diff --git a/README.md b/README.md index 4dada97..15045ed 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,7 @@ + main + +This repository contains several jupyter notebooks + # hack-5-python notebooks for learning python variables, lists, and for loops + main diff --git a/notebooks/.ipynb_checkpoints/14.1-visualization-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/14.1-visualization-checkpoint.ipynb new file mode 100644 index 0000000..28f0ddd --- /dev/null +++ b/notebooks/.ipynb_checkpoints/14.1-visualization-checkpoint.ipynb @@ -0,0 +1,1682 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "8417caef-af4b-4d18-86a0-8b34987b02c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not load conda plugin `conda-libmamba-solver`:\n", + "\n", + "dlopen(/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/libmambapy/bindings.cpython-310-darwin.so, 0x0002): Library not loaded: @rpath/libarchive.13.dylib\n", + " Referenced from: /Users/riyarampalli/miniforge3/lib/libmamba.2.0.0.dylib\n", + " Reason: tried: '/Users/riyarampalli/miniforge3/lib/libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/libmambapy/../../../libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/libmambapy/../../../libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/bin/../lib/libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/bin/../lib/libarchive.13.dylib' (no such file), '/usr/local/lib/libarchive.13.dylib' (no such file), '/usr/lib/libarchive.13.dylib' (no such file, not in dyld cache)\n", + "done\n", + "doneing environment: \\ \n", + "\n", + "\n", + "==> WARNING: A newer version of conda exists. <==\n", + " current version: 23.3.1\n", + " latest version: 25.1.1\n", + "\n", + "Please update conda by running\n", + "\n", + " $ conda update -n base -c conda-forge conda\n", + "\n", + "Or to minimize the number of packages updated during conda update use\n", + "\n", + " conda install conda=25.1.1\n", + "\n", + "\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /Users/riyarampalli/miniforge3\n", + "\n", + " added / updated specs:\n", + " - matplotlib\n", + " - scikit-learn\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " brotli-1.1.0 | hd74edd7_2 19 KB conda-forge\n", + " brotli-bin-1.1.0 | hd74edd7_2 16 KB conda-forge\n", + " contourpy-1.3.1 | py310h48ca7d4_0 249 KB\n", + " cycler-0.12.1 | pyhd8ed1ab_1 13 KB conda-forge\n", + " fonttools-4.56.0 | py310hc74094e_0 2.2 MB conda-forge\n", + " joblib-1.4.2 | pyhd8ed1ab_1 215 KB conda-forge\n", + " kiwisolver-1.4.8 | py310h313beb8_0 65 KB\n", + " libbrotlicommon-1.1.0 | hd74edd7_2 67 KB conda-forge\n", + " libbrotlidec-1.1.0 | hd74edd7_2 28 KB conda-forge\n", + " libbrotlienc-1.1.0 | hd74edd7_2 273 KB conda-forge\n", + " matplotlib-3.10.0 | py310hca03da5_0 8 KB\n", + " matplotlib-base-3.10.0 | py310h7ef442a_0 7.2 MB\n", + " munkres-1.1.4 | pyh9f0ad1d_0 12 KB conda-forge\n", + " numpy-1.24.0 | py310h5d7c261_0 5.0 MB conda-forge\n", + " pyparsing-3.2.1 | pyhd8ed1ab_0 91 KB conda-forge\n", + " scikit-learn-1.2.2 | py310h98c5782_1 6.6 MB conda-forge\n", + " scipy-1.7.3 | py310h6ecf4ae_0 20.1 MB conda-forge\n", + " threadpoolctl-3.5.0 | pyhc1e730c_0 23 KB conda-forge\n", + " unicodedata2-16.0.0 | py310h078409c_0 400 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 42.5 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " brotli conda-forge/osx-arm64::brotli-1.1.0-hd74edd7_2 \n", + " brotli-bin conda-forge/osx-arm64::brotli-bin-1.1.0-hd74edd7_2 \n", + " contourpy pkgs/main/osx-arm64::contourpy-1.3.1-py310h48ca7d4_0 \n", + " cycler conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_1 \n", + " fonttools conda-forge/osx-arm64::fonttools-4.56.0-py310hc74094e_0 \n", + " joblib conda-forge/noarch::joblib-1.4.2-pyhd8ed1ab_1 \n", + " kiwisolver pkgs/main/osx-arm64::kiwisolver-1.4.8-py310h313beb8_0 \n", + " libbrotlicommon conda-forge/osx-arm64::libbrotlicommon-1.1.0-hd74edd7_2 \n", + " libbrotlidec conda-forge/osx-arm64::libbrotlidec-1.1.0-hd74edd7_2 \n", + " libbrotlienc conda-forge/osx-arm64::libbrotlienc-1.1.0-hd74edd7_2 \n", + " matplotlib pkgs/main/osx-arm64::matplotlib-3.10.0-py310hca03da5_0 \n", + " matplotlib-base pkgs/main/osx-arm64::matplotlib-base-3.10.0-py310h7ef442a_0 \n", + " munkres conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n", + " pyparsing conda-forge/noarch::pyparsing-3.2.1-pyhd8ed1ab_0 \n", + " scikit-learn conda-forge/osx-arm64::scikit-learn-1.2.2-py310h98c5782_1 \n", + " threadpoolctl conda-forge/noarch::threadpoolctl-3.5.0-pyhc1e730c_0 \n", + " unicodedata2 conda-forge/osx-arm64::unicodedata2-16.0.0-py310h078409c_0 \n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " numpy 1.22.4-py310h0a343b5_0 --> 1.24.0-py310h5d7c261_0 \n", + "\n", + "The following packages will be DOWNGRADED:\n", + "\n", + " scipy 1.7.3-py310ha0d8a01_1 --> 1.7.3-py310h6ecf4ae_0 \n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "matplotlib-base-3.10 | 7.2 MB | | 0% \n", + "fonttools-4.56.0 | 2.2 MB | | 0% \u001b[A\n", + "\n", + "brotli-1.1.0 | 19 KB | | 0% \u001b[A\u001b[A\n", + "\n", + "\n", + "joblib-1.4.2 | 215 KB | | 0% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "numpy-1.24.0 | 5.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "libbrotlicommon-1.1. | 67 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cycler-0.12.1 | 13 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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"donefying transaction: / \n", + "done\n" + ] + } + ], + "source": [ + "!conda install -c conda-forge matplotlib scikit-learn -y\n", + "#make sure to add bang (!)\n", + "#if running install in jupyter notebook make sure to add -your to bypass y/n push" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "97f9dfc1-673e-4a33-9f52-e6d7cc843d53", + "metadata": {}, + "outputs": [], + "source": [ + "#include a few imports that we will need:\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "markdown", + "id": "e5802551-e11b-43ea-a542-5aedd64b3916", + "metadata": {}, + "source": [ + "We have been loading and cleaning the iris data by hand up to this point, but now we are going to make use of a nice feature in scikit-learn, which provides the iris data as a pre-loaded dataset." + ] + }, + { + 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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)species
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
..................
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
\n", + "

150 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + ".. ... ... ... ... \n", + "145 6.7 3.0 5.2 2.3 \n", + "146 6.3 2.5 5.0 1.9 \n", + "147 6.5 3.0 5.2 2.0 \n", + "148 6.2 3.4 5.4 2.3 \n", + "149 5.9 3.0 5.1 1.8 \n", + "\n", + " species \n", + "0 setosa \n", + "1 setosa \n", + "2 setosa \n", + "3 setosa \n", + "4 setosa \n", + ".. ... \n", + "145 virginica \n", + "146 virginica \n", + "147 virginica \n", + "148 virginica \n", + "149 virginica \n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = load_iris()\n", + "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n", + "iris_df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n", + "iris_df" + ] + }, + { + "cell_type": "markdown", + "id": "d69fdbc9-8058-43f2-80c3-68352e65e01e", + "metadata": {}, + "source": [ + "# Exploring the data 1-D at a time with histograms¶\n", + "\n", + "Let's say we want to visualize the differences between the different measurements for each of the species. A simple way to do this is with histograms. Here we will use the pd.groupby method to group the data by species ID." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "642b8733-dd2c-4209-9d00-43357d537637", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EAEhOgAAAyQkQACA5AQIAJCdAAIDkBAgAkJwAAQCSEyAAQHICBABIToAAAMkJEAAgOQECACQnQACA5AQIAJCcAAEAkhMgAEByAgQASC73AKmrq4tjjz02qqqqolevXnHmmWfG6tWr874MAFDCcg+QZ555JiZOnBgvvvhiLFq0KLZs2RJjx46Njz/+OO9LAQAlqjLvE/7pT39q8fzuu++OXr16xfLly+Ob3/xm3pcDAEpQ0X8GpKGhISIievToUexLAQAlIvc7IP8ty7KYOnVqjBo1KoYOHdrqPk1NTdHU1NT8vLGxsZhDAgD2AkW9A/KjH/0oXn311Vi4cOFO96mrq4vu3bs3P/r161fMIQEAe4GiBcjVV18djzzySCxevDgOOeSQne43ffr0aGhoaH7U19cXa0gAwF4i97dgsiyLq6++Oh566KFYsmRJDBw4cJf7FwqFKBQKeQ8DANiL5R4gEydOjAULFsTvf//7qKqqig0bNkRERPfu3WO//fbL+3IAQAnK/S2YOXPmRENDQ4wePTr69u3b/Lj//vvzvhQAUKKK8hYMAMCu+C4YACA5AQIAJCdAAIDkBAgAkJwAAQCSEyAAQHICBABIToAAAMkJEAAgOQECACQnQACA5AQIAJCcAAEAkhMgAEByAgQASE6AAADJCRAAIDkBAgAkJ0AAgOQECACQnAABAJITIABAcgIEAEiusr0HUMoG1D7W3kMoqpqB/ZNcpypq27T/qnXrizQSoJTVzKtp7yHQBu6AAADJCRAAIDkBAgAkJ0AAgOQECACQnAABAJITIABAcgIEAEhOgAAAyQkQACA5AQIAJCdAAIDkBAgAkJwAAQCSEyAAQHICBABIToAAAMkJEAAgOQECACQnQACA5AQIAJCcAAEAkhMgAEByAgQASK5oAXLHHXfEwIEDo0uXLjF8+PD4y1/+UqxLAQAlpigBcv/998fkyZPj+uuvj5dffjm+8Y1vxGmnnRbr168vxuUAgBJTlAC57bbb4oc//GFcdtllccQRR8Ts2bOjX79+MWfOnGJcDgAoMZV5n/DTTz+N5cuXR21tbYvtY8eOjeeff36H/ZuamqKpqan5eUNDQ0RENDY25j20iIjY1vRJUc5bjrb+e2t7D6FVjU1Zew9hB3vrnxVAa4rxf+z2c2bZ7v0bnXuAfPDBB7F169bo3bt3i+29e/eODRs27LB/XV1dzJw5c4ft/fr1y3tolInu7T2AVv29vQcAsNu6/0/x/iXdvHlzdO/+xefPPUC2q6ioaPE8y7IdtkVETJ8+PaZOndr8fNu2bfHPf/4zevbs2er+e7vGxsbo169f1NfXR7du3dp7OEl11Ll31HlHmHtHnHtHnXdEx5377s47y7LYvHlzVFdX79Z5cw+Qgw46KDp16rTD3Y6NGzfucFckIqJQKEShUGix7YADDsh7WMl169atQ/0F/W8dde4ddd4R5t4R595R5x3Rcee+O/PenTsf2+X+Q6j77rtvDB8+PBYtWtRi+6JFi+LEE0/M+3IAQAkqylswU6dOjYsuuihGjBgRJ5xwQsydOzfWr18fV155ZTEuBwCUmKIEyDnnnBMffvhh3HzzzfH+++/H0KFD449//GMceuihxbjcXqVQKMRNN920w9tKHUFHnXtHnXeEuXfEuXfUeUd03LkXa94V2e5+XgYAICe+CwYASE6AAADJCRAAIDkBAgAkJ0C+hLq6uqioqIjJkyfvdJ8lS5ZERUXFDo9//OMf6QaagxkzZuwwhz59+uzymGeeeSaGDx8eXbp0icMOOyzuvPPORKPNT1vnXS7rvd27774bF154YfTs2TO6du0axxxzTCxfvnyXx5TDuke0fe7lsPYDBgxodQ4TJ07c6THlst5tnXs5rHdExJYtW+KGG26IgQMHxn777ReHHXZY3HzzzbFt27ZdHpfHuhftV7GXu6VLl8bcuXPjqKOO2q39V69e3eI3yB188MHFGlrRDBkyJJ566qnm5506ddrpvuvWrYvTTz89Lr/88pg/f3789a9/jauuuioOPvjg+O53v5tiuLlpy7y3K4f13rRpU4wcOTJOPvnkePzxx6NXr17x1ltv7fI3FZfLuu/J3Lcr5bVfunRpbN36/79Y8bXXXotTTz01zj777Fb3L5f1jmj73Lcr5fWOiLj11lvjzjvvjHnz5sWQIUNi2bJlcckll0T37t1j0qRJrR6T27pntNnmzZuzQYMGZYsWLcpOOumkbNKkSTvdd/HixVlEZJs2bUo2vmK46aabsqOPPnq397/uuuuywYMHt9h2xRVXZMcff3zOIyuuts67XNY7y7Js2rRp2ahRo9p0TLms+57MvZzWfrtJkyZlhx9+eLZt27ZWXy+X9W7NF829XNZ73Lhx2aWXXtpi21lnnZVdeOGFOz0mr3X3FswemDhxYowbNy5OOeWU3T5m2LBh0bdv3xgzZkwsXry4iKMrnjfeeCOqq6tj4MCBce6558batWt3uu8LL7wQY8eObbHtW9/6Vixbtiw+++yzYg81V22Z93blsN6PPPJIjBgxIs4+++zo1atXDBs2LO66665dHlMu674nc9+uHNY+IuLTTz+N+fPnx6WXXrrTLwYtl/X+vN2Z+3alvt6jRo2Kp59+OtasWRMREa+88ko899xzcfrpp+/0mLzWXYC00X333RcrVqyIurq63dq/b9++MXfu3HjggQfiwQcfjK9+9asxZsyYePbZZ4s80nwdd9xxce+998YTTzwRd911V2zYsCFOPPHE+PDDD1vdf8OGDTt8+WDv3r1jy5Yt8cEHH6QYci7aOu9yWe+IiLVr18acOXNi0KBB8cQTT8SVV14Z11xzTdx77707PaZc1n1P5l5Oax8R8fDDD8e//vWv+MEPfrDTfcplvT9vd+ZeLus9bdq0OO+882Lw4MHRuXPnGDZsWEyePDnOO++8nR6T27q36X5JB7d+/fqsV69e2cqVK5u3fdFbMK0ZP358dsYZZ+Q8urQ++uijrHfv3tkvfvGLVl8fNGhQNmvWrBbbnnvuuSwisvfffz/FEIvii+bdmlJd786dO2cnnHBCi21XX331Lm+zlsu678ncW1Oqa59lWTZ27Nhs/Pjxu9ynXNb783Zn7q0pxfVeuHBhdsghh2QLFy7MXn311ezee+/NevTokd1zzz07PSavdXcHpA2WL18eGzdujOHDh0dlZWVUVlbGM888E7/61a+isrKyxQ8w7crxxx8fb7zxRpFHW1z7779/1NTU7HQeffr0iQ0bNrTYtnHjxqisrIyePXumGGJRfNG8W1Oq6923b9848sgjW2w74ogjYv369Ts9plzWfU/m3ppSXft33nknnnrqqbjssst2uV+5rPd/2925t6YU1/vaa6+N2traOPfcc6OmpiYuuuiimDJlyi7v8ue17gKkDcaMGROrVq2KlStXNj9GjBgRF1xwQaxcuXK3Ph0REfHyyy9H3759izza4mpqaoq///3vO53HCSecEIsWLWqx7cknn4wRI0ZE586dUwyxKL5o3q0p1fUeOXJkrF69usW2NWvW7PJLJctl3fdk7q0p1bW/++67o1evXjFu3Lhd7lcu6/3fdnfurSnF9f7kk09in31apkCnTp12+THc3NZ9j+/bkGXZjm/B1NbWZhdddFHz81/+8pfZQw89lK1ZsyZ77bXXstra2iwisgceeKAdRrvnfvzjH2dLlizJ1q5dm7344ovZ+PHjs6qqquztt9/OsmzHea9duzbr2rVrNmXKlOxvf/tb9pvf/Cbr3Llz9rvf/a69prBH2jrvclnvLMuyl156KausrMxuueWW7I033sh++9vfZl27ds3mz5/fvE+5rvuezL1c1n7r1q1Z//79s2nTpu3wWrmu93ZtmXu5rPeECROyr3zlK9mjjz6arVu3LnvwwQezgw46KLvuuuua9ynWuguQL+nzATJhwoTspJNOan5+6623ZocffnjWpUuX7MADD8xGjRqVPfbYY+kH+iWdc845Wd++fbPOnTtn1dXV2VlnnZW9/vrrza9/ft5ZlmVLlizJhg0blu27777ZgAEDsjlz5iQe9ZfX1nmXy3pv94c//CEbOnRoVigUssGDB2dz585t8Xq5rnuWtX3u5bL2TzzxRBYR2erVq3d4rZzXO8vaNvdyWe/GxsZs0qRJWf/+/bMuXbpkhx12WHb99ddnTU1NzfsUa90rsizLdv9+CQDAl+dnQACA5AQIAJCcAAEAkhMgAEByAgQASE6AAADJCRAAIDkBAgAkJ0AAgOQECACQnAABAJITIABAcv8PUZUREX6owe0AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define a variable for the feature we wish to plot\n", + "feature = \"sepal length (cm)\"\n", + "# Group the data in the dataframe by species ID\n", + "gb = iris_df.groupby(\"species\")\n", + "# Iterate through the groupby object\n", + "for sp, dat in gb:\n", + " # For each species, plot the data for the given feature as a histogram\n", + " plt.hist(dat[feature], label=sp)" + ] + }, + { + "cell_type": "markdown", + "id": "17050e4b-3eae-4265-9591-b9d86ea9c494", + "metadata": {}, + "source": [ + "# Set the alpha channel¶\n", + "\n", + "The initial result looks good, but hist defaults to plotting all histograms with zero opacity, so it's not clear what is happening in regions where the histograms overlap. Matplotlib plotting functions can take an opacity parameter called alpha which takes values between 0 and 1. Try setting the alpha to a small value and replotting." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "24f3d128-3496-4c26-99f8-058b14bf86a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.06) #higher alpha values mean less transparency \n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "bf1710af-eec4-4267-a99f-300a85176e28", + "metadata": {}, + "source": [ + "# Change the colors¶\n", + "\n", + "By default, matplotlib will choose different colors for each histogram, and this is fine, but you might want to control the colors for each histogram to unify color schemes across your plots, and also to beautify them. You can specify colors in several different ways, but the most straightforward way is to pick from the large list of matplotlib named colors. Here are some examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "db4f953a-f9b8-449b-a6b2-8e98926ba22f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "# Create a dictionary mapping species IDs to color names, which we will access inside the for loop\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.3, color=cdict[sp])" + ] + }, + { + "cell_type": "markdown", + "id": "3d6fb1f8-2b69-42c9-af6f-6a9c90d23625", + "metadata": {}, + "source": [ + "# Add a legend¶\n", + "\n", + "If you add labels to the hist call (as we have done), then matplotlib can easily generate a legend for your figure with the plt.legend() method." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "128535c2-e9a9-49d8-8883-83e51279ccfa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.5, color=cdict[sp])\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "f0ca14bc-d1d7-4b28-97e8-5f6c929ed8b4", + "metadata": {}, + "source": [ + "# Add axis labels and title¶\n", + "\n", + "Finally, we will wrap up this figure by providing axis labels and a plot title, which we can do with the plt.title(), and plt.xlabel()/plt.ylabel() methods.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3f3a30a2-1473-450d-a427-7d7823a85ac5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'sepal length (cm)')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.5, color=cdict[sp])\n", + "\n", + "plt.title(f'Histogram of {feature} by Species')\n", + "plt.ylabel('Count')\n", + "plt.xlabel(feature)" + ] + }, + { + "cell_type": "markdown", + "id": "587df83d-9096-4026-b80d-8e84e4780697", + "metadata": {}, + "source": [ + "# Saving figures to a file¶\n", + "\n", + "Once you are happy with your figure, you can either right-click on the image in the notebook and \"copy output to clipboard\", but this will copy a low-res version of the image, which is fine for presentations but not great for publications. For this reason matplotlib provides a savefig() method for saving the resulting figure in several different possible output formats, also allowing to control the output resolution (using the dpi argument, for example). Here is an example of saving as a standard resolution PNG file.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6307ad79-ecf9-4b9c-aa39-72e7dffc5095", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XOnfurLFjx2rNmjXat2+fqlWrpokTJ2rHjh1pbv/mtpR6svK+P3XqVKZe9xudPn1a169f14QJE1KNnxI2U8a/nc9IZI41bveAy3Nzc9PgwYM1ePBgnTt3TitWrNBrr72mNm3aKC4uTt7e3umum/Ihefz48VQfKMeOHVPJkiUd+qV8SN3oxIkTaR6lSetfhLNmzVKLFi00adIkh/bz589nPMkccONcb3bs2DEVKFBAxYsXz/K4hQsXVnR0tKKjo/XXX3/Zj9Z07NhRu3btsr+GkZGR6tKlS5pjVK5c2eF5Wv9qP3HihCpUqCBJ2r59u7Zu3arp06crIiLC3mffvn1Zrj89KXVPmDAh3btLshpESpQooevXr+vMmTMOv/wze5QiJ6TM6+YwVqpUKUlKdTF8bknZfko9Gbl+/bpOnz7tEGpSXrMb23x9fRUREaGPP/5YQ4YM0bRp0/TUU0+pWLFi2aqxXLlyev755zVw4EDt2LFD1atXT7X9G504ccJeT1be96VKlcry6168eHEVLFhQ3bp1U9++fdPsExYWJun2PiORORyhQY4rVqyYunbtqr59++rMmTP2L0m7+V/KKe6//35J/wSNG23YsEE7d+5UeHi4JKlhw4by8PDQvHnzHPqtX7/+lof4b2Sz2ey1pNi2bVuefLlZ5cqVVaZMGc2ePdvhTpCLFy/qq6++st/5dDsCAgLUvXt3Pfnkk9q9e7cuXbqkypUrq2LFitq6dWuaR+Hq1asnHx8fh3E+//xzh+dr167V4cOH7XdbpYTFm1/Ljz766Lbqv1GTJk1UrFgx/fHHH+nWnXJkLLNSboG/+X00d+7cVH3Te8/erpCQEHl5eWn//v0O7Y0bN5avr68mT56c6k6h3HDgwAGVKFEi06Hw5vfE7NmzJSnVHXgvvfSS/v77b3Xt2lXnzp1L83TRzc6fP68LFy6kuSzl9GVQUJBD+5w5cxxep8OHD2vt2rX2erLyvm/btq1WrVqV6tRrRry9vdWyZUv99ttvqlWrVprjp3UUL73PSNwejtAgR3Ts2FE1atRQvXr1VKpUKR0+fFjjx49XSEiIKlasKEmqWbOmJOn9999XRESE3N3dVblyZVWuXFnPP/+8JkyYoAIFCqht27Y6dOiQ3njjDQUHB2vQoEGS/jnFM3jwYMXExKh48eJ6+OGH9eeffyo6OlqBgYEO16RkpEOHDnr77bc1YsQINW/eXLt379Zbb72lsLAwXb9+PXdeoP+vQIECevfdd/X000+rQ4cO6t27txITEzV27FidO3fOftttVjVs2FAdOnRQrVq1VLx4ce3cuVOfffaZQ0D66KOP1LZtW7Vp00bdu3dXmTJldObMGe3cuVObN2/W//3f/zmMuXHjRvXq1UuPPvqo4uLiNHz4cJUpU8Z+qL5KlSq66667NGzYMBlj5Ofnp2+++UaxsbG39yLdoEiRIpowYYIiIiJ05swZde3aVf7+/jp16pS2bt2qU6dOpTrSdisPPvigmjRpopdfflkJCQmqW7eu1q1bp5kzZ0pyvD04vffszeEvqwoVKqRGjRrZT8/dON/33ntPvXr10gMPPKDnnntOAQEB2rdvn7Zu3aoPPvjgtrZ7s/Xr16t58+a3vLYlpeb33ntPFy5cUP369bV27VqNHDlSbdu2TXWdXaVKlfTggw9qyZIlatq0qWrXrn3L8Xfv3q02bdroiSeeUPPmzRUYGKizZ8/q22+/1ZQpU9SiRQs1btzYYZ2TJ0/q4Ycf1nPPPaf4+HiNGDFCnp6eioyMtPfJ7Pv+rbfe0pIlS3TffffptddeU82aNXXu3DktXbpUgwcPVpUqVdKs+/3331fTpk3VrFkzvfjiiwoNDdX58+e1b98+ffPNN/r+++8lZe4zErfJqZckwyWk3FmyYcOGNJe3b9/+lnc5vffee6Zx48amZMmS9ts7n332WXPo0CGH9SIjI01QUJApUKCAkWS/TTopKcmMGTPGVKpUybi7u5uSJUuaZ555xsTFxTmsn5ycbEaOHGnKli1rChUqZGrVqmUWL15sateu7XBXTkZ3CCUmJpohQ4aYMmXKGE9PT3PPPfeYhQsXmoiICId5ptzRMHbsWIf10xv7Vq/jjRYuXGgaNmxoPD09TeHChU14eLj5+eefM7WdtAwbNszUq1fPFC9e3Hh4eJjy5cubQYMGmb///tuh39atW81jjz1m/P39jbu7uyldurS5//77zeTJk1PNY/ny5aZbt26mWLFixsvLy7Rr187s3bvXYbw//vjDtGrVyvj4+JjixYubRx991Bw5ciTV3UK3c9u2McasWbPGtG/f3vj5+Rl3d3dTpkwZ0759e4fXJuUupxtv809vzDNnzpgePXqYYsWKGW9vb9OqVSuzfv36NO/kSe89GxISYtq3b59qDs2bN3e44y49n3zyiSlYsKA5duxYqmXfffedad68uSlcuLDx9vY21apVM2PGjLEvj4iIMIULF05z29WrV0/Vnlat+/btS3WnVHpStrdt2zbTokUL4+XlZfz8/MyLL76Y7h1S06dPN5LM3Llzbzm+McacPXvWjBw50tx///2mTJkyplChQqZw4cLm7rvvNiNHjjSXLl2y90352fjss8/MSy+9ZEqVKmU8PDxMs2bN0rw9PTPve2P+uVW/Z8+epnTp0sbd3d0EBQWZxx57zPz111/GmLTvckpp79mzpylTpoxxd3c3pUqVMo0bN3a4IzCzn5HIPgINLO/AgQOmUKFCZtSoUc4uJV/ISjDLT1K+H+jmYJlbLl++bEqVKpXq1u288vrrr5ty5cqlup05p3Tp0sUEBQU5fB9RTslK2Medg1NOsJStW7dqzpw5aty4sYoWLardu3fr3XffVdGiRfXss886uzxYxJw5c3T06FHVrFlTBQoU0Pr16zV27Fjdd999qU5r5BZPT09FR0crKipK/fr1U+HChfNku9I/35Q8ceJETZgwIUf/FEhiYqI2b96sX3/9VQsWLNC4cePk7u6eY+MDGSHQwFIKFy6sjRs36pNPPtG5c+fk6+urFi1aaNSoUdm67RZ3Jh8fH82dO1cjR47UxYsXFRgYqO7du2vkyJF5Wsfzzz+vc+fO6cCBA/brdfLCwYMHFRkZaf8emZxy/Phx+z82evfurf79++fo+EBGbMbkwaX0AAAAuYjbtgEAgOURaAAAgOURaAAAgOXl+4uCk5OTdezYMfn4+GTqy6MAAIDzGWN0/vx5BQUFZeqLU/N9oDl27JiCg4OdXQYAAMiGuLi4TP3h0HwfaFK+ojwuLk5FixZ1cjUAACAzEhISFBwcnOk/NZLvA03KaaaiRYsSaAAAsJjMXi7CRcEAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDynBpofvjhB3Xs2FFBQUGy2WxauHChfdm1a9c0dOhQ1axZU4ULF1ZQUJD+9a9/6dixY84rGAAAuCSnBpqLFy+qdu3a+uCDD1Itu3TpkjZv3qw33nhDmzdv1vz587Vnzx499NBDTqgUAAC4Mpsxxji7COmfPz61YMECde7cOd0+GzZsUIMGDXT48GGVK1cuU+MmJCTI19dX8fHx/HFKAAAsIqu/vy11DU18fLxsNpuKFSvm7FIAAIALcXN2AZl15coVDRs2TE899VSGSS0xMVGJiYn25wkJCXlRHgAAcCJLBJpr167piSeeUHJysj788MMM+8bExCg6OjqPKkNWLPr1Uo6N9VAD7xwbCwBgfS5/yunatWt67LHHdPDgQcXGxt7yPFpkZKTi4+Ptj7i4uDyqFAAAOItLH6FJCTN79+7VqlWrVKJEiVuu4+HhIQ8PjzyoDgAAuAqnBpoLFy5o37599ucHDx7Uli1b5Ofnp6CgIHXt2lWbN2/W4sWLlZSUpBMnTkiS/Pz8VKhQIWeVDQAAXIxTA83GjRvVsmVL+/PBgwdLkiIiIhQVFaVFixZJku6++26H9VatWqUWLVrkVZkAAMDFOTXQtGjRQhl9DY6LfEUOAABwcS5/UTAAAMCtEGgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlOTXQ/PDDD+rYsaOCgoJks9m0cOFCh+XGGEVFRSkoKEheXl5q0aKFduzY4ZxiAQCAy3JqoLl48aJq166tDz74IM3l7777rsaNG6cPPvhAGzZsUOnSpdWqVSudP38+jysFAACuzM2ZG2/btq3atm2b5jJjjMaPH6/hw4erS5cukqQZM2YoICBAs2fPVu/evfOyVAAA4MJc9hqagwcP6sSJE2rdurW9zcPDQ82bN9fatWvTXS8xMVEJCQkODwAAkL859QhNRk6cOCFJCggIcGgPCAjQ4cOH010vJiZG0dHRuVobkNeSVi/Lk+0UbNEmT7YD5zi9bbyzS0hTiVoDnV0C8gGXPUKTwmazOTw3xqRqu1FkZKTi4+Ptj7i4uNwuEQAAOJnLHqEpXbq0pH+O1AQGBtrbT548meqozY08PDzk4eGR6/UBAADX4bJHaMLCwlS6dGnFxsba265evao1a9aocePGTqwMAAC4Gqceoblw4YL27dtnf37w4EFt2bJFfn5+KleunAYOHKjRo0erYsWKqlixokaPHi1vb2899dRTTqwaAAC4GqcGmo0bN6ply5b254MHD5YkRUREaPr06Xr11Vd1+fJl9enTR2fPnlXDhg21fPly+fj4OKtkAADggpwaaFq0aCFjTLrLbTaboqKiFBUVlXdFAQAAy3HZa2gAAAAyi0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsz83ZBQBAfnR623hnlwDcUThCAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALM+lA83169f1+uuvKywsTF5eXipfvrzeeustJScnO7s0AADgQtycXUBGxowZo8mTJ2vGjBmqXr26Nm7cqB49esjX11cDBgxwdnkAAMBFuHSgWbdunTp16qT27dtLkkJDQzVnzhxt3LjRyZUBAABX4tKnnJo2baqVK1dqz549kqStW7fqp59+Urt27dJdJzExUQkJCQ4PAACQv7n0EZqhQ4cqPj5eVapUUcGCBZWUlKRRo0bpySefTHedmJgYRUdH52GVcIZFv17KkXEeauCdI+Mga5JWL8uxsc4kfJPh8gKhFXJsWwBcl0sfoZk3b55mzZql2bNna/PmzZoxY4b+/e9/a8aMGemuExkZqfj4ePsjLi4uDysGAADO4NJHaF555RUNGzZMTzzxhCSpZs2aOnz4sGJiYhQREZHmOh4eHvLw8MjLMgEAgJO59BGaS5cuqUABxxILFizIbdsAAMCBSx+h6dixo0aNGqVy5cqpevXq+u233zRu3Dj17NnT2aUBAAAX4tKBZsKECXrjjTfUp08fnTx5UkFBQerdu7fefPNNZ5cGAABciEsHGh8fH40fP17jx493dikAAMCFufQ1NAAAAJlBoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJaXrUBTvnx5nT59OlX7uXPnVL58+dsuCgAAICuyFWgOHTqkpKSkVO2JiYk6evTobRcFAACQFW5Z6bxo0SL7/y9btky+vr7250lJSVq5cqVCQ0NzrDgAAIDMyFKg6dy5syTJZrMpIiLCYZm7u7tCQ0P13nvv5VhxAAAAmZGlQJOcnCxJCgsL04YNG1SyZMlcKQoAACArshRoUhw8eDCn6wAAAMi2bAUaSVq5cqVWrlypkydP2o/cpPj0009vuzAAAIDMylagiY6O1ltvvaV69eopMDBQNpstp+sCAADItGwFmsmTJ2v69Onq1q1bTtcDAACQZdn6HpqrV6+qcePGOV0LAABAtmQr0PTq1UuzZ8/O6VoAAACyJVunnK5cuaIpU6ZoxYoVqlWrltzd3R2Wjxs3LkeKAwAAyIxsBZpt27bp7rvvliRt377dYRkXCAMAgLyWrUCzatWqnK4DAAAg27J1DQ0AAIArydYRmpYtW2Z4aun777/PdkEAAABZla1Ak3L9TIpr165py5Yt2r59e6o/WgkAAJDbshVo/vOf/6TZHhUVpQsXLtxWQQAAAFmVo9fQPPPMM/wdJwAAkOdyNNCsW7dOnp6eOTkkAADALWXrlFOXLl0cnhtjdPz4cW3cuFFvvPFGjhQGAACQWdkKNL6+vg7PCxQooMqVK+utt95S69atc6QwAACAzMpWoJk2bVpO15Guo0ePaujQoVqyZIkuX76sSpUq6ZNPPlHdunXzrAYAAODashVoUmzatEk7d+6UzWZTtWrVVKdOnZyqS5J09uxZNWnSRC1bttSSJUvk7++v/fv3q1ixYjm6HQAAYG3ZCjQnT57UE088odWrV6tYsWIyxig+Pl4tW7bU3LlzVapUqRwpbsyYMQoODnY4IhQaGpojYwMAgPwjW3c59e/fXwkJCdqxY4fOnDmjs2fPavv27UpISNBLL72UY8UtWrRI9erV06OPPip/f3/VqVNHU6dOzbHxAQBA/pCtQLN06VJNmjRJVatWtbdVq1ZNEydO1JIlS3KsuAMHDmjSpEmqWLGili1bphdeeEEvvfSSZs6cme46iYmJSkhIcHgAAID8LVunnJKTk+Xu7p6q3d3dXcnJybdd1I3bqVevnkaPHi1JqlOnjnbs2KFJkybpX//6V5rrxMTEKDo6OsdqsKJFv17KsbEeauCdY2PlN0mrlzm7BAB3mNPbxju7hFRK1Bro7BIkZfMIzf33368BAwbo2LFj9rajR49q0KBBCg8Pz7HiAgMDVa1aNYe2qlWr6siRI+muExkZqfj4ePsjLi4ux+oBAACuKVtHaD744AN16tRJoaGhCg4Ols1m05EjR1SzZk3NmjUrx4pr0qSJdu/e7dC2Z88ehYSEpLuOh4eHPDw8cqwGAADg+rIVaIKDg7V582bFxsZq165dMsaoWrVqeuCBB3K0uEGDBqlx48YaPXq0HnvsMf3666+aMmWKpkyZkqPbAQAA1palU07ff/+9qlWrZr/QtlWrVurfv79eeukl1a9fX9WrV9ePP/6YY8XVr19fCxYs0Jw5c1SjRg29/fbbGj9+vJ5++ukc2wYAALC+LB2hGT9+vJ577jkVLVo01TJfX1/17t1b48aNU7NmzXKswA4dOqhDhw45Nh4AAMh/snSEZuvWrXrwwQfTXd66dWtt2rTptosCAADIiiwFmr/++ivN27VTuLm56dSpU7ddFAAAQFZkKdCUKVNGv//+e7rLt23bpsDAwNsuCgAAICuyFGjatWunN998U1euXEm17PLlyxoxYgTXuwAAgDyXpYuCX3/9dc2fP1+VKlVSv379VLlyZdlsNu3cuVMTJ05UUlKShg8fnlu1AgAApClLgSYgIEBr167Viy++qMjISBljJEk2m01t2rTRhx9+qICAgFwpFAAAID1Z/mK9kJAQfffddzp79qz27dsnY4wqVqyo4sWL50Z9AAAAt5StbwqWpOLFi6t+/fo5WQsAAEC2ZOuPUwIAALgSAg0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8N2cXANe26NdLzi7BMnYfvZZjY5XyWZpjY2XJom+cs91clHxoX55sp0BohTzZTn6bjySd3jY+z7aVWSVqDXR2CcgijtAAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLs1SgiYmJkc1m08CBA51dCgAAcCGWCTQbNmzQlClTVKtWLWeXAgAAXIwlAs2FCxf09NNPa+rUqSpevLizywEAAC7GEoGmb9++at++vR544IFb9k1MTFRCQoLDAwAA5G9uzi7gVubOnavNmzdrw4YNmeofExOj6OjoXK7qfxb9eilHxnmogXeOjOPKKuz9Pk+2s6/i/Znuezv7r8LRa9leFwCQs1z6CE1cXJwGDBigWbNmydPTM1PrREZGKj4+3v6Ii4vL5SoBAICzufQRmk2bNunkyZOqW7euvS0pKUk//PCDPvjgAyUmJqpgwYIO63h4eMjDwyOvSwUAAE7k0oEmPDxcv//+u0Nbjx49VKVKFQ0dOjRVmAEAAHcmlw40Pj4+qlGjhkNb4cKFVaJEiVTtAADgzuXS19AAAABkhksfoUnL6tWrnV0CAABwMRyhAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlufm7AIA5K7T55NzZJwSPvz7JyPJh/Y5u4QclZfzKRBaIc+2hfyLTygAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5Lh1oYmJiVL9+ffn4+Mjf31+dO3fW7t27nV0WAABwMS4daNasWaO+fftq/fr1io2N1fXr19W6dWtdvHjR2aUBAAAX4ubsAjKydOlSh+fTpk2Tv7+/Nm3apPvuu89JVQEAAFfj0oHmZvHx8ZIkPz+/dPskJiYqMTHR/jwhISHX6wIAAM5lmUBjjNHgwYPVtGlT1ahRI91+MTExio6OzsPKgJx3+nyys0sAAEtx6WtobtSvXz9t27ZNc+bMybBfZGSk4uPj7Y+4uLg8qhAAADiLJY7Q9O/fX4sWLdIPP/ygsmXLZtjXw8NDHh4eeVQZAABwBS4daIwx6t+/vxYsWKDVq1crLCzM2SUBAAAX5NKBpm/fvpo9e7a+/vpr+fj46MSJE5IkX19feXl5Obk6AADgKlz6GppJkyYpPj5eLVq0UGBgoP0xb948Z5cGAABciEsfoTHGOLsEAABgAS59hAYAACAzCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDy3JxdAP6x6NdLzi4h1xXQd3mynUp7M7+dZLXLxUoAWNXpbeOdXQKyiCM0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8iwRaD788EOFhYXJ09NTdevW1Y8//ujskgAAgAtx+UAzb948DRw4UMOHD9dvv/2mZs2aqW3btjpy5IizSwMAAC7C5QPNuHHj9Oyzz6pXr16qWrWqxo8fr+DgYE2aNMnZpQEAABfh0oHm6tWr2rRpk1q3bu3Q3rp1a61du9ZJVQEAAFfj5uwCMvL3338rKSlJAQEBDu0BAQE6ceJEmuskJiYqMTHR/jw+Pl6SlJCQkCs1XrpwKVfGzY8uXLrq7BJSSZZr7r8Ccr3XqlBBl/73DyyswIUrzi4Bt8E9l36/pvzeNsZkqr9LB5oUNpvN4bkxJlVbipiYGEVHR6dqDw4OzpXaYHWfOLsAALC4yFwd/fz58/L19b1lP5cONCVLllTBggVTHY05efJkqqM2KSIjIzV48GD78+TkZJ05c0YlSpRINwS5soSEBAUHBysuLk5FixZ1djl55k6dt8Tc78S536nzlu7cud+p85YyP3djjM6fP6+goKBMjevSgaZQoUKqW7euYmNj9fDDD9vbY2Nj1alTpzTX8fDwkIeHh0NbsWLFcrPMPFG0aNE77k0v3bnzlpj7nTj3O3Xe0p079zt13lLm5p6ZIzMpXDrQSNLgwYPVrVs31atXT40aNdKUKVN05MgRvfDCC84uDQAAuAiXDzSPP/64Tp8+rbfeekvHjx9XjRo19N133ykkJMTZpQEAABfh8oFGkvr06aM+ffo4uwyn8PDw0IgRI1KdRsvv7tR5S8z9Tpz7nTpv6c6d+506byn35m4zmb0fCgAAwEXxxRIAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQuIiYmRjabTQMHDky3z+rVq2Wz2VI9du3alXeF5oCoqKhUcyhdunSG66xZs0Z169aVp6enypcvr8mTJ+dRtTkrq3PPL/tcko4ePapnnnlGJUqUkLe3t+6++25t2rQpw3Xyy37P6tzzy34PDQ1Ncx59+/ZNd538sM+zOu/8sr8l6fr163r99dcVFhYmLy8vlS9fXm+99ZaSk5MzXC8n9rslbtvO7zZs2KApU6aoVq1ameq/e/duh29XLFWqVG6VlmuqV6+uFStW2J8XLFgw3b4HDx5Uu3bt9Nxzz2nWrFn6+eef1adPH5UqVUqPPPJIXpSbo7Iy9xRW3+dnz55VkyZN1LJlSy1ZskT+/v7av39/ht/inV/2e3bmnsLq+33Dhg1KSkqyP9++fbtatWqlRx99NM3++WWfZ3XeKay+vyVpzJgxmjx5smbMmKHq1atr48aN6tGjh3x9fTVgwIA018mx/W7gVOfPnzcVK1Y0sbGxpnnz5mbAgAHp9l21apWRZM6ePZtn9eWGESNGmNq1a2e6/6uvvmqqVKni0Na7d29z77335nBluS+rc88v+3zo0KGmadOmWVonv+z37Mw9v+z3mw0YMMDcddddJjk5Oc3l+WWf3+xW885P+7t9+/amZ8+eDm1dunQxzzzzTLrr5NR+55STk/Xt21ft27fXAw88kOl16tSpo8DAQIWHh2vVqlW5WF3u2bt3r4KCghQWFqYnnnhCBw4cSLfvunXr1Lp1a4e2Nm3aaOPGjbp27Vpul5rjsjL3FFbf54sWLVK9evX06KOPyt/fX3Xq1NHUqVMzXCe/7PfszD2F1ff7ja5evapZs2apZ8+e6f6h4Pyyz2+UmXmnyA/7u2nTplq5cqX27NkjSdq6dat++ukntWvXLt11cmq/E2icaO7cudq8ebNiYmIy1T8wMFBTpkzRV199pfnz56ty5coKDw/XDz/8kMuV5qyGDRtq5syZWrZsmaZOnaoTJ06ocePGOn36dJr9T5w4keqvqwcEBOj69ev6+++/86LkHJPVueeXfX7gwAFNmjRJFStW1LJly/TCCy/opZde0syZM9NdJ7/s9+zMPb/s9xstXLhQ586dU/fu3dPtk1/2+Y0yM+/8tL+HDh2qJ598UlWqVJG7u7vq1KmjgQMH6sknn0x3nRzb71k6noMcc+TIEePv72+2bNlib7vVKae0dOjQwXTs2DGHq8tbFy5cMAEBAea9995Lc3nFihXN6NGjHdp++uknI8kcP348L0rMNbeae1qsuM/d3d1No0aNHNr69++f4SHl/LLfszP3tFhxv9+odevWpkOHDhn2yS/7/EaZmXdarLq/58yZY8qWLWvmzJljtm3bZmbOnGn8/PzM9OnT010np/Y7R2icZNOmTTp58qTq1q0rNzc3ubm5ac2aNfrvf/8rNzc3hwvKMnLvvfdq7969uVxt7ipcuLBq1qyZ7jxKly6tEydOOLSdPHlSbm5uKlGiRF6UmGtuNfe0WHGfBwYGqlq1ag5tVatW1ZEjR9JdJ7/s9+zMPS1W3O8pDh8+rBUrVqhXr14Z9ssv+zxFZuedFqvu71deeUXDhg3TE088oZo1a6pbt24aNGhQhmcicmq/E2icJDw8XL///ru2bNlif9SrV09PP/20tmzZkqk7XyTpt99+U2BgYC5Xm7sSExO1c+fOdOfRqFEjxcbGOrQtX75c9erVk7u7e16UmGtuNfe0WHGfN2nSRLt373Zo27Nnj0JCQtJdJ7/s9+zMPS1W3O8ppk2bJn9/f7Vv3z7Dfvlln6fI7LzTYtX9fenSJRUo4BgtChYsmOFt2zm237N9XAk57uZTTsOGDTPdunWzP//Pf/5jFixYYPbs2WO2b99uhg0bZiSZr776ygnVZt/LL79sVq9ebQ4cOGDWr19vOnToYHx8fMyhQ4eMMannfeDAAePt7W0GDRpk/vjjD/PJJ58Yd3d38+WXXzprCtmW1bnnl33+66+/Gjc3NzNq1Cizd+9e8/nnnxtvb28za9Yse5/8ut+zM/f8st+NMSYpKcmUK1fODB06NNWy/LrPjcnavPPT/o6IiDBlypQxixcvNgcPHjTz5883JUuWNK+++qq9T27tdwKNC7k50ERERJjmzZvbn48ZM8bcddddxtPT0xQvXtw0bdrUfPvtt3lf6G16/PHHTWBgoHF3dzdBQUGmS5cuZseOHfblN8/bGGNWr15t6tSpYwoVKmRCQ0PNpEmT8rjqnJHVueeXfW6MMd98842pUaOG8fDwMFWqVDFTpkxxWJ6f93tW556f9vuyZcuMJLN79+5Uy/LzPs/KvPPT/k5ISDADBgww5cqVM56enqZ8+fJm+PDhJjEx0d4nt/a7zRhjMn88BwAAwPVwDQ0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag2AHNe9e3d17tw53eXTp09XsWLF8qyeWwkNDdX48eOzvN7p06fl7++vQ4cO5XhNKU6ePKlSpUrp6NGjubYNID8g0AC4Y+R0kIqJiVHHjh0VGhqaY2PezN/fX926ddOIESNybRtAfkCgAYBsuHz5sj755JNs/SXlrOrRo4c+//xznT17Nte3BVgVgQbIZ7788kvVrFlTXl5eKlGihB544AFdvHjRvnzatGmqWrWqPD09VaVKFX344Yf2ZYcOHZLNZtPcuXPVuHFjeXp6qnr16lq9erW9T1JSkp599lmFhYXJy8tLlStX1vvvv3/bdX/zzTeqW7euPD09Vb58eUVHR+v69ev25TabTR9//LEefvhheXt7q2LFilq0aJHDGIsWLVLFihXl5eWlli1basaMGbLZbDp37pxWr16tHj16KD4+XjabTTabTVFRUfZ1L126pJ49e8rHx0flypXTlClTMqx3yZIlcnNzU6NGjRzad+zYofbt26to0aLy8fFRs2bNtH//fkn/OxU3evRoBQQEqFixYvZ5vvLKK/Lz81PZsmX16aefOoxZs2ZNlS5dWgsWLMjOSwvcGW7vz1ABcCXHjh0zbm5uZty4cebgwYNm27ZtZuLEieb8+fPGGGOmTJliAgMDzVdffWUOHDhgvvrqK+Pn52emT59ujDHm4MGDRpIpW7as+fLLL80ff/xhevXqZXx8fMzff/9tjDHm6tWr5s033zS//vqrOXDggJk1a5bx9vY28+bNs9cRERFhOnXqlG6d06ZNM76+vvbnS5cuNUWLFjXTp083+/fvN8uXLzehoaEmKirK3ielrtmzZ5u9e/eal156yRQpUsScPn3aXru7u7sZMmSI2bVrl5kzZ44pU6aMkWTOnj1rEhMTzfjx403RokXN8ePHzfHjx+2vS0hIiPHz8zMTJ040e/fuNTExMaZAgQJm586d6c5hwIAB5sEHH3Ro+/PPP42fn5/p0qWL2bBhg9m9e7f59NNPza5du+yvi4+Pj+nbt6/ZtWuX+eSTT4wk06ZNGzNq1CizZ88e8/bbbxt3d3dz5MgRh7Efe+wx071793TrAe50BBogH9m0aZORZA4dOpTm8uDgYDN79myHtrfffts0atTIGPO/QPPOO+/Yl1+7ds2ULVvWjBkzJt3t9unTxzzyyCP251kNNM2aNTOjR4926PPZZ5+ZwMBA+3NJ5vXXX7c/v3DhgrHZbGbJkiXGGGOGDh1qatSo4TDG8OHD7YEmre2mCAkJMc8884z9eXJysvH398/wL/526tTJ9OzZ06EtMjLShIWFmatXr6a5TkREhAkJCTFJSUn2tsqVK5tmzZrZn1+/ft0ULlzYzJkzx2HdQYMGmRYtWqRbD3Cnc3PesSEAOa127doKDw9XzZo11aZNG7Vu3Vpdu3ZV8eLFderUKcXFxenZZ5/Vc889Z1/n+vXr8vX1dRjnxtMobm5uqlevnnbu3Glvmzx5sj7++GMdPnxYly9f1tWrV3X33Xdnu+5NmzZpw4YNGjVqlL0tKSlJV65c0aVLl+Tt7S1JqlWrln154cKF5ePjo5MnT0qSdu/erfr16zuM26BBg0zXcOPYNptNpUuXto+dlsuXL8vT09OhbcuWLWrWrJnc3d3TXa969eoqUOB/Z/sDAgJUo0YN+/OCBQuqRIkSqbbt5eWlS5cuZXo+wJ2GQAPkIwULFlRsbKzWrl2r5cuXa8KECRo+fLh++eUXeyiYOnWqGjZsmGq9W7HZbJKkL774QoMGDdJ7772nRo0aycfHR2PHjtUvv/yS7bqTk5MVHR2tLl26pFp2Y2i4OSjYbDYlJydLkowx9hpTGGMyXUNGY6elZMmSqS7S9fLyytZ2MrPtM2fOqFSpUrccH7hTcVEwkM/YbDY1adJE0dHR+u2331SoUCEtWLBAAQEBKlOmjA4cOKAKFSo4PMLCwhzGWL9+vf3/r1+/rk2bNqlKlSqSpB9//FGNGzdWnz59VKdOHVWoUMF+0Wt23XPPPdq9e3equipUqOBwNCMjVapU0YYNGxzaNm7c6PC8UKFCSkpKuq1aU9SpU0d//PGHQ1utWrX0448/6tq1azmyjRtt375dderUyfFxgfyCQAPkI7/88otGjx6tjRs36siRI5o/f75OnTqlqlWrSpKioqIUExOj999/X3v27NHvv/+uadOmady4cQ7jTJw4UQsWLNCuXbvUt29fnT17Vj179pQkVahQQRs3btSyZcu0Z88evfHGG6mCRFa9+eabmjlzpqKiorRjxw7t3LlT8+bN0+uvv57pMXr37q1du3Zp6NCh2rNnj7744gtNnz5d0v+OLoWGhurChQtauXKl/v7779s6hdOmTRvt2LHD4ShNv379lJCQoCeeeEIbN27U3r179dlnn2n37t3Z3o70zx1YmzZtUuvWrW9rHCA/I9AA+UjRokX1ww8/qF27dqpUqZJef/11vffee2rbtq0kqVevXvr44481ffp01axZU82bN9f06dNTHaF55513NGbMGNWuXVs//vijvv76a5UsWVKS9MILL6hLly56/PHH1bBhQ50+fVp9+vS5rbrbtGmjxYsXKzY2VvXr19e9996rcePGKSQkJNNjhIWF6csvv9T8+fNVq1YtTZo0ScOHD5ckeXh4SJIaN26sF154QY8//rhKlSqld999N9s116xZU/Xq1dMXX3xhbytRooS+//57XbhwQc2bN1fdunU1derUDK+pyYyvv/5a5cqVU7NmzW5rHCA/s5msnGQGkK8dOnRIYWFh+u23327rIl9XMWrUKE2ePFlxcXG5Mv53332nIUOGaPv27Zk+NZYdDRo00MCBA/XUU0/l2jYAq+OiYAD5xocffqj69eurRIkS+vnnnzV27Fj169cv17bXrl077d27V0ePHlVwcHCubOPkyZPq2rWrnnzyyVwZH8gvOEIDwM7qR2gGDRqkefPm6cyZMypXrpy6deumyMhIubnxbzcgvyPQAAAAy+OiYAAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHn/D3i1ug3PEZzCAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.5, color=cdict[sp])\n", + "\n", + "plt.title(f'Histogram of {feature} by Species')\n", + "plt.ylabel('Count')\n", + "plt.xlabel(feature) #can I define x axis here? do I need to use feature? \n", + "plt.savefig('sepal_length.raw')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5c764743-ceac-4b87-830b-ebaa93be28a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'eps': 'Encapsulated Postscript',\n", + " 'jpg': 'Joint Photographic Experts Group',\n", + " 'jpeg': 'Joint Photographic Experts Group',\n", + " 'pdf': 'Portable Document Format',\n", + " 'pgf': 'PGF code for LaTeX',\n", + " 'png': 'Portable Network Graphics',\n", + " 'ps': 'Postscript',\n", + " 'raw': 'Raw RGBA bitmap',\n", + " 'rgba': 'Raw RGBA bitmap',\n", + " 'svg': 'Scalable Vector Graphics',\n", + " 'svgz': 'Scalable Vector Graphics',\n", + " 'tif': 'Tagged Image File Format',\n", + " 'tiff': 'Tagged Image File Format',\n", + " 'webp': 'WebP Image Format'}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.gcf().canvas.get_supported_filetypes()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7af7bd17-9346-4359-a7b4-e84a2342578c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/icebroken-challenges-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/icebroken-challenges-checkpoint.ipynb new file mode 100644 index 0000000..f52fdd3 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/icebroken-challenges-checkpoint.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e572f07e-31a5-4a3b-a150-d398399abc45", + "metadata": {}, + "source": [ + "# Challenge: Find all elements smaller than a given number¶\n", + "\n", + "This function is supposed to return a list of all values less than a given integer value. The function optionally takes a max_value, and it requires to pass in a list of numbers. Your goal is to get this function to work correctly. This code block has two errors, which you can solve consecutively." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "27e566cb-1ad3-45c4-808d-0dd964c420e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 []\n", + "2 [2]\n", + "12 [2]\n", + "7 [2]\n", + "3 [2, 3]\n", + "8 [2, 3]\n" + ] + }, + { + "data": { + "text/plain": [ + "[2, 3]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_smaller(my_list, max_value = 5):\n", + " \"\"\"Return a list of all values smaller than max_value\"\"\"\n", + " low = [] #creates an empty list\n", + " for element in my_list:\n", + " if element < max_value:\n", + " low.append(element) #will add element if it is smaller than max value\n", + " print(element, low)\n", + " return low #returns appended list \n", + "\n", + "my_list = [5, 2, 12, 7, 3, 8]\n", + "\n", + "get_smaller(my_list = my_list)" + ] + }, + { + "cell_type": "markdown", + "id": "1c09a781-2f8f-4da6-9ea1-c37dcaf7d8d9", + "metadata": {}, + "source": [ + "# Challenge: Implement FizzBuzz¶\n", + "\n", + "FizzBuzz is a classical programming challenge in which you are given the task to take consecutive integer values and print \"Fizz\" for each integer divisible by 3, \"Buzz\" for integers divisible by 5, and \"FizzBuzz\" for integers divisible by both 3 and 5, and remain silent for all other integers. In this case we have given you most of the code but there is an error, please fix it and verify that it works. This code block has two errors, which you can solve consecutively." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "97c6ad72-e5cb-4845-ad8f-8c0f7ee92f8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 fizz\n", + "5 buzz\n", + "6 fizz\n", + "9 fizz\n", + "10 buzz\n", + "12 fizz\n" + ] + } + ], + "source": [ + "def fizzbuzz(max_num):\n", + " \"This method implements FizzBuzz\"\n", + " three_mul = 'fizz' #for each integer divisible by 3\n", + " five_mul = 'buzz' #for each integer divisible by 5\n", + " num1 = 3\n", + " num2 = 5 \n", + "\n", + " # Google for 'range in python' to see what it does\n", + " for i in range(1,max_num):\n", + " # % or modulo division gives you the remainder \n", + " if i%num1==0 and i%num2==0:\n", + " print(i,three_mul+five_mul)\n", + " elif i%num1==0:\n", + " print(i,three_mul)\n", + " elif i%num2==0:\n", + " print(i,five_mul)\n", + "\n", + "fizzbuzz(max_num = 13) #need to define default value of max_num OUTSIDE of function" + ] + }, + { + "cell_type": "markdown", + "id": "da0656ef-c9e3-4142-92b4-515c57b6cbdb", + "metadata": {}, + "source": [ + "# Challenge: Sum of positive integers¶\n", + "\n", + "This function takes one argument, numbers, which should be a list of numbers. The function should sum all positive numbers in this list. Why doesn't it work?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5be8883f-c1bc-4765-b08c-66857cd5d617", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sum_positive(numbers):\n", + " total = 0 \n", + " for num in numbers:\n", + " if num > 0:\n", + " total += num #just needed an equal sign \n", + " return total\n", + "\n", + "sum_positive([1,2,3,4,5,6,7])" + ] + }, + { + "cell_type": "markdown", + "id": "3e40e102-244e-4c3a-8754-fa3664812576", + "metadata": {}, + "source": [ + "# Challenge: Add two positive numbers¶\n", + "\n", + "This function is supposed to accept two required arguments, add them together and return the sum. There are two at least two conditions that this function should handle which it currently doesn't: 1) If one of the passed in values is not strictly positive; and 2) If one of the values is not a number. Your goal is to get this function to work correctly. There are several ways to do this so we will discuss alternative strategies." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0c16cf9a-b655-4fe7-9f12-0a15a706ebb9", + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "bad input values", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#print(\"bad input values\")\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbad input values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#this is defining a and b (?)\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(result, \u001b[38;5;28mtype\u001b[39m(result))\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(result \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m)\n", + "Cell \u001b[0;32mIn[20], line 6\u001b[0m, in \u001b[0;36madd\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a \u001b[38;5;241m+\u001b[39m b\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#print(\"bad input values\")\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbad input values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mException\u001b[0m: bad input values" + ] + } + ], + "source": [ + "def add(a, b):\n", + " if a > 0 and b > 0:\n", + " return a + b\n", + " else:\n", + " #print(\"bad input values\")\n", + " raise Exception(\"bad input values\")\n", + " \n", + "result = add(-1, 5) #this is defining a and b (?)\n", + "print(result, type(result))\n", + "print(result + 2)" + ] + }, + { + "cell_type": "markdown", + "id": "a817ace2-01e0-403d-9aea-30361bf70c4b", + "metadata": {}, + "source": [ + "# Challenge: Implement a 'Person' class¶\n", + "\n", + "Here is a simple class that represents information about a \"Person\". This code block implements the Person class, instantiates a new person and tries to print it to the screen. Your goal is to fix this class to generate the expected output." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "adfd4e66-9290-43c6-aafb-09adcacdb64a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phylo is 10 years old\n" + ] + } + ], + "source": [ + "class Person:\n", + " def __init__(self, name, age = 10):\n", + " '''Initialize the Person object'''\n", + " self.name = name\n", + " self.age = age\n", + " \n", + " def __repr__(self):\n", + " '''Describe the string representation of a Person'''\n", + " \n", + " return f\"{self.name} is {self.age} years old\"\n", + "\n", + "p = Person(\"Phylo\") #can either set age here (name, age) OR set it above in arguments \n", + "print(p)" + ] + }, + { + "cell_type": "markdown", + "id": "c2dd21ab-c403-4fca-86c6-66d0d315ad1e", + "metadata": {}, + "source": [ + "# Challenge: Implement a simple Dog class¶\n", + "\n", + "This class describes a Dog with the ability to bark(). The expected output is Woof!. Your goal is to fix this class." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1e972be9-a6a8-4079-9710-6cfa83ee78fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phylo says Woof!\n" + ] + } + ], + "source": [ + "class Dog:\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " def bark(self):\n", + " print(f\"{self.name} says Woof!\")\n", + "\n", + "d = Dog(\"Phylo\")\n", + "d.bark()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75855be9-4da8-49b5-83fd-5dd5b57c7d07", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/iris_pd-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/iris_pd-checkpoint.ipynb new file mode 100644 index 0000000..ae5ff33 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/iris_pd-checkpoint.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "id": "1f3695ff-f432-4bee-9d64-ed7cacf6ab80", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import scipy.stats as stats #for the challenge exercise!!!" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ec4b1c8d-d80a-401d-ba8e-fa0a3fe2be83", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://eaton-lab.org/data/iris-data-dirty.csv\" #fetched from linked page on assigment\n", + "\n", + "response = requests.get(url)\n", + "\n", + "\"\"\"\n", + "Load the iris data into a pandas DataFrame. \n", + "This version of the data does not have 'headers' to indicate the meanings of the columns.\n", + "The 5 columns in the dataset are sepal length and width, petal length and width, and species ID.\n", + "\"\"\"\n", + "column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species_ID']\n", + "df = pd.read_csv(url, header = None, names = column_names)\n", + "\n", + "#print(df.head()) -> prints only first couple rows\n", + "\n", + "\"\"\"\n", + "Fix rows with misspelled species IDs and remove NA values\n", + "\"\"\"\n", + "df['species_ID'] = df['species_ID'].replace({\n", + " 'Iris-setsa': 'Iris-setosa', # Correct 'Iris-setsa' to 'Iris-setosa'\n", + " 'Iris-versicolour': 'Iris-versicolor', # Correct 'Iris-versicolour' to 'Iris-versicolor'\n", + " 'Iris-versicolour*': 'Iris-versicolor', # If you still have an asterisk variant\n", + " 'Iris-setosa*': 'Iris-setosa', # If you still have an asterisk variant\n", + " 'Iris-virginica*': 'Iris-virginica' # If you still have an asterisk variant\n", + "})\n", + "\n", + "df = df.dropna()\n", + "#print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "dd051236-256f-42d9-9279-6b6ab4c78278", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " sepal_length \\\n", + " count mean std min 25% 50% 75% max \n", + "species_ID \n", + "Iris-setosa 50.0 5.006000 0.352490 4.3 4.800 5.0 5.2 5.8 \n", + "Iris-versicolor 48.0 5.935417 0.522062 4.9 5.600 5.9 6.3 7.0 \n", + "Iris-virginica 50.0 6.588000 0.635880 4.9 6.225 6.5 6.9 7.9 \n", + "\n", + " sepal_width ... petal_length petal_width \\\n", + " count mean ... 75% max count \n", + "species_ID ... \n", + "Iris-setosa 50.0 3.418000 ... 1.575 1.9 50.0 \n", + "Iris-versicolor 48.0 2.770833 ... 4.600 5.1 48.0 \n", + "Iris-virginica 50.0 2.974000 ... 5.875 6.9 50.0 \n", + "\n", + " \n", + " mean std min 25% 50% 75% max \n", + "species_ID \n", + "Iris-setosa 0.244000 0.107210 0.1 0.2 0.2 0.3 0.6 \n", + "Iris-versicolor 1.322917 0.200255 1.0 1.2 1.3 1.5 1.8 \n", + "Iris-virginica 2.026000 0.274650 1.4 1.8 2.0 2.3 2.5 \n", + "\n", + "[3 rows x 32 columns]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Use the describe() method to show summary statistics over the numerical valued columns. \n", + "\"\"\"\n", + "species_summary = df.groupby('species_ID').describe()\n", + "print(species_summary)" + ] + }, + { + "cell_type": "markdown", + "id": "1dd57802-ad2f-4172-819d-e354be88d8f5", + "metadata": {}, + "source": [ + "From inspecting the summary statistics I am able to draw **several** conclusions:\n", + "1. Iris-setosa has the smallest mean sepal length (5.01). This suggests to me that the sepal length for this species is smaller in comparison to the other species. Iris-versicolor has a slightly larger mean sepal length (5.49). Iris-vriginica has the largest mean sepal length (6.59), with a wider range amongst its values indicating that the species has more variability in sepal length.\n", + "2. Iriso-setosa has the largest mean sepal width (3.42) compared to the other species, suggesting to me that its sepals are broader on average. Iris-versicolor has a smaller mean sepal width (2.77); Iris-virginica has the smallest (2.97). This points out interesting *pattern* - as sepal length increases, width decreases!\n", + "3. Iris-setosas the smalelst mean petal length (1.46); Iris-versicolor has a significantly larger mean petal length (4.26); Iris-virginica has the largest mean petal length (5.55).\n", + "4. Iris-setosa has the smallest mean petal width (0.244) while Iris-virginica has the largest mean petal width (2.03).\n", + "\n", + "**Conclusions:**\n", + "These summary statistics indicate to me that there are clear differences between the three species based on the measurements of sepal and petal dimensions. Iris-setosa seems to be consistently smaller, while Iris-virgina is larger, and experiences more variation. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e58698d9-8644-4594-8f05-721a1b56e5b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-test for sepal length between setosa and virginica: t-statistic = -15.386195820079404, p-value = 6.892546060674106e-28\n" + ] + } + ], + "source": [ + "#CHALLENGE:\n", + "\"\"\"\n", + "Perform formal tests for differences in group means using ttest_ind from scipy.stats.\n", + "Hint: You can access the data for a specific group using the get_group() method of the groupby object.\n", + "\"\"\"\n", + "grouping = df.groupby('species_ID')\n", + "setosa = grouping.get_group('Iris-setosa')\n", + "versicolor = grouping.get_group('Iris-versicolor')\n", + "virginica = grouping.get_group('Iris-virginica')\n", + "\n", + "#only did one-comparison here to get a sense of how the function works:\n", + "t_stat,p_value = stats.ttest_ind(setosa['sepal_length'], virginica['sepal_length'])\n", + "print(f\"T-test for sepal length between setosa and virginica: t-statistic = {t_stat}, p-value = {p_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03f9783d-bc6b-4fda-83a9-aac1db5c89e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/myFirstScript-checkpoint.py b/notebooks/.ipynb_checkpoints/myFirstScript-checkpoint.py new file mode 100644 index 0000000..a010972 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/myFirstScript-checkpoint.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python + +import argparse +import random +import string + +def random_password(length=20, verbose=False): + if args.verbose: + print(f"Generating password length: {length}") + #Define the set of available characters to sample from + chars = string.ascii_letters + string.digits + string.punctuation + + #Generate a sample from this list + password = random.choices(chars, k=length) + return "".join(password) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-l", help="Password length in characters", + dest='length', type=int, default=20) + parser.add_argument("-v", "--verbose", help="increase output verbosity", + action="store_true") + args = parser.parse_args() + + if args.verbose: + print(args) + print(random_password(length=args.length, verbose=args.verbose)) \ No newline at end of file diff --git a/notebooks/.ipynb_checkpoints/nb-15.0-debugging-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nb-15.0-debugging-checkpoint.ipynb new file mode 100644 index 0000000..82a7bea --- /dev/null +++ b/notebooks/.ipynb_checkpoints/nb-15.0-debugging-checkpoint.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "05322757-c6ff-447c-91bc-f938c14eab23", + "metadata": {}, + "source": [ + "This notebook will give some practice in debugging python code, and reading, understanding, and responding to python error messages in small functions.\n", + "\n", + "# Methodology\n", + "\n", + "This tutorial involves identifying and fixing small errors or bugs in python code. For this set of challenges you will open a new jupyter notebook, copy the code from each of the challenges, and attempt to fix each of them according to the directions. You may use whatever to do this that are at your disposal.\n", + "\n", + "Hint: Remember that using print() statements inside functions is a good way to easily trace execution." + ] + }, + { + "cell_type": "markdown", + "id": "42215c88-34cb-4ffb-985f-e1aad7f3edfd", + "metadata": {}, + "source": [ + "# Challenge: Load a dataset from a csv\n", + "\n", + "The following code will download a small dataset that we will use in the machine learning exercise today. There is a bug in this code that you'll need to find and fix in order to proceed with the exercise later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cb579605-9b61-430f-a803-a5e9c8c13724", + "metadata": {}, + "outputs": [], + "source": [ + "import requests #requesting access to file \n", + "import pandas as pd #do I need this package -> YES (otherwise 'pd' not defined)\n", + "\n", + "url = \"https://raw.githubusercontent.com/eaton-lab/hack-the-planet/refs/heads/master/docs/data/penguin_data.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5ca6edde-608f-4e3e-80f1-1d0cabfddbbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "with open(\"penguin_data.csv\", 'w') as outfile:\n", + " outfile.write(requests.get(url).text)\n", + "\n", + "data = \"penguin_data.csv\" #issue is that this needs quotes\n", + "df = pd.read_csv(data)\n", + "\n", + "print(df.head)" + ] + }, + { + "cell_type": "markdown", + "id": "9624c46b-df3f-44ef-ba6f-20fbcd7e7e9e", + "metadata": {}, + "source": [ + "# Challenge: Testing multiple conditions in an if statement\n", + "\n", + "Here is a quick function to test whether either of the two first input values are less than the third (optional) value that defaults to 4.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fb33815e-d428-444f-b247-4781bfaa6059", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Both aren't less\n" + ] + } + ], + "source": [ + "def either_less(a, b, c=4):\n", + " if a < c and b < c: #needed to switch 'OR' to 'AND'\n", + " print(\"Both are less\")\n", + " else:\n", + " print(\"Both aren't less\")\n", + "\n", + "either_less(3, 5) #printing 'both are less' BUT they aren't " + ] + }, + { + "cell_type": "markdown", + "id": "555804be-3ab0-4bf6-a008-8694e4b4b412", + "metadata": {}, + "source": [ + "# Challenge: Fix all the bugs in one shot\n", + "\n", + "Here is a different kind of challenge. This code should just print a message to the console but there are many small problems with it. See how many of the bugs you can fix before running this cell to test it. Keep track of how many bugs you find before you get this code to run. How many was it total?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "67881cd6-148f-4226-9268-02ad341e1688", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n" + ] + } + ], + "source": [ + "is_user = True #had to define a variable with value 'True'\n", + "\n", + "if is_user == True:\n", + " print(\"Hello World\")\n", + "else: #included this to test code output\n", + " print(\"Error\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ac2f35a-c2d4-4a57-8e56-db8a7a24b348", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/nb-15.1-intro-scikit-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nb-15.1-intro-scikit-checkpoint.ipynb new file mode 100644 index 0000000..1e0ca6d --- /dev/null +++ b/notebooks/.ipynb_checkpoints/nb-15.1-intro-scikit-checkpoint.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "8ff058c4-2710-49b2-8885-c338f000dbe7", + "metadata": {}, + "outputs": [], + "source": [ + "#import necessary packages:\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn import datasets\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "markdown", + "id": "acaff501-db44-4af6-80eb-88f100bb5442", + "metadata": {}, + "source": [ + "# Fetch the Iris data\n", + "\n", + "We have been loading and cleaning the iris data by hand up to this point. Now we are going to make use of a nice feature in scikit-learn, which provides the iris data as a pre-loaded dataset.The iris data will be loaded as a dictionary with several keys." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e1707235-2ed9-4efe-8d0c-0c572f499852", + "metadata": {}, + "outputs": [], + "source": [ + "iris = datasets.load_iris() #this returns a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1f0cc6f5-7bee-4a3c-be49-878ea6572c72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(150, 4)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.keys() #outputs the key names in a list format; contained within keys is an array\n", + "iris[\"data\"].shape #numpy array with 4 columns (aka measurements) and 150 rows (aka observations)\n", + "#machine learning algorithm (SciKit) will be applied to RAW data" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c9080568-6efe-4bb4-8be8-c5fb8bb366e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris[\"target\"] #array of integer values; created an encoding of 'target' names as integer values" + ] + }, + { + "cell_type": "markdown", + "id": "6e2c05ea-f45b-43e2-88ba-cd5c3187c37e", + "metadata": {}, + "source": [ + "# Data Cleaning\n", + "\n", + "Previously we had been removing rows that had contained missing values from the iris data. This might not be the best strategy because for a given row with missing data, it may still have values in the columns that are not missing, and throwing away data feels bad. In the case where you want to retain all your data you can elect to impute missing values, which means to fill them in using some strategy which is hopefully defensible. A very simple and defensible strategy is to impute missing data with the mean value for a given column. Much more detailed information about imputation is available on the scikit.impute documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e28ff38e-7c87-40c8-a83c-ded9d0b1aa15", + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning data:\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "imputer = SimpleImputer(strategy='mean') #if there is any missing data, fill it in with the mean value for that column\n", + "imputed_data = imputer.fit_transform(iris.data) #calling function 'fit_transform' which passes in iris data" + ] + }, + { + "cell_type": "markdown", + "id": "d840ff30-e63d-4b3f-a44c-05ea5e9738e2", + "metadata": {}, + "source": [ + "# Visualizing raw data\n", + "\n", + "As we did last week, we can use matplotlib scatter to visualize the relationship between 2 of the dimensions of data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e643f56f-7d1b-4359-abb2-e1ad0e6aee46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Sepal Width')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(iris.data[:, 0], iris.data[:, 1], c=data.target) #target is a vector of 0s 1s and 2s; color each data point based on index in this vector\n", + "plt.xlabel('Sepal Length')\n", + "plt.ylabel('Sepal Width')" + ] + }, + { + "cell_type": "markdown", + "id": "fad2d3e7-b336-48fa-b879-06ff189f7138", + "metadata": {}, + "source": [ + "# Unsupervised learning: Dimension reduction & clustering\n", + "\n", + "The nature of 'Big Data' is that it is hugely multidimensional, so plotting 2 dimension at a time might be a pointless operation. Unsupervised learning is a class of methods that takes unlabeled high-dimensional data and collapses it into a smaller number of dimensions that can capture in some way the relationship between the data points. I sometimes think of unsupervised learning as 'exploratory data analysis', because it's a way of looking at the structure of the data through different lenses. Unsupervised learning also includes clustering methods for finding groupings of data-points that are 'similar' given some statistical model. " + ] + }, + { + "cell_type": "markdown", + "id": "c2b5231d-a35f-49ae-a3a7-34a2eab7b33e", + "metadata": {}, + "source": [ + "# Principle component analysis\n", + "\n", + "Principal component analysis is a dimensionality reduction method used to transform and project data points onto fewer orthogonal axes that can explain the greatest amount of variance in the data.\n", + "\n", + "We begin by constructing a PCA object, specifying to retain 3 components, and then calling fit_transform() passing in the iris data. fit_transform() is actually a shortcut for calling pca.fit(iris.data).transform(iris.data), because fitting and transforming are two different actions, but in some cases (as with PCA) it isn't necessary (or useful) to fit a model to data without transforming it as well." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "80bc47e6-01cd-483e-8df9-63b3fb5200b8", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca = PCA(n_components=3) #telling it how many components to maintain \n", + "X_r = pca.fit_transform(iris.data) #X_r contains the transformed data from the PCA algorithm!!!" + ] + }, + { + "cell_type": "markdown", + "id": "ca00e162-86f7-4bb6-ab9c-a474e1a99bf9", + "metadata": {}, + "source": [ + "One key piece of information with PCA is the amount of variance explained along each orthogonal PC axis. By definition the variance explained monotonically decreases from the first to the last PC, meaning the first axis captures the most variation, and the second axis captures the second most, and so on. This allows you to 'read the tea leaves' a bit in interpreting the relationship between samples in PC-space." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2eeabda9-ec33-4f05-afeb-81500250118c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variance ratio (first two components): [0.92461872 0.05306648 0.01710261]\n" + ] + } + ], + "source": [ + "## Percentage of variance explained for each component\n", + "print(f\"Explained variance ratio (first two components): {pca.explained_variance_ratio_}\")\n", + "\n", + "#what percent of variance is explained by each component? " + ] + }, + { + "cell_type": "markdown", + "id": "88445409-fcd6-433b-91ec-803ab368c000", + "metadata": {}, + "source": [ + "We can now plot the transformed data to see a 2-dimensional representation of our 4-dimensional iris dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "6c30fbf6-9005-48c2-b324-736ac3c5cbaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = [\"navy\", \"turquoise\", \"darkorange\"]\n", + "\n", + "for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):\n", + " plt.scatter(X_r[iris.target == i, 0], X_r[iris.target == i, 1], color=color, label=target_name)\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "a77fa6d9-31f4-4652-9e62-62595a705fec", + "metadata": {}, + "source": [ + "# Challenge: Plot PCs other than 1 and 2\n", + "\n", + "In the previous example we plotted PCs 1 and 2, which by definition explain the most variance in the data. Because we asked for n_components=3 we actually have 3 PC dimensions to play with, so try plotting dimensions other than 1 and 2. Try plotting 2 and 3, for example, but first think about how the relationship between the points will change when looking at these PCs" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "8183dc00-5ecf-4fe2-b797-3b1ef2bbfb69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = [\"navy\", \"turquoise\", \"darkorange\"]\n", + "\n", + "for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):\n", + " plt.scatter(X_r[iris.target == i, 1], X_r[iris.target == i, 2], color=color, label=target_name) \n", + " #taking PC data, look for all rows where iris.target = i -> pull out the 1th column which is the 2nd PC\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8edaa2ae-a212-4060-8089-ad9662c4b3f4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "297a358e-f0f4-4c6d-b0be-1d53438e07e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/nb-5.0-arithmetic-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nb-5.0-arithmetic-checkpoint.ipynb new file mode 100644 index 0000000..9da8f3b --- /dev/null +++ b/notebooks/.ipynb_checkpoints/nb-5.0-arithmetic-checkpoint.ipynb @@ -0,0 +1,765 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook 3.1: Arithmetic operations\n", + "\n", + "### Python as a calculator\n", + "One of the simplest uses of a scientific programming language is the ability to use it as a calculator. Python has a lot of tools for doing arithmetic operations builtin to the standard library, and many extensions available through third-party packages. Here we will focus on the standard tools in Python. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numeric types\n", + "Numeric types are typically stored as either an `integer` or a `float`. The first is a whole number while the latter has a floating point decimal. There is little difference between the two for most purposes, but it can be important to distinguish the type for some purposes. For example, only integers can be used for indexing (selecting the nth element from a list), and integer and float types can differ in the amount of memory that they take up.\n", + "\n", + "First things first, we must learn how to assign a numeric type to a variable. As you can see below, the use of the `int()` or `float()` specifiers are superflous in this case, since Python can figure out whether you are saving an int versus float based on whether or not there is a decimal in the value. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# assign an integer variable\n", + "x = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# also assigns an integer variable\n", + "y = int(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# assign a float variable\n", + "z = 3.195" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# also assigns a float variable\n", + "xx = float(9.999999999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting between types\n", + "The `int()` and `float()` functions can be useful for converting between types. Gotcha: In the latter case beware that the conversion of a float to int rounds down by default. This is for reasons of speed. You can use the `round()` function instead if you want to be more explicity.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert int 3 to a float 3.0\n", + "float(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert float 3.0 to an int 3\n", + "int(3.51)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(3.51)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Boolean type\n", + "A boolean type is a simple True or False statement. This type is used when comparing objects or values to return whether or not they are the same. Binary statements of this type are *very* common in programming so expect that you will see boolean types frequently. In Python `True` and `False` have special meaning to represent the boolean values. Not `true` or `TRUE`, but `True`, with only the first letter capitalized. When it comes to booleans the case is important, since only the `True` and `False` have special meaning. \n", + "\n", + "As a shortcut the integers 0 and 1 can be used in place of True and False to represent booleans. You can see this is the example below. Here use use the single `=` to assign values to variables. We then use the double `==` to ask whether to objects are the same. This operation is answered with a boolean object." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# True can be stored as True or as 1\n", + "x = True\n", + "y = 1\n", + "x == y" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# False can be stored as False or as 0\n", + "x = False\n", + "y = 0\n", + "x == y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparisons\n", + "There are several other comparison expressions available in addition to `==`. Not all of these make sense for every type of object. For example, the `==` operator applies to any object, since we can ask whether anything is the same as anything else. But the `>` operator on the other hand is asking whether one item is greater than another. This makes sense for numerics, but not for strings. Instead, there are other operators that are often used for comparing other types of objects. Examples include the `in` operator, shown below, to ask whether an item is contained inside another one (such as a list). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "x = 10\n", + "y = 3\n", + "z = \"orange\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x > y" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x >= y" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y < x" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x == z" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z != y" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'>' not supported between instances of 'str' and 'int'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# this is expected to raise an error, since we cannot compare\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# a string and an integer using the greater-than operator.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'" + ] + } + ], + "source": [ + "# this is expected to raise an error, since we cannot compare\n", + "# a string and an integer using the greater-than operator.\n", + "z > y" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z in [x, y, z]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z not in [x, y]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exceptions\n", + "In the example above you saw your first example of a Exception. This one is called a TypeError, meaning that you used a type that was inappropriate for the given operation. Always read the message that accompanies an exception because it will tell you what is causing the error. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operations\n", + "A range of artithmetic operations are available and work as you would expect." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 + 3" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 - 3" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 * 3" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 ** 3" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.5" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 / 2 ## standard division " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "27 // 5 ## floor division rounds down to nearest whole number" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "27 % 3 ## returns the remainder of division" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "27 % 5 ## returns the remainder of division " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also function calls that can perform arithmetic operations on a large number of values at a time. We will learn more about these after you are introduced to object types for storing iterable data, such as lists and tuples." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum([3, 2, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order of operations\n", + "The order of operations can be controlled with parentheses." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 * (3 + 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 * 3 + 3" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.375" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(3 / 2) ** 3" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.375" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 / 2 ** 3" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13.0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "10 + ((3 ** 3) / 9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking types\n", + "You can ask what the type of an object is by using either the `type()` function or the `isinstance()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# type returns the type as a result\n", + "x = 3\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# isinstance returns a boolean as the result\n", + "isinstance(x, int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Assessment:\n", + " None. Be sure to save the completed notebook before closing.\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/.ipynb_checkpoints/nb-5.1-strings-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nb-5.1-strings-checkpoint.ipynb new file mode 100644 index 0000000..089be0e --- /dev/null +++ b/notebooks/.ipynb_checkpoints/nb-5.1-strings-checkpoint.ipynb @@ -0,0 +1,1334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook 5.1: String operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A \"string\" is the name used in Python for 'strings of characters' such as words, sentences, or paragraphs of text. It is one of the most basic data types and one that Python is very good at working with. In fact, the ease with which Python can be used to manipulate text is one of the primary reasons it bas become such a popular language for both scientific programming as well and web development. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a string object\n", + "Below we assign a string object to a variable name using the `=` sign." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "mystring = \"a string of text\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexing and slicing\n", + "Strings are an *indexed* datatype that is *immutable*. This means that we can select portions of the text using indexed numbering, but we cannot modify individual parts of it. When you take an index or slice of a variable a new variable is *returned*. Unless you store that returned variable, however, it is not saved, and the original string variable is unchanged unless you overwrite the variable name. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# indexing: select the first element of a string (Python index starts at 0)\n", + "mystring[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'t'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# indexing: select the last element (negative indexes start from the end)\n", + "mystring[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# indexing: select a range of elements with a slice (e.g., first:last)\n", + "mystring[0:8]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# indexing: the above could also be written as [:8] meaning from start to index 8\n", + "mystring[:8]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ext'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# indexing: this means index -3 until the end.\n", + "mystring[-3:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strings can be combined or reassigned\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# create new string objects and assign to variables\n", + "string1 = \"a new string \"\n", + "string2 = \"bigger than the last string\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a new string bigger than the last string'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# combine them and assign the result to another variable\n", + "newstring = string1 + string2\n", + "newstring" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### You cannot mutate string objects\n", + "Attempts to assign a value to an index or slice of a string will raise an error because strings are *immutable*. We can take an index or slice of a string to return part of it, but cannot change it *in place*. Instead, we can replace the variable storing a string object with a new string. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'str' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# error: you cannot assign to an index inside a string.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmystring\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" + ] + } + ], + "source": [ + "# error: you cannot assign to an index inside a string.\n", + "mystring[0] = \"A\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How do you modify strings then?\n", + "You assign a new string to the same named variable. This can be done using indexing or concatenation, or several other ways as well as we'll see. This may seem a little nuanced, but you'll see later how this varies among different objects, some of which are mutable and others which are not. The example below creates a new string by adding \"A\" to the previous string indexed from 1 until the end." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## you could do this instead\n", + "newstring = \"A\" + mystring[1:]\n", + "newstring" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Built-in string functions\n", + "String variables are a type of data object in Python, and just like all objects in Python this means that they have more information than just the value that we assigned to it. For example, string objects store the length of the string that is stored, and they have many `functions` that can be called to manipulate that string. These `'attributes'` and `'functions'` can be accessed by using tab-completion on an object in jupyter. After the period at the end of `mystring` below hit tab to see the options displayed. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (4153749568.py, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[11], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m mystring.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "# put your cursor after the period below and press to see available options\n", + "mystring." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example builtins\n", + "The examples below call `functions` associated with string objects." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this capitalizes the first character of the string\n", + "mystring.capitalize()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'string', 'of', 'text']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this splits the string into a 'list' where ever there is whitespace\n", + "mystring.split()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' a string of text '" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this centers the text across a width of 40\n", + "mystring.center(40)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this prints the index of the searched word starting from the left\n", + "mystring.find(\"of\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifying strings\n", + "In the examples above we applied a function to `mystring` which returned a new string object that was printed into the output location below the cell. The new string object was not stored, and so it is lost.\n", + "\n", + "Because the returned value wasn't saved it was \"garbage-collected\" by Python, meaning that it's space in memory was erased. If we want to store the result we need to assign it to a variable. This is done below, where you see `mystring` is changed after we run the command." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# here mystring is replaced by a new string where the first character is capitalized.\n", + "mystring = mystring.capitalize()\n", + "mystring" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string of text'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's create a new variable like the original that is not capitalized\n", + "lower_string = mystring.lower()\n", + "lower_string" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `print()` function\n", + "\n", + "The fact that a variable name appears when you simply execute a cell that has the object on the last line is a convenience of jupyter. It is showing you what the object is. In the case of a string it shows the content with quotes around it to indicate it is a string.\n", + "\n", + "A more formal way to show the contents of a string is by using the `print()` function. You can see that the first example below simply returns an object, showing quotes around the text to indicate it is a string object. The second way, using print, prints just the text. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you return a variable's value by entering just the variable\n", + "mystring" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A string of text\n" + ] + } + ], + "source": [ + "# or you can use the print() function, which prints it to stdout\n", + "print(mystring)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A string of text a string of text\n" + ] + } + ], + "source": [ + "# In Python3 but not Py2 print of multiple strings is concatenated\n", + "print(mystring, lower_string)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single, double, and triple quotes\n", + "What is the difference between single, double, or triple quotes when creating strings. \n", + "There is little difference, but by providing redundancy \n", + "it is easier to find ways to write strings that include quotes inside of them. For example, to write a string that contains single quotes you can wrap it in double quotes. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello world\n", + "printing in single quotes is the same as in double quotes\n", + "'you can make strings that include single quotes by putting them inside doubles'\n", + "\"you can make strings that include double quotes by putting them inside singles\"\n", + "\n", + "multi-line string with mixed single and double\n", + "quotes can all be captured inside triple-quotes.\n", + "This also interprets the starting line as a newline.\n", + "\n" + ] + } + ], + "source": [ + "## examples of printing strings\n", + "print(\"hello world\")\n", + "print('printing in single quotes is the same as in double quotes')\n", + "print(\"'you can make strings that include single quotes by putting them inside doubles'\")\n", + "print('\"you can make strings that include double quotes by putting them inside singles\"')\n", + "print(\"\"\"\n", + "multi-line string with mixed single and double\n", + "quotes can all be captured inside triple-quotes.\n", + "This also interprets the starting line as a newline.\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Formatted printing\n", + "There are several ways to insert string variables into another string to automate the process of writing long strings in Python. We will focus on two methods: fstrings and formatting. \n", + "\n", + "These allow you to substitute variables into a string when printing by using curly braces to indicate the position where elements should be inserted. Let's see some examples." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the dog is meaner than the cat\n" + ] + } + ], + "source": [ + "# substite variables into the curly braces\n", + "dog = \"dog\"\n", + "cat = \"cat\"\n", + "print(\"the {} is meaner than the {}\".format(dog, cat))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the cat is meaner than the dog\n" + ] + } + ], + "source": [ + "# use indexing to indicate the order of filling\n", + "dog = \"dog\"\n", + "cat = \"cat\"\n", + "print(\"the {1} is meaner than the {0}\".format(dog, cat))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "This is a long string over\n", + "multiple lines. But I can still \n", + "insert \"dog\" here or \"cat\" here, \n", + "it's easy as pie.\n", + "\n" + ] + } + ], + "source": [ + "# the variables do not need to be assigned before hand\n", + "print(\"\"\"\n", + "This is a long string over\n", + "multiple lines. But I can still \n", + "insert \"{}\" here or \"{}\" here, \n", + "it's easy as {}.\n", + "\"\"\".format('dog', 'cat', 'pie'))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the final answer is: 0.00123\n" + ] + } + ], + "source": [ + "# You can substitute strings, ints, floats, etc.\n", + "print(\"the final answer is: {}\".format(0.00123))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### F-strings\n", + "In the fstring format you need to add an 'f' before the string, and then it will allow you to substitute variables into the string simply by inserting them directory into the string inside of curly brackets. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the dog is meaner than the cat\n" + ] + } + ], + "source": [ + "# f-strings works great for a one-line string\n", + "dog = \"dog\"\n", + "cat = \"cat\"\n", + "print(f\"the {dog} is meaner than the {cat}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "This is a long string over\n", + "multiple lines. But I still want to \n", + "insert \"dog\" here or \"cat\" here.\n", + "\n" + ] + } + ], + "source": [ + "# f-strings \n", + "print(f\"\"\"\n", + "This is a long string over\n", + "multiple lines. But I still want to \n", + "insert \"{dog}\" here or \"{cat}\" here.\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parsing a string document\n", + "In many cases an entire data set, or document page, will be stored as a string, and so it is really useful to know some common workflows for parsing strings of text into other usable forms. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# a string that is like a full page document\n", + "page = \"\"\"\n", + "This is a multi-line document.\n", + "This is the second line.\n", + "The last line is here.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThis is a multi-line document.\\nThis is the second line.\\nThe last line is here.\\n'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the string variable looks like this. Newlines are represented as '\\n'\n", + "page" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'This is a multi-line document.\\nThis is the second line.\\nThe last line is here.'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# strip() removes the newline characters at the beginning and end\n", + "page.strip()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['This is a multi-line document.',\n", + " 'This is the second line.',\n", + " 'The last line is here.']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# split can take an argument to split on a specific character .\n", + "# Here we enter '\\n' to split on new lines. \n", + "# This parses the string into a 'list' object of lines. \n", + "# We'll discuss lists more later. \n", + "page.strip().split('\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Details on strings versus bytes\n", + "Although strings seem like a simple data type, being just characters of text, when you dive in deeper you find they are actually quite complex. This is particularly true in Python3 which, as opposed to Python2, has two distinct types called `strings` and `bytes`, which I have been referring to so far simply as strings. Technically, bytes are the representations of text that can be written to your hard disk, whereas strings are simply a mapping of those bytes to a certain type of decoding. In this way, the same set of bytes can represent different strings depending on the encoding type. This is particularly useful for representing the full range of characters from non-latin based characters, and emojis, and other things that exist in the wider world of text. Simply, a string is for showing to humans while a bytestring is for storing on disk. Let's see some examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# define a unicode string.\n", + "\"a string\"" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a unicode string'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# explicitly define a unicode string\n", + "u\"a unicode string\"" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "b'a byte string'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# explicitly define a byte string\n", + "b\"a byte string\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You're probably thinking, huh, these all look the same so far. That's true. It's because the default encoding for strings is called `\"utf-8\"`. Strings are simply the utf-8 representation of a byte string. What if we decode the bytestring with a different encoding?" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "this is bb decoded with utf-8: 'a unicode string'\n", + "this is bb decoded with utf-16: '⁡湵捩摯⁥瑳楲杮'\n", + "\n" + ] + } + ], + "source": [ + "bb = b\"a unicode string\"\n", + "\n", + "print(f\"\"\"\n", + "this is bb decoded with utf-8: '{bb.decode(\"utf-8\")}'\n", + "this is bb decoded with utf-16: '{bb.decode(\"utf-16\")}'\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'\\xe4\\xb8\\xad\\xe6\\x96\\x87'\n" + ] + } + ], + "source": [ + "# encode this string back into a bytestring\n", + "print('中文'.encode('utf-8'))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'蓏콯캁澽苏'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# decde this bytestring back into a utf-16 string\n", + "b'\\xcf\\x84o\\xcf\\x81\\xce\\xbdo\\xcf\\x82'.decode('utf-16')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The bytes versus strings concept is a bit complex, and it is OK if you don't fully grasp all of it right now. For the most part, if you are only working with simple unicode characters, the only thing you need to know about bytes for now that you can use the `.decode()` function call to convert them back to strings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comments (#)\n", + "Written code often contains comment strings or lines. These are not meant to be interpreted by the program, but instead to provide hints to the person reading the code. You've seen this already in the lecture notes and example code we have examined, including in most cells of this notebook. You can see that the jupyter notebook interpreter colors comments as a lighter blue-grey color. In the examples below you can see that the comments are not interpreted when written in-line either. " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newstring = \"a new string\" ## creating a new string variable\n", + "newstring = newstring.capitalize() ## return capiltalized version\n", + "newstring.startswith(\"A\") ## ask whether it starts with an \"A\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using print() for debugging\n", + "\n", + "A common use for the `print()` function when writing code is to use it for *debugging*. Essentially, this is a way of asking \"what is happening in the code right now?\". It is a good way to ensure that the code is running how you want it to, or to find the bugs in your code if they exist. We'll use `print` in this way below while discussing `for-loops`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strings are iterable\n", + "Strings, like many other Python data objects, are *iterable*, which means that we can sample sequential elements from them. The elements in a string are bytes (i.e., characters). We can write a for-loop like below to iterate over the elements in the string object. \n", + "\n", + "A `for-loop` is a very common procedure in programming. It is used to repeat some procedure a specified number of times. The syntax for writing a for-loop in python is `for x in y:` where `x` becomes a variable name that will be updated every loop. The `y` variable must be an iterable object, such as a string. This line must end with a colon, and the next line must be indented in. By convention, indentation is usually 4 spaces in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "p\n", + "p\n", + "l\n", + "e\n", + "s\n", + " \n", + "o\n", + "r\n", + "a\n", + "n\n", + "g\n", + "e\n", + " \n", + "g\n", + "r\n", + "a\n", + "p\n", + "e\n", + "s\n" + ] + } + ], + "source": [ + "## define a string variable\n", + "stringvar = \"apples orange grapes\"\n", + "\n", + "## a for-loop iterating over elements in stringvar\n", + "for x in stringvar:\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More on for-loops\n", + "We've learned about operators for things like addition, subtraction, and for performing comparisons, such as `=`, `>`, and `<`. Below we combine these in a for-loop to perform a more complex operation. Following the format we used above to write a for-loop, it's important to recognize that the variable in the loop is being reassigned on every iteration. In this loop we name the variable `char`, and if `char` then test if `char` is a vowel by asking if it `is in` a string that contains only vowel characters. If the `==` comparison returns True then the print function is called." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "e\n", + "o\n", + "a\n", + "e\n", + "a\n", + "e\n" + ] + } + ], + "source": [ + "## find vowels in stringvar\n", + "for char in stringvar:\n", + " for vowel in \"aeiou\":\n", + " if char == vowel:\n", + " print(char)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A simpler way to do the same thing" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "e\n", + "o\n", + "a\n", + "e\n", + "a\n", + "e\n" + ] + } + ], + "source": [ + "## find vowels in stringvar\n", + "for char in stringvar:\n", + " if char in \"aeiou\":\n", + " print(char)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenges" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Try to complete all of the challenges below.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A. Create a variable named 'varstring' and assign it the value \"apples orange grapes\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apples orange grapes\n" + ] + } + ], + "source": [ + "#defining new variable\n", + "varstring = \"apples orange grapes\"\n", + "print(varstring)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "B. Use indexing to create a new variable of only the first two fruits\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apples orange \n" + ] + } + ], + "source": [ + "first_varstring = varstring[:14]\n", + "print(first_varstring)\n", + "\n", + "#realized I could also do this:\n", + "#first_varstring = varstring.split()[:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C. Use indexing to create a new variable of only the last two fruits\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "orange grapes\n" + ] + } + ], + "source": [ + "second_varstring = varstring[7:20]\n", + "print(second_varstring)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "D. Split the string on whitespace to create a list\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apples', 'orange', 'grapes']" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# splitting string\n", + "varstring.split() #don't need '\\n' bc that splits by line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "E. Iterate over varstring and print every element that is not a vowel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p\n", + "p\n", + "l\n", + "s\n", + " \n", + "r\n", + "n\n", + "g\n", + " \n", + "g\n", + "r\n", + "p\n", + "s\n" + ] + } + ], + "source": [ + "#find letters that are NOT vowels\n", + "for char in varstring:\n", + " if char not in \"aeiou\":\n", + " print(char)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "F. Create a variable that is assigned the following string and print it: `They asked, \"what's your name?\"`" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "They asked, \"what's your name?\" \n" + ] + } + ], + "source": [ + "your_name = \"\"\"They asked, \"what's your name?\" \"\"\"\n", + "print(your_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "G. Count the number of differences between these two DNA strings\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "dna1 = \"ACAGAGTTGCCAGGAGATGACAGAAAGGTGTGGGTTACAACTCTCTCTAATTTAAGGGCCAATTAACATT\"\n", + "dna2 = \"ACAGAGTCGCCAGGAGATGACAGAAAGGTCTGGGTTACAACTCTCTCTAAAATAAGGGCCAATTAACGTT\"" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "#find differences\n", + "#parsing through strings\n", + "differ = sum(a != b for a, b in zip(dna1, dna2)) \n", + "#printing count of differerences\n", + "print(differ)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/.ipynb_checkpoints/nb-5.2-lists-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nb-5.2-lists-checkpoint.ipynb new file mode 100644 index 0000000..b7b7d12 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/nb-5.2-lists-checkpoint.ipynb @@ -0,0 +1,919 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook 5.2: Sequences (tuples, lists, and range)\n", + "\n", + "### container types\n", + "Here we will cover several objects that can be used as containers to store other object types. In particular, we'll cover `tuples` and `lists`. We will then also cover a special function called `range`, which is used to iterate over a range of numbers, which can be especially useful for indexing elements in a container. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tuples\n", + "A tuple consists of a number of values separated by commas, and is represented as a container in which elements are enclosed by parentheses. Although a tuple can be created like in the cell below by assigning comma-separated values, it is more clear to surround the elements with parentheses to show they are in fact a tuple. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2, 3)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a tuple variable \n", + "a = 1, 2, 3\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2, 3)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# another way to create a tuple variable\n", + "b = (1, 2, 3)\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2, 3)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# another way to create a tuple\n", + "c = tuple((1, 2, 3))\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What can tuples store?\n", + "Tuples can store a variety of data objects, such as strings, integers, floats, even other tuples and lists. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "tup1 = (1, 2, 3)\n", + "tup2 = ('a', 'b', 'c')\n", + "tup3 = (4, 5, 'd', 'e')\n", + "tup4 = (tup1, tup2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2, 3)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tup1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1, 2, 3), ('a', 'b', 'c'))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tup4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexing and slicing\n", + "Tuples can be indexed and sliced just like string or list. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2, 3)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tup4[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is special about tuples?\n", + "For the most part tuples are very similar to lists, but offer a bit less functionality, so for that reason they are typically used less often than lists. However, when used correctly they offer a number of advantages. The main difference between tuples and lists is that tuples are *immutable*, just like strings. This means that although we can index elements in the tuple, we cannot modify individual elements of it. This is shown below. \n", + "\n", + "This turns out to be important for some obscure reasons now, but which we will learn more about in a later session about dicts and sets." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# error: *try* to modify a tuple element in place\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtup3\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], + "source": [ + "# error: *try* to modify a tuple element in place\n", + "tup3[0] = 'a'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lists\n", + "Lists are one the most commonly used data types in Python. It is a sort of general use container. Lists can be indexed and sliced like strings and tuples, but in addition they can also be mutated. Finally, a *mutable* object! This means that you can change elements of the list and the list object will still be represented as being the same object in memory. We'll show many examples of this below. But first, let's create a list. Lists are represented by objects enclosed by brackets and can store any other type of data object. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# store a list variable\n", + "mylist = ['a', 'b', 'c', 'd']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'b']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# index or slice to view elements in the list\n", + "mylist[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# assign (mutate) new values to indexed positions in the list\n", + "mylist[0] = \"apple\"\n", + "mylist[-1] = \"dandelion\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apple', 'b', 'c', 'dandelion']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# see the new mutated list\n", + "mylist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Another way to create a list" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'p', 'p', 'l', 'e']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list('apple')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List built-ins\n", + "Lists also have many builtin functions that can be used to operate on it, such as sampling objects from the list, counting them, sorting them, etc. Let's try out some of these below. As we've done several times previously, type a list variable name below with a period at the end of it, and press the `` key to see the many available options. You'll notice as you execute the commands below that they modify the list in-place, meaning that it doesn't return a new list but instead modifies your existing list. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (3522878973.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[14], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m mylist.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "mylist." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apple', 'b', 'c', 'dandelion', 'dog', 'dog']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add an element to the end of the list\n", + "mylist.append(\"dog\")\n", + "mylist" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apple', 'b', 'c', 'dandelion', 'dog', 'dog']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sorts the list (alphanumerically, by default)\n", + "mylist.sort()\n", + "mylist" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get index of the element 'c'\n", + "mylist.index(\"c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['dog', 'dog', 'dandelion', 'c', 'b', 'apple']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reverse order of the list\n", + "mylist.reverse()\n", + "mylist" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# empties the list\n", + "mylist.clear()\n", + "mylist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iteration\n", + "Both tuples and lists can be iterated over. Unlike string objects where the elements to be iterated over were byte characters, in tuples or lists the elements in the container are iterated over. For example, below the three string objects in the list are the elements. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "species_A\n", + "species_B\n", + "species_C\n" + ] + } + ], + "source": [ + "mylist = [\"species_A\", \"species_B\", \"species_C\"]\n", + "for item in mylist:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lists as stacks\n", + "A common use of lists is to store objects that have been passed through some type of filter process. Lists are nice for this because you can start with an empty list and sequentially add objects to it to build it up. Example below. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "vowels = []\n", + "for item in \"abcdefghijklmnopqrstuvwxyz\":\n", + " if item in \"aeiou\":\n", + " vowels.append(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'e', 'i', 'o', 'u']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vowels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List comprehension\n", + "A more compact way to assign values to a list while iterating over a for-loop or conditional statement is to use a method called *list comprehension*. This is essentially a way of rewriting a multi-line for-loop statement into a single line. The point of list comprehension is to make your code more compact and easier to read. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'e', 'i']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vowels = [i for i in \"abcdefghi\" if i in \"aeiou\"]\n", + "vowels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `range` sequence\n", + "The sequence object `range` is a special highly efficient operator for iterating over numeric values. It has the form `range(start, stop, step)`, and returns an object that generates numbers on the fly as they are sampled. This makes it highly efficient since if you tell it to generate a billion numbers it doesn't need to generate them ahead of time but instead generates them only as they are needed. We will discuss more other types of Python generators in the future, but for now `range` is important since it is often used in conjunction with sequence type objects to sample their index. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "## sample 10 values\n", + "for idx in range(0, 10):\n", + " print(idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "2\n", + "4\n", + "6\n", + "8\n", + "10\n", + "12\n", + "14\n", + "16\n", + "18\n" + ] + } + ], + "source": [ + "## sample 20 values by 2\n", + "for idx in range(0, 20, 2):\n", + " print(idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 10000000000, 100)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## examine a range object\n", + "ra = range(0, int(1e10), 100)\n", + "ra" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## query the range object.\n", + "## It doesn't need to generate all 1e10 values to know 100 is in it\n", + "500 in ra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index a list using range\n", + "It is often useful to use range to index the elements of an object. This can be done by accessing the length of the object, if it is a sequence, to know how many items are in it. Let's use the example from earlier when we were learning about strings to count the differences in DNA between two sequences. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "dna1 = \"ACAGAGTTGCCAGGAGATGACAGAAAGGTGTGGGTTACAACTCTCTCTAATTTAAGGGCCAATTAACATT\"\n", + "dna2 = \"ACAGAGTCGCCAGGAGATGACAGAAAGGTCTGGGTTACAACTCTCTCTAAAATAAGGGCCAATTAACGTT\"" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## get length of dna1\n", + "len(dna1)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 70)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## a range object over this length\n", + "range(len(dna1))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "29\n", + "50\n", + "51\n", + "67\n" + ] + } + ], + "source": [ + "## a for-loop to iterate over the range and compare dna strings at each index\n", + "for idx in range(len(dna1)):\n", + " if dna1[idx] != dna2[idx]:\n", + " print(idx)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Challenges" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A. Create a list that contains the three objects in the cell below. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "a = (1, 10, 100)\n", + "b = ['dog', 'cat', 'rat']\n", + "c = \"Columbia University\"" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1, 10, 100), ['dog', 'cat', 'rat'], 'Columbia University']\n" + ] + } + ], + "source": [ + "mylist = [a, b, c]\n", + "print(mylist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "B. Find all values between 0 and 200 that are divisible by 17 and store them in a list." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187]\n" + ] + } + ], + "source": [ + "#parsing values between 0 - 200\n", + "#adding values to an empty list\n", + "div_by_17 =[value for value in range(0,200) if value % 17 == 0]\n", + " #checking that only values that have remainder of zero are added to list\n", + "#printing list output\n", + "print(div_by_17)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C. Rewrite the for-loop below using list-comprehension" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[91, 92, 93, 94, 95, 96, 97, 98, 99]\n" + ] + } + ], + "source": [ + "store = []\n", + "for idx in range(100):\n", + " if idx > 90:\n", + " store.append(idx)\n", + "print(store) #added this to check my work below" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[91, 92, 93, 94, 95, 96, 97, 98, 99]\n" + ] + } + ], + "source": [ + "store = [idx for idx in range(100) if idx > 90]\n", + "print(store) #values match original for-loop output!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "D. Write a for-loop nested within a for-loop to iterate over each element in the list, and then over eaach character in the element. As you iterate over the characters use an if statement to call `print()` on the character only if it is capitalized. " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "mylist = [\"Camel\", \"hOrse\", \"DonkEy\", \"elephant\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C\n", + "O\n", + "D\n", + "E\n" + ] + } + ], + "source": [ + "for word in mylist: #looks at each word in list\n", + " for char in word: #look at each character of each word\n", + " if char.isupper(): #if character is uppercase\n", + " print(char)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### optional extended reading on Python objects\n", + "https://docs.python.org/3/tutorial/datastructures.html?highlight=tuples" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/.ipynb_checkpoints/nb-5.3-seesion5-challenges-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nb-5.3-seesion5-challenges-checkpoint.ipynb new file mode 100644 index 0000000..6f609fa --- /dev/null +++ b/notebooks/.ipynb_checkpoints/nb-5.3-seesion5-challenges-checkpoint.ipynb @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook 5.3: Session 5 challenges\n", + "\n", + "Here are more challenges to test your knowledge of variables, lists, and for loops." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Challenges" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A. Create code that includes a loop which iterates over the a list of integers and prints the sum. Use\n", + "this list as a test case [1, 5, 2, 6] and print the results in this form:\n", + "```\n", + "The sum of the numbers in the list is 14\n", + "```\n", + "\n", + "Hint: Use the += operator, and a variable which is instantiated outside the loop, but modified inside it." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The sum of the numbers in the list is 14\n" + ] + } + ], + "source": [ + "test_case = [1,5,2,6] #defined my list\n", + "amount = 0 #realized I couldn't create a var. named 'sum' bc of built-in sum function\n", + "\n", + "for num in test_case: #started for-loop \n", + " amount += num #adding integers to 'amount'\n", + "#printing output of for-loop; want it ouside loop for TOTAL sum\n", + "print(f\"The sum of the numbers in the list is {amount}\") #used a f-string to re-format output " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "B. Write python code to print the following DNA sequence pattern using a loop.\n", + "```\n", + "CATG\n", + "CATGCATG\n", + "CATGCATGCATG\n", + "CATGCATGCATGCATG\n", + "CATGCATGCATGCATGCATG\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CATG\n", + "CATGCATG\n", + "CATGCATGCATG\n", + "CATGCATGCATGCATG\n", + "CATGCATGCATGCATGCATG\n" + ] + } + ], + "source": [ + "DNA_seq = \"CATG\" #created a string \n", + "i = 0 #repetition factor\n", + "#trying out while-loop;\n", + "while i <= 5: #created while-loop; there are five lines (i.e. 5 repetitions needed)\n", + " print(DNA_seq * i) #multiplying current seq. by i\n", + " i += 1 #increasing repetition increment for TOTAL of 5 repetitions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C. Create code with a nested loop based on the list of lists [[\"apple\", \"orange\"], [\"carrot\", \"cabbage\"], [\"chicken\", \"beef\"]] and produces the following output:\n", + "```\n", + "apple\n", + "orange\n", + "carrot\n", + "cabbage\n", + "chicken\n", + "beef\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apple\n", + "orange\n", + "carrot\n", + "cabbage\n", + "chicken\n", + "beef\n" + ] + } + ], + "source": [ + "list_lists = [[\"apple\", \"orange\"], [\"carrot\", \"cabbage\"], [\"chicken\", \"beef\"]]\n", + "for item in list_lists: #'item' here is my OUTER list\n", + " for items in item: #while 'items' is my INNER list\n", + " print(items) #printing individuals 'items'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "D. Using the same input list of lists as above modify create new code so it prints the following output:\n", + "```\n", + "apple\n", + "carrot\n", + "chicken\n", + "orange\n", + "cabbage\n", + "beef\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apple\n", + "carrot\n", + "chicken\n", + "orange\n", + "cabbage\n", + "beef\n" + ] + } + ], + "source": [ + "#found this challenging!\n", + "list_lists = [[\"apple\", \"orange\"], [\"carrot\", \"cabbage\"], [\"chicken\", \"beef\"]]\n", + "\n", + "index = 0 #assigning a starting value\n", + "done = False #checking each time as to whether OUTER list is finished \n", + "\n", + "while not done: #starting while loop \n", + " done = True #meaning I have gone through entire OUTER/INNER list(s)\n", + " for item in list_lists: #'item' here is my OUTER list\n", + " if index < len(item): #checking if INNER list contains item\n", + " print(item[index]) #printing item\n", + " done = False #if there is still more printing we are NOT done \n", + " index += 1 #updating index with new value " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "E. Write a for loop to print the first 20 even numbers starting at 0 (hint: use range())." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "2\n", + "4\n", + "6\n", + "8\n", + "10\n", + "12\n", + "14\n", + "16\n", + "18\n", + "20\n", + "22\n", + "24\n", + "26\n", + "28\n", + "30\n", + "32\n", + "34\n", + "36\n", + "38\n" + ] + } + ], + "source": [ + "for num in range(0,40,2): #starting at 0, stopping at 40; step count by 2\n", + " #ended at 40 to ensure we can get 20 EVEN numbers \n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "F. Write a for loop to print the first 20 odd numbers starting at 101, but print them in descending order." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "101\n", + "99\n", + "97\n", + "95\n", + "93\n", + "91\n", + "89\n", + "87\n", + "85\n", + "83\n", + "81\n", + "79\n", + "77\n", + "75\n", + "73\n", + "71\n", + "69\n", + "67\n", + "65\n", + "63\n", + "61\n" + ] + } + ], + "source": [ + "for num in range(101,60,-2): #starting at 101, stopping at 60; step count decrease by 2\n", + " #ended at 60 to ensure we can get 20 ODD numbers; had to do some trial-error here to figure out range \n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "G. Using what you have learned in the previous exercises, write code to take a the following \n", + "DNA sequence and print only the bases with even index. For example if the\n", + "input sequence is \"GCACGG\" the output should be \"GAG\" (remembering that 0 is also even)." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "seq = \"\"\"\n", + ">Calumma_boettgeri_voucher_FGZC_462\n", + "GGAAGTATTAACCAGACACAACTACGAAAACTAATGGCCTACTCATCAATTACTAACC\n", + "TAGGGTGGACAATAATTATTTTCACAACCGCACCACACATTGCTACACTAAACATTAT\n", + "TATCTACATAATTATACTCATCCCACTATTCATACTCATTAAAAAAATATCTATAAAA\n", + "ACACTGCGAGACTCAACAACTACATGAACTACGTCCCCGATAGCAAACACCCTACTAA\n", + "TGCTAATACTTCTGTCACTAAGTGGTCTTCCCCCCCTCACAGGTTTCACCCCAAAACT\n", + "TTTAATTTTAAACGAACTAATTTTACAAAATTTAACACCCGCTGCAACAATAATAGCC\n", + "ATACTATCTCTAATTGGACTATTTTTTTATATCCGAACAAC\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "GATTACAAAACAGAATAGCTCCTATATACTGGGAATATTTCCACCCAAATCAATACTA\n", + "ACAAATAATACCCATAATATAAATTTTAAAATCAATACATCTACAGCCGTGAAACTCA\n", + "GTAATCGCCAGGTTCCCCCCGTTACCAATTTATTACACATTAAATTAACGTCAATAAC\n", + "TCACCATGATTTTTTTCACA\n", + "\n" + ] + } + ], + "source": [ + "even_bases = \"\" #creating empty string\n", + "for char in range(0, len(seq), 2): #starting at index 0, ending at sequence end; step count by 2\n", + " even_bases += seq[char] #adding new base 'character' to empty string \n", + "\n", + "print(even_bases) #printing entire string of EVEN indexed bases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/.ipynb_checkpoints/sepal_length-checkpoint.png b/notebooks/.ipynb_checkpoints/sepal_length-checkpoint.png new file mode 100644 index 0000000..638d70d Binary files /dev/null and b/notebooks/.ipynb_checkpoints/sepal_length-checkpoint.png differ diff --git a/notebooks/.ipynb_checkpoints/sepal_length-checkpoint.svg b/notebooks/.ipynb_checkpoints/sepal_length-checkpoint.svg new file mode 100644 index 0000000..b23aab7 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/sepal_length-checkpoint.svg @@ -0,0 +1,1318 @@ + + + + + + + + 2025-03-06T11:10:50.140663 + image/svg+xml + + + Matplotlib v3.10.0, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/notebooks/.ipynb_checkpoints/timeseries-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/timeseries-checkpoint.ipynb new file mode 100644 index 0000000..e16a24b --- /dev/null +++ b/notebooks/.ipynb_checkpoints/timeseries-checkpoint.ipynb @@ -0,0 +1,743 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a1ab8076-3476-4d06-b988-f8709e0dfb77", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.0\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "\n", + "# One of the modules reports prodigious warnings so we will just hide these ;)\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66fd8892-871d-46b7-9138-c9e64be2e5d1", + "metadata": {}, + "outputs": [], + "source": [ + "time = np.arange(144)\n", + "trend = time * 2.65 +100 #slope and y-intercept " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4a1c9367-925b-4823-8ec4-73d3781a23b4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, trend, color='tab:red')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Trend vs Time\");" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "479787b3-1983-463b-ba49-2515bd4d8126", + "metadata": {}, + "outputs": [], + "source": [ + "seasonal = 20 + np.sin( time * 0.5) * 20" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cc7b5c5b-e9f5-4661-a63e-65a2d9e9dbbe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, seasonal, color='tab:orange')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Seasonality\");" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3da6c045-b058-4f85-b32c-fc5a7d706f84", + "metadata": {}, + "outputs": [], + "source": [ + "residuals = np.random.normal(loc=0.0, scale=3, size=len(time))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bc71cceb-0a50-44bd-8a51-09bcb093ab62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, residuals, color='tab:green')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Residuals\");\n", + "#residuals equivalent to stochasticity in a system; the trend will/should ALWAYS dominate" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9ec67d4f-db49-4118-a87d-7659f6ac978f", + "metadata": {}, + "outputs": [], + "source": [ + "additive = trend + seasonal + residuals\n", + "#residuals are 'error' terms " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cb34edcd-2fd2-49da-aaa2-56d38ed150b7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cNSVOKR/l1X4pt/ZJebVPyqt9Ul7tl3Jbs6TlFDL5130APNanCbe2r3/BdrUlr2XtY7W6zb+goIA9e/YQGhpKZGQkISEhpaZGFBYWsnLlypKCPCYmBqPRWKpNYmIiO3fuvGTRLiIiIiIiIlXr5V/2kJpTSLNgL/5xXSNbh1Nj2HSkfeLEidx0003Ur1+f5ORkXnrpJTIzM7nnnnswGAyMHz+eyZMn06RJE5o0acLkyZNxd3dnxIgRAPj4+DB27Fgef/xxAgIC8Pf3Z+LEiURHR9OnTx9bdk1ERERERET+su5gCt/En8BggMlDo3HWMvFlZtOi/cSJE9xxxx2kpKRQp04dOnfuzPr164mIiADgiSeeIC8vjwcffJC0tDQ6derE4sWL8fLyKjnHW2+9hZOTE8OGDSMvL4/evXszc+ZMHB0dbdUtERERERER+Uu+ycwzC3YAcFenCGIi/C7zCvlfNi3a582bd8nnDQYDcXFxxMXFXbSNq6srU6dOZerUqRUcnYiIiIiIiFytacsOcvRsLsHeLvyrfzNbh1PjaE6CiIiIiIiIVIp9SVl8uPIQAJMGReHtav8LzFU0Fe0iIiIiIiJS4SwWK099t50ii5XYlsH0jwqxdUg1kop2ERERERERqXBf/HmMLcfT8XRxYtLgVrYOp8aqVvu0i4iIiIiI2Cur1cobi/eTnJVP10aBdG0cQJCXq63DqhRbE9KZ/MteAP7VrxmhPm42jqjmUtEuIiIiIiJSBVYfSGHa8oMAfLXpBADNQ7zo1jiQaxsH0jHSHw+Xml+iHUnJYczMjeSZzHRvEshdnSNsHVKNVvN/IkRERERERGqAT9ceAaBtuC8ms4VdpzLZm5TF3qQsZqw5gtHRQLv6flzbOJBujQNpU88HJ8eadUdzclY+Iz/9k9ScQqLr+vDBXTE4OhhsHVaNpqJdRERERESkkh1MzmLFvjMYDPDO7W2JCPDgbHYB6w6dZe3BFFYfSOFkeh4bjqSy4Ugqby7Zj5eLE50bBdA+wo/ouj60quuDj1v1XX09K9/E6M82kpCaR0SAO5+O6oCnHcwcsDV9B0VERERERCrZZ2uPAtCnRTARAR4ABHi6cFObMG5qE4bVauV4ai5rDqaw5kAK6w6dJSPPxJLdp1my+3TJeSIC3Imq60N0XR+GtqtLkHf1uCe+sMjCA3Pi2XUqk0BPZz4f05E6Xi62DssuqGgXERERERGpROm5hXy7ufge9jHdIi/YxmAwEBHgQUSAB3d2isBssbLrVAZrD55l+4l0dpzM4ERaHsfO5nLsbC4/b09k5tqjzL2vEw3reFZld85jsViZ+PU21h48i7uzI5+N6ljywYRcPRXtIiIiIiIilejLDQnkmyy0CPWmc0P/Mr3G0cFA63q+tK7nW3IsPbeQnScz2Xkqg682JXD4TA7Dpq/ny/s60STYq5KivzST2ULcD7v4YdspnBwMfHhXDNH1fGwSi72qWasaiIiIiIiI1CAms4XP/zgKwJhuDTAYrnxRNl93Z65tEsgDPRrx9f1daBHqTUp2Abd/tJ49iZkVFHHZnUjLZdj0P/jiz+MAvH5ba65rWqfK47B3KtpFREREREQqyaKdSSRm5BPo6cxNbcIq7LwBni58eV8nouv6cDankDs+Xs+OExkVdv7L+W1XEje+s5otx9PxdnXiw7tiuLldvSq7fm2iol1ERERERKSSnNvm7a7OEbgaHSv03L7uzsy5txPt6vuSnmtixCfr2Xw8rUKv8XcFRWYm/biL+2fHk5lfRJtwX35+pDv9o0Iq9bq1mYp2ERERERGRSrD5eBpbjqfj7OjAnZ0iKuUaPm5GZo/tRIcGfmTlF3H3J3+y+sCZSrlWQmout37wR8lK+Pd1j+Tr+7sQ7u9eKdeTYiraRUREREREKsG54nZQ27BK3f7M08WJWWM60rVRADmFZkZ+uoFXftlDQZG5wq5hMlsYM3MjO05m4Otu5JOR7Xl2QEucnVRSVjZ9h0VERERERCpYYkYev+xIBGB0twaVfj13Zyc+HdWBOzvVx2qF6asOc/N76ziYnFUh55+34TgHkrPx93Dm50e606dlcIWcVy5PRbuIiIiIiEgF+/yPY5gtVjo39KdVWNVsgeZqdOTlm6P5eGR7/D2c2Z2YyYB31zB7/TGsVusVnzcz38RbSw8A8FifJtT1dauokKUMVLSLiIiIiIhUoF92JDLzr6nxY7pFVvn1+7YMZtGj3eneJJCCIgvPf7+Te2dtIiW74IrO997yg6TmFNKojgd3dKxfwdHK5ahoFxERERERqQAms4X/+2k3D36xmTyTmWsbB9K7hW2mkQd5uzJrdEf+PbAlzo4O/L43mf5vr2b5vuRynSchNZfP1hwF4NkBLXByVAlZ1fQdFxERERERuUpJGfnc/tF6Zqwp3uLt/usaMnN0BxwdDDaLycHBwJhrI1n4cDeaBnuSkl3A6M82EvfDLvJNZVuk7rVFeyk0W+jWOIBezYIqOWK5EBXtIiIiIiIiV2HNgRQGvLua+GNpeLk4Mf3uGJ6+sfqMSrcI9eaHh69lVNcGAMxcd5TB09ayNynzkq+LP5bGT9sTMRjg2RtbYjDY7gOI2qx6/BSJiIiIiIjUMFarlfeWH+TuT//kbE4hLUK9+emRa+nXKsTWoZ3H1ehI3KBWfDa6A4GeLuw7ncWgqWuZseYIRWbLee2tVisv/bwbgNti6tEyzLuqQ5a/qGgXEREREREpp4IiMxO+2sbrv+3DaoXh7cNZ8GBXIgI8bB3aJfVqFsSi8d3p3TyIwr/uwe8+ZTlTfz9AclZ+SbufdySy5Xg6bkZHHo9tZsOIxcnWAYiIiIiIiNQkqTmF3D97ExuPpuHoYGDSoFbc1TnC1mGVWaCnC5/c0545fx7nzcX7SMzI540l+3nn9wP0iwrhjg71efXXvQA80KMRwd6uNo64dqs2I+2vvPIKBoOB8ePHlxwbNWoUBoOh1Ffnzp1Lva6goIBx48YRGBiIh4cHgwYN4sSJE1UcvYiIiIiI1AaHzmRz8/tr2Xi0+P71maM71KiC/RyDwcDdnSP44+nevDW8DdfU96XIYuXn7YncNeNPTqTlEeztwn3XVf2WdVJatSjaN27cyEcffUTr1q3Pe65///4kJiaWfP3yyy+lnh8/fjwLFixg3rx5rFmzhuzsbAYOHIjZXLbVEEVERERERMpi3aEUbn5vLcfO5lLPz43vHuxK9yZ1bB3WVXE1OnJzu3p892A3fn7kWu7oWB83oyMAT9/QAndnTc62NZtnIDs7mzvvvJOPP/6Yl1566bznXVxcCAm58EIOGRkZzJgxg9mzZ9OnTx8A5syZQ3h4OEuXLqVfv36VGruIiIiIiFyY1WrFagXruX//ddxYTVZUL68ftp1iwvytFFmsXFPfl49GtifQ08XWYVWoVmE+vDI0mqdvbM7pjHyaBHvZOiShGhTtDz30EAMGDKBPnz4XLNpXrFhBUFAQvr6+9OjRg5dffpmgoOL9AePj4zGZTMTGxpa0DwsLIyoqinXr1l20aC8oKKCgoKDkcWZm8VYHJpMJk8lUkd2rUOdiq84xSvkpr/ZLubVPyqt9Ul7tk/JqG6k5hTz05VY2HUu/4PPNgj2Z0LcJvZoGXvEWYlWd26z8Ip75bgdFFisDokN47eZWuBgd7PZny80RGvi7Vnn/att7tqz9NFitVuvlm1WOefPm8fLLL7Nx40ZcXV3p2bMnbdu25e233wZg/vz5eHp6EhERwZEjR3j++ecpKioiPj4eFxcX5s6dy+jRo0sV4ACxsbFERkYyffr0C143Li6OSZMmnXd87ty5uLu7V3g/RURERERqgwIzvLfbkWPZly/GG3tbGBxhob5nFQR2lX4/aeCH444Eu1l5qo0ZB21XLhUgNzeXESNGkJGRgbf3xbfUs9lIe0JCAo8++iiLFy/G1fXCqxEOHz685N9RUVG0b9+eiIgIfv75Z4YOHXrRc1ut1kt+avf0008zYcKEkseZmZmEh4cTGxt7yW+WrZlMJpYsWULfvn0xGo22DkcqiPJqv5Rb+6S82ifl1T4pr1WroMjC/XO2cCz7LH7uRj4dGUOorysGwGAAAwYKiszM+uM4s9Yf52AmvLHDgYHRITzetwn1/NzKfK2qzG1BkYXJb64GChjfP4qB19St1OvVZrXtPXtuxvfl2Kxoj4+PJzk5mZiYmJJjZrOZVatWMW3aNAoKCnB0dCz1mtDQUCIiIjhw4AAAISEhFBYWkpaWhp+fX0m75ORkunbtetFru7i44OJy/v0nRqOxRvxw1JQ4pXyUV/ul3Non5dU+Ka/2SXmtfGaLlae+3sLaQ2dxd3bks9EdaRvue8G2zw5sxT3dInlz8X6+23KSn3YksXh3MhNim/JAj0blum5V5HbB1gROZxUQ7O3CLTH1MTrVzHvya5La8p4tax9t9hPXu3dvduzYwdatW0u+2rdvz5133snWrVvPK9gBzp49S0JCAqGhoQDExMRgNBpZsmRJSZvExER27tx5yaJdREREREQqhtVqJe6HXfy0PRGjo4Hpd8dctGA/p56fO28Ob8tP466lW+MACs0WXv11L/M2HK+aoMvIYrEyfdUhAMZeG4mzCnaxAZuNtHt5eREVFVXqmIeHBwEBAURFRZGdnU1cXBy33HILoaGhHD16lGeeeYbAwEBuvvlmAHx8fBg7diyPP/44AQEB+Pv7M3HiRKKjo0tWkxcRERERkcrzzu8HmL3+GAYDvDW8bbm2QIuq68OcsZ14a+kB3v39AM99v5Nwf3e6NQ6sxIjLbume0xw6k4OXqxN3dKxv63Cklqq2HxU5OjqyY8cOBg8eTNOmTbnnnnto2rQpf/zxB15e/9164K233mLIkCEMGzaMbt264e7uzo8//njBkXoREREREak4s/84yttLi29dfXFQKwa2Div3OQwGA4/1acKQtmEUWaw8MCeeA6ezKjrUKzJ91WEA7uocgZer/U/XlurJ5lu+/a8VK1aU/NvNzY3ffvvtsq9xdXVl6tSpTJ06tRIjExERERGR/7XxaCpxP+4GYHyfJtzdpcEVn8tgMPDara05mZ7HxqNpjJm1kQUPdrPpPugbj6YSfywNZ0cHRndrYLM4RKrtSLuIiIiIiFRPqTmFjJu7BbPFyuC2YTzau8lVn9PFyZHpd7cnIsCdhNQ8/vH5JvJN5gqI9spMX1l8L/stMXUJ8rrwblciVUFFu4iIiIiIlJnFYuXxr7aSlJlPw0APXr45+pLbLZeHv4czn47qgI+bkc3H05n49TYsFmuFnLs89p/OYumeZAwGuK97wyq/vsj/UtEuIiIiIiJl9vHqwyzfdwYXJwfeu/MaPF0q9o7bRnU8+fCuGIyOBn7ansiIT9Yz9fcDrD2YQnZBUYVe62I++ute9v6tQmhYx7NKrilyMdXqnnYREREREam+4o+lMeW3fQC8cFMrWoR6V8p1ujQK4JWhrfnXN9tYfziV9YdTAXAwQLMQb9qFe9OgsFIuTWJGHgu3ngTg/nLuGy9SGVS0i4iIiIjIZaXnFvLIl8X3sd/UJow7OoZX6vVujalH63o+rD2Ywubj6Ww+lsbJ9Dz2JGayJzETd0dHYjplEBNZcdvD5ZvMvPTzHkxmK50b+l92v3mRqqCiXURERERELslqtTLx6+2cTM+jQYA7k2+OqrD72C+labAXTYO9GN2t+PHpzHw2H0vjw5WH2HYig3tmxjNzdAfaN/C/6mttP5HOY/O3cuhMDgDjrr/6xfVEKoLuaRcRERERkUuavuowS/ecxtnRgWkjrrHZnuXB3q7cEB3KzFExNPKykl1QxMhPN/DHobNXfE6T2cI7Sw8w9P11HDqTQ5CXC5+N7kC3xhU3gi9yNVS0i4iIiIjIBVksVl5btJdXf90LwPMDWxBV18fGUYGnixMPtDDTrVEAuYVmRn22gZX7z5T7PIfOZHPrB+t4a+l+iixWBrQO5bfx19GrWVAlRC1yZTQ9XkREREREzpNXaGbCV1v5dWcSAI9c35i7OkfYOKr/cnaE6Xe25dGvdvD73mTum7WJ9+68hr4tgy/6GovFyoHkbDYdSyX+aBq/7Ewk32TB29WJ/xsSxaA2YVUy7V+kPFS0i4iIiIhIKclZ+dw3axPbTmRgdDTw2i2tGXpNPVuHdR4XoyMf3BXDo/O28OvOJP45J5529X3xdjXi7WbE29UJbzcjBoOBbQnpbD6eRlZ+6W3jujcJZMqtrQn1cbNRL0QuTUW7iIiIiIiU2JuUydiZmziZnoevu5Hpd8XQqWGArcO6KGcnB6be0Y7Hv97Gwq2n2Hg07ZLt3Z0daRvuS/sIPzpGBtC1UQAODhpdl+pLRbuIiIiIiACw9mAK98+OJ7ugiIaBHnw6qgMNAj1sHdZlOTk68Pbwtozs0oDTmflk5pnIzDeRmVdEVr6JfJOFFqFexET40yLUCydHLe0lNYeKdhERERERId9k5rH5W8kuKKJLwwA+uOsafN2dbR1WmRkMBmIi/GwdhkiFU9EuIiIiIiJ8HX+C5KwCQn1c+Wx0B1yNjrYOSUTQlm8iIiIiIrVeYZGFD1ccAuCBHo1UsItUIxppFxERERGpIIkZeTz93Q6KzFbu6Fif2FbBGGvA/dPfbznJyfQ8Aj1dGN4h3NbhiMj/UNEuIiIiIlIB1h1KYdzcLZzNKQRgzcEUgrxcuL1jfe7oGF5ttxQrMlt4b8VBAO6/rqFG2UWqmer/sZ+IiIiISDVmtVqZvvIQd33yJ2dzCmkZ6s1DvRoR6OlCclYB7/5+gGtfW84Ds+PZdSrD1uGe56ftiRw7m4ufu5ERnerbOhwR+RuNtIuIiIiIXKGsfBP/+no7i3YlAXDLNfV4+eYoXI2OPNq7KYt3JzH7j2P8eSSVRbuSWHswhd8f70GQt6uNIy9msViZtrx4lP3e7g3xcFF5IFLd6F0pIiIiInIJJrOFoyk5mMxWzBYrJosFs8VKVr6Jl37ew+EzORgdDcQNasWIjvUxGAwAODs5MLB1GANbh7H/dBaPzd/KrlOZvPjTbqaNuMbGvSq2aFcSB5Oz8XZ1YmSXCFuHIyIXoKJdREREROQidp7M4J9fxJOQmnfRNqE+rrx/5zW0q3/xPcKbBnvx2i2tGTRtDT9tT+S29mfo0bROZYRcZlarlanLikfZR3WLxMvVaNN4ROTCdE+7iIiIiMgFfBN/gls+WEdCah7uzo4EebkQ5uNKfX93GgZ60DTYk0Ftwvhx3LWXLNjPiarrw6iukQA8//1O8k3myu7CJf2+J5k9iZl4ODsyplsDm8YiIhenkXYRERERkf9RUGTmxR9388WfxwG4vnkQbw1ri4/71Y9ET4htyi87Ejmemsu0ZQeZ2K/ZVZ/zShSPsh8A4O4uDfB1d7ZJHCJyeRppFxERERH5S2JGHsOnr+eLP49jMMBjfZryycj2FVKwA3i6OBE3qCUA01cd4mByVoWct7xWH0hh24kMXI0O3Ns90iYxiEjZVJui/ZVXXsFgMDB+/PiSY1arlbi4OMLCwnBzc6Nnz57s2rWr1OsKCgoYN24cgYGBeHh4MGjQIE6cOFHF0YuIiIhITbfhSCoD313D1oR0vF2d+HRUBx7t0wQHB0OFXqdfqxB6Nw/CZLby7IKdWK3WCj3/5SSk5vLUt9sBGNExgkBPlyq9voiUT7Uo2jdu3MhHH31E69atSx2fMmUKb775JtOmTWPjxo2EhITQt29fsrL++4nk+PHjWbBgAfPmzWPNmjVkZ2czcOBAzGbb3iMkIiIiIjXHkZQcxs7cyNmcQlqEevPTuO70ahZUKdcyGIpXmnc1OvDnkVS+ia+6AafEjDxGfLKeUxn5NKzjwcPXN66ya4vIlbF50Z6dnc2dd97Jxx9/jJ/ffxfwsFqtvP322zz77LMMHTqUqKgoZs2aRW5uLnPnzgUgIyODGTNm8MYbb9CnTx/atWvHnDlz2LFjB0uXLrVVl0RERESkBsktLOKB2fFkFRTRPsKP7/7ZlfoB7pV6zXB/d8b3aQrA5F/2kJpTWKnXA0jOzGfEx3+SkJpHRIA7c+/tjL+H7mUXqe5svhDdQw89xIABA+jTpw8vvfRSyfEjR46QlJREbGxsyTEXFxd69OjBunXruP/++4mPj8dkMpVqExYWRlRUFOvWraNfv34XvGZBQQEFBQUljzMzMwEwmUyYTKaK7mKFORdbdY5Ryk95tV/KrX1SXu2T8mqfypJXq9XKk9/sYN/pLOp4OvPO8NY4GSyYTJZKj29kp3p8F3+C/cnZPPnNNt4e1hpnp8oZUzubXcCdn27iSEoOdX1d+XxUDAHujjX2Z17vWftU2/Ja1n7atGifN28emzdvZuPGjec9l5SUBEBwcHCp48HBwRw7dqykjbOzc6kR+nNtzr3+Ql555RUmTZp03vHFixfj7l65n6pWhCVLltg6BKkEyqv9Um7tk/Jqn5RX+3SpvK5KNPDjUUccsHJHRC6bVv9ehZHBjUFwMNmRJXuSGfTmEsY0M+NZwdul55hg2m5HTuUa8HG2MiYym63rlrO1Yi9jE3rP2qfaktfc3NwytbNZ0Z6QkMCjjz7K4sWLcXV1vWg7g6H0wh9Wq/W8Y393uTZPP/00EyZMKHmcmZlJeHg4sbGxeHt7l7EHVc9kMrFkyRL69u2L0VjBv83FZpRX+6Xc2ifl1T4pr/bpcnnddCyNhX9uAqw8dUNzRneNqPoggehrUnh0/nYOZRXxwSEvPrqrHU2CPM9rV2S28Ouu0/y68zT1/d3o1awO19T3xeh44dF5i8XKoZQcnvh2J6dyMwn0dGbu2A5EBnpUdpcqnd6z9qm25fXcjO/LsVnRHh8fT3JyMjExMSXHzGYzq1atYtq0aezbtw8oHk0PDQ0taZOcnFwy+h4SEkJhYSFpaWmlRtuTk5Pp2rXrRa/t4uKCi8v5q2QajcYa8cNRU+KU8lFe7Zdya5+UV/ukvNqnC+U1OTOfR+Zvp8hi5aY2Ydx3XaPLDgxVlt4tQ1nwoCdjZ23ieGouwz/awNQR7ej510J4BUVmvtt8kg9XHuLY2f+OzM1YewxvVyd6NAuid/MgOjX050hKDpuPpRF/LI3Nx9PJyCuefuvv4czc+zrTNNjLJn2sLHrP2qfaktey9tFmRXvv3r3ZsWNHqWOjR4+mefPmPPnkkzRs2JCQkBCWLFlCu3btACgsLGTlypW89tprAMTExGA0GlmyZAnDhg0DIDExkZ07dzJlypSq7ZCIiIiI1Agms4WH527hTFYBTYM9eXVotM0K9nOaBHvx/UPdeGB2PBuOpjJm5kaeubEFBoOBj1cdJikzHwA/dyN3dKxPUmY+K/adITWnkB+3neLHbacueF5XowMxEX48P7Cl3RXsIrWFzYp2Ly8voqKiSh3z8PAgICCg5Pj48eOZPHkyTZo0oUmTJkyePBl3d3dGjBgBgI+PD2PHjuXxxx8nICAAf39/Jk6cSHR0NH369KnyPomIiIhI9Wa1Wnnpp91sOJqKl4sTH94Vg4eLzddmBopHw+fc24lnF+zg6/gTvPTznpLnQrxdue+6htzRMRx35+J4zRYrWxPSWbb3NL/vSWZvUhZ1fd24JsKPa+r7EhPhR4tQ74tOnxeRmqF6/Ia6iCeeeIK8vDwefPBB0tLS6NSpE4sXL8bL67+fEr711ls4OTkxbNgw8vLy6N27NzNnzsTR0dGGkYuIiIhIdfTx6sPM+qN4UeP/DGtDwzrn3ztuS85ODky5tTVNgj159de9hPu7888ejbj5mrq4OJX++9bRwUBMhB8xEX78q19z8k1mXI36G1jE3lSron3FihWlHhsMBuLi4oiLi7voa1xdXZk6dSpTp06t3OBEREREpEZbuPUkk3/ZC8BzA1rQr1WIjSO6MIPBwD+ua8TwDvXxdHHC0aFsU/dVsIvYp2pVtIuIiIiIVIZ1h1KY+PU2AMZ0i+Te7g1tHNHl+bjZ/0JcInJ5usFFREREROzavqQs7v88HpPZyo3RITw3oIWtQxIRKTMV7SIiIiJit9ILYOzszWQVFNGxgT9vDmuLQxmnm4uIVAeaHi8iIiIi7D6VyaZjqUQEeNCojgdhPm41vrjNzDPx4R5HTucV0DjIk49Gxui+bxGpcVS0i4iIiNRyO09mMHz6H+QUmkuOuRodaBjoSaMgT1qEetGjaR1ahnrbfD/z8njtt/0k5hkI8nJh5ugO+Lo72zokEZFyU9EuIiIiUosdO5vDqM82kFNopmEdDxwNBo6ezSHfZGF3Yia7EzP5cRtMWbSPYG8XejStQ69mQXRrEoi3a/VdKC0r38SP2xMBeOPWaOr5uds4IhGRK6OiXURERKSWSskuYOSnG0jJLqRlqDfz7++Ml6uRIrOFhLQ8DiVnc+hMNhuPprH2YAqnMwv4atMJvtp0AicHAwNah/Kf29pgdKx+yyT9tD2RPJOFYDcrnSL9bB2OiMgVU9EuIiIiUgvlFBQx+rONHDubS7i/GzPHdMDrr5FzJ0cHIgM9iAz0oA/B3N8D8k1mNh5NZfneM6zYn8zhMzks3HqKYG9Xnrmx+q3GPn9jAgCdgyw1akq/iMjfVb+PRUVERESkUhUWWXhgTjw7Tmbg7+HMrNEdCfJyveRrXI2OdG9Sh3/f1JJlj/fkvRHXAPDRqsMs23u6KsIus31JWWxNSMfJwUCHOlZbhyMiclVUtIuIiIjUIhaLlSe/3c7qAym4GR35dFQHGtbxLPd5BrQOZVTXBgBM+GobiRl5FRzplTs3yt6rWR28qu9t9yIiZaKiXURERKSWsFqtTP5lDwu2nMTRwcD7d11D23DfKz7f0zc2J7quD+m5Jh75cgtFZkvFBXuFCorMLNhyAoDbYuraOBoRkaunol1ERESkFrBarbz6614+WXMEgFeHRtOrWdBVndPFyZFpI9rh5eLExqNpvLlkf0WEelWW7k4mLddEsLcL3RsH2DocEZGrpqJdRERExM5ZrVZeXbSX6asOA/B/g1txW/vwCjl3RIAHr97SGoD3Vxxi5f4zFXLeKzV/U/HU+Ftj6uFUDVe1FxEpL/0mExEREbFjVquVKb/tY/rK4oL9xcGtuLtLgwq9xoDWodzVuT4AE+Zv5XRmfoWev6xOpuex+kDxhwbDKuhDCRERW1PRLiIiImKnrFYrr/+2jw9WHAKKC/aRFVywn/PcgJa0CPXmbE4hw6f/wZuL9xF/LA2zpepWb/9m0wmsVujSMICIAI8qu66ISGXSPu0iIiIidshqtfKfxft4/6+CfdKgyivYoXhLuPdGtGPoB+s4ejaXd5cd5N1lB/F1N3Jdkzr0bFaHXs2C8PNwrpTrWyxWvvpravzwDhplFxH7oaJdRERExA59vPow7y0vLthfuKkl9/y1PVtlaljHk2WP9+T3PadZsf8Mq/afIT3XxA/bTvHDtlM4ORi4tkkgA1uHEdsqGG/XituPbe2hFE6m5+Hl6kT/qJAKO6+IiK2paBcRERGxM8fP5vLG4uKV3J+9sQWju0VW2bX9PZy5rX04t7UPp8hsYUtCOsv3JrNsbzJ7k7JYse8MK/adwfk7B3o2q8NNbcLo2zIYV6PjVV333N7sQ9rWvepziYhUJyraRUREROzMpB93UVBkoVvjAO7tXnUF+985OTrQoYE/HRr480T/5hw6k81P2xL5cfspDiZns3j3aRbvPk3zEC8+G92BUB+3K7pOWk4hi3edBjQ1XkTsj4p2ERERETuyZPdpft+bjNHRwKRBURgMBluHVKJRHU8e7dOER3o3Zt/pLH7cdor5GxPYm5TF0PfXMWtMR5oGe132PGezC9h1KpNdpzLZeSqDrcfTKTRbaBnqTVRdnyroiYhI1VHRLiIiImIn8grNxP2wC4B7uzekcZCnjSO6MIPBQPMQb5qHeHNHx/rc8+kGDp3J4dYP1vHxyPZ0ahhw3muyC4r4/I+jfLH+OCfT88573sEA9/doWBXhi4hUKRXtIiIiInbi/RUHOZmeR5iPK+Oub2zrcMqknp873/6zK/fO2sSmY2ncPWMDbw1vy4DWocB/i/WPVx0mLddU8rrIQA9ahnkTFeZDq7DiEXb/SlqZXkTEllS0i4iIiNiBIyk5TF95GIB/39QSd+ea82eer7szc+7txKPztvDbrtM8/OVmTqY3x2S28vHqw6T/VaxHBnrwcK/GxLYKxqsCV54XEanOas5vcxERERG5IKvVyr8X7qTQbKFH0zr0a1XztjxzNTry/p0xTPpxF5//cYzJv+wtea5hoAfjejfmptZhODk62DBKEZGqZ9Pfeh988AGtW7fG29sbb29vunTpwq+//lry/KhRozAYDKW+OnfuXOocBQUFjBs3jsDAQDw8PBg0aBAnTpyo6q6IiIiI2MyinUmsPpCCs6MDcYNaVavF58rD0cHApEGteKJ/MwyG4mL97eFtWTKhBze3q6eCXURqJZuOtNerV49XX32Vxo2L77maNWsWgwcPZsuWLbRq1QqA/v3789lnn5W8xtm59L1K48eP58cff2TevHkEBATw+OOPM3DgQOLj43F01B6dIiIiYt9yC4t48afdADzQoyGRgR42jujqGAwGHuzZmBEd6+PlasTRoWZ+ACEiUlFsWrTfdNNNpR6//PLLfPDBB6xfv76kaHdxcSEk5MJTvDIyMpgxYwazZ8+mT58+AMyZM4fw8HCWLl1Kv379KrcDIiIiIjb28s97SMzIp56fG//sWTMWnysLX3ctKiciAtXonnaz2czXX39NTk4OXbp0KTm+YsUKgoKC8PX1pUePHrz88ssEBQUBEB8fj8lkIjY2tqR9WFgYUVFRrFu37qJFe0FBAQUFBSWPMzMzATCZTJhMpgu+pjo4F1t1jlHKT3m1X8qtfVJe7VNNzevcDQl88edxDAaYdFMLnAwWTCaLrcOqNmpqXuXylFv7VNvyWtZ+GqxWq7WSY7mkHTt20KVLF/Lz8/H09GTu3LnceOONAMyfPx9PT08iIiI4cuQIzz//PEVFRcTHx+Pi4sLcuXMZPXp0qQIcIDY2lsjISKZPn37Ba8bFxTFp0qTzjs+dOxd3d/eK76SIiIhIBTuQYeD9PQ5YrAYG1jfTt65N/6QTEZFyys3NZcSIEWRkZODt7X3RdjYv2gsLCzl+/Djp6el8++23fPLJJ6xcuZKWLVue1zYxMZGIiAjmzZvH0KFDL1q09+3bl0aNGvHhhx9e8JoXGmkPDw8nJSXlkt8sWzOZTCxZsoS+fftiNGqbE3uhvNov5dY+Ka81Q0p2AX8eScPJwYCbsyOuRgfcjI64Gh0J9HTG729Tr2taXo+l5nLrh3+SnmfiptYhvHFrdI1dfK4y1bS8Stkpt/aptuU1MzOTwMDAyxbtNp8e7+zsXLIQXfv27dm4cSPvvPPOBUfJQ0NDiYiI4MCBAwCEhIRQWFhIWloafn5+Je2Sk5Pp2rXrRa/p4uKCi4vLeceNRmON+OGoKXFK+Siv9ku5tU/Ka/VkMluYufYoby/dT06h+YJtHAwwpG1dHrq+MY3qeJZ6ribkNSvfxANfbCU9z0Sbej68fltbnI1afPdSakJe5coot/aptuS1rH28on0zioqKWLp0KdOnTycrKwuAU6dOkZ2dfSWnK8VqtZ43cn7O2bNnSUhIIDQ0FICYmBiMRiNLliwpaZOYmMjOnTsvWbSLiIiI/Vl7MIUb3lnNy7/sIafQTOMgT9pH+NEqzJuGdTwI83HF192IxQrfbTlJnzdXMu7LLew/nWXr0MvMbLHy6LytHEzOJtjbhY9GtsdVBbuIiF0r90j7sWPH6N+/P8ePH6egoIC+ffvi5eXFlClTyM/Pv+iU9At55plnuOGGGwgPDycrK4t58+axYsUKFi1aRHZ2NnFxcdxyyy2EhoZy9OhRnnnmGQIDA7n55psB8PHxYezYsTz++OMEBATg7+/PxIkTiY6OLllNXkREROzbyfQ8Jv+8h593JAIQ4OHMk/2bc2tMPRwusF3YjhMZvLvsAEt2n+bHbaf4cdsp+rcKpnUNqH2nLNrLsr3JuDg58NHd7Qn2drV1SCIiUsnKXbQ/+uijtG/fnm3bthEQEFBy/Oabb+bee+8t17lOnz7N3XffTWJiIj4+PrRu3ZpFixbRt29f8vLy2LFjB59//jnp6emEhobSq1cv5s+fj5eXV8k53nrrLZycnBg2bBh5eXn07t2bmTNnao92ERGRWuC3XUmMn7eVPJMZBwOM7NKAx/o0xcf94lMOo+v58PHI9uw+lcm05Qf4ZUcSi3adZhFObC7cwqN9mhFdz6cKe1E2P20/xfRVhwGYcmtr2oT72jYgERGpEuUu2tesWcPatWtxdi69gEtERAQnT54s17lmzJhx0efc3Nz47bffLnsOV1dXpk6dytSpU8t1bREREanZ0nMLefq7HeSZzHRs4M+kwa1oEVr2BWVbhnnz/p0x7EvK4t3f9/PLjkSW7j3D0r1nuL55EOOub0y7+n6XP1EVSM0p5N8LdwHwQI9GDG5b18YRiYhIVSl30W6xWDCbz1/Y5cSJE6VGwEVEREQq038W7yM1p5CmwZ58cV8njI5XtFQPzUK8eHtYa9o4nmC3NZwftieybG8yy/Ym071JIOP7NCUmwrbF+4s/7iI1p5BmwV5M6NvUprGIiEjVKvf/bn379uXtt98ueWwwGMjOzuaFF14o2V9dREREpDLtOJHBF38eB+DFwVFXXLD/r2A3eP3WaJY93pPbYurh6GBg9YEUbvlgHXE/7CLvIqvRV7Zle0/z/dZTOBjgtVtb4+x09X0VEZGao9y/9d96662SfdTz8/MZMWIEDRo04OTJk7z22muVEaOIiIhICYvFynMLd2K1wpC2YXRuGHD5F5VDg0APXr+tDSsm9uTWmHoAzFx3lAHvrmbL8bQKvdblZBcU8dyCnQCM6RZJW93HLiJS65R7enxYWBhbt27lyy+/ZPPmzVgsFsaOHcudd96Jm5tbZcQoIiIiUuKrTQlsS0jH08WJZ25sUWnXCfd35z+3tWFg61Ce/HY7h1NyuOWDdfyzZyMe7d20Ska8pyzay6mMfML93ZgQq2nxIiK1UbmLdiheJG7MmDGMGTOmouMRERERuai0nEJeW7QXgMf6NiWoCrY869ksiMXje/DCDzv5fusp3lt+iGV7z/Cf21rTKqzyVpnfeDSVz/84BsCrQ1vj7nxFf7aJiEgNV+7f/p9//vklnx85cuQVByMiIiJyKa8v3kdaronmIV7c0yWiyq7r427k7dvbEdsqhGcX7GBPYiYD3l1DnxbB3N+jIe0j/DAYzt8T/krlm8w8+e12AIa1r0e3xoEVdm4REalZrmif9v9lMpnIzc3F2dkZd3d3Fe0iIiJSKbYlpPPlhv8uPudUAYvPldeN0aF0aOBP3I+7ireI23OapXtO066+L/df14i+LYNxdPhv8V5YZCExI4/krAKah3jh5Xrx/eP/17RlBzl8Joc6Xi48e2PLyuqOiIjUAOUu2tPSzl+A5cCBA/zzn//kX//6V4UEJSIiIvK/zBYrz/+1+NzQdnXpGOlvs1jqeLnw3ohrOHQmm09WH+HbzSfYcjydB+bEExnoQVRdH06m5XIyvbhYt1r/+7pP7+lAdL1LT6lfcyCFD1ceAuD/BrfCx71shb6IiNinCvmIukmTJrz66qvnjcKLiIiIVIS5fx5j+4kMvFyceOrG5rYOB4BGdTx5ZWg0a5+8nod7Ncbb1YkjKTn8uO0Um4+nczqzuGB3cXLAx83ImawChk3/g6W7T1/wfFarlY9XHWbkp39SZLFyY3QI/aNCq7hXIiJS3VTYiiaOjo6cOnWqok4nIiIiAsCxszm88mvx4nMTYpsS5FX5i8+VRx0vFyb2a8YDPRvx47ZTZOcXUdfPjbq+btT1cyPAw5nsgiIe/GIzqw+k8I/Zm/j3wJaM6hZZco6cgiKe+HY7P29PBGDoNXWZfHO0rbokIiLVSLmL9h9++KHUY6vVSmJiItOmTaNbt24VFpiIiIiI2WLl8a+2kVtoplOkP/d0aWDrkC7K08WJOzrWv+BzXq5GPh3Vgee/38m8jQnE/bibY6m5PDegJQmpudw/O559p7NwcjDwwk0tuatzRIUubCciIjVXuYv2IUOGlHpsMBioU6cO119/PW+88UZFxSUiIiLCJ6sPs+lYGp4uTvzntjY4ONTcQtbo6MArQ6OpH+DOlEX7+GztUfYmZrHzVAZZ+UXU8XLhgzuvoX0D292vLyIi1U+5i3aLxVIZcYiIiIiUsi8pizcW7wfg+YEtCPd3t3FEV89gMPBgz8aE+7nz+Nfb+OPwWQDaR/jx/p3XVMm+8yIiUrNU2D3tIiIiUrW+2pTAR6sOc1tMPUZ1a4CLk6OtQ6owhUUWJny1lUKzheubBzGsfbitQ6pQN7UJI9THlX8v3EXXRgE80b85zk5Vv4WdiIhUf2Uq2idMmFDmE7755ptXHIyIiIiUzYHTWTz3/U4Kiyy88ute5vx5jKf6t+DG6BC7uBd62rID7DqVia+7kVeHRttFn/6ufQN/fnm0u63DEBGRaq5MRfuWLVvKdDJ7/A9VRESkuikyW5j49TYKiyy0rufD6cx8ElLzeGjuZmIi/Hh2QAuuqe9n6zCv2LaEdN5bUbxP+UtDojRlXEREarUyFe3Lly+v7DhERESkjKavOsy2Exl4uTrx0d3t8XZz4qNVh5m+8jDxx9IY+v46bowOYVCbunRrHICXq9HWIZdZvsnMY19txWyxclObMAa2DrN1SCIiIjale9pFRERqkL1Jmby9tHhxtribWhHiUzwKPb5PU27vUJ83Fu/jm80n+GVHEr/sSMLJwUD7Bn70aBpEz2Z1aB7iVa1nxn206jCHz+QQ5OXC/w1uZetwREREbO6KivaNGzfy9ddfc/z4cQoLC0s9991331VIYCIiIlKa6a9p8SazlT4tghh6Td1Sz4f4uPL6bW0Y1a0BX286wcr9ZziSksP6w6msP5zKa4v20jDQg89GdyAiwMNGvbi4rHwTM9YcAeDZAS3wdXe2cUQiIiK2V+5lSufNm0e3bt3YvXs3CxYswGQysXv3bpYtW4aPj09lxCgiIiLABysOsfNkJj5uRibffPHF2VqF+RA3qBXLJ/Zk5b968uLgVlzfPAhXowOHU3K4d9YmsvJNVRz95X3+xzEy8kw0quOhafEiIiJ/KXfRPnnyZN566y1++uknnJ2deeedd9izZw/Dhg2jfv36lRGjiIhIrbf7VCbv/n4AgBcHtyrz4mwRAR6M7NKAT0d1YOW/ehHk5cKB5Gwem78Vi8VamSGXS05BEZ+sPgzAuOub4OhQfafwi4iIVKVyF+2HDh1iwIABALi4uJCTk4PBYOCxxx7jo48+qvAARUREarvCouJp8UUWK/1aBTOozZWNQgd7u/LRyPY4OzmwdE8ybyzZV8GRXrk564+RlmsiMtCDga1DbR2OiIhItVHuot3f35+srCwA6taty86dOwFIT08nNze3YqMTERGp5fJNZv71zTZ2J2bi527kpSFXt2d523BfXrslGoD3lh9i4daTFRXqFcsrNPPRquJR9od6NcbJsdx/noiIiNitMv+vuHXrVgC6d+/OkiVLABg2bBiPPvoo9913H3fccQe9e/eulCBFRERqo9OZ+Qz/aD0Lt57C0cHAq7e0po6Xy1Wf9+Z29bi/R0MAnvhmOztOZFz1Oa/GF38e42xOIfX93RncVveyi4iI/K8yF+3XXHMNMTExtGjRgjvuuAOAp59+mokTJ3L69GmGDh3KjBkzKi1QERGR2mRrQjo3TV3DtoR0fN2NfD6mI/1ahVTY+Z/o15xezepQUGThvs83kZyZX2HnLo98k5kPV54bZW+EUaPsIiIipZT5f8a1a9dyzTXX8J///IdGjRpx1113sXLlSp544gl++OEH3nzzTfz8/Mp18Q8++IDWrVvj7e2Nt7c3Xbp04ddffy153mq1EhcXR1hYGG5ubvTs2ZNdu3aVOkdBQQHjxo0jMDAQDw8PBg0axIkTJ8oVh4iISHXy3eYTDJv+B8lZBTQN9mThQ93o1jiwQq/h6GDgnTva0aiOB0mZ+dw9YwPTVx5iw5FU8grNFXqtS/lyw3FSsguo6+vGze3qVdl1RUREaooyF+1dunTh448/JikpiQ8++IATJ07Qp08fGjVqxMsvv3xFhXK9evV49dVX2bRpE5s2beL6669n8ODBJYX5lClTePPNN5k2bRobN24kJCSEvn37ltxTDzB+/HgWLFjAvHnzWLNmDdnZ2QwcOBCzuer+4BAREakIZouVyb/sYcJX2ygsstCnRTDfPdit0vZU93Y18sk9HfBxM7LvdBav/LqXYdP/ICruNwZOXc3z3+9kzYGUSrk2nBtlPwTAg70a4eykUXYREZG/cyrvC9zc3Ljnnnu45557OHToEJ999hnTp08nLi6Ovn378ssvv5T5XDfddFOpxy+//DIffPAB69evp2XLlrz99ts8++yzDB06FIBZs2YRHBzM3Llzuf/++8nIyGDGjBnMnj2bPn36ADBnzhzCw8NZunQp/fr1u+B1CwoKKCgoKHmcmZkJgMlkwmSqfvvWnnMutuoco5Sf8mq/lFv7VJl5nbr8UMmCbA/2aMij1zfCwcFaqT9D9Xyc+f6fnfl1VxJbEzLYmpBBclYBO09msvNkJrPXH2PSTS0Y0TG8wq8978/jnM4sINTHlcGtQ2z6XtH71T4pr/ZLubVPtS2vZe2nwWq1XtUmrdnZ2XzxxRc888wzpKenX/EIt9ls5uuvv+aee+5hy5YtuLq60qhRIzZv3ky7du1K2g0ePBhfX19mzZrFsmXL6N27N6mpqaWm5rdp04YhQ4YwadKkC14rLi7ugs/NnTsXd3f3K4pfRETkaqQWwOQtjpisBoY3NNM12DZ7qFutkF4IR7MNbD9rYPPZ4tHvYQ3NdKugmKxWSMmHabsdSS80cGukme4h1WfPeBERkaqQm5vLiBEjyMjIwNvb+6Ltyj3Sfs7KlSv59NNP+fbbb3F0dGTYsGGMHTu23OfZsWMHXbp0IT8/H09PTxYsWEDLli1Zt24dAMHBwaXaBwcHc+zYMQCSkpJwdnY+71764OBgkpKSLnrNp59+mgkTJpQ8zszMJDw8nNjY2Et+s2zNZDKxZMkS+vbti9FotHU4UkGUV/ul3NqnysrruHnbMFlP0ynSj/8b1f6qtnWrKFarlVcX7efTdcf46rAj0VEtub1D+e87zzeZ2XEyk83H09makM7mhHRSc4pHF4K9XIi7+1pcjI4VHX656P1qn5RX+6Xc2qfaltdzM74vp1xFe0JCAjNnzmTmzJkcOXKErl27MnXqVIYNG4aHx5Xdb9esWTO2bt1Keno63377Lffccw8rV64sef7vf7RYrdbL/iFzuTYuLi64uJy/ZY7RaKwRPxw1JU4pH+XVfim39qki87ruYAqLdp3GwQCTBkfh7OxcIeetCM/f1AqDgwMz1hzh+R924+TkyB0d65f59RuOpHLf55vIyCs9BdDZ0YHoej48fUNzPN1dKzrsK6b3q31SXu2Xcmufaktey9rHMhftffv2Zfny5dSpU4eRI0cyZswYmjVrdsUBnuPs7Ezjxo0BaN++PRs3buSdd97hySefBIpH00NDQ0vaJycnl4y+h4SEUFhYSFpaWqnR9uTkZLp27XrVsYmIiFS2IrOFuB+LF2C9u3MEzUOq14wvg8HAcwNaYLXCp2uP8PR3O3AwwPAOly/cd5zIYMzMjWQXFBHo6UKHBn7ERPjRrr4fUXW9cXGy7ei6iIhITVDmot3NzY1vv/2WgQMH4uhYef/JWq1WCgoKiIyMJCQkhCVLlpTc015YWMjKlSt57bXXAIiJicFoNLJkyRKGDRsGQGJiIjt37mTKlCmVFqOIiEhFmbP+GPtPZ+PnbuSxvk1tHc4FGQwGnh/YAitWPlt7lKe+24HJbOXOTvUvOrPtwOksRn76J9kFRXSK9GfWmI642ngKvIiISE1U5qL9hx9+qPCLP/PMM9xwww2Eh4eTlZXFvHnzWLFiBYsWLcJgMDB+/HgmT55MkyZNaNKkCZMnT8bd3Z0RI0YA4OPjw9ixY3n88ccJCAjA39+fiRMnEh0dXbKavIiISHWVmlPIm0v2A/B4bDN83avPtPi/MxgM/HtgS6xWmLnuKM99v5Pf95zm/4ZEUc+v9CKuCam53DXjT9JyTbSp58Mn97RXwS4iInKFrnghuopw+vRp7r77bhITE/Hx8aF169YsWrSIvn37AvDEE0+Ql5fHgw8+SFpaGp06dWLx4sV4eXmVnOOtt97CycmJYcOGkZeXR+/evZk5c2alzgYQERGpCP9ZvI/M/CJahHqX6z5xWzEYDLxwU0sCPZ159/eDLN93hr5vruLx2KaM6toAJ0cHTmfmc+cnf3I6s4CmwZ7MHN0RL1f7vy9RRESksti0aJ8xY8YlnzcYDMTFxREXF3fRNq6urkydOpWpU6dWcHQiIiKVZ+fJDL7ccByASYNa4ehg+9Xiy8JgMPDw9U3oHxXKMwt2sOFIKi/9vIfvt57kyf7NefHH3RxPzSUiwJ05Yzvh51F9Zw+IiIjUBA62DkBERKS2sVqtxP2wC6sVBrUJo2Okv61DKrfGQZ7Mu68zr90SjY+bkZ0nM7l7xgYOJGcT4u3KnLGdCPKuPqvCi4iI1FQq2kVERKrYt5tPsulYGm5GR56+sbmtw7liDg4Ghneoz9IJPRjcNgwAfw9n5tzbiXB/98u8WkRERMrCptPjRUREapvTmfm8+NcWb+N6NybUx83GEV29Ol4uvHN7Ox7o0YhATxfqeLnYOiQRERG7oZF2ERGp1g6czmLKor1sS0i3dShXzWq18vR3O8jML6JNPR/+0b2hrUOqUC1CvVWwi4iIVDCNtIuISLW1Yl8yD8/dQnZBEe+vOETXRgE80KMR3ZsEXnR/8Ors280nWbY3GWdHB/5zWxucHPXZuYiIiFyainYREamWZq8/RtwPuzBbrEQGepCQmsu6Q2dZd+gsrcK8ub9HI26MCqkxhW9SRj6T/poWP75vE5oEe13mFSIiIiIq2kVEpJoxW6y88ssePllzBIBbrqnHK0OjOZNdwCerDzNvQwK7TmXyyJdbeN3fjX/2aMwtMXVxcXK0ceQXVzwtfjtZ+UW0Cfe1u2nxIiIiUnlqxvCEiIjUCrmFRTwwJ76kYJ8Y25T/3NYaZycH6vq68cJNrVj31PVM6NsUfw9nElLzeGbBDnq+voJZ646SbzLbuAcX9k38CZbvO1M8Lf7W1jVmdoCIiIjYnkbaRUTEpjJyTRxKyeZgcjaz/zjGjpMZODsV3/M9qE3Yee39PJx5pHcT7uvekC83HGf6qkMkZuTzwg+7mLb8IP/o3pA7O9fH3bl6/BeXmJHHiz/uBuCxvk01LV5ERETKpXr8RSMiIrVGVr6JacsOsjUhnUNnckjJLij1vL+HMx+PjCEmwv+S53FzdmTMtZGM6FSfr+NP8OGKQ5xMz+PlX/YwfdUh3r2jHV0bBVZmVy7LarXy1Lc7yCoonhZ/X/dIm8YjIiIiNY+KdhERqVIv/ribr+NPlDoW6uNKozqeNAn2ZEy3SML93ct8PlejI3d3jmB4+3AWbDnBe8sPcTw1l5EzNjD55miGdQiv6C6U2ax1R1m5/wzOTg68cZumxYuIiEj5qWgXEZEqs+NEBt9sLi7YJw1qRbv6vjSs44mny9X/d+Ts5MDwDvUZ3LYuE7/exk/bE3ni2+0cSsnmyX7NcXCo2i3iNh1N5aWf9wDwZP/mNA7StHgREREpP33kLyIiVcJqtfLiT7uwWmFI2zDu6dqA1vV8K6Rg/1+uRkfevb0dj1zfGIDpKw/z4BebySusukXqkrPyefCLzRRZrAxoHcqYbg2q7NoiIiJiX1S0i4hIlfhlRxIbj6bhanTgyRuaV+q1HBwMTIhtxlvD2+Ds6MCiXUkMm/4HpzPzK/W6ACazhYfnbiE5q4DGQZ5MuaU1BkPVjvKLiIiI/dD0eBERqXT5JjOTfymeKv5Aj0aE+rhVyXVvblePen7u/OPzTew4mUG/t1fRpWEA7er70q6+H9F1fXA1Vuz+7lMW7WXDkVQ8XZz48K4YPCp4JoGIiIjULvpLQkREKt2MNUc4mZ5HqI8r91/XqEqv3aGBP98/1I2xszZxMDmbX3cm8evOJACcHAy0CPWmV/MgHurVCBenqyvgf96eyMeri/eY/89trWkc5HnV8YuIiEjtpqJdREQqVXJmPu8tPwjAUzc0x825Yke2yyIiwINfHunOluNpbElIZ8vxNDYfT+dMVgE7Tmaw42QGK/ef4cO7rrniWQAHk7P41zfbALi/R0P6R4VWZBdERESkllLRLiIilWrKb/vILTTTrr4vg9qE2SwOZycHOjUMoFPDAKB4YbxTGfmsPZjCyz/vYVtCOjdNXcO0EdfQ+a82ZXX8bC7/mB1PbqGZLg0D+Fdss8rogoiIiNRCWohOREQqzY4TGXzz157szw9sWa0WZDMYDNT1dWNY+3B+fPhaWoR6k5JdyJ2f/MmMNUewWq2XPYfFYuXzP47S7+1VHD6TQ4i3K1NHtNN+7CIiIlJh9FeFiIhUinNbvEHxFm/X1PezcUQXVz/Ane/+2ZXBbcMwW6z830+7GT9/6yW3iTuRlsedn/zJvxfuIs9kplOkP18/0IVAT5cqjFxERETsnabHi4hIpfhh26kq2+KtIrg5O/L28La0qefLy7/sYeHWU6w9eJa24b5E1/Uhup43UXV98HVxYO1pA89MW0dOoRlXowNP9W/OyC4NcHCoPjMJRERExD6oaBcRkQqXkWfi/34q3uLtwZ6Nq2yLt6tlMBgYc20kLcO8eXjuFlKyC1i65zRL95wuaePt6kRmviNgpkMDP16/tQ0NAj1sF7SIiIjYNRXtIiJS4f7z2z5SsgtoWMeD+3s0tHU45da5YQCrn+jF9hPp7DyVyc6/Vpg/dCabzPwijAYr/+rfnLHdG+Go0XURERGpRCraRUSkQm1NSGfOn8cAeGlI1FXvfW4rbs6OpVabB8gpKGLXyTT2xa/j9q4RKthFRESk0tl0IbpXXnmFDh064OXlRVBQEEOGDGHfvn2l2owaNQqDwVDqq3PnzqXaFBQUMG7cOAIDA/Hw8GDQoEGcOHGiKrsiIiJAkdnCswt2YLXC0HZ16doo0NYhVSgPFyfahfvi7WzrSERERKS2sGnRvnLlSh566CHWr1/PkiVLKCoqIjY2lpycnFLt+vfvT2JiYsnXL7/8Uur58ePHs2DBAubNm8eaNWvIzs5m4MCBmM0XX/VXREQq3ud/HGPXqUy8XZ14ZkALW4cjIiIiUuPZdHr8okWLSj3+7LPPCAoKIj4+nuuuu67kuIuLCyEhIRc8R0ZGBjNmzGD27Nn06dMHgDlz5hAeHs7SpUvp169f5XVARERKJGbk8cbi4tlST93QQlufiYiIiFSAanVPe0ZGBgD+/v6ljq9YsYKgoCB8fX3p0aMHL7/8MkFBQQDEx8djMpmIjY0taR8WFkZUVBTr1q27YNFeUFBAQUFByePMzEwATCYTJpOpwvtVUc7FVp1jlPJTXu3XxXJrMlswOpZtopPFYiWroAgfN2OFx1fR4hbuJKfQTLtwH25pG2K3P9N6z9on5dU+Ka/2S7m1T7Utr2Xtp8FqtVorOZYysVqtDB48mLS0NFavXl1yfP78+Xh6ehIREcGRI0d4/vnnKSoqIj4+HhcXF+bOncvo0aNLFeEAsbGxREZGMn369POuFRcXx6RJk847PnfuXNzd3Su+cyIiQJEFfjruwMpEA428rfSrZ6WxtxXDBdYys1hhe6qBRQkOJOXBqKYW2gZUi1/XF7QrzcBHex1xwMrE1mbqagc0ERERkUvKzc1lxIgRZGRk4O3tfdF21Wak/eGHH2b79u2sWbOm1PHhw4eX/DsqKor27dsTERHBzz//zNChQy96PqvViuFCfwkDTz/9NBMmTCh5nJmZSXh4OLGxsZf8ZtmayWRiyZIl9O3bF6Ox+o+6Sdkor/brf3N7NLWACV9vZ+/pbAAOZBo4sBti6vvyUM+GXNs4AIPBgMViZfGeZN5ffoh9f7UFWJnqxZN3dquWq5XnFhbx+tR1QD6juzXgvv7NbB1SpdJ71j4pr/ZJebVfyq19qm15PTfj+3KqRdE+btw4fvjhB1atWkW9evUu2TY0NJSIiAgOHDgAQEhICIWFhaSlpeHn51fSLjk5ma5du17wHC4uLri4nH+vpdForBE/HDUlTikf5dU+Wawwd9Mppiw+QGGRBX8PZ54b0IKtCenM25hA/PF0xny+mTb1fBjUti5fb0pgb1IWAF4uTtzTtQGf/3GUwym5/L7vLANah9q4R+d7eeFuTqTnE+bjyoTY5hiN1eK/lkqn96x9Ul7tk/Jqv5Rb+1Rb8lrWPtr0Lyur1cq4ceNYsGABK1asIDIy8rKvOXv2LAkJCYSGFv/hGhMTg9FoZMmSJQwbNgyAxMREdu7cyZQpUyo1fhGRS0nOKmD6Hgf2ZhQvztarWR1eu7U1QV6uDL2mHg/1asxHqw7zxZ/H2HYig20nitf18HJxYvS1kYztFomPuxEHBwPv/n6A95Yf5MbokIvOIrKFhVtP8tWmEzgY4I1hbfFwqR0Fu4iIiEhVselfVw899BBz585l4cKFeHl5kZSUBICPjw9ubm5kZ2cTFxfHLbfcQmhoKEePHuWZZ54hMDCQm2++uaTt2LFjefzxxwkICMDf35+JEycSHR1dspq8iEhVW3MghXFfbiYt1wEXJweeHdCCuztHlCq4g71deX5gS/7ZsxEz1hxh3aGz9GgSyJhrI/F1/+9G4KO7NuCT1YfZnZjJin1n6NU8yBZdOs+RlBye+W4HAOOub0KXRgE2jkhERETE/ti0aP/ggw8A6NmzZ6njn332GaNGjcLR0ZEdO3bw+eefk56eTmhoKL169WL+/Pl4eXmVtH/rrbdwcnJi2LBh5OXl0bt3b2bOnImjo2NVdkdEBIDZfxwl7sfdmC1W6rpb+XhsZ1rW9bto+0BPF57s3/yiz/t5OHNnp/p8vPoI05YfpGezOjYfbS8oMvPw3M3kFJrpFOnPI72b2DQeEREREXtl8+nxl+Lm5sZvv/122fO4uroydepUpk6dWlGhiYiUm8ls4cUfdzN7/TEABrcJpbtrAk2CPK/63Pd1b8isdceIP5bGn0dS6dzQtqPar/yyl12nMvH3cOad29tVywXyREREROxB2TYKFhGRS8rINTHqsw3MXn8MgwGe6N+M12+JwlhBv2WDvF25rX3xQp3vLT9YMSe9Qr/tSmLmuqMAvHFbG0J8XG0aj4iIiIg9U9EuInKVDp3JZsj7a1l78Czuzo5MvyuGB3s2rvAp7A/0aISjg4HVB1LYlpBeoecuq5PpeTzxzXYA7useWW3urxcRERGxVyraRUSuwvYT6dz83lqOpORQ19eNbx7oSmyrkEq5Vri/O4PbhAG2GW0vMlt45MstZOSZaBPuy7/6Xfw+fBERERGpGCraRUSu0MHkbEZ9tpHM/CLa1ffl+4e60TLMu1Kv+WCvRhgMsHj3afafzqrUa/3dx6uPEH8sDS8XJ6be3g5nJ/0XIiIiIlLZ9BeXiMgVOJmex90z/iQ1p5DW9XyYPbYTdbxcKv26jYO86P/XSP77f422ZxcUcTA5m7UHU/g2/gQ/bT912YU+y+vA6SzeWrIfgBcGtaJ+gHuFnl9ERERELsymq8eLiNREZ7MLuHvGnyRm5NOwjgczR3fE06Xqfp0+2LMxv+5MYuG2U/y+J5msgqLz2uzskclTN1TM9PUis4WJ32yn0GyhV7M63HJN3Qo5r4iIiIhcnkbaRUTKIbugiFGfbeTwmRzCfFyZM7YT/h7OVRpDdD0f+rQIxmqlpGD3dHGicZAnHSP9Afhw5SFmrj1SIdebseYI2xLS8XJ14pWhrW2+R7yIiIhIbaKRdhGRMso3mblv1iZ2nMzA38OZ2fd2IszXzSaxvHtHW3adysTP3ZkQH9dSI/3Tlh3gP4v3M+mn3QR5u3JjdOgVX+dgcjZv/DUt/vmBLbW9m4iIiEgVU9EuIlIGFouVR+dt4Y/DZ/FwdmTW6I40quNps3jcnZ3o0MD/gs891KsxpzMLmL3+GOPnbyXAw5lODQPOaxd/LJW3lx6gwGTh4esb071JYKlRdLPFyr++2UZhkYUeTetwW0y9SuuPiIiIiFyYpseLiJTB/E0J/LbrNM5ODnx8T3ui6/nYOqSLMhgMxA1qRWzLYAqLLNz7+Sb2Jf13pfmdJzMY/dkGbvngD1YfSGHD0VRGfrqBER//yZbjaSXtPl1zhC3H0/FyceKVodGaFi8iIiJiAxppFxG5jIxcE6//tg+AJ/s3p2ujQBtHdHmODgbevaMdd33yJ5uOpXHPpxt4c1gbvvjzOD/vSCxpc1tMPdycHfli/XH+OHyWm99fR9+WwdwWU4//LC7u83MDW9jsNgARERGR2k5Fu4jIZby5ZB+pOYU0CfJkZJcIW4dTZq5GRz65pz23fvgHB5OzGfHJnwAYDHBT6zAe69uUyEAPAMZeG8k7Sw/w7eYTLNl9miW7TwPQvUkgw9qH26wPIiIiIrWdpseLiFzCnsRMZq8/BsCkQa0wOtasX5u+7s7MGtOREO/iBeT6tgzm10e78+4d7UoKdoB6fu68flsbFj92HTdEFe8D7+XqxKu3aLV4EREREVvSSLuIXLFT6Xk8+e126vm5c0/XCJqHeNs6pApltVp54YddWKxwY3QIXRtX/2nxF1LX143fxl9Ham5hqUL9QhoHefHBXTEcPpONq9FR0+JFREREbExFu4hcEavVylPf7WD1gRQAvtxwnK6NAhjVtQG9WwTj6FDzR2d/3J7IhiOpuBodeObGFrYO56r4uBvxcTeWuX1DG66MLyIiIiL/paJdRK7Id5tPsmr/GZydHOjZtA5L95xm3aGzrDt0lnp+btzTpQHDO4bj7Vr2QrE6ySkoYvLPewD4Z4/G1PNzt3FEIiIiIlIb1aybM0WkWjiTVcCLP+0G4LE+TfloZHtWP3k9D/RohK+7kRNpebz8yx66v7ac95YfJLugyMYRl997yw+SlJlPPT837u/R0NbhiIiIiEgtpaJdRMot7oddZOSZaBXmzX3dI4Hi+6afuqE5fzzVm1eHRtOojgcZecVbpXV/bRkfrjxEbmHNKN6PpuTwyeojADw/sCWuRkcbRyQiIiIitZWKdhEpl0U7k/h5RyKODgZeu6U1Tn9bTd3N2ZHbO9Zn8WM9eOf2tkQGepCWa+LVX/fS/bXlfLTqEPuSssg3mW3Ug0uzWq3830+7KTRb6N4kkNiWwbYOSURERERqMd3TLiJllpFr4vmFOwG4/7qGRNX1uWhbRwcDg9vWZUB0KN9vPcW7vx/geGouk3/Zy+Rf9gIQ6uNKgwAPGgR60DLMm9s7hNt8S7X3Vxzi973JODkYeOGmVtruTERERERsSkW7iJTZ5F/2cCargIaBHjzSu0mZXuPk6MCtMfUY3DaMBZtPMnfDcQ6dySYrv4jEjHwSM/L54/BZAPYmZvLyzdGV2YVL+ib+BK//tg8onhbfOEgrqIuIiIiIbaloF5EyWXswhfmbEgB47dbW5b7P2+jowLAO4QzrEI7VaiUt18SRlByOpuSwNymTj1cf4csNx7mnawOaBntVRhcuaeX+Mzz17XYA7u/RkHu6NqjyGERERERE/k5Fu4hckNVqJTmrgF2nMth5MpN5G44DMLJLBB0a+F/VuQ0GA/4ezvh7OBMT4QdAQmoei3YlMfmXPcwc3fGq4y+PHScy+OeceIosVoa0DePJfs2r9PoiIiIiIhejol1ESlitVmatO8ryfWfYdSqTlOyCUs/X9XXjif6VU9A+dUNzft97mhX7zrBq/xmua1qnUq7zdwmpuYyeuZHcQjPdGgcw5dY2ODjoPnYRERERqR5suuLTK6+8QocOHfDy8iIoKIghQ4awb9++Um2sVitxcXGEhYXh5uZGz5492bVrV6k2BQUFjBs3jsDAQDw8PBg0aBAnTpyoyq6I2IX4Y2nE/biblfvPkJJdgIMBmgR5cnO7ujw3oAULHuqKp0vlfNbXINCDkV0aAPDyz3swW6yVcp3/lZpTyD2fbiAlu4AWod58eFcMzk7aVENEREREqg+b/nW6cuVKHnroIdavX8+SJUsoKioiNjaWnJyckjZTpkzhzTffZNq0aWzcuJGQkBD69u1LVlZWSZvx48ezYMEC5s2bx5o1a8jOzmbgwIGYzdVzSymR6uqrv+5Z79msDt892JVdk/qzZEIP3hrelnu7NyTIy7VSrz/u+sb4uBnZdzqrJJbKkpFnYszMjRxOyaGurxszR3fAy9VYqdcUERERESkvmxbtixYtYtSoUbRq1Yo2bdrw2Wefcfz4ceLj44HiUfa3336bZ599lqFDhxIVFcWsWbPIzc1l7ty5AGRkZDBjxgzeeOMN+vTpQ7t27ZgzZw47duxg6dKltuyeSI2SW1jEz9sTAXiwZ2Ouqe+Hm3P5Fpu7Wr7uzjz616r0byzeT3ZBUaVc52x2AXd8tJ6tCen4uhuZNaYDwd6V+4GEiIiIiMiVqFb3tGdkZADg71+8yNWRI0dISkoiNja2pI2Liws9evRg3bp13H///cTHx2MymUq1CQsLIyoqinXr1tGvX7/zrlNQUEBBwX/v1c3MzATAZDJhMpkqpW8V4Vxs1TlGKb/qktcft54kp9BMhL87bet62iye4TFhzFp3lGOpuby3bD8T+pRta7mySszIZ9TMeA6n5BDg4czMUTFE+LlWSn+rS26lYimv9kl5tU/Kq/1Sbu1TbctrWftZbYp2q9XKhAkTuPbaa4mKigIgKSkJgODg4FJtg4ODOXbsWEkbZ2dn/Pz8zmtz7vV/98orrzBp0qTzji9evBh3d/er7ktlW7Jkia1DkEpg67x+vMsRMBDlkcWvv/5q01h6Bxr4NNWRT1YdJjjrAH4uFXPelHx4b7cjqQUGfJ2tPNAkl8ObV3O4Yk5/UbbOrVQO5dU+Ka/2SXm1X8qtfaotec3NzS1Tu2pTtD/88MNs376dNWvWnPecwVB6JWer1Xresb+7VJunn36aCRMmlDzOzMwkPDyc2NhYvL29ryD6qmEymViyZAl9+/bFaNS9t/aiOuT1WGouB/9Yg8EATw7vRaiPbaeK32C1suPTTWw8msZWcziv3xh91ec8cDqbl2fFk1pQQIS/O7NGx1DX160Cor246pBbqXjKq31SXu2T8mq/lFv7VNvyem7G9+VUi6J93Lhx/PDDD6xatYp69eqVHA8JCQGKR9NDQ0NLjicnJ5eMvoeEhFBYWEhaWlqp0fbk5GS6du16weu5uLjg4nL+0J3RaKwRPxw1JU4pH1vmdeG24lkp1zYOpH6gl01i+LvnB7Zk0LS1fL8tkY4NA7m9Q/glt2LLyjcxfeVhNh9Pw9vViJ+HM/4eRvzcnXE1OvLG4n2k5ZpoFuzF7Hs7Vvqiev9L71n7pLzaJ+XVPimv9ku5tU+1Ja9l7aNNi3ar1cq4ceNYsGABK1asIDIystTzkZGRhISEsGTJEtq1awdAYWEhK1eu5LXXXgMgJiYGo9HIkiVLGDZsGACJiYns3LmTKVOmVG2HRGogs8XKt/HFWyTe1j7cxtH8V+t6vgxvH878TQk8s2AH8zcl8H+DW9G6nm+pdhaLlW/iTzDlt33n7Sv/d23q+TBrTEd83Z0rMXIRERERkYpj06L9oYceYu7cuSxcuBAvL6+Se9B9fHxwc3PDYDAwfvx4Jk+eTJMmTWjSpAmTJ0/G3d2dESNGlLQdO3Ysjz/+OAEBAfj7+zNx4kSio6Pp06ePLbsnUiOsO5TCqYx8vF2diG0ZfPkXVKGXbo6iSbAnby89wLaEdAa/t5bbO9TnX/2a4e/hzMajqUz6cRc7TxZPLYoM9ODe7pGYLVbSckyk5RaSmlNIWm4h9f3deeqG5trWTURERERqFJsW7R988AEAPXv2LHX8s88+Y9SoUQA88cQT5OXl8eCDD5KWlkanTp1YvHgxXl7/ncL71ltv4eTkxLBhw8jLy6N3797MnDkTR8eq3a5KpCb6elPxKPvgtnVxNVav94zR0YF7uzdkUJswXvl1Lwu2nOTLDcf5dWci19T3Y9neZAC8XJx4tE8TRnZpgLOTTXeyFBERERGpUDafHn85BoOBuLg44uLiLtrG1dWVqVOnMnXq1AqMTsT+ZeSaWLSreIbLbe3rXaa17QR5u/LW8Lbc0bE+/164k71JWSzbm4zBALd3qM/jsU0J9KygJeZFRERERKqRarEQnUhNlJiRx/SVhxnYOpT2DfxtHc4V+WH7KQqLLDQL9iK6ro+tw7msjpH+/DTuWr7ccJwtx9MZ2z2SVmHVP24RERERkSulol3kCuw6lcGYmRs5nVnAwq0nWfZ4T/w8at7iZt9sSgCKR9kvt41ideHk6MDdXRpwdxdbRyIiIiIiUvl086dIOS3fl8ywD//gdGbxSuVpuSam/LbXxlGV3/7TWWw7kYGTg4Eh7eraOhwREREREbkAFe0i/yMhNZeT6XkXff6LP49x76xN5BSa6dY4gE9HtQfgyw0JbD6eVlVhVoiv/xpl79U8SPeDi4iIiIhUU5oeL/KX+GOp3P7RekxmK02CPOnZrA69mgXRvoE/Tg4GXvttL9NXHgbg1ph6TL45GmcnB26Nqcc38Sd4bsFOfni4G06O1f+zsHyTmQVbTgIwrBrtzS4iIiIiIqWpaBcBcgqKeGz+Nkzm4h0NDiRncyA5m49XH8HD2ZFwf3f2JmUB8Hjfpjx8feOSe8CfvqE5S3afZndiJnPWH2NUt0ib9aOsPlhxiJTsQsJ8XOnZrI6twxERERERkYuo/kOCIlXgpZ93czw1l7q+bqx+ohdT72jHLdfUI9DThZxCM3uTsjA6GnhreBvG9W5SatG2AE8XnujfDIA3Fu8nOTPfVt0ok+Nnc/lg5SEAnh3QEmMNmBkgIiIiIlJbaaRdar2lu0/z5YYEDAZ4Y1gbwv3dCfd356Y2YVgsVnYnZvLHobN0iPSnbbjvBc9xe4f6fLXpBNsS0nn5lz28c3u7qu1EObz4024Kiyx0bRTAjdEhtg5HREREREQuQUNsUqulZBfw1HfbAbive0M6Nwwo9byDg4Gouj7cd13DixbsAI4OBl4aHIWDARZuPcW6gymVGfYVW743maV7TuPkYGDSoFY1Zps3EREREZHaSkW71FpWq5Wnv9tBSnYhzUO8eDy26VWdL7qeD3d3jgDguYU7KSyyVESYFaagyMykH3cBMLpbA5oEe9k4IhERERERuRwV7VJrfb3pBEt2n8bZ0YG3hrfFxcnxqs85IbYZgZ4uHD6Tw+u/7cVqtVZApBXjk9VHOHo2lyAvFx7p3cTW4YiIiIiISBmoaJda6fjZ3JJR58djm9Ii1LtCzuvjZuSFm1oC8PHqI/xn8b5qUbifTM9j6rIDADxzYwu8XI02jkhERERERMpCRbvUOmaLlQlfbSWn0EzHSH/u7d6wQs9/U5sw/j2wuHB/b/khXltk+8L95Z93k2+y0LGBP4Pbhtk0FhERERERKTsV7VLrvLf8IJuOpeHp4sQbt7XB0aHiF2Mbc20kcX+NuH+48hCv/mq7qfJrDqTwy44kHAwwabAWnxMRERERqUm05ZvUKvHHUnnn9+Jp4i8NiSLc373SrjWqWyQODgb+vXAX01cdxmyx8uyAFlVWNGcXFDFvw3E+/GtP9pFdGlTYbQAiIiIiIlI1VLRLrZGZb+LReVsxW6zc3K4uQ9rVrfRrjuzSAAeDgee+38kna45gscLzAyu3cE/OzOezdUeZs/4YWflFAEQGevBY36tbHV9ERERERKqeinapFaxWK88u2MmJtDzC/d14cXCrKrv2XZ0jcDAYeGbBDj5de4T03EJevjkaN+fLr1ZvtVrLXOAfO5vDBysO8d3mkxSai7eba1jHg/uva8iQdnUrZHV8ERERERGpWirapVb4bvNJftx2CkcHA+/c3q7KV08f0ak+jg7wzIKdfLflJHuTsph+d8xFp+cfP5vLiz/tZsORszx5Q3NGdKx/yeJ98a4kHptfvLgeQPsIP/5xXUP6tAjGoRLu2RcRERERkaqhol3s3tGUHP69cCcAj/VpwjX1/WwSx/AO9Qn3d2fc3C3sTsxk4NQ1vHtHO7pG+pa0yTeZ+XDlId5fcYjCouLR8mcX7GTDkVQm3xyNh0vpt6zFYmXa8oO8uWQ/AB0b+PPkDc2IifCvsn6JiIiIiEjl0erxYtdMZguPzttCTqGZTpH+/LNnY5vG07VRID+Ou5Y24b5k5JkY9dkG3l9xGIsVlu07Q9+3VvL20gMUFlno1jiAR3o3wdHBwMKtpxg0bQ37T2eVnCunoIiH5m4uKdhHdW3AF/d1UsEuIiIiImJHNNIudu2tJfvZdiIDHzcjbw1vWynbu5VXmK8bX93fmbgfdvPlhuO89ftBAlwcObt+CwAh3q48N7AFA6JDMRgMdG8SyMNzN3PoTA6Dpq3hpSHRdIr0577PN7E3KQujo4GXhkQxvEN9G/dMREREREQqmop2sVtHU3KYvuowAK8OjSbM183GEf2Xi5MjrwyNpk09H55fuJOzBeDkYGBs90geub5JqWnwHRr488sj3Rk/fyurD6Qw8ettuBodyDdZCPR0Yfrd12h0XURERETETqloF7v13vKDmC1WejWrww3RobYO54Ju71if5sEeTF24jom3XEuLuhe+3z7A04WZozvy3vKDvLV0P/kmC63r+TD97hhCfarPhxEiIiIiIlKxVLSLXTp+NpfvtpwE4JHeTWwczaW1CvNmUISFxkGel2zn6GDgkd5N6NoogG0nMrizU31cjdrGTURERETEntl0IbpVq1Zx0003ERYWhsFg4Pvvvy/1/KhRozAYDKW+OnfuXKpNQUEB48aNIzAwEA8PDwYNGsSJEyeqsBdSHb2/oniU/bqmdWhno9XiK0v7Bv6MvTZSBbuIiIiISC1g06I9JyeHNm3aMG3atIu26d+/P4mJiSVfv/zyS6nnx48fz4IFC5g3bx5r1qwhOzubgQMHYjabKzt8qaYSUnP5Jr74g5tHq/kou4iIiIiIyKXYdHr8DTfcwA033HDJNi4uLoSEhFzwuYyMDGbMmMHs2bPp06cPAHPmzCE8PJylS5fSr1+/Co/ZXh1MzubrTQm0q+9L/6jqef93WX2w8hBFFivXNg4kJsK+RtlFRERERKR2qfb3tK9YsYKgoCB8fX3p0aMHL7/8MkFBQQDEx8djMpmIjY0taR8WFkZUVBTr1q27aNFeUFBAQUFByePMzEwATCYTJpOpEntzdc7FVlExWq1WNh1L55M1R1m27wxQfN/0Z/cY6NIwoEKuUdUSM/L5elMCAA/2iKzW+TynovMq1Ydya5+UV/ukvNon5dV+Kbf2qbbltaz9NFitVmslx1ImBoOBBQsWMGTIkJJj8+fPx9PTk4iICI4cOcLzzz9PUVER8fHxuLi4MHfuXEaPHl2qAAeIjY0lMjKS6dOnX/BacXFxTJo06bzjc+fOxd3dvUL7VR1ZrLAj1cDvpxw4ll28b7kBKwGukJJvwMPJyuPRZgJcbRzoFfj6sANrTjvQ2NvCuFYWW4cjIiIiIiJyQbm5uYwYMYKMjAy8vb0v2q5aj7QPHz685N9RUVG0b9+eiIgIfv75Z4YOHXrR11mtVgwGw0Wff/rpp5kwYULJ48zMTMLDw4mNjb3kN8vWTCYTS5YsoW/fvhiNxis6x65TmUz8ZgcHz+QA4OzkwM1tQxnTtQFhvq7c8clGdp7K5OtEP+bf1xE355qz2FlSZj4TN6wGrMTd2pFOkTVj7/KKyKtUT8qtfVJe7ZPyap+UV/ul3Nqn2pbXczO+L6daF+1/FxoaSkREBAcOHAAgJCSEwsJC0tLS8PP7773LycnJdO3a9aLncXFxwcXF5bzjRqOxRvxwXEmcFouVT9ce4bVFezGZrfi4Gbm7cwT3dG1AHa//fi8+Gtmem6auYU9SFs//uIe3h7e95Acg1cmMtfsxma10bOBPtyZBNSbuc2rKz5+Un3Jrn5RX+6S82ifl1X4pt/aptuS1rH206erx5XX27FkSEhIIDS1eKC0mJgaj0ciSJUtK2iQmJrJz585LFu21zZmsAkbP3MhLP+/BZLbSr1UwK//Vk4n9mpUq2AHCfN14785rcHIwsHDrKWasOWKjqMsnOTOfuRuOA8X7ste0gl1ERERERORCbDrSnp2dzcGDB0seHzlyhK1bt+Lv74+/vz9xcXHccssthIaGcvToUZ555hkCAwO5+eabAfDx8WHs2LE8/vjjBAQE4O/vz8SJE4mOji5ZTb62W7X/DBO+2kZKdgEuTg78+6aWjOhY/5JFbeeGATw3oAVxP+7mlV/30jLUm66NA6sw6vL7cOVhCossxET40a1xzVxET0RERERE5O9sWrRv2rSJXr16lTw+d5/5PffcwwcffMCOHTv4/PPPSU9PJzQ0lF69ejF//ny8vLxKXvPWW2/h5OTEsGHDyMvLo3fv3sycORNHx5pzL3ZlsFisTPltHx+uPARAs2Avpo5oR9Ngr8u8stg9XRuw42Qm324+wUNzN/PDw9cS7l89F+lbcyCFWX8cBTTKLiIiIiIi9sWmRXvPnj251OL1v/3222XP4erqytSpU5k6dWpFhlajmcwWJn69jYVbTwEwsksEz9zYAldj2T/IMBgMvHxzFAeSs9h+IoPbPvyD129rTfcmdSor7CtyJCWHB7+Ix2yxMrRdXa5rUr1nBIiIiIiIiJRHjbqnXS4vr9DM/bPjWbj1FE4OBt4e3pYXB0eVq2A/x9XoyId3xdCwjgdJmfncPWMDLyzcSV6huRIiL7+MPBNjZ20kM7+IdvV9mTw0WqPsIiIiIiJiV1S025GMPBMjP/2TZXuTcXFy4OOR7RnSru5VnTPM142fx3Xnni4RAMz64xgDpq5mW0J6BUR85YrMFsZ9uYXDZ3II9XFl+t0xV/TBhIiIiIiISHWmot1OnMkq4I6P1rPxaBperk7MubcTvZoHVci53ZwdmTQ4is/HdCTY24XDZ3IY+sE63l66n5PpeWTmm7BYLn6bQ2WY/MteVu0/g5vRkY9HtifIy7VKry8iIiIiIlIVatQ+7XJhJ9JyueuTPzl6NpdAT2dmjelIqzCfCr/OdU3r8Nv463h+4S5+3HaKt5ce4O2lBwAwGMDTxQlvVyOBns7847pGDGgdWuExAMzbcJxP1xZvRffmsDZE1a34voqIiIiIiFQHGmmv4ZIz8xk+fT1Hz+ZS19eNrx/oWikF+zm+7s5MvaMd79zelogAd5wdi3+ErFbIyi/iZHoe205k8NDczby5eF+Fj8D/efgszy/cCcCEvk25IbpyPhgQERERERGpDjTSXoPlFhYxdtYmTqbnERnowZf3dSbEp2qmiQ9uW5fBbYvvl883mcnKLyIz30RWfhE/bTvFJ2uO8O6ygxxIzuaNYW1wd776H7Uftp3iiW+2YTJbGdg6lHHXN77qc4qIiIiIiFRnKtprKLPFyiNfbmXHyQz8PZz5bFSHKivY/87V6Iir0ZE6Xi4AtA33pVmIF88s2MGvO5M4nprLJ/e0J9TH7YrOb7ZYmfLbXqavPAxAj6Z1eP3WNlopXkRERERE7J6mx9dQL/+8h6V7TuPs5MDHI2NoEOhh65BKua19OHPv64y/hzO7TmUyaNpathxPK/d5MnJNjJm5saRgf6BHIz4d1QE3Z60ULyIiIiIi9k8j7TXQ7PWlF2KLifC3cUQX1qGBPwsf6sa9szax73QWw6evp3GQJwGezvi5O+Pv4UyAhzMBni7U9XOjvr87Yb6uuDgVF+QHTmdx3+ebOHo2F1ejA6/d0rpkSr6IiIiIiEhtoKK9htmZZmDG+r0APNG/GQNbh9k4oksL93fn2we7Mn7eFpbuSWZ3YuYl2xsMEOLtSrifO7tOZZBTaKaurxvT747RKvEiIiIiIlLrqGivQXadymTWfgcsVri9Qzj/7NHI1iGViaeLEx+PbM/epCySMvNJyykk9X++zmQVkJCWS0JqHnkmM4kZ+SRm5APQKdKf9++8hgBPFxv3QkREREREpOqpaK8hUrIL+MecLRRaDHRrFMD/DYmqUQuxGQwGWoR60yLU+6JtrFYrZ3MKSUjN5XhqLo4OBvq1CsHoqKUXRERERESkdlLRXkP4uztzyzVhfL/hMFNvb22XhazBYCDQ04VATxfa1fezdTgiIiIiIiI2p6K9hnBwMDChTxMa5h3Ay9Vo63BERERERESkCtjfcK2d005nIiIiIiIitYeKdhEREREREZFqSkW7iIiIiIiISDWlol1ERERERESkmlLRLiIiIiIiIlJNqWgXERERERERqaZUtIuIiIiIiIhUUyraRURERERERKopFe0iIiIiIiIi1ZSKdhEREREREZFqSkW7iIiIiIiISDXlZOsAqgOr1QpAZmamjSO5NJPJRG5uLpmZmRiNRluHIxVEebVfyq19Ul7tk/Jqn5RX+6Xc2qfaltdz9ee5evRiVLQDWVlZAISHh9s4EhEREREREalNsrKy8PHxuejzBuvlyvpawGKxcOrUKby8vDAYDLYO56IyMzMJDw8nISEBb29vW4cjFUR5tV/KrX1SXu2T8mqflFf7pdzap9qWV6vVSlZWFmFhYTg4XPzOdY20Aw4ODtSrV8/WYZSZt7d3rfghrm2UV/ul3Non5dU+Ka/2SXm1X8qtfapNeb3UCPs5WohOREREREREpJpS0S4iIiIiIiJSTalor0FcXFx44YUXcHFxsXUoUoGUV/ul3Non5dU+Ka/2SXm1X8qtfVJeL0wL0YmIiIiIiIhUUxppFxEREREREammVLSLiIiIiIiIVFMq2kVERERERESqKRXtIiIiIiIiItWUivYa5P333ycyMhJXV1diYmJYvXq1rUOScnjllVfo0KEDXl5eBAUFMWTIEPbt21eqjdVqJS4ujrCwMNzc3OjZsye7du2yUcRSXq+88goGg4Hx48eXHFNOa66TJ09y1113ERAQgLu7O23btiU+Pr7keeW25ikqKuK5554jMjISNzc3GjZsyIsvvojFYilpo7zWDKtWreKmm24iLCwMg8HA999/X+r5suSxoKCAcePGERgYiIeHB4MGDeLEiRNV2Av5u0vl1WQy8eSTTxIdHY2HhwdhYWGMHDmSU6dOlTqH8lr9XO79+r/uv/9+DAYDb7/9dqnjtT2vKtpriPnz5zN+/HieffZZtmzZQvfu3bnhhhs4fvy4rUOTMlq5ciUPPfQQ69evZ8mSJRQVFREbG0tOTk5JmylTpvDmm28ybdo0Nm7cSEhICH379iUrK8uGkUtZbNy4kY8++ojWrVuXOq6c1kxpaWl069YNo9HIr7/+yu7du3njjTfw9fUtaaPc1jyvvfYaH374IdOmTWPPnj1MmTKF119/nalTp5a0UV5rhpycHNq0acO0adMu+HxZ8jh+/HgWLFjAvHnzWLNmDdnZ2QwcOBCz2VxV3ZC/uVRec3Nz2bx5M88//zybN2/mu+++Y//+/QwaNKhUO+W1+rnc+/Wc77//nj///JOwsLDznqv1ebVKjdCxY0frAw88UOpY8+bNrU899ZSNIpKrlZycbAWsK1eutFqtVqvF8v/t3VtMFGcfx/HfwrKIFVd2razULNWEIAJJFW5qSU2jMcZDMKa1Hoo0eEMiimKJVhNrL9zWG5uaqg01eqMEbmw8XLSllYJoUpqlVO1FPXQLnghp0tI2iFD2ea+c911BhbeRndHvJ9mEeZ4ny3/zy4T5zww7URMIBMyHH35orenr6zNer9d8+umn8SoTI/DXX3+ZrKws09DQYObNm2cqKyuNMWTqZNu2bTNFRUUPnSdbZ1qyZIkpKyuLGVuxYoV56623jDHk6lSSzOeff25tjyTHP/74wyQlJZm6ujprza1bt0xCQoL54osvxqx2PNyDuQ6ntbXVSDIdHR3GGHJ1goflevPmTfPCCy+Yy5cvm8zMTPPRRx9Zc+RqDFfaHaC/v1/hcFgLFy6MGV+4cKEuXLgQp6rwb/X09EiSfD6fJCkSiairqysm5+TkZM2bN4+cbW7Dhg1asmSJFixYEDNOps516tQpFRYW6o033tCUKVM0e/ZsffbZZ9Y82TpTUVGRvvnmG125ckWS9OOPP6qlpUWLFy+WRK5Pi5HkGA6HNTAwELMmIyNDeXl5ZO0gPT09crlc1l1Q5OpM0WhUJSUlqq6uVm5u7pB5cpXc8S4Aj/fbb79pcHBQ6enpMePp6enq6uqKU1X4N4wxqqqqUlFRkfLy8iTJynK4nDs6Osa8RoxMXV2d2tra9P333w+ZI1Pn+uWXX3To0CFVVVVpx44dam1t1aZNm5ScnKx169aRrUNt27ZNPT09mjlzphITEzU4OKg9e/Zo9erVkthnnxYjybGrq0sej0dpaWlD1nBs5Qx9fX3avn271qxZo4kTJ0oiV6fau3ev3G63Nm3aNOw8udK0O4rL5YrZNsYMGYMzVFRU6OLFi2ppaRkyR87OcePGDVVWVuqrr77SuHHjHrqOTJ0nGo2qsLBQoVBIkjR79mz99NNPOnTokNatW2etI1tnqa+v17Fjx1RbW6vc3Fy1t7dr8+bNysjIUGlpqbWOXJ8O/0+OZO0MAwMDWrVqlaLRqA4ePPjY9eRqX+FwWB9//LHa2tpGndGzlCu3xzvA5MmTlZiYOORMUnd395CzyLC/jRs36tSpU2psbNS0adOs8UAgIEnk7CDhcFjd3d0qKCiQ2+2W2+1WU1OT9u/fL7fbbeVGps4zdepUzZo1K2YsJyfH+vJP9ldnqq6u1vbt27Vq1Srl5+erpKREW7Zs0QcffCCJXJ8WI8kxEAiov79fv//++0PXwJ4GBga0cuVKRSIRNTQ0WFfZJXJ1onPnzqm7u1vBYNA6luro6NDWrVv14osvSiJXiabdETwejwoKCtTQ0BAz3tDQoLlz58apKoyWMUYVFRU6ceKEzp49q+nTp8fMT58+XYFAICbn/v5+NTU1kbNNzZ8/X5cuXVJ7e7v1Kiws1Nq1a9Xe3q4ZM2aQqUO98sorQx7JeOXKFWVmZkpif3Wq3t5eJSTEHvokJiZaj3wj16fDSHIsKChQUlJSzJo7d+7o8uXLZG1j9xv2q1ev6uuvv5bf74+ZJ1fnKSkp0cWLF2OOpTIyMlRdXa0vv/xSErlK3B7vGFVVVSopKVFhYaFefvll1dTUqLOzU+Xl5fEuDSO0YcMG1dbW6uTJk0pNTbWuAHi9XqWkpFjP9w6FQsrKylJWVpZCoZDGjx+vNWvWxLl6DCc1NdX6ToL7nnvuOfn9fmucTJ1py5Ytmjt3rkKhkFauXKnW1lbV1NSopqZGkthfHWrZsmXas2ePgsGgcnNz9cMPP2jfvn0qKyuTRK5O8vfff+vatWvWdiQSUXt7u3w+n4LB4GNz9Hq9Wr9+vbZu3Sq/3y+fz6d33nlH+fn5Q75UFGPnUblmZGTo9ddfV1tbm86cOaPBwUHrWMrn88nj8ZCrTT1uf33w5EtSUpICgYCys7Mlsb9K4pFvTnLgwAGTmZlpPB6PmTNnjvWoMDiDpGFfR48etdZEo1Hz3nvvmUAgYJKTk82rr75qLl26FL+iMWr/+8g3Y8jUyU6fPm3y8vJMcnKymTlzpqmpqYmZJ1vn+fPPP01lZaUJBoNm3LhxZsaMGWbnzp3m3r171hpydYbGxsZh/6aWlpYaY0aW4927d01FRYXx+XwmJSXFLF261HR2dsbh0+C+R+UaiUQeeizV2NhovQe52s/j9tcHPfjIN2PI1WWMMWN0fgAAAAAAAIwC/9MOAAAAAIBN0bQDAAAAAGBTNO0AAAAAANgUTTsAAAAAADZF0w4AAAAAgE3RtAMAAAAAYFM07QAAAAAA2BRNOwAAAAAANkXTDgAAHmn37t166aWX4l0GAADPJJcxxsS7CAAAEB8ul+uR86Wlpfrkk0907949+f3+MaoKAADcR9MOAMAzrKury/q5vr5eu3bt0s8//2yNpaSkyOv1xqM0AAAgbo8HAOCZFggErJfX65XL5Roy9uDt8W+//baWL1+uUCik9PR0TZo0Se+//77++ecfVVdXy+fzadq0aTpy5EjM77p165befPNNpaWlye/3q7i4WL/++uvYfmAAAByGph0AAIza2bNndfv2bTU3N2vfvn3avXu3li5dqrS0NH333XcqLy9XeXm5bty4IUnq7e3Va6+9pgkTJqi5uVktLS2aMGGCFi1apP7+/jh/GgAA7IumHQAAjJrP59P+/fuVnZ2tsrIyZWdnq7e3Vzt27FBWVpbeffddeTwenT9/XpJUV1enhIQEHT58WPn5+crJydHRo0fV2dmpb7/9Nr4fBgAAG3PHuwAAAOA8ubm5Skj477n/9PR05eXlWduJiYny+/3q7u6WJIXDYV27dk2pqakx79PX16fr16+PTdEAADgQTTsAABi1pKSkmG2XyzXsWDQalSRFo1EVFBTo+PHjQ97r+eeff3KFAgDgcDTtAADgiZszZ47q6+s1ZcoUTZw4Md7lAADgGPxPOwAAeOLWrl2ryZMnq7i4WOfOnVMkElFTU5MqKyt18+bNeJcHAIBt0bQDAIAnbvz48WpublYwGNSKFSuUk5OjsrIy3b17lyvvAAA8gssYY+JdBAAAAAAAGIor7QAAAAAA2BRNOwAAAAAANkXTDgAAAACATdG0AwAAAABgUzTtAAAAAADYFE07AAAAAAA2RdMOAAAAAIBN0bQDAAAAAGBTNO0AAAAAANgUTTsAAAAAADZF0w4AAAAAgE39B/rr4w1iXDfpAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, additive, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Residuals\");" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a3778a9-98b5-4c98-9f0b-fdfa1d14cad2", + "metadata": {}, + "outputs": [], + "source": [ + "multiplicative = trend * seasonal # * np.abs(residuals) # <- Plotting w/o residuals to show the pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "abf7ea82-c880-43c2-8d85-1f5d3b431357", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, multiplicative, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Multiplicative\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6ce91721-133d-48b8-9143-c0a196d3dcad", + "metadata": {}, + "outputs": [], + "source": [ + "slope, intercept = np.polyfit(np.arange(len(additive)), additive, 1) # estimate line coefficient\n", + "#polynomial 1 ^\n", + "trend = np.arange(len(additive)) * slope + intercept # linear trend\n", + "#plot trend ^\n", + "detrended = additive - trend # remove the trend\n", + "#we are identfying slope and intercept of trend AND THEN pull out de-trended (seasonality, green)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "65a7cfcb-e6a2-479d-b975-70a0083bd5de", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "plt.plot(additive, label='Original')\n", + "plt.plot(trend, label='Trend')\n", + "plt.plot(detrended, label='Detrended')\n", + "plt.grid()\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8f9a74b5-63d9-40b2-9b39-20336298851e", + "metadata": {}, + "outputs": [], + "source": [ + "additive_decomposition = seasonal_decompose(x=additive, model='additive', period = 12)\n", + "#x is the timeseries, the model is additive, and we are providing the model with a period of time " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "76bdd05d-81ef-4d24-9cce-07eafb943d5d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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B0qVL8eMf/xj33HMPVqxYASCY7L1kyRK89NJL6Onpwdy5c/Hcc89h8uTJ4de53W7cd999WLVqFfr6+nDuuefi+eefR3FxcTyaRURElNK8/gA+39+JNqsLAQHwCwK8vgD+t7cdnx/oRORQVE2HA+9ubQYAFJq1eP7rs6LOTT2eyCB2WrEZf7l1Lsxpia+9CgAmrQrTRPo9KfnFHMhu2rQJL730EqZOnTro8eXLl+Ppp5/GK6+8gnHjxuGxxx7D+eefj71798JoNAIAFi1ahPfeew+vv/46srKysHjxYlx22WWoqqqCQiHunSQREVGy2ttqw5ubG/D2lqZB25Yebm5ZJq6bXYIsgxo7Gy3Y0WRBVV0Pmi0uLPzdejx6xWTcOKckrrmg1c1W3PSHL9Hr9GJaSTr+8p05kgWxRDGlFtjtdsycORPPP/88HnvsMUyfPh0rVqyAIAgoLCzEokWL8MADDwAIjr7m5eVh2bJluO2222CxWJCTk4OVK1fi+uuvBwA0NzejpKQEH374IS688MJhtYGpBUREdKLY3tiLJe9Vo6quJ/xYjlGDacVmyGUyKOQyyGTA2FwjrplZhNFZR+ahWl1e3Pe3bfi4ug0AcN2sYvzia5Uxpxp02t14aW0NVq6vQ5/Xj2kl6Vh56xxJdsGiE1vCUgvuvPNOXHrppTjvvPPw2GOPhR8/dOgQWltbccEFF4Qf02g0OPPMM7Fu3TrcdtttqKqqgtfrHfScwsJCVFZWYt26dUcNZN1uN9xud/jfVqs1ll+BiIhIclaXF0/9ey/+8mUdBAFQymU4d2IuFs4uwZnjcqBUDH/lv0mrwovfmIXfranBk//egzerGrGpthtfP3U0rp1VjHTdQC1Xjy+A6hYr/IEASrP0yNSrB43e+vwBNPe68OqGunAACwCnlmfipW/OZhBLkos6kH399dfx1VdfYdOmTUf8rLW1FQCQl5c36PG8vDzU1dWFn6NWq5GRkXHEc0KvH8rSpUuxZMmSaJtNRESUNEKbB/z8vWq024KDNFdOL8TDl0xErkkb9fvKZDLcftYYTC0244ertqC2y4nHPtiN5f/ei8umFCDPrEVVbQ+2NfbC7QuEX2fUKlGapYdCLkOrxYV2mwsR+xFgWkk6Fp1XgbPG5bB0FSWFqALZhoYG3HPPPfj444+h1R79RDv8Sy4IwnG/+Md7zkMPPYR77703/G+r1YqSkpJhtpyIiCg5VNV14/EP94TTCEqzdHjsa1NwekV23D7jtLHZWHP/2fjn1ma8+mUdqlus+Ef/lrEhGToV0lQKNFtcsLl82NFkGfRzpVyGaSXpuOucsQxgKelEFchWVVWhvb0ds2bNCj/m9/uxdu1aPPvss9i7dy+A4KhrQUFB+Dnt7e3hUdr8/Hx4PB709PQMGpVtb2/H/Pnzj/rZGo0GGo0mmmYTEdFJqKbDDpc3gAy9Chk6tehlqYbTnuUf7cVHu4Kzj1qVHLctGIPbzxojStsMGiVumjsKN84pwbZGC97c3ACvP4BZozMwuzQT5dl6yGQyuLx+1Hc7cajTAUEACsxaFKRrka3XQC5n8ErJKapA9txzz8WOHTsGPfbtb38bEyZMwAMPPIDy8nLk5+dj9erVmDFjBgDA4/FgzZo1WLZsGQBg1qxZUKlUWL16NRYuXAgAaGlpwc6dO7F8+fJYficiIiLsbbVh+Ud78J897YMeT1MpUJCuRWmWHqOzdBidqcO5E/MSUij/q/oe3PjSl3D7ApDLgIWzS/Cj88chL4Y0guGSyWSYXpJ+1JJcWpUC4/KMGJdnFL0tRPESVSBrNBpRWVk56DG9Xo+srKzw44sWLcLjjz+OiooKVFRU4PHHH4dOp8NNN90EADCbzbj11luxePFiZGVlITMzE/fddx+mTJmC8847L8Zfi4iITlbNvX14ZvU+vPVVIwICoJDLkKFTo9fpgS8goM/rR02HAzUdjvBrlv5rD+49fxxuPb1sRAurRqLd5sLtr1bB7QtgTlkmHvtaJYNGohiJtt3F/fffj76+Ptxxxx3hDRE+/vjjcA1ZAHjmmWegVCqxcOHC8IYIr7zyCmvIEhHRiDX19uHFNQfx+qYGePoXMF0yJR/3XTAe5TkGCIIAm9uHbrsHjT19qOt2oK7Liaq6HlTV9WDpv/bggx0tWHbNVEwsiG85R48vgDv/+hXarG6MzTXgT7ecwh2niOKAW9QSEVFKq+ty4Pn/HcRbXzXC17/Efm5ZJh68eAJmjMo4zquDi4zf3NyIX3xQDZvLFy59NTbXgPJsA8pz9CjPMcRU9P+n7+zEyi/rYNQq8e6dp6E8xxD1exGd6EYS2zGQJSKilCQIAv6yvg6/eL86HMDOH5OFu8+pwKnlmSNeXd9mdeGn7+wMbyRwuGyDGuU5BozJ0eO0sdm4cHI+VMdJQ3B5/fh7VSN+8s5OyGTAH781G+dMyDvma4hOdgxkiYjohOby+vHTd3bizapGAMDpY7Pxo/MrMGt0ZkzvKwgCNtX2YHtjL2o6HajpsKOmwxGu8RqpwKzF108djRvnjIJOrcC2hl5sPNSNTXU9aOx2osPmhs3tCz//3vPH4YfnVsTUPqKTAQNZIiI6YbVaXLjt1Spsa+iFXAY8ePEEfO+MclHrm9pcXhzqDC4Q291ixVtfNaLT7gEAqJVyQAA8/sCQr1Ur5Vg4uxg/v6KSZayIhoGBLBERnXAEQcB724O7YHXa3TCnqfDsTTNwRkVOwtvi9vnx/rYWvLzuEHY2BbdKzzFqMKcsE3PLMjE214Bcoxa5Jg2MGiU3ESAagZHEdlwySUREAIBOuxtr9nZg46FueP0BKBUyKBVyqBVyjMszYk5ZBsbkGCQJyqqbrXj0vV3YeKgbADAh34iXvjEbo7LEr/06FI1SgWtmFePqmUXY12aHRinH6CwdA1aiBGMgS0R0ErO5vPjT57X4z542bG+0HPf5mXo1Zo/OwJyyTMwpy8SkApNodVcBwO72YflHe/Dql3UICMFdsO44ayy+v6Bc8h26gOAmA+PzWQuWSCoMZImITlIHO+z4/l8242DExgCVRSYsqMhBuk4Fr1+Azy/A6fFha0Mvtjb0otvhwcfVbeGV/Tq1AjNHZeDsCbm4ZEo+CsxpcWvf7hYr7vzrV6jpDLbv0qkF+PElE1GUHr/PIKLUxhxZIqKT0H/3tOGeVVthc/uQb9Li3vPH4azxOcg9xlapbp8fO5ss2HioB5tqu7G5thtWl2/Qc2aOSsclUwpwRkUOKnINUS1uEgQBb2xqwCP/3AW3L4B8kxZPL5yG+WOzR/xeRJR6uNiLiIiGJAgCnvvfATy1eh8EATilNAPP3zwLOUbNiN8rEBCwt82GdQe78NHOFmyu60HkFcWoUWL6qHRML0mHSiGHzeWFzeWD0+PH7NIMXDWjCEbt4E0GDnU6sOKTfXh3azMA4MxxOXjm+unI1Ktj+r2JKHUwkCUioiO021z4vze3Y82+DgDAN04djZ9eNilYPioO2qwufLSzFf/e1Yot9b3o8/qP+Xy9WoGrZxbj+lNKsLvFijc3N2JjbXAxl0Iuw+ILxuEHC8awZBXRSYaBLBGRRA522PHh9haMztbj9LHZSTOSuLq6DQ+8tR3dDg80SjmWXDEZN8wZJdrn+fwB7G2z4av6XuxstEAuB4xaFYya4NKMd7Y2DcrNDZHLgqOwd50zNubNDYgoNTGQJSJKsFBe55L3qsMjkTIZMLnQhNPGZmN8nhGjs3QYnaVHll6dsDJNLq8fv3i/Gn/dUA8AmFhgwm9umI6KPGlX2guCgHUHu/CX9bVYXd2GUZk6XDe7BNfMLEa++eh5ukR04mMgS0SUQBanFw+9vR0f7mgFAEwvSYfL68eeVtuQzzdqlfjGqaNx1zljoVOLVzymz+PHd/+yCV8c6AIAfO+MMtx34XholNKXrYrkDwiQy8AarEQEgBsiEBElhNPjw/vbW7Bi9T40W1xQymW478Lx+P4Z5ZDLZWi3ufDFgU5sPNSN2k4n6rudaLb0weby4flPD+LtLU348SUTcdnUgrgHcU6PD995ZRO+rOmGXq3AC1+fhQXjEr8D1nAomANLRFHiiCwR0QjtarZg1cZ6vLulGTZ3sPxUaZYOv75hBqaVpB/ztS6vH5/u7cAvP6xGQ3cfAGBuWSYWjMtBvkmLfLMWRelpMe0SZXf78J2XN2FjbTcMGiX+/J1TmG9KRCmDqQVERCLw+AJ45J87sWpjQ/ixUZk6XH9KCb41vxQGzfAnuVxeP15cU4PnPz0Aty9wxM+nlaTj7rPH4tyJuccNaF1eP9qsLrRYXGi1uLDyyzpU1fXAqFHiL7fOwYxRGcP/JYmIJMZAlogozrodHvzg1SpsPNQNuQy4eEoBbpozCvPKs2IqD9XY48TfNjeisceJNmswEG3o7oPHHwxuJxaYcPc5Y3HBpLwjtoJt7HHit/85gLe+aoQvMLgrN2mVWHnr3OOOEBMRJRsGskREcbS31YZb/7wJjT19MGqU+M1NM3D2+FzRPq/T7sYfPjuEletr4fAEKyCY01Q4Z0IuzpuYhwkFRrzyRS1e31QPrz/YhWtVchSY05Bv0qI4Iw3fPaMc4/OlrUxARBQNBrJERHHyrx0tuO/NbXB4/BidpcMfvzUbY3MTEyD2Oj340xe1ePXLOnQ7PEM+57SxWbj3/HGYOSqDq/6J6ITAQJaIUo4gCKiq68Ff1tdhe2MvxucbMXt0JmaVZqCy0By33aeGq8/jx8/fr8aqjcH6q/PKs/D8zTORIcEGB/5A8Nh8srsNq6vbcKjTgdmjM3DvBeMwf0x2wttDRCQmBrJElPRcXj+6HR70OD3Y2tCLlevrjlp3VaOUY1pxOmaVZmD26AzMGp2BdJ14AeXuFit+uGoL9rfbIZMBPzhzDO49fxxUisQG00fj9PiQplJwBJaITkisI0tESckfELD833vw6vq6cO5nJK1KjiunFeGCyXnY327H5toeVNV1o8fpxcbabmys7QYQ3Mb0vIl5+Pqpo3H62OyYFltF8voD+NPnh/DU6n3w+ALINWrwzPXTcdrY5Br1FHMTBSKiVMIRWSJKCLvbh3tWbcF/9rSHH1PKZcjQq5Fv0uLK6YW4blYJzDrVoNcJgoCaTgc213b3B7Y9qOl0hH9emqXDwlNKMC7XiHxzsA5rpk494uC2qq4HD7+9IzwqfM6EXDx57VRkGTQx/NZERDRSTC0goqTS3NuH77yyCXtabdAo5Vh+7VScMyEXBo0yqunx/W02/HVDPd6qagxvSBApTaXAhZPzsPCUEpxaduzyWI09Tjz/6UGs2lgPQQAydCo8dMlEXDermFP3REQSYCBLRElja0MvvveXzeiwuZFt0OD335wVtwL9To8P725txn/3tKPVEtwQoMvhRmSvVpKZhqtmFGNMjh65Ri1yjBoAAlZXt+NfO1uwvdESfu51s4rx0CUTkSnBgi4iIgpiIEtESWHVxno88u4uePwBTMg34g/fmo3iDJ2on+n1B7CjyYK/VzXiva3NQ47YRpLLgDllmVh03jicWp4latuIiOj4GMgSkaRcXj8eeXcX3tgc3Mr1/El5eHrhNBi1quO8Mr76PH78a2cLPt3bgTarCx12NzqsbvR5/Zg3JgsXVebjgkn5/aO0RESUDFi1gOgk4PL68enedry3rQWNvX2YVmzGKaWZOKU0E+k6FbY19GLjoeBKf4fbh19dNw3lOQbR29XY48Qdf/0K2xstkMmA+y4Yj9vPHBO3ygIjkaZW4OqZxbh6ZvGgxwVBYP4rEdEJgCOyRCkkEBDw+YFOvL2lCR/vah2yhBUQrAbgCww+tfNMGrzx/XkozdaL1rbXNtZj6Ye74fD4kaFT4dc3zMCCcTmifB4REZ2YOCJLdILpsLnxZlUDXt/YgPpuZ/jxovQ0XDa1ABMLTNja0ItNtd3Y3WKFLyAg26DB3PJMzCnNxF831GFfmx03/v5LvP79UzE6K77BbF2XAw++tQPra7oAALNGZ2DF9dNRkiluPiwREZ3cOCJLlMQaup1Y8cl+/HNbE7z+4Klq1Cpx1YwiXDm9CDNK0o+Ysre5vOh1elGckRaePu+wuXHj77/EgXY7Cs1avHHbvHCQKQgCBAFRTf0LgoCVX9Zh6Yd70Of1I02lwP0Xjcc355VCIUEqARERpT4u9iJKce1WF5793wGs2lgfDmCnl6TjprmjcPnUQqSpFSN/T5sLN7z0JWo6HDBqlTBqlLC7fXB4/NCpFbj19DJ874xy6DXDm6jpcXhw/1vbsbq6DQAwrzwLy66ZilFZHIUlIqLoMZAlSlGBgIAX1hzEb/+7Hy5vAABwRkU27j1/XFxqr7ZZXbjxpS8H7YwVKdugwaLzKrBwdgn2t9uw/mAXvqzpQpvVjRmj0jGvPAunlmdhX5sNi97YihaLC2qFHA9ePAHfPq2UC6iIiChmDGSJjsHt86Pb4UG+SZtUgZfT48Piv23Dv3a2AgBmjErH/104HvPHZMf1c1xeP7Y3WqBRyqHXKGHQKLG5rhtP/nsv6rqC+bcqhSw8EjwUmQwQBKA8W4/f3DgDlUXmuLaRiIhOXgxkiY7i073tuPdv29Dt8CBTr8bUYjOmFqdjxqh0zB6dkfA6pyGNPU587y9V2N1ihUohwy+urMT1p5QkNND2+AJ4bUMdfvPfA+h2eKBXK3BKWSbmlWehMD0Nm2u7sb6mC/va7ACAa2cVY8kVk4edikBERDQcCQlkly5din/84x/Ys2cP0tLSMH/+fCxbtgzjx48PP0cQBCxZsgQvvfQSenp6MHfuXDz33HOYPHly+Dlutxv33XcfVq1ahb6+Ppx77rl4/vnnUVxcPNTHxvTL0snL5w/gqdX78MKnB4/6HLkMmFxoxtyyTEwpNiNDp0a6ToX0NDXyzBpolCPPSx2OdQc6cfeqLehyeJBtUON3X5+F2aWZonzWcDg9PjT29KEsWw+VQn7EzztsbvR5/MyFJSIiUSQkkL3oootwww034JRTToHP58PDDz+MHTt2oLq6Gnp9sLTPsmXL8Mtf/hKvvPIKxo0bh8ceewxr167F3r17YTQaAQC333473nvvPbzyyivIysrC4sWL0d3djaqqKigUxw8cGMjSsTjcPjT0OPHTd3ZiU20PAOCb80bj/y4cj4MdDmxv7MXWhl5U1fWEp9WHkqVX47mbZ8Z1C9MD7XYs+2hPeLHUpAITfv+t2ShKT4vbZxAREaUaSVILOjo6kJubizVr1mDBggUQBAGFhYVYtGgRHnjgAQDB0de8vDwsW7YMt912GywWC3JycrBy5Upcf/31AIDm5maUlJTgww8/xIUXXnjcz2UgS5ECAQHLPtqDf+1sRafdDWfEhgEGjRLLrpmKS6cWDPnaFksfNtR0Y8OhLtR0OGDp88LS50W3wwO3LwCVQobl107FVTOGN1twNG1WF379n/14Y1MD/AEBchlww5xR+MmlE6FTc5qeiIhObpJsiGCxWAAAmZnBKdFDhw6htbUVF1xwQfg5Go0GZ555JtatW4fbbrsNVVVV8Hq9g55TWFiIyspKrFu3bshA1u12w+12h/9ttVrj9SvQCeC5/x3Ai2trBj2mUyswrTgdS6+ecsxdrQrMafjajCJ8bUbRoMddXj/u/dtWfLijFT96Yxsauvtw9zljR5y/eqDdjt+vrcHbW5rg8QcrEpw3MQ8PXDQeFXnGEb0XERERxSmQFQQB9957L04//XRUVlYCAFpbgyuv8/LyBj03Ly8PdXV14eeo1WpkZGQc8ZzQ6w+3dOlSLFmyJB7NphPMmn0dePqTfQCAhy+ZiPMn5SHHqIl5MZJWpcCzN87Esow9eHFtDZ5evQ9722y4ec4ozC7NhFoZzCMVBAEHO+xYs68TjT1OaFUKaJUKpKnl2HioB5/sbgu/5+zRGbj/ogmYUyZdLiwREVGqi0sge9ddd2H79u34/PPPj/jZ4aNWgiAcdyTrWM956KGHcO+994b/bbVaUVJSEkWr6UTS0O3EPa9vgSAAN5xSgu8tKI/r+8vlMjx0yUSUZOrws3d34oPtLfhgewv0agVOG5uNTL0an+3vRFNv31HfQyYLjsDetqBc0sVcREREJ4qYA9m7774b//znP7F27dpBlQby8/MBBEddCwoGchLb29vDo7T5+fnweDzo6ekZNCrb3t6O+fPnD/l5Go0GGo0m1mZTHLi8fvxzWzPe2NSADJ0aT1wzBdmGxP+/cXn9uP2vVeh1ejG12IxHr5h8/BdF6eunjsaEfCNe39SAT/d2oNPuxsfVAyOtaqUcc8syManQBI8vAJc3AJfXjwydGjefOgpjcgyitY2IiOhkE3UgKwgC7r77brz99tv49NNPUVZWNujnZWVlyM/Px+rVqzFjxgwAgMfjwZo1a7Bs2TIAwKxZs6BSqbB69WosXLgQANDS0oKdO3di+fLl0TaN4qjd5sIzq/eh1eLC6Cw9RmfpUJKhw6babryxuQG9Tm/4uVf81oLffWMWphanJ6x9Pn8AP/7HDuxssiJDp8LzN8+EViVOmayQ2aWZmF2aiUBAwK5mK/63tx12tw/zxmTh1LKsqLaPJSIiopGLOpC988478dprr+Hdd9+F0WgM57SazWakpaVBJpNh0aJFePzxx1FRUYGKigo8/vjj0Ol0uOmmm8LPvfXWW7F48WJkZWUhMzMT9913H6ZMmYLzzjsvPr8hRe3DHS14+O0d6AkHqx1HPKcoPQ0LZ5fg3a1NqOl04Nrfrccvv1aJ62aLn+5hcXpx16qv8Nn+TshkwG9unIHijMTVNpXLZZhSbMaUYu5qRUREJIWoy28dLYf15Zdfxi233AJgYEOEF198cdCGCKEFYQDgcrnwf//3f3jttdcGbYgw3LxXlt+KP4vTi0f+uRPvbG0GAEwsMOGmOSVo7O1DXacT9d1O5Jk0uHnuaJw9IRcKuQxWlxf3vrEVn+xuBwAsnF2MReeNQ6FINVEPtNvw3T9vRm2XE2kqBX513bSjltUiIiKi1MEtailqa/d14P6/b0er1QW5DLjjrLH44bkV4ZX5xxIICPjNf/djxSf7AQAqhQxXzyjG7WeNOWbZq5Fotbiwdl8Hfv5+NexuH4rS0/DSN2dhciFHRYmIiE4EDGRpxJweH5Z+uAcrvwyWRivL1uOphdMwc1TGcV55pA01XVjxyX6sr+kCENz69aLKfFx/yiicPjYbCvnAaL7d7cP6g11QKmQ4bUz2EQGz3e3D+9ua8dmBTmyp60GzxRX+2ZyyTLxw80xkSbDAjIiIiMTBQJZGpKquB4v/thW1/Vu0fmveaDx48cSYFy1V1XXj2f8ewP/2DuTWFpi1uHZWMcxpKvxvbzs2HuqG1x/8Cmbq1bhiWiGunlkEf0DA6xsb8N725kG7c8llwIR8E86blIe7zh47rJFiIiIiSh0MZGnY/rWjBXev2gJfQECBWYsnr52G0yuy4/oZ1c1WvLGpHu9sbYalz3vEz0uzdHB4/OiwuYd4NTAmR48rpxdhdmkGphWnx7zBARERESUvBrJJrMfhwe4WK8pzDMg3ayVty/vbm3HP61vhDwi4uDIfT1wzFeY0lWif5/L68cnuNryzpQkev4Azx+XgnAm5KMvWw+cP4LMDnfjHV034eFewAsalUwtw45xRmD06Y8TbwRIREVFqYiCbRHocHnxc3YoNh7qxtb4XNZ0OAMHC+XeeNRa3nVkuet3Toby7tQk/emMrAgJw9cwiPHnttEG5q1JyeYOpBFIcFyIiIpLWSGI7ztGKwOnxYXV1G/65tRlr9nXAFxh8r5Bj1KDD5sYzn+zDP7Y0YskVk3HW+NyEte/vVY24/+/bEBCCZbKWXj01aYJYgAEsERERDQ8D2Thq7HHi5S9q8camBtjdvvDjkwpMOHdiLmaOysD0knSk61R4f3sLHvugGnVdTtzy8ibMLcvEFdMLcdHkfNFW4Td0O/HLD3bjo/6p+xvnjMIvv1YJeRIFsURERETDxdSCONjRaMFLn9Xgwx0t8PePvo7K1OHK6YW4YlohKvKMQ77O7vZhxep9eHldbfh1CrkM88qzMLnQBLVSDo1SDo1SgcmFJpxanhVV0Nnn8eOFNQfx4pqDcPsCUMhluG1BOe67YDyDWCIiIkoqzJFNkPouJ5Z9tAcf7GgJP3b62Gx894wynDkuZ9gLlBq6nfhgRws+2N6CHU2Woz6vNEuHm+aOwrWzSpCpVw/rfd/Y1IC/bW5Ae39FgPljsvDI5ZMxPn/o4JqIiIhISgxkRWbp8+L5/x3Ay1/UwuMPQC4DrpxehO+dUY5JhbG1oa7LgY93taHd5oLbF4DHF4DN5cOafR3hdAW1Qo4JBUYYNEroNcr+vxXB/1YroVUpsGZfBz4/0Bl+36L0NPzk0om4qDKfFQCIiIgoaXGxl4g+qW7D/W9tR7fDAyA4AvvwpRMxsSA+QfToLD2+t6D8iMcdbh/+ua0Zr22ox44mC7Y3Hn3kNkQmC7bvhlNG4bxJudAouYiKiIiIThwMZEeoIF2LHqcHY3L0+Mmlk3DW+OGnEMRCr1HixjmjcOOcUdjbakNjjxN2tw8Otx8Ot6//v/v/9vhRnq3HtbOKUZKpE71tRERERFJgakEUvjjQiTllmVApuD0qERERUTwxtUBkp42N7xauRERERDRyHFIkIiIiopSU8iOyocwIq9UqcUuIiIiIKFahmG442a8pH8jabDYAQElJicQtISIiIqJ4sdlsMJvNx3xOyi/2CgQCaG5uhtFoTFh9VKvVipKSEjQ0NEi2m1gq4/GLDY9fbHj8YsdjGBsev9jw+MUmFY6fIAiw2WwoLCyEXH7sLNiUH5GVy+UoLi6W5LNNJlPSfglSAY9fbHj8YsPjFzsew9jw+MWGxy82yX78jjcSG8LFXkRERESUkhjIEhEREVFKYiAbBY1Gg0ceeQQajUbqpqQkHr/Y8PjFhscvdjyGseHxiw2PX2xOtOOX8ou9iIiIiOjkxBFZIiIiIkpJDGSJiIiIKCUxkCUiIiKilMRAloiIiIhSEgNZIiIiIkpJDGSJiIiIKCUxkCUiIiKilMRAloiIiIhSEgNZIiIiIkpJDGSJiIiIKCUxkCUiIiKilMRAloiIiIhSEgNZIiIiIkpJDGSJiOJMJpMN68+nn34qaTsfffRRyGQySdtARBQLpdQNICI60axfv37Qv3/xi1/gf//7H/773/8OenzSpEmJbBYR0QmHgSwRUZydeuqpg/6dk5MDuVx+xOOHczqd0Ol0YjaNiOiEwtQCIiIJnHXWWaisrMTatWsxf/586HQ6fOc73wEAWK1W3HfffSgrK4NarUZRUREWLVoEh8Mx6D1kMhnuuusurFy5EhMnToROp8O0adPw/vvvH/F5H3zwAaZPnw6NRoOysjL86le/SsjvSUQkJo7IEhFJpKWlBV//+tdx//334/HHH4dcLofT6cSZZ56JxsZG/PjHP8bUqVOxa9cu/OxnP8OOHTvwySefDMpr/eCDD7Bp0yb8/Oc/h8FgwPLly3HVVVdh7969KC8vBwD85z//wZVXXol58+bh9ddfh9/vx/Lly9HW1ibVr05EFBcMZImIJNLd3Y0333wT55xzTvixJ554Atu3b8eGDRswe/ZsAMC5556LoqIiXHvttfjoo49w8cUXh5/f19eHTz75BEajEQAwc+ZMFBYW4m9/+xsefPBBAMDDDz+MvLw8rF69GlqtFgBw4YUXorS0NEG/KRGROJhaQEQkkYyMjEFBLAC8//77qKysxPTp0+Hz+cJ/LrzwwiErHZx99tnhIBYA8vLykJubi7q6OgCAw+HApk2bcPXVV4eDWAAwGo24/PLLxfvliIgSgCOyREQSKSgoOOKxtrY2HDhwACqVasjXdHZ2Dvp3VlbWEc/RaDTo6+sDAPT09CAQCCA/P/+I5w31GBFRKmEgS0QkkaFquGZnZyMtLQ1/+tOfhnxNdnb2iD4jIyMDMpkMra2tR/xsqMeIiFIJA1kioiRy2WWX4fHHH0dWVhbKyspifj+9Xo85c+bgH//4B5588slweoHNZsN7770X8/sTEUmJObJERElk0aJFGD9+PBYsWICnn34an3zyCT7++GP84Q9/wMKFC7Fhw4YRv+cvfvELtLa24vzzz8c777yDt956C+eeey70er0IvwERUeJwRJaIKIno9Xp89tlneOKJJ/DSSy/h0KFDSEtLw6hRo3DeeedFVWkgFMD+5Cc/wfXXX4/8/Hzccccd6Ovrw5IlS+L/SxARJYhMEARB6kYQEREREY0UUwuIiIiIKCUxkCUiIiKilMRAloiIiIhSEgNZIiIiIkpJDGSJiIiIKCUxkCUiIiKilJTydWQDgQCam5thNBqH3O6RiIiIiFKHIAiw2WwoLCyEXH7sMdeUD2Sbm5tRUlIidTOIiIiIKI4aGhpQXFx8zOekfCBrNBoBBH9Zk8kkcWuIiIiIKBZWqxUlJSXhGO9YUj6QDaUTmEwmBrJEREREJ4jhpIymfCBLREREdLJxenyobrZiW6MFOxp7cf9FE1CYniZ1sxKOgSwRERFREnN5/djdYsWOJgu2N1qwo9GC/e02BISB51wwOZ+BLBERERElXiAgoNnShwPtdhzqdKCuy4m6Lgfqup2o73LCFxm19ss3aTGl2IypRWaMyzt+PumJiIEsERERUYJ4/QHUdTlwoN0+8KfDjoPtDvR5/Ud9XZZejanFZkwpTsfUIjOmFJuRZ9ImsOXJiYEsERERUZw5PT4cbHfgQIdtUNBad5TRVQBQKWQozdKjPEeP0mw9RmfqMTpLh7JsPQrMWtbLHwIDWSIiIqIodTs8Q4yu2tHU23fU1+jVCozJNWBsjiH4d/+fUZk6qBTcdHUkGMgSERERHUfkgqvdLdZw4Nrj9B71NVl69UCgmjMQsHJ0NX4YyBIRERFF8PoD2Ntq668S0IvtjRbsbbUdNSWgKD0tHKSOjQhcM/TqBLf85MNAloiIiE5a/oCAgx12bGvoDZe3qm6xwuMLHPHczP4FV5MLTajINWJsrgHlOXro1AynpMIjT0RERCcFnz+A2i4ndjUHA9btjb3Y2WQdslqAUavE1GIzpkZUCShKT2NKQJJhIEtEREQnlEBAQFNvX3jh1d5WG/a02rCvzQb3ECOtOrUClUXmcMA6rTgdo7N0DFpTAANZIiIiSklunx+1nc7wwquDHcG/azrtcHmPDFgBIE2lwIQCI6YW9Y+2FptRnmOAQs6gNRUxkCUiIqKkZnV5cfCw8lYHOxyo63LgKOuvoFbIUZ6jx5j+agETC4yYkG/CqEwd5AxaTxgMZImIiEhygiCgw+4OjqxGBK372+xot7mP+jqjVomxuYZwwBoqc1WSqeMo60mAgSwRERElTCh/dX/74B2vDrTbYXX5jvq6PJNmUKAaClxzjBrmsp7EGMgSERGRKDrt7vBCq72tVuxttWFfm33IKgEAIJcBozJ1wUA1MmjNNcCkVSW49ZQKGMgSERFRTAIBATWdDuxqtmBHowW7+4PWTrtnyOeH81cP2/GqLFsPrUqR4NZTKmMgS0RERMPm8wdwsMOBnU0W7GiyYFezBdXNVjg8R46yyvpHWMfnGTEh34jx+SaMzzeiNEsHpUIuQevpRMNAloiIiIbk9Piwu8WG6hYrqputqG62YE/r0LVYtSo5JhWYUFlkxqQCEyYUmDAuz8Bdr0hUcfl2LV26FD/+8Y9xzz33YMWKFQCCqw+XLFmCl156CT09PZg7dy6ee+45TJ48Ofw6t9uN++67D6tWrUJfXx/OPfdcPP/88yguLo5Hs4iIiGgYPL4A6rsd/bVYHdjdYkV1ixWHOh0QhihvpVcrMLnQjMlFJkwpMqOyyIzybD1HWSnhYg5kN23ahJdeeglTp04d9Pjy5cvx9NNP45VXXsG4cePw2GOP4fzzz8fevXthNBoBAIsWLcJ7772H119/HVlZWVi8eDEuu+wyVFVVQaFgjgwREVE82VxeHOxwBMtbdQxsIlDf5YTvKAVZc4waTC40YVKBCZP6/y7N0rMWKyWFmAJZu92Om2++Gb///e/x2GOPhR8XBAErVqzAww8/jKuvvhoA8Oc//xl5eXl47bXXcNttt8FiseCPf/wjVq5cifPOOw8A8Oqrr6KkpASffPIJLrzwwliaRkREdFISBAHtNvegna5Cf7dZj16PVa9WYEx/WauKPAMmF5oxscCIXKM2ga0nGpmYAtk777wTl156Kc4777xBgeyhQ4fQ2tqKCy64IPyYRqPBmWeeiXXr1uG2225DVVUVvF7voOcUFhaisrIS69atYyBLRER0DIIgoM3qxs4mC/b112Q92OFATbsdNvfR67HmGDUYm2PAmFx9/9/BigH5Ji3rsVLKiTqQff311/HVV19h06ZNR/ystbUVAJCXlzfo8by8PNTV1YWfo1arkZGRccRzQq8fitvthts9cEdptVqj/RWIiIhSgiAIaLG4sLPJEq4WsKPJik770COschkwOiu4PWtkwDomxwBzGuux0okjqkC2oaEB99xzDz7++GNotUefcjj8zk4QhOPe7R3vOUuXLsWSJUtG1mAiIqIU4fMHUNM5sOBqd4sNu5os6HIcWZNVLgMqco2YUGActHnA6CwdNEquNaETX1SBbFVVFdrb2zFr1qzwY36/H2vXrsWzzz6LvXv3AgiOuhYUFISf097eHh6lzc/Ph8fjQU9Pz6BR2fb2dsyfP/+on/3QQw/h3nvvDf/barWipKQkml+DiIhIUk6PDzsaLdjVbMXuFit2t1qxr80OzxDlrRRyGSpyDZhSZMaUYjMmFwbLXKWpGbDSySuqQPbcc8/Fjh07Bj327W9/GxMmTMADDzyA8vJy5OfnY/Xq1ZgxYwYAwOPxYM2aNVi2bBkAYNasWVCpVFi9ejUWLlwIAGhpacHOnTuxfPnyo362RqOBRqOJptlERESSEQQBtV1ObKnvwVf1PdhS34s9rTb4h6gWoFcrMKHAhIkFRkwsCFYKmFhg4q5XRIeJKpA1Go2orKwc9Jher0dWVlb48UWLFuHxxx9HRUUFKioq8Pjjj0On0+Gmm24CAJjNZtx6661YvHgxsrKykJmZifvuuw9TpkwJVzEgIiJKRW6fH7WdThxoD1YL2NbYiy31Pehxeo94bp5Jg6nF6f0BazBwLcnQsbwV0TCItt3G/fffj76+Ptxxxx3hDRE+/vjjcA1ZAHjmmWegVCqxcOHC8IYIr7zyCmvIEhFRSrC6vMGarP11WUP/Xd/txFBlWdUKOSqLTJg5KgMzRmVg5uh0FJjTEt9wohOETBCG2rMjdVitVpjNZlgsFphMJqmbQ0REJ5ih6rKG/rTbjl6X1ahRhktbTSowYeboDEwsMHIRFtFxjCS24wbIRERECI6u1nY6cKjTgZoOB2q7gv99qMNxzLqseSZNsFpAf9WAUPWAHKOGdVmJRMZAloiITho+fwC1XQ4caO8PVDv6A9dOx1FrsgKD67IGg1Z9uNSVScu6rERSYSBLREQnpF6nJ1yHdc9xSluF5Bg1KMvWoyxLj7IcffC/s/Wsy0qUpBjIEhFRyutxeLCtsRfbGizY1tiL3S1WtFhcQz5Xp1agIteA0uyBQLU824DSbB2MHF0lSikMZImIKKW4vH7sarZiW0MvtjX2YmtDL+q6nEM+tyQzDRPyTSxtRXSCYiBLRERJyx8QUNNhx9aGYMC6rbEXe1ps8A1R26osW49pxWZMLU7HlGIzxucbmb9KdIJjIEtEREmj1eLC1oYebG2wYFtDL3Y0WWAfomJAtkGN6SXpmFacjmkl6ZhabEa6Ti1Bi4lISgxkiYgo4QRBQIvFhT2twcVYoTSBNuuRlQPSVApMKTZHBK5mFKWnsbQVETGQJSIi8VicXtR1O1Db5UR9V/Dvui4H9rTaYHMdOdKqkMswPs+IaSXpmF5ixrSSdIzNMUCpkEvQeiJKdgxkiYgoJna3D4c6HKjptKOmvy5rXZcDdd1O9Dq9R32dUi7DmBwDxucbMbU4GLRWFpqRpmaZKyIaHgayRER0XF5/AA3dzvCuVzWdDtR02HGo03HMbVqBYG3W0iwdRmUG67GOztJhXJ4RY3IMUCs50kpE0WMgS0REg1hdXuxqsmJnkwU7mizY2WxBfZdzyEoBIdkGdbgea1mOHqVZwaB1VKYOeg0vNUQkDvYuREQnMYvTi+qWYNC6vcmCnU0WHOp0DPlcrUqOsmwDyrP1KO/f9ao8x4CybD3MaSxzRUSJx0CWiOgk4Pb5cajTgX1tduxrtWF3ixW7W6xoPsruV0XpaagsMmFKkRmVRWaMyzMi36TlRgJElFQYyBIRnUA8vgBquxzY12bDvjY79rfZsK/NhtouJ/xHSQ0ozkhDZaEZU4qDQWtloQlZBk2CW05ENHIMZImIUpDXH0BdV/8Ia5sN+/v/PtTpOGouq1GrxLg8I8blGcLbtk4o4O5XRJS6GMgSESUxnz+A+m7nwOhqezA1oKbTDq9/6IDVoFGiIs+AcbnG4N95RozLMyLPpOEmAkR0QmEgS0SUBCxOLw522nGw3Y6aTgcOtttxsMOO+m7nUQNWnVqBilwDKvpHWUMBa4FZy4CViE4KDGSJiBIkEBDQ1NuH/e021HQ4cLDDjoPtwY0EOu2eo74uTaXA2FxDxOiqARW5RhSlp3HxFRGd1BjIEhHFmSAIaLa4+nNXbdjbasf+dhsOtNvh9PiP+roCsxblOXqMyQmWuBqTa0B5jgEFrBZARDQkBrJERDHotLtR3WztrxIQrBRwoN0Ou9s35PPVCnkwWM01YEx/sDqmvxYrNw4gIhoZ9ppERMMgCAJaLC7sbLJgZ7MV1c0W7GyyotU6dB1WpVyG8hx9MH81tz8dIM+I0iwdlApuy0pEFA8MZImIDiMIAuq7ndjZZMXO5uBuV7uareh2HJnHKpMBZVl6jM83hhddjc8zojRbDxUDViIiUTGQJaKTWiAgoK7biR1NFuxo7MWO/qDV5joyNUAhl6Ei1xDeNKCyyIyJBSamBBARSYS9LxGdNCKD1p1NFmxv7MWuJitsQ+SzqpVyTMw3YnKRGZWFZkwuNGF8vhFalUKClhMR0VAYyBLRCanX6cHBDgdqOuzY327HjkYLdjZbhhxpVSvlmFRgwpQiM6YUBbdprcgzMDWAiCjJMZAlopTl8wfQ0NPXv4mAPVybtabDga4h8lmB/pHWAhOmFJkwtSidQSsRUQpjIEtESS9ydHXg72PvegUA+aaBuqyVRcGc1nF5RgatREQnCAayRJQ0vP4ADnU6sLvFit0tNuxusWJPqxVtVvdRX6NVyVGWbQgHrGNy9CjPNqAsRw8DF2EREZ3Q2MsTkSQ8vgD2tFqxtaEX2xst2N1ixf42Ozz+wJDPP3zXq/KcYPBaaOY2rUREJysGskQkOkEQ0NjThy0Nvdha34utDT3Y2WyFx3dk0KpXKzChwISJBUZMyDdhYkGwWgBHV4mI6HC8MhBR3Fn6vNjeGApae7GtsRed9iMXX5nTVJheko5pJemYVGDCpAITijM4wkpERMPDQJaIYuLxBbC31YatDT3Y2mDB1oYeHOxwHPE8pVyGSYUmTC9Jx4xR6ZhekoHSLB1kMgatREQUHQayRDRsFqcX1S3W4J/m4N8H2m1DVg4oyUzD9JIMTC9Jx/QSMyYXmrmZABERxRUDWSI6gtPjw8F2Bw502HCg3Y69rXbsbrGiqbdvyOebtEpMK0nHjJJ0TB+VjmnF6cgyaBLcaiIiOtkwkCU6Sbl9frRaXGjq6UNdtxMH2u3hP0cLWAGgOCMtmM9aaAr/XZSexhQBIiJKOAayRCcgQRBg7fOhqbcPTb19aO7/u6m3D009wX+3245emxUAMvVqjM0xYGyeAWNzDJhUGKwgYE5TJei3ICIiOjYGskQpzOH2oabDgZrOgR2vajocqO92wu72Hff1WpUcRelpKMrQBYPW3IE/mXp1An4DIiKi6DGQJUoB/oCAQ512bG+0YEeTBXtbbTjYYT/mjldAcFS1KD0NRelpKExPQ1FGGorStShK16EwXYtMvZopAURElLIYyBIlmVDQuqPJgh2NVuxo6sWuZiucHv+Qz8/Sq1Hevy1reU5wx6uybB2K0nVIU7NKABERnbgYyBJJyOcP4FCnAzubg0HrziYLdjZbhgxa01QKTC40YUqxGZMKTBiTa8CYbAPMOuasEhHRyYmBLFGC2Fxe7Gm1YXdEDda9rTa4h9imNRS0VhaZMbXYjClFZpTnGKDgjldERERhUQeyS5cuxT/+8Q/s2bMHaWlpmD9/PpYtW4bx48eHnyMIApYsWYKXXnoJPT09mDt3Lp577jlMnjw5/By324377rsPq1atQl9fH84991w8//zzKC4uju03I5JAICCg2dKH2k4nDnU5UNfpQG2XA/vb7ajrcg75Gr1agYkFwaB1Sn/gyqCViIjo+KIOZNesWYM777wTp5xyCnw+Hx5++GFccMEFqK6uhl6vBwAsX74cTz/9NF555RWMGzcOjz32GM4//3zs3bsXRqMRALBo0SK89957eP3115GVlYXFixfjsssuQ1VVFRQK5vdR8gkEBLRYXajrdOBQlwO1nQ4c6nSititYLcAzxAhrSIFZG669OrEgWId1VKYOcgatREREIyYTBOHIvSWj0NHRgdzcXKxZswYLFiyAIAgoLCzEokWL8MADDwAIjr7m5eVh2bJluO2222CxWJCTk4OVK1fi+uuvBwA0NzejpKQEH374IS688MLjfq7VaoXZbIbFYoHJZIrHr0IEIDij0GJxYV+brf+PHfvbbNjfbj/qwisAUClkKMnUoSxLj9Ls4J8x2XpMLDAhgyWtiIiIjmkksV3ccmQtFgsAIDMzEwBw6NAhtLa24oILLgg/R6PR4Mwzz8S6detw2223oaqqCl6vd9BzCgsLUVlZiXXr1g0ZyLrdbrjdAyWHrFZrvH4FOkkJgoAOuxv72+zY22rD/nZb8O82O2xHqcWqlMswKlMXDFSz9CjN1qE0S4+ybD0K09OYFkBERJQAcQlkBUHAvffei9NPPx2VlZUAgNbWVgBAXl7eoOfm5eWhrq4u/By1Wo2MjIwjnhN6/eGWLl2KJUuWxKPZdBLqdniwr82G/W027O0fZd3XZkOv0zvk85VyGcqy9RiXZ+z/Y0BFnhGlWTooFfIEt56IiIgixSWQveuuu7B9+3Z8/vnnR/zs8GLrgiActwD7sZ7z0EMP4d577w3/22q1oqSkJIpW04nM6vIGg9VW+6DUgE770BsIyGXA6Cw9xuUZIoJWI8qy9VArGbASERElo5gD2bvvvhv//Oc/sXbt2kGVBvLz8wEER10LCgrCj7e3t4dHafPz8+HxeNDT0zNoVLa9vR3z588f8vM0Gg00Gk2szaYTiNcfwJ4WG76q7wn/aejuO+rzSzLTMC7XiHH5/SOsuUaMzTVAq+LiQiIiolQSdSArCALuvvtuvP322/j0009RVlY26OdlZWXIz8/H6tWrMWPGDACAx+PBmjVrsGzZMgDArFmzoFKpsHr1aixcuBAA0NLSgp07d2L58uXRNo1OYIIgoMPmxrZGC6rqgkHr9sZeuLxHVgooMGvD6QChEdaxuQboNSyfTEREdCKI+op+55134rXXXsO7774Lo9EYzmk1m81IS0uDTCbDokWL8Pjjj6OiogIVFRV4/PHHodPpcNNNN4Wfe+utt2Lx4sXIyspCZmYm7rvvPkyZMgXnnXdefH5DSllunx8H2u3Y3WLDnhYrdrdasafFhi6H54jnmrRKzBiVgZmjMjBrdAamFJthTuOOV0RERCeyqAPZF154AQBw1llnDXr85Zdfxi233AIAuP/++9HX14c77rgjvCHCxx9/HK4hCwDPPPMMlEolFi5cGN4Q4ZVXXmEN2ZOIxxdAbZcDB9rtONBux/52O/a2WnGwwwF/4MjqcHIZMCbHgJmjMjBzdDpmjc5AebaBtViJiIhOMnGrIysV1pFNHX0ePw52hIJVWzhoretyDhmwAsGR1okFpv4/RkzIN2FcnhFpat7oEBERnYgkqSNLBAzsenWow4FDnXbUdDpQ0+HAwQ47GnuOvgBLr1ZgbJ4RY3MMqMgzoCLXgIkFJhSYtcetckFEREQnJwayFBVBENDQ3YfqFguqm6040GFHTYcDtV2OIRdehWTq1RibY8DYPEM4aB2ba0C+iQErERERjQwDWToujy+AfW02VLdYUd1sRXWLFbubrcfe9SpLh/JsA8pzgrtdlWfrMTbXgCwDS6cRERFRfDCQpUHsbh92Nlmwq3kgaD3QboPXf2QOq1ohx7h8AyYVBPNWx+QYUJatR3FGGne9IiIiItExkD2Jubx+VLdYsaPRgm2NvdjeaMHBDjuGWv5n0ioxudCMSYUmTCowYVKhCWNyDNz1ioiIiCTDQPYk4fUH0wO2N1qwvT9o3dtqg2+IagEFZi0qi8yYHBG0FqWnMYeViIiIkgoD2RNQICCgptOObQ0W7GgKjrZWN1vh9h25CCtTr8bUYjOmFqdjWrEZU4rNyDVqJWg1ERER0cgwkE1xvU4P9rUFNxDY3WrD7hYr9rba4PT4j3iuUaPElP6gNRi8mjnSSkRERCmLgWyKsPR5sb/Nhn1tduxrs2F/e/C/O2zuIZ+vVclRWTg4aC3N0nP3KyIiIjphMJBNMm1WFz6ubkNTTx8ae5xo7OlDY08fOu1DB6wAUJSehnF5wQ0EJhSYMDHfiLJsPSsHEBER0QmNgWySaezpw0/f2TnkzwrMWlTkGTEu14BxecbwZgJGrSrBrSQiIiKSHgPZJDM6S4fzJuahOCMt/KcoXYfR2TqYGLASERERhTGQTTLZBg3+8K3ZUjeDiIiIKOkxiZKIiIiIUlLKj8gK/dtQWa1WiVtCRERERLEKxXTCUFuNHiblA1mbzQYAKCkpkbglRERERBQvNpsNZrP5mM+RCcMJd5NYIBBAc3MzjEZjwgr7W61WlJSUoKGhASaTKSGfeSLh8YsNj19sePxix2MYGx6/2PD4xSYVjp8gCLDZbCgsLIRcfuws2JQfkZXL5SguLpbks00mU9J+CVIBj19sePxiw+MXOx7D2PD4xYbHLzbJfvyONxIbwsVeRERERJSSGMgSERERUUpiIBsFjUaDRx55BBqNRuqmpCQev9jw+MWGxy92PIax4fGLDY9fbE6045fyi72IiIiI6OTEEVkiIiIiSkkMZImIiIgoJTGQJSIiIqKUxECWiIiIiFISA1kiIiIiSkkMZImIiIgoJTGQJSIiIqKUxECWiIiIiFISA1kiIiIiSkkMZImIiIgoJTGQJSIiIqKUxECWiIiIiFISA1kiIiIiSkkMZInopLNhwwZcddVVGDVqFDQaDfLy8jBv3jwsXrxY6qZJ5pZbbkFpaemgx0pLS3HLLbeE/93c3IxHH30UW7duTWjbiIiORil1A4iIEumDDz7AFVdcgbPOOgvLly9HQUEBWlpasHnzZrz++ut46qmnpG5i0nj77bdhMpnC/25ubsaSJUtQWlqK6dOnS9cwIqJ+DGSJ6KSyfPlylJWV4d///jeUyoEu8IYbbsDy5cslbFnymTFjhtRNICI6JqYWENFJpaurC9nZ2YOC2BC5fHCX+MYbb2DevHnQ6/UwGAy48MILsWXLlkHP2bx5M2644QaUlpYiLS0NpaWluPHGG1FXVzfoeU6nE/fddx/Kysqg1WqRmZmJ2bNnY9WqVYOe989//hPz5s2DTqeD0WjE+eefj/Xr1w96zqOPPgqZTIZdu3bhxhtvhNlsRl5eHr7zne/AYrEMeu5zzz2HBQsWIDc3F3q9HlOmTMHy5cvh9XqPe6wiUws+/fRTnHLKKQCAb3/725DJZJDJZHj00UexcuVKyGSyI9oJAD//+c+hUqnQ3Nx83M8jIhopBrJEdFKZN28eNmzYgB/+8IfYsGHDUQO6xx9/HDfeeCMmTZqEv/3tb1i5ciVsNhvOOOMMVFdXh59XW1uL8ePHY8WKFfj3v/+NZcuWoaWlBaeccgo6OzvDz7v33nvxwgsv4Ic//CE++ugjrFy5Etdddx26urrCz3nttddw5ZVXwmQyYdWqVfjjH/+Inp4enHXWWfj888+PaOM111yDcePG4a233sKDDz6I1157DT/60Y8GPefgwYO46aabsHLlSrz//vu49dZb8eSTT+K2224b0XGbOXMmXn75ZQDAT37yE6xfvx7r16/Hd7/7XVx//fXIz8/Hc889N+g1Pp8PL774Iq666ioUFhaO6POIiIZFICI6iXR2dgqnn366AEAAIKhUKmH+/PnC0qVLBZvNJgiCINTX1wtKpVK4++67B73WZrMJ+fn5wsKFC4/6/j6fT7Db7YJerxd+/etfhx+vrKwUvva1rx31dX6/XygsLBSmTJki+P3+QZ+Zm5srzJ8/P/zYI488IgAQli9fPug97rjjDkGr1QqBQOCon+H1eoW//OUvgkKhELq7u8M/+9a3viWMHj160PNHjx4tfOtb3wr/e9OmTQIA4eWXXz7ivR955BFBrVYLbW1t4cfeeOMNAYCwZs2ao/7eRESx4IgsEZ1UsrKy8Nlnn2HTpk144okncOWVV2Lfvn146KGHMGXKFHR2duLf//43fD4fvvnNb8Ln84X/aLVanHnmmfj000/D72e32/HAAw9g7NixUCqVUCqVMBgMcDgc2L17d/h5c+bMwb/+9S88+OCD+PTTT9HX1zeoXXv37kVzczO+8Y1vDEpxMBgMuOaaa/Dll1/C6XQOes0VV1wx6N9Tp06Fy+VCe3t7+LEtW7bgiiuuQFZWFhQKBVQqFb75zW/C7/dj37598TikAIDbb78dAPD73/8+/Nizzz6LKVOmYMGCBXH7HCKiSFzsRUQnpdmzZ2P27NkAAK/XiwceeADPPPMMli9fDrPZDADhnNDDRQaaN910E/7zn//gpz/9KU455RSYTCbIZDJccsklg4LV3/zmNyguLsYbb7yBZcuWQavV4sILL8STTz6JioqKcIpBQUHBEZ9XWFiIQCCAnp4e6HS68ONZWVmDnqfRaAAg/Ln19fU444wzMH78ePz6179GaWkptFotNm7ciDvvvPOIYDoWeXl5uP766/Hiiy/iwQcfxK5du/DZZ5/hxRdfjNtnEBEdjoEsEZ30VCoVHnnkETzzzDPYuXMnrrzySgDA3//+d4wePfqor7NYLHj//ffxyCOP4MEHHww/7na70d3dPei5er0eS5YswZIlS9DW1hYenb388suxZ8+ecFDa0tJyxOc0NzdDLpcjIyNjRL/XO++8A4fDgX/84x+Dfg+x6sDec889WLlyJd5991189NFHSE9Px8033yzKZxERAQxkiegk09LSMuSoZygNoLCwEBdeeCGUSiUOHjyIa6655qjvJZPJIAhCeCQ05A9/+AP8fv9RX5eXl4dbbrkF27Ztw4oVK+B0OjF+/HgUFRXhtddew3333QeZTAYAcDgceOutt8KVDEYi9B6R7RMEYdD0/0gcPuJ7uFmzZmH+/PlYtmwZdu7cie9///vQ6/VRfRYR0XAwkCWik8qFF16I4uJiXH755ZgwYQICgQC2bt2Kp556CgaDAffccw9KS0vx85//HA8//DBqampw0UUXISMjA21tbdi4cWN4dNVkMmHBggV48sknkZ2djdLSUqxZswZ//OMfkZ6ePuhz586di8suuwxTp05FRkYGdu/ejZUrVw4KUJcvX46bb74Zl112GW677Ta43W48+eST6O3txRNPPDHi3/X888+HWq3GjTfeiPvvvx8ulwsvvPACenp6ojp2Y8aMQVpaGv76179i4sSJMBgMKCwsHFSR4J577sH1118PmUyGO+64I6rPISIaNqlXmxERJdIbb7wh3HTTTUJFRYVgMBgElUoljBo1SvjGN74hVFdXD3ruO++8I5x99tmCyWQSNBqNMHr0aOHaa68VPvnkk/BzGhsbhWuuuUbIyMgQjEajcNFFFwk7d+48YsX/gw8+KMyePVvIyMgQNBqNUF5eLvzoRz8SOjs7j/jMuXPnClqtVtDr9cK5554rfPHFF4OeE6pa0NHRMejxl19+WQAgHDp0KPzYe++9J0ybNk3QarVCUVGR8H//93/Cv/71LwGA8L///S/8vOFULRAEQVi1apUwYcIEQaVSCQCERx55ZNDP3W63oNFohIsuumiIo09EFF8yQRAEaUNpIiI6Ubz33nu44oor8MEHH+CSSy6RujlEdIJjIEtERDGrrq5GXV0d7rnnHuj1enz11VfhHF0iIrGwjiwREcXsjjvuwBVXXIGMjAysWrWKQSwRJQRHZImIiIgoJXFEloiIiIhSEgNZIiIiIkpJDGSJiIiIKCWl/IYIgUAAzc3NMBqNXFxARERElOIEQYDNZkNhYSHk8mOPuaZ8INvc3IySkhKpm0FEREREcdTQ0IDi4uJjPiflA1mj0Qgg+MuaTCaJW0NEREREsbBarSgpKQnHeMeS8oFsKJ3AZDIxkCUiIiI6QQwnZZSLvZJQqpf2PRHafyL8DqmM7ZcW2y8ttl9avAakFgaySeZQpwNnLP8fbnzpS7RY+qRuzoh1Ozy45Def49LffIZ9bTapmzNiLq8f3/zTRpz55KfYUNMldXNGLBAQsPhv23DKL/+DD3e0SN2cqDz98V5M//lq/HldbUp2xq9tqMe0JR/jyX/vgT+Qeu3/7542zPzFatz/923o8/ilbs6IbW/sxalL/4Pv/nkzehweqZszYo09Tpz9q09x3e/WoaHbKXVzRszq8uJrz32BC59Zi13NFqmbM2JefwDf/fNmnL7sf/h8f6fUzRkxQRDw8Ns7MPuxT/DOliapm5MQKb+zl9VqhdlshsViSfnUgj6PH1c9/wX2tAYDwGyDGr+5cQbmj8mWuGXDEwgIuOWVTVi7rwMAoFMrsOyaqbh8WqHELRu+h9/egb9uqAcAKOQyPHTxBNx6elnKVMT43ZqDeOJfe8L//v6Cctx/4XgoFalxz/r+9mbc9dqW8L+/Nr0Qj189BTp1amRBVdV14/oXv4SvP4A9fWw2fn3DdGQZNBK3bHjquhy47Lefw+byAQAmFpjw4tdnYVSWTuKWDU+v04NLf/M5mnqDgwBF6Wn43ddnYUqxWeKWDY/b58fC363HtsZgAJiuU+HXN8zAmeNyJG7Z8AiCgB+8WoV/72oDAGiUcjx+1RRcM+vYi3WSyWPvV+MPnx8CAMhlwH0XjsftZ45JmWvAn9fV4pF/7gr/+1vzRuPhSydBrUyNa0DISGK71PrNTnA/e3cn9rTakG1QY2KBCZ12D77+hw14cc3BlBiZevZ/B7B2Xwe0KjlOKc2A0+PH3au24BfvV8PrD0jdvON6e0sj/rqhHjJZMADxBwQ89sFu3L1qCxxun9TNO64va7rw5L/3AgDOqAje/Ly0tgZf/+MGdNjcUjZtWA522PHA37cDAOaPyYJCLsM7W5tx9fPrUNvpkLh1x9dld+POv26BLyBg1ugMpKkU+PxAJy7/7efY2tArdfOOy+X14/ZXv4LN5cPEAhOy9GrsbrHist9+hv/taZe6eccVCAi492/b0NTbh5LMNIzO0qGptw/X/G4d3thUL3XzhuWXH+zGtkYLzGkqTCkyo9fpxS0vb8Rv/7MfgRQY3f/j54fw711tUCvkmFuWCbcvgMVvbsNP3tkBty/5R/c/2tkSDmLPqMhGQACWf7QXt62sgtXllbh1x7elvgePfVANYOAa8Of1dbjx91+izeqSsmmiYiCbJP62qQFvVjVCLgN+c+MM/OP2+bh6ZhECArD0X3uw5L1qqZt4TJ/v78Qzn+wDADz2tSlY9b1TcftZYwAEO7cfrKxK6mB8X5sNP/7HTgDAD8+pwMpb5+DRyydBKZfh/e0tWPji+qTuiNttLty9agv8AQFXzyjCX74zB8/fPBN6tQJf1nTjimc/T+ppVqfHh9tfrYLD48fcskz85Ttz8Np35yLboMGeVhsu/+3nONhhl7qZR+UPCFj0xla0Wl0oz9Hjz9+Zg3fvOg3l2Xo0W1xY+Lv1WH8wuVNVlry3C9UtVmTq1fjTLbPx/g9Px4xR6bC6fPj2K5uSfpryhTUH8d897VAr5fjd12fhn3edjvMm5sLjC+CBt3bg2f/ul7qJx/Tu1ib8ZX0dAGDF9dPx5g/m4cY5oyAIwFOr9+Ghf+yQuIXHtqm2G0v7Z4N+evkkrPreqVh0XgVkMuDVL+vxnVc2JXUwfqjTgf97M3gj/f0F5Vh561w8cfUUqBVyfFzdhmueX5fUqTY9Dg/u/OtX8PoFXFyZj798Zw7+8M3ZMGqVqKrrwaW/+RztJ2gwy0A2CexqtuCn7waDqMUXjMf8MdlIUyvw1HXTsOSKyQCA1zbWw+NLzlHNVosL97y+BYIA3HBKCa6dVQylQo4HLpqAF78xCwq5DP/Z045mS3KeRHa3Dz94tQp9Xj/OqMjGD8+tgEwmwy2nleH1758Ko1aJXc1WfFXXK3VTh+TzB/DDVVvQYXNjXJ4Bj11VCZlMhkumFODdu05HUXoaWiwurN7dJnVThyQIAn7y9k7sa7Mjx6jBb2+aAaVCjrnlWfjgh6djcqEJNrcP7yZxIPWb/+zHZ/s7kaZS4HdfnwWDRolxeUa8e9dpWDAuBx5/AH/b3CB1M4/q71WNWLWxATIZ8JsbZqDAnIYCcxre+P48XNs/LbzyyzqJW3l06w524qmPg7MRv7hyMiYXmmFOU+Glb8zGD88ZCwDhIDEZHWi3hQPVu84ei7Mn5EKrUmDp1VOw/JqpAIA3qxrg9CTnzFCn3Y27XvsK/oCAK6cX4utzR0Eul2HReePwp1tOgVopxxcHulCTpDMrwdmIKtjcPswpzcT/XTgeAHDDnFF48wfzkKFTYX+7HV8m6bqJQP+NdLPFhbJsPZZfOxUymQznTcrDe3edjtIsHTrtbny0q1XqpopC1EB27dq1uPzyy1FYWAiZTIZ33nln0M8FQcCjjz6KwsJCpKWl4ayzzsKuXbuGfrMTlMvrxx1//QpuXwBnj8/B7WeOCf9MJpPhm/NGw5ymgscXwN7W5Fs8JQgCfrhqC7ocHkwqMOHR/sA75MLJ+ZhYEKwDty1Jp1d/9u5O1HQ4kG/SYsX106GQD+RCzS7NxOljg1M02xt7JWrhsf32vwfwZU039GoFXvj6rEH5pGNzDbhsagGA5G3/W1814R9bmiCXAb+9cQZyjdrwz/JMWtwwZxQAYGtjci4cWX+wC7/pH+375VWVGJc3UPfQqFXhO6eVAkje7//BDjt+8k4wiPrReeNwesVATr5aKQ/PrOxqtiRlilCPw4MfrtqCgABcO6sYC2cPbJAjl8tw+1ljoZDL0G5zozUJb6Y9vgBuf/UrOD1+zB+ThR+dP27QzxeeUoJ8kxYBAdjZZJWolUcnCMGUjjarG2NzDXj8qimD8knPHp+Laf05ysl6Dvzi/epwWt9vb5oBVcSagmkl6Th7fC4AYFuS9qEvfVaDNfs6oFHK8fzNM2HUqsI/K83W48rpRQCAbQ3J2YfGStRA1uFwYNq0aXj22WeH/Pny5cvx9NNP49lnn8WmTZuQn5+P888/HzZb8gVsYvmypgt1XU5kGzR45vrpkMsHJ5TLZDJMK0kHAGxNwpOoptOBjbXdUCvleOHrM6FVKY54zrTidADJ2Ym5vP7wlOlvb5ox5KKc0PFP1k7sjU3Bkb6fX1mJMTmGI34ebn+SdmKh/MUfnluBU8uzjvj59IjvTzKmp7y5uQGCAFwzsxhXzzxyUUvo+1/T6YDFmXx5du9ubYbLG8Cp5Zm46+yxR/y8LEsPo1YJlzeQlJVI/rOnHZ12D8qy9fjFlZVHLMpJUyswvv/mIhlzlbfU92B/uz28sEshP3JR0bSS5A0EWywurN3XAYVchhdungm95siFmeFrQBL2oT5/AG9WNQIAnl44HXkm7RHPGehDexPYsuELXQN+etkkTCw4cmHU9CS/hsVK1ED24osvxmOPPYarr776iJ8JgoAVK1bg4YcfxtVXX43Kykr8+c9/htPpxGuvvSZms5LK7pbgheHU8kyk69RDPmd6Et/N7m4JjhBMLDBhdJZ+yOeEA/EkbP/eVhsCApClV2P26IwhnzMQiCdfINjj8KC1P+/pwsr8IZ8TOv67W6xweZMrx0sQBOzpPwcuOkr7x+cboVbKYenzoq4r+coRVfefA0drf4ZejdH9q/63N/UmqlnDFjqHL5iUf8SNNBAc1UzmcyDU/jPH5SBNfeSNNJDcN6Oh9s8enYEc49DVLZJ5MCPU/rE5BlTkDb0LUzIHgjWdDnh8AejVivDs2+EGvj+WpLuZdnp8qO0KpmwcrQ+a2h9DHOywp8SitZGSLEf20KFDaG1txQUXXBB+TKPR4Mwzz8S6deuO+jq32w2r1TroTyqLDASPJpk7gVD7JxUcfRu50N3gjiZL0tXVjDz+RyuvMqXYDJkMaOrtQ7stuaYmQ+0flamDYYiREAAoNGuRbdDAFxDCQVeyaOzpg83tg1ohH3I0GQhOb08uDJ4fyRaIeHyB8CK0icc4B5J5VmJ4fVDy30xPOkb7pyd1+4M3csc6/tNT4vtz/GtAdYs16RbNhto/ocA05I0cEPzdVAoZuh0eNPYkV333Pa02CAKQY9Qg+yhl/rIMGpRkpkEQgJ1JmqIVC8kC2dbWYNJxXl7eoMfz8vLCPxvK0qVLYTabw39KSkqO+txUUD2MTnhqfyd2oMMOW5LdTVU3H/8iOCbHAL1aAafHjwPtybXyfDidsEGjREVuMMjanmQjUtXDaL9MJkvaC/mu/u/P2FzDoLy0w4UCwWQb1d/fboPXL8CkVaIoPe2ozxuYlUiu74+lzxu+MB+rD0rWqWFBECLOgeMPBmxvtCTdyvnhtL+y/2a6sacPnfbkKqU3nPYXZ6QhU6+G1y+EA/dkMZw+VKNUhM+PZOuDhnMNBiL60CQ7h+NB8qoFh4+CCYJwzMLDDz30ECwWS/hPQ0PyrgQ+HpfXj5r+0ZxJhUf/EuYYNShKD95N7WhKrgthqFM61kVQIZehsig5A6nwjcQxjj8wcDORbBfygRuhYxd8n5qkIzq7h3n8k3VEMHI07Vj9VnixS2Ny5fnu6T/+RelpMOtUR31eKBDc12ZLqprKrVYXep1eKOQyVOQNPaIPBKe901QK2N0+1HQmz820zx/A3rbj96EmrQrl2cHUrWRbtBm+BhzjHJbJZOHp7WQ7h0OBYMr3ocMMZJOt/fEgWSCbnx/M5Th89LW9vf2IUdpIGo0GJpNp0J9Uta8tmJ+ZqVcj9yi5USGhC/n2JJoWiMzPnHCckygZk80j8zOPezebpHm+A4HU0UcTgME5XslkONPawEAnvKvZmlQr54fb/smFZijkMnTY3OFzJhkMZ0YCCFaPGFg5nzzfoVD7x+Toh1xoGqJUyDGl/2Y6mUbFD0XkZ47KPPbuacm4aDMyP3O453AyXQOAaPrQXpFbNDLDPYeT8fsTL5IFsmVlZcjPz8fq1avDj3k8HqxZswbz58+XqlkJFfkFPN72d8l4NzWc/MyQZOwEhpOfGZKMK+c9vgAOtA8zEO8fDTnU6UCvM3k2RtjdOrxOuDRLD5NWCXeSlaEb7mhI5Mr55DqHh/f9ASJGxZPoHI6q/Ul0/EMzKuPzjUfNzwxJxsGA4eRnhkxPwrUeHTY3Ou1uyGTB/wfHEkrP2tFkgS9JbqYDASG8pf3x+qDKIhPksuAsRjKWoYuFqIGs3W7H1q1bsXXrVgDBBV5bt25FfX09ZDIZFi1ahMcffxxvv/02du7ciVtuuQU6nQ433XSTmM1KGgNTGsPphNMBJFcnMJz83pBQ+/e02JJm5Xyo/RV5x87PBAZWzltdPtQmycr5A+12eP0CjFolijOOnp8JAOk6NUpDK+eTZFTW6vKiofv4+ZlA/8r5JLuQR+ZnHi81AkjOPNnQjcTI+qDkaX9UfWiSfH+A4ac2AYMHM5LlZnokx39g5bwjaVbOh25Ey7L1g+pvD6U82wCDJlSGLjnSU+q6nXB6/NAo5SjLHrpqUIhOrQzXuE6mcyAeRA1kN2/ejBkzZmDGjBkAgHvvvRczZszAz372MwDA/fffj0WLFuGOO+7A7Nmz0dTUhI8//hhG47HvjE4UIxlNmFJkhlwGNFtcSbPN3HCS/EMiV86HFvhIbbhJ8sBhK+eT5GZiOBUXIiXbzVAoraPQrD1q6blIyTYrEZmfOTb32CP6QPKtnPf5A+HRnOGcA9OTcMHdcFM7gIHvz+4kWjk/kmvAhAIj1Ao5epwDN4BSG8nxD62cB4AdSXIzPZJrmFwekeebJIHg7ogRfeVxBmOA5BwVjwdRA9mzzjoLgiAc8eeVV14BEEwAf/TRR9HS0gKXy4U1a9agsrJSzCYlDUEQIqZVj38S6TVKVOSG7qaSoxMYbm4RkJwr50fSCQPJl+M13GntkIH2J8v3Z4THP8lGBIebnxkyLcnK0I0kPxMYWDnf1JscK+edHh8ODTM/EwiunM9KspXzIzkHNEoFJvbfTCfLyvPh5meGJFv1kRH3oeHqF70itWhkwsc/f6TtT44+NF4kr1pwsmrs6YPN5YNKITtufmZIMuV4jSQ/MyTpAsFh5meGJNvd7EjbH7lgLRmmJkd+IxH8/u9rt8GeBCvnRzKaBgAVuUbo1P0r5zukn5ocSX4mEFw5H+qrkuFCvrc/PzPboDnqRgKRIndJTIZzuMPmRoctmJ854Tj5mSHJtDnOSPIzQ5KuD406EE+OQDDa9m9r7E26MnSxYCArkXB+Zm4w93I4kqkE1EjyM0OmJlEnZhtBfmZIaFppZxKsnBcEYdhlY0ImF5qglMvQaXejJQmS/UeSHwgAuSYtCszaYFHvJFg5P5L8QKC/DF1haOV8r1jNGrbhlE06XOgcSIYL+Ui/PwCSqgRUOD8z6/j5mSHJVAJqJPmZIcl0DXN5/TjYERzRH24fGhpM2tdmg9Mj/c10uA8qHF77x+UZoFXJYXMNzGacCBjISmSko1HA4LtZqUfURpqfCQyMqNV2OSVfOR8aSRhufiYwsHLekwQr59usbvQMo35mJK1KEV6ZK/WF0OcfOIYjOQdCIwrJMCIYzTmcTGX0Yu2DpDbS0SggubZ6je77kw4A2Nks/cr5keZnAgMr59usbslXzh9ot8MfEJChUyHPdPwRfQDIN2mRa9TAnwRrPXqdHjRbQuUvh3cOKBXy8M10MpzD8cJAViLRdMLJtHJ+pLlFQHKtnB/JQq+QyJXzUo+ohY5/efbw8jNDkuVCXtvlgNsXgE6twOhh5GeGJEue7EjzM0OSaeX8SBa6hEROTUp/Mz2yaW1goP01HQ5Y+qRdOR/NNaA8Ww9jkqycH2l+JpBcK+cjrwHDHYxJpvSU0Pe/OCMNJu3RNzM5XLK0P54YyEpkJKWrQlQKOSqTZOX8cLb1G0qynETRjIYAybNyPpogBEiePdtDoxnDzc8MCY1oSn0jsWeE+ZkhkSvnpSxD12kfeX4mMLByvtfpRX23dDfTgYAQ1TmcqVeHF7ZJvXI+mnNYLpdhapLU8x0IBEd2DUiWUf2o+9AkGcyItv0DgxnSzwrFCwNZCUTmZ0b9JZTwJBIEIWJEdni5OSHJsuBruFujHi5ZRtSiyQ8EIlbON0q7cj6a0TQgWIYutHK+wybdyvlovz+DV85LNzUZTX4mcNjKeQn7oPr+/Ey1Uh7eunW4kuEcHpSfOdJzOEluRgfOgRFeA5Lg+APRzSoCSXgNi3IwY3dz8pShixUDWQmE8jMLzFpk6IeXnxmSDHeD0eRnhkQWhZdqanKk9TMjhfJ897fbJV05H+2I8thcA3RqBRwePw60Szc1GW37jVoVxvavnJfyQh7NtDAweGpSynM42uMPDKycT4b2j88bfn5mSOgc3lLfG+9mDVsoPzNdp0K+STui1ybD9yea/MyQcJ57g0WylfORgzEjPQem9H9/Grr70CVhGbpo21+SmYYMnQoefyBpytDFioGsBGK5iMwoyQAA7Gq2SLZqMtr8TCC4cl6tkKPT7pYszzfa/EwguHK+KD0NggBU1fWI1MJjc3p8ONQZys8c2UVEIZeFLySbarvj3bRhi+kcGJUOANhUJ2X7oxtRBoAZ/YHI5lppvj9A9NPCADBjVLAPkrL90d5IAAPtr6rrliyQqo7ILx1ufmZI6Puzt80Gi1OaPN9o8zOB4Mp5nVoBm9sXHlBItKbePlj7y18OZzOTSOY0Vfg1myQ6B7z+APb350iPtA+SyWQR57B0fWg8MZCVQCwXkZLMNBSlp8HrFyQ7iaLNzQGCK+dDgcgXBzrj2axhq+7vhEeanxkyb0wWAGCdRO0fqJ+pRq5xZKM5QET7D0rT/k67G+1R5GeGDBz/rng3bViizc8MiTz+UgVSI62BGynU/p3NFsmqj8TSB00tNkOvVqDH6Q3XYk60aBabhuSatBiTo4cgAOtrpDkHYjn+SoUcc8oyAUjXB4W+/2NyDMMufxlpXrm0fejBDjs8/gAMmuGXv4wUar9U1+B4YyArgWjzS4Hg3dT8MdJ+CaPNzww5fWw2AOnaH21uUUio/Z9L1v7ogxAAOG1sqBPukiSQCh3/0iw99Jrh52eGzB8TPP5SBVKx5GcCwanhUCBVLUGerNvnx8H+DRmiOYfzTFqMzTUEA6mD0gRSsYyIqyICKcn7oCj70NPYh8YkXsdf6vZPLIhuMGZ+/zVgw6FueHzSlnGLBwayCeYPCNjbNvytXYdyekVydGLRBlLz+zuB9TXSBlJRt7//RqK6xYoeR+IDqVgvIlOL02HQKNErUSAVy7QwEAykKiQMpGLJzwSCgdRcCUd09rfZ4YsyPzPktNDNtATttzi9aOoNLpadEPXNXKgPTfz3Z3B+ZnTnQLj9ko1oxtqHBtu/UaJAKtY+dF55FuSyYBk3KerhxjqYMTHfhEy9Gk6PX/JFa/HAQDbBDnU64PIGkKZSYHTWyEdzgIGpPSkCqT6PH7VR5meGTCs2J0kgFV0nkBsZSEkwtRdr+1UKOeZKOCIV7oRHUH/ycFJeyGMNQgBEzKpI+P2JIj8zJHQzKkV6RygdoCg9Dea0keVnhoS+P1IEUs0WF6wuH5TykednhpwqYSAVS35myIR8o6SBVKx9qFmnQmVRcEZVmj40tvbL5bJwHHEipBcwkE2w0B7rY3MNUEQxJQAAuUYtxuVJE0jVdjkQEIB0nSqq/EwgmCMlVSBld/vQZg2uNB1pxYVIUk7t1fTfSMTS/vnhQDDxgUjoHKjIiz0QlCKQOth//MfF0H4pA6macPuj//6EA6lOB5r7R0cTpaYj9vaPzzMiS69Gn9ef8NX/oe9/abYeGuXIFsuGmNNUmCJRINXU0wePPwCtSh5VfiYgbSDl8QXCNZBj6kPHSHczHY9z4LQx0t2MxhsD2QQLnUCjska2Wv5woZMo0Tk6df2VBka62v9wUuUY1fe3P1OvHvFq20hSBbI2lxfd/aPwo2L4fxDKk914qCvhtQTr+s+B0TGcA3MlDKRC36FYjv/4PCOyDcFAakt9Yhdthtsf5YwQ0B9I9Ve/SPQ5UNcdvIhHO6MFDA6kUrUPnS9RHxQ6f0dl6qLKzwyRaq1EU28fAgKQplIgxzD8zUwOF9n+RJaSdPv8aLYE+7xRmdGfA6FrwFf1PXBIWEoyHhjIJlh9d+wXQWDgJEr0yvmG/vaXxCmQ3VTbndBAqj5O7Z9bngm5DKjtcobz9RIh1P5MvRrGGALxUCDl8gYSWk/T0udFb3/JoFj+H5jTVJgqUSAVj5vRYCAlzah4vPqg08YMLBpMpHj1Qaneh54ekV6TyEAqft+fYPu31PcmNJCKbH+0qTUAMLs0A2qlHG1Wd3hzi0Ro6umDIAA6tQLZhpHVoY80KlOH4ow0+AICNqZ4GS4GsgkWr7vxueWZUMhlCQ+kBkZDYmv/uDwDsg2ahAdS9aH2x3j8TVppAql4jAYCoeoXib+Qhy7i2QY1DFFULIgUWX0hUSxOLyx9wUA8boFgwkcE43MOnybRiFS8Z4W2NiQ2kAq3P8bjP2u0NIFUff/3J5bRQCB4IyhFIBVuf4zHX6tSYFZ/PdZELtqsi1MgLpPJItILUjtPloFsgsUrtcCoVWFqceJzpAYuIrF1YjKZbCAQkaL9MR5/QJoRnXhMy4eEjn8iRwTr4hSIAwMjOokMpEI3cjlGzYi2dh1KZCCVqF3iep0eWF3BzyrJiD2Q0ijlaLe5w+W8xCYIQvhmLtZzoCRTh5LM/kDqUOICqXidw1qVArNHSxBIpXofGqcbIUCaCkLxGswABspwSbHoNJ4YyCaQPyCgsUecC3mixGtqHhhofyJz1OLZ/vkRgWDCAql4dmJjBgIpmysxOwSFAsF4tH9mRCCVqO1243n8SzJ1GJWp6w+kEnMhCbU/16hBmjq6hUYhWpUCs0uDgdTn+xNzDvc4vbD1B/2p2AcFA/H4nQPhtQYJOv5A/FILgIE8388TGEjVxWkwCRhYdLr+YBf8CSolKcY1oLrFKul2u7FiIJtALZY+eP0CVAoZCszRrfaMND9iajURgZTPH0BTTzCNIR5346H2b2u0JCyQCnXC8bgbnzkqGEh12NzYn6BAqiGOF5FQIOVP4IhUuP0xLNQJiQykEnUzF8/vDxAxKp6gC3l9HEf0gciV24ltf55JM+LtsYeS6AVT3Q4PHB4/ZDKgOMYRcWAgkPqyJjGBlCAIcZtVBAbavzuBgVQ8+9ApRWYYNUpYXT7sbLLE/H7DEc9zOMeowfj+6itS7RIXDwxkEyg0JVCSoYu69FakmaMyoFUlLpBqsbjgCwhQK+VRF1KPVJyhw+isxAVSgwPx+ARSp5QmtoxYPFZsR0p0Yfh4TusBkfVkExRIdcXvIg5EBIIJDsRjzW8MCR3/L2u64POLX0YsnN8bp/aHAqk9rTZ0JiCQCo0G5pu0cQnEpxSZYdQmLpDqtHvgDAfisQ/GZBs04W2qExFIRQbi8ehDlRGbmySqDFdonUc8BgMAaTcHiRcGsgkUzykNIPGBVF04EE+LqexKpESeRM29wUBco5Qj1xh92ZVIiWy/1x9Ac2+w+Hm8RtQGFkwlKBCPY34dMDA1nLBAKk6LHUMSHkjFaaFXSCiQsrl82Nks/uYm8b6RGBRIJeBmKJ75jUAwkDo1gYFUKIgqNKdFXQP3cInsQzvsbjg9fshlwQ014uH0sYmraT0oEI/zrJAUuwzGCwPZBIpnblFIIkd0xGh/IvN8I/Nj4xeI9+9ZnYBAqrm3D/7+QDyW+oeR5pUPBFIdNnEDKY8vgJZw/cP4fIcqi8ww9QdSOxIwItXQHd/2Zxk04d15ElF9Id7nsEIuC3+HUrYPSmB6gTh9aOKPf0lmfIJAIDK9Rvz2h9IKCsxpUCvjE/5ElpJ0ecUtJdlhc8PlDUAuAwrjFIjPKQtWQKrrcoaPT6phIJtA8b4bBwZGdDYe6hY9Tzbe09rAwHa7e9tssDjFzZOti1PprUiTC/sDKbcPe1ptcXvfoUQm+ccrEM+KGJHaLHIJnEGFyOM0Iq6Qy8JTe5tEbn+8CpEfLnQOb0pAek28RzSBwX2Q2OJZtSMk3P4ElICK94wEMJDnW1XXI3qebLyq1kSaU5YFhVyG+m4n2qzibrcrxvEfm2tAjlEDty8g+s106PtfmB6/QDyyApLYfahYGMgmkBiB4Ph8IxRyGayuga1XxSJGIJ6pV4enePa1ixsIinERV8hl4RG1vWIHsiJcxAGE9wzf2yZ2ID6wWjuW+oeHqyzsb3+ruHnijXEqRH64yqL+74/Ix9/t86OlP1CI581c6PuzT+T2A+L0QaH213Y6RB9Ri3d+IwCMyTFAo5TD5R3YelUsYvShBo0Spf3vJ3ofKkIgK5PJMLkwQdcAEdoPRPShCTiHxcBANkEEQYhr2YwQrUox0AmIHojEv/3AwH7RieoE4t3+8f0jmmJfyENle+JRdihSaNWq6O2PY+mzSOPzg98f8Y9/fAqRH25cxPEXc1aloTsYiOvVCmTq4xeIV/S3v8XiCm8WIQaX14/W/kA8nudwrlEDc5oKAQGi18MVow9SyGWoSFQfKkJqBJDAPlSsPihRfWgcS7dFGhc6/iJ/f8TCQDZBLH1e2PoLkYvWCYj4JRQEIZw/E++7wXEJ7sTi3v7+TkzsG4l4J/mHhI6/2BfBeBWyP1zo+O9vt4k6tSpGfiMQHFGTy4Bep1fUPOXI0mfxDMTNaSoUmINVTPaLeA6EanAbNMq4BuIymSwhgUifx4/2/v+/cT+HE3wzKlofKnYf1B3/1Agg8e2PZ2oTEBmIJ6aMZLwlRSD7/PPPo6ysDFqtFrNmzcJnn30mdZPiLp6FyA83Pk/8qcl4FyKPND4BncCg+odx7gQmJOhudmBaSZz213Y5RZ1aFSs1YnSWPjy1KuZiBbGm9bQqBUqzg/9PxTyHB0pXxbf9wMDNtLjtF2dEHIhov4jpKQ39gbhRq0S6ThXX956QgOPv9PjCN1rxDgQTNaIp1jkcOaIs5qyKWH1o6Pg39fYlrKZ7PEkeyL7xxhtYtGgRHn74YWzZsgVnnHEGLr74YtTX10vdtLgSa0oGSMzUaugiGK9C5JES0Ql0Ozywu31xq38YKTS12mxxwSpSJxAZiMf7RiI0teoPCKgRcc/2cB3lOLd/0NSqiOdAfRx3JTvchASMise7/F+kcCAiZvtFSg0CEjMrJGYgPi4Bxz/U/5i0SpjjHIgPXAPsCIg0q+Jw+8Il7uLdB43NDc6q9Di96BCxjJ4YOeIAYNapwrXhU3FUVvJA9umnn8att96K7373u5g4cSJWrFiBkpISvPDCC1I3La4aRLyIRE4riTW1KtaUDDAwtdoj4tRqqP3xKkQeKRFTq12OgULk8Sx9AwyeWt3bJk4tUDHqH0ZKxNRefcTUfLwlov3x3NHocIlIrxFrWhtIzKyQqO3vDwRrOh1w+8SZVakXaUYo9J5qpRx9Xn945DreQu+brlPBnBbfQDy4ViV4XPaJNKpvd/vQ5fAAECmOSFCKmRgkDWQ9Hg+qqqpwwQUXDHr8ggsuwLp164Z8jdvthtVqHfQnFcR7R5pIoU5AzKlVMVarhiRialWs/MaQgUBEnE4sNJpTYNLGrRB5pHH5ocUi4rS/w+5GnzdYiDweW3MebrzIgZTYgXgiplbFmlYFIqfmxZtViefWqIcLLTgVc2p1YKFO/K8B+SYtjFqlqLMqYh5/hVyGilxxF6zFe1fBw4l9Mxe6BmfoVDBp4xuIA8D4vMQsmhWDpIFsZ2cn/H4/8vLyBj2el5eH1tbWIV+zdOlSmM3m8J+SkpJENDVmYl5EBnUCIn0J60S8iAPij4iIefyByAu5ODdWA2V7xD3+YnVioU44noXII4m96rY9ohB5UZxTU4DIqW1xplYDAUHUWZWxuQbIRJ5aFXMwIF2nRp4pWNtYrKlVsfIbgcQsWBM7EExUHyTGjAoQOaIp9jVApPYnaMGaGCRPLQBwRL6QIAhHzSF66KGHYLFYwn8aGhoS0cSYNYiU3xgido6amHfjgPirbhM2IitaJxzfHaUOJ3YnJvbxD33/D4k0tRpqf2F6GlSK+HebozN14anVxp6+uL9/h90Nty8AhVyGgnRt3N9f7KnVQEBAQ09izoGU7YNEnhpOWPtFupEYaH/8b0SByFkhsdsv7mAMR2RHKDs7GwqF4ojR1/b29iNGaUM0Gg1MJtOgP8luUCFysUcExb6bFfkkEq0TEPlufILIU6tibKYRKXT8xZpaFXtEvMAcnFr1BQQc6oz/1KrY7Vcq5BibI96sSqj9RSIF4oC46R1tNhc8vgCUchkKRQjEAXEX3PkDAhrjvL3x4SaIHIiIPZgh9mBMnYgzEsBAH7q/zSbKrIrYI+IVuUbIZMH1GJ0iLlgTg6SBrFqtxqxZs7B69epBj69evRrz58+XqFXxF1mIPCuO9Q8jibnqNrIQuViBVLgWqFidgAjb00aKnFrttHvi/v5i30hETq3ub4//zYTYF8FBC9ZEuBCKmd8YIuaISHhaXqTjD4ib3hEOxDPSoBQpEBdzRLbV6oLHHwrExRkRFHNWyB8QwnV8RbsG9H9/DnbY4fEF4v7+4XNYpHOgNEsHtUIOp8ePpt74z6qI3YemqRXh62OqbYwgeWrBvffeiz/84Q/405/+hN27d+NHP/oR6uvr8YMf/EDqpsVNKLelRISyKyGhi3hNhyPunUAoLcKgUSIjzmVXQkqzglOrYnQCLq8/vH2vWIHgoKlVMQIRkaeVAHFL+NSJtCNNJDFv5hJ5/EUJxEVObQLEHZEV+0YOSMyNRHFGGhRyca4Boe9PQ3cfHP01v+OlubcPXr8AlUIWLtMUb4VmLYya4KxKbVd8Z1V8/kA4ZUes75BSIccYEResiVl+LiRRm/vEm+SB7PXXX48VK1bg5z//OaZPn461a9fiww8/xOjRo6VuWtyItaNRpIKITqCmM74japG5OWIF4pFTq3vi3AmEAnExCpFHEmur3T6Pf6AQuYjfoVAgEu/jDwD1/dOqYk3rAeIuGBSzdFKImFPbYlZcCAnVsxZjVkXs/EBgYFal0x7/qdXIXdXEkqlXI8cYWrAmTh9akqETLRCXyWThm9F490EtFhd8AQFqpVy0QBwYWPkf70DQ5w+EB3hEvQakaAkuyQNZALjjjjtQW1sLt9uNqqoqLFiwQOomxdXAalXxOrHITiDeX0Kx8wNDxBoRiWy/WIE4IN6q28hC5Ok6cVJTAPFGNCMLkYs1LQaIO5qQiBFBMadWE3EOj87SQ62QwyHCrIqYK/5DdGpl+P9vvGclxM5vDBGrDxJzM41IYs0KhWckMtIgFykQB8TrQ5t7XfD3B+J5RvECcY7I0lEl4iIIiJfjJXZuTohYU6tiJ/mHjBNpwd1AfqO47Rc7EBejEHmk0I1QvKdWxS5EHlJo1sIg0tSqWNszR1Ip5CjPESe9JhE5yoB4F/JEBOKAePWsExeIizOiKdb23ocTa1aoLmJXQTED8fERee5ibrUbbwxkEyAR02JA5NSkeKkFYhJrq12xS5+FTBCpE0jU8a/IE2dqNVHtj5xajeeCNbELkYfIZDJR0lNsLi+6ExCIAxF9kFg304k6h1O9D0rR9os1opm4a9jAWhWvP36zKolqf1m2HiqFTJRZFTExkBXZoELkiZqWEWtEMEGjIQc77HHtBBKxYjv4/uJMrSZqRHzQ1Gocv0OJmpEAxCnhI3Yh8khipNeEvj9ZejUMGmXc3ncoYlQusLq86HEGS8Kl7KxQgtKzRJsVErn8X0jo/K3vdsLpid+sSn23+ItNgWB5O71aAY8/EL7uxEOi+lCVQo4xOam3wxcDWZG12wYKkYtVdiUkNJoTz04gshC52J1wqBPw+gXUxrEWqNi7koVETq3G80KYqGk9QJwctYGLYOLaH88LuRTHP57fHzG3lz6cGAsGQ+3PNogfiA/cSNjjNqticXph6esPxMWeVelfNd9hc4dH4WMlCELCAvEsgwbZBjUEATgQx1mVRLVfrAVriWo/IP5262JgICuygx3BL4OYhchDgp1A/9RqnDYWaOzpCxciLzCLl2QOHLZgLU6BiNvnHyhEnogLuQgjIqHvUCIDkXhuTHGwPTEj+oA46Smh45+Ii4gYecrh9icwEI/n1Gr4+5+A9pdmBadW7W4fmi2uuLzngf725xg10KnFDcT1GiVK+neuitd3qMPuhs3lg0wWrFogtnjfzPkDAwMjCT2H4xjIJrQPSsEdvhjIimxTbTcAYHpJekI+L3Qhj1cnEGr/lGKzaIXII8U7WX57owUefwDZBg2KRB4RB+I/otli6UNjTx/kMmBKkTku73ks8d4v3OMLYEtDDwBg+qj0uLznsYwTYURwc21/+xNwDoeOf10cZ1U2JbD9YkytDvShGXF5v2NRK+Uozw71ofE5BzYn+hoQ5z409P0fn2dEmloRl/c8lngHsrtbrHB4/DBqlSjr/38rpnjPCvU4POGc/2nF6XF5z2MRow8VGwNZkW08FOzE5pRlJuTz4n0SSdb+OJ1EofbPLcsUtfRWSLxHNEPtn1xohlHEhUYhAyOC8Zla3dlsgcsbQIZOFa4TLKaK/vbHa2q13eZCTacDMhkwe7T450C2QYMsffymVn3+AKrqgoHInLKsmN/veORyWfj/QbymJhPeB8V50WxkH5QIYl0DEtX+eM9qbehv/ymlmaLVwI0UmZ4SD6EbubG5BmT1z7iKKXQNONhuhy+Oa1XExEBWRB5fAF/Vhy4iCeoE4jw1GTqJ5pQmpv3xXnU70ImJP5oDDHRi8eoENkZ0wolQlq2HUh6/qdVN/e2fXZopatmYEINGieKM+E2tRo5GmUXcTCNSPIuS726xwe72wahVht9XbPHc4avH4QkHBAk7h/Pil54SCAjhPjRR5/D4OC+4C/dBCQ5k43UN2HioC0Dijn/oRqK2ywGX1x/z+yX6GlCckQZd/6xKbX9ubrJjICuiRI9GAQOdwO6W2EtAJXo0Cojv1KrPH8BXCRyNAgZPrR6Kw4K18I1Egi4iauXAqtU9LbFPrSZ6NAcYuBlK1fbHc2pvY21iR6OAgT4oHsc/0aNRADA+3wQgOCUdq71tNlhdPujUCkwuNMX8fsMReSMU6w5rlj4vdvenWCRqMCO0YK3N6kZPjLMqgiCEU2sS1YfmGAdmVeIRjIfOgUT1QZGzKnvilF4jNgayIoq8k0rEaBQATMg3QSGXodPuRqs1thG1TYd6wu+ZqNGobIMG+SYtBAHY2RTbSRQajTIlcDRKLpdhcmEwl3V7oyWm9+qWYDQKACYXBS+4sbbfHxDCgVSiLiIABo5/U2ztBwZG9BN1IwQAlf250DtiPP7AwGhUIo9/uP1xOP6JTisAgMr+7//+djv6PLGNqIXaP2t0RkLWGADAmBwDNEo5bG5fzBtrVNV1QxCCMzW5Im7tGsmoVaEsO7gwNNZz+GCHHd0OD7QqeULWGIRMLorPNcDu9mFnc/+NRCLPgf6brnj0QYnAQFZEUnTCaWpFeGpvW0NvTO8VuggmcjQKAKaVBDuBWNu/ob/9sxM4GgVEtL+xN6b3Cd2JVyRwNAoYWJQSa/v3ttpgc/mgVyswqSAxo1FARPtj/P5Y+rzhEYlTyhJ3IzG9ZCAQjCU9RRCEhE9LAsFAUC4L7m/fFuPN9MYEj0YBQL5Ji1yjBv6AgF3NsV3IpRjRVynk4ZuJWM/h8I1cAr8/ADCtOF7XgGD7Z5RkQK1MXLgzPU7t/6quB/6AgOKMNNHLd0aa1t+Hbo2x/YnCQFYk/ojcqLkJHM0BIr+EMXbCCZ6SCQm3P8ZOWIobCWCg/bHfSEjU/v6VsdsaemNKTwl9/2eVZiZsNAoApvZfRA52OGB1eaN+n9BoVHm2Hrki7m9+uPJsAwwaJfq8/ph2KDvQbkeP05vw0SidWhlOj4jlHLC7fdjZPyKXyEBcJpPF5UIuCJEzEgm+BoTP4diuAZvYh0ZlWpwGA6Rqf2gwYEeTBf4Y01MSgYGsSCJHoyYWJGZaO2R6HEY0Lc6I0agE341PjwikohW5yEKqQLC6xQq3L/qpSak6sQkFRqgVcvQ4vWjojn6HsnD7E5gWAQTrKYdqacYyNbZBgtFMIJieMjUOIzqh9s8cldjRKCA+o/pVdT0ICEj4aBQQ2f7ovz+1XU502NxQK+Th/5+JEpoViiUQ7/P4w1PjUgaC0d5MR85IJHpWcWr/NWB/ux12d/RrPTZKNCI+JscAvVoBp8cf140pxMJAViShaflEj0YBA53A9sbeqO+mNkeMRoX2r0+UymIzZLLgZgyddndU73GwIzgalaZSoLIwsReR4ow0ZOnV8PoFVDdHl+drd/vC05qJDqQ0SgUm9udIhWrAjpQgCJLkl4aEbiZiuZBLdSMBxGdqT6obOSBO7Zfy+Ie/P9F9/4GBa8D0knRoVeLXX40UCsSrm6O/md5S3wNfQECBWRuuBJIokwpMUMpl6LR70NgT3c10Y08fWiwuKOUyzBiV2JvpHGOwbrkgBK/D0XB5/eFZyUSfAwq5DFPilB6RCAxkRSJFbldIRa4ROrUCDo8/vCPISEl5ETdpVeGV89GeROHcqFHpCR+NipyajLb9odGokszEj0YBkTle0Y1IHep0oNPuhlqZ+NEoYOBCHm0g5fT4wqO50gZSvVG9XhAEbKiRZjQHGGj/9gZL1CvnpRpNAxC+iDd096ErypvpDRL2oaMydUjXqeDxB7CnJbqV85HtT0QN7khalQIT+/Pqox3VD31/phSbE7KRw+EGcvWj60O3N1rg8QU38wktfkuk0DVsCwPZk1NwSkOa/FIgeDcVSvaP9kIoxWrzSNNiTC+QMhAHItof5dRkeLV5aeJHM4HYc7wid7RL9GgUMHhEMJqpya31vfAFBBRKMBoFDFwE97XZoipD19jTh1arNKNRADAuzwCtKrhyviaKMnQurz/cd0kxom9OU6E8p3/lfJTnsJQj4jKZLKIP6o3qPaRsPxD7ol/JrwExt39gsXWibySA+KT4JQoDWRFIPRoFADNiGBGUejQKGNjOdGsUF5HI3KjU7cSkG40CBgKpnU0WeKNYOb9B4vZXFpqhkMvQYYuuDJ2Uo1EAkG/WIt+kRSDKMnSh9k+VaDRKqRhYYBbNObCtoRcefwA5Rg1KE7C//FBiGdVv7u1DQ3cfFHIZZo5O/I0EEFv7Izfzka4PCh63aEc0pZwVBRDzjYSUI/rAwDV4b5st5jJ0YmMgK4JQEDK9JB0aZeIvIkBsI2pbBo1GSXQRiWHlfEN3cDRKpZBhRgL2Zx9KqBOr6XTA4hzZynmX1x/uvBO1m87hSrP0MGmVcPsCUe0wJUXZp0ixlqFL9G5GQ4nlZmigfqw0I/pAbBfyyEUuUtxIALEtWAuNZk4uNMGgUcaxVcMXSxm6HU3BzXwy9epwmleixVKGrt3qwqH+zXxmJWgzn8NVFpmjLkMXubW0VH1oPMvQiY2BrAikHk0DBgLZPS22EW+TJ/WdIBDcnUatlMPS50XdCLfJC9WPnVIkzWgUAGTo1RjdP5K0val3RK/dmgSjUXK5LOqboabePjT2SDsaBURfhi4ZRqOA2MrQDcxISH/8owrEJZ7WBmIrQydV/dVIsZShG7gRzZDsRiKWMnSh78+EfBPMaYnZzOdwek30Zeh2NVvh9PgTupnP4eJVhi4RGMiKIBkCwUKzFtkGDXwBAbtGuHI+GUZz1Ep5eEvHkQZSmySq3Xi4aEdENkk8rR0SbZ5yqP2VEo5GAdGXodvR1Au3T9rRKCD6HLV2qwu1XU5JR6OAiJXzIyxDFzkaJWUfGksZOqlTm4DYytAlwzUgljJ0m5JgMAmIflR/kwRbSw8lHmXoEoGBbJw19/ahqbd/NEqCRRYhMpksfCEfyd2UxxfAlvrg86XshIGBQCrUnuFKhhFxIPqV51LndoVEezeeDKNpQPRl6MILNSWc1gaiL0MXOv4TJRyNAqIvQzdoNCpPmtEoIPoydF12d7j2plTTwiHR9EH+gIDNddLPSADR90HJMJgEnADtj0MZukRgIBtnnx/oBBDMj9FLOBoFRDeiVlXXA7cvgGyDGmNyEl/yI1I0d7ONPU7UdjkhlwGzElyI/3CRU9vDnZp0ef2SrxYOCeVojrSo9xf954DUI+LRlqEbaL+0xz/aMnTJ0v5oy9B9HtF+uYSjUUB0ZejWHQyOZo7PMyJDrxalXcMVzYKvHU0W2Fw+GDVKTJBoWjskmkC8w+bGnv68/mS5kRhJGTqfP4Ava0Ij4tK2Px5l6BKBgWycrd3XAQA4syJb4pYMrDocSSC4dn+w/WdU5Eg6GgUMdMK7mq3w+IaX7L92X/AiOGNUBkxa6UajgOBCj2BRbzeaLcNL9t9c2wOXN4Bco0bS0SgAyDVqw0W9hzs1WdflQF2XE0q5DPPGSBvIKuSy8Mr54V4I+zz+8IjmgnE5YjVt2EaaniIIQvgcODMJ2h9NGbrP+vugpDj+0fSh+0LtT4JrQBRl6ELtP21sdsI38zncjP7jP5IydJ8fCLZ/UoEp4Zv5HC6aMnTbGnthc/lgTlOFdwiTSjzK0CUCA9k48geE8GjCGUnQCU8tSgcA1HU50ePwDOs1n4UDWek74dFZOpjTVPCMYOV8MrVfq1JgQsHIkv2T6UYCiFg5P8wL+dr9we//zNEZkubHhow0ENxwqAseXwCFZq3kMxJA5IKv4V1EajodaOrtg1ohx9xyaUdzgJFXXnC4feH82DMqpO9DQ4H4cMvQCYKAz/rPgWRo/+QoytCF+9AkCMTzTCMvQ/fZvtA1WPr2R1OGbk1/+08fmy1pfmxIrJvLJAID2Tja2WRBr9MLo0YZ/p8vJbNOhfL+HUGGE4h02t3hziIZOuFBqyaH0X6fPxC+kUiG0Rxg5OkdyTSaA0Tf/mQYDQRGXoYuNJq5YFxy3EiMtAxd6PjPLs2ATi39jcRIy9CtP9gFr19ASWaaZBU7Io20DN3+djtarS5olHLJp4WBkZehs7q8+Kp/TcKCJLgGACO7GQoEhPDN9JnJ0v4RlqFLtmtALGXoEoWBbByFvoDzx2ZBJfGUTMhAjtrxR3Q+7+8AkmFKJmT6CFatRk7JTJN4SiZkJMn+bVYX9rTaIJMlx40EMLISSl5/AOv78wOT5yKYDmD4ZejWJtG0NjDyMnQDF8HkaP9Iy9CFj3+SzEiMtAxd6PjPLc+SZEe7oYykDN26A13wBwSUZ+tRkin9jQQwsjJ0u1ut6LS7kaZSSL5GImQkfWiv04Pt/b9nspzDsZShSxRRo61f/vKXmD9/PnQ6HdLT04d8Tn19PS6//HLo9XpkZ2fjhz/8ITye4U2DJ5tkuwgCwLTiUI7g8VcdJttFEBhZIJhsUzLAwN3sjibLcVfOh6YkpxSZkSnxIpGQKf1FvZstLrQfZ2ryq7oe2N0+ZOnV4dJpUhtchu7YF/Lm3j4caLdDLgNOG5McoyGRZeiOdw64fX58WdOf35skNxJAxIKdYVQfCZ0DSdUHjaD9a0J9aBKkNoUMVK8ZxjUgCa9h00dw/EMzKvPGZEm2GdHhIsvQHe9m+osDXQgIQEWuAQXmxG+NPZRYytAliqiBrMfjwXXXXYfbb799yJ/7/X5ceumlcDgc+Pzzz/H666/jrbfewuLFi8VsliiScUoGGAgEtzT0HnN3lMgpmWSZ0gAG2n+ww37cPN9km5IBgDE5BujVCjg9fuxuOXaOV7j9SfT90WuUqMgNTk2GSvIcTegieHpFtuSrzUMiy9Btrj1O+/uP//SSdJh10i4UjBQKpDbXdR/zeZtre9Dn9SPHqMHEAmkXCkYKncPH+/40dDtxqNMBpVyG+RIvFIwUan/Vcdrv8vrDpf+SJbUGiCxDZzlmPd/gQsHk60On9Jeha+rtQ+txFs2uTcIbicgydDuajn0znYyDSdGWoUskUQPZJUuW4Ec/+hGmTJky5M8//vhjVFdX49VXX8WMGTPw/+2dd3hUZfbHv3d6eu8FEggECD30rjRFkUVRsWJdVFRk7e4q60+XFV3XtYEdO1hQsSEgvUMCoSaUJKT3nsn0+/vjzr2ZJDPJJNPukPN5nnkecudm8s7LzL3nPe/3fM/MmTPxn//8Bx988AEaGrrfX9yTHLgovi0ZgLMBC/aVo06tx5FObuTZZY2oatLCVyHFaA92Y2pPuL8Sg2ICwbLA1rPlNs+z3JIRy7Y8wFXOT+rPXVS3nC6zeZ7JolBQTBcxoLVwrrPxAxbZNBHNP9D6edhyxvbnBxBnNhBoDSq2ninv1MJnt0Whoxi25Xmmmcd/4GI1GjvpMMVnM0clhiDAw44jloxLDoVcyiC3qhkXKmzrZA/n1UBrMCEmSIX+kZ5rpNGeAZEBiAxQQq0zYv+Fapvn5VerUVTbArmUwfhk8SwkAlRyjDQH41vO2L4GqXUGYbEnpu8wwzB2XUNZlhVlRhwApvQPx9QBEQj0oC91Z3hUyHngwAGkpaUhNjZWODZnzhxotVpkZGRY/R2tVouGhoY2DzHAr6TEUC1viVwqwcxBUQCAPzr5EvFfoPHJ4tmS4Zk7JBoA8Mcp2+Pnt2T6R/ojNlgcWzI8c9O48W/uZP5PldSjplkHf6VMsJwRC/z4/zxbYdMGraZZJ2QbxPYdmGP+/GRcqrUpj2jjOCKyQHxS/3D4K2Uob9B2qhMUCtVENv7+kQHoF+EHndGE7dkVNs8T6zU0UCUXFqObO7kGWY5fTAsJiYQRvgP2jD+9T6goCgUtEa6hnYz/YC5XKBgX7IOkcM87jlhieQ+wpTO9UNGE0noNFDKJR1sbW+PxOQPx2d1jMWNgpKeHYhWPBrJlZWWIiopqcywkJAQKhQJlZdY/sKtWrUJQUJDwSEhIcMdQO0XMKykAuMriImAroyPGLRmeq4Zy499zvspmRkeM2/I8Vw6KgkzC4Fx5k01jfj4bOKGfeAoFeUYlhiAiQIlGrQH7LlZZPWfP+UqwLJAaHYDIQJWbR9g50UEqYXFgazGXVVSH+hY9AlUyQVcuFpQyKa5I5W4gtm7kFY0aQboyWYTf4a4CkTaFgiK+hv7eWSDoBfeALWfKbErMxLitzTN3SAwAruNVjQ2JmdgcRyyZOiACKrkEhTUtNlvG89K+cUmh8FGIK5kkdrp9x1y5ciUYhun0cfToUbtfz9oHjmVZmx/EZ555BvX19cKjsLCwu2/B6eRXq1FYI74tGZ5J/cPhp5CirEFjtfJWrTMI+kExXsRSIv2RHM5ldHbkVHZ4vu1CQnw38SAfOSZ2kdHZJeKbCJfRMWf1bYxfTCb81uCz+ray4vxNfHKK503grWEZCFrL6PDemWlxgQj3F4fjiCVXpXGByM6cSrToOuo0jxfWoVFrQIivHGlx4lpIAMDMQVGQMFxzlsKaju4RpfUtOFfOFQpO7i++a9DYpFCE+MpRq9YLDT8s0RlMOJDLLyTEN/7EMF8MjgmE0cRimw2JUKv1n/jG76uQYfoAbjFqazEt5mSM2On2FXvZsmU4e/Zsp4+0tDS7Xis6OrpD5rW2thZ6vb5DppZHqVQiMDCwzcPTWG7JeLotrTVUcimuMMsLrN3ID+ZWQ2c0IT5EfFsyALfY4W/k1gIpyy2ZcR5ui2qLqzrJSDVq9Mg0F5KIxfuwPXwgsuVMeQf3Bc4EXryBONAaCB7MrbFaNCj2m8j0gRFQyiQoqFHjbGlHnaalbZUYGRIbiPgQH7TojcKizZLWhUSEaBxHLAnzVwrXFmuBCL+QGBYfjGBfcTiOWCKTSjBrsO3F6NFLNVDrjFxNQrTn76nWuKoTiVZhjRq5Vc2QShghaSA25naS1dfojTiUJ94dCbHT7UA2PDwcqampnT5UKvu2FidMmIBTp06htLRUOLZlyxYolUqMHj26u0PzGGLekuGZO8R2RkfMWzI8/EVgR05FBwsT/sYo5i2ZWYO5jM7J4voOGZ0DF6thMLHoG+aLRBGYwFtjbFIogn3lqGnWCZXZPNlljaho1EIllyBdJN6N7ekT5odB5oxO+6LBerVesLYSQ0c+a/gqZEK2e/Op0jbPmUysaAvVeBiGsbgGlXZ4XszSJp7OApFdIl/IAW11mu0lZq36avE4jrSHH/9eKxIzfiE3MiHY463JbXHFoEjIpQwuVDR1KBo8kl8Djd6EqEAlBkSJp1DQW3DpHlpBQQGOHz+OgoICGI1GHD9+HMePH0dTE6cTnD17NgYPHozbb78dx44dw59//onHH38c9913nygyrfZguSUjtiIFS/iMzqVqNbLbdahpzeaId/xD44IQF+wDtc4o3PR4WltCinf84f5KjDEL+NtndMTU0tIWcqkEs2wUDe4RcaGgJVfZyOrvv1gFEwv0i/BDnMgKBS3hteLtM1JnShtQ06yDn0KKUYniXEgAtosGa5t1OCEUCor3O2CraNBoYrHvQmsgKFY6KxoUU1taW6RE2S4aFNrSivjz01nRoOU9QKzJJDHj0kD2+eefx8iRI/HCCy+gqakJI0eOxMiRIwUNrVQqxa+//gqVSoVJkybhxhtvxIIFC/Daa6+5clhO5cO9ueYtGQUGx4g3+PZTyoRsgWVGYVNWCXIrxb0lA3AZHWuVt4fzarD/orizUTzWCl4uVjZhU1YJAO8aP5/RqWrS4vODlwCId1ubhx+/ZdFgi86ItbtzAYh//q9IjYJc2rZo0Ghi8eaf5wFwhYIKmfj0vTy2igbf2n4BLAsMjApAdJC4CgUtsVU0+NmBfK41uUocrcltYatocMvpMqEAaXJ/cX8HrF1DjxfWYUcOF9iKUd9ribWiwcIaNb7PKAIg/muQWHHpVW/dunVgWbbDY/r06cI5iYmJ+OWXX6BWq1FdXY233noLSqX4ihWs8X1GEVZvzgEAPHplimi3ZHjaZ6T2X6jC3745DgC4e1Jf0W7J8PAZqW1ny6Ez9z6/99Mj0BtZzBkSJfQUFytCRqeAy+iUN2hwx0eHUd+ix/CEYNEWSvHwGR2+aLBZa8BdnxxBYU0LEkJ9sHBUnKeH2Ckpkf5IjmgtGjQYTVj2VSayCusQ5CPHnRP6enqInRLkI8fEfq0ZHZZlsXLTaWw5Uw6FVIIHpvfz8Ag7x1rR4Ae7c/HxvjwAwCNXpnhsbPbSXqf564lSvPjLGQDAshn9RVkoaMlV7YoGj+bX4OGvjwEAbhufKJrW5LZoXzSYW9mEu9cdgdZgwrQBEaJeSAAdiwarm7S44+PDqG7WYVBMIGYPtl4bRHSOuL91ImZnTgWe+v4EAOCvU5Nxu8hvggBwZSpnA5VT3ohfTpTg/s8zoDeymDc0Bk9fNcjTw+uSUYkhCPdXokFjwMbMItz58WE0aAwY3ScE/7t5pOi3ZGKDfTA8IRgsC3yfWYwlnxxBcV0LksL98PGd6aLOpgFc0eAMc0bn56xSLP0iAyeL6xHqp8Bnd48TZZGLJe11ms/9cAp/ZldAKZPgozvT0VeEhY7tscxIvbPjAj4/eAkMA/z3phEY3Udc3pPWsCwa/C6jCC//dhYA8NzVgzBvWIwnh2YXvA3Uwdwa/H6yFI9tOA6WBW4f3wf3T0328Oi6ZtpAzgaqoEaNTVkluOfTo9AaTLgyNRIrrx3i6eF1iWXR4HeZRbjj48OoadZhWHwQ3r11lOjvAZZFgxszi3H3uiPIq2pGXLAPPr1rDFRy8UqzxAzD2nLn9RIaGhoQFBSE+vp6t+lqswrrsPiDg1DrjFgwIhav3zhC9NlYnjs+PtxGYzo+ORTr7hrrNV+g5344iS8PFQg/94/0x3dLJ4g+iOJZu+si/v17tvBzRIASGx+YKKpucJ3x28lSPPhlpvCzj1yK9fePF9pgip2TRfW49u29ws8SBlh722jMNge4YqeqSYuxL2+DZa3OymsHY8mkJM8NqhvojSaMeXkb6tStxTr3Tk7C368Z7MFRdY+r/7cHZyzaTc8dEo13bh0lSrcFa/z186P443RrwePIxGB8de940RbKtuelX87gw715ws99wnzx/QMTRWk7Z41P9+fjhU2nhZ9DfOX47oGJ6BdBRV6WdCe2E3cKSITkVTXj7nVHoNYZMSUlHKtvGO41QSzQ6l4AcOb179+R7jVBLNCakQKA6EAVPr17rNcEsUDb+fdXyvDJkjFeE8QCnE+s0pw5lkoYvHvbKK8JYgHOZ9WyoOulBUO9JogF2hYNAsCD0/t5TRALtO00CADzh8fi2avFvxtkieU1aGxSKN64eYTXBLFA2/EnR/jh4zvHeE0QC7Qdf7i/Ap/dPdZrgligVWIGcImAj5eMoSDWQSiQ7SYniupQq9YhLS4Qa24bLfrt4PbMTYtGkI8ciaG++PTusaLXxbZnfHIY+oT5IsRXjk/vHivqKnNr9A33w7ikUKjkErx3+2hRmr93hp9ShvnDYyGVMHjl+mGibVloC4ZhcNMYrhvgilkDcMu4RA+PqPvcPJYb/03pCXhizkAPj6b73JieAIbhClteW+RdiQAAuG5ELFRyCQbFBOIDL0sEAFynwXB/BWKDVPjs7rEI8fOeRADAScxSIv0RoJLhkyVj0SdM/JIgS6KDVJg+MAIKqQTv3DoSI0XsNOItkLSgB+zIqUBabJDohfG2aNDooZBKvO4CzNOiM8JgMiHAy4JwHq3BiBad0asyyZYYjCbUt+gR5kVZEEtMJhbVzTqv/f6yLIvKJi0i/JWi1wTaorJRizA/hdcFsTy1zTr4KWVel8jgadToIZUw8FWIr4GPPWj0RuiMJq9LxPDoDCY0aw1et4hwJ92J7SiQJQiCIAiCIEQDaWQJgiAIgiCIyx7v3FewgE8oNzQ0dHEmQRAEQRAEIXb4mM4e0YDXB7KNjVy71YSEBA+PhCAIgiAIgnAWjY2NCArqvCja6zWyJpMJJSUlCAgIcFvhQ0NDAxISElBYWEi63B5A8+cYNH+OQfPnODSHjkHz5xg0f47hDfPHsiwaGxsRGxsLiaRzFazXZ2QlEgni4+M98rcDAwNF+yHwBmj+HIPmzzFo/hyH5tAxaP4cg+bPMcQ+f11lYnmo2IsgCIIgCILwSiiQJQiCIAiCILwSCmR7gFKpxAsvvACl0jsN1T0NzZ9j0Pw5Bs2f49AcOgbNn2PQ/DnG5TZ/Xl/sRRAEQRAEQfROKCNLEARBEARBeCUUyBIEQRAEQRBeCQWyBEEQBEEQhFdCgSxBEARBEAThlVAgSxAEQRAEQXglFMgSBEEQBEEQXgkFsgRBEARBEIRXQoEsQRAEQRAE4ZVQIEsQBEEQBEF4JRTIEgRBEARBEF4JBbIEQRAEQRCEV0KBLEEQBEEQBOGVUCBLEARBEARBeCUUyBIEQbRj3bp1YBhGeMhkMsTExODmm2/G+fPnXfI3V65cCYZh7Dq3b9++WLJkiUvG0ZPxEARBeAqZpwdAEAQhVj755BOkpqZCo9Fg3759ePnll7Fjxw5kZ2cjJCTEqX/r3nvvxdy5c536mgRBEJc7FMgSBEHYIC0tDenp6QCA6dOnw2g04oUXXsCPP/6Iu+66y6l/Kz4+HvHx8U59TYIgiMsdkhYQBEHYCR/UlpeXC8eOHj2K+fPnIzQ0FCqVCiNHjsQ333zT5vfUajUef/xxJCUlQaVSITQ0FOnp6fj666+Fc6xt5ev1ejz55JOIjo6Gr68vJk+ejMOHD3cYly0ZAC+RyM/PF45t2LABs2fPRkxMDHx8fDBo0CA8/fTTaG5u7vL9b9++HdOnT0dYWBh8fHyQmJiI66+/Hmq1usvfJQiCcAWUkSUIgrCTvLw8AMCAAQMAADt27MDcuXMxbtw4rF27FkFBQVi/fj1uuukmqNVqQce6YsUKfP7553jppZcwcuRINDc349SpU6iuru70791333347LPP8Pjjj2PWrFk4deoUFi5ciMbGxh6/h/Pnz+Pqq6/G8uXL4efnh+zsbLzyyis4fPgwtm/fbvP38vPzMW/ePEyZMgUff/wxgoODUVxcjM2bN0On08HX17fHYyIIgugpFMgSBEHYwGg0wmAwCBrZl156CVOnTsX8+fMBAA8++CCGDBmC7du3QybjLqdz5sxBVVUVnn32Wdxxxx2QSCTYt28fZs+ejccee0x47Xnz5nX6t7Ozs/Hpp5/isccew+rVqwEAs2bNQlRUFG699dYev6e///3vwr9ZlsWkSZMwaNAgTJs2DSdOnMCwYcOs/l5GRgY0Gg1effVVDB8+XDh+yy239HgsBEEQjkLSAoIgCBuMHz8ecrkcAQEBmDt3LkJCQvDTTz9BJpPhwoULyM7OFoJKg8EgPK6++mqUlpYiJycHADB27Fj8/vvvePrpp7Fz5060tLR0+bd37NgBAB2C1htvvFEImntCbm4ubrnlFkRHR0MqlUIul2PatGkAgLNnz9r8vREjRkChUOD+++/Hp59+itzc3B6PgSAIwllQIEsQBGGDzz77DEeOHMH27dvx17/+FWfPnsXixYsBtOpkH3/8ccjl8jaPBx98EABQVVUFAHjzzTfx1FNP4ccff8SMGTMQGhqKBQsWdGrlxcsOoqOj2xyXyWQICwvr0ftpamrClClTcOjQIbz00kvYuXMnjhw5go0bNwJApwF2v379sG3bNkRGRuKhhx5Cv3790K9fP/zvf//r0VgIgiCcAUkLCIIgbDBo0CChwGvGjBkwGo348MMP8d1332Ho0KEAgGeeeQYLFy60+vsDBw4EAPj5+eGf//wn/vnPf6K8vFzIzl577bXIzs62+rt8sFpWVoa4uDjhuMFg6KCtValUAACtVgulUikc5wNpnu3bt6OkpAQ7d+4UsrAAUFdX1+VcAMCUKVMwZcoUGI1GHD16FG+99RaWL1+OqKgo3HzzzXa9BkEQhDOhjCxBEISdrF69GiEhIXj++eeRkpKClJQUZGVlIT093eojICCgw2tERUVhyZIlWLx4MXJycmxW/E+fPh0A8OWXX7Y5/s0338BgMLQ51rdvXwDAiRMn2hz/+eef2/zMOxtYBrsA8N5773X+xtshlUoxbtw4vPPOOwCAzMzMbv0+QRCEs6CMLEEQhJ2EhITgmWeewZNPPomvvvoK7733Hq666irMmTMHS5YsQVxcHGpqanD27FlkZmbi22+/BQCMGzcO11xzDYYNG4aQkBCcPXsWn3/+OSZMmGCz2n/QoEG47bbb8MYbb0Aul2PmzJk4deoUXnvtNQQGBrY59+qrr0ZoaCjuuecevPjii5DJZFi3bh0KCwvbnDdx4kSEhIRg6dKleOGFFyCXy/Hll18iKyury/e+du1abN++HfPmzUNiYiI0Gg0+/vhjAMDMmTN7Mp0EQRAOQxlZgiCIbvDwww8jMTERL774IqZOnYrDhw8jODgYy5cvx8yZM/HAAw9g27ZtbYK7K664Aps2bcJdd92F2bNnY/Xq1bjjjjs6ZEzb89FHH2HFihVYt24d5s+fj2+++Qbff/99h65igYGB2Lx5MwICAnDbbbdh6dKlSEtLw3PPPdfmvLCwMPz666/w9fXFbbfdhrvvvhv+/v7YsGFDl+97xIgRMBgMeOGFF3DVVVfh9ttvR2VlJTZt2oTZs2d3YwYJgiCcB8OyLOvpQRAEQRAEQRBEd6GMLEEQBEEQBOGVUCBLEARBEARBeCUUyBIEQRAEQRBeCQWyBEEQBEEQhFdCgSxBEARBEAThlVAgSxAEQRAEQXglXt8QwWQyoaSkBAEBAULXGoIgCIIgCMI7YVkWjY2NiI2NhUTSec7V6wPZkpISJCQkeHoYBEEQBEEQhBMpLCxEfHx8p+d4fSDL9zIvLCzs0LaRIAiCIAiC8C4aGhqQkJAgxHid4fWBLC8nCAwMpECWIAiCIAjiMsEeySgVexFdsvtcJTIu1Xp6GARBEARBEG2gQJbolDMlDbjj48NY8slhGIwmTw+HIAiCIAhCgAJZolM+3Z8PAGjUGFBc1+LZwRAEQRAEQVhAgSxhk9pmHX48Xiz8nFvV7MHREARBEARBtIUCWcIm648UQmtolRPkVlIgSxAEQRCEeKBAlrCKwWjC5wfyAQAJoT4AgLyqJg+OiCAIgiAIoi0UyBJW2Xa2HCX1GoT6KbB0Wj8AlJElCIIgCEJcUCBLWOWTffkAgMVjE5Aazfnzeksge768ESeL6j09DIIgCIIgXIxLA9lVq1ZhzJgxCAgIQGRkJBYsWICcnJw257Asi5UrVyI2NhY+Pj6YPn06Tp8+7cphEV1wpqQBh/JqIJUwuG18H/SL8AMAlDVo0Kw1eHh0nWM0sVj8wUFcv2Y/CmvUnh4OQRAEQRAuxKWB7K5du/DQQw/h4MGD2Lp1KwwGA2bPno3m5tbM3urVq/H666/j7bffxpEjRxAdHY1Zs2ahsbHRlUMjOoG33Jo7JBoxQT4I9lUg1E8BAMgTuXNBWYMGVU066Iwm/HCsuOtfIAiCIAjCa3FpILt582YsWbIEQ4YMwfDhw/HJJ5+goKAAGRkZALhs7BtvvIHnnnsOCxcuRFpaGj799FOo1Wp89dVXrhwaYYM6davl1pJJfYXjSeFcVlbsgWxBdWsWdmNmEViW9eBoCIIgCIJwJW7VyNbXc7rF0NBQAEBeXh7Kysowe/Zs4RylUolp06Zh//797hwaYeaHY8XQGkxIjQ5Aep8Q4XiyOZAVu062oKZ1fPnVamQWUGtdgiAIgrhccVsgy7IsVqxYgcmTJyMtLQ0AUFZWBgCIiopqc25UVJTwXHu0Wi0aGhraPAjnwLIs1h8uBAAsHpsIhmGE55LMOtlckVtwFbTTxX6fSfICgiAIgrhccVsgu2zZMpw4cQJff/11h+csAyaAC6jaH+NZtWoVgoKChEdCQoJLxtsbOV5Yh5zyRihlEiwYEdfmueRwfwDilxZcMksLZgyMAAD8klUCjd7Y5pwGjR5VTVq3j40gCIIgCOfilkD24YcfxqZNm7Bjxw7Ex8cLx6OjowGgQ/a1oqKiQ5aW55lnnkF9fb3wKCwsdN3Aexl8Nnbe0BgE+crbPMc7F+RWNotad8o7Fdw0JgGxQSo0aAz482xFm+dn/mcXZry2Ew0avaeGSRAEQfQSGjV6VDRqPD2MyxaXBrIsy2LZsmXYuHEjtm/fjqSkpDbPJyUlITo6Glu3bhWO6XQ67Nq1CxMnTrT6mkqlEoGBgW0ehOM0aQ34+UQJAC4IbE9imC8kDHdepYizmZfMgWyfMD8sGMlllTdmFgHgMrF3rzuCikYtGjUGnCkhWQpBEAThGorrWvB/v5zBuH/9iamrdyC3UtzSPG/FpYHsQw89hC+++AJfffUVAgICUFZWhrKyMrS0tADgJAXLly/Hv/71L/zwww84deoUlixZAl9fX9xyyy2uHBrRjl+ySqDWGZEc7oexSaEdnlfKpIgP8QUg3oKv+hY96tRcljUx1BcLR3HZ/53nKlFWr8FDX2bifEXrheRCBV1UCIIgCOdysbIJKzYcx7TVO/DR3jyodUZo9Cb8fsp67Q/hGC4NZNesWYP6+npMnz4dMTExwmPDhg3COU8++SSWL1+OBx98EOnp6SguLsaWLVsQEBDgyqER7fj6CCcruGlMgk19cnKEuJ0LeFlBuL8CfkoZ+kf6Y3hCMIwmFje+dwB7zlfBRy7FtAGcfpYCWYIgCMJZsCyLLw9dwtX/24ONx4phMLGY2C8Mi8cmAgB25lR08QpET5C58sXt0VIyDIOVK1di5cqVrhwK0QlnSxuQVVgHmYTB9aPjbZ6XFO6HnTmVyBOpcwFf6JUY6iscu2FUHLIK61BQowbDAG8uHolatQ67zlXifAU13SAIgvAkBqMJ7+3ORVWTFgtGxGFYfJDNZIpY+PZoIbZnV2DqgAjMGRKNUD8F6tV6PL3xhJB1nZISjifmDMSw+GAU17Xg68MFyLhUizq1DsG+Cg+/g8sLlwayhHewwZyNnTU4CuH+SpvnJUdwzgVizcgWWOhjea4ZFov/++UsdEYT/j5vMGYNjsIxs7fs+XJxBuQEQRC9gZpmHR7+OhP7LlQDAD7Zl4+BUQFYlB6PhaPihY6SYmP1HzmobNTi91Nl+PuPpzAhOQx5Vc0ormuBTMLgqbmpuGdyEiQSLiCPC/bBwKgA5JQ3Yvf5KswfHuvhd3B5QYFsL0drMAqtXG82b3/YIlnk3b34ZggJFhnZED8FPrwzHdXNWsFSrH8kF5BXNGpRr9Z3cGggCILwJowmFtllDci8VIujl2qRVViHCf3CsWrhUE8PzSZnShpw/+dHUVTbAl+FFFNSwrEjpxI55Y146dezWLc/H38snwo/pbjCFIPRJNg3Dojyx7nyJuy9UAWA2w18a/FIDE8I7vB701MjkFPeiJ3ZFRTIOhlxfUIIt3OurAn1LXqE+MoxuX94p+fyGtmCGjX0RhPkUrc2husSISNrEcgCwFSzJpYnQCVHTJAKpfUaXKhsxOg+HYvbCIIgvAG1zoCr/7cH+dVtm8HkVxfg6atSEeQjvoX67ydL8dg3x6HRm9AnzBfv356OgdEBqG/R4+esEry+9RyKaluw90IV5gyJ9vRw21DTrAPLAlIJg98fnYrCGjV+O1WKJo0BD0zvhwCV9fmeMTAS7+3Kxc5zlTCZWCFbSziOuCIRwu1cNNuBDIgKgLSLL1Z0oAo+cikMJlYorBITgkY2zLeLM1uzsiQvIAjCmzmUW4P8ajUUMgmmpITjsZkDEBnAScTOlorPYlCjN+Jv32ZBozdh6oAIbHpoMgZGc8XdQT5y3Da+D64ZFgMA2JlT6cmhWqWikcvGhvkpIJUw6Bvuhwen98eTc1NtBrEAMLpPCAJUMtQ065BVVOem0fYOKJDt5fCBbD9zYNcZDMMgKVyczgV6owkldZytW/uMrDVSIrkL53mROxewLIvNp0rxwe5cGIwmTw+HIAiRsf8it629cGQcPr9nHB6dmYIR5q3t0yL0yr5Y2QS1zoggHzk+WTLGqrRrurkz4+5zlaJrwMP7qEcE2K4nsYZcKsHUFO597RBhgO7NUCDbyxEC2YiuA1nAwoJLZM4FxbUtMLGASi6x6wKTEsW9XzFbcNWpdVj29TEs/SITL/92VrBIIwiC4Nl/kSuUmtAvTDg2JDYIAHC6pN4jY+oMfhdsQJS/zV3ACcnhUMgkKK5rEd01urKxZ4Es0Bqgkw2Xc6FAtpdzsYLLrPItaLtCrAVffEevxFBfu6xbUiLFHcjuu1CFuW/swa8nSoVjb/15Hi06owdHRRCEmKht1uGMWT5gGcgOjuU6Xoqxe+G5cs72cECUba94H4UU48yNecQmLxAC2U4cfmwxzRzIniiqF16HcBwKZHsxRhMrBKT2Z2S58y6KTFpQUNPRQ7YzeI1scV0LmrQGl42rJ3y4Jxe3fngIZQ0aJIf74bulExAf4oOKRi0+O5Dv6eERBCESDuVVg2W5hXlkgEo4PsQcyF6oaILWIK7Frz2BLABMHxgJANh5TlzZS0cyspEBKgyN47LllJV1HhTI9mKKatXQGU1QyiSIC/ax63d4aUFOWaOosrIF1dxYEkPtyywH+yoEz9yLIsrK6o0m/HfrOQDA4rEJ+OWRyUjvG4rlMwcAANbsuogGjd6TQ3SYU8X1+Pfv2ZdtdjmvqhmPbTiOgmrxFUQSlxcHzLKCiRbZWACICVIhxFcOg4nFuTLxXN8A4Fx5a4FxZ/Db8EfyatEsomRDTzWyPDNSzQG6yDLN3gwFsr0YXh+bHOFvtxVISmQAogNVqG/RY+4bu7Fm50VRFCG1ZmTtC8iBVnmBmAq+Mi/VollnRJifAi8vGApfBeeQ95eRcegf6Y86tR4f7snz8Ch7TrPWgPs+O4q1uy5i/ZECTw/HJby1/Tx+OFaM17fmeHooxGWONX0swBXmCvKCUvHoZNU6g3CtHhDV+S5gcrgfEkJ9oDOahPcpBhzJyALADL6Q7Xwl9CK4d14OUCDbi+muPhbgtEvf/HUCJvcPh9Zgwiubs3HdO/s8bvPCW29ZdvXqCr7gS0ytavec5yqQJ6eEt1lcSCUM/jaLy8p+tCcX1U3eqa96Z8cFlNZrAHA64MuRI/k1AIDt2RV0oxIZnAtIGRat3Y/F7x/EK5uz8cfpMlQ0aDw9tG5T0ajB+YomMAwwLimsw/OtBV/i0cnyNQlhfgqEdaExZRgG0wfw2UvxbMNXOaCRBYBh8cEI9VOgUWNAVmGdE0fWe6FAthfTXccCnsQwX3x+z1i8esMwBPnIcbqkAXevOwKTyTM2KSzb6mtrj4csj1DwJSIv2d3nue0m3qbFkrlp0RgaF4RmnRFrdl5099AcJreyCR/syRV+PpRbI4psvjMpq9egsIazgWvQGHAkr8bDIyJ4jhfW4ab3DmLpFxk4kl+LA7nVWLPzIv76eQbG/utPPP/TKU8PsVvwsoLBMYEIsdLKldfJiimQtVdWwMPLC3aJyIbL0YysVMJgdJ8QANxnknAcCmR7Md3xkG0PwzBYlJ6ArSumQsIApfUaVDV7JktY3axDs84IhgHiQ+yXFvQXmZdsTbMOJ4u5bcApKR27rDEMg8fnDAQAfHbwEnIrxTFue2BZFv/8+Qz0RhZTB0QgyEeORq1BeL+XC4fz2wauW86Ue2gkBI/BaMLfvsnCgnf24XB+DVRyCZbN6I9VC4fipvQEYYt7w5FCaPTeo9u2pY/lGRzDBbJnSxtg9FCSoT2thV723XMm9AuDQipBUW2LKAqMW3RGNJr1uj0NZAEIPr9ZRZfX9c9TUCDbi+EvDN2RFrQnMkAlVMuW1nlme47XXMUEqqCUSe3+PV5aUFirFsUNbO+FKrAskBodgMhAldVzpqaEY2K/MOgMJtz76VHUq72j8Gvb2QrsOlcJuZTBymsHY3wyZ60jJu2bM+AzsLxN3dYz5aLJJPVWfjtVhu8zi8AwwPWj4rHj8el4fM5ALB6biFduGIY/lk9FdKAKWoMJh7wog25LH8uTHOEPlVwCtc6IS9WeDwKB1kA2xc6MrK9ChnHJvA2X5+UFVWZJl0ougb9S1uPXGRbPyT5OUIcvp0CBbC+lplmHmmYdACA5vPsZWUtigrmgi++s5W746vAEO623eML8FAjxlYNlW7PTnmT3ObOsYEBHWQEPwzB44+YRiA1SIbeqGQ9+lSF6HaZGb8SLv5wGANw7JRnJEf6Y1J/LOF9uOlleH/vwlf2hknOG7mdE2Ca0N8F/r+6dnIT/3DgcMUFtd20YhsE083dul5dUkhfVqlFQo4ZUwmBM31Cr50glDFKjxSUvOFfGBbJ8S1p7EP5vznn+/6bCQlZgj1+5LYbFBQPgajtqzfdhoudQINtL4bel44J94KOwP4tpjVjzjaGk3rMZ2T7d0McC3A2Mb1Xr6cYILMtiTyf6WEsiA1T48M4x8FVIse9CNVZuOi3qrN+He3JRWNOC6EAVls3oD6B1O/TopVpRZMOdQX2LHjnmjNPk/hGYYv5/3HKa5AWewvJ7Nc1cOGSNaYIW0/NZP3vgZQXD4oMQoOrY4pVHTDrZRo1euEcMiLQ/kOV1sgdzq7F6czbKPViY50gzBEuCfOVCu/cTl5m8yhNQINtLcUQf257YYF5a4JmMLO9YYG8zBEv6884FHi74OlfehPIGLVRyCdL7hnR5/uDYQPzv5pFgGODLQwVYtz/f9YPsASYTiy8PcTZbT84dCD/zdly/CH9EBiihM5iQeanWk0N0GhmXasCyQFK4HyIClJg1OAoAJy8gPMP5Cu57pZR1/r2a1D8cUgmDi5XNQuGomOlKH8vTasHl+UCWr0WIClQiyNd28N2efhH+mD4wAnoji3d3XsTkV7ZjxTfHBZmCO3HUQ9YSQV5ABV8OQ4FsL4XPQDqij+Xht+pKPZaRNTdD6Ib1Fk+rl6xnLbj4rNG4pDCo5PZlyGcNjsLTc1MBAP/3yxlcEJGNGM+R/BqU1msQoJTh6qExwnGGYYSbsDWdrFiKU7rD4TwuIB9jDpiuTI2EhOGCiKJa8QdHDRo9jubX4IuDl7Dqt7PIKRPf56m78HZ2Y5NCO/1eBfnIMdJcgMM7h4gVlmVb9bHJHYtCLeEtuM6U1Ht814aXFdjrWMDDMAw+unMM1t42GmP6hkBvZLExsxjXvrUXdWr3bss76lhgybD4YABAFulkHYYC2V5Ka6GX8zKyJfUe0sh2sz2tJf1F0hSB139ZcyvojPunJmNS/zCYWK6gSmz8lFUCgLMOax9ITOR1shfb6mT//Xs2Bv1jMzIueU/hDQAcNetjec1imL8S6X24f4s5K3u2tAHTX92BYSu34Ia1B/D3H0/hvd25uPXDg16RnewMe+U6ALxGJ/v5wUsoa9BAIZUINk62SI0OgIQBqpp0gr7TU/DWWyndkBXwSCUM5qZF49ulE/HjQ5MQEaCE1mDCGTdLJlqlBdaLcbvDiARukZFV5PlFhrdDgWwvpacestYQMrIecC2ob9GjvIG7uPTtpkYWaL2oXqpWe6wnuUZvxGFztfS0Tgq9rMEwDK5M5bawxVY4pTea8PvJUgDA/BGxHZ7nM7IniurRaG67u/tcJdbuugid0YRP919y32AdRKM34oTZSsey+MYb5AXv785FvlmeExukwvSBEegX4YeqJh3u/OSw27NezkJrMOJgLpe5nDKg6wUir5Pdf7EaOoM4Cyh/PFaM53/iCicfvqJ/l/UNKrlUuMa7O+hrD7/rNTDasXvOiIRgIXue7eZdA2dmZAfHBEEqYVDZqEWZFzbkEBMUyPZCNHqjkGnpF+kEaYE5I1vRqHG7wf1Jc/CQGOqLYN+OpuBdERWoRJCPHEYT67Gt1MN5NdAaTIgOVAkZ4u7AOwAcya/xWDBujb3nq1Cr1iPcX4EJyR21fPEhvugT5gujicXhvBrUq/V48rsTwvNbz5SLqsd6Z2QV1kFnNCEiQNmm6JAPZA/l1aCqSYvssgb8eKwY7+++KIpq5QaNHr+f4hYb3y6dgP3PXIl1d43FV/eNR0yQCrmVzfjr5xmi+lzZS0Z+LTR67v9koB3b2WmxQQj1U6BJa0Bmgfh029vOlONv32YBAJZM7ItlV/S36/daC748W1TEX1/ttd7qjFSz64G7r9nO1Mj6KKSCzCKrkAq+HIEC2V7IpWo1TCwQoJI5XH0JAOF+SsilDEwsUO7m7SteXzTcvELvLgzT2mXlsIc8JFttt8J7ZOkyIMof4f5KaPQmHCuoc/Loes5Px4sBANcMi4VMav1SM7Efb8NVjZU/n0ZZgwZJ4X5IDPVFi96IbWfFl8msU+vw+YF8wVMSaLXdGts3tM3/Yd9wPwyI8ofRxGLMy9sw9409WL7hOP71Wzbe3H7e7WNvz68nSqHRm9A/0h/pFtvUUYEqfHLXGPgrZTiUV4Mnvzvhsc59PWWPeYdiSn/7vlcSCYOpZmmPGKyeLDmYW42HvsqE0cRi4cg4PH/NYLuvFWJoVVunbpU2pDihwHgAH8i6ueCryokZWQAYHs/LC+qc8nq9FQpkeyGWsgJHvPB4JBIG0UGe8ZLlAzf+gtATxiZxW8GeCGRZlsUOs9H3FDt0fNZoUzglEnlBi84odLW6dnhHWQEPP+5vjxbih2PFkDDAf24cjgVmKcKm4yWuH2w3YFkWD32ViX/8dBrXr9kveBgfyW9b6GXJdSPizL8L+CtlQoHlARE0g/guowgAsGh0fIdrQWp0INbcNgoyCYOfjpfgie9OoMlLMuRAqz7WHlkBj2DDJSKdbEWjBvd9ehRagwkzB0XhlRuGQSKx/7o9WAQWXLw+Ni7Yp1O7MHvhM7LnyhvdtsBiWdap0gKgNQFDjREcgwLZXsjFCufpY3l4naw7A1mWZYVe1SN6mJEFWgPZI/k1bs86nSiqx8XKZihlkk4bIXTFpP5cQLhPBMERAGw7Ww61zoj4EB+MSgy2eR7flYhv+7h0Wj+MSgwRNLW7zlWKYgue54/T5dh3gZvjS9VqXL92P06X1AsWYulWzOn/OjUZ3z8wAXuenIGTK2dj/f0TAHDZJE/qTy9WNiHjUi2kEgZ/GRln9ZwpKRFYtXAoAOD7zCLMfWO3KALwrqhu0uJUMRe48dIbe+AXk2dKG1AhEt3i5wcuoVFrwJDYQLx9y0jIbexu2CItltNiFtSoPdbhq7Wjl3PuOX3D/KCQcl3Limrdc89paDFAZ5bOhft3X8ZmjdYOX/Vet+MhJiiQ7YW0esg6ro/liTVnZN1pwVVar0FVkxZSCSNsn/WEtNggqOQS1Kr1bu/w9W1GIQCuqj/Ip+eZCn6LPquwThRZs01mt4L5w2M7zfqH+yuF7MqgmEAsnzkAANA/MgCDYwJhMLH4/VSZ6wdsBxq9ES/9egYAcOu4RKRGB6CyUYuF7+5Ho9aAAKUMg8z97S2RSSUY3ScUCaG+YBgGEQFKJEf4gWWBo/me02J+b87GThsQYbMlMgAsSk/A1/eNR3yID4pqW7D4g4NYuek0WnTi1c3yC7rU6AChhbY9hPsrMTSOu5bsPl8Fg9GEwho1juTXeESv3aIz4ouDXNHjshn97bbmsyTIVy7sfPxqLr50N+fNgaw9WmV7kEklgge6u+QFlU3cvS3IR96tVuidMSAqAEqZBI0aA/JE0kbYG6FAthfiTOstnthg3rnAfRnZLHM2NjU6wKHuZAqZBKMSuS1hd/Za1+iNwtb5otEJDr1WQqgvEkN9YTCxOJzXNmNWVq8R5AvuoF6tF7ZmrbkVtOehGf2R3icEb948AgpZ6yXpOvPv8lpbT/PB7lwU1XIdyp6bNwgb7p+A9D4h0Jor3Ef1CYHUzi3fcbycJd8zumyjifPiBIAbRsd3ef6EfmHYvHwqFo9NBACs25+Pu9cdEW11/x472j3bgncOef6nU0j9x2ZMWb0Di9YewBX/2el2E/7vM4tQq9YjIdQHs4dE9/h15pk9nH894ZlANqfceYVePK0FX+6RTFQ4WVYAAHKpBGlxfFa2zmmv29ugQLaXwbKsU623eGKC3d+m9riDhV6WeEInu+VMORo0BsQF+3TZocceBHnBhdZAtkVnxA1r9+OuT44gw00dtDafLoXOaMKAKH+h13tnXDs8Ft89MLHDTe4as7b2cH4NSj3kUcxTWt+Cd3deBAA8c3UqfBUyBPnK8fk943BFKtf6dOYg2y1Q28NbdHmqwHDvhSqUNWgQ7CvHlXaO218pw6qFQ7HOXAR2ILcaz/1wUnQemFxbWnOhVzd9mQFgjjlgVOuMMJhYKKQSBKhkKG/Q4sb3DghyJldjMrH4eG8eAODuSUl2L5KsMWdINKQSBqdLGpBX5f7MH9850VkZWQAYaA5k3WXB5az2tO3h5QXkXNBzKJDtZZTWa6DWGSGVMD1qIGCLVmmB+zOyI8wdUhzBMpB1143526OcrOD6UXHdKt6wRasDQGvB19s7zgsaMnfZ73x7lNuy5oucekpcsA/G9g0FywK/ZHkmk8Sz6rdstOiNGNM3BPMtitd8FFJ8eEc6tq2YhlvH9bH79fjP26nieo9sWfOfvQUj4rq9TTp9YCTeumUkJAzwbUYR1uy66Ioh9phz5U1cwwCZpI2nr70MjQ/CpmWTsOH+8TjwzBXI/r+52P3EDAxPCEadWo9bPzjolqLKHTkVyK1qRoBKhkXpju3YhPgpBK3wb26WF5TVa1DdrIOEQY/sBW3BB8XusuBydqEXz3Dq8OUwFMj2Mk4Vc8FMSqR/m21cR3F3UwSjiRU8ZJ2RkR2ZEAK5lEFZg8YtxQMldS3Ya74Z3uCgrICHz+pmlzWiqkmLCxWNeH93rvB8fpXruzRlXKrB0Uu1kEsZXD+q6y3rrriWdy/I8px7wZH8GmzKKgHDAC9cO6SD5lciYdA/0r9bi5H4EF/EBfvAYGLdbplWr9YLjhL2yAqsMWNgJFbOHwIAWL05x2Nb1tZYt5/LYk7pH94jTSnAtQ8dlxyGmCAfSCQMQvwU+OrecZjUPwzNOiOWfHIE27Ndaw33wR7uu3vL2ET4K2UOv941ZnnBL27+vzpm9uRNjQ50SALWHj4jm1fV7BafY5cFsub715mSBujd7MN+uSCKQPbdd99FUlISVCoVRo8ejT179nh6SJctJ82B7DAH7KqswbeprW7WQaN3/UXlYmUTmnVG+CqkTlnl+yikQpGHO3SyGzOLwLKcVjKxBx3JrBFmUTi1/2I1/vHjaeiNLHzNNw93VCy/t4u7+f5lZJxgyeYI84bGQCZhcLK4XliEuZt3dlwAANw8JkHQszmDsR7QyTZo9Hj8uyzoDCakRgcIZvk94Y4JfXHXpL4AgBXfHBda9NpDXlWzS64ThTVqYUfggen9nPrafkoZPl4yBnOGREFnNOGfP59x6utbcqq4HgdzayCTMLhzYl+nvObsIVGQSRicLW1wa1HrMX7nrBP3kp4QE6RCgEoGg4lFbqXrr22uCmT7hvkiUCWD1mDyWFMeb8fjgeyGDRuwfPlyPPfcczh27BimTJmCq666CgUFBZ4e2mUJ30ZzqBO24y0J8pHDx5z9cIdzwXFzFmtoXJBD2jFLxiZxGc32xVLOhmXZVv9OB7cM28NvH67enI0DudVQySX4xzWDAQD5Lg5kL1Q0Yau5gcH9U5Od8pqhfgpMN3t7Llp7AB/tzYPRjTY1RbVqwRz/r1OdGxi16mTdY2eVU9aI697eh61nyqGQSvD47IEO+0j/fd5gXJkaCa3BhFs+PIRvjhR2+Ttrdl7EjNd24vmfTjn0t63x7s6LMJhYTO4fbtUKzVGUMilWXz8cAGe/Vt+id/rfAICPzNrYecNihEJaRwn2VWCyWTP8mxuzsvy1eqQTds4sYRjGrfICoauXkzWyDMMIXr/uLia8XPB4IPv666/jnnvuwb333otBgwbhjTfeQEJCAtasWePpoV12sCzbmpF1YmYJ4L6MfKtadzgX8IVejvjHtmdskns6fB3Jr0V+tRp+CimuHtrzSmRrTDYHsrw84uErUoRjhTUtLg0CP9idC5bl2rL2j3ReUcdLC4ZifHIoWvRG/N8vZ7Bo7X7BzsfVfHOkECzLFdL1DXeeXR3QmpE9VlDn8q3Rn44XY8E7+5BX1YzYIBW+WToBM83tcx1BKmHw5uKRmDU4CjqDCU9+fwLPbDxp8/18c6QQr2zOBsDtGjiTolo1vjPb2T06M8Wpr21JkK9cqAlwRQBV2ajFz2YpzT2Tk5z62vPcLC/QG004UVwHABiZ2LFZiKMMdGOHL1dlZAEgJZJv8OBe+8fLBY8GsjqdDhkZGZg9e3ab47Nnz8b+/fs9NKrLl+K6FtQ06yCXMkiNcV6gwRMb5D7ngiwnNEJoz+g+oWAYIL9a7VIzdL7QZt6wGPgqHNe+WTI2KRQyc4a6X4Qf7puSjNhgH8ilDHRGk8saVpQ3aPDDMc7Oaek052RjeaKDVPjq3vF4+S9p8FfKkFlQh3lv7nW5C4PBaMIG8/8VbzvlTPpF+CHMTwGtwSTovXnq1DqnFR1uzy7Ho+uPo0VvxOT+4fjlkSlO/d74KWV477bReHz2ADAM8PXhAtz43kFcqGgbXGw9U46nN54Qfi6qbUGDxnkZzXd3XoTeyGJS/7AeFXl1B94v+Gyp862ftmeXw2BiMSw+CMOcvHM2e3A05FIGOeWNblkM5pQ1QqM3IUAlQ7KTF4KApQWXlwey5kYR7b8zhH14NJCtqqqC0WhEVFTbzEBUVBTKyqyboGu1WjQ0NLR5EPbB3ywHRgc4zdDZkpgg92RkNXqjYLnijEIvniAfOQaZ7aJcpVs0mVih0MYZxVDt8VPKMH1gJORSBi8tGAqFTAKphEGC2aHiUrVrCr4+3pcHndGEMX1DMLqP84MIiYTBreP6YMtjUzEuKRQ6owkf7snt+hcdYHt2BcobtAjzU2D2YOdmzgFuF0OQF5g/byzLYvXmbIx4cSvW7c93yt/ZeobzEL52eCw+vXssQv2c05XIEomEwbIrUrDurrEI8pEjq7AOM1/fjRvW7Mc3Rwqx61wlln2VCRPLtcONCuSCgXNOCkCK61qEBeKjVw5wymt2hisD2W1nuf+vmYMcz5i3J8hXLnQv23CkEFtOl+Ffv53FjWsPYNVvZ53+945ZJByc4czSngFukhbojSbUmLvwuSKQ5es8zldQRrYneFxaAKCDTotlWZvarVWrViEoKEh4JCQ4V2N4OXPCLCsYGhfsktePdZOX7OmSehhNLCIClELw7Cxc7Sd7obIJ9S16+MilGNXH+VttAPD2LSOx96krhPavANfSEXCNTrZBo8dXBzlNu7N1pO2JDfbBC9dy1fLbzpajxoXta78+zL2nG9LjnerwYUn7z9u7Oy8KfrVfHLzklKzsSfPW7tVp0U7Tk9ti2oAI/LxsMmYOioKEAY5eqsWT35/AnR8fhtZgwsxBUVi1cKgQCDrLA/TdHRegN7KY2C9MmFNXIgSyTg6gNHoj9po9cO319+0uvLzgw715uP/zDLy/OxeH82vw3u5cVJl1oM6CdyxwhawAgOBTXVzXgkYnZvfbU9OsA8tyUpoQX+cvBPmAvKBG7ZZi6csNjway4eHhkEqlHbKvFRUVHbK0PM888wzq6+uFR2Fh18UFBAffOcTZjgU8vHOBq71kj5uNo4fHBztcrNIeVweyR8yZt5GJwd3umW4vKrkUUe1ajvKBrCucC74+VIBGrQEpkf5CcwBXMjg2EENiA6E3si7r+lVUq8ZOc5HXzWOcLyvg4T9vR/Nr8dHePLz6Rw4AQMJwHfjOljoWKGn0RiFbNdRF3/v2JIb54sM703HgmSvx5NyBSDJvKY/tG4q3bxkJmVQiBCDZTujKVFrfgm+EbKzrtLGW8NKsnLIGp+rOD1ysRoveiNggFQZbaXfsDGYNiRKyiimR/lg8NlHQ/B53shWcUOjlZMcCniBfOaLN1zpXFkrxsoIwP4VLFoNhfgqE+MrBsnB7m/TLAY8GsgqFAqNHj8bWrVvbHN+6dSsmTpxo9XeUSiUCAwPbPIiuYVm21bHAyYVePLyXrKt0mDyt+ljnvw9+qzenvBHFLngfR/O5DIUrKqo7o284Jy3Id4G0gPd4vXdKkku2D62xyOx/ylstORu+yGtivzAhEHMFg2ICEaCUoUlrwP/9wtk5PXJFf6G71M8nHPPPzSlrhN7IIsRXjjgnVb/bS1SgCg9O74/tf5uGbSum4sv7xgm+roPMgWC2g4E6AHx5sAB6I4txSaEYl+x4hzx76BvmB5VcAo3e5NRdjm1m148rBkU6fZHOE6iSY9cT05H1/GxsXTENqxYOFdxOnNm1rE6tQ665i5gzmtbYYoAbOny5Uh8LcLvSfMHXBZIXdBuPSwtWrFiBDz/8EB9//DHOnj2Lxx57DAUFBVi6dKmnh3ZZcalajUaNAQqZRKj0dDZCRtbFTRH4i60z9bE8EQFKoZvUsxud336Tz8imu0hWYIs+LsrINmr0gk5w+kDXZ2N5rhsRB4VUgjOlDU7vWObqIi9LpBIGo/u2fhaWTOyLx2YNwLXm7mE/Z5U49BkU5EQu2L2wF4Zh0D8yoM0ORGtGttGh96e3+L9ylt+qPUglDAby78EJwTjAJRu2Z3P62CtTna+PtYRvsczDe7weK3ReASV/nU4K90OIC3TZPHzBl7P01tZwdSALAP3NBV/nybmg23g8kL3pppvwxhtv4MUXX8SIESOwe/du/Pbbb+jTx/52j0TX8De0wTGBLtvS5jOyjVqDy/RKJ4vqUVCjhkzCOL2il+dfC7kiqV3nKoUtS2dQVs91DZMwrttqs0XfsNZiL5MTt0KPFdTBxAKJob4d5AyuJMRPgZmDucC5fVZ286lSLH7/YI8zGztyKlHeoEWonwKzh7g2oACAuebs603pCXj+msFgGAYzBkbCTyFFUW2LQ1myU0WusdtzlOQIP8ilDJq0Boc66W07U47KRi0iApSY5QQ7se4wyBxAOavg63RJA0rrNfCRS9vo290B72JxorDeadeHYy7yj20P7yXr0oysizxkLUkRCr7IuaC7eDyQBYAHH3wQ+fn50Gq1yMjIwNSpUz09pMuOky7WxwJcxXygirOTclVThI/2cpXq1wyLQZCPvIuze0b/SH88PpurfH7pl7NOkxgcvcRlYwfFBCJA5Zqx2yIu2AcyCQOtwYQyJ1qLCRnmvu7NMAPAInNr35+OF0Nn4Fo77sipwLKvjuFAbjVe+rX7nZdYlsW7Oy+YXz/eJe4e7blpTAIOPXslXrlhmCDN8FFIhcDs56yee362ZmTFFcjKpRLBa9iRAOTLQ1xB3o3p8S5boNvC2c4FfDZ2ckrPW+v2lIFRAfCRS9GoNThNo+mqjl7tsfSSdfYOGo87MrK8tICcC7qPKAJZwvW4Wh/LIzgXuEBfWlrfIhh53zvFuV6l7blncjJGJQajUWvA09+fcMoFktfHutrj0hoyqUSw4HKmpo8PZMd64D1NSQlHZIAStWo9tmeX41hBLR78IhMGc0ZpZ04lMgu6t1W65Uw5jhXUwUcudboZvS0YhrGazeblBb+cKOlRQZFGbxQKYFy5gO0pfEYzu4eBYH5VM/ZeqALDuLYgzxbOdl7406yPnekit4LOkEklwmLnmBN0siYTK9QyjExw7SK3f6Q/JAxQp9a7zDHHLYGsWVpwqVrt8gYplxsUyPYCTCZW6FPvqu14HsFL1gUXlHX782EwsRifHOrUnvfWkEoYvLZoOJQyCfacr8LXhx2XGHgyewkAfcKc6yWrM5iEbW93F68B3M13odmLd83Oi7hr3RG06I2YOiACC0fGAQDe2Hbe7tczGE1Ybe46de+UJES6USphjSkpEQhUyVDRqBU+O93hTClXUR/urxQqu8UEX/nf00CQt0ebPiBCWKS5Ez4TWFzXgnq1Y1KqigYNsszJhhlucP6wBi8BcEbBV151M+pb9FDKJC5pvmOJSi4V7mu7zU4jzsYdgWxkgBIBKhmMJhZ5Va5tJ365QYFsLyC3qhnNOiN85FL0i3BdBTYAxJgzss5uitCsNeAr8zbivZNdm43lSY7wxxNzBgIAXv71jEN91Zu0BmELMt0FDQPswdlesqdL6qHRmxDiK3f558oWi9K5QDarqB51aj2GJwRjza2j8NisAZBJGOw+V2l3B7DvMopwsbIZIb5y3D/VPZ+xzlDIJLgqjfP85FuWWoNlueyXwWhqc/yksAsT6LFCr87gC77O2mHB1V63qTUY8W0Gp42+ZZxn6imCfFqdIOx5D53BywqGxwchMsAziw5eJ+sMCy5eHzssPsgtkg/e9o+fR2fCsixKG7j7mSs1spxzARV89QQKZHsBvCH6kNhAyFx8UeH9CIud7FzwzdFCNGoMSA73c4tXKc9dk5IQF+yDZp0Rp4t7Xh1/rKAWJhaID/FBtJObONgLn5HNd9JqvzXDHOqxQKlfhD9GmTV4yRF++GTJGPgpZUgI9cUNZouuN7ad6/J1WnRG/Nd83rIrUtyuYbYFLy/4/VQZ9O0CVZ7PD17Cde/sw0u/tu3MJMiJXLwL01P4TF1+VTNadLa3Us+WNmDoyj9w9f/2YGdOBViWxeZTZahp1iEmSIUZAyPcNeQOCPICB3WyfDevK13QzcteeC1rdlkD1DqDQ6913Ox+4MxWyJ3B3xP2Xahy+rb84bwaFNa0QCGVICXKtdll0sn2DApkewGtNzTX6+R4jawzmyIYTSw+3pcHALh7svu8SgFOYjA4lrtZOWK4fcSD+lie1qYI3ZMW1DbrrGpNW9+TZ6QSPC9el4Y7J/TBF/eMa9N+9aEZ/SGTMNhzvqrLrflP9uehvEGLuGAf3Dbe/XpLW4xPDkWYnwI1zTrsv1jd4XmWZfH5gUsAuK32aovOTPwCVmyOBTwR/kqE+SlgYjuv1P5wTx6adUacKW3Akk+O4JYPDuG9XVzR501jEly+OO+MwTG8c0H3rg06gwlnSxuwMbMI//rtLPac57bEXdXNyx5ignwQFaiEiW3N5vcUwbHARR292jMkNhBRgUqodUYcynVuMxu+096i9HiXtHe2hNfJXiDngm5BgWwvgL8ouaPgg7fgcqZGduuZMhTWtCDYV47rzZpIdzLAfHE558Aq+aiH9bEA0De8VVrQneK1Fd8cx8J39wuNDwAugDpqkZH1JGlxQfjndWnCIoonIdQXi9I5Z4P/brWdla1t1mGN+Wb1+JwBbnEqsBeZVIKrzS1FrVnBZRXVC9kbrcEkVPGrdQbBfkxsjgU8DMO06mRtBIL1LXr8epL73F07PBYKqQQHcqtxprQBUgnjkSIvS1Jj7JdH8NSr9Ziyejuu+t8erPgmC+/vzoXWYELfMF+XdfOylxFO0MkeK6gVZFTuysjylnWAc+UFp4rrsetcJSSM69tvA1zhGkDSgu5CgexljsFowukS7qIyNC7Y5X+Pb4pQUtfiNCuUD/dw2djbxvWBj8L9QQbfB/t8DzOyeqNFUZSH9LEAZ8EllTDQ6E2oaLSvp3qDRo895t7vr/2RI2xvX6xsRq1aD5VcgrRYcQZKALDsiv6QSxnsv1iNdfvyOmgtC2vU+OvnGWjUGDAoJhDXDY/z0Ehtc8s4Llj7/WQpCmvaZtO/y+CCW76Y67MD+dDojThT0gATC0QFKt3q79tdutLJbsoqgUZvQkqkP968eQR2PDEdN4yOB8Nw9miekunw8NKCnLJGu50lDufXoLxBC4VMgrF9Q3HnhD5YtXAo1t8/weNaZj6D2tNAtqpJiwe/zISJBeYNi+mwuHQlljpZZ917+AXu/OGxSAxzfUEhL13Iq2q2KSUiOkKB7GXOufImtOiN8FfKkOzCVps8scE+UMgk0BpMTmmHWtGowdFLtWAY4I4Jninq4HVL58qbenSBPFvaALXOiECVTBDzewKFTCIUp1jqZGuadTiU23HbGgD2na8S7KwKatRC8wF+q35EQjAUMvFeRuKCfXD7+L4AgJU/n8Ff1uzHiaI6sCyLLw5ewpw3duNwfg18FVL8c/4Qt8pW7GVQTCCmpITDxEKQ2ACcvdam41y2ctX1QxEbpEJVkw6bjpdY2O0Fe2LIdpMa3XlGdr3ZmeDmsYlgGAZxwT54bdFwnH1xLl7+y1C3jdMWfUJ94SOXQmsw2V1pzjvIzB8ei2+WTsA/r0vD4rGJHg/KAccysgajCQ9/dQyl9Rr0i/DDK9cPc+7gumBS/3AopBIU1KhxsdLxOoCLlU347RRn9/jA9P4Ov549xAap4KeQwmBind6F8XJGvHcgwilkmE34RyYGu+UmLZdKMMSsKc1ygo0LX0E7IDLAY3ZIyRF+kDDcNmelnZlMS3gtaXrfUI8HSu0tuLQGI2567wBuev8gduZ03JLbmcNp96ICuWrdN/88D43eKASyntT82suzV6fi+WsGI0ApQ1ZhHa57Zx/mvrEHf//xFNQ6I8YmhWLzo1MxNkm874V3UdhwpFCwetp2thwNGgNig1SYmhKBJZP6AgA+3JuLE+YGKK72jXaU1la1DR0WiSeL6nG6pAEKqUSwU+NRyaWQimDRIZEwgg2XvY0R+B2ytFjPygisMTQuCBKGk4aVdVMe9uqWHBzIrYafQor3bh8Nf6XMRaO0jp9ShnHJ3Hd4e3a5w6+3dudFsCwwc1CUy9q6t4dhGPQXdgBJXmAvFMhe5hw1Ww+N7uM+beZwc5V0lvlm6gi8Obe7W7paopJLhUKpcz24uPCLCU/qY3mSzFn5PPNq/90dFwWNZXsNJsuy2HmOC25fXsBl/MoaNPji4CWhuYOn9bH2IJNKcPfkJPz5+DQsHBkHluW6AKnkEjx/zWCsv2+8W7YNHWFy/3CkRgdArTPiy8Nccdd3Zvup60fHc3rRsYnwU0hxrrwJv50qAyDORgiWpERxZva1an0HucvXR7hs7Jy0aIS4uMjGEVobI9gbyHIZ2SEiXGT4KWWClIp3HrCH30+WCgV4q28YLnRtczdXOsmGq7iuBT8cKwYAPDjD9dpYS1pb1VIgay8UyF7mCAGHG7WZ/PaUMzKyx8zV8p4MZIHWatLuOhcczK0WrHU80f2qPX0E54JmnC9vFNqxAsC2MxVtjN3PljaivEELH7kUk1PC8ejMFABcVragRg0JA8H6yhuIDFDh9ZtG4Ju/TsD9U5Px2yNT3O6C0VMYhsF95m526/blo7BGLZi/8wWQgSo5bjIXP/Ete13dOMRRVHKpsLiyzGiqdQZBNrF4TIJHxmYv3XEuqGrSorReA4ZpDYDFBn+ttbfDl0ZvxFPfnwAA3DclCfOGxbhoZF1zRSpnX3Y0vxYNmp77fn+wOxcGE4sJyWEY5SbnBR4+kHXEJae3QYHsZUxZvQbFdS2QMK7vd20JnwU6VdLgkGDdYDQJWj932bjYQij46oYtyqnietz76VHoDCbMGhzl1qy4LfqaM4+5lc14ZuNJ6I0srkiNRGp0AHRGE349WSqcu8MsNZjYLwwquRTXj4pHUrgfGjScx2RqdKBo/Fa7w9ikUDx79SAkR3hOr9wTrh0ei6hAJSoatXjgywyYWM76rK+F9v2uSX3Bx+WxQSqXdiJyFqlWWr3+cqIUTVoD+oT5YnxymKeGZhf8YmHXucpOG1cArbKCpHA/t2+920t3GyOcKKpHg8aAcH8lnpqb6rqB2UFimC/6R/rDYGKx51xVj15j/4UqfH6Q2/VwdzYWsLTgooysvVAgexlz1LylPSgm0K0Xzb5hfghUyaAzmJDjQB/ynPJGqHVGBChl6O/hoIOvJrVXWpBb2YQ7Pz6MJq0B45ND8dbikR6vSAZaM7LZZY04eqkWfgop/m9BGhaO4jSIGzOLhHN3mfWx083bdTKpBMvNWVnA8/6xvQ2FTIK7JiUBAE4VcwER3/SBJyHUV+gGJlbbrfYMMusPvzpUgDe2ncOh3Gqh/eyN6Qmiz5iPSAjGotHxMJpYPLr+WBubuvbwhV5idvoYkcB9r08U1UOj77q5AK+XH5sU4lFPXx7eveDPHuhkC2vUeOirTBhNLBaOisPk/uHOHl6X8MXFuZXNHbr1Edbx/KeOcBmtsgL3BhwSCYPhTvEj5H53hJsK1TrDcrunK+eCkroW3P7RYVQ36zA0Lggf3JEOlVwc3qQJoT6wnMon5gxEXLAPrhsRBwnDaaovVTejXq1HhlnWMX1Aa+eka4fFCluik1M811Gpt7LYrIMFAB+5FPOGxXY455mrU3HdiFg8fEVKh+fEyMT+4ZAwnCvGG9vO46b3D+JYQR2kEgaLRrvfN7q7MAyDV64fhkWj42FigeXrj+Gn48VWz+X1sWlx4pQVANy1LiZIhRa90WoBaHsEP2kPWgtawvvJ7sqp7GC31xktOiPu/zwDtWo9hsUH4V9/GeqR5ENcsA9Ucgl0RhMKahx3/ukNUCB7GXP0kucM6/mCrxMOFHwJ3WHcZKrdGckRfpBKGDRqDChv6Ny54JGvj6G4rgXJEX5Yd9cYUW2/K2VSwdtxREIwbp/QFwAQFajCJHP24YdjxdhzoRJGE4v+kf5ICG0thJJIGHx69xi8d/tozPRgF6LeSpCPXPCVnTcsxupOS3yIL/5380jR62N5RiWGYOfjM/DyX9JwzbAYhPtzhV3XjYj1mFNJd5FIuGD2xnQumH1sw3GrMgM+ky7mjKxEwmD+CG6BxBc82cJkYpFxyfNdCy1J7xuCQJUM1c06HLRhK9gelmXx5PcncLa0AeH+Cqy9bbTHkg8SCSNkZbMd2NHsTVAge5nSrDUIxQeeqJYfLhR89bzV4bFCvtDL81vYSplUsK7qTIRfp9YJThHrloxFmL/4NIp/GRmHuGAfrL5hWBsLI15e8MOxYuzINssKBnTMukYGqDBnSLQopBK9kcfnDMR/Fg3H89cO9vRQnEZimC9uHdcHb98yCkeem4kDz1zhdh9SR5FIGPx74TDclJ4AEwv8/cdT0Bpat+brW/RChm2IiANZgLtGAMCO7ErUqXU2zztf0YQGjQG+CikGxXjGqaA9cqkE1w7nAvFvM4q6OJvjwz15+DmrBDIJg3dvHe3WRg7W4DP2J4sdaxXcW6BA9jLleGEdjCYWccE+QttYdzLcrM87V9GIJq2h279fp9Yh12xq7a42h10xQGiMYDuQ5bPIyeF+orV0+tvsgdj71AyhgI1nzpBo+CqkuFStFrJJM1Ip6yo2lDIprh8dj0ARZfqdCcMwiAnygVwEesvuIpEw+NfCoYgKVKK+RS8sCAHgjLnQKyHUB0G+4v6/S40OFApAfztZZvM8Xh87MjFYFPpYHr419W8nS7t0LzCaWLz553kAwPPXDhaFnzS/m3KKAlm7EM8nj3AqvD7WU5XykYEqxASpwLI9+zLy2tqkcD/ReEgOiOq6D3ZmgXiyyJ1hLZvqq5Bhblo0AEBnNMFXIRWF9y1BeBNSCYPrRnAZzR8ttuYF/9gYcWdjefis7I+dyAvEpo/lGR4fhAFR/tAaTPglq7TTc89XNKJRa4CfQopbx3mme2R7hpk78p0oqndau93LGQpkRci7Oy/Yba5ti6MiMOEXGiP0oOBLTPpYHsG5oBMLLl4vNqpPsDuG5HR4T1KAa/molImjSI0gvIkF5kB2e3arN7PgWCDiQi9L5o+IBcMAh/NrUGij6OioyPSxPAzDYNFoLivbvtFLe/h7zfCEYFF0iwOAAdH+kEsZ1LfoUVTb4unhiB4KZEXG7ydLsXpzDha8sw/f26nvaY/RxApfTk96l/I6Wd4L1hosy+KfP5/Gcz+cbGM1IoaOXu3ht+IvlDdZXSUbjCYhaBeDZ2xPGJ8chmhzgc30geRKQBA9YXBsYAdv5lNmaYEYO3pZIybIBxPMHr7WLMVK61tQVOt+n3J7WTAyDjIJg+OFdTjfiRws85I4mu5YopRJhba4pJPtGgpkRca45DBMHRABjd6Ev32bhWc2nrDLy8+S7LIGNGkN8FfKhF7mnmB4AnfB7syC63xFEz7Zl48vDxUIOiWTicVxEW7RJ4X7QSZh0Kg1oKyhYx/ynPJGNOuM8FfKhKpTb0MqYfDaouG4a1LfNtlZgiC6xwKLrXm1zoCLlZwkScyOBe3h38PGzKIOi3devjY41r0+5fYSEaAUNP6dFX0JSZME8dxrAGCoecFDgWzXUCArMkL9FFi3ZAxWzBoAhgG+PlyI69fsR0G1/X5yGRYrTE9ulQyNCwLDcH2rKxutW1b9ebbVp/CtHRew93wVcqu4SliVXILUaPEEhAqZROiiZK0xQiYvh/DwvDvK5JRwvHDtENF43xKEN3Kdxdb81jPlYFkgKlDpFd3WeOamRUMpk+BiZbPQlYxHrPpYS240F31tzCyy2mWyvkUvdNASU0YWAIaadbInO9nRJDgokBUhEgmDR65MwWd3j0WonwKnSxpw+8eH7G732toIwbMXmACVXOjIZctPdru5+0pcsA9YFli+4Rj+OM0dGxYvrkpYwLLgq+NW1bFL4ssiEwThGSy35ldvzgHgXdlYAAhUyTFzcBSAjp6yvD5WzAWh0wdGINxfgaomHXbmVHZ4npeC9QnzFZ1VomVGlgq+OkdcUQLRhikpEfj1kckI91fgUrUam4533sebR1gpi+AC0+onW9fhudpmnZA9/vyesUiNDkBVkw6vbeEu+mJbIQOt7QOtWXDxnbBGiXDcBEG4H77yv7iOK9jxFn2sJX8Z0eovzb+PRo0eZ0u5DK2nEyadIZdKsNAskbJW9CW4zIioqJiHCr7shwJZkRMT5IN7JicD4NwMumq5dzS/BiX1GkgljCj8V3k/2Swr2yO7zlXCxAKp0QFIjvDHO7eOgq9CCn7xKTbNEtBa8NVeWlDVpMUls/yDMrIEQQCtW/M8abHe4VhgydQBEUiO8ENNsw6L3z+I4roWHCuog4nlPHGjg8TdfY1vc7w9uwIVjW1rG/ii6FEiLM5VyqRCjQvpZDuHAlkv4LbxiQhUyXCxshl/nLZtTl1a34KlX2QCAOYNjYGfCAT4Qka2qK6NKwHAXVgA4AqzIL9fhD9e/kua8LwYM5u8tOBCRVvnAv6CmBLpjyAfcZudEwThHgJUcswyb80D8Jq2wZYoZBJ8cc849AnzRUGNGovfP4hfTnC7g2NEnI3lSYkKwKjEYBhNLL450pqVNZlYoRBZjEkToPXz0t755+O9eVj8/kGU1lOmFqBA1isIUMmxZGJfAMDbOy5Y1cu06Iy4/7MMVDVpkRodgFULh7p5lNZJjQ5EmJ8CdWo9vs9srRw1GE3YmcMFslcOau0e9ZeR8fj3wqFYfcMwUfZZ7xvuB7mUQZPW0Kb4gZdIeKvtFkEQroFv/Rzur0CMyLOXtogN9sHX941HYigXzH5zlLuWjxaBfM0e+EYHXx8uhNG8q5lb1Yz6Fj1XVCyS9rrtGWqlw1dZvQb//j0bB3KrsWJDVpe7tL0BCmS9hLsmJcFXIcXpkgbsPNdWtM6yLJ78/gROFtcj1E+BD+5IF0U2FuBW8w9M7wcA+N+280Lv8YxLtWjQGBDiK8eIdqvhm8cmCtWmYkMulWDmIC7D8tT3J4QCvExBH+sdF3aCINzDjIGReP6awfjvTSOsdtTzFmKDfbD+fi6Y5RFbIwRbzBsWg2BfOYrrWrDDvBN4zHzNHhYXLNp2yNYKvtbuugid+b5zILcaH+3N89j4xII4//eIDoT4KXDruEQAwLs7LgjHuT7RF/BzVglkEgbv3joKCRYXGjFw2/g+iApUoqReg68OFQBolRXMGBjpdVZV/5w/BEE+cpwuacA7Oy5AbzQJrgze2tGLIAjXwDAM7p6chCkp3t9ghA9mU6MDMKZviOBKI3ZUcqmglf3i0CUA4my6054B0f5QSCWob9GjsKYFFQ0afH2Yu4fymf5X/8gRCu96Ky4LZPPz83HPPfcgKSkJPj4+6NevH1544QXodLo25xUUFODaa6+Fn58fwsPD8cgjj3Q4h+C4d0oyFFIJjuTX4ouDl/DPn09jwqo/8d9t5wAA/7xuCMab7V7EhEouxSNXpgAA3tlxAWqdAX/y+lgLWYG3EBmowovXDQEAvL39Ar7LKIJGb0KQjxzJ4d5xYScIgugJscE++P3RKfh26URIvCgJcYtZXrDrXCUKa9StbdBFHMi27/D1/u5caA0mjO4Tgv8sGo6Zg6KgM5qwfP3xbjdOupxwWSCbnZ0Nk8mE9957D6dPn8Z///tfrF27Fs8++6xwjtFoxLx589Dc3Iy9e/di/fr1+P777/G3v/3NVcPyaqICVViUzq0q//7jKXyyLx8VjVoEqmR4Ys5AQQckRm5MT0BiqC+qmnR48eczuFDRBJmE8dosxfzhsbgqLRoGE4u//3gKAHdB9KYLO0EQRE/wRolEUrgfpqSEg2WBD/bkIqeMy2KK3WWGL/jada4CX5p3NB++oj8YhsEr1w9FuL8SOeWNePWPHE8O06MwrBuddl999VWsWbMGubm5AIDff/8d11xzDQoLCxEbGwsAWL9+PZYsWYKKigoEBnZtVdLQ0ICgoCDU19fbdb63U1ijxrw390BvZDFrcBTmD4/FlAHhUMrE34VpY2YRVnyTJfw8ITkMX98/3oMjcozqJi1m/3c3qpu5HYQVswYImWeCIAhCXGw+VYalX2SAYQCW5Rrx7Hv6Ck8Pq1O+OlSAZ384Kfw8PD4IPz40SVhM7MiuwF3rjgAAvrhnHCanhHtknM6mO7GdWzWy9fX1CA1tFYcfOHAAaWlpQhALAHPmzIFWq0VGRoY7h+Y1JIT6Yv8zV+LY87Pw5uKRmDk4yiuCWAC4bkQcUiJbt9552y1vJcxf2cYujBwLCIIgxMvMQZGIDlQJXuUjRCwr4BkW39ay7ZErU9pkxGekRuL28X0QLrLOZO7EbYHsxYsX8dZbb2Hp0qXCsbKyMkRFRbU5LyQkBAqFAmVl1v1StVotGhoa2jx6G/5KGVRy7wheLZFKGPxt9gDhZ2/Ux7ZnbloMHps5AAtGxGJskndU8BIEQfRGZFIJFo9NFH72BpeZAVEBUJhdFYbEBlpNAD179SD8sXzKZZON7S7dDmRXrlwJhmE6fRw9erTN75SUlGDu3LlYtGgR7r333jbPWdPasCxrU4OzatUqBAUFCY+EBHHaNBHWmTMkGvdMTsKD0/shOdzP08NxCo/OTMEbN48UrYULQRAEwXHz2ATBKUfMhV48CplE2O1bPnOA1djIRyFFWC/OyHZbI1tVVYWqqqpOz+nbty9UKs74uaSkBDNmzMC4ceOwbt06SCStN/vnn38eP/30E7KyWnWTtbW1CA0Nxfbt2zFjxowOr63VaqHVaoWfGxoakJCQ0Gs0sgRBEARB9JxvjxaisLYFj81M8YrCtYpGDQprWnqVfK07Gtluu+aHh4cjPNy+9HVxcTFmzJiB0aNH45NPPmkTxALAhAkT8PLLL6O0tBQxMTEAgC1btkCpVGL06NFWX1OpVEKp7L0rD4IgCIIges4ikTbcsUVkgAqRAd7ZFc4duMy1oKSkBNOmTUNiYiI+++wzSKWtms7o6GgAnP3WiBEjEBUVhVdffRU1NTVYsmQJFixYgLfeesuuv9PbXAsIgiAIgiAuZ1yakbWXLVu24MKFC7hw4QLi4+PbPMfHzlKpFL/++isefPBBTJo0CT4+Prjlllvw2muv2f13+NfqjUVfBEEQBEEQlxt8TGdPrtWtPrKuoKioiAq+CIIgCIIgLjMKCws7JEPb4/WBrMlkQklJCQICAtwm2uYLzAoLC0nO0ANo/hyD5s8xaP4ch+bQMWj+HIPmzzG8Yf5YlkVjYyNiY2M71Fe1x2XSAnchkUi6jNZdRWBgoGg/BN4AzZ9j0Pw5Bs2f49AcOgbNn2PQ/DmG2OcvKCio65Pg5s5eBEEQBEEQBOEsKJAlCIIgCIIgvBIKZHuAUqnECy+8QH62PYTmzzFo/hyD5s9xaA4dg+bPMWj+HONymz+vL/YiCIIgCIIgeieUkSUIgiAIgiC8EgpkCYIgCIIgCK+EAlmCIAiCIAjCK6FAliAIgiAIgvBKKJDtJu+++y6SkpKgUqkwevRo7Nmzx9NDEiWrVq3CmDFjEBAQgMjISCxYsAA5OTltzmFZFitXrkRsbCx8fHwwffp0nD592kMjFjerVq0CwzBYvny5cIzmr2uKi4tx2223ISwsDL6+vhgxYgQyMjKE52kObWMwGPD3v/8dSUlJ8PHxQXJyMl588UWYTCbhHJq/Vnbv3o1rr70WsbGxYBgGP/74Y5vn7ZkrrVaLhx9+GOHh4fDz88P8+fNRVFTkxnfhOTqbP71ej6eeegpDhw6Fn58fYmNjcccdd6CkpKTNa/Tm+QO6/gxa8te//hUMw+CNN95oc9wb55AC2W6wYcMGLF++HM899xyOHTuGKVOm4KqrrkJBQYGnhyY6du3ahYceeggHDx7E1q1bYTAYMHv2bDQ3NwvnrF69Gq+//jrefvttHDlyBNHR0Zg1axYaGxs9OHLxceTIEbz//vsYNmxYm+M0f51TW1uLSZMmQS6X4/fff8eZM2fwn//8B8HBwcI5NIe2eeWVV7B27Vq8/fbbOHv2LFavXo1XX30Vb731lnAOzV8rzc3NGD58ON5++22rz9szV8uXL8cPP/yA9evXY+/evWhqasI111wDo9HorrfhMTqbP7VajczMTPzjH/9AZmYmNm7ciHPnzmH+/PltzuvN8wd0/Rnk+fHHH3Ho0CHExsZ2eM4r55Al7Gbs2LHs0qVL2xxLTU1ln376aQ+NyHuoqKhgAbC7du1iWZZlTSYTGx0dzf773/8WztFoNGxQUBC7du1aTw1TdDQ2NrIpKSns1q1b2WnTprGPPvooy7I0f/bw1FNPsZMnT7b5PM1h58ybN4+9++672xxbuHAhe9ttt7EsS/PXGQDYH374QfjZnrmqq6tj5XI5u379euGc4uJiViKRsJs3b3bb2MVA+/mzxuHDh1kA7KVLl1iWpflrj605LCoqYuPi4thTp06xffr0Yf/73/8Kz3nrHFJG1k50Oh0yMjIwe/bsNsdnz56N/fv3e2hU3kN9fT0AIDQ0FACQl5eHsrKyNvOpVCoxbdo0mk8LHnroIcybNw8zZ85sc5zmr2s2bdqE9PR0LFq0CJGRkRg5ciQ++OAD4Xmaw86ZPHky/vzzT5w7dw4AkJWVhb179+Lqq68GQPPXHeyZq4yMDOj1+jbnxMbGIi0tjebTCvX19WAYRthhofnrGpPJhNtvvx1PPPEEhgwZ0uF5b51DmacH4C1UVVXBaDQiKiqqzfGoqCiUlZV5aFTeAcuyWLFiBSZPnoy0tDQAEObM2nxeunTJ7WMUI+vXr0dmZiaOHDnS4Tmav67Jzc3FmjVrsGLFCjz77LM4fPgwHnnkESiVStxxxx00h13w1FNPob6+HqmpqZBKpTAajXj55ZexePFiAPQZ7A72zFVZWRkUCgVCQkI6nEP3mLZoNBo8/fTTuOWWWxAYGAiA5s8eXnnlFchkMjzyyCNWn/fWOaRAtpswDNPmZ5ZlOxwj2rJs2TKcOHECe/fu7fAczad1CgsL8eijj2LLli1QqVQ2z6P5s43JZEJ6ejr+9a9/AQBGjhyJ06dPY82aNbjjjjuE82gOrbNhwwZ88cUX+OqrrzBkyBAcP34cy5cvR2xsLO68807hPJo/++nJXNF8tkWv1+Pmm2+GyWTCu+++2+X5NH8cGRkZ+N///ofMzMxuz4fY55CkBXYSHh4OqVTaYVVSUVHRYZVNtPLwww9j06ZN2LFjB+Lj44Xj0dHRAEDzaYOMjAxUVFRg9OjRkMlkkMlk2LVrF958803IZDJhjmj+bBMTE4PBgwe3OTZo0CChOJM+g53zxBNP4Omnn8bNN9+MoUOH4vbbb8djjz2GVatWAaD56w72zFV0dDR0Oh1qa2ttntPb0ev1uPHGG5GXl4etW7cK2ViA5q8r9uzZg4qKCiQmJgr3lEuXLuFvf/sb+vbtC8B755ACWTtRKBQYPXo0tm7d2ub41q1bMXHiRA+NSrywLItly5Zh48aN2L59O5KSkto8n5SUhOjo6DbzqdPpsGvXLppPAFdeeSVOnjyJ48ePC4/09HTceuutOH78OJKTk2n+umDSpEkdLN/OnTuHPn36AKDPYFeo1WpIJG1vEVKpVLDfovmzH3vmavTo0ZDL5W3OKS0txalTp2g+0RrEnj9/Htu2bUNYWFib52n+Ouf222/HiRMn2txTYmNj8cQTT+CPP/4A4MVz6KEiM69k/fr1rFwuZz/66CP2zJkz7PLly1k/Pz82Pz/f00MTHQ888AAbFBTE7ty5ky0tLRUearVaOOff//43GxQUxG7cuJE9efIku3jxYjYmJoZtaGjw4MjFi6VrAcvS/HXF4cOHWZlMxr788svs+fPn2S+//JL19fVlv/jiC+EcmkPb3HnnnWxcXBz7yy+/sHl5eezGjRvZ8PBw9sknnxTOoflrpbGxkT127Bh77NgxFgD7+uuvs8eOHROq6u2Zq6VLl7Lx8fHstm3b2MzMTPaKK65ghw8fzhoMBk+9LbfR2fzp9Xp2/vz5bHx8PHv8+PE29xStViu8Rm+eP5bt+jPYnvauBSzrnXNIgWw3eeedd9g+ffqwCoWCHTVqlGAnRbQFgNXHJ598IpxjMpnYF154gY2OjmaVSiU7depU9uTJk54btMhpH8jS/HXNzz//zKalpbFKpZJNTU1l33///TbP0xzapqGhgX300UfZxMREVqVSscnJyexzzz3XJnCg+Wtlx44dVq95d955J8uy9s1VS0sLu2zZMjY0NJT18fFhr7nmGragoMAD78b9dDZ/eXl5Nu8pO3bsEF6jN88fy3b9GWyPtUDWG+eQYVmWdUfmlyAIgiAIgiCcCWlkCYIgCIIgCK+EAlmCIAiCIAjCK6FAliAIgiAIgvBKKJAlCIIgCIIgvBIKZAmCIAiCIAivhAJZgiAIgiAIwiuhQJYgCIIgCILwSiiQJQiCIAiCILwSCmQJgiAIgiAIr4QCWYIgCIIgCMIroUCWIAiCIAiC8EookCUIgiAIgiC8kv8HeI40jFnMyScAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A helper function to plot the original time series, and the 3 decomposed components\n", + "def seas_decomp_plots(original, decomposition):\n", + " _, axes = plt.subplots(4, 1, sharex=True, sharey=False, figsize=(7, 6))\n", + " axes[0].plot(original)\n", + " axes[0].set_title('Original')\n", + " axes[1].plot(decomposition.trend)\n", + " axes[1].set_title('Trend')\n", + " axes[2].plot(decomposition.seasonal)\n", + " axes[2].set_title('Seasonality')\n", + " axes[3].plot(decomposition.resid)\n", + " axes[3].set_title('Residuals')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "seas_decomp_plots(additive, additive_decomposition)\n", + "#if value for period > 12 then " + ] + }, + { + "cell_type": "markdown", + "id": "daee768f-f450-4552-84bf-ad433c27902b", + "metadata": {}, + "source": [ + "### Challenge Exercise\n", + "Implement 'seasonal_decompose' for the multiplicative model.\n", + "Evaluate the results." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a9d2f2ca-e9f6-4037-8344-c1f00fcf82ed", + "metadata": {}, + "outputs": [], + "source": [ + "multiplicative_decomp = seasonal_decompose(x = multiplicative, model = 'multiplicative', period = 5) # <- Plotting w/o residuals to show the pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5cb3e935-9896-4c20-9e7b-32dd3fce23d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A helper function to plot the original time series, and the 3 decomposed components\n", + "def seas_decomp_plots(original, decomposition):\n", + " _, axes = plt.subplots(4, 1, sharex=True, sharey=False, figsize=(7, 6))\n", + " axes[0].plot(original)\n", + " axes[0].set_title('Original')\n", + " axes[1].plot(decomposition.trend)\n", + " axes[1].set_title('Trend')\n", + " axes[2].plot(decomposition.seasonal)\n", + " axes[2].set_title('Seasonality')\n", + " axes[3].plot(decomposition.resid)\n", + " axes[3].set_title('Residuals')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "seas_decomp_plots(multiplicative, multiplicative_decomp)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3c049081-8880-4cdf-8e17-13681b52240c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "144" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Get the length of our input time series\n", + "max_x = additive.shape[-1]\n", + "max_x" + ] + }, + { + "cell_type": "markdown", + "id": "2a61df0e-cf2f-4107-a903-d501d826ceb0", + "metadata": {}, + "source": [ + "First we need to split our additive data into a test and train set, with the idea that we will train ARIMA on the train data. Once the model is fitted, you can use it to forecast future values by calling the predict method, then we can compare the prediction to the ‘true’ values to see how well it did." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "43609e64-82df-4f20-b935-b287530c1eb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We'll split at index 120, which will leave 24 timepoints to predict in the train set\n", + "split_idx = 120 #this is the number of values for 'test data'\n", + "\n", + "# For plotting we need x-coords for the full dataset so make a list of [1, 2, .., max_x]\n", + "xs = list(range(max_x))\n", + "\n", + "# Split the train data and the x-coords\n", + "train = additive[:split_idx] #taking additive data and splitting based on first x elements\n", + "xs_train = xs[:split_idx]\n", + "\n", + "# Split the test data and the remaining x-coords\n", + "test = additive[split_idx:] #opposite (of above) for the test data! \n", + "xs_test = xs[split_idx:]\n", + "\n", + "# Plot the test and train data to see the 'truth' value of the test set\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, test, color = \"red\")\n", + "plt.title(\"Train/Test split for additive Data\")\n", + "plt.ylabel(\"Value\")\n", + "plt.xlabel(\"Time\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8f4c12b7-a828-4e2d-9889-61eff5a64614", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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QEJC/uuSWlKCIiEixsWHDBpKSkggICOCee+65ZflWrVpx2223ceHCBZZmvx6cV4sXW/7U8E6hUIIiIiLFxpL/78Xo1asXTk63/ggzmUwMGjQIyOcwz99/w8aNlq+VoBQKJSgiIlIsZGVlWXtBetswByR7mGfVqlX8/fffeQu+bJlliftGjaBWrbzVITZRgiIiIsVCbGwsJ0+exMvLiw4dOuT6vgYNGtC4cWM8PT3ZvXt33oJreKfQ2eGlcBERkYL34/+v4Nq9e/cbblh7I9999x3VqlXDw8PD9sDJybB6teVrJSiFRgmKiIgUC9nzT2wZ3slWt27dvAdeuBBSU6FuXQgJyXs9YhMN8YiIiMP7888/2bdvH87OzvTo0aNwg3/+ueXPxx+3rIEihUIJioiIOLzs4Z22bdtSvnz5wgu8fz9s2QLOzjB0aOHFFSUoIiLi2LKysvjiiy8A6NOnj30qPXcud+Vmz7b8GRoKVarYJ7bkihIUERFxaIsWLWL//v2UK1eOIUOG5K+ytDR48kmoVg2OHLl12a++snz9xBP5iys2U4IiIiIOyzAM/vvf/wLwzDPP4Ovrm78K3dzg8GG4dAkmTbp52aVLLQu0BQZCt275iys2U4IiIiIOa9myZezatYuyZcsyZswY+1T65puWP+fNs8wxuZHsybGPPQY32ZRQCoYSFBERcUiGYfD6668DMGrUKCpUqGCfiu+5B/r0gaws+M9/rl/m0KH/rX3y+OP2iSs2UYIiIiIOadWqVcTGxuLp6clzzz1n38rfeMPyyvAPP0BMTM5rZrMlgTEM6NIFate2b2zJFSUoIiLikN59910ARowYQcWKFe1beXAwPPqo5euJE/93Pj0d+ve3DP0EBv7vLR4pdCbDMIyifghbJSUl4evri9lsxsfHp6gfR0RE7Cw9PR1vb29SU1M5cOAA9erVs3+Qw4ehfn3IyICHH4bWrSE6Gr78Ejw94ZdfoEkT+8ctxWz5/FYPioiIOJx9+/aRmpqKr69v/papv5latSB74m1EBIwaZUlOTCb45hslJ0VM05JFRMThxPz/vJBmzZphKsjl5d95B7p3h02bYPNmy9DOyy9Dr14FF1NyRQmKiIjYbPfu3Tg7O9OwYcMCqT87QWnatGmB1G9lMkHHjpZDHIqGeERExCYHDhygWbNmNGrUiG+//bZAYlzZgyKlk3pQRETEJq+88grp6ekADBo0CMMwePjhh+1Wf2pqKrt37waUoJRm6kEREZFci4mJYeHChZhMJnr27ElWVhaPPPII33zzjd1i7N27l/T0dCpUqMBtt91mt3qleFGCIiIiuTZ+/HgABg8ezJIlS3j88cfJyspiyJAhHDt2zC4xCm2CrDg0JSgiIpIra9asYc2aNbi6uvLaa6/h5OTEZ599xj333ENGRgY///yzXeIU2gRZcWiagyIiItdlGAYrV67k5MmTXLp0ic//f/O8ESNGWIdenJyc6N69O9u2bWPdunUMHz4833E1QVZAK8mKiMgNzJo1iyeffDLHOS8vLw4fPkylSpWs5zZu3Ejbtm2pVKkSCQkJ+RqWuXz5Mt7e3mRkZHD06FGqV6+e57rE8djy+a0eFBERucalS5d49dVXAWjdujXVqlXD09OT/v3750hOAJo3b46HhweJiYns378/X2uj7N69m4yMDCpWrEhQUFC+2iDFmxIUERG5xocffkhCQgI1a9Zk7dq1uLm53bCsu7s7rVu3JjIyknXr1uUrQbly/okmyJZumiQrIiI5nDt3jrfeeguA119//abJSbYOHToAsG7dunzF1vwTyaYERUREcnj77bc5f/48wcHBDBw4MFf3ZCcoGzZsIDMzM8+xY2NjASUoogRFRESuEB8fz7Rp0wCYPHkyzs7OubqvSZMm+Pj4cP78eXbu3Jmn2CdPnmTv3r2AEhTJZ4ISHh6OyWQiLCzMes4wDCZNmkRgYCAeHh60a9eOffv25bgvNTWV0aNH4+/vj5eXF7169eLEiRP5eRQREbGDDz/8kJSUFFq2bEloaGiu73NxcaFt27YArF27Nk+xZ8+eTVZWFm3atKFq1ap5qkNKjjwnKNHR0cyaNYs777wzx/m3336b999/n48++ojo6GgCAgLo3LkzycnJ1jJhYWEsXryYiIgINm3axIULFwgNDc1Xt6CIiOSPYRgsXLgQgDFjxtg8STU/81AyMjL47LPPAHjqqadsvl9KICMPkpOTjbp16xqRkZFG27ZtjTFjxhiGYRhZWVlGQECAMWXKFGvZy5cvG76+vsbMmTMNwzCM8+fPG66urkZERIS1zMmTJw0nJydj1apVuYpvNpsNwDCbzXl5fBERuY6dO3cagFGmTBkjOTnZ5vt37dplAIanp6eRmppq071Lly41AMPPz8+4fPmyzbGleLDl8ztPPSgjR46kR48edOrUKcf5uLg4EhIS6NKli/Wcu7s7bdu2ZfPmzYBlAlR6enqOMoGBgQQHB1vLXC01NZWkpKQch4iI2Nf3338PQLdu3ShbtqzN9wcHB+Pv78+lS5f49ddfbbr3008/BeBf//oX7u7uNseWksfmBCUiIoLt27cTHh5+zbWEhAQAKleunON85cqVrdcSEhJwc3OjfPnyNyxztfDwcHx9fa2HFu8REbG/H374AYB+/frl6X4nJyd69eoFwPvvv5/r+44ePcqKFSsA+Pe//52n2FLy2JSgHD9+nDFjxjB//nzKlClzw3JXj1sahnHLscyblRk/fjxms9l6HD9+3JbHFhGRW9i/fz+///47rq6u9OzZM8/1vPjiizg5ObF8+XLrK8Ng+R2/bds2Ll26dM09n3/+OYZh0KFDB+rWrZvn2FKy2JSgxMbGkpiYSNOmTXFxccHFxYWoqCg+/PBDXFxcrD0nV/eEJCYmWq8FBASQlpbGuXPnbljmau7u7vj4+OQ4RETEfrJ7Tzp37oyvr2+e66lXr5517ZTXX38dsCQnI0eOpHnz5jRq1Ijo6Ghr+X379lk3IdTkWLmSTQlKx44d2bNnDzt37rQezZo145FHHmHnzp3UqlWLgIAAIiMjrfekpaURFRVFq1atAMvyxa6urjnKxMfHs3fvXmsZEREpXNkJyoMPPpjvuiZOnIjJZGLp0qXs2LGDd999lxkzZgDw559/0qpVK9544w2GDRvGnXfeSUJCAkFBQfTu3TvfsaUEye+M3Cvf4jEMw5gyZYrh6+trLFq0yNizZ48xcOBAo0qVKkZSUpK1zFNPPWVUq1bNWLNmjbF9+3ajQ4cORqNGjYyMjIxcxdRbPCIi9nPo0CEDMJydnY2///7bLnUOGjTIAIz69esbgAEYb7zxhtG/f3/r99nHAw88YPz55592iSuOzZbPb7tvFvjCCy+QkpLC008/zblz52jevDmrV6/G29vbWuaDDz7AxcWFAQMGkJKSQseOHZk7d26uVywUERH7ye49ad++PX5+fnapc+LEiXzzzTccOHAAgGeeeYaJEydiGAbdu3dn7NixhISEMGXKFFq2bGmXmFKymAzDMIr6IWyVlJSEr68vZrNZ81FERPKpcePG7Nq1i5kzZ/Lkk0/ard5HHnmEr7/+mj59+vD999/n+EeokYuXJ6TkseXzWwmKiEgptmvXLho3boybmxvx8fFUqFDBbnUnJyezbt06unfvnqsdkaXks+Xz2+5DPCIiUnzMmzcPgJ49e9o1OQHw9vbWxFfJM+1mLCJSSmVkZDB//nwAhgwZUsRPI5KTEhQRkVIqMjKS06dP4+/vT7du3Yr6cURyUIIiIlJKffXVVwAMGjRIc0TE4ShBEREphcxmM0uWLAE0vCOOSQmKiIgD2Lx5M9HR0RTWi5ULFy7k8uXLNGjQgCZNmhRKTBFbKEERESli0dHRtG7dmnvuuYcGDRrw1ltv3XB3d3v5+uuvAUvvidYjEUekBEVEpIhl7+YL8Mcff/DSSy9Rt25dZs+eXSA9KoZhEBMTA0CPHj3sXr+IPShBEREpQikpKXz77bcALFmyhM8//5ymTZty4cIFnnjiCXr37s3p06ftGvPUqVMkJyfj7OxMvXr17Fq3iL0oQRERKUI//vgjZrOZGjVq0LNnT4YNG8Zvv/3GO++8g5ubG8uWLaNJkyb8888/dou5f/9+AGrXrq23d8RhKUERESlCc+fOBSxzQZycLL+SnZ2dGTduHDExMVSvXp1Tp05Z37ixh99//x2AO+64w251itibEhQRkSJy8uRJIiMjARg6dOg110NCQnjiiScAS0+LvWQnKA0aNLBbnSL2pgRFRKSIzJ8/n6ysLFq3bk3t2rWvWyZ7L5vVq1dz8eJFu8RVD4oUB0pQRESKgGEY1uGdxx577IblQkJCuO2227h8+bK1tyW/lKBIcaAERUSkCPz222/88ccfeHh40L9//xuWM5lM9OnTB8Au81DOnj1LYmIiALfffnu+6xMpKEpQRESKwKeffgpAv3798PHxuWnZ7GGe5cuXk5GRka+42b0n1atXp2zZsvmqS6QgKUERESlk//zzDxEREQCMGDHiluVbt25NhQoVOHv2LJs3b85XbA3vSHGhBEVEpJDNnTuXy5cv07hxY1q2bHnL8i4uLoSGhgL5H+ZRgiLFhRIUEZFClJWVxYwZMwB4+umnc70PTvYwz48//piv5e+VoEhxoQRFRKQQrVmzhj///BMfHx8GDRqU6/u6dOmCu7s7hw8fZtmyZXmOn72KrBIUcXRKUERECtEnn3wCWF4t9vLyyvV9ZcuWpV+/foClN2XMmDGkpKTYFPvChQscO3YM0CJt4viUoIiIFJJjx45Zez9yMzn2arNmzeKpp54C4MMPP6RJkybs2bMn1/cfOHAAgIoVK+Ln52dzfJHCpARFRKSQfPXVV2RlZdGhQ4c8rUHi5eXFjBkzWLFiBVWqVOGPP/6gZcuWuZ44q/knUpwoQRERKSRr164FYMCAAfmqp3v37uzZs4eOHTty8eJF+vbty5tvvnnLybNKUKQ4UYIiIlIILl++zJYtWwBo3759vuvz8/Nj5cqVjBo1CoCJEyfyxhtv3PSe7Amymn8ixYESFBGRQrB161ZSU1OpUqUKdevWtUudrq6uTJ8+nalTpwLw7rvvcv78+euWvXjxIrt37wbUgyLFgxIUEZFCsGHDBgDatWuX67VPcmv06NEEBweTnJxsXWMl299//82rr75K9erVOXz4MADBwcF2jS9SEJSgiIgUguwExR7DO1dzcnLihRdeAGDatGnW149///136tevz+uvv84///xDnTp1mD9/PlWqVLH7M4jYmxIUEZEClpKSYp1/0q5duwKJ8fDDD1O9enVOnz7Nl19+ydmzZwkNDeWff/6hQYMGLFy4kD/++INHHnmkQOKL2JsSFBGRArZ161bS0tIIDAykTp06BRLD1dWVcePGAfDOO+/Qr18/Dh8+TM2aNdmwYQMPPvggzs7OBRJbpCAoQRERKWAFOf/kSo8//jh+fn4cPnyYqKgovL29WbZsGRUrViywmCIFRQmKiEgBW79+PVAw80+u5OXlxTPPPANY5qVERETQsGHDAo0pUlBcivoBRERKskuXLvHbb78BBTf/5ErPPvsshw8fpkuXLtx///0FHk+koNjUgzJjxgzuvPNOfHx88PHxoWXLlqxcudJ6/bHHHsNkMuU4WrRokaOO1NRURo8ejb+/P15eXvTq1YsTJ07YpzUiIg4me/5J1apVqV27doHH8/b2Zu7cuTbtlCziiGxKUKpVq8aUKVOIiYkhJiaGDh060Lt3b/bt22ct061bN+Lj463HihUrctQRFhbG4sWLiYiIYNOmTVy4cIHQ0FAyMzPt0yIREQeSPbxT0PNPREoam4Z4evbsmeP7N998kxkzZrB161brOKe7uzsBAQHXvd9sNjN79mzmzZtHp06dAJg/fz5BQUGsWbOGrl275qUNIiIOK3v34g4dOhTxk4gUL3meJJuZmUlERAQXL16kZcuW1vMbNmygUqVK1KtXj+HDh5OYmGi9FhsbS3p6Ol26dLGeCwwMJDg4mM2bN98wVmpqKklJSTkOERFH98cff7Br1y5cXFzo3bt3UT+OSLFic4KyZ88eypYti7u7O0899RSLFy+2bjzVvXt3FixYwLp163jvvfeIjo6mQ4cOpKamApCQkICbmxvly5fPUWflypVJSEi4Yczw8HB8fX2tR1BQkK2PLSJS6L799lsAunTpgp+fXxE/jUjxYvNbPPXr12fnzp2cP3+eH374gaFDhxIVFUWDBg146KGHrOWCg4Np1qwZNWrU4KeffuKBBx64YZ2GYdx0bHb8+PE899xz1u+TkpKUpIiIQzMMg4iICMCyyquI2MbmBMXNzc26EmKzZs2Ijo5m2rRpfPrpp9eUrVKlCjVq1ODQoUMABAQEkJaWxrlz53L0oiQmJtKqVasbxnR3d8fd3d3WRxURITExkdDQUP7++28CAgKoUqUK3bt354knnijQuLt37+aPP/7A3d1dwzsieZDvdVAMw7AO4Vzt7NmzHD9+3LoxVdOmTXF1dSUyMpIBAwYAEB8fz969e3n77bfz+ygiIjkYhsGwYcOIjo4GIC4uDoBFixZx+fJlRo0aVWCxs3tPevTogY+PT4HFESmpbEpQJkyYQPfu3QkKCiI5OZmIiAg2bNjAqlWruHDhApMmTaJfv35UqVKFI0eOMGHCBPz9/enbty8Avr6+DBs2jLFjx+Ln50eFChUYN24cISEh1rd6RETs5bPPPmP58uW4ubnxzTffAJaJ/NOnT2fMmDHUqVOHbt262T2uhndE7MCwweOPP27UqFHDcHNzMypWrGh07NjRWL16tWEYhnHp0iWjS5cuRsWKFQ1XV1ejevXqxtChQ41jx47lqCMlJcUYNWqUUaFCBcPDw8MIDQ29psytmM1mAzDMZrNN94lI6XHw4EHD09PTAIx3333Xej4rK8v417/+ZQCGj4+PsW/fPrvH3rp1qwEYXl5exsWLF+1ev0hxZcvnt8kwDKNIM6Q8SEpKwtfXF7PZrK5TEblGeno6rVu3Ztu2bbRv3541a9bg5PS/lxbT0tLo1KkTv/zyCzVr1uS3336z64Z6zz77LFOnTmXQoEEsWLDAbvWKFHe2fH5rs0ARKXEWLVrEtm3b8PX15csvv8yRnIBlsv+iRYuoVasWcXFx9OnTh8uXL9sltmEYLF68GCDHm40iYhslKCJS4qxZswaAJ5544oZLEvj7+/PTTz/h6+vL5s2befzxx7FHh/Kff/7J0aNHcXNzo2PHjvmuT6S0UoIiIiXOhg0bAGjfvv1Ny91+++0sWrQIFxcXvvnmGyZNmpTv2JGRkQC0atUKLy+vfNcnUlopQRGREuXEiRP8+eefODk50bp161uW79ChAzNnzgTg9ddf56effspX/Ozem86dO+erHpHSTgmKiJQoUVFRADRp0gRfX99c3TNs2DDGjBkDwKhRo7h06VKeYmdkZLBu3TpACYpIfilBEZESJbfDO1d78803CQoK4siRI0yePDlPsWNiYjCbzZQvX54mTZrkqQ4RsVCCIiIlSnaC0q5dO5vu8/LyYtq0aQC8/fbbHDhwwObY2fNPOnTogLOzs833i8j/KEERkRLD1vknV+vTpw/3338/6enpjBo1yua3erITFA3viOSfEhQRKTGy5580bdo0T4s4mkwmpk+fTpkyZVizZg2ff/55ru+9cOECW7ZsAZSgiNiDEhQRKTHyOrxzpVq1avGf//wHgBEjRuT6rZ6oqCgyMjKoVasWtWrVynN8EbFQgiIiJYY9EhSAF198kSFDhpCZmUn//v2tPSPHjx/n66+/5siRI9fckz28o41PRezDpt2MRUQcVX7nn1zJycmJzz//nDNnzrBy5Up69OhBtWrV2LNnDwD16tVj//791omwhmGwevVqQMM7IvaiHhQRKRGye0/yOv/kaq6urixcuJB77rmHc+fOsWfPHkwmE25ubhw8eJCFCxday/7888/8/vvvuLu7a3l7ETtRgiIiJUL2EIut65/cjJeXFytXrmTSpEksWLCAxMREJk6cCMDkyZPJysoiKyuLCRMmADBy5EjKly9vt/gipZnJsMfuWIXMlu2aRaTky8rKokqVKiQmJrJu3Tq7JilXO3fuHDVq1CA5OZkff/yRy5cv89BDD+Ht7c3hw4fx9/cvsNgixZ0tn9/qQRGRYm/nzp0kJiZStmxZ7r333gKNVb58eUaOHAnAG2+8wSuvvALA2LFjlZyI2JESFBEp9lauXAlAx44dcXNzK/B4zz77LB4eHsTExHDw4EH8/f157rnnCjyuSGmiBEVEir3sBKV79+6FEq9SpUr8+9//tn4/YcIEvL29CyW2SGmhOSgiYjenTp3CMAyqVq1aaDHPnTuHv78/WVlZHD16lOrVqxdK3BMnThASEkLFihXZvXs3ZcqUKZS4IsWZLZ/fWgdFROzi0qVLNG7cmMuXL7Nt2zZuv/32QokbGRlJVlYWDRo0KLTkBKBatWr8+eefuLm5KTkRKQAa4hERu4iKiuLMmTMkJyfz0EMPkZKSUihxV61aBRTe8M6V/Pz8NLQjUkCUoIiIXWQnCgC7d+8ulEmjhmEUaYIiIgVHCYqI2MXPP/8MWDbYM5lMzJw5k++++65AY+7atYv4+Hi8vLzyvby9iDgWJSgikm9HjhzhwIEDODs7Ex4ezvjx4wF44oknCjRJyX57p0OHDri7uxdYHBEpfEpQRCTfsntPWrZsia+vL6+99hrt27e3zkcZOHAgZ8+etXvc7P13unbtave6RaRoKUERkXzLngeSnSi4uLiwatUqXnnlFZydnYmIiCAkJIRjx47ZLaZhGMTExADQokULu9UrIo5BCYqI5Et6ejpr164FoFu3btbzbm5uvP7662zZsoU6deoQHx/PjBkz7BY3Li6Of/75Bzc3N0JCQuxWr4g4BiUoIpIvW7ZsITk5GX9/f5o0aXLN9bvvvps333wTgG+//RZ7rQ2Z3XvSqFGjQlneXkQKlxIUEcmX7PknXbp0wcnp+r9SevTogaenJ3FxcURHR9slbnaC0qxZM7vUJyKORQmKiOTL1fNPrsfLy4tevXoBll4Ue8hOdO6++2671CcijkUJiojkWXx8PNu3bwcsPSg389BDDwHw3XffkZWVla+4WVlZxMbGAupBESmplKCISJ699957ALRq1YqAgICblu3WrRs+Pj6cOHGCzZs35yvuwYMHSU5OxsPDgzvuuCNfdYmIY1KCIiJ5Eh8fz8cffwzAK6+8csvyZcqUoU+fPkD+h3my5580adIEFxfteSpSEilBEZE8mTJlCpcvX6Zly5a5Xigte5hn4cKFZGZm5jl29vwTDe+IlFw2JSgzZszgzjvvxMfHBx8fH1q2bGldahosCydNmjSJwMBAPDw8aNeuHfv27ctRR2pqKqNHj8bf3986ce7EiRP2aY2IFIoTJ07w6aefAvD6669jMplydV+nTp0oX748p0+fZuHChXmOn92DogmyIiWXTQlKtWrVmDJlCjExMcTExNChQwd69+5tTULefvtt3n//fT766COio6MJCAigc+fOJCcnW+sICwtj8eLFREREsGnTJi5cuEBoaGi+/jUlIoUrPDyc1NRU7rvvPjp27Jjr+9zc3Ky9KAMHDuT+++9nx44dNsXOyMiw3qMeFJESzMin8uXLG59//rmRlZVlBAQEGFOmTLFeu3z5suHr62vMnDnTMAzDOH/+vOHq6mpERERYy5w8edJwcnIyVq1aleuYZrPZAAyz2ZzfxxcRGx07dsxwdXU1AGPDhg0235+UlGQ8+eSThouLiwEYgDFjxoxc379r1y4DMHx8fIzMzEyb44tI0bHl8zvPc1AyMzOJiIjg4sWLtGzZkri4OBISEnK8auju7k7btm2tM/ZjY2NJT0/PUSYwMJDg4OCbzupPTU0lKSkpxyEiReOHH34gPT2dNm3a0LZtW5vv9/b2ZubMmfz+++/0798fgEmTJpGWlpar+7OHd5o2bXrDheFEpPiz+f/uPXv2ULZsWdzd3XnqqadYvHgxDRo0ICEhAYDKlSvnKF+5cmXrtYSEBNzc3ChfvvwNy1xPeHg4vr6+1iMoKMjWxxYRO9m4cSMAoaGh+aqnTp06LFiwgMDAQE6fPs3ixYtzdZ8WaBMpHWxOUOrXr8/OnTvZunUrI0aMYOjQoezfv996/erJcoZh3HIC3a3KjB8/HrPZbD2OHz9u62OLiB0YhmFNUO6777581+fq6srw4cMB+OSTT25a9uzZszz33HN88cUXgOafiJR0Nicobm5u1KlTh2bNmhEeHk6jRo2YNm2adZGmq3tCEhMTrb0qAQEBpKWlce7cuRuWuR53d3frm0PZh4gUvv3793P27Fk8PT2vuzFgXgwfPhxnZ2c2btzI3r17r1tmzpw51K5dmw8++IC0tDTuv/9+evfubZf4IuKY8j2AaxgGqamp1KxZk4CAACIjI63X0tLSiIqKolWrVoBlzNjV1TVHmfj4ePbu3WstIyKOK7v3pGXLlnbbQbhq1arWBdxmzJhxzfVTp04xfPhwzGYzjRo1YtWqVSxfvlw7GIuUcDYtwThhwgS6d+9OUFAQycnJREREsGHDBlatWoXJZCIsLIzJkydTt25d6taty+TJk/H09GTQoEEA+Pr6MmzYMMaOHYufnx8VKlRg3LhxhISE0KlTpwJpoIjYjz2Hd6709NNP88MPP/DVV18xZcoUvL29rde+/PJLMjMzadmyJZs2bdLEWJFSwqYE5fTp0wwePJj4+Hh8fX258847WbVqFZ07dwbghRdeICUlhaeffppz587RvHlzVq9eneOXzQcffICLiwsDBgwgJSWFjh07MnfuXJydne3bMhGxK8MwiIqKAsjT2zs30759e+rXr8+BAweYP38+I0aMACybAn7++ecAPPnkk0pOREoRk2EYRlE/hK2SkpLw9fXFbDZrPopIIfnzzz+pW7cubm5unD9/Hg8PD7vW/+GHHzJmzBhuu+029u7di5eXF+vWraNjx474+Phw6tQpvLy87BpTRAqXLZ/f+ueIiORK9vDOPffcY/fkBOBf//oX1atX58iRI0yYMAHA2nsyaNAgJScipYwSFBHJlezhHXvPP8nm7e3NrFmzAJg+fTo//vgjP/zwAwBPPPFEgcQUEcelBEVEcqWgJsheqWvXrjz++OMYhsGDDz5IWload911F02bNi2wmCLimJSgiMgtHTt2jCNHjuDs7FzgSwK89957BAYGkpGRAaj3RKS0UoIiIreU3XvSpEmTHG/lFYRy5cpZh3quXKZAREoXm14zFpHSae3atYD9Xy++kR49erBs2TL8/PwoV65cocQUEceiBEVEbsowDH7++WeAHDuRF7T8bkYoIsWbhnhEiqFt27bx5ZdfkpWVVeCx9uzZQ3x8PB4eHrRp06bA44mIgHpQRIqdpKQkunXrxrlz59i3bx9vv/12gcbL7j1p164dZcqUKdBYIiLZ1IMiUszMmDHDuiP4O++8w7Rp0wo0XnaC0rVr1wKNIyJyJSUoIsXIpUuXeO+99wDo2LEjAM8++yzfffddgcS7ePEiv/zyC6AERUQKlxIUkWJk1qxZnDlzhpo1a7Jq1SpGjRqFYRgMHjzY+iqwPUVFRZGWlkb16tWpX7++3esXEbkRJSgixcTly5d55513ABg/fjwuLi5MnTqVfv36kZaWRt++fTl06JBdY145vGMymexat4jIzShBESkm5s6dy6lTp6hWrRpDhgwBwNnZmXnz5tG8eXP++ecfevTowdmzZ+0WMztB6datm93qFBHJDSUoIg7u4MGDTJgwwbrD74svvoi7u7v1uoeHBz/++CM1atTg0KFDPPDAA6SlpeU77tGjRzlw4ADOzs7W+S4iIoVFCYqIg0pNTaVr167Ur1+f8PBwzp07R3BwMMOGDbumbOXKlVm+fDne3t5s3LiRyZMn5zt+du9JixYt8PX1zXd9IiK2UIIi4qDWr1/P6tWrcXJyonv37nz33XfExMTg4eFx3fLBwcHWPWymTp2K2WzOV/yffvoJ0Ns7IlI0lKCIOKjs13sHDx7MihUr6N+/f46hnesZMGAADRs2xGw2M3369DzHTk5Otvag9O7dO8/1iIjklRIUEQeVnaDYsry8k5MTEydOBOCDDz4gOTk5T7FXrFhBamoqderUISQkJE91iIjkhxIUEQeUmprKtm3bAGjdurVN9/bv35/69evzzz//8Mknn+Qp/g8//ABAv3799HqxiBQJJSgiDigmJobU1FQqVapEvXr1bLrX2dmZl19+GYD33nuPixcv2nR/SkoKK1asACwJiohIUVCCIuKAsod3WrdunacejIEDB1K7dm3OnDnDM888Y927Jzd+/vlnLl68SPXq1WnWrJnNsUVE7EEJiogDysv8kyu5uLjw2muvAfDFF19Qq1YtwsPDc9Wb8v333wMa3hGRoqUERcTBZGZm8uuvvwJ5T1AAHnnkEZYtW0ZISAjnz59nwoQJ9OrVC8MwbnhPamoqy5YtAzS8IyJFSwmKiIPZu3cvZrOZsmXL0qhRo3zVFRoayo4dO1iwYAEeHh6sW7fOOr/ketauXUtSUhJVqlShZcuW+YotIpIfSlBEHEz28E6rVq1wcXHJd33Ozs4MGjSIZ555BoCXX36ZrKys65bNHt7p27cvTk769SAiRUe/gUQczJUTZO3phRdewNfXl127dvHdd99dc33//v3Mnz8fgIceesiusUVEbKUERcSBGIaR7wmyN1KhQgWef/55AF555RXS09NzxB0xYgTp6en07NnT7rFFRGylBEXEgcTFxREfH4+rqyvNmze3e/1jxoyhUqVK/Pnnn8yZM8d6/ssvv2Tjxo14enoyffp0vb0jIkVOCYqIA4mKigKgWbNmN9wUMD/Kli1rXcQtLCyMZ599ll27djFu3DgAJk2aRI0aNeweV0TEVkpQRBzI+vXrAWjfvn2BxXjyySfp0KEDKSkpTJ06lcaNG3P27FlCQkIICwsrsLgiIrZQgiLiIAzDYN26dQB06NChwOK4u7uzZs0afv75Z9q2bQuAyWRi5syZuLq6FlhcERFbmIybrdrkoJKSkvD19cVsNuPj41PUjyNiFwcPHqR+/fq4ublx/vz5AhniuZ6YmBiysrK45557CiWeiJRetnx+53+RBRGxi+zek1atWhVacgJovx0RcUg2DfGEh4dz99134+3tTaVKlejTpw8HDhzIUeaxxx7DZDLlOFq0aJGjTGpqKqNHj8bf3x8vLy969erFiRMn8t8akWIse/5JQQ7viIgUFzYlKFFRUYwcOZKtW7cSGRlJRkYGXbp0uWYDsm7duhEfH289rl5aOywsjMWLFxMREcGmTZu4cOECoaGhZGZm5r9FIsVQVlZWoUyQFREpLmwa4lm1alWO7+fMmUOlSpWIjY3lvvvus553d3cnICDgunWYzWZmz57NvHnz6NSpEwDz588nKCiINWvW0LVrV1vbIFLs7du3jzNnzuDp6am5ICIi5PMtHrPZDFhWqLzShg0bqFSpEvXq1WP48OEkJiZar8XGxpKenk6XLl2s5wIDAwkODmbz5s3XjZOamkpSUlKOQ6QkyZ5/0qZNG9zc3Ir4aUREil6eExTDMHjuuedo3bo1wcHB1vPdu3dnwYIFrFu3jvfee4/o6Gg6dOhAamoqAAkJCbi5uVG+fPkc9VWuXJmEhITrxgoPD8fX19d6BAUF5fWxRW7JMAwyMjKue+306dMUxItvhfF6sYhIcZLnBGXUqFHs3r2bb775Jsf5hx56iB49ehAcHEzPnj1ZuXIlBw8e5KeffrppfYZh3HB57fHjx2M2m63H8ePH8/rYIjdlGAahoaEEBASwffv2HNfCwsIICAhg7ty5do2ZmZlpXUFWCYqIiEWeEpTRo0ezdOlS1q9fT7Vq1W5atkqVKtSoUYNDhw4BEBAQQFpaGufOnctRLjExkcqVK1+3Dnd3d3x8fHIcIgXhm2++YcWKFZw9e5aePXta3y6bMWMG06ZNA7hlsm2rHTt2YDab8fX15a677rJr3SIixZVNCYphGIwaNYpFixaxbt06atasect7zp49y/Hjx6lSpQoATZs2xdXVlcjISGuZ+Ph49u7dS6tWrWx8fBH7uXDhgnW3X09PT06dOkXPnj1ZtmwZo0ePtpaLjY21a9w1a9YA0K5dO5ydne1at4hIcWVTgjJy5Ejmz5/P119/jbe3NwkJCSQkJJCSkgJYfsGPGzeOLVu2cOTIETZs2EDPnj3x9/enb9++APj6+jJs2DDGjh3L2rVr2bFjB48++ighISHWt3pEisJ///tfTp06Ra1atdi+fTuVKlVi586d9OrVi8zMTPr06QPAkSNHOHv2rN3iLl26FEBvsImIXMmwAXDdY86cOYZhGMalS5eMLl26GBUrVjRcXV2N6tWrG0OHDjWOHTuWo56UlBRj1KhRRoUKFQwPDw8jNDT0mjI3YzabDcAwm822PL7IDR04cMBwdXU1AGPp0qWGYRjGli1bjDJlyhiA0bx5cyMlJcWoXbu2ARirV6+2S9yEhATDZDIZgHHixAm71Cki4qhs+fy2aR0U4xZvL3h4ePDzzz/fsp4yZcowffp0pk+fbkt4kQLz7LPPkp6eTvfu3QkNDQWgRYsWrFixgkWLFjFx4kTKlClD06ZN+euvv4iNjaVz5875jrt8+XIMw6BZs2ZUrVo13/WJiJQU2s1YSr1ffvmFFStW4OrqytSpU3O8Tda+fXumT59uncDdtGlTwH7zUH788UcAevXqZZf6RERKCiUoUuq99tprAAwbNox69erdtKw9E5RLly5ZJ4v37t073/WJiJQkSlCkVPvll19Yu3Ytrq6ujB8//pblmzRpAkBcXBz//PNPvmJHRkZy+fJlbrvtNkJCQvJVl4hISaMERUq1K3tPqlevfsvy5cuXp1atWgDXLORmqyuHd260SKGISGmlBEVKLVt7T7LZY5gnMzOT5cuXAxreERG5HiUoUmrZ2nuSzR4JytatWzlz5gzlypWjTZs2ea5HRKSkUoIipVJMTEyeek/APglK9uJs999/P66urnmuR0SkpFKCIqXS7NmzAejfv79NvSfwv4myhw8fvmZPqdxauXIlAD179szT/SIiJZ0SFCl1Ll26xNdffw1YhndsVaFCBes+VNkTZf/++28yMjJydf+pU6fYs2cPJpPJLou9iYiUREpQpNRZtGgRSUlJ3HbbbbRr1y5PdWQP87z44ovUrl2bihUr8uCDD+bq3tWrVwPQrFkz/Pz88hRfRKSkU4Iipc4XX3wBwL/+9S+cnPL2v0CzZs0AyzyUw4cPA5bXhrO/vpnsBKVLly55ii0iUhooQZFS5fDhw6xfvx6TycRjjz2W53qeeOIJBg8ezLhx41ixYgXt27cH4KuvvrrpfVlZWdbVY7V7sYjIjdm0WaBIcTdnzhwAOnfubPPk2Cv5+fnlSEb++ecf1q9fz1dffcV//vOfG/bMbN++nb///htvb29atGiR5/giIiWdelCk1MjMzGTu3LkAPP7443atu2/fvnh7exMXF8emTZus55csWcLHH39MVlYWgHW37w4dOuj1YhGRm1CCIqXGhg0bOHHiBBUqVKBPnz52rdvT05P+/fsD8OWXXwKW5KRv376MGjWKKVOmAP+bf6LhHRGRm1OCIqXGmjVrAMvaI+7u7navP3tOy8KFC4mNjWXIkCHWaxMnTmThwoVs3rwZUIIiInIrSlCk1IiKigLI86vFt9K6dWtq1apFcnIy9913H8nJybRt25YnnngCwzB4+OGHycjIoE6dOtYNB0VE5PqUoEipcOnSJaKjowG47777CiSGyWSy9ppcunSJoKAgvvvuOz766COaN29unYei14tFRG5NCYqUClu2bCEjI4Nq1apZV4EtCEOGDMHFxQV3d3cWLVpEpUqVcHd35/vvv6dixYoAhIaGFlh8EZGSQq8ZS6HKzMzk0qVLeHt7F2rc7OGdtm3bYjKZCixOzZo1+eWXX/D09OTOO++0nq9WrRpbtmwhJiaGbt26FVh8EZGSQj0oUqh69+5NQECA9XXbwnJlglLQWrRokSM5yVa7dm0eeuihAk2QRERKCiUoUmjOnj3LihUruHTpEn369GHDhg2FEvfy5cv89ttvQMHNPxEREftSgiKFZt26dRiGAViShtDQUOtrtwVp27ZtpKamUrlyZerVq1fg8UREJP+UoEihyd6D5sknn6Rz585cvHiR7t27s3v37gKNW1jzT0RExH6UoEihMAzDmqD07NmTJUuWcN9995GUlESvXr04c+ZMgcXOTlA0vCMiUnwoQZFC8ddff3HkyBFcXV1p27Ytnp6eLFmyhDp16nD06FEefPBB0tLS7B43LS3NOoxUGBNkRUTEPpSgSKHI7j1p2bIlZcuWBaB8+fIsXboUb29vNm7cyOjRo61zVOwlNjaWlJQU/Pz8aNCggV3rFhGRgqMERQpFdoLSuXPnHOfvuOMOvvnmG0wmE7NmzaJDhw589tlnnD171q5x27Rpg5OT/nMXESku9BtbClxGRgbr1q0Drk1QAHr06MH777+PyWRiw4YN/Pvf/yYgIIDPPvssX3ENw2D+/PmAZf0VEREpPpSgSIGLiYnBbDZTrlw5mjVrdt0yYWFhHD58mClTptCwYUMyMjKYOXNmvuJu2bKFQ4cO4enpSb9+/fJVl4iIFC4lKFLgsodZOnTogLOz8w3L3Xbbbbz44ousWrUKgJ07d5KcnJznuF999RUA/fr1K/Sl9UVEJH+UoEiBysrKsiYc1xveuZ5q1apx2223kZWVxdatW/MU9/Lly3z77bcADB06NE91iIhI0VGCIgUiLS2NuXPnEhISYn3NN7cJCsC9994LwK+//pqn+EuXLuX8+fMEBQXRvn37PNUhIiJFRwmK2N3FixcJDg7mX//6F/v378fHx4epU6dSu3btXNfRunVrADZt2pSnZ/jyyy8BGDx4sN7eEREphmz6zR0eHs7dd9+Nt7c3lSpVok+fPhw4cCBHGcMwmDRpEoGBgXh4eNCuXTv27duXo0xqaiqjR4/G398fLy8vevXqxYkTJ/LfGnEI0dHRHDp0iLJlyzJlyhSOHTvGmDFjbKojO0HZunUrGRkZNt2bkJBg3S15yJAhNt0rIiKOwaYEJSoqipEjR7J161YiIyPJyMigS5cuXLx40Vrm7bff5v333+ejjz4iOjqagIAAOnfunGOyY1hYGIsXLyYiIoJNmzZx4cIFQkNDyczMtF/LpMj88ccfgGVp+RdffBFfX1+b62jQoAHlypXj4sWL7Nq1y6Z7v/76azIzM2nevDn169e3ObaIiDgAIx8SExMNwIiKijIMwzCysrKMgIAAY8qUKdYyly9fNnx9fY2ZM2cahmEY58+fN1xdXY2IiAhrmZMnTxpOTk7GqlWrchXXbDYbgGE2m/Pz+FJAxowZYwDG2LFj81XP/fffbwDG1KlTc31PZmamUbt2bQMwZsyYka/4IiJiX7Z8fudrcN5sNgNQoUIFAOLi4khISKBLly7WMu7u7rRt29Y6UTI2Npb09PQcZQIDAwkODraWuVpqaipJSUk5DnFcv//+OwC33357vurJnihryzyU5cuX89dff1GuXDkeffTRfMUXEZGik+cExTAMnnvuOVq3bk1wcDBgGfsHqFy5co6ylStXtl5LSEjAzc2N8uXL37DM1cLDw/H19bUeQUFBeX1sKQTZQzx33HFHvurJnofy66+/5nqPnqlTpwIwfPhw654/IiJS/OQ5QRk1ahS7d+/mm2++ueaayWTK8b1hGNecu9rNyowfPx6z2Ww9jh8/ntfHlgJ24cIFjh07BuS/B+Xuu+/G1dWV+Ph44uLibll+586drF+/HmdnZ0aPHp2v2CIiUrTylKCMHj2apUuXsn79eqpVq2Y9HxAQAHBNT0hiYqK1VyUgIIC0tDTOnTt3wzJXc3d3x8fHJ8chjungwYMA+Pv74+fnl6+6PDw8aNq0KZC7YZ5p06YB8OCDD6qXTUSkmLMpQTEMg1GjRrFo0SLWrVtHzZo1c1yvWbMmAQEB1qXNwbJgV1RUFK1atQKgadOmuLq65igTHx/P3r17rWWk+Mqef5Lf4Z1sVw7zXO3s2bPs2rWL1NRUEhIS+PrrrwHLW2IiIlK82ZSgjBw5kvnz5/P111/j7e1NQkICCQkJpKSkAJahnbCwMCZPnszixYvZu3cvjz32GJ6engwaNAgAX19fhg0bxtixY1m7di07duzg0UcfJSQkhE6dOtm/hVKosuef5Hd4J1t2grJkyRLOnj1rPf/333/TuHFjGjduTNmyZbnrrrtIS0ujRYsWtGjRwi6xRUSk6LjYUnjGjBkAtGvXLsf5OXPm8NhjjwHwwgsvkJKSwtNPP825c+do3rw5q1evzrFZ2wcffICLiwsDBgwgJSWFjh07Mnfu3JtuJCfFg70myGbr2rUrt99+O3/88QdPPfUU3333HQCPP/44J06cwMnJiYyMDOuw4tixY+0SV0REipbJyO3rEQ4kKSkJX19fzGaz5qM4mODgYPbt28eKFSvo3r27XeqMiYmhZcuWZGRk8NVXX5GUlMSoUaNwc3Nj27Zt+Pn5sWvXLjIzM+nZs+ctJ2SLiEjRsOXz26YeFJGbycjI4NChQ4D9hngAmjVrxquvvsorr7zCyJEjSUtLA+Cdd96hUaNGADkma4uISPGnXdTEbuLi4khLS6NMmTLUqFHDrnW/9NJLtGzZkuTkZFJTU+nRo4deJRYRKcGUoIjdZM8/qV+/vt13EHZxcWHevHmUK1eOatWqMWfOHA3liIiUYEpQSoE//viDRx991Dr8UlDs/Yrx1WrXrs1ff/3Fvn37qFixYoHEEBERx6A5KKXAk08+ycaNG3F1dWXOnDkFFsferxhfT/a+TyIiUrKpB6WE27RpExs3bgRg/fr1BRrLXpsEioiIKEEp4d58803r10ePHs3VnjZ5YRiG3ddAERGR0ksJSgmSkJDA1q1brd9v376dVatW4ezsTK1atYCC60VJTEzk/PnzmEwm6tatWyAxRESk9FCCUoL06NGDli1b8vDDD3P27FkmT54MwMCBA3n44YcB2LBhQ4HEzh7eqVmzJh4eHgUSQ0RESg8lKCVEXFwc27dvB+Dbb7+lYcOGLFq0CLCsIdK+fXvA0oNSEIsH//bbbwDceeeddq9bRERKH73FU0KsXLkSgAYNGpCVlWWdD9K3b18aNmxIzZo1cXV15cSJE/z111/UqVPHrvGze2batm1r13pFRKR0Ug9KCbFixQoAhgwZwvbt2xk7dix333034eHhAHh6elp3+bX3PJSMjAw2bdoEKEERERH7UIJSAqSkpLBu3ToA7r//fjw8PHj33XfZtm0b9evXt5a7cpjHnnbs2MGFCxcoV66chnhERMQulKCUAFFRUaSkpFCtWjWCg4NvWK6g5qFkD++0adMGZ2dnu9UrIiKllxKUEiB7eOf++++/6f40LVq0wN3dnYSEBA4cOGC3+FFRUQC0a9fObnWKiEjppgSlmDMMg59++gmwJCg3U6ZMGVq2bAnY73XjzMxMfvnlF0DzT0RExH6UoBRzhw4d4vDhw7i5udGxY8dbls8e5sl+6ye/du7cSVJSEj4+PjRu3NgudYqIiChBKeayh3fatm1L2bJlb1m+X79+APz000+cOHEi3/Gzh3c0/0REROxJCUoxd+X8k9xo2LAh7dq1IzMzk08//TTf8bX+iYiIFAQlKMXYhQsXrD0Y3bt3z/V9I0eOBGDWrFmkpqbmOb7mn4iISEFRglKMRUZGkpaWRp06dahXr16u7+vduzdVq1YlMTGRH374Ic/x9+zZw/nz5ylbtixNmjTJcz0iIiJXU4JSjC1btgyAnj173vT14qu5urry5JNPAvDRRx/lKbZhGHz88ccAtG7dGhcX7ZogIiL2owSlmMrKyrK+XhwaGmrz/cOHD8fV1ZUtW7ZYNxm0xZtvvsnnn3+OyWSyDhmJiIjYixKUYio6OprExER8fHxo06aNzfcHBATw4IMPArb3onz22We88sorAEybNi1PCZKIiMjNKEEpprKHd7p164arq2ue6hg9ejQA8+fP5/DhwzmuHT58mNjY2GvuWbFiBU899RQAL7/8srUOERERe1KCUkwtX74csMw/yauWLVvSpUsX0tPTefXVV63nT5w4QbNmzbjnnnuIjo62nk9PT2f06NFkZWXx+OOP88Ybb+S9ASIiIjehBKUYOnbsGLt27cLJycmm14uvZ/LkyQAsWLCAPXv2WJOPc+fOkZWVxfPPP2/dWPDLL7/k8OHDVKxYkWnTptk0MVdERMQWSlCKoezJsa1atcLPzy9fdTVt2pQHH3wQwzCYOHEin3zyCZGRkZQpUwZ3d3eioqL46aefSE1NtfaYvPTSS7latVZERCSvlKAUQ9nzT+w1OfW///0vzs7OLF26lLFjxwLwzjvvEBYWBsCLL77IrFmzOHbsGFWqVGHEiBF2iSsiInIjJiO7/74YSUpKwtfXF7PZjI+PT1E/TqG6ePEifn5+pKamsm/fPho0aGCXep944glmz54NQOfOnVm1ahVJSUnUqVOHs2fP4uLiQkZGBtOnT2fUqFF2iSkiIqWLLZ/f6kEpZqKjo0lNTSUoKIg77rjDbvW++uqreHt74+fnx5w5c3BycqJcuXLW14kzMjIICgpi+PDhdospIiJyI0pQipnst2qaN29u10mqQUFB7Nu3j71791K1alXr+REjRlCrVi0AXnnlFdzd3e0WU0RE5Ea0Pnkxk52gNGvWzO51BwUFXXPOzc2N1atXExMTw4ABA+weU0RE5HqUoBQzMTExANx9992FFrN27drUrl270OKJiIjYPMSzceNGevbsSWBgICaTiSVLluS4/thjj2EymXIcLVq0yFEmNTWV0aNH4+/vj5eXF7169eLEiRP5akhp8PfffxMXFweg3YNFRKREszlBuXjxIo0aNbrp/i3dunUjPj7eeqxYsSLH9bCwMBYvXkxERASbNm3iwoULhIaGkpmZaXsLSpHs3pN69epRrly5on0YERGRAmTzEE/37t1vuXqpu7s7AQEB171mNpuZPXs28+bNo1OnToBlL5igoCDWrFlD165dbX2kIpWWlsYvv/xC69atC3wCaVEM74iIiBSFAnmLZ8OGDVSqVIl69eoxfPhwEhMTrddiY2NJT0+nS5cu1nOBgYEEBwezefPm69aXmppKUlJSjsMRpKen06dPHzp16mRdMr4gFeQEWREREUdi9wSle/fuLFiwgHXr1vHee+8RHR1Nhw4dSE1NBSAhIQE3NzfKly+f477KlSuTkJBw3TrDw8Px9fW1Htd726SwZWVlMWzYMFauXAnAvHnzKOg177ITFPWgiIhISWf3BOWhhx6iR48eBAcH07NnT1auXMnBgwet+8fciGEYN1zXY/z48ZjNZutx/Phxez+2zV544QXmzZuHs7Mz7u7uxMXFERsbW2DxTp06RXx8PE5OTjRu3LjA4oiIiDiCAl+orUqVKtSoUYNDhw4BEBAQQFpaGufOnctRLjExkcqVK1+3Dnd3d3x8fHIcRemjjz7ivffeA2DOnDn06tULgIULFxZYzOzek4YNG+Ll5VVgcURERBxBgScoZ8+e5fjx41SpUgWw7J7r6upKZGSktUx8fDx79+6lVatWBf04+RYfH89LL70EwFtvvcXgwYOtC5gtXLiwwIZ5NEFWRERKE5vf4rlw4QJ//vmn9fu4uDh27txJhQoVqFChApMmTaJfv35UqVKFI0eOMGHCBPz9/enbty8Avr6+DBs2jLFjx+Ln50eFChUYN24cISEh1rd6HNmECRO4ePEizZs3Z9y4cQDcf//9eHp6Wod5CmISqybIiohIaWJzD0pMTAx33XUXd911FwDPPfccd911F//5z39wdnZmz5499O7dm3r16jF06FDq1avHli1b8Pb2ttbxwQcf0KdPHwYMGMC9996Lp6cny5Ytw9nZ2X4tKwAxMTHMnTsXgGnTpuHkZPnxeXp60qNHD6BghnkMw9AEWRERKVVMRkG/elIAbNmu2V4Mw6BNmzb8+uuvPProo8ybNy/H9e+//57+/ftTs2ZN/vrrL7tu5BcXF0etWrVwdXUlOTlZG/aJiEixZMvnt3YzzqXvvvuOX3/9FU9PT6ZMmXLN9SuHebZv327X2FFRUQA0atRIyYmIiJQKSlByITMzkwkTJgDw0ksvUbVq1WvKXDnM880339g1fkREBID1bSEREZGSTglKLixZsoTDhw/j5+fHc889d8NyjzzyCACffPIJx44ds0vsM2fOsGbNGgAefvhhu9QpIiLi6JSg5EL2micjRoy46RokvXr1ok2bNqSkpNw0kbHFDz/8QGZmJk2bNqVu3bp2qVNERMTRKUG5hS1btrBlyxbc3NwYOXLkTcuaTCY+/vhjnJ2d+eGHH/j555/zHT97eEe9JyIiUpooQbmF999/H7AM39xoh+YrhYSEMHr0aABGjx5t3YMoL06ePMnGjRsBrIvBiYiIlAZKUG4iLi6ORYsWAdg0ZDNp0iQCAgI4dOgQgwcP5vPPPycqKork5GSb4mevTHvvvfdSvXp1m+4VEREpzpSg3MTUqVPJysqia9euBAcH5/o+X19f3n33XcCSZAwfPpx27doREhLCpUuXcl1P9ttAAwcOtO3BRUREijklKDdw8eJFvvjiCwDGjh1r8/2DBg1i8eLFjB49mq5du+Lj48PRo0eZNWtWru4/fPgw27Ztw8nJiQcffNDm+CIiIsWZEpQbWLJkCRcuXKB27dp52iPIZDLRp08fPvzwQ1atWmV9E+itt94iJSXllvd//vnnALRv3/6GuzyLiIiUVEpQbmD+/PkAPProo3ZZtn7IkCHcdtttJCQk8Omnn9607JkzZ/jwww8BbvnmkIiISEmkBOU6Tp8+zerVq4H/Lb6WX25ubrz88svArXtR3nrrLS5evEjTpk3p06ePXeKLiIgUJ0pQruPbb78lKyuL5s2b23VxtNz0osTHx/Pxxx8D8MYbb9h100EREZHiQgnKdVw5vGNPV/aivPbaazzxxBN8/PHHREdHk5WVBcDkyZO5fPkyrVq1olu3bnaNLyIiUlyYDMMwivohbGXLds22OnDgALfffjvOzs7Ex8dTsWJFu9aflpZG06ZN2bt3b47zlStXJjQ0lHnz5pGWlsa6deto3769XWOLiIgUJVs+v10K6ZmKjQULFgDQtWtXuycnYOlF2bJlC6tXr2b79u3s2LGDTZs2cfr0aWbPng1Ahw4dlJyIiEippgTlCoZhFNjwzpXKli3LAw88wAMPPABYelWioqJYsmQJ+/fvt77BIyIiUlppiOcKmzdv5t5776Vs2bKcPn0aT09Pu9UtIiJS2mmIJ4/q1q3L+++/z4ULF5SciIiIFCH1oIiIiEihsOXzW68Zi4iIiMNRgiIiIiIORwmKiIiIOBwlKCIiIuJwlKCIiIiIw1GCIiIiIg5HCYqIiIg4HCUoIiIi4nCUoIiIiIjDUYIiIiIiDkcJioiIiDgcJSgiIiLicJSgiIiIiMNxKeoHyIvsDZiTkpKK+ElEREQkt7I/t7M/x2+mWCYoycnJAAQFBRXxk4iIiIitkpOT8fX1vWkZk5GbNMbBZGVlcerUKby9vTGZTHatOykpiaCgII4fP46Pj49d63Z0arvarraXHmq72l4UbTcMg+TkZAIDA3Fyuvksk2LZg+Lk5ES1atUKNIaPj0+p+w83m9qutpc2arvaXtoUZdtv1XOSTZNkRURExOEoQRERERGHowTlKu7u7rz66qu4u7sX9aMUOrVdbS9t1Ha1vbQpTm0vlpNkRUREpGRTD4qIiIg4HCUoIiIi4nCUoIiIiIjDUYIiIiIiDkcJyhU++eQTatasSZkyZWjatCm//PJLUT+S3YWHh3P33Xfj7e1NpUqV6NOnDwcOHMhRxjAMJk2aRGBgIB4eHrRr1459+/YV0RMXnPDwcEwmE2FhYdZzJbntJ0+e5NFHH8XPzw9PT08aN25MbGys9XpJbXtGRgYTJ06kZs2aeHh4UKtWLV5//XWysrKsZUpK2zdu3EjPnj0JDAzEZDKxZMmSHNdz087U1FRGjx6Nv78/Xl5e9OrVixMnThRiK/LuZu1PT0/nxRdfJCQkBC8vLwIDAxkyZAinTp3KUUdxbf+t/u6v9OSTT2IymZg6dWqO847WdiUo/+/bb78lLCyMl19+mR07dtCmTRu6d+/OsWPHivrR7CoqKoqRI0eydetWIiMjycjIoEuXLly8eNFa5u233+b999/no48+Ijo6moCAADp37mzdA6kkiI6OZtasWdx55505zpfUtp87d457770XV1dXVq5cyf79+3nvvfcoV66ctUxJbftbb73FzJkz+eijj/j99995++23eeedd5g+fbq1TElp+8WLF2nUqBEfffTRda/npp1hYWEsXryYiIgINm3axIULFwgNDSUzM7OwmpFnN2v/pUuX2L59O6+88grbt29n0aJFHDx4kF69euUoV1zbf6u/+2xLlizht99+IzAw8JprDtd2QwzDMIx77rnHeOqpp3Kcu/32242XXnqpiJ6ocCQmJhqAERUVZRiGYWRlZRkBAQHGlClTrGUuX75s+Pr6GjNnziyqx7Sr5ORko27dukZkZKTRtm1bY8yYMYZhlOy2v/jii0br1q1veL0kt71Hjx7G448/nuPcAw88YDz66KOGYZTctgPG4sWLrd/npp3nz583XF1djYiICGuZkydPGk5OTsaqVasK7dnt4er2X8+2bdsMwDh69KhhGCWn/Tdq+4kTJ4yqVasae/fuNWrUqGF88MEH1muO2Hb1oABpaWnExsbSpUuXHOe7dOnC5s2bi+ipCofZbAagQoUKAMTFxZGQkJDjZ+Hu7k7btm1LzM9i5MiR9OjRg06dOuU4X5LbvnTpUpo1a0b//v2pVKkSd911F5999pn1eklue+vWrVm7di0HDx4EYNeuXWzatIn7778fKNltv1Ju2hkbG0t6enqOMoGBgQQHB5eon0U2s9mMyWSy9iSW5PZnZWUxePBgnn/+eRo2bHjNdUdse7HcLNDe/v77bzIzM6lcuXKO85UrVyYhIaGInqrgGYbBc889R+vWrQkODgawtvd6P4ujR48W+jPaW0REBNu3byc6OvqaayW57YcPH2bGjBk899xzTJgwgW3btvHMM8/g7u7OkCFDSnTbX3zxRcxmM7fffjvOzs5kZmby5ptvMnDgQKBk/71fKTftTEhIwM3NjfLly19TpqT9Lrx8+TIvvfQSgwYNsm6aV5Lb/9Zbb+Hi4sIzzzxz3euO2HYlKFcwmUw5vjcM45pzJcmoUaPYvXs3mzZtuuZaSfxZHD9+nDFjxrB69WrKlClzw3Ilse1ZWVk0a9aMyZMnA3DXXXexb98+ZsyYwZAhQ6zlSmLbv/32W+bPn8/XX39Nw4YN2blzJ2FhYQQGBjJ06FBruZLY9uvJSztL2s8iPT2dhx9+mKysLD755JNbli/u7Y+NjWXatGls377d5nYUZds1xAP4+/vj7Ox8TZaYmJh4zb82SorRo0ezdOlS1q9fT7Vq1aznAwICAErkzyI2NpbExESaNm2Ki4sLLi4uREVF8eGHH+Li4mJtX0lse5UqVWjQoEGOc3fccYd1EnhJ/nt//vnneemll3j44YcJCQlh8ODBPPvss4SHhwMlu+1Xyk07AwICSEtL49y5czcsU9ylp6czYMAA4uLiiIyMtPaeQMlt/y+//EJiYiLVq1e3/u47evQoY8eO5bbbbgMcs+1KUAA3NzeaNm1KZGRkjvORkZG0atWqiJ6qYBiGwahRo1i0aBHr1q2jZs2aOa7XrFmTgICAHD+LtLQ0oqKiiv3PomPHjuzZs4edO3daj2bNmvHII4+wc+dOatWqVWLbfu+9917zOvnBgwepUaMGULL/3i9duoSTU85fdc7OztbXjEty26+Um3Y2bdoUV1fXHGXi4+PZu3dvifhZZCcnhw4dYs2aNfj5+eW4XlLbP3jwYHbv3p3jd19gYCDPP/88P//8M+CgbS+SqbkOKCIiwnB1dTVmz55t7N+/3wgLCzO8vLyMI0eOFPWj2dWIESMMX19fY8OGDUZ8fLz1uHTpkrXMlClTDF9fX2PRokXGnj17jIEDBxpVqlQxkpKSivDJC8aVb/EYRslt+7Zt2wwXFxfjzTffNA4dOmQsWLDA8PT0NObPn28tU1LbPnToUKNq1arG8uXLjbi4OGPRokWGv7+/8cILL1jLlJS2JycnGzt27DB27NhhAMb7779v7Nixw/qWSm7a+dRTTxnVqlUz1qxZY2zfvt3o0KGD0ahRIyMjI6OompVrN2t/enq60atXL6NatWrGzp07c/z+S01NtdZRXNt/q7/7q139Fo9hOF7blaBc4eOPPzZq1KhhuLm5GU2aNLG+eluSANc95syZYy2TlZVlvPrqq0ZAQIDh7u5u3HfffcaePXuK7qEL0NUJSklu+7Jly4zg4GDD3d3duP32241Zs2bluF5S256UlGSMGTPGqF69ulGmTBmjVq1axssvv5zjQ6mktH39+vXX/f976NChhmHkrp0pKSnGqFGjjAoVKhgeHh5GaGiocezYsSJoje1u1v64uLgb/v5bv369tY7i2v5b/d1f7XoJiqO13WQYhlEYPTUiIiIiuaU5KCIiIuJwlKCIiIiIw1GCIiIiIg5HCYqIiIg4HCUoIiIi4nCUoIiIiIjDUYIiIiIiDkcJioiIiDgcJSgiIiLicJSgiIiIiMNRgiIiIiIORwmKiIiIOJz/A/BcCN547xizAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pmdarima.arima import auto_arima\n", + "\n", + "# Train the model on the training data\n", + "model = auto_arima(train) #create model \n", + "model.fit(train)\n", + "\n", + "# Predict the next n timepoints corresponding to the length of the test set\n", + "forecast = model.predict(n_periods=len(test)) #predict the model \n", + "\n", + "# Plot the results\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, forecast, color = \"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "22405758-7d36-4e6e-aa9e-11851fcd8eab", + "metadata": {}, + "source": [ + "### Challenge Exercise\n", + "The ARIMA model does pretty well when the size of the training set is large (>100 samples) and the size of the forecast is small. Try increasing the size of the n_periods to forecast and plotting this. Think about how the model will behave as predictions go farther into the future. How long will it take before the predictions start to look like noise?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4a689af9-21cd-4564-8c0d-2154fef12bc4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Lets say you want to predict 100 timepoints in the future you can update the xs_test x-coords like this\n", + "n_periods = 150 #have to define this first!\n", + "\n", + "#we have already trained model and fit it to the data:\n", + "forecast = model.predict(n_periods= n_periods) #predict the model\n", + "\n", + "xs_test = list(range(split_idx, split_idx +n_periods)) #range gives us increment of integers starting at max_x and going to something even bigger\n", + "\n", + "# Plot the results\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, forecast, color = \"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "32b93f76-3908-45b2-88ef-6927863f78c7", + "metadata": {}, + "source": [ + "### Challenge Exercises\n", + "\n", + "The statsmodels library has several different timeseries datasets built in. Here you will load two different time series example datasets and work through the analysis that we just practiced above.\n", + "\n", + "Step 1. Plot the two timeseries \n", + "Step 2. Determine if time series look like additive or multiplicative models\n", + "Step 3. Decompose each time series into component parts (assume a period of 12)\n", + "Step 4. Split each dataset into test and train sets AND train an ARIMA model. Predict the held-out test set.\n", + "\n", + "**How does ARIMA perform on each of these datasets?**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ec1bcf9c-e58c-4d72-b705-4c5a644cad0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "144\n", + "468\n" + ] + } + ], + "source": [ + "import statsmodels.api as smf\n", + "ts_A = sm.datasets.get_rdataset(\"AirPassengers\", \"datasets\").data[\"value\"].values\n", + "print(len(ts_A)) #length of timeseries A\n", + "ts_B = sm.datasets.get_rdataset(\"co2\", \"datasets\").data[\"value\"].values\n", + "print(len(ts_B)) #length of timeseries B\n", + "#This step is useful so we can know how to split up the train/test data for each " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7f6e8bd-b7d2-4d4b-9fdd-d6c9008bef0e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b589c82-14d4-469c-822d-628df9d18b3c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f3e5009e-5781-4181-af09-bf39fa38c3b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot timeseries A:\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(time, ts_A, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59717533-a8c6-46bb-9d18-c74c829cb85a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "667881bf-ddd2-4b94-8544-64ba009fe19e", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must have same first dimension, but have shapes (144,) and (468,)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[28], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#Plot timeseries B:\u001b[39;00m\n\u001b[1;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts_B\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtab:blue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValue\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/matplotlib/pyplot.py:3829\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3821\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3822\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3823\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3827\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3828\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3829\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m 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+ "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (144,) and (468,)" + ] + }, + { + "data": { + "image/png": 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srCx+85vfxIEDB4Yfzc3N8dnPfjYOHDgQixYturDdAwAAwBSR0xXwiIiWlpa48847o7a2NhYvXhw//elPo7u7O5qbmyPig4+P//73v4+f/exnMW3atJg3b96I9VdddVWUlpaOGgcAAICPs5wDvLGxMfr7+2PTpk3R09MT8+bNi/b29qiqqoqIiJ6eno+8JzgAAABcanK+D/jF4D7gAAAApHTR7wMOAAAA5EeAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAJ5BfjmzZujuro6SktLo6amJnbv3n3OuS+++GLceuut8alPfSrKyspi8eLF8ctf/jLvDQMAAMBUlHOAt7W1xZo1a2LDhg3R1dUV9fX1sXTp0uju7h5z/q5du+LWW2+N9vb26OzsjJtuuimWL18eXV1dF7x5AAAAmCoKsizLclmwaNGiWLBgQWzZsmV4bO7cubFixYpobW09r5/x+c9/PhobG+PBBx88r/mDg4NRXl4eAwMDUVZWlst2AQAAIGcT0aE5XQE/efJkdHZ2RkNDw4jxhoaG2Lt373n9jDNnzsSJEyfiyiuvzOWlAQAAYEorymVyX19fnD59OioqKkaMV1RURG9v73n9jB/96Efx7rvvxsqVK885Z2hoKIaGhoafDw4O5rJNAAAAmHTy+iNsBQUFI55nWTZqbCzPPvtsfP/734+2tra46qqrzjmvtbU1ysvLhx9z5szJZ5sAAAAwaeQU4DNnzozCwsJRV7uPHz8+6qr4X2pra4tVq1bFL37xi7jllls+dO769etjYGBg+HH06NFctgkAAACTTk4BXlxcHDU1NdHR0TFivKOjI+rq6s657tlnn4277747nnnmmbj99ts/8nVKSkqirKxsxAMAAACmspy+Ax4R0dLSEnfeeWfU1tbG4sWL46c//Wl0d3dHc3NzRHxw9fr3v/99/OxnP4uID+K7qakp/vVf/zW+9KUvDV89v+yyy6K8vHwc3woAAABMXjkHeGNjY/T398emTZuip6cn5s2bF+3t7VFVVRURET09PSPuCf6Tn/wkTp06Fd/+9rfj29/+9vD4XXfdFU8//fSFvwMAAACYAnK+D/jF4D7gAAAApHTR7wMOAAAA5EeAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABIQ4AAAAJCAAAcAAIAEBDgAAAAkIMABAAAgAQEOAAAACQhwAAAASECAAwAAQAICHAAAABLIK8A3b94c1dXVUVpaGjU1NbF79+4Pnb9z586oqamJ0tLSuO666+KJJ57Ia7MAAAAwVeUc4G1tbbFmzZrYsGFDdHV1RX19fSxdujS6u7vHnH/kyJFYtmxZ1NfXR1dXVzzwwAOxevXqeOGFFy548wAAADBVFGRZluWyYNGiRbFgwYLYsmXL8NjcuXNjxYoV0draOmr+d7/73XjllVfi0KFDw2PNzc3x61//Ovbt23derzk4OBjl5eUxMDAQZWVluWwXAAAAcjYRHVqUy+STJ09GZ2dnrFu3bsR4Q0ND7N27d8w1+/bti4aGhhFjt912W2zdujXef//9mD59+qg1Q0NDMTQ0NPx8YGAgIj74LwAAAAAm2tn+zPGa9YfKKcD7+vri9OnTUVFRMWK8oqIient7x1zT29s75vxTp05FX19fVFZWjlrT2toaGzduHDU+Z86cXLYLAAAAF6S/vz/Ky8vH5WflFOBnFRQUjHieZdmosY+aP9b4WevXr4+Wlpbh53/84x+jqqoquru7x+2Nw2QzODgYc+bMiaNHj/qqBR9bzjmXAuecS4FzzqVgYGAgrr322rjyyivH7WfmFOAzZ86MwsLCUVe7jx8/Puoq91lXX331mPOLiopixowZY64pKSmJkpKSUePl5eX+B87HXllZmXPOx55zzqXAOedS4JxzKZg2bfzu3p3TTyouLo6ampro6OgYMd7R0RF1dXVjrlm8ePGo+Tt27Ija2toxv/8NAAAAH0c5p3xLS0s8+eSTsW3btjh06FCsXbs2uru7o7m5OSI++Ph4U1PT8Pzm5uZ48803o6WlJQ4dOhTbtm2LrVu3xn333Td+7wIAAAAmuZy/A97Y2Bj9/f2xadOm6OnpiXnz5kV7e3tUVVVFRERPT8+Ie4JXV1dHe3t7rF27Nh5//PGYNWtWPProo/G1r33tvF+zpKQkHnrooTE/lg4fF845lwLnnEuBc86lwDnnUjAR5zzn+4ADAAAAuRu/b5MDAAAA5yTAAQAAIAEBDgAAAAkIcAAAAEhg0gT45s2bo7q6OkpLS6OmpiZ27979ofN37twZNTU1UVpaGtddd1088cQTiXYK+cvlnL/44otx6623xqc+9akoKyuLxYsXxy9/+cuEu4X85Pr7/KzXXnstioqK4otf/OLEbhDGQa7nfGhoKDZs2BBVVVVRUlISn/nMZ2Lbtm2Jdgv5yfWcb9++PebPnx+XX355VFZWxj333BP9/f2Jdgu52bVrVyxfvjxmzZoVBQUF8fLLL3/kmvFo0EkR4G1tbbFmzZrYsGFDdHV1RX19fSxdunTE7cz+ryNHjsSyZcuivr4+urq64oEHHojVq1fHCy+8kHjncP5yPee7du2KW2+9Ndrb26OzszNuuummWL58eXR1dSXeOZy/XM/5WQMDA9HU1BRf+cpXEu0U8pfPOV+5cmX8+7//e2zdujX++7//O5599tm48cYbE+4acpPrOd+zZ080NTXFqlWr4vXXX4/nnnsufvWrX8W9996beOdwft59992YP39+PPbYY+c1f9waNJsEFi5cmDU3N48Yu/HGG7N169aNOf+f/umfshtvvHHE2De/+c3sS1/60oTtES5Urud8LJ/73OeyjRs3jvfWYNzke84bGxuz733ve9lDDz2UzZ8/fwJ3CBcu13P+b//2b1l5eXnW39+fYnswLnI95//8z/+cXXfddSPGHn300Wz27NkTtkcYLxGRvfTSSx86Z7wa9KJfAT958mR0dnZGQ0PDiPGGhobYu3fvmGv27ds3av5tt90W+/fvj/fff3/C9gr5yuec/6UzZ87EiRMn4sorr5yILcIFy/ecP/XUU/HGG2/EQw89NNFbhAuWzzl/5ZVXora2Nn74wx/GNddcEzfccEPcd9998ac//SnFliFn+Zzzurq6OHbsWLS3t0eWZfHWW2/F888/H7fffnuKLcOEG68GLRrvjeWqr68vTp8+HRUVFSPGKyoqore3d8w1vb29Y84/depU9PX1RWVl5YTtF/KRzzn/Sz/60Y/i3XffjZUrV07EFuGC5XPOf/e738W6deti9+7dUVR00f+RBB8pn3N++PDh2LNnT5SWlsZLL70UfX198a1vfSvefvtt3wNnUsrnnNfV1cX27dujsbEx/vznP8epU6fiq1/9avz4xz9OsWWYcOPVoBf9CvhZBQUFI55nWTZq7KPmjzUOk0mu5/ysZ599Nr7//e9HW1tbXHXVVRO1PRgX53vOT58+HXfccUds3LgxbrjhhlTbg3GRy+/zM2fOREFBQWzfvj0WLlwYy5Yti0ceeSSefvppV8GZ1HI55wcPHozVq1fHgw8+GJ2dnfHqq6/GkSNHorm5OcVWIYnxaNCLfrlh5syZUVhYOOrfph0/fnzUv2E46+qrrx5zflFRUcyYMWPC9gr5yuecn9XW1harVq2K5557Lm655ZaJ3CZckFzP+YkTJ2L//v3R1dUV3/nOdyLig1DJsiyKiopix44dcfPNNyfZO5yvfH6fV1ZWxjXXXBPl5eXDY3Pnzo0sy+LYsWNx/fXXT+ieIVf5nPPW1tZYsmRJ3H///RER8YUvfCGuuOKKqK+vj4cfftgnVJnyxqtBL/oV8OLi4qipqYmOjo4R4x0dHVFXVzfmmsWLF4+av2PHjqitrY3p06dP2F4hX/mc84gPrnzffffd8cwzz/gOFZNerue8rKwsfvOb38SBAweGH83NzfHZz342Dhw4EIsWLUq1dThv+fw+X7JkSfzhD3+Id955Z3jst7/9bUybNi1mz549ofuFfORzzt97772YNm1kWhQWFkbE/79KCFPZuDVoTn+ybYL8/Oc/z6ZPn55t3bo1O3jwYLZmzZrsiiuuyP7nf/4ny7IsW7duXXbnnXcOzz98+HB2+eWXZ2vXrs0OHjyYbd26NZs+fXr2/PPPX6y3AB8p13P+zDPPZEVFRdnjjz+e9fT0DD/++Mc/Xqy3AB8p13P+l/wVdKaCXM/5iRMnstmzZ2d/93d/l73++uvZzp07s+uvvz679957L9ZbgI+U6zl/6qmnsqKiomzz5s3ZG2+8ke3Zsyerra3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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot timeseries B:\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(time, ts_B, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4012a103-9da6-4e4b-8e08-d9af9f6d03be", + "metadata": {}, + "outputs": [], + "source": [ + "#For timeseries A:\n", + "\"\"\"\n", + "Split index at 110, which will leave out 34 timepoints to predict in the training set\n", + "\"\"\"\n", + "split_idx = 110 #this is the number of values for 'test data'\n", + "\n", + "# For plotting we need x-coords for the full dataset so make a list of [1, 2, .., max_x]\n", + "xs = list(range(max_x))\n", + "\n", + "# Split the train data and the x-coords\n", + "train = additive[:split_idx] #taking additive data and splitting based on first x elements\n", + "xs_train = xs[:split_idx]\n", + "\n", + "# Split the test data and the remaining x-coords\n", + "test = additive[split_idx:] #opposite (of above) for the test data! \n", + "xs_test = xs[split_idx:]\n", + "\n", + "# Plot the test and train data to see the 'truth' value of the test set\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, test, color = \"red\")\n", + "plt.title(\"Train/Test split for additive Data\")\n", + "plt.ylabel(\"Value\")\n", + "plt.xlabel(\"Time\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7052739e-7572-428b-a2eb-5870cbe57792", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c27c5ec0-e147-420f-b7d6-d56f9be9c290", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "895ab6d6-ac4e-4204-909c-3fa9eb67622c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/wf-sim-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/wf-sim-checkpoint.ipynb new file mode 100644 index 0000000..d6ac0c6 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/wf-sim-checkpoint.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "73e1e51f-f815-4c64-992d-4e3d85cc8182", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'foobarfizzbuzz'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def my_cool_function(arg1, arg2):\n", + " arg1 += 'bar'\n", + " arg2 += 'buzz'\n", + " return arg1 + arg2\n", + "my_cool_function('foo', 'fizz')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "037f7223-d84b-4e85-88c5-4f346b286988", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#initializing the population\n", + "#first argument is size of population in haploid individuals (N)\n", + "#second argument is frequency of derived alleles (f)\n", + "\n", + "def init_function(N, f):\n", + " derived = int(N*f) #determines derived alleles\n", + " ancestral = N - derived #determines ancestral alleles\n", + " population = [1] * derived + [0] * ancestral #derived/ancestral allele frequency for entire pop\n", + " return population #returns 23 list items, and assigns them 0 or 1 to fit value of f\n", + "init_function(N=10, f=0.5) #setting values for N and f" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0711d723-5143-406a-984b-e6101ff1b674", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 1, 0, 1, 1, 1, 1, 1, 1]\n" + ] + } + ], + "source": [ + "import random #have to import this package first \n", + "\n", + "ancestral_pop = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0] #manually initializing with nums. from above\n", + "\n", + "#retrieving one WF generation\n", + "def step_function(wf_pop): #my function!!!\n", + " N = len(wf_pop) #retrieving population size (in this case it is 10!)\n", + " derived = sum(wf_pop) / N #chance of a derived allele being past to next generation \n", + " #(I have N = 10, derived alleles = 5 THUS 50% chance of allele being passed on\n", + "\n", + " next_gen = [1 if random.random() < derived else 0 for _ in range(N)] \n", + " #^ will probabilistically assign 1 or 0 to the derived allele frequency \n", + " #if random num is less than 50% (or 0.5) then it will be assigned a 1; otherwise 0 for ancestral\n", + " return next_gen\n", + " \n", + "#simulating next generation\n", + "next_gen = step_function(wf_pop=ancestral_pop) \n", + "#^calling on step_function and passing my ancestral_pop list as an argument to the param 'wf_pop'\n", + "\n", + "print(next_gen)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d9d52c13-0ace-431a-9596-4574e621c1df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generations: 10; freq(a): 0.0541\n" + ] + } + ], + "source": [ + "#COPIED FROM ABOVE: this function initialized my population w a certain N and f for each individual\n", + "def init_function(N, f):\n", + " derived = int(N*f) #determines derived alleles\n", + " ancestral = N - derived #determines ancestral alleles\n", + " population = [1] * derived + [0] * ancestral #derived/ancestral allele frequency for entire pop\n", + " return population #returns 23 list items, and assigns them 0 or 1 to fit value of f\n", + "\n", + "#COPIED FROM ABOVE: this function allows me to simulate generations esp. allele frequencies\n", + "def step_function(wf_pop): #my function!!!\n", + " N = len(wf_pop) #retrieving population size (in this case it is 10!)\n", + " derived = sum(wf_pop) / N #chance of a derived allele being past to next generation \n", + " #(I have N = 10, derived alleles = 5 THUS 50% chance of allele being passed on\n", + "\n", + " next_gen = [1 if random.random() < derived else 0 for _ in range(N)] \n", + " #^ will probabilistically assign 1 or 0 to the derived allele frequency \n", + " #if random num is less than 50% (or 0.5) then it will be assigned a 1; otherwise 0 for ancestral\n", + " return next_gen\n", + "\n", + "\n", + "#Bringing it ALL together - initializes AND loops through each generation \n", + "##spits out final derived allele frequency after 10 gens!!!!!\n", + "def wf_function(N, f, ngens): #N = pop size., f = derived allele freq., ngens = # generations to run\n", + " population = init_function(N, f)\n", + " #simulating for _ generations:\n", + " for _ in range(ngens):\n", + " population = step_function(population)\n", + " final_derived_freq = sum(population) / N\n", + "\n", + " print(f\"generations: {ngens}; freq(a): {final_derived_freq:.4f}\") #formats so string/int can print together\n", + "wf_function(N = 37, f = 0.3, ngens = 10) #output" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7d62ce89-0973-4f6a-9a8d-f59dad1f799e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 0, 0, 0, 0, 0, 0, 1, 0, 0]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random #need this library for later!!\n", + "\n", + "class Population: #defined my first python class!!\n", + " def __init__(self, N = 10, f = 0.2): #special class method which is called when creating instance of a new class\n", + " #defining attributes and setting them equal to the passed-in values:\n", + " self.N = N #we set \"reasonable default values\" for this\n", + " self.f = f #we set \"reasonable default values\" for this\n", + " #reasonable values means class instance will take default values that we define for arguments!!!\n", + "\n", + " derived_count = round(N*f)\n", + " self.pop = [0] * (N - derived_count) + [1] * derived_count \n", + " def __repr__(self):\n", + " return f\"Population(N={self.N}, f={self.f})\"\n", + " \n", + " def step(self, ngens=1):#Population class is already aware of N and f, so we only need to pass in 1 new argument\n", + " for i in range(ngens): #new argument; calling on functions in random library \n", + " \n", + " #original step() function didn't know about the pop so we had it calculate len(pop) to set value for k \n", + " #wildly inefficient, as we know pop size is fixed and constant for a given simulation, \n", + " #we took advantage of N attribute of the Population object to get past pointless recalculation:\n", + " self.pop = random.choices(self.pop, k = self.N)\n", + " \n", + " #pass (python keyword that means \"don't do anything (here)\")\n", + "\n", + "p = Population() #when I type p it will \"run through\" the class and carry it all the functions\n", + "p.step(ngens = 10) #setting a \"reasonable default value\" for ngen\n", + "p.pop" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "653372e1-1a61-474e-96a7-644755f9616b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 0 1 1 0 1 0 1 1]\n", + "False\n" + ] + } + ], + "source": [ + "import random #need this library for later!!\n", + "import numpy as np\n", + "\n", + "#Further challenges:\n", + "\"\"\"\n", + "Create a new isMonomorphic() method for your Population class to test whether the population is fixed for either the ancestral or derived state.\n", + "Method should return TRUE if population is monomorphic for either state \n", + "FALSE if otherwise \n", + "\"\"\"\n", + "#COPIED FROM ABOVE -----:\n", + "\n", + "class Population: #defined my first python class!!\n", + " def __init__(self, N = 10, f = 0.2, with_np = False): #special class method which is called when creating instance of a new class\n", + " #defining attributes and setting them equal to the passed-in values:\n", + " self.N = N #we set \"reasonable default values\" for this\n", + " self.f = f #we set \"reasonable default values\" for this\n", + " self.with_np = with_np #I set a default value for this; making attribute a class object\n", + " #reasonable values means class instance will take default values that we define for arguments!!!\n", + " \n", + " derived_count = round(N*f)\n", + " \n", + " #this if-else generates a population based on whether 'with_np' = T/F\n", + " if self.with_np: \n", + " self.pop = np.array([0] * (N - derived_count) + [1] * derived_count) #this is a numpy array \n", + " else:\n", + " self.pop = [0] * (N - derived_count) + [1] * derived_count #use regular list \n", + " \n", + " def __repr__(self):\n", + " return f\"Population(N={self.N}, f={self.f})\"\n", + " \n", + " def step(self, ngens=1):#Population class is already aware of N and f, so we only need to pass in 1 new argument\n", + " for _ in range(ngens): #new argument; calling on functions in random library \n", + " #if with_np is true it'll use numpy rc, otherwise just uses random choice\n", + " if self.with_np:\n", + " self.pop = np.random.choice(self.pop, size = self.N)\n", + " #original step() function didn't know about the pop so we had it calculate len(pop) to set value for k \n", + " #wildly inefficient, as we know pop size is fixed and constant for a given simulation, \n", + " #we took advantage of N attribute of the Population object to get past pointless recalculation:\n", + " else:\n", + " self.pop = random.choices(self.pop, k = self.N)\n", + " \n", + " def isMonomorphic(self):\n", + " return len(set(self.pop)) == 1 #returns true if ALL elements in self.pop are the same \n", + " #pass (python keyword that means \"don't do anything (here)\")\n", + "\n", + "p = Population(with_np = True) #when I type p it will \"run through\" the class and carry it all the functions\n", + "p.step(ngens = 10) #setting a \"reasonable default value\" for ngen\n", + "print(p.pop)\n", + "print(p.isMonomorphic())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "17dbeb2a-7b75-4425-bf2d-ee6a65627247", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Instantiate two new objects of type Population, one called pop and the other pop_np.\n", + "Set the population size to 100 and set the with_np flag based on name of these objects.\n", + "\"\"\"\n", + "pop = Population(N = 100, with_np = False)\n", + "pop_np = Population(N = 100, with_np = True)\n", + "print(pop.pop)\n", + "print(pop_np.pop) #this is my numpy array " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "04734676-ad85-40d1-892c-1ef60ea14fdb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.2 ms ± 15.5 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit #automatically \"prints\" timeing result output \n", + "pop.step(ngens = 1000) " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c74b44b3-7b2b-4a50-a62a-2d188d165980", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.44 ms ± 26.7 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit #automatically \"prints\" timeing result output \n", + "pop_np.step(ngens = 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "ef6f9a9f-26f1-4096-b00f-d2722257e804", + "metadata": {}, + "source": [ + "After testing the performance of step() using both the list-based and numpy-array based populations, using %%timeit, I observed the following results:\n", + "1. for the **list-based** population, the execution time was 6.2 ms ± 15.5 μs per loop.\n", + "2. for the **numpy-array based** population, the execution time was 6.44 ms ± 26.7 μs per loop\n", + "\n", + "**Conclusion:**\n", + "The execution time difference between the two populations is quite small, with the list-based population excuting slightly faster. Thus, I conclude that for this specific test, the list-based implementation seems to be marginally faster than the numpy-array based implementation.However, given the marginal difference in execution times it is hard to favor one over the other in this context.\n", + "*Note:* if the number of generations or population size were to be >1000, perhaps the performance benefits of using numpy-arrays would become more apparent?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e78d1480-6064-4ff7-a1bb-9fcd97355c16", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/wf-sim-checkpoint.py b/notebooks/.ipynb_checkpoints/wf-sim-checkpoint.py new file mode 100644 index 0000000..3783fe4 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/wf-sim-checkpoint.py @@ -0,0 +1,79 @@ +import random #need this library for later!! +import numpy as np +import argparse + +class Population: #defined my first python class!! + def __init__(self, N = 10, f = 0.2, with_np = False): #default values introduced here + #defining attributes and setting them equal to the passed-in values: + self.N = N #we set "reasonable default values" for this + self.f = f #we set "reasonable default values" for this + self.with_np = with_np #I set a default value for this; making attribute a class object + + derived_count = round(N*f) + + #this if-else generates a population based on whether 'with_np' = T/F + if self.with_np: + self.pop = np.array([0] * (N - derived_count) + [1] * derived_count) + else: + self.pop = [0] * (N - derived_count) + [1] * derived_count #use regular list + + def __repr__(self): + return f"Population(N={self.N}, f={self.f})" + + def step(self, ngens=1):#Pop class is aware of N and f, so we only need to pass in 1 new argument + for _ in range(ngens): #new argument; calling on functions in random library + #if with_np is true it'll use numpy rc, otherwise just uses random choice + if self.with_np: + self.pop = np.random.choice(self.pop, size = self.N) + else: + self.pop = random.choices(self.pop, k = self.N) + + def isMonomorphic(self): + return len(set(self.pop)) == 1 #returns true if ALL elements in self.pop are the same + + def get_population(self): #huhhhhh??? + return self.pop + +def init_function(N, f): + derived_count = round(N*f) #determines derived alleles + return [0] * (N - derived_count) + [1] * derived_count + +def run_sim(N, f, ngens, verbose = False): + population = init_function(N,f) + + for generation in range(ngens): + # Simulate one step (generation) of the population + derived_freq = sum(population) / N + next_gen = [1 if random.random() < derived_freq else 0 for _ in range(N)] + population = next_gen + + # Optionally print verbose output + if verbose: + print(f"Generation {generation + 1}: Population = {population}") + + final_derived_freq = sum(population) / N + print(f"After {ngens} generations, the derived allele frequency is: {final_derived_freq:.4f}") + +def main(): + # Set up argparse for command-line arguments + parser = argparse.ArgumentParser(description="Run a Wright-Fisher simulation") + + # Arguments for the simulation + parser.add_argument('--N', type=int, default=10, help='Population size (N)') + parser.add_argument('--f', type=float, default=0.2, help='Frequency of derived allele (f)') + parser.add_argument('--ngens', type=int, default=10, help='Number of generations') + + # Verbose flag for additional debugging + parser.add_argument('--verbose', action='store_true', help='Print verbose debugging information') + + args = parser.parse_args() + + # Run the simulation with user-defined arguments + run_sim(args.N, args.f, args.ngens, verbose=args.verbose) + + +if __name__ == "__main__": + main() + + + diff --git a/notebooks/14.1-visualization.ipynb b/notebooks/14.1-visualization.ipynb new file mode 100644 index 0000000..28f0ddd --- /dev/null +++ b/notebooks/14.1-visualization.ipynb @@ -0,0 +1,1682 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "8417caef-af4b-4d18-86a0-8b34987b02c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not load conda plugin `conda-libmamba-solver`:\n", + "\n", + "dlopen(/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/libmambapy/bindings.cpython-310-darwin.so, 0x0002): Library not loaded: @rpath/libarchive.13.dylib\n", + " Referenced from: /Users/riyarampalli/miniforge3/lib/libmamba.2.0.0.dylib\n", + " Reason: tried: '/Users/riyarampalli/miniforge3/lib/libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/libmambapy/../../../libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/libmambapy/../../../libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/bin/../lib/libarchive.13.dylib' (no such file), '/Users/riyarampalli/miniforge3/bin/../lib/libarchive.13.dylib' (no such file), '/usr/local/lib/libarchive.13.dylib' (no such file), '/usr/lib/libarchive.13.dylib' (no such file, not in dyld cache)\n", + "done\n", + "doneing environment: \\ \n", + "\n", + "\n", + "==> WARNING: A newer version of conda exists. <==\n", + " current version: 23.3.1\n", + " latest version: 25.1.1\n", + "\n", + "Please update conda by running\n", + "\n", + " $ conda update -n base -c conda-forge conda\n", + "\n", + "Or to minimize the number of packages updated during conda update use\n", + "\n", + " conda install conda=25.1.1\n", + "\n", + "\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /Users/riyarampalli/miniforge3\n", + "\n", + " added / updated specs:\n", + " - matplotlib\n", + " - scikit-learn\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " brotli-1.1.0 | hd74edd7_2 19 KB conda-forge\n", + " brotli-bin-1.1.0 | hd74edd7_2 16 KB conda-forge\n", + " contourpy-1.3.1 | py310h48ca7d4_0 249 KB\n", + " cycler-0.12.1 | pyhd8ed1ab_1 13 KB conda-forge\n", + " fonttools-4.56.0 | py310hc74094e_0 2.2 MB conda-forge\n", + " joblib-1.4.2 | pyhd8ed1ab_1 215 KB conda-forge\n", + " kiwisolver-1.4.8 | py310h313beb8_0 65 KB\n", + " libbrotlicommon-1.1.0 | hd74edd7_2 67 KB conda-forge\n", + " libbrotlidec-1.1.0 | hd74edd7_2 28 KB conda-forge\n", + " libbrotlienc-1.1.0 | hd74edd7_2 273 KB conda-forge\n", + " matplotlib-3.10.0 | py310hca03da5_0 8 KB\n", + " matplotlib-base-3.10.0 | py310h7ef442a_0 7.2 MB\n", + " munkres-1.1.4 | pyh9f0ad1d_0 12 KB conda-forge\n", + " numpy-1.24.0 | py310h5d7c261_0 5.0 MB conda-forge\n", + " pyparsing-3.2.1 | pyhd8ed1ab_0 91 KB conda-forge\n", + " scikit-learn-1.2.2 | py310h98c5782_1 6.6 MB conda-forge\n", + " scipy-1.7.3 | py310h6ecf4ae_0 20.1 MB conda-forge\n", + " threadpoolctl-3.5.0 | pyhc1e730c_0 23 KB conda-forge\n", + " unicodedata2-16.0.0 | py310h078409c_0 400 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 42.5 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " brotli conda-forge/osx-arm64::brotli-1.1.0-hd74edd7_2 \n", + " brotli-bin conda-forge/osx-arm64::brotli-bin-1.1.0-hd74edd7_2 \n", + " contourpy pkgs/main/osx-arm64::contourpy-1.3.1-py310h48ca7d4_0 \n", + " cycler conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_1 \n", + " fonttools conda-forge/osx-arm64::fonttools-4.56.0-py310hc74094e_0 \n", + " joblib conda-forge/noarch::joblib-1.4.2-pyhd8ed1ab_1 \n", + " kiwisolver pkgs/main/osx-arm64::kiwisolver-1.4.8-py310h313beb8_0 \n", + " libbrotlicommon conda-forge/osx-arm64::libbrotlicommon-1.1.0-hd74edd7_2 \n", + " libbrotlidec conda-forge/osx-arm64::libbrotlidec-1.1.0-hd74edd7_2 \n", + " libbrotlienc conda-forge/osx-arm64::libbrotlienc-1.1.0-hd74edd7_2 \n", + " matplotlib pkgs/main/osx-arm64::matplotlib-3.10.0-py310hca03da5_0 \n", + " matplotlib-base pkgs/main/osx-arm64::matplotlib-base-3.10.0-py310h7ef442a_0 \n", + " munkres conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n", + " pyparsing conda-forge/noarch::pyparsing-3.2.1-pyhd8ed1ab_0 \n", + " scikit-learn conda-forge/osx-arm64::scikit-learn-1.2.2-py310h98c5782_1 \n", + " threadpoolctl conda-forge/noarch::threadpoolctl-3.5.0-pyhc1e730c_0 \n", + " unicodedata2 conda-forge/osx-arm64::unicodedata2-16.0.0-py310h078409c_0 \n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " numpy 1.22.4-py310h0a343b5_0 --> 1.24.0-py310h5d7c261_0 \n", + "\n", + "The following packages will be DOWNGRADED:\n", + "\n", + " scipy 1.7.3-py310ha0d8a01_1 --> 1.7.3-py310h6ecf4ae_0 \n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "matplotlib-base-3.10 | 7.2 MB | | 0% \n", + "fonttools-4.56.0 | 2.2 MB | | 0% \u001b[A\n", + "\n", + "brotli-1.1.0 | 19 KB | | 0% \u001b[A\u001b[A\n", + "\n", + "\n", + "joblib-1.4.2 | 215 KB | | 0% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "numpy-1.24.0 | 5.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "libbrotlicommon-1.1. | 67 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cycler-0.12.1 | 13 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pyparsing-3.2.1 | 91 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "libbrotlidec-1.1.0 | 28 KB | | 0% 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"cell_type": "code", + "execution_count": 6, + "id": "97f9dfc1-673e-4a33-9f52-e6d7cc843d53", + "metadata": {}, + "outputs": [], + "source": [ + "#include a few imports that we will need:\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "markdown", + "id": "e5802551-e11b-43ea-a542-5aedd64b3916", + "metadata": {}, + "source": [ + "We have been loading and cleaning the iris data by hand up to this point, but now we are going to make use of a nice feature in scikit-learn, which provides the iris data as a pre-loaded dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5eb31f84-86ef-4620-913e-0021fa160297", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)species
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
..................
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
\n", + "

150 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + ".. ... ... ... ... \n", + "145 6.7 3.0 5.2 2.3 \n", + "146 6.3 2.5 5.0 1.9 \n", + "147 6.5 3.0 5.2 2.0 \n", + "148 6.2 3.4 5.4 2.3 \n", + "149 5.9 3.0 5.1 1.8 \n", + "\n", + " species \n", + "0 setosa \n", + "1 setosa \n", + "2 setosa \n", + "3 setosa \n", + "4 setosa \n", + ".. ... \n", + "145 virginica \n", + "146 virginica \n", + "147 virginica \n", + "148 virginica \n", + "149 virginica \n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = load_iris()\n", + "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n", + "iris_df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n", + "iris_df" + ] + }, + { + "cell_type": "markdown", + "id": "d69fdbc9-8058-43f2-80c3-68352e65e01e", + "metadata": {}, + "source": [ + "# Exploring the data 1-D at a time with histograms¶\n", + "\n", + "Let's say we want to visualize the differences between the different measurements for each of the species. A simple way to do this is with histograms. Here we will use the pd.groupby method to group the data by species ID." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "642b8733-dd2c-4209-9d00-43357d537637", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define a variable for the feature we wish to plot\n", + "feature = \"sepal length (cm)\"\n", + "# Group the data in the dataframe by species ID\n", + "gb = iris_df.groupby(\"species\")\n", + "# Iterate through the groupby object\n", + "for sp, dat in gb:\n", + " # For each species, plot the data for the given feature as a histogram\n", + " plt.hist(dat[feature], label=sp)" + ] + }, + { + "cell_type": "markdown", + "id": "17050e4b-3eae-4265-9591-b9d86ea9c494", + "metadata": {}, + "source": [ + "# Set the alpha channel¶\n", + "\n", + "The initial result looks good, but hist defaults to plotting all histograms with zero opacity, so it's not clear what is happening in regions where the histograms overlap. Matplotlib plotting functions can take an opacity parameter called alpha which takes values between 0 and 1. Try setting the alpha to a small value and replotting." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "24f3d128-3496-4c26-99f8-058b14bf86a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.06) #higher alpha values mean less transparency \n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "bf1710af-eec4-4267-a99f-300a85176e28", + "metadata": {}, + "source": [ + "# Change the colors¶\n", + "\n", + "By default, matplotlib will choose different colors for each histogram, and this is fine, but you might want to control the colors for each histogram to unify color schemes across your plots, and also to beautify them. You can specify colors in several different ways, but the most straightforward way is to pick from the large list of matplotlib named colors. Here are some examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "db4f953a-f9b8-449b-a6b2-8e98926ba22f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yAgQASE6AAADJCRAAIDkBAgAkJ0AAgOQECACQnAABAJITIABAcgIEAEhOgAAAyQkQACA5AQIAJCdAAIDkKtt7AEBL/1u/Obdz9e3VKbdzlZvs3TXtPYTcpZpTxf4Dk1yH8uYOCACQnAABAJITIABAcgIEAEhOgAAAyQkQACA5AQIAJCdAAIDkBAgAkJwAAQCSEyAAQHICBABIToAAAMkJEAAgOQECACQnQACA5AQIAJCcAAEAkhMgAEByAgQASE6AAADJCRAAIDkBAgAkJ0AAgORyD5Da2to44ogjokePHlFVVRWnn356rFixIu/LAAAlLPcAefrpp2PixInxwgsvxPz582PTpk0xduzY+Pjjj/O+FABQoirzPuGf/vSnFs/vuOOOqKqqiiVLlsTXvva1vC8HAJSgon8GpL6+PiIievfuXexLAQAlIvc7IP8ty7KYMmVKjBkzJoYPH97qPo2NjdHY2Nj8vKGhoZhDAgB2A0UNkO9///vx8ssvx7PPPrvNfWpra2PGjBnFHAYU3f/Wb27vIQCUlKK9BXPllVfGI488EgsWLIgBAwZsc7/p06dHfX1986Ourq5YQwIAdhO53wHJsiyuvPLKeOihh2LhwoUxePDg7e5fKBSiUCjkPQwAYDeWe4BMnDgx7rnnnvjd734XPXr0iLVr10ZERK9evaJbt255Xw4AKEG5vwUzZ86cqK+vj+OPPz769+/f/Lj//vvzvhQAUKKK8hYMAMD2+C0YACA5AQIAJCdAAIDkBAgAkJwAAQCSEyAAQHICBABIToAAAMkJEAAgOQECACQnQACA5AQIAJCcAAEAkhMgAEByAgQASE6AAADJCRAAIDkBAgAkJ0AAgOQECACQnAABAJITIABAcgIEAEiusr0HUMqWv72xvYdQVNmnLya5zp4r23adim5fKdJIgFK24Z9PtvcQaAN3QACA5AQIAJCcAAEAkhMgAEByAgQASE6AAADJCRAAIDkBAgAkJ0AAgOQECACQnAABAJITIABAcgIEAEhOgAAAyQkQACA5AQIAJCdAAIDkBAgAkJwAAQCSEyAAQHICBABIToAAAMkJEAAgOQECACRXtAC59dZbY/DgwdG1a9cYNWpU/OUvfynWpQCAElOUALn//vtj8uTJce2118ZLL70UX/3qV+OUU06JNWvWFONyAECJKUqA3HzzzfG9730vLrnkkjj44INj9uzZUVNTE3PmzCnG5QCAElOZ9wk3btwYS5YsiWnTprXYPnbs2Hjuuee22r+xsTEaGxubn9fX10dERENDQ95Di4iIDes3FuW85ajp40/bewitqmja0N5D2Er26e7536pbZaf2HgJlqGL9x+09BL6gpiL8jd3ydzvLsp3aP/cA+eCDD2Lz5s3Rt2/fFtv79u0ba9eu3Wr/2tramDFjxlbba2pq8h4aAFBk69evj169eu1wv9wDZIuKiooWz7Ms22pbRMT06dNjypQpzc+bmpriX//6V/Tp06fV/Xd3DQ0NUVNTE3V1ddGzZ8/2Hk5SHXXuHXXeEebeEefeUecd0XHnvrPzzrIs1q9fH9XV1Tt13twDZN99941OnTptdbdj3bp1W90ViYgoFApRKBRabNt7773zHlZyPXv27FD/g/63jjr3jjrvCHPviHPvqPOO6Lhz35l578ydjy1y/xBqly5dYtSoUTF//vwW2+fPnx/HHHNM3pcDAEpQUd6CmTJlSlxwwQUxevToOProo2Pu3LmxZs2auPzyy4txOQCgxBQlQM4666z48MMP48Ybb4z3338/hg8fHn/84x/jgAMOKMbldiuFQiF+8pOfbPW2UkfQUefeUecdYe4dce4ddd4RHXfuxZp3Rbaz35cBAMiJ34IBAJITIABAcgIEAEhOgAAAyQmQL6C2tjYqKipi8uTJ29xn4cKFUVFRsdXj9ddfTzfQHNxwww1bzaFfv37bPebpp5+OUaNGRdeuXePAAw+M2267LdFo89PWeZfLem/x7rvvxvnnnx99+vSJ7t27x+GHHx5LlizZ7jHlsO4RbZ97Oaz9oEGDWp3DxIkTt3lMuax3W+deDusdEbFp06a47rrrYvDgwdGtW7c48MAD48Ybb4ympqbtHpfHuhftn2Ivd4sWLYq5c+fGYYcdtlP7r1ixosW/ILfffvsVa2hFM2zYsHjyySebn3fqtO0fOlu9enWceuqpcemll8a8efPir3/9a1xxxRWx3377xbe+9a0Uw81NW+a9RTms90cffRTHHntsnHDCCfHYY49FVVVVvPXWW9v9l4rLZd13Ze5blPLaL1q0KDZv3tz8/NVXX42TTz45zjzzzFb3L5f1jmj73Lco5fWOiJg1a1bcdtttcdddd8WwYcNi8eLFcdFFF0WvXr1i0qRJrR6T27pntNn69euzIUOGZPPnz8+OO+64bNKkSdvcd8GCBVlEZB999FGy8RXDT37yk2zEiBE7vf8111yTDR06tMW2yy67LDvqqKNyHllxtXXe5bLeWZZlU6dOzcaMGdOmY8pl3Xdl7uW09ltMmjQpO+igg7KmpqZWXy+X9W7NjuZeLus9bty47OKLL26x7YwzzsjOP//8bR6T17p7C2YXTJw4McaNGxcnnXTSTh8zcuTI6N+/f5x44omxYMGCIo6ueN54442orq6OwYMHx9lnnx2rVq3a5r7PP/98jB07tsW2r3/967F48eL47LPPij3UXLVl3luUw3o/8sgjMXr06DjzzDOjqqoqRo4cGbfffvt2jymXdd+VuW9RDmsfEbFx48aYN29eXHzxxdv8YdByWe/P25m5b1Hq6z1mzJh46qmnYuXKlRERsXz58nj22Wfj1FNP3eYxea27AGmj++67L5YuXRq1tbU7tX///v1j7ty58cADD8SDDz4YX/rSl+LEE0+MZ555psgjzdeRRx4Zd999dzz++ONx++23x9q1a+OYY46JDz/8sNX9165du9WPD/bt2zc2bdoUH3zwQYoh56Kt8y6X9Y6IWLVqVcyZMyeGDBkSjz/+eFx++eVx1VVXxd13373NY8pl3Xdl7uW09hERDz/8cPz73/+O7373u9vcp1zW+/N2Zu7lst5Tp06Nc845J4YOHRqdO3eOkSNHxuTJk+Occ87Z5jG5rXub7pd0cGvWrMmqqqqyZcuWNW/b0VswrRk/fnx22mmn5Ty6tDZs2JD17ds3+/nPf97q60OGDMlmzpzZYtuzzz6bRUT2/vvvpxhiUexo3q0p1fXu3LlzdvTRR7fYduWVV273Nmu5rPuuzL01pbr2WZZlY8eOzcaPH7/dfcplvT9vZ+bemlJc73vvvTcbMGBAdu+992Yvv/xydvfdd2e9e/fO7rzzzm0ek9e6uwPSBkuWLIl169bFqFGjorKyMiorK+Ppp5+OX/7yl1FZWdniA0zbc9RRR8Ubb7xR5NEW15577hmHHnroNufRr1+/WLt2bYtt69ati8rKyujTp0+KIRbFjubdmlJd7/79+8chhxzSYtvBBx8ca9as2eYx5bLuuzL31pTq2r/zzjvx5JNPxiWXXLLd/cplvf/bzs69NaW43j/84Q9j2rRpcfbZZ8ehhx4aF1xwQVx99dXbvcuf17oLkDY48cQT45VXXolly5Y1P0aPHh3nnXdeLFu2bKe+HRER8dJLL0X//v2LPNriamxsjH/84x/bnMfRRx8d8+fPb7HtiSeeiNGjR0fnzp1TDLEodjTv1pTqeh977LGxYsWKFttWrly53R+VLJd135W5t6ZU1/6OO+6IqqqqGDdu3Hb3K5f1/m87O/fWlOJ6f/LJJ7HHHi1ToFOnTtv9Gm5u677L923Ismzrt2CmTZuWXXDBBc3Pf/GLX2QPPfRQtnLlyuzVV1/Npk2blkVE9sADD7TDaHfdD37wg2zhwoXZqlWrshdeeCEbP3581qNHj+ztt9/Osmzrea9atSrr3r17dvXVV2d///vfs1//+tdZ586ds9/+9rftNYVd0tZ5l8t6Z1mWvfjii1llZWV20003ZW+88Ub2m9/8JuvevXs2b9685n3Kdd13Ze7lsvabN2/OBg4cmE2dOnWr18p1vbdoy9zLZb0nTJiQ7b///tmjjz6arV69OnvwwQezfffdN7vmmmua9ynWuguQL+jzATJhwoTsuOOOa34+a9as7KCDDsq6du2a7bPPPtmYMWOyP/zhD+kH+gWdddZZWf/+/bPOnTtn1dXV2RlnnJG99tprza9/ft5ZlmULFy7MRo4cmXXp0iUbNGhQNmfOnMSj/uLaOu9yWe8tfv/732fDhw/PCoVCNnTo0Gzu3LktXi/Xdc+yts+9XNb+8ccfzyIiW7FixVavlfN6Z1nb5l4u693Q0JBNmjQpGzhwYNa1a9fswAMPzK699tqssbGxeZ9irXtFlmXZzt8vAQD44nwGBABIToAAAMkJEAAgOQECACQnQACA5AQIAJCcAAEAkhMgAEByAgQASE6AAADJCRAAIDkBAgAk9/8AWCJvwyQFiAwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "# Create a dictionary mapping species IDs to color names, which we will access inside the for loop\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.3, color=cdict[sp])" + ] + }, + { + "cell_type": "markdown", + "id": "3d6fb1f8-2b69-42c9-af6f-6a9c90d23625", + "metadata": {}, + "source": [ + "# Add a legend¶\n", + "\n", + "If you add labels to the hist call (as we have done), then matplotlib can easily generate a legend for your figure with the plt.legend() method." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "128535c2-e9a9-49d8-8883-83e51279ccfa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.5, color=cdict[sp])\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "f0ca14bc-d1d7-4b28-97e8-5f6c929ed8b4", + "metadata": {}, + "source": [ + "# Add axis labels and title¶\n", + "\n", + "Finally, we will wrap up this figure by providing axis labels and a plot title, which we can do with the plt.title(), and plt.xlabel()/plt.ylabel() methods.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3f3a30a2-1473-450d-a427-7d7823a85ac5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'sepal length (cm)')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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XOnfurLFjx2rNmjXat2+fqlWrpokTJ2rHjh1pbv/mtpR6svK+P3XqVKZe9xudPn1a169f14QJE1KNnxI2U8a/nc9IZI41bveAy3Nzc9PgwYM1ePBgnTt3TitWrNBrr72mNm3aKC4uTt7e3umum/Ihefz48VQfKMeOHVPJkiUd+qV8SN3oxIkTaR6lSetfhLNmzVKLFi00adIkh/bz589nPMkccONcb3bs2DEVKFBAxYsXz/K4hQsXVnR0tKKjo/XXX3/Zj9Z07NhRu3btsr+GkZGR6tKlS5pjVK5c2eF5Wv9qP3HihCpUqCBJ2r59u7Zu3arp06crIiLC3mffvn1Zrj89KXVPmDAh3btLshpESpQooevXr+vMmTMOv/wze5QiJ6TM6+YwVqpUKUlKdTF8bknZfko9Gbl+/bpOnz7tEGpSXrMb23x9fRUREaGPP/5YQ4YM0bRp0/TUU0+pWLFi2aqxXLlyev755zVw4EDt2LFD1atXT7X9G504ccJeT1be96VKlcry6168eHEVLFhQ3bp1U9++fdPsExYWJun2PiORORyhQY4rVqyYunbtqr59++rMmTP2L0m7+V/KKe6//35J/wSNG23YsEE7d+5UeHi4JKlhw4by8PDQvHnzHPqtX7/+lof4b2Sz2ey1pNi2bVuefLlZ5cqVVaZMGc2ePdvhTpCLFy/qq6++st/5dDsCAgLUvXt3Pfnkk9q9e7cuXbqkypUrq2LFitq6dWuaR+Hq1asnHx8fh3E+//xzh+dr167V4cOH7XdbpYTFm1/Ljz766Lbqv1GTJk1UrFgx/fHHH+nWnXJkLLNSboG/+X00d+7cVH3Te8/erpCQEHl5eWn//v0O7Y0bN5avr68mT56c6k6h3HDgwAGVKFEi06Hw5vfE7NmzJSnVHXgvvfSS/v77b3Xt2lXnzp1L83TRzc6fP68LFy6kuSzl9GVQUJBD+5w5cxxep8OHD2vt2rX2erLyvm/btq1WrVqV6tRrRry9vdWyZUv99ttvqlWrVprjp3UUL73PSNwejtAgR3Ts2FE1atRQvXr1VKpUKR0+fFjjx49XSEiIKlasKEmqWbOmJOn9999XRESE3N3dVblyZVWuXFnPP/+8JkyYoAIFCqht27Y6dOiQ3njjDQUHB2vQoEGS/jnFM3jwYMXExKh48eJ6+OGH9eeffyo6OlqBgYEO16RkpEOHDnr77bc1YsQINW/eXLt379Zbb72lsLAwXb9+PXdeoP+vQIECevfdd/X000+rQ4cO6t27txITEzV27FidO3fOftttVjVs2FAdOnRQrVq1VLx4ce3cuVOfffaZQ0D66KOP1LZtW7Vp00bdu3dXmTJldObMGe3cuVObN2/W//3f/zmMuXHjRvXq1UuPPvqo4uLiNHz4cJUpU8Z+qL5KlSq66667NGzYMBlj5Ofnp2+++UaxsbG39yLdoEiRIpowYYIiIiJ05swZde3aVf7+/jp16pS2bt2qU6dOpTrSdisPPvigmjRpopdfflkJCQmqW7eu1q1bp5kzZ0pyvD04vffszeEvqwoVKqRGjRrZT8/dON/33ntPvXr10gMPPKDnnntOAQEB2rdvn7Zu3aoPPvjgtrZ7s/Xr16t58+a3vLYlpeb33ntPFy5cUP369bV27VqNHDlSbdu2TXWdXaVKlfTggw9qyZIlatq0qWrXrn3L8Xfv3q02bdroiSeeUPPmzRUYGKizZ8/q22+/1ZQpU9SiRQs1btzYYZ2TJ0/q4Ycf1nPPPaf4+HiNGDFCnp6eioyMtPfJ7Pv+rbfe0pIlS3TffffptddeU82aNXXu3DktXbpUgwcPVpUqVdKs+/3331fTpk3VrFkzvfjiiwoNDdX58+e1b98+ffPNN/r+++8lZe4zErfJqZckwyWk3FmyYcOGNJe3b9/+lnc5vffee6Zx48amZMmS9ts7n332WXPo0CGH9SIjI01QUJApUKCAkWS/TTopKcmMGTPGVKpUybi7u5uSJUuaZ555xsTFxTmsn5ycbEaOHGnKli1rChUqZGrVqmUWL15sateu7XBXTkZ3CCUmJpohQ4aYMmXKGE9PT3PPPfeYhQsXmoiICId5ptzRMHbsWIf10xv7Vq/jjRYuXGgaNmxoPD09TeHChU14eLj5+eefM7WdtAwbNszUq1fPFC9e3Hh4eJjy5cubQYMGmb///tuh39atW81jjz1m/P39jbu7uyldurS5//77zeTJk1PNY/ny5aZbt26mWLFixsvLy7Rr187s3bvXYbw//vjDtGrVyvj4+JjixYubRx991Bw5ciTV3UK3c9u2McasWbPGtG/f3vj5+Rl3d3dTpkwZ0759e4fXJuUupxtv809vzDNnzpgePXqYYsWKGW9vb9OqVSuzfv36NO/kSe89GxISYtq3b59qDs2bN3e44y49n3zyiSlYsKA5duxYqmXfffedad68uSlcuLDx9vY21apVM2PGjLEvj4iIMIULF05z29WrV0/Vnlat+/btS3WnVHpStrdt2zbTokUL4+XlZfz8/MyLL76Y7h1S06dPN5LM3Llzbzm+McacPXvWjBw50tx///2mTJkyplChQqZw4cLm7rvvNiNHjjSXLl2y90352fjss8/MSy+9ZEqVKmU8PDxMs2bN0rw9PTPve2P+uVW/Z8+epnTp0sbd3d0EBQWZxx57zPz111/GmLTvckpp79mzpylTpoxxd3c3pUqVMo0bN3a4IzCzn5HIPgINLO/AgQOmUKFCZtSoUc4uJV/ISjDLT1K+H+jmYJlbLl++bEqVKpXq1u288vrrr5ty5cqlup05p3Tp0sUEBQU5fB9RTslK2Medg1NOsJStW7dqzpw5aty4sYoWLardu3fr3XffVdGiRfXss886uzxYxJw5c3T06FHVrFlTBQoU0Pr16zV27Fjdd999qU5r5BZPT09FR0crKipK/fr1U+HChfNku9I/35Q8ceJETZgwIUf/FEhiYqI2b96sX3/9VQsWLNC4cePk7u6eY+MDGSHQwFIKFy6sjRs36pNPPtG5c+fk6+urFi1aaNSoUdm67RZ3Jh8fH82dO1cjR47UxYsXFRgYqO7du2vkyJF5Wsfzzz+vc+fO6cCBA/brdfLCwYMHFRkZaf8emZxy/Phx+z82evfurf79++fo+EBGbMbkwaX0AAAAuYjbtgEAgOURaAAAgOURaAAAgOXl+4uCk5OTdezYMfn4+GTqy6MAAIDzGWN0/vx5BQUFZeqLU/N9oDl27JiCg4OdXQYAAMiGuLi4TP3h0HwfaFK+ojwuLk5FixZ1cjUAACAzEhISFBwcnOk/NZLvA03KaaaiRYsSaAAAsJjMXi7CRcEAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDynBpofvjhB3Xs2FFBQUGy2WxauHChfdm1a9c0dOhQ1axZU4ULF1ZQUJD+9a9/6dixY84rGAAAuCSnBpqLFy+qdu3a+uCDD1Itu3TpkjZv3qw33nhDmzdv1vz587Vnzx499NBDTqgUAAC4Mpsxxji7COmfPz61YMECde7cOd0+GzZsUIMGDXT48GGVK1cuU+MmJCTI19dX8fHx/HFKAAAsIqu/vy11DU18fLxsNpuKFSvm7FIAAIALcXN2AZl15coVDRs2TE899VSGSS0xMVGJiYn25wkJCXlRHgAAcCJLBJpr167piSeeUHJysj788MMM+8bExCg6OjqPKkNWLPr1Uo6N9VAD7xwbCwBgfS5/yunatWt67LHHdPDgQcXGxt7yPFpkZKTi4+Ptj7i4uDyqFAAAOItLH6FJCTN79+7VqlWrVKJEiVuu4+HhIQ8PjzyoDgAAuAqnBpoLFy5o37599ucHDx7Uli1b5Ofnp6CgIHXt2lWbN2/W4sWLlZSUpBMnTkiS/Pz8VKhQIWeVDQAAXIxTA83GjRvVsmVL+/PBgwdLkiIiIhQVFaVFixZJku6++26H9VatWqUWLVrkVZkAAMDFOTXQtGjRQhl9DY6LfEUOAABwcS5/UTAAAMCtEGgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlEWgAAIDlOTXQ/PDDD+rYsaOCgoJks9m0cOFCh+XGGEVFRSkoKEheXl5q0aKFduzY4ZxiAQCAy3JqoLl48aJq166tDz74IM3l7777rsaNG6cPPvhAGzZsUOnSpdWqVSudP38+jysFAACuzM2ZG2/btq3atm2b5jJjjMaPH6/hw4erS5cukqQZM2YoICBAs2fPVu/evfOyVAAA4MJc9hqagwcP6sSJE2rdurW9zcPDQ82bN9fatWvTXS8xMVEJCQkODwAAkL859QhNRk6cOCFJCggIcGgPCAjQ4cOH010vJiZG0dHRuVobkNeSVi/Lk+0UbNEmT7YD5zi9bbyzS0hTiVoDnV0C8gGXPUKTwmazOTw3xqRqu1FkZKTi4+Ptj7i4uNwuEQAAOJnLHqEpXbq0pH+O1AQGBtrbT548meqozY08PDzk4eGR6/UBAADX4bJHaMLCwlS6dGnFxsba265evao1a9aocePGTqwMAAC4Gqceoblw4YL27dtnf37w4EFt2bJFfn5+KleunAYOHKjRo0erYsWKqlixokaPHi1vb2899dRTTqwaAAC4GqcGmo0bN6ply5b254MHD5YkRUREaPr06Xr11Vd1+fJl9enTR2fPnlXDhg21fPly+fj4OKtkAADggpwaaFq0aCFjTLrLbTaboqKiFBUVlXdFAQAAy3HZa2gAAAAyi0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsj0ADAAAsz83ZBQBAfnR623hnlwDcUThCAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALI9AAwAALM+lA83169f1+uuvKywsTF5eXipfvrzeeustJScnO7s0AADgQtycXUBGxowZo8mTJ2vGjBmqXr26Nm7cqB49esjX11cDBgxwdnkAAMBFuHSgWbdunTp16qT27dtLkkJDQzVnzhxt3LjRyZUBAABX4tKnnJo2baqVK1dqz549kqStW7fqp59+Urt27dJdJzExUQkJCQ4PAACQv7n0EZqhQ4cqPj5eVapUUcGCBZWUlKRRo0bpySefTHedmJgYRUdH52GVcIZFv17KkXEeauCdI+Mga5JWL8uxsc4kfJPh8gKhFXJsWwBcl0sfoZk3b55mzZql2bNna/PmzZoxY4b+/e9/a8aMGemuExkZqfj4ePsjLi4uDysGAADO4NJHaF555RUNGzZMTzzxhCSpZs2aOnz4sGJiYhQREZHmOh4eHvLw8MjLMgEAgJO59BGaS5cuqUABxxILFizIbdsAAMCBSx+h6dixo0aNGqVy5cqpevXq+u233zRu3Dj17NnT2aUBAAAX4tKBZsKECXrjjTfUp08fnTx5UkFBQerdu7fefPNNZ5cGAABciEsHGh8fH40fP17jx493dikAAMCFufQ1NAAAAJlBoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJZHoAEAAJaXrUBTvnx5nT59OlX7uXPnVL58+dsuCgAAICuyFWgOHTqkpKSkVO2JiYk6evTobRcFAACQFW5Z6bxo0SL7/y9btky+vr7250lJSVq5cqVCQ0NzrDgAAIDMyFKg6dy5syTJZrMpIiLCYZm7u7tCQ0P13nvv5VhxAAAAmZGlQJOcnCxJCgsL04YNG1SyZMlcKQoAACArshRoUhw8eDCn6wAAAMi2bAUaSVq5cqVWrlypkydP2o/cpPj0009vuzAAAIDMylagiY6O1ltvvaV69eopMDBQNpstp+sCAADItGwFmsmTJ2v69Onq1q1bTtcDAACQZdn6HpqrV6+qcePGOV0LAABAtmQr0PTq1UuzZ8/O6VoAAACyJVunnK5cuaIpU6ZoxYoVqlWrltzd3R2Wjxs3LkeKAwAAyIxsBZpt27bp7rvvliRt377dYRkXCAMAgLyWrUCzatWqnK4DAAAg27J1DQ0AAIArydYRmpYtW2Z4aun777/PdkEAAABZla1Ak3L9TIpr165py5Yt2r59e6o/WgkAAJDbshVo/vOf/6TZHhUVpQsXLtxWQQAAAFmVo9fQPPPMM/wdJwAAkOdyNNCsW7dOnp6eOTkkAADALWXrlFOXLl0cnhtjdPz4cW3cuFFvvPFGjhQGAACQWdkKNL6+vg7PCxQooMqVK+utt95S69atc6QwAACAzMpWoJk2bVpO15Guo0ePaujQoVqyZIkuX76sSpUq6ZNPPlHdunXzrAYAAODashVoUmzatEk7d+6UzWZTtWrVVKdOnZyqS5J09uxZNWnSRC1bttSSJUvk7++v/fv3q1ixYjm6HQAAYG3ZCjQnT57UE088odWrV6tYsWIyxig+Pl4tW7bU3LlzVapUqRwpbsyYMQoODnY4IhQaGpojYwMAgPwjW3c59e/fXwkJCdqxY4fOnDmjs2fPavv27UpISNBLL72UY8UtWrRI9erV06OPPip/f3/VqVNHU6dOzbHxAQBA/pCtQLN06VJNmjRJVatWtbdVq1ZNEydO1JIlS3KsuAMHDmjSpEmqWLGili1bphdeeEEvvfSSZs6cme46iYmJSkhIcHgAAID8LVunnJKTk+Xu7p6q3d3dXcnJybdd1I3bqVevnkaPHi1JqlOnjnbs2KFJkybpX//6V5rrxMTEKDo6OsdqsKJFv17KsbEeauCdY2PlN0mrlzm7BAB3mNPbxju7hFRK1Bro7BIkZfMIzf33368BAwbo2LFj9rajR49q0KBBCg8Pz7HiAgMDVa1aNYe2qlWr6siRI+muExkZqfj4ePsjLi4ux+oBAACuKVtHaD744AN16tRJoaGhCg4Ols1m05EjR1SzZk3NmjUrx4pr0qSJdu/e7dC2Z88ehYSEpLuOh4eHPDw8cqwGAADg+rIVaIKDg7V582bFxsZq165dMsaoWrVqeuCBB3K0uEGDBqlx48YaPXq0HnvsMf3666+aMmWKpkyZkqPbAQAA1palU07ff/+9qlWrZr/QtlWrVurfv79eeukl1a9fX9WrV9ePP/6YY8XVr19fCxYs0Jw5c1SjRg29/fbbGj9+vJ5++ukc2wYAALC+LB2hGT9+vJ577jkVLVo01TJfX1/17t1b48aNU7NmzXKswA4dOqhDhw45Nh4AAMh/snSEZuvWrXrwwQfTXd66dWtt2rTptosCAADIiiwFmr/++ivN27VTuLm56dSpU7ddFAAAQFZkKdCUKVNGv//+e7rLt23bpsDAwNsuCgAAICuyFGjatWunN998U1euXEm17PLlyxoxYgTXuwAAgDyXpYuCX3/9dc2fP1+VKlVSv379VLlyZdlsNu3cuVMTJ05UUlKShg8fnlu1AgAApClLgSYgIEBr167Viy++qMjISBljJEk2m01t2rTRhx9+qICAgFwpFAAAID1Z/mK9kJAQfffddzp79qz27dsnY4wqVqyo4sWL50Z9AAAAt5StbwqWpOLFi6t+/fo5WQsAAEC2ZOuPUwIAALgSAg0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8N2cXANe26NdLzi7BMnYfvZZjY5XyWZpjY2XJom+cs91clHxoX55sp0BohTzZTn6bjySd3jY+z7aVWSVqDXR2CcgijtAAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLI9AAAADLs1SgiYmJkc1m08CBA51dCgAAcCGWCTQbNmzQlClTVKtWLWeXAgAAXIwlAs2FCxf09NNPa+rUqSpevLizywEAAC7GEoGmb9++at++vR544IFb9k1MTFRCQoLDAwAA5G9uzi7gVubOnavNmzdrw4YNmeofExOj6OjoXK7qfxb9eilHxnmogXeOjOPKKuz9Pk+2s6/i/Znuezv7r8LRa9leFwCQs1z6CE1cXJwGDBigWbNmydPTM1PrREZGKj4+3v6Ii4vL5SoBAICzufQRmk2bNunkyZOqW7euvS0pKUk//PCDPvjgAyUmJqpgwYIO63h4eMjDwyOvSwUAAE7k0oEmPDxcv//+u0Nbjx49VKVKFQ0dOjRVmAEAAHcmlw40Pj4+qlGjhkNb4cKFVaJEiVTtAADgzuXS19AAAABkhksfoUnL6tWrnV0CAABwMRyhAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlufm7AIA5K7T55NzZJwSPvz7JyPJh/Y5u4QclZfzKRBaIc+2hfyLTygAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5BBoAAGB5Lh1oYmJiVL9+ffn4+Mjf31+dO3fW7t27nV0WAABwMS4daNasWaO+fftq/fr1io2N1fXr19W6dWtdvHjR2aUBAAAX4ubsAjKydOlSh+fTpk2Tv7+/Nm3apPvuu89JVQEAAFfj0oHmZvHx8ZIkPz+/dPskJiYqMTHR/jwhISHX6wIAAM5lmUBjjNHgwYPVtGlT1ahRI91+MTExio6OzsPKgJx3+nyys0sAAEtx6WtobtSvXz9t27ZNc+bMybBfZGSk4uPj7Y+4uLg8qhAAADiLJY7Q9O/fX4sWLdIPP/ygsmXLZtjXw8NDHh4eeVQZAABwBS4daIwx6t+/vxYsWKDVq1crLCzM2SUBAAAX5NKBpm/fvpo9e7a+/vpr+fj46MSJE5IkX19feXl5Obk6AADgKlz6GppJkyYpPj5eLVq0UGBgoP0xb948Z5cGAABciEsfoTHGOLsEAABgAS59hAYAACAzCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQAAMDy3JxdAP6x6NdLzi4h1xXQd3mynUp7M7+dZLXLxUoAWNXpbeOdXQKyiCM0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8gg0AADA8iwRaD788EOFhYXJ09NTdevW1Y8//ujskgAAgAtx+UAzb948DRw4UMOHD9dvv/2mZs2aqW3btjpy5IizSwMAAC7C5QPNuHHj9Oyzz6pXr16qWrWqxo8fr+DgYE2aNMnZpQEAABfh0oHm6tWr2rRpk1q3bu3Q3rp1a61du9ZJVQEAAFfj5uwCMvL3338rKSlJAQEBDu0BAQE6ceJEmuskJiYqMTHR/jw+Pl6SlJCQkCs1XrpwKVfGzY8uXLrq7BJSSZZr7r8Ccr3XqlBBl/73DyyswIUrzi4Bt8E9l36/pvzeNsZkqr9LB5oUNpvN4bkxJlVbipiYGEVHR6dqDw4OzpXaYHWfOLsAALC4yFwd/fz58/L19b1lP5cONCVLllTBggVTHY05efJkqqM2KSIjIzV48GD78+TkZJ05c0YlSpRINwS5soSEBAUHBysuLk5FixZ1djl55k6dt8Tc78S536nzlu7cud+p85YyP3djjM6fP6+goKBMjevSgaZQoUKqW7euYmNj9fDDD9vbY2Nj1alTpzTX8fDwkIeHh0NbsWLFcrPMPFG0aNE77k0v3bnzlpj7nTj3O3Xe0p079zt13lLm5p6ZIzMpXDrQSNLgwYPVrVs31atXT40aNdKUKVN05MgRvfDCC84uDQAAuAiXDzSPP/64Tp8+rbfeekvHjx9XjRo19N133ykkJMTZpQEAABfh8oFGkvr06aM+ffo4uwyn8PDw0IgRI1KdRsvv7tR5S8z9Tpz7nTpv6c6d+506byn35m4zmb0fCgAAwEXxxRIAAMDyCDQAAMDyCDQAAMDyCDQAAMDyCDQuIiYmRjabTQMHDky3z+rVq2Wz2VI9du3alXeF5oCoqKhUcyhdunSG66xZs0Z169aVp6enypcvr8mTJ+dRtTkrq3PPL/tcko4ePapnnnlGJUqUkLe3t+6++25t2rQpw3Xyy37P6tzzy34PDQ1Ncx59+/ZNd538sM+zOu/8sr8l6fr163r99dcVFhYmLy8vlS9fXm+99ZaSk5MzXC8n9rslbtvO7zZs2KApU6aoVq1ameq/e/duh29XLFWqVG6VlmuqV6+uFStW2J8XLFgw3b4HDx5Uu3bt9Nxzz2nWrFn6+eef1adPH5UqVUqPPPJIXpSbo7Iy9xRW3+dnz55VkyZN1LJlSy1ZskT+/v7av39/ht/inV/2e3bmnsLq+33Dhg1KSkqyP9++fbtatWqlRx99NM3++WWfZ3XeKay+vyVpzJgxmjx5smbMmKHq1atr48aN6tGjh3x9fTVgwIA018mx/W7gVOfPnzcVK1Y0sbGxpnnz5mbAgAHp9l21apWRZM6ePZtn9eWGESNGmNq1a2e6/6uvvmqqVKni0Na7d29z77335nBluS+rc88v+3zo0KGmadOmWVonv+z37Mw9v+z3mw0YMMDcddddJjk5Oc3l+WWf3+xW885P+7t9+/amZ8+eDm1dunQxzzzzTLrr5NR+55STk/Xt21ft27fXAw88kOl16tSpo8DAQIWHh2vVqlW5WF3u2bt3r4KCghQWFqYnnnhCBw4cSLfvunXr1Lp1a4e2Nm3aaOPGjbp27Vpul5rjsjL3FFbf54sWLVK9evX06KOPyt/fX3Xq1NHUqVMzXCe/7PfszD2F1ff7ja5evapZs2apZ8+e6f6h4Pyyz2+UmXmnyA/7u2nTplq5cqX27NkjSdq6dat++ukntWvXLt11cmq/E2icaO7cudq8ebNiYmIy1T8wMFBTpkzRV199pfnz56ty5coKDw/XDz/8kMuV5qyGDRtq5syZWrZsmaZOnaoTJ06ocePGOn36dJr9T5w4keqvqwcEBOj69ev6+++/86LkHJPVueeXfX7gwAFNmjRJFStW1LJly/TCCy/opZde0syZM9NdJ7/s9+zMPb/s9xstXLhQ586dU/fu3dPtk1/2+Y0yM+/8tL+HDh2qJ598UlWqVJG7u7vq1KmjgQMH6sknn0x3nRzb71k6noMcc+TIEePv72+2bNlib7vVKae0dOjQwXTs2DGHq8tbFy5cMAEBAea9995Lc3nFihXN6NGjHdp++uknI8kcP348L0rMNbeae1qsuM/d3d1No0aNHNr69++f4SHl/LLfszP3tFhxv9+odevWpkOHDhn2yS/7/EaZmXdarLq/58yZY8qWLWvmzJljtm3bZmbOnGn8/PzM9OnT010np/Y7R2icZNOmTTp58qTq1q0rNzc3ubm5ac2aNfrvf/8rNzc3hwvKMnLvvfdq7969uVxt7ipcuLBq1qyZ7jxKly6tEydOOLSdPHlSbm5uKlGiRF6UmGtuNfe0WHGfBwYGqlq1ag5tVatW1ZEjR9JdJ7/s9+zMPS1W3O8pDh8+rBUrVqhXr14Z9ssv+zxFZuedFqvu71deeUXDhg3TE088oZo1a6pbt24aNGhQhmcicmq/E2icJDw8XL///ru2bNlif9SrV09PP/20tmzZkqk7XyTpt99+U2BgYC5Xm7sSExO1c+fOdOfRqFEjxcbGOrQtX75c9erVk7u7e16UmGtuNfe0WHGfN2nSRLt373Zo27Nnj0JCQtJdJ7/s9+zMPS1W3O8ppk2bJn9/f7Vv3z7Dfvlln6fI7LzTYtX9fenSJRUo4BgtChYsmOFt2zm237N9XAk57uZTTsOGDTPdunWzP//Pf/5jFixYYPbs2WO2b99uhg0bZiSZr776ygnVZt/LL79sVq9ebQ4cOGDWr19vOnToYHx8fMyhQ4eMMannfeDAAePt7W0GDRpk/vjjD/PJJ58Yd3d38+WXXzprCtmW1bnnl33+66+/Gjc3NzNq1Cizd+9e8/nnnxtvb28za9Yse5/8ut+zM/f8st+NMSYpKcmUK1fODB06NNWy/LrPjcnavPPT/o6IiDBlypQxixcvNgcPHjTz5883JUuWNK+++qq9T27tdwKNC7k50ERERJjmzZvbn48ZM8bcddddxtPT0xQvXtw0bdrUfPvtt3lf6G16/PHHTWBgoHF3dzdBQUGmS5cuZseOHfblN8/bGGNWr15t6tSpYwoVKmRCQ0PNpEmT8rjqnJHVueeXfW6MMd98842pUaOG8fDwMFWqVDFTpkxxWJ6f93tW556f9vuyZcuMJLN79+5Uy/LzPs/KvPPT/k5ISDADBgww5cqVM56enqZ8+fJm+PDhJjEx0d4nt/a7zRhjMn88BwAAwPVwDQ0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag0AALA8Ag2AHNe9e3d17tw53eXTp09XsWLF8qyeWwkNDdX48eOzvN7p06fl7++vQ4cO5XhNKU6ePKlSpUrp6NGjubYNID8g0AC4Y+R0kIqJiVHHjh0VGhqaY2PezN/fX926ddOIESNybRtAfkCgAYBsuHz5sj755JNs/SXlrOrRo4c+//xznT17Nte3BVgVgQbIZ7788kvVrFlTXl5eKlGihB544AFdvHjRvnzatGmqWrWqPD09VaVKFX344Yf2ZYcOHZLNZtPcuXPVuHFjeXp6qnr16lq9erW9T1JSkp599lmFhYXJy8tLlStX1vvvv3/bdX/zzTeqW7euPD09Vb58eUVHR+v69ev25TabTR9//LEefvhheXt7q2LFilq0aJHDGIsWLVLFihXl5eWlli1basaMGbLZbDp37pxWr16tHj16KD4+XjabTTabTVFRUfZ1L126pJ49e8rHx0flypXTlClTMqx3yZIlcnNzU6NGjRzad+zYofbt26to0aLy8fFRs2bNtH//fkn/OxU3evRoBQQEqFixYvZ5vvLKK/Lz81PZsmX16aefOoxZs2ZNlS5dWgsWLMjOSwvcGW7vz1ABcCXHjh0zbm5uZty4cebgwYNm27ZtZuLEieb8+fPGGGOmTJliAgMDzVdffWUOHDhgvvrqK+Pn52emT59ujDHm4MGDRpIpW7as+fLLL80ff/xhevXqZXx8fMzff/9tjDHm6tWr5s033zS//vqrOXDggJk1a5bx9vY28+bNs9cRERFhOnXqlG6d06ZNM76+vvbnS5cuNUWLFjXTp083+/fvN8uXLzehoaEmKirK3ielrtmzZ5u9e/eal156yRQpUsScPn3aXru7u7sZMmSI2bVrl5kzZ44pU6aMkWTOnj1rEhMTzfjx403RokXN8ePHzfHjx+2vS0hIiPHz8zMTJ040e/fuNTExMaZAgQJm586d6c5hwIAB5sEHH3Ro+/PPP42fn5/p0qWL2bBhg9m9e7f59NNPza5du+yvi4+Pj+nbt6/ZtWuX+eSTT4wk06ZNGzNq1CizZ88e8/bbbxt3d3dz5MgRh7Efe+wx071793TrAe50BBogH9m0aZORZA4dOpTm8uDgYDN79myHtrfffts0atTIGPO/QPPOO+/Yl1+7ds2ULVvWjBkzJt3t9unTxzzyyCP251kNNM2aNTOjR4926PPZZ5+ZwMBA+3NJ5vXXX7c/v3DhgrHZbGbJkiXGGGOGDh1qatSo4TDG8OHD7YEmre2mCAkJMc8884z9eXJysvH398/wL/526tTJ9OzZ06EtMjLShIWFmatXr6a5TkREhAkJCTFJSUn2tsqVK5tmzZrZn1+/ft0ULlzYzJkzx2HdQYMGmRYtWqRbD3Cnc3PesSEAOa127doKDw9XzZo11aZNG7Vu3Vpdu3ZV8eLFderUKcXFxenZZ5/Vc889Z1/n+vXr8vX1dRjnxtMobm5uqlevnnbu3Glvmzx5sj7++GMdPnxYly9f1tWrV3X33Xdnu+5NmzZpw4YNGjVqlL0tKSlJV65c0aVLl+Tt7S1JqlWrln154cKF5ePjo5MnT0qSdu/erfr16zuM26BBg0zXcOPYNptNpUuXto+dlsuXL8vT09OhbcuWLWrWrJnc3d3TXa969eoqUOB/Z/sDAgJUo0YN+/OCBQuqRIkSqbbt5eWlS5cuZXo+wJ2GQAPkIwULFlRsbKzWrl2r5cuXa8KECRo+fLh++eUXeyiYOnWqGjZsmGq9W7HZbJKkL774QoMGDdJ7772nRo0aycfHR2PHjtUvv/yS7bqTk5MVHR2tLl26pFp2Y2i4OSjYbDYlJydLkowx9hpTGGMyXUNGY6elZMmSqS7S9fLyytZ2MrPtM2fOqFSpUrccH7hTcVEwkM/YbDY1adJE0dHR+u2331SoUCEtWLBAAQEBKlOmjA4cOKAKFSo4PMLCwhzGWL9+vf3/r1+/rk2bNqlKlSqSpB9//FGNGzdWnz59VKdOHVWoUMF+0Wt23XPPPdq9e3equipUqOBwNCMjVapU0YYNGxzaNm7c6PC8UKFCSkpKuq1aU9SpU0d//PGHQ1utWrX0448/6tq1azmyjRtt375dderUyfFxgfyCQAPkI7/88otGjx6tjRs36siRI5o/f75OnTqlqlWrSpKioqIUExOj999/X3v27NHvv/+uadOmady4cQ7jTJw4UQsWLNCuXbvUt29fnT17Vj179pQkVahQQRs3btSyZcu0Z88evfHGG6mCRFa9+eabmjlzpqKiorRjxw7t3LlT8+bN0+uvv57pMXr37q1du3Zp6NCh2rNnj7744gtNnz5d0v+OLoWGhurChQtauXKl/v7779s6hdOmTRvt2LHD4ShNv379lJCQoCeeeEIbN27U3r179dlnn2n37t3Z3o70zx1YmzZtUuvWrW9rHCA/I9AA+UjRokX1ww8/qF27dqpUqZJef/11vffee2rbtq0kqVevXvr44481ffp01axZU82bN9f06dNTHaF55513NGbMGNWuXVs//vijvv76a5UsWVKS9MILL6hLly56/PHH1bBhQ50+fVp9+vS5rbrbtGmjxYsXKzY2VvXr19e9996rcePGKSQkJNNjhIWF6csvv9T8+fNVq1YtTZo0ScOHD5ckeXh4SJIaN26sF154QY8//rhKlSqld999N9s116xZU/Xq1dMXX3xhbytRooS+//57XbhwQc2bN1fdunU1derUDK+pyYyvv/5a5cqVU7NmzW5rHCA/s5msnGQGkK8dOnRIYWFh+u23327rIl9XMWrUKE2ePFlxcXG5Mv53332nIUOGaPv27Zk+NZYdDRo00MCBA/XUU0/l2jYAq+OiYAD5xocffqj69eurRIkS+vnnnzV27Fj169cv17bXrl077d27V0ePHlVwcHCubOPkyZPq2rWrnnzyyVwZH8gvOEIDwM7qR2gGDRqkefPm6cyZMypXrpy6deumyMhIubnxbzcgvyPQAAAAy+OiYAAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHkEGgAAYHn/D3i1ug3PEZzCAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.5, color=cdict[sp])\n", + "\n", + "plt.title(f'Histogram of {feature} by Species')\n", + "plt.ylabel('Count')\n", + "plt.xlabel(feature)" + ] + }, + { + "cell_type": "markdown", + "id": "587df83d-9096-4026-b80d-8e84e4780697", + "metadata": {}, + "source": [ + "# Saving figures to a file¶\n", + "\n", + "Once you are happy with your figure, you can either right-click on the image in the notebook and \"copy output to clipboard\", but this will copy a low-res version of the image, which is fine for presentations but not great for publications. For this reason matplotlib provides a savefig() method for saving the resulting figure in several different possible output formats, also allowing to control the output resolution (using the dpi argument, for example). Here is an example of saving as a standard resolution PNG file.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6307ad79-ecf9-4b9c-aa39-72e7dffc5095", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature = \"sepal length (cm)\"\n", + "gb = iris_df.groupby(\"species\")\n", + "cdict = {'setosa':'cornflowerblue',\n", + " 'versicolor':'salmon',\n", + " 'virginica':'goldenrod'}\n", + "for sp, dat in gb:\n", + " plt.hist(dat[feature], label=sp, alpha=0.5, color=cdict[sp])\n", + "\n", + "plt.title(f'Histogram of {feature} by Species')\n", + "plt.ylabel('Count')\n", + "plt.xlabel(feature) #can I define x axis here? do I need to use feature? \n", + "plt.savefig('sepal_length.raw')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5c764743-ceac-4b87-830b-ebaa93be28a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'eps': 'Encapsulated Postscript',\n", + " 'jpg': 'Joint Photographic Experts Group',\n", + " 'jpeg': 'Joint Photographic Experts Group',\n", + " 'pdf': 'Portable Document Format',\n", + " 'pgf': 'PGF code for LaTeX',\n", + " 'png': 'Portable Network Graphics',\n", + " 'ps': 'Postscript',\n", + " 'raw': 'Raw RGBA bitmap',\n", + " 'rgba': 'Raw RGBA bitmap',\n", + " 'svg': 'Scalable Vector Graphics',\n", + " 'svgz': 'Scalable Vector Graphics',\n", + " 'tif': 'Tagged Image File Format',\n", + " 'tiff': 'Tagged Image File Format',\n", + " 'webp': 'WebP Image Format'}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.gcf().canvas.get_supported_filetypes()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7af7bd17-9346-4359-a7b4-e84a2342578c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/__pycache__/wf_sim.cpython-310.pyc b/notebooks/__pycache__/wf_sim.cpython-310.pyc new file mode 100644 index 0000000..41da205 Binary files /dev/null and b/notebooks/__pycache__/wf_sim.cpython-310.pyc differ diff --git a/notebooks/icebroken-challenges.ipynb b/notebooks/icebroken-challenges.ipynb new file mode 100644 index 0000000..f52fdd3 --- /dev/null +++ b/notebooks/icebroken-challenges.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e572f07e-31a5-4a3b-a150-d398399abc45", + "metadata": {}, + "source": [ + "# Challenge: Find all elements smaller than a given number¶\n", + "\n", + "This function is supposed to return a list of all values less than a given integer value. The function optionally takes a max_value, and it requires to pass in a list of numbers. Your goal is to get this function to work correctly. This code block has two errors, which you can solve consecutively." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "27e566cb-1ad3-45c4-808d-0dd964c420e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 []\n", + "2 [2]\n", + "12 [2]\n", + "7 [2]\n", + "3 [2, 3]\n", + "8 [2, 3]\n" + ] + }, + { + "data": { + "text/plain": [ + "[2, 3]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_smaller(my_list, max_value = 5):\n", + " \"\"\"Return a list of all values smaller than max_value\"\"\"\n", + " low = [] #creates an empty list\n", + " for element in my_list:\n", + " if element < max_value:\n", + " low.append(element) #will add element if it is smaller than max value\n", + " print(element, low)\n", + " return low #returns appended list \n", + "\n", + "my_list = [5, 2, 12, 7, 3, 8]\n", + "\n", + "get_smaller(my_list = my_list)" + ] + }, + { + "cell_type": "markdown", + "id": "1c09a781-2f8f-4da6-9ea1-c37dcaf7d8d9", + "metadata": {}, + "source": [ + "# Challenge: Implement FizzBuzz¶\n", + "\n", + "FizzBuzz is a classical programming challenge in which you are given the task to take consecutive integer values and print \"Fizz\" for each integer divisible by 3, \"Buzz\" for integers divisible by 5, and \"FizzBuzz\" for integers divisible by both 3 and 5, and remain silent for all other integers. In this case we have given you most of the code but there is an error, please fix it and verify that it works. This code block has two errors, which you can solve consecutively." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "97c6ad72-e5cb-4845-ad8f-8c0f7ee92f8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 fizz\n", + "5 buzz\n", + "6 fizz\n", + "9 fizz\n", + "10 buzz\n", + "12 fizz\n" + ] + } + ], + "source": [ + "def fizzbuzz(max_num):\n", + " \"This method implements FizzBuzz\"\n", + " three_mul = 'fizz' #for each integer divisible by 3\n", + " five_mul = 'buzz' #for each integer divisible by 5\n", + " num1 = 3\n", + " num2 = 5 \n", + "\n", + " # Google for 'range in python' to see what it does\n", + " for i in range(1,max_num):\n", + " # % or modulo division gives you the remainder \n", + " if i%num1==0 and i%num2==0:\n", + " print(i,three_mul+five_mul)\n", + " elif i%num1==0:\n", + " print(i,three_mul)\n", + " elif i%num2==0:\n", + " print(i,five_mul)\n", + "\n", + "fizzbuzz(max_num = 13) #need to define default value of max_num OUTSIDE of function" + ] + }, + { + "cell_type": "markdown", + "id": "da0656ef-c9e3-4142-92b4-515c57b6cbdb", + "metadata": {}, + "source": [ + "# Challenge: Sum of positive integers¶\n", + "\n", + "This function takes one argument, numbers, which should be a list of numbers. The function should sum all positive numbers in this list. Why doesn't it work?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5be8883f-c1bc-4765-b08c-66857cd5d617", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sum_positive(numbers):\n", + " total = 0 \n", + " for num in numbers:\n", + " if num > 0:\n", + " total += num #just needed an equal sign \n", + " return total\n", + "\n", + "sum_positive([1,2,3,4,5,6,7])" + ] + }, + { + "cell_type": "markdown", + "id": "3e40e102-244e-4c3a-8754-fa3664812576", + "metadata": {}, + "source": [ + "# Challenge: Add two positive numbers¶\n", + "\n", + "This function is supposed to accept two required arguments, add them together and return the sum. There are two at least two conditions that this function should handle which it currently doesn't: 1) If one of the passed in values is not strictly positive; and 2) If one of the values is not a number. Your goal is to get this function to work correctly. There are several ways to do this so we will discuss alternative strategies." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0c16cf9a-b655-4fe7-9f12-0a15a706ebb9", + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "bad input values", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#print(\"bad input values\")\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbad input values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#this is defining a and b (?)\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(result, \u001b[38;5;28mtype\u001b[39m(result))\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(result \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m)\n", + "Cell \u001b[0;32mIn[20], line 6\u001b[0m, in \u001b[0;36madd\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a \u001b[38;5;241m+\u001b[39m b\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#print(\"bad input values\")\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbad input values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mException\u001b[0m: bad input values" + ] + } + ], + "source": [ + "def add(a, b):\n", + " if a > 0 and b > 0:\n", + " return a + b\n", + " else:\n", + " #print(\"bad input values\")\n", + " raise Exception(\"bad input values\")\n", + " \n", + "result = add(-1, 5) #this is defining a and b (?)\n", + "print(result, type(result))\n", + "print(result + 2)" + ] + }, + { + "cell_type": "markdown", + "id": "a817ace2-01e0-403d-9aea-30361bf70c4b", + "metadata": {}, + "source": [ + "# Challenge: Implement a 'Person' class¶\n", + "\n", + "Here is a simple class that represents information about a \"Person\". This code block implements the Person class, instantiates a new person and tries to print it to the screen. Your goal is to fix this class to generate the expected output." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "adfd4e66-9290-43c6-aafb-09adcacdb64a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phylo is 10 years old\n" + ] + } + ], + "source": [ + "class Person:\n", + " def __init__(self, name, age = 10):\n", + " '''Initialize the Person object'''\n", + " self.name = name\n", + " self.age = age\n", + " \n", + " def __repr__(self):\n", + " '''Describe the string representation of a Person'''\n", + " \n", + " return f\"{self.name} is {self.age} years old\"\n", + "\n", + "p = Person(\"Phylo\") #can either set age here (name, age) OR set it above in arguments \n", + "print(p)" + ] + }, + { + "cell_type": "markdown", + "id": "c2dd21ab-c403-4fca-86c6-66d0d315ad1e", + "metadata": {}, + "source": [ + "# Challenge: Implement a simple Dog class¶\n", + "\n", + "This class describes a Dog with the ability to bark(). The expected output is Woof!. Your goal is to fix this class." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1e972be9-a6a8-4079-9710-6cfa83ee78fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phylo says Woof!\n" + ] + } + ], + "source": [ + "class Dog:\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " def bark(self):\n", + " print(f\"{self.name} says Woof!\")\n", + "\n", + "d = Dog(\"Phylo\")\n", + "d.bark()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75855be9-4da8-49b5-83fd-5dd5b57c7d07", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/iris_pd.ipynb b/notebooks/iris_pd.ipynb new file mode 100644 index 0000000..ae5ff33 --- /dev/null +++ b/notebooks/iris_pd.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "id": "1f3695ff-f432-4bee-9d64-ed7cacf6ab80", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import scipy.stats as stats #for the challenge exercise!!!" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ec4b1c8d-d80a-401d-ba8e-fa0a3fe2be83", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://eaton-lab.org/data/iris-data-dirty.csv\" #fetched from linked page on assigment\n", + "\n", + "response = requests.get(url)\n", + "\n", + "\"\"\"\n", + "Load the iris data into a pandas DataFrame. \n", + "This version of the data does not have 'headers' to indicate the meanings of the columns.\n", + "The 5 columns in the dataset are sepal length and width, petal length and width, and species ID.\n", + "\"\"\"\n", + "column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species_ID']\n", + "df = pd.read_csv(url, header = None, names = column_names)\n", + "\n", + "#print(df.head()) -> prints only first couple rows\n", + "\n", + "\"\"\"\n", + "Fix rows with misspelled species IDs and remove NA values\n", + "\"\"\"\n", + "df['species_ID'] = df['species_ID'].replace({\n", + " 'Iris-setsa': 'Iris-setosa', # Correct 'Iris-setsa' to 'Iris-setosa'\n", + " 'Iris-versicolour': 'Iris-versicolor', # Correct 'Iris-versicolour' to 'Iris-versicolor'\n", + " 'Iris-versicolour*': 'Iris-versicolor', # If you still have an asterisk variant\n", + " 'Iris-setosa*': 'Iris-setosa', # If you still have an asterisk variant\n", + " 'Iris-virginica*': 'Iris-virginica' # If you still have an asterisk variant\n", + "})\n", + "\n", + "df = df.dropna()\n", + "#print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "dd051236-256f-42d9-9279-6b6ab4c78278", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " sepal_length \\\n", + " count mean std min 25% 50% 75% max \n", + "species_ID \n", + "Iris-setosa 50.0 5.006000 0.352490 4.3 4.800 5.0 5.2 5.8 \n", + "Iris-versicolor 48.0 5.935417 0.522062 4.9 5.600 5.9 6.3 7.0 \n", + "Iris-virginica 50.0 6.588000 0.635880 4.9 6.225 6.5 6.9 7.9 \n", + "\n", + " sepal_width ... petal_length petal_width \\\n", + " count mean ... 75% max count \n", + "species_ID ... \n", + "Iris-setosa 50.0 3.418000 ... 1.575 1.9 50.0 \n", + "Iris-versicolor 48.0 2.770833 ... 4.600 5.1 48.0 \n", + "Iris-virginica 50.0 2.974000 ... 5.875 6.9 50.0 \n", + "\n", + " \n", + " mean std min 25% 50% 75% max \n", + "species_ID \n", + "Iris-setosa 0.244000 0.107210 0.1 0.2 0.2 0.3 0.6 \n", + "Iris-versicolor 1.322917 0.200255 1.0 1.2 1.3 1.5 1.8 \n", + "Iris-virginica 2.026000 0.274650 1.4 1.8 2.0 2.3 2.5 \n", + "\n", + "[3 rows x 32 columns]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Use the describe() method to show summary statistics over the numerical valued columns. \n", + "\"\"\"\n", + "species_summary = df.groupby('species_ID').describe()\n", + "print(species_summary)" + ] + }, + { + "cell_type": "markdown", + "id": "1dd57802-ad2f-4172-819d-e354be88d8f5", + "metadata": {}, + "source": [ + "From inspecting the summary statistics I am able to draw **several** conclusions:\n", + "1. Iris-setosa has the smallest mean sepal length (5.01). This suggests to me that the sepal length for this species is smaller in comparison to the other species. Iris-versicolor has a slightly larger mean sepal length (5.49). Iris-vriginica has the largest mean sepal length (6.59), with a wider range amongst its values indicating that the species has more variability in sepal length.\n", + "2. Iriso-setosa has the largest mean sepal width (3.42) compared to the other species, suggesting to me that its sepals are broader on average. Iris-versicolor has a smaller mean sepal width (2.77); Iris-virginica has the smallest (2.97). This points out interesting *pattern* - as sepal length increases, width decreases!\n", + "3. Iris-setosas the smalelst mean petal length (1.46); Iris-versicolor has a significantly larger mean petal length (4.26); Iris-virginica has the largest mean petal length (5.55).\n", + "4. Iris-setosa has the smallest mean petal width (0.244) while Iris-virginica has the largest mean petal width (2.03).\n", + "\n", + "**Conclusions:**\n", + "These summary statistics indicate to me that there are clear differences between the three species based on the measurements of sepal and petal dimensions. Iris-setosa seems to be consistently smaller, while Iris-virgina is larger, and experiences more variation. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e58698d9-8644-4594-8f05-721a1b56e5b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-test for sepal length between setosa and virginica: t-statistic = -15.386195820079404, p-value = 6.892546060674106e-28\n" + ] + } + ], + "source": [ + "#CHALLENGE:\n", + "\"\"\"\n", + "Perform formal tests for differences in group means using ttest_ind from scipy.stats.\n", + "Hint: You can access the data for a specific group using the get_group() method of the groupby object.\n", + "\"\"\"\n", + "grouping = df.groupby('species_ID')\n", + "setosa = grouping.get_group('Iris-setosa')\n", + "versicolor = grouping.get_group('Iris-versicolor')\n", + "virginica = grouping.get_group('Iris-virginica')\n", + "\n", + "#only did one-comparison here to get a sense of how the function works:\n", + "t_stat,p_value = stats.ttest_ind(setosa['sepal_length'], virginica['sepal_length'])\n", + "print(f\"T-test for sepal length between setosa and virginica: t-statistic = {t_stat}, p-value = {p_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03f9783d-bc6b-4fda-83a9-aac1db5c89e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/myFirstScript.py b/notebooks/myFirstScript.py new file mode 100644 index 0000000..a010972 --- /dev/null +++ b/notebooks/myFirstScript.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python + +import argparse +import random +import string + +def random_password(length=20, verbose=False): + if args.verbose: + print(f"Generating password length: {length}") + #Define the set of available characters to sample from + chars = string.ascii_letters + string.digits + string.punctuation + + #Generate a sample from this list + password = random.choices(chars, k=length) + return "".join(password) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-l", help="Password length in characters", + dest='length', type=int, default=20) + parser.add_argument("-v", "--verbose", help="increase output verbosity", + action="store_true") + args = parser.parse_args() + + if args.verbose: + print(args) + print(random_password(length=args.length, verbose=args.verbose)) \ No newline at end of file diff --git a/notebooks/nb-15.0-debugging.ipynb b/notebooks/nb-15.0-debugging.ipynb new file mode 100644 index 0000000..82a7bea --- /dev/null +++ b/notebooks/nb-15.0-debugging.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "05322757-c6ff-447c-91bc-f938c14eab23", + "metadata": {}, + "source": [ + "This notebook will give some practice in debugging python code, and reading, understanding, and responding to python error messages in small functions.\n", + "\n", + "# Methodology\n", + "\n", + "This tutorial involves identifying and fixing small errors or bugs in python code. For this set of challenges you will open a new jupyter notebook, copy the code from each of the challenges, and attempt to fix each of them according to the directions. You may use whatever to do this that are at your disposal.\n", + "\n", + "Hint: Remember that using print() statements inside functions is a good way to easily trace execution." + ] + }, + { + "cell_type": "markdown", + "id": "42215c88-34cb-4ffb-985f-e1aad7f3edfd", + "metadata": {}, + "source": [ + "# Challenge: Load a dataset from a csv\n", + "\n", + "The following code will download a small dataset that we will use in the machine learning exercise today. There is a bug in this code that you'll need to find and fix in order to proceed with the exercise later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cb579605-9b61-430f-a803-a5e9c8c13724", + "metadata": {}, + "outputs": [], + "source": [ + "import requests #requesting access to file \n", + "import pandas as pd #do I need this package -> YES (otherwise 'pd' not defined)\n", + "\n", + "url = \"https://raw.githubusercontent.com/eaton-lab/hack-the-planet/refs/heads/master/docs/data/penguin_data.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5ca6edde-608f-4e3e-80f1-1d0cabfddbbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "with open(\"penguin_data.csv\", 'w') as outfile:\n", + " outfile.write(requests.get(url).text)\n", + "\n", + "data = \"penguin_data.csv\" #issue is that this needs quotes\n", + "df = pd.read_csv(data)\n", + "\n", + "print(df.head)" + ] + }, + { + "cell_type": "markdown", + "id": "9624c46b-df3f-44ef-ba6f-20fbcd7e7e9e", + "metadata": {}, + "source": [ + "# Challenge: Testing multiple conditions in an if statement\n", + "\n", + "Here is a quick function to test whether either of the two first input values are less than the third (optional) value that defaults to 4.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fb33815e-d428-444f-b247-4781bfaa6059", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Both aren't less\n" + ] + } + ], + "source": [ + "def either_less(a, b, c=4):\n", + " if a < c and b < c: #needed to switch 'OR' to 'AND'\n", + " print(\"Both are less\")\n", + " else:\n", + " print(\"Both aren't less\")\n", + "\n", + "either_less(3, 5) #printing 'both are less' BUT they aren't " + ] + }, + { + "cell_type": "markdown", + "id": "555804be-3ab0-4bf6-a008-8694e4b4b412", + "metadata": {}, + "source": [ + "# Challenge: Fix all the bugs in one shot\n", + "\n", + "Here is a different kind of challenge. This code should just print a message to the console but there are many small problems with it. See how many of the bugs you can fix before running this cell to test it. Keep track of how many bugs you find before you get this code to run. How many was it total?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "67881cd6-148f-4226-9268-02ad341e1688", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n" + ] + } + ], + "source": [ + "is_user = True #had to define a variable with value 'True'\n", + "\n", + "if is_user == True:\n", + " print(\"Hello World\")\n", + "else: #included this to test code output\n", + " print(\"Error\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ac2f35a-c2d4-4a57-8e56-db8a7a24b348", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nb-15.1-intro-scikit.ipynb b/notebooks/nb-15.1-intro-scikit.ipynb new file mode 100644 index 0000000..1e0ca6d --- /dev/null +++ b/notebooks/nb-15.1-intro-scikit.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "8ff058c4-2710-49b2-8885-c338f000dbe7", + "metadata": {}, + "outputs": [], + "source": [ + "#import necessary packages:\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn import datasets\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "markdown", + "id": "acaff501-db44-4af6-80eb-88f100bb5442", + "metadata": {}, + "source": [ + "# Fetch the Iris data\n", + "\n", + "We have been loading and cleaning the iris data by hand up to this point. Now we are going to make use of a nice feature in scikit-learn, which provides the iris data as a pre-loaded dataset.The iris data will be loaded as a dictionary with several keys." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e1707235-2ed9-4efe-8d0c-0c572f499852", + "metadata": {}, + "outputs": [], + "source": [ + "iris = datasets.load_iris() #this returns a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1f0cc6f5-7bee-4a3c-be49-878ea6572c72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(150, 4)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.keys() #outputs the key names in a list format; contained within keys is an array\n", + "iris[\"data\"].shape #numpy array with 4 columns (aka measurements) and 150 rows (aka observations)\n", + "#machine learning algorithm (SciKit) will be applied to RAW data" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c9080568-6efe-4bb4-8be8-c5fb8bb366e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris[\"target\"] #array of integer values; created an encoding of 'target' names as integer values" + ] + }, + { + "cell_type": "markdown", + "id": "6e2c05ea-f45b-43e2-88ba-cd5c3187c37e", + "metadata": {}, + "source": [ + "# Data Cleaning\n", + "\n", + "Previously we had been removing rows that had contained missing values from the iris data. This might not be the best strategy because for a given row with missing data, it may still have values in the columns that are not missing, and throwing away data feels bad. In the case where you want to retain all your data you can elect to impute missing values, which means to fill them in using some strategy which is hopefully defensible. A very simple and defensible strategy is to impute missing data with the mean value for a given column. Much more detailed information about imputation is available on the scikit.impute documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e28ff38e-7c87-40c8-a83c-ded9d0b1aa15", + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning data:\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "imputer = SimpleImputer(strategy='mean') #if there is any missing data, fill it in with the mean value for that column\n", + "imputed_data = imputer.fit_transform(iris.data) #calling function 'fit_transform' which passes in iris data" + ] + }, + { + "cell_type": "markdown", + "id": "d840ff30-e63d-4b3f-a44c-05ea5e9738e2", + "metadata": {}, + "source": [ + "# Visualizing raw data\n", + "\n", + "As we did last week, we can use matplotlib scatter to visualize the relationship between 2 of the dimensions of data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e643f56f-7d1b-4359-abb2-e1ad0e6aee46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Sepal Width')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(iris.data[:, 0], iris.data[:, 1], c=data.target) #target is a vector of 0s 1s and 2s; color each data point based on index in this vector\n", + "plt.xlabel('Sepal Length')\n", + "plt.ylabel('Sepal Width')" + ] + }, + { + "cell_type": "markdown", + "id": "fad2d3e7-b336-48fa-b879-06ff189f7138", + "metadata": {}, + "source": [ + "# Unsupervised learning: Dimension reduction & clustering\n", + "\n", + "The nature of 'Big Data' is that it is hugely multidimensional, so plotting 2 dimension at a time might be a pointless operation. Unsupervised learning is a class of methods that takes unlabeled high-dimensional data and collapses it into a smaller number of dimensions that can capture in some way the relationship between the data points. I sometimes think of unsupervised learning as 'exploratory data analysis', because it's a way of looking at the structure of the data through different lenses. Unsupervised learning also includes clustering methods for finding groupings of data-points that are 'similar' given some statistical model. " + ] + }, + { + "cell_type": "markdown", + "id": "c2b5231d-a35f-49ae-a3a7-34a2eab7b33e", + "metadata": {}, + "source": [ + "# Principle component analysis\n", + "\n", + "Principal component analysis is a dimensionality reduction method used to transform and project data points onto fewer orthogonal axes that can explain the greatest amount of variance in the data.\n", + "\n", + "We begin by constructing a PCA object, specifying to retain 3 components, and then calling fit_transform() passing in the iris data. fit_transform() is actually a shortcut for calling pca.fit(iris.data).transform(iris.data), because fitting and transforming are two different actions, but in some cases (as with PCA) it isn't necessary (or useful) to fit a model to data without transforming it as well." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "80bc47e6-01cd-483e-8df9-63b3fb5200b8", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca = PCA(n_components=3) #telling it how many components to maintain \n", + "X_r = pca.fit_transform(iris.data) #X_r contains the transformed data from the PCA algorithm!!!" + ] + }, + { + "cell_type": "markdown", + "id": "ca00e162-86f7-4bb6-ab9c-a474e1a99bf9", + "metadata": {}, + "source": [ + "One key piece of information with PCA is the amount of variance explained along each orthogonal PC axis. By definition the variance explained monotonically decreases from the first to the last PC, meaning the first axis captures the most variation, and the second axis captures the second most, and so on. This allows you to 'read the tea leaves' a bit in interpreting the relationship between samples in PC-space." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2eeabda9-ec33-4f05-afeb-81500250118c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variance ratio (first two components): [0.92461872 0.05306648 0.01710261]\n" + ] + } + ], + "source": [ + "## Percentage of variance explained for each component\n", + "print(f\"Explained variance ratio (first two components): {pca.explained_variance_ratio_}\")\n", + "\n", + "#what percent of variance is explained by each component? " + ] + }, + { + "cell_type": "markdown", + "id": "88445409-fcd6-433b-91ec-803ab368c000", + "metadata": {}, + "source": [ + "We can now plot the transformed data to see a 2-dimensional representation of our 4-dimensional iris dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "6c30fbf6-9005-48c2-b324-736ac3c5cbaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = [\"navy\", \"turquoise\", \"darkorange\"]\n", + "\n", + "for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):\n", + " plt.scatter(X_r[iris.target == i, 0], X_r[iris.target == i, 1], color=color, label=target_name)\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "a77fa6d9-31f4-4652-9e62-62595a705fec", + "metadata": {}, + "source": [ + "# Challenge: Plot PCs other than 1 and 2\n", + "\n", + "In the previous example we plotted PCs 1 and 2, which by definition explain the most variance in the data. Because we asked for n_components=3 we actually have 3 PC dimensions to play with, so try plotting dimensions other than 1 and 2. Try plotting 2 and 3, for example, but first think about how the relationship between the points will change when looking at these PCs" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "8183dc00-5ecf-4fe2-b797-3b1ef2bbfb69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = [\"navy\", \"turquoise\", \"darkorange\"]\n", + "\n", + "for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):\n", + " plt.scatter(X_r[iris.target == i, 1], X_r[iris.target == i, 2], color=color, label=target_name) \n", + " #taking PC data, look for all rows where iris.target = i -> pull out the 1th column which is the 2nd PC\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8edaa2ae-a212-4060-8089-ad9662c4b3f4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "297a358e-f0f4-4c6d-b0be-1d53438e07e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nb-5.0-arithmetic.ipynb b/notebooks/nb-5.0-arithmetic.ipynb index 8f26507..9da8f3b 100644 --- a/notebooks/nb-5.0-arithmetic.ipynb +++ b/notebooks/nb-5.0-arithmetic.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -70,9 +70,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# convert int 3 to a float 3.0\n", "float(3)" @@ -80,9 +91,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# convert float 3.0 to an int 3\n", "int(3.51)" @@ -90,9 +112,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "round(3.51)" ] @@ -109,9 +142,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# True can be stored as True or as 1\n", "x = True\n", @@ -121,9 +165,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# False can be stored as False or as 0\n", "x = False\n", @@ -141,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -152,54 +207,121 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x > y" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x >= y" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y < x" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x == z" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "z != y" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "'>' not supported between instances of 'str' and 'int'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# this is expected to raise an error, since we cannot compare\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# a string and an integer using the greater-than operator.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'" + ] + } + ], "source": [ "# this is expected to raise an error, since we cannot compare\n", "# a string and an integer using the greater-than operator.\n", @@ -208,18 +330,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "z in [x, y, z]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "z not in [x, y]" ] @@ -242,72 +386,160 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 + 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 - 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 * 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "27" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 ** 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.5" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 / 2 ## standard division " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "27 // 5 ## floor division rounds down to nearest whole number" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "27 % 3 ## returns the remainder of division" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "27 % 5 ## returns the remainder of division " ] @@ -321,9 +553,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sum([3, 2, 1])" ] @@ -338,45 +581,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "2 * (3 + 3)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "2 * 3 + 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.375" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(3 / 2) ** 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.375" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 / 2 ** 3" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "13.0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "10 + ((3 ** 3) / 9)" ] @@ -391,9 +689,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# type returns the type as a result\n", "x = 3\n", @@ -402,9 +711,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# isinstance returns a boolean as the result\n", "isinstance(x, int)" @@ -423,7 +743,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -437,9 +757,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/nb-5.1-strings.ipynb b/notebooks/nb-5.1-strings.ipynb index d07dd20..089be0e 100644 --- a/notebooks/nb-5.1-strings.ipynb +++ b/notebooks/nb-5.1-strings.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,9 +41,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# indexing: select the first element of a string (Python index starts at 0)\n", "mystring[0]" @@ -51,9 +62,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'t'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# indexing: select the last element (negative indexes start from the end)\n", "mystring[-1]" @@ -61,9 +83,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# indexing: select a range of elements with a slice (e.g., first:last)\n", "mystring[0:8]" @@ -71,9 +104,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# indexing: the above could also be written as [:8] meaning from start to index 8\n", "mystring[:8]" @@ -81,9 +125,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ext'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# indexing: this means index -3 until the end.\n", "mystring[-3:]" @@ -98,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -109,9 +164,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a new string bigger than the last string'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# combine them and assign the result to another variable\n", "newstring = string1 + string2\n", @@ -128,9 +194,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'str' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# error: you cannot assign to an index inside a string.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmystring\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" + ] + } + ], "source": [ "# error: you cannot assign to an index inside a string.\n", "mystring[0] = \"A\"" @@ -146,9 +224,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "## you could do this instead\n", "newstring = \"A\" + mystring[1:]\n", @@ -165,9 +254,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (4153749568.py, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[11], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m mystring.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ "# put your cursor after the period below and press to see available options\n", "mystring." @@ -183,9 +281,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# this capitalizes the first character of the string\n", "mystring.capitalize()" @@ -193,9 +302,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'string', 'of', 'text']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# this splits the string into a 'list' where ever there is whitespace\n", "mystring.split()" @@ -203,9 +323,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' a string of text '" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# this centers the text across a width of 40\n", "mystring.center(40)" @@ -213,9 +344,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# this prints the index of the searched word starting from the left\n", "mystring.find(\"of\")" @@ -233,9 +375,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# here mystring is replaced by a new string where the first character is capitalized.\n", "mystring = mystring.capitalize()\n", @@ -244,9 +397,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string of text'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# let's create a new variable like the original that is not capitalized\n", "lower_string = mystring.lower()\n", @@ -266,9 +430,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A string of text'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# you return a variable's value by entering just the variable\n", "mystring" @@ -276,9 +451,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A string of text\n" + ] + } + ], "source": [ "# or you can use the print() function, which prints it to stdout\n", "print(mystring)" @@ -286,9 +469,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A string of text a string of text\n" + ] + } + ], "source": [ "# In Python3 but not Py2 print of multiple strings is concatenated\n", "print(mystring, lower_string)" @@ -306,9 +497,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello world\n", + "printing in single quotes is the same as in double quotes\n", + "'you can make strings that include single quotes by putting them inside doubles'\n", + "\"you can make strings that include double quotes by putting them inside singles\"\n", + "\n", + "multi-line string with mixed single and double\n", + "quotes can all be captured inside triple-quotes.\n", + "This also interprets the starting line as a newline.\n", + "\n" + ] + } + ], "source": [ "## examples of printing strings\n", "print(\"hello world\")\n", @@ -334,9 +541,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the dog is meaner than the cat\n" + ] + } + ], "source": [ "# substite variables into the curly braces\n", "dog = \"dog\"\n", @@ -346,9 +561,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the cat is meaner than the dog\n" + ] + } + ], "source": [ "# use indexing to indicate the order of filling\n", "dog = \"dog\"\n", @@ -358,9 +581,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "This is a long string over\n", + "multiple lines. But I can still \n", + "insert \"dog\" here or \"cat\" here, \n", + "it's easy as pie.\n", + "\n" + ] + } + ], "source": [ "# the variables do not need to be assigned before hand\n", "print(\"\"\"\n", @@ -373,9 +609,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the final answer is: 0.00123\n" + ] + } + ], "source": [ "# You can substitute strings, ints, floats, etc.\n", "print(\"the final answer is: {}\".format(0.00123))" @@ -391,9 +635,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the dog is meaner than the cat\n" + ] + } + ], "source": [ "# f-strings works great for a one-line string\n", "dog = \"dog\"\n", @@ -403,9 +655,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "This is a long string over\n", + "multiple lines. But I still want to \n", + "insert \"dog\" here or \"cat\" here.\n", + "\n" + ] + } + ], "source": [ "# f-strings \n", "print(f\"\"\"\n", @@ -426,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -440,9 +704,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThis is a multi-line document.\\nThis is the second line.\\nThe last line is here.\\n'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# the string variable looks like this. Newlines are represented as '\\n'\n", "page" @@ -450,9 +725,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'This is a multi-line document.\\nThis is the second line.\\nThe last line is here.'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# strip() removes the newline characters at the beginning and end\n", "page.strip()" @@ -460,9 +746,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['This is a multi-line document.',\n", + " 'This is the second line.',\n", + " 'The last line is here.']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# split can take an argument to split on a specific character .\n", "# Here we enter '\\n' to split on new lines. \n", @@ -481,9 +780,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a string'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# define a unicode string.\n", "\"a string\"" @@ -491,9 +801,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a unicode string'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# explicitly define a unicode string\n", "u\"a unicode string\"" @@ -501,9 +822,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "b'a byte string'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# explicitly define a byte string\n", "b\"a byte string\"" @@ -518,9 +850,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "this is bb decoded with utf-8: 'a unicode string'\n", + "this is bb decoded with utf-16: '⁡湵捩摯⁥瑳楲杮'\n", + "\n" + ] + } + ], "source": [ "bb = b\"a unicode string\"\n", "\n", @@ -532,9 +875,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'\\xe4\\xb8\\xad\\xe6\\x96\\x87'\n" + ] + } + ], "source": [ "# encode this string back into a bytestring\n", "print('中文'.encode('utf-8'))" @@ -542,9 +893,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'蓏콯캁澽苏'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# decde this bytestring back into a utf-16 string\n", "b'\\xcf\\x84o\\xcf\\x81\\xce\\xbdo\\xcf\\x82'.decode('utf-16')" @@ -567,9 +929,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "newstring = \"a new string\" ## creating a new string variable\n", "newstring = newstring.capitalize() ## return capiltalized version\n", @@ -597,9 +970,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "p\n", + "p\n", + "l\n", + "e\n", + "s\n", + " \n", + "o\n", + "r\n", + "a\n", + "n\n", + "g\n", + "e\n", + " \n", + "g\n", + "r\n", + "a\n", + "p\n", + "e\n", + "s\n" + ] + } + ], "source": [ "## define a string variable\n", "stringvar = \"apples orange grapes\"\n", @@ -619,9 +1019,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "e\n", + "o\n", + "a\n", + "e\n", + "a\n", + "e\n" + ] + } + ], "source": [ "## find vowels in stringvar\n", "for char in stringvar:\n", @@ -639,9 +1053,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "e\n", + "o\n", + "a\n", + "e\n", + "a\n", + "e\n" + ] + } + ], "source": [ "## find vowels in stringvar\n", "for char in stringvar:\n", @@ -674,10 +1102,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apples orange grapes\n" + ] + } + ], + "source": [ + "#defining new variable\n", + "varstring = \"apples orange grapes\"\n", + "print(varstring)" + ] }, { "cell_type": "markdown", @@ -688,10 +1128,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apples orange \n" + ] + } + ], + "source": [ + "first_varstring = varstring[:14]\n", + "print(first_varstring)\n", + "\n", + "#realized I could also do this:\n", + "#first_varstring = varstring.split()[:2]" + ] }, { "cell_type": "markdown", @@ -702,10 +1156,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "orange grapes\n" + ] + } + ], + "source": [ + "second_varstring = varstring[7:20]\n", + "print(second_varstring)" + ] }, { "cell_type": "markdown", @@ -716,10 +1181,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "['apples', 'orange', 'grapes']" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# splitting string\n", + "varstring.split() #don't need '\\n' bc that splits by line" + ] }, { "cell_type": "markdown", @@ -730,10 +1209,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p\n", + "p\n", + "l\n", + "s\n", + " \n", + "r\n", + "n\n", + "g\n", + " \n", + "g\n", + "r\n", + "p\n", + "s\n" + ] + } + ], + "source": [ + "#find letters that are NOT vowels\n", + "for char in varstring:\n", + " if char not in \"aeiou\":\n", + " print(char)" + ] }, { "cell_type": "markdown", @@ -744,10 +1248,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "They asked, \"what's your name?\" \n" + ] + } + ], + "source": [ + "your_name = \"\"\"They asked, \"what's your name?\" \"\"\"\n", + "print(your_name)" + ] }, { "cell_type": "markdown", @@ -758,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -766,6 +1281,27 @@ "dna2 = \"ACAGAGTCGCCAGGAGATGACAGAAAGGTCTGGGTTACAACTCTCTCTAAAATAAGGGCCAATTAACGTT\"" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "#find differences\n", + "#parsing through strings\n", + "differ = sum(a != b for a, b in zip(dna1, dna2)) \n", + "#printing count of differerences\n", + "print(differ)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -776,7 +1312,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -790,9 +1326,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/nb-5.2-lists.ipynb b/notebooks/nb-5.2-lists.ipynb index 6ddfc79..b7b7d12 100644 --- a/notebooks/nb-5.2-lists.ipynb +++ b/notebooks/nb-5.2-lists.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ "(1, 2, 3)" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -115,7 +115,7 @@ "(1, 2, 3)" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ "((1, 2, 3), ('a', 'b', 'c'))" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -163,7 +163,7 @@ "(1, 2, 3)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# error: *try* to modify a tuple element in place\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# error: *try* to modify a tuple element in place\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtup3\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m\n", "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -224,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -233,7 +233,7 @@ "['a', 'b']" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -265,7 +265,7 @@ "['apple', 'b', 'c', 'dandelion']" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -293,7 +293,7 @@ "['a', 'p', 'p', 'l', 'e']" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -312,27 +312,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (3522878973.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[14], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m mylist.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ "mylist." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['apple', 'b', 'c', 'dandelion', 'dog']" + "['apple', 'b', 'c', 'dandelion', 'dog', 'dog']" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -345,16 +354,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['apple', 'b', 'c', 'dandelion', 'dog']" + "['apple', 'b', 'c', 'dandelion', 'dog', 'dog']" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -367,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -376,7 +385,7 @@ "2" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -388,16 +397,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['dog', 'dandelion', 'c', 'b', 'apple']" + "['dog', 'dog', 'dandelion', 'c', 'b', 'apple']" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -410,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -419,7 +428,7 @@ "[]" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -469,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -481,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -490,7 +499,7 @@ "['a', 'e', 'i', 'o', 'u']" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -509,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -518,7 +527,7 @@ "['a', 'e', 'i']" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -538,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -566,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -594,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -603,7 +612,7 @@ "range(0, 10000000000, 100)" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -625,7 +634,7 @@ "True" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -646,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -656,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -665,7 +674,7 @@ "70" ] }, - "execution_count": 33, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -677,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -686,7 +695,7 @@ "range(0, 70)" ] }, - "execution_count": 34, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -698,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -736,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -747,10 +756,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1, 10, 100), ['dog', 'cat', 'rat'], 'Columbia University']\n" + ] + } + ], + "source": [ + "mylist = [a, b, c]\n", + "print(mylist)" + ] }, { "cell_type": "markdown", @@ -761,10 +781,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187]\n" + ] + } + ], + "source": [ + "#parsing values between 0 - 200\n", + "#adding values to an empty list\n", + "div_by_17 =[value for value in range(0,200) if value % 17 == 0]\n", + " #checking that only values that have remainder of zero are added to list\n", + "#printing list output\n", + "print(div_by_17)" + ] }, { "cell_type": "markdown", @@ -775,22 +810,42 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[91, 92, 93, 94, 95, 96, 97, 98, 99]\n" + ] + } + ], "source": [ "store = []\n", "for idx in range(100):\n", " if idx > 90:\n", - " store.append(idx)" + " store.append(idx)\n", + "print(store) #added this to check my work below" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[91, 92, 93, 94, 95, 96, 97, 98, 99]\n" + ] + } + ], + "source": [ + "store = [idx for idx in range(100) if idx > 90]\n", + "print(store) #values match original for-loop output!!!" + ] }, { "cell_type": "markdown", @@ -801,7 +856,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -810,10 +865,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C\n", + "O\n", + "D\n", + "E\n" + ] + } + ], + "source": [ + "for word in mylist: #looks at each word in list\n", + " for char in word: #look at each character of each word\n", + " if char.isupper(): #if character is uppercase\n", + " print(char)" + ] }, { "cell_type": "markdown", @@ -826,7 +897,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -840,9 +911,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/nb-5.3-seesion5-challenges.ipynb b/notebooks/nb-5.3-seesion5-challenges.ipynb index 87f8487..6f609fa 100644 --- a/notebooks/nb-5.3-seesion5-challenges.ipynb +++ b/notebooks/nb-5.3-seesion5-challenges.ipynb @@ -31,10 +31,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The sum of the numbers in the list is 14\n" + ] + } + ], + "source": [ + "test_case = [1,5,2,6] #defined my list\n", + "amount = 0 #realized I couldn't create a var. named 'sum' bc of built-in sum function\n", + "\n", + "for num in test_case: #started for-loop \n", + " amount += num #adding integers to 'amount'\n", + "#printing output of for-loop; want it ouside loop for TOTAL sum\n", + "print(f\"The sum of the numbers in the list is {amount}\") #used a f-string to re-format output " + ] }, { "cell_type": "markdown", @@ -52,10 +68,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CATG\n", + "CATGCATG\n", + "CATGCATGCATG\n", + "CATGCATGCATGCATG\n", + "CATGCATGCATGCATGCATG\n" + ] + } + ], + "source": [ + "DNA_seq = \"CATG\" #created a string \n", + "i = 0 #repetition factor\n", + "#trying out while-loop;\n", + "while i <= 5: #created while-loop; there are five lines (i.e. 5 repetitions needed)\n", + " print(DNA_seq * i) #multiplying current seq. by i\n", + " i += 1 #increasing repetition increment for TOTAL of 5 repetitions" + ] }, { "cell_type": "markdown", @@ -74,10 +110,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apple\n", + "orange\n", + "carrot\n", + "cabbage\n", + "chicken\n", + "beef\n" + ] + } + ], + "source": [ + "list_lists = [[\"apple\", \"orange\"], [\"carrot\", \"cabbage\"], [\"chicken\", \"beef\"]]\n", + "for item in list_lists: #'item' here is my OUTER list\n", + " for items in item: #while 'items' is my INNER list\n", + " print(items) #printing individuals 'items'" + ] }, { "cell_type": "markdown", @@ -96,10 +152,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apple\n", + "carrot\n", + "chicken\n", + "orange\n", + "cabbage\n", + "beef\n" + ] + } + ], + "source": [ + "#found this challenging!\n", + "list_lists = [[\"apple\", \"orange\"], [\"carrot\", \"cabbage\"], [\"chicken\", \"beef\"]]\n", + "\n", + "index = 0 #assigning a starting value\n", + "done = False #checking each time as to whether OUTER list is finished \n", + "\n", + "while not done: #starting while loop \n", + " done = True #meaning I have gone through entire OUTER/INNER list(s)\n", + " for item in list_lists: #'item' here is my OUTER list\n", + " if index < len(item): #checking if INNER list contains item\n", + " print(item[index]) #printing item\n", + " done = False #if there is still more printing we are NOT done \n", + " index += 1 #updating index with new value " + ] }, { "cell_type": "markdown", @@ -110,10 +193,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "2\n", + "4\n", + "6\n", + "8\n", + "10\n", + "12\n", + "14\n", + "16\n", + "18\n", + "20\n", + "22\n", + "24\n", + "26\n", + "28\n", + "30\n", + "32\n", + "34\n", + "36\n", + "38\n" + ] + } + ], + "source": [ + "for num in range(0,40,2): #starting at 0, stopping at 40; step count by 2\n", + " #ended at 40 to ensure we can get 20 EVEN numbers \n", + " print(num)" + ] }, { "cell_type": "markdown", @@ -124,10 +238,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "101\n", + "99\n", + "97\n", + "95\n", + "93\n", + "91\n", + "89\n", + "87\n", + "85\n", + "83\n", + "81\n", + "79\n", + "77\n", + "75\n", + "73\n", + "71\n", + "69\n", + "67\n", + "65\n", + "63\n", + "61\n" + ] + } + ], + "source": [ + "for num in range(101,60,-2): #starting at 101, stopping at 60; step count decrease by 2\n", + " #ended at 60 to ensure we can get 20 ODD numbers; had to do some trial-error here to figure out range \n", + " print(num)" + ] }, { "cell_type": "markdown", @@ -156,6 +302,32 @@ "\"\"\"" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "GATTACAAAACAGAATAGCTCCTATATACTGGGAATATTTCCACCCAAATCAATACTA\n", + "ACAAATAATACCCATAATATAAATTTTAAAATCAATACATCTACAGCCGTGAAACTCA\n", + "GTAATCGCCAGGTTCCCCCCGTTACCAATTTATTACACATTAAATTAACGTCAATAAC\n", + "TCACCATGATTTTTTTCACA\n", + "\n" + ] + } + ], + "source": [ + "even_bases = \"\" #creating empty string\n", + "for char in range(0, len(seq), 2): #starting at index 0, ending at sequence end; step count by 2\n", + " even_bases += seq[char] #adding new base 'character' to empty string \n", + "\n", + "print(even_bases) #printing entire string of EVEN indexed bases" + ] + }, { "cell_type": "code", "execution_count": null, @@ -180,7 +352,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/notebooks/penguin_data.csv b/notebooks/penguin_data.csv new file mode 100644 index 0000000..f10a090 --- /dev/null +++ b/notebooks/penguin_data.csv @@ -0,0 +1,151 @@ +culmen.length,culmen.depth,flipper.length,body.mass,species +39.1,18.7,181,3750,Adelie +39.5,17.4,186,3800,Adelie +40.3,18,195,3250,Adelie +36.7,19.3,193,3450,Adelie +39.3,20.6,190,3650,Adelie +38.9,17.8,181,3625,Adelie +39.2,19.6,195,4675,Adelie +34.1,18.1,193,3475,Adelie +42,20.2,190,4250,Adelie +37.8,17.1,186,3300,Adelie +37.8,17.3,180,3700,Adelie +41.1,17.6,182,3200,Adelie +38.6,21.2,191,3800,Adelie +34.6,21.1,198,4400,Adelie +36.6,17.8,185,3700,Adelie +38.7,19,195,3450,Adelie +42.5,20.7,197,4500,Adelie +34.4,18.4,184,3325,Adelie +46,21.5,194,4200,Adelie +37.8,18.3,174,3400,Adelie +37.7,18.7,180,3600,Adelie +35.9,19.2,189,3800,Adelie +38.2,18.1,185,3950,Adelie 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222ooo222!!![[[ ///222!!![[[ ///jjj222!!!jjj{{{ !!!222!!! ...ooo+++ + + +ooo+++``` + + +ooo``` + + +ooo + + +ooo%%%???ooo///vvv(((ggg888///www<<>>jjjzzz666~~~---PPPaaafff///___OOOOOObbb///ZZZYYYRRR^^^___OOO???ooohhhKKKlllBBBCCC///___OOO:::bbbwww---||| //////!!!___OOO???ooo<<<||| fff |||///___OOO{{{vvv///RRR___OOO???ooo GGG&&&000>>>777)))```iiixxx///___OOO::: 666$$$@@@hhh///888FFF + + + ===___OOO???ooo''')))555{{{ppp%%% \ No newline at end of file diff --git a/notebooks/sepal_length.svg b/notebooks/sepal_length.svg new file mode 100644 index 0000000..b23aab7 --- /dev/null +++ b/notebooks/sepal_length.svg @@ -0,0 +1,1318 @@ + + + + + + + + 2025-03-06T11:10:50.140663 + image/svg+xml + + + Matplotlib v3.10.0, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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b/notebooks/sl-wf-sim.py @@ -0,0 +1,73 @@ +import streamlit as st +import pandas as pd +import numpy as np +import time # <- We'll need this later as well + +from wf_sim import Population + +st.title('Simple Wright-Fisher Simulation of Genetic Drift') + +# Create a NEW Population +# These are the default values for pop. size (N) and derived allele frequency (f). +# N=10, f=0.2 +p = Population() + +"""Basic code to simulate wf and use streamlit LineChart +to show the change through time""" + +# Initialize chart with allele frequency of the derived allele. +#`line_chart` expects a list, so we must wrap `p.f` in square brackets + +chart = st.line_chart([p.f]) + +#for Challenge #1: +threshold = 0.001 #defining this as my threshold allele frequency +prev_freq = None #var to hold previous allele frequency + +#for Challenge #2: +progress_bar = st.sidebar.progress(0) +status_text = st.sidebar.empty() +last_rows = np.random.randn(1, 1) + +# Initially we'll run a loop 60 times +for i in range(1, 60): + # Step 1 wf generation + p.step(ngens=1) + # Calculate the current derived allele frequency + freq = np.sum(p.pop)/len(p.pop) + # Update the chart to add the current allele frequency + chart.add_rows([freq]) + +#check frequency change + if prev_freq is not None and abs(freq - prev_freq) <= threshold: + print("Frequency has stabilized. Exiting loop.") + break + +status_text.text(f"{i}% complete") +progress_bar.progress(i) + +prev_freq = freq +time.sleep(0.05) + +# Add a button to rerun the simulation +st.button("Rerun") + +"""Streamlit can detect changes in the source code and suggest +user to re-run the code to incorporate the changes.""" + +# Challenge 1: End the visualization when no longer 'interesting' +# Challenge 2: Add a status message + + +# Streamlit widgets automatically run the script from top to bottom. Since +# this button is not connected to any other logic, it just causes a plain +# rerun. + +# Challenge 3: Add slider to set number of wf generations to simulate +# Challenge 4: Add a progress bar +# Challenge 5: Add sliders to set N and f parameters for the pop + + + + + diff --git a/notebooks/timeseries.ipynb b/notebooks/timeseries.ipynb new file mode 100644 index 0000000..e16a24b --- /dev/null +++ b/notebooks/timeseries.ipynb @@ -0,0 +1,743 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a1ab8076-3476-4d06-b988-f8709e0dfb77", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/riyarampalli/miniforge3/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.0\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "\n", + "# One of the modules reports prodigious warnings so we will just hide these ;)\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66fd8892-871d-46b7-9138-c9e64be2e5d1", + "metadata": {}, + "outputs": [], + "source": [ + "time = np.arange(144)\n", + "trend = time * 2.65 +100 #slope and y-intercept " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4a1c9367-925b-4823-8ec4-73d3781a23b4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, trend, color='tab:red')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Trend vs Time\");" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "479787b3-1983-463b-ba49-2515bd4d8126", + "metadata": {}, + "outputs": [], + "source": [ + "seasonal = 20 + np.sin( time * 0.5) * 20" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cc7b5c5b-e9f5-4661-a63e-65a2d9e9dbbe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, seasonal, color='tab:orange')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Seasonality\");" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3da6c045-b058-4f85-b32c-fc5a7d706f84", + "metadata": {}, + "outputs": [], + "source": [ + "residuals = np.random.normal(loc=0.0, scale=3, size=len(time))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bc71cceb-0a50-44bd-8a51-09bcb093ab62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, residuals, color='tab:green')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Residuals\");\n", + "#residuals equivalent to stochasticity in a system; the trend will/should ALWAYS dominate" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9ec67d4f-db49-4118-a87d-7659f6ac978f", + "metadata": {}, + "outputs": [], + "source": [ + "additive = trend + seasonal + residuals\n", + "#residuals are 'error' terms " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cb34edcd-2fd2-49da-aaa2-56d38ed150b7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, additive, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Residuals\");" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a3778a9-98b5-4c98-9f0b-fdfa1d14cad2", + "metadata": {}, + "outputs": [], + "source": [ + "multiplicative = trend * seasonal # * np.abs(residuals) # <- Plotting w/o residuals to show the pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "abf7ea82-c880-43c2-8d85-1f5d3b431357", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.plot(time, multiplicative, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()\n", + "plt.title(\"Multiplicative\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6ce91721-133d-48b8-9143-c0a196d3dcad", + "metadata": {}, + "outputs": [], + "source": [ + "slope, intercept = np.polyfit(np.arange(len(additive)), additive, 1) # estimate line coefficient\n", + "#polynomial 1 ^\n", + "trend = np.arange(len(additive)) * slope + intercept # linear trend\n", + "#plot trend ^\n", + "detrended = additive - trend # remove the trend\n", + "#we are identfying slope and intercept of trend AND THEN pull out de-trended (seasonality, green)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "65a7cfcb-e6a2-479d-b975-70a0083bd5de", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "plt.plot(additive, label='Original')\n", + "plt.plot(trend, label='Trend')\n", + "plt.plot(detrended, label='Detrended')\n", + "plt.grid()\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8f9a74b5-63d9-40b2-9b39-20336298851e", + "metadata": {}, + "outputs": [], + "source": [ + "additive_decomposition = seasonal_decompose(x=additive, model='additive', period = 12)\n", + "#x is the timeseries, the model is additive, and we are providing the model with a period of time " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "76bdd05d-81ef-4d24-9cce-07eafb943d5d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A helper function to plot the original time series, and the 3 decomposed components\n", + "def seas_decomp_plots(original, decomposition):\n", + " _, axes = plt.subplots(4, 1, sharex=True, sharey=False, figsize=(7, 6))\n", + " axes[0].plot(original)\n", + " axes[0].set_title('Original')\n", + " axes[1].plot(decomposition.trend)\n", + " axes[1].set_title('Trend')\n", + " axes[2].plot(decomposition.seasonal)\n", + " axes[2].set_title('Seasonality')\n", + " axes[3].plot(decomposition.resid)\n", + " axes[3].set_title('Residuals')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "seas_decomp_plots(additive, additive_decomposition)\n", + "#if value for period > 12 then " + ] + }, + { + "cell_type": "markdown", + "id": "daee768f-f450-4552-84bf-ad433c27902b", + "metadata": {}, + "source": [ + "### Challenge Exercise\n", + "Implement 'seasonal_decompose' for the multiplicative model.\n", + "Evaluate the results." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a9d2f2ca-e9f6-4037-8344-c1f00fcf82ed", + "metadata": {}, + "outputs": [], + "source": [ + "multiplicative_decomp = seasonal_decompose(x = multiplicative, model = 'multiplicative', period = 5) # <- Plotting w/o residuals to show the pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5cb3e935-9896-4c20-9e7b-32dd3fce23d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A helper function to plot the original time series, and the 3 decomposed components\n", + "def seas_decomp_plots(original, decomposition):\n", + " _, axes = plt.subplots(4, 1, sharex=True, sharey=False, figsize=(7, 6))\n", + " axes[0].plot(original)\n", + " axes[0].set_title('Original')\n", + " axes[1].plot(decomposition.trend)\n", + " axes[1].set_title('Trend')\n", + " axes[2].plot(decomposition.seasonal)\n", + " axes[2].set_title('Seasonality')\n", + " axes[3].plot(decomposition.resid)\n", + " axes[3].set_title('Residuals')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "seas_decomp_plots(multiplicative, multiplicative_decomp)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3c049081-8880-4cdf-8e17-13681b52240c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "144" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Get the length of our input time series\n", + "max_x = additive.shape[-1]\n", + "max_x" + ] + }, + { + "cell_type": "markdown", + "id": "2a61df0e-cf2f-4107-a903-d501d826ceb0", + "metadata": {}, + "source": [ + "First we need to split our additive data into a test and train set, with the idea that we will train ARIMA on the train data. Once the model is fitted, you can use it to forecast future values by calling the predict method, then we can compare the prediction to the ‘true’ values to see how well it did." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "43609e64-82df-4f20-b935-b287530c1eb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We'll split at index 120, which will leave 24 timepoints to predict in the train set\n", + "split_idx = 120 #this is the number of values for 'test data'\n", + "\n", + "# For plotting we need x-coords for the full dataset so make a list of [1, 2, .., max_x]\n", + "xs = list(range(max_x))\n", + "\n", + "# Split the train data and the x-coords\n", + "train = additive[:split_idx] #taking additive data and splitting based on first x elements\n", + "xs_train = xs[:split_idx]\n", + "\n", + "# Split the test data and the remaining x-coords\n", + "test = additive[split_idx:] #opposite (of above) for the test data! \n", + "xs_test = xs[split_idx:]\n", + "\n", + "# Plot the test and train data to see the 'truth' value of the test set\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, test, color = \"red\")\n", + "plt.title(\"Train/Test split for additive Data\")\n", + "plt.ylabel(\"Value\")\n", + "plt.xlabel(\"Time\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8f4c12b7-a828-4e2d-9889-61eff5a64614", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pmdarima.arima import auto_arima\n", + "\n", + "# Train the model on the training data\n", + "model = auto_arima(train) #create model \n", + "model.fit(train)\n", + "\n", + "# Predict the next n timepoints corresponding to the length of the test set\n", + "forecast = model.predict(n_periods=len(test)) #predict the model \n", + "\n", + "# Plot the results\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, forecast, color = \"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "22405758-7d36-4e6e-aa9e-11851fcd8eab", + "metadata": {}, + "source": [ + "### Challenge Exercise\n", + "The ARIMA model does pretty well when the size of the training set is large (>100 samples) and the size of the forecast is small. Try increasing the size of the n_periods to forecast and plotting this. Think about how the model will behave as predictions go farther into the future. How long will it take before the predictions start to look like noise?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4a689af9-21cd-4564-8c0d-2154fef12bc4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Lets say you want to predict 100 timepoints in the future you can update the xs_test x-coords like this\n", + "n_periods = 150 #have to define this first!\n", + "\n", + "#we have already trained model and fit it to the data:\n", + "forecast = model.predict(n_periods= n_periods) #predict the model\n", + "\n", + "xs_test = list(range(split_idx, split_idx +n_periods)) #range gives us increment of integers starting at max_x and going to something even bigger\n", + "\n", + "# Plot the results\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, forecast, color = \"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "32b93f76-3908-45b2-88ef-6927863f78c7", + "metadata": {}, + "source": [ + "### Challenge Exercises\n", + "\n", + "The statsmodels library has several different timeseries datasets built in. Here you will load two different time series example datasets and work through the analysis that we just practiced above.\n", + "\n", + "Step 1. Plot the two timeseries \n", + "Step 2. Determine if time series look like additive or multiplicative models\n", + "Step 3. Decompose each time series into component parts (assume a period of 12)\n", + "Step 4. Split each dataset into test and train sets AND train an ARIMA model. Predict the held-out test set.\n", + "\n", + "**How does ARIMA perform on each of these datasets?**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ec1bcf9c-e58c-4d72-b705-4c5a644cad0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "144\n", + "468\n" + ] + } + ], + "source": [ + "import statsmodels.api as smf\n", + "ts_A = sm.datasets.get_rdataset(\"AirPassengers\", \"datasets\").data[\"value\"].values\n", + "print(len(ts_A)) #length of timeseries A\n", + "ts_B = sm.datasets.get_rdataset(\"co2\", \"datasets\").data[\"value\"].values\n", + "print(len(ts_B)) #length of timeseries B\n", + "#This step is useful so we can know how to split up the train/test data for each " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7f6e8bd-b7d2-4d4b-9fdd-d6c9008bef0e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b589c82-14d4-469c-822d-628df9d18b3c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f3e5009e-5781-4181-af09-bf39fa38c3b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot timeseries A:\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(time, ts_A, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59717533-a8c6-46bb-9d18-c74c829cb85a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "667881bf-ddd2-4b94-8544-64ba009fe19e", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must have same first dimension, but have shapes (144,) and (468,)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[28], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#Plot timeseries B:\u001b[39;00m\n\u001b[1;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts_B\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtab:blue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValue\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/matplotlib/pyplot.py:3829\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3821\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3822\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3823\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3827\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3828\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3829\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m 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+ "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (144,) and (468,)" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot timeseries B:\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(time, ts_B, color='tab:blue')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4012a103-9da6-4e4b-8e08-d9af9f6d03be", + "metadata": {}, + "outputs": [], + "source": [ + "#For timeseries A:\n", + "\"\"\"\n", + "Split index at 110, which will leave out 34 timepoints to predict in the training set\n", + "\"\"\"\n", + "split_idx = 110 #this is the number of values for 'test data'\n", + "\n", + "# For plotting we need x-coords for the full dataset so make a list of [1, 2, .., max_x]\n", + "xs = list(range(max_x))\n", + "\n", + "# Split the train data and the x-coords\n", + "train = additive[:split_idx] #taking additive data and splitting based on first x elements\n", + "xs_train = xs[:split_idx]\n", + "\n", + "# Split the test data and the remaining x-coords\n", + "test = additive[split_idx:] #opposite (of above) for the test data! \n", + "xs_test = xs[split_idx:]\n", + "\n", + "# Plot the test and train data to see the 'truth' value of the test set\n", + "plt.plot(xs_train, train, color = \"black\")\n", + "plt.plot(xs_test, test, color = \"red\")\n", + "plt.title(\"Train/Test split for additive Data\")\n", + "plt.ylabel(\"Value\")\n", + "plt.xlabel(\"Time\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7052739e-7572-428b-a2eb-5870cbe57792", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c27c5ec0-e147-420f-b7d6-d56f9be9c290", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "895ab6d6-ac4e-4204-909c-3fa9eb67622c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/wf-sim.ipynb b/notebooks/wf-sim.ipynb new file mode 100644 index 0000000..d6ac0c6 --- /dev/null +++ b/notebooks/wf-sim.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "73e1e51f-f815-4c64-992d-4e3d85cc8182", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'foobarfizzbuzz'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def my_cool_function(arg1, arg2):\n", + " arg1 += 'bar'\n", + " arg2 += 'buzz'\n", + " return arg1 + arg2\n", + "my_cool_function('foo', 'fizz')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "037f7223-d84b-4e85-88c5-4f346b286988", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#initializing the population\n", + "#first argument is size of population in haploid individuals (N)\n", + "#second argument is frequency of derived alleles (f)\n", + "\n", + "def init_function(N, f):\n", + " derived = int(N*f) #determines derived alleles\n", + " ancestral = N - derived #determines ancestral alleles\n", + " population = [1] * derived + [0] * ancestral #derived/ancestral allele frequency for entire pop\n", + " return population #returns 23 list items, and assigns them 0 or 1 to fit value of f\n", + "init_function(N=10, f=0.5) #setting values for N and f" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0711d723-5143-406a-984b-e6101ff1b674", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 1, 0, 1, 1, 1, 1, 1, 1]\n" + ] + } + ], + "source": [ + "import random #have to import this package first \n", + "\n", + "ancestral_pop = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0] #manually initializing with nums. from above\n", + "\n", + "#retrieving one WF generation\n", + "def step_function(wf_pop): #my function!!!\n", + " N = len(wf_pop) #retrieving population size (in this case it is 10!)\n", + " derived = sum(wf_pop) / N #chance of a derived allele being past to next generation \n", + " #(I have N = 10, derived alleles = 5 THUS 50% chance of allele being passed on\n", + "\n", + " next_gen = [1 if random.random() < derived else 0 for _ in range(N)] \n", + " #^ will probabilistically assign 1 or 0 to the derived allele frequency \n", + " #if random num is less than 50% (or 0.5) then it will be assigned a 1; otherwise 0 for ancestral\n", + " return next_gen\n", + " \n", + "#simulating next generation\n", + "next_gen = step_function(wf_pop=ancestral_pop) \n", + "#^calling on step_function and passing my ancestral_pop list as an argument to the param 'wf_pop'\n", + "\n", + "print(next_gen)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d9d52c13-0ace-431a-9596-4574e621c1df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generations: 10; freq(a): 0.0541\n" + ] + } + ], + "source": [ + "#COPIED FROM ABOVE: this function initialized my population w a certain N and f for each individual\n", + "def init_function(N, f):\n", + " derived = int(N*f) #determines derived alleles\n", + " ancestral = N - derived #determines ancestral alleles\n", + " population = [1] * derived + [0] * ancestral #derived/ancestral allele frequency for entire pop\n", + " return population #returns 23 list items, and assigns them 0 or 1 to fit value of f\n", + "\n", + "#COPIED FROM ABOVE: this function allows me to simulate generations esp. allele frequencies\n", + "def step_function(wf_pop): #my function!!!\n", + " N = len(wf_pop) #retrieving population size (in this case it is 10!)\n", + " derived = sum(wf_pop) / N #chance of a derived allele being past to next generation \n", + " #(I have N = 10, derived alleles = 5 THUS 50% chance of allele being passed on\n", + "\n", + " next_gen = [1 if random.random() < derived else 0 for _ in range(N)] \n", + " #^ will probabilistically assign 1 or 0 to the derived allele frequency \n", + " #if random num is less than 50% (or 0.5) then it will be assigned a 1; otherwise 0 for ancestral\n", + " return next_gen\n", + "\n", + "\n", + "#Bringing it ALL together - initializes AND loops through each generation \n", + "##spits out final derived allele frequency after 10 gens!!!!!\n", + "def wf_function(N, f, ngens): #N = pop size., f = derived allele freq., ngens = # generations to run\n", + " population = init_function(N, f)\n", + " #simulating for _ generations:\n", + " for _ in range(ngens):\n", + " population = step_function(population)\n", + " final_derived_freq = sum(population) / N\n", + "\n", + " print(f\"generations: {ngens}; freq(a): {final_derived_freq:.4f}\") #formats so string/int can print together\n", + "wf_function(N = 37, f = 0.3, ngens = 10) #output" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7d62ce89-0973-4f6a-9a8d-f59dad1f799e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 0, 0, 0, 0, 0, 0, 1, 0, 0]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random #need this library for later!!\n", + "\n", + "class Population: #defined my first python class!!\n", + " def __init__(self, N = 10, f = 0.2): #special class method which is called when creating instance of a new class\n", + " #defining attributes and setting them equal to the passed-in values:\n", + " self.N = N #we set \"reasonable default values\" for this\n", + " self.f = f #we set \"reasonable default values\" for this\n", + " #reasonable values means class instance will take default values that we define for arguments!!!\n", + "\n", + " derived_count = round(N*f)\n", + " self.pop = [0] * (N - derived_count) + [1] * derived_count \n", + " def __repr__(self):\n", + " return f\"Population(N={self.N}, f={self.f})\"\n", + " \n", + " def step(self, ngens=1):#Population class is already aware of N and f, so we only need to pass in 1 new argument\n", + " for i in range(ngens): #new argument; calling on functions in random library \n", + " \n", + " #original step() function didn't know about the pop so we had it calculate len(pop) to set value for k \n", + " #wildly inefficient, as we know pop size is fixed and constant for a given simulation, \n", + " #we took advantage of N attribute of the Population object to get past pointless recalculation:\n", + " self.pop = random.choices(self.pop, k = self.N)\n", + " \n", + " #pass (python keyword that means \"don't do anything (here)\")\n", + "\n", + "p = Population() #when I type p it will \"run through\" the class and carry it all the functions\n", + "p.step(ngens = 10) #setting a \"reasonable default value\" for ngen\n", + "p.pop" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "653372e1-1a61-474e-96a7-644755f9616b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 0 1 1 0 1 0 1 1]\n", + "False\n" + ] + } + ], + "source": [ + "import random #need this library for later!!\n", + "import numpy as np\n", + "\n", + "#Further challenges:\n", + "\"\"\"\n", + "Create a new isMonomorphic() method for your Population class to test whether the population is fixed for either the ancestral or derived state.\n", + "Method should return TRUE if population is monomorphic for either state \n", + "FALSE if otherwise \n", + "\"\"\"\n", + "#COPIED FROM ABOVE -----:\n", + "\n", + "class Population: #defined my first python class!!\n", + " def __init__(self, N = 10, f = 0.2, with_np = False): #special class method which is called when creating instance of a new class\n", + " #defining attributes and setting them equal to the passed-in values:\n", + " self.N = N #we set \"reasonable default values\" for this\n", + " self.f = f #we set \"reasonable default values\" for this\n", + " self.with_np = with_np #I set a default value for this; making attribute a class object\n", + " #reasonable values means class instance will take default values that we define for arguments!!!\n", + " \n", + " derived_count = round(N*f)\n", + " \n", + " #this if-else generates a population based on whether 'with_np' = T/F\n", + " if self.with_np: \n", + " self.pop = np.array([0] * (N - derived_count) + [1] * derived_count) #this is a numpy array \n", + " else:\n", + " self.pop = [0] * (N - derived_count) + [1] * derived_count #use regular list \n", + " \n", + " def __repr__(self):\n", + " return f\"Population(N={self.N}, f={self.f})\"\n", + " \n", + " def step(self, ngens=1):#Population class is already aware of N and f, so we only need to pass in 1 new argument\n", + " for _ in range(ngens): #new argument; calling on functions in random library \n", + " #if with_np is true it'll use numpy rc, otherwise just uses random choice\n", + " if self.with_np:\n", + " self.pop = np.random.choice(self.pop, size = self.N)\n", + " #original step() function didn't know about the pop so we had it calculate len(pop) to set value for k \n", + " #wildly inefficient, as we know pop size is fixed and constant for a given simulation, \n", + " #we took advantage of N attribute of the Population object to get past pointless recalculation:\n", + " else:\n", + " self.pop = random.choices(self.pop, k = self.N)\n", + " \n", + " def isMonomorphic(self):\n", + " return len(set(self.pop)) == 1 #returns true if ALL elements in self.pop are the same \n", + " #pass (python keyword that means \"don't do anything (here)\")\n", + "\n", + "p = Population(with_np = True) #when I type p it will \"run through\" the class and carry it all the functions\n", + "p.step(ngens = 10) #setting a \"reasonable default value\" for ngen\n", + "print(p.pop)\n", + "print(p.isMonomorphic())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "17dbeb2a-7b75-4425-bf2d-ee6a65627247", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Instantiate two new objects of type Population, one called pop and the other pop_np.\n", + "Set the population size to 100 and set the with_np flag based on name of these objects.\n", + "\"\"\"\n", + "pop = Population(N = 100, with_np = False)\n", + "pop_np = Population(N = 100, with_np = True)\n", + "print(pop.pop)\n", + "print(pop_np.pop) #this is my numpy array " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "04734676-ad85-40d1-892c-1ef60ea14fdb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.2 ms ± 15.5 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit #automatically \"prints\" timeing result output \n", + "pop.step(ngens = 1000) " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c74b44b3-7b2b-4a50-a62a-2d188d165980", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.44 ms ± 26.7 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit #automatically \"prints\" timeing result output \n", + "pop_np.step(ngens = 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "ef6f9a9f-26f1-4096-b00f-d2722257e804", + "metadata": {}, + "source": [ + "After testing the performance of step() using both the list-based and numpy-array based populations, using %%timeit, I observed the following results:\n", + "1. for the **list-based** population, the execution time was 6.2 ms ± 15.5 μs per loop.\n", + "2. for the **numpy-array based** population, the execution time was 6.44 ms ± 26.7 μs per loop\n", + "\n", + "**Conclusion:**\n", + "The execution time difference between the two populations is quite small, with the list-based population excuting slightly faster. Thus, I conclude that for this specific test, the list-based implementation seems to be marginally faster than the numpy-array based implementation.However, given the marginal difference in execution times it is hard to favor one over the other in this context.\n", + "*Note:* if the number of generations or population size were to be >1000, perhaps the performance benefits of using numpy-arrays would become more apparent?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e78d1480-6064-4ff7-a1bb-9fcd97355c16", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/wf_sim.py b/notebooks/wf_sim.py new file mode 100644 index 0000000..3783fe4 --- /dev/null +++ b/notebooks/wf_sim.py @@ -0,0 +1,79 @@ +import random #need this library for later!! +import numpy as np +import argparse + +class Population: #defined my first python class!! + def __init__(self, N = 10, f = 0.2, with_np = False): #default values introduced here + #defining attributes and setting them equal to the passed-in values: + self.N = N #we set "reasonable default values" for this + self.f = f #we set "reasonable default values" for this + self.with_np = with_np #I set a default value for this; making attribute a class object + + derived_count = round(N*f) + + #this if-else generates a population based on whether 'with_np' = T/F + if self.with_np: + self.pop = np.array([0] * (N - derived_count) + [1] * derived_count) + else: + self.pop = [0] * (N - derived_count) + [1] * derived_count #use regular list + + def __repr__(self): + return f"Population(N={self.N}, f={self.f})" + + def step(self, ngens=1):#Pop class is aware of N and f, so we only need to pass in 1 new argument + for _ in range(ngens): #new argument; calling on functions in random library + #if with_np is true it'll use numpy rc, otherwise just uses random choice + if self.with_np: + self.pop = np.random.choice(self.pop, size = self.N) + else: + self.pop = random.choices(self.pop, k = self.N) + + def isMonomorphic(self): + return len(set(self.pop)) == 1 #returns true if ALL elements in self.pop are the same + + def get_population(self): #huhhhhh??? + return self.pop + +def init_function(N, f): + derived_count = round(N*f) #determines derived alleles + return [0] * (N - derived_count) + [1] * derived_count + +def run_sim(N, f, ngens, verbose = False): + population = init_function(N,f) + + for generation in range(ngens): + # Simulate one step (generation) of the population + derived_freq = sum(population) / N + next_gen = [1 if random.random() < derived_freq else 0 for _ in range(N)] + population = next_gen + + # Optionally print verbose output + if verbose: + print(f"Generation {generation + 1}: Population = {population}") + + final_derived_freq = sum(population) / N + print(f"After {ngens} generations, the derived allele frequency is: {final_derived_freq:.4f}") + +def main(): + # Set up argparse for command-line arguments + parser = argparse.ArgumentParser(description="Run a Wright-Fisher simulation") + + # Arguments for the simulation + parser.add_argument('--N', type=int, default=10, help='Population size (N)') + parser.add_argument('--f', type=float, default=0.2, help='Frequency of derived allele (f)') + parser.add_argument('--ngens', type=int, default=10, help='Number of generations') + + # Verbose flag for additional debugging + parser.add_argument('--verbose', action='store_true', help='Print verbose debugging information') + + args = parser.parse_args() + + # Run the simulation with user-defined arguments + run_sim(args.N, args.f, args.ngens, verbose=args.verbose) + + +if __name__ == "__main__": + main() + + + diff --git a/parallel.py b/parallel.py new file mode 100644 index 0000000..efebc24 --- /dev/null +++ b/parallel.py @@ -0,0 +1,26 @@ +import numpy as np +import sys +import time +from concurrent.futures import ProcessPoolExecutor, as_completed + +## Use sys.argv to pass in an integer value for the number +## of consecutive integers to operate on +length = int(sys.argv[1]) + +def task(n): + time.sleep(1) + return n * n #square of number passed in + +if __name__ == '__main__': + + numbers = range(0, length) #takes integer value passed in + ## create a pool of workers + ## max_workers determines the number of parallel process to run + with ProcessPoolExecutor(max_workers=4) as executor: #this is number of parallel processeors!!! + ## Submit all the jobs to the executor + results = [executor.submit(task, num) for num in numbers] #send tasks to CPU worker pool + + ## As jobs finish, pull the results and print them + for future in as_completed(results): #taking list and detecting when job is finished + #detects completion when worker pools finish their 'tasks' + print(f"Result: {future.result()}") \ No newline at end of file