Plotting

We provide a lot of plotting functions which can be used to recreate our plots or create completely new visualizations. If you are familiar with matplotlib it should be no problem to use them extensively.

We provide three different types of functions here:

• High level functions These can be used create figures similar to our paper Dehning et al. arXiv:2004.01105. The are neat little one liners which create a good looking plot from our model, but do not have a lot of customization options.

• Low level functions These extend the normal matplotlib plotting functions and can be used to plot arbitrary data. They have a lot of customization options, it could take some time to get nicely looking plots with these functions though.

• Helper functions These are mainly functions that manipulate data or retrieve data from our model. These do not have to be used most of the time and are only documented here for completeness.

If one just wants to recreate our figures with a different color. The easiest was is to change the default rc parameters.

High level functions

Low level functions

Example

In this example we want to use the low level time series function to plot the new daily cases and deaths reported by the Robert Koch institute.

import datetime
import matplotlib.pyplot as plt
import covid19_inference as cov19

# Data retrieval i.e. download new data from RobertKochInstitue
rki = cov19.data_retrieval.RKI()
rki.download_all_available_data()

new_deaths = rki.get_new(
    value = "deaths",
    data_begin=datetime.datetime(2020,3,15), #arbitrary data
    data_end=datetime.datetime.today())

new_cases = rki.get_new(
    value = "confirmed",
    data_begin=datetime.datetime(2020,3,15),
    data_end=datetime.datetime.today())

# Create a multiplot
fig, axes = plt.subplots(2,1, figsize=(12,6))

# Plot the new cases onto axes[0]
cov19.plot._timeseries(
    x=new_cases.index,
    y=new_cases,
    ax=axes[0],
    what="model", #We define model here to get a line instead of data points
)

# Plot the new deaths onto axes[1]
cov19.plot._timeseries(
    x=new_deaths.index,
    y=new_deaths,
    ax=axes[1],
    what="model", #We define model here to get a line instead of data points
)

# Label the plots

axes[0].set_title("New cases")

axes[1].set_title("New deaths")

# Show the figure
fig.show()

Helper functions