Lectures/09-Statistics.ipynb

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    "# ETHZ: 227-0966-00L\n",
    "# Quantitative Big Imaging\n",
    "# April 18, 2019\n",
    "\n",
    "## Statistics and Reproducibility"
   ]
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    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 8)\n",
    "plt.rcParams[\"figure.dpi\"] = 150\n",
    "plt.rcParams[\"font.size\"] = 14\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "plt.style.use('ggplot')\n",
    "sns.set_style(\"whitegrid\", {'axes.grid': False})"
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    "# Literature / Useful References\n",
    "\n",
    "### Books\n",
    "- Jean Claude, Morphometry with R\n",
    " - [Online](http://link.springer.com/book/10.1007%2F978-0-387-77789-4) through ETHZ\n",
    " - __Chapter 3__\n",
    " - [Buy it](http://www.amazon.com/Morphometrics-R-Use-Julien-Claude/dp/038777789X)\n",
    "- John C. Russ, âThe Image Processing Handbookâ,(Boca Raton, CRC Press)\n",
    " - Available [online](http://dx.doi.org/10.1201/9780203881095) within domain ethz.ch (or proxy.ethz.ch / public VPN) \n",
    "- [Hypothesis Testing Chapter](http://www.sagepub.com/upm-data/40007_Chapter8.pdf)\n",
    "- Grammar of Graphics: Leland and Wilkinson - http://www.springer.com/gp/book/9780387245447"
   ]
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    "### Videos / Podcasts\n",
    "- Google/Stanford Statistics Intro\n",
    " - https://www.youtube.com/watch?v=YFC2KUmEebc\n",
    "- MCB 140 P-value lecture at UC Berkeley (Audio)\n",
    " - https://itunes.apple.com/us/itunes-u/mcb-140-fall-2007-general/id461120088?mt=10\n",
    "- Correlation and Causation (Video)\n",
    " - https://www.youtube.com/watch?v=YFC2KUmEebc\n",
    "- Last Week Tonight: Scientific Studies\n",
    " - https://www.youtube.com/watch?v=0Rnq1NpHdmw\n",
    "- Credibility Crisis \n",
    " - https://www.datacamp.com/community/podcast/credibility-crisis-in-data-science"
   ]
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    "### Slides\n",
    "- How to solve NLP problems\n",
    " - https://twitter.com/sleepinyourhat/status/1105946169165955073?s=20\n",
    "- Data Visualization\n",
    " - https://socviz.co/lookatdata.html\n",
    "- P-Values with Puppies\n",
    " - https://hackernoon.com/explaining-p-values-with-puppies-af63d68005d0\n",
    "\n",
    "### Model Evaluation\n",
    "\n",
    "- [Julia Evans - Recalling with Precision](https://www.youtube.com/watch?v=ryZL4XNUmwo)\n",
    "- [Stripe's Next Top Model](https://github.com/stripe/topmodel)\n",
    "\n",
    "### Iris Dataset\n",
    "\n",
    "- The Iris dataset was used in Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems: http://rcs.chemometrics.ru/Tutorials/classification/Fisher.pdf"
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   "source": [
    "### Papers / Sites\n",
    "- [Matlab Unit Testing Documentation](http://www.mathworks.ch/ch/help/matlab/matlab-unit-test-framework.html\n",
    ")\n",
    "- [Databases Introduction](http://swcarpentry.github.io/sql-novice-survey/)\n",
    "- [Visualizing Genomic Data](http://circos.ca/documentation/course/visualizing-genomic-data.pdf) (General Visualization Techniques)\n",
    "- [NIMRod Parameter Studies](http://www.messagelab.monash.edu.au/nimrod)\n",
    "\n",
    "- M.E. Wolak, D.J. Fairbairn, Y.R. Paulsen (2012) Guidelines for Estimating Repeatability. Methods in Ecology and Evolution 3(1):129-137.\n",
    "- David J.C. MacKay, Bayesian Interpolartion (1991) [http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.9072]"
   ]
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    "# Previously on QBI ...\n",
    "\n",
    "- Image Enhancment \n",
    " - Highlighting the contrast of interest in images\n",
    " - Minimizing Noise\n",
    "- Understanding image histograms\n",
    "- Automatic Methods\n",
    "- Component Labeling\n",
    "- Single Shape Analysis\n",
    "- Complicated Shapes\n",
    "- Dynamic Experiments"
   ]
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    "# Quantitative \"Big\" Imaging\n",
    "\n",
    "\n",
    "The course has covered imaging enough and there have been a few quantitative metrics, but \"big\" has not really entered.\n",
    "\n",
    "What does __big__ mean?\n",
    "\n",
    "- Not just / even large\n",
    "- it means being ready for _big data_\n",
    "- volume, velocity, variety (3 V's)\n",
    "- scalable, fast, easy to customize\n",
    "\n",
    "\n",
    "So what is \"big\" imaging\n",
    "\n",
    "#### doing analyses in a disciplined manner\n",
    "\n",
    " - fixed steps\n",
    " - easy to regenerate results\n",
    " - no _magic_\n",
    " \n",
    "#### having everything automated\n",
    "\n",
    " - 100 samples is as easy as 1 sample\n",
    " \n",
    "#### being able to adapt and reuse analyses\n",
    "\n",
    " - one really well working script and modify parameters\n",
    " - different types of cells\n",
    " - different regions"
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    "# Objectives\n",
    "\n",
    "1. Scientific Studies all try to get to a single number\n",
    " - Make sure this number is describing the structure well (what we have covered before)\n",
    " - Making sure the number is meaningful (__today!__)\n",
    "1. How do we compare the number from different samples and groups?\n",
    " - Within a sample or same type of samples\n",
    " - Between samples\n",
    "1. How do we compare different processing steps like filter choice, minimum volume, resolution, etc?\n",
    "1. How do we evaluate our parameter selection?\n",
    "1. How can we ensure our techniques do what they are supposed to do?\n",
    "1. How can we visualize so much data? Are there rules?"
   ]
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    "# Outline\n",
    "\n",
    "- Motivation (Why and How?)\n",
    "- Scientific Goals\n",
    "- Reproducibility\n",
    "- Predicting and Validating\n",
    "- Statistical metrics and results\n",
    "- Parameterization\n",
    " - Parameter sweep\n",
    " - Sensitivity analysis\n",
    "- Unit Testing\n",
    "- Visualization"
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    "# What do we start with?\n",
    "\n",
    "Going back to our original cell image\n",
    "\n",
    "1. We have been able to get rid of the noise in the image and find all the cells (lecture 2-4)\n",
    "1. We have analyzed the shape of the cells using the shape tensor (lecture 5)\n",
    "1. We even separated cells joined together using Watershed (lecture 6)\n",
    "1. We have created even more metrics characterizing the distribution (lecture 7)\n",
    "\n",
    "We have at least a few samples (or different regions), large number of metrics and an almost as large number of parameters to _tune_\n",
    "\n",
    "### How do we do something meaningful with it?"
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    "# Correlation and Causation\n",
    "\n",
    "\n",
    "One of the most repeated criticisms of scientific work is that correlation and causation are confused. \n",
    "\n",
    "1. Correlation \n",
    " - means a statistical relationship\n",
    " - very easy to show (single calculation)\n",
    "2. Causation \n",
    " - implies there is a mechanism between A and B\n",
    " - very difficult to show (impossible to prove)"
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    "# Controlled and Observational\n",
    "\n",
    "There are two broad classes of data and scientific studies. \n",
    "\n",
    "### Observational\n",
    "\n",
    " - Exploring large datasets looking for trends\n",
    " - Population is random\n",
    " - Not always hypothesis driven\n",
    " - Rarely leads to causation\n",
    "\n",
    "We examined 100 people and the ones with blue eyes were on average 10cm taller\n",
    "\n",
    "In 100 cake samples, we found a 0.9 correlation between cooking time and bubble size\n",
    "\n",
    "### Controlled\n",
    "\n",
    " - Most scientific studies fall into this category\n",
    " - Specifics of the groups are controlled\n",
    " - Can lead to causation\n",
    "\n",
    "We examined 50 mice with gene XYZ off and 50 gene XYZ on and as the foot size increased by 10%\n",
    "\n",
    "We increased the temperature and the number of pores in the metal increased by 10%\n",
    " "
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    "# Simple Model: Magic / Weighted Coin\n",
    "\n",
    "\n",
    "\n",
    "Since most of the experiments in science are usually specific, noisy, and often very complicated and are not usually good teaching examples\n",
    "\n",
    "- Magic / Biased Coin\n",
    " - You buy a _magic_ coin at a shop\n",
    " - How many times do you need to flip it to _prove_ it is not fair?\n",
    " - If I flip it 10 times and another person flips it 10 times, is that the same as 20 flips?\n",
    " - If I flip it 10 times and then multiply the results by 10 is that the same as 100 flips?\n",
    " - If I buy 10 coins and want to know which ones are fair what do I do?\n",
    " "
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    "# Simple Model: Magic / Weighted Coin\n",
    "\n",
    "\n",
    "1. Each coin represents a stochastic variable $\\mathcal{X}$ and each flip represents an observation $\\mathcal{X}_i$.\n",
    "1. The act of performing a coin flip $\\mathcal{F}$ is an observation $\\mathcal{X}_i = \\mathcal{F}(\\mathcal{X})$\n",
    "\n",
    "We normally assume\n",
    "\n",
    "1. A _fair_ coin has an expected value of $E(\\mathcal{X})=0.5 \\rightarrow$ 50% Heads, 50% Tails\n",
    "1. An _unbiased_ flip(er) means \n",
    " - each flip is independent of the others \n",
    "\n",
    "$$ P(\\mathcal{F}_1(\\mathcal{X})*\\mathcal{F}_2(\\mathcal{X}))= P(\\mathcal{F}_1(\\mathcal{X}))*P(\\mathcal{F}_2(\\mathcal{X}))$$\n",
    "\n",
    " - the expected value of the flip is the same as that of the coin\n",
    " \n",
    "$$ E(\\prod_{i=0}^\\infty \\mathcal{F}_i(\\mathcal{X})) = E(\\mathcal{X}) $$"
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    "# Simple Model to Reality\n",
    "\n",
    "\n",
    "### Coin Flip\n",
    "\n",
    "1. Each flip gives us a small piece of information about the flipper and the coin\n",
    "1.  More flips provides more information\n",
    " - Random / Stochastic variations in coin and flipper cancel out\n",
    " - Systematic variations accumulate\n",
    "\n",
    "\n",
    "\n",
    "### Real Experiment\n",
    "\n",
    "1. Each measurement tells us about our sample, out instrument, and our analysis\n",
    "2. More measurements provide more information\n",
    " - Random / Stochastic variations in sample, instrument, and analysis cancel out\n",
    " - _Normally_ the analysis has very little to no stochastic variation\n",
    " - Systematic variations accumulate"
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    "# Iris: A more complicated model\n",
    "\n",
    "\n",
    "Coin flips are very simple and probably difficult to match to another experiment. A very popular dataset for learning about such values beyond 'coin-flips' is called the Iris dataset which covers a number of measurements from different plants and the corresponding species."
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>1.5</td>\n",
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       "47                4.6               3.2                1.4               0.2   \n",
       "94                5.6               2.7                4.2               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
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    "%matplotlib inline\n",
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "data = load_iris()\n",
    "iris_df = pd.DataFrame(data['data'], columns=data['feature_names'])\n",
    "iris_df['target'] = data['target_names'][data['target']]\n",
    "iris_df.sample(3)"
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\n",
      "text/plain": [
       "<Figure size 799.125x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_df, hue='target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Reproducibility\n",
    "\n",
    "\n",
    "A very broad topic with plenty of sub-areas and deeper meanings. We mean two things by reproducibility\n",
    "\n",
    "### Analysis\n",
    "\n",
    "The process of going from images to numbers is detailed in a clear manner that _anyone_, _anywhere_ could follow and get the exact (within some tolerance) same numbers from your samples\n",
    "\n",
    " - No platform dependence\n",
    " - No proprietary or \"in house\" algorithms\n",
    " - No manual _clicking_, _tweaking_, or _copying_\n",
    " - One script to go from image to result\n",
    " \n",
    "\n",
    "\n",
    "### Measurement\n",
    "\n",
    "Everything for analysis + taking a measurement several times (noise and exact alignment vary each time) does not change the statistics _significantly_\n",
    "\n",
    "- No sensitivity to mounting or rotation\n",
    "- No sensitivity to noise\n",
    "- No dependence on exact illumination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Reproducible Analysis\n",
    "\n",
    "\n",
    "The basis for reproducible scripts and analysis are scripts and macros. Since we will need to perform the same analysis many times to understand how reproducible it is.\n",
    "\n",
    "```bash\n",
    "IMAGEFILE=$1\n",
    "THRESHOLD=130\n",
    "matlab -r \"inImage=$IMAGEFILE; threshImage=inImage>$THRESHOLD; analysisScript;\"\n",
    "```\n",
    "- __or__ \n",
    "```java -jar ij.jar -macro TestMacro.ijm blobs.tif```\n",
    "- __or__\n",
    "```Rscript -e \"library(plyr);...\"```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Comparing Groups: Intraclass Correlation Coefficient\n",
    "\n",
    "\n",
    "The intraclass correlation coefficient basically looking at how similar objects within a group are compared to between groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "format": "column",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.swarmplot(data=iris_df, \n",
    "               x='target', \n",
    "               y='sepal width (cm)')\n",
    "g.set_title('Low Group Similarity');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "format": "column",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ojnqHTD8ghOjXvl5RSdFuY8BRW2uALRtrOf28IRQXNpOQaGd3fgPFe4xg39zk45N3i5kyy+j2CODzBdm8oYZTz8qhosxDTKyVcRMTB800A/siwV0I0a9VVXYaXeoPYrebmbMgA4B1qyNf5TU3+6jsNCIVjFb77PkZ0atoPyPBXQjRrw0ZHkdhQftUAc4YCyVFzXy1rJyERDsZWTERa6QmpzoYMdrNdl0fLrNYTDQ2ennthXxcLiszj0slOdVJcWET61ZV4fcFmTQ9meGj9j8B2UAiwV0I0a9Nnp5MW6uf7bqe2DgrSckOVn5RAUBZSQsJSTYmTE2iML+R5FQncxZk4E60M7cxg80barE7zGTlusKjWAFKipo4e/GwiC6VxYVNnH/pCNIHSQ8aCe5CiH7NZDJFrI/6yvO7IrbX1XhZdE4y80/KiiifPCOFyTNSAHj/zT0R21qa/WxaVxPRpTIYhF3b6yW4CyFENJQUNVNa1ExGdkx4aby62jbydzQQG2clPsFGeWn77I1Wq4mWFh8FKxtITnUwZHgcJpOJpkYvO7fWY3dYuoxiBUhLd3Ypc8Xa2LSuGpPJxKixbhxOC35/kF3b62lq9DFiVDzuRHuX4/ojCe5CiH5j/Zoqli8tC3+ePT+drBwXb7xYEG5lZ+e6SEiyU1fTZszuOD6BN18qMPq7A5OmJTNpejKvPLeT1tDApeRUB2kZTirKPJjNMGN2GmpiIqXFLehNxjw0ucNiWbeqgqZGYzjO2pWVXHjFSD5cUsSe/EYAVn5ezrkXDx8QrXsJ7kKIfmPtyspOn6uoLPNEpE+KC5u58IoRmE0mYuNsvPXf9sAOkLe+miDBcGAHqK5s5YzzhxDvtuOMsYRHsS48LZtZc9II+IMU7GyIeHHbUO9l3aqqcGAHQot6VHHymblH+taPOAnuQoh+Ixjs/DnYpQzAhAmLxYTZbOp2O91MDBYMgsVqHNOR2WzCZIJupxLr5uTdXq8fkuAuhOg3ps5KiejVMnVWClk5seTvbCAQar1nZrv46J0iqitbsdnMjB7nDk8QBjBuUhKTpiezbUs9bW1G6z0pxcHXKyooK/FgsZiYNSeNqbNSWPZRKZs31BAMwrARcbhirTQ3+QCIi7cxdVYq5aUeivYYLXqzxcSk6cm99es4LKZgP3kMrV69Ojhz5sy+roYQoo8V7m6ktKiFjOwYhgwzFs+oqW5l17Z64uJt7M5vZEeHPuxWq4nTzxtCaXELSSkORoyOx2Qy0VDvZYeuw+6wUFfbxvpOg50WLsrmk3eLI8rmnpBBEDABY8Yn4Iyx4vcF2K7raWr0MmKMm6RkR7R/BQdl9erVzJw5s8twW2m5CyH6ldyhceQOjVwRKSnZQdLsNAA2rauO2ObzBXHF2ph5XOQx8W4b045JBeC9NyK7QgIRPW72am72Mfv4yFGsFqsZNXHgLeghwV0IMaAMGxVPeWl7GsadaGPzhmr2FDSRnOrkuPnpxLvt5K2vYfOGGuwOc5feLU6nhQmTk9iyoSY8mRjAsJEyQlUIIfrEtFmpECS8hqrFamLD18ZC17XVbdTXtjHzuDQ++7AkfEx5SQvHzE0jf0cDrlgrs+akkZLm5Izzh4bmhw8weXoKmdmDZ6ZICe5CiAHFbDYxY3YaM0JpmuefilxDtbLcww5dF1Hm8wVxJ9i54LKREeW5w+LIHTZ4FsXuKJprqAohRFRUlLaw8oty9KZaEpMiR4zGuCykdDP61GIxsWp5OXnrqvF6B/dCHSAtdyHEAJO/o4H33tgT7m+eMzSWpGQHNdWtOGMsLDglm9xhsZQWNVOwsxGLxcTocW4+fLsoPBhK59Vx/qXDB/Wc7hLchRADyoavqyIGEhXtbuKbV4/CZDbhclmxWI2ExOnnDaW5yYfFauKLj0sjRrmWl7ZQVtwyqFdjkrSMEGJA6TzCFACTCU+zH1+ntVI9LT7aWgOYLV2P6a5sMJGWuxBiQJk2K5XiwubwiNXho+J47T/5NDf5sFpNzD8li+Ej41nyym7KSoy+7KPGurHZzXhDI1Zzh8YOiMm/Dsd+g7tSygF8F7gAUIAf2AK8CDyttW7bz+FCCHHE5QyN5eJvj2L3LqMr5Po1VeEpA3y+IF98XEpDnTcc2AF2bK3n9POG0FDvxRVrHVT92fdln2kZpdQJwEpgPPA34EzgdOB+YAqwSil1Um9UUgghOkpItDN5egpDR8TT2OCL2NbaGqCuprXLMV5vgEnTkhk5xo1lkKdkYP8t93OA47XuMImDYRPwplLKDdwOfBStygnRlzaV1HP/J9spqW/h5LHp/GjBKKwWeU11JLV6/HyxtJSi3U2kpDmZd2Im8W4ba76qRG+qJcZl4Zi56RF90f2+AF9+Vk7+jnrciXayclzU1bQnETJzXIydkMi2Le2hy243U1HWwopl5cTEWpl9fDrZubHs0HWsWVFJwB9k8vRkJkxNprLcw/JPS6mvbWP4KDfHzU/H6wvy+cellBQ2kZYRw7wTM4nrZgGQ/kQmDhOiG22+AOc8+jnVzd5w2feOH8G1c0b0Ya0Gn4/eKWLb5vYBR2kZTiZMSWLp++2jS61WE5d9ZwwxMUZb9KtlZaxd2T4JmCvWwsRpyRQWNJGc4mDmcWnEuKzs3FYfmn7AgjvBFnGMzWbmzAuG8voL+RE9b874xhA+fb+Epsb2bwMzZqdSW9PGzq3tD4vM7BjOu6R//Fs45InDlFKZwFVAxDyXWutfHLHaCdHPbKtojAjsACsKaiS4H2FFu5siPleUedi9qzGizOcLUlbcwvBR8d0e09zkZ/jIeGYcmxZRPnKMm5Fj3AC822niMK83wNbNdV3mZt+p6yMCO0BhQVOXNE9pcQs+XwCrtf9+k+tJb5nXgUJgx8GeXCn1NbD3sbxLa331wZ5DiN7S6vPz+oYSCqqbmTkkEafNjKfDSMax6YNzmHpfSklz0tzUHszdCTbSMpzs2t4QLjOZwO8P8PknpcTF20hKcVBR1j5xmN1hpramlbwNNSSnOFETE7FYTFRVetiaV4fDYSax07qnJpPRY2bz+pqI8qxcF7u2N4Tngd9bR6vNRPGe5nBZYrK9Xwd26Flwt2utLzjYEyulnABa64UHe6wQfeFXb2xi6XZjmbf/rClk8bQcPtxaTk2zl+OGJ3PdXGm1H2nzTszkg7cKqSz3EO+2ceKiHFIznFSWt7Jrez02u5kx4xL4cElRuJWdmu4kO9dFcWEzLpeV4aPieP/NovA5SwqbmDorlVf/vSs8cCnebWXYyDgKdjbicJiZPT+DkWPczJidyvrVVQQCQcZNSmLshETsDgvLPiyludlH9hAXx8xLo7XFz/tvFVJd2Yo7wahnf3fAnLtS6gngL1rrjQdzYqXUbOD/gAKMh8itWusv97W/5NxFX6pobOXMhz+PKJuU5eaxy2bg8QaIc8iQkGhq9fixO8wR0wG0tfmxWMx8sKSQ/A4teYALLhuBO8GOzW7m5X/tpLqyPW1iMoGamMiWjbURx5x2Ti45Q2KxWM0RvWV8vgDBoJGH3ysQCOJtC+BwWiLO4fH4cXSqZ187nMU6PgfWKqVKgHASUms9ct+HANAM3Ac8DowB3lZKKa21b/+HCdH7bBYzFrMJf4e1N2NsFsrqWymt9zA5OwF76Gt4vcfL5tIGVHociS77vk4pDkLnIApgtxtltm7SHyYTVFZ4cCfYIoIyGCNYbfaux1itZiorPLhirSQmta+mVFvdit8fJD0zJhy0zWZTt3VydlPWX/UkuN8EXMbB59y3Atu11kFgq1KqCsgCui6JIkQfS4yxcdnMIfxz5W4A7BYz6fF2vvHYcoJAepyDhy+dzp6aFm55fQMebwCH1cxvzprASWPT+7byg9zUWSkU7GqgrdXIgw8dEceSV3bT0uzHZIKxExKoKPeER6xOOyaFcZOS2KHrw4ObMrJi+OyjEhrqjPbppGnJzDkhg3df3xN+gZuRFcNZFw7r8rAYqHoS3Gu11i8cwrmvASYDP1BKZQNuoGT/hwjRd25YOJoTx6axq6qJMWlxXP3sava248sbW3lyeT55pfXhl6ytvgB/+XibBPcoS0lzcunVo9mzq5HYeBub1lbR0uwHIBiEbVvq+calw6msaCU5xRGeVuCSq0aFc+x78hvZuLb95enGtdUkJNojeuaUlbSwNa+WiVMHxgLYB9KT4P6RUuo+4GUgnNjSWq85wHFPAE8rpZYBQeAaScmI/m5ydgKTsxPQZQ34O72PKm9opbwhsktcZWMbwWCwX+VgB6OYGCtjJxjrmK78vDxiW8AfxGo1M67TOqd2u4Ux4xIAY4rfzupqu45i3dvSHwx6EtwvC/33wg5lQWC/OffQvDOX7W8fIfrKkk0lPPr5LjxeP4un53Ld3BHkldbzhw+2kl/VxLyRKYxIcbGrqr372xkTMxmW7OKlte09MxaNz5DA3svGjE+ImDcmJc3JyuUV7MlvJDnFwfyTs0hOdbDyi4rwIKYRoyK7scbF25g8IwWdVxeeTMxshpFj3b16L9HUoxGqSqk4rXVjqHujW2tdfsCDDpL0lhG9Jb+qiYuf/IqO//LvOWci93+ynbIOLfOzJ2US77BRXNfCKSqd0ydk4vMHeG7VHr4uqmVyVgKXHzMEh3XgvGQbLPSmWvJ3NOBOtNHU6GNHh1lSEhLtTD82hU/ea88Cm0xw/EmZFBY0ERNrZdqsFOLddqoqPKxfU0XAH2TC1GSyBuD87oczQvVi4G6MHi9DgWVKqWu11m8c+WoKEX1ri+ro3KRZtqMqIrADbClt4PmrZ0eUWS1mvj17GN9mWJRrKfZHTUxEhdIwzz+5LWJbXW0bewoiR7EGg2CzWzjtnCER5SlpzgHRZ/1Q9CQt8yvgRACt9Val1EzgNUCCu+iX8krreW19MU6bhYun55KTGENFYyv/Xr2HOo+XadkJXY45ZmgiXxVUU9XUPgHVmPQ4Hvt8F8X1Rst93shUAsEgr64vZm1hLZOyErhgWjZWc3vvCo/Xz4tfF7K9oom5I5NZND6zV+75aJaeFUN9XftUEXHxNrJzXRGteYD0jK7rqg5mPQnuFq114d4PWus9SqnB0VdIDDrbyhv5znOr8Ya6xb2TV8pzVx7Ld55bTXGdMWT9zQ2lfHPmEJbkleLx+rlwag5nTsoiKzGGe97V7KlpZu7IFHZUNvF2XplxzMZS7jlnInmlDTwb6i75dl4ZO6uauOVUFb7+r9/K45NtFQAsySulsrGNy48Z2pu/gqPOnAWZeFr8FBY0kZhsZ+Fp2aRlxFBd2cqWjbXY7GaOnZdOQoe+7UeDngT3cqXU9Ri9X4LAlUBZVGslxCF6O680HNgBqpu9/HPl7nBgB/AHgzS1+Xj/h8cTBMyhF6IzhyTx8neOwx8IsrOyicueWRFx7tc3lJBXGtkafGNDCTefMhaTyURti5elocC+12sbiiW4R5kr1spZFwwjEAhGLMF3/ElZzDsx86h94d2TFvj1GKsxtQCe0M/fj2alhDhU7piu7ZW0uK4tNrfTytqiOr7Mr8bnb58kaldVE5/tqMRkgs4hISHGRoLT1uU8JfUePtpaTqPHi6PTAJjO+4vo6W5t1aM1sEMPWu5a623ATKVUEuDTWjcc6Bgh+sr5U3J4c2MpBdVGF8bZw5K4dOYQNhTX8eFWo1WdGe9gU0kDz640BksPT3bx+GUzeWltIY8s2wVArN3CqePSeW+L0TEswWnlqtnDKKhu5ldvbsIfCGIxmZg3MoVvPLacQBBsFhOnj8/kjY1GLw2H1cz1xx9olg4homOfXSFDE4b9Smtduo/tWcA9R2oaX+kKKY4Urz/AioIaYmxmpucmhltv64vqqG3xEggGuenVDRHHXDd3OE9/VRCR0pkzIpkfzh9FcZ2HY4YlhScPK2vwsLG4nnEZ8Vz9r1XUdJj3fWRqLL8/bxI7K5qYMSRR5p4RUXcoXSEfwFhObyfwJrAdI40zCjgDGAtcF4W6CnFQAsFgOG8OxiRgc0ckd/lKPiUngWAwyNt5XdsrlY1tEYEdoLbFi8qIZ0x6XMT5M+KdpI11EAgEqfdEjmisbfYyPDmW4cmxB6wnIKNbo+xo/v3uM7hrrdcqpY4BLgYWA+MwXqhqtKd2AAAgAElEQVRq4CXgRa11YF/HCxFtdS1e7ng7j893VDE02cWvFo1jWk4C93+ynZfXFeG0WvjuvBFcND2XJZtKeWDpduo8XhaNyyApxkZNi9HitllMLJ6eQ1FdCysK2ucfmZjp5rx/fEFZfSsnjk3j16ePo7rZy+1v5bG+uI4JmfEsGJXKxx1eop43JatLPWua27hjyWaW76pieEosty0ax6RsN3/5eBuvrCvGZbfw/eNH8o2pg7O/dV8IBIIsX1oWGqFq9JYZNympr6vVq2QNVTFg3f3uFl5dXxz+nBJr538WjOKOtzdH7PfA4qn8+OX1EXPFfGfucJpa/bR4/Zw/JZuJWW6a2ny8sKaQXVVNHDssmfs+3EpTmz98zLePHUpeaT2rdrfPEz4+M54zJmSSV1LPzKFJnDs5q0vr/Pa38ljS4dtCRryD78wZwd3vbQmXmYAXrp3dbYtfHLzNG2r49IPIeQovvWrUoOwOeTjzuQvRL20sjpwMqqqpLaLlvdcn2yu6TAJWWNPCb8+eGFEWa7dy9XHDAdhUUh8R2AE2FNd36Qq5ubSBZ66Ytd+v/htLIutZ1tDKqt2R9QwCeSX1EtyPkPLSlq5lZZ5BGdz3RQYjiQFrWm7kLIDp8Q7mjUqJKDMBp43LwNqpm9yQpBjuenczt725idV7jEBb7/Hy4Kc7uOmV9Wwpq8ftjGz7TB+SyPRO15yS7eapLwv42SvreW7VHnwBI1O5ZFMpN726gfs/2c6EzMjJqHISnMwZGTmtrNkEU3Iizy0OXWZ25BwxJhNkZsX0UW36hrTcxYD1wwWjaGj18en2SoanuLj5FMXELDcFVc288HUhMTYL188bwYwhSdx9zkT+/ukOalu8LBqfwb9X76Gh1WiZf7ClnCevmMlfP97G14VGK/uT7ZUsnpbD2sJaSuo9nKzSuXr2MOo8Xu56Zwtri2qZlOUmxmbl4WU7Afh0eyVlDR6GJbu49z0drufYtDhOUel8vrOKkamx/PJUxdj0OHZXN/PyumJiQzn33MSjK/hE09gJCdTWtLJ5Qy0Oh5lj5qUTn3B09Vzq6ayQOUA6HcZ19GA+94MiOXfRW97cWMKdnfLy503O4rUNkTnacRnx/PPbx+zzPF5/gPl/WRqR8kl22RiW7Ao/JPZ64ZrZjEiRlIs48g5nVsi7gJ9hTDmw91/xAedzF6K/So3t2oLLSnDisJpp9QUi9vu6sJaSuhbmjEghKdRnvaC6mXWhlnuiyxYx2VhKrIOU2Mi8rtVsIjFGRqqK3tWTtMwVwHCttcwnIwaFY4cnc+KYtHAXRpUexyUzhuC0Wvjb0u0EgsZUA1aLme8+b3xBjbVbePiS6eyqaubOt/MIBI2vsedMzgrPZxNjs3DjwtGkxTn4urCWqqY2TBg9c5JkMJPoZQdMyyilVmqt9/3d9AiRtIzobbqsAY/Xz+SchHD3xdJ6DwXVzaTH27nkyRUR876fotLJK62PmIQs2WXjuatms72ikQmZ8cSH5pLxeP2sL64jJyGGHMmliyg66LSMUmpG6MevlVL3A88B4XHWRzrnLkRvafB4iXfaUBnxXbalxzuIc1gprfd0WdCjqc3XpXtks9dPUoyViVnu8PQEAE6bhYlZbpyySpPoI/tLy7zc6fO5HX6WnLsYcHRZA79+axO7qpoZnRrL3edMYmRq+0vO5buquOvdLZQ3tDI9N5FJWW42lhj92k3AhVNzGJ/ZwJPL88PHnDA6jYueXMHummbGpsdxzzmTSIix8as3NrKioIbUWDu3nKo4YUxa796sOOr1JC2T23GxjlDZRK31piNZEUnLiGi7/JkVbC1vDH+empPA45cZ/+bafAHOeuRzalvaJwFbNC6dcZluSuo8nKTSmDkkiWAwyHtbyvi6sI5JWW6e+Sqf/Or2ATOzhiYxLNnFyx0W0Y61W1jy/Xm47NLzWBx5h5KW2TvK4i2l1EKMxksQsGO06sdFoZ5CREUwGIwI7AC6vH326rIGT0RgB9hR2cRd50yKKDOZTCwan8mi8Zm0+QJdulRuLW+g1RuZumlq81NU62FMetyRuBUhemR/TYnngVNDP1d1KPdhTBwmxIBhMpk4ZmgSKzsM+5+Rm8hfPtqGLm/g2GFJ5CQ4KerwsnRilpu73tlMcZ2HU1Q6F0zLodXn56kvC4w1VLMTmJLtZn1x+5QExw5LZmiyiw0l7WVpcXZGpESOmBQi2vY3K+QiAKXUk1rra3qvSkJExx1nTuCPH2g2lNQzPTeRmuY2nlttLNixek8tZ0/MJCuhlYLqJo4flcrKghoKa42Uy96HQl5pfXiw0+o9tcwflcL8UankldYzc0giN52icNksNLX6+HhrBTmJMfz0pDFYLTLTh+hdPcm5L+hUFASagW1ad1pe/DBIzl30psZWHyf+7dOIsky3gzeunwfAlrIGvvV/KyO2zxqaxObSyAnFLGYTy3+68KidM1z0vcOZFfIvwFRgE+AHJgMlgEspda3W+rV9HaiUSgdWA6dqrbfsaz8hjjR/IMiyHZWUN7ayYHQqGfFOwJjtcX1xHZOz3CS5bBGrKGW5nXyVX01+dRPjM+KxmE34A+2Nn+wEJ7XNbWyvbAqXZcY7wrNFTs9NDHevLKpt4fOdVeQkxnS7cIgQ0daT4F4A/Exr/QmAUupY4CfATcDrQLfBXSllAx7FWFhbiF71i9c28On2SgAeWLqDf3xzBmv21PCXj7eH9zlvchbvbC6j1RcgyWUj2WXnRy+uBYwpA86amMlbm0rxB4IMSYzh2jnDKa7z8ItXN9DQ6iPWbmH6kESufW41YPQ4+PUZ48lOcHLDi+toCy28ffakTG4/Y0Lv/gLEUa8nwX3k3sAOoLVeoZQaq7UuVErt77j7gEeAXx5eFYU4ONvKG8OBHaDF6+e5Vbv5YmdVxH7L86tZ8v157K5uJi3OwXn/WB7e5gsEKapt4c3r51Le2IpKN1ry2QkxvPW9eeyobGRosotzH/0ifEwQeHJ5PsOSXeHADvDWxlKunzeSTLczavcsRGc9ecvjVUqdtvdD6Oc2pVQa0O1sSEqpq4AKrfW7R6SWQhwEb6Dr6o9ef7DLGqk+fwCXzUJqnAOz2VjjtKM2f4A4h5XUWAeWDvPBO2xmUuMcOKxmfIHIY7z+AF5/5PWD0GU/IaKtJy33HwAvK6WCGA8DD8aaqjdhtMy7cw0QVEqdAkwD/k8pda7WuuvKxEIcYRMy3UzLSWBtkTHtrtVs4qLpOQxNiuHJLwvC+80bmcpZj3xBdXMbw5NdzB2Rwue7jNa9CRibHs/pDy2jqc3PlOwE/nj+ZMobW7n5tQ0U13nIiHewcEwa725un1PvmzOHMCTJxardNeyN5/NHpcpc7aLX9XQ+dwvGi1QfsFlr7T/AIR2P/QT43oFeqEpvGXEkebx+lmwqpayxlVPGpocHEH2yrYINxXVMzUng9x9spbyhNXzMscOSOHNCJvnVzUzPTeQXr22ImAJ48bQc8krrySttH/w0PNnF948fyabSemYNTWLOCGMlqE0l9SzdXkFuYgynj8/EbpWukCI6Dmc+9wzge0AyocU6lFJorW844rUU4ghx2ixcMC2nS/nCMWksHJNGvccbEdgB8qubOWtSFmD0Z+8Y2AF2VjWxq6o5oqygupkTx6ZxkkqPKJ+Y5WZiVuTyekL0pp6kZZ7F6Nf+NXSZKO+AtNYLD/YYIaLN7bQxOcsdMZJ0Rm4iv35zEwXVzcwbkUJanJ2KxvaFOI4fmUKyy84HujxcNndkinRzFP1ST4J7rtZ6fNRrIkQvu/e8Sdz/yXa2ljdy3PBkVhZUszPUMt9c1sAFU7Ipa2wNTT+QxmWzhtLU5iPWYWFtaOKwGxeO7uO7EKJ7PernrpSK1Vo3HXhXIQaOjHgn94QmBiusbeE/ayImP2VDST3PXXVsRJnbaeO2RdLWEf1fT4J7CbA29GI0PCBJcu5iMEmNtRNrt0RMLTAkKYZ38kopqfdwwui0iLnfhejvevIKPx8j716IMTvk3j9CDBpOm4VbTlXE2o2Vk4Ylu2ho9fHrt/J46LOdXP7MClZ1mFFSiP7ugC13rfWdSqkYYDTG/DJOrXXzAQ4TYsA5fUImC0anUt7YCkG46Mmvwtt8gSDPrdrDrKFJfVhDIXrugC13pdRsYAfwFpAN7FFKzY12xYToCy67leHJsd32gDGboLa5jfxqef0k+r+epGXuA04BqkLL7X0LuD+qtRKijw1LdrGww7qnNouJxBgbZzz8ORc98RVXPbuqy8pNQvQnPQnuLq113t4PWusl9OxFrBAD2r3nTuSecybyPwtG8ZdvTOH1DSXhOWI2ldTzzxUFBziDEH2nJ0Haq5RKIjSASR1gKkghBgur2cyp4zIA+Cq/ussIvr2rNAnRH/UkuN8NLAUylVLPA6cB341qrYToZR/qcp5ZUUAgEOTyY4ZyxoRMtpY38MDSHRTXeVg4OoWkGBs1HVIxHdM2QvQ3Pekt84ZSajPGYtkW4Dda680HOEyIAWNbeSO3vrExPIvj7W/lkRnv4NY3NlHZZEw/8H8rm7l0Zi7lDa1UNbVx5oRMzpiQ2Ye1FmL/9hnclVIzOhXt7RcWo5SaobVeE71qCdF7vsyvouN060HgrU2l4cC+19byRh69tPP/FkL0T/trub+8n21BYOQRrosQfWJkalyXsqm5CeEl+PYaJSNUxQCyz+CutR7RmxURoq/MHZHMpTNzeenrIoJBOG9KFmdNzMJiMnHfh9toaPUxPTeR6+bK/xJi4OjRYh29QRbrEH2tsdVHMBgk3tm+emSbL0BDq4+UWHsf1kyIfdvXYh2yPIwQIeUNrZR1WsCjtsVLcV0Lvm7WZRWiP5PBSOKo5wsEuPm1jXy6vRKA44Yn86dvTOG51bt55LNd+INBshOc/P3i6bIWqhgwDqa3TATpLSMGi6XbKsOBHeDL/GpeXlvEI8uMwA5QXOfhieW7uP2MCX1VTSEOivSWEUe90npPl7KCmmb8gWCn/Vq77CdEfyW9ZcRR74QxaTy8bGe426PVbGLxtBxW7a6hoLp9duvTxqXv6xRC9DsHzLkrpVIxZoKMA0wYo1RHa60vj3LdhOgVuYkxPHTxdJ5fvQd/IMglM3MZnRbHgxdN46kv80NrqKZz3pTsvq6qED3WkxeqL2AsrzcReB9jGoLPolkpIXrblJwEpuQkRJRlup388rRxfVQjIQ5PT7pCDtNanwUsAR4E5gHyL14IIfqxngT30tB/twGTtNZFgG0/+wshhOhjPUnLlCulbgKWA3cqpeoBV3SrJYQQ4nD0pOV+PdCqtV4GrAJ+A/wiqrUSop8oqG7my11VeLz+vq6KEAelJy33C7XWfwPQWt8M3KyUuvlABymlLMBjgAL8wNVa6x2HU1khetNDn+3gqS+NpfRSYu08csl0hqfIzJBiYNjfCNXvYaRffqKU6jjm2gZ8D/j9Ac59DoDWep5SaiHwZ+C8w6qtEL2kvKGVZ75qXyO1qqmNJ7/M5zdnTezDWgnRc/truXuByRgBfnKHch/wswOdWGv9qlLqzdDHYUDZoVZSiN5W09xGpwGqVHVavEOI/mx/I1SfAJ5QSp2vtX71UE6utfYppZ4BvgEsPsQ6CtHrxqbHMSYtjm0VjeGysybKsnpi4DjgfO5KqXjgdxh92y8C7gV+prVu3O+BkefIxFimb4LWuqm7fWQ+d9HfVDe18ezK3ZTUezhZpXOKkukHRP+zr/nce/JC9X6gBMgAPIAb+Adw2f4OUkp9C8jVWt8LNAMBjBerQgwIybF2blg4uq+rIcQh6UlXyOla618BXq11M3A5MK0Hx/0XmK6U+hR4F/ix1rrr9HtCCCGOuJ603Du3ti0YrfD9CqVfLj6USgkhhDg8PWm5f6qU+j0Qo5RahNEi/zi61RJCCHE4ehLcbwYagTrgbmA9cFM0KyWEEOLwHDAto7X2Ar9VSj0I+LXW9dGvlhBCiMNxwJa7UmqcUmolUA5UKaWWKqWGRr9qQgghDlVP0jJPAY9jjFSNA14CnohmpYQQQhyenvSWcWmtH+3w+QGl1HXRqpAQQojD15OW+xal1Ny9H5RSk4Bd0auSEEKIw9WTlvswYKlSah3GpGHTgVKl1HoArfWUKNZPCCHEIehJcD/g3O1CCCH6l550hVzaGxURQghx5PQk5y6EEGKAkeAuhBCDkAR3IYQYhCS4CyHEICTBXQghBiEJ7kIIMQhJcBdCiEFIgrsQQgxCEtyFEGIQkuAuhBCDkAR3IYQYhCS4CyHEICTBXQghBiEJ7kIIMQhJcBdCiEGoJ4t1HBKllA14EhgOOIC7tNavR+t6Qggh2kWz5X4FUKW1ng+cATwYxWsJIYToIGotd+BF4KUOn31RvJYQQogOohbctdaNAEqpeIwgf1u0rtUf+KsrqX36Abzbt+CYdiwJ3/4hJquV+ucfo+WrT7HmDCPxqh9hzcrt66oKIY4C0Wy5o5QaArwCPKS1fi6a1+prVb+/ldaNawDwFuwg2OrBkpRK/b+fMMp2bcObv53MR17EZDL1ZVWFEEeBaL5QzQDeA36ktf4wWtfpDwLNjeHAvlfLis+wJKVElPkK8/EV78aWM6w3qyeEOApF84XqrUAS8Gul1CehPzFRvF6fMTldWFIzIspsQ0Zgyx0euZ8rFktKei/WTAhxtIpmzv1G4MZonb8/MZnNJN1wG9V/+l8CdTVYMnNI/O7PMLti8RbswJu/HVNsHEk/uAWzc1A+34QQ/UxUc+5Hk5iZc8h+5i38leVYMrIxmY0vRZl//ze+0iLMiSmYnc4+rqUQ4mghI1QPQ8DjoW37FgKtHgBMNjuWjGy8BTvw19WE97Nm5hwwsHt378RXWR5R5qsoxbsn/4jXWwgx+EnL/RB51q6g6t5bCDTWY45PIOXW32PNHkLFbT/Ct2cXWK0kXPkj3Bdcsd/zBBobqLj9Rtq2rAezmbjzvknSd35C9d/vpent/0IwiGPKLFJv/4ukdIQQPSYt90NU89DvCDTWAxBoqKPmkT9Q//zjRmAH8Pmoe/oB/NWV+z1Pw5v/MQI7QCBA4yv/ovG912la8jIEgwC0rl9F0zuvRO1ehBCDjwT3Q+QrK4747C8twldaFLmT34+vonS/5/F3Pgbw7tRdr9fNfkIIsS9HTXAPBoPUPf84Jdd9g7KfX4Mn1C+9+fOPKL3hckq+fzGNb7/c4/O55p4U8Tlm3sm4jj85osySkU3TR0sovvocKn79I7wFOwBoePNFSr53EaU3XIE5OS3iGFNsPHHnXIIpxtWh0ETM3BO71KFtxxbKb/0+xdecS+0T9xP0+fDX11J1368pvuosKu+5GX9VBcFAgLpnH6X42vMo+8V3aM1b1+P7FEIMTKZg6Kt/X1u9enVw5syZUTt/4zuvUPPA3eHPphgXaXc/TPnPr4GAP1yedtffcU6ffcDzBVqaqX/uMVq3rMcxfiruy67D7Iyh4c0XafnsfSwZWZisdprebU+nWDJzSLz+Jqru/HH7icwWEq65Ec/KZZjj4nFfcg32UYrWrZtoeOFpAi1NxJ25GNe8yIdJ0Oul+JpzCHRI+7i/9T28O7fS8vlH4TLH1GOJmXMCtY/8sf3eY+PJfuYtzB0fIEKIAWn16tXMnDmzy7D3o+aFqmfNlxGfgy3NNH3wekRgB/B8/WWPgrs5xkXitV278ceffRHxZ18EQOn/XBaxzV9aRMuy9yMPCPgxOx2k3/NQRLFj7EQct/2RffEW7IgI7GDco3fntoiy1nUrMDkcEWXBpgba9Eac047d5/mFEAPbgErLNK/4jOKrz6HoyrNo+uTtgzrWNnJMZIHZjHNq1+BmSUmn7p+PUPfso/jKjXx5oLmJhlefo+bxv9C6ZSNgpHmal75LzaP30fxZe8Bu3byemsf/QsPr/8Y2dGTkJePc2MdP7Vo5p4vaJ/9G/QtP46+vBcBfW039v5+g9ukHw90hg14vjW//l5p//AlfRSmmTr1n7CPGdLlP67CR2EeO7XSTli5128tfXUnpDVdQ9M1TqX74D93uI4To/wZMWsazeQMVP786oizltvtwzVnYo/MHPC1U//E2Wr76FJMrlsSrfkTcmYup++fDNPz3WYJ+H675p9Ky6guCoV4wZncimQ/9h8q7bmrv0WI2k/abv+FZt4qGF58On9996Xewj59M5Z0/gUAAAPvYSWC10Ja3DnNSCsk/uhXnMfOoeeAemj58C5PNiuvEs2j68E3wtgFgzR1G+n1PUXbjFfhDL21NDicZf/0ndc8+Qsvn7dP0xJ65mJZlHxCor8UxZRYpt/yOQG0Vlffegm/PLiyZOaTcfA+23OFU/eFXeFYuwxQbT+K1NxK36Pxuf097zjsOfO2zMzuPOZ60O/7ao9+xEKL37SstM2CCe+mPv413W15EmSV7CNmPHVwXwUBjAyaHA5PN3l7m8UDAT+M7r1D3RGQgi//GFTS88mxEWcycE/Gs/YpgS3O4zBznxj5hKp4Vn0Xsm/Hg81hS0jDHxmGytGfBAs1NYLFQ+/hfaVryUsQx8YuvpOGlZyLKYs9cTNPb7d0jAWxjJpBx35MEWpqwxCdE7O+vq8HsToyYgTLQUI/JGYPJZuv2d9O09D2q/3BrZKHZzJA3VnS7vxCi7+0ruA+YtIypmxGeZodR5qssw7NxDcFQ6xeM4OlZvwp/XW24LOj3GyNBK8oizuMrLcRXVNDtKFJTXHy3dTE5YjqVxXRbR5PZbIxYralur0cwiLcwH19JYfgeIo5xxXa9V5cLLJbIMqcTf3WlMcWw1xsuDzQ14s3fQaChrv2afh/e3TvxV0bee9vOrbRt22ycz+3ucl3MA+afiBCigwHzQjXpx3dQdu25EWWJP7yF+pf/Sd3TDxovJpNTSb/7Ify11VT+9mcEm5vAZiflJ7fjmDyT8l9ej6+wAIC4cy8l8bqfUnXPzbQs/xgA29iJWHOG4Ssy9rEOHUn8ed/Et3snzUvfBYzAG3/Bt3BMnEbNg/caFTGZcF92HfaRCs/Kz8MteufsBZTf+gMCtVVgtpB47Y3EnnYeFbf9kDZt5O4dM+diTkwmUGsEf8fUY4m/8Ft4Vi6jbbORCrKkpBN/3mUQDNLw8j+Na1ptWDNyKLn2XAgEsKRmkHbPw/jLi6m8+xcEW5ox2R0k//y32NVEKn75PXzFe8BkIv6CK0i48odU/vbneFYuM647eQapd/7NeID4218yO2cdf0T/HoUQvWPABHf/7h1dytry1lH3fw+He7wEqiup+9c/8JUWGYEdwNtGzT/+jGvh6eHADtD4+r+x5gwNB3YA79ZNJFz3U6wpaYCJmNnzjQB5013EnnouvsoyYo45HktiMvaRY7GPn0rblg04xk/BNmwUAFmPvULLymVYM7JpeO15I7ADBPzUPfN3gq2t4cAO0Lr6C5J/+Xto82COc+OcOReTxUL67/6BZ+UyAi3NxBy3ALMrjsRrbiTmuIV49+zCNlJR/rOrwvl9f2UZ9c8/hnfn1vDDJdjWSu1jfyJm9glGYAfjAfHfZ7GkZYUDO0DrhjXU//fZiMAO4N2x5eD/soQQfW7gBPeq8i5lvuI94PNG7ldZ3mXfQH0N/squI0X3ttAj9q2rwXV+ZBdGk8nUbfdI+/DR2IePjiizJKUQd9p5ANQ+9UDEtmBbK76yriNNg00NXV5wmqxWYrp5WeyYMBXHhKm07drWJRAb914RWVZd2WVCMoJBvN3cu2/3zi5lgaaGLmVCiP5vwCRUY45dEDlq02Ih7szF2EaPj9jPtXARroWnRx47ZyGxJ50VUWZJSSf+3Mu6nDMY8FN81dkUX30OjaEXnZ51qyj9n8so+ubJ1Dz8B4JeL77yUir+9wYKL15Ixe034qssJ+hto/rvv6PomydTesMV2NXEiGva1STizrgAzO25c1NozveiyxdRcv1impd/AkDz0ncpue4Cir51OnXPPw5A2w5N2c+vofDiE6l/6RmsQ0ZE3vuJZ+BauCiybP5pxJ4Y+fuwZGQTf+6lmDrm+6023Jdei8ke2SfeeYykZYQYiAZMbxkwhts3vPocwbY24s6+COfkmUZ/8Beewle8h5g5C4lbdD5Bv4+GV5+ndf1K7KMnEH/RlZidMTR/8THNH76JOTEZ9+IrsWblRpzTPnEqdY/+KeKaqXf9nap7ftGe5gESrvwhnq+/onX9qnCZc8Zx2MdPpf5fj4bLzHFu3Fd8D8/qL7DmDsN90dVYEhLxrFtJ45KXMTkcWDJzaehwDFYrafc8QsUt3w2nXACSb76HuqcfDHePBHCdfDbm2Dh8pUW45p1M7ClnE/T5aHjlWVo3rsGuJhF/4bcxO5w0f/YBTR8vwZKSjvuiK7GmZ9G2LY+GV58nGPARf/YlOCZOo23Xdqr/fDv+qgpijj2e5B//70H/XQohes+A7wrZG+qef5z6Zx+JKIs9/RtdZmR0zjgOz9oVEcEXqw3HxGm0rlsZsW/6fU/iGD9ln9es+vPtNH/4VuQ1z1zcpXuk68Qzaf54SUSZdehIsh5+4YD3JYQYvAZ8V8jeYB8zoUuZc+acLsP37WMmdNnXPrZrmckZg6+0iOq/3kn9i08TCL3obNuhqXn499Q+cT/WzJzIC5rNxMye36UejkkzMCenHrC+QggBYLnjjjv6ug4AlJSU3JGdnd2ndbBlD4FAgLbtmzHZ7LgvuYb4MxdjzR1BW946gq0eYo4/hcRrf4xz8ixat2wgUFOJbcwEUn52JzGz5uEr3o2vqABLajox80+l/pkH8e7cSuvaFbRty8M+ahzlP72Kts3rjT+7d+KadzLePbswx7lJ/O7PiF1wGmZ3Am1bNhIMBok780Lcl1yNfewE2vLWEWioxznjOJJ+KGuyCnG0KykpITs7+87O5ZKW6UYw1AvF1GHQUDAYBL8fk/X/27v3IK3qOo7j7112WW6Cin3l8O4AAAjaSURBVIN4KS9lH9OwDHUd5CYCYwXpSBbapKjJkFKO2QimNUwqlI42OhHahaAbKYYZlilmXDLxAnhB8Wtjojukoo6i6Cq77vbH7yw+u8vFbfbZy3k+rxmG5+w55znnPL/z+z6/53fO73ua32DUWFfXasRnY3099OjB5u+cy7ann2g2b0ddLntfPIs+J54M5T2ajShtbGiAxoZmI1t3tk0zK00lnxWyLcpajASFdDskFa0/rpZBtuHdWt57fA09Bg2mvG+L0a0VFZQP2LPVe5T326NVAAeyh2y37jlzYDez3XFwb0d1NRvZPHPq9tGmvUeOp6x3n+2DivpPOot+X5xM7cp7qN/0AgBVQ4bS69gTOm2fzSyfHNzb0Zu3zt8e2AFqVy1j3xt/S/2mGioO+Oj21LuD597Cu+tWU1bVi6ohQ7MWuplZ+3Fwb0cNBUnKgJTBsaGRPiPGNvtzWWUlvY9rfUeMmVl7KWqTUVK1pOXF3EZX0nfcxGbTlQd9jC2LfkHNxGpenHY6761f10l7Zmalpmgtd0mXAl8D3t7dsnnRZ8Q4ynpW8c7KZVTsux/1r73CO/cuBaC+5jlenTOT/Rfc6QuiZlZ0xeyWeRY4DfhNEbfR5fSuHknv6pEAvDS9eQKyhjdeo/7lTVQeeHAn7JmZlZKidctExB+But0umGNVRzZ/Xmr53vtQMfjATtobMyslvqBaRAPOvpCGN7dQ++AKKg44iL0umNlqEJSZWTE40hRReZ9+DJwxu7N3w8xKkG+wNjPLoaK23CNiI3B8MbdhZmatueVuZpZDDu5mZjnk4G5mlkMO7mZmOeTgbmaWQ13qPvc1a9Z09i6YmeVCl3nMnpmZtR93y5iZ5ZCDu5lZDjm4m5nlkIO7mVkOObibmeWQg7uZWQ45uBeBpCGSRnb2ftj/T9LJkqa2cZ1ZkqYVa59KWVvKQ9JgST/dxfzPSPp+++1d1+T73ItA0izgpYi4qbP3xTqOy926ki41QrWrk/QJYAHp2bD1wFnAdGAk6VfQ9cC/gCnANklrgQHAVcC7wGvAuUAlcEu2TiUwLSKekDQHOAbYA9gQEed01LHlhaQlwA0RsULSscAs4CXgMNLnfUVELJe0HngGeA/4CXAdqVxfB74KTAIOj4iZkq4ATiXVl3kRcbOkS4DJpPNgZUTMaLEf1wHDs8nfR8QNkhYAA7N/X4iI14v1OXR3OyjHe4F5wE3AUlJd+iuwHJgLvAVsJtWzWcAfIuJ4SY8DK4CjgEbgFOBoUp2bLOk84BtAD+COiJglaTpwGqlubgFOi4htHXLg7cjdMm0zDlgDjAWuJp0Ah0TECcCJwOXA26QvgOuBh4GfkU6OUaST7ArgONJJ8zngW0B/Sf2B1yNiHDAMOF7SAR13aLnxc+Ds7PUU4G/AqxExklSx52bz+gFXRsQZpMC9BBgFzAf2anozSUeTyqmaVC5HSBoCfDmbHgYcJmlCwToTgENID6oZDpyZrQNwX0QMc2DfrZbleHnBvMHA+Ii4hhTsp0TEGODZHbxPf2BRVv82kcoSAEmDgJnACGAoMCCrhwOBsRExghTgj23H4+owbrm3zS+BGaSAsQV4FBgqaXk2vxI4qGD5fYA3I2JTNr0SmA1cSmpJ3kFqLV4F1AKDJC0CtpKCT2UxDyan7gaulbQ3qdKWA8MlVWfzKyQNzF5H9v9sUvD4OykAPFjwfgIeioj3gXeAiySdDqyOiDoASauAIwvW+SSwKiIagTpJq4EjWmzTdq1lOa4tmPdcQUt6/4h4Mnu9ivRrqqV12f81QK+Cvx8KrI+I2mz6YgBJ24BFkrYCB9JN66Fb7m1zCqnSngQsBs4B/hERo4ExwK3Af4AG0mf7KqlVvl+2/ihSV8Bo4MWIGE8K7LNJLYqPZC3J7wK9gbKOOaz8iIgGUtnMA/4EbCC13EaTPuPFpK4XSOUEqRtmQUScCDwJFF64exr4rKRySZWSlpHKsFpShaQyUrfcMwXrbCDrkpFUSWrd/7vFNm0XdlCO7xfMLvwMayQ1fXHu7JGeO7uw+CxwuKQqAEm3SRoFnBoRXwG+SarH3bIeOri3zSPA1VlLbRrwJWBrNr0GaIyIt7LX00lB/HxgiaT7Sd05VwKPAedLegC4FpgDPAQcmrXybiN9SezfgceWJ/NJXWbzgZtJFXgF6XrI81ngKPQwsDBbZgzw66YZEfEo6Zfa/cA/gd9FxGOkL/L7SeW2kRSAmta5E3guK9/VwG0RUdjytA+nsBx35gJgvqR7Sd2ddR/2zSPiFeBHwIqsrNaSzoW3JT0CLANepJvWQ98tY2bdlqQLgVsj4hVJVwHbIuIHnb1fXYH73M2sO3sZuCfrH9/CBxdhS55b7mZmOeQ+dzOzHHJwNzPLIQd3M7MccnC3kiHpHkn7dMB2vi7pgmJvx2xXHNytlIzroO0MB/p00LbMdsh3y1hJkPQrUo6S9cA1pGRRPYFBwMKI+J6k0cANpPxA/Ug5RS4GziMlplpJGr14sKSepAEwo0hJp9aR8gSdREpTUQvMjoimXDZmHcotdysJBRk2x5Ayc54dEceQhqxfVtBd8yngjIg4ijTCeAopyA8lZetsMpOUEXJoRHwa+C/ww4i4Hfgz8GMHdutMHsRkpaYRmAhMkHQmKclXGdA3m18TEc9nrz8PLI6INwAkzSW1zAEmAHsC4yRB+hWwuUOOwOxDcHC3UtMXeAC4nZRFcD4p5W9TcqitBcvW0zxpVGHyqh7ARRFxF4CkfjTPOGjWqdwtY6XkfVISqP6kh3YsJXW9VJGCdUt/ASZJGpBNn8cHGQbvBqZL6impnJR/fE42r55umibW8sPB3UrJYmAh6aLq05I2kLpongI+3nLhiLiPFLQfyLIEDiDldIeU3XMj6ULqU6QW/iXZvLuAaZIuK9qRmO2G75Yx2wlJxwDDIuLGbPrbQHWW69usS3Ofu9nOPQPMkDSV1B3zAs0f5GHWZbnlbmaWQ+5zNzPLIQd3M7MccnA3M8shB3czsxxycDczy6H/AVAtYjvPfGuMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.swarmplot(data=iris_df, \n",
    "               x='target', \n",
    "               y='petal length (cm)')\n",
    "g.set_title('High Group Similarity');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Intraclass Correlation Coefficient Definition\n",
    "\n",
    "$$ ICC = \\frac{S_A^2}{S_A^2+S_W^2} $$\n",
    "\n",
    "where \n",
    "- $S_A^2$ is the variance among groups or classes\n",
    " - Estimate with the standard deviations of the mean values for each group \n",
    "- $S_W^2$ is the variance within groups or classes.\n",
    " - Estimate with the average of standard deviations for each group\n",
    " \n",
    "- 1 means 100% of the variance is between classes\n",
    "- 0 means 0% of the variance is between classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Intraclass Correlation Coefficient: Values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "format": "tab",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def icc_calc(value_name, group_name, data_df):\n",
    "    data_agg = data_df.groupby(group_name).agg({value_name: ['mean', 'var']}).reset_index()\n",
    "    data_agg.columns = data_agg.columns.get_level_values(1)\n",
    "    S_w = data_agg['var'].mean()\n",
    "    S_a = data_agg['mean'].var()\n",
    "    return S_a/(S_a+S_w)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
    "sns.swarmplot(data=iris_df, \n",
    "               x='target', \n",
    "               y='sepal width (cm)',\n",
    "             ax=ax1)\n",
    "ax1.set_title('Low Group Similarity\\nICC:{:2.1%}'.format(\n",
    "    icc_calc('sepal width (cm)', 'target', iris_df)));\n",
    "\n",
    "sns.swarmplot(data=iris_df, \n",
    "               x='target', \n",
    "               y='petal length (cm)',\n",
    "             ax=ax2)\n",
    "ax2.set_title('High Group Similarity\\nICC:{:2.1%}'.format(\n",
    "    icc_calc('petal length (cm)', 'target', iris_df)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Comparing Groups: Tests\n",
    "\n",
    "\n",
    "Once the reproducibility has been measured, it is possible to compare groups. The idea is to make a test to assess the likelihood that two groups are the same given the data\n",
    "\n",
    "1. List assumptions\n",
    "1. Establish a null hypothesis\n",
    " - Usually both groups are the same\n",
    "1. Calculate the probability of the observations given the truth of the null hypothesis\n",
    " - Requires knowledge of probability distribution of the data\n",
    " - Modeling can be exceptionally complicated\n",
    " \n",
    "\n",
    "\n",
    "### Loaded Coin\n",
    "We have 1 coin from a magic shop\n",
    "- our assumptions are\n",
    " - we flip and observe flips of coins accurately and independently\n",
    " - the coin is invariant and always has the same expected value\n",
    "- our null hypothesis is the coin is unbiased $E(\\mathcal{X})=0.5$\n",
    "- we can calculate the likelihood of a given observation given the number of flips (p-value)\n",
    "\n",
    "\n",
    "How good is good enough?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Comparing Groups: Student's T Distribution\n",
    "\n",
    "Since we do not usually know our distribution very well _or_ have enough samples to create a sufficient probability model\n",
    "\n",
    "### [Student T Distribution](http://en.wikipedia.org/wiki/Student's_t-distribution)\n",
    "We assume the distribution of our stochastic variable is normal (Gaussian) and the t-distribution provides an estimate for the mean of the underlying distribution based on few observations.\n",
    "\n",
    "- We estimate the likelihood of our observed values assuming they are coming from random observations of a normal process\n",
    "\n",
    "\n",
    "\n",
    "### Student T-Test\n",
    "\n",
    "Incorporates this distribution and provides an easy method for assessing the likelihood that the two given set of observations are coming from the same underlying process (null hypothesis)\n",
    "\n",
    "- Assume unbiased observations\n",
    "- Assume normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Multiple Testing Bias\n",
    "\n",
    "\n",
    "Back to the magic coin, let's assume we are trying to publish a paper, we heard a p-value of < 0.05 (5%) was good enough. That means if we get 5 heads we are good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "format": "column",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>n_flips</th>\n",
       "      <th>Probability of # Heads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>6.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_flips Probability of # Heads\n",
       "0        1                  50.0%\n",
       "1        4                   6.2%\n",
       "2        5                   3.1%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>n_friends</th>\n",
       "      <th>Probability of 5 Heads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>27.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>47.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>71.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>92.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_friends Probability of 5 Heads\n",
       "0          1                   3.1%\n",
       "1         10                  27.2%\n",
       "2         20                  47.0%\n",
       "3         40                  71.9%\n",
       "4         80                  92.1%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import ttest_ind\n",
    "from IPython.display import display\n",
    "all_heads_df = pd.DataFrame({'n_flips': [1, 4, 5]})\n",
    "all_heads_df['Probability of # Heads'] = all_heads_df['n_flips'].map(\n",
    "    lambda x: '{:2.1%}'.format(0.5**x))\n",
    "display(all_heads_df)\n",
    "friends_heads_df = pd.DataFrame({'n_friends': [1, 10, 20, 40, 80]})\n",
    "friends_heads_df['Probability of 5 Heads'] = friends_heads_df['n_friends'].map(\n",
    "    lambda n_friends: '{:2.1%}'.format((1-(1-0.5**5)**n_friends)))\n",
    "display(friends_heads_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Clearly this is not the case, otherwise we could keep flipping coins or ask all of our friends to flip until we got 5 heads and publish\n",
    "\n",
    "The p-value is only meaningful when the experiment matches what we did. \n",
    "- We didn't say the chance of getting 5 heads ever was < 5%\n",
    "- We said if we have exactly 5 observations and all of them are heads the likelihood that a fair coin produced that result is <5%\n",
    "\n",
    "Many [methods](http://en.wikipedia.org/wiki/Multiple_comparisons_problem) to correct, most just involve scaling $p$. The likelihood of a sequence of 5 heads in a row if you perform 10 flips is 5x higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Multiple Testing Bias: Experiments\n",
    "\n",
    "\n",
    "This is very bad news for us. We have the ability to quantify all sorts of interesting metrics \n",
    "- cell distance to other cells\n",
    "- cell oblateness\n",
    "- cell distribution oblateness\n",
    "\n",
    "So lets throw them all into a magical statistics algorithm and push the __publish__ button\n",
    "\n",
    "\n",
    "With our p value of less than 0.05 and a study with 10 samples in each group, how does increasing the number of variables affect our result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "format": "column",
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>Var_00</th>\n",
       "      <th>Var_01</th>\n",
       "      <th>Var_02</th>\n",
       "      <th>Var_03</th>\n",
       "      <th>Var_04</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.96</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var_00  Var_01  Var_02  Var_03  Var_04  Group\n",
       "0   -0.96    0.53   -0.10   -0.76    0.86      1\n",
       "1    0.30   -0.72   -0.54   -0.55   -0.48      1\n",
       "2   -0.77    0.26   -0.23   -0.37    0.26      1\n",
       "3   -0.41    0.89   -0.70   -0.85    0.41      1\n",
       "4   -0.86   -0.39   -0.34   -0.38   -0.12      1\n",
       "5    0.53   -0.05   -0.99    0.40    0.26      2\n",
       "6   -0.94   -0.83    0.41   -0.09    0.41      2\n",
       "7    0.86   -0.18   -0.92    0.24   -0.28      2\n",
       "8    0.84    0.83   -0.46   -0.39   -0.97      2\n",
       "9    0.08    0.34   -0.09    0.07    0.82      2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('precision', 2)\n",
    "np.random.seed(2017)\n",
    "\n",
    "def random_data_maker(rows, cols):\n",
    "    data_df = pd.DataFrame(\n",
    "        np.random.uniform(-1, 1, size=(rows, cols)),\n",
    "        columns=['Var_{:02d}'.format(c_col) for c_col in range(cols)])\n",
    "    data_df['Group'] = [1]*(rows-rows//2)+[2]*(rows//2)\n",
    "    return data_df\n",
    "\n",
    "\n",
    "rand_df = random_data_maker(10, 5)\n",
    "\n",
    "rand_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "format": "column",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_356c286c_615f_11e9_b61c_f40f24258857row0_col0 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_356c286c_615f_11e9_b61c_f40f24258857\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >P-Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_356c286c_615f_11e9_b61c_f40f24258857level0_row0\" class=\"row_heading level0 row0\" >Var_03</th>\n",
       "                        <td id=\"T_356c286c_615f_11e9_b61c_f40f24258857row0_col0\" class=\"data row0 col0\" >0.0057</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_356c286c_615f_11e9_b61c_f40f24258857level0_row1\" class=\"row_heading level0 row1\" >Var_00</th>\n",
       "                        <td id=\"T_356c286c_615f_11e9_b61c_f40f24258857row1_col0\" class=\"data row1 col0\" >0.08</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_356c286c_615f_11e9_b61c_f40f24258857level0_row2\" class=\"row_heading level0 row2\" >Var_04</th>\n",
       "                        <td id=\"T_356c286c_615f_11e9_b61c_f40f24258857row2_col0\" class=\"data row2 col0\" >0.73</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_356c286c_615f_11e9_b61c_f40f24258857level0_row3\" class=\"row_heading level0 row3\" >Var_01</th>\n",
       "                        <td id=\"T_356c286c_615f_11e9_b61c_f40f24258857row3_col0\" class=\"data row3 col0\" >0.82</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_356c286c_615f_11e9_b61c_f40f24258857level0_row4\" class=\"row_heading level0 row4\" >Var_02</th>\n",
       "                        <td id=\"T_356c286c_615f_11e9_b61c_f40f24258857row4_col0\" class=\"data row4 col0\" >0.92</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112689358>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "\n",
    "def show_significant(in_df, cut_off=0.05):\n",
    "    return in_df.sort_values('P-Value').style.apply(lambda x: ['background-color: yellow' if v<cut_off else '' for v in x])\n",
    "\n",
    "\n",
    "def all_ttest(in_df):\n",
    "    return pd.DataFrame(\n",
    "        {'P-Value': {c_col: ttest_ind(\n",
    "            a=in_df[in_df['Group'] == 1][c_col],\n",
    "            b=in_df[in_df['Group'] == 2][c_col]\n",
    "        ).pvalue\n",
    "            for c_col in\n",
    "            in_df.columns if 'Group' not in c_col}})\n",
    "\n",
    "\n",
    "show_significant(all_ttest(rand_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_357eec74_615f_11e9_b800_f40f24258857row0_col0 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_357eec74_615f_11e9_b800_f40f24258857row1_col0 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_357eec74_615f_11e9_b800_f40f24258857\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >P-Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row0\" class=\"row_heading level0 row0\" >Var_15</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row0_col0\" class=\"data row0 col0\" >0.014</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row1\" class=\"row_heading level0 row1\" >Var_03</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row1_col0\" class=\"data row1 col0\" >0.045</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row2\" class=\"row_heading level0 row2\" >Var_14</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row2_col0\" class=\"data row2 col0\" >0.096</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row3\" class=\"row_heading level0 row3\" >Var_01</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row3_col0\" class=\"data row3 col0\" >0.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row4\" class=\"row_heading level0 row4\" >Var_07</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row4_col0\" class=\"data row4 col0\" >0.26</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row5\" class=\"row_heading level0 row5\" >Var_18</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row5_col0\" class=\"data row5 col0\" >0.4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row6\" class=\"row_heading level0 row6\" >Var_13</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row6_col0\" class=\"data row6 col0\" >0.4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row7\" class=\"row_heading level0 row7\" >Var_10</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row7_col0\" class=\"data row7 col0\" >0.44</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row8\" class=\"row_heading level0 row8\" >Var_04</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row8_col0\" class=\"data row8 col0\" >0.5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row9\" class=\"row_heading level0 row9\" >Var_11</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row9_col0\" class=\"data row9 col0\" >0.55</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row10\" class=\"row_heading level0 row10\" >Var_06</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row10_col0\" class=\"data row10 col0\" >0.57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row11\" class=\"row_heading level0 row11\" >Var_05</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row11_col0\" class=\"data row11 col0\" >0.66</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row12\" class=\"row_heading level0 row12\" >Var_09</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row12_col0\" class=\"data row12 col0\" >0.71</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row13\" class=\"row_heading level0 row13\" >Var_02</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row13_col0\" class=\"data row13 col0\" >0.71</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row14\" class=\"row_heading level0 row14\" >Var_12</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row14_col0\" class=\"data row14 col0\" >0.74</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row15\" class=\"row_heading level0 row15\" >Var_00</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row15_col0\" class=\"data row15 col0\" >0.87</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row16\" class=\"row_heading level0 row16\" >Var_19</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row16_col0\" class=\"data row16 col0\" >0.87</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row17\" class=\"row_heading level0 row17\" >Var_08</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row17_col0\" class=\"data row17 col0\" >0.89</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row18\" class=\"row_heading level0 row18\" >Var_17</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row18_col0\" class=\"data row18 col0\" >0.89</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_357eec74_615f_11e9_b800_f40f24258857level0_row19\" class=\"row_heading level0 row19\" >Var_16</th>\n",
       "                        <td id=\"T_357eec74_615f_11e9_b800_f40f24258857row19_col0\" class=\"data row19 col0\" >0.92</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112659518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2019)\n",
    "show_significant(all_ttest(random_data_maker(150, 20)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d86759c8ddc042cdb7a8ac0de043d93b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=15), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11467f780>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from tqdm import tqdm_notebook # progressbar\n",
    "out_list = []\n",
    "for n_vars in tqdm_notebook(range(1, 150, 10)):\n",
    "    for _ in range(50):\n",
    "        p_values = all_ttest(random_data_maker(100, n_vars)).values\n",
    "        out_list += [{'Variables in Study': n_vars,\n",
    "                      'Significant Variables Found': np.sum(p_values < 0.05),\n",
    "                     'raw_values': p_values}]\n",
    "var_found_df = pd.DataFrame(out_list)\n",
    "sns.swarmplot(data=var_found_df, x='Variables in Study', y='Significant Variables Found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146ad668>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=var_found_df,\n",
    "            x='Variables in Study', y='Significant Variables Found')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Multiple Testing Bias: Correction\n",
    "\n",
    "Using the simple correction factor (number of tests performed), we can make the significant findings constant again. \n",
    "$$ p_{cutoff} = \\frac{0.05}{\\textrm{# of Tests}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114fb6160>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_found_df['Corrected Significant Count'] = var_found_df['raw_values'].map(lambda p_values: \n",
    "                                                                             np.sum(p_values<0.05/len(p_values)))\n",
    "\n",
    "var_found_df.groupby('Variables in Study').agg('mean').reset_index().plot('Variables in Study', [\n",
    "    'Significant Variables Found',\n",
    "    'Corrected Significant Count'\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "So no harm done there we just add this correction factor right?\n",
    "Well what if we have exactly one variable with shift of 1.0 standard deviations from the other. In a dataset where we check $n$ variables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11843ec50>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABqmjbdDKeWUJB9JMpDkR0n+pNb6zW4PBgDQS52sJP1+khm11t9MsjLJbd0dCQCg9zqJpN9O8q9JUmv9UpKFXZ0IAGAK6CSSXpzk+0dsHyiljHuZDgDgeNZJ7PxPkhcdsX1KrXX/0f6FkZGRYxoK/l9uXtvrCSbchZN4ru/kTZNynsn8TByr/+71AMc1fxYe/zqJpAeSvCHJplLKoiRfPdrOg4ODfRMxGABAL3USSZ9KsqyU8u9J+pJc3d2RAAB6r+/gwYO9ngEAYMrxMEkAgAaRBADQIJIAABo872iKKKW8Msl/JHlZrfWHvZ7nZFZK+cUkd+XQM8L6k7y71vpgb6c6OflapKmplDI9yceTzE1yWpL31Vo/09OhSJKUUs5MMpJkWa31672e53hnJWkKKKW8OIe+7uVHvZ6FJMm7k3y+1vraJFcl+XBvxzmp+VqkqektSb5ba31NkouSfKjH85CxeF2bZG+vZzlRiKQeK6X0Jbkjyaoke3o8Dof8VQ79RpMcWm21stc7vhZpavpkkpuO2D7qA4aZNGuSfDTJt3s9yInC5bZJVEq5Jsmf/dTLTybZWGv9cimlB1Od3F7gv8nVtdb/LKXMzKHLbu+a/Mk4rPm1SOM99Z/uqrX+IElKKS9KsjnJe3s7EaWUq5I8U2v9bCnlPb2e50ThOUk9Vkr5ZpKdhzcXJXmo1rqkhyORpJTy60k2JvnzWuu9vZ7nZFVKuT3Jl2qtmw5v76y1/kqPxyJJKWV2Dj1s+CO11o/3ep6TXSlla5KDh/95VZJvJHljrXW0p4Md56wk9Vit9dznfi6lPJHkd3o2DEmSUsqv5dDlhMtrrV/u9TwnuZ/ra5GYHKWUlyX5XJJra62f7/U8JEf+z3Up5QtJ3i6Qjp1Igp/1gSQzkvzN4Uug36+1/l5vRzpp+VqkqWlVkpckuamU8ty9SRfVWt0wzAnF5TYAgAZ/uw0AoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADf8HYvOifDp4zLkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_df = random_data_maker(50, 10)\n",
    "really_different_var = np.concatenate([\n",
    "    np.random.normal(loc=0, scale=1.0, size=(table_df.shape[0]//2)),\n",
    "    np.random.normal(loc=1, scale=1.0, size=(table_df.shape[0]//2))\n",
    "])\n",
    "table_df['Really Different Var'] = really_different_var\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(10, 5))\n",
    "ax1.hist(table_df.query('Group==1')['Really Different Var'], np.linspace(-5, 5, 20), label='Group 1')\n",
    "ax1.hist(table_df.query('Group==2')['Really Different Var'], np.linspace(-5, 5, 20), label='Group 2', alpha=0.5);\n",
    "ax1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "out_p_value = []\n",
    "for _ in range(200):\n",
    "    out_p_value += [ttest_ind(np.random.normal(loc=0, scale=1.0, size=(table_df.shape[0]//2)),\n",
    "          np.random.normal(loc=1, scale=1.0, size=(table_df.shape[0]//2))).pvalue]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, m_axs = plt.subplots(2, 3, figsize=(20, 10))\n",
    "for c_ax, var_count in zip(m_axs.flatten(), np.linspace(1, 140, 9).astype(int)):\n",
    "    c_ax.hist(np.clip(np.array(out_p_value)*var_count, 0.01, 0.3), np.linspace(0.01, 0.3, 30))\n",
    "    c_ax.set_ylim(0, 100)\n",
    "    c_ax.set_title('p-value after multiple correction\\n for {} variables'.format(var_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% Likelihood')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_find_df = pd.DataFrame({'Variables': np.linspace(1, 100, 30).astype(int)})\n",
    "var_find_df['Likelihood of Detecting Really Different Variable'] = var_find_df['Variables'].map(\n",
    "    lambda var_count: np.mean(np.array(out_p_value)*var_count<0.05)\n",
    ")\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(15, 5))\n",
    "var_find_df.plot('Variables', 'Likelihood of Detecting Really Different Variable', ax=ax1)\n",
    "ax1.set_ylabel('% Likelihood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Predicting and Validating\n",
    "\n",
    "\n",
    "![Validation Graphic](http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png)\n",
    "- Borrowed from http://peekaboo-vision.blogspot.ch/2013/01/machine-learning-cheat-sheet-for-scikit.html\n",
    "\n",
    "### Main Categories\n",
    "\n",
    "- Classification\n",
    "- Regression\n",
    "- Clustering\n",
    "- Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Overview\n",
    "\n",
    "\n",
    "Basically all of these are ultimately functions which map inputs to outputs. \n",
    "\n",
    "The input could be \n",
    "\n",
    "- an image\n",
    "- a point\n",
    "- a feature vector\n",
    "- or a multidimensional tensor\n",
    "\n",
    "The output is\n",
    "\n",
    "- a value (regression)\n",
    "- a classification (classification)\n",
    "- a group (clustering)\n",
    "- a vector / matrix / tensor with _fewer_ degrees of input / less noise as the original data (dimensionality reduction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Overfitting\n",
    "\n",
    "The most serious problem with machine learning and such approachs is overfitting your model to your data. Particularly as models get increasingly complex (random forest, neural networks, deep learning, ...), it becomes more and more difficult to apply common sense or even understand exactly what a model is doing and why a given answer is produced. \n",
    "\n",
    "```python\n",
    "magic_classifier = {}\n",
    "# training\n",
    "magic_classifier['Dog'] = 'Animal'\n",
    "magic_classifier['Bob'] = 'Person'\n",
    "magic_classifier['Fish'] = 'Animal'\n",
    "```\n",
    "\n",
    "Now use this classifier, on the training data it works really well\n",
    "\n",
    "```python\n",
    "magic_classifier['Dog'] == 'Animal' # true, 1/1 so far!\n",
    "magic_classifier['Bob'] == 'Person' # true, 2/2 still perfect!\n",
    "magic_classifier['Fish'] == 'Animal' # true, 3/3, wow!\n",
    "```\n",
    "\n",
    "On new data it doesn't work at all, it doesn't even execute.\n",
    "\n",
    "```python\n",
    "magic_classifier['Octopus'] == 'Animal' # exception?! but it was working so well\n",
    "magic_classifier['Dan'] == 'Person' # exception?! \n",
    "```\n",
    "\n",
    "The above example appeared to be a perfect trainer for mapping names to animals or people, but it just memorized the inputs and reproduced them at the output and so didn't actually learn anything, it just copied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Validation\n",
    "\n",
    "\n",
    "Relevant for each of the categories, but applied in a slightly different way depending on the group. The idea is two divide the dataset into groups called training and validation or ideally training, validation, and testing. The analysis is then \n",
    "\n",
    "- developed on __training__\n",
    "- iteratively validated on __validation__\n",
    "- ultimately tested on __testing__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Concrete Example: Classifying Flowers\n",
    "\n",
    "\n",
    "Here we return to the iris data set and try to automatically classify flowers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "95                 5.7               3.0                4.2               1.2   \n",
       "11                 4.8               3.4                1.6               0.2   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "\n",
       "         target  \n",
       "95   versicolor  \n",
       "11       setosa  \n",
       "133   virginica  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "data = load_iris()\n",
    "iris_df = pd.DataFrame(data['data'], columns=data['feature_names'])\n",
    "iris_df['target'] = data['target_names'][data['target']]\n",
    "iris_df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Old Slides \n",
    "https://rawgit.com/Quantitative-Big-Imaging/Quantitative-Big-Imaging-2017/master/Lectures/08-Slides.html#(37)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Parameters\n",
    "\n",
    "```{r, show_chain_block}\n",
    "library(igraph)\n",
    "make.im.proc.chain<-function(root.node=\"Raw\\nImages\",filters=c(),filter.parms=c(),\n",
    "                             segmentation=c(),segmentation.parms=c(),\n",
    "                             analysis=c(),analysis.parms=c()) {\n",
    "  node.names<-c(\"Raw\\nImages\",\n",
    "                filter.parms,filters,\n",
    "                segmentation.parms,segmentation,\n",
    "                analysis.parms,analysis\n",
    "                \n",
    "                )\n",
    "  \n",
    "  c.mat<-matrix(0,length(node.names),length(node.names))\n",
    "  colnames(c.mat)<-node.names\n",
    "  rownames(c.mat)<-node.names\n",
    "  \n",
    "  \n",
    "  for(cFilt in filters) {\n",
    "    c.mat[\"Raw\\nImages\",cFilt]<-1\n",
    "    for(cParm in filter.parms) c.mat[cParm,cFilt]<-1\n",
    "    for(cSeg in segmentation) {\n",
    "        c.mat[cFilt,cSeg]<-1\n",
    "        for(cParm in segmentation.parms) c.mat[cParm,cSeg]<-1\n",
    "        for(cAnal in analysis) {\n",
    "          c.mat[cSeg,cAnal]<-1\n",
    "          for(cParm in analysis.parms) c.mat[cParm,cAnal]<-1\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  \n",
    "  \n",
    "  g<-graph.adjacency(c.mat,mode=\"directed\")\n",
    "  V(g)$degree <- degree(g)\n",
    "  V(g)$label <- V(g)$name\n",
    "  V(g)$color <- \"lightblue\"\n",
    "  V(g)[\"Raw\\nImages\"]$color<-\"lightgreen\"\n",
    "  for(cAnal in analysis) V(g)[cAnal]$color<-\"pink\"\n",
    "  V(g)$size<-30\n",
    "  for(cParam in c(filter.parms,segmentation.parms,analysis.parms)) {\n",
    "    V(g)[cParam]$color<-\"grey\"\n",
    "    V(g)[cParam]$size<-25\n",
    "  }\n",
    "  E(g)$width<-2\n",
    "  g\n",
    "  }\n",
    "```\n",
    "How does a standard image processing chain look?\n",
    "```{r , fig.height=9}\n",
    "g<-make.im.proc.chain(filters=c(\"Gaussian\\nFilter\"),\n",
    "                      filter.parms=c(\"3x3\\nNeighbors\",\"0.5 Sigma\"),\n",
    "                      segmentation=c(\"Threshold\"),\n",
    "                      segmentation.parms=c(\"100\"),\n",
    "                      analysis=c(\"Shape\\nAnalysis\",\"Thickness\\nAnalysis\")\n",
    "                      )\n",
    "plot(g)#,layout=layout.circle) #, layout=layout.circle)# layout.fruchterman.reingold)# layout.kamada.kawai) \n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "- Green are the images we start with (measurements)\n",
    "- Blue are processing steps\n",
    "- Gray are use input parameters\n",
    "- Pink are the outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Qualitative vs Quantitative\n",
    "\n",
    "\n",
    "Given the complexity of the tree, we need to do some pruning\n",
    "\n",
    "### Qualitative Assessment\n",
    " - Evaluating metrics using visual feedback\n",
    " - Compare with expectations from other independent techniques or approach\n",
    " - Are there artifacts which are included in the output?\n",
    " - Do the shapes look correct?\n",
    " - Are they distributed as expected?\n",
    " - Is their orientation meaningful?\n",
    " \n",
    "\n",
    "\n",
    "![Porosity](ext-figures/poros.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Quantitative Metrics\n",
    "\n",
    "\n",
    "With a quantitative approach, we can calculate the specific shape or distribution metrics on the sample with each parameter and establish the relationship between parameter and metric. \n",
    "\n",
    "### Parameter Sweep\n",
    "\n",
    "The way we do this is usually a parameter sweep which means taking one (or more) parameters and varying them between the reasonable bounds (judged qualitatively).\n",
    "\n",
    "\n",
    "\n",
    "```{r, load-metrics}\n",
    "source('../common/shapeAnalysisProcess.R')\n",
    "source('../common/commonReportFunctions.R')\n",
    "# read and correct the coordinate system\n",
    "thresh.fun<-function(x) {\n",
    "  pth<-rev(strsplit(x,\"/\")[[1]])[2]\n",
    "  t<-strsplit(pth,\"_\")[[1]][3]\n",
    "  as.numeric(substring(t,2,nchar(t)))\n",
    "}\n",
    "readfcn<-function(x) cbind(compare.foam.corrected(x,\n",
    "                                                  checkProj=F\n",
    "                                                  #force.scale=0.011 # force voxel size to be 11um\n",
    "                                                  ),\n",
    "                           thresh=thresh.fun(x) # how to parse the sample names\n",
    "                           )\n",
    "# Where are the csv files located\n",
    "rootDir<-\"../common/data/mcastudy\" \n",
    "clpor.files<-Sys.glob(paste(rootDir,\"/a*/lacun_0.csv\",sep=\"/\")) # list all of the files\n",
    "\n",
    "# Read in all of the files\n",
    "all.lacun<-ldply(clpor.files,readfcn,.parallel=T)\n",
    "```\n",
    "\n",
    "```{r , fig.height=5}\n",
    " ggplot(all.lacun,aes(y=VOLUME*1e9,x=thresh))+\n",
    "  geom_jitter(alpha=0.1)+geom_smooth()+\n",
    "  theme_bw(24)+labs(y=\"Volume (um3)\",x=\"Threshold Value\",color=\"Threshold\")+ylim(0,1000)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Is it always the same?\n",
    "\n",
    "\n",
    "\n",
    "```{r , fig.height=5}\n",
    " ggplot(subset(all.lacun,thresh %% 1000==0),aes(y=VOLUME*1e9,x=as.factor(thresh)))+\n",
    "  geom_violin()+\n",
    "  theme_bw(24)+labs(y=\"Volume (um3)\",x=\"Threshold Value\",color=\"Threshold\")+ylim(0,1000)\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "```{r , fig.height=5}\n",
    " ggplot(all.lacun,aes(y=PCA1_Z,x=thresh))+\n",
    "  geom_jitter(alpha=0.1)+geom_smooth()+\n",
    "  theme_bw(24)+labs(y=\"Orientation\",x=\"Threshold Value\",color=\"Threshold\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Sensitivity\n",
    "\n",
    "Sensitivity is defined in control system theory as the change in the value of an output against the change in the input.\n",
    "$$ S = \\frac{|\\Delta \\textrm{Metric}|}{|\\Delta \\textrm{Parameter}|} $$\n",
    "\n",
    "Such a strict definition is not particularly useful for image processing since a threshold has a unit of intensity and a metric might be volume which has $m^3$ so the sensitivity becomes volume per intensity. \n",
    "\n",
    " \n",
    "\n",
    "### Practical Sensitivity\n",
    "\n",
    "A more common approach is to estimate the variation in this parameter between images or within a single image (automatic threshold methods can be useful for this) and define the sensitivity based on this variation. It is also common to normalize it with the mean value so the result is a percentage.\n",
    "\n",
    "$$ S = \\frac{max(\\textrm{Metric})-min(\\textrm{Metric})}{avg(\\textrm{Metric})} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Sensitivity: Real Measurements\n",
    "\n",
    "\n",
    "In this graph it is magnitude of the slope. The steeper the slope the more the metric changes given a small change in the parameter\n",
    "\n",
    "```{r , fig.height=5}\n",
    "poresum<-function(all.data) ddply(all.data,.(thresh),function(c.sample) {\n",
    "  data.frame(Count=nrow(c.sample),\n",
    "             Volume=mean(c.sample$VOLUME*1e9),\n",
    "             Stretch=mean(c.sample$AISO),\n",
    "             Oblateness=mean(c.sample$OBLATENESS),\n",
    "             #Lacuna_Density_mm=1/mean(c.sample$DENSITY_CNT),\n",
    "             Length=mean(c.sample$PROJ_PCA1*1000),\n",
    "             Width=mean(c.sample$PROJ_PCA2*1000),\n",
    "             Height=mean(c.sample$PROJ_PCA3*1000),\n",
    "             Orientation=mean(abs(c.sample$PCA1_Z)))\n",
    "})\n",
    "comb.summary<-cbind(poresum(all.lacun),Phase=\"Lacuna\")\n",
    "splot<-ggplot(comb.summary,aes(x=thresh))\n",
    "splot+geom_line(aes(y=Count))+geom_point(aes(y=Count))+scale_y_log10()+\n",
    "  theme_bw(24)+labs(y=\"Object Count\",x=\"Threshold\",color=\"Phase\")\n",
    "```\n",
    "\n",
    "Comparing Different Variables we see that the best (lowest) value for the count sensitivity is the highest for the volume and anisotropy. \n",
    "\n",
    "```{r , fig.height=5}\n",
    "calc.sens<-function(in.df) {\n",
    "  data.frame(sens.cnt=100*with(in.df,(max(Count)-min(Count))/mean(Count)),\n",
    "             sens.vol=100*with(in.df,(max(Volume)-min(Volume))/mean(Volume)),\n",
    "             sens.stretch=100*with(in.df,(max(Stretch)-min(Stretch))/mean(Stretch))\n",
    "             )\n",
    "}\n",
    "sens.summary<-ddply.cutcols(comb.summary,.(cut_interval(thresh,5)),calc.sens)\n",
    "ggplot(sens.summary,aes(x=thresh))+\n",
    "  geom_line(aes(y=sens.cnt,color=\"Count\"))+\n",
    "  geom_line(aes(y=sens.vol,color=\"Volume\"))+\n",
    "  geom_line(aes(y=sens.stretch,color=\"Anisotropy\"))+\n",
    "  labs(x=\"Threshold\",y=\"Sensitivity (%)\",color=\"Metric\")+\n",
    "  theme_bw(20)\n",
    "```\n",
    "\n",
    "### Which metric is more important?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Unit Testing\n",
    "\n",
    "In computer programming, unit testing is a method by which individual units of source code, sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures, are tested to determine if they are fit for use.\n",
    "\n",
    "- Intuitively, one can view a unit as the smallest testable part of an application\n",
    "- Unit testing is possible with every language\n",
    "- Most (Java, C++, Matlab, R, Python) have built in support for automated testing and reporting\n",
    "\n",
    "The first requirement for unit testing to work well is to have you tools divided up into small independent parts (functions)\n",
    "- Each part can then be tested independently (unit testing)\n",
    " - If the tests are well done, units can be changed and tested independently\n",
    " - Makes upgrading or expanding tools _easy_\n",
    "- The entire path can be tested (integration testing)\n",
    " - Catches mistakes in integration or _glue_\n",
    "\n",
    "\n",
    "\n",
    "Ideally with realistic but simulated test data\n",
    "- The utility of the testing is only as good as the tests you make"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Example\n",
    "\n",
    "- Given the following function\n",
    "\n",
    "```function vxCnt=countVoxs(inImage)```\n",
    "\n",
    "- We can right the following tests\n",
    " - testEmpty2d\n",
    " \n",
    "```assert countVoxs(zeros(3,3)) == 0```\n",
    "\n",
    " - testEmpty3d\n",
    " \n",
    "```assert countVoxs(zeros(3,3,3)) == 0```\n",
    "\n",
    " - testDiag3d\n",
    " \n",
    "```assert countVoxs(eye(3)) == 3```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Unit Testing: Examples\n",
    "\n",
    "\n",
    "- Given the following function\n",
    "```function shapeTable=shapeAnalysis(inImage)```\n",
    "We should decompose the task into sub-components\n",
    "- ```function clImage=componentLabel(inImage)```\n",
    "\n",
    "- ```function objInfo=analyzeObject(inObject)```\n",
    " - ```function vxCnt=countVoxs(inObject)``` \n",
    " - ```function covMat=calculateCOV(inObject)```\n",
    " - ```function shapeT=calcShapeT(covMat)```\n",
    " - ```function angle=calcOrientation(shapeT)```\n",
    " - ```function aniso=calcAnisotropy(shapeT)```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Unit Testing in ImageJ\n",
    "\n",
    "\n",
    "[On this page](https://github.com/imagej/ij1-tests/blob/master/src/test/java/ij/VirtualStackTest.java)\n",
    "\n",
    "<iframe src=\"https://github.com/imagej/ij1-tests/blob/master/src/test/java/ij/VirtualStackTest.java\" width='100%' height='800'></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Unit Testing in KNIME\n",
    "\n",
    "[Read more](https://tech.knime.org/community/developers) and [Here](https://www.knime.org/files/kos-11/KNIME_Testing.pdf)\n",
    "The Java-based unit-testing can be used (JUnit) before any of the plugins are compiled, additionally entire workflows can be made to test the objects using special testing nodes like\n",
    "- difference node (check the if two values are different)\n",
    "\n",
    "![KNIME Tests](ext-figures/KnimeTests.png)\n",
    "\n",
    "- disturber node (insert random / missing values to determine fault tolerance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Unit Testing in Python\n",
    "\n",
    "## PyTest\n",
    "Packages like PyTest are well suited for larger projects where you make a set of specific tests to run each time the project is updated. \n",
    "\n",
    "### Scikit Image\n",
    "https://github.com/scikit-image/scikit-image/tree/master/skimage\n",
    "\n",
    "- Test Watershed https://github.com/scikit-image/scikit-image/blob/16d3fd07e7d882d7f6b74e8dc4028ff946ac7e63/skimage/morphology/tests/test_watershed.py#L79\n",
    "\n",
    "- Test Connected Components https://github.com/scikit-image/scikit-image/blob/16d3fd07e7d882d7f6b74e8dc4028ff946ac7e63/skimage/morphology/tests/test_ccomp.py#L13\n",
    "\n",
    "```python\n",
    "class TestWatershed(unittest.TestCase):\n",
    "    eight = np.ones((3, 3), bool)\n",
    "\n",
    "    def test_watershed01(self):\n",
    "        \"watershed 1\"\n",
    "        data = np.array([[0, 0, 0, 0, 0, 0, 0],\n",
    "                            [0, 0, 0, 0, 0, 0, 0],\n",
    "                            [0, 0, 0, 0, 0, 0, 0],\n",
    "                               [0, 1, 1, 1, 1, 1, 0],\n",
    "                               [0, 1, 0, 0, 0, 1, 0],\n",
    "                               [0, 1, 0, 0, 0, 1, 0],\n",
    "                               [0, 1, 0, 0, 0, 1, 0],\n",
    "                               [0, 1, 1, 1, 1, 1, 0],\n",
    "                               [0, 0, 0, 0, 0, 0, 0],\n",
    "                               [0, 0, 0, 0, 0, 0, 0]], np.uint8)\n",
    "        markers = np.array([[ -1, 0, 0, 0, 0, 0, 0],\n",
    "                               [0, 0, 0, 0, 0, 0, 0],\n",
    "                               [0, 0, 0, 0, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 0, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 0, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 1, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 0, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 0, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 0, 0, 0, 0],\n",
    "                                  [  0, 0, 0, 0, 0, 0, 0]],\n",
    "                                 np.int8)\n",
    "        out = watershed(data, markers, self.eight)\n",
    "        expected = np.array([[-1, -1, -1, -1, -1, -1, -1],\n",
    "                      [-1, -1, -1, -1, -1, -1, -1],\n",
    "                      [-1, -1, -1, -1, -1, -1, -1],\n",
    "                      [-1,  1,  1,  1,  1,  1, -1],\n",
    "                      [-1,  1,  1,  1,  1,  1, -1],\n",
    "                      [-1,  1,  1,  1,  1,  1, -1],\n",
    "                      [-1,  1,  1,  1,  1,  1, -1],\n",
    "                      [-1,  1,  1,  1,  1,  1, -1],\n",
    "                      [-1, -1, -1, -1, -1, -1, -1],\n",
    "                      [-1, -1, -1, -1, -1, -1, -1]])\n",
    "        error = diff(expected, out)\n",
    "        assert error < eps\n",
    "```\n",
    "\n",
    "## DocTests\n",
    "\n",
    "Keep the tests in the code itself: https://github.com/scikit-image/scikit-image/blob/16d3fd07e7d882d7f6b74e8dc4028ff946ac7e63/skimage/filters/thresholding.py#L886\n",
    "```python\n",
    "def apply_hysteresis_threshold(image, low, high):\n",
    "    \"\"\"Apply hysteresis thresholding to `image`.\n",
    "    This algorithm finds regions where `image` is greater than `high`\n",
    "    OR `image` is greater than `low` *and* that region is connected to\n",
    "    a region greater than `high`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    image : array, shape (M,[ N, ..., P])\n",
    "        Grayscale input image.\n",
    "    low : float, or array of same shape as `image`\n",
    "        Lower threshold.\n",
    "    high : float, or array of same shape as `image`\n",
    "        Higher threshold.\n",
    "    Returns\n",
    "    -------\n",
    "    thresholded : array of bool, same shape as `image`\n",
    "        Array in which `True` indicates the locations where `image`\n",
    "        was above the hysteresis threshold.\n",
    "    Examples\n",
    "    --------\n",
    "    >>> image = np.array([1, 2, 3, 2, 1, 2, 1, 3, 2])\n",
    "    >>> apply_hysteresis_threshold(image, 1.5, 2.5).astype(int)\n",
    "    array([0, 1, 1, 1, 0, 0, 0, 1, 1])\n",
    "    References\n",
    "    ----------\n",
    "    .. [1] J. Canny. A computational approach to edge detection.\n",
    "           IEEE Transactions on Pattern Analysis and Machine Intelligence.\n",
    "           1986; vol. 8, pp.679-698.\n",
    "           DOI: 10.1109/TPAMI.1986.4767851\n",
    "    \"\"\"\n",
    "    low = np.clip(low, a_min=None, a_max=high)  # ensure low always below high\n",
    "    mask_low = image > low\n",
    "    mask_high = image > high\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Unit Testing Jupyter\n",
    "Working primarily in notebooks makes regular testing more difficult but not impossible. If we employ a few simple tricks we can use doctesting seamlessly inside of Jupyter. We can make what in python is called an annotatation to setup this code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import doctest\n",
    "import copy\n",
    "import functools\n",
    "\n",
    "def autotest(func):\n",
    "    globs = copy.copy(globals())\n",
    "    globs.update({func.__name__: func})\n",
    "    doctest.run_docstring_examples(\n",
    "        func, globs, verbose=True, name=func.__name__)\n",
    "    return func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
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    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding tests in add_5\n",
      "Trying:\n",
      "    add_5(5)\n",
      "Expecting:\n",
      "    10\n",
      "ok\n"
     ]
    }
   ],
   "source": [
    "@autotest\n",
    "def add_5(x):\n",
    "    \"\"\"\n",
    "    Function adds 5\n",
    "    >>> add_5(5)\n",
    "    10\n",
    "    \"\"\"\n",
    "    return x+5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "format": "column",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding tests in simple_label\n",
      "Trying:\n",
      "    test_img = np.eye(3)\n",
      "Expecting nothing\n",
      "ok\n",
      "Trying:\n",
      "    test_img\n",
      "Expecting:\n",
      "    array([[1., 0., 0.],\n",
      "           [0., 1., 0.],\n",
      "           [0., 0., 1.]])\n",
      "ok\n",
      "Trying:\n",
      "    simple_label(test_img)\n",
      "Expecting:\n",
      "    array([[1, 0, 0],\n",
      "           [0, 1, 0],\n",
      "           [0, 0, 1]])\n",
      "ok\n",
      "Trying:\n",
      "    test_img[1,1] = 0\n",
      "Expecting nothing\n",
      "ok\n",
      "Trying:\n",
      "    simple_label(test_img)\n",
      "Expecting:\n",
      "    array([[1, 0, 0],\n",
      "           [0, 0, 0],\n",
      "           [0, 0, 2]])\n",
      "ok\n"
     ]
    }
   ],
   "source": [
    "from skimage.measure import label\n",
    "import numpy as np\n",
    "@autotest\n",
    "def simple_label(x):\n",
    "    \"\"\"\n",
    "    Label an image\n",
    "    >>> test_img = np.eye(3)\n",
    "    >>> test_img\n",
    "    array([[1., 0., 0.],\n",
    "           [0., 1., 0.],\n",
    "           [0., 0., 1.]])\n",
    "    >>> simple_label(test_img)\n",
    "    array([[1, 0, 0],\n",
    "           [0, 1, 0],\n",
    "           [0, 0, 1]])\n",
    "    >>> test_img[1,1] = 0\n",
    "    >>> simple_label(test_img)\n",
    "    array([[1, 0, 0],\n",
    "           [0, 0, 0],\n",
    "           [0, 0, 2]])\n",
    "    \"\"\"\n",
    "    return label(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Unit Testing Matlab\n",
    "https://www.mathworks.com/help/matlab/matlab-unit-test-framework.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Test Driven Programming\n",
    "\n",
    "\n",
    "Test Driven programming is a style or approach to programming where the tests are written before the functional code. Like very concrete specifications. It is easy to estimate how much time is left since you can automatically see how many of the tests have been passed. You and your collaborators are clear on the utility of the system.\n",
    "\n",
    "1. shapeAnalysis must give an anisotropy of 0 when we input a sphere\n",
    "1. shapeAnalysis must give the center of volume within 0.5 pixels\n",
    "1. shapeAnalysis must run on a 1000x1000 image in 30 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Continuous Integration\n",
    "Conntinuous integration is the process of running tests automatically everytime changes are made. This is possible to setup inside of many IDEs and is offered as a commercial service from companies like CircleCI and Travis. We use them for the QBI course to make sure all of the code in the slides are correct. Projects like scikit-image use them to ensure changes that are made do not break existing code without requiring manual checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Visualization\n",
    "\n",
    "\n",
    "One of the biggest problems with _big_ sciences is trying to visualize a lot of heterogeneous data. \n",
    "\n",
    "- Tables are difficult to interpret\n",
    "- 3D Visualizations are very difficult to compare visually \n",
    "- Contradictory necessity of simple single value results and all of the data to look for trends and find problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Bad Graphs\n",
    "\n",
    "There are too many graphs which say\n",
    "\n",
    "- *my data is very complicated*\n",
    "- *I know how to use __ toolbox in Matlab/R/Mathematica*\n",
    "\n",
    "![3d Plots](ext-figures/badImage1.png)\n",
    "![Spectroscopy](ext-figures/badPlot4.png)\n",
    "\n",
    "\n",
    "- Most programs by default make poor plots\n",
    "- Good visualizations takes time\n",
    "\n",
    "![Linkage](ext-figures/badImage3.png)\n",
    "\n",
    "![Linkage 2](ext-figures/badImage2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Key Ideas\n",
    "\n",
    "\n",
    "1. What is my message? \n",
    "1. Does the graphic communicate it clearly?\n",
    "1. Is a graphic representation really necessary?\n",
    " - \n",
    "1. Does every line / color serve a purpose?\n",
    " - Pretend ink is very expensive\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Simple Rules\n",
    "\n",
    "1. Never use 3D graphics when it can be avoided (unless you want to be deliberately misleading), our visual system is not well suited for comparing heights of different \n",
    "![Dumb 3d](ext-figures/3dplot.png)\n",
    "1. Pie charts can also be hard to interpret\n",
    "1. Background color should almost always be white (not light gray)\n",
    "1. Use color palettes adapted to human visual sensitivity "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# What is my message\n",
    "\n",
    "\n",
    "- Plots to \"show the results\" or \"get a feeling\" are usually not good\n",
    "\n",
    "```{r, fig.height=7}\n",
    "xd<-runif(80)\n",
    "test.data<-data.frame(x=xd,y=xd+runif(80),z=runif(80))\n",
    "plot(test.data)\n",
    "```\n",
    "\n",
    "\n",
    "- Focus on a single, simple message\n",
    " - X is a little bit correlated with Y\n",
    "```{r, fig.height=7}\n",
    "ggplot(test.data,aes(x,y))+\n",
    "  geom_point()+geom_smooth(method=\"lm\")+\n",
    "  coord_equal()+\n",
    "  labs(title=\"X is weakly correlated with Y\")+\n",
    "  theme_bw(20)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Does my graphic communicate it clearly?\n",
    "\n",
    "- Too much data makes it very difficult to derive a clear message\n",
    "```{r, fig.height=7}\n",
    "xd<-runif(5000)\n",
    "test.data<-data.frame(x=xd,y=(xd-0.5)*runif(5000))\n",
    "ggplot(test.data,aes(x,y))+\n",
    "  geom_point()+\n",
    "  coord_equal()+\n",
    "  theme_bw(20)\n",
    "```\n",
    "\n",
    "- Filter and reduce information until it is extremely simple\n",
    "\n",
    "```{r, fig.height=4}\n",
    "\n",
    "ggplot(test.data,aes(x,y))+\n",
    "  stat_binhex(bins=20)+\n",
    "  geom_smooth(method=\"lm\",aes(color=\"Fit\"))+\n",
    "  coord_equal()+\n",
    "  theme_bw(20)+guides(color=F)\n",
    "```\n",
    "\n",
    "```{r, fig.height=4}\n",
    "\n",
    "ggplot(test.data,aes(x,y))+\n",
    "  geom_density2d(aes(color=\"Contour\"))+\n",
    "  geom_smooth(method=\"lm\",aes(color=\"Linear Fit\"))+\n",
    "  coord_equal()+\n",
    "  labs(color=\"Type\")+\n",
    "  theme_bw(20)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Grammar of Graphics\n",
    "\n",
    "\n",
    "- What is a grammar?\n",
    " - Set of rules for constructing and validating a sentence\n",
    " - Specifies the relationship and order between the words constituting the sentence\n",
    "- How does this apply to graphics?\n",
    " - If we develop a consistent way of expressing graphics (sentences) in terms of elements (words) we can compose and decompose graphics easily\n",
    "\n",
    "\n",
    "- The most important modern work in graphical grammars is âThe Grammar of Graphicsâ by Wilkinson, Anand, and Grossman (2005). This work built on earlier work by Bertin (1983) and proposed a grammar that can be used to describe and construct a wide range of statistical graphics.\n",
    "\n",
    "- This can be applied in R using the ggplot2 library (<small>H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009.</small>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Grammar Explained\n",
    "\n",
    "Normally we think of plots in terms of some sort of data which is fed into a plot command that produces a picture\n",
    "- In Excel you select a range and plot-type and click \"Make\"\n",
    "- In Matlab you run ```plot(xdata,ydata,color/shape)``` \n",
    "\n",
    "1. These produces entire graphics (sentences) or at least phrases in one go and thus abstract away from the idea of grammar. \n",
    "1. If you spoke by finding entire sentences in a book it would be very ineffective, it is much better to build up word by word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Grammar\n",
    "\n",
    "Separate the graph into its component parts\n",
    "\n",
    "\n",
    "1. Data Mapping\n",
    " - $var1 \\rightarrow x$, $var2 \\rightarrow y$\n",
    "\n",
    "![Graph Decomposed](ext-figures/grammarOfGraphics.png)\n",
    "\n",
    "1. Points\n",
    "1. Axes / Coordinate System\n",
    "1. Labels / Annotation\n",
    "\n",
    "Construct graphics by focusing on each portion independently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Wrapping up\n",
    "\n",
    "\n",
    "- I am not a statistician\n",
    "- This is not a statistics course\n",
    "- If you have questions or concerns, Both ETHZ and Uni Zurich offer __free__ consultation with real statisticians\n",
    " - They are rarely bearers of good news\n",
    " \n",
    "\n",
    "- Simulations (even simple ones) are very helpful (see [StatisticalSignificanceHunter\n",
    "](knime://LOCAL/Exercise%209%20StatsRepro/StatisticalSignificanceHunter\n",
    "))\n",
    "\n",
    "- Try and understand the tests you are performing\n",
    " "
   ]
  },
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