implement method and classes for post-proc

Changes to be committed:
	modified:   src/pytom3d/scan.py
	modified:   src/pytom3d/stats.py
	modified:   src/pytom3d/util.py
	modified:   src/pytom3d/viewer.py -> add canvas contours and plots

src/pytom3d/scan.py

@@ -18,6 +18,30 @@ def __init__(self, **kwargs: Dict['str', Any]) -> None:
         except KeyError:
             self.name = "Untitled"
 
+        try:
+            self.color = kwargs.pop("color")
+        except KeyError:
+            self.color = "k"
+
+        try:
+            self.line = kwargs.pop("line")
+        except KeyError:
+            self.line = "-"
+
+        try:
+            self.alpha = kwargs.pop("alpha")
+        except KeyError:
+            self.alpha = 0.3
+
+        try:
+            self.err_bar = kwargs.pop("err_bar")
+        except KeyError:
+            self.err_bar = False
+
+    def config_aspect(self, color, line, alpha):
+        self.color = color
+        self.line = line
+        self.alpha = alpha
 
     def load_file(self, reader: callable, path: str, **kwargs: Dict['str', Any]) -> None:
         """
@@ -37,8 +61,7 @@ def load_file(self, reader: callable, path: str, **kwargs: Dict['str', Any]) ->
         self.y = data.iloc[:, 1].to_numpy()
         self.y_err = data.iloc[:, 2].to_numpy()
 
-
-    def load_data(self, x: np.ndarray, y: np.ndarray, y_err) -> None:
+    def load_data(self, x: np.ndarray, y: np.ndarray, y_err: np.ndarray = None) -> None:
         """
         Load data directly.
 
@@ -53,7 +76,7 @@ def load_data(self, x: np.ndarray, y: np.ndarray, y_err) -> None:
         """
         self.x = x
         self.y = y
-        self.y_err = y
+        self.y_err = y_err
 
 
 def export_line_scan(reader: callable, path: str, *scans: List, **kwargs:  Dict['str', Any]) -> None:

src/pytom3d/stats.py

@@ -2,7 +2,7 @@
 import numpy as np
 
 
-def running_mean(inp: List[int], out: int, up_to_id = None, *list_path: List[str]) -> np.ndarray:
+def running_mean(out: int, up_to_id = None, *list_path: List[str]) -> np.ndarray:
     """
     Calculate the running mean of specified columns across multiple files.
 
@@ -40,7 +40,7 @@ def running_mean(inp: List[int], out: int, up_to_id = None, *list_path: List[str
     return cumulative_sum/N
 
 
-def running_std(inp: List[int], out: int, up_to_id = None, ddof: int = 1, *list_path: List[str]) -> np.ndarray:
+def running_std(out: int, up_to_id = None, ddof: int = 1, *list_path: List[str]) -> np.ndarray:
     """
     Calculate the running standard deviation of specified columns across multiple files.
 
@@ -56,7 +56,7 @@ def running_std(inp: List[int], out: int, up_to_id = None, ddof: int = 1, *list_
         Index of the last file to include in the calculation. If None, all files are included. Default is None.
 
     ddof : int, optional
-        Delta degrees of freedom. Default is 1.
+        Degrees of freedom. Default is 1 (unbiased variance estimator).
 
     list_path : List[str]
         Variable number of file paths containing the data.
@@ -74,7 +74,7 @@ def running_std(inp: List[int], out: int, up_to_id = None, ddof: int = 1, *list_
     else:
         max_id = up_to_id + 1
 
-    mean = running_mean(inp, out, max_id - 1, *list_path)
+    mean = running_mean(out, max_id - 1, *list_path)
     for r in range(0, max_id):
         data = np.load(list_path[r])
         cumulative_sum += (data[:, out] - mean)**2

src/pytom3d/util.py

@@ -7,17 +7,7 @@
 import re
 from typing import Tuple, List, Any
 
-
-def summation(x, y):
-    return x+y
-
-
-def distance(x, y):
-    return (x**2+y**2)**0.5
-
-
-def distance2(x, y):
-    return (abs(2*x)+y**2)**0.5
+from pytom3d.stats import running_mean, running_std
 
 
 def export_regressor(regressor, folder: str = "./", filename: str = "my_regressor", extension: str = ".rg",
@@ -463,3 +453,55 @@ def wrapper(self, *args, **kwargs) -> None:
 
         self.history_.append(event)
     return wrapper
+
+
+def contour_data_wrapper(path: str, match: str) -> Tuple[np.ndarray]:
+    """
+    Wrapper function for generating contour data.
+
+    Parameters
+    ----------
+    path : str
+        Path to the directory containing data files.
+    match : str
+        A string used to match the desired data files.
+
+    Returns
+    -------
+    Tuple[np.ndarray]
+        A tuple containing the x-coordinates, y-coordinates, mean value, and standard deviation.
+
+    """
+    data = recursive_search(path, match=match, pop_first=True, take_first=False)
+
+    mean = running_mean(3, None, *data)
+    std = running_std(3, None, 1, *data)
+    x, y = get_coordinates([0], *data), get_coordinates([1], *data)
+
+    return x.reshape(-1), y.reshape(-1), mean, std
+
+
+def scan_data_wrapper(path: str, match: str) -> Tuple[np.ndarray]:
+    """
+    Wrapper function for generating scan data.
+
+    Parameters
+    ----------
+    path : str
+        Path to the directory containing data files.
+    match : str
+        A string used to match the desired data files.
+
+    Returns
+    -------
+    Tuple[np.ndarray]
+        A tuple containing the x-coordinates, mean value, and standard deviation.
+
+    """
+    data = recursive_search(path, match=match, pop_first=True, take_first=False)
+
+    mean = running_mean(3, None, *data)
+    std = running_std(3, None, 1, *data)
+    x = get_coordinates([0], *data)
+
+    return x.reshape(-1), mean, std