"""
============
Scatter plot
============

"""

###############################
# 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/master/docs/tutorials.
#
# Make sure to place the data file in the same folder as this script/notebook.

import matplotlib.pyplot as plt

import pyam

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

###############################
# Show relation of variables
# **************************
#
# In the first example, we show the relation between two variables,
# biomass and fossil energy use.

data = df.filter(region="World")

data.plot.scatter(
    x="Primary Energy|Biomass", y="Primary Energy|Fossil", color="scenario"
)
plt.tight_layout()
plt.show()

###############################
# Show a scatter plot of meta indicators
# **************************************
#
# In the second example, we show the relation between two meta indicators
# computed from the timeseries data.
#
# Cumulative CO2 emissions
# ========================
#
# The first indicator computes the cumulative CO2 emissions from 2020
# until the end of the century.

co2 = (
    df.filter(region="World", variable="Emissions|CO2")
    .convert_unit("Mt CO2/yr", "Gt CO2/yr")
    .timeseries()
)

df.set_meta(
    meta=co2.apply(pyam.timeseries.cumulative, first_year=2020, last_year=2100, axis=1),
    name="cumulative_co2",
)

###############################
# Temperature at the end of the century
# =====================================
#
# The second indicator takes the value of the temperature variable
# in the latest year and assigns it as a meta indicator.

temperature_var = "AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED"
df.set_meta_from_data(name="temperature", variable=temperature_var, year=2100)

###############################
# Draw the scatter plot!
# ======================
#

df.plot.scatter(x="cumulative_co2", y="temperature", color="scenario")