STY: update pre-commit version and apply relevant fixes

.pre-commit-config.yaml

@@ -1,22 +1,23 @@
+exclude: '(v2)/.*'
 default_language_version:
     python: python3
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/ambv/black
-    rev: 23.1.0
+    rev: 24.4.2
     hooks:
       - id: black
   - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.6.1
+    rev: 0.7.1
     hooks:
       - id: nbstripout

pyCHX/Badpixels.py

@@ -1,4 +1,5 @@
 """Dev@Octo12,2017"""
+
 import numpy as np
 
 damaged_4Mpixel = np.array(

pyCHX/Create_Report.py

@@ -77,7 +77,6 @@ def add_image_string(
 
 
 class create_pdf_report(object):
-
     """Aug 16, YG@CHX-NSLS-II
      Create a pdf report by giving data_dir, uid, out_dir
      data_dir: the input data directory, including all necessary images

pyCHX/chx_Fitters2D.py

@@ -11,10 +11,7 @@ def gauss_func(x, xc, amp, sigma, baseline):
 
 
 def gauss2D_func(x, y, xc, amp, sigmax, yc, sigmay, baseline):
-    return (
-        amp * np.exp(-((x - xc) ** 2) / 2.0 / sigmax**2) * np.exp(-((y - yc) ** 2) / 2.0 / sigmay**2)
-        + baseline
-    )
+    return amp * np.exp(-((x - xc) ** 2) / 2.0 / sigmax**2) * np.exp(-((y - yc) ** 2) / 2.0 / sigmay**2) + baseline
 
 
 def extract_param(bestfits, key):
@@ -75,7 +72,7 @@ def __call__(self, x, y, vx, vy, **kwargs):
 
         # make the parameters from the kwargs
         for key in self.params.keys():
-            if key in kwargs.keys() and key is not "XY":
+            if key in kwargs.keys() and key != "XY":
                 params[key].value = kwargs[key]
             else:
                 # then guess
@@ -142,7 +139,7 @@ def guess(self, **kwargs):
 
         if kwargs is not None:
             for key in kwargs.keys():
-                if key in paramsdict and key is not "xy":
+                if key in paramsdict and key != "xy":
                     paramsdict[key] = kwargs[key]
 
         return paramsdict
@@ -189,7 +186,7 @@ def __call__(self, XY, img, **kwargs):
 
         # make the parameters from the kwargs
         for key in self.params.keys():
-            if key in kwargs.keys() and key is not "XY":
+            if key in kwargs.keys() and key != "XY":
                 params[key].value = kwargs[key]
             else:
                 # then guess
@@ -334,7 +331,7 @@ def guess(self, img, XY=None, **kwargs):
         paramsdict["sigmay"] = 1
         # print(  paramsdict )
         for key in kwargs.keys():
-            if key in paramsdict and key is not "xy":
+            if key in paramsdict and key != "xy":
                 paramsdict[key] = kwargs[key]
         # print(  paramsdict )
         return paramsdict

pyCHX/chx_correlationc.py

@@ -4,7 +4,6 @@
 This module is for computation of time correlation by using compressing algorithm
 """
 
-
 from __future__ import absolute_import, division, print_function
 
 import logging

pyCHX/chx_correlationp.py

@@ -3,6 +3,7 @@
 [email protected]
 This module is for parallel computation of time correlation
 """
+
 from __future__ import absolute_import, division, print_function
 
 import logging

pyCHX/chx_correlationp2.py

@@ -5,6 +5,7 @@
 Feb 20, 2018
 The chx_correlationp2 is for dedug g2
 """
+
 from __future__ import absolute_import, division, print_function
 
 import logging

pyCHX/chx_libs.py

@@ -3,6 +3,7 @@
 [email protected]
 This module is for the necessary packages for the XPCS analysis
 """
+
 ## Import all the required packages for  Data Analysis
 from databroker import Broker
 from databroker.assets.path_only_handlers import RawHandler

pyCHX/chx_speckle.py

@@ -681,9 +681,7 @@ def fit_xsvs1(
 
             # print ( rois )
             if func == "bn":
-                result = mod.fit(
-                    spe_cts_all[j, i][rois], bin_values=bin_edges[j, i][:-1][rois], K=5 * 2**j, M=12
-                )
+                result = mod.fit(spe_cts_all[j, i][rois], bin_values=bin_edges[j, i][:-1][rois], K=5 * 2**j, M=12)
             elif func == "gm":
                 result = mod.fit(
                     spe_cts_all[j, i][rois], bin_values=bin_edges[j, i][:-1][rois], K=K_mean[i] * 2**j, M=20

pyCHX/chx_specklecp.py

@@ -1778,9 +1778,7 @@ def fit_xsvs1(
 
             # print ( rois )
             if func == "bn":
-                result = mod.fit(
-                    spe_cts_all[j, i][rois], bin_values=bin_edges[j, i][:-1][rois], K=5 * 2**j, M=12
-                )
+                result = mod.fit(spe_cts_all[j, i][rois], bin_values=bin_edges[j, i][:-1][rois], K=5 * 2**j, M=12)
             elif func == "gm":
                 result = mod.fit(
                     spe_cts_all[j, i][rois], bin_values=bin_edges[j, i][:-1][rois], K=K_mean[i] * 2**j, M=20