update,
This commit is contained in:
louiscklaw
7
Man1130/gitUpdate.bat
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Man1130/gitUpdate.bat
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git status .
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@pause
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git add .
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git commit -m"update Man1130,"
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start git push
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22
Man1130/jupyter/Man1130-python-comission/.gitignore
vendored
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22
Man1130/jupyter/Man1130-python-comission/.gitignore
vendored
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**~*
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**.cache
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||||
**.Trash*
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||||
# Created by https://www.toptal.com/developers/gitignore/api/jupyternotebooks
|
||||
# Edit at https://www.toptal.com/developers/gitignore?templates=jupyternotebooks
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||||
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||||
### JupyterNotebooks ###
|
||||
# gitignore template for Jupyter Notebooks
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||||
# website: http://jupyter.org/
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||||
.ipynb_checkpoints
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*/.ipynb_checkpoints/*
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||||
# IPython
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profile_default/
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ipython_config.py
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# Remove previous ipynb_checkpoints
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# git rm -r .ipynb_checkpoints/
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||||
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# End of https://www.toptal.com/developers/gitignore/api/jupyternotebooks
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||||
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{
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||||
"cells": [
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"# Supervised Learning\n",
|
||||
"## KNN\n",
|
||||
"> - KNN is a non-parametric learning algorithm (No assumption is made on the data) \n",
|
||||
"> - KNN can be used for classification (discrte) and regression (continuous label) \n",
|
||||
"> - All training data has to be present to determine the label of new data \n",
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"> - Sensitive to irrelavant features \n",
|
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"> - Sensitive to scale of data \n",
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"### Issues:\n",
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"> - Choose number of neighors *k* \n",
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"> - Choose distance metric "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data\n",
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"### Breast Cancer\n",
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"> **Label**: Malignant or Benign \n",
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"> **30 Features**: Radius, Texture, Perimeter, Area, Smoothness, etc"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data"
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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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"metadata": {},
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/html": [
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||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
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||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
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" }\n",
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"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>mean radius</th>\n",
|
||||
" <th>mean texture</th>\n",
|
||||
" <th>mean perimeter</th>\n",
|
||||
" <th>mean area</th>\n",
|
||||
" <th>mean smoothness</th>\n",
|
||||
" <th>mean compactness</th>\n",
|
||||
" <th>mean concavity</th>\n",
|
||||
" <th>mean concave points</th>\n",
|
||||
" <th>mean symmetry</th>\n",
|
||||
" <th>mean fractal dimension</th>\n",
|
||||
" <th>...</th>\n",
|
||||
" <th>worst radius</th>\n",
|
||||
" <th>worst texture</th>\n",
|
||||
" <th>worst perimeter</th>\n",
|
||||
" <th>worst area</th>\n",
|
||||
" <th>worst smoothness</th>\n",
|
||||
" <th>worst compactness</th>\n",
|
||||
" <th>worst concavity</th>\n",
|
||||
" <th>worst concave points</th>\n",
|
||||
" <th>worst symmetry</th>\n",
|
||||
" <th>worst fractal dimension</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>17.99</td>\n",
|
||||
" <td>10.38</td>\n",
|
||||
" <td>122.80</td>\n",
|
||||
" <td>1001.0</td>\n",
|
||||
" <td>0.11840</td>\n",
|
||||
" <td>0.27760</td>\n",
|
||||
" <td>0.3001</td>\n",
|
||||
" <td>0.14710</td>\n",
|
||||
" <td>0.2419</td>\n",
|
||||
" <td>0.07871</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>25.38</td>\n",
|
||||
" <td>17.33</td>\n",
|
||||
" <td>184.60</td>\n",
|
||||
" <td>2019.0</td>\n",
|
||||
" <td>0.1622</td>\n",
|
||||
" <td>0.6656</td>\n",
|
||||
" <td>0.7119</td>\n",
|
||||
" <td>0.2654</td>\n",
|
||||
" <td>0.4601</td>\n",
|
||||
" <td>0.11890</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>20.57</td>\n",
|
||||
" <td>17.77</td>\n",
|
||||
" <td>132.90</td>\n",
|
||||
" <td>1326.0</td>\n",
|
||||
" <td>0.08474</td>\n",
|
||||
" <td>0.07864</td>\n",
|
||||
" <td>0.0869</td>\n",
|
||||
" <td>0.07017</td>\n",
|
||||
" <td>0.1812</td>\n",
|
||||
" <td>0.05667</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>24.99</td>\n",
|
||||
" <td>23.41</td>\n",
|
||||
" <td>158.80</td>\n",
|
||||
" <td>1956.0</td>\n",
|
||||
" <td>0.1238</td>\n",
|
||||
" <td>0.1866</td>\n",
|
||||
" <td>0.2416</td>\n",
|
||||
" <td>0.1860</td>\n",
|
||||
" <td>0.2750</td>\n",
|
||||
" <td>0.08902</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>19.69</td>\n",
|
||||
" <td>21.25</td>\n",
|
||||
" <td>130.00</td>\n",
|
||||
" <td>1203.0</td>\n",
|
||||
" <td>0.10960</td>\n",
|
||||
" <td>0.15990</td>\n",
|
||||
" <td>0.1974</td>\n",
|
||||
" <td>0.12790</td>\n",
|
||||
" <td>0.2069</td>\n",
|
||||
" <td>0.05999</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>23.57</td>\n",
|
||||
" <td>25.53</td>\n",
|
||||
" <td>152.50</td>\n",
|
||||
" <td>1709.0</td>\n",
|
||||
" <td>0.1444</td>\n",
|
||||
" <td>0.4245</td>\n",
|
||||
" <td>0.4504</td>\n",
|
||||
" <td>0.2430</td>\n",
|
||||
" <td>0.3613</td>\n",
|
||||
" <td>0.08758</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>11.42</td>\n",
|
||||
" <td>20.38</td>\n",
|
||||
" <td>77.58</td>\n",
|
||||
" <td>386.1</td>\n",
|
||||
" <td>0.14250</td>\n",
|
||||
" <td>0.28390</td>\n",
|
||||
" <td>0.2414</td>\n",
|
||||
" <td>0.10520</td>\n",
|
||||
" <td>0.2597</td>\n",
|
||||
" <td>0.09744</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>14.91</td>\n",
|
||||
" <td>26.50</td>\n",
|
||||
" <td>98.87</td>\n",
|
||||
" <td>567.7</td>\n",
|
||||
" <td>0.2098</td>\n",
|
||||
" <td>0.8663</td>\n",
|
||||
" <td>0.6869</td>\n",
|
||||
" <td>0.2575</td>\n",
|
||||
" <td>0.6638</td>\n",
|
||||
" <td>0.17300</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>20.29</td>\n",
|
||||
" <td>14.34</td>\n",
|
||||
" <td>135.10</td>\n",
|
||||
" <td>1297.0</td>\n",
|
||||
" <td>0.10030</td>\n",
|
||||
" <td>0.13280</td>\n",
|
||||
" <td>0.1980</td>\n",
|
||||
" <td>0.10430</td>\n",
|
||||
" <td>0.1809</td>\n",
|
||||
" <td>0.05883</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>22.54</td>\n",
|
||||
" <td>16.67</td>\n",
|
||||
" <td>152.20</td>\n",
|
||||
" <td>1575.0</td>\n",
|
||||
" <td>0.1374</td>\n",
|
||||
" <td>0.2050</td>\n",
|
||||
" <td>0.4000</td>\n",
|
||||
" <td>0.1625</td>\n",
|
||||
" <td>0.2364</td>\n",
|
||||
" <td>0.07678</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>5 rows × 30 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
|
||||
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
|
||||
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
|
||||
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
|
||||
"3 11.42 20.38 77.58 386.1 0.14250 \n",
|
||||
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
|
||||
"\n",
|
||||
" mean compactness mean concavity mean concave points mean symmetry \\\n",
|
||||
"0 0.27760 0.3001 0.14710 0.2419 \n",
|
||||
"1 0.07864 0.0869 0.07017 0.1812 \n",
|
||||
"2 0.15990 0.1974 0.12790 0.2069 \n",
|
||||
"3 0.28390 0.2414 0.10520 0.2597 \n",
|
||||
"4 0.13280 0.1980 0.10430 0.1809 \n",
|
||||
"\n",
|
||||
" mean fractal dimension ... worst radius worst texture worst perimeter \\\n",
|
||||
"0 0.07871 ... 25.38 17.33 184.60 \n",
|
||||
"1 0.05667 ... 24.99 23.41 158.80 \n",
|
||||
"2 0.05999 ... 23.57 25.53 152.50 \n",
|
||||
"3 0.09744 ... 14.91 26.50 98.87 \n",
|
||||
"4 0.05883 ... 22.54 16.67 152.20 \n",
|
||||
"\n",
|
||||
" worst area worst smoothness worst compactness worst concavity \\\n",
|
||||
"0 2019.0 0.1622 0.6656 0.7119 \n",
|
||||
"1 1956.0 0.1238 0.1866 0.2416 \n",
|
||||
"2 1709.0 0.1444 0.4245 0.4504 \n",
|
||||
"3 567.7 0.2098 0.8663 0.6869 \n",
|
||||
"4 1575.0 0.1374 0.2050 0.4000 \n",
|
||||
"\n",
|
||||
" worst concave points worst symmetry worst fractal dimension \n",
|
||||
"0 0.2654 0.4601 0.11890 \n",
|
||||
"1 0.1860 0.2750 0.08902 \n",
|
||||
"2 0.2430 0.3613 0.08758 \n",
|
||||
"3 0.2575 0.6638 0.17300 \n",
|
||||
"4 0.1625 0.2364 0.07678 \n",
|
||||
"\n",
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||||
"[5 rows x 30 columns]"
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||||
]
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||||
},
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||||
"execution_count": 1,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.datasets import load_breast_cancer\n",
|
||||
"\n",
|
||||
"breast_cancer = load_breast_cancer()\n",
|
||||
"X = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)\n",
|
||||
"X.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create an X with 2 features only\n",
|
||||
"\n",
|
||||
"X = X[['mean area', 'mean compactness']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
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"metadata": {},
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||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['malignant', 'benign'], dtype='<U9')"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"breast_cancer.target_names"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
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||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['malignant', 'malignant', 'malignant', 'malignant', 'malignant', ..., 'malignant', 'malignant', 'malignant', 'malignant', 'benign']\n",
|
||||
"Length: 569\n",
|
||||
"Categories (2, object): ['malignant', 'benign']"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y = pd.Categorical.from_codes(codes=breast_cancer.target, categories=breast_cancer.target_names)\n",
|
||||
"y"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>benign</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" benign\n",
|
||||
"0 0\n",
|
||||
"1 0\n",
|
||||
"2 0\n",
|
||||
"3 0\n",
|
||||
"4 0"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y = pd.get_dummies(y, drop_first=True) \n",
|
||||
"y.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(426, 1)\n",
|
||||
"(143, 1)\n",
|
||||
"<class 'pandas.core.frame.DataFrame'>\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=1)\n",
|
||||
"print(ytrain.shape)\n",
|
||||
"print(ytest.shape)\n",
|
||||
"print(type(ytest))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.neighbors import KNeighborsClassifier\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"knn = KNeighborsClassifier(n_neighbors=5, metric='euclidean')\n",
|
||||
"knn.fit(Xtrain, ytrain.to_numpy().ravel())\n",
|
||||
"ypred = knn.predict(Xtest).reshape([143,1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Prediction</th>\n",
|
||||
" <th>Actual</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Prediction Actual\n",
|
||||
"0 1 1\n",
|
||||
"1 1 0\n",
|
||||
"2 1 1\n",
|
||||
"3 0 0\n",
|
||||
"4 0 0"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.DataFrame(np.hstack([ypred,ytest]),columns=['Prediction','Actual']).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[42 13]\n",
|
||||
" [ 9 79]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import confusion_matrix\n",
|
||||
"print(confusion_matrix(ytest, ypred))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.11.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,151 @@
|
||||
Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
|
||||
1,5.1,3.5,1.4,0.2,Iris-setosa
|
||||
2,4.9,3.0,1.4,0.2,Iris-setosa
|
||||
3,4.7,3.2,1.3,0.2,Iris-setosa
|
||||
4,4.6,3.1,1.5,0.2,Iris-setosa
|
||||
5,5.0,3.6,1.4,0.2,Iris-setosa
|
||||
6,5.4,3.9,1.7,0.4,Iris-setosa
|
||||
7,4.6,3.4,1.4,0.3,Iris-setosa
|
||||
8,5.0,3.4,1.5,0.2,Iris-setosa
|
||||
9,4.4,2.9,1.4,0.2,Iris-setosa
|
||||
10,4.9,3.1,1.5,0.1,Iris-setosa
|
||||
11,5.4,3.7,1.5,0.2,Iris-setosa
|
||||
12,4.8,3.4,1.6,0.2,Iris-setosa
|
||||
13,4.8,3.0,1.4,0.1,Iris-setosa
|
||||
14,4.3,3.0,1.1,0.1,Iris-setosa
|
||||
15,5.8,4.0,1.2,0.2,Iris-setosa
|
||||
16,5.7,4.4,1.5,0.4,Iris-setosa
|
||||
17,5.4,3.9,1.3,0.4,Iris-setosa
|
||||
18,5.1,3.5,1.4,0.3,Iris-setosa
|
||||
19,5.7,3.8,1.7,0.3,Iris-setosa
|
||||
20,5.1,3.8,1.5,0.3,Iris-setosa
|
||||
21,5.4,3.4,1.7,0.2,Iris-setosa
|
||||
22,5.1,3.7,1.5,0.4,Iris-setosa
|
||||
23,4.6,3.6,1.0,0.2,Iris-setosa
|
||||
24,5.1,3.3,1.7,0.5,Iris-setosa
|
||||
25,4.8,3.4,1.9,0.2,Iris-setosa
|
||||
26,5.0,3.0,1.6,0.2,Iris-setosa
|
||||
27,5.0,3.4,1.6,0.4,Iris-setosa
|
||||
28,5.2,3.5,1.5,0.2,Iris-setosa
|
||||
29,5.2,3.4,1.4,0.2,Iris-setosa
|
||||
30,4.7,3.2,1.6,0.2,Iris-setosa
|
||||
31,4.8,3.1,1.6,0.2,Iris-setosa
|
||||
32,5.4,3.4,1.5,0.4,Iris-setosa
|
||||
33,5.2,4.1,1.5,0.1,Iris-setosa
|
||||
34,5.5,4.2,1.4,0.2,Iris-setosa
|
||||
35,4.9,3.1,1.5,0.1,Iris-setosa
|
||||
36,5.0,3.2,1.2,0.2,Iris-setosa
|
||||
37,5.5,3.5,1.3,0.2,Iris-setosa
|
||||
38,4.9,3.1,1.5,0.1,Iris-setosa
|
||||
39,4.4,3.0,1.3,0.2,Iris-setosa
|
||||
40,5.1,3.4,1.5,0.2,Iris-setosa
|
||||
41,5.0,3.5,1.3,0.3,Iris-setosa
|
||||
42,4.5,2.3,1.3,0.3,Iris-setosa
|
||||
43,4.4,3.2,1.3,0.2,Iris-setosa
|
||||
44,5.0,3.5,1.6,0.6,Iris-setosa
|
||||
45,5.1,3.8,1.9,0.4,Iris-setosa
|
||||
46,4.8,3.0,1.4,0.3,Iris-setosa
|
||||
47,5.1,3.8,1.6,0.2,Iris-setosa
|
||||
48,4.6,3.2,1.4,0.2,Iris-setosa
|
||||
49,5.3,3.7,1.5,0.2,Iris-setosa
|
||||
50,5.0,3.3,1.4,0.2,Iris-setosa
|
||||
51,7.0,3.2,4.7,1.4,Iris-versicolor
|
||||
52,6.4,3.2,4.5,1.5,Iris-versicolor
|
||||
53,6.9,3.1,4.9,1.5,Iris-versicolor
|
||||
54,5.5,2.3,4.0,1.3,Iris-versicolor
|
||||
55,6.5,2.8,4.6,1.5,Iris-versicolor
|
||||
56,5.7,2.8,4.5,1.3,Iris-versicolor
|
||||
57,6.3,3.3,4.7,1.6,Iris-versicolor
|
||||
58,4.9,2.4,3.3,1.0,Iris-versicolor
|
||||
59,6.6,2.9,4.6,1.3,Iris-versicolor
|
||||
60,5.2,2.7,3.9,1.4,Iris-versicolor
|
||||
61,5.0,2.0,3.5,1.0,Iris-versicolor
|
||||
62,5.9,3.0,4.2,1.5,Iris-versicolor
|
||||
63,6.0,2.2,4.0,1.0,Iris-versicolor
|
||||
64,6.1,2.9,4.7,1.4,Iris-versicolor
|
||||
65,5.6,2.9,3.6,1.3,Iris-versicolor
|
||||
66,6.7,3.1,4.4,1.4,Iris-versicolor
|
||||
67,5.6,3.0,4.5,1.5,Iris-versicolor
|
||||
68,5.8,2.7,4.1,1.0,Iris-versicolor
|
||||
69,6.2,2.2,4.5,1.5,Iris-versicolor
|
||||
70,5.6,2.5,3.9,1.1,Iris-versicolor
|
||||
71,5.9,3.2,4.8,1.8,Iris-versicolor
|
||||
72,6.1,2.8,4.0,1.3,Iris-versicolor
|
||||
73,6.3,2.5,4.9,1.5,Iris-versicolor
|
||||
74,6.1,2.8,4.7,1.2,Iris-versicolor
|
||||
75,6.4,2.9,4.3,1.3,Iris-versicolor
|
||||
76,6.6,3.0,4.4,1.4,Iris-versicolor
|
||||
77,6.8,2.8,4.8,1.4,Iris-versicolor
|
||||
78,6.7,3.0,5.0,1.7,Iris-versicolor
|
||||
79,6.0,2.9,4.5,1.5,Iris-versicolor
|
||||
80,5.7,2.6,3.5,1.0,Iris-versicolor
|
||||
81,5.5,2.4,3.8,1.1,Iris-versicolor
|
||||
82,5.5,2.4,3.7,1.0,Iris-versicolor
|
||||
83,5.8,2.7,3.9,1.2,Iris-versicolor
|
||||
84,6.0,2.7,5.1,1.6,Iris-versicolor
|
||||
85,5.4,3.0,4.5,1.5,Iris-versicolor
|
||||
86,6.0,3.4,4.5,1.6,Iris-versicolor
|
||||
87,6.7,3.1,4.7,1.5,Iris-versicolor
|
||||
88,6.3,2.3,4.4,1.3,Iris-versicolor
|
||||
89,5.6,3.0,4.1,1.3,Iris-versicolor
|
||||
90,5.5,2.5,4.0,1.3,Iris-versicolor
|
||||
91,5.5,2.6,4.4,1.2,Iris-versicolor
|
||||
92,6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
93,5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
94,5.0,2.3,3.3,1.0,Iris-versicolor
|
||||
95,5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
96,5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
97,5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
98,6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
99,5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
100,5.7,2.8,4.1,1.3,Iris-versicolor
|
||||
101,6.3,3.3,6.0,2.5,Iris-virginica
|
||||
102,5.8,2.7,5.1,1.9,Iris-virginica
|
||||
103,7.1,3.0,5.9,2.1,Iris-virginica
|
||||
104,6.3,2.9,5.6,1.8,Iris-virginica
|
||||
105,6.5,3.0,5.8,2.2,Iris-virginica
|
||||
106,7.6,3.0,6.6,2.1,Iris-virginica
|
||||
107,4.9,2.5,4.5,1.7,Iris-virginica
|
||||
108,7.3,2.9,6.3,1.8,Iris-virginica
|
||||
109,6.7,2.5,5.8,1.8,Iris-virginica
|
||||
110,7.2,3.6,6.1,2.5,Iris-virginica
|
||||
111,6.5,3.2,5.1,2.0,Iris-virginica
|
||||
112,6.4,2.7,5.3,1.9,Iris-virginica
|
||||
113,6.8,3.0,5.5,2.1,Iris-virginica
|
||||
114,5.7,2.5,5.0,2.0,Iris-virginica
|
||||
115,5.8,2.8,5.1,2.4,Iris-virginica
|
||||
116,6.4,3.2,5.3,2.3,Iris-virginica
|
||||
117,6.5,3.0,5.5,1.8,Iris-virginica
|
||||
118,7.7,3.8,6.7,2.2,Iris-virginica
|
||||
119,7.7,2.6,6.9,2.3,Iris-virginica
|
||||
120,6.0,2.2,5.0,1.5,Iris-virginica
|
||||
121,6.9,3.2,5.7,2.3,Iris-virginica
|
||||
122,5.6,2.8,4.9,2.0,Iris-virginica
|
||||
123,7.7,2.8,6.7,2.0,Iris-virginica
|
||||
124,6.3,2.7,4.9,1.8,Iris-virginica
|
||||
125,6.7,3.3,5.7,2.1,Iris-virginica
|
||||
126,7.2,3.2,6.0,1.8,Iris-virginica
|
||||
127,6.2,2.8,4.8,1.8,Iris-virginica
|
||||
128,6.1,3.0,4.9,1.8,Iris-virginica
|
||||
129,6.4,2.8,5.6,2.1,Iris-virginica
|
||||
130,7.2,3.0,5.8,1.6,Iris-virginica
|
||||
131,7.4,2.8,6.1,1.9,Iris-virginica
|
||||
132,7.9,3.8,6.4,2.0,Iris-virginica
|
||||
133,6.4,2.8,5.6,2.2,Iris-virginica
|
||||
134,6.3,2.8,5.1,1.5,Iris-virginica
|
||||
135,6.1,2.6,5.6,1.4,Iris-virginica
|
||||
136,7.7,3.0,6.1,2.3,Iris-virginica
|
||||
137,6.3,3.4,5.6,2.4,Iris-virginica
|
||||
138,6.4,3.1,5.5,1.8,Iris-virginica
|
||||
139,6.0,3.0,4.8,1.8,Iris-virginica
|
||||
140,6.9,3.1,5.4,2.1,Iris-virginica
|
||||
141,6.7,3.1,5.6,2.4,Iris-virginica
|
||||
142,6.9,3.1,5.1,2.3,Iris-virginica
|
||||
143,5.8,2.7,5.1,1.9,Iris-virginica
|
||||
144,6.8,3.2,5.9,2.3,Iris-virginica
|
||||
145,6.7,3.3,5.7,2.5,Iris-virginica
|
||||
146,6.7,3.0,5.2,2.3,Iris-virginica
|
||||
147,6.3,2.5,5.0,1.9,Iris-virginica
|
||||
148,6.5,3.0,5.2,2.0,Iris-virginica
|
||||
149,6.2,3.4,5.4,2.3,Iris-virginica
|
||||
150,5.9,3.0,5.1,1.8,Iris-virginica
|
||||
|
@@ -0,0 +1,19 @@
|
||||
[[source]]
|
||||
url = "https://pypi.org/simple"
|
||||
verify_ssl = true
|
||||
name = "pypi"
|
||||
|
||||
[packages]
|
||||
jupyter = "*"
|
||||
notebook = "*"
|
||||
pandas = "*"
|
||||
quandl = "*"
|
||||
seaborn = "*"
|
||||
sklearn = "*"
|
||||
scikit-learn = "*"
|
||||
pydot = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
[requires]
|
||||
python_version = "3.11"
|
||||
1334
Man1130/jupyter/Man1130-python-comission/course_materials/Note/Pipfile.lock
generated
Normal file
1334
Man1130/jupyter/Man1130-python-comission/course_materials/Note/Pipfile.lock
generated
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,8 @@
|
||||
10 + 10
|
||||
'10 + 10'
|
||||
'10 + 10'
|
||||
10 + 10
|
||||
10 + '10'
|
||||
'Hello world'
|
||||
type(10)
|
||||
type('abc')
|
||||
@@ -0,0 +1,6 @@
|
||||
# x = 5
|
||||
# x
|
||||
# x = 10
|
||||
# y = 20
|
||||
# x + y
|
||||
# x = y
|
||||
@@ -0,0 +1,29 @@
|
||||
# print()
|
||||
# print('hello world!')
|
||||
# year = 2017
|
||||
# print('This year is '+year)
|
||||
# print('This year is '+str(year))
|
||||
# yearStr = '2017'
|
||||
# print('This year is '+yearStr)
|
||||
# print('This year is '+(yearStr+1))
|
||||
# print('This year is '+str(int(yearStr)+1))
|
||||
#
|
||||
# format()
|
||||
# year = 2017
|
||||
# print('This year is '+year)
|
||||
# print('This year is '+str(year))
|
||||
# print('This year is {}'.format(year)
|
||||
# print('This year is {}, and next year is {}'.format(year,year+1))
|
||||
# print('This year is {}, and I will go abroad at the end of {}'.format(2017,2017))
|
||||
# print('This year is {y}, and I will go abroad at the end of {y}'.format(y=2017))
|
||||
#
|
||||
# input()
|
||||
# input('How tall are you? ')
|
||||
# height = input('How tall are you?')
|
||||
# print('You are {} meter tall'.format(int(height)/100))
|
||||
#
|
||||
#
|
||||
#
|
||||
height = input('What\'s your height? ')
|
||||
height = int(input('What\'s your height? '))
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
def say_hello():
|
||||
print('Hello World!')
|
||||
|
||||
|
||||
def area_of_square(length):
|
||||
return length**2
|
||||
|
||||
|
||||
def area_of_circle(radius):
|
||||
pi = 3.1415926
|
||||
return pi*radius**2
|
||||
|
||||
# pi can also be defined outside of the function
|
||||
# good and bad?
|
||||
# what if someone change the value outside?
|
||||
|
||||
|
||||
def area_of_rectangle(length,width):
|
||||
return length*width
|
||||
|
||||
|
||||
|
||||
def app():
|
||||
age = int(input('What\'s your age?'))
|
||||
print('Your age is {}'.format(age))
|
||||
print('-------')
|
||||
app()
|
||||
|
||||
app()
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
|
||||
|
||||
def bmi_test(bmi):
|
||||
if bmi < 18.5:
|
||||
print('You\'d better eat more!')
|
||||
elif bmi < 24:
|
||||
print('Good job!')
|
||||
else:
|
||||
print('You\'d better to some exercises')
|
||||
|
||||
|
||||
|
||||
bmi_test(20)
|
||||
|
||||
# multiple statement
|
||||
# multiple
|
||||
@@ -0,0 +1,52 @@
|
||||
# BMI = Weight / Height^2 kg and m
|
||||
|
||||
def convert_to_m(height):
|
||||
return height/100
|
||||
|
||||
|
||||
def calculate_bmi(height, weight):
|
||||
#print(weight)
|
||||
#print(height)
|
||||
return weight/height**2
|
||||
|
||||
|
||||
def bmi_test(bmi):
|
||||
print('Your bmi is {}'.format(round(bmi, 2)))
|
||||
if bmi < 18.5:
|
||||
print('You\'d better eat more!')
|
||||
elif bmi < 25:
|
||||
print('Good job!')
|
||||
elif bmi < 30:
|
||||
print('You\'d better do some exercises')
|
||||
else:
|
||||
print('You\'d better consult doctor')
|
||||
|
||||
|
||||
def bmi_app():
|
||||
try:
|
||||
age = int(input('What\'s your age?'))
|
||||
except ValueError:
|
||||
print("You need to enter 0 - 100 as your age")
|
||||
try:
|
||||
age = int(input('What\'s your age?'))
|
||||
except:
|
||||
print("Your input is still wrong. Quitting the app")
|
||||
return
|
||||
|
||||
if age < 18:
|
||||
print("Sorry I can't help you.")
|
||||
else:
|
||||
height = float(input('What\'s your height (in cm)? '))
|
||||
height = convert_to_m(height)
|
||||
weight = float(input('what\'s your weight (in kg) '))
|
||||
bmi = calculate_bmi(height, weight)
|
||||
bmi_test(bmi)
|
||||
|
||||
print('-------------------------------------------')
|
||||
bmi_app()
|
||||
|
||||
|
||||
bmi_app()
|
||||
|
||||
# how to improve?
|
||||
# Q1 what if the weight is not integer? e.g. 62.5
|
||||
@@ -0,0 +1,29 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
|
||||
colors = ['red', 'blue', 'green']
|
||||
|
||||
colors
|
||||
|
||||
colors[0] # zero base indexing
|
||||
colors[1]
|
||||
colors[2]
|
||||
|
||||
colors.append('purple')
|
||||
colors
|
||||
|
||||
|
||||
colors.remove('green')
|
||||
colors
|
||||
|
||||
|
||||
colors.extend(['green', 'yellow'])
|
||||
|
||||
'green' in colors
|
||||
|
||||
colors[3]
|
||||
|
||||
type(colors)
|
||||
|
||||
help(colors)
|
||||
@@ -0,0 +1,13 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
import random
|
||||
|
||||
random.randint(0,3)
|
||||
|
||||
|
||||
colors = [random.randint(0,2)]
|
||||
colors = [random.randint(0,len(colors))]
|
||||
|
||||
|
||||
random.choice(colors)
|
||||
@@ -0,0 +1,28 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
colors = ['red','blue','green']
|
||||
|
||||
for color in colors:
|
||||
print('I love {}'.format(color))
|
||||
|
||||
range(3)
|
||||
|
||||
for i in range(3):
|
||||
print(i)
|
||||
|
||||
for i in range(5):
|
||||
print(i)
|
||||
|
||||
# break, pass, continue
|
||||
for i in range(3):
|
||||
cmd = input('Enter command: ')
|
||||
if cmd == 'break':
|
||||
break
|
||||
elif cmd == 'pass':
|
||||
pass
|
||||
print('command is pass')
|
||||
elif cmd == 'continue':
|
||||
continue
|
||||
print('command is continue')
|
||||
|
||||
@@ -0,0 +1,26 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
import random
|
||||
|
||||
|
||||
def guess_lucky_number():
|
||||
colors = ['red', 'blue', 'green', 'purple', 'yellow']
|
||||
lucky_color = random.choice(colors)
|
||||
|
||||
for i in range(3):
|
||||
print('There are {} colors'.format(colors))
|
||||
guess = input('Guess your lucky color: ')
|
||||
if guess != lucky_color:
|
||||
colors.remove(guess)
|
||||
print('Seems like {} is not your lucky color :( \n'.format(guess))
|
||||
else:
|
||||
break
|
||||
|
||||
if guess == lucky_color:
|
||||
print('Great! {} is your lucky color!! '.format(lucky_color))
|
||||
else:
|
||||
print('Actually, {} is your lucky color '.format(lucky_color))
|
||||
|
||||
|
||||
guess_lucky_number()
|
||||
@@ -0,0 +1,19 @@
|
||||
vocabs = {}
|
||||
|
||||
|
||||
type(vocabs)
|
||||
|
||||
vocabs['programming'] = 'the activity of job of writing computer programs'
|
||||
vocabs
|
||||
|
||||
|
||||
vocabs['python'] = 'a very large snake that kills animals for food'
|
||||
|
||||
del(vocabs['programming'])
|
||||
|
||||
vocabs.clear()
|
||||
|
||||
vocabs['programming'] = ['the activity of job of writing computer programs','_______ming']
|
||||
|
||||
vocabs = {'programming': 'the activity of job of writing computer programs',
|
||||
'python': 'a very large snake'}
|
||||
@@ -0,0 +1,19 @@
|
||||
vocabs = {'programming': ['the activity or job of writing computer programs','_______ming'],
|
||||
'python': ['a very large snake that kills animals for food by wrapping itself around them and crushing them', '___hon'],
|
||||
'fun': ['pleasure, enjoyment, or entertainment', '__n']}
|
||||
|
||||
|
||||
vocabs.keys()
|
||||
|
||||
vocabs.values()
|
||||
|
||||
vocabs.items()
|
||||
|
||||
for key in vocabs.keys():
|
||||
print(key)
|
||||
|
||||
for value in vocabs.values():
|
||||
print(value)
|
||||
|
||||
for key,value in vocabs.items():
|
||||
print('{} ({})\n {}'.format(key,value[1],value[0]))
|
||||
@@ -0,0 +1,36 @@
|
||||
vocabs = {'programming': ['the activity or job of writing computer programs','_______ming'],
|
||||
'python': ['a very large snake that kills animals for food by wrapping itself around them and crushing them', '___hon'],
|
||||
'fun': ['pleasure, enjoyment, or entertainment', '__n']}
|
||||
|
||||
|
||||
def new_word():
|
||||
try:
|
||||
word, definition, hint = input('Enter a new word (word|definition|hint): ').split('|')
|
||||
print('{} ({})\n {}'.format(word,hint,definition))
|
||||
except ValueError:
|
||||
print('Please make sure your format is correct!')
|
||||
except:
|
||||
print('Something is wrong')
|
||||
|
||||
new_word()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def new_word():
|
||||
try:
|
||||
word, definition, hint = input("Enter a new words (word|definition|hint)").split("|")
|
||||
print("{}".format(word))
|
||||
print(" - {} ({})".format(definition, hint))
|
||||
print('----------------------')
|
||||
except ValueError:
|
||||
print("Value Error")
|
||||
new_word()
|
||||
except:
|
||||
print("Something is wrong")
|
||||
new_word()
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,13 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python -m pip install pipenv
|
||||
|
||||
pipenv sync
|
||||
|
||||
pipenv run \
|
||||
jupyter-notebook \
|
||||
--allow-root \
|
||||
--ip=0.0.0.0
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
pipenv install jupyter
|
||||
pipenv install jupyter notebook
|
||||
|
||||
pipenv install pandas
|
||||
pipenv install quandl
|
||||
|
||||
pipenv install seaborn
|
||||
pipenv install scikit-learn
|
||||
|
||||
# jupyter-notebook
|
||||
|
||||
|
|
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
docker run -it \
|
||||
-v $PWD:/app \
|
||||
-w /app \
|
||||
-v /var/run/docker.sock:/var/run/docker.sock \
|
||||
-v ~/.ssh/id_rsa:/home/node/.ssh/id_rsa:ro \
|
||||
-v ~/.ssh/known_host:/home/node/.ssh/known_hosts:ro \
|
||||
-p 8889:8888 \
|
||||
--rm \
|
||||
python:latest \
|
||||
bash
|
||||
|
||||
@@ -0,0 +1,52 @@
|
||||
"state","abbreviation"
|
||||
"Alabama","AL"
|
||||
"Alaska","AK"
|
||||
"Arizona","AZ"
|
||||
"Arkansas","AR"
|
||||
"California","CA"
|
||||
"Colorado","CO"
|
||||
"Connecticut","CT"
|
||||
"Delaware","DE"
|
||||
"District of Columbia","DC"
|
||||
"Florida","FL"
|
||||
"Georgia","GA"
|
||||
"Hawaii","HI"
|
||||
"Idaho","ID"
|
||||
"Illinois","IL"
|
||||
"Indiana","IN"
|
||||
"Iowa","IA"
|
||||
"Kansas","KS"
|
||||
"Kentucky","KY"
|
||||
"Louisiana","LA"
|
||||
"Maine","ME"
|
||||
"Montana","MT"
|
||||
"Nebraska","NE"
|
||||
"Nevada","NV"
|
||||
"New Hampshire","NH"
|
||||
"New Jersey","NJ"
|
||||
"New Mexico","NM"
|
||||
"New York","NY"
|
||||
"North Carolina","NC"
|
||||
"North Dakota","ND"
|
||||
"Ohio","OH"
|
||||
"Oklahoma","OK"
|
||||
"Oregon","OR"
|
||||
"Maryland","MD"
|
||||
"Massachusetts","MA"
|
||||
"Michigan","MI"
|
||||
"Minnesota","MN"
|
||||
"Mississippi","MS"
|
||||
"Missouri","MO"
|
||||
"Pennsylvania","PA"
|
||||
"Rhode Island","RI"
|
||||
"South Carolina","SC"
|
||||
"South Dakota","SD"
|
||||
"Tennessee","TN"
|
||||
"Texas","TX"
|
||||
"Utah","UT"
|
||||
"Vermont","VT"
|
||||
"Virginia","VA"
|
||||
"Washington","WA"
|
||||
"West Virginia","WV"
|
||||
"Wisconsin","WI"
|
||||
"Wyoming","WY"
|
||||
|
@@ -0,0 +1,53 @@
|
||||
state,area (sq. mi)
|
||||
Alabama,52423
|
||||
Alaska,656425
|
||||
Arizona,114006
|
||||
Arkansas,53182
|
||||
California,163707
|
||||
Colorado,104100
|
||||
Connecticut,5544
|
||||
Delaware,1954
|
||||
Florida,65758
|
||||
Georgia,59441
|
||||
Hawaii,10932
|
||||
Idaho,83574
|
||||
Illinois,57918
|
||||
Indiana,36420
|
||||
Iowa,56276
|
||||
Kansas,82282
|
||||
Kentucky,40411
|
||||
Louisiana,51843
|
||||
Maine,35387
|
||||
Maryland,12407
|
||||
Massachusetts,10555
|
||||
Michigan,96810
|
||||
Minnesota,86943
|
||||
Mississippi,48434
|
||||
Missouri,69709
|
||||
Montana,147046
|
||||
Nebraska,77358
|
||||
Nevada,110567
|
||||
New Hampshire,9351
|
||||
New Jersey,8722
|
||||
New Mexico,121593
|
||||
New York,54475
|
||||
North Carolina,53821
|
||||
North Dakota,70704
|
||||
Ohio,44828
|
||||
Oklahoma,69903
|
||||
Oregon,98386
|
||||
Pennsylvania,46058
|
||||
Rhode Island,1545
|
||||
South Carolina,32007
|
||||
South Dakota,77121
|
||||
Tennessee,42146
|
||||
Texas,268601
|
||||
Utah,84904
|
||||
Vermont,9615
|
||||
Virginia,42769
|
||||
Washington,71303
|
||||
West Virginia,24231
|
||||
Wisconsin,65503
|
||||
Wyoming,97818
|
||||
District of Columbia,68
|
||||
Puerto Rico,3515
|
||||
|
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
1026
Man1130/jupyter/Man1130-python-comission/course_materials/heart.csv
Normal file
1026
Man1130/jupyter/Man1130-python-comission/course_materials/heart.csv
Normal file
File diff suppressed because it is too large
Load Diff
1026
Man1130/jupyter/Man1130-python-comission/heart.csv
Normal file
1026
Man1130/jupyter/Man1130-python-comission/heart.csv
Normal file
File diff suppressed because it is too large
Load Diff
13
Man1130/jupyter/Man1130-python-comission/meta.md
Normal file
13
Man1130/jupyter/Man1130-python-comission/meta.md
Normal file
@@ -0,0 +1,13 @@
|
||||
---
|
||||
tags: [docker, jupyter, python, kaggle, data-science]
|
||||
---
|
||||
|
||||
# man1130
|
||||
|
||||
### task 1
|
||||
|
||||
- HKD 200
|
||||
|
||||
### task 2
|
||||
|
||||
- HKD 250
|
||||
1
Man1130/jupyter/Man1130-python-comission/notes.md
Normal file
1
Man1130/jupyter/Man1130-python-comission/notes.md
Normal file
@@ -0,0 +1 @@
|
||||
# helloworld
|
||||
13
Man1130/jupyter/Man1130-python-comission/package.json
Normal file
13
Man1130/jupyter/Man1130-python-comission/package.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"name": "jupyter-bokeh",
|
||||
"version": "1.0.0",
|
||||
"description": "",
|
||||
"main": "index.js",
|
||||
"scripts": {
|
||||
"test": "echo \"Error: no test specified\" && exit 1",
|
||||
"gitUpdate": "git add . && git commit -m\"update Man1130-python-comission,\" && git push"
|
||||
},
|
||||
"keywords": [],
|
||||
"author": "",
|
||||
"license": "ISC"
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
File diff suppressed because one or more lines are too long
BIN
Man1130/jupyter/Man1130-python-comission/slides/Project.pdf
Normal file
BIN
Man1130/jupyter/Man1130-python-comission/slides/Project.pdf
Normal file
Binary file not shown.
98
Man1130/jupyter/Man1130-python-comission/slides/academic.css
Normal file
98
Man1130/jupyter/Man1130-python-comission/slides/academic.css
Normal file
@@ -0,0 +1,98 @@
|
||||
/* @theme academic */
|
||||
|
||||
@import 'gaia';
|
||||
@import url('https://fonts.googleapis.com/css2?family=Noto+Sans+JP:wght@400;700&display=swap');
|
||||
@import url('https://fonts.googleapis.com/css2?family=Source+Code+Pro&display=swap');
|
||||
|
||||
:root {
|
||||
--color-background: #fff;
|
||||
--color-foreground: #333;
|
||||
--color-highlight: #800000;
|
||||
}
|
||||
|
||||
section {
|
||||
background-image: none;
|
||||
font-family: 'Noto Sans JP', sans-serif;
|
||||
padding-top: 90px;
|
||||
padding-left: 40px;
|
||||
padding-right: 40px;
|
||||
}
|
||||
|
||||
/* https://github.com/marp-team/marpit/issues/271 */
|
||||
section::after {
|
||||
font-weight: 700;
|
||||
content: attr(data-marpit-pagination) '/' attr(data-marpit-pagination-total);
|
||||
}
|
||||
|
||||
ul ul {
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
section.lead h1 {
|
||||
color: #800000;
|
||||
text-align: left;
|
||||
}
|
||||
section.lead h1 strong {
|
||||
-webkit-text-stroke: 1px #800000;
|
||||
}
|
||||
section.lead h2 {
|
||||
color: #800000;
|
||||
text-align: left;
|
||||
}
|
||||
section.lead h2 strong {
|
||||
-webkit-text-stroke: 1px #800000;
|
||||
}
|
||||
section.lead h3 {
|
||||
color: #800000;
|
||||
text-align: left;
|
||||
}
|
||||
section.lead h3 strong {
|
||||
-webkit-text-stroke: 1px #800000;
|
||||
}
|
||||
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
|
||||
header {
|
||||
background-color: #800000;
|
||||
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|
||||
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|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
blockquote {
|
||||
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|
||||
border-top: 0.1em dashed #555;
|
||||
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|
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|
||||
bottom: 20px;
|
||||
}
|
||||
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|
||||
content: "";
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
img[alt~="center"] {
|
||||
display: block;
|
||||
margin: 0 auto;
|
||||
}
|
||||
|
||||
code {
|
||||
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|
||||
}
|
||||
BIN
Man1130/jupyter/Man1130-python-comission/slides/bg.jpg
(Stored with Git LFS)
Normal file
BIN
Man1130/jupyter/Man1130-python-comission/slides/bg.jpg
(Stored with Git LFS)
Normal file
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1790
Man1130/jupyter/Man1130-python-comission/slides/colors.css
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Man1130/jupyter/Man1130-python-comission/slides/colors.css
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330
Man1130/jupyter/Man1130-python-comission/slides/dracula.css
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330
Man1130/jupyter/Man1130-python-comission/slides/dracula.css
Normal file
@@ -0,0 +1,330 @@
|
||||
@charset "UTF-8";
|
||||
/*!
|
||||
* Marp Dracula theme.
|
||||
* @theme dracula
|
||||
* @author Daniel Nicolas Gisolfi
|
||||
*
|
||||
* @auto-scaling true
|
||||
* @size 4:3 960px 720px
|
||||
* @size 16:9 1280px 720px
|
||||
*/
|
||||
|
||||
@import url("https://fonts.googleapis.com/css?family=Lato:400,900|IBM+Plex+Sans:400,700");
|
||||
|
||||
:root {
|
||||
--dracula-background: #282a36;
|
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--dracula-current-line: #44475a;
|
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--dracula-foreground: #f8f8f2;
|
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--dracula-comment: #6272a4;
|
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--dracula-cyan: #8be9fd;
|
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--dracula-green: #50fa7b;
|
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--dracula-orange: #ffb86c;
|
||||
--dracula-pink: #ff79c6;
|
||||
--dracula-purple:#bd93f9;
|
||||
--dracula-red: #ff5555;
|
||||
--dracula-yellow: #f1fa8c;
|
||||
}
|
||||
|
||||
.hljs {
|
||||
display: block;
|
||||
overflow-x: auto;
|
||||
padding: 0.5em;
|
||||
background: var(--dracula-background);
|
||||
}
|
||||
|
||||
/* Dracula Foreground */
|
||||
.hljs,
|
||||
.hljs-subst,
|
||||
.hljs-typing,
|
||||
.hljs-variable,
|
||||
.hljs-template-variable {
|
||||
color: var(--dracula-foreground);
|
||||
}
|
||||
|
||||
/* Dracula Comment */
|
||||
.hljs-comment,
|
||||
.hljs-quote,
|
||||
.hljs-deletion {
|
||||
color: var(--dracula-comment);
|
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}
|
||||
|
||||
/* Dracula Cyan */
|
||||
.hljs-meta .hljs-doctag,
|
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.hljs-built_in,
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.hljs-selector-tag,
|
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.hljs-section,
|
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.hljs-link,
|
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.hljs-class {
|
||||
color: var(--dracula-cyan);
|
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}
|
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|
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|
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/* Dracula Green */
|
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.hljs-title {
|
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color: var(--dracula-green);
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}
|
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|
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/* Dracula Orange */
|
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.hljs-params {
|
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color: var(--dracula-orange);
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}
|
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|
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/* Dracula Pink */
|
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.hljs-keyword {
|
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|
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/* Dracula Purple */
|
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.hljs-literal,
|
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.hljs-number {
|
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color: var(--dracula-purple);
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}
|
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|
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/* Dracula Red */
|
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.hljs-regexp {
|
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color: var(--dracula-red);
|
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}
|
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|
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/* Dracula Yellow */
|
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.hljs-string,
|
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.hljs-name,
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.hljs-type,
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.hljs-attr,
|
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.hljs-symbol,
|
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.hljs-bullet,
|
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.hljs-addition,
|
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.hljs-template-tag {
|
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color: var(--dracula-yellow);
|
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}
|
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|
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.hljs-keyword,
|
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.hljs-selector-tag,
|
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.hljs-literal,
|
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.hljs-title,
|
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.hljs-section,
|
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.hljs-doctag,
|
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.hljs-type,
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.hljs-strong {
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font-weight: bold;
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}
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.hljs-params,
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.hljs-emphasis {
|
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h1,
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h2,
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h3,
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h4,
|
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h5,
|
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|
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margin: 0.5em 0 0 0;
|
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color: var(--dracula-pink);
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h1 strong,
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h2 strong,
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h5 strong,
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|
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|
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|
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p,
|
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|
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|
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|
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|
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ul > li,
|
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|
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margin: 0.3em 0 0 0;
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color: var(--dracula-cyan);
|
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|
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ul > li > p,
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|
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|
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|
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|
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|
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|
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pre code {
|
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box-sizing: border-box;
|
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|
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|
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padding: 0.5em;
|
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font-size: 0.7em;
|
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|
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pre code svg[data-marp-fitting=svg] {
|
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max-height: calc(580px - 1em);
|
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}
|
||||
|
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blockquote {
|
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margin: 1em 0 0 0;
|
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padding: 0 1em;
|
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position: relative;
|
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color: var(--dracula-orange);
|
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|
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blockquote::after, blockquote::before {
|
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|
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display: block;
|
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font-family: "Times New Roman", serif;
|
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font-weight: bold;
|
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position: absolute;
|
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|
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blockquote::before {
|
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top: 0;
|
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|
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|
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transform: rotate(180deg);
|
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|
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blockquote > *:first-child {
|
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margin-top: 0;
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|
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mark {
|
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background: transparent;
|
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}
|
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|
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table {
|
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border-spacing: 0;
|
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border-collapse: collapse;
|
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margin: 1em 0 0 0;
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|
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table th,
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table td {
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padding: 0.2em 0.4em;
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section {
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padding: 70px;
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color: var(--dracula-foreground);
|
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background-color: var(--dracula-background);
|
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}
|
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section > *:first-child,
|
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section > header:first-child + * {
|
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margin-top: 0;
|
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|
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section a,
|
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section mark {
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|
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|
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section code {
|
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background: var(--dracula-current-line);
|
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color: var(--dracula-current-green);
|
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}
|
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section h1 strong,
|
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section h2 strong,
|
||||
section h3 strong,
|
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section h4 strong,
|
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section h5 strong,
|
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section h6 strong {
|
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color: var(--dracula-current-line);
|
||||
}
|
||||
section pre > code {
|
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background: var(--dracula-current-line);
|
||||
}
|
||||
section header,
|
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section footer,
|
||||
section section::after,
|
||||
section blockquote::before,
|
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section blockquote::after {
|
||||
color: var(--dracula-comment);
|
||||
}
|
||||
section table th,
|
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section table td {
|
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border-color: var(--dracula-current-line);
|
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}
|
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section table thead th {
|
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background: var(--dracula-current-line);
|
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color: var(--dracula-yellow);
|
||||
}
|
||||
section table tbody > tr:nth-child(even) td,
|
||||
section table tbody > tr:nth-child(even) th {
|
||||
background: var(--dracula-current-line);
|
||||
}
|
||||
|
||||
header,
|
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footer,
|
||||
section::after {
|
||||
box-sizing: border-box;
|
||||
font-size: 66%;
|
||||
height: 70px;
|
||||
line-height: 50px;
|
||||
overflow: hidden;
|
||||
padding: 10px 25px;
|
||||
position: absolute;
|
||||
color: var(--dracula-comment);
|
||||
}
|
||||
|
||||
header {
|
||||
left: 0;
|
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right: 0;
|
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top: 0;
|
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}
|
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|
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footer {
|
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left: 0;
|
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right: 0;
|
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bottom: 0;
|
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}
|
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|
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section::after {
|
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right: 0;
|
||||
bottom: 0;
|
||||
font-size: 80%;
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
npx @marp-team/marp-cli@latest -w slides.md
|
||||
|
||||
@REM -o slides.pdf
|
||||
@@ -0,0 +1,88 @@
|
||||
---
|
||||
|
||||
marp: true
|
||||
title: Marp CLI example
|
||||
description: Hosting Marp slide deck on the web
|
||||
theme: uncover
|
||||
paginate: true
|
||||
_paginate: false
|
||||
|
||||
footer: '2022 project presentation'
|
||||
|
||||
style: |
|
||||
section {
|
||||
background-color: #ccc;
|
||||
}
|
||||
|
||||
footer {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
# <!--fit--> Marp CLI 例子
|
||||
|
||||
托管 Marp slide deck 在服务器上
|
||||
|
||||
https://github.com/yhatt/marp-cli-example
|
||||
|
||||
<style scoped>a { color: #eee; }</style>
|
||||
|
||||
<!-- 这是一个注释文件 This is presenter note. You can write down notes through HTML comment. -->
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
|
||||
##### <!--fit--> [Marp CLI](https://github.com/marp-team/marp-cli) + [GitHub Pages](https://github.com/pages) | [Netlify](https://www.netlify.com/) | [Vercel](https://vercel.com/)
|
||||
|
||||
##### <!--fit--> 👉 The easiest way to host<br />your Marp deck on the web
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
## **[GitHub Pages](https://github.com/pages)**
|
||||
|
||||
#### Ready to write & host your deck!
|
||||
|
||||
[](https://github.com/yhatt/marp-cli-example/generate)
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
## **[Netlify](https://www.netlify.com/)**
|
||||
|
||||
#### Ready to write & host your deck!
|
||||
|
||||
[](https://app.netlify.com/start/deploy?repository=https://github.com/yhatt/marp-cli-example)
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
## **[Vercel](https://vercel.com/)**
|
||||
|
||||
#### Ready to write & host your deck!
|
||||
|
||||
[](https://vercel.com/import/project?template=https://github.com/yhatt/marp-cli-example)
|
||||
|
||||
---
|
||||
|
||||
### <!--fit--> :ok_hand:
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
### Created by Yuki Hattori ([@yhatt](https://github.com/yhatt))
|
||||
|
||||
https://github.com/yhatt/marp-cli-example
|
||||
411
Man1130/jupyter/Man1130-python-comission/slides/slides.html
Normal file
411
Man1130/jupyter/Man1130-python-comission/slides/slides.html
Normal file
File diff suppressed because one or more lines are too long
223
Man1130/jupyter/Man1130-python-comission/slides/slides.md
Normal file
223
Man1130/jupyter/Man1130-python-comission/slides/slides.md
Normal file
@@ -0,0 +1,223 @@
|
||||
---
|
||||
|
||||
marp: true
|
||||
title: Marp CLI example
|
||||
description: Hosting Marp slide deck on the web
|
||||
theme: uncover
|
||||
paginate: true
|
||||
_paginate: false
|
||||
backgroundImage: url('https://www.google.com/url?sa=i&url=https%3A%2F%2Fcommons.wikimedia.org%2Fwiki%2FFile%3AHelloWorld.svg&psig=AOvVaw0d3lmyaMphPi0ANeGIEJOw&ust=1670049479380000&source=images&cd=vfe&ved=0CBAQjRxqFwoTCJjxx6Gp2vsCFQAAAAAdAAAAABAE')
|
||||
|
||||
footer: '2022 project presentation'
|
||||
|
||||
style: |
|
||||
section {
|
||||
background-color: #ccc;
|
||||
padding: 0 10vw;
|
||||
}
|
||||
|
||||
footer {
|
||||
text-align: center;
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **slide topic?**
|
||||
|
||||
##### overview of the dataset
|
||||
- Male and Female proportion
|
||||
- male by age
|
||||
- female by age
|
||||
- The types of chest pain experienced among the patients
|
||||
- pie chart
|
||||
- showing the correlation between
|
||||
chest pain and target Confusion Matrix ?
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **Decision Tree**
|
||||
|
||||
##### overview of the dataset
|
||||
- Decision Tree 果張圖
|
||||
- Performance Analysis
|
||||
- confusion matrix
|
||||
- ROC
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **Naive Bayes**
|
||||
|
||||
##### overview of the dataset
|
||||
- Naive Bayes 果張圖
|
||||
- Performance Analysis
|
||||
- confusion matrix
|
||||
- ROC
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **Logistic Regression**
|
||||
|
||||
##### overview of the dataset
|
||||
- Logistic Regression 果張圖
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis**
|
||||
- Performance Analysis
|
||||
- confusion matrix
|
||||
- ROC
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis con't**
|
||||
- pick a sample, bayes modeling:
|
||||
- naive_bayes_scramble.ipynb
|
||||
- column selection
|
||||
- compare column vs accurancy
|
||||
|
||||
- the performance/accurancy of fewer column MAY BE better than more column
|
||||
- possible cause
|
||||
- column noise/ input data accurancy ?
|
||||
- modal overfitting ?
|
||||
- extreme case ?
|
||||
|
||||
<!-- - Q: Evaluation -->
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis con't**
|
||||
- how to improve ?
|
||||
- the choice of the columns may be better if other facuity involved.
|
||||
- more labelled data improves accuracy
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis con't**
|
||||
- disclaimer ?
|
||||
- no model can introduce 100% accurancy
|
||||
- why ?
|
||||
- extreme case
|
||||
- chaos theory ?
|
||||
- will never take all ~~ervery~~ matters into account
|
||||
|
||||
- however, the model can be considered if accuracy above nn% in general
|
||||
|
||||
<!-- Q:What’s the problem you are trying to solve -->
|
||||
|
||||
|
||||
<!-- - How’s your program solve the problem -->
|
||||
|
||||
|
||||
<!-- - Demonstrate how your program work/ Go through the data analysis -->
|
||||
<!-- - What are the interesting libraries that you used? -->
|
||||
<!-- - Any complex logic in your project that you want to show off -->
|
||||
<!-- - Any other interesting thing -->
|
||||
<!-- - It’s not a must for you to show your code in presentation. -->
|
||||
<!-- - Do that if you think that help you to explain things -->
|
||||
<!-- - We don’t need everyone to speak. The whole group share the same score. -->
|
||||
<!-- - Evaluation -->
|
||||
<!-- - Good presentation flow, Clarity of your points, Pace of your presentation -->
|
||||
---
|
||||
|
||||
### **why these method ?**
|
||||
- [x]unsupervised modeling
|
||||
- cross out reason
|
||||
- data is already labelled
|
||||
- data is small amount and discrete
|
||||
|
||||
- [x] Knn
|
||||
- [x] Kmeans
|
||||
|
||||
<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->
|
||||
|
||||
---
|
||||
|
||||
### **why these method ?**
|
||||
- supervised modeling / supervised learning
|
||||
- data is already labelled
|
||||
- []Decision Tree
|
||||
- []Naive Bayes
|
||||
- performance ? accuracy ? ROI ?
|
||||
|
||||
- [x]multi dimensional/column difficult to understand/maintain
|
||||
- [x]Logistic Regression
|
||||
- [x]SVM
|
||||
|
||||
<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->
|
||||
|
||||
---
|
||||
|
||||
### notes only
|
||||
|
||||
<!-- Q: What’s the problem you are trying to solve -->
|
||||
<!-- Q: How’s your program solve the problem -->
|
||||
<!-- A: to predict humans heart disease possiblilies by modelling -->
|
||||
|
||||
<!-- Q: Demonstrate how your program work/ Go through the data analysis -->
|
||||
<!-- 可能你要捉一捉佢路, if i were you, i won't go through the data analysis, -->
|
||||
<!-- because the sample steps every one can google e.g. data cleaning, training model, performance analysis, not the main point -->
|
||||
|
||||
<!-- Q: What are the interesting libraries that you used? -->
|
||||
<!-- Q: Any complex logic in your project that you want to show off -->
|
||||
<!-- Q: Any other interesting thing -->
|
||||
<!-- bayes scrambling can help -->
|
||||
|
||||
|
||||
<!-- Q: Evaluation -->
|
||||
<!-- Performance Analysis ? -->
|
||||
|
||||
<!-- X -->
|
||||
<!-- Q: It’s not a must for you to show your code in presentation. -->
|
||||
<!-- - ? Do that if you think that help you to explain things -->
|
||||
|
||||
<!-- Q: We don’t need everyone to speak. The whole group share the same score. -->
|
||||
|
||||
<!-- Q: Good presentation flow, Clarity of your points, Pace of your presentation -->
|
||||
BIN
Man1130/jupyter/Man1130-python-comission/slides/slides.pdf
Normal file
BIN
Man1130/jupyter/Man1130-python-comission/slides/slides.pdf
Normal file
Binary file not shown.
@@ -0,0 +1,21 @@
|
||||
[[source]]
|
||||
url = "https://pypi.org/simple"
|
||||
verify_ssl = true
|
||||
name = "pypi"
|
||||
|
||||
[packages]
|
||||
jupyter = "*"
|
||||
notebook = "*"
|
||||
pandas = "*"
|
||||
quandl = "*"
|
||||
seaborn = "*"
|
||||
sklearn = "*"
|
||||
scikit-learn = "*"
|
||||
pydot = "*"
|
||||
bokeh = "*"
|
||||
jupyter-bokeh = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
[requires]
|
||||
python_version = "3"
|
||||
1403
Man1130/jupyter/Man1130-python-comission/tryout/jupyter-bokeh-helloworld/Pipfile.lock
generated
Normal file
1403
Man1130/jupyter/Man1130-python-comission/tryout/jupyter-bokeh-helloworld/Pipfile.lock
generated
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "b6bf609f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"helloworld\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('helloworld')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9b24b4df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1cc7f2dc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,16 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python -m pip install --upgrade pip
|
||||
|
||||
python -m pip install pipenv
|
||||
|
||||
pipenv sync
|
||||
|
||||
pipenv install jupyter_bokeh
|
||||
|
||||
pipenv run \
|
||||
jupyter-notebook \
|
||||
--allow-root \
|
||||
--ip=0.0.0.0
|
||||
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "b6bf609f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"helloworld\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('helloworld')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9b24b4df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1cc7f2dc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
pipenv install jupyter
|
||||
pipenv install jupyter notebook
|
||||
|
||||
pipenv install pandas
|
||||
pipenv install quandl
|
||||
|
||||
pipenv install seaborn
|
||||
pipenv install scikit-learn
|
||||
|
||||
# jupyter-notebook
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
### to spin up dev environment
|
||||
|
||||
```
|
||||
./start_docker.sh
|
||||
|
||||
// inside docker
|
||||
|
||||
./dev.sh
|
||||
|
||||
open host browser:
|
||||
http://127.0.0.1:8888/?token=ttttt
|
||||
|
||||
```
|
||||
|
||||
### to develop
|
||||
|
||||
|
||||
start from fresh python docker image
|
||||
|
||||
```
|
||||
./start_docker.sh
|
||||
|
||||
./init.sh
|
||||
```
|
||||
|
||||
|
||||
### stack
|
||||
- python
|
||||
- bokeh
|
||||
|
||||
@@ -0,0 +1,18 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
docker run -it \
|
||||
-v $PWD:/app \
|
||||
-w /app \
|
||||
-v /var/run/docker.sock:/var/run/docker.sock \
|
||||
-v ~/.ssh/id_rsa:/home/node/.ssh/id_rsa:ro \
|
||||
-v ~/.ssh/known_host:/home/node/.ssh/known_hosts:ro \
|
||||
-p 8888:8888 \
|
||||
--rm \
|
||||
python:3.10 \
|
||||
bash
|
||||
|
||||
# -u 1000:1000 \
|
||||
# -e XDG_CACHE_HOME=/app/.cache \
|
||||
|
||||
@@ -0,0 +1,25 @@
|
||||
[[source]]
|
||||
url = "https://pypi.org/simple"
|
||||
verify_ssl = true
|
||||
name = "pypi"
|
||||
|
||||
[packages]
|
||||
jupyter = "*"
|
||||
notebook = "*"
|
||||
pandas = "*"
|
||||
quandl = "*"
|
||||
seaborn = "*"
|
||||
sklearn = "*"
|
||||
scikit-learn = "*"
|
||||
pydot = "*"
|
||||
bokeh = "*"
|
||||
jupyter-bokeh = "*"
|
||||
shap = "*"
|
||||
ipywidgets = "*"
|
||||
widgetsnbextension = "*"
|
||||
pandas-profiling = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
[requires]
|
||||
python_version = "3"
|
||||
1740
Man1130/jupyter/Man1130-python-comission/tryout/jupyter-shap-helloworld/Pipfile.lock
generated
Normal file
1740
Man1130/jupyter/Man1130-python-comission/tryout/jupyter-shap-helloworld/Pipfile.lock
generated
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,18 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python -m pip install --upgrade pip
|
||||
|
||||
python -m pip install pipenv
|
||||
|
||||
pipenv sync
|
||||
|
||||
python -m pipenv install shap
|
||||
python -m pipenv install ipywidgets widgetsnbextension pandas-profiling
|
||||
|
||||
|
||||
pipenv run \
|
||||
jupyter-notebook \
|
||||
--allow-root \
|
||||
--ip=0.0.0.0
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
pipenv install jupyter
|
||||
pipenv install jupyter notebook
|
||||
|
||||
pipenv install pandas
|
||||
pipenv install quandl
|
||||
|
||||
pipenv install seaborn
|
||||
pipenv install scikit-learn
|
||||
|
||||
# jupyter-notebook
|
||||
|
||||
@@ -0,0 +1,27 @@
|
||||
### to spin up dev environment
|
||||
|
||||
```
|
||||
./start_docker.sh
|
||||
|
||||
// inside docker
|
||||
|
||||
./dev.sh
|
||||
|
||||
open host browser:
|
||||
http://127.0.0.1:8888/?token=98ab80de026fe83fd8e03c8e344b31e7575ec4a084c59f21
|
||||
|
||||
```
|
||||
|
||||
### to develop
|
||||
|
||||
|
||||
start from fresh python docker image
|
||||
|
||||
```
|
||||
./start_docker.sh
|
||||
|
||||
./init.sh
|
||||
```
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,18 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
docker run -it \
|
||||
-v $PWD:/app \
|
||||
-w /app \
|
||||
-v /var/run/docker.sock:/var/run/docker.sock \
|
||||
-v ~/.ssh/id_rsa:/home/node/.ssh/id_rsa:ro \
|
||||
-v ~/.ssh/known_host:/home/node/.ssh/known_hosts:ro \
|
||||
-p 8888:8888 \
|
||||
--rm \
|
||||
python:3.10 \
|
||||
bash
|
||||
|
||||
# -u 1000:1000 \
|
||||
# -e XDG_CACHE_HOME=/app/.cache \
|
||||
|
||||
@@ -0,0 +1,25 @@
|
||||
[[source]]
|
||||
url = "https://pypi.org/simple"
|
||||
verify_ssl = true
|
||||
name = "pypi"
|
||||
|
||||
[packages]
|
||||
jupyter = "*"
|
||||
notebook = "*"
|
||||
pandas = "*"
|
||||
quandl = "*"
|
||||
seaborn = "*"
|
||||
sklearn = "*"
|
||||
scikit-learn = "*"
|
||||
pydot = "*"
|
||||
bokeh = "*"
|
||||
jupyter-bokeh = "*"
|
||||
voila = "*"
|
||||
bqplot = "*"
|
||||
ipympl = "*"
|
||||
ipyvolume = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
[requires]
|
||||
python_version = "3"
|
||||
1563
Man1130/jupyter/Man1130-python-comission/tryout/jupyter-voila-helloworld/Pipfile.lock
generated
Normal file
1563
Man1130/jupyter/Man1130-python-comission/tryout/jupyter-voila-helloworld/Pipfile.lock
generated
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,19 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python -m pip install --upgrade pip
|
||||
|
||||
python -m pip install pipenv
|
||||
|
||||
python -m pipenv install voila
|
||||
python -m pipenv install bqplot
|
||||
python -m pipenv install ipympl
|
||||
python -m pipenv install ipyvolume
|
||||
|
||||
pipenv sync
|
||||
|
||||
pipenv run \
|
||||
jupyter-notebook \
|
||||
--allow-root \
|
||||
--ip=0.0.0.0
|
||||
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
pipenv install jupyter
|
||||
pipenv install jupyter notebook
|
||||
|
||||
pipenv install pandas
|
||||
pipenv install quandl
|
||||
|
||||
pipenv install seaborn
|
||||
pipenv install scikit-learn
|
||||
|
||||
# jupyter-notebook
|
||||
|
||||
@@ -0,0 +1,248 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# So easy, *voilà*!\n",
|
||||
"\n",
|
||||
"In this example notebook, we demonstrate how Voilà can render Jupyter notebooks with interactions requiring a roundtrip to the kernel."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Jupyter Widgets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "9d234bc95ca5460ea86e664a957764e3",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(FloatSlider(value=4.0, description='$x$'), FloatText(value=16.0, description='$x^2$', disabled=…"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import ipywidgets as widgets\n",
|
||||
"\n",
|
||||
"slider = widgets.FloatSlider(description='$x$')\n",
|
||||
"text = widgets.FloatText(disabled=True, description='$x^2$')\n",
|
||||
"\n",
|
||||
"def compute(*ignore):\n",
|
||||
" text.value = str(slider.value ** 2)\n",
|
||||
"\n",
|
||||
"slider.observe(compute, 'value')\n",
|
||||
"\n",
|
||||
"slider.value = 4\n",
|
||||
"\n",
|
||||
"widgets.VBox([slider, text])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Basic outputs of code cells"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>sepal_length</th>\n",
|
||||
" <th>sepal_width</th>\n",
|
||||
" <th>petal_length</th>\n",
|
||||
" <th>petal_width</th>\n",
|
||||
" <th>species</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>5.1</td>\n",
|
||||
" <td>3.5</td>\n",
|
||||
" <td>1.4</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" <td>setosa</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>4.9</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>1.4</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" <td>setosa</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>4.7</td>\n",
|
||||
" <td>3.2</td>\n",
|
||||
" <td>1.3</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" <td>setosa</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>4.6</td>\n",
|
||||
" <td>3.1</td>\n",
|
||||
" <td>1.5</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" <td>setosa</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>3.6</td>\n",
|
||||
" <td>1.4</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" <td>setosa</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>145</th>\n",
|
||||
" <td>6.7</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>5.2</td>\n",
|
||||
" <td>2.3</td>\n",
|
||||
" <td>virginica</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>146</th>\n",
|
||||
" <td>6.3</td>\n",
|
||||
" <td>2.5</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.9</td>\n",
|
||||
" <td>virginica</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>147</th>\n",
|
||||
" <td>6.5</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>5.2</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>virginica</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>148</th>\n",
|
||||
" <td>6.2</td>\n",
|
||||
" <td>3.4</td>\n",
|
||||
" <td>5.4</td>\n",
|
||||
" <td>2.3</td>\n",
|
||||
" <td>virginica</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>149</th>\n",
|
||||
" <td>5.9</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>5.1</td>\n",
|
||||
" <td>1.8</td>\n",
|
||||
" <td>virginica</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>150 rows × 5 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" sepal_length sepal_width petal_length petal_width species\n",
|
||||
"0 5.1 3.5 1.4 0.2 setosa\n",
|
||||
"1 4.9 3.0 1.4 0.2 setosa\n",
|
||||
"2 4.7 3.2 1.3 0.2 setosa\n",
|
||||
"3 4.6 3.1 1.5 0.2 setosa\n",
|
||||
"4 5.0 3.6 1.4 0.2 setosa\n",
|
||||
".. ... ... ... ... ...\n",
|
||||
"145 6.7 3.0 5.2 2.3 virginica\n",
|
||||
"146 6.3 2.5 5.0 1.9 virginica\n",
|
||||
"147 6.5 3.0 5.2 2.0 virginica\n",
|
||||
"148 6.2 3.4 5.4 2.3 virginica\n",
|
||||
"149 5.9 3.0 5.1 1.8 virginica\n",
|
||||
"\n",
|
||||
"[150 rows x 5 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
|
||||
"iris"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -0,0 +1,84 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# So easy, *voilà*!\n",
|
||||
"\n",
|
||||
"In this example notebook, we demonstrate how Voilà can render custom Jupyter widgets such as [bqplot](https://github.com/bloomberg/bqplot). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import warnings\n",
|
||||
"warnings.filterwarnings('ignore')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "f771faf1649e425480af29391b9a238e",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from bqplot import pyplot as plt\n",
|
||||
"\n",
|
||||
"plt.figure(1, title='Line Chart')\n",
|
||||
"np.random.seed(0)\n",
|
||||
"n = 200\n",
|
||||
"x = np.linspace(0.0, 10.0, n)\n",
|
||||
"y = np.cumsum(np.random.randn(n))\n",
|
||||
"plt.plot(x, y)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,202 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This demo uses Voilà to render a notebook to a custom HTML page using gridstack.js for the layout of each output. In the cell metadata you can change the default cell with and height (in grid units between 1 and 12) by specifying.\n",
|
||||
" * `grid_row`\n",
|
||||
" * `grid_columns`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"n = 200\n",
|
||||
"\n",
|
||||
"x = np.linspace(0.0, 10.0, n)\n",
|
||||
"y = np.cumsum(np.random.randn(n)*10).astype(int)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import ipywidgets as widgets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "fb68ca2d6f7f40c2adb9d01d134a4373",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Label(value='Selected: 0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"label_selected = widgets.Label(value=\"Selected: 0\")\n",
|
||||
"label_selected"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"grid_columns": 8,
|
||||
"grid_rows": 4
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "227e65a70eab4eb38b0b2f25d8237656",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Figure(axes=[Axis(orientation='vertical', scale=LinearScale()), Axis(scale=LinearScale(max=70.0, min=-131.0))]…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from bqplot import pyplot as plt\n",
|
||||
"import bqplot\n",
|
||||
"\n",
|
||||
"fig = plt.figure( title='Histogram')\n",
|
||||
"np.random.seed(0)\n",
|
||||
"hist = plt.hist(y, bins=25)\n",
|
||||
"hist.scales['sample'].min = float(y.min())\n",
|
||||
"hist.scales['sample'].max = float(y.max())\n",
|
||||
"display(fig)\n",
|
||||
"fig.layout.width = 'auto'\n",
|
||||
"fig.layout.height = 'auto'\n",
|
||||
"fig.layout.min_height = '300px' # so it shows nicely in the notebook\n",
|
||||
"fig.layout.flex = '1'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"grid_columns": 12,
|
||||
"grid_rows": 6
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5fee6ee9af0a4dc7a557f8d16a5b7150",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top':…"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from bqplot import pyplot as plt\n",
|
||||
"import bqplot\n",
|
||||
"\n",
|
||||
"fig = plt.figure( title='Line Chart')\n",
|
||||
"np.random.seed(0)\n",
|
||||
"n = 200\n",
|
||||
"p = plt.plot(x, y)\n",
|
||||
"fig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig.layout.width = 'auto'\n",
|
||||
"fig.layout.height = 'auto'\n",
|
||||
"fig.layout.min_height = '300px' # so it shows nicely in the notebook\n",
|
||||
"fig.layout.flex = '1'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"brushintsel = bqplot.interacts.BrushIntervalSelector(scale=p.scales['x'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def update_range(*args):\n",
|
||||
" label_selected.value = \"Selected range {}\".format(brushintsel.selected)\n",
|
||||
" mask = (x > brushintsel.selected[0]) & (x < brushintsel.selected[1])\n",
|
||||
" hist.sample = y[mask]\n",
|
||||
" \n",
|
||||
"brushintsel.observe(update_range, 'selected')\n",
|
||||
"fig.interaction = brushintsel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"celltoolbar": "Edit Metadata",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,119 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Which multiplication table do you want to learn?\n",
|
||||
"\n",
|
||||
"In this example notebook we demonstrate how Voilà can render different Jupyter widgets using [GridspecLayout](https://ipywidgets.readthedocs.io/en/latest/examples/Layout%20Templates.html#Grid-layout)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "19dcc46af51049c9a9e26568b76aa789",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"GridspecLayout(children=(Dropdown(index=1, layout=Layout(grid_area='widget001'), options=(1, 2, 3, 4, 5, 6, 7,…"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from ipywidgets import GridspecLayout, Button, BoundedIntText, Valid, Layout, Dropdown\n",
|
||||
"\n",
|
||||
"def create_expanded_button(description, button_style):\n",
|
||||
" return Button(description=description, button_style=button_style, layout=Layout(height='auto', width='auto'))\n",
|
||||
" \n",
|
||||
"rows = 11\n",
|
||||
"columns = 6\n",
|
||||
"\n",
|
||||
"gs = GridspecLayout(rows, columns)\n",
|
||||
"\n",
|
||||
"def on_result_change(change):\n",
|
||||
" row = int(change[\"owner\"].layout.grid_row)\n",
|
||||
" gs[row, 5].value = gs[0, 0].value * row == change[\"new\"]\n",
|
||||
" \n",
|
||||
"def on_multipler_change(change):\n",
|
||||
" for i in range(1, rows):\n",
|
||||
" gs[i, 0].description = str(change[\"new\"])\n",
|
||||
" gs[i, 4].max = change[\"new\"] * 10\n",
|
||||
" gs[i, 4].value = 1\n",
|
||||
" gs[i, 4].step = change[\"new\"]\n",
|
||||
" gs[i, 5].value = False\n",
|
||||
"\n",
|
||||
"gs[0, 0] = Dropdown(\n",
|
||||
" options=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
|
||||
" value=2,\n",
|
||||
")\n",
|
||||
"gs[0, 0].observe(on_multipler_change, names=\"value\")\n",
|
||||
"multiplier = gs[0, 0].value\n",
|
||||
"\n",
|
||||
"for i in range(1, rows):\n",
|
||||
" gs[i, 0] = create_expanded_button(str(multiplier), \"\")\n",
|
||||
" gs[i, 1] = create_expanded_button(\"*\", \"\")\n",
|
||||
" gs[i, 2] = create_expanded_button(str(i), \"info\")\n",
|
||||
" gs[i, 3] = create_expanded_button(\"=\", \"\")\n",
|
||||
"\n",
|
||||
" gs[i, 4] = BoundedIntText(\n",
|
||||
" min=0,\n",
|
||||
" max=multiplier * 10,\n",
|
||||
" layout=Layout(grid_row=str(i)),\n",
|
||||
" value=1,\n",
|
||||
" step=multiplier,\n",
|
||||
" disabled=False\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" gs[i, 5] = Valid(\n",
|
||||
" value=False,\n",
|
||||
" description='Valid!',\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" gs[i, 4].observe(on_result_change, names='value')\n",
|
||||
"\n",
|
||||
"gs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "b6bf609f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"helloworld\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('helloworld')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9b24b4df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1cc7f2dc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,76 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# So easy, *voilà*!\n",
|
||||
"\n",
|
||||
"In this example notebook, we demonstrate how Voilà can render notebooks making use of ipywidget's `@interact`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "954b4c8acda94613bc8dde7e41ad5f9e",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(VBox(children=(IntSlider(value=0), IntSlider(value=0))), Output()))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from ipywidgets import HBox, VBox, IntSlider, interactive_output\n",
|
||||
"from IPython.display import display\n",
|
||||
"\n",
|
||||
"a = IntSlider()\n",
|
||||
"b = IntSlider()\n",
|
||||
"\n",
|
||||
"def f(a, b):\n",
|
||||
" print(\"{} * {} = {}\".format(a, b, a * b))\n",
|
||||
"\n",
|
||||
"out = interactive_output(f, { \"a\": a, \"b\": b })\n",
|
||||
"\n",
|
||||
"display(HBox([VBox([a, b]), out]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,66 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# So easy, *voilà*!\n",
|
||||
"\n",
|
||||
"In this example notebook, we demonstrate how Voilà can render custom Jupyter widgets such as [ipyvolume](https://github.com/maartenbreddels/ipyvolume). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "module 'collections' has no attribute 'Mapping'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn [2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mipyvolume\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mipv\u001b[39;00m\n\u001b[1;32m 2\u001b[0m ipv\u001b[38;5;241m.\u001b[39mexamples\u001b[38;5;241m.\u001b[39mexample_ylm();\n",
|
||||
"File \u001b[0;32m~/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages/ipyvolume/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m absolute_import\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_version\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m styles\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mwidgets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransferfunction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n",
|
||||
"File \u001b[0;32m~/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages/ipyvolume/styles.py:56\u001b[0m\n\u001b[1;32m 47\u001b[0m light \u001b[38;5;241m=\u001b[39m create(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlight\u001b[39m\u001b[38;5;124m\"\u001b[39m, \\\n\u001b[1;32m 48\u001b[0m {\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackground-color\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 52\u001b[0m }\n\u001b[1;32m 53\u001b[0m })\n\u001b[1;32m 55\u001b[0m default \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m---> 56\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdict_deep_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdefault\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_defaults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 57\u001b[0m utils\u001b[38;5;241m.\u001b[39mdict_deep_update(default, light)\n\u001b[1;32m 59\u001b[0m dark \u001b[38;5;241m=\u001b[39m create(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdark\u001b[39m\u001b[38;5;124m\"\u001b[39m, \\\n\u001b[1;32m 60\u001b[0m {\n\u001b[1;32m 61\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackground-color\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#000001\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# for some reason we cannot set it to black!?!\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 70\u001b[0m }\n\u001b[1;32m 71\u001b[0m })\n",
|
||||
"File \u001b[0;32m~/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages/ipyvolume/utils.py:19\u001b[0m, in \u001b[0;36mdict_deep_update\u001b[0;34m(d, u)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdict_deep_update\u001b[39m(d, u):\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m u\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(v, \u001b[43mcollections\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMapping\u001b[49m):\n\u001b[1;32m 20\u001b[0m r \u001b[38;5;241m=\u001b[39m dict_deep_update(d\u001b[38;5;241m.\u001b[39mget(k, {}), v)\n\u001b[1;32m 21\u001b[0m d[k] \u001b[38;5;241m=\u001b[39m r\n",
|
||||
"\u001b[0;31mAttributeError\u001b[0m: module 'collections' has no attribute 'Mapping'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import ipyvolume as ipv\n",
|
||||
"ipv.examples.example_ylm();"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,289 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "913fbbd3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# JSON"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "e23193bb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "4c030553429b482b8a3a0d24347f745c",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Button(description='Output JSON', style=ButtonStyle())"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "31ece561ecab413f832f1d5033de4194",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from ipywidgets import Button, Output\n",
|
||||
"from IPython import display\n",
|
||||
"\n",
|
||||
"button = Button(description='Output JSON')\n",
|
||||
"output = Output()\n",
|
||||
"obj = {\n",
|
||||
" \"abcde\": 1234,\n",
|
||||
" \"nested\": list(range(10))\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"@output.capture()\n",
|
||||
"def on_click(change):\n",
|
||||
" display.display(display.JSON(obj))\n",
|
||||
" \n",
|
||||
" \n",
|
||||
"button.on_click(on_click)\n",
|
||||
"display.display(button)\n",
|
||||
"output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "690bd908",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/json": {
|
||||
"abcde": 1234,
|
||||
"nested": [
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4,
|
||||
5,
|
||||
6,
|
||||
7,
|
||||
8,
|
||||
9
|
||||
]
|
||||
},
|
||||
"text/plain": [
|
||||
"<IPython.core.display.JSON object>"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"application/json": {
|
||||
"expanded": false,
|
||||
"root": "root"
|
||||
}
|
||||
},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"display.JSON(obj)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "97caeade",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Fasta"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "27bbbb6c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "60c37cb6500547cf81751809d825af34",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Button(description='Output FASTA', style=ButtonStyle())"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "0cfaf056ab234c0c8c4ff914d8334efb",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fasta_button = Button(description='Output FASTA')\n",
|
||||
"fasta_output = Output()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def Fasta(data=''):\n",
|
||||
" bundle = {}\n",
|
||||
" bundle['application/vnd.fasta.fasta'] = data\n",
|
||||
" bundle['text/plain'] = data\n",
|
||||
" display.display(bundle, raw=True)\n",
|
||||
" \n",
|
||||
" \n",
|
||||
"@fasta_output.capture()\n",
|
||||
"def on_click(change):\n",
|
||||
" Fasta(\"\"\">SEQUENCE_1\n",
|
||||
"MTEITAAMVKELRESTGAGMMDCKNALSETNGDFDKAVQLLREKGLGKAAKKADRLAAEG\n",
|
||||
"LVSVKVSDDFTIAAMRPSYLSYEDLDMTFVENEYKALVAELEKENEERRRLKDPNKPEHK\n",
|
||||
"IPQFASRKQLSDAILKEAEEKIKEELKAQGKPEKIWDNIIPGKMNSFIADNSQLDSKLTL\n",
|
||||
"MGQFYVMDDKKTVEQVIAEKEKEFGGKIKIVEFICFEVGEGLEKKTEDFAAEVAAQL\n",
|
||||
">SEQUENCE_2\n",
|
||||
"SATVSEINSETDFVAKNDQFIALTKDTTAHIQSNSLQSVEELHSSTINGVKFEEYLKSQI\n",
|
||||
"ATIGENLVVRRFATLKAGANGVVNGYIHTNGRVGVVIAAACDSAEVASKSRDLLRQICMH\"\"\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"fasta_button.on_click(on_click)\n",
|
||||
"display.display(fasta_button)\n",
|
||||
"fasta_output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a441bd16",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# GeoJSON"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "31371b2f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "093f167a6c8642ca9e9bc1cdda6e73d1",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Button(description='Output GeoJSON', style=ButtonStyle())"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "d78a22c950e34fd48c514bddad9c444b",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from IPython.display import GeoJSON\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"geojson_button = Button(description='Output GeoJSON')\n",
|
||||
"geojson_output = Output()\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"@geojson_output.capture()\n",
|
||||
"def on_click(change):\n",
|
||||
" obj = GeoJSON({\n",
|
||||
" \"type\": \"Feature\",\n",
|
||||
" \"geometry\": {\n",
|
||||
" \"type\": \"Point\",\n",
|
||||
" \"coordinates\": [-118.4563712, 34.0163116]\n",
|
||||
" }\n",
|
||||
" })\n",
|
||||
" display.display(obj)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"geojson_button.on_click(on_click)\n",
|
||||
"display.display(geojson_button)\n",
|
||||
"geojson_output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9c0381b4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4099a46a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,69 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "ebaa184b-765f-4261-9c5c-285bbde8fad5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "dfda01818965434d8417413b6c2020d5",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(HBox(children=(Button(description='1', style=ButtonStyle()), Button(description='2', style=Butt…"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import ipywidgets as widgets\n",
|
||||
"def generate(n):\n",
|
||||
" widget = []\n",
|
||||
" for i in range(n):\n",
|
||||
" inner = []\n",
|
||||
" for j in range(n):\n",
|
||||
" inner.append(widgets.Button(description=f'{i*n+j+1}'))\n",
|
||||
" widget.append(widgets.HBox(inner))\n",
|
||||
"\n",
|
||||
" return widgets.VBox(widget)\n",
|
||||
"generate(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8b59589a-7c71-4b1a-896a-b1139cbcf70f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,107 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-08-18T12:06:29.243603Z",
|
||||
"start_time": "2020-08-18T12:06:29.240780Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from urllib.parse import parse_qs\n",
|
||||
"import IPython.display"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-08-18T12:06:29.250356Z",
|
||||
"start_time": "2020-08-18T12:06:29.246161Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_string = os.environ.get('QUERY_STRING', '')\n",
|
||||
"parameters = parse_qs(query_string)\n",
|
||||
"print(\"query string parameters:\", parameters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-08-18T12:06:29.258506Z",
|
||||
"start_time": "2020-08-18T12:06:29.253815Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parameters is a dict of lists\n",
|
||||
"username = parameters.get('username', ['Kim'])[0]\n",
|
||||
"print(f'Hi {username}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-08-18T12:06:29.275717Z",
|
||||
"start_time": "2020-08-18T12:06:29.261315Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"server = os.environ.get('SERVER_NAME', 'localhost') \n",
|
||||
"url = \"http://\" + server\n",
|
||||
"\n",
|
||||
"port = os.environ.get('SERVER_PORT', '')\n",
|
||||
"if port:\n",
|
||||
" url += \":\" + port\n",
|
||||
"\n",
|
||||
"path = os.environ.get('SCRIPT_NAME', '')\n",
|
||||
"url += path\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"IPython.display.HTML(data=f\"\"\"\n",
|
||||
"<a href=\"{url}?username=Riley\">Link to myself as user Riley</a>\n",
|
||||
"\"\"\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -0,0 +1,135 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"hi\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('hi')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "583e6dc5fecc4802a205e91b768f0261",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(FloatSlider(value=14.0, description='x'), FloatText(value=196.0, description='$x^2$', disabled=…"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import ipywidgets as widgets\n",
|
||||
"slider = widgets.FloatSlider(description='x')\n",
|
||||
"text = widgets.FloatText(disabled=True, description='$x^2$')\n",
|
||||
"text.disabled\n",
|
||||
"def compute(*ignore):\n",
|
||||
" text.value = str(slider.value**2)\n",
|
||||
"slider.observe(compute, 'value')\n",
|
||||
"slider.value = 14\n",
|
||||
"widgets.VBox([slider, text])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"voila\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('voila')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"1+2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"celltoolbar": "Slideshow",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.8"
|
||||
},
|
||||
"voila": {
|
||||
"template": "reveal"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user