Dev

.github/workflows/cd.yml

@@ -57,7 +57,7 @@ jobs:
           ls -ltrh
           ls -ltrh dist
       - name: Publish to Test PyPI
-        uses: pypa/gh-action-pypi-publish@v1.9.0
+        uses: pypa/gh-action-pypi-publish@v1.10.2
         with:
           repository-url: https://test.pypi.org/legacy/
           verbose: true
@@ -95,5 +95,5 @@ jobs:
           name: artifact
           path: dist
 
-      - uses: pypa/gh-action-pypi-publish@v1.9.0
+      - uses: pypa/gh-action-pypi-publish@v1.10.2
         if: startsWith(github.ref, 'refs/tags')

.github/workflows/ci.yml

@@ -74,8 +74,9 @@ jobs:
         if:
           ${{ matrix.python-version == '3.10' && matrix.os == 'ubuntu-latest'}}
         uses: codecov/codecov-action@v4
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         with:
-          token: ${{ secrets.CODECOV_TOKEN }}
           files: ./coverage.xml
           flags: unittests
           name: codecov-umbrella

README.md

@@ -47,10 +47,10 @@ x = torch.tensor([
     5.0, -0.2, 0.0, 0.8, 0.0, 1., 1.0, 10.0
 ])  # fmt: skip
 
-minisim = caustics.LensSource(
+sim = caustics.LensSource(
     lens=sie, source=src, lens_light=lnslt, pixelscale=0.05, pixels_x=100
 )
-plt.imshow(minisim(x, quad_level=3), origin="lower")
+plt.imshow(sim(x), origin="lower")
 plt.axis("off")
 plt.show()
 ```
@@ -63,7 +63,7 @@ plt.show()
 newx = x.repeat(20, 1)
 newx += torch.normal(mean=0, std=0.1 * torch.ones_like(newx))
 
-images = torch.vmap(minisim)(newx)
+images = torch.vmap(sim)(newx)
 
 fig, axarr = plt.subplots(4, 5, figsize=(20, 16))
 for ax, im in zip(axarr.flatten(), images):
@@ -76,7 +76,7 @@ plt.show()
 ### Automatic Differentiation
 
 ```python
-J = torch.func.jacfwd(minisim)(x)
+J = torch.func.jacfwd(sim)(x)
 
 # Plot the new images
 fig, axarr = plt.subplots(3, 7, figsize=(20, 9))

docs/requirements.txt

@@ -1,3 +1,4 @@
+emcee
 ipywidgets
 jupyter-book
 matplotlib

docs/source/_toc.yml

@@ -21,11 +21,10 @@ chapters:
       - file: tutorials/InvertLensEquation
       - file: tutorials/Parameters
       - file: tutorials/Simulators
-      - file: tutorials/Playground
   - file: examples/index
     sections:
       - file: examples/Example_ImageFit_LM
-      - file: examples/Example_ImageFit_NUTS
+      - file: examples/Example_ImageFit_MCMC
       - file: examples/Example_QSOLensFit
   - file: contributing
   - file: frequently_asked_questions

docs/source/examples/Example_ImageFit_LM.ipynb

@@ -56,6 +56,7 @@
     "cosmology.to(dtype=torch.float32)\n",
     "\n",
     "upsample_factor = 1\n",
+    "quad_level = 3\n",
     "thx, thy = caustics.utils.meshgrid(\n",
     "    pixelscale / upsample_factor,\n",
     "    upsample_factor * numPix,\n",
@@ -117,6 +118,7 @@
     "    pixels_x=numPix,\n",
     "    pixelscale=pixelscale,\n",
     "    upsample_factor=upsample_factor,\n",
+    "    quad_level=quad_level,\n",
     "    z_s=2.0,\n",
     ")"
    ]
@@ -128,16 +130,18 @@
    "source": [
     "## Sample some mock data\n",
     "\n",
-    "Here we write out the true values for all the parameters in the model. In total there are 21 parameters, so this is quite a complex model already! We then plot the data so we can see what it is we re trying to fit.\n",
-    "\n",
-    "Note that when we sample the simulator we call it with `quad_level=7`. This means the simulator will use gaussian quadrature sub-pixel integration to ensure the brightness of each pixel is very accurately computed."
+    "Here we write out the true values for all the parameters in the model. In total there are 21 parameters, so this is quite a complex model already! We then plot the data so we can see what it is we re trying to fit."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "7",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Generate the mock data\n",
@@ -178,7 +182,7 @@
     "print(true_params)\n",
     "\n",
     "# simulate lens, crop extra evaluation for PSF\n",
-    "true_system = sim(allparams, quad_level=7)  # simulate at high resolution\n",
+    "true_system = sim(allparams)\n",
     "\n",
     "fig, axarr = plt.subplots(1, 2, figsize=(15, 8))\n",
     "axarr[0].imshow(\n",
@@ -231,7 +235,7 @@
     "res = caustics.utils.batch_lm(\n",
     "    batch_inits,\n",
     "    obs_system.reshape(-1).repeat(10, 1),\n",
-    "    lambda x: sim(x, quad_level=3).reshape(-1),\n",
+    "    lambda x: sim(x).reshape(-1),\n",
     "    C=variance.reshape(-1).repeat(10, 1),\n",
     ")\n",
     "best_fit = res[0][np.argmin(res[2].numpy())]\n",
@@ -242,7 +246,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "10",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "print(best_fit, allparams)\n",
@@ -288,7 +296,7 @@
    "outputs": [],
    "source": [
     "# Compute jacobian\n",
-    "J = torch.func.jacfwd(lambda x: sim(x, quad_level=3))(best_fit)\n",
+    "J = torch.func.jacfwd(lambda x: sim(x))(best_fit)\n",
     "fig, axarr = plt.subplots(3, 7, figsize=(21, 9))\n",
     "for i, ax in enumerate(axarr.flatten()):\n",
     "    ax.imshow(J[..., i], origin=\"lower\")\n",
@@ -310,7 +318,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "14",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
    "outputs": [],
    "source": [
     "def corner_plot_covariance(\n",