rerun workflow

dockerHDDM_Workflow.ipynb

@@ -11,14 +11,15 @@
     "\n",
     "- Wanke Pan ([email protected]) @Nanjing Normal University\n",
     "- Hu Chuan-Peng (corresponding author, [email protected]) @Nanjing Normal University\n",
+    "- Ru-Yuan Zhang (corresponding author, [email protected]) @Shanghai Jiao Tong University\n",
     "\n",
     "This is a supplementary notebook for the introductary paper *A Hitchhiker’s Guide to Bayesian Hierarchical Drift-Diffusion Modeling with dockerHDDM*. \n",
     "\n",
-    "This notebook was tested in docker image [`hcp4715/hddm:0.9.8-amd64`](https://hub.docker.com/r/hcp4715/hddm/tags), where the packages `hddm` and `kabuki` were rectfied to RC version as below code box.\n",
+    "This notebook was tested in docker image [`hcp4715/hddm:0.9.8-amd64`](https://hub.docker.com/r/hcp4715/hddm/tags), where the packages `kabuki` were rectfied to RC version as below code box.\n",
     "\n",
     "Preprint of this manuscript: https://psyarxiv.com/6uzga/\n",
     "\n",
-    "![workflow](./figs/fig4/workflow.png)"
+    "Github repository at: https://github.com/hcp4715/dockerHDDM"
    ]
   },
   {
@@ -31,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "d5205ced",
    "metadata": {
     "scrolled": true
@@ -44,7 +45,7 @@
       "The current HDDM version is:  0.9.8RC\n",
       "The current kabuki version is:  0.6.5RC3\n",
       "The current PyMC version is:  2.3.8\n",
-      "The current ArviZ version is:  0.14.0\n"
+      "The current ArviZ version is:  0.15.1\n"
      ]
     }
    ],
@@ -75,54 +76,6 @@
     "print(\"The current ArviZ version is: \", az.__version__)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9f8c78f3-73f1-4965-a3e1-56a36d59877e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2min 1s, sys: 9.51 s, total: 2min 10s\n",
-      "Wall time: 2min 10s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "m0_infdata = az.from_netcdf(\"model_fitted/m0.nc\")\n",
-    "m1_infdata = az.from_netcdf(\"model_fitted/m1.nc\")\n",
-    "m2_infdata = az.from_netcdf(\"model_fitted/m2.nc\")\n",
-    "m3_infdata = az.from_netcdf(\"model_fitted/m3.nc\")\n",
-    "m4_infdata = az.from_netcdf(\"model_fitted/m4.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "98679226-30c0-427e-b870-bb28c08013f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8.13 s, sys: 194 ms, total: 8.33 s\n",
-      "Wall time: 8.55 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "m0 = hddm.load(\"model_fitted/m0.hddm\")\n",
-    "m1 = hddm.load(\"model_fitted/m1.hddm\")\n",
-    "m2 = hddm.load(\"model_fitted/m2.hddm\")\n",
-    "m3 = hddm.load(\"model_fitted/m3.hddm\")\n",
-    "m4 = hddm.load(\"model_fitted/m4.hddm\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "03047e29",
@@ -164407,7 +164360,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.ravel()[0].figure.savefig(\"figs/fig6a.pdf\")"
+    "axes.ravel()[0].figure.savefig(\"fig6a.pdf\")"
    ]
   },
   {
@@ -165832,7 +165785,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "154e5507",
+   "id": "d49561a2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165849,7 +165802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "656765cc",
+   "id": "1a38226c",
    "metadata": {},
    "source": [
     "First, let's spend some time generating posterior predictions. Note that we set `n_ppc` is 500 means we generate 500 samples for each draws in each parameters. "
@@ -165906,7 +165859,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2b39874-7d80-452e-af72-2d64dea76a1e",
+   "id": "04473921",
    "metadata": {},
    "outputs": [
     {
@@ -165947,7 +165900,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.ravel()[0].figure.savefig(\"figs/fig7a1.pdf\")"
+    "axes.ravel()[0].figure.savefig(\"fig7a1.pdf\")"
    ]
   },
   {
@@ -165994,7 +165947,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.ravel()[0].figure.savefig(\"figs/fig7a2.pdf\")"
+    "axes.ravel()[0].figure.savefig(\"fig7a2.pdf\")"
    ]
   },
   {
@@ -166052,12 +166005,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.ravel()[0].figure.savefig(\"figs/fig7b1.pdf\")"
+    "axes.ravel()[0].figure.savefig(\"fig7b1.pdf\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "96c97eab",
    "metadata": {},
    "outputs": [
     {
@@ -166099,7 +166053,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.ravel()[0].figure.savefig(\"figs/fig7b2.pdf\")"
+    "axes.ravel()[0].figure.savefig(\"fig7b2.pdf\")"
    ]
   },
   {
@@ -166126,7 +166080,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aa606c40",
+   "id": "b5762ceb",
    "metadata": {},
    "outputs": [
     {
@@ -166171,7 +166125,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.figure.savefig(\"figs/fig8a.pdf\")"
+    "axes.figure.savefig(\"fig8a.pdf\")"
    ]
   },
   {
@@ -166241,7 +166195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3945a07",
+   "id": "91498072",
    "metadata": {},
    "outputs": [
     {
@@ -166281,7 +166235,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84965ad3",
+   "id": "ae13df5b",
    "metadata": {},
    "outputs": [
     {
@@ -166315,7 +166269,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes.ravel()[0].figure.savefig(\"figs/fig8b.pdf\")"
+    "axes.ravel()[0].figure.savefig(\"fig8b.pdf\")"
    ]
   },
   {