freecodecamp-data-analysis/sea-level-predictor
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sea-level-predictor/test_module.py

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init
2020-09-29 16:50:39 +02:00
import unittest
import sea_level_predictor
import matplotlib as mpl
fixed test_plot_data_points and test_plot_lines (#3) * 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
2021-04-19 18:58:56 +02:00
import numpy as np
init
2020-09-29 16:50:39 +02:00
# the test case
class LinePlotTestCase(unittest.TestCase):
def setUp(self):
self.ax = sea_level_predictor.draw_plot()
def test_plot_title(self):
actual = self.ax.get_title()
expected = "Rise in Sea Level"
self.assertEqual(actual, expected, "Expected line plot title to be 'Rise in Sea Level'")
def test_plot_labels(self):
actual = self.ax.get_xlabel()
expected = "Year"
self.assertEqual(actual, expected, "Expected line plot xlabel to be 'Year'")
actual = self.ax.get_ylabel()
expected = "Sea Level (inches)"
self.assertEqual(actual, expected, "Expected line plot ylabel to be 'Sea Level (inches)'")
actual = self.ax.get_xticks().tolist()
expected = [1850.0, 1875.0, 1900.0, 1925.0, 1950.0, 1975.0, 2000.0, 2025.0, 2050.0, 2075.0]
self.assertEqual(actual, expected, "Expected x tick labels to be '1850.0, 1875.0, 1900.0, 1925.0, 1950.0, 1975.0, 2000.0, 2025.0, 2050.0, 2075.0'")
def test_plot_data_points(self):
actual = self.ax.get_children()[0].get_offsets().data.tolist()
fixed test_plot_data_points and test_plot_lines (#3) * 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
2021-04-19 18:58:56 +02:00
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.")
init
2020-09-29 16:50:39 +02:00
def test_plot_lines(self):
actual = self.ax.get_lines()[0].get_ydata().tolist()
fixed test_plot_data_points and test_plot_lines (#3) * 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
2021-04-19 18:58:56 +02:00
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.")
init
2020-09-29 16:50:39 +02:00
actual = self.ax.get_lines()[1].get_ydata().tolist()
fixed test_plot_data_points and test_plot_lines (#3) * 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
2021-04-19 18:58:56 +02:00
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.")
init
2020-09-29 16:50:39 +02:00
if __name__ == "__main__":
unittest.main()
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