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berti_r
2025-07-18 11:09:57 +02:00
parent aecc9fe30e
commit 7e894a0628
21 changed files with 756 additions and 218 deletions
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4
Config/config.json
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@@ -1,9 +1,9 @@
{
"Number_of_cycles": 50,
"Number_of_cycles": 200,
"Amplitude_mm": 1,
"Time_in_beam_s": 5,
"Time_out_of_beam_s": 10,
"Exposure_time": 0.0004,
"Exposure_time": 0.0002,
"Img_Processing": 1,
"pixel_size_mu": 0.275
}

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Scripts/__pycache__/metrology_functions.cpython-313.pyc
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Scripts/__pycache__/myutility.cpython-313.pyc
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Scripts/metrology_functions.py
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@@ -8,6 +8,7 @@ import sys
import scipy.ndimage as spnd
import threading as th
from scipy.optimize import curve_fit
from skimage.io import imshow
#error chatchign and hard code catch stuff
@@ -82,25 +83,31 @@ def gaussian_2d(coords, amplitude, x0, y0, sigma_x, sigma_y, offset):
return amplitude * np.exp(
-(((x - x0) ** 2) / (2 * sigma_x ** 2) + ((y - y0) ** 2) / (2 * sigma_y ** 2))
) + offset
def compute_gradient(img, pos, h=1e-2):
def compute_gradient(img, pos):
x, y = pos
x_int, y_int = int(x), int(y)
# Ensure position is within bounds
# Ensure within bounds
if x_int <= 0 or x_int >= img.shape[1] - 1 or y_int <= 0 or y_int >= img.shape[0] - 1:
return np.array([0.0, 0.0])
# Use central difference
dx = (img[y_int, x_int + 1] - img[y_int, x_int - 1]) / 2
dy = (img[y_int + 1, x_int] - img[y_int - 1, x_int]) / 2
# Cast to float to avoid overflow
img = img.astype(np.float32)
dx = (img[y_int, x_int + 1] - img[y_int, x_int - 1]) / 2.0
dy = (img[y_int + 1, x_int] - img[y_int - 1, x_int]) / 2.0
return np.array([dx, dy])
def gradient_ascent(img, start_pos, lr=0.8, max_iter=1000, tol=1e-4):
def gradient_ascent(img, start_pos, lr=0.8, max_iter=1000, tol=1e-30):
pos = np.array(start_pos, dtype=float)
path = [tuple(pos)]
for i in range(max_iter):
grad = compute_gradient(img, pos)
if np.linalg.norm(grad) < tol:
print(f"grad: {grad}")
print(f"itteration {i}")
break
pos += lr * grad
@@ -140,32 +147,47 @@ def band_pass_filter(img_float, low_radius, high_radius):
def __process_img(img , retimg=0):
img_np = np.array(img)
img = cv2.GaussianBlur(img,(5,5),0)
img = spnd.zoom(img, 4,order=3)
z = np.float32(img)
#z = band_pass_filter(z,4,25)
# z = band_pass_filter(z, 5, 15)
z = spnd.zoom(z, 4, order=3)
z = cv2.GaussianBlur(z,(11,11),4)
"""img = cv2.GaussianBlur(img,(9,9),7)
img = cv2.GaussianBlur(img, (9, 9), 3)
img = cv2.GaussianBlur(img, (11, 11), 9)
img = cv2.GaussianBlur(img, (13, 13), 11)
img = cv2.GaussianBlur(img, (15, 15), 13)
img = cv2.GaussianBlur(img, (17, 17), 13)"""
z = np.array(img)
#z = band_pass_filter(z,5,10)
# Create a test image (e.g., 2D Gaussian)
x = np.linspace(0, z.shape[0], z.shape[0])
y = np.linspace(0, z.shape[1], z.shape[1])
x, y = np.meshgrid(x, y)
# z = gaussian_2d((x, y), 10, 25, 25, 5, 5, 1) + np.random.normal(0, 0.2, x.shape)
# Flatten and fit
x_gues, y_gues = image_center_of_mass(img, plot=False, verbose=False)
"""
initial_guess = (180, x_gues, y_gues, 10, 10, 1)
popt, _ = curve_fit(gaussian_2d, (x.ravel(), y.ravel()), z.ravel(), p0=initial_guess)
x_gues, y_gues = image_center_of_mass(z, plot=False, verbose=False)
max_a = np.max(z)-2
initial_guess = (max_a, x_gues, y_gues, 10, 10, 0)
popt, _ = curve_fit(gaussian_2d, (x.ravel(), y.ravel()), z.ravel(), p0=initial_guess,maxfev=10000)
x = popt[1]
y = popt[2]"""
result , path = gradient_ascent(img, (x_gues, y_gues))
y = popt[2]
#result , path = gradient_ascent(img, (x_gues, y_gues))
#x,y = result
#y,x = np.unravel_index(np.argmax(z), z.shape)
#ret1, th1 = cv2.threshold(img, 196, 255, cv2.THRESH_BINARY)
#x, y = image_center_of_mass(img, plot=False, verbose=False)
if retimg:
return x,y,img
return x,y,z
return x, y
def aquire_avg(camera_a, nr=10):
@@ -514,7 +536,7 @@ camera_seting.set_ev_time(init_exposure_time())
print(camera_seting.get_exposure_time())"""
image_test()

2
Scripts/myutility.py
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@@ -139,3 +139,5 @@ def get_latest_measurement_dir(static_dir_yes_no = 0):
return result
# Match measurement folders like 20250715_170343_repeatibility_0
return result

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data/data20250718_alignment_tests/20250718_094348_static_0/conf_20250718.json Normal file
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@@ -0,0 +1 @@
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data/data20250718_alignment_tests/20250718_094348_static_0/static_0.dat Normal file
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data/data20250718_alignment_tests/20250718_100656_static_0/conf_20250718.json Normal file
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@@ -0,0 +1 @@
{"Number_of_cycles": 100, "Amplitude_mm": 1, "Time_in_beam_s": 5, "Time_out_of_beam_s": 10, "Exposure_time": 0.0002, "Img_Processing": 1, "pixel_size_mu": 0.275}

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data/data20250718_alignment_tests/20250718_100656_static_0/static_0.dat Normal file
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data/data20250718_alignment_tests/20250718_103652_static_0/conf_20250718.json Normal file
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data/data20250718_alignment_tests/20250718_105043_static_0/conf_20250718.json Normal file
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{"Number_of_cycles": 20, "Amplitude_mm": 1, "Time_in_beam_s": 5, "Time_out_of_beam_s": 10, "Exposure_time": 0.0002, "Img_Processing": 1, "pixel_size_mu": 0.275}

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data/data20250718_alignment_tests/20250718_105043_static_0/static_0.dat Normal file
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data/data20250718_alignment_tests/20250718_105332_static_0/conf_20250718.json Normal file
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{"Number_of_cycles": 200, "Amplitude_mm": 1, "Time_in_beam_s": 5, "Time_out_of_beam_s": 10, "Exposure_time": 0.0002, "Img_Processing": 1, "pixel_size_mu": 0.275}

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182 0.000000 0.000000 24.755 40.191 1752829210.725
183 0.000000 0.000000 24.799 40.197 1752829210.881
184 0.000000 0.000000 24.662 40.178 1752829211.038
185 0.000000 0.000000 24.800 40.207 1752829211.196
186 0.000000 0.000000 24.775 40.232 1752829211.356
187 0.000000 0.000000 24.834 40.184 1752829211.513
188 0.000000 0.000000 24.857 40.108 1752829211.679
189 0.000000 0.000000 24.813 40.342 1752829211.837
190 0.000000 0.000000 24.916 40.206 1752829211.996
191 0.000000 0.000000 24.823 40.212 1752829212.153
192 0.000000 0.000000 24.806 40.274 1752829212.309
193 0.000000 0.000000 24.864 40.237 1752829212.466
194 0.000000 0.000000 24.858 40.156 1752829212.659
195 0.000000 0.000000 24.806 40.170 1752829212.815
196 0.000000 0.000000 24.805 40.142 1752829212.972
197 0.000000 0.000000 24.864 40.167 1752829213.129
198 0.000000 0.000000 24.811 40.276 1752829213.285
199 0.000000 0.000000 24.728 40.258 1752829213.442
200 0.000000 0.000000 24.788 40.240 1752829213.600

2
notebooks/Untitled.ipynb
View File

@@ -191,7 +191,7 @@
" return np.array([dx, dy])\n",
"\n",
"# 3. Gradient ascent\n",
"def gradient_ascent(img, start_pos, lr=0.8, max_iter=1000, tol=1e-4):\n",
"def gradient_ascent(img, start_pos, lr=0.8, max_iter=100, tol=1e-4):\n",
" pos = np.array(start_pos, dtype=float)\n",
" path = [tuple(pos)]\n",
"\n",

2
notebooks/controller_improvements_log.csv
View File

@@ -18,3 +18,5 @@ timestamp,description,measurement_id,notes
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
2025-07-18 09:55:17.959710,Tuned PID gains for better damping,meas_20250710_120000,Observed 30% overshoot reduction
2025-07-18 10:01:12.270546,Tuned PID gains for better damping,meas_20250710_120000,Observed 30% overshoot reduction
1 timestamp description measurement_id notes
18 2025-07-16 16:07:33.382085 Tuned PID gains for better damping meas_20250710_120000 Observed 30% overshoot reduction
19 2025-07-16 17:25:52.318083 Tuned PID gains for better damping meas_20250710_120000 Observed 30% overshoot reduction
20 2025-07-17 15:43:44.147022 Tuned PID gains for better damping meas_20250710_120000 Observed 30% overshoot reduction
21 2025-07-18 09:55:17.959710 Tuned PID gains for better damping meas_20250710_120000 Observed 30% overshoot reduction
22 2025-07-18 10:01:12.270546 Tuned PID gains for better damping meas_20250710_120000 Observed 30% overshoot reduction

246
notebooks/sample.ipynb
View File

@@ -87,10 +87,6 @@
"metadata": {
"jupyter": {
"source_hidden": true
},
"ExecuteTime": {
"end_time": "2025-07-18T07:08:19.394842Z",
"start_time": "2025-07-18T07:08:19.144708Z"
}
},
"source": [
@@ -299,195 +295,8 @@
"\n",
"\n"
],
"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 0x000001EEF4ADD6A0>)\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": "6828982f64e5493793d19e4eefbbbae1"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "396ab16c07ce435ab7d0c2da4e9d7f92"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"BoundedIntText(value=10, description='Nr of cycles:', max=1000, min=1)"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "36a81552307445258c2468c5fb0bd6d1"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6df23067d8534062841a367c50a1dba3"
}
},
"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": "c45b50ce47d443ba87483511c8c1d8c7"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "72c6cd1366a14323a2c23111d76895e1"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"ToggleButton(value=True, description='Processing', tooltip='Toggle processing on/off')"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bb2bb30d673b443398a79cb749f93dd7"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e27b10214ab34805abd55d7f597e0303"
}
},
"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": "c521adfeb1134ede8a43ff85992c80d0"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "76f186ccdfd44632955d378d0cb8d21c"
}
},
"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": "f6e7b5a8a82c4219984394c8b34d1a52"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4e8cb912dda4474c9e30d8eaa4a23df4"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 1
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
@@ -743,6 +552,44 @@
"source": [
"pooling = 0\n",
"\n",
"STATIC_LOG_FILE = \"static_tests_log.csv\"\n",
"\n",
"def log_static_test(x_std, y_std, x_p2v, y_p2v, nr_of_cycles=0):\n",
" masiv = myu.get_latest_measurement_dir(1)\n",
" singel = masiv.split(\"\\\\\")\n",
"\n",
" new_entry = pd.DataFrame([{\n",
" \"day_time\":singel[-1],\n",
" \"x_std\": x_std,\n",
" \"y_std\": y_std,\n",
" \"x_p2v\": x_p2v,\n",
" \"y_p2v\": y_p2v,\n",
" \"pooling\": pooling,\n",
" \"nr of measurements\": nr_of_cycles\n",
" }])\n",
" if os.path.exists(STATIC_LOG_FILE):\n",
" old_log = pd.read_csv(STATIC_LOG_FILE)\n",
" new_log = pd.concat([old_log, new_entry], ignore_index=True)\n",
" else:\n",
" new_log = new_entry\n",
" new_log.to_csv(STATIC_LOG_FILE, index=False)\n",
" print(\"Static test logged.\")\n",
"\n",
"def remove_duplicate_static_tests(log_file=\"static_tests_log.csv\"):\n",
" if not os.path.exists(log_file):\n",
" print(f\"No such file: {log_file}\")\n",
" return\n",
"\n",
" # Load the log\n",
" df = pd.read_csv(log_file)\n",
"\n",
" # Drop duplicate rows\n",
" df_clean = df.drop_duplicates(keep='first')\n",
"\n",
" # Save cleaned log back\n",
" df_clean.to_csv(log_file, index=False)\n",
" print(f\"Removed duplicates. {len(df) - len(df_clean)} rows deleted.\")\n",
"\n",
"def get_pixel_size():\n",
" config = myu.load_object(config_path)\n",
" return config.get(\"pixel_size_mu\")\n",
@@ -750,7 +597,7 @@
"\n",
"axis_path_1 = myu.get_latest_measurement_dir(1)\n",
"print(axis_path_1)\n",
"#axis_path_1 = r\"C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250717_alignment_tests\\20250717_085115_static_0\" #uncomment for specific path\n",
"axis_path_1 = r\"C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\data20250718_alignment_tests\\20250718_105332_static_0\" #uncomment for specific path\n",
"axis_data_file_path_1 = myu.find_newest_dat_file(axis_path_1)\n",
"print(axis_data_file_path_1)\n",
"#mf.analyze_repeatability(axis_data_file_path_1,1.1)\n",
@@ -770,9 +617,10 @@
"\n",
"if pooling == 1:\n",
" x_vals1, y_vals1, times1 = myu.load_xy_data(axis_data_file_path_1)\n",
" times = times1[:100]\n",
"\n",
" x_vals = pool_average_1d(x_vals1,10)\n",
" y_vals = pool_average_1d(y_vals1,10)\n",
" times = times1[:len(x_vals)]\n",
" x_vals = x_vals*get_pixel_size()\n",
" y_vals = y_vals*get_pixel_size()\n",
"if pooling == 0:\n",
@@ -792,6 +640,10 @@
"x_p2v = max_x-min_x\n",
"y_p2v = max_y-min_y\n",
"\n",
"\n",
"log_static_test(std_x, std_y, x_p2v, y_p2v,len(x_vals))\n",
"remove_duplicate_static_tests()\n",
"\n",
"print(f'Statistics| X | Y |\\n'\n",
" f' STD |{std_x:.2f}|{std_y:.2f}|\\n'\n",
" f' P2V |{x_p2v:.2f}|{y_p2v:.2f}|\\n ')\n",
@@ -866,7 +718,9 @@
"plot_initial()\n",
"update_plot()\n",
"plt.tight_layout()\n",
"plt.show()\n"
"plt.show()\n",
"\n",
"pd.read_csv(STATIC_LOG_FILE)\n"
],
"outputs": [],
"execution_count": null

12
notebooks/static_tests_log.csv Normal file
View File

@@ -0,0 +1,12 @@
day_time,x_std,y_std,x_p2v,y_p2v,pooling,nr of measurements
20250718_094348_static_0,0.2529374357425167,0.253971418864409,1.4139999999999873,1.4279999999999973,,
20250718_100656_static_0,0.3188139386852471,0.16410581068323,1.7569999999999908,1.0209999999999866,,
20250718_103652_static_0,0.4591714930175879,0.4881253194621204,1.848999999999989,2.2269999999999754,,
20250718_105043_static_0,0.1761264886381379,0.135490996010805,0.8899999999999864,0.8019999999999925,,
20250718_105043_static_0,0.1761264886381379,0.135490996010805,0.8899999999999864,0.8019999999999925,0.0,
20250718_105043_static_0,0.004922500000001,0.0037124999999988,0.2098450000000049,0.2074250000000006,1.0,
20250718_105332_static_0,0.0367441106443858,0.032679132404143,0.339672500000006,0.3233099999999922,1.0,
20250718_105332_static_0,0.0367441106443858,0.032679132404143,0.339672500000006,0.3233099999999922,1.0,20.0
20250718_105332_static_0,0.2235625969946675,0.1905487798439019,1.4199999999999875,1.299999999999983,0.0,200.0
20250718_105941_static_0,0.0562077341208484,0.0659889337313464,0.485000000000003,0.5350000000000037,0.0,200.0
20250718_105941_static_0,0.2235625969946675,0.1905487798439019,1.4199999999999875,1.299999999999983,0.0,200.0
1 day_time x_std y_std x_p2v y_p2v pooling nr of measurements
2 20250718_094348_static_0 0.2529374357425167 0.253971418864409 1.4139999999999873 1.4279999999999973
3 20250718_100656_static_0 0.3188139386852471 0.16410581068323 1.7569999999999908 1.0209999999999866
4 20250718_103652_static_0 0.4591714930175879 0.4881253194621204 1.848999999999989 2.2269999999999754
5 20250718_105043_static_0 0.1761264886381379 0.135490996010805 0.8899999999999864 0.8019999999999925
6 20250718_105043_static_0 0.1761264886381379 0.135490996010805 0.8899999999999864 0.8019999999999925 0.0
7 20250718_105043_static_0 0.004922500000001 0.0037124999999988 0.2098450000000049 0.2074250000000006 1.0
8 20250718_105332_static_0 0.0367441106443858 0.032679132404143 0.339672500000006 0.3233099999999922 1.0
9 20250718_105332_static_0 0.0367441106443858 0.032679132404143 0.339672500000006 0.3233099999999922 1.0 20.0
10 20250718_105332_static_0 0.2235625969946675 0.1905487798439019 1.4199999999999875 1.299999999999983 0.0 200.0
11 20250718_105941_static_0 0.0562077341208484 0.0659889337313464 0.485000000000003 0.5350000000000037 0.0 200.0
12 20250718_105941_static_0 0.2235625969946675 0.1905487798439019 1.4199999999999875 1.299999999999983 0.0 200.0