"""
==================
Stacked bar charts
==================

"""

# sphinx_gallery_thumbnail_number = 4

###############################
# Read in tutorial data and show a summary
# ****************************************
#
# This gallery uses the scenario data from the first-steps tutorial.
#
# If you haven't cloned the **pyam** GitHub repository to your machine,
# you can download the file from
# https://github.com/IAMconsortium/pyam/tree/main/docs/tutorials.
#
# Make sure to place the data file in the same folder as this script/notebook.

import matplotlib.pyplot as plt

import pyam
from pyam.plotting import add_net_values_to_bar_plot

df = pyam.IamDataFrame("tutorial_data.csv")
df

###############################
# Show stacked bar chart by categories
# ************************************
#
# First, we generate a simple stacked bar chart
# of all components of primary energy supply for one scenario.
#
# Calling :func:`tight_layout() <matplotlib.pyplot.tight_layout>` ensures
# that the final plot looks nice and tidy.

args = dict(model="WITCH-GLOBIOM 4.4", scenario="CD-LINKS_NPi2020_1000")
data = df.filter(**args, variable="Primary Energy|*", region="World")

data.plot.bar(stacked=True, title="Primary energy mix")
plt.legend(loc=1)
plt.tight_layout()
plt.show()

###############################
# Flip the direction of a stacked bar chart
# *****************************************
#
# We can flip that round for a horizontal chart.

data.plot.bar(stacked=True, orient="h", title="Primary energy mix")
plt.legend(loc=1)
plt.tight_layout()
plt.show()

###############################
# Show stacked bar chart by regions
# *********************************
#
# We don't just have to plot subcategories of variables,
# any data or meta indicators from the IamDataFrame can be used.
# Here, we show the contribution by region to total CO2 emissions.

data = df.filter(**args, variable="Emissions|CO2").filter(region="World", keep=False)

data.plot.bar(
    bars="region", stacked=True, title="CO2 emissions by region", cmap="tab20"
)
plt.legend(loc=1)
plt.tight_layout()
plt.show()

###############################
# Add indicators to show net values
# *********************************
#
# Sometimes, stacked bar charts have negative entries.
# In that case, it helps to add a line showing the net value.

fig, ax = plt.subplots()
data.plot.bar(
    ax=ax, bars="region", stacked=True, title="CO2 emissions by region", cmap="tab20"
)
add_net_values_to_bar_plot(ax)
plt.legend(loc=1)
plt.tight_layout()
plt.show()