Scatter Plot w Fit Lines Vignette

CHANGELOG.rst

@@ -9,6 +9,7 @@ The format is based on `Keep a Changelog <https://keepachangelog.com/en/1.0.0/>`
 Unreleased
 ----------
 
+-  Add a vignette for testing vector data plots. (@nkorinek, #208)
 -  Changed how `assert_lines` checks the limits of lines (@nkorinek, #243)
 -  Changed changelog to an rst file. (@nkorinek, #266)
 -  Add a vignette for testing vector data plots. (@nkorinek, #208)

dev-requirements.txt

@@ -12,3 +12,4 @@ pip==19.0.3
 descartes==1.1.0
 seaborn>=0.9.1
 pillow==7.1.2
+seaborn>=0.9.1

examples/plot_line_testing.py

@@ -0,0 +1,165 @@
+"""
+Test Regression and 1:1 Lines On Plots Using Matplotcheck
+===========================================================
+
+You can use MatPlotcheck to test lines on plots. In this example you will
+learn how to test that a 1:1 line or a regression line is correct as
+rendered on a scatter plot.
+"""
+
+################################################################################
+# To begin, import the required Python packages.
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from scipy import stats
+import pandas as pd
+import matplotcheck.base as pt
+
+################################################################################
+# Create Example Data
+# -------------------
+# Below you create some data to add to a scatter plot. You will use the
+# points to calculate and create a linear regression fit line to your
+# plot. You will also add a 1:1 line to your plot. This will allow you to
+# compare the slope of the regression output to a standard 1:1 fit.
+
+# Create Pandas DataFrame containing data points
+col1 = np.random.randint(25, size=15)
+col2 = np.random.randint(25, size=15)
+data = pd.DataFrame({'data1':col1, 'data2':col2})
+
+# Plot data points, regression line, and one-to-one (1:1) line for reference
+fig, ax = plt.subplots()
+
+# Use Seaborn to calculate and plot a regression line + associated points
+sns.regplot('data1', 'data2',
+            data=data,
+            color='purple',
+            ax=ax,
+            scatter=True)
+
+# Add 1:1 line to your plot
+ax.plot((0, 1), (0, 1), transform=ax.transAxes, ls='--', c='k')
+
+ax.set(xlabel='data1',
+       ylabel='data2',
+       title='Example Data Regression Plot',
+       xlim=(0, 25),
+       ylim=(0, 25));
+
+################################################################################
+# Test Line Plots Using a matplotcheck.PlotTester Object
+# ------------------------------------------------------------
+# Once you have your plot, you can test the lines on the plot using
+# MatPlotCheck. To begin, create a  ``matplotcheck.PlotTester``
+# object. MatPlotCheck can test to see if there is a 1:1 and / or a regression
+# line on your plot. It will also test that the line has the correct
+# y-intercept and slope.
+
+# Convert matplotlib plot axes object into a matplotcheck PlotTester object
+line_plot_tester = pt.PlotTester(ax)
+
+################################################################################
+# Test For Regression and 1:1 Lines on a Plot
+# --------------------------------------------
+# You can use the method ``assert_lines_of_type()`` to test if a 1:1 or
+# linear regression line (or both line types) are present in the plot.
+
+# Check regression line is accurate to the points in the plot
+line_plot_tester.assert_lines_of_type(line_types=['linear-regression'])
+
+# Check onetoone line is present in the data. Set check_coverage to false since
+# the onetoone line goes past the extent of the points.
+line_plot_tester.assert_lines_of_type(
+        line_types=['onetoone'], check_coverage=False
+    )
+
+################################################################################
+# Test Slope and Y Intercept
+# --------------------------
+# You can also test the slope and y-intercept of a line to ensure
+# that the line is correct. If you made
+# your line from a list of vertices, you can use the
+# ``line_figure_tests.get_slope_yintercept()`` method of the PlotTester object,
+# to get the slope and y-intercept. However, if you made your line
+# from a regression function, it will take an extra step to get the slope and
+# intercept data. In this example, ``stats.linregress`` is used to calculate
+# the slope and intercept data. Once you have created that data, you can plug
+# it into the ``assert_line()`` function to ensure your line in your plot has
+# the correct values.
+
+# Get slope and y intercept data of regression line for testing
+slope_data, intercept_data, _, _, _ = stats.linregress(
+        data.data1, data.data2
+    )
+
+# Check that slope and y intercept are correct (expected) values
+line_plot_tester.assert_line(slope_exp=slope_data, intercept_exp=intercept_data)
+
+
+################################################################################
+# Test Other Aspects of the Plot
+# ------------------------------
+# Plots with linear regression lines and one to one lines generally have other
+# important aspects to the plots aside from the lines themselves, such as the
+# points the regression is based off of, or the labels of the plot. You can
+# test those aspects as well to ensure they are accurate.
+
+# Checking key words are found in the title
+line_plot_tester.assert_title_contains(
+        strings_expected=[["Example"], ["Regression"], ["Plot"]]
+    )
+
+# Check labels contain key words
+line_plot_tester.assert_axis_label_contains(
+        axis="x", strings_expected=["data1"]
+    )
+line_plot_tester.assert_axis_label_contains(
+        axis="y", strings_expected=["data2"]
+    )
+
+# Checking point data matches the expected data
+line_plot_tester.assert_xydata(
+        xy_expected=data, xcol=['data1'], ycol=['data2'], points_only=True
+    )
+
+################################################################################
+#
+# .. note::
+#    Matplotcheck can be used to test plots in Jupyter Notebooks as well. The
+#    main difference is how you access the axes objects from the plot that you
+#    want to test. Below is an example of how you could access the axes of a
+#    plot you want to test in a Jupyter Notebook.
+
+# First, import the Notebook module from Matplotcheck
+import matplotcheck.notebook as nb
+
+# Plot the data
+fig, ax = plt.subplots()
+
+# Points and regression line
+sns.regplot('data1', 'data2',
+            data=data,
+            color='purple',
+            ax=ax)
+
+# 1:1 line
+ax.plot((0, 1), (0, 1), transform=ax.transAxes, ls='--', c='k')
+
+ax.set(xlabel='data1',
+       ylabel='data2',
+       title='Example Data Regression Plot',
+       xlim=(0, 25),
+       ylim=(0, 25));
+
+# Here is where you access the axes objects of the plot for testing.
+# You can add the code line below to the end of any plot cell to store all axes
+# objects created by matplotlib in that cell.
+axis_object = nb.convert_axes(plt, which_axes="current")
+
+# This object can then be turned into a PlotTester object.
+line_plot_tester_2 = pt.PlotTester(axis_object)
+
+# Now you can run the tests as you did earlier!

matplotcheck/base.py

@@ -930,8 +930,8 @@ def assert_xydata(
             the assertion being run."""
             try:
                 np.testing.assert_array_max_ulp(
-                    xy_data["x"].to_numpy(dtype=np.float64),
-                    xy_expected[xcol].to_numpy(dtype=np.float64),
+                    xy_data["x"].to_numpy(dtype=np.float64).flatten(),
+                    xy_expected[xcol].to_numpy(dtype=np.float64).flatten(),
                     5,
                 )
             except AssertionError:
@@ -944,8 +944,8 @@ def assert_xydata(
                 )
             try:
                 np.testing.assert_array_max_ulp(
-                    xy_data["y"].to_numpy(dtype=np.float64),
-                    xy_expected[ycol].to_numpy(dtype=np.float64),
+                    xy_data["y"].to_numpy(dtype=np.float64).flatten(),
+                    xy_expected[ycol].to_numpy(dtype=np.float64).flatten(),
                     5,
                 )