Get rid of the legacy demo data

.gitignore

@@ -31,4 +31,5 @@ docs/source/examples/_notebooks
 .#*
 \#*#
 # Exclude files starting with exclude (local test and development)
-exclude*
+exclude*
+data/demo_data.fits

data/readme.txt

@@ -1,5 +1,4 @@
 The data directory contains a few data sets used in example python files and notebooks:
-- data/demo: Contains 10 image files created using fake_data_creator.py and containing a single fake object.
-- data/demo_times.dat: The external time file for the images in data/demo.
+- data/demo_image.fits: Contains an image file created using fake_data_creator.py and containing a single fake object.
 - data/small: Contains 10 small image files created using fake_data_creator.py and containing a single fake object.
 - data/fake_results: Contains the results of running the KBMOD_Demo notebook on the data in data/demo. For a description of the files see the KBMOD documentation.

notebooks/KBMOD_Demo.ipynb

@@ -18,8 +18,11 @@
     "\n",
     "from pathlib import Path\n",
     "\n",
+    "from kbmod.configuration import SearchConfiguration\n",
+    "from kbmod.fake_data.demo_helper import make_demo_data\n",
     "from kbmod.run_search import *\n",
-    "from kbmod.search import *"
+    "from kbmod.search import *\n",
+    "from kbmod.work_unit import WorkUnit"
    ]
   },
   {
@@ -33,13 +36,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are at least two file paths you need to setup in order to run kbmod:\n",
-    "1. The im_filepath provides a path to the input images.\n",
-    "1. The res_filepath provides a path to the directory where the output results will be stored.\n",
+    "In order to run KBMOD you need to have the location of the input data and a `res_filepath` that provides a path to the directory where the output results will be stored. Input data can come from a variety of formats including Rubin’s Bulter, fits files, and `WorkUnit` files. In this demo we use the `WorkUnit` file which is an internal storage format used. For more information on generating a `WorkUnit` from the Butler or fits, see the standardizer notebooks.\n",
     "\n",
-    "A time and psf file can optionally be specified.\n",
-    "\n",
-    "If you already have data files, you can use those. Below we use the data in `data/demo`. You can also create your own fake data using `fake_data_creator.py`."
+    "If you already have data files, you can use those. Below we create and use data in `data/demo_data.fits`."
    ]
   },
   {
@@ -48,9 +47,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define the path for the data.\n",
-    "im_filepath = \"../data/demo\"\n",
-    "print(os.listdir(im_filepath))"
+    "input_filename = \"../data/demo_data.fits\"\n",
+    "\n",
+    "# Create the fake data usering a helper function.\n",
+    "if not Path(input_filename).is_file():\n",
+    "    make_demo_data(filename=input_filename)"
    ]
   },
   {
@@ -108,21 +109,19 @@
     "results_suffix = \"DEMO\"\n",
     "\n",
     "# The demo data has an object moving at x_v=10 px/day\n",
-    "# and y_v = 0 px/day. So we search velocities [0, 20].\n",
+    "# and y_v = 0 px/day. So we search velocities [0, 19].\n",
     "v_min = 0\n",
     "v_max = 20\n",
-    "v_steps = 21\n",
+    "v_steps = 20\n",
     "v_arr = [v_min, v_max, v_steps]\n",
     "\n",
-    "# and angles [-0.5, 0.5]\n",
+    "# and angles [-0.5, 0.5)\n",
     "ang_below = 0.5\n",
     "ang_above = 0.5\n",
-    "ang_steps = 11\n",
+    "ang_steps = 10\n",
     "ang_arr = [ang_below, ang_above, ang_steps]\n",
     "\n",
     "input_parameters = {\n",
-    "    # Input parameters\n",
-    "    \"im_filepath\": im_filepath,\n",
     "    # Grid search parameters\n",
     "    \"v_arr\": v_arr,\n",
     "    \"ang_arr\": ang_arr,\n",
@@ -134,12 +133,15 @@
     "    \"do_mask\": True,  # <-- Apply the masks specified in the FITS files.\n",
     "    \"mask_num_images\": 10,\n",
     "    # Basic filtering (always applied)\n",
-    "    \"num_obs\": 7,  # <-- Filter anything with fewer than 7 observations\n",
+    "    \"num_obs\": 15,  # <-- Filter anything with fewer than 15 observations\n",
     "    \"lh_level\": 10.0,  # <-- Filter anything with a likelihood < 10.0\n",
     "    # SigmaG clipping parameters\n",
     "    \"sigmaG_lims\": [15, 60],  # <-- Clipping parameters (lower and upper percentile)\n",
     "    \"gpu_filter\": True,  # <-- Apply clipping and filtering on the GPU\n",
     "    \"clip_negative\": True,\n",
+    "    # Some basic stamp filtering limits.\n",
+    "    \"mom_lims\": [37.5, 37.5, 1.5, 1.0, 1.0],\n",
+    "    \"peak_offset\": [3.0, 3.0],\n",
     "    # Override the ecliptic angle for the demo data since we\n",
     "    # know the true angle in pixel space.\n",
     "    \"average_angle\": 0.0,\n",
@@ -160,7 +162,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# print(config)"
+    "print(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We load the data as a `WorkUnit` object. In general `WorkUnit`'s include a copy of their own configuration so they have all the information they need for a full run. We overwrite the stored configuration with the one we defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_data = WorkUnit.from_fits(input_filename)\n",
+    "input_data.config = config"
    ]
   },
   {
@@ -188,7 +207,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once we have defined the search parameters, we can create a ``SearchRunner`` and use one of the run_search functions. In this case we use ``run_search_from_config`` which uses the config to search for the input files."
+    "Once we have defined the search parameters, we can create a `SearchRunner` and use one of the run_search functions. In this case we use `run_search_from_work_unit` which uses the `WorkUnit` to define both the image data and the configuration information."
    ]
   },
   {
@@ -198,7 +217,7 @@
    "outputs": [],
    "source": [
     "rs = SearchRunner()\n",
-    "results = rs.run_search_from_config(config)"
+    "results = rs.run_search_from_work_unit(input_data)"
    ]
   },
   {
@@ -289,13 +308,11 @@
    "outputs": [],
    "source": [
     "# Turn on filtered tracking\n",
-    "input_parameters[\"track_filtered\"] = True\n",
+    "input_data.config.set(\"track_filtered\", True)\n",
     "\n",
     "# Turn up filtering of stamp filtering. This will require 100% of the stamp's flux\n",
     "# to be at the center pixel and effectively filter every candidate trajectory.\n",
-    "input_parameters[\"center_thresh\"] = 1.0\n",
-    "\n",
-    "config = SearchConfiguration.from_dict(input_parameters)"
+    "input_data.config.set(\"center_thresh\", 1.0)"
    ]
   },
   {
@@ -312,7 +329,7 @@
    "outputs": [],
    "source": [
     "rs = SearchRunner()\n",
-    "results = rs.run_search_from_config(config)\n",
+    "results = rs.run_search_from_work_unit(input_data)\n",
     "print(f\"Search found {len(results)} results.\")"
    ]
   },
@@ -355,7 +372,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we search for one of the expected trajectories (starting at pixel (106, 44) at the first time step) by using the table's search functionality."
+    "Now we search for one of the expected trajectories (starting at pixel (50, 40) at the first time step) by using the table's search functionality."
    ]
   },
   {
@@ -364,7 +381,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "subset = results.table[(results.table[\"x\"] == 106) & (results.table[\"y\"] == 44)]\n",
+    "subset = results.table[(results.table[\"x\"] == 50) & (results.table[\"y\"] == 40)]\n",
     "print(subset)"
    ]
   },
@@ -374,6 +391,13 @@
    "source": [
     "As we can see all of the potential trajectories were filtered by the stamp filter. We can use this information to help tune different filtering stages."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

notebooks/Kbmod_Reference.ipynb

@@ -40,7 +40,7 @@
     "import matplotlib.pyplot as plt\n",
     "import os\n",
     "\n",
-    "im_path = \"../data/demo/\"\n",
+    "im_file = \"../data/demo_image.fits\"\n",
     "res_path = \"./results\""
    ]
   },
@@ -183,7 +183,7 @@
    "source": [
     "from kbmod.data_interface import load_deccam_layered_image\n",
     "\n",
-    "im = load_deccam_layered_image(im_path + \"000000.fits\", p)\n",
+    "im = load_deccam_layered_image(im_file, p)\n",
     "print(f\"Loaded a {im.get_width()} by {im.get_height()} image at time {im.get_obstime()}\")"
    ]
   },