make seperate fit_dist function

if pars are not return (e.g. with the norm distribution)

src/spei/si.py

@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
 
 from numpy import linspace, std
 from pandas import DatetimeIndex, Grouper, Series
@@ -51,35 +51,47 @@ def compute_si_ppf(
     for _, grval in dfval.groupby(Grouper(freq=inf_freq)):
         data = get_data_series(grval)
         if prob_zero:
-            cdf = compute_cdf_probzero(data=data.values, dist=dist)
+            p0 = (data == 0.0).sum() / len(data)
+            pars, loc, scale = fit_dist(data=data[data != 0.0], dist=dist)
+            cdf_sub = compute_cdf(data=data, dist=dist, loc=loc, scale=scale, pars=pars)
+            cdf = p0 + (1 - p0) * cdf_sub
+            cdf[data == 0.0] = p0
         else:
-            cdf = compute_cdf(data=data.values, dist=dist)
+            pars, loc, scale = fit_dist(data=data, dist=dist)
+            cdf = compute_cdf(data=data, dist=dist, loc=loc, scale=scale, pars=pars)
         ppf = norm.ppf(cdf)
         si.loc[data.index] = ppf
     return si
 
 
-def compute_cdf(
-    data: Union[Series, NDArrayFloat], dist: ContinuousDist
-) -> NDArrayFloat:
-    *pars, loc, scale = dist.fit(data, scale=std(data))
-    cdf = dist.cdf(data, pars, loc=loc, scale=scale)
-    return cdf
+def fit_dist(data: Union[Series, NDArrayFloat], dist: ContinuousDist) -> Tuple:
+    """Fit a Scipy Continuous Distribution"""
+    fit_tuple = dist.fit(data, scale=std(data))
+    if len(fit_tuple) == 2:
+        loc, scale = fit_tuple
+        pars = None
+    else:
+        *pars, loc, scale = fit_tuple
+    return pars, loc, scale
 
 
-def compute_cdf_probzero(
-    data: Union[Series, NDArrayFloat], dist: ContinuousDist
+def compute_cdf(
+    data: Union[Series, NDArrayFloat],
+    dist: ContinuousDist,
+    loc: float,
+    scale: float,
+    pars: Optional[Tuple[float]] = None,
 ) -> NDArrayFloat:
-    p0 = (data == 0.0).sum() / len(data)
-    *pars, loc, scale = dist.fit(data[data != 0.0], scale=std(data))
-    cdf_sub = dist.cdf(data, pars, loc=loc, scale=scale)
-    cdf = p0 + (1 - p0) * cdf_sub
-    cdf[data == 0.0] = p0
+    """Compute cumulative density function of a Scipy Continuous Distribution"""
+    if pars is not None:
+        cdf = dist.cdf(data, pars, loc=loc, scale=scale)
+    else:
+        cdf = dist.cdf(data, loc=loc, scale=scale)
     return cdf
 
 
 def compute_cdf_nsf(data: Union[Series, NDArrayFloat]) -> NDArrayFloat:
-    """Normal Scores Transform"""
+    """Compute cumulative density function using the Normal Scores Transform"""
     n = data.size
     cdf = linspace(1 / (2 * n), 1 - 1 / (2 * n), n)
     return cdf