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@ -5,9 +5,11 @@ if [ "${date}" == "" ] ; then
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date="yesterday"
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fi
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SourceHost=cryptoval3.cvtt.vpn
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SourceHost=cloud21.cvtt.vpn
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SourceUser=cvtt
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SourceRootDir=/localdisk/cvtt/archive/md_archive
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SourceRootDir=/opt/store/cvtt/md_archive/crypto/cloud21
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#SourceRootDir=/opt/store/cvtt/md_archive/crypto/cvttdata
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SourceFile=$(date -d ${date} "+%Y%m%d.mktdata.db")
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SourceFileZip="${SourceFile}.gz"
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SourceFilePath=$(date -d ${date} "+${SourceRootDir}/%Y/%m/${SourceFileZip}")
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@ -1,5 +1,5 @@
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ROOT_DIR=/opt/jupyter_gpu/pytorch
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DATA_DIR=/opt/jupyter_gpu/data
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JUPYTER_PORT=8888
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PUID=1000
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PGID=1000
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# PUID=1000
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# PGID=1000
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@ -18,6 +18,7 @@ RUN apt-get install graphviz -y
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# Set environment variables, etc.
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#ENV MY_ENV_VAR=myvalue
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COPY cvttpy /cvttpy
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# Set the working directory
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WORKDIR /workspace
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@ -10,23 +10,19 @@ version: '3.8'
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#==================================================
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services:
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jupyter:
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user: "${UID}:${GID}"
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build: ${ROOT_DIR}
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image: cvtt/jupyter_gpu_pt:v1.1.5
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# user: "${UID}:${GID}"
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image: cloud21.cvtt.vpn:5500/jupyter_gpu_pytorch:latest
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container_name: jupyter_gpu_pt
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runtime: nvidia
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restart: unless-stopped
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environment:
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- JUPYTER_ENABLE_LAB=yes
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- PYTHONPATH=/cvtt/prod
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# security_opt:
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# - no-new-privileges:true
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volumes:
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- ${ROOT_DIR}/notebooks:/workspace
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- ${ROOT_DIR}/jupyter_settings/user-settings:/root/.jupyter/lab/user-settings
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- ${ROOT_DIR}/jupyter_settings/workspaces:/root/.jupyter/lab/workspaces
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- ${ROOT_DIR}/.cache/torch:/root/.cache/torch
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- ${ROOT_DIR}/cvtt:/cvtt
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- ${DATA_DIR}:/workspace/data
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ports:
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@ -246,7 +246,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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@ -20,6 +20,13 @@ RUN pip install plotly
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RUN pip install seaborn
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RUN pip install statsmodels
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RUN pip install scikit-learn
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RUN pip install Arch
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RUN pip install Quantlib
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RUN pip install Backtrader
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RUN pip install pyfolio
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RUN pip install XGBoost
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# Install additional applications
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RUN apt update
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RUN apt-get install graphviz -y
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "89251b34-9738-4fa3-b2db-7d6a7dde7d48",
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"metadata": {},
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"outputs": [],
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@ -24,7 +24,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"id": "eb24b805-ee73-4db4-b1de-0c43bea7d86f",
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"metadata": {},
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"outputs": [],
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@ -42,7 +42,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 5,
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"id": "8dcb0ec1-4e55-4d13-91fc-57c24fac6460",
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"metadata": {},
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"outputs": [
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@ -246,7 +246,7 @@
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"[5760 rows x 9 columns]"
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]
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},
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"execution_count": 14,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -210,25 +210,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": 179,
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"execution_count": 2,
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"id": "316c0cfb-c73d-4dad-9d49-eb80daa229ec",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"========================================\n",
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" date close_xrp volume_xrp vwap_xrp close_eth volume_eth vwap_eth \\\n",
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"0 1-0-0 0.5173 28809.165339 0.517217 0.376204 37.497964 0.376108 \n",
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"1 1-0-1 0.5170 10184.511212 0.517060 0.375942 11.703305 0.376013 \n",
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"2 1-0-10 0.5176 15515.348778 0.517601 0.375938 11.922897 0.375935 \n",
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"\n",
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" close_ltc volume_ltc vwap_ltc close_btc volume_btc vwap_btc \n",
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"0 0.8317 110.056253 0.831662 0.675060 2.045049 0.674969 \n",
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"1 0.8312 176.868598 0.831441 0.674947 0.646759 0.675009 \n",
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"2 0.8311 58.951033 0.831037 0.675507 1.201335 0.675478 \n",
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"test sample size: 16698\n"
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"ename": "NameError",
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"evalue": "name 'df_concat' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# BTC\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_concat_btc \u001b[38;5;241m=\u001b[39m \u001b[43mdf_concat\u001b[49m[df_concat[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minstrument_id\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPAIR-BTC-USD\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Reset Index\u001b[39;00m\n\u001b[1;32m 5\u001b[0m df_concat_btc \u001b[38;5;241m=\u001b[39m df_concat_btc\u001b[38;5;241m.\u001b[39mreset_index(drop \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'df_concat' is not defined"
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]
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}
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],
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@ -323,6 +317,36 @@
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"print (\"test sample size: \", sample_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b193e3d7-7907-4137-a63f-4695f76aae21",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'sample' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msample.shape[0] shape: \u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[43msample\u001b[49m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])\n",
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"\u001b[0;31mNameError\u001b[0m: name 'sample' is not defined"
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]
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}
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],
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"source": [
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"print ('sample.shape[0] shape: ', sample.shape[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "982cc6d4-28f7-4338-b51d-17666c90541c",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 177,
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