Merge branch 'main' into dust_map3

.github/workflows/pre-commit-ci.yml

@@ -31,5 +31,5 @@ jobs:
         extra_args: --all-files --verbose
       env:
         SKIP: "check-lincc-frameworks-template-version,no-commit-to-branch,check-added-large-files,validate-pyproject,sphinx-build,pytest-check"
-    - uses: pre-commit-ci/lite-action@v1.0.3
+    - uses: pre-commit-ci/lite-action@v1.1.0
       if: failure() && github.event_name == 'pull_request' && github.event.pull_request.draft == false

src/tdastro/astro_utils/obs_utils.py

@@ -0,0 +1,49 @@
+import numpy as np
+
+
+def phot_eff_function(snr):
+    """
+    Photometric detection efficiency as a simple step function of snr.
+
+    Parameters
+    ----------
+    snr: `list` or `numpy.ndarray`
+        Signal to noise ratio of a list of observations.
+
+    Returns
+    -------
+    eff: `list` or `numpy.ndarray`
+        The photometric detection efficiency given snr.
+    """
+
+    snr = np.array(snr)
+    eff = np.where(snr > 5, 1.0, 0.0)
+
+    return eff
+
+
+def spec_eff_function(peak_imag):
+    """
+    Spectroscopic follow-up efficiency as a function of peak i band magnitude.
+
+    Parameters
+    ----------
+    peak_imag: `list` or `numpy.ndarray`
+        Peak magnitude in i band.
+
+    Returns
+    -------
+    eff: `list` or `numpy.ndarray`
+        The spectroscopic efficiency given peak i band magnitude.
+        Based on Equation (17) in Kessler et al. 2019
+        s0, s1, s2 are fitted using data from Figure 4 in Kessler et al. 2019
+    """
+
+    s0 = 1.0
+    s1 = 2.36
+    s2 = 51.9
+
+    peak_imag = np.array(peak_imag)
+    eff = s0 * np.power((1.0 + np.exp(s1 * peak_imag - s2)), -1)
+
+    return eff

src/tdastro/base_models.py

@@ -472,7 +472,7 @@ def _sample_helper(self, graph_state, seen_nodes, rng_info=None):
             An object mapping graph parameters to their values. This object is modified
             in place as it is sampled.
         seen_nodes : `dict`
-            A dictionary mapping nodes seen during this sampling run to their ID.
+            A dictionary mapping nodes strings seen during this sampling run to their object.
             Used to avoid sampling nodes multiple times and to validity check the graph.
         rng_info : numpy.random._generator.Generator, optional
             A given numpy random number generator to use for this computation. If not
@@ -482,9 +482,12 @@ def _sample_helper(self, graph_state, seen_nodes, rng_info=None):
         ------
         Raise a ``KeyError`` if the sampling encounters an error with the order of dependencies.
         """
-        if self in seen_nodes:
+        node_str = str(self)
+        if node_str in seen_nodes:
+            if seen_nodes[node_str] != self:
+                raise ValueError(f"Duplicate node label {node_str}.")
             return  # Nothing to do
-        seen_nodes[self] = self.node_pos
+        seen_nodes[node_str] = self
 
         # Run through each parameter and sample it based on the given recipe.
         # As of Python 3.7 dictionaries are guaranteed to preserve insertion ordering,