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

# In[1]:


get_ipython().run_line_magic('config', "InlineBackend.figure_formats = ['svg']")
import oscovida as ov


# In[2]:


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


# ### Total cumulative numbers
# 
# The first graph returned by the `overview` function (see the [overview](tutorial-overview-graphs.nbdata.html)
# and the explanation of the [observables](tutorial-observables.nbdata.html))
# shows the cumulative numbers for COVID cases and deaths. Here we show how it is created internally.

# Firstly, we import the plotting framework and some special functions:

# In[3]:


import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter, FuncFormatter, FixedLocator
from matplotlib.dates import DateFormatter, MONDAY, WeekdayLocator


# We will need an empty Matplotlib graph object to pass onto the plotting functions.

# In[4]:


fig1, ax1 = plt.subplots();


# Now let's add some data to our empty graph

# In[5]:


country = "Iran"
weeks = 30
cases, deaths = ov.get_country_data(country)


# Now we populate the empty plot we have with the data. We call the ploting function twice with two different series of data:

# In[6]:


ov.plot_time_step(ax=ax1, series=cases)   # add cases to the graph
ov.plot_time_step(ax=ax1, series=deaths)  # add deaths to the graph
ax1.set_title("Total numbers, log scale")
ax1.legend()
ax1
fig1


# Additionaly, one may turn off the logarithmic scale with `logscale=False` and pick another line style:

# In[7]:


fig2, ax2 = plt.subplots();
ov.plot_time_step(ax=ax2, series=cases, style='*', logscale=False)   # add cases to the graph
ov.plot_time_step(ax=ax2, series=deaths, style='--', logscale=False)  # add deaths to the graph
ax2.set_title("Total numbers, linear scale")
ax2.legend();


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