterior

  • dir_save (str, optional) – where to save the the figures (expecting a folder). Does not save if None
    Default: None

  • observed (pd.DataFrame, optional) – modelParams dataframe for the corresponding observed values for the variable e.g. modelParams.pos_tests_dataframe

covid19_npis.plot.time_series._timeseries(x, y, ax=None, what='data', draw_ci_95=None, draw_ci_75=None, draw_ci_50=None, **kwargs)[source]

low-level function to plot anything that has a date on the x-axis.

Parameters

  • x (array of datetime.datetime) – times for the x axis

  • y (array, 1d or 2d) – data to plot. if 2d, we plot the CI as fill_between (if CI enabled in rc params) if 2d, then first dim is realization and second dim is time matching x if 1d then first tim is time matching x

  • ax (mpl axes element, optional) – plot into an existing axes element. default: None

  • what (str, optional) – what type of data is provided in x. sets the style used for plotting: * data for data points * fcast for model forecast (prediction) * model for model reproduction of data (past)

  • kwargs (dict, optional) – directly passed to plotting mpl.

Returns

Return type

ax