test_usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from csdids.ATTgt import ATTgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "import warnings\n",
    "findspark.init()\n",
    "\n",
    "\n",
    "from pyspark.sql import  SparkSession, Row\n",
    "from pyspark.sql.functions import \\\n",
    "\tlit, col, when, expr, countDistinct,\\\n",
    "\tmonotonically_increasing_id, desc\n",
    "spark = SparkSession.builder.appName('test_preprocesdid')\\\n",
    "    .config('spark.master', 'local[4]')\\\n",
    "    .config('spark.executor.memory', '1g')\\\n",
    "    .config(\"spark.sql.shuffle.partitions\", 1)\\\n",
    "    .config('spark.driver.memory','4g')\\\n",
    "    .config('spark.driver.cores', '5')\\\n",
    "    .config('spark.rdd.compress', True)\\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:30<00:00,  3.34s/it]\n",
      "100%|██████████| 9/9 [00:30<00:00,  3.36s/it]\n",
      "100%|██████████| 9/9 [00:29<00:00,  3.23s/it]\n",
      "100%|██████████| 9/9 [00:29<00:00,  3.24s/it]\n",
      "100%|██████████| 9/9 [00:29<00:00,  3.29s/it]\n",
      "100%|██████████| 5/5 [02:28<00:00, 29.65s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 19s\n",
      "Wall time: 2min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import os\n",
    "\n",
    "name = r'D:\\Workflow\\work\\csdid_r\\R\\5g10t.csv'\n",
    "\n",
    "yname, gname, idname, tname = 'Y', 'G', 'id', 'period'\n",
    "\n",
    "\n",
    "data = spark.read.csv(name, header=True, inferSchema=True)\n",
    "g5 = ATTgt(data=data, tname=tname, gname=gname, yname=yname, idname=idname)\n",
    "g5.fit(bstrap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Time</th>\n",
       "      <th>ATT(g, t)</th>\n",
       "      <th>Post</th>\n",
       "      <th>Std. Error</th>\n",
       "      <th>[95% Pointwise</th>\n",
       "      <th>Conf. Band]</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9102</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0299</td>\n",
       "      <td>0.8192</td>\n",
       "      <td>1.0013</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.9856</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>1.1159</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.1465</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>0.9842</td>\n",
       "      <td>1.3087</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7958</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0670</td>\n",
       "      <td>1.5918</td>\n",
       "      <td>1.9999</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4.2931</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>4.0544</td>\n",
       "      <td>4.5318</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4.3900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0870</td>\n",
       "      <td>4.1252</td>\n",
       "      <td>4.6548</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4.4759</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>4.7954</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4.5609</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1165</td>\n",
       "      <td>4.2061</td>\n",
       "      <td>4.9157</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4.6524</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1313</td>\n",
       "      <td>4.2525</td>\n",
       "      <td>5.0522</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1092</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.1971</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.2992</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>1.2123</td>\n",
       "      <td>1.3861</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5296</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>1.3962</td>\n",
       "      <td>1.6630</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.2077</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>2.0441</td>\n",
       "      <td>2.3714</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4.7688</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0741</td>\n",
       "      <td>4.5434</td>\n",
       "      <td>4.9943</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4.9001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>4.6278</td>\n",
       "      <td>5.1724</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0421</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1082</td>\n",
       "      <td>4.7128</td>\n",
       "      <td>5.3715</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5.1906</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1172</td>\n",
       "      <td>4.8337</td>\n",
       "      <td>5.5476</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>5.3417</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1321</td>\n",
       "      <td>4.9394</td>\n",
       "      <td>5.7439</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1696</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.2536</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5914</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.4962</td>\n",
       "      <td>0.6867</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.7568</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0330</td>\n",
       "      <td>1.6563</td>\n",
       "      <td>1.8574</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4994</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>2.3455</td>\n",
       "      <td>2.6533</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0916</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>4.8873</td>\n",
       "      <td>5.2959</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2788</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>5.0285</td>\n",
       "      <td>5.5290</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5.4633</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>5.1764</td>\n",
       "      <td>5.7502</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5.6295</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>5.2945</td>\n",
       "      <td>5.9645</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5.8398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1263</td>\n",
       "      <td>5.4554</td>\n",
       "      <td>6.2242</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2622</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>0.1781</td>\n",
       "      <td>0.3462</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.6981</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.6085</td>\n",
       "      <td>0.7877</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.4631</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>1.3355</td>\n",
       "      <td>1.5907</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2.6726</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0444</td>\n",
       "      <td>2.5373</td>\n",
       "      <td>2.8079</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5.3312</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0644</td>\n",
       "      <td>5.1352</td>\n",
       "      <td>5.5271</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5.5906</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0791</td>\n",
       "      <td>5.3498</td>\n",
       "      <td>5.8315</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5.8271</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0937</td>\n",
       "      <td>5.5420</td>\n",
       "      <td>6.1123</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6.0784</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1074</td>\n",
       "      <td>5.7514</td>\n",
       "      <td>6.4054</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>6.3586</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1223</td>\n",
       "      <td>5.9862</td>\n",
       "      <td>6.7311</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3032</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.2187</td>\n",
       "      <td>0.3876</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.7788</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.6801</td>\n",
       "      <td>0.8774</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5937</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>1.4802</td>\n",
       "      <td>1.7071</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0706</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>3.9094</td>\n",
       "      <td>4.2318</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5.4074</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0622</td>\n",
       "      <td>5.2182</td>\n",
       "      <td>5.5967</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5.7191</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>5.4924</td>\n",
       "      <td>5.9457</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6.0084</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0839</td>\n",
       "      <td>5.7530</td>\n",
       "      <td>6.2638</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6.2930</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0996</td>\n",
       "      <td>5.9896</td>\n",
       "      <td>6.5964</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6.6090</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1096</td>\n",
       "      <td>6.2753</td>\n",
       "      <td>6.9427</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Group  Time  ATT(g, t)  Post  Std. Error  [95% Pointwise  Conf. Band]   \n",
       "0       2     2     0.9102     1      0.0299          0.8192       1.0013  *\n",
       "1       2     3     0.9856     1      0.0428          0.8554       1.1159  *\n",
       "2       2     4     1.1465     1      0.0533          0.9842       1.3087  *\n",
       "3       2     5     1.7958     1      0.0670          1.5918       1.9999  *\n",
       "4       2     6     4.2931     1      0.0784          4.0544       4.5318  *\n",
       "5       2     7     4.3900     1      0.0870          4.1252       4.6548  *\n",
       "6       2     8     4.4759     1      0.1050          4.1563       4.7954  *\n",
       "7       2     9     4.5609     1      0.1165          4.2061       4.9157  *\n",
       "8       2    10     4.6524     1      0.1313          4.2525       5.0522  *\n",
       "9       3     2     0.1092     0      0.0289          0.0214       0.1971  *\n",
       "10      3     3     1.2992     1      0.0286          1.2123       1.3861  *\n",
       "11      3     4     1.5296     1      0.0438          1.3962       1.6630  *\n",
       "12      3     5     2.2077     1      0.0538          2.0441       2.3714  *\n",
       "13      3     6     4.7688     1      0.0741          4.5434       4.9943  *\n",
       "14      3     7     4.9001     1      0.0894          4.6278       5.1724  *\n",
       "15      3     8     5.0421     1      0.1082          4.7128       5.3715  *\n",
       "16      3     9     5.1906     1      0.1172          4.8337       5.5476  *\n",
       "17      3    10     5.3417     1      0.1321          4.9394       5.7439  *\n",
       "18      4     2     0.1696     0      0.0276          0.0855       0.2536  *\n",
       "19      4     3     0.5914     0      0.0313          0.4962       0.6867  *\n",
       "20      4     4     1.7568     1      0.0330          1.6563       1.8574  *\n",
       "21      4     5     2.4994     1      0.0505          2.3455       2.6533  *\n",
       "22      4     6     5.0916     1      0.0671          4.8873       5.2959  *\n",
       "23      4     7     5.2788     1      0.0822          5.0285       5.5290  *\n",
       "24      4     8     5.4633     1      0.0942          5.1764       5.7502  *\n",
       "25      4     9     5.6295     1      0.1100          5.2945       5.9645  *\n",
       "26      4    10     5.8398     1      0.1263          5.4554       6.2242  *\n",
       "27      5     2     0.2622     0      0.0276          0.1781       0.3462  *\n",
       "28      5     3     0.6981     0      0.0294          0.6085       0.7877  *\n",
       "29      5     4     1.4631     0      0.0419          1.3355       1.5907  *\n",
       "30      5     5     2.6726     1      0.0444          2.5373       2.8079  *\n",
       "31      5     6     5.3312     1      0.0644          5.1352       5.5271  *\n",
       "32      5     7     5.5906     1      0.0791          5.3498       5.8315  *\n",
       "33      5     8     5.8271     1      0.0937          5.5420       6.1123  *\n",
       "34      5     9     6.0784     1      0.1074          5.7514       6.4054  *\n",
       "35      5    10     6.3586     1      0.1223          5.9862       6.7311  *\n",
       "36      6     2     0.3032     0      0.0277          0.2187       0.3876  *\n",
       "37      6     3     0.7788     0      0.0324          0.6801       0.8774  *\n",
       "38      6     4     1.5937     0      0.0373          1.4802       1.7071  *\n",
       "39      6     5     4.0706     0      0.0529          3.9094       4.2318  *\n",
       "40      6     6     5.4074     1      0.0622          5.2182       5.5967  *\n",
       "41      6     7     5.7191     1      0.0744          5.4924       5.9457  *\n",
       "42      6     8     6.0084     1      0.0839          5.7530       6.2638  *\n",
       "43      6     9     6.2930     1      0.0996          5.9896       6.5964  *\n",
       "44      6    10     6.6090     1      0.1096          6.2753       6.9427  *"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g5.sum_gt().summary2_gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "   ATT Std. Error [95.0%  Conf. Int.]  \n",
      "4.3129     0.0656 4.1842       4.4415 *\n",
      "\n",
      "\n",
      "---\n",
      "Signif. codes: `*' confidence band does not cover 0\n",
      "Control Group:  None , \n",
      "Anticipation Periods:  0\n",
      "Estimation Method:  Doubly Robust\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Overall summary of ATT's based on group/cohort aggregation:\n",
      "   ATT Std. Error [95.0%  Conf. Int.]  \n",
      "4.5278     0.0652    4.4       4.6556 *\n",
      "\n",
      "\n",
      "Group Effects:\n",
      "   Group  Estimate  Std. Error  [95.0% Simult.   Conf. Band   \n",
      "0      2    3.0234      0.0760           2.8743      3.1724  *\n",
      "1      3    3.7850      0.0781           3.6320      3.9380  *\n",
      "2      4    4.5084      0.0745           4.3625      4.6544  *\n",
      "3      5    5.3098      0.0784           5.1561      5.4634  *\n",
      "4      6    6.0074      0.0881           5.8348      6.1800  *\n",
      "---\n",
      "Signif. codes: `*' confidence band does not cover 0\n",
      "Control Group:  None , \n",
      "Anticipation Periods:  0\n",
      "Estimation Method:  Doubly Robust\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Overall summary of ATT's based on calendar time aggregation:\n",
      "   ATT Std. Error [95.0%  Conf. Int.]  \n",
      "3.6279     0.0571  3.516       3.7398 *\n",
      "\n",
      "\n",
      "Time Effects (calendar):\n",
      "   Time  Estimate  Std. Error  [95.0% Simult.   Conf. Band   \n",
      "0     2    0.9102      0.0277           0.8560      0.9644  *\n",
      "1     3    1.1426      0.0288           1.0860      1.1991  *\n",
      "2     4    1.4758      0.0344           1.4084      1.5432  *\n",
      "3     5    2.2933      0.0454           2.2043      2.3823  *\n",
      "4     6    4.9784      0.0634           4.8542      5.1026  *\n",
      "5     7    5.1758      0.0765           5.0258      5.3257  *\n",
      "6     8    5.3635      0.0890           5.1891      5.5378  *\n",
      "7     9    5.5508      0.1026           5.3496      5.7519  *\n",
      "8    10    5.7607      0.1113           5.5424      5.9789  *\n",
      "---\n",
      "Signif. codes: `*' confidence band does not cover 0\n",
      "Control Group:  None , \n",
      "Anticipation Periods:  0\n",
      "Estimation Method:  Doubly Robust\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "g5.compute_aggte(na_rm=False, typec='simple')\n",
    "g5.compute_aggte(na_rm=False, typec='group')\n",
    "g5.compute_aggte(na_rm=False, typec='calendar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g5.plot_attgt();\n",
    "g5.plot_aggte(typec='calendar');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "csdid_r-1-mgsrfE",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}