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Added fiber_camera control to fiber_search
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@pgriffin17
pgriffin17 committed May 31, 2023
1 parent 0739455 commit f581612
Showing 1 changed file with 208 additions and 1 deletion.
209 changes: 208 additions & 1 deletion operations/bin/fiber_search.ipynb
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"import numpy as np\n",
"from ipywidgets import Button\n",
"from IPython.display import display, clear_output\n",
"import time\n",
"from matplotlib.colors import LogNorm\n",
"import matplotlib.patches as mpatches\n",
"\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"\n",
"# Constants\n",
"meter = 1\n",
Expand Down Expand Up @@ -56,7 +62,139 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Connect to mount"
"## Connect to camera"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Connect to ASCOM camera\n",
"fiber_camera = Dispatch('ASCOM.ASICamera2.Camera')\n",
"\n",
"# Open setup dialog\n",
"fiber_camera.SetupDialog()\n",
"\n",
"if fiber_camera.Connected:\n",
" print('Camera is already connected')\n",
"else:\n",
" fiber_camera.Connected = True\n",
" if fiber_camera.Connected:\n",
" print('Camera is connected')\n",
" else:\n",
" print('Camera is not connected, try again')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Camera setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Function to take and download image\n",
"def take_image(exposure_time):\n",
" fiber_camera.StartExposure(exposure_time, True)\n",
" while fiber_camera.ImageReady == False:\n",
" time.sleep(0.1)\n",
" img = fiber_camera.ImageArray\n",
" img = np.array(img)\n",
" img = np.rot90(img, 1)\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img = take_image(0.1)\n",
"\n",
"\n",
"# Plot image\n",
"plt.imshow(img, cmap='viridis')\n",
"plt.show()\n",
"\n",
"value = 16\n",
"print('Number of pixels at value ', value, ': ', np.count_nonzero(img == value))\n",
"\n",
"# Plot histogram of image with log scale on y-axis\n",
"plt.hist(img.ravel(), bins=256, range=(0.0, 65536.0), fc='k', ec='k')\n",
"plt.yscale('log')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create widget to draw circle on image and reposition it, as well as save\n",
"# the x, y, and r values\n",
"def draw_circle(x, y, r):\n",
" plt.imshow(img, cmap='viridis')\n",
" circle = plt.Circle((x, y), r, color='r', fill=False)\n",
" plt.gcf().gca().add_artist(circle)\n",
" # Make figure larger\n",
" plt.gcf().set_size_inches(10, 10)\n",
" plt.show()\n",
" return [x, y, r]\n",
"\n",
"# Get max x and y dimensions from camera\n",
"max_x = fiber_camera.CameraXSize\n",
"max_y = fiber_camera.CameraYSize\n",
"\n",
"circle_selector = interactive(draw_circle, x=widgets.IntSlider(min=0, max=max_x, step=1, value=max_x/2),\n",
" y=widgets.IntSlider(min=0, max=max_y, step=1, value=max_y/2), \n",
" r=widgets.IntSlider(min=0, max=1024, step=1, value=256),\n",
" continuous_update=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(circle_selector)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print average, median, and standard deviation of pixels in box around fiber (circle is too hard)\n",
"\n",
"[fiber_x, fiber_y, fiber_r] = circle_selector.result\n",
"\n",
"# Get box around fiber\n",
"fiber_box = img[fiber_y-fiber_r:fiber_y+fiber_r, fiber_x-fiber_r:fiber_x+fiber_r]\n",
"\n",
"# Print average, median, and standard deviation of pixels in box around fiber\n",
"print('Average: %.2f' % np.average(fiber_box))\n",
"print('Median: %2d' % np.median(fiber_box))\n",
"print('Standard deviation: %2d' % np.std(fiber_box))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connect to mount"
]
},
{
Expand Down Expand Up @@ -163,6 +301,7 @@
" for j in range(len(y)):\n",
" xrot[i,j], yrot[i,j] = np.dot([[np.cos(theta), np.sin(theta)], [-np.sin(theta), np.cos(theta)]], np.array([x[i,j], y[i,j]]))\n",
"delta_ras = xrot.flatten(); delta_decs = yrot.flatten()\n",
"delta_coords = np.array([delta_ras, delta_decs]).T\n",
"\n",
"# Plot grid\n",
"fig,ax = plt.subplots(figsize=(6,6))\n",
Expand All @@ -181,6 +320,74 @@
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Make numpy array to store average, median, and standard deviation of pixels in box around fiber\n",
"# for multiple images. We will append to this array each time we take an image.\n",
"# We will use this array to plot the average, median, and standard deviation of pixels in box around fiber\n",
"# as a function of exposure time.\n",
"fiber_stats = np.empty((0, 3), float)\n",
"\n",
"# On function call, take image, show image, and print average, median, and standard deviation of pixels in box around fiber\n",
"def take_image_and_print_stats(exposure_time, fiber_x, fiber_y, fiber_r, fiber_stats):\n",
" '''Take image, plot image, and print average, median, and standard deviation of pixels in box around fiber\n",
"\n",
" Parameters:\n",
" exposure_time (float): Exposure time in seconds\n",
" '''\n",
" # This is supposed to wait to clear the current output until new output is available, but it doesn't seem to work\n",
" clear_output(wait=True) \n",
"\n",
" # Take image, plot image\n",
" img = take_image(exposure_time)\n",
"\n",
" # Set parameters for log stretch based on image statistics\n",
" # Set minimum value to 1\n",
" min_value = np.percentile(img, 5)\n",
" # Set maximum value to 99th percentile\n",
" max_value = np.percentile(img, 99)\n",
"\n",
" # Plot image, set log stretch\n",
" plt.imshow(img, cmap='inferno', norm=LogNorm(vmin=min_value, vmax=max_value))\n",
" \n",
" # Use linear stretch\n",
" #plt.imshow(img, cmap='inferno', vmin=min_value, vmax=max_value)\n",
" \n",
" # Show box around fiber\n",
" plt.plot([fiber_x-fiber_r, fiber_x+fiber_r, fiber_x+fiber_r, fiber_x-fiber_r, fiber_x-fiber_r],\n",
" [fiber_y-fiber_r, fiber_y-fiber_r, fiber_y+fiber_r, fiber_y+fiber_r, fiber_y-fiber_r], 'r', linewidth=1)\n",
" # Make figure larger\n",
" plt.gcf().set_size_inches(15, 10)\n",
" plt.colorbar()\n",
" plt.gcf()\n",
"\n",
" # Statistics\n",
" [fiber_x, fiber_y, fiber_r] = circle_selector.result\n",
" fiber_box = img[fiber_y-fiber_r:fiber_y+fiber_r, fiber_x-fiber_r:fiber_x+fiber_r]\n",
" print('Average: %.2f' % np.average(fiber_box))\n",
" print('Median: %2d' % np.median(fiber_box))\n",
" print('Standard deviation: %2d' % np.std(fiber_box))\n",
"\n",
" # Append average, median, and standard deviation of pixels in box around fiber to fiber_stats\n",
" fiber_stats = np.append(fiber_stats, [[np.average(fiber_box), np.median(fiber_box), np.std(fiber_box)]], axis=0)\n",
" return fiber_stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in range(1, 10):\n",
" fiber_stats = take_image_and_print_stats(0.001, fiber_x, fiber_y, fiber_r, fiber_stats)\n",
" time.sleep(5)"
]
},
{
"attachments": {},
"cell_type": "markdown",
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