Merge pull request #51 from miguelangel/fix_50

Fix #50 - Rename notebook folder to notebooks.

download_data.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-DATA_PATH=./notebook/data
+DATA_PATH=./notebooks/data
 mkdir -p $DATA_PATH
 wget https://query.data.world/s/qlidesn2wqntqjqie5nc7p3lsuvcon -O $DATA_PATH/security_master.csv.gz
 wget https://query.data.world/s/i2xn3byzbx3msm4gebstjjkqusanpt -O $DATA_PATH/stock_price_hist.csv.gz

notebook/04_portfolio_trade.ipynb → notebooks/04_portfolio_trade.ipynb

@@ -226,7 +226,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In case that there is no direct dataframe method for a particular logic, a Numba GPU kernel can be used to implement it. Some examples of customized GPU kernels in Numba can be found [here](https://github.com/rapidsai/gQuant/blob/master/notebook/05_customize_nodes.ipynb).\n",
+    "In case that there is no direct dataframe method for a particular logic, a Numba GPU kernel can be used to implement it. Some examples of customized GPU kernels in Numba can be found [here](https://github.com/rapidsai/gQuant/blob/master/notebooks/05_customize_nodes.ipynb).\n",
     "\n",
     "If we use customized GPU kernel functions inside the `process` method to process the dataframe instead of _normal_ dataframe API functions calls,  we need to add `self.delayed_process = True` in the `columns_setup` method to let gQuant handle the dask graph integration problem. If we use  _normal_ dataframe API functions inside the `process` method, nothing needs to be done as `self.delayed_process = False` by default.\n",
     "\n",

notebook/06_xgboost_trade.ipynb → notebooks/06_xgboost_trade.ipynb

@@ -183,7 +183,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The features used for XGBoost algorithm are prepared in the `node_technical_indicator` node, where `cuIndicator` module is used to compute the technical indicators in the GPU for all the stock symbols. `node_xgboost_strategy` is the node that is used to compute the trading signals from the stock technical indicators. Each of the gQuant node is implemented by overwriting \"columns_setup\" and \"process\" methods of the Node base class. Please refer to [customize nodes notebook](https://github.com/rapidsai/gQuant/blob/master/notebook/05_customize_nodes.ipynb) for details. Following is the source code for \"XGBoostStrategyNode\":"
+    "The features used for XGBoost algorithm are prepared in the `node_technical_indicator` node, where `cuIndicator` module is used to compute the technical indicators in the GPU for all the stock symbols. `node_xgboost_strategy` is the node that is used to compute the trading signals from the stock technical indicators. Each of the gQuant node is implemented by overwriting \"columns_setup\" and \"process\" methods of the Node base class. Please refer to [customize nodes notebook](https://github.com/rapidsai/gQuant/blob/master/notebooks/05_customize_nodes.ipynb) for details. Following is the source code for \"XGBoostStrategyNode\":"
    ]
   },
   {

notebook/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb → notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb

@@ -129,7 +129,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We create a worfklow by defining tasks and specifying their configuration (parameters) and inputs (for basics tutorial on gQuant refer to [01_tutorial.ipynb](https://github.com/rapidsai/gQuant/blob/master/notebook/01_tutorial.ipynb) and custom plugins [05_customize_nodes.ipynb](https://github.com/rapidsai/gQuant/blob/master/notebook/05_customize_nodes.ipynb)). The workflow to calculate the mortgage features and delinquecy is defined in the `mortgage_etl_workflow_def` function in `mortgage_common` module. Its code is shown below."
+    "We create a worfklow by defining tasks and specifying their configuration (parameters) and inputs (for basics tutorial on gQuant refer to [01_tutorial.ipynb](https://github.com/rapidsai/gQuant/blob/master/notebooks/01_tutorial.ipynb) and custom plugins [05_customize_nodes.ipynb](https://github.com/rapidsai/gQuant/blob/master/notebooks/05_customize_nodes.ipynb)). The workflow to calculate the mortgage features and delinquecy is defined in the `mortgage_etl_workflow_def` function in `mortgage_common` module. Its code is shown below."
    ]
   },
   {
@@ -1258,9 +1258,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py36-rapids",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "py36-rapids"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1272,7 +1272,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,