Create helper functions for analyzing results

notebooks/kbmod_analysis_demo.ipynb

@@ -14,6 +14,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
     "from kbmod.analysis.plot_results import *\n",
@@ -148,6 +149,66 @@
     "ResultsVisualizer.plot_result_row(row0)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Histograms of Results\n",
+    "\n",
+    "We can plot the histograms of individual attributes of the results. First we need to load a result set with more than one entry. Then we plot the histogram of number of observations in the trajectory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2 = load_result_list_from_files(\"../data/fake_results_noisy/\", \"DEMO\")\n",
+    "print(f\"Loaded {results2.num_results()} results.\")\n",
+    "\n",
+    "counts = results2.get_result_values(\"obs_count\")\n",
+    "_ = plt.hist(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `ResultsVisualizer` class provides a helper function to plotting a 2-d histogram of the trajectory's starting positions on the image. This requires knowing the image size. The demo data consists of 256 by 256 images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ResultsVisualizer.plot_starting_pixel_histogram(results2, 256, 256)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we could manually create a histogram of the pixel velocities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "xv_vals = results2.get_result_values(\"trajectory.vx\")\n",
+    "yv_vals = results2.get_result_values(\"trajectory.vy\")\n",
+    "_, _, _, img = ax.hist2d(xv_vals, yv_vals, bins=10)\n",
+    "fig.colorbar(img, ax=ax)\n",
+    "_ = ax.set_xlabel(\"vx (pixels per day)\")\n",
+    "_ = ax.set_ylabel(\"vy (pixels per day)\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -159,9 +220,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Jeremy's KBMOD",
    "language": "python",
-   "name": "python3"
+   "name": "kbmod_jk"
   },
   "language_info": {
    "codemirror_mode": {
@@ -173,9 +234,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

notebooks/kbmod_results_and_filtering.ipynb

@@ -85,6 +85,25 @@
     "results.sort(key=\"final_likelihood\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting Individual Attributes\n",
+    "\n",
+    "The `ResultList`class provides a helper function `get_result_values()` that allows a user to extract all of the values for a given attribute of the results. For example we could extract all of the flux values and create a histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flux_values = results.get_result_values(\"trajectory.flux\")\n",
+    "plt.hist(flux_values)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -300,9 +319,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

src/kbmod/analysis/plot_results.py

@@ -1,5 +1,4 @@
 import math
-
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -156,3 +155,24 @@ def plot_result_row(row, times=None, title=None, fig=None):
         else:
             ax = fig_bot.add_axes([0, 0, 1, 1])
             ax.text(0.5, 0.5, "No Individual Stamps")
+
+    @staticmethod
+    def plot_starting_pixel_histogram(results, height, width):
+        """Plot a histogram of the starting pixels of each found trajectory.
+
+        Parameters
+        ----------
+        results : `ResultList`
+            The results to analyze.
+        height : `int`
+            The image height in pixels
+        width : `int`
+            The image width in pixels
+        """
+        fig, ax = plt.subplots()
+
+        x_vals = results.get_result_values("trajectory.x")
+        y_vals = results.get_result_values("trajectory.y")
+        _, _, _, img = ax.hist2d(x_vals, y_vals, bins=[height, width])
+        fig.colorbar(img, ax=ax)
+        ax.set_title("Histogram of Starting Pixel")

src/kbmod/result_list.py

@@ -674,6 +674,40 @@ def sort(self, key="final_likelihood", reverse=True):
         self.results.sort(key=lambda x: getattr(x, key), reverse=reverse)
         return self
 
+    def get_result_values(self, attribute):
+        """Return the values of the ResultRows' attribute as a list.
+        Subattributes can be extracted as "attribute.subattribute",
+        such as "trajectory.x"
+
+        Examples:
+            trj_list = res.get_trajectory_values("trajectory")
+            x_values = res.get_trajectory_values("trajectory.x")
+            stamps = res.get_trajectory_values("stamp")
+
+        Parameter
+        ---------
+        attribute : `str`
+            The name of the attribute to extract.
+
+        Returns
+        -------
+        values : `list`
+            A list of the results' values.
+
+        Raises
+        ------
+        Raises an ``AttributeError`` if the attribute does not exist.
+        """
+        att_list = attribute.split(".")
+
+        values = []
+        for row in self.results:
+            obj = row
+            for att in att_list:
+                obj = getattr(obj, att)
+            values.append(obj)
+        return values
+
     def compute_predicted_skypos(self, wcs):
         """Compute the predict sky position for each result's trajectory
         at each time step.

tests/test_result_list.py

@@ -264,6 +264,42 @@ def test_sort(self):
         for i, val in enumerate(expected_order):
             self.assertEqual(rs.results[i].trajectory.x, val)
 
+    def test_get_result_values(self):
+        rs = ResultList(self.times)
+        rs.append_result(ResultRow(make_trajectory(x=0, lh=1.0, obs_count=1), self.num_times))
+        rs.append_result(ResultRow(make_trajectory(x=1, lh=-1.0, obs_count=2), self.num_times))
+        rs.append_result(ResultRow(make_trajectory(x=2, lh=5.0, obs_count=3), self.num_times))
+        rs.append_result(ResultRow(make_trajectory(x=3, lh=4.0, obs_count=5), self.num_times))
+        rs.append_result(ResultRow(make_trajectory(x=4, lh=6.0, obs_count=4), self.num_times))
+
+        # Test getting a list of trajectories.
+        trjs = rs.get_result_values("trajectory")
+        self.assertEqual(len(trjs), 5)
+        for i in range(5):
+            self.assertTrue(type(trjs[i]) is Trajectory)
+
+        # Stamps should all be None
+        stamps = rs.get_result_values("stamp")
+        self.assertEqual(len(stamps), 5)
+        for i in range(5):
+            self.assertTrue(stamps[i] is None)
+
+        # We can extract sub-attributes
+        x_vals = rs.get_result_values("trajectory.x")
+        self.assertEqual(len(x_vals), 5)
+        for i in range(5):
+            self.assertEqual(x_vals[i], i)
+
+        vx_vals = rs.get_result_values("trajectory.vx")
+        self.assertEqual(len(vx_vals), 5)
+        for i in range(5):
+            self.assertEqual(vx_vals[i], 0.0)
+
+        # We get an error if we try to extract an attribute that doesn't exist.
+        self.assertRaises(AttributeError, rs.get_result_values, "")
+        self.assertRaises(AttributeError, rs.get_result_values, "Not There")
+        self.assertRaises(AttributeError, rs.get_result_values, "trajectory.z")
+
     def test_filter(self):
         rs = ResultList(self.times)
         for i in range(10):