attempt to fix tests, remove unused cumulative function

hyswap/cumulative.py

@@ -69,7 +69,7 @@ def calculate_daily_cumulative_values(df, data_column_name,
     years = df['index_year'].unique()
 
     # make an empty dataframe to hold cumulative values for each year
-    cdf = pd.DataFrame([])
+    cdf = pd.DataFrame()
     selected_columns = [
         data_column_name,
         'index_month_day',
@@ -100,50 +100,3 @@ def calculate_daily_cumulative_values(df, data_column_name,
     cdf = cdf[['index_month_day', 'index_year', 'index_doy', 'cumulative']]
     return cdf
 
-
-def _tidy_cumulative_dataframe(cdf, year_type):
-    """Tidy a cumulative dataframe.
-
-    Parameters
-    ----------
-    cdf : pandas.DataFrame
-        DataFrame containing cumulative values, rows are years, columns are
-        days of year.
-    year_type : str
-        The type of year to use. Must be one of 'calendar', 'water', or
-        'climate'. Default is 'calendar' which starts the year on January 1
-        and ends on December 31. 'water' starts the year on October 1 and
-        ends on September 30 of the following year which is the "water year".
-        For example, October 1, 2010 to September 30, 2011 is "water year
-        2011". 'climate' years begin on April 1 and end on March 31 of the
-        following year, they are numbered by the ending year. For example,
-        April 1, 2010 to March 31, 2011 is "climate year 2011".
-
-    Returns
-    -------
-    cdf : pandas.DataFrame
-        DataFrame containing cumulative values, rows are dates, columns include
-        years, day of year (doy), and cumulative values.
-    """
-    # convert cdf to dataframe organized with full dates on the index
-    cdf2 = cdf.stack().reset_index()
-    cdf2.columns = ["index_year", "index_doy", "cumulative"]
-    # create date column
-    if year_type == "calendar":
-        cdf2["date"] = pd.to_datetime(
-            cdf2["index_year"].astype(str) + "-" +
-            cdf2["index_doy"].astype(str),
-            format="%Y-%j")
-    elif year_type == "water":
-        cdf2["date"] = pd.to_datetime(
-            cdf2["index_year"].astype(str) + "-" +
-            cdf2["index_doy"].astype(str),
-            format="%Y-%j") + pd.DateOffset(days=273)
-    elif year_type == "climate":
-        cdf2["date"] = pd.to_datetime(
-            cdf2["index_year"].astype(str) + "-" +
-            cdf2["index_doy"].astype(str),
-            format="%Y-%j") + pd.DateOffset(days=90)
-    # set date to index
-    cdf2 = cdf2.set_index("date")
-    return cdf2

hyswap/exceedance.py

@@ -39,20 +39,18 @@ def calculate_exceedance_probability_from_distribution(x, dist,
     distribution with a mean of 1 and a standard deviation of 0.25.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
-        >>> exceedance.calculate_exceedance_probability_from_distribution(
-        ...     1, 'lognormal', 1, 0.25)
-        0.6132049428659028
+        >>> np.round(exceedance.calculate_exceedance_probability_from_distribution(
+        ...     1, 'lognormal', 1, 0.25), 3).item()
+        0.613
 
     Calculating the exceedance probability of a value of 1 from a normal
     distribution with a mean of 1 and a standard deviation of 0.25.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
         >>> exceedance.calculate_exceedance_probability_from_distribution(
-        ...     1, 'normal', 1, 0.25)
+        ...     1, 'normal', 1, 0.25).item()
         0.5
     """
     # type check
@@ -119,20 +117,18 @@ def calculate_exceedance_probability_from_values(x, values_to_compare,
     of 1, 2, 3, and 4.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
         >>> exceedance.calculate_exceedance_probability_from_values(
-        ...     1, [1, 2, 3, 4], method='linear')
+        ...     1, [1, 2, 3, 4], method='linear').item()
         1.0
 
     Calculating the exceedance probability of a value of 5 from a set of values
     of 1, 2, 3, and 4.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
         >>> exceedance.calculate_exceedance_probability_from_values(
-        ...     5, [1, 2, 3, 4])
+        ...     5, [1, 2, 3, 4]).item()
         0.0
 
     Fetch some data from NWIS and calculate the exceedance probability

hyswap/percentiles.py

@@ -611,14 +611,13 @@ def calculate_fixed_percentile_from_value(value, percentile_df):
     Calculate the percentile associated with a value from some synthetic data.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
         >>> data = pd.DataFrame({'values': np.arange(1001),
         ...                      'date': pd.date_range('2020-01-01', '2022-09-27')}).set_index('date')  # noqa: E501
         >>> pcts_df = percentiles.calculate_fixed_percentile_thresholds(
         ...     data, 'values', percentiles=[5, 10, 25, 50, 75, 90, 95])
         >>> new_percentile = percentiles.calculate_fixed_percentile_from_value(
-        ...     500, pcts_df)
+        ...     500, pcts_df).item()
         >>> new_percentile
         50.0
 

hyswap/rasterhydrograph.py

@@ -62,12 +62,11 @@ def format_data(df, data_column_name, date_column_name=None,
     Formatting synthetic daily data for a raster hydrograph.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
         >>> df = pd.DataFrame({'date': pd.date_range('1/1/2010', '12/31/2010'),
         ...                    'data': np.random.rand(365)})
         >>> df_formatted = rasterhydrograph.format_data(df, 'data', 'date')
-        >>> df_formatted.index[0]
+        >>> df_formatted.index[0].item()
         2010
         >>> len(df_formatted.columns)
         365

hyswap/runoff.py

@@ -28,10 +28,9 @@ def convert_cfs_to_runoff(cfs, drainage_area, frequency="annual"):
     Convert 14 cfs to mm/yr for a 250 km2 drainage area.
 
     .. doctest::
-        :skipif: True  # docstrings test fails with np.float64
 
         >>> mmyr = runoff.convert_cfs_to_runoff(14, 250)
-        >>> np.round(mmyr)
+        >>> np.round(mmyr).item()
         50.0
     """
     # convert frequency string to value

tests/test_cumulative.py

@@ -4,23 +4,6 @@
 import pandas as pd
 from hyswap import cumulative
 
-
-def test_tidy_cumulative_dataframe():
-    """Test the _tidy_cumulative_dataframe function."""
-    # make a dataframe
-    cdf = pd.DataFrame(
-        index=np.arange(2000, 2003),
-        columns=np.arange(1, 367),
-        data=np.arange(1, 1099).reshape(3, 366))
-    # test the function
-    cdf = cumulative._tidy_cumulative_dataframe(cdf, 'calendar')
-    assert cdf.shape == (1098, 3)
-    assert cdf.columns.tolist() == ['index_year', 'index_doy', 'cumulative']
-    assert cdf.index.year.unique().tolist() == [2000, 2001, 2002, 2003]
-    assert cdf['index_doy'].tolist() == list(range(1, 367)) * 3
-    assert cdf['cumulative'].tolist() == list(range(1, 1099))
-
-
 class TestDailyCumulativeValues:
 
     def test_calculate_daily_cumulative_values(self):