Fixed an issue when calling the moving block bootstrap algorithm with 'method=OLS'.

toarstats/trends/interface.py

@@ -63,7 +63,7 @@ def calculate_trend(method, data, quantiles=None):
         if not all(0 < quantile < 1 for quantile in quantile_list):
             raise ValueError("The quantiles must be strictly between 0 and 1.")
     anomalies_series = calculate_anomalies(data_in)
-    if method == "quant_reg":
+    if method == "quant":
         fit = {
             quantile: quant_reg(anomalies_series, quantile)
             for quantile in quantile_list

toarstats/trends/ols.py

@@ -21,7 +21,7 @@ def ols(data):
     :return: The trend with its uncertainty and p value
     """
     fit = smf.ols("value~datetime", data).fit(method="qr").params
-    mbb = moving_block_bootstrap("OLS", data, quantile)
+    mbb = moving_block_bootstrap("OLS", data)
     fit_se = np.nanstd(mbb, axis=0)
     fit_pv = 2*scipy.stats.t.sf(x=abs(fit/fit_se), df=len(data)-2)
     return {"trend": fit, "uncertainty": fit_se, "p_value": fit_pv}

toarstats/trends/utils.py

@@ -42,13 +42,14 @@ def calculate_anomalies(data):
     }).sort_values("datetime")
 
 
-def moving_block_bootstrap(method, data, quantile, num_samples=1000):
+def moving_block_bootstrap(method, data, quantile=None, num_samples=1000):
     """Perform the moving block bootstrap algorithm.
 
     :param method: either ``"OLS"`` or ``"quant"``
     :param data: data containing a list of date time values and
                  associated parameter values
-    :param quantile: a single quantile, must be between 0 and 1
+    :param quantile: a single quantile, must be between 0 and 1 if
+                     ``method="quant"``
     :param num_samples: number of sampled trends
 
     :return: A list of sampled trends