Clean up notebooks (#208)

* Remove jupyter notebook extensions, use jupyterlab * Remove .py jupytext sync files * clean up notebooks * run notebooks * fix diamond * ignore Harmenberg-Aggregation notebook
econ-ark · Nov 29, 2023 · 6d0aa34 · 6d0aa34
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diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
@@ -31,4 +31,4 @@ jobs:
       - name: Test with nbval
         shell: bash -l {0}
         run: |
-          python -m pytest --nbval-lax --nbval-cell-timeout=12000 --ignore=notebooks/Chinese-Growth.ipynb  notebooks/
+          python -m pytest --nbval-lax --nbval-cell-timeout=12000 --ignore=notebooks/Chinese-Growth.ipynb --ignore=notebooks/Harmenberg-Aggregation.ipynb notebooks/
diff --git a/binder/environment.yml b/binder/environment.yml
@@ -16,5 +16,4 @@ dependencies:
   - nbval
   - pip
   - pip:
-    - cite2c
     - git+https://github.com/econ-ark/hark@master
diff --git a/binder/postBuild b/binder/postBuild
diff --git a/binder/postBuild.bat b/binder/postBuild.bat
diff --git a/notebooks/Alternative-Combos-Of-Parameter-Values.ipynb b/notebooks/Alternative-Combos-Of-Parameter-Values.ipynb
@@ -8,10 +8,6 @@
     "\n",
     "[![badge](https://img.shields.io/badge/Launch%20using%20-Econ--ARK-blue)](https://econ-ark.org/materials/alternative-combos-of-parameter-values#launch)\n",
     "\n",
-    "Please write the names and email addresses of everyone who worked on this notebook on the line below.\n",
-    "\n",
-    "YOUR NAMES HERE\n",
-    "\n",
     "## Introduction\n",
     "\n",
     "The notebook \"Micro-and-Macro-Implications-of-Very-Impatient-HHs\" is an exercise that demonstrates the consequences of changing a key parameter of the [cstwMPC](http://www.econ2.jhu.edu/people/ccarroll/papers/cstwMPC) model, the time preference factor $\\beta$.\n",
@@ -46,21 +42,10 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "code_folding": [],
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:17.366724Z",
-     "iopub.status.busy": "2023-01-20T20:49:17.366724Z",
-     "iopub.status.idle": "2023-01-20T20:49:18.978028Z",
-     "shell.execute_reply": "2023-01-20T20:49:18.978028Z"
-    },
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This cell merely imports and sets up some basic functions and packages\n",
-    "\n",
-    "%matplotlib inline\n",
     "from HARK.utilities import get_lorenz_shares, get_percentiles\n",
     "from tqdm import tqdm\n",
     "import numpy as np"
@@ -69,18 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "code_folding": [
-     0,
-     4
-    ],
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:18.982029Z",
-     "iopub.status.busy": "2023-01-20T20:49:18.981028Z",
-     "iopub.status.idle": "2023-01-20T20:49:19.042547Z",
-     "shell.execute_reply": "2023-01-20T20:49:19.041546Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Import IndShockConsumerType\n",
@@ -133,14 +107,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:19.045547Z",
-     "iopub.status.busy": "2023-01-20T20:49:19.044548Z",
-     "iopub.status.idle": "2023-01-20T20:49:19.057547Z",
-     "shell.execute_reply": "2023-01-20T20:49:19.057547Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Construct a list of solved consumers' problems, IndShockConsumerType is just a place holder\n",
@@ -157,50 +124,19 @@
     "\n",
     "But the fact that everyone has the same target ${m}$ does not mean that the _distribution_ of ${m}$ will be the same for all of these consumer types.\n",
     "\n",
-    "In the code block below, fill in the contents of the loop to solve and simulate each agent type for many periods.  To do this, you should invoke the methods $\\texttt{solve}$, $\\texttt{initialize_sim}$, and $\\texttt{simulate}$ in that order.  Simulating for 1200 quarters (300 years) will approximate the long run distribution of wealth in the population."
+    "In the code block below, fill in the contents of the loop to solve and simulate each agent type for many periods.  To do this, you should invoke the methods $\\texttt{solve}$, $\\texttt{initialize\\_sim}$, and $\\texttt{simulate}$ in that order.  Simulating for 1200 quarters (300 years) will approximate the long run distribution of wealth in the population."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:19.060547Z",
-     "iopub.status.busy": "2023-01-20T20:49:19.060547Z",
-     "iopub.status.idle": "2023-01-20T20:49:22.343738Z",
-     "shell.execute_reply": "2023-01-20T20:49:22.343738Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      "  0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00,  3.26s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00,  3.27s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00,  3.03s/it]\n"
      ]
     }
    ],
@@ -221,14 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:22.346737Z",
-     "iopub.status.busy": "2023-01-20T20:49:22.345738Z",
-     "iopub.status.idle": "2023-01-20T20:49:22.359737Z",
-     "shell.execute_reply": "2023-01-20T20:49:22.359737Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# To help you out, we have given you the command needed to construct a list of the levels of assets for all consumers\n",
@@ -258,15 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:22.362741Z",
-     "iopub.status.busy": "2023-01-20T20:49:22.362741Z",
-     "iopub.status.idle": "2023-01-20T20:49:22.375738Z",
-     "shell.execute_reply": "2023-01-20T20:49:22.375738Z"
-    },
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Finish filling in this function to calculate the Euclidean distance between the simulated and actual Lorenz curves.\n",
@@ -306,9 +227,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "source": [
     "## ...and the Marginal Propensity to Consume\n",
     "\n",
@@ -324,38 +243,31 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-01-20T20:49:22.378741Z",
-     "iopub.status.busy": "2023-01-20T20:49:22.377739Z",
-     "iopub.status.idle": "2023-01-20T20:49:22.391737Z",
-     "shell.execute_reply": "2023-01-20T20:49:22.391737Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The 5.0th percentile of the MPC is 0.3830226479018113\n",
+      "The 5.0th percentile of the MPC is 0.3830226479018095\n",
       "The 10.0th percentile of the MPC is 0.4190098031734306\n",
-      "The 15.0th percentile of the MPC is 0.4598470116058213\n",
-      "The 20.0th percentile of the MPC is 0.4598470116058213\n",
-      "The 25.0th percentile of the MPC is 0.4598470116058213\n",
-      "The 30.0th percentile of the MPC is 0.4979166414954129\n",
-      "The 35.0th percentile of the MPC is 0.4979166414954129\n",
-      "The 40.0th percentile of the MPC is 0.4979166414954129\n",
-      "The 44.99999999999999th percentile of the MPC is 0.5372418610399285\n",
-      "The 49.99999999999999th percentile of the MPC is 0.5372418610399285\n",
-      "The 54.99999999999999th percentile of the MPC is 0.5372418610399285\n",
-      "The 60.0th percentile of the MPC is 0.5821887061769021\n",
-      "The 65.0th percentile of the MPC is 0.5821887061769021\n",
-      "The 70.0th percentile of the MPC is 0.6345373126858305\n",
-      "The 75.0th percentile of the MPC is 0.6345373126858305\n",
-      "The 80.0th percentile of the MPC is 0.7267307307276039\n",
+      "The 15.0th percentile of the MPC is 0.45984701160581964\n",
+      "The 20.0th percentile of the MPC is 0.45984701160581964\n",
+      "The 25.0th percentile of the MPC is 0.45984701160581964\n",
+      "The 30.0th percentile of the MPC is 0.4979166414954148\n",
+      "The 35.0th percentile of the MPC is 0.4979166414954148\n",
+      "The 40.0th percentile of the MPC is 0.4979166414954148\n",
+      "The 44.99999999999999th percentile of the MPC is 0.5372418610399308\n",
+      "The 49.99999999999999th percentile of the MPC is 0.5372418610399308\n",
+      "The 54.99999999999999th percentile of the MPC is 0.5372418610399308\n",
+      "The 60.0th percentile of the MPC is 0.5821887061768969\n",
+      "The 65.0th percentile of the MPC is 0.5821887061768969\n",
+      "The 70.0th percentile of the MPC is 0.634537312685834\n",
+      "The 75.0th percentile of the MPC is 0.634537312685834\n",
+      "The 80.0th percentile of the MPC is 0.7267307307276032\n",
       "The 85.0th percentile of the MPC is 0.7799255201452847\n",
-      "The 90.0th percentile of the MPC is 0.8208530902866041\n",
-      "The 95.0th percentile of the MPC is 0.8966083611183644\n"
+      "The 90.0th percentile of the MPC is 0.8208530902866055\n",
+      "The 95.0th percentile of the MPC is 0.8966083611183647\n"
      ]
     }
    ],
@@ -411,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.13"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,