Adding tests for timeseries module

CHANGELOG.rst

@@ -25,6 +25,8 @@ Unreleased
    in them (@nkorinek, #182)
 -  added contributors file and updated README to remove that information
    (@nkorinek, #121)
+- Improved handling of datasets with different shapes in base.assert_xy() (@ryla5068, #233)
+- Bug fix for handling object datatypes in base.assert_xy() (@ryla5068, #232)
 
 0.1.2
 -----

matplotcheck/base.py

@@ -8,14 +8,13 @@
 """
 
 import numpy as np
-import matplotlib.dates as mdates
 import matplotlib
 from matplotlib.backend_bases import RendererBase
 import math
 from scipy import stats
 import pandas as pd
-import geopandas as gpd
 import numbers
+import geopandas as gpd
 
 
 class InvalidPlotError(Exception):
@@ -766,7 +765,7 @@ def assert_no_legend_overlap(self, message="Legends overlap eachother"):
 
     """ BASIC PLOT DATA FUNCTIONS """
 
-    def get_xy(self, points_only=False, xtime=False):
+    def get_xy(self, points_only=False):
         """Returns a pandas dataframe with columns "x" and "y" holding the x
         and y coords on Axes `ax`
 
@@ -777,9 +776,6 @@ def get_xy(self, points_only=False, xtime=False):
         points_only : boolean
             Set ``True`` to check only points, set ``False`` to check all data
             on plot.
-        xtime : boolean
-            Set equal to True if the x axis of the plot contains datetime
-            values
 
         Returns
         -------
@@ -820,9 +816,6 @@ def get_xy(self, points_only=False, xtime=False):
         xy_data = xy_data[xy_data["x"] >= lims[0]]
         xy_data = xy_data[xy_data["x"] <= lims[1]].reset_index(drop=True)
 
-        # change to datetime dtype if needed
-        if xtime:
-            xy_data["x"] = mdates.num2date(xy_data["x"])
         return xy_data
 
     def assert_xydata(
@@ -831,7 +824,6 @@ def assert_xydata(
         xcol=None,
         ycol=None,
         points_only=False,
-        xtime=False,
         xlabels=False,
         tolerence=0,
         message="Incorrect data values",
@@ -855,11 +847,6 @@ def assert_xydata(
         points_only : boolean,
             Set ``True`` to check only points, set ``False`` tp check all data
             on plot.
-        xtime : boolean
-            Set ``True`` if the a-axis contains datetime values. Matplotlib
-            converts datetime objects to seconds? This parameter will ensure
-            the provided x col values are converted if they are datetime
-            elements.
         xlabels : boolean
             Set ``True`` if using x axis labels rather than x data. Instead of
             comparing numbers in the x-column to expected, compares numbers or
@@ -869,7 +856,8 @@ def assert_xydata(
             For example: Given a tolerance ``tolerence=0.1``, an expected value
             ``e``, and an actual value ``a``, this asserts
             ``abs(a - e) < (e * 0.1)``. (This uses `np.testing.assert_allclose`
-            with ``rtol=tolerence`` and ``atol=inf``.)
+            with ``rtol=tolerence`` and ``atol=inf``.) If using tolerance for
+            datetime data, units for tolerance will be in days.
         message : string
             The error message to be displayed if the xy-data does not match
             `xy_expected`
@@ -905,7 +893,7 @@ def assert_xydata(
                 xy_expected, xcol=xcol, ycol=ycol, message=message
             )
             return
-        xy_data = self.get_xy(points_only=points_only, xtime=xtime)
+        xy_data = self.get_xy(points_only=points_only)
 
         # Make sure the data are sorted the same
         xy_data, xy_expected = (
@@ -914,8 +902,6 @@ def assert_xydata(
         )
 
         if tolerence > 0:
-            if xtime:
-                raise ValueError("tolerance must be 0 with datetime on x-axis")
             np.testing.assert_allclose(
                 xy_data["x"],
                 xy_expected[xcol],
@@ -933,20 +919,41 @@ def assert_xydata(
             """We use `assert_array_max_ulp()` to compare the
             two datasets because it is able to account for small errors in
             floating point numbers, and it scales nicely between extremely
-            small or large numbers. We catch this error and throw our own so
-            that we can use our own message."""
+            small or large numbers. Because of the way that matplotlib stores
+            datetime data, this is essential for comparing high-precision
+            datetime data (i.e. millisecond or lower).
+
+            We catch this error and raise our own that is more relevant to
+            the assertion we are running."""
             try:
                 np.testing.assert_array_max_ulp(
-                    np.array(xy_data["x"]), np.array(xy_expected[xcol])
+                    xy_data["x"].to_numpy(dtype=np.float64),
+                    xy_expected[xcol].to_numpy(dtype=np.float64),
+                    5,
                 )
             except AssertionError:
+                # xy_data and xy_expected do not contain the same data
                 raise AssertionError(message)
+            except ValueError:
+                # xy_data and xy_expected do not have the same shape
+                raise ValueError(
+                    "xy_data and xy_expected do not have the same shape"
+                )
             try:
                 np.testing.assert_array_max_ulp(
-                    np.array(xy_data["y"]), np.array(xy_expected[ycol])
+                    xy_data["y"].to_numpy(dtype=np.float64),
+                    xy_expected[ycol].to_numpy(dtype=np.float64),
+                    5,
                 )
+
             except AssertionError:
+                # xy_data and xy_expected do not contain the same data
                 raise AssertionError(message)
+            except ValueError:
+                # xy_data and xy_expected do not have the same shape
+                raise ValueError(
+                    "xy_data and xy_expected do not have the same shape"
+                )
 
     def assert_xlabel_ydata(
         self, xy_expected, xcol, ycol, message="Incorrect Data"
@@ -1014,7 +1021,7 @@ def assert_xlabel_ydata(
         if x_is_numeric:
             try:
                 np.testing.assert_array_max_ulp(
-                    np.array(xy_data["x"]), np.array(xy_expected[xcol])
+                    np.array(xy_data["x"]), np.array(xy_expected[xcol]), 5
                 )
             except AssertionError:
                 raise AssertionError(message)
@@ -1026,7 +1033,7 @@ def assert_xlabel_ydata(
         # Testing y-data
         try:
             np.testing.assert_array_max_ulp(
-                np.array(xy_data["y"]), np.array(xy_expected[ycol])
+                np.array(xy_data["y"]), np.array(xy_expected[ycol]), 5
             )
         except AssertionError:
             raise AssertionError(message)
@@ -1166,7 +1173,7 @@ def get_num_bins(self):
             overlapping or stacked histograms in the same
             `matplotlib.axis.Axis` object, then this returns the number of bins
             with unique edges. """
-        x_data = self.get_xy(xtime=False)["x"]
+        x_data = self.get_xy()["x"]
         unique_x_data = list(set(x_data))
         num_bins = len(unique_x_data)
 

matplotcheck/tests/test_timeseries_module.py

@@ -1,9 +1,75 @@
-'''
-def test_assert_xydata_timeseries(pt_time_line_plt, pd_df_timeseries):
-    """Commenting this out for now as this requires a time series data object
-    this is failing because the time data needs to be in seconds like how
-    mpl saves it. """
-    pt_time_line_plt.assert_xydata(
-        pd_df_timeseries, xcol='time', ycol='A', xtime=True
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pytest
+from matplotcheck.timeseries import TimeSeriesTester
+
+
+@pytest.fixture
+def pd_df_timeseries():
+    """Create a pandas dataframe for testing, with timeseries in one column"""
+    return pd.DataFrame(
+        {
+            "time": pd.date_range(start="1/1/2018", periods=100),
+            "A": np.random.randint(0, 100, size=100),
+        }
     )
-'''
+
+
+@pytest.fixture
+def pt_time_line_plt(pd_df_timeseries):
+    """Create timeseries line plot for testing"""
+    fig, ax = plt.subplots()
+    pd_df_timeseries.plot("time", "A", kind="line", ax=ax)
+    axis = plt.gca()
+
+    return TimeSeriesTester(axis)
+
+
+def test_assert_xydata_timeseries(pt_time_line_plt):
+    """Tests that assert_xydata() correctly passes with matching timeseries
+    data."""
+    data = pt_time_line_plt.get_xy()
+    pt_time_line_plt.assert_xydata(data, xcol="x", ycol="y")
+
+
+def test_assert_xydata_timeseries_fails(pt_time_line_plt):
+    """Tests that assert_xydata() correctly fails without matching timeseries
+    data."""
+    data = pt_time_line_plt.get_xy()
+    data.loc[0, "x"] = 100
+    with pytest.raises(AssertionError, match="Incorrect data values"):
+        pt_time_line_plt.assert_xydata(data, xcol="x", ycol="y")
+
+
+def test_assert_xydata_timeseries_truncation_error(
+    pt_time_line_plt, pd_df_timeseries
+):
+    """Tests that assert_xydata() handles floating-point truncation error
+    gracefully for timeseries data."""
+
+    pt1 = pt_time_line_plt
+
+    # Create second plottester object with slightly different time values
+    # The change in values here should be small enough that it gets truncated
+    # in matplotlib's conversion of datetime data
+    for i in range(len(pd_df_timeseries)):
+        pd_df_timeseries.loc[i, "time"] = pd_df_timeseries.loc[
+            i, "time"
+        ] + pd.Timedelta(1)
+    fig, ax2 = plt.subplots()
+    pd_df_timeseries.plot("time", "A", kind="line", ax=ax2)
+    pt2 = TimeSeriesTester(ax2)
+
+    # Test that the two datasets assert as equal
+    data1 = pt1.get_xy()
+    pt2.assert_xydata(data1, xcol="x", ycol="y")
+
+
+def test_assert_xydata_timeseries_roundoff_error(pt_time_line_plt):
+    """Tests that assert_xydata() handles floating-point roundoff error
+    gracefully for timeseries data."""
+    data = pt_time_line_plt.get_xy()
+    data.loc[0, "x"] = data.loc[0, "x"] + 0.00000000001
+
+    pt_time_line_plt.assert_xydata(data, xcol="x", ycol="y")