"""
The Basics of Testing Plots
===========================

These are some examples of using the basic functionality of MatPlotCheck.

"""

###############################################################################
# Setup
# -----
# You will start by importing the required packages. You will be using
# ``matplotlib.pyplot`` to create our plots, but any ``matplotlib`` based
# plotter (such as ``pandas.DataFrame.plot``) can be used.

import matplotlib.pyplot as plt
import matplotcheck.base as mpc
import matplotcheck.notebook as nb
import pandas as pd

###############################################################################
# Plot
# ----
# Now you will create some data and plot it.

months = [
    "Jan",
    "Feb",
    "Mar",
    "Apr",
    "May",
    "Jun",
    "Jul",
    "Aug",
    "Sep",
    "Oct",
    "Nov",
    "Dec",
]
percip = [
    0.75,
    0.83,
    2.2,
    2.87,
    2.80,
    2.20,
    1.77,
    1.85,
    1.69,
    1.54,
    1.22,
    0.94,
]

fig, ax = plt.subplots()
ax.bar(months, percip, color="blue")
ax.set(
    title="Average Monthly Precipitation in Boulder, CO",
    xlabel="Month",
    ylabel="Percipitation (in)",
)

plot_1_hold = nb.convert_axes(plt, which_axes="current")
###############################################################################
#
# .. note::
#    To use matplotcheck to test a plot, you need a copy of the
#    ``Matplotlib.axis.Axis`` object that the plot is stored in. This can be
#    prove difficult when testing plots in a Jupyter Notebook, where a
#    ``Matplotlib.axis.Axis`` object will not persisit beyond the cell it was
#    created in. In this case, ``nb.convert_axes()`` is used to hold the most
#    recently created plot in the variable ``plot_1_hold``. There are other ways
#    to do this, but in a Jupyter Notebook you *must* use ``nb.convert_axes()``
#    to save the ``Axis`` object.
#
# Testing the plot
# ----------------
# Now you can use matplotcheck to check that the plot has certain attributes
# that you expect. You need to create a ``PlotTester`` object, and then you can
# run some tests.

plot_tester_1 = mpc.PlotTester(plot_1_hold)


# Test that the plot is a bar plot
plot_tester_1.assert_plot_type("bar")

# Test that the plot title contains specific words
plot_tester_1.assert_title_contains(["average", "monthly precip", "boulder"])

# Test that the axis labels contain specific words
plot_tester_1.assert_axis_label_contains(axis="x", strings_expected=["month"])
plot_tester_1.assert_axis_label_contains(axis="y", strings_expected=["percip", "in"])

###############################################################################
# Now you can create a DataFrame to store the data that you expect to see in our
# plot. Then you test wether that data exists in the plot.

expected_x_data = [
    "Jan",
    "Feb",
    "Mar",
    "Apr",
    "May",
    "Jun",
    "Jul",
    "Aug",
    "Sep",
    "Oct",
    "Nov",
    "Dec",
]
expected_y_data = [
    0.75,
    0.83,
    2.20,
    2.87,
    2.80,
    2.20,
    1.77,
    1.85,
    1.69,
    1.54,
    1.22,
    0.94,
]
expected_data = pd.DataFrame({"Months": expected_x_data, "Percip": expected_y_data})

plot_tester_1.assert_xydata(expected_data, xcol="Months", ycol="Percip", xlabels=True)

###############################################################################
# Since no errors have been raised by the above assertions, you know that all
# those tests passed. Now you can run some tests that will fail.


# Test that the plot is a scatter plot
try:
    plot_tester_1.assert_plot_type("scatter")
except AssertionError as message:
    print("AssertionError:", message)

# Test that the title contains specific strings
try:
    plot_tester_1.assert_title_contains(["Denver", "wind speed"])
except AssertionError as message:
    print("AssertionError:", message)

# Test that the axis labels contain specific strings
try:
    plot_tester_1.assert_axis_label_contains(axis="x", strings_expected=["year"])
except AssertionError as message:
    print("AssertionError:", message)
try:
    plot_tester_1.assert_axis_label_contains(axis="y", strings_expected=["wind speed"])
except AssertionError as message:
    print("AssertionError:", message)


###############################################################################
# In some cases, you may not want the error message to display exactly what
# words you are expecting. If you'd like to set your own error message for an
# assertion, you can use the ``message`` flag. For more details, see the
# documentation for :py:func:`base.assert_plot_type`.

try:
    plot_tester_1.assert_plot_type(
        plot_type="line", message="Make sure you have the correct plot type!"
    )
except AssertionError as message:
    print("AssertionError:", message)

###############################################################################
#
# .. note::
#    Some assert functions use a slightly different flag to set a custom error
#    message, and some have special functionality. See the documantation for
#    details.