clean up camel case names

wqio/bootstrap.py

@@ -13,7 +13,7 @@
     "BootstrappedFitEstimate", ["xhat", "yhat", "lower", "upper", "xlog", "ylog"]
 )
 
-__all__ = ["BCA", "percentile"]
+__all__ = ["BCA", "percentile", "fit"]
 
 
 def _acceleration(data):

wqio/features.py

@@ -590,7 +590,7 @@ def probplot(
             ax.set_yticklabels([])
 
         if rotateticklabels:
-            viz.rotateTickLabels(ax, 45, "x", ha="right")
+            viz.rotate_tick_labels(ax, 45, "x", ha="right")
 
         if bestfit:
             utils.fit_line()
@@ -868,7 +868,7 @@ def exclude(self):
     def medianCIsOverlap(self):
         overlap = True
         if self.influent.hasData and self.effluent.hasData:
-            overlap = utils.checkIntervalOverlap(
+            overlap = utils.check_interval_overlap(
                 self.influent.median_conf_interval,
                 self.effluent.median_conf_interval,
                 oneway=False,
@@ -1302,7 +1302,7 @@ def probplot(
             ax.set_ylabel(ylabel)
 
         if rotateticklabels:
-            viz.rotateTickLabels(ax, 45, "x")
+            viz.rotate_tick_labels(ax, 45, "x")
 
         return fig
 
@@ -1552,7 +1552,7 @@ def scatterplot(
 
         # include the line of equality, if requested
         if one2one:
-            viz.one2one(
+            viz.one2one_line(
                 ax,
                 linestyle="-",
                 linewidth=1.25,
@@ -1601,8 +1601,8 @@ def scatterplot(
 
                 ax.annotate(
                     r"$\log(y) = {} \, \log(x) + {}$".format(
-                        utils.sigFigs(modelres.params[1], n=3),
-                        utils.sigFigs(modelres.params[0], n=3),
+                        utils.sig_figs(modelres.params[1], n=3),
+                        utils.sig_figs(modelres.params[0], n=3),
                     ),
                     (txt_x, txt_y),
                     xycoords="axes fraction",

wqio/hydro.py

@@ -197,7 +197,7 @@ def __init__(
         # tease out start/stop info
         self.start = self.data.index[0]
         self.end = self.data.index[-1]
-        self._season = utils.getSeason(self.start)
+        self._season = utils.get_season(self.start)
 
         # storm duration (hours)
         duration = self.end - self.start

wqio/ros.py

@@ -582,7 +582,13 @@ def ROS(
         output = df[[result, censorship]].assign(final=df[result])
 
     # normal ROS stuff
-    elif is_valid_to_ros(df, censorship, as_obj=False):
+    elif is_valid_to_ros(
+        df,
+        censorship,
+        max_fraction_censored=max_fraction_censored,
+        min_uncensored=min_uncensored,
+        as_obj=False,
+    ):
         output = _do_ros(
             df,
             result,

wqio/samples.py

@@ -103,7 +103,7 @@ def __init__(
         self._markersize = None
         self._linestyle = None
         self._yfactor = None
-        self._season = utils.getSeason(self.starttime)
+        self._season = utils.get_season(self.starttime)
         self.storm = storm
 
     @property

wqio/tests/test_viz.py

@@ -605,7 +605,7 @@ def test_rotateTickLabels_xaxis():
     fig, ax = pyplot.subplots()
     ax.set_xticks([1, 2, 3])
     ax.set_xticklabels(["AAA", "BBB", "CCC"])
-    viz.rotateTickLabels(ax, 60, "x")
+    viz.rotate_tick_labels(ax, 60, "x")
     return fig
 
 
@@ -614,7 +614,7 @@ def test_rotateTickLabels_yaxis():
     fig, ax = pyplot.subplots()
     ax.set_yticks([1, 2, 3])
     ax.set_yticklabels(["AAA", "BBB", "CCC"])
-    viz.rotateTickLabels(ax, -30, "y")
+    viz.rotate_tick_labels(ax, -30, "y")
     return fig
 
 
@@ -626,7 +626,7 @@ def test_rotateTickLabels_both():
 
     ax.set_yticks([1, 2, 3])
     ax.set_yticklabels(["AAA", "BBB", "CCC"])
-    viz.rotateTickLabels(ax, 45, "both")
+    viz.rotate_tick_labels(ax, 45, "both")
     return fig
 
 
@@ -692,7 +692,7 @@ def test_one2one():
     fig, ax = pyplot.subplots()
     ax.set_xlim([-2, 5])
     ax.set_ylim([-3, 3])
-    viz.one2one(ax, label="Equality", lw=5, ls="--")
+    viz.one2one_line(ax, label="Equality", lw=5, ls="--")
     ax.legend()
     return fig
 

wqio/tests/utils_tests/test_dateutils.py

@@ -1,11 +1,11 @@
-from datetime import datetime
 import warnings
+from datetime import datetime
 
-import pytest
 import pandas
+import pytest
 
-from wqio.utils import dateutils
 from wqio.tests import helpers
+from wqio.utils import dateutils
 
 
 @pytest.mark.parametrize(
@@ -22,7 +22,7 @@
 )
 def test_getSeason(datemaker, datestring, expected):
     date = datemaker(datestring)
-    season = dateutils.getSeason(date)
+    season = dateutils.get_season(date)
     assert season == expected
 
 
@@ -45,21 +45,21 @@ def test_getSeason(datemaker, datestring, expected):
 def test_makeTimestamp_basic(row, expected, error):
     with warnings.catch_warnings(), helpers.raises(error):
         warnings.simplefilter("always")
-        tstamp = dateutils.makeTimestamp(row)
+        tstamp = dateutils.make_timestamp(row)
         assert tstamp == pandas.Timestamp(expected)
 
 
 def test_makeTimestamp_customcols():
     row = {"mydate": "2012-05-25", "mytime": "16:54"}
-    tstamp = dateutils.makeTimestamp(row, datecol="mydate", timecol="mytime")
+    tstamp = dateutils.make_timestamp(row, datecol="mydate", timecol="mytime")
     assert tstamp == pandas.Timestamp("2012-05-25 16:54")
 
 
 @pytest.mark.parametrize(
     "datemaker", [pandas.Timestamp, lambda x: datetime.strptime(x, "%Y-%m-%d")]
 )
 @pytest.mark.parametrize("datestring", ["2005-10-02", "2006-09-02"])
-def test_getWaterYear(datemaker, datestring):
+def test_get_wateryear(datemaker, datestring):
     date = datemaker(datestring)
-    wateryear = dateutils.getWaterYear(date)
+    wateryear = dateutils.get_wateryear(date)
     assert wateryear == "2005/2006"

wqio/tests/utils_tests/test_numutils.py

@@ -43,9 +43,9 @@
         (numpy.inf * -1, 3, False, False, False, "NA", None),
     ],
 )
-def test_sigFigs(value, N, tex, forceint, pval, expected, error):
+def test_sig_figs(value, N, tex, forceint, pval, expected, error):
     with helpers.raises(error):
-        result = numutils.sigFigs(value, N, tex=tex, forceint=forceint, pval=pval)
+        result = numutils.sig_figs(value, N, tex=tex, forceint=forceint, pval=pval)
         assert result == expected
 
 
@@ -58,8 +58,8 @@ def test_sigFigs(value, N, tex, forceint, pval, expected, error):
         (0.00257, "=", 2, "=0.0026"),
     ],
 )
-def test_formatResult(num, qual, N, expected):
-    assert numutils.formatResult(num, qual, N) == expected
+def test_format_result(num, qual, N, expected):
+    assert numutils.format_result(num, qual, N) == expected
 
 
 @pytest.mark.parametrize(
@@ -143,7 +143,7 @@ def test_processAndersonDarlingResults(which):
 
     expected = {"bad": "<85.0%", "good": "99.0%"}
 
-    assert numutils.processAndersonDarlingResults(ARs[which]) == expected[which]
+    assert numutils.process_AD_result(ARs[which]) == expected[which]
 
 
 @pytest.mark.parametrize(
@@ -287,7 +287,7 @@ def test_normalize_units_bad_conversion(units_norm_data):
 )
 def test_test_pH2concentration(pH, expected, error):
     with helpers.raises(error):
-        assert abs(numutils.pH2concentration(pH) - expected) < 0.0001
+        assert abs(numutils.pH_to_concentration(pH) - expected) < 0.0001
 
 
 @helpers.seed
@@ -501,9 +501,9 @@ def test_fit_line_with_xhat(fit_data):
 )
 def test_checkIntervalOverlap_single(oneway, expected):
     result = [
-        numutils.checkIntervalOverlap([1, 2], [3, 4], oneway=oneway),
-        numutils.checkIntervalOverlap([1, 4], [2, 3], oneway=oneway),
-        numutils.checkIntervalOverlap([1, 3], [2, 4], oneway=oneway),
+        numutils.check_interval_overlap([1, 2], [3, 4], oneway=oneway),
+        numutils.check_interval_overlap([1, 4], [2, 3], oneway=oneway),
+        numutils.check_interval_overlap([1, 3], [2, 4], oneway=oneway),
     ]
     assert result == expected
 
@@ -513,7 +513,7 @@ def test_checkIntervalOverlap(oneway, expected):
     x = numpy.array([[1, 2], [1, 4], [1, 3]])
     y = numpy.array([[3, 4], [2, 3], [2, 4]])
     nptest.assert_array_equal(
-        numutils.checkIntervalOverlap(x, y, oneway=oneway, axis=1),
+        numutils.check_interval_overlap(x, y, oneway=oneway, axis=1),
         numpy.array(expected, dtype=bool),
     )
 

wqio/utils/dateutils.py

@@ -9,7 +9,7 @@
 _logger = logging.getLogger(__name__)
 
 
-def getSeason(date):
+def get_season(date):
     """Defines the season from a given date.
 
     Parameters
@@ -50,7 +50,7 @@ def getSeason(date):
     return season
 
 
-def makeTimestamp(row, datecol="sampledate", timecol="sampletime", issuewarnings=False):
+def make_timestamp(row, datecol="sampledate", timecol="sampletime", issuewarnings=False):
     """Makes a pandas.Timestamp from separate date/time columns
 
     Parameters
@@ -114,7 +114,7 @@ def makeTimestamp(row, datecol="sampledate", timecol="sampletime", issuewarnings
     return tstamp
 
 
-def getWaterYear(date):
+def get_wateryear(date):
     """Returns the water year of a given date
 
     Parameters
@@ -132,7 +132,7 @@ def getWaterYear(date):
     >>> import datetime
     >>> import wqio
     >>> x = datetime.datetime(2005, 11, 2)
-    >>> print(wqio.utils.getWaterYear(x))
+    >>> print(wqio.utils.get_wateryear(x))
     2005/2006
 
     """

wqio/utils/numutils.py

@@ -13,7 +13,7 @@
 TheilStats = namedtuple("TheilStats", ("slope", "intercept", "low_slope", "high_slope"))
 
 
-def sigFigs(x, n, expthresh=5, tex=False, pval=False, forceint=False):
+def sig_figs(x, n, expthresh=5, tex=False, pval=False, forceint=False):
     """Formats a number with the correct number of sig figs.
 
     Parameters
@@ -41,10 +41,10 @@ def sigFigs(x, n, expthresh=5, tex=False, pval=False, forceint=False):
 
     Examples
     --------
-    >>> print(sigFigs(1247.15, 3))
+    >>> print(sig_figs(1247.15, 3))
     1,250
 
-    >>> print(sigFigs(1247.15, 7))
+    >>> print(sig_figs(1247.15, 7))
     1,247.150
 
     """
@@ -54,7 +54,7 @@ def sigFigs(x, n, expthresh=5, tex=False, pval=False, forceint=False):
 
     # check on the number provided
     elif x is not None and not numpy.isinf(x) and not numpy.isnan(x):
-        # check on the sigFigs
+        # check on the sig_figs
         if n < 1:
             raise ValueError("number of sig figs must be greater than zero!")
 
@@ -99,7 +99,7 @@ def sigFigs(x, n, expthresh=5, tex=False, pval=False, forceint=False):
     return out
 
 
-def formatResult(result, qualifier, sigfigs=3):
+def format_result(result, qualifier, sigfigs=3):
     """Formats a results with its qualifier
 
     Parameters
@@ -118,12 +118,12 @@ def formatResult(result, qualifier, sigfigs=3):
 
     Example
     -------
-    >>> formatResult(1.23, '<', sigfigs=4)
+    >>> format_result(1.23, '<', sigfigs=4)
     '<1.230'
 
     """
 
-    return f"{qualifier}{sigFigs(result, sigfigs)}"
+    return f"{qualifier}{sig_figs(result, sigfigs)}"
 
 
 def process_p_vals(pval):
@@ -145,7 +145,7 @@ def process_p_vals(pval):
     if pval is None:
         out = "NA"
     elif 0 < pval < 0.001:
-        out = formatResult(0.001, "<", sigfigs=1)
+        out = format_result(0.001, "<", sigfigs=1)
     elif pval > 1 or pval < 0:
         raise ValueError(f"p-values must be between 0 and 1 (not {pval})")
     else:
@@ -220,7 +220,7 @@ def anderson_darling(data):
     return ADResult(**values)
 
 
-def processAndersonDarlingResults(ad_results):
+def process_AD_result(ad_result):
     """Return a nice string of Anderson-Darling test results
 
     Parameters
@@ -235,7 +235,7 @@ def processAndersonDarlingResults(ad_results):
 
     """
 
-    AD, crit, sig = ad_results
+    AD, crit, sig = ad_result
     try:
         ci = 100 - sig[crit < AD][-1]
         return f"{ci:0.1f}%"
@@ -355,7 +355,7 @@ def normalize_units(
     return normalized
 
 
-def pH2concentration(pH, *args):
+def pH_to_concentration(pH, *args):
     """Converts pH values to proton concentrations in mg/L
 
     Parameters
@@ -528,7 +528,7 @@ def fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None, through_or
     return xhat, yhat, results
 
 
-def checkIntervalOverlap(interval1, interval2, oneway=False, axis=None):
+def check_interval_overlap(interval1, interval2, oneway=False, axis=None):
     """Checks if two numeric intervals overlaps.
 
     Parameters
@@ -560,7 +560,7 @@ def checkIntervalOverlap(interval1, interval2, oneway=False, axis=None):
     if oneway:
         return first_check
     else:
-        return first_check | checkIntervalOverlap(interval2, interval1, oneway=True, axis=axis)
+        return first_check | check_interval_overlap(interval2, interval1, oneway=True, axis=axis)
 
 
 def winsorize_dataframe(df, **limits):