Fixed static files distribution with pip install

MANIFEST

@@ -1,2 +1 @@
-include methcomp/static/seg.csv
-include methcomp/static/seg600.png
+include methcomp/static/*

methcomp/glucose.py

@@ -1226,6 +1226,7 @@ def __init__(
         reference,
         test,
         units,
+        file_name,
         x_title,
         y_title,
         graph_title,
@@ -1238,6 +1239,7 @@ def __init__(
         self.reference: np.array = np.asarray(reference)
         self.test: np.array = np.asarray(test)
         self.units = units
+        self.file_name = file_name
         self.graph_title: str = graph_title
         self.x_title: str = x_title
         self.y_title: str = y_title
@@ -1284,7 +1286,7 @@ def _calc_error_score(self):
         _zones = []
         from . import static  # temporary fix
 
-        data = np.loadtxt(pkg_resources.open_text(static, "seg.csv"))
+        data = np.loadtxt(pkg_resources.open_text(static, self.file_name))
         _zones = np.array([data.T[int(p), int(t)] for p, t in zip(pred, ref)])
 
         return _zones
@@ -1424,6 +1426,7 @@ def seg(
     reference,
     test,
     units,
+    file_name,
     x_label=None,
     y_label=None,
     title=None,
@@ -1489,6 +1492,7 @@ def seg(
         reference,
         test,
         units,
+        file_name,
         x_label,
         y_label,
         title,
@@ -1510,7 +1514,7 @@ def seg(
     return ax
 
 
-def segscores(reference, test, units):
+def segscores(reference, test, units, file_name="seg.csv"):
     """Provides the raw error values as depicted by the
     surveillance error grid analysis for each point in the reference and test datasets.
 
@@ -1534,7 +1538,7 @@ def segscores(reference, test, units):
 
     # obtain zones from a Clarke reference object
     _zones = _SEG(
-        reference, test, units, None, None, None, None, None, None, None
+        reference, test, units, file_name, None, None, None, None, None, None, None
     )._calc_error_score()
 
     return _zones

setup.py

@@ -68,4 +68,7 @@
         packages=PACKAGES,
         classifiers=CLASSIFIERS,
         include_package_data=True,
+        package_data={
+            'methcomp': ['static/*'],  # Include all files in the static folder
+        },
     )

smoothen_seg.py

@@ -0,0 +1,17 @@
+import numpy as np
+import pandas as pd
+from scipy.ndimage import gaussian_filter
+
+# Step 1: Load the matrix from a CSV file
+matrix = pd.read_csv('methcomp/static/seg.csv', header=None, delimiter=' ', dtype=str)
+matrix = matrix.apply(pd.to_numeric, errors='coerce').fillna(0).values
+
+sigmas = [1.0, 2.0, 5.0]
+
+for sigma in sigmas:
+    # Step 2: Apply a Gaussian filter to smoothen the matrix
+    smoothed_matrix = gaussian_filter(matrix, sigma=sigma)
+
+    # Step 3: Save the smoothed matrix as a new CSV file
+    pd.DataFrame(smoothed_matrix).to_csv(f'methcomp/static/smoothed_seg_{sigma}.csv', index=False, header=False)
+