Merge pull request #174 from astro-informatics/documentation

add explicit doc link to readme

Author: CosmoMatt

README.md

@@ -121,6 +121,8 @@ flmn = s2fft.wigner.forward_jax(f, L, N)
 f = fft.wigner.inverse_jax(flmn, L, N)
 ```
 
+For further details on usage see the [documentation](https://astro-informatics.github.io/s2fft/) and associated [notebooks](https://astro-informatics.github.io/s2fft/tutorials/spherical_harmonic/spherical_harmonic_transform.html).
+
 ## Benchmarking :hourglass_flowing_sand:
 
 We benchmarked the spherical harmonic and Wigner transforms implemented

notebooks/spherical_harmonic_transform.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,9 +50,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e24324bbdc364d63ae9f34f68d19aba4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value=\"<iframe src='http://localhost:38091/index.html?ui=P_0x7f3bd2a6dfd0_0&reconnect=auto' style='widt…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotting_functions.plot_sphere(f, L, sampling, isnotebook=True)"
    ]
@@ -71,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,18 +164,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 4.2041881386973936e-13\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Mean absolute error = {np.nanmean(np.abs(np.real(f_recov) - f))}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error using precomputes = 4.2041881386973936e-13\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Mean absolute error using precomputes = {np.nanmean(np.abs(np.real(f_recov_pre) - f))}\")"
    ]

notebooks/wigner_transform.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,26 +131,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 5.126898555332371e-14\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Mean absolute error = {np.nanmean(np.abs(flmn_recov - flmn))}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error using precomputes = 5.126898555332371e-14\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Mean absolute error using precomputes = {np.nanmean(np.abs(flmn_recov_pre - flmn))}\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.0 ('s2fft')",
+   "display_name": "Python 3.8.16 64-bit ('s2fft')",
    "language": "python",
    "name": "python3"
   },
@@ -164,12 +180,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.8.16"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "3425e24474cbe920550266ea26b478634978cc419579f9dbcf479231067df6a3"
+    "hash": "d6019e21eb0d27eebd69283f1089b8b605b46cb058a452b887458f3af7017e46"
    }
   }
  },