regular backup

ToolFunction/ToolFunction.py

@@ -1,5 +1,6 @@
 import glob
 from datetime import date
+import copy
 
 import numpy as np
 from uncertainties import unumpy as unp
@@ -8,14 +9,37 @@ import xarray as xr
 
 
 def get_mask(dataArray):
+    """generate a bool mask array for given dataArray
+
+    :param dataArray: The given dataArray
+    :type dataArray: xarray DataArray
+    :return: the mask array
+    :rtype: numpy array of bool elements
+    """
     return np.ones(dataArray.shape, dtype=bool)
 
 
 def remove_bad_shots(dataArray, **kwargs):
+    """copy and remove bad shots from the dataArray
+
+    :param dataArray: The given dataArray
+    :type dataArray: xarray DataArray
+    :return: The dataArray after removement
+    :rtype: xarray DataArray
+    """
+    dataArray = copy.deepcopy(dataArray)
     dataArray.loc[dict(kwargs)] = np.nan
+    return dataArray
 
 
 def auto_rechunk(dataSet):
+    """rechunk the dataSet or dataArray using auto rechunk function
+
+    :param dataSet: The given dataArray or dataSet
+    :type dataSet: xarray DataArray or xarray DataSet
+    :return: The chuncked dataArray or dataSet
+    :rtype: xarray DataArray or xarray DataSet
+    """
     kwargs = {
         key: "auto"
         for key in dataSet.dims
@@ -24,6 +48,15 @@ def auto_rechunk(dataSet):
 
 
 def copy_chunk(dataSet, dataChunk):
+    """copy the chunk and apply to another dataArray or dataSet
+
+    :param dataSet: The dataArray or dataSet will be chunked
+    :type dataSet: xarray DataArray or xarray DataSet
+    :param dataChunk: The dataArray or dataSet giving the chunk
+    :type dataChunk: xarray DataArray or xarray DataSet
+    :return: The chuncked dataArray or dataSet
+    :rtype: xarray DataArray or xarray DataSet
+    """
     kwargs = {
         key: dataChunk.chunksizes[key]
         for key in dataChunk.chunksizes
@@ -33,6 +66,17 @@ def copy_chunk(dataSet, dataChunk):
 
 
 def get_h5_file_path(folderpath, maxFileNum=None, filename='*.h5',):
+    """_summary_
+
+    :param folderpath: _description_
+    :type folderpath: _type_
+    :param maxFileNum: _description_, defaults to None
+    :type maxFileNum: _type_, optional
+    :param filename: _description_, defaults to '*.h5'
+    :type filename: str, optional
+    :return: _description_
+    :rtype: _type_
+    """
     filepath = np.sort(glob.glob(folderpath + filename))
     if maxFileNum is None:
         return filepath

fit_test_20230703.ipynb

@@ -2033,6 +2033,13 @@
     "dataSet_cropOD.to_numpy()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,