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fixed test_plot_data_points and test_plot_lines (#3)

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* Fixed test_plot_data_points to match given data

Some of the expected values in test_plot_data_points have extra digits.  The second data point had a y value of 0.22047244100000002 instead of 0.220472441 as given in the csv data file.  These cause errors with assertEqual.  It works fine with just this change, but it is still checking floats for equality which could be brittle. I also modified the assert line to use np.testing.assert_almost_equal which checks lists in an element-by-element fashion with tolerance and should be more robust.

* fixed test_plot_lines

Question asks for regression line "Through the year 2050" but the expected lists stops at the y-value for 2049.  Added datapoint for 2050 in both lines.  assertEqual was throwing errors for differences in 10th digit, changing this to np.testing.assert_almost_equal checks the first 7 digits which is a more robust way to check a regression calculated value.

* Added numpy import at top

added numpy import at top for np.testing.assert_almost_equal
main
Chris DiCarlo 3 years ago committed by GitHub
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commit d23928ece0
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1 changed files with 7 additions and 6 deletions
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test_module.py
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@ -1,6 +1,7 @@
import unittest
import sea_level_predictor
import matplotlib as mpl
import numpy as np
# the test case
@ -26,15 +27,15 @@ class LinePlotTestCase(unittest.TestCase):
def test_plot_data_points(self):
actual = self.ax.get_children()[0].get_offsets().data.tolist()
expected = [[1880.0, 0.0], [1881.0, 0.22047244100000002], [1882.0, -0.440944881], [1883.0, -0.232283464], [1884.0, 0.590551181], [1885.0, 0.531496062], [1886.0, 0.43700787399999996], [1887.0, 0.216535433], [1888.0, 0.299212598], [1889.0, 0.362204724], [1890.0, 0.440944881], [1891.0, 0.374015748], [1892.0, 0.499999999], [1893.0, 0.6850393690000001], [1894.0, 0.303149606], [1895.0, 0.767716535], [1896.0, 0.46850393700000004], [1897.0, 0.6732283459999999], [1898.0, 1.043307086], [1899.0, 1.338582676], [1900.0, 1.125984251], [1901.0, 1.1102362190000001], [1902.0, 1.291338581], [1903.0, 1.6062992109999998], [1904.0, 1.2007874], [1905.0, 0.9842519679999999], [1906.0, 1.251968503], [1907.0, 1.196850392], [1908.0, 1.098425196], [1909.0, 1.27559055], [1910.0, 1.2716535420000001], [1911.0, 1.598425195], [1912.0, 1.476377951], [1913.0, 1.5472440930000002], [1914.0, 1.795275589], [1915.0, 2.10629921], [1916.0, 2.031496061], [1917.0, 1.854330707], [1918.0, 1.791338581], [1919.0, 1.854330707], [1920.0, 1.905511809], [1921.0, 1.988188974], [1922.0, 1.952755904], [1923.0, 1.999999998], [1924.0, 1.7125984230000002], [1925.0, 1.791338581], [1926.0, 2.0472440919999997], [1927.0, 2.003937006], [1928.0, 1.850393699], [1929.0, 1.905511809], [1930.0, 2.062992124], [1931.0, 2.0472440919999997], [1932.0, 2.271653541], [1933.0, 2.440944879], [1934.0, 2.228346454], [1935.0, 2.448818895], [1936.0, 2.295275588], [1937.0, 2.519685037], [1938.0, 2.6220472409999998], [1939.0, 2.826771651], [1940.0, 2.618110234], [1941.0, 3.098425194], [1942.0, 3.098425194], [1943.0, 3.098425194], [1944.0, 2.84645669], [1945.0, 2.95669291], [1946.0, 3.2519685010000003], [1947.0, 3.374015745], [1948.0, 3.562992122], [1949.0, 3.51181102], [1950.0, 3.598425193], [1951.0, 3.9724409410000003], [1952.0, 3.8700787360000004], [1953.0, 4.043307082], [1954.0, 3.929133854], [1955.0, 3.964566925], [1956.0, 3.763779524], [1957.0, 4.291338578], [1958.0, 4.346456688], [1959.0, 4.358267712], [1960.0, 4.503937003], [1961.0, 4.748031491], [1962.0, 4.543307082], [1963.0, 4.480314956], [1964.0, 4.1692913339999995], [1965.0, 4.6102362160000006], [1966.0, 4.397637791], [1967.0, 4.452755901000001], [1968.0, 4.484251963999999], [1969.0, 4.751968499], [1970.0, 4.67716535], [1971.0, 4.881889759], [1972.0, 5.240157475], [1973.0, 5.003937003], [1974.0, 5.472440939], [1975.0, 5.409448812999999], [1976.0, 5.370078735], [1977.0, 5.303149601], [1978.0, 5.555118105], [1979.0, 5.362204719], [1980.0, 5.5984251910000005], [1981.0, 6.0866141670000005], [1982.0, 5.858267711], [1983.0, 6.188976372000001], [1984.0, 6.153543301], [1985.0, 5.74803149], [1986.0, 5.771653537000001], [1987.0, 5.795275585], [1988.0, 5.980314955], [1989.0, 6.157480308999999], [1990.0, 6.2322834579999995], [1991.0, 6.334645663], [1992.0, 6.35826771], [1993.0, 6.291338576], [1994.0, 6.499999992999999], [1995.0, 6.618110229], [1996.0, 6.787401568], [1997.0, 7.066929127000001], [1998.0, 6.665354324], [1999.0, 7.011811016], [2000.0, 7.062992119], [2001.0, 7.287401567000001], [2002.0, 7.3818897560000005], [2003.0, 7.7598425120000005], [2004.0, 7.740157472000001], [2005.0, 7.74409448], [2006.0, 7.9173228270000005], [2007.0, 7.996062984], [2008.0, 8.350393692], [2009.0, 8.586614164], [2010.0, 8.901574794], [2011.0, 8.96456692], [2012.0, 9.326771643999999], [2013.0, 8.980314951]]
self.assertEqual(actual, expected, "Expected different data points in scatter plot.")
expected = [[1880.0, 0.0], [1881.0, 0.220472441], [1882.0, -0.440944881], [1883.0, -0.232283464], [1884.0, 0.590551181], [1885.0, 0.531496062], [1886.0, 0.437007874], [1887.0, 0.216535433], [1888.0, 0.299212598], [1889.0, 0.362204724], [1890.0, 0.440944881], [1891.0, 0.374015748], [1892.0, 0.499999999], [1893.0, 0.685039369], [1894.0, 0.303149606], [1895.0, 0.767716535], [1896.0, 0.468503937], [1897.0, 0.673228346], [1898.0, 1.043307086], [1899.0, 1.338582676], [1900.0, 1.125984251], [1901.0, 1.110236219], [1902.0, 1.291338581], [1903.0, 1.606299211], [1904.0, 1.2007874], [1905.0, 0.984251968], [1906.0, 1.251968503], [1907.0, 1.196850392], [1908.0, 1.098425196], [1909.0, 1.27559055], [1910.0, 1.271653542], [1911.0, 1.598425195], [1912.0, 1.476377951], [1913.0, 1.547244093], [1914.0, 1.795275589], [1915.0, 2.10629921], [1916.0, 2.031496061], [1917.0, 1.854330707], [1918.0, 1.791338581], [1919.0, 1.854330707], [1920.0, 1.905511809], [1921.0, 1.988188974], [1922.0, 1.952755904], [1923.0, 1.999999998], [1924.0, 1.712598423], [1925.0, 1.791338581], [1926.0, 2.047244092], [1927.0, 2.003937006], [1928.0, 1.850393699], [1929.0, 1.905511809], [1930.0, 2.062992124], [1931.0, 2.047244092], [1932.0, 2.271653541], [1933.0, 2.440944879], [1934.0, 2.228346454], [1935.0, 2.448818895], [1936.0, 2.295275588], [1937.0, 2.519685037], [1938.0, 2.622047241], [1939.0, 2.826771651], [1940.0, 2.618110234], [1941.0, 3.098425194], [1942.0, 3.098425194], [1943.0, 3.098425194], [1944.0, 2.84645669], [1945.0, 2.95669291], [1946.0, 3.251968501], [1947.0, 3.374015745], [1948.0, 3.562992122], [1949.0, 3.51181102], [1950.0, 3.598425193], [1951.0, 3.972440941], [1952.0, 3.870078736], [1953.0, 4.043307082], [1954.0, 3.929133854], [1955.0, 3.964566925], [1956.0, 3.763779524], [1957.0, 4.291338578], [1958.0, 4.346456688], [1959.0, 4.358267712], [1960.0, 4.503937003], [1961.0, 4.748031491], [1962.0, 4.543307082], [1963.0, 4.480314956], [1964.0, 4.169291334], [1965.0, 4.610236216], [1966.0, 4.397637791], [1967.0, 4.452755901], [1968.0, 4.484251964], [1969.0, 4.751968499], [1970.0, 4.67716535], [1971.0, 4.881889759], [1972.0, 5.240157475], [1973.0, 5.003937003], [1974.0, 5.472440939], [1975.0, 5.409448813], [1976.0, 5.370078735], [1977.0, 5.303149601], [1978.0, 5.555118105], [1979.0, 5.362204719], [1980.0, 5.598425191], [1981.0, 6.086614167], [1982.0, 5.858267711], [1983.0, 6.188976372], [1984.0, 6.153543301], [1985.0, 5.74803149], [1986.0, 5.771653537], [1987.0, 5.795275585], [1988.0, 5.980314955], [1989.0, 6.157480309], [1990.0, 6.232283458], [1991.0, 6.334645663], [1992.0, 6.35826771], [1993.0, 6.291338576], [1994.0, 6.499999993], [1995.0, 6.618110229], [1996.0, 6.787401568], [1997.0, 7.066929127], [1998.0, 6.665354324], [1999.0, 7.011811016], [2000.0, 7.062992119], [2001.0, 7.287401567], [2002.0, 7.381889756], [2003.0, 7.759842512], [2004.0, 7.740157472], [2005.0, 7.74409448], [2006.0, 7.917322827], [2007.0, 7.996062984], [2008.0, 8.350393692], [2009.0, 8.586614164], [2010.0, 8.901574794], [2011.0, 8.96456692], [2012.0, 9.326771644], [2013.0, 8.980314951]]
np.testing.assert_almost_equal(actual, expected, 7, "Expected different data points in scatter plot.")
def test_plot_lines(self):
actual = self.ax.get_lines()[0].get_ydata().tolist()
expected = [-0.5421240249263661, -0.4790794409142336, -0.41603485690208686, -0.3529902728899543, -0.2899456888778218, -0.22690110486568926, -0.16385652085355673, -0.1008119368414242, -0.037767352829277456, 0.025277231182855076, 0.08832181519498761, 0.15136639920712014, 0.21441098321925267, 0.2774555672313852, 0.34050015124351773, 0.4035447352556645, 0.466589319267797, 0.5296339032799295, 0.5926784872920621, 0.6557230713041946, 0.7187676553163271, 0.7818122393284739, 0.8448568233406064, 0.9079014073527389, 0.9709459913648715, 1.033990575377004, 1.0970351593891365, 1.1600797434012833, 1.2231243274134158, 1.2861689114255483, 1.3492134954376809, 1.4122580794498134, 1.475302663461946, 1.5383472474740927, 1.6013918314862252, 1.6644364154983577, 1.7274809995104903, 1.7905255835226228, 1.8535701675347553, 1.9166147515468879, 1.9796593355590346, 2.042703919571167, 2.1057485035832997, 2.168793087595432, 2.2318376716075647, 2.2948822556196973, 2.357926839631844, 2.4209714236439766, 2.484016007656109, 2.5470605916682416, 2.610105175680374, 2.6731497596925067, 2.7361943437046534, 2.799238927716786, 2.8622835117289185, 2.925328095741051, 2.9883726797531835, 3.051417263765316, 3.1144618477774486, 3.1775064317895954, 3.240551015801728, 3.3035955998138604, 3.366640183825993, 3.4296847678381255, 3.492729351850258, 3.5557739358624048, 3.6188185198745373, 3.68186310388667, 3.7449076878988024, 3.807952271910935, 3.8709968559230674, 3.934041439935214, 3.9970860239473467, 4.060130607959479, 4.123175191971612, 4.186219775983744, 4.249264359995877, 4.312308944008024, 4.375353528020156, 4.438398112032289, 4.501442696044421, 4.564487280056554, 4.627531864068686, 4.690576448080819, 4.7536210320929655, 4.816665616105098, 4.879710200117231, 4.942754784129363, 5.005799368141496, 5.068843952153628, 5.131888536165775, 5.194933120177907, 5.25797770419004, 5.3210222882021725, 5.384066872214305, 5.4471114562264376, 5.510156040238584, 5.573200624250717, 5.636245208262849, 5.699289792274982, 5.762334376287114, 5.825378960299247, 5.8884235443113795, 5.951468128323526, 6.014512712335659, 6.077557296347791, 6.140601880359924, 6.203646464372056, 6.266691048384189, 6.329735632396336, 6.392780216408468, 6.455824800420601, 6.518869384432733, 6.581913968444866, 6.644958552456998, 6.708003136469145, 6.771047720481278, 6.83409230449341, 6.897136888505543, 6.960181472517675, 7.023226056529808, 7.086270640541954, 7.149315224554087, 7.2123598085662195, 7.275404392578352, 7.338448976590485, 7.401493560602617, 7.46453814461475, 7.527582728626896, 7.590627312639029, 7.653671896651161, 7.716716480663294, 7.7797610646754265, 7.842805648687559, 7.905850232699706, 7.968894816711838, 8.03193940072397, 8.094983984736103, 8.158028568748236, 8.221073152760368, 8.284117736772515, 8.347162320784648, 8.41020690479678, 8.473251488808913, 8.536296072821045, 8.599340656833178, 8.66238524084531, 8.725429824857457, 8.78847440886959, 8.851518992881722, 8.914563576893855, 8.977608160905987, 9.040652744918134, 9.103697328930252, 9.166741912942399, 9.229786496954517, 9.292831080966664, 9.35587566497881, 9.41892024899093, 9.481964833003076, 9.545009417015194, 9.608054001027341, 9.671098585039488, 9.734143169051606, 9.797187753063753, 9.860232337075871, 9.923276921088018, 9.986321505100136, 10.049366089112283, 10.11241067312443]
self.assertEqual(actual, expected, "Expected different line for first line of best fit.")
expected = [-0.5421240249263661, -0.4790794409142336, -0.41603485690208686, -0.3529902728899543, -0.2899456888778218, -0.22690110486568926, -0.16385652085355673, -0.1008119368414242, -0.037767352829277456, 0.025277231182855076, 0.08832181519498761, 0.15136639920712014, 0.21441098321925267, 0.2774555672313852, 0.34050015124351773, 0.4035447352556645, 0.466589319267797, 0.5296339032799295, 0.5926784872920621, 0.6557230713041946, 0.7187676553163271, 0.7818122393284739, 0.8448568233406064, 0.9079014073527389, 0.9709459913648715, 1.033990575377004, 1.0970351593891365, 1.1600797434012833, 1.2231243274134158, 1.2861689114255483, 1.3492134954376809, 1.4122580794498134, 1.475302663461946, 1.5383472474740927, 1.6013918314862252, 1.6644364154983577, 1.7274809995104903, 1.7905255835226228, 1.8535701675347553, 1.9166147515468879, 1.9796593355590346, 2.042703919571167, 2.1057485035832997, 2.168793087595432, 2.2318376716075647, 2.2948822556196973, 2.357926839631844, 2.4209714236439766, 2.484016007656109, 2.5470605916682416, 2.610105175680374, 2.6731497596925067, 2.7361943437046534, 2.799238927716786, 2.8622835117289185, 2.925328095741051, 2.9883726797531835, 3.051417263765316, 3.1144618477774486, 3.1775064317895954, 3.240551015801728, 3.3035955998138604, 3.366640183825993, 3.4296847678381255, 3.492729351850258, 3.5557739358624048, 3.6188185198745373, 3.68186310388667, 3.7449076878988024, 3.807952271910935, 3.8709968559230674, 3.934041439935214, 3.9970860239473467, 4.060130607959479, 4.123175191971612, 4.186219775983744, 4.249264359995877, 4.312308944008024, 4.375353528020156, 4.438398112032289, 4.501442696044421, 4.564487280056554, 4.627531864068686, 4.690576448080819, 4.7536210320929655, 4.816665616105098, 4.879710200117231, 4.942754784129363, 5.005799368141496, 5.068843952153628, 5.131888536165775, 5.194933120177907, 5.25797770419004, 5.3210222882021725, 5.384066872214305, 5.4471114562264376, 5.510156040238584, 5.573200624250717, 5.636245208262849, 5.699289792274982, 5.762334376287114, 5.825378960299247, 5.8884235443113795, 5.951468128323526, 6.014512712335659, 6.077557296347791, 6.140601880359924, 6.203646464372056, 6.266691048384189, 6.329735632396336, 6.392780216408468, 6.455824800420601, 6.518869384432733, 6.581913968444866, 6.644958552456998, 6.708003136469145, 6.771047720481278, 6.83409230449341, 6.897136888505543, 6.960181472517675, 7.023226056529808, 7.086270640541954, 7.149315224554087, 7.2123598085662195, 7.275404392578352, 7.338448976590485, 7.401493560602617, 7.46453814461475, 7.527582728626896, 7.590627312639029, 7.653671896651161, 7.716716480663294, 7.7797610646754265, 7.842805648687559, 7.905850232699706, 7.968894816711838, 8.03193940072397, 8.094983984736103, 8.158028568748236, 8.221073152760368, 8.284117736772515, 8.347162320784648, 8.41020690479678, 8.473251488808913, 8.536296072821045, 8.599340656833178, 8.66238524084531, 8.725429824857457, 8.78847440886959, 8.851518992881722, 8.914563576893855, 8.977608160905987, 9.040652744918134, 9.103697328930252, 9.166741912942399, 9.229786496954517, 9.292831080966664, 9.35587566497881, 9.41892024899093, 9.481964833003076, 9.545009417015194, 9.608054001027341, 9.671098585039488, 9.734143169051606, 9.797187753063753, 9.860232337075871, 9.923276921088018, 9.986321505100136, 10.049366089112283, 10.11241067312443, 10.175455257136548]
np.testing.assert_almost_equal(actual, expected, 7, "Expected different line for first line of best fit.")
actual = self.ax.get_lines()[1].get_ydata().tolist()
expected = [7.06107985777146, 7.227507131103323, 7.393934404435242, 7.560361677767105, 7.726788951098968, 7.89321622443083, 8.059643497762693, 8.226070771094555, 8.392498044426418, 8.55892531775828, 8.725352591090143, 8.891779864422006, 9.058207137753925, 9.224634411085788, 9.39106168441765, 9.557488957749513, 9.723916231081375, 9.890343504413238, 10.0567707777451, 10.223198051076963, 10.389625324408826, 10.556052597740688, 10.72247987107255, 10.88890714440447, 11.055334417736333, 11.221761691068195, 11.388188964400058, 11.55461623773192, 11.721043511063783, 11.887470784395646, 12.053898057727508, 12.220325331059371, 12.386752604391233, 12.553179877723153, 12.719607151055015, 12.886034424386878, 13.05246169771874, 13.218888971050603, 13.385316244382466, 13.551743517714328, 13.718170791046191, 13.884598064378054, 14.051025337709916, 14.217452611041836, 14.383879884373698, 14.55030715770556, 14.716734431037423, 14.883161704369286, 15.049588977701148, 15.216016251033011]
self.assertEqual(actual, expected, "Expected different line for second line of best fit.")
expected = [7.06107985777146, 7.227507131103323, 7.393934404435242, 7.560361677767105, 7.726788951098968, 7.89321622443083, 8.059643497762693, 8.226070771094555, 8.392498044426418, 8.55892531775828, 8.725352591090143, 8.891779864422006, 9.058207137753925, 9.224634411085788, 9.39106168441765, 9.557488957749513, 9.723916231081375, 9.890343504413238, 10.0567707777451, 10.223198051076963, 10.389625324408826, 10.556052597740688, 10.72247987107255, 10.88890714440447, 11.055334417736333, 11.221761691068195, 11.388188964400058, 11.55461623773192, 11.721043511063783, 11.887470784395646, 12.053898057727508, 12.220325331059371, 12.386752604391233, 12.553179877723153, 12.719607151055015, 12.886034424386878, 13.05246169771874, 13.218888971050603, 13.385316244382466, 13.551743517714328, 13.718170791046191, 13.884598064378054, 14.051025337709916, 14.217452611041836, 14.383879884373698, 14.55030715770556, 14.716734431037423, 14.883161704369286, 15.049588977701148, 15.216016251033011, 15.382443524364874]
np.testing.assert_almost_equal(actual, expected, 7, "Expected different line for second line of best fit.")
if __name__ == "__main__":
unittest.main()

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