Merge pull request #104 from Autostronomy/dev

Updates to LM implementation, sampling, live PSF

.github/workflows/testing.yaml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
         os: [ubuntu-latest, windows-latest, macOS-latest]
     steps:
       - uses: actions/checkout@master

autophot/__init__.py

@@ -5,7 +5,7 @@
 from . import models, image, plots, utils, fit, AP_config
 
 # meta data
-__version__ = "0.10.3"
+__version__ = "0.10.4"
 __author__ = "Connor Stone"
 __email__ = "[email protected]"
 

autophot/fit/__init__.py

@@ -1,10 +1,14 @@
 from .base import *
 from .lm import *
+from .oldlm import *
 from .gradient import *
 from .iterative import *
-from .hmc import *
+try:
+    from .hmc import *
+    from .nuts import *
+except AssertionError as e:
+    print("Could not load HMC or NUTS due to:", str(e))
 from .mhmcmc import *
-from .nuts import *
 
 """
 base: This module defines the base class BaseOptimizer, 

autophot/fit/iterative.py

@@ -54,6 +54,10 @@ def __init__(
 
         self.method = method
         self.method_kwargs = method_kwargs
+        if "relative_tolerance" not in method_kwargs and isinstance(method, LM):
+            # Lower tolerance since it's not worth fine tuning a model when it's neighbors will be shifting soon anyway
+            self.method_kwargs["relative_tolerance"] = 1e-3
+            self.method_kwargs["max_iter"] = 15
         #          # pixels      # parameters
         self.ndf = self.model.target[self.model.window].flatten("data").size(0) - len(
             self.current_state

autophot/fit/nuts.py

@@ -99,7 +99,7 @@ def __init__(
         super().__init__(model, initial_state, max_iter=max_iter, **kwargs)
 
         self.inv_mass = kwargs.get("inv_mass", None)
-        self.epsilon = kwargs.get("epsilon", 1e-3)
+        self.epsilon = kwargs.get("epsilon", 1e-4)
         self.progress_bar = kwargs.get("progress_bar", True)
         self.prior = kwargs.get("prior", None)
         self.warmup = kwargs.get("warmup", 100)