save conf

Config/config.json

@@ -1,9 +1,9 @@
 {
-    "Number_of_cycles": 50,
+    "Number_of_cycles": 500,
     "Amplitude_mm": 1,
     "Time_in_beam_s": 5,
     "Time_out_of_beam_s": 10,
     "Exposure_time": 0.0004,
-    "Img_Processing": 1,
-    "pixel_size_mu": 0.275
+    "Img_Processing": 0,
+    "pixel_size_mu": 1.1
 }

Scripts/metrology_functions.py

@@ -39,6 +39,7 @@ from utils import get_datestr, get_timestr
 import ad
 import myutility as myu
 import cv2
+import shutil
 
 workdir = \
     os.path.expanduser(rf'C:\Users\berti_r\Python_Projects\StagePerformaceDocu\data\data{get_datestr()}_alignment_tests')
@@ -50,6 +51,9 @@ plc = mfl.plc('5.17.17.136.1.1', 852)
 plc.connect()
 axis1 = mfl.axis(plc, 1)
 #insert try catch later
+def get_pixel_size():
+    config = myu.load_object(config_path)
+    return config.get("pixel_size_mu")
 
 def init_nr_of_cycles():
     config = myu.load_object(config_path)
@@ -61,6 +65,8 @@ def init_exposure_time():
 def init_image_processing_yes_no():
     config = myu.load_object(config_path)
     return config.get("Img_Processing",0)
+def safe_meas_settings(save_path):
+    shutil.copy(save_path, config_path)
 
 def gaussian_2d(coords, amplitude, x0, y0, sigma_x, sigma_y, offset):
     x, y = coords
@@ -128,6 +134,7 @@ def run_repeatability_series(
 
     savedir = os.path.join(workdir,
         f'{get_timestr()}_repeatibility_{motor_pv_prefix}')
+    safe_meas_settings(savedir)
     savefile = os.path.join(savedir,
                             f'repeatibility_{motor_pv_prefix}.dat')
     os.makedirs(savedir)
@@ -159,7 +166,22 @@ def run_repeatability_series(
         start_pos_rbv = 4 #????
         meas_pos_rbv = 5  #????
 #---------------------------------------------capture------------------------------------------
-        com_x, com_y = aquire_avg(camera)
+        x_array = []
+        y_array = []
+        for nr_img in range(10):
+            sleep(0.1)
+            im = camera.get_image()
+            sleep(0.1)
+
+            if (1 == init_image_processing_yes_no()):
+
+                com_x_tmp, com_y_tmp = __process_img(im)
+            else:
+                com_x_tmp, com_y_tmp = image_center_of_mass(im, plot=False, verbose=False)
+            x_array.append(com_x_tmp)
+            y_array.append(com_y_tmp)
+        com_x = np.average(x_array)
+        com_y = np.average(y_array)
 
         data_str = " {:6d} {:18f} {:18f} {:8.3f} {:8.3f} {:14.3f}\n".format(
                 i, start_pos_rbv, meas_pos_rbv, com_x, com_y, time.time())
@@ -328,7 +350,7 @@ def image_test(motor_on_off=0):
     # Create a new figure and axis
     fig, ax = plt.subplots()
     ax.imshow(im)
-    ax.set_title(f"Center of Mass @{x:.2f}, {y:.2f}")
+    ax.set_title(f"Center of Mass @{x*get_pixel_size():.2f}, {y*get_pixel_size():.2f}")
     ax.scatter([x], [y], c='red', s=50)
     ax.axis('off')
     plt.show()
@@ -345,7 +367,7 @@ def camera_thread_function( img_queue):
 
     camera.start()
     start_time = time.time()
-    for i in range(100):
+    for i in range(10):
         img_queue.put((camera.get_image(),time.time()))
     stop_time = time.time()
     elapsed_time = stop_time - start_time
@@ -361,6 +383,7 @@ def static_test(motor_on_off=0):
 
     savedir = os.path.join(workdir,
         f'{get_timestr()}_static_{motor_on_off}')
+    safe_meas_settings(savedir)
     savefile = os.path.join(savedir,
                             f'static_{motor_on_off}.dat')
     os.makedirs(savedir)

data/data20250717_alignment_tests/20250717_151233_repeatibility_0/repeatibility_0.dat

@@ -0,0 +1 @@
+      0           4.000000           5.000000  101.519  172.972 1752757974.096

data/data20250717_alignment_tests/20250717_151520_repeatibility_0/repeatibility_0.dat

@@ -0,0 +1,50 @@
+      0           4.000000           5.000000  101.454  173.133 1752758141.111
+      1           4.000000           5.000000  101.673  173.077 1752758159.189
+      2           4.000000           5.000000  100.577  172.981 1752758177.195
+      3           4.000000           5.000000  101.014  172.910 1752758195.266
+      4           4.000000           5.000000  101.455  173.074 1752758213.312
+      5           4.000000           5.000000  101.057  173.065 1752758231.329
+      6           4.000000           5.000000  101.485  173.020 1752758249.341
+      7           4.000000           5.000000  101.700  173.063 1752758267.372
+      8           4.000000           5.000000  101.512  173.075 1752758285.437
+      9           4.000000           5.000000  101.605  173.223 1752758303.492
+     10           4.000000           5.000000  101.965  173.162 1752758321.501
+     11           4.000000           5.000000  102.299  173.142 1752758339.528
+     12           4.000000           5.000000  101.873  173.142 1752758357.569
+     13           4.000000           5.000000  101.823  172.996 1752758375.605
+     14           4.000000           5.000000  101.942  172.953 1752758393.624
+     15           4.000000           5.000000  101.893  172.895 1752758411.611
+     16           4.000000           5.000000  101.894  172.932 1752758429.632
+     17           4.000000           5.000000  102.235  172.788 1752758447.690
+     18           4.000000           5.000000  102.158  172.769 1752758465.716
+     19           4.000000           5.000000  101.608  172.562 1752758483.702
+     20           4.000000           5.000000  101.523  172.496 1752758501.707
+     21           4.000000           5.000000  101.612  172.602 1752758519.713
+     22           4.000000           5.000000  101.627  172.491 1752758537.682
+     23           4.000000           5.000000  101.670  172.415 1752758555.651
+     24           4.000000           5.000000  101.728  172.319 1752758573.627
+     25           4.000000           5.000000  102.157  172.250 1752758591.710
+     26           4.000000           5.000000  102.035  172.325 1752758609.994
+     27           4.000000           5.000000  102.151  172.089 1752758628.014
+     28           4.000000           5.000000  101.936  172.048 1752758646.023
+     29           4.000000           5.000000  102.097  172.066 1752758664.076
+     30           4.000000           5.000000  102.551  171.946 1752758682.350
+     31           4.000000           5.000000  102.265  171.850 1752758700.392
+     32           4.000000           5.000000  102.179  172.031 1752758718.400
+     33           4.000000           5.000000  102.205  172.001 1752758736.636
+     34           4.000000           5.000000  102.488  171.891 1752758754.596
+     35           4.000000           5.000000  102.373  171.817 1752758772.816
+     36           4.000000           5.000000  101.992  171.735 1752758790.769
+     37           4.000000           5.000000  101.865  171.697 1752758808.794
+     38           4.000000           5.000000  102.280  171.700 1752758826.811
+     39           4.000000           5.000000  102.427  171.533 1752758844.783
+     40           4.000000           5.000000  102.449  171.462 1752758862.897
+     41           4.000000           5.000000  102.432  171.340 1752758880.888
+     42           4.000000           5.000000  101.798  171.300 1752758899.091
+     43           4.000000           5.000000  101.118  171.156 1752758917.287
+     44           4.000000           5.000000  101.724  170.839 1752758935.313
+     45           4.000000           5.000000  102.012  170.879 1752758953.363
+     46           4.000000           5.000000  102.281  170.488 1752758971.446
+     47           4.000000           5.000000  102.079  170.657 1752758989.591
+     48           4.000000           5.000000  102.303  170.552 1752759007.690
+     49           4.000000           5.000000  101.703  170.506 1752759025.814

notebooks/Untitled.ipynb

@@ -98,6 +98,54 @@
     }
    ],
    "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-17T13:46:01.465152Z",
+     "start_time": "2025-07-17T13:46:01.461007Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def pool_average_1d(data, pool_size=10):\n",
+    "    data = np.asarray(data)\n",
+    "    remainder = len(data) % pool_size\n",
+    "\n",
+    "    if remainder != 0:\n",
+    "        # Truncate the extra values that don't fit into a full block\n",
+    "        data = data[:len(data) - remainder]\n",
+    "\n",
+    "    # Reshape and average\n",
+    "    pooled = data.reshape(-1, pool_size).mean(axis=1)\n",
+    "    return pooled\n",
+    "\n",
+    "# Example usage\n",
+    "data = np.arange(103)  # 1D array with 103 elements\n",
+    "pooled = pool_average_1d(data, pool_size=10)\n",
+    "\n",
+    "print(\"Original:\", data)\n",
+    "print(\"Pooled:\", pooled)\n"
+   ],
+   "id": "5824fc6dfbfacfdc",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
+      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
+      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
+      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
+      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
+      "  90  91  92  93  94  95  96  97  98  99 100 101 102]\n",
+      "Pooled: [ 4.5 14.5 24.5 34.5 44.5 54.5 64.5 74.5 84.5 94.5]\n"
+     ]
+    }
+   ],
+   "execution_count": 1
   }
  ],
  "metadata": {

notebooks/controller_improvements_log.csv

@@ -17,3 +17,4 @@ timestamp,description,measurement_id,notes
 2025-07-16 16:01:07.665920,Tuned PID gains for better damping,meas_20250710_120000,Observed 30% overshoot reduction
 2025-07-16 16:07:33.382085,Tuned PID gains for better damping,meas_20250710_120000,Observed 30% overshoot reduction
 2025-07-16 17:25:52.318083,Tuned PID gains for better damping,meas_20250710_120000,Observed 30% overshoot reduction
+2025-07-17 15:43:44.147022,Tuned PID gains for better damping,meas_20250710_120000,Observed 30% overshoot reduction

notebooks/sample.ipynb

@@ -26,7 +26,15 @@
     "\n",
     "## Improved image prcesing for optical position measurement\n",
     "\n",
-    "I found that the lense flair effects the center of mass considerably. To fix that I implemented a threshhold. this introduces an undesiarable descritisation effect sinc pixels suddenly get added remove when passing the threshhold, another option would be to ron a gradient decent optimizer at a lover exposure time with som gausian blur for preprocesing. this would work great under the assumption the intencity distribution gausian\n",
+    "| Aproach                             | Result                                                           |\n",
+    "|-------------------------------------|------------------------------------------------------------------|\n",
+    "| Center of mass on raw image         | heavily influenced by lensflare                                  |\n",
+    "| Treshhold -> CM                     | Not influenced by lenseflare but introduced discretisation error |\n",
+    "| Gausian Blur -> Threshhold -> CM    | Not influenced by lenseflare but introduced discretisation error |\n",
+    "| Gausian Blur -> Upscale 3d > TH >CM | Bad                                                              |\n",
+    "| 5                                   | Base                                                             |\n",
+    "\n",
+    "\n",
     "\n",
     "|                  Befor                   |                  After                   |\n",
     "|:----------------------------------------:|:----------------------------------------:|\n",
@@ -79,6 +87,10 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-07-17T14:13:34.725425Z",
+     "start_time": "2025-07-17T14:13:34.499524Z"
     }
    },
    "source": [
@@ -287,8 +299,195 @@
     "\n",
     "\n"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Scripts\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Config\\config.json\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Config\\config.json\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\templates\\motion_libs\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Config\\measurement.json\n",
+      "Constructor for PLC\n",
+      "Connect to PLC\n",
+      "is_open()=True\n",
+      "get_local_address()=None\n",
+      "read_device_info()=('Plc30 App', <pyads.structs.AdsVersion object at 0x000001E1DC595550>)\n",
+      "GVL_APP.nAXIS_NUM=3\n",
+      "Constructor for axis\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Dropdown(description='Test Type:', options=('Image Test', 'Repeatability Test', 'Static Test'), value='Image T…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "24e6d309a6fa4bf08355a4d9b16c5140"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c7afe3bdcf394f1d867c3330e0203656"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "BoundedIntText(value=500, description='Nr of cycles:', max=1000, min=1)"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "674c2ad7cc27438dbb50381677c4460b"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "3a7767c1f7c0403a90815507626cb7d4"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(value='0.0004', description='Exposure [s]:')"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "d34235c2d58a42a6a07c6c7c83553e4a"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c58b963fbf24430e86e5a41e792940e0"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ToggleButton(value=False, description='Processing', tooltip='Toggle processing on/off')"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "d2935b4152c343c0af0327dbfbc9a318"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c4007ab71ec94cd682c3eb9a7a089eff"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Button(description='Set exposure time', style=ButtonStyle())"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "de9fd9d529d4439f86053c7cd04f764a"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "51f5422cd751446ebd8a43237487e3f8"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Button(description='Start Measurement', style=ButtonStyle())"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "a6aecc0a05de4ccb8836e160d385376d"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "748a252c15184b26bb7202e02156790a"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -557,10 +756,28 @@
     "#mf.analyze_repeatability(axis_data_file_path_1,1.1)\n",
     "\n",
     "\n",
-    "x_vals, y_vals, times = myu.load_xy_data(axis_data_file_path_1)\n",
+    "def pool_average_1d(data, pool_size=10):\n",
+    "    data = np.asarray(data)\n",
+    "    remainder = len(data) % pool_size\n",
+    "\n",
+    "    if remainder != 0:\n",
+    "        # Truncate the extra values that don't fit into a full block\n",
+    "        data = data[:len(data) - remainder]\n",
+    "\n",
+    "    # Reshape and average\n",
+    "    pooled = data.reshape(-1, pool_size).mean(axis=1)\n",
+    "    return pooled\n",
+    "\n",
+    "x_vals1, y_vals1, times1 = myu.load_xy_data(axis_data_file_path_1)\n",
+    "times = times1[:10]\n",
+    "x_vals = pool_average_1d(x_vals1)\n",
+    "y_vals = pool_average_1d(y_vals1)\n",
     "x_vals = x_vals*get_pixel_size()\n",
     "y_vals = y_vals*get_pixel_size()\n",
     "\n",
+    "\n",
+    "\n",
+    "\n",
     "#Calc statistics\n",
     "rms_x = np.sqrt(np.mean(np.square(x_vals)))\n",
     "rms_y = np.sqrt(np.mean(np.square(y_vals)))\n",
@@ -631,8 +848,8 @@
     "    line_x.set_data(times[ind_min:ind_max:step], x_vals[ind_min:ind_max:step])\n",
     "    line_y.set_data(times[ind_min:ind_max:step], y_vals[ind_min:ind_max:step])\n",
     "\n",
-    "    ax1.relim()\n",
-    "    ax2.relim()\n",
+    "    #ax1.relim()\n",
+    "    #ax2.relim()\n",
     "    ax1.autoscale_view()\n",
     "    ax2.autoscale_view()\n",
     "    fig_static.canvas.draw_idle()\n",