diff --git a/Config/config.json b/Config/config.json
index 28deddd..4013e18 100644
--- a/Config/config.json
+++ b/Config/config.json
@@ -1,10 +1,10 @@
 {
-    "Number_of_cycles": 1,
+    "Number_of_cycles": 30,
     "Amplitude_mm": 1,
     "Time_in_beam_s": 5,
     "Time_out_of_beam_s": 10,
     "Exposure_time": 0.0001,
-    "Img_Processing": 0,
-    "pixel_size_mu": 1.1,
+    "Img_Processing": 1,
+    "pixel_size_mu": 0.275,
     "long_time_interval": 50
 }
\ No newline at end of file
diff --git a/Config/measurement.json b/Config/measurement.json
index 149b814..0e184f0 100644
--- a/Config/measurement.json
+++ b/Config/measurement.json
@@ -1,4 +1,4 @@
-{"std_test_mov": [-1, 1],
-"std_test_wait": [4, 4],
+{"std_test_mov": [-10, 10],
+"std_test_wait": [10, 50],
 "backlsh_meas_mov":  [10, -10, -10, 10],
 "backlash_meas_wait": [1]}
\ No newline at end of file
diff --git a/Scripts/__pycache__/metrology_functions.cpython-313.pyc b/Scripts/__pycache__/metrology_functions.cpython-313.pyc
index d7a5390..ff34f02 100644
Binary files a/Scripts/__pycache__/metrology_functions.cpython-313.pyc and b/Scripts/__pycache__/metrology_functions.cpython-313.pyc differ
diff --git a/Scripts/metrology_functions.py b/Scripts/metrology_functions.py
index 9ed0d79..1ea3915 100644
--- a/Scripts/metrology_functions.py
+++ b/Scripts/metrology_functions.py
@@ -344,7 +344,7 @@ def run_repeatability_series(
         # add multithreading for simultanious movement of y and x axis
         for mov, wait in zip(x_coordinates, wait_x):
             axis1.moveRelative(mov)
-            axis4.moveRelative(wait)
+            axis4.moveRelative(mov)
             sleep(wait)
         start_pos_rbv = 4  # ????
         meas_pos_rbv = 5  # ????
diff --git a/data/data20250807_alignment_tests/20250807_164741_repeatibility_0/im_00000.tif b/data/data20250807_alignment_tests/20250807_164741_repeatibility_0/im_00000.tif
new file mode 100644
index 0000000..53c77fa
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_164741_repeatibility_0/im_00000.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_164741_repeatibility_0/repeatibility_0.dat b/data/data20250807_alignment_tests/20250807_164741_repeatibility_0/repeatibility_0.dat
new file mode 100644
index 0000000..0954644
--- /dev/null
+++ b/data/data20250807_alignment_tests/20250807_164741_repeatibility_0/repeatibility_0.dat
@@ -0,0 +1 @@
+      0           4.000000           5.000000      nan      nan 1754578076.625
diff --git a/data/data20250807_alignment_tests/20250807_171103_repeatibility_0/im_00000.tif b/data/data20250807_alignment_tests/20250807_171103_repeatibility_0/im_00000.tif
new file mode 100644
index 0000000..4775e4c
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171103_repeatibility_0/im_00000.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171103_repeatibility_0/repeatibility_0.dat b/data/data20250807_alignment_tests/20250807_171103_repeatibility_0/repeatibility_0.dat
new file mode 100644
index 0000000..4f93871
--- /dev/null
+++ b/data/data20250807_alignment_tests/20250807_171103_repeatibility_0/repeatibility_0.dat
@@ -0,0 +1 @@
+      0           4.000000           5.000000   38.672   31.960 1754579478.929
diff --git a/data/data20250807_alignment_tests/20250807_171648_repeatibility_0/im_00000.tif b/data/data20250807_alignment_tests/20250807_171648_repeatibility_0/im_00000.tif
new file mode 100644
index 0000000..984ef8c
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171648_repeatibility_0/im_00000.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171648_repeatibility_0/repeatibility_0.dat b/data/data20250807_alignment_tests/20250807_171648_repeatibility_0/repeatibility_0.dat
new file mode 100644
index 0000000..15925f7
--- /dev/null
+++ b/data/data20250807_alignment_tests/20250807_171648_repeatibility_0/repeatibility_0.dat
@@ -0,0 +1 @@
+      0           4.000000           5.000000   39.402  123.381 1754579835.605
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00000.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00000.tif
new file mode 100644
index 0000000..8e503f1
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00000.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00001.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00001.tif
new file mode 100644
index 0000000..d132b5d
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00001.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00002.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00002.tif
new file mode 100644
index 0000000..d277d0a
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00002.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00003.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00003.tif
new file mode 100644
index 0000000..3503966
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00003.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00004.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00004.tif
new file mode 100644
index 0000000..17a005c
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00004.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00005.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00005.tif
new file mode 100644
index 0000000..38f5b31
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00005.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00006.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00006.tif
new file mode 100644
index 0000000..32b62d5
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00006.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00007.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00007.tif
new file mode 100644
index 0000000..ddd6a1d
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00007.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00008.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00008.tif
new file mode 100644
index 0000000..f83243f
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00008.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00009.tif b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00009.tif
new file mode 100644
index 0000000..3b2e525
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/im_00009.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/repeatibility_0.dat b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/repeatibility_0.dat
new file mode 100644
index 0000000..2dc89bb
--- /dev/null
+++ b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/repeatibility_0.dat
@@ -0,0 +1,10 @@
+      0           4.000000           5.000000   39.602  123.560 1754579924.630
+      1           4.000000           5.000000   39.839  123.335 1754579947.566
+      2           4.000000           5.000000   39.524  123.444 1754579970.500
+      3           4.000000           5.000000   39.676  123.448 1754579993.441
+      4           4.000000           5.000000   39.463  123.454 1754580016.368
+      5           4.000000           5.000000   39.284  123.168 1754580039.294
+      6           4.000000           5.000000   39.014  123.363 1754580062.248
+      7           4.000000           5.000000   39.509  123.262 1754580085.189
+      8           4.000000           5.000000   39.648  123.327 1754580108.126
+      9           4.000000           5.000000   39.348  123.233 1754580131.038
diff --git a/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/repeatibility_0.pdf b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/repeatibility_0.pdf
new file mode 100644
index 0000000..c304452
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_171817_repeatibility_0/repeatibility_0.pdf differ
diff --git a/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/im_00000.tif b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/im_00000.tif
new file mode 100644
index 0000000..6f5ebb3
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/im_00000.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/im_00001.tif b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/im_00001.tif
new file mode 100644
index 0000000..9540e8c
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/im_00001.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/repeatibility_0.dat b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/repeatibility_0.dat
new file mode 100644
index 0000000..ee17099
--- /dev/null
+++ b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/repeatibility_0.dat
@@ -0,0 +1,2 @@
+      0           4.000000           5.000000   39.152  123.274 1754580250.833
+      1           4.000000           5.000000   39.252  123.275 1754580273.761
diff --git a/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/repeatibility_0.pdf b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/repeatibility_0.pdf
new file mode 100644
index 0000000..1603093
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_172343_repeatibility_0/repeatibility_0.pdf differ
diff --git a/data/data20250807_alignment_tests/20250807_172706_repeatibility_0/im_00000.tif b/data/data20250807_alignment_tests/20250807_172706_repeatibility_0/im_00000.tif
new file mode 100644
index 0000000..7b84883
Binary files /dev/null and b/data/data20250807_alignment_tests/20250807_172706_repeatibility_0/im_00000.tif differ
diff --git a/data/data20250807_alignment_tests/20250807_172706_repeatibility_0/repeatibility_0.dat b/data/data20250807_alignment_tests/20250807_172706_repeatibility_0/repeatibility_0.dat
new file mode 100644
index 0000000..faa3d73
--- /dev/null
+++ b/data/data20250807_alignment_tests/20250807_172706_repeatibility_0/repeatibility_0.dat
@@ -0,0 +1 @@
+      0           4.000000           5.000000  156.702  136.263 1754580454.989
diff --git a/notebooks/Merasurement.ipynb b/notebooks/Merasurement.ipynb
index 885f45b..f13ce84 100644
--- a/notebooks/Merasurement.ipynb
+++ b/notebooks/Merasurement.ipynb
@@ -46,8 +46,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:39.737165Z",
-     "start_time": "2025-08-07T14:46:39.717225Z"
+     "end_time": "2025-08-07T15:25:00.071051Z",
+     "start_time": "2025-08-07T15:25:00.054037Z"
     }
    },
    "cell_type": "code",
@@ -214,8 +214,8 @@
      "source_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.198976Z",
-     "start_time": "2025-08-07T14:46:39.752407Z"
+     "end_time": "2025-08-07T15:25:00.488266Z",
+     "start_time": "2025-08-07T15:25:00.114173Z"
     }
    },
    "source": [
@@ -441,7 +441,7 @@
       "Connect to PLC\n",
       "is_open()=True\n",
       "get_local_address()=None\n",
-      "read_device_info()=('Plc30 App', <pyads.structs.AdsVersion object at 0x000001B3EA4FCAD0>)\n",
+      "read_device_info()=('Plc30 App', <pyads.structs.AdsVersion object at 0x00000236A01E8980>)\n",
       "GVL_APP.nAXIS_NUM=4\n",
       "Constructor for axis\n",
       "Constructor for axis\n",
@@ -456,7 +456,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "83e3ccb968024d1f976fe04a2e06c5e1"
+       "model_id": "d497a6f1aab14aab9074c4d4f8ce73c7"
       }
      },
      "metadata": {},
@@ -470,7 +470,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "36fa96ce72024c42854b2393573c0b8b"
+       "model_id": "aac30b59dae9481d8b9550ccc855ed71"
       }
      },
      "metadata": {},
@@ -479,12 +479,12 @@
     {
      "data": {
       "text/plain": [
-       "BoundedIntText(value=1, description='Nr of cycles:', max=10000, min=1)"
+       "BoundedIntText(value=30, description='Nr of cycles:', max=10000, min=1)"
       ],
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "1e4b16cb6e034531a4596ee0ec6bece4"
+       "model_id": "a336dcea39f14b95ba23debcb8da0006"
       }
      },
      "metadata": {},
@@ -498,7 +498,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "c4b4b49dcd0347b78065cbf8630cee0e"
+       "model_id": "3dca11160d46474883605827a664d8f6"
       }
      },
      "metadata": {},
@@ -512,7 +512,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "04ed163cdaaa4c77bbef4b842afa3d92"
+       "model_id": "ea495b8693c94947a4c8a2052d131462"
       }
      },
      "metadata": {},
@@ -526,7 +526,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "f7645aa4bdec48e1a501eec8dd977227"
+       "model_id": "33397d619c05403dadffa51fffdc32f8"
       }
      },
      "metadata": {},
@@ -540,7 +540,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "f99b5722744a4a15b808af31ac866aa6"
+       "model_id": "c1c77f69024a4c289c76b7a2751fb8b8"
       }
      },
      "metadata": {},
@@ -554,7 +554,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "2ac3889f0cd84b6c8337d3476769ce34"
+       "model_id": "67ac0dacbbea433285ffb7757a53e7a9"
       }
      },
      "metadata": {},
@@ -568,7 +568,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "84d074dcdce6441fba8fef29c78cfe62"
+       "model_id": "4266725ef5ff433dbd9fb820b9bdd4e0"
       }
      },
      "metadata": {},
@@ -582,7 +582,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "81a8b334b03c4ba9913eefba17f63807"
+       "model_id": "6eb62c53809a45f981b049088b3d20b6"
       }
      },
      "metadata": {},
@@ -596,7 +596,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "edd1ca5338e24d98b9023287c9fa5266"
+       "model_id": "660676492735425b88f083ec2d7b5fec"
       }
      },
      "metadata": {},
@@ -610,7 +610,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "4e4f4c7fc3ab4314a7b372e14e84db2f"
+       "model_id": "ad23358f27db4520b4b68401e639bc1c"
       }
      },
      "metadata": {},
@@ -636,8 +636,8 @@
      "source_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.901706Z",
-     "start_time": "2025-08-07T14:46:40.217986Z"
+     "end_time": "2025-08-07T15:25:01.255274Z",
+     "start_time": "2025-08-07T15:25:00.701067Z"
     }
    },
    "source": [
@@ -657,20 +657,40 @@
      "text": [
       "Using daily folder: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250807_alignment_tests\n",
       "static repeatibility measurement folder\n",
-      "C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250807_alignment_tests\\20250807_164424_repeatibility_0\\repeatibility_0.dat\n"
+      "C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250807_alignment_tests\\20250807_172343_repeatibility_0\\repeatibility_0.dat\n",
+      "Repeatability results\n",
+      "---------------------\n",
+      "Direction   Peak2valley          RMS       1-sima      3-sigma\n",
+      "X              0.110000     0.055000     0.055000     0.165000\n",
+      "Y              0.001100     0.000550     0.000550     0.001650\n",
+      "\n"
      ]
     },
     {
-     "ename": "IndexError",
-     "evalue": "invalid index to scalar variable.",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[3]\u001B[39m\u001B[32m, line 8\u001B[39m\n\u001B[32m      6\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m config.get(\u001B[33m\"\u001B[39m\u001B[33mpixel_size_mu\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      7\u001B[39m \u001B[38;5;28mprint\u001B[39m(axis_data_file_path)\n\u001B[32m----> \u001B[39m\u001B[32m8\u001B[39m \u001B[43mmf\u001B[49m\u001B[43m.\u001B[49m\u001B[43manalyze_repeatability\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis_data_file_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43mget_pixel_size\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\Scripts\\metrology_functions.py:514\u001B[39m, in \u001B[36manalyze_repeatability\u001B[39m\u001B[34m(input_file, pixel_size, units)\u001B[39m\n\u001B[32m    511\u001B[39m neg_dir = start_pos > meas_pos\n\u001B[32m    512\u001B[39m bidir = np.any(pos_dir) \u001B[38;5;129;01mand\u001B[39;00m np.any(neg_dir)\n\u001B[32m--> \u001B[39m\u001B[32m514\u001B[39m t = ts - \u001B[43mts\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[32m    518\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mcalc_repeatability\u001B[39m(com_vals):\n\u001B[32m    520\u001B[39m     mean_val = np.mean(com_vals)\n",
-      "\u001B[31mIndexError\u001B[39m: invalid index to scalar variable."
+     "data": {
+      "text/plain": [
+       "<Figure size 600x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plot saved in C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250807_alignment_tests\\20250807_172343_repeatibility_0\\repeatibility_0.pdf\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "execution_count": 3
@@ -697,8 +717,8 @@
      "source_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.903770600Z",
-     "start_time": "2025-08-07T14:41:22.827290Z"
+     "end_time": "2025-08-07T15:25:01.543691Z",
+     "start_time": "2025-08-07T15:25:01.278908Z"
     }
    },
    "source": [
@@ -787,7 +807,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "dcf2a884722741068f737e9a723f02ee"
+       "model_id": "f3631cba5bc84d90862f0532578de82a"
       }
      },
      "metadata": {},
@@ -812,8 +832,8 @@
      "source_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.905159500Z",
-     "start_time": "2025-08-07T14:41:23.264721Z"
+     "end_time": "2025-08-07T15:25:01.901609Z",
+     "start_time": "2025-08-07T15:25:01.649916Z"
     }
    },
    "source": [
@@ -909,11 +929,7 @@
      "output_type": "stream",
      "text": [
       "Using daily folder: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250807_alignment_tests\n",
-      "static repeatibility measurement folder\n",
-      "no measruments on that day\n",
-      "going one day back\n",
-      "no measruments on that day\n",
-      "going one day back\n"
+      "static repeatibility measurement folder\n"
      ]
     },
     {
@@ -935,7 +951,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "b61fa533325641368acc4d107a124a0a"
+       "model_id": "eaa633d6b36d4eebabc5a44f3562ed41"
       }
      },
      "metadata": {},
@@ -965,8 +981,8 @@
      "source_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.905159500Z",
-     "start_time": "2025-08-07T14:41:23.620325Z"
+     "end_time": "2025-08-07T15:25:02.727453Z",
+     "start_time": "2025-08-07T15:25:01.939616Z"
     }
    },
    "source": [
@@ -1195,7 +1211,7 @@
      "source_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.906170200Z",
+     "end_time": "2025-08-07T15:25:02.746908300Z",
      "start_time": "2025-08-07T14:12:03.811306Z"
     }
    },
@@ -1627,7 +1643,7 @@
     },
     "tags": [],
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.906170200Z",
+     "end_time": "2025-08-07T15:25:02.748898600Z",
      "start_time": "2025-08-07T14:12:03.840798Z"
     }
    },
@@ -1663,7 +1679,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.907172Z",
+     "end_time": "2025-08-07T15:25:02.749926800Z",
      "start_time": "2025-08-07T14:12:03.861481Z"
     }
    },
@@ -1750,7 +1766,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-07T14:46:40.907172Z",
+     "end_time": "2025-08-07T15:25:02.749926800Z",
      "start_time": "2025-08-07T14:12:04.069811Z"
     }
    },