#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('config', "InlineBackend.figure_formats = ['svg']")
import oscovida as ov
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, MONDAY, WeekdayLocator, DayLocator

ov.display_binder_link("tutorial-overview-plot_reproduction_number.ipynb")


# ## Reproduction number
# 
# The forth and the fifth graphs returned by the `overview` function 
# (see the [overview](tutorial-overview-graphs.nbdata.html)
# and the explanation of the [observables](tutorial-observables.nbdata.html))
# show the R-value and the growth factor for COVID cases and deaths respectively. Here we show how it is created internally.
# 
# (Find more on reproduction number [here](r-value.html).)
# 
# As earlier, we retrieve cases and deaths data, create an empty graph and populate the graph with the number calculated by a function `plot_reproduction_number`.
# 
# This function has the following parameters:
# * `ax` — an axis object;
# * `series` — the data source;
# * `labels` — a pair of strings: region label and identifier of cases or deaths;
# * `color_g` and `color_R` — two colors, here we use the scheme `CN` where `N` is a number digit, but one may use [any colormap](https://matplotlib.org/tutorials/colors/colormaps.html).

# In[2]:


fig, ax = plt.subplots();    # create an empty graph
country = "US"
region = "California"

cases, deaths = ov.get_country_data(country, region)    # get actual data

# pass the data to the plotting function
ov.plot_reproduction_number(ax=ax, series=cases, labels=("USA: California","cases"), color_g="C0", color_R="C4");
ax.set_title("COVID Cases: R-value and Growth Factor");

# nicer X-axis formatting: put a tick every Monday
ax.get_xaxis().set_major_locator(DayLocator(bymonthday=(1))) 
ax.get_xaxis().set_major_formatter(DateFormatter('%d %b'))



# # Other tutorials
# 
# You can find [more tutorials here](tag-tutorial.html).