diff --git a/content/GA_2_3/P7.ipynb b/content/GA_2_3/Analysis_solution.ipynb similarity index 100% rename from content/GA_2_3/P7.ipynb rename to content/GA_2_3/Analysis_solution.ipynb diff --git a/content/GA_2_3/P7_solution.pdf b/content/GA_2_3/GA_2_3_solution.pdf similarity index 100% rename from content/GA_2_3/P7_solution.pdf rename to content/GA_2_3/GA_2_3_solution.pdf diff --git a/content/GA_2_4/Analysis.ipynb b/content/GA_2_4/Analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..da129992f07506cd026befbdc28e8ba46ef4c812 --- /dev/null +++ b/content/GA_2_4/Analysis.ipynb @@ -0,0 +1,1071 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Project 8: Global Mean Sea Level\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 25px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Friday December 8, 2023.*" + ] + }, + { + "attachments": { + "fig_sea_level_rise.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": { + "cell_id": "82b7494301c646b2858f959260412e27", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Overview\n", + "\n", + "In this project we will use time series analysis techniques to analyze real data on _global_ sea level (i.e., the data represents the average value over the entire globe). In particular, we will take into account seasonal effects of the time series in our analysis. Specifically, the tasks are:\n", + "\n", + "- Tasks 1-2: as a warm up you will create and analyze some elementary noise + signals using AR(1)\n", + "- Tasks 3-5: explore sea level data, then reconstruct it using a linear trend and seasonality evaluated with a simple monthly average\n", + "- Task 6: evaluate frequency components of seasonality using least-squares harmonic estimation (LS-HE)\n", + "- Tasks 7-8: using the results from Task 6, apply BLUE and evaluate annualy rate of sea level rise\n", + "- Task 9: apply AR(3) and consider how to make a prediction (no prediction is actually made)\n", + "\n", + "**Note that a only a subset of your answers here are required to be included in the `Report.md` file.**\n", + "\n", + "## Global Mean Sea Level (GMSL) measurements\n", + "\n", + "_The following text is provided to explain the GMSL data._\n", + "\n", + "The Earth's temperature is rising due to the accumulation of greenhouse gases in the atmosphere, resulting in two inter-related consequences regarding sea-level rise. The direct consequence is the accelerated melting of polar ice sheets and glaciers. This leads to an expansion of seawater, contributing to the overall rise in sea levels. Additionally, as an indirect effect, the warming of oceans causes seawater to thermally expand, further worsening the rise in sea levels. The impact of sea-level rise is already evident in various parts of the world, presenting significant challenges to coastal communities and ecosystems. For example, coastal cities are particularly vulnerable due to their concentrated infrastructure and populations near shorelines (see below).\n", + "\n", + "\n", + "\n", + "Satellite altimetry (SA) provides a powerful tool to monitor the global sea-level changes. It employs specific satellite missions equipped with altimeters to measure the distance between the satellite and the sea surface. This measurement relies on the travel time of signals, the time taken for a radar or laser pulse emitted from the satellite to reach the sea surface and then return to the satellite. In this context, we aim to analyze a specific dataset of SA information.\n", + "\n", + "The data are the Global Mean Sea Level (GMSL) measurements made since 1993 until mid 2020 by satellite altimetry. It consists of a few satellite missions such as TOPEX/Poseidon (launched August, 1992), Jason-1 (launched December, 2001), Jason-2 (launched June, 2008) and Jason-3 (launched January 2016). The unit is in mm, and it shows a steady increase in GMSL of around 3.5 ± 0.4 mm/year over the above period. The details, along with the data, can be found [here](\n", + "https://www.cmar.csiro.au/sealevel/sl_hist_last_decades.html).\n", + "\n", + "The goal of this project is to apply the Time Series Analysis theories presented in Week 2.4 to this time series. We just only use here data from 1993 Jan. to 2019 Dec. (so exactly 27 years).\n", + "\n", + "In particular we are interested to identify the components of time series, check the stationarity of the time series, make statistical judgment whether the estimated trend in sea-level is significant (you may know that there are argues about climate change, sea level rise, and global warming), identify the appropriate functional model ($Y=\\mathrm{Ax}+\\epsilon$) and estimate the stochastic model parameters of the ARMA process. \n", + "\n", + "Most of the exercises in this notebook consist of both coding and answering (open) questions. Typically, as you work your way through the exercises, you can often re-use code, or part of it, from earlier exercises.\n", + "\n", + "File needed for the exercise: `CSIRO_Alt_seas_inc.txt`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as signal\n", + "from statsmodels.graphics.tsaplots import plot_acf \n", + "from scipy.stats import norm\n", + "from scipy.stats.distributions import chi2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "711425f304ea4230a43e722a165e6aba", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Tasks 1-2: Simulated data with AR(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "24e525319d2d49bcaf27de4aaa59d220", + "deepnote_cell_type": "markdown" + }, + "source": [ + "We intend to simulate (as a time series) 1000 samples at 1-second intervals (so $m=1000$), using a first-order auto-regressive AR(1) random process $s(t)$ as follows:\n", + "\n", + "$$\n", + "S(t)\n", + "= \\beta S(t-1)+e(t)\n", + "$$\n", + "\n", + "with $t = 1, …, 1000$ and where $\\beta=0.9$ is the given AR(1) parameter, and we further assume\n", + "\n", + "$$\n", + "\\mathbb{E}(S(t))=0 \\text{,} \\hspace{2mm} \\mathbb{D}(S(t))=\\sigma^2=1.\n", + "$$ \n", + "\n", + "Please note that, as a convention, for Time Series Analysis and Observation Theory $m$ represents the number of samples, which is represented as $N$ for the Signal Processing, so $m=N$.\n", + "\n", + "You may simulate the data using a normal distribution. To do so, you will use the above recursive form, which needs initialization. To initialize the first data, you can use <code>S[0] = np.random.normal(...)</code> using the normal distribution. To use the above recursive formula you need to simulate $e(t)$, requiring to have its standard deviation $\\sigma_{e}$ of the white noise process. It is given from the following equation:\n", + "\n", + "$$\n", + "\\sigma_{e} = \\sigma \\sqrt{(1-\\beta^2)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "48e8df4114544ca4914a15c77372ab30", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1:</b> \n", + "\n", + "Create a synthetic time series and evaluate it by completing the following steps:\n", + "<ol>\n", + " <li>Simulate and plot (versus time) the AR(1) time series based on the above-specified values. By the visual inspection, explain the time-correlated pattern of the simulated time series (i.e. describe what does it show, how does it look like; for example compared to white noise).</li>\n", + " <li>Plot the normalized Auto-Covariance Function (ACF) of the generated time series. \n", + " <li>Plot the power spectral density (PSD)/periodogram of the generated time series. Repeat the simulation several times to see if you can observe a particular pattern in the PSD. Explain your observations.</li>\n", + " <li>Take $\\beta = 0$ to simulate a white noise process and repeat steps 1-3 (do not need to provide any 'plot' here). Compare the results with the case $\\beta = 0.9$. In which case can you observe a flat PSD? \n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "7047abf2b93f427a86e5ea387783d174", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 2334, + "execution_start": 1696691527706, + "source_hash": null, + "tags": [] + }, + "outputs": [], + "source": [ + "# np.random.seed(0) # For reproducibility\n", + "\n", + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "Fs = 1.0\n", + "\n", + "beta = YOUR_CODE_HERE\n", + "sigma = YOUR_CODE_HERE \n", + "sigma_e = YOUR_CODE_HERE\n", + "\n", + "s = np.zeros(m)\n", + "s[0] = np.random.normal(loc=0, scale=sigma, size=None)\n", + "\n", + "for i in range(1, m):\n", + " s[i] = YOUR_CODE_HERE\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: y(t)')\n", + "plt.title(f'Time series (beta = {beta:.2f})')\n", + "plt.legend()\n", + "\n", + "# Calculate ACF\n", + "plot_acf(YOUR_CODE_HERE, lags=100, alpha=0.05, color = 'red')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (sec)')\n", + "plt.title(f'ACF (beta = {beta:.2f})')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal\n", + "frequencies, psd = signal.periodogram(YOUR_CODE_HERE, fs=YOUR_CODE_HERE, scaling='density', return_onesided=False)\n", + "\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(8, 3))\n", + "plt.loglog(YOUR_CODE_HERE, YOUR_CODE_HERE, color='blue', label='psd')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.title(f'Power Spectral Density (PSD) of the signal (beta = {beta:.2f})')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.ylim([1e-4, 1e3])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "be7d4044f42541a497ef4ab68deb94d4", + "deepnote_cell_type": "markdown" + }, + "source": [ + "We now intend to simulate (as a time series) 1000 samples of a harmonic wave plus white noise, again at 1-second intervals. We take $m=1000$ and use the following equation (signal + noise):\n", + "\n", + "$$\n", + "Y(t)=A\\sin(2\\pi ft + \\theta)+S(t)\n", + "$$\n", + "\n", + "where $S(t)$ is the white noise process of Task 1 $(\\beta=0)$. The amplitude, initial phase and frequency of the sine wave are given as $A=1$, $\\theta=\\frac{\\pi}{4}$, and $f=0.05$ Hz, respectively. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "ae5841495b8e44a8ba4fc5c8ef424297", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2:</b> \n", + " \n", + "It is required to:\n", + "\t\n", + "<ol>\n", + " <li>Plot the normalized Auto-Covariance Function (ACF) of the generated time series. What is the ACF of a sine wave?</li>\n", + " <li>Plot the power spectral density (PSD)/periodogram of the generated time series.</li>\n", + " <li>Increase $\\sigma=1$ to $\\sigma=10$ (increase noise) and repeat steps 1 and 2 (no plot needed here to present). Discuss your observations.</li>\n", + "</ol>\n", + "</p>\n", + "</div>\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "eda81652ecc94c5fb34a766079d1f935", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 912, + "execution_start": 1696691528530, + "source_hash": null, + "tags": [] + }, + "outputs": [], + "source": [ + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "Fs = 1.0\n", + "\n", + "beta = YOUR_CODE_HERE\n", + "sigma = YOUR_CODE_HERE\n", + "sigma_e = YOUR_CODE_HERE\n", + "\n", + "A = 1\n", + "Theta = np.pi / 4\n", + "f = 0.05\n", + "\n", + "s = np.zeros(m)\n", + "s[0] = np.random.randn(1)*sigma\n", + "\n", + "for i in range(1, m):\n", + " s[i] = YOUR_CODE_HERE\n", + "\n", + "Y = YOUR_CODE_HERE\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: y(t)')\n", + "plt.title(f'Signal with $A$={A}, $\\Theta$={Theta*180/np.pi:.1f}°, '\n", + " f'$f$={f} Hz, $\\sigma$={sigma}')\n", + "plt.legend()\n", + "\n", + "# Calculate ACF\n", + "plot_acf(YOUR_CODE_HERE, lags=100, alpha=0.05, color = 'red')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (sec)')\n", + "plt.title(f'Auto-covariance (sigma={sigma})')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal\n", + "frequencies, psd = signal.periodogram(YOUR_CODE_HERE, fs=YOUR_CODE_HERE, scaling='density',\n", + " return_onesided=False)\n", + "\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(8, 3))\n", + "plt.loglog(YOUR_CODE_HERE, YOUR_CODE_HERE, color='blue', label='psd')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.title(f'Power Spectral Density of the signal (sigma={sigma})')\n", + "plt.ylim([1e-3, 1e3])\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "d9370df3a6a043dc8077c6c93c6a9bf5", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Tasks 3-5: exploration and (simple) reconstruction of sea level data\n", + "\n", + "The descrition about the data is given above.\n", + "\n", + "### Read and plot the data\n", + "\n", + "The data consists of monthly global mean sea levels. We use exactly 27 years of monthly data, so $m=27\\times 12=324$. \n", + "\n", + "We use the package pandas to import the data and do a bit of pre-processing. Once imported as a dataframe object, we the method `iloc` (integer-based indexing) toextract the time and sea level data as ndarrays. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "4d5981d7086e4a3fbd2792e17e87d660", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 291, + "execution_start": 1696691529410, + "source_hash": null + }, + "outputs": [], + "source": [ + "dat = pd.read_csv('CSIRO_Alt_seas_inc.txt', names=['month','sl'])\n", + "\n", + "noy = 27 # number of years\n", + "nom = 12 # months in a year\n", + "dat = dat.loc[0: nom*noy - 1] # keep first 27 years of data\n", + "t0 = dat.iloc[:, 0] - dat.iloc[0, 0] # create time-array\n", + " # time relative to t0 [yr],\n", + " # so t0[0] = 0\n", + "t1 = dat.iloc[:, 0] # time-array, original time instances\n", + " # (so t1[0] = 1993.042)\n", + "m = len(dat) # number of observations\n", + " # (m as the number of observations)\n", + "T = (t0[m - 1] - t0[0])*m / (m - 1) # observation record length\n", + " # (as N*dt; sample-and-hold convention)\n", + "dt = T/m # Delta t [yr]\n", + "Y = dat.iloc[:,1] # observed sea-level height \n", + "\n", + "# plot observed time-series, as it is, versus epoch-time in [year]\n", + "plt.plot(t1, Y, color='red', label='sea level')\n", + "plt.xlabel('Time [yr]')\n", + "plt.ylabel('Global mean sea level [mm]')\n", + "plt.title('Global Mean Sea-Level (GMSL)')\n", + "plt.grid()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "6269ce2d660941329800f428fc492995", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3:</b> \n", + "\n", + "For this Task, the objective is to visually analyze the trend pattern of the global mean sea level height data. While these pattern is apparent through a visual inspection of the data, we intend to enhance clarity by applying some straightforward and simple measures. Specifically, you will compute the annual mean by averaging the twelve monthly values for each of the twenty-seven years under consideration. It is therefore asked to:\n", + "\n", + "<ol>\n", + " <li>Calculate yearly averages (for 12 months of a year calculate its mean and take that mean value for all months). This can result in an array of (<code>y_mean_array</code>) 324 entries, which is indeed based on only 27 annual means. </li>\n", + " <li>Plot the original data and the yearly averaged data in a single figure.</li>\n", + " <li>Based on your visual inspection, do you conclude that the sea-level rises in this plot? If so, what is your initial estimate of sea level rise (i.e., just a rough estimate using the plot only; no need to use code!)? Report your estimate in units of mm/yr.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Hint for Tasks 3-5:</b> \n", + "\n", + "\n", + "You may want to use either the matrix form <code>y_mat</code> or the vector form <code>y</code>, depending on the question asked, to will see which one is more convenient. For example, to compute the mean over rows or columns of the matrix, for a given matrix M you may use <code>M.mean(axis=1)</code> or <code>M.mean(axis=0)</code>: check the difference; or to repeat arrays using <code>np.repeat(a, repeats, axis=0)</code>, and <code>np.tile(a, repeats)</code>: check the difference.\n", + "\n", + "Read the documentation for more information: <code>[np.repeat](https://numpy.org/doc/stable/reference/generated/numpy.repeat.html)</code> and <code>[np.tile](https://numpy.org/doc/stable/reference/generated/numpy.tile.html)</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "6c7fa94a64534499a26ab2c6c27fc304", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 560, + "execution_start": 1696691529562, + "source_hash": null + }, + "outputs": [], + "source": [ + "Y_mat = np.reshape(Y.values, (noy,nom))\n", + "\n", + "Y_mean = YOUR_CODE_HERE\n", + "\n", + "Y_mean_array = YOUR_CODE_HERE\n", + "\n", + "plt.figure(figsize=(8,4))\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE,'r-', label='Monthly')\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE,'b.', label='Annual mean')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title('Original and mean values (averaged over months)')\n", + "plt.xlabel('Time [yr]')\n", + "plt.ylabel('Global mean sea level [mm]')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "9ad97a741727470a9720566efe855fc9", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4:</b> \n", + " \n", + "In this Task you investigate the seasonality (monthly variations of sea-levels). For every individual year, you have already calculated the mean sea level. You can now subtract it from the original monthly values of that year (so showing the deviation from the mean value of that particular year). This process yields the specific year's seasonal variations, consequently centering them around a zero mean. Subsequently, you determine the average magnitude of these seasonal variations across the span of 27 years. It is then required to:\n", + "\n", + "<ol>\n", + " <li>Calculate/plot an array containing 324 entries derived from subtracting the yearly averages (<code>y_mean_array</code>) from the original observations y (seasonality variations over all 27 years).\n", + " <li>Calculate/plot the average seasonal sea-level variations over 12 months of the year (so 12 values in total, averaged over 27 years). For simplicity, you may need to reshape the previous array to a matrix form first.</li>\n", + " <li>Compute the difference between the maximum and minimum values of the averaged seasonalities of the sea levels.</li>\n", + " <li>Explain why we would expect to have such seasonal variations in GMSL.</li>\n", + " <li>Compute/plot an array (<code>m_mean_array</code>) that contains the above 12 seasonal values (repeated and therefore identical for all 27 years), making in total 324 entries.</li>\n", + " \n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "7f3b913564af4aa8b7fbc96a90c0f8f4", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 435, + "execution_start": 1696691530041, + "source_hash": null + }, + "outputs": [], + "source": [ + "# calculate average of different months of a year\n", + "all_seasonality = YOUR_CODE_HERE\n", + "plt.figure(figsize=(16,4))\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE, 'b-o', label='average')\n", + "plt.title('Seasonal variations (over all years)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Seasonal variations [mm]')\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "all_seasonality_mat = np.reshape(all_seasonality.values, (27,12))\n", + "m_mean = YOUR_CODE_HERE\n", + "\n", + "# create x-axis from January (1) to December (12)\n", + "months = YOUR_CODE_HERE\n", + "\n", + "# plot average of different months of a year\n", + "plt.figure(figsize=(8,4))\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE, 'b-o', label='average')\n", + "plt.title('Seasonal variations (in a year)')\n", + "plt.xlabel('Month of year')\n", + "plt.ylabel('Global mean sea level [mm]')\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "# create array with monthly average of each month repeated 27 times\n", + "m_mean_array = np.tile(YOUR_CODE_HERE, 27)\n", + "plt.figure(figsize=(16,4))\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE, 'b-o', label='average')\n", + "plt.title('Average seasonal variations over 27 years')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Average seasonal variations [mm]')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "c0d339ac9452451fb2b1978e5fa3ea47", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 5:</b> \n", + "\n", + "We have a total of $m=12\\times 27=324$ monthly samples. The aim of this Task is to reconstruct the original dataset while utilizing a reduced amount of information (parameters). To achieve this, you exclusively employ the 27 mean annual values and 12 mean seasonal values, resulting in a combined count of $27+12=39$ values. To generate the data for a specific year, you combine/add the annual mean of that year with the set of 12 zero-mean seasonal values. This, for example, can be linked to array <code>y_mean_array</code> and <code>m_mean_array</code>. It is required to:\n", + "<ol>\n", + " <li>Reconstruct the 324 samples using the above-mentioned $27+12=39$ values (<code>recon_y</code>).</li>\n", + " <li>Plot the original and reconstructed data in a single figure, and compare the results.</li>\n", + " <li>Plot the difference between the original and reconstructed data: <code>y-recon_y</code> (residuals). Do you see a trend in the residuals?</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "2a851b23b78f49afbb09e2b050fd9ea0", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 760, + "execution_start": 1696691530266, + "source_hash": null + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,5))\n", + "\n", + "plt.plot(t1, YOUR_CODE_HERE, 'r-',\n", + " label='before reconstruction')\n", + "\n", + "recon_Y = YOUR_CODE_HERE\n", + "plt.plot(t1, recon_Y, 'b-',\n", + " label='after reconstruction')\n", + "\n", + "plt.plot(t1, YOUR_CODE_HERE, 'g-',\n", + " label='difference')\n", + "\n", + "plt.title('Reconstructed data')\n", + "plt.xlabel('time [yr]')\n", + "plt.ylabel('Global mean sea level [mm]')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 6: Least-squares harmonic estimation (LS-HE)\n", + "\n", + "In this task we rely on concepts from Sensing and Observation Theory as well as Signal Processing: the least-squares harmonic estimation (LS-HE) method utilizes hypothesis testing and hence making a power spectral density (PSD) to **identify the most statistically significant frequency components in a time series.**\n", + "\n", + "Previously we have used the FFT PSD, which is a special case of LS-HE. There are several advantages of LS-HE over FFT PSD: as a generalized form of the FFT PSD, LS-HE is limited neither to evenly spaced data nor to integer frequencies. With LS-HE, we may in addition include the following terms in the PSD estimation:\n", + "\n", + "1. the linear trend $Y=\\mathrm{Ax}$, as an already available deterministic part of the model, and\n", + "2. the covariance matrix $\\Sigma_{Y}$, as a stochastic part of the model.\n", + "\n", + "\n", + "In the function `LS-HE(A,Y,t,sigma)` we consider $\\Sigma_{Y}=\\sigma^2 I$ to ba a scaled identity matrix. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "1ef4fd7d394f4809b8eef70d3f58dc76", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\"> <p>\n", + "\n", + "<b>Python function for model identification using LS-HE (detection of seasonality)</b>\n", + "\n", + "<em>Note: it is optional to follow all steps here, but it is important to be able to use the function</em> <code>LSHE(A,Y,t,sigma)</code>.\n", + "\n", + "Least Squares Harmonic Estimation (LS-HE) uses hypothesis testing to compute the Power Spectral Density (PSD). The goal is to detect any potential seasonality (or periodic pattern) in the time series $Y$. An alternative has already been obtained from DFT and periodogram (Week 2.3 on Signal Processing). This is a generalization of the DFT formulation, with an initial design matrix $\\mathrm{A}$ available. We will simplify the test statistic $T_{q=2}$ to be able to implement and use it here in LS-HE function.\n", + "\n", + "<b>Implementation of $T_{q=2}$ test statistics</b>\n", + " \n", + "Considering \n", + "\n", + "$$\n", + "\\Sigma_{\\hat \\epsilon}= \\Sigma_Y - A (A^T \\Sigma_Y^{-1} A)^{-1} A^T\n", + "$$\n", + "\n", + "If we assume $\\Sigma_Y=\\sigma^2 I$, we then have\n", + "\n", + "$$\n", + " T_q =\\hat \\epsilon^T C(C^T \\Sigma_{\\hat \\epsilon} C)^{-1} C^T \\hat \\epsilon\n", + "$$\n", + "\n", + "The following function <code>LSHE(A,Y,t,sigma)</code> is an implementation of the above formula.\n", + "</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "67e90689736b4c6dbacd8c2c8474e687", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 276, + "execution_start": 1696691530544, + "source_hash": null + }, + "outputs": [], + "source": [ + "def LSHE(A, Y, t, sigma):\n", + " \"\"\"\n", + " Least squares harmonic estimation (LS-HE),\n", + " by AR Amiri-Simkooei, CCJM Tiberius, PJG Teunissen.\n", + " Assessment of noise in GPS coordinate time series:\n", + " methodology and results,\n", + " J. of Geophy. Res.: Solid Earth 112 (B7)\n", + " Here we assume the variance matrix of observation is\n", + " Sigma_Y = sigma^2*I, a scalled identity matrix.\n", + " \n", + " INPUT:\n", + " A: the initial design matrix\n", + " y: the vector of observations\n", + " t: time instances of observations, assuming t[0]=0\n", + " sigma: the standard deviation of observations in\n", + " Sigma_Y = sigma**2*I\n", + "\n", + " OUTPUT:\n", + " P: the periods at which PSDs are calculated\n", + " F: the frequencies at which PSDs are calculated\n", + " PSD: the power spectral density calculated at P or F\n", + " \"\"\"\n", + " m, n = np.shape(A)\n", + "\n", + " # Generating a series of periods to be tested\n", + " # starting period (just above the Nyquist period)\n", + " P = np.ones(10**6)*2.001*(t[1] - t[0])\n", + "\n", + " # the maximum period to be tested\n", + " Pmax = 2*(t[m - 1] - t[0])\n", + "\n", + " i = 0\n", + " while P[i]<Pmax:\n", + " # to make sure that we check all possible frequencies/periods\n", + " P[i+1] = P[i]*(1 + 0.1*P[i]/Pmax) \n", + " i = i + 1\n", + "\n", + " # Keep the periods from the minimum to the maximum tested period \n", + " P = P[0:i] \n", + "\n", + " # Computing frequencies from the periods\n", + " F = 1 / P\n", + "\n", + " # mt number of frequencies/periods to be tested\n", + " mt = len(F)\n", + " AtAinv = np.linalg.inv(A.T @ A)\n", + " Aty = A.T @ Y\n", + " xhat = AtAinv @ Aty\n", + " eps_hat = Y - A @ xhat\n", + " PSD= np.zeros(mt)\n", + " C = np.zeros((len(t),2))\n", + " for j in np.arange(0,mt):\n", + " wt = 2 * np.pi * F[j] * t \n", + " C[:,0] = np.cos(wt)\n", + " C[:,1] = np.sin(wt)\n", + " CtA = C.T @ A\n", + " MAT = C.T @ C - CtA @ AtAinv @ CtA.T\n", + " CtE = C.T @ eps_hat\n", + " PSD[j] = CtE.T @ np.linalg.inv(MAT) @ CtE\n", + " PSD = PSD / sigma**2\n", + " \n", + " return P, F, PSD " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "fc9b13e37adc420c942782404f484426", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 6:</b> \n", + "\n", + "To identify the seasonality signal, LS-HE requires an initial design matrix $\\mathrm{A}$ and some other input. In time series analysis, the functional model is usually based on the linear regression model $Y(t)=y_0 + r t$. From this model, you can make the initial $Y=\\mathrm{Ax}+\\epsilon$ linear model, where $\\mathrm{A}$ is the $m\\times 2$ design matrix (see Weeks 1.2 and 1.3), and $\\mathrm{x}=[y_0, r]^T$ is the vector of two unknown parameters. For this application the standard deviation of data is assumed to be $\\sigma= 4$ mm. It is required to:\n", + "\n", + "<ol>\n", + " <li>Establish an initial $m\\times 2$ design matrix based on $t_0$ (see above for the definition of $t_0$).</li>\n", + " <li>Use the required input for the function <code>LSHE</code> to compute/plot the PSD of the GMSL time series (plot versus frequency).</li>\n", + " <li>Identify the first three important periodic signals (3 highest peaks) in the PSD. Determine the frequencies (in cycle/year) of the detected peaks: $f_1=?$, $f_2=?$ and $f_3=?$. Explain possible causes for the detected signals.</li>\n", + " <li>Test if the 3 identified signals are statistically significant in $1-\\alpha=0.999$ confidence level.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note: once you create the plot, you should be able to identify three peaks, where you will need 2 decimals of precision for the peak with the lowest frequency (the other two peaks can be reported as integers). The following code may help you identify the values.</p></div>\n", + "\n", + "```\n", + "vector_1 = np.array([0, 1, 2, 3, 4, 5, 6])\n", + "vector_2 = np.array([10, 11, 12, 13, 14, 15, 16])\n", + "use_these_indices = np.where((vector_1>1) & (vector_1<6) & (vector_2>12))\n", + "print(vector_1[use_these_indices])\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "7cd0ecf3448444a4a4502ceb9db6c0b3", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 2307, + "execution_start": 1696691530551, + "source_hash": null + }, + "outputs": [], + "source": [ + "A0 = YOUR_CODE_HERE\n", + "sigma = YOUR_CODE_HERE\n", + "Sigma_Y = YOUR_CODE_HERE\n", + "Sigma_Y_inv = np.linalg.inv(Sigma_Y)\n", + "\n", + "P, F, PSD = YOUR_CODE_HERE\n", + "plt.figure()\n", + "plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE, color='b', label='psd')\n", + "\n", + "# the chi-squared test shows that annual and semi-annual signals\n", + "# are statistically significant (alpha = 0.001)\n", + "plt.axhline(chi2.ppf(YOUR_CODE_HERE, df=YOUR_CODE_HERE), linestyle='--',\n", + " color='r', label='confidence level')\n", + "plt.xlabel('Frequency [cycle/year]')\n", + "plt.ylabel('PSD: test statistics $T_q$')\n", + "plt.title('$T_q$ test statistics')\n", + "plt.grid()\n", + "plt.legend()\n", + "#plt.xlim([0.02, 0.06])\n", + "f1 = 1\n", + "f2 = 2\n", + "f3 = 0.04" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "88fb13205e9849e292677995e95d388e", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 7:</b> \n", + "\n", + "This Task applies the best linear unbiased estimation (BLUE) to estimate and test the rate $r$ for sea-level rise based on the initial model $y(t)=y_0 + r t$, with the three identified seasonal signals included. Each detected signal can represent a particular sine wave as: $a \\cos 2 \\pi f t + b \\sin 2 \\pi f t$ (see Chapter on Components of Time Series). You then need to make the final functional model $Y=\\mathrm{Ax}+\\epsilon$, with $\\mathrm{A}$ being an $m \\times 8$ matrix. Having the linear model $Y=\\mathrm{Ax}+\\epsilon$ along with the covariance matrix of observations $\\Sigma_Y=\\sigma^2 I$, $\\sigma = 4$ mm, you can estimate the BLUEs of $x$ (unknowns), $y$ (observations) and $e$ (residuals): $\\hat{X}=(A^T \\Sigma_Y^{-1}A)^{-1}A^T \\Sigma_Y^{-1}y$, $\\hat{Y}=A \\hat{x}$, and $\\hat{\\epsilon}=Y-\\hat{Y}$. Further you can estimate the covariance matrix of $\\hat{X}$: $\\Sigma_{\\hat{X}}=(A^T \\Sigma_Y^{-1}A)^{-1}$. It is required to:\n", + "\n", + "<ol>\n", + " <li>Establish the linear model of observation equations $Y=\\mathrm{Ax}+\\epsilon$ with $m\\times 8$ design matrix $\\mathrm{A}$.</li>\n", + " <li>Explain the unknown elements of $x=[x_1,...,x_8]^T$.</li>\n", + " <li>Obtain the BLUE of $x$ and its covariance matrix $\\Sigma_{\\hat{X}}$, and also the BLUE of $Y$ and $\\epsilon$: $\\hat{Y}$ and $\\hat{\\epsilon}$ along with their covariance matrices.</li>\n", + " <li>Plot the original data $Y$, the estimated $\\hat{Y}=\\mathrm{A\\hat{X}} $, the estimated trend (from columns 1 and 2 of $\\mathrm{A}$, which is $\\mathrm{A_1}$), and the estimated seasonality (from columns 3-8 of $\\mathrm{A}$, which is $\\mathrm{A_2}$). $\\textbf{Hint:}$ To separate the trend from seasonality note that if $\\mathrm{A}=[\\mathrm{A_1,A_2}]$ is partitioned column-wise and $x=[x_1^T, x_2^T]^T$ is partitioned row-wise, we then have $\\hat{Y}=\\mathrm{A\\hat{X}} = \\mathrm{A_1\\hat{X_1} + A_2\\hat{X_2}} = \\hat{Y}_1 + \\hat{Y}_2$.</li>\n", + " <li>What is the total amplitude of the combined annual + semi-annual signals?</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "53cc9d8e850044c48f6de110696acc32", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 194, + "execution_start": 1696691532653, + "source_hash": null + }, + "outputs": [], + "source": [ + "A = YOUR_CODE_HERE # Note: there are many ways to do this;\n", + " # and you will need more than one line\n", + "\n", + "# BLUE estimate of x\n", + "Xhat = YOUR_CODE_HERE\n", + "\n", + "# covariance matrix of xhat\n", + "Sigma_Xhat = YOUR_CODE_HERE\n", + "\n", + "# BLUE estimate of y\n", + "Yhat = YOUR_CODE_HERE\n", + "\n", + "# covariance matrix of yhat\n", + "Sigma_Yhat = YOUR_CODE_HERE\n", + "\n", + "# BLUE estimate of e (residuals)\n", + "eps_hat = YOUR_CODE_HERE\n", + "\n", + "# covariance matrix of eps_hat\n", + "Qeps_hat = YOUR_CODE_HERE\n", + "\n", + "# separate the seasonality and trend (third-order polynomial)\n", + "yhat_trend = YOUR_CODE_HERE\n", + "yhat_season = YOUR_CODE_HERE\n", + "\n", + "plt.figure()\n", + "plt.plot(t1, Y,'b', label='Original')\n", + "plt.plot(t1, Yhat,'r',label='Least squares fit')\n", + "plt.plot(t1, yhat_trend,'k--', label='Trend')\n", + "plt.plot(t1, yhat_season,'k-', label='Seasonality')\n", + "plt.xlabel('Time [year]')\n", + "plt.ylabel('Time series value [mm]')\n", + "plt.title('Original, LS fit and seasonality of the signal')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depending on how you have formulated the entries of $x=[x_1,...,x_8]^T$, one of $x_i$'s, $i=1,...,8$ is the rate $r$ for sea-level rise (assume $x_j=r$). We then need to determine $\\hat{x}_j=\\hat{r}$, and its standard deviation $\\sigma_{\\hat{x}_j}=\\sigma_{\\hat{r}}$. Based on the information provided, we need to decide if the estimated sea-level rise is statistically significant. The test can be performed using the statistical significance test given in Weeks 1.3 and 1.4 (Sensing and Observation Theory). Two hypotheses are put forward: Null hypothesis $H_0$: $r=0$ versus alternative hypothesis $H_a$: $r\\neq 0$. We need to test the null hypothesis versus the alternative one. This can be done by two different but equivalent forms from duality between 'confidence intervals' and 'hypothesis tests'. We can obtain a confidence interval by inverting a hypothesis test, and vice versa. \n", + "\n", + "##### Duality between 'confidence intervals' and 'hypothesis tests' (make your own choice for the next Task)\n", + "In the first scenario, we may make a 'confidence interval' around the estimated $\\hat{r}$ for $r$. This is performed as follows:\n", + "$$\n", + " \\hat{r} - z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}} \\le r \\le \\hat{r} + z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}}\n", + "$$\n", + "where $z_{\\frac{\\alpha}{2}}$ is the critical value obtained from the Standard Normal Distribution in a given significance level $\\alpha$. If the reference value specified as $r=0$ in $H_0$ lies inside the above interval, we can reject the null hypothesis. By a few simple operations, the above confidence internal can be inverted to make a 'hypothesis test' as follows (second scenario): \n", + "$$\n", + "r - z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}} \\le \\hat{r} \\le r + z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}}\n", + "$$\n", + "which accepts the null hypothesis if $\\hat{r}$ lies in the above interval." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "3242fedf52b04a679c449022b7e01570", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 8:</b> \n", + "\n", + "<ol>\n", + " <li> Use $\\hat{X}$ and its covariance matrix $\\Sigma_{\\hat{x}}$ to determine the BLUE estimate for the sea level rate (r) and its precision. What are the values?</li>\n", + " <li>Determine a 99.9% confidence interval for the unknown rate, assuming the original observations are normally distributed (first scenario). You can alternatively also apply/use the second scenario.</li>\n", + " <li>If the null hypothesis states that 'the rate for sea level rise is not statistically significant', do you accept or reject the null hypothesis?</li>\n", + "</ol> \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "35cafb24ca6e4d55b54d534943bf45fb", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 147, + "execution_start": 1696691533000, + "source_hash": null + }, + "outputs": [], + "source": [ + "YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After detrending the observations denoted as $Y$, our next step involves addressing the residuals $\\hat{\\epsilon}$, where $\\hat{\\epsilon} = Y - A\\hat{X}$. By eliminating the linear trend $\\mathrm{Ax}$ from the original time series $Y$, the resulting residuals are expected to exhibit stationarity. While statistical tests are available to determine whether a time series is stationary, we choose for a visual inspection in this context. For a sationary time series $\\hat{\\epsilon}$, we calculate its normalized auto-covariance function (ACF). Various methods can be employed to compute the ACF of a time series using <code>plot_acf</code> from <code>statsmodels</code>.\n", + "\n", + "The temporal correlation within $\\hat{\\epsilon}$ provides insights into the temporal dependencies among residual entries, thereby offering a valuable tool for prediction. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "037151be471a437c9185737253535af7", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 9:</b> \n", + "\n", + "In the context of prediction (although we will not implement prediction in this project), it is essential to identify the type of random process represented by the residuals, which follow an ARMA(p,q) model. For this exercise, we assume an ARMA(3,0)=AR(3) model, implying that we set p=3 and q=0. A Python function is provided (see below), which is designed to estimate the parameters of an AR(p) process ($\\beta_1,...,\\beta_p$). This function will subsequently be used for the AR(3) process by setting $p=3$. It is required to:\n", + "\n", + "<ol>\n", + " <li>Plot the BLUE residulas of the fitted (detrended) model. Is it now a stationary time series (just by visual inspection)?</li>\n", + " <li>Plot the normalized auto-covariance function (ACF) of the residuals $\\hat{\\epsilon}$.</li>\n", + " <li>Check the given function <code>AR_estimation</code>, which estimate the AR(p) parameters and its standard deviation.</li>\n", + " <li>Use the function <code>AR_estimation</code> to estimate the three parameters $\\beta_1$, $\\beta_2$ and $\\beta_3$ of AR(3) of the residuals $\\hat{\\epsilon}$. Please provide the three parameters of the AR(3) along with their standard deviations.</li>\n", + " <li>Explain how you can use the information obtained from the functional model $Y=\\mathrm{Ax}$ and stochastic model AR(3) to predict sea-level for the coming year (you do not need to do the actual prediction).</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "4e0deefc15e2406482d055ab879323c3", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 372, + "execution_start": 1696691533012, + "source_hash": null + }, + "outputs": [], + "source": [ + "def AR_estimation(S, p):\n", + " \"\"\"\n", + " This function computes the AR(p) parameters beta_1,...,beta_p \n", + " for an AR(p) process Y (stationary S: for example epsilon hat).\n", + " \n", + " INPUT:\n", + " S: m x 1 observations (time series)\n", + " p: order of AR\n", + " OUTPUT:\n", + " Beta: Parameters Beta\n", + " S_Beta: Standard deviation of Beta \n", + " Sigma_e: Standard deviation of white noise \n", + " \"\"\"\n", + " m = len(S)\n", + " # make the design matrix\n", + " A = np.zeros((m-p, p))\n", + " for i in range(1, p+1):\n", + " A[:,i-1] = S[p-i:m-i]\n", + "\n", + " # removing the first p data from s\n", + " S = S[p:m]\n", + " m, p = A.shape\n", + "\n", + " # least squares estimate of Beta\n", + " Beta = np.linalg.inv(A.T @ A) @ A.T @ S\n", + "\n", + " # least squares estimate of residuals (white noise)\n", + " Ehat = S - A @ Beta\n", + "\n", + " # estimation of variance of data (white noise)\n", + " Sig2 = (Ehat.T @ Ehat) / (m - p)\n", + "\n", + " # covariance matrix of Beta\n", + " Sigma_Beta = Sig2 * np.linalg.inv(A.T @ A)\n", + "\n", + " # standard deviation of Beta\n", + " std_Beta = np.sqrt(np.diag(Sigma_Beta))\n", + "\n", + " # standard deviation of white noise\n", + " Sigma_e = np.sqrt(Sig2)\n", + " \n", + " return Beta, std_Beta, Sigma_e" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "4e7ceb0a961a4332ad792a6bb8e1780c", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 1195, + "execution_start": 1696691533016, + "source_hash": null + }, + "outputs": [], + "source": [ + "YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "deepnote": {}, + "deepnote_execution_queue": [], + "deepnote_notebook_id": "aa9b740477e34ea98fd2031f67fc974e", + "deepnote_persisted_session": { + "createdAt": "2023-10-07T15:30:07.197Z" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_4/Analysis_solution.ipynb b/content/GA_2_4/Analysis_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..67fd9b897abb1021c37c5471fc4f42ab952d18fe --- /dev/null +++ b/content/GA_2_4/Analysis_solution.ipynb @@ -0,0 +1,1654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Project 8: Global Mean Sea Level\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 25px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Friday December 8, 2023.*" + ] + }, + { + "attachments": { + "fig_sea_level_rise.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": { + "cell_id": "82b7494301c646b2858f959260412e27", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Overview\n", + "\n", + "In this project we will use time series analysis techniques to analyze real data on _global_ sea level (i.e., the data represents the average value over the entire globe). In particular, we will take into account seasonal effects of the time series in our analysis. Specifically, the tasks are:\n", + "\n", + "- Tasks 1-2: as a warm up you will create and analyze some elementary noise + signals using AR(1)\n", + "- Tasks 3-5: explore sea level data, then reconstruct it using a linear trend and seasonality evaluated with a simple monthly average\n", + "- Task 6: evaluate frequency components of seasonality using least-squares harmonic estimation (LS-HE)\n", + "- Tasks 7-8: using the results from Task 6, apply BLUE and evaluate annualy rate of sea level rise\n", + "- Task 9: apply AR(3) and consider how to make a prediction (no prediction is actually made)\n", + "\n", + "**Note that a only a subset of your answers here are required to be included in the `Report.md` file.**\n", + "\n", + "## Global Mean Sea Level (GMSL) measurements\n", + "\n", + "_The following text is provided to explain the GMSL data._\n", + "\n", + "The Earth's temperature is rising due to the accumulation of greenhouse gases in the atmosphere, resulting in two inter-related consequences regarding sea-level rise. The direct consequence is the accelerated melting of polar ice sheets and glaciers. This leads to an expansion of seawater, contributing to the overall rise in sea levels. Additionally, as an indirect effect, the warming of oceans causes seawater to thermally expand, further worsening the rise in sea levels. The impact of sea-level rise is already evident in various parts of the world, presenting significant challenges to coastal communities and ecosystems. For example, coastal cities are particularly vulnerable due to their concentrated infrastructure and populations near shorelines (see below).\n", + "\n", + "\n", + "\n", + "Satellite altimetry (SA) provides a powerful tool to monitor the global sea-level changes. It employs specific satellite missions equipped with altimeters to measure the distance between the satellite and the sea surface. This measurement relies on the travel time of signals, the time taken for a radar or laser pulse emitted from the satellite to reach the sea surface and then return to the satellite. In this context, we aim to analyze a specific dataset of SA information.\n", + "\n", + "The data are the Global Mean Sea Level (GMSL) measurements made since 1993 until mid 2020 by satellite altimetry. It consists of a few satellite missions such as TOPEX/Poseidon (launched August, 1992), Jason-1 (launched December, 2001), Jason-2 (launched June, 2008) and Jason-3 (launched January 2016). The unit is in mm, and it shows a steady increase in GMSL of around 3.5 ± 0.4 mm/year over the above period. The details, along with the data, can be found [here](\n", + "https://www.cmar.csiro.au/sealevel/sl_hist_last_decades.html).\n", + "\n", + "The goal of this project is to apply the Time Series Analysis theories presented in Week 2.4 to this time series. We just only use here data from 1993 Jan. to 2019 Dec. (so exactly 27 years).\n", + "\n", + "In particular we are interested to identify the components of time series, check the stationarity of the time series, make statistical judgment whether the estimated trend in sea-level is significant (you may know that there are argues about climate change, sea level rise, and global warming), identify the appropriate functional model ($Y=\\mathrm{Ax}+\\epsilon$) and estimate the stochastic model parameters of the ARMA process. \n", + "\n", + "Most of the exercises in this notebook consist of both coding and answering (open) questions. Typically, as you work your way through the exercises, you can often re-use code, or part of it, from earlier exercises.\n", + "\n", + "File needed for the exercise: `CSIRO_Alt_seas_inc.txt`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as signal\n", + "from statsmodels.graphics.tsaplots import plot_acf \n", + "from scipy.stats import norm\n", + "from scipy.stats.distributions import chi2\n", + "\n", + "\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interact" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "711425f304ea4230a43e722a165e6aba", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Tasks 1-2: Simulated data with AR(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "24e525319d2d49bcaf27de4aaa59d220", + "deepnote_cell_type": "markdown" + }, + "source": [ + "We intend to simulate (as a time series) 1000 samples at 1-second intervals (so $m=1000$), using a first-order auto-regressive AR(1) random process $s(t)$ as follows:\n", + "\n", + "$$\n", + "S(t)\n", + "= \\beta S(t-1)+e(t)\n", + "$$\n", + "\n", + "with $t = 1, …, 1000$ and where $\\beta=0.9$ is the given AR(1) parameter, and we further assume\n", + "\n", + "$$\n", + "\\mathbb{E}(S(t))=0 \\text{,} \\hspace{2mm} \\mathbb{D}(S(t))=\\sigma^2=1.\n", + "$$ \n", + "\n", + "Please note that, as a convention, for Time Series Analysis and Observation Theory $m$ represents the number of samples, which is represented as $N$ for the Signal Processing, so $m=N$.\n", + "\n", + "You may simulate the data using a normal distribution. To do so, you will use the above recursive form, which needs initialization. To initialize the first data, you can use <code>S[0] = np.random.normal(...)</code> using the normal distribution. To use the above recursive formula you need to simulate $e(t)$, requiring to have its standard deviation $\\sigma_{e}$ of the white noise process. It is given from the following equation:\n", + "\n", + "$$\n", + "\\sigma_{e} = \\sigma \\sqrt{(1-\\beta^2)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "48e8df4114544ca4914a15c77372ab30", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1:</b> \n", + "\n", + "Create a synthetic time series and evaluate it by completing the following steps:\n", + "<ol>\n", + " <li>Simulate and plot (versus time) the AR(1) time series based on the above-specified values. By the visual inspection, explain the time-correlated pattern of the simulated time series (i.e. describe what does it show, how does it look like; for example compared to white noise).</li>\n", + " <li>Plot the normalized Auto-Covariance Function (ACF) of the generated time series. \n", + " <li>Plot the power spectral density (PSD)/periodogram of the generated time series. Repeat the simulation several times to see if you can observe a particular pattern in the PSD. Explain your observations.</li>\n", + " <li>Take $\\beta = 0$ to simulate a white noise process and repeat steps 1-3 (do not need to provide any 'plot' here). Compare the results with the case $\\beta = 0.9$. In which case can you observe a flat PSD? \n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "cell_id": "7047abf2b93f427a86e5ea387783d174", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 2334, + "execution_start": 1696691527706, + "source_hash": null, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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i4uLi9Ea7jh07Fk8//TQ+/PBDPP/88/j++++xY8cO7N271+HXZyA6Gg9atMKyEVNQQ52OLt1awWNkDIM6IiIiMsqlpjvZvXs3OnToYJA+bNgwxMfHIzo6GpcuXcLu3bt1+/bs2YPx48fj1KlTCAwMxOTJkzFq1Ci947/77ju8/fbbuHDhAurUqYOZM2eid+/eFpdLo9FApVJBrVbbrin2H1k5eWj4zs8AgD+nd0Y5T5eKxYmIiOghWRNnuFRgV1YxsCMiIiJ7sSbOkHUfOyIiIqJHCQM7IiIiIplgYEdEREQkEwzsiIiIiGSCgR0RERGRTDCwIyIiIpIJBnZEREREMsHAjoiIiEgmGNgRERERyQQDOyIiIiKZYGBHREREJBMM7IiIiIhkgoEdERERkUwwsCMiIiKSCXdnF4BKITkZWLoUuHQJCAkBhg8HQkOdXSoiIiJyMgZ2rmbZMuDllwFJAoQo+Hf2bCAuDoiOdnbpiIiIyInYFOtCpOTkgqBOqwXy8/X/HTECOHfO2UUkIiIiJ2Jg50Lcl8cX1NAZI0kFtXZERET0yGJg50Kky5cLml+NEaKgzx0RERE9shjYuRARHGy+xi4kxKHlISIiorKFgZ0LyRsWDQgBo3V2QhT0syMiIqJHFgM7FyJCQwv60SkUyJMUyJcUEG5ugEJRkF63rrOLSERERE7E6U5cTXQ0HrRohWUjpqCGOh1durWCx8gYBnVERETEwM4Vibp1MbtdNADgz+md4eHJ20hERERsiiUiIiKSDQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZIKBHREREZFMMLAjIiIikgkGdkREREQywcCOiIiISCYY2BERERHJBFePl4vkZGDpUuDSJSAkBBg+HAgNdXapiIiIyIFcrsZu0aJFqFWrFry8vBAREYHffvvNZN7o6GhIkmSwPf7447o88fHxRvM8ePDAEZdjG8uWAWFhwEcfAd98U/BvWBgQH+/skhEREZEDuVRgt27dOowbNw5vvfUWjh8/jrZt26JLly5ISUkxmn/BggVITU3VbVeuXEGVKlXQt29fvXw+Pj56+VJTU+Hl5eWIS3poUnIy8PLLgFYL5Ofr/ztiBHDunLOLSERERA7iUoHd3LlzMWLECLz88sto0KAB5s+fj6CgIHz++edG86tUKvj7++u2I0eO4M6dO3jppZf08kmSpJfP39/fEZdjE+7L4wFJMr5TkoC4OIeWh4iIiJzHZQK7nJwcHD16FFFRUXrpUVFR2L9/v0XniIuLwzPPPIPg4GC99MzMTAQHB6NGjRro1q0bjh8/brNy25t0+TIghPGdQhT0uSMiIqJHgssMnrh58yby8/Ph5+enl+7n54e0tLQSj09NTcVPP/2E1atX66WHhYUhPj4ejRo1gkajwYIFC9CmTRucOHECoSYGH2RnZyM7O1v3WqPRlOKKbEMEB5uvsQsJcWh5iIiIyHlcpsaukFQsiBFCGKQZEx8fj0qVKqFnz5566a1atcLgwYPRpEkTtG3bFt988w3q1auHhQsXmjzXrFmzoFKpdFtQUFCprsUW8oZFA0LAaJ2dEAX97IiIiOiR4DKBna+vL9zc3Axq59LT0w1q8YoTQmDp0qUYMmQIPD09zeZVKBR48sknkZycbDJPbGws1Gq1brty5YrlF2JjIjS0oB+dQoE8SYF8SQHh5gYoFAXpdes6rWxERETkWC4T2Hl6eiIiIgIJCQl66QkJCYiMjDR77J49e3Du3DmMsKD2SgiBpKQkBAQEmMyjVCrh4+OjtzlVdDQenDyFxS17Y0vYU8ib8AZw9iwQHe3cchEREZFDuUwfOwCYMGEChgwZgubNm6N169ZYvHgxUlJSMGrUKAAFNWnXrl3DihUr9I6Li4tDy5YtER4ebnDOadOmoVWrVggNDYVGo8Enn3yCpKQkfPbZZw65JlsRdetidrtoAMCf0zvDw9Olbi0RERHZgEv99e/fvz9u3bqF6dOnIzU1FeHh4di6datulGtqaqrBnHZqtRrr16/HggULjJ4zIyMDI0eORFpaGlQqFZo2bYpff/0VLVq0sPv1EBEREdmSSwV2ADB69GiMHj3a6L54IystqFQqZGVlmTzfvHnzMG/ePFsVj4iIiMhpXKaPHRERERGZx8COiIiISCYY2BERERHJBAM7IiIiIplgYEdEREQkEwzsiIiIiGSCgR0RERGRTDCwIyIiIpIJl5ugmKyQnAwsXQpcugSEhADDhwOhoc4uFREREdkJAzu5WrYMePllQJIAIQr+nT0biIsDoqOdXToiIiKyAzbFypCUnFwQ1Gm1QH6+/r8jRgDnzjm7iERERGQHDOxkyH15fEENnTGSVFBrR0RERLLDwE6GpMuXC5pfjRGioM8dERERyQ4DOxkSwcHma+xCQhxaHiIiInIMBnYylDcsGhACRuvshCjoZ0dERESyw8BOhkRoaEE/OoUCeZIC+ZICws0NUCgK0uvWdXYRiYiIyA443YlcRUfjQYtWWDZiCmqo09GlWyt4jIxhUEdERCRjDOxkTNSti9ntogEAf07vDA9P3m4iIiI5Y1MsERERkUwwsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTnNjsUZOcDCxdCly6VLBm7PDhQGios0tFRERENsDA7lGybBnw8suAJBWsGStJwOzZBcuMRUc7u3RERET0kNgU+4iQkpMLgjqtFsjP1/93xAjg3DlnF5GIiIgeEgO7R4T78viCGjpjJKmg1o6IiIhcGgO7R4R0+XJB86sxQhT0uSMiIiKXxsDuESGCg83X2IWEOLQ8REREZHsM7B4RecOiASFgtM5OiIJ+dkREROTSGNg9IkRoaEE/OoUCeZIC+ZICws0NUCgK0uvWdXYRiYiI6CFxupNHSXQ0HrRohWUjpqCGOh1durWCx8gYBnVEREQywcDuESPq1sXsdtEAgD+nd4aHJ38EiIiI5IJNsUREREQywcCOiIiISCZcLrBbtGgRatWqBS8vL0REROC3334zmXf37t2QJMlgO3PmjF6+9evXo2HDhlAqlWjYsCE2btxo78soW5KTgdhY4MUXC/5NTnZ2iYiIiKgUXCqwW7duHcaNG4e33noLx48fR9u2bdGlSxekpKSYPe7s2bNITU3VbaFFFr1PTExE//79MWTIEJw4cQJDhgxBv379cPDgQXtfTtmwbBkQFgZ89BHwzTcF/4aFAfHxzi4ZERERWcmlAru5c+dixIgRePnll9GgQQPMnz8fQUFB+Pzzz80eV61aNfj7++s2Nzc33b758+ejU6dOiI2NRVhYGGJjY9GxY0fMnz/fzlfjfFw/loiISF5cJrDLycnB0aNHERUVpZceFRWF/fv3mz22adOmCAgIQMeOHbFr1y69fYmJiQbn7Ny5c4nnlAOuH0tERCQvLjPXxc2bN5Gfnw8/Pz+9dD8/P6SlpRk9JiAgAIsXL0ZERASys7Px9ddfo2PHjti9ezeefvppAEBaWppV5wSA7OxsZGdn615rNJrSXpZTcf1YIiIieXGZwK6QVKyGSQhhkFaofv36qF+/vu5169atceXKFXz88ce6wM7acwLArFmzMG3atNIUv0zh+rFERETy4jJNsb6+vnBzczOoSUtPTzeocTOnVatWSC4y6tPf39/qc8bGxkKtVuu2K1euWPz+ZQnXjyUiIpIXlwnsPD09ERERgYSEBL30hIQEREZGWnye48ePIyAgQPe6devWBufcvn272XMqlUr4+Pjoba6I68cSERHJi0s1xU6YMAFDhgxB8+bN0bp1ayxevBgpKSkYNWoUgIKatGvXrmHFihUACka8hoSE4PHHH0dOTg5WrlyJ9evXY/369bpzjh07Fk8//TQ+/PBDPP/88/j++++xY8cO7N271ynX6HBcP5aIiEg2XCqw69+/P27duoXp06cjNTUV4eHh2Lp1K4KDgwEAqampenPa5eTkYOLEibh27Rq8vb3x+OOPY8uWLXjuued0eSIjI7F27Vq8/fbbmDJlCurUqYN169ahZcuWDr8+Z+H6sURERPLgcn/BR48ejdGjRxvdF19sUt1JkyZh0qRJJZ6zT58+6NOnjy2KJx/JycDSpQUjY0NCgOHDgSITOxMREVHZ43KBHTnAsmUFExdLUsEgCkkCZs8u6HcXHe3s0hEREZEJLjN4ghyDq1EQERG5LgZ2pIerURAREbkuBnakh6tREBERuS4GdqSHq1EQERG5LgZ2pIerURAREbkuBnakh6tREBERuS5Od0KGuBoFERGRS2JgR0ZxNQoiIiLXw6ZYIiIiIplgNQxZhkuMERERlXkM7KhEbsvjgVGvcIkxIiKiMo5NsWRWyO1r8Bz1CpcYIyIicgEM7MisficTuMQYERGRi2BgR2bVUKdziTEiIiIXwcCOzLqqqsYlxoiIiFwEAzsy65tGnbjEGBERkYtgYEdmXapSHTlfLuYSY0RERC6A051QifKHDsODyDZcYoyIiKiMY2BHFuESY0RERGUfm2KJiIiIZILVLlR6XGaMiIioTGFgR6WzbBnw8stcZoyIiKgMYVMsWU1KTi4I6rjMGBERUZnCwI6s5r48nsuMERERlUEM7Mhq0uXLXGaMiIioDGJgR1YTwcFcZoyIiKgMYmBHVssbFs1lxoiIiMogBnZkNREaWtCPjsuMERERlSmc7oRKJzoaD1q04jJjREREZQgDOyo1LjNGRERUtrAploiIiEgmGNgRERERyQTbzsi2uH4sERGR0zCwI9vh+rFEREROxaZYsgmuH0tEROR8DOzIJrh+LBERkfNZHNi98847yMrK0r2+c+eOXQpEronrxxIRETmfxYHdzJkzkZmZqXsdHByMCxcu2KVQ5ixatAi1atWCl5cXIiIi8Ntvv5nMu2HDBnTq1AmPPfYYfHx80Lp1a/z88896eeLj4yFJksH24MEDe1+KrHD9WCIiIuezOLATxWpjir92hHXr1mHcuHF46623cPz4cbRt2xZdunRBSkqK0fy//vorOnXqhK1bt+Lo0aPo0KEDunfvjuPHj+vl8/HxQWpqqt7m5eXliEuSDa4fS0RE5HwuNSp27ty5GDFiBF5++WUAwPz58/Hzzz/j888/x6xZswzyz58/X+/1+++/j++//x4//vgjmjZtqkuXJAn+/v52Lbvc6daPHTECeQKQACgUEiQhuH4sERGRg1hcYydJEu7evQuNRgO1Wg1JkpCZmQmNRqO32UtOTg6OHj2KqKgovfSoqCjs37/fonNotVrcvXsXVapU0UvPzMxEcHAwatSogW7duhnU6BWXnZ3tsOt2KdHReHDyFBa37I0tYU8hb8IbwNmznOqEiIjIQSyusRNCoF69enqvi9Z6CSEgSRLy8/NtW8J/3Lx5E/n5+fDz89NL9/PzQ1pamkXnmDNnDu7du4d+/frp0sLCwhAfH49GjRpBo9FgwYIFaNOmDU6cOIFQExPrzpo1C9OmTSv9xciY0fVjOWkxERGRQ1gc2O3atcue5bCYVKyDfmFAWZI1a9Zg6tSp+P7771GtWjVdeqtWrdCqVSvd6zZt2qBZs2ZYuHAhPvnkE6Pnio2NxYQJE3SvNRoNgoKCrL2URwMnLSYiInIYiwO7du3a2bMcJfL19YWbm5tB7Vx6erpBLV5x69atw4gRI/Dtt9/imWeeMZtXoVDgySefRHJyssk8SqUSSqXS8sI/ovQmLS5uxAjgqafY946IiMiGLO5jd/36dUycONFofzK1Wo3//ve/uHHjhk0LV5SnpyciIiKQkJCgl56QkIDIyEiTx61ZswbR0dFYvXo1unbtWuL7CCGQlJSEgICAhy7zo46TFhMRETmWxYHd3LlzodFo4OPjY7BPpVLh7t27mDt3rk0LV9yECRPw1VdfYenSpTh9+jTGjx+PlJQUjBo1CkBBE+nQoUN1+desWYOhQ4dizpw5aNWqFdLS0pCWlga1Wq3LM23aNPz888+4cOECkpKSMGLECCQlJenOSaXHSYuJiIgcy+LAbtu2bXpBU3FDhw7F5s2bbVIoU/r374/58+dj+vTpeOKJJ/Drr79i69atCA4OBgCkpqbqzWn35ZdfIi8vD//3f/+HgIAA3TZ27FhdnoyMDIwcORINGjRAVFQUrl27hl9//RUtWrSw67U8CjhpMRERkWNZ3Mfu4sWLqFmzpsn9NWrUwCUH1MCMHj0ao0ePNrovPj5e7/Xu3btLPN+8efMwb948G5SMissbFg2POR9DoGBeOz2ctJiIiMjmLK6x8/b2Nhu4Xbp0Cd7e3rYoE8mEbtJihQJ5kgL5kgLCzQ1QKDhpMRERkR1YHNi1bNkSX3/9tcn9K1asYPMlGTI1aXGbNkBsLPDiiwX/mhmFTERERJaxuCl24sSJ6NSpE1QqFf773//qphi5ceMGZs+ejfj4eGzfvt1uBSXXZTBp8aqvObcdERGRHVgc2HXo0AGfffYZxo4di3nz5sHHxweSJEGtVsPDwwMLFy7Ef/7zH3uWlWSAc9sRERHZj8WBHQC88sor6NatG7755hucO3dOt8xYnz59UKNGDXuVkWTEorntZs1yaJmIiIjkwqrADgCqV6+O8ePHG6SfPHkScXFxmD9/vi3KRTLFue2IiIjsx+LBE8ZoNBp8+eWXaNGiBZo0aWLR9CL0aOPcdkRERPZTqsBuz549GDp0KAICAjB69Gj85z//wV9//YWkpCQbF4/kJm9YNCAEjNbZcW47IiKih2JxU2xqaiqWLVuGpUuX4t69e3jxxRexZ88etG7dGkOHDkVddngnC+jmthsxAnmiYOJihUKCJATntiOisiM5GVi6tKB7SEgIMHw4EBpqkE0IASEAUfh/ANp/0gr2AwKGeQr34Z/9utcFSbpzF31dNE9BuoluLY4mTL/895pKuEZh3TGiWF5AGMln/DwG/zfyOZrqMWSKbwUl/FVe1h1kJxYHdrVq1ULfvn3x2WefoVOnTlAoHqoVlx5l0dF40KIVlo2YghrqdHTp1goeI2OgrV0H2nwttEUehID+gxEo/tD495WtH3Emf7GNpBd9MOg/MIqez3hZS3xQl3Bh9nq0W/Jgs/QPi7UPSXtxRjGEhRdv68/IlvevrLDsmgrz6v++GQsACp817ufPo9Lar+FxJQU5NWoiv6IP/GbPMJiS6drEt+B2VwPPK1eQXSMI6X0G4n5IHRteIbmqil5WD1mwG4tLEhwcjL1796JmzZoIDg5GWFiYPctFLkirFXiQm697na7JhrtbDu7ez9OlnU7VwNPNDfe8HtPNbbf8pSfhKbnBa9dhVPt2NZTXUpBdvSbS+w7EAz40iciOHvtuNer8b9y/QRwAaLUGyyAKANVnzwAUbgWvJAkBixci5Y234K7R8LlFZYbFgd3Zs2exb98+xMXF4cknn0S9evUwePBgAIBkqjM8ydK97Dxk5eTjdmaOLu14yh0oJIVeYHfx5j14ebjppWnu58HLQz8A1AojD1dJQuCShTg/awH+fuFFh1wXET1avC6dR53/jYNUbF5NYxWDur9y2ny9fDU/0g/2+NwiZ7Oq7rBNmzZo06YNPvnkE6xZswZLly5Ffn4+Ro8ejYEDB6Jnz5547LHH7FVWcpIb6mxk5vxb6/bHNY1BwJaTJ+DlUbrze1+6YPLhWid2LO5GtMSDkNqlOzkRURFel87rWgaUV68ABnVzxlKMMxXs1Zn8Gqpu+xFZ9RqwBo8crlQd5SpUqICYmBgkJibi1KlTiIiIwNtvv43AwEBbl48c7F52Hq5l3MeZ1Lu6tEu37uHm3Wy7vWfAhtVGp0CRAECSUO3bVXZ7byJ6dDz23Wo8EdUagV99iqpbv0eFE0f1gjJbKHySVdqdgMCvPsUTUa3x2Po1Nn0PInMeegREgwYN8PHHH+PatWtYt26dLcpEDiSEgDorV/f6j2sapNzKgvp+rpmjbMvr2hWzkxYrr6U4rCxEJE9Fm12l/PyCf82Mxii+x5phJhIASQhI+fmAVos6sWPhdelCaYpNZDWbDeNwd3dH7969bXU6coDLN7OQlZsHTZHBDc7woHqQ2UmLs6vXdGyBiEh2qn1rumVAQL/5tTCIy5MUBVMySQC0WqP5Smq2LTx/3YmjkV0jiAMsyO7KzvhccoiiNXFpmgfw8nBzYmkKpPYeiJpxnxl/uAqB9L6DnFMwInJpRfvTlTt9yux8KVoAQlJAoSgYwHV+/P+wfd8Z1FCno8lTTYBKlVFzznv/zr9pItgzfnItKpw4igq/H+MAC7I7BnaPiMzsPFy6mYUbmgfOLoqB+yG1cX7WAtSJHas3aTGEwPlZCzhwgoisZjDSvnAzRqFAkn8orqr80OSpJrjdfwgyqgdjtvthAMCy6Cfh5eGG1I7P4djUj40GewpRMEWKsSBPAvTenwPDyJ4Y2D0iTv0zkrWs+vuFF3GzyZN6D83b/YfwoUdEVjM3jYmpZtfx3d7A5cqBuiAOuYaDKu4H19LNv1k82Kv/92X858IRCCFKbK5l8yzZEwM7GdM8cNwACFsw9tAs2pTChx8RWaKk/nRaSBCSpGsZOPPePFxWl25Wh6LPrZ9U5xH29vh/Wx5gppaQzbNkJxYFdtYMitiwYUOpC0O2c0PzQG/KElfESYuJqDSU11JMBlRCknC+Sg2crlZLr9kV8Ycf+n3Teg2AullLXctDO8978DmZZHRKFaPNs2++jgrHj8A9U8MvslRqFgV2KpVK938hBDZu3AiVSoXmzZsDAI4ePYqMjAyOii0jLt68B839vDKzPmdpcNJiIiqt7Oo1TY60lxQKJIS2xOx20WabXUuraA3e2vZV0bJrG4tG00oo+Pvqt25FQdn5RZZKyaLAbtmyZbr/T548Gf369cMXX3wBN7eCPluFq0/4+PjYp5RklXRNdpnuT2cJc5MWi38mLU757xTHF4yIyqSi3TbyKlQEhDA50n5d4yiHlMnowDAzzbMcZEG2YHUfu6VLl2Lv3r26oA4A3NzcMGHCBERGRuKjjz6yaQHJMjl52pIzuRBOWkxEljLWbaPw3zxIeiPtH6Y/XWkUHxhmrnm2OA6yoNKwOrDLy8vD6dOnUb9+fb3006dPQ6uVV3DhKrLz8nEmzbX70xVndtJiAMqrVxA6LoYPOqJHnLkRsJAkrG0SBZ/sLJv3p7NGaZtnAXCQBVnN6sDupZdewvDhw3Hu3Dm0atUKAHDgwAF88MEHeOmll2xeQDLvQW4+Lt/Kwv0c26536GxmJy3WalHh9+N80BGR+RGwCgU0XhXwducxdulPVxpsniV7szqw+/jjj+Hv74958+YhNTUVABAQEIBJkybhjTfesHkBybzTqXehMFOz5aqMPvz+meldAvSaMfigI3p0mR0BKwRqqNMdXKKSFW+ejWgUjMDvVkEUPt/+YXKQBfsZkxlWB3YKhQKTJk3CpEmToNFoAICDJpwoJ0/r8gMlTLG0bwofdESPlqIDJZRXr8DUol6SJOGqqppjC2eh4vN23o9oYXEtHvsZkzmlmqA4Ly8Pu3fvxvnz5zFw4EAAwPXr1+Hj44MKFSrYtIBk6F52nrOL4DBFH34HT34Fn5PHjWfkg47okWAwUAIwumaro0fAPiyrBllIUsGULkRGWB3YXb58Gc8++yxSUlKQnZ2NTp06oWLFipg9ezYePHiAL774wh7lpH/IcaCEpcwOqBAC5U6fQs2PZnAwBZFMmR0oASBPUjh1BOzDsnSQBYRAet9Bji8guQSFtQeMHTsWzZs3x507d+Dt7a1L79WrF3bu3GnTwpGh3HyBvHwXnnn4IaT2Hqibm6qowged98VzCPzqUzwR1RqPrV/jhBISkT2ZGygBhQK/B4RiS9hTSBk+GknbDyCt1wCHl9FWCvsZQ6FAnqRAvqSAcHMDFAqkvPEWqn27CqHjYlDzoxnwunTe2cWlMsTqGru9e/di37598PT01EsPDg7GtWvXbFYwouIMBlQIAQWKLLj9z7d4DqYgkpfCPnW+P67X/Z4XJwBcVfnh9R6TyswI2IdVvHm2yVNNAFUl1Jwzk0stkklWB3ZarRb5+Ya/LFevXkXFihVtUigiU4o+6DolH0Td21cNOhhzMAWRcxUd3PCwc03q9akz8renUFkeKPEwjDXPcqlFMsfqwK5Tp06YP38+Fi9eDKDglykzMxPvvvsunnvuOZsXkKi4wgddDXU66t65ZnzkGAdTENmcsYANgF5aXsWKqDn3fZvUKJnrU+fKAyVKi0stkiWsDuzmzZuHDh06oGHDhnjw4AEGDhyI5ORk+Pr6Ys0a9msix7mqqgYhScYnOuCosUeWLWuL6F/Glu0KXPxJwU6FQm+EavHfSQGgzpuvo8LxI3DP1Ji9L5ZMZVK41JYAILm5ueRAidIwu9SiVovKO37izz1ZH9gFBgYiKSkJa9aswbFjx6DVajFixAgMGjRIbzAFkb1906gTXj20weTqFG7qDC47JnPFgzhTtUUpb7wFd43GbE0Tf0ZMK7HmLF9/wvDiJBRMFuy3bkXBvTFxXwzuX5EVF4oTAK77PIb8/gOctlSYo5U0M4D3hWR4XzynC7oz2j2D/AoV+PP9iCnVPHbe3t4YPnw4hg8fbuvyEFnsUpXqOPPePIS9Pf7fST0Vkq5ztd+3K9m5WMZMzWdmrLao5kczAIVbwStjNU38Q2iW2dGoFqTp0ostjaV3XwCT988YraTADw3bocGEt2UxUMIS5pZaNPb5VtqdUPAzbuYLDn/G5cfq6U7c3NzQoUMH3L59Wy/9xo0bcHOz/woIixYtQq1ateDl5YWIiAj89ttvZvPv2bMHERER8PLyQu3atY3Os7d+/Xo0bNgQSqUSDRs2xMaNG+1VfLKxtF4DcHDrfixu2Rtbwp7C9RcGApIESQhI+fmQtFpI+fmAVos6sWPhdemCs4tMpeR16TxqfjQDoeNiUOvtCboaJN19NjFaUirctEV+HoQw/BkRApV2J6Dq1u85bc4/Cj9zc6NRS8vgvpi5f8amOJIg/z51xRmdAsVEDd6/n++/z8CaH81A4JJP+TNuA0WfRzU/mgH38+ecXSQdq2vshBDIzs5G8+bN8cMPPyA8PFxvnz2tW7cO48aNw6JFi9CmTRt8+eWX6NKlC/7880/UrGnYn+rixYt47rnnEBMTg5UrV2Lfvn0YPXo0HnvsMbzwwgsAgMTERPTv3x8zZsxAr169sHHjRvTr1w979+5Fy5Yt7Xo9ZBtFR43tuf0zOxfLkEHtnIkmOktXTTZb0/SITptjtlnbzGhUY0zWKJXAXB4tACEpdJMPT372dVyuLO8+dcYUnwKlY04qyl08X2Lg/e/Pd7F1ti3s+0j/MtbfFEsWAnFxQHS0s4tnfWAnSRLWr1+PDz74AJGRkfj666/x/PPP6/bZ09y5czFixAi8/PLLAID58+fj559/xueff45Zs2YZ5P/iiy9Qs2ZNzJ8/HwDQoEEDHDlyBB9//LEusJs/fz46deqE2NhYAEBsbCz27NmD+fPnczCIC2LnYvkpabUBe3mUvgxY06xt0J/VRJpuFQhza55aSqFAkn8orqr80OSpJkh9YRC+23Xz4c7pwop/ma25dFGpzmOq7yO7rZhm9nk0YgTw1FNA3bpOKVuhUtXYubm5YcGCBXj88cfRv39/vP3227pgy15ycnJw9OhRvPnmm3rpUVFR2L9/v9FjEhMTERWlX1XfuXNnxMXFITc3Fx4eHkhMTMT48eMN8hQGg1a5dw+wdXN0Th68cx78e34BKLLu6dIUWfeg8HCDIjffrmkAHP6epSlbdjV/yzsXL1mIC9Nm4+bzfUt5c8helJcvotqmb+B57SqU168YzWPqa2Rpa4uM0mpRefsWKC9fRE71Gkjv2Q/ZwbVKe7YySXn5osWBs8Fo1H+OyYOkt5TXhf+biJ0HklFdk46mjwchYOM3EEJr0X0xFTzGdh6DlEoB+GJwMwCAd85VAGX7eeSI97zxXE+j/e4sZaxvXp03X0fFwwfglnlXtj/3peW3Zrnp/qaSBHzxBTBtmu3f+N49y/MKK0mSJG7cuKF7vWvXLlG1alXxzDPPCIVCYe3pLHbt2jUBQOzbt08vfebMmaJevXpGjwkNDRUzZ87US9u3b58AIK5fvy6EEMLDw0OsWrVKL8+qVauEp6enybI8ePBAqNVq3XblyhUBQKj1G4m4cePGjRs3btweelOj4DuVWq0uMV6yevBEcHCw3iCJ9u3b48CBA7h69aq1pyqV4s29QgizTcDG8hdPt/acs2bNgkql0m1BQUEWl5+IiIjIXqxuir148aJBWt26dXH8+HHcuHHDJoUyxtfXF25ubkhLS9NLT09Ph5+fn9Fj/P39jeZ3d3dH1apVzeYxdU6goB/ehAkTdK81Gk1BcHf9OuDjY9V1WSszOw+nrqnt+h5yoEy5hGob18Hz2lWU++tPeF+8AEkYdi4WkoTzlQNx9rEQNGrdCLdfGAB1YE2MWnkMAPDF4Gbw8nDDg9z8EtMAlOo4W6fV2LIetadORr4oGDkooWBdXe0/X1YkhULX7JYPqSCPQgFJCJyd8gF6aWrbrWzeKReR9P6nqK5JR6PWjQCVCkELP9KVVQEAQljeRCdJSH9h4L9NRr36I7tmiNmfh8J8AHRpbvcyUWnvbkChPzjDaDnc3LCkWQ/Me2qQXT4je/8cFf/5sPozVyhw4oddRj9naxm7L7Y4Lxl+tnkVfRD0yWyDAUiWNt/a8uceKBvPypLKtvzpynjyhU4muykYdC1QKIDjx4E6dui7rdEAgZYNFirVPHbGeHl5ITg42FanM+Dp6YmIiAgkJCSgV69euvSEhATd4I3iWrdujR9//FEvbfv27WjevDk8PDx0eRISEvT62W3fvh2RkZEmy6JUKqFUKg13lC9fsNmTex605fLs+x4ycD/scVyOnQ6gYK4s768+BYwN7BMCtW9fR6071yGdO4CgFYtx5r15uO9Z8IupLVceWg83aHPzcd/Ty2wagBLz2CNNmZyM1xLXFiyxdi0Ygd+tgqTVGvxyu/1TW438fN0fbHfop9Wf8SaqvfwFLlcOtHl5q237AWFTJqBZYcCZnFgwFQP0H0QCpol/OngX/nGytJN30Z+HooqmeV26gGrfroLyWgrKnT5V0BfTyEhDodWiw4Uj8M+8hZDMJrjdfzCyqodYfa9MHQvY7+dImX4DtadONvj5EDARxAEQReb/K/zM74c9XuJnbglT94UenrHP9la33rqf8bwKFeH3zUoIrYV9H4WAf+Yt3Pf0euifS8A5z0pry+a3dZPpWRZg+DxCXBzQuHFJt6Z0rBiZblFgV6VKFfz111/w9fVF5cqVzTZTFp/fzpYmTJiAIUOGoHnz5mjdujUWL16MlJQUjBo1CkBBTdq1a9ewYsUKAMCoUaPw6aefYsKECYiJiUFiYiLi4uL0RruOHTsWTz/9ND788EM8//zz+P7777Fjxw7s3bvXbtdBjpXedyAClyw02alegih48U9wE/b2eAT/E9yUNd6XLmDSnnjUUKej9u0mgEqFmnPfR/PC2pczvxU8YEpgaroPAWDe5jm4qvJD7dsFgYeUq9V7z9v9B+NB9RCryh1y+xrCPpqgH1AIYbKDvlEKBTIbN0N2jaB/RjUPsuk0JA9CautGv+q+DBgrnxCoc+sqat++CumvfagZ9xnOvDcPgPlv6YUjTwvvVeGx58fFYtL+s7rP907Ltpi055uH+ryLKvoz02DbPZhdoqvYH6qUN96Gu0ZdZCS5bT9zcqyiP+MAkNn0SdSJHVtw34v26DJCAlBDfQOf/DDbJj+XZVXR3xffnFTTz1NJwv3aochq8HjB8pUjhqP6k3YK6qxkUWA3b948VKxYEQBKN1rURvr3749bt25h+vTpSE1NRXh4OLZu3aqrKUxNTUVKyr8Lv9eqVQtbt27F+PHj8dlnnyEwMBCffPKJbqoTAIiMjMTatWvx9ttvY8qUKahTpw7WrVvHOexk5EFIHZyftcCiB1jhFBf9f9+um06grDAIDM7utbrGq0RaLZ64fhZNUv8qCDz+CW6aFzbZmghGTAWAhWmdkg+a/LyNMVV7dO7jRQ4JLKz+MvDWOLzXpDN8srOMfh71LhqpSf3n2DpzZ2KkpCj4fM/8hppLFqJRYbO5mc/bkj+qxX9mCptdjSr2h4pBnPz9/cKLuBvR0qJaPGi1aJya/O+zwcIvNK7Ef8MahE2ZYNnvi0KBO8900QXKgZW8HFbOklgU2A0bNszo/51h9OjRGD16tNF98fHxBmnt2rXDsWPHzJ6zT58+6NOnjy2KR2VU8QdYSU1tnZIP2qzGpLSKfnM0GhhYW+NlgcLZ6iGE2SZbvWDEVABYJE1hpH9jSYw1uzoq0LD6y4AQGJj0M4QEo5+HqZrUwnvlXuzzKd5sbvB5WxBcG/2ZgZnAv9gfKno0lFiLB+jmNNT9nLpA64a1jLYqwEw3BSGQ3neQI4toMYsCO41GY/EJfew8eICotOzd1PYwStvEWtq53Ex2/rXg/AbBiJkAUC/NirJCknCj/9Ais+E7vvbImi8Dxmrxil97aWtSTX3eJQXX5n5mXO0PFTlO8Z975dUrqPD7cb0VK4Cy3bphjcJnr7lWBWPdFBz5RdNaFgV2lSpVKnFVicIpQvKtXHqGyBls0dRmq1o8ezWxiqKdeoWAUPwzu5Ek/btslkJRYt8aS5gLAM2WsfBfIx30y8LM95Z+GSiutJ+HpSwKrmH9QJSy+oeKHKvoz33ouBhU+N14q5cQAjXU6Y4smk0VffaabVVwsW4KFgV2u3btsnc5iBzqoZvabFSL533pgm7W/9I2sVpS45UR+TQq7f9VrxM8gBK/lduD7trc3Fyqg35JXwYs8TDHlsSqYNLOA1FIPrKr1zQ6MhRw7QEVRp+9prhYNwWLArt27drZuxxEDvewTW226F8SsGG16eVpjLBmSoriNV6aNoa/x4UPKq9L5/FEVOtSN9la5Z9vv3ee6eJSAYU1XwbM0dWUAYBWa1Gz+cN83s4eiEKuzdwXGlceUGHq2VucK3ZTKPU8dllZWUhJSUFOTo5eemN7zeFCZAcP09QmoD81iLFpKsyNFC1xOL0J9piSwmjQYqzJFjAZjMDSNEnC2S9XumRQYdUoQpRck5rno0LNOe8ZBIq6ZnPAouCvJGx2pdIy+mwAXH5Ahde1KyafvcVbFVzt98XqwO7vv//GSy+9hJ9++snofvaxsy93hQQ3hYR8ben7Q5FxVje1FZ0axNg0FSWMFC1pOL2jBxUUD1qMNdkaDUaMBYCm0lzwIVlciaMIS7j24jWptzt30/t8izebG3zegHXBdRkYiEKuTS4DKooOUvPyND6nIwCXbVUoZHVgN27cONy5cwcHDhxAhw4dsHHjRty4cQPvvfce5syZY48yUhFeHm5oEFARZ9LuIi+fwZ0tWdvUpjc1yD8sWd3BWAf3h2litaXiQUuh4mnFgxFjAaCpNFd7SJbE0oDY1LUb+8yLN5sX/7wtDq7L0EAUcm0WD6goI9NFFWcwSO2f3xlTX6BdtVUBKEVg98svv+D777/Hk08+CYVCgeDgYHTq1Ak+Pj6YNWsWunbtao9yUhEVvTzQIMAHZ1I1yGVwZ1PWNLWV1BRmaWd2U8Ppy/KgAksDQFNpcmPN52Gr81saXJeVnxmSD7MDKko5XVTxKZ9sueJKSfOAGvsC7cq/N1YHdvfu3UO1atUAFCw19vfff6NevXpo1KhRiRMBk+1UULqjYaAPTqdqkJPH4M6WHmbZnVJzseH05Hz2DiaJTHnY6aJSew/UO5+p5fZKOxDDYMUVc3M6KhTIbNxUViPErQ7s6tevj7NnzyIkJARPPPEEvvzyS4SEhOCLL75AQECAPcpIJpTzdEfDABX+TNUgJ8/6mf3JMpb2L3koLjacnogeXbZYmSWuVgTuKcuZXW6vpKX6bLLiCoDsGkFInr/EZp+Ps0lCWFf1sGrVKuTm5iI6OhrHjx9H586dcevWLXh6eiI+Ph79+/e3V1nLLI1GA5VKBbVa7ZSVNx7k5uPPVA2ycxncOULh1CAooXnWqpGiCgWSth9w+W+KRPTo8Lp0wWC6KMnIdFHFFT4HtZKkG0BmrGG3oP+xVBAQKhS6c+cXDkgzkmbufEbL4uaG6y+Peegv1YGVvBBctfxDncMca+IMqwO74rKysnDmzBnUrFkTvr6+D3Mql+XswA4AsvPy8ed1DR4wuHOIx9avQZ3YsXorO0AIwMjqDjAyUhTs4E5EMlLzoxkI/OpTSKWYGcPS6XuM5bM0zVi6Lb9Uyyqwo7IR2AFATp4WZ9I0uJfNKWccoei3VUtWd2AHdyKSK0tbMpypcMRr8TkdbfGl2qUDOyEEvvvuO+zatQvp6enQFqt23bBhg/UldnFlJbADgHytwIW/M3EzM6fkzERERDZiqiWjNLVxdllxxY5zOpalwM7qwRNjx47F4sWL0aFDB/j5+RXMBUNlhptCQqhfRZRT3seV21k2H7xJRERkjKXTRZkiitSk2eKPl7EVVx6FLi9W19hVqVIFK1euxHPPPWevMrmcslRjV1RGVg6S0zM5kTERETmFXi2eVqsL2MzVphUGhMaadY0eW8L5HNHlxaVr7FQqFWrXZn8gV1CpnCcaVVfhwt/3oL6f6+ziEBHRI6Z4LZ5bZiYq/bpTf9UUI7VpFi/VxxVXDFhdY7d8+XJs27YNS5cuhbe3t73K5VLKao1dUer7ubhyOwt3H+Q5uyhERPQIKz7wzFRtmrF8QNkckFaWauysDuyysrLQu3dv7Nu3DyEhIfDw8NDb/yiuPuEKgV2h2/dycOV2FrJyOHKWiIjIFspSYGd1U2x0dDSOHj2KwYMHc/CEC6pS3hNVyntCfT8XNzOzcfteDvvgERERyYTVgd2WLVvw888/46mnnrJHechBVN4eUHl7oFZVgTtZObiZmQP1/VzkaxnkERERuSqrA7ugoKAy39xIllMoJFStoETVCkoIIaB5kAfN/Vyo7+ciMzuP06UQERG5EIW1B8yZMweTJk3CpUuX7FAcciZJkqDy9kBQlXIIr65C8+DKaBjgg5pVy6FqBU94ulv940JEREQOZHWN3eDBg5GVlYU6deqgXLlyBoMnbt++bbPCkXO5uymgKqeAqty/9zg3X4usnHxk5+bj/j/bg1wtsnPzwVZcIiIi57I6sJs/f74dikGuwsNNAZW3AvD2MNiXm69FTl7BlpuvRa5WID9fIFerRV6+QJ5WC60WyBcC+VoB7T//srmXiIjINqwK7HJzc7F7925MmTKFkxSTAQ83BTzcFCivtO44IQS0ArpAryANEChIL5yRR+jyF/6nyDlgOjq0ZeBo7FRFZwwSeulFj9N7YfRcxspp6roe5pps8XFYOkvSoxy0W3rt5n52rT2vuTNZObOVXdmjJIaXJ/TShd7/xb9LmeLfL5fFnzuFxxT9v1bwyyiVbVYFdh4eHti4cSOmTJlir/LQI0iSJLhJgBskeLg5uzRERCUr/EJaPOgD9ANGAf08KLYPRfYXHgsUCfgNvxPq5bPpNdkg5BZGyqv35VcY5hUo+rkV3f/vl/p/jzPMa/Q8xT9HGP/MiqeV9jMoS33QrW6K7dWrFzZt2oQJEybYozxERERlXuEXUli0vD2R41gd2NWtWxczZszA/v37ERERgfLl9Wdafv31121WOCIiIiKynNVLitWqVcv0ySQJFy5ceOhCuRpXWlKMiIiIXItdlxS7ePFiqQtGRERERPbzUL39hBBlaqQVERER0aOsVIHdihUr0KhRI3h7e8Pb2xuNGzfG119/beuyEREREZEVrG6KnTt3LqZMmYIxY8agTZs2EEJg3759GDVqFG7evInx48fbo5xEREREVIJSDZ6YNm0ahg4dqpe+fPlyTJ069ZHsg8fBE0RERGQv1sQZVjfFpqamIjIy0iA9MjISqamp1p6OiIiIiGzE6sCubt26+OabbwzS161bh9DQUJsUioiIiIisZ3VgN23aNLzzzjt49tlnMWPGDLz33nt49tlnMW3aNEyfPt0eZQQA3LlzB0OGDIFKpYJKpcKQIUOQkZFhMn9ubi4mT56MRo0aoXz58ggMDMTQoUNx/fp1vXzt27eHJEl624ABA+x2HURERET2YnVg98ILL+DgwYPw9fXFpk2bsGHDBvj6+uLQoUPo1auXPcoIABg4cCCSkpKwbds2bNu2DUlJSRgyZIjJ/FlZWTh27BimTJmCY8eOYcOGDfjrr7/Qo0cPg7wxMTFITU3VbV9++aXdroOIiIjIXqwePOEMp0+fRsOGDXHgwAG0bNkSAHDgwAG0bt0aZ86cQf369S06z+HDh9GiRQtcvnwZNWvWBFBQY/fEE09g/vz5pS4fB08QERGRvdh18IQzJCYmQqVS6YI6AGjVqhVUKhX2799v8XnUajUkSUKlSpX00letWgVfX188/vjjmDhxIu7evWurohMRERE5jMXz2CkUCkiSZDaPJEnIy8t76EIVl5aWhmrVqhmkV6tWDWlpaRad48GDB3jzzTcxcOBAvWh30KBBqFWrFvz9/fHHH38gNjYWJ06cQEJCgslzZWdnIzs7W/dao9FYcTVERERE9mFxYLdx40aT+/bv34+FCxdavbzY1KlTMW3aNLN5Dh8+DABGg0ohRInBJlAwkGLAgAHQarVYtGiR3r6YmBjd/8PDwxEaGormzZvj2LFjaNasmdHzzZo1q8RyExERETmaxYHd888/b5B25swZxMbG4scff8SgQYMwY8YMq958zJgxJY5ADQkJwe+//44bN24Y7Pv777/h5+dn9vjc3Fz069cPFy9exC+//FJi23SzZs3g4eGB5ORkk4FdbGwsJkyYoHut0WgQFBRk9rxERERE9mb1kmIAcP36dbz77rtYvnw5OnfujKSkJISHh1t9Hl9fX/j6+paYr3Xr1lCr1Th06BBatGgBADh48CDUarXRyZILFQZ1ycnJ2LVrF6pWrVrie506dQq5ubkICAgwmUepVEKpVJZ4LiIiIiJHsmrwhFqtxuTJk1G3bl2cOnUKO3fuxI8//liqoM4aDRo0wLPPPouYmBgcOHAABw4cQExMDLp166Y3IjYsLEzXZJyXl4c+ffrgyJEjWLVqFfLz85GWloa0tDTk5OQAAM6fP4/p06fjyJEjuHTpErZu3Yq+ffuiadOmaNOmjV2viYiIiMjWLK6xmz17Nj788EP4+/tjzZo1Rptm7WnVqlV4/fXXERUVBQDo0aMHPv30U708Z8+ehVqtBgBcvXoVP/zwAwDgiSee0Mu3a9cutG/fHp6enti5cycWLFiAzMxMBAUFoWvXrnj33Xfh5uZm/4siIiIisiGL57FTKBTw9vbGM888Yzbo2bBhg80K5yo4jx0RERHZizVxhsU1dkOHDrVoBCoREREROYfFgV18fLwdi0FERERED8slVp4gIiIiopIxsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZIKBHREREZFMMLAjIiIikgkGdkREREQywcCOiIiISCYY2BERERHJBAM7IiIiIplgYEdEREQkEwzsiIiIiGSCgR0RERGRTDCwIyIiIpIJBnZEREREMsHAjoiIiEgmGNgRERERyQQDOyIiIiKZYGBHREREJBMM7IiIiIhkgoEdERERkUwwsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZIKBHREREZFMMLAjIiIikgkGdkREREQywcCOiIiISCZcJrC7c+cOhgwZApVKBZVKhSFDhiAjI8PsMdHR0ZAkSW9r1aqVXp7s7Gy89tpr8PX1Rfny5dGjRw9cvXrVjldCREREZB8uE9gNHDgQSUlJ2LZtG7Zt24akpCQMGTKkxOOeffZZpKam6ratW7fq7R83bhw2btyItWvXYu/evcjMzES3bt2Qn59vr0shIiIisgt3ZxfAEqdPn8a2bdtw4MABtGzZEgCwZMkStG7dGmfPnkX9+vVNHqtUKuHv7290n1qtRlxcHL7++ms888wzAICVK1ciKCgIO3bsQOfOnW1/MURERER24hI1domJiVCpVLqgDgBatWoFlUqF/fv3mz129+7dqFatGurVq4eYmBikp6fr9h09ehS5ubmIiorSpQUGBiI8PNzsebOzs6HRaPQ2IiIiImdzicAuLS0N1apVM0ivVq0a0tLSTB7XpUsXrFq1Cr/88gvmzJmDw4cP4z//+Q+ys7N15/X09ETlypX1jvPz8zN73lmzZun6+qlUKgQFBZXyyoiIiIhsx6mB3dSpUw0GNxTfjhw5AgCQJMngeCGE0fRC/fv3R9euXREeHo7u3bvjp59+wl9//YUtW7aYLVdJ542NjYVardZtV65csfCKiYiIiOzHqX3sxowZgwEDBpjNExISgt9//x03btww2Pf333/Dz8/P4vcLCAhAcHAwkpOTAQD+/v7IycnBnTt39Grt0tPTERkZafI8SqUSSqXS4vclIiIicgSnBna+vr7w9fUtMV/r1q2hVqtx6NAhtGjRAgBw8OBBqNVqswFYcbdu3cKVK1cQEBAAAIiIiICHhwcSEhLQr18/AEBqair++OMPzJ49uxRXREREROQ8LtHHrkGDBnj22WcRExODAwcO4MCBA4iJiUG3bt30RsSGhYVh48aNAIDMzExMnDgRiYmJuHTpEnbv3o3u3bvD19cXvXr1AgCoVCqMGDECb7zxBnbu3Injx49j8ODBaNSokW6ULBEREZGrcInpTgBg1apVeP3113UjWHv06IFPP/1UL8/Zs2ehVqsBAG5ubjh58iRWrFiBjIwMBAQEoEOHDli3bh0qVqyoO2bevHlwd3dHv379cP/+fXTs2BHx8fFwc3Nz3MURERER2YAkhBDOLoSr02g0UKlUUKvV8PHxcXZxiIiISEasiTNcoimWiIiIiErGwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZIKBHREREZFMMLAjIiIikgkGdkREREQywcCOiIiISCYY2BERERHJBAM7IiIiIplgYEdEREQkEwzsiIiIiGSCgR0RERGRTDCwIyIiIpIJBnZEREREMsHAjoiIiEgmGNgRERERyQQDOyIiIiKZYGBHREREJBMM7IiIiIhkgoEdERERkUwwsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZIKBHREREZFMMLAjIiIikgkGdkREREQywcCOiIiISCYY2BERERHJBAM7IiIiIplgYEdEREQkEy4T2N25cwdDhgyBSqWCSqXCkCFDkJGRYfYYSZKMbh999JEuT/v27Q32DxgwwM5XQ0RERGR77s4ugKUGDhyIq1evYtu2bQCAkSNHYsiQIfjxxx9NHpOamqr3+qeffsKIESPwwgsv6KXHxMRg+vTputfe3t42LDkRERGRY7hEYHf69Gls27YNBw4cQMuWLQEAS5YsQevWrXH27FnUr1/f6HH+/v56r7///nt06NABtWvX1ksvV66cQV4iIiIiV+MSTbGJiYlQqVS6oA4AWrVqBZVKhf3791t0jhs3bmDLli0YMWKEwb5Vq1bB19cXjz/+OCZOnIi7d++aPVd2djY0Go3eRkRERORsLlFjl5aWhmrVqhmkV6tWDWlpaRadY/ny5ahYsSJ69+6tlz5o0CDUqlUL/v7++OOPPxAbG4sTJ04gISHB5LlmzZqFadOmWXcRRERERHbm1Bq7qVOnmhzgULgdOXIEQMFAiOKEEEbTjVm6dCkGDRoELy8vvfSYmBg888wzCA8Px4ABA/Ddd99hx44dOHbsmMlzxcbGQq1W67YrV65YcdVERERE9uHUGrsxY8aUOAI1JCQEv//+O27cuGGw7++//4afn1+J7/Pbb7/h7NmzWLduXYl5mzVrBg8PDyQnJ6NZs2ZG8yiVSiiVyhLPRURERORITg3sfH194evrW2K+1q1bQ61W49ChQ2jRogUA4ODBg1Cr1YiMjCzx+Li4OERERKBJkyYl5j116hRyc3MREBBQ8gUQERERlSEuMXiiQYMGePbZZxETE4MDBw7gwIEDiImJQbdu3fRGxIaFhWHjxo16x2o0Gnz77bd4+eWXDc57/vx5TJ8+HUeOHMGlS5ewdetW9O3bF02bNkWbNm3sfl1EREREtuQSgR1QMHK1UaNGiIqKQlRUFBo3boyvv/5aL8/Zs2ehVqv10tauXQshBF588UWDc3p6emLnzp3o3Lkz6tevj9dffx1RUVHYsWMH3Nzc7Ho9RERERLYmCSGEswvh6jQaDVQqFdRqNXx8fJxdHCIiIpIRa+IMl6mxIyIiIiLzGNgRERERyQQDOyIiIiKZYGBHREREJBMM7IiIiIhkgoEdERERkUwwsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZIKBHREREZFMMLAjIiIikgkGdkREREQywcCOiIiISCYY2BERERHJBAM7IiIiIplgYEdEREQkEwzsiIiIiGSCgR0RERGRTDCwIyIiIpIJBnZEREREMsHAjoiIiEgmGNgRERERyQQDOyIiIiKZYGBHREREJBMM7IiIiIhkgoEdERERkUwwsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIiIimWBgR0RERCQTDOyIiIiIZMJlAruZM2ciMjIS5cqVQ6VKlSw6RgiBqVOnIjAwEN7e3mjfvj1OnTqllyc7OxuvvfYafH19Ub58efTo0QNXr161wxUQERER2ZfLBHY5OTno27cvXn31VYuPmT17NubOnYtPP/0Uhw8fhr+/Pzp16oS7d+/q8owbNw4bN27E2rVrsXfvXmRmZqJbt27Iz8+3x2UQERER2Y0khBDOLoQ14uPjMW7cOGRkZJjNJ4RAYGAgxo0bh8mTJwMoqJ3z8/PDhx9+iFdeeQVqtRqPPfYYvv76a/Tv3x8AcP36dQQFBWHr1q3o3LmzRWXSaDRQqVRQq9Xw8fF5qOsjIiIiKsqaOMNlauysdfHiRaSlpSEqKkqXplQq0a5dO+zfvx8AcPToUeTm5urlCQwMRHh4uC4PERERkatwd3YB7CUtLQ0A4Ofnp5fu5+eHy5cv6/J4enqicuXKBnkKjzcmOzsb2dnZutdqtRpAQURNREREZEuF8YUljaxODeymTp2KadOmmc1z+PBhNG/evNTvIUmS3mshhEFacSXlmTVrltFyBwUFla6QRERERCW4e/cuVCqV2TxODezGjBmDAQMGmM0TEhJSqnP7+/sDKKiVCwgI0KWnp6fravH8/f2Rk5ODO3fu6NXapaenIzIy0uS5Y2NjMWHCBN1rrVaL27dvo2rVqiUGjaWh0WgQFBSEK1eusA+fE/E+OB/vgfPxHpQNvA/O58h7IITA3bt3ERgYWGJepwZ2vr6+8PX1tcu5a9WqBX9/fyQkJKBp06YACkbW7tmzBx9++CEAICIiAh4eHkhISEC/fv0AAKmpqfjjjz8we/Zsk+dWKpVQKpV6aZZOwfIwfHx8+AtcBvA+OB/vgfPxHpQNvA/O56h7UFJNXSGX6WOXkpKC27dvIyUlBfn5+UhKSgIA1K1bFxUqVAAAhIWFYdasWejVqxckScK4cePw/vvvIzQ0FKGhoXj//fdRrlw5DBw4EEDBhzRixAi88cYbqFq1KqpUqYKJEyeiUaNGeOaZZ5x1qURERESl4jKB3TvvvIPly5frXhfWwu3atQvt27cHAJw9e1Y3kAEAJk2ahPv372P06NG4c+cOWrZsie3bt6NixYq6PPPmzYO7uzv69euH+/fvo2PHjoiPj4ebm5tjLoyIiIjIRlwmsIuPj0d8fLzZPMVHi0iShKlTp2Lq1Kkmj/Hy8sLChQuxcOFCG5TSPpRKJd59912D5l9yLN4H5+M9cD7eg7KB98H5yuo9cLkJiomIiIjIONlOUExERET0qGFgR0RERCQTDOyIiIiIZIKBnQtYtGgRatWqBS8vL0REROC3335zdpFka9asWXjyySdRsWJFVKtWDT179sTZs2f18gghMHXqVAQGBsLb2xvt27fHqVOnnFRi+Zs1a5Zu+qJCvAeOce3aNQwePBhVq1ZFuXLl8MQTT+Do0aO6/bwP9pWXl4e3334btWrVgre3N2rXro3p06dDq9Xq8vAe2Navv/6K7t27IzAwEJIkYdOmTXr7Lfm8s7Oz8dprr8HX1xfly5dHjx49cPXqVcddhKAybe3atcLDw0MsWbJE/Pnnn2Ls2LGifPny4vLly84umix17txZLFu2TPzxxx8iKSlJdO3aVdSsWVNkZmbq8nzwwQeiYsWKYv369eLkyZOif//+IiAgQGg0GieWXJ4OHTokQkJCROPGjcXYsWN16bwH9nf79m0RHBwsoqOjxcGDB8XFixfFjh07xLlz53R5eB/s67333hNVq1YVmzdvFhcvXhTffvutqFChgpg/f74uD++BbW3dulW89dZbYv369QKA2Lhxo95+Sz7vUaNGierVq4uEhARx7Ngx0aFDB9GkSRORl5fnkGtgYFfGtWjRQowaNUovLSwsTLz55ptOKtGjJT09XQAQe/bsEUIIodVqhb+/v/jggw90eR48eCBUKpX44osvnFVMWbp7964IDQ0VCQkJol27drrAjvfAMSZPniyeeuopk/t5H+yva9euYvjw4XppvXv3FoMHDxZC8B7YW/HAzpLPOyMjQ3h4eIi1a9fq8ly7dk0oFAqxbds2h5SbTbFlWE5ODo4ePYqoqCi99KioKOzfv99JpXq0FE54XaVKFQDAxYsXkZaWpndPlEol2rVrx3tiY//3f/+Hrl27GqwCw3vgGD/88AOaN2+Ovn37olq1amjatCmWLFmi28/7YH9PPfUUdu7cib/++gsAcOLECezduxfPPfccAN4DR7Pk8z569Chyc3P18gQGBiI8PNxh98RlJih+FN28eRP5+fnw8/PTS/fz80NaWpqTSvXoEEJgwoQJeOqppxAeHg4Aus/d2D25fPmyw8soV2vXrsWxY8dw+PBhg328B45x4cIFfP7555gwYQL+97//4dChQ3j99dehVCoxdOhQ3gcHmDx5MtRqNcLCwuDm5ob8/HzMnDkTL774IgD+LjiaJZ93WloaPD09UblyZYM8jvq7zcDOBUiSpPdaCGGQRrY3ZswY/P7779i7d6/BPt4T+7ly5QrGjh2L7du3w8vLy2Q+3gP70mq1aN68Od5//30ABcs4njp1Cp9//jmGDh2qy8f7YD/r1q3DypUrsXr1ajz++ONISkrCuHHjEBgYiGHDhuny8R44Vmk+b0feEzbFlmG+vr5wc3MziPLT09MNvjGQbb322mv44YcfsGvXLtSoUUOX7u/vDwC8J3Z09OhRpKenIyIiAu7u7nB3d8eePXvwySefwN3dXfc58x7YV0BAABo2bKiX1qBBA6SkpADg74Ij/Pe//8Wbb76JAQMGoFGjRhgyZAjGjx+PWbNmAeA9cDRLPm9/f3/k5OTgzp07JvPYGwO7MszT0xMRERFISEjQS09ISEBkZKSTSiVvQgiMGTMGGzZswC+//IJatWrp7a9Vqxb8/f317klOTg727NnDe2IjHTt2xMmTJ5GUlKTbmjdvjkGDBiEpKQm1a9fmPXCANm3aGEz189dffyE4OBgAfxccISsrCwqF/p9pNzc33XQnvAeOZcnnHRERAQ8PD708qamp+OOPPxx3TxwyRINKrXC6k7i4OPHnn3+KcePGifLly4tLly45u2iy9OqrrwqVSiV2794tUlNTdVtWVpYuzwcffCBUKpXYsGGDOHnypHjxxRc5vYCdFR0VKwTvgSMcOnRIuLu7i5kzZ4rk5GSxatUqUa5cObFy5UpdHt4H+xo2bJioXr26brqTDRs2CF9fXzFp0iRdHt4D27p79644fvy4OH78uAAg5s6dK44fP66bYsySz3vUqFGiRo0aYseOHeLYsWPiP//5D6c7IX2fffaZCA4OFp6enqJZs2a6qTfI9gAY3ZYtW6bLo9Vqxbvvviv8/f2FUqkUTz/9tDh58qTzCv0IKB7Y8R44xo8//ijCw8OFUqkUYWFhYvHixXr7eR/sS6PRiLFjx4qaNWsKLy8vUbt2bfHWW2+J7OxsXR7eA9vatWuX0b8Bw4YNE0JY9nnfv39fjBkzRlSpUkV4e3uLbt26iZSUFIddgySEEI6pGyQiIiIie2IfOyIiIiKZYGBHREREJBMM7IiIiIhkgoEdERERkUwwsCMiIiKSCQZ2RERERDLBwI6IiIhIJhjYEREREckEAzsiIieIi4tDVFSUQ95r8+bNaNq0qW6NUSKSLwZ2RPTIio6ORs+ePR3+vtnZ2XjnnXcwZcoUh7xft27dIEkSVq9e7ZD3IyLnYWBHRORg69evR4UKFdC2bVuHvedLL72EhQsXOuz9iMg5GNgREZkwd+5cNGrUCOXLl0dQUBBGjx6NzMxMvTxLlixBUFAQypUrh169emHu3LmoVKmS2fOuXbsWPXr00EvbvXs3WrRogfLly6NSpUpo06YNLl++rNv/448/IiIiAl5eXqhduzamTZuGvLw83f6MjAyMHDkSfn5+8PLyQnh4ODZv3qzb36NHDxw6dAgXLlx4iE+EiMo6d2cXgIiorFIoFPjkk08QEhKCixcvYvTo0Zg0aRIWLVoEANi3bx9GjRqFDz/8ED169MCOHTssal797bffMGjQIN3rvLw89OzZEzExMVizZg1ycnJw6NAhSJIEAPj5558xePBgfPLJJ2jbti3Onz+PkSNHAgDeffddaLVadOnSBXfv3sXKlStRp04d/Pnnn3Bzc9O9R3BwMKpVq4bffvsNtWvXtuXHRERliCSEEM4uBBGRM0RHRyMjIwObNm2yKP+3336LV199FTdv3gQADBgwAJmZmXo1Y4MHD8bmzZuRkZFh9BwZGRmoXLkyfv31V11T7O3bt1G1alXs3r0b7dq1Mzjm6aefRpcuXRAbG6tLW7lyJSZNmoTr169j+/bt6NKlC06fPo169eqZLH+zZs3w/PPP491337XoeonI9bAplojIhF27dqFTp06oXr06KlasiKFDh+LWrVu4d+8eAODs2bNo0aKF3jHFXxd3//59AICXl5curUqVKoiOjkbnzp3RvXt3LFiwAKmpqbr9R48exfTp01GhQgXdFhMTg9TUVGRlZSEpKQk1atQwG9QBgLe3N7Kysqz6DIjItTCwIyIy4vLly3juuecQHh6O9evX4+jRo/jss88AALm5uQAAIYSuubRQSY0gVatWhSRJuHPnjl76smXLkJiYiMjISKxbtw716tXDgQMHAABarRbTpk1DUlKSbjt58iSSk5Ph5eUFb29vi67p9u3beOyxxyzKS0SuiX3siIiMOHLkCPLy8jBnzhwoFAXfgb/55hu9PGFhYTh06JDBceZ4enqiYcOG+PPPPw3msWvatCmaNm2K2NhYtG7dGqtXr0arVq3QrFkznD17FnXr1jV6zsaNG+Pq1av466+/TNbaPXjwAOfPn0fTpk3Nlo+IXBsDOyJ6pKnVaiQlJemlValSBXXq1EFeXh4WLlyI7t27Y9++ffjiiy/08r322mt4+umnMXfuXHTv3h2//PILfvrpJ4NavOI6d+6MvXv3Yty4cQCAixcvYvHixejRowcCAwNx9uxZ/PXXXxg6dCgA4J133kG3bt0QFBSEvn37QqFQ4Pfff8fJkyfx3nvvoV27dnj66afxwgsvYO7cuahbty7OnDkDSZLw7LPPAgAOHDgApVKJ1q1b2+aDI6KySRARPaKGDRsmABhsw4YNE0IIMXfuXBEQECC8vb1F586dxYoVKwQAcefOHd05Fi9eLKpXry68vb1Fz549xXvvvSf8/f3Nvu/p06eFt7e3yMjIEEIIkZaWJnr27CkCAgKEp6enCA4OFu+8847Iz8/XHbNt2zYRGRkpvL29hY+Pj2jRooVYvHixbv+tW7fESy+9JKpWrSq8vLxEeHi42Lx5s27/yJEjxSuvvGKDT42IyjKOiiUisqGYmBicOXMGv/32m9l8/fr10zW72tvff/+NsLAwHDlyBLVq1bL7+xGR83DwBBHRQ/j4449x4sQJnDt3DgsXLsTy5csxbNiwEo/76KOPUKFCBQeUsKCpd9GiRQzqiB4BrLEjInoI/fr1w+7du3H37l3Url0br732GkaNGuXsYhHRI4qBHREREZFMsCmWiIiISCYY2BERERHJBAM7IiIiIplgYEdEREQkEwzsiIiIiGSCgR0RERGRTDCwIyIiIpIJBnZEREREMsHAjoiIiEgm/h+8NSrelQ7EnwAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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pwZEjRwAAe/fuxZNPPqlaz86dO9G6dWu0b98epaWlqp/+/ftDIpHg4MGDGtvt06cPateubdZ+xcbGIjMzE0lJSZgxYwZatWqFHTt2YPDgwXrvOH/++ec1LnWGhoaie/fuqktYV65cwd9//40XXngBADTaGxsbizt37uDixYsAgF9//RVRUVFo0aKFWW015Omnn9aZlp2djUmTJiE4OBiOjo6QyWSqy38XLlwo1/YMYYypHltyHASDBw/WeN62bVsA6tKA9u3bw8nJCS+99BI2bNiAa9eulbvNr732GrKzs7F161YA/HL/6tWrMWDAADRo0MDosrdv30adOnV0Ln0L3nrrLchkMri4uCA8PByZmZlYu3atxqXUOXPmIDMzE19++SVefvlleHh4YM2aNQgPD8d3331n0b6Ij7+Yv7+/yR5YhM+v9jncpUsXtGjRQnUOW1tUVBRq1aqleh4QEAB/f3+NEqp9+/Zh2LBhcHNz0/kcFRUV4cSJEwbXL5QKjRw5Elu2bDGrJ5qCggKcOnUKQ4cOhZOTk2q6h4cHBg0aVKb9APg5sHDhQrRs2RJOTk5wdHSEk5MTLl++XKZzUigD8vf31/t6q1at0K5dO41pzz//PPLy8lS9HFj6t9WQNWvWoGPHjnBxcVH9vdm3b59Z+zVv3jycPHnS5I92GY8hhs5HU6+1a9cOvXr1wpIlS7B161Y8ePAAx44dw6RJkyCVSnVKjyzdjjnnISkbCnyJTXz99dc4efIkzpw5g9u3b+OPP/5Ajx49APBaRUdHR9SpU0djGYlEgrp166pqGQHgySefxIkTJ1BQUIC9e/eiT58+8PX1RXh4OPbu3YuMjAxkZGRoBL53797FH3/8AZlMpvFTq1YtMMZ06lYN1Uwa4urqiqFDh2LJkiU4dOgQrly5gpYtWyIxMRHnzp3TmLdu3bo6y4v38e7duwCAGTNm6LR3ypQpAKBq771791C/fn2L2qrNzc0Nnp6eGtOUSiWio6Oxfft2vPnmm9i3bx9+//13VYDw+PHjcm1Tn4KCAuTm5iIoKAiAZcdB4Ovrq/Hc2dlZo72NGjXC3r174e/vj/j4eDRq1AiNGjXCihUrytzuDh06ICIiQlUvunPnTly/ft2sbrYeP34MFxcXg6+/9tprOHnyJNLS0nD16lXcuXMHL730ks58AQEBGD9+PNasWYM//vgDhw4dgpOTE1577TWz90P7+Iu5uLiYfM+N1RsHBQVpnMPWpP2eA/x9F9qbm5uL0tJSfPrppzqfI+ELhLG69V69emHHjh0oLS3FmDFjUL9+fbRu3drol4p///0XjDEEBATovKZvmjn7AQAJCQmYM2cOhg4dip9//hm//fYbTp48iXbt2pXpnBSWMfQZNPS3CoDG3ytL/rbqs3TpUkyePBldu3bFtm3bcOLECZw8eRJPPfWUWfsVEhKC9u3bm/xp3Lix0fXUrl0bEolE72f1/v37AAAfHx+j69i6dSt69OiBkSNHonbt2oiKisLw4cPRvn171KtXTzWfUP8vvrdAvC192zHnPCRl42jvBpDqqUWLFujUqZPe13x9fVFaWop79+5pBL+MMWRlZamyLgDQt29fzJkzB6mpqdi3bx/mzp2rmr5nzx6EhYWpngv8/Pzg6uqKL7/8Uu/2/fz8NJ4b+yZujpCQELz00kuYNm0azp07h1atWqley8rK0pk/KytL9Y9PaMvMmTMxfPhwvesXbkSpU6eOxo1DZaFvX//66y+cPXsW69evx9ixY1XTr1y5Uq5tGfPLL79AoVCobnix5DhYIiIiAhEREVAoFDh16hQ+/fRTTJs2DQEBAXj22WfL1PZXX30VI0aMwOnTp7Fy5Uo0bdoU/fr1M7mcn5+f0f5B69evb/CcMaZXr16Ijo7Gjh07kJ2dbTCjJ6Z9/MXu37+vc45oEz6/d+7c0fkydvv2bZPL20rt2rUhlUoxevRoxMfH651H+JthyJAhQzBkyBAUFxfjxIkTWLRoEZ5//nk0aNAA3bp107tNiUSi+vImpu/8N9emTZswZswYLFy4UGN6Tk6O0b6QDRHeEyGo02bobxUAjb9Xlvxt1WfTpk2IjIzE6tWrNabn5+ebXBbgN5IaugFZrHfv3kYz0K6urmjcuLHemwX//PNPuLq6omHDhka34e/vj+TkZGRnZyMrKwuhoaFwdXXFqlWr8Mwzz6jma9OmjWq9Xbt2VU3PyspCTk4OWrdurbNuc85DUjYU+JIK17dvX3z44YfYtGkTXn/9ddX0bdu2oaCgQCOI7dKlCzw9PbF8+XJkZWWpAownn3wSixcvxpYtW9CyZUuNzNXAgQOxcOFC+Pr6mvwnZ4n8/HxIJBJ4eHjovCZcotPOoH333XdISEhQBZz//PMPjh07hjFjxgDgwVyTJk1w9uxZnX9w2mJiYrBx40ZcvHjRYBConfU0h9A2YVmBuZcKLZWZmYkZM2bAy8sLL7/8MgDLjkNZSKVSdO3aFc2bN8c333yD06dPGwx8TR3DYcOGISQkBNOnT8ehQ4ewbNkys748NW/eHN999x0ePnwILy8vi/fh7t27qFOnjs4lVIVCgcuXL8PNzc2sgEjf8Re7du2a3n/EYn369AHAgxjxF9WTJ0/iwoULmDVrlhl7pEt87F1dXS1e3s3NDVFRUThz5gzatm2rUXpQlrb07t0b3t7e2L17N86cOaM38HV3d0enTp2wY8cOfPTRR6ptPnr0SG+PE+aSSCQ65+Qvv/yCW7dumcxm6iOUSF29elXv6+fOncPZs2c1yh2+/fZb1KpVCx07dgRg2d9W7Qy2QN9+/fHHHzh+/DiCg4NN7se8efPMusIiLiUxZNiwYVi+fDlu3Lih2nZ+fj62b9+OwYMHw9HRvBDJ399f9YXzk08+QUFBgUYbn3rqKbi4uGD9+vUaga/QQ83QoUN11mnOeUjKhgJfUuH69euH/v3746233kJeXh569OiBP/74A3PnzkWHDh00unuSSqXo3bs3fv75Z4SFhaFRo0YAgB49esDZ2Rn79u3Dq6++qrH+adOmYdu2bejVqxdef/11tG3bFkqlEpmZmdizZw+mT5+u8cfHXBcvXkT//v3x7LPPonfv3ggMDMS///6LX375BevWrUNkZCS6d++usUx2djaGDRuGF198EQ8fPsTcuXPh4uKCmTNnquZZu3YtYmJi0L9/f4wbNw716tXD/fv3ceHCBZw+fVpVTzp//nz8+uuv6NWrF9555x20adMGDx48wK5du5CQkIDmzZujUaNGcHV1xTfffIMWLVrAw8MDQUFBei9pC4Tl3n77bTDG4OPjg59//hkpKSkWHyNtf/31l6oOMDs7G4cPH8ZXX30FqVSKpKQkjYy/ucfBXGvWrMH+/fsxYMAAhISEoKioSJWpEpfGaBP+2axbtw61atWCi4sLwsLCVFkvqVSK+Ph4vPXWW3B3d9epczUkMjISjDH89ttviI6OtmhfAN6N19q1a/H888+jc+fO8PLyws2bN/H555/j3LlzePfdd3UCPUuOP8AvaV++fBmvvPKK0bY0a9YML730Ej799FM4ODggJiYG169fx5w5cxAcHKzxhdYSQmZs8eLFiImJgVQqtTiAXbFiBXr27ImIiAhMnjwZDRo0QH5+Pq5cuYKff/5ZdY+BPu+++y5u3ryJvn37on79+njw4AFWrFgBmUyG3r17G1xu/vz5GDBgAPr374/XXnsNCoUCS5YsgYeHh8EMqykDBw7E+vXr0bx5c7Rt2xZpaWlYsmRJmcud6tevj4YNG+LEiRM6fzMB/qV98ODBmDdvHgIDA7Fp0yakpKRg8eLFcHNzA2DZ39Y2bdrg4MGD+PnnnxEYGIhatWqhWbNmGDhwIP73v/9h7ty56N27Ny5evIj58+cjLCzMaPdhggYNGpispzfXjBkzsHHjRgwYMADz58+Hs7MzPvjgAxQVFel0uyd82RBfCfvss88A8LKqBw8e4Ndff8UXX3yBhQsXqr4sALxkYvbs2ZgzZw58fHwQHR2NkydPYt68eZg4cSJatmypsS1zz0NSRva7r45UR+bcTc4Y7/LlrbfeYqGhoUwmk7HAwEA2efJkjW6mBEJXaNoj3PTr148B0OlRgTHeXdPs2bNZs2bNmJOTk6p7o9dff12j035Y0Cn+v//+yxYsWMD69OnD6tWrx5ycnJi7uztr3749W7BggcbAF0KvDhs3bmSvvvoqq1OnDnN2dmYRERHs1KlTOus+e/YsGzlyJPP392cymYzVrVuX9enTh61Zs0Zjvhs3brAJEyawunXrMplMxoKCgtjIkSM1Bgf47rvvWPPmzZlMJtPosWDs2LHM3d1d776dP3+e9evXj9WqVYvVrl2bjRgxgmVmZur0eGBprw7Cj5OTE/P392e9e/dmCxcuNDjqlTnHwdBnTDjmQm8Mx48fZ8OGDWOhoaHM2dmZ+fr6st69e+t8XrT3kTHGli9fzsLCwphUKtV7x/z169cZADZp0iSjx0FMoVCwBg0a6PSEYKg7M23nz59n06dPZ506dWJ16tRhjo6OrHbt2qx3795s48aNGvOW9fh/8cUXTCaTmTWwiEKhYIsXL2ZNmzZlMpmM+fn5sf/7v//T6a7Mkl4diouL2cSJE1mdOnWYRCLR+KwZOldDQ0N1eg/IyMhgEyZMYPXq1WMymYzVqVOHde/enS1YsMDo9nfu3MliYmJU57e/vz+LjY1lhw8f1li3vs9EUlISa9OmDXNycmIhISHsgw8+YK+++iqrXbu2xnzm7se///7L4uLimL+/P3Nzc2M9e/Zkhw8fZr1799boscCSASzmzJnDateureoRR7ztAQMGsB9++IG1atWKOTk5sQYNGrClS5fqrMPcv63p6emsR48ezM3NTaOXheLiYjZjxgxWr1495uLiwjp27Mh27NjBxo4dy0JDQ03ug7VduXKFDR06lHl6ejI3NzfWt29flpaWpjNfaGioTvvWrl3LWrRowdzc3JiHhweLiIhgO3bsMLitFStWsKZNm6o+I3PnztUZ4Y0xy85DYjkJYwZu7SWElNnBgwcRFRWFrVu3atR6kerh008/xauvvoq//vpLo6bblI8//hjvv/8+bt26VaZL+bYWERGBkJAQfPPNN/ZuSpUnl8tVNznt2bPH3s0BwOuvw8LC8PXXX2PUqFH2bg4xgM5D26JeHQghxExnzpzB9u3bMX/+fAwZMsSioBcA4uPj4eXlZfYoYhUpNTUVJ0+exP/+9z97N6VKiouLw/fff49Dhw5h8+bNiI6OxoULF/Dmm2/au2kqQUFBmDZtGt5//30olUp7N4foQeeh7VX7Gt/8/Hz06dMHcrkcCoUCr776Kl588UV7N4sQUgUNGzYMWVlZiIiIwJo1ayxe3sXFBRs3btQ7bLW95ebm4uuvvzZ5JzvRLz8/HzNmzMC9e/cgk8nQsWNHJCcnG60nt4fZs2fDzc0Nt27dMutmMlKx6Dy0vWpf6qBQKFBcXAw3NzcUFhaidevWOHnypN5+FAkhhBBCSPVV7UsdpFKp6o7UoqIiKBQKgyMWEUIIIYSQ6qvSB76pqakYNGgQgoKCIJFIsGPHDp15Vq1ahbCwMNVQn4cPH9Z4/cGDB2jXrh3q16+PN998kzqFJoQQQgipgSp94FtQUIB27dph5cqVel/fvHkzpk2bhlmzZuHMmTOIiIhATEwMMjMzVfN4e3vj7NmzyMjIwLfffqt3hB1CCCGEEFK9VakaX4lEgqSkJI1RTrp27YqOHTtqDH/YokULDB06FIsWLdJZx+TJk9GnTx+MGDFC7zaKi4tRXFyseq5UKnH//n34+vqWe2hbQgghhBBifYwx5OfnIygoSGeES7Eq3atDSUkJ0tLS8Pbbb2tMj46OxrFjxwDwYT5dXV3h6emJvLw8pKamYvLkyQbXuWjRIrz33ns2bTchhBBCCLG+GzduGB3hsEoHvjk5OVAoFAgICNCYHhAQgKysLADAzZs3ERcXB8YYGGOYOnUq2rZta3CdM2fOREJCgur5w4cPERISgoyMDLPG/i4vuVyOAwcOICoqCjKZzObbI6S6oXOIkPKhc4hURfn5+QgLCzMZq1XpwFegXYLAGFNNCw8PR3p6utnrcnZ2hrOzMxITE5GYmAiFQgGAj7Xt6elptTYbIpfL4ebmBl9fX/qDQ0gZ0DlESPnQOUSqIuGzaqostdLf3GaMn58fpFKpKrsryM7O1skCE0IIIYSQmq1KB75OTk4IDw9HSkqKxvSUlBR07969XOuOj4/H+fPncfLkyXKthxBCCCGEVA6VvtTh0aNHuHLliup5RkYG0tPT4ePjg5CQECQkJGD06NHo1KkTunXrhnXr1iEzMxOTJk0q13a1Sx0IIYQQQkjVVukD31OnTiEqKkr1XLjxbOzYsVi/fj1GjRqF3NxczJ8/H3fu3EHr1q2RnJyM0NDQcm03Pj4e8fHxyMvLg5eXV7nWRQghhBBiikKhgFwut3czKiWZTAapVFru9VT6wDcyMtLkEMNTpkzBlClTrLpdyvgSQgghpCIwxpCVlYUHDx7YuymVmre3N+rWrVuucRUqfeBrL5TxJYQQQkhFEIJef39/uLm50YBZWhhjKCwsRHZ2NgAgMDCwzOuiwJcQQgghxE4UCoUq6PX19bV3cyotV1dXALznLn9//zKXPVTpXh1sKTExES1btkTnzp3t3RRCCCGEVFNCTa+bm5udW1L5CceoPHXQFPgaQN2ZEUIIIaSiUHmDadY4RhT4EkIIIYSQGoECXwOo1IEQQgghpOJERkZi2rRpNt0GBb4GUKkDIYQQQkj1QoEvIYQQQgipESjwJYQQQgghFouMjMTUqVMxdepUeHt7w9fXF7Nnz1YNPLZq1So0adIELi4uCAgIwDPPPKNatqCgAGPGjIGHhwcCAwPx8ccfV0ibqR9fA2jkNkIIIYTYA2NAYWHFb9fNDbC044QNGzYgLi4Ov/32G06dOoWXXnoJoaGh6NChA1599VVs3LgR3bt3x/3793H48GHVcm+88QYOHDiApKQk1K1bF++88w7S0tLQvn176+6UFgp8DaCR2wghhBBiD4WFgIdHxW/30SPA3d2yZYKDg7Fs2TJIJBI0a9YMf/75J5YtW4YFCxbA3d0dAwcORK1atVTBMN/OI3zxxRf4+uuv0a9fPwA8gK5fv761d0kHlToQQgghhJAyeeKJJzT61+3WrRsuX76Mvn37IjQ0FA0bNsTo0aPxzTffoPC/NPbVq1dRUlKCbt26qZbz8fFBs2bNbN5eyvgSQgghhFQibm48+2qP7VqLh4cHTp8+jYMHD2LPnj149913MW/ePJw8eVJVA2wPFPgSQgghhFQiEonlJQf2cuLECZ3nTZo0gVQqBQA8+eSTePLJJzF37lx4e3tj//79iI6Ohkwmw4kTJxASEgIA+Pfff3Hp0iX07t3bpu2lwJcQQgghhJTJjRs3kJCQgJdffhmnT5/Gp59+io8//hg7d+7EtWvX0KtXL9SuXRvJyclQKpVo1qwZPDw8EBcXhzfeeAO+vr4ICAjArFmz4OBg+wpcCnwNoF4dCCGEEEKMGzNmDB4/fowuXbpAKpXilVdewUsvvYSjR49i+/btmDdvHoqKitCkSRN89913aNWqFQBgyZIlePToEQYPHoxatWph+vTpePjwoc3bS4GvAdSrAyGEEEKIcTKZDMuXL8fq1as1pvfs2RMHDx40uJyHhwc2btyIjRs3qqa98cYbtmqmCvXqQAghhBBCagQKfAkhhBBCSI1Q7Usdbty4gdGjRyM7OxuOjo6YM2cORowYYe9mEUIIIYRUacZKGSqrah/4Ojo6Yvny5Wjfvj2ys7PRsWNHxMbGwr2q9BNCCCGEEEKsotoHvoGBgQgMDAQA+Pv7w8fHB/fv36fAlxBCCCGVhj0HdagqrHGMKn2Nb2pqKgYNGoSgoCBIJBLs2LFDZ55Vq1YhLCwMLi4uCA8Px+HDh/Wu69SpU1AqlQgODrZxqwkhhBBCTJPJZACgGs6XGCYcI+GYlUWlz/gWFBSgXbt2GD9+PJ5++mmd1zdv3oxp06Zh1apV6NGjB9auXYuYmBicP39eNRoIAOTm5mLMmDH4/PPPK7L5hBBCCCEGSaVSeHt7Izs7GwDg5uYGiURi51ZVLowxFBYWIjs7G97e3qpR4cqi0ge+MTExiImJMfj60qVLERcXh4kTJwIAli9fjt27d2P16tVYtGgRAKC4uBjDhg3DzJkz0b17d6PbKy4uRnFxsep5Xl4eAEAul0Mul5d3d0wStlER2yKkOqJziJDyoXOo4vn6+kKhUODu3bv2bkql5unpCV9fX72fTXM/r5U+8DWmpKQEaWlpePvttzWmR0dH49ixYwD4t4Rx48ahT58+GD16tMl1Llq0CO+9957O9D179sDNzc06DTdDSkpKhW2LkOqIziFCyofOoYonkUjKlc2szhQKhdEaX3NLRap04JuTkwOFQoGAgACN6QEBAcjKygIAHD16FJs3b0bbtm1V9cEbN25EmzZt9K5z5syZSEhIUD3Py8tDcHAwoqOj4enpaZsdEZHL5UhJSUG/fv3KVcNCSE1F5xAh5UPnEKmKhCv0plTpwFegXQvDGFNN69mzJ5RKpdnrcnZ2hrOzMxITE5GYmAiFQgGAF1JX5B+Ait4eIdUNnUOElA+dQ6QqMfezWul7dTDGz88PUqlUld0VZGdn62SBCSGEEEJIzValA18nJyeEh4fr1CGlpKSYvInNlPj4eJw/fx4nT54s13oIIYQQQkjlUOlLHR49eoQrV66onmdkZCA9PR0+Pj4ICQlBQkICRo8ejU6dOqFbt25Yt24dMjMzMWnSpHJtV7vUgRBCCCGEVG2VPvA9deoUoqKiVM+FG8/Gjh2L9evXY9SoUcjNzcX8+fNx584dtG7dGsnJyQgNDS3XduPj4xEfH4+8vDx4eXmVa12EEEIIIcT+Kn3gGxkZaXKIuilTpmDKlClW3S5lfAkhhBBCqpcqXeNrS1TjSwghhBBSvVDgSwghhBBCagQKfA1ITExEy5Yt0blzZ3s3hRBCCCGEWAEFvgZQqQMhhBBCSPVCgS8hhBBCCKkRKPA1gEodCCGEEEKqFwp8DaBSB0IIIYSQ6oUCX0IIIYQQUiNQ4EsIIYQQQmoECnwNoBpfQgixXEYGMH06cPOmvVtCCCG6KPA1gGp8CSHEcsuXA0uXAp9/bu+WEEKILgp8CSHEDq5dA+LigL//tndLrOv6df774UO7NoMQQvSiwJcQQqxAqQRu3TJ//rVrgS+/BD75xHZtsgehxKGoyL7tIIQQfSjwJYQQK3jvPaB+feCXX8ybX8iMZmTYrEl2IQS+jx/btx2EEKIPBb6EEFJOCgXP4ALAoUPmLXPjBv9dnQLf4mIgO5s/psCXEFIZUeBrAPXqQAgx1+HDwN27/LGQyTVFyIz+8w/AmE2aVeFu31Y/plIHQkhlRIGvAdSrAyHEXFu3qh//84/p+RUKdZBYVKQOmqs6cRdmlPElhFRGFPgSQuzm/n0gPh74/Xd7t6TsFApg2zb1c3Myvnfu8OUsWaYqoMCXEFLZUeBLCNFx8iTwxBPAvn223c7cucCqVcCIEVU3UBLKHNzd+fPsbKCw0PgyQn2vwFZ1vkuWAP37A/n5tlm/Ngp8CSGVnaO9G1ARhg0bhoMHD6Jv37744YcfyrSO48cBNzfeZRFj/Ed4bM1ppaUS/PlnfRQWSuDuDri48B9nZ8OPnZ0BB/oKQ6yEMWDyZCAtjfcze/Ei/4xZW24u784LADIzgY8+AubMsf52bE0ocxg5kmd+8/J4uUOLFoaX0Q58bZHxVSqBBQt4e375BXj2WetvQ5s48KUaX0JIZVQjAt9XX30VEyZMwIYNG8q8jqeesmKDjHIEEG7xUjKZ8SDZWOBszuvmzOvoCEgk1j8ipGL99BMPegEewCUmAgkJ1t/OqlU8M+rtDTx4AHzwATBhAlCvnvW3ZSviMocRI/hx++OPyhH4XrnCg16Af3Evb+Cbnw98/TUwfDgQGKh/Hsr4EkIquxoR+EZFReHgwYPlWkfDhjy4lEj4j4OD5m9rTQOUyMrKgaenH4qLHVBUxLsIKiqCzmPxneByOf+pqEua+kgk5QucXVyAPn2AqCj77UNNp1QC777LH7dty4O4BQuA8eOB2rWtt53Hj4FPP+WPExOB1auBI0eAt98GNm603nZs7cgRXuZQuzbQty/QoAE/ZqYCWSFArFMHuHfPNoGv+L7cY8fKv77PPgOmTwf++ou/X/pQ4EsIqewqfeCbmpqKJUuWIC0tDXfu3EFSUhKGDh2qMc+qVauwZMkS3LlzB61atcLy5csRERFh1XacOQN4elp1lXrJ5QokJx9HbGwsZDLD9QuMAaWlusGwJY+tMa9crtmmx4/L9w9vwQLgpZeAjz8GPDzKvh5SNtu28cDN0xPYuxeIjATOn+fZ2MWLrbedr7/mAV9oKC8RaNYM6NwZ2LSJ3+z2xBPW25YtbdnCfw8dCjg58f0BTAeyQsY3IgLYvt02Nb6nTqkfp6fz7Lqbm/FlUlOB77/n73WtWpqvXbzIf587Z3j5ii51YIx/2a+Iv82EkOqh0ge+BQUFaNeuHcaPH4+nn35a5/XNmzdj2rRpWLVqFXr06IG1a9ciJiYG58+fR0hIiB1aXDEkEp6Blsl0/0FVJKXSeHBsSXB96xbw3XfAunU86Pr6a6BHD/vtW0VizP5lIgoFv9kMAF5/nWcjFy8GBg0CVqzgAak1TimFgn+xEbbj6AiEhwPjxgFffQVMm8YzlJW9bl27zAHgGV/AdJdm2oHvP//wc8ma+ywOfEtL+fNevYwvM28ecOAA/+IxZozma5mZ/PeVK/qXlct5bxWCisj4vvsusGgRHzSkpvytIISUT6UPfGNiYhATE2Pw9aVLlyIuLg4TJ04EACxfvhy7d+/G6tWrsWjRIou3V1xcjOLiYtXzvP+K5ORyOeTi9KaNCNuoiG1Zi6Mjz85aI0M7frwEcXFSXLsmQa9eDNOnKzF3rhJOTuVfd2W1c6cEkydLERvLsGaNwm4B8LffSnDhgiNq12aYOrUUcjkQHQ1EREhx+LADZs9W4osvFKZXZEJSkgSXL/PtjBlTqrpqMG8esHWrI377TYKvvy7FCy9YPqrDnTvAsGEOAJ5Aw4alaN683M01KDVVgrt3HeHtzdCrF9+P+vUlAByRkaGEXG74WN244QhAgk6dSiGVSlFSIsGNG3IEBVmnbaWlwOnTfBstWjBcuCDBkSMKdOumNLrcrVt8mYsXFZDLNee9fp2/ducO8OCBXNWLhXqfAMZkqudFRUBJidymn+e9e6VQKBzw228KdOlifN+I+ari/yFCzP28VvrA15iSkhKkpaXh7bff1pgeHR2NY2Usalu0aBHee+89nel79uyBm6nrhFaUkpJSYduqbD74wBGff94GBw6EYMkSKbZufYRp09LQoIEdC5ht5NdfG+Czz9pCqZTgq68kkEjOYfDgaxXeDoVCgnfe6QPAA7GxF3D06GXVa4MGeePw4d7YtEmC8PAjaNAgr8zbYQx4990IAD548slLSE39W+P1oUObYNOmlpgxQw5X131wcTE/0JbLHTB7dg9cvOgDIACdOpVi9OgLGDDgmtmZVKUSOHQoGKdP+2PkyEsIDjb8mVu3rg2AhggPz8TevekAgBs3vABE4tKlEiQn7zbQTgmysgYBAK5e3Qtf3whkZ7vj+++Po3nzf83eX2P++acWCgv7wMWlFF26XMSFC63w44/ZaNXKeIfJt2/HAHDC0aO3kZx8WjWdMeD69QEQ/mVs2KD7Obh4sTaAXnB3L0FBAf+m+uOPu+DkZLuA9OLF/gBccOrUJSQnX7LZdmqqmvx/iFQ9hab6kfyPhLGqM1imRCLRqPG9ffs26tWrh6NHj6J79+6q+RYuXIgNGzbg4n9Faf3798fp06dRUFAAHx8fJCUlGRyKWF/GNzg4GDk5OfCsgEIyuVyOlJQU9OvXDzKZzPQC1VhSkgTx8VLk5Ejg5MTw3ntKTJumhFRq75aVn1IJzJ7tgI8+4jvTubMSJ086wNGRYd8+Bbp1q9jT8uuvJZg40RF+fgyXLpXqZO+fe06Kbdsc0L+/Ej//XPas79GjEkRFOcLZmeHKlVIEBGi+XlQEtGvniIwMCd55R4F588wPmqZMccDnn0vh7c1Qv34O/vqrDgCgRw8l1q1ToEkT48sfOybB9OkOSEvjUXLdugz795eicWPdeRUKoEEDR9y9K8FPP5Xiqaf4+5WbCwQG8vM2L08OFxfdZf/5B2jSRAYnJ4a8vFL07y/FoUMOWL++FM8/b533XXg/e/VSYsECJXr1ckSdOgw3b5YazMCWlAAeHrzt3bopceiQ+n2+fx+oW1f992jz5lIMG6bZ1m3bJHjuOUd06qTEqVP8GN69K7fqTZFijx8DXl68TdOmKfDhh5TxtRb6P0Sqory8PPj5+eHhw4fG4zVWhQBgSUlJque3bt1iANixY8c05luwYAFr1qxZuba1cuVK1qJFC9a0aVMGgD18+LBc6zNXSUkJ27FjByspKamQ7VV2d+4wNnCg0NMxYxERjF27Zu9WlU9REWPPPafep/nzGVMqGXv2Wf68fn3G7t2ruPaUlDAWFsa3vWSJ/nkuX2bM0ZHPs3dv2bc1eDBfx4svGp7nhx/4PC4ujF2/bt5616zhy0gkjP38s5wlJe1giYmlzMNDva6lSxkrLdVd9p9/1MceYKxWLcYaNeKPQ0MZu3FDd5mDB/nr3t6MFRerpyuVTLXNixf1t/XwYf56w4b8+fjx/PmCBebtqzni4/k6p0/nnzcnJ/788mXDy9y8qT4Gdetqvnb6tPo1gLHFi3WXX7aMvzZyJGMODvzxrVvW2ydt58+r2xMXZ7vt1ET0f4hURQ8fPjQrXqvkt48Y5+fnB6lUiqysLI3p2dnZCNBOJZEqqW5d3q/sZ5/xGuLDh3k3W19+qdmdW1Xx4AHvE/q773ht9Pr1fNAGiYTf1NesGb8z/v/+j2eFK8JXX/FeBQICgClT9M/TuDEf1AIA3nyzbG37+2/+XkokvFssQ4YPB3r35tnft94yvd6jR4FXXuGPFy4E+vdnkEiAF19U4s8/eTdjRUW8L+JevdS9Ezx6xG+OataM92TAlwEuX+bdlDVuzLOz/frxHijEhEErhN4cBBKJ6Z4dhBvb6tfnv4Ub4qzZpZnQlVmnTrzLwPD/ugY3VgF29676cVaW5uhzwo1tAn03uAk9OgQHA66u/LEte3YQ94Tx8KHttkMIqV6qdODr5OSE8PBwnTqklJQUjdKHsoiPj8f58+dxUtwZJrELiQSYOBE4exbo2ZMHLHFxPOgQ/7Ou7DIz+Z3nBw/ynjiSk4GxY9Wv16oF/PADDxp27wbef9/2bSou5l3IAcDMmca7u5ozh7fx9Glg82bLtyX05DB4MA82DZFIgOXL+e/Nm3kQasitW8DTT/MeBUaM0A2UGzQAUlKAtWt5248dA9q3B157jbfhf//jwVnv3ny/1q3jXwDq1uU9i9SvzwP2/v3VwZW4N4eRI3XbZCqQFQLf4GDz5rdUSQk/VwAe+AKA8Ofw+HHDy2Vnaz4XB5ZC4CuUGRkLfOvXh6rEw5Y9O4iPFwW+hBBzVfrA99GjR0hPT0d6ejoAICMjA+np6cj87y9xQkICPv/8c3z55Ze4cOECXn/9dWRmZmLSpEnl2m5iYiJatmxpsBaYVLyGDXnQuHgxz7L99BPQpg2wY4e9W2ZaejrvIur8eSAoiGeu+/XTna91a/XgAHPn8uDLlj77jAdi9eoBL79sfN46ddSB5Tvv8KDZXFlZvHs6AHjjDdPzt2/Pv+wAvHszfRnmoiKeHb57l38OvvxSf5dwEgnvG/qvv/gxLyoCPvkEuH0bCAvjQeyBA3ybYqGh/PjXqcP78R44kGdBjxzh++PtzbPJ2kx1aWYo8LVWX77nzvH3xtsbaNSIT+vWjf82N+Or3R5hX7p04b9NBb5CxteWgS9lfAkhZVJBpRdlduDAAQZA52fs2LGqeRITE1loaChzcnJiHTt2ZIcOHbLa9s2tGbEWqq0yz9mzjLVtq67xGzeOsQp6iyy2a5e67rN1a8YyM00vM3Ein79OHV57aQuFhYwFBvLtrFpl3jKPHqmXWbbM/G3NnMmX6d7d/GWysni9LcDYl19qvqZUMjZhAn+tdm3Grl5Vv2bsHFIqGfvsM8Y6dWLsgw8Ye/zYdDvOnGHMy4tv66mneH2y8JnT58MP+evPP6//9aFD+euJifx5ZiZ/LpPpr0G21Lp1fH1PPqmedusWn+bgYPg8Edot/KxYoX5txAg+bdYsdS219rELDeWvHT/OWJMm/PHhw+XfH0Oeflrd1nLe0kG00P8hUhVVmxrfyMhIMMZ0ftavX6+aZ8qUKbh+/TqKi4uRlpaGXqZ6aTcDZXwrt7Ztgd9/5xlIiYTXyrZtyzPClclXXwEDBvDyjKgonukVMn3GfPIJz0Deuwc8+6zmCHnWsmYN7/c2JISXjpjD3R2YP58//t//eM2yKfn56iy2OdleQUAAL68AeIZZPBz36tU8w+vgwOtzGzY0b51C2czJk/yzo6/XBW3t2/OyFDc3YNcuniUH1INWaLO01CEoiNd7aw8AUVbi+l5BUBDPYCuV/LzRRzvje03Uq56Q8Q0P56OkMaaZcVUqedkJUHGlDuLtm/M5JIQQoAqUOtgL1fhWfs7OfCjd1FR+yfqff4A+ffiNUxUxXKoxjPEBGSZM4DWh//d/PGjy9jZveVdXfgOVpye/tD5rlnXbV1DAjx3Ag0tLBggZNw5o0YJ3cWXOMMZffMEDk6ZNeX2vJV59lV+uz8riN64B/P1+7TX++IMP+CAbtta9Oy+pEY6Ttzfw5JP65zW31EG4uU0qVY+IZyhYPnuWl4qYc0OnMGKbOPAFTNf5CjW+YWH8tzjwFWp8Q0Oh6t5NXO6Qnc0HzXBw4PXRVOpACKmsKPAlVV7PnjwwePFFHhgsXcozU2fO2Kc9cjnPoArjoLzzDg9aLB19rnFjnjEGgCVLgB9/tF4bV67kwUrDhpo32JnD0VEd8C5frg7k9JHLgWXL+OPp0y0fktfZWX1T3NKlPOgdMYIHWc8+C8yYYdn6yqNfP55d9vDgwzcbej+FwPf2bd066OJidYApzvybqvMdOZK/T7t2GW9jURHw55/8sfbFKlN1vkLG94knNNtSXMy/eAA8QNcX+Ar1vYGB/PNh68D34UPgX9FYH3yUOP747l1+haUqOnWqatyzQEhVRoGvAVTqULXUqsXvyP/5Z36J/Px5fiPO++/zIKmi5OXxm6C++ooHeWvX8jaUddjW4cOB11/nj8eO1czClaeNH37IH8+dC5Slf/qBA4GICB5wzJ1reL6tW3m20N8fGDOmbO0dPJjfRFZSwstFsrOBdu14Jrmih3ceNoxnr4WeMPTx8+OBH2O6XwqEANHFBfD1VU83Vh5x8yZw6b9ByUwFRX/8wT/vderoltQIGd8TJ/TfLKgd+F67prkPbm68zcYCXyGLLZQ62OrKixCUiwfHEILhRo2AyEjbbNeWGONfVoYN413qEUJsgwJfA6jUoWoaOJBnvIYP5wHA7Nk8QNN3F7q13b7N+4nds4cHCT/9xHsTKK/Fi3m27uFDnu0sbzCxYgUvU2jWDHjhhbKtQyLhWWiA11cLWUYxxtTzvPKKefW0hra1bBn/IqFU8uBrxw7jXa/ZkqmRAyUSw+UO4vpecdAulBfoC3wPH1Y//vln430oi+t7tb8UtG3LA/IHD3gXbdqETHSXLnzZwkI+TShzCAnh080JfG2d8RWOU6NGUI0y+PAhDxgLCvgX36pG3Fd0bq792kFIdWdR4FtQUIB3330XrVu3hoeHB2rVqoW2bdti/vz5Zo+RTIit1anD+8PdsIHXyJ44wTOEa9bYbtCLc+d4puzsWZ7dPHSI39RmDTIZ78/W15f3NStkgMvi33/VpQPz5pkO4ozp2hV45hl+TN9+W/f1fft4N25uburBL8qqTRvez7CPD7BlizqwrKwMZXDFgzyYMz/AyzsEd+7wz4Ahhup7Af45Eroj067zVSrVgW/9+uoA9to1zcAXqByBr5DxDQsDvLz444cP1cHj48cVNwCMtVy4oH5saUkQIcR8Zp9eJSUl6N27Nz788EM0adIEr7zyCuLj4xEWFob3338fffv2hdwWt57bCZU6VG0SCb+0/uef/PJ4YSEPvmJjeWbWmg4c4ANT3LjBs6gnTugPPMojOBj45hu+X2vW8MdlsWwZDxBatdI/+IKlFi7kNZ3Jyfw4iAnlFBMnal7WL6sFC3gmrE+f8q/L1gyN3qZ9Y5vAWI2vkPH18eG/f/rJ8HaFwNfQny2hr2JxkAXwL0QKBX/s76/uJSMjQ521FvZJCHyvX1fX1Yp7dAAqrtTBUOAL2PbGOlsQZ+GF40oIsT6zA9/Vq1fj5s2bOHv2LJKSkrBo0SJ88MEH+PHHH3H27FlkZGRgzZo1tmxrhaJSh+ohJIQPQrBsGb9Ratcunj3cssU66//2W/WoXj168OFzhcvW1ta/v7p7r5desvxybm4uvxkN4DfeWSOr1KSJeuAL8VDG6el8xDSptHwZ6qrKUAZXuysz7flv3NCsSc/J4VcTAH6TJMDLHfQRX+IXhijWJgxocfWq5nShvtfbm9+0JwS++jK+devyLL5SyYNihQL47TfN/bJXxlc8+lxVuwgp/jJiyeAwhBDLmP2vb/v27ZgzZw6a6RlrtHnz5pg1axZ++OEHqzaOEGtwcOCjf50+zQOC+/eBUaOA55/XvDPcEowBixbxGllhuNy9e62T2TTm3Xd5N1qFhbzMwJK715cs4X3htm/Pb6CxZps8PHi2UfhC8dFH/PeIEZW/LMEWzKnxFQsK4qUIpaWaVySE4ZpbtOA3Nzo48C8VQjAqduwYD0aDgviPPkK2VjvwFQLGgAD+Wxz4amd8tet8N23itbU+PsBTT/HpZQl8Cwv5lZJnnjE9rzkZ34IC87ddGYgzvhT4EmI7Zge+58+fR6SRW2WjoqJwvireUUBqjJYteW3jnDk8E/nddzz7m5Ji2XpKS4EpU9QZuIQE3s1VWW/esoRUysscgoJ4hmjSJPPqlu/eBT79lD+eP9+6NYT+/jzbC/BjcuUKPx6AZQNWVCemSh20A18HB/3LCPW9vXrx3iKEnhn0ZX2FbuOM9ZUszviKPzdCxtffn/8WrlpkZOhmfAF14Hv+PK8VB/iAIEIQKgS+lpQ6pKQAaWl8CGkhy62PePAMY4GvuRnflBTe84ol5QWFherSEGuhUgdCKobZ//4ePHgAXyPpLF9fXzysRr2IU41v9SST8cDv6FF+mf7WLT4AwiuvmPePsqCAZ0vXrOGZrxUr+M1iFXkzir8/v9lNCILXrTO9zOLFfP86d+Y9X1hbQgK/BJ6RwY+nQsFrcTt2tP62qgIh43vrluaoe4ZubhMvIw58hfpeYTBKIajVDnzT04Fff+WfQ2N9GzdowOcpLFT3zQsYzvhevWo88F2yhLe3bl1g6lT162UZuU3cR/HWrYbnu32bt9/BgbeprBnf3FzeF3R0NO/9Zft289qZl8e3K2S3raGgQPPqAGV8CbEds/9dK5VKSI3cAu7g4ACFtb8C2xHV+FZvXbvyAS7i4/nzlSuBDh0MD+cK8KxYZCSwcyf/x/7DD3xkMXvo2VM98tqrr/JMmSG3b6uHDJ4/3zZ934qHMhaycTU12wvwANLFhZceCMFuYaG6myrtm9sA3Rvc8vPVPThERPDfgwbx3wcOaA7hLAwoMnKkOqurj5OTOugWlztoZ3yFwPfGDR6ESSSabRYCX2G52bM1u5eztNSBMc3Ad8sWw1cyhK7zmjblx7isGd+33uJfIAXGzn2xK1f4+2hoIJCyEPppFlDGlxDbMTvwZYyhb9++6Nixo96ffv362bKdhFiduzsPeHfv5qUDly7xS8lz52pm6QDg4kXel+6pU7yOd/9+3lewPU2fDgwZwv9JjhhhuF550SJ+ybl7d36DnK2MHw80b84ft2lj221VdhKJ7jDEQgDs7q5/6GrtjK9QsxsWpg5WmzXjVypKSnh/0QAPxITaan3dymnTV+ernfH199cMZIUaZO11ALxE48UXNbdhaanDpUt8v52c+M+FC4bLHf74g/9u25b/LmvgKwTQwpDXQo8Ypgh19dYsdxB6xRBQxpcQ23E0d8a5xoZn+s/TTz9drsYQYg/R0fyfYHw8r02dP593z7VxIw/kjh7ll5jv3+eZsF9/5dkme5NI+OARHTvyLOH48UBSkmZGNzNTXQrxv//ZdqQzR0fg8895Bvqjjyp+VLXKpkEDdUAHGB68QqA9iIVQ3ytkewG+3KBBfPjmn34Cnn6alxsolbyrvnbtTLerUSPex7KxjK9EwtsjBJ9C/bFAHPjOnas7fLOlpQ5CtrdXL/XgL1u2AK1b686rHfgKXyIePLCs1EGYd8QI/iXi9GkeyAoXNoWMs/Z7Jb6h9NEjdeBdHjk5ms8p40uI7Vg18CWkqvLx4Te7DRnCb1w7dYqXPkycCHz2Gc/AdOnCayuF4KAy8Pbm9ZDduwM//sjrjcU1nsJNO5GRFdP/bY8exssuahLtnh0M3dimPb8Q+GrX9woGD+aB7y+/8HWuX8+nm5PtBdSlEOIBKLQzvgD/kicEvuL6XoCXPYwYwa+MjB6tuw1LSx2EwPepp3i9sBD4vveebuApZGq1M75ZWZrBrqmMrxD4RkTwLHxBAb+y07IlD2i7duX9XWt3fSguMcnLs07gqz1SG2V8CbGdct+Sc+jQISQnJ+PfsvYLRUgl8uyz/B9r//78Mu3Klfyf0ODBvK6yMgW9gvBwfpMdwIMfIWC6dg348kv+WKi/JRVHO5A1N/C9cYMHXkLfuNqBb48eQO3aPFh64QX+xaZHD83MsDH6+vIVMr7iwFfcH7V24CuR8IAwKYln+rVZUurw+DFw8CB/HBPDM9rOzjwI1R4Ku6RE3d+tduCrPSy5scD38WN15jYggH/JBdTlDvv28R4rfvxRt9ZYnPEVB8HloZ3xLSwEvv6an8OEEOsyO/BdsmSJRtaXMYannnoKUVFRGDhwIFq0aIFzxvqgqWKoV4eaq149Xs6wahV//Prr/I5vcc1jZfPyyzwIUih48J6dzUsbSkuBfv3MD4qI9Wh3T6Y9rK+2unV5wKdQ8M9bSQmfJi4rAHigGRvLHwtfcmbONL9dxmp8xV/shBvcxPtiLktKHVJTeYAcHMz7K/b05AEwoNu7w8WLPMvs6akOxoXAVwjeBcZKHYRsr0zGlxdGWhSuVghdHJaU6O6DOPDNyzO9f+bQDnzXruX9Nhu7UdGaEhKAoUOr3jDPhJSF2YHvd999h5YtW6qe//DDD0hNTcXhw4eRk5ODTp064b333rNJI+2BenWo2SQSPsTxzZv8srKRDk0qBWEo4xYteC8OAwfyjBHAA2BS8SzN+Ir78hXeu1699NcDi/vqbdNGHQibQwhoc3P5DWEFBeogUbvUQaCd8TXFklKHX3/lv596Sr2vQu8Vhw5pziuu7xXmNVRqYCzjKwS+fn58PULgK2R89+5Vz6t9MVOc5bV2xle4gVD4rFQEpZL3Af3jj+peRAipzswOfDMyMtBWuLYEIDk5GU8//TR69OgBHx8fzJ49G8ePH7dJIwkhpnl48C7W3NyAkyf5P7QBA3itIql4QuB78ybPvJsKfMXL7N/PfxvK1Pfvrw6S3n7bshsJa9VSZ3avXlVne11c+GdIYKzUwRRLAl8hyBT3AtKjB//9+++a9a5C4NumjXqaocDXnIxvnTr8tzDE85kz/IvKxYvqee/f11zWFhlfocbX0Ih7tvTggfpxRfZHToi9mP0xl8vlcHZ2Vj0/fvw4ugvDCAEICgpCjvb1GkJIhWrZUnNAC6rttZ+6dXlvBwoF767KksBXqCvVru8VeHnxvpnfeov33Wsp8Q1u4vpecQAdFsbbL5NZPuy0UOpgqsY3P5/X0gKaQX7TpjwbW1ysmYXUvrENKF/GV/gC0LQpD/ofP+bHVEw742vLGt969XRfs2T0u7IQ31hXWlr29WzfzsszTPWmce0aL6sQhuMG+OedwgdSUcwOfBs3bozU//rXyczMxKVLl9C7d2/V6zdv3jQ6sps97dy5E82aNUOTJk3w+eef27s5hNjUCy8AGzbwUd1q6shplYEwshjAAzZhYEtzAl+A99ihrzsvQVwcH8RE381lpohvcNPuykzg7s6Dma1beU2tJczN+Kal8aAnJERz+xKJOusrDpC0uzIDDLdNCHzXrOGlE+LMpnbG18GBnzeAbi8OxkodrF3jqy/wFTLyYkolD1I3beI14+XpSUUc+BYW8s+DucM9iz39NC/REYZyN+SVV3hZhfiLzssv8/dC6JuaEFsyO/CdPHkypk6diri4OMTExKBbt24aNb/79+9HB+HW2EqktLQUCQkJ2L9/P06fPo3Fixfjvva1K0KqmTFjgOeft3criBDIHj3Kf3t58VIDQ8TlBRERtrv0LL7BTV9XZoIBA3gXf5YyN/AVRkvTdw9xz578t3DscnPVAz2IvxBIpZolGkIwK2Qely3joy0KddOAbuALAJ98ojkMsRBQ2yrjKwx+oVQaL3XQvmkPAJ58kg9mMno0Pyb6sv7ffgt88YXpdogD32vX+JWK7t2Nf5s6dYrfRCseXlsgHg1PH30dQH32Gf/97rvGlyXEGsz+s/ryyy9jxYoVuH//Pnr16oVt27ZpvH779m1MmDDB6g0sr99//x2tWrVCvXr1UKtWLcTGxmL37t32bhYhpAYQblYTel8w1KODQJzxNVTmYA3mZHzLQyh1UCiMXz4X7h3WF/gKGd+jR3lWWChzCAvTzfKKyx2EYyhkLYVM7/ffq+fRF/g6OQHbtvHL8NHRvDcUwHjgW9aM7+7dvJTj3Xf5lQChN4XAQN15s7I0nzPGb/oTd3WmnctRKHgGe+JE3eGQtYkDX6EXjfPnJQaHjAb4+7V5s/4+nO/eNT6inbEeQspTakGIuSzKJ8TFxSEpKQmrV69G3bp1NV5btWoVhg0bZtXGAUBqaioGDRqEoKAgSCQS7NixQ2eeVatWISwsDC4uLggPD8dh4b8MeEBeT3T9qH79+rilPT4kIYTYgBCECQGesTIH8fyAbbugEwe+xjK+ZSVkfAHjWV/huHTpovtax448gM7J4cHbgQN8uvjGNoG+wFfI+AolJsePqzOU+gJfgN8YmpTEA1Phi4C1e3X44w+eWX7wAFiwQF3mUKuW/qsB2hnf0lLdbse0j7G4VEHIqhsiDnzFI989fmy6hkYcfLu761+nNvE5oH1szQl85XLqdo2Uj9mBr1KpxJIlS9CjRw906dIF77zzDopsXXUPoKCgAO3atcPKlSv1vr5582ZMmzYNs2bNwpkzZxAREYGYmBhkZmYC4P0Na5PU9LFUCSEVQgjChCFoTQW+AQE829ijh23rs4XA9+ZN9chy1gx8hYwvYDjwvXePb1siUfeqIObsrM4Eb9rEh2YG9F/WFwe+QkaxsJDfHCfuFUKo3xWCfe3AV6x2bf7b2hlfcQ0sY7z7QQDw9eX7rE078NX3b1d7pDfxMdceBESbOEgVly48fKgeh9rQSHJC3+aMabbL2Jcd8WcjI0PzNXHg+/336kFcBHI5H0aeutcn5WH2bRGLFy/G7Nmz0bdvX7i6umLp0qXIycnBOvEt5DYQExODGKE3cz2WLl2KuLg4TJw4EQCwfPly7N69G6tXr8aiRYtQr149jQzvzZs30dVI/07FxcUoFp3lef/9ZZPL5ZDL5eXdHZOEbVTEtgipjirTOVSvngTiP7NBQQrI5cbTVTt3qh/bahe8vYFatRyRny/Bb78xABL4+JRCLjdyfdtCzs6OKC6WID9frgoixY4f58emaVMGV9dSvfvarZsDDh+WYsEC/rxXLyVGjFDozOvpKYWQxwkOVgCQoqCAISenFIBMNd933zG8/nop7t1zBCBB7dqG99nT0wGAFLm5Ssjl6mv3jx7xZQHg4UP+mlwOTJ/ugHPnJHj0SILatRlGjlRiwgTddV+8qF4eAI4d4+319VXCwUEJ7X/Lt29rfmZ4llkGbeLPO89y83lOntRsv7Z79/h+AppBcF6eM+RyORYscMDixQ44eFCB8HBhf/i63dwY5PJSFBUBCoVMtKzc4GdXLldv79q1UrRpw1Trk8v5+tLSJHjuOX4cSkrUK7pwAbh2jc/76JFc7xcFUnOZ+zff7MB3/fr1+PTTTzFlyhQAwK5duzB06FCsXbvWbhnUkpISpKWl4W2tQeqjo6Nx7NgxAECXLl3w119/4datW/D09ERycjLeNVJBv2jRIr0DcezZswduFTh0V4owdBAhpEwqwzl0754LAHUHtffvn0VycgWOTmCEn19v5Od74+5d/vf7+vXfkJxsvT6lpNJYADLs3p2KevUe6bz+/ffNADRHYOBNJCfrHznByckfQDcAgKOjEiNGHMCvv+quq6AgHAAvoM7OPgngCdy7V4CffjoB4Ek4OirAmATp6Q747LNU3LnTC4AMFy4cQl6e7voA4ObNEAAdcOlSNjZsOIuDB0PQv/915Ob2AcAjruvXc5GcfAxnz/phzZoeoqUlOHpUiYCAZI0u4hgDMjMHApDC07MYeXnO2L79HoC6YOwezp/PBKCZzkxPz0Jy8inVc+3PlCA5OVn1+MaNWgD6AAB++02BnTuTDd4o+ddfnQDodifx8KETUlJSMH8+v7tx4sQHeP/9/+40BJ9WXPwvkpMPIy9PBkA9ikpKylFcu/ZQ7/YuX24BoCkA4OjRs5DJbqrW9/BhIZKT9yI1tR6ATjr7dfu2O4AnAQBJSXvh6Vmif6dIjVRoZnckZge+//zzDwYOHKh63r9/fzDGdGpoK1JOTg4UCgUCtK7RBQQEIOu/OwIcHR3x8ccfIyoqCkqlEm+++abRbtdmzpyJhIQE1fO8vDwEBwcjOjoanpb26VMGcrkcKSkp6NevH2Qy3W/1hBDjKtM5pFAAkyczlJby6Cc2ti369tVTpGoHGzZINS41DxzYxWj3aZby9HREYSHQuXMvtG+v+/q6dTzrN2RIEGJj6+rOAKBbN6iyvQkJDC+/rP+Ov507HXDkCODoyDB4cDjefx+QSNzRoUMkAKBuXQe0aMGQkgLk5UWisJBv++mne8HHR3/7S0okWLkSkMn8cfr0k/jmGymaNWuKkhJ1BOnk5IvY2Fg8fMjf37ZtGWbPVmDkSEcUFzsiKipWY6jze/eAkhK+7aeekmHLFuDyZf7/q3nzOnjiCd3/TVJpIGJFQ/Ndvqy/veJ50tLU0XZhoQytW8ca7It5xQr9w1Lm5Tmjn3CHH4DAQB+NbQBAvXreiI2NxX+VhSoXL0bg228l2LJFoVPek5qqPn5Nm7ZDbKy6bzpnZzfExsaioEDdfvE2hT6fAaBr1yc1ekFhjF8hcVJXaJAaJs/M2iOzA9+SkhK4iu5YkEgkcHJy0igLsBftjDNjTGPa4MGDMVg8xqcRzs7OcHZ2RmJiIhITE6H47/ZUmUxWof9EK3p7hFQ3leEcksl4H7XCTUBhYY6oLKd1kyaaz+vXl1m1bcK/C4VCd72MqW9se+IJKWQy/cGXvz8fUOLiRWDuXMPzCaUUfn4SeHvzjRUUSFBQwP/FeXlJEBUlQUoK8NNPfB1SKVCnjsxgJlSo/33wwAFXr/LH//wj1ahlzc93gEzmoOo5onlzCZ55xhGOjrxeNT9fplF/LB4spGNHB2zZAlXQXKeOA9zcdBtz+LADRo50wKZN/AYyQz0miD/rJVqJ0MJCw++tod498/KcNNbp7Mz3VczdnU/TDgOELzWvvOKAX37Rv34AKC7WPB9KSyWQyWQawatUqn6PxDe1FRWp9+mXX/gw7c7O/IZNO+XiiJ2Z+/feoq7P58yZo3G5v6SkBO+//z68RGf20qVLLVllufj5+UEqlaqyu4Ls7GydLDAhhNhDaKg68DV1c1tFEvryBXh/wYYyn2Ul3MSk70anzEye/XR0hN5ssNgHH5jelvAvqE4d9Q1XhYXqHh28vNS9ZBw/zn/7+RnvJ1l8c5twj/SVK5rzCL06CD0z+Pnxm/V8fPgNdPfv83pqR0d+PMSj97Voobmu4GD9N7cBwI4dQGoqEBNjeCQ3pRJISeHr1b7i+0h/NQcAwz0w5OVppk6FmEIcfArH2tBobdrHC9AM3LXbKdzcJn5fiovVX6LEAba4Rw3hYnRxMR/RULhKYA6lknf91qwZMG+e+cuRqsvswLdXr164KB7AHED37t1xTdSfSUXX+jo5OSE8PBwpKSkaXamlpKRgSFl6XReJj49HfHw88vLyNAJ7QgixhHCJ2ccHqMDbBEwSenYAeMAo1Z9MLTNjg1gI2d42bTTv8i8rceArdKsll6uDOi8v3hOAs7M6eDLWowOgGfgKAbR2ICdcWRW6AfPz47+FwPfWLd5LR926QHq6OvCtX58PLy7w9QXGj9e8lK9N6F3CUOD766/qAHD7ds3XjAW+hsoiHz501gg0hSysOMg1FfgaGnXO0LaFwFf8WXz8WP1Z0sy269+mdldnjPEfQ19yjhxR9/FMgW/NYHbge/DgQRs2w7BHjx7hiuivTUZGBtLT0+Hj44OQkBAkJCRg9OjR6NSpE7p164Z169YhMzMTkyZNKtd2tUsdCCGkLITAtzJlewHNwNeag1cI9AUrAmMDV5RFZCQf/GHYMM0vF3fu8N9eXuru0YQhkM0NfMVlA0IHQRIJD6YKC3kGU5zxBdTZ87Q0Xt5w9y4P6m7e5NODgzVH6fvwQ76MdsZ39mxg5Ure5+/Nm8DrrxvOzO/fr36co3WPorHAV7ssQpCX56TRlZswNLa4jFLIAhtav3iYaIE5GV/Nkgb1Y0MZXzHtHkwHDODdtJ09C73lHtbulVWpBKKi+OdHz7ADpBIowyjvFevUqVOIiopSPRduPBs7dizWr1+PUaNGITc3F/Pnz8edO3fQunVrJCcnI9TY8DBmoIwvIcQahAEXtC9t21v9+jwQkMut24evwFipg1BuYKRnSYu0bs2DUiEgdXDgAYjQR67wJzwiwvzA18ODZx715T78/dX1uvn56kBTWKdw//SFC+pl8vM1Sx2kUiA5mbd7/Hg+XfvGrEGDePD+xRe81tkYcU9O/3VqpKIvMGWMB72GAt/MTE9cuiS+SU69HwLh2BjK+OpjTuArbpP486Mv8NUOdLWf//or/33yJNC9u257xBeqlcryDxN+8SIvS7HW+oj1Vfq3JDIyEowxnZ/169er5pkyZQquX7+O4uJipKWloZcVxvpMTExEy5Yt0Zl6yiaElMPgwTzzs2KFvVuiSSoFGjbkj22Z8dUOfIuL1aOJ9expve0JAYxEoi53EGd8Ac3R8Ezts0QCvf0PAzywFbKH4sBXO+MrDnzz8tQZX2Ho6pgYPqyw0HbtjK+rK68RNoe4Z4VDhzRf0xf4Pv88Py7agW9UFODjw3DvnhsmTVLXHAjrEGd8hUDVloGvOCMrfiy0Q7vrVkOjuhmqxBRPl8t52co77+ivTzaHuD3Ghn0m9lPpA197iY+Px/nz53FSuCZHCCFlIJUCQ4bYJrgsL6HcwRYZX0OBb1oaD379/XV7lrAWodxBO/Dt3l0d6JjK+AKGA18PD0Do3TIvz3Dg+/ff6mXy8jQzvvqUJ/AVRuADdEdE0xf4fv+9/hHZmjQBVqzg0emVK+qoUAhuxRnfDRuA2FjDPUMAusGfOTW+hgJffRlf7cBXvD3xY0NjG4gD35IS4LXXgEWLgA4d9M9vijiwp6GVKycKfAkhpIbqxseGQNu2xucrC6HUQbuG8vBh/rtnT8NZuPISMr7apQ5eXkC7dvyxOYGvoXpaDw+gVi3+2FjgKw76hTpdQJ3x1aZd6uDiYn7gKx5uWJuxGl9tvr76v6Tpy/gCvJTg008Nr0+7Flc746svULSk1MFY4CsOPHv3Nn0ciov5TYiAZcdMTLw/dItQ5USBrwFU6kAIqe5mzgTOnQPGjrX+ug1lfIUaW2uWOWgTMr5CT5fi2zRmzQL69OH1s6YYyvjWqqXO+N68qc5UCrW9+gLma9d4QCeRGO5ntjwZX303kgksCeJ8fAAvL91r9IYCXwA6A1iI5ebyAHTVKh5Uage++jKx5pQ6CPurXaohDna1A0/hS5eYeJ6SEsNfSswl3j5lfCsnCnwNoFIHQkh1J5XybrVskXnVF/gqlcDR/0a9rYjAVwhqxIHvM88A+/aZF+CIA1/xwJ3ijK9QVuDurt5nfYGvUPbg76+/dwFAN+NrSeCrj9ATg6UZX32DlOordTDH48fAwYNAfDwvH/jrL/VrhYW6gatCYV7GV8jmG6vx1Q589fXgIF6+uFjzS0lZAlcqdaj8yhT4hoWFaQxlCABPPvkkGgp3ShBCCKnR9HVnduEC74/Wzc30wBXlIZQ6CMraMY848BVf/BPX+Apd2QtlDoD+wFcIkMXzadPO+FpS6qCPULJgecZXd7qwDkP9/gL6a7YLC9XdwAHqUgLhNe3ANT9fc5o40BYHvr/8woNp7Uy3uNRBO/DVV9Ms3lZJiWYJjNB/syUo8K38ytSd2dixY1FHq0Bq2LBhyNHuPLAKo358CSGk7PR1ZyaUOXTrZjjraQ3aA4VYI/Dt2pVnigH9GV9xQCuUPIgJNbjGRsgTHxMnJ94VVnkD39u3Let1wddX//ES6nH1BY+C6Gjg8mXNaY8fGw4g9WV8b9zQnPbCC/yGyI8/1t32qlW6I88Jga9Coe4vWqAv4yveVkmJ5uAZQgmLJSjwrfwsCnwZY8jMzMSbb76pMXQxwEsDqhPqx5cQQspOX6lDRdT3AtbP+Pr5aQ74Ia7xNTfja07g6+Cg7ltZOH7WzPju3s2D94ULDS/j5cWDbicnBUpKNIfzKyw0PuBDp0660x4/Nlx//M8/wLZtmtMyM3WD4aVLeeCrb9vao90Jge/MmcCSJZqvmcr4FhdrBquGeoIwhmp8Kz+LSh0YY2jSpAluia9bEEIIIVr0lTqIe3SwJe2Mr76aVXMIgW9oqGaXb6YyvvqCW2HAC2OBL6Audyhr4CveV+HCrBD4PvUUDwaXLze8jIcH/+3mphv1FRQYzvj2769eVsxY4KtUAq+8ojlNX+Ar0LdtQze3aQe9gOka35ISzYwtZXwtk5rKr4ycOmXvlhhnUeDr4OCAJk2aIFf72gIhhBAiol3qcOMGz/BJpcATT9h229YKfCMj+ZDTL7wA1K2rnu7hoQ5ghUBHHPh6empeMhfTVwYhJtzgJhw/d3fD6wLUAbJAfKuNduArSEnRfO7tDfzf//Eb/4Qhtt3cdKO+R490g0eZjGfyN2/WPe4AzxIb63FC2z//WCfw1UffoBTi5a2R8RUHy2UNfDMyeIbbkhIVQ3JygMRE430tW0vv3nxwmqeesv22ysPim9s+/PBDvPHGG/hLfGtmNUTdmRFCSNlplzoIvTl06KA/M2hN4lIHd3d17waWatiQByGvv64b+A4YoDmvOPA1NuqbpRlficR41jc0VLfNAkM3t128qPncyQnYuBHYulXdw4eLizqCE4bdffRIN/h0dAR69OAlEtpBOMDf/3//Ndx+bcYCX30ZW+32GBstbeVKIDtbc5q1M77i9RkKfC9dAoYPB06fVj/fulXd9h49gBkzgIQEy7ev7emngalTgWefLf+6zFURQXZ5WBz4/t///R9+//13tGvXDq6urvDx8dH4qS6oOzNCCCk77VKHtDT+29bZXkAz82itWzTEgzrUqsUDeGEwDEC3twZD/w5N/ZsUMr7iINJY4OvhoQ5MAfMCX/Eob+JtiimV6j7uhOBaX8ZX/KVC3w2Lxkod9MnLK1/Gd98+3RvsxLRzdsYCX32DY8yZA3z1lXraP/+o+4vWXsZQ4BsRASQlqc+FZs2AkSN5TxWAesRB7fpnwU8/ASEhvLTAFGEe7Sy/LQlXKyori78HL9cuDiKEEEK0aJc6CMGW+CYxWxFnfK0V+MpkvEwhN1edsR4/Hpg2jT82FPhKpZrBlKUZX8B44OvszPdX6PZLnJk2tzszfYGvXK6OpgMCeOY7IkJ3PnGwq68kw9LA9+FDIDlZ/2vmBL5XrgBNmxpev/axFC+/cCEPQgXaGd/ffwcWLOCPx4/nbRVKQ4RsranA9949ddZZO7A+cQIYOFD93FBV6YgRvN29exvOcG/fzodftgftbvkqG4sD37G2GOKHEEJItaJd6iAEvtqX5m3BFhlfAOjenWfOWrTgz194QR34Cje7CYRa3rAwzdpScwNfcdbMWODr4qIZ+IoDcCHwlcuNd0OmL/AtLVUHvsYG+xBnfPWVlFha4yuUxOijr/TA2H4ZcuIEz7C+/75m8HniBP8RaAem2iUbQo8eAA9yHRxMB76//qr5XFzHq12CYoizs+GsuODpp81bly24uPArA3K57nlRGZRpAIurV69i9uzZeO6555D931eXXbt24dy5c1ZtHCGEkKpJu9ShOgS+27fzy9CBgfy5nx/vauuZZ4CoKM15hQBXe1AHa5c6ODtr7q/45jlxd/vG6mxNZXzNDXytkfE1RKHQvx5TAaC20lJg6FB+s+WYMcCnnxqfV0x7hEN9g2WYCny1j5G43v2HHwy3RUzfTYQV6do1XqZhqBTD2ZmfI56elg2eUlEsDnwPHTqENm3a4LfffsP27dvx6L+9+uOPPzB37lyrN5AQQkjVIy51KCpSd+dVEYGvLUodAB7kaQehr7/Ob0zSDh7/7/+A8HB+SVzMVK8OZS11EGgPsyysz9j4UvoCX8bUUZ6xwFdc6mCNjK8hkZH6a1qN3cymT2mpZjAmfC4NzStmLPAV5rU08LWG7Gzeq4bwJaAsI87l5qr7mhacOKH/c/PSS8Bvv/EvfPo4Oqrf8z/+sLwttmZx4Pv2229jwYIFSElJgZPobImKisLx48et2jh7ol4dCCGk7MSlDjdu8Mfu7qYzntZgq4yvJaKjeX+mvXtrTi9LxnfgQF67GxKiO7+zs2ZWWbzvbm7qjOK9e6a3KTZt2mk4OTGsWgXUq2d4WVMZ3wcPrNOfrTD4SXmVlprfHu1Sh4oIfLXLHcLDgf37jS/TowfvtWHOHP5FpCyDngQE8LIc4ca6vXv5CIthYbpfXKr6IL0WB75//vknhg0bpjO9Tp061ap/X+rVgRBCyk4c+AplDiEhusGDLdgq41sW4j6EZTLdUeW06avxHTqUByTiG5/E83/yCS+12LZNs1cHFxf1/ot7HtCmL/Bt1+4e7t8vxeTJ5md89QV1lnRlVhFKS83PEhvL+CqV+gNfU/34OpiIupo313x++jTQt6/hdgDqGvIPPyxbF2yAulRDyF/u3Ml/P3rEryJ89pl63qo+MIfFga+3tzfuCF8JRM6cOYN6xr4WEkIIqTGEwLe4WH0JtSLKHIDKkfEVODurg0MfH9OBv76Mr0Bf0OzszDOy+/fzvmG9vPgwvleu8CBLuNnt5k3T2zQ0vTw3t1mjzMGaDAW+PXroTjOW8c3O1gwAzc34lmVQDG1l/fI4frzmEOIC8fFYsIDPJ+6JBODlDb//zh9bEvhaWopSESwOfJ9//nm89dZbyMrKgkQigVKpxNGjRzFjxgyMGTPGFm0khBBSxYgzlpcu8d81MfCVSNRZX3PKPPTV+AoMBb7aWrRQdxsnBL5CuYk+hgJfQVCQ4dfMKXUwh60HNRGUluoGdQAQHKx/XjFxwNmsmebrwmNxYKkvQNQ3CIelyhr4rl+v/2Y+8fE4c4bPt2uX7nxdu/Lf4v3ats146UO1CHzff/99hISEoF69enj06BFatmyJXr16oXv37pg9e7Yt2lhuw4YNQ+3atfGMoUpsQgghViUO3P7+m/+uqMC3MpU6ANYLfPUFh6b6TC1Pxlf8uqFRxEzd3GZu4CvugcKWUlP1lwPoe2/kch64CYGeOODMy9PM3grrLCxUT9MX+Jal+zXB+fPAK6+o63DLQl/Ji7425eUZXod4v555Rn+2vDKzOPCVyWT45ptvcOnSJWzZsgWbNm3C33//jY0bN0Jqi9sVreDVV1/F119/be9mEEJIjSGTqTOAwg07NTHjC1gW+AplBfoykOZmfMWsEfgCwMcfqzN+YqYyvubW+GoPAPL99+YtZ6nFi83bPsCD2WHDeAa9qEg30yoOfIWsqa0C3/PngVat+LDL5cmi6htdT1+bDJUzjBqlewOecEVHcPVq2dpWUSwewOLy5cto0qQJGjVqhEYVMQSPFURFReHgwYP2bgYhhNQoLi68g37hH2FNz/ia6soM4Hfm9+3Lu+/Spm8oWHMD3/KUOgj0Bd6manzN7cfV3CGfAR6AWvsSur7AVy4HfvyRP969W7e3BHMzvnfuAL16AS+/XPZ2x8aWbTlt2u/RtWvAzz/rzmeonVu2WLa9alHq0KxZM9SrVw/PP/881q5di4vmDjViQGpqKgYNGoSgoCBIJBLs2LFDZ55Vq1YhLCwMLi4uCA8Px+HDh8u1TUIIIbYnXK4XsmEVFfiKywQqU+BrTsbX3R3o109/Zk5fRtXcwNfSXh0MtU2bqV4dzKUdeBrr/cIWF5cNZXwF16/rlnuYE/iuWMF75bhyBXjjjbIP6CD0jFJen33Gs8eCRo3Uow+KWRqw/vRTuZpVoSwOfO/cuYOPPvoInp6eWLZsGVq0aIHAwEA8++yzWLNmjcUNKCgoQLt27bBy5Uq9r2/evBnTpk3DrFmzcObMGURERCAmJgaZmZmqecLDw9G6dWudn9u3b1vcHkIIIdYhDkAdHdUjntmao6M6cDIny2prlgS+xogDPiHgNTfwNcbcwFfftkxlfM2l/T65uam71jK2TWsxlPEVTJvGuxYz9Lq+wPePP/hyQm8IgPnDEtvKvXu8ZMIUS7ssGzJE//TKmPG1+OMTEBCA5557Ds899xwA4MqVK1iwYAG++eYbbN26FZMmTbJofTExMYiJiTH4+tKlSxEXF4eJEycCAJYvX47du3dj9erVWLRoEQAgLS3N0t0wqLi4GMWigpe8/yq85XI55Nboh8QEYRsVsS1CqiM6hyoPZ2dHALwwsn59BqWytML6AF2+XIJbtyQIDFRapQup8nj+eQmuXXPAwIGKcrWFj6bG/227ujIUF0vg6KiAXG74oHp7q5cxRCrVXIehc0gikUI7X+boqIRczlP6/L3Vk6o2Q+3aCgDqyN7JSY5WrQA3N0cUFmoW1zo6MgifK+F5aWn5Ooj28pJDu+1FRZpt0paXVwrh2D5+LIdcDhQUqI/R7du6y2/eXK5m6ti9W90GS6jfW/3vl1KpeYzLqrS0FHJ5xUS/5v7Nt/hoPXr0CEeOHMHBgwdx6NAhpKeno0WLFnjllVfQW3uImnIqKSlBWloa3n77bY3p0dHROHbsmFW3JVi0aBHee+89nel79uyBWwUOkJ2SklJh2yKkOqJzyP5KSyMB8FoDd/dcJCcfrbBt16nDf5KTK2yTRs2cyS+Xaw8La4k//ggE0AUAIJEUA3DBxYt/IDk50+AyN254AOhr8HUAuH79EpKTL+lM1z6H7t7tCEDzrrucnLtITuYpzeJiBwCDVK+5usrx+LF5gXBW1p8A2quenzixH5cvFwGIhXZwplSWakzz8CjGgwd6CqAt8Mcf+wH015h29ux5AG0MLhMXpw6hUlOP4fbtB7h5sycAnr6+ePFvAGakV8th8OCyBaft2z/ElClnYeizIZdrHuOyOn78BPLzK2Zws0Jxut0IiwPf2rVrw8fHB6NHj8bs2bPRs2dPeNmoiConJwcKhQIBAQEa0wMCApBlrGBJS//+/XH69GkUFBSgfv36SEpKMjgU8cyZM5EgKuTJy8tDcHAwoqOj4SkegsdG5HI5UlJS0K9fP8j0FXkRQoyic6jyWLhQqgr0OnTwQay17tCpoTp04KNzubszeHo6499/gU6d2iA2trXBZe7d411gGdO6dVPExjZWPTd0Dm3dqpv9rFcvQPW+aifcmjZ1xNmz/LGbG9PI3EqlDAqF+nnv3q2xerV62UGD+sDbG3B1dYR2POPiojktKMi53ANljBjRBy++qDktLKyl2cufOxeBTZskEA9g26xZc8MLWImjowNKSixf7vx5P0ydavgLkYODdepJnnjiCfTqVTEZ3zxjfbCJWLxnAwYMwJEjR7Bx40bcuHEDmZmZiIyMRIsWLSxupLkkWn2IMMZ0phmze/dus+d1dnaGs7MzEhMTkZiYCMV/d2XIZLIK/Sda0dsjpLqhc8j+xDW+YWEOkMksvq2EiISE8G7JatWSQMjduLs76r0RTuDvr9kLgr4eEVxdpZDJdINa7XNI33ZkMvX7qn3TWePGElXg6+4u0QhWpVKJxsAJfn6a4YiXlwwymf5tOjpq/v/n5Rzl4+kpg6Oj5g1tcrn5d9F99ZXuZ7siunjVLgOxFl5WU35SqfHPpzWZ+/fe4r9CO3bsQE5ODlJSUtCzZ0/s27cPkZGRqFu3Lp599lmLG2qMn58fpFKpTnY3OztbJwtMCCGkchEHvhXVo0N1V68ev1nO3JvbHB2B2rXVz8WPBebe3KYvjhPnoBy0IoqGDdWPtXtp0L5BTXxzm4ODuk3m9G5R1pHMBN98w9ehva3yjrJWUfXstmDvunhbKvPX77Zt26Jnz57o3r07unTpgtzcXGzfvt2abYOTkxPCw8N16oxSUlLQvXt3q25LW3x8PM6fP4+TJ0/adDuEEFJdUeBrO0OG8MEuOnUyPa94VDTtvmgB87sHMxX4ahP34qE96px4XR99pDmvOJjXn/FVP7bggq5BQtu0vwCUN/CtysGjtdpu7gAmFcniwHfZsmUYMmQIfHx80KVLF3z33Xdo1qwZkpKSkGNswGYDHj16hPT0dKSnpwMAMjIykJ6eruquLCEhAZ9//jm+/PJLXLhwAa+//joyMzMt7j3CUomJiWjZsqXBWmBCCCHGiQPfkBD7taM6+t//gMxMoG5d0/MKo8EB+gNfc7ucsjTwFW9LO+Mrzg5Pn264WzRTgW90dPkzvsJ969bO+JZneOLqQrtuujKwuMb3m2++QWRkJF588UX06tWr3Dd8nTp1ClFRUarnwo1lY8eOxfr16zFq1Cjk5uZi/vz5uHPnDlq3bo3k5GSE2jh9EB8fj/j4eOTl5dns5j1CCKnOxCONUeBrfeYGfOJjry/wNZe+/nONtUH8r9NYxlf7ufixuQN5WGrOHP7lAVAH5dbO+FLgC42b/SoLiwPfU6dOWbUBkZGRYCa+bk6ZMgVTpkyx6nZN0b65jRBCiGWEjG9AgP7hdknFCBb1QFaejG9zPZ0UlDXjqx28ioNqU4GvNQawEO+LrTK+laUrPaKpTB+fBw8e4IsvvsCFCxcgkUjQokULxMXFVavMKGV8CSGkfITAl+p77Usc+Oq7uc3cwPfFF4EbN/iQyn3/6wnL1oGvviD33XeB558Hxo/nz8syOpi43cKXMmtnfC9cKN/yxDYsrvE9deoUGjVqhGXLluH+/fvIycnBsmXL0KhRI5zWHs+PEEJIjSUEFBT42pe1Sh1kMmDRIqBPH/U0e2R8O3cG8vKAL74w2WSDxDfTCdvQDnypVKF6sjjwff311zF48GBcv34d27dvR1JSEjIyMjBw4EBMmzbNBk20D7q5jRBCyqdnT571HTDA3i2p2ayV8dXH3Bpf7VIX7a7PxMGu+DVDNb61apX9praPPgLE/9qFKxPawXl5M77a3n/fuusjZVOmGt/PPvsMjqKvZ46OjnjzzTfRyZx+VaoIKnUghJDy6d+fZ+asUZNJyk4c+OoLFsvT36y5ga94YAhA9zMhXo+tb26bPl39u6REnf2tVUtzPmsHvub2l0xsy+KMr6enp6qrMbEbN26glvanhhBCSI1GQa/9iXtUyM/XfX3w4LKv21jgK37vtfuFNRa8Whr4lifz+8kn6ufaPU9YO/CVyYC9e4ElS3Qz3qTiWHzoR40ahbi4OGzevBk3btzAzZs38f3332PixIl47rnnbNFGu6BSB0IIIdWFjw//HROjnrZwIZCTU76u5swNOrUzvsYCX3FQqC9LaquRgG0d+Do68psCZ8zQH9CTimHxd/GPPvoIEokEY8aMQel/n2SZTIbJkyfjgw8+sHoD7YVKHQghhFQXly/zHhlatlRPk0o1hwouC3MD37JmfPV1g2erwLespQ5SKWBOz6figUT69qXuzuzF4sDXyckJK1aswKJFi3D16lUwxtC4cWO4CR3hEUIIIaRS8fHhP+Ib2azRTb25ga+fn+ZzcwNf8eh/AluVCdgy4/vLL8BTT6mfb9gAJCbyx/PmWW87xDSzPz6FhYWIj49HvXr14O/vj4kTJyIwMBBt27aloJcQQgipAsSBanluajPXDz/wXj3mzNGcXp7AV3tZS3qlGD7c8GvGAt/WrQ0vZ84XiNhYzYDdzw+YOxcICzO9LLEuswPfuXPnYv369RgwYACeffZZpKSkYPLkybZsm11RjS8hhJDqzBqBr6mM79NPAzt3Wjfja06pQ7NmutP+/BP4/nvDyxgqdWjWjC9rC7a+ya1tW9uuvyoy+5Bv374dX3zxBdatW4dPPvkEv/zyC3bs2FFth/SNj4/H+fPncfLkSXs3hRBCCLG6igh8DTH35jZzAl9xG1q1Av7+G/jrL+DJJzXna93a+E1l2hlfYQCL8vZMEhdn+LWyHj9zde9u2/VXRWYHvjdu3EBERITqeZcuXeDo6Ijbt2/bpGGEEEIIsZ2KrPHVJgSlprorMyfwFY/C9tdfPEPr6Ah8841lbYqKAgICdKefO2fZerStXm34NVtnfG0dWFdFZh9yhUIBJ61+RRwdHVU9OxBCCCGk6ihvjw5A2QOrOXOAlSt5dlabpRnfTz7hNbQ//6w53d+f/5irSRMgKwv48UfzlzElNNR4ltnWgWm3brZdf1VkdgKfMYZx48bB2dlZNa2oqAiTJk2Cu2icv+3bt1u3hYQQQgixmk2beC8DL79c/nWVNXBzcQHi4/W/Jg5s9d07rx341q3L98dazCltcHAwr1TE1I13tg58e/a07fqrIrMD37Fjx+pM+7//+z+rNoYQQgghtvXCC/zHGmwRuJkqdbB1sGjO4BL9+gG7d5uez1RwbOt9oRHidEkYs6QjkJojMTERiYmJUCgUuHTpEl544SGcnDzNXr4sR5UxQKlU4ubNm6hfvz4czPjElnU7NX2ZitwWLVOx75FCocTdu3cREBBg1jlU1u3QMhW7LVqm4t4jxpTIybkPX18fSCT6z6FDh9SPe/dWbyc1VT29Vy/NZfS9Jm7b4cPqx8ItReJpAkuymEeOqB/36GHeMkeP6k7r3h04dsz87YoZKzc4frxs6zRXUBBg71uxKirKFAYce/jwITw9DcdrFPiaoB657SEA8wNfQgghhJCarrIFvuXspKPmmDtX/9CJhpTl8oVEwm8i/Pvvv9G8eXNIzeissKzbqS7LVNZ2VbdlKmu79C2jUJTizz//Qps2rSGV6v8TV1n3p7K2q7otU1nbVVmWKS0txZkzZ9ChQwc4igpezdnOyJHqx1u2aC6zbh2QksIfb92qu+yIEerHP/zAf+/dC6xZozmf8Jo57Xr6afXjbdvMW+bECeDDDzWnbd9uePCLjRt1u0IbNkz9eMcOw9vavh34+mv+eOpUfsOfIZ98Auzbx2++69ZNnS3+9FPglVf0L7N2rXVqucvqpZfst21DKONrgrnfIKxFLpcjOTkZsbGxkJlTaEQI0UDnECHlU55zSBxQakcXxcU8EG3TBli0yPCy/fsDu3bxx6mp6lIKQ+s1xt8fuHfPsuXS0oBOnXS3Kd63gQP5wBwAUFioW4sszFunDpCdbXhbSUnqgFp7G9pu3gTq1eOPv/kGEG6zunEDaN8eyM3VXebOHXV3b2+9BSxebHj9tnDnDr/5sCKYG69V+7LnGzduIDIyEi1btkTbtm2xVd/XTEIIIYTYlLMzDxb1Bb1i4oud9vjuam6vDvoea7Nmrw7idom3KZUaboN2sK6tTRvzt18WFRX0WqLaB76Ojo5Yvnw5zp8/j7179+L1119HQUGBvZtFCCGEEJFBg/jv119XT7NH4CvepqOj/q7SxAGlscDXVK8Owo16+gbO0FaWwFcceOurnqyJ1/yrfeAbGBiI9u3bAwD8/f3h4+OD+/fv27dRhBBCCNGQlMQv54uHGi7vcMFlIZerH//zDx8cQ5u1Al8/P+D+fb4dbV27aj4XHwvx9s0NfPXNI35dXJ9dndk98E1NTcWgQYMQFBQEiUSCHXqqwFetWoWwsDC4uLggPDwch/X1b2KGU6dOQalUIjg4uJytJoQQQoi28vRLK5Wqa1gFtWuXrz1lERqqfiweDlnM3MDXnIxq7dq8DETbunWazw19CTAW+IqZem/8/IAOHUyvp6qze+BbUFCAdu3aYaWBWxk3b96MadOmYdasWThz5gwiIiIQExODzMxM1Tzh4eFo3bq1zs9tUed1ubm5GDNmDNZpf5IIIYQQYhXWHpAhNBT44APLelUqL29vnoHNyTG8P+LpxvbZnNHdDNEOmssb+JrahlRaM0of7N6dWUxMDGJiYgy+vnTpUsTFxWHixIkAgOXLl2P37t1YvXo1Fv1XIZ+WlmZ0G8XFxRg2bBhmzpyJ7t27m5y3uLhY9TwvLw8Av8tVLr7+YSPCNipiW4RUR3QOEVI+5TmHJBJHAJIyL69PQgKQleWA5culFq+XsbK1R8j0ai6iLv5lTAkhd6h/vbL/5mOQy0vN3q54GyUlcq1tylXtkcslEEI4pVL+X9eNuhG4j48cbds6wsEBqFWrVGN9ANCunRLnz/P9kEgUUCod9K6nrCry77C527J74GtMSUkJ0tLS8Pbbb2tMj46OxjEzh1BhjGHcuHHo06cPRo8ebXL+RYsW4b333tOZvmfPHrjpGzTcRlKEzg4JIWVC5xAh5VOWc4ixQRACp+TkZKu1JSOjFYDGFq+3pOQpAM5Was8Q1aO7d+8AqGdkvXxeuVxh4XbV2zhy5AiAKNXzXbuSVdnl9PQgAJ0BACkpu9C5c1Ncv95MZ227diVj3jz++NAhdwDqAurBg68gNvYSvvuOFzFnZl5DXp4/AC+jLWzU6AGuXvU2a2+s+RkwpbCw0Kz5KlU/vhKJBElJSRg6dCgA4Pbt26hXrx6OHj2qkalduHAhNmzYgIsXL5pc55EjR9CrVy+0bdtWNW3jxo1oY6APD30Z3+DgYOTk5FRYP74pKSno168f9UFKSBnQOURI+ZTnHHJzc0RpKY/OeMbSOt56ywHLlkktXm9QkCNycqzTHicn9bF45hklfvjBweB6hXldXBjy8szP+CYlSTBqFM9J/vabHF27ameAuS1bJPi//+PzFRbK4eAAfPWVBJMna+YzxctcuQK0bKm7PqGtM2YosHu3A/78UzfjGxenxBdfqPdXfCyMseZnwJS8vDz4+flVj5HbJFoFNIwxnWmG9OzZE0oLimycnZ3h7OyMxMREJCYmQqFQAABkMlmF/hOt6O0RUt3QOURI+ZTlHBL/a7bm+SeuYS3req3ZHqlU3SBj61UqJRZtd/Bg9WPt5cTPxfW+Li4ySCTApEnA5MkwuIx2M7TX7+xseLTYwEDz9ldbRcdN5rD7zW3G+Pn5QSqVIisrS2N6dnY2Aszp9I4QQgghxMrMvYnP0mvq4gDf3GWtdUOhvn5+BaNG8d8tWlhnW/ZUqQNfJycnhIeH69QZpaSkmLxJrbzi4+Nx/vx5nDx50qbbIYQQQqoLa/fqIDAWlNmDqf1s0oT/7t/fsvWaG/iW5TibCqSN9erQujVw6xaQnm75disbu5c6PHr0CFeuXFE9z8jIQHp6Onx8fBASEoKEhASMHj0anTp1Qrdu3bBu3TpkZmZi0qRJNm2XdqkDIYQQQoyzVeA7Ywbw/feAGfeoa+jUCdi1q+zdfRliaj/37wc2bQJefNGy9ZrbTlvcnWWqO7OgIOtv0x7sHvieOnUKUVHquxYTEhIAAGPHjsX69esxatQo5ObmYv78+bhz5w5at26N5ORkhIp7mLaB+Ph4xMfHIy8vD15exu9wJIQQQojtAt86dYDr1y1f//r1wMKFwEsvWbc9ptpRvz6g1SGVxeut6K4HHB2pH98KERkZCVMdS0yZMgVTpkypoBZxlPElhBBCLGOrwLes6w4IAFassM72d+4E4uKAr78GNm60zjq12fL4mWKP4aHtoVLX+NoT1fgSQgghltEecrg6GTAAuHMHiI62fumEPtbOvnp7qx+fO6d+/OqrQMOGwMSJ+re5ZYt122FvFPgSQgghxCp27AB69QIOHLB3S2xDyMhWRGY2NBR4+mnrra9OHWDzZuCnn4CWLdXTV6zgffx6eekPfNu1s14bKoMakti2HJU6EEIIIZZp1Qo4dMjerbA9Wwa+Fy8Cjx4B/v6Ak5P+ecrasdXIkfqnC/ujL/DVt68//wxMnw5culS2dtgTZXwNoFIHQgghhOhjy8C3aVOgY0f+2FDgGxzMb/Z78MB27RDo29eBA3mAXhVRxpcQQgghxAIVdROaocAX4KUQ1mZuxrcqo4yvAYmJiWjZsiU6d+5s76YQQgghpBKpiJvbAKB584rZjjHVLfCljK8B1I8vIYQQQvSpqGBw6lTek8RTT1XM9mpCxpcCX0IIIYSQSsjJCViypOK2VxMCXyp1IIQQQgixQEWVOlQ0awS+H39snbbYCmV8DaDuzAghhBCiT3XLghpj6b6OHs17nGjVyibNKTcKfA2gGl9CCCGE6FNdA19rZHwdHIBPPrFOe2yhmibrCSGEEEJsgwJfwyr7saHAlxBCCCHEAvXr27sFtlETAl8qdSCEEEIIscC0aXzksiFD7N0S26PAlxBCCCGkBnN1Bdavt3crKkZ1C3yp1MEAGrmNEEIIITWJpaUO8+dbNn9lQIGvAfHx8Th//jxOnjxp76YQQgghhNicpYHv22/r9mlMgS8hhBBCCLG5UaPKt7ylga9MBuTnA7/+at78lQEFvoQQQggh1cC33wKBgRW7TTc3XvMsoMDXzvLz89G5c2e0b98ebdq0wWeffWbvJhFCCCGEWJ2DQ/kCX+rOrBpwc3PDoUOH4ObmhsLCQrRu3RrDhw+Hr6+vvZtGCCGEEGJV5Qk8yxr4iper7IFvtc/4SqVSuLm5AQCKioqgUCjA9L2zhBBCCCE1WE3I+No98E1NTcWgQYMQFBQEiUSCHTt26MyzatUqhIWFwcXFBeHh4Th8+LBF23jw4AHatWuH+vXr480334Sfn5+VWk8IIYQQUn1RxtfKCgoK0K5dO6xcuVLv65s3b8a0adMwa9YsnDlzBhEREYiJiUFmZqZqnvDwcLRu3Vrn5/bt2wAAb29vnD17FhkZGfj2229x9+7dCtk3QgghhJCqoiaUOti9xjcmJgYxMTEGX1+6dCni4uIwceJEAMDy5cuxe/durF69GosWLQIApKWlmbWtgIAAtG3bFqmpqRgxYoTeeYqLi1FcXKx6npeXBwCQy+WQy+Vmbac8hG1UxLYIqY7oHCKkfOgcquqkEPKalr+HjgA0I9fSUjlMraa0VAIhpCwtldsl+DV3X+0e+BpTUlKCtLQ0vP322xrTo6OjcezYMbPWcffuXbi6usLT0xN5eXlITU3F5MmTDc6/aNEivPfeezrT9+zZo6oVrggpKSkVti1CqiM6hwgpHzqHqqa8vF4AagMAkpOTLVq2qKg/ABeNaSkpe+DuXmp0uT/+8APQAwCwa9evkEor/l6qwsJCs+ar1IFvTk4OFAoFAgICNKYHBAQgKyvLrHXcvHkTcXFxYIyBMYapU6eibdu2BuefOXMmEhISVM/z8vIQHByM6OhoeHp6lm1HLCCXy5GSkoJ+/fpBJpPZfHuEVDd0DhFSPnQOVW0LFkhVj2NjYy1a1slJNyzs3z8apsIfFxd1ijc2NgZSqZGZbUS4Qm9KpQ58BRKtnDljTGeaIeHh4UhPTzd7W87OznB2dkZiYiISExOhUCgAADKZrEL/AFT09gipbugcIqR86ByqmsThkaXvn74aXycnGUytxlEUTTo5yXSGMa4I5u6r3W9uM8bPzw9SqVQnu5udna2TBSaEEEIIqensfXOZvbdvSqUOfJ2cnBAeHq5TZ5SSkoLu3bvbdNvx8fE4f/48Tp48adPtEEIIIYRUBtYY5qCyB752L3V49OgRrly5onqekZGB9PR0+Pj4ICQkBAkJCRg9ejQ6deqEbt26Yd26dcjMzMSkSZNs2i7tUgdCCCGEkOrMGt2ZVXZ2D3xPnTqFqKgo1XPhxrKxY8di/fr1GDVqFHJzczF//nzcuXMHrVu3RnJyMkJDQ23arvj4eMTHxyMvLw9eXl423RYhhBBCiDVYe8ji6sbugW9kZKTJIYSnTJmCKVOmVFCLOMr4EkIIIaSqqeylBvZWqWt87YlqfAkhhBBSk9SEUgcKfAkhhBBCiF7VLYNs91KHyopKHQghhBBSk4gztxMnAq6uQAUOWlshKPA1gG5uI4QQQkhVY62b2z77rPxtqYyo1IEQQgghhJS5VpdqfKuBxMREtGzZEp07d7Z3UwghhBBCzBIcbO8WVG4U+BpAvToQQgghpKr55BNg2DBg927Ll61d2/rtqWwo8CWEEEIIqSYCAoDt24HoaMuX3bED6NABSE62bLmqVOpAN7cRQgghhBC0awecPm3vVtgWZXwNoBpfQgghhBDT6te3dwvMR4GvAVTjSwghhBBiWqtWwPr1wJ499m6JaVTqQAghhBBCymXsWHu3wDyU8SWEEEIIITUCBb6EEEIIIaRGoMCXEEIIIYTUCBT4GkC9OhBCCCGEVC8U+BpAvToQQgghhFQvFPgSQgghhJAagQJfQgghhBBSI9SYwLewsBChoaGYMWOGvZtCCCGEEELsoMYEvu+//z66du1q72YQQgghhBA7qRGB7+XLl/H3338jNjbW3k0hhBBCCCF2YvfANzU1FYMGDUJQUBAkEgl27NihM8+qVasQFhYGFxcXhIeH4/DhwxZtY8aMGVi0aJGVWkwIIYQQQqoiuwe+BQUFaNeuHVauXKn39c2bN2PatGmYNWsWzpw5g4iICMTExCAzM1M1T3h4OFq3bq3zc/v2bfz4449o2rQpmjZtWlG7RAghhBBCKiFHezcgJiYGMTExBl9funQp4uLiMHHiRADA8uXLsXv3bqxevVqVxU1LSzO4/IkTJ/D9999j69atePToEeRyOTw9PfHuu+/qnb+4uBjFxcWq5w8fPgQA3L9/H3K53OL9s5RcLkdhYSFyc3Mhk8lsvj1Cqhs6hwgpHzqHSFWUn58PAGCMGZ+RVSIAWFJSkup5cXExk0qlbPv27Rrzvfrqq6xXr14Wr/+rr75i06dPNzrP3LlzGQD6oR/6oR/6oR/6oR/6qWI/N27cMBrn2T3ja0xOTg4UCgUCAgI0pgcEBCArK8sm25w5cyYSEhJUz5VKJe7fvw9fX19IJBK9y3Tu3NmsEd7MmS8vLw/BwcG4ceMGPD09LWt8NWDusaxIFdUma2/HGusr6zosXY7OIeuhc6jynEMVdf5YsgydQ6ZVtnOoIttTlc8hxhjy8/MRFBRkdL5KHfgKtANOxpjBINSYcePGmZzH2dkZzs7OGtO8vb2NLiOVSs3642DufADg6elZI//gWHKMKkpFtcna27HG+sq6DkuXo3PIeugcqjznUEWdP5YsQ+eQaZXtHKrI9lT1c8jLy8vkPHa/uc0YPz8/SKVSnexudna2ThbYnuLj4606X01WGY9RRbXJ2tuxxvrKug5Ll6NzyHoq4zGqqedQRZ0/lixTGT8flU1lO0YV2Z7qcg4ZI/mvtrZSkEgkSEpKwtChQ1XTunbtivDwcKxatUo1rWXLlhgyZEi17KIsLy8PXl5eePjwYaX6xklIVUHnECHlQ+cQqc7sXurw6NEjXLlyRfU8IyMD6enp8PHxQUhICBISEjB69Gh06tQJ3bp1w7p165CZmYlJkybZsdW24+zsjLlz5+qUWxBCzEPnECHlQ+cQqc7snvE9ePAgoqKidKaPHTsW69evB8AHsPjwww9x584dtG7dGsuWLUOvXr0quKWEEEIIIaQqs3vgSwghhBBCSEWo1De3EUIIIYQQYi0U+BJCCCGEkBqBAl9CCCGEEFIjUOBLCCGEEEJqBAp8q6j8/Hx07twZ7du3R5s2bfDZZ5/Zu0mEVCk3btxAZGQkWrZsibZt22Lr1q32bhIhVc6wYcNQu3ZtPPPMM/ZuCiFmoV4dqiiFQoHi4mK4ubmhsLAQrVu3xsmTJ+Hr62vvphFSJdy5cwd3795F+/btkZ2djY4dO+LixYtwd3e3d9MIqTIOHDiAR48eYcOGDfjhhx/s3RxCTKKMbxUllUrh5uYGACgqKoJCoQB9hyHEfIGBgWjfvj0AwN/fHz4+Prh//759G0VIFRMVFYVatWrZuxmEmI0CXxtJTU3FoEGDEBQUBIlEgh07dujMs2rVKoSFhcHFxQXh4eE4fPiwRdt48OAB2rVrh/r16+PNN9+En5+flVpPiP1VxDkkOHXqFJRKJYKDg8vZakIqj4o8hwipKijwtZGCggK0a9cOK1eu1Pv65s2bMW3aNMyaNQtnzpxBREQEYmJikJmZqZonPDwcrVu31vm5ffs2AMDb2xtnz55FRkYGvv32W9y9e7dC9o2QilAR5xAA5ObmYsyYMVi3bp3N94mQilRR5xAhVQojNgeAJSUlaUzr0qULmzRpksa05s2bs7fffrtM25g0aRLbsmVLWZtISKVmq3OoqKiIRUREsK+//toazSSk0rLl/6EDBw6wp59+urxNJKRCUMbXDkpKSpCWlobo6GiN6dHR0Th27JhZ67h79y7y8vIAAHl5eUhNTUWzZs2s3lZCKiNrnEOMMYwbNw59+vTB6NGjbdFMQiota5xDhFRFjvZuQE2Uk5MDhUKBgIAAjekBAQHIysoyax03b95EXFwcGGNgjGHq1Klo27atLZpLSKVjjXPo6NGj2Lx5M9q2bauqfdy4cSPatGlj7eYSUulY4xwCgP79++P06dMoKChA/fr1kZSUhM6dO1u7uYRYDQW+diSRSDSeM8Z0phkSHh6O9PR0G7SKkKqjPOdQz549oVQqbdEsQqqM8pxDALB7925rN4kQm6JSBzvw8/ODVCrV+VadnZ2t8+2bEKKLziFCyofOIVJTUeBrB05OTggPD0dKSorG9JSUFHTv3t1OrSKk6qBziJDyoXOI1FRU6mAjjx49wpUrV1TPMzIykJ6eDh8fH4SEhCAhIQGjR49Gp06d0K1bN6xbtw6ZmZmYNGmSHVtNSOVB5xAh5UPnECF62LNLierswIEDDIDOz9ixY1XzJCYmstDQUObk5MQ6duzIDh06ZL8GE1LJ0DlESPnQOUSILgljNM4tIYQQQgip/qjGlxBCCCGE1AgU+BJCCCGEkBqBAl9CCCGEEFIjUOBLCCGEEEJqBAp8CSGEEEJIjUCBLyGEEEIIqREo8CWEEEIIITUCBb6EEEIIIaRGoMCXEEKITZSUlKBx48Y4evSoVde7c+dOdOjQAUql0qrrJYRUfxT4EkKIGcaNGweJRKLzc+XKFXs3rdJat24dQkND0aNHD9U0iUSCHTt26Mw7btw4DB061Kz1Dhw4EBKJBN9++62VWkoIqSko8CWEEDM99dRTuHPnjsZPWFiYznwlJSV2aF3l8+mnn2LixIk2Wff48ePx6aef2mTdhJDqiwJfQggxk7OzM+rWravxI5VKERkZialTpyIhIQF+fn7o168fAOD8+fOIjY2Fh4cHAgICMHr0aOTk5KjWV1BQgDFjxsDDwwOBgYH4+OOPERkZiWnTpqnm0Zch9fb2xvr161XPb926hVGjRqF27drw9fXFkCFDcP36ddXrQjb1o48+QmBgIHx9fREfHw+5XK6ap7i4GG+++SaCg4Ph7OyMJk2a4IsvvgBjDI0bN8ZHH32k0Ya//voLDg4OuHr1qt5jdfr0aVy5cgUDBgyw8CgD169f15tdj4yMVM0zePBg/P7777h27ZrF6yeE1FwU+BJCiBVs2LABjo6OOHr0KNauXYs7d+6gd+/eaN++PU6dOoVdu3bh7t27GDlypGqZN954AwcOHEBSUhL27NmDgwcPIi0tzaLtFhYWIioqCh4eHkhNTcWRI0fg4eGBp556SiPzfODAAVy9ehUHDhzAhg0bsH79eo3gecyYMfj+++/xySef4MKFC1izZg08PDwgkUgwYcIEfPXVVxrb/fLLLxEREYFGjRrpbVdqaiqaNm0KT09Pi/YHAIKDgzWy6mfOnIGvry969eqlmic0NBT+/v44fPiwxesnhNRgjBBCiEljx45lUqmUubu7q36eeeYZxhhjvXv3Zu3bt9eYf86cOSw6Olpj2o0bNxgAdvHiRZafn8+cnJzY999/r3o9NzeXubq6stdee001DQBLSkrSWI+Xlxf76quvGGOMffHFF6xZs2ZMqVSqXi8uLmaurq5s9+7dqraHhoay0tJS1TwjRoxgo0aNYowxdvHiRQaApaSk6N3327dvM6lUyn777TfGGGMlJSWsTp06bP369QaP12uvvcb69OmjMx0Ac3Fx0TiO7u7uzNHRkQ0ZMkRn/sePH7OuXbuygQMHMoVCofFahw4d2Lx58wy2gRBCtDnaN+wmhJCqIyoqCqtXr1Y9d3d3Vz3u1KmTxrxpaWk4cOAAPDw8dNZz9epVPH78GCUlJejWrZtquo+PD5o1a2ZRm9LS0nDlyhXUqlVLY3pRUZFGGUKrVq0glUpVzwMDA/Hnn38CANLT0yGVStG7d2+92wgMDMSAAQPw5ZdfokuXLti5cyeKioowYsQIg+16/PgxXFxc9L62bNkyPPnkkxrT3nrrLSgUCp154+LikJ+fj5SUFDg4aF6kdHV1RWFhocE2EEKINgp8CSHETO7u7mjcuLHB18SUSiUGDRqExYsX68wbGBiIy5cvm7VNiUQCxpjGNHFtrlKpRHh4OL755hudZevUqaN6LJPJdNYrdAfm6upqsh0TJ07E6NGjsWzZMnz11VcYNWoU3NzcDM7v5+enCqy11a1bV+c41qpVCw8ePNCYtmDBAuzatQu///67TmAPAPfv39fYR0IIMYUCX0IIsYGOHTti27ZtaNCgARwddf/UNm7cGDKZDCdOnEBISAgA4N9//8WlS5c0Mq916tTBnTt3VM8vX76skeXs2LEjNm/eDH9//zLV0wJAmzZtoFQqcejQIZ1MrCA2Nhbu7u5YvXo1fv31V6SmphpdZ4cOHbB69WowxiCRSCxu07Zt2zB//nz8+uuveuuIhYx2hw4dLF43IaTmopvbCCHEBuLj43H//n0899xzqt4H9uzZgwkTJkChUMDDwwNxcXF44403sG/fPvz1118YN26czuX8Pn36YOXKlTh9+jROnTqFSZMmaWRvX3jhBfj5+WHIkCE4fPgwMjIycOjQIbz22mu4efOmWW1t0KABxo4diwkTJmDHjh3IyMjAwYMHsWXLFtU8UqkU48aNw8yZM9G4cWONEg19oqKiUFBQgHPnzllw1Li//voLY8aMwVtvvYVWrVohKysLWVlZuH//vmqeEydOwNnZ2WQ7CCFEjAJfQgixgaCgIBw9ehQKhQL9+/dH69at8dprr8HLy0sV3C5ZsgS9evXC4MGD8eSTT6Jnz54IDw/XWM/HH3+M4OBg9OrVC88//zxmzJihUWLg5uaG1NRUhISEYPjw4WjRogUmTJiAx48fW5QBXr16NZ555hlMmTIFzZs3x4svvoiCggKNeeLi4lBSUoIJEyaYXJ+vry+GDx+utwTDlFOnTqGwsBALFixAYGCg6mf48OGqeb777ju88MILRsstCCFEm4RpF48RQgixm8jISLRv3x7Lly+3d1N0HD16FJGRkbh58yYCAgJMzv/nn3/iySef1HvzXXncu3cPzZs3x6lTp/QOIEIIIYZQxpcQQohRxcXFuHLlCubMmYORI0eaFfQCvHb4ww8/1BhMwxoyMjKwatUqCnoJIRajm9sIIYQY9d133yEuLg7t27fHxo0bLVp27NixVm9Ply5d0KVLF6uvlxBS/VGpAyGEEEIIqRGo1IEQQgghhNQIFPgSQgghhJAagQJfQgghhBBSI1DgSwghhBBCagQKfAkhhBBCSI1AgS8hhBBCCKkRKPAlhBBCCCE1AgW+hBBCCCGkRqDAlxBCCCGE1Aj/D6F3GxBpl617AAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 800x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# np.random.seed(0) # For reproducibility\n", + "\n", + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "Fs = 1.0\n", + "\n", + "beta = 0.9\n", + "sigma = 1 \n", + "sigma_e = sigma*np.sqrt(1 - beta ** 2)\n", + "\n", + "s = np.zeros(m)\n", + "s[0] = np.random.normal(loc=0, scale=sigma, size=None)\n", + "\n", + "for i in range(1, m):\n", + " s[i] = beta*s[i - 1] + np.random.normal(loc=0, scale=sigma_e, size=None)\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, s, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: y(t)')\n", + "plt.title(f'Time series (beta = {beta:.2f})')\n", + "plt.legend()\n", + "\n", + "# Calculate ACF\n", + "plot_acf(s, lags=100, alpha=0.05, color = 'red')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (sec)')\n", + "plt.title(f'ACF (beta = {beta:.2f})')\n", + "plt.tight_layout()\n", + "# plt.show();\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal\n", + "frequencies, psd = signal.periodogram(s, fs=Fs, scaling='density', return_onesided=False)\n", + "\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(8, 3))\n", + "plt.loglog(frequencies, psd, color='blue', label='psd')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.title(f'Power Spectral Density (PSD) of the signal (beta = {beta:.2f})')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.ylim([1e-4, 1e3]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + " \n", + "- The time series clearly shows the time correlation as it is not randomly scattered in time (seems to contain a seasonal signal, but it is not; it is due to the high temopral correlation introduced as $\\beta=0.9$ is close to 1).\n", + "\n", + "- For the AR(1) process ($\\beta=0.9$), the PSD values at lower frequencies are larger than those at higher frequencies. It means the contribution of low frequencies in time series variations is larger than that of higher frequencies. \n", + " \n", + "- In the context of white noise, a flat PSD indicates that all frequencies contribute equally to the variations observed in the time series. This concept aligns conceptually with 'white light', where all wavelengths (colors) are present.\n", + " \n", + "</p>\n", + "</div>\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "be7d4044f42541a497ef4ab68deb94d4", + "deepnote_cell_type": "markdown" + }, + "source": [ + "We now intend to simulate (as a time series) 1000 samples of a harmonic wave plus white noise, again at 1-second intervals. We take $m=1000$ and use the following equation (signal + noise):\n", + "\n", + "$$\n", + "Y(t)=A\\sin(2\\pi ft + \\theta)+S(t)\n", + "$$\n", + "\n", + "where $S(t)$ is the white noise process of Task 1 $(\\beta=0)$. The amplitude, initial phase and frequency of the sine wave are given as $A=1$, $\\theta=\\frac{\\pi}{4}$, and $f=0.05$ Hz, respectively. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "ae5841495b8e44a8ba4fc5c8ef424297", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2:</b> \n", + " \n", + "It is required to:\n", + "\t\n", + "<ol>\n", + " <li>Plot the normalized Auto-Covariance Function (ACF) of the generated time series. What is the ACF of a sine wave?</li>\n", + " <li>Plot the power spectral density (PSD)/periodogram of the generated time series.</li>\n", + " <li>Increase $\\sigma=1$ to $\\sigma=10$ (increase noise) and repeat steps 1 and 2 (no plot needed here to present). Discuss your observations.</li>\n", + "</ol>\n", + "</p>\n", + "</div>\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "cell_id": "eda81652ecc94c5fb34a766079d1f935", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 912, + "execution_start": 1696691528530, + "source_hash": null, + "tags": [] + }, + "outputs": [], + "source": [ + "### SOLUTION\n", + "def plotsignal(sigma):\n", + " m = 1000\n", + " t = np.arange(1, m + 1)\n", + " Fs = 1.0\n", + "\n", + " beta = 0.0\n", + "# sigma = 1 #change to 10\n", + " sigma_e = sigma * np.sqrt(1 - beta ** 2)\n", + "\n", + " A = 1\n", + " Theta = np.pi / 4\n", + " f = 0.05\n", + "\n", + " s = np.zeros(m)\n", + " s[0] = np.random.normal(loc=0, scale=sigma, size=None)\n", + "\n", + " for i in range(1, m):\n", + " s[i] = beta * s[i - 1] + np.random.normal(loc=0, scale=sigma_e, size=None)\n", + "\n", + " Y = A * np.sin(2 * np.pi * f * t + Theta) + s\n", + "\n", + " # Create the first plot (Time series data)\n", + " plt.figure(figsize=(8, 6))\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, Y, '-', color='blue', label='signal')\n", + " plt.grid(True)\n", + " plt.box(True)\n", + " plt.xlabel('Time (sec)')\n", + " plt.ylabel('TS data: y(t)')\n", + " plt.title(f'Signal with $A$={A}, $\\Theta$={Theta*180/np.pi:.1f}°, '\n", + " f'$f$={f} Hz, $\\sigma$={sigma}')\n", + " plt.legend()\n", + "\n", + " # Calculate ACF\n", + " plot_acf(Y, lags=100, alpha=0.05, color = 'red')\n", + " plt.ylabel('Normalized ACF')\n", + " plt.xlabel('Lag (sec)')\n", + " plt.title(f'Auto-covariance (sigma={sigma})')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Calculate and plot power spectral density (PSD) of the generated signal\n", + " frequencies, psd = signal.periodogram(Y, fs=Fs, scaling='density',\n", + " return_onesided=False)\n", + "\n", + " # Create the second plot (Power spectral density)\n", + " plt.figure(figsize=(8, 3))\n", + " plt.loglog(frequencies, psd, color='blue', label='psd')\n", + " plt.ylabel('Power: PSD')\n", + " plt.xlabel('Frequency (Hz)')\n", + " plt.title(f'Power Spectral Density of the signal (sigma={sigma})')\n", + " plt.ylim([1e-3, 1e3])\n", + " plt.grid(True)\n", + " plt.box(True)\n", + " plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6208c7638ebc4da9aefe69527fa08904", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=6, description='sigma', max=11, min=1), Output()), _dom_classes=('widget…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "@interact(sigma=(1,11,1))\n", + "def samples_slideplot(sigma):\n", + " plotsignal(sigma);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "bc2f33f70ec84252af1fa66da20dcb1a", + "deepnote_cell_type": "markdown" + }, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "bf178a1f09b04b61ac0bac471b9ee216", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "- The ACF of a sine wave typically appears as a cosine wave. However, for the generated time series, the ACF may not precisely exhibit a cosine wave due to the presence of both the signal and added noise.\n", + "\n", + "- Increasing the noise level from $\\sigma=1$ to $\\sigma=10$ will hide the periodic ACF in the noise level. In fact the precision of ACF gets poorer in this case. Similar circumstances can occur for the Power Spectral Density (PSD).\n", + "\n", + " </p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "d9370df3a6a043dc8077c6c93c6a9bf5", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Tasks 3-5: exploration and (simple) reconstruction of sea level data\n", + "\n", + "The descrition about the data is given above.\n", + "\n", + "### Read and plot the data\n", + "\n", + "The data consists of monthly global mean sea levels. We use exactly 27 years of monthly data, so $m=27\\times 12=324$. \n", + "\n", + "We use the package pandas to import the data and do a bit of pre-processing. Once imported as a dataframe object, we the method `iloc` (integer-based indexing) toextract the time and sea level data as ndarrays. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "cell_id": "4d5981d7086e4a3fbd2792e17e87d660", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 291, + "execution_start": 1696691529410, + "source_hash": null + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dat = pd.read_csv('CSIRO_Alt_seas_inc.txt', names=['month','sl'])\n", + "\n", + "noy = 27 # number of years\n", + "nom = 12 # months in a year\n", + "dat = dat.loc[0: nom*noy - 1] # keep first 27 years of data\n", + "t0 = dat.iloc[:, 0] - dat.iloc[0, 0] # create time-array\n", + " # time relative to t0 [yr],\n", + " # so t0[0] = 0\n", + "t1 = dat.iloc[:, 0] # time-array, original time instances\n", + " # (so t1[0] = 1993.042)\n", + "m = len(dat) # number of observations\n", + " # (m as the number of observations)\n", + "T = (t0[m - 1] - t0[0])*m / (m - 1) # observation record length\n", + " # (as N*dt; sample-and-hold convention)\n", + "dt = T/m # Delta t [yr]\n", + "Y = dat.iloc[:,1] # observed sea-level height \n", + "\n", + "# plot observed time-series, as it is, versus epoch-time in [year]\n", + "plt.plot(t1, Y, color='red', label='sea level')\n", + "plt.xlabel('Time [yr]')\n", + "plt.ylabel('Global mean sea level [mm]')\n", + "plt.title('Global Mean Sea-Level (GMSL)')\n", + "plt.grid()\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "6269ce2d660941329800f428fc492995", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3:</b> \n", + "\n", + "For this Task, the objective is to visually analyze the trend pattern of the global mean sea level height data. While these pattern is apparent through a visual inspection of the data, we intend to enhance clarity by applying some straightforward and simple measures. Specifically, you will compute the annual mean by averaging the twelve monthly values for each of the twenty-seven years under consideration. It is therefore asked to:\n", + "\n", + "<ol>\n", + " <li>Calculate yearly averages (for 12 months of a year calculate its mean and take that mean value for all months). This can result in an array of (<code>y_mean_array</code>) 324 entries, which is indeed based on only 27 annual means. </li>\n", + " <li>Plot the original data and the yearly averaged data in a single figure.</li>\n", + " <li>Based on your visual inspection, do you conclude that the sea-level rises in this plot? If so, what is your initial estimate of sea level rise (i.e., just a rough estimate using the plot only; no need to use code!)? Report your estimate in units of mm/yr.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Hint for Tasks 3-5:</b> \n", + "\n", + "\n", + "You may want to use either the matrix form <code>y_mat</code> or the vector form <code>y</code>, depending on the question asked, to will see which one is more convenient. For example, to compute the mean over rows or columns of the matrix, for a given matrix M you may use <code>M.mean(axis=1)</code> or <code>M.mean(axis=0)</code>: check the difference; or to repeat arrays using <code>np.repeat(a, repeats, axis=0)</code>, and <code>np.tile(a, repeats)</code>: check the difference.\n", + "\n", + "Read the documentation for more information: <code>[np.repeat](https://numpy.org/doc/stable/reference/generated/numpy.repeat.html)</code> and <code>[np.tile](https://numpy.org/doc/stable/reference/generated/numpy.tile.html)</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "cell_id": "6c7fa94a64534499a26ab2c6c27fc304", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 560, + "execution_start": 1696691529562, + "source_hash": null + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Global mean sea level [mm]')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Y_mat = np.reshape(Y.values, (noy,nom))\n", + "\n", + "### SOLUTION\n", + "\n", + "# Calculate yearly average (ymean), 27 values (for 27 years)\n", + "Y_mean = Y_mat.mean(axis=1)\n", + "\n", + "# Create an array with the annual avaeage of each year repeating 12 times\n", + "Y_mean_array = np.repeat(Y_mean,12, axis=0) \n", + "\n", + "plt.figure(figsize=(8,4))\n", + "plt.plot(t1, Y,'r-', label='Monthly')\n", + "plt.plot(t1, Y_mean_array,'b.', label='Annual mean')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title('Original and mean values (averaged over months)')\n", + "plt.xlabel('Time [yr]')\n", + "plt.ylabel('Global mean sea level [mm]')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "f68b68c87c984611a50da72b0fe81af6", + "deepnote_cell_type": "markdown" + }, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "b7220de48a0d4ca8b130db8f887623cc", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 600, + "execution_start": 1696691529789, + "source_hash": null + }, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "- Yes, sea level seems to be rising of around $20$ mm every $5$ years, which becomes approximately $4$ mm/year.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "9ad97a741727470a9720566efe855fc9", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4:</b> \n", + " \n", + "In this Task you investigate the seasonality (monthly variations of sea-levels). For every individual year, you have already calculated the mean sea level. You can now subtract it from the original monthly values of that year (so showing the deviation from the mean value of that particular year). This process yields the specific year's seasonal variations, consequently centering them around a zero mean. Subsequently, you determine the average magnitude of these seasonal variations across the span of 27 years. It is then required to:\n", + "\n", + "<ol>\n", + " <li>Calculate/plot an array containing 324 entries derived from subtracting the yearly averages (<code>y_mean_array</code>) from the original observations y (seasonality variations over all 27 years).\n", + " <li>Calculate/plot the average seasonal sea-level variations over 12 months of the year (so 12 values in total, averaged over 27 years). For simplicity, you may need to reshape the previous array to a matrix form first.</li>\n", + " <li>Compute the difference between the maximum and minimum values of the averaged seasonalities of the sea levels.</li>\n", + " <li>Explain why we would expect to have such seasonal variations in GMSL.</li>\n", + " <li>Compute/plot an array (<code>m_mean_array</code>) that contains the above 12 seasonal values (repeated and therefore identical for all 27 years), making in total 324 entries.</li>\n", + " \n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "cell_id": "7f3b913564af4aa8b7fbc96a90c0f8f4", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 435, + "execution_start": 1696691530041, + "source_hash": null + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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R/v7+WLp0KapXrw4DAwN4eHik+bq+vr7YvXs36tevjwkTJqBgwYLYuHEj9uzZg9mzZyN//vwaxbl7924sWbIEbdu2RbFixSBJEvz9/fH27Vt4e3tn+Nx69eph9erVuH37NkqVKqXReTWRXW00YMAAzJ49G+fPn8cff/yR6vEFCxagTp068PT0xA8//AA3Nze8e/cOd+/exT///IPDhw8DEHV9/f39MWDAAHTs2BGPHz/GlClT4ODgoBpyk1lnzpxBoUKFULFiRY2eR5QryTqtjIiyzZYtWyQfHx+pZMmSUr58+SRjY2PJ1dVV6t69u3T9+vVUx+/YsUOqX7++ZG1tLZmamkpFixaVOnbsKB08eFB1zJMnT6QOHTpINjY2kpWVldS0aVPp6tWrUtGiRaWePXuqjhs9erTk4eEh2djYSKamplKxYsWk4cOHSy9fvkx1zi+//FIyMzOTLC0tpYYNG0onT55UOya5OsGLFy/U9q9Zs0YCIIWEhKj2/fPPP1LlypUlMzMzycnJSRo1apS0b9++NGe6Z6Y6QbKxY8dKACQXFxe1agrJTp06JdWsWVOysLCQChcuLPXr10+6cOGCBEBas2aN2nktLS3TPEdaMWX2/bx+/Vrq2LGjVKBAAUmhUEgpf5Xjo+oEkiRJV65ckVq1aiXlz59fMjExkSpXrqwWpyR9mJH/119/qe0PCQlRe183b96UunbtKhUvXlwyNzeX8ufPL33xxRfS2rVr076YKUREREj58uVLVXkiveoEaV275O+PT8lsG31KvXr1pIIFC0rR0dFpPh4SEiL16dNHcnJykoyNjaXChQtLtWrVkqZOnap23MyZMyU3NzfJ1NRUKlu2rLRy5co03wsAaeDAgWmeS6lUSkWLFk1VWYQor+KKXURElGMGDx6MQ4cO4dq1a5keLyyX8PBwFC1aFIMHD06zpm5OO3ToEBo3boxr166hTJkycodDJDsmsURElGOeP3+OUqVKYdWqVejYsaPc4aTpyZMnuH//PubMmYPDhw/j9u3bcHJykjss1K9fHyVKlPjscd9EuQVLbBERUY6xt7fHxo0bdbpE1B9//IF69erh2rVr2Lhxo04ksG/evIGXlxemTZsmdyhEOoM9sURERESkd9gTS0RERER6h0ksEREREekdJrFEREREpHfy1GIHSqUST58+hZWVlc6XdiEiIiLKiyRJwrt37+Do6AgDg/T7W/NUEvv06VO4uLjIHQYRERERfcLjx4/h7Oyc7uN5Kom1srICIC6KtbW1zNHol4SEBPz7779o3LgxjI2N5Q6HUmDb6Ca2i+5i2+gmtovuyum2iYyMhIuLiypvS0+eSmKThxBYW1szidVQQkICLCwsYG1tzV8uOoZto5vYLrqLbaOb2C66S662+dTQT07sIiIiIiK9wySWiIiIiPQOk1giIiIi0jt5akxsZkiShMTERCQlJckdik5JSEiAkZERYmNjZb02hoaGMDIyYok0IiKiPI5JbArx8fEICwtDdHS03KHoHEmSUKRIETx+/Fj2BNLCwgIODg4wMTGRNQ4iIiKSD5PY/1MqlQgJCYGhoSEcHR1hYmIie7KmS5RKJaKiopAvX74MCw9rkyRJiI+Px4sXLxASEoKSJUvKFgsRERHJi0ns/8XHx0OpVMLFxQUWFhZyh6NzlEol4uPjYWZmJmviaG5uDmNjYzx8+FAVDxER6bekJCAwUIFjx5xgaalA/fqAoaHcUZGuYzfWR9izp/vYRkREuYe/P+DmBnh7G2HePA94exvBzU3sJ8oIswEiIiKShb8/0LEj8OSJ+v7QULGfiSxlRG+S2MTERIwfPx7u7u4wNzdHsWLFMHnyZCiVSrlDIyIiIg0lJQFDhwKSlPqx5H3DhonjiNKiN2NiZ82ahWXLluHPP/9E+fLlce7cOfTu3Rv58+fH0KFD5Q5PJSkJOH4cCAsDHBwAT0+O6yEiIvrY8eOpe2BTkiTg8WNxXL16ORYW6RG9SWJPnz6NNm3aoEWLFgAANzc3bNq0CefOnZM5sg/8/cV/lSl/KJ2dgQULgPbt5YuLiIhI14SFZe64fv2AHj2AFi2AqlUBTougZHqTxNapUwfLli3D7du3UapUKVy6dAknTpzA/Pnz031OXFwc4uLiVPcjIyMBiML9CQkJascmJCRAkiQolcosDVHw9wc6dVL8/yOQD6W5QkMldOwIbN0q6XUiK/3/sx1JkpCQkACFQiHbBCulUqmKw5Dd3Krv5Y+/p0lebBfdxbbRDYULK5CZNOTePcDXV9yKFJHQtKmEZs2UaNRIgpWV9uOknP+Zyex5FJKU1mgU3SNJEsaOHYtZs2bB0NAQSUlJmDZtGsaMGZPucyZOnIhJkyal2u/n55eqjJaRkRGKFCkCFxcXmJiYQJKAzK55kJQEfPWVNcLCFEiZwCZTKCQ4OEg4fToyU0MLLCwATUrUHjx4EL/++itu3LgBQ0ND1KhRAzNnzoS7uzsaN26MWrVqYeLEiarjX758ibJly8Lf3x+enp6Ij4/H1KlT8ffffyMiIgJly5bFxIkTUadOHQDieo0ZMwbLly/HxIkTcffuXZw/fx6vXr3ClClTcPnyZSQkJKBixYqYPn06KleurDrX7du3MWTIEAQHB8PNzQ0zZ85Eu3btsGHDBlWv+tOnTzF+/HgcPnwYBgYG+OqrrzBz5ky4urqm+X7j4+Px+PFjPHv2DImJiZm/UEREpDMSEoBu3VogPj69RFZCwYKx6Nz5Ji5etEdwsB1iYz8ca2SkRPnyL1G9+nN4eDyHo+P7nAmctC46Oho+Pj6IiIiAtbV1usfpTU/sli1bsGHDBvj5+aF8+fIIDg7GsGHD4OjoiJ49e6b5nDFjxmDEiBGq+5GRkXBxcUHjxo1TXZTY2Fg8fvwY+fLlg5mZGd6/B5yds6enUZIUePpUgaJFC2Tq+MhIJSwtNXl9CSNHjkTFihXx/v17+Pr6omfPnrhw4QK6d++OX3/9FXPnzlUt3rB+/XrY29ujWbNmMDAwwDfffIOHDx9i06ZNcHR0xI4dO9CxY0dcunQJJUuWhJmZGWJiYvDbb79h5cqVsLW1hbOzM168eIHevXujevXqAIB58+ahc+fOuHXrFqysrKBUKtGjRw+4uLjg9OnTePfuHUaNGgVA1Hu1trZGdHQ02rZtizp16iAwMBBGRkaYNm0aOnXqhODg4DRX5YqNjYW5uTnq1q3LOrEQ/7EGBATA29sbxsbGcodD/8d20V1sG90wbpwB4uMNAST3pX3ovVEoxL6lS43Rrl0FAEBcnITjxxOxb58C+/YZ4O5dA1y6ZIdLl+ywenVFlCghoXlzJZo1k+DpKYGLOmafnP6ZSf7k/JMkPeHs7CwtWrRIbd+UKVOk0qVLZ/o1IiIiJABSREREqsdiYmKk69evSzExMZIkSVJUlCSJYeU5f4uK+rxrFR4eLgGQrly5IoWHh0tGRkbSsWPHVI/XrFlTGjVqlCRJknT37l1JoVBIoaGhaq/RsGFDacyYMZIkSdKaNWskANKxY8ekpKSkdM+bmJgoWVlZSf/8848kSZK0b98+ycjISAoLC1MdExAQIAGQtm/fLkmSJK1atUoqXbq0pFQqVcfExcVJ5ubm0oEDB9I8z8dtldfFx8dLO3bskOLj4+UOhVJgu+guto38Vqz48Ddv8GBJcnZW/zvo4iJJ27Zl/Bq3bknSvHmS1LChJBkZqT/fykqS2reXpFWrJCnFnyDKopz+mckoX0tJb3pio6OjU43BNDQ01FqJLQsLICoqc8ceOwY0b/7p4/buBerWzdy5NXHv3j388ssvOHPmDF6+fKm6Jo8ePUKFChXg7e2NjRs3wtPTEyEhITh9+jSWLl0KALhw4QIkSUKpUqXUXjMuLg6FChVS3TcxMUGFChXUjgkPD8eECRNw+PBhPH/+HElJSYiOjsajR48AALdu3YKLiwuKFCmies4XX3yh9hrnz5/H3bt3YfXRwKbY2Fjcu3dPswtBREQ6799/gR9+ENu+vsDEicBvvwFHjiRi375gNGtWBfXrG31y+F2pUuI2fDgQGQkEBAB79oi/tc+fi7kqyXVmq1cXE8NatAA8PDg5LLfQmyS2VatWmDZtGlxdXVG+fHlcvHgR8+bNQ58+fbRyPoUCmf5Iv3FjUYUgNDTtencKhXi8cWPtlNtq1aoVXFxcsHLlSjg6OkKpVKJChQqIj48HAHTr1g1Dhw7F77//rhqOkTxuValUwtDQEOfPn081SSpfvnyqbXNzc9VwhGS9evXCixcvMH/+fBQtWhSmpqaoWbOm6rySJKV6zseUSiWqV6+OjRs3pnqscOHCml8MIiLSWVeuiEUMkpKA7t1FEguIv41eXhLevw+Fl1dljf9WWlsDHTqIm1IJXLggEto9e4CgIOD8eXGbPBmwsxMdTy1aAN7eQP782f8+KWfoTRL7+++/45dffsGAAQMQHh4OR0dHfPfdd5gwYYLcocHQUJTR6thRJKwpE9nkHG7+fO0ksK9evcKNGzewfPlyeHp6AgBOnDihdkzbtm3x3XffYf/+/fDz80P37t1Vj1WtWhVJSUkIDw9XPT+zjh8/jiVLlqD5/7uhHz9+jJcvX6oeL1OmDB49eoTnz5/D3t4eABAUFKT2GtWqVcOWLVtgZ2eX4eBtIiLSb0+fiuTx3TtR9/WPPzSbxJxZBgait9XDQyTJz58D+/YBu3eLXuDwcGDtWnEzMhL13JN7aUuX1k5MpB1606FuZWWF+fPn4+HDh4iJicG9e/cwderUNCf+yKF9e+DvvwEnJ/X9zs5iv7bKa9nY2KBQoUJYsWIF7t69i8OHD6tNZgMAS0tLtGnTBr/88gtu3LgBHx8f1WOlSpVCt27d0KNHD/j7+yMkJARBQUGYNWsW9u7dm+G5S5QogfXr1+PGjRv477//0K1bN5ibm6se9/b2RvHixdGzZ09cvnwZJ0+exLhx4wBA1UPbrVs32Nraok2bNjh+/DhCQkIQGBiIoUOH4klGVbCJiEhvREUBLVuKOuplyoiP+XPqz7e9PdCrl/hb/PIlcOgQMGKESFgTE4EjR4CRI4GyZYESJUS993//BVJU6CQdpTdJrD5o3x548ED8QPj5ia8hIdpd6MDAwACbN2/G+fPnUaFCBQwfPhxz5sxJdVy3bt1w6dIleHp6pipdtWbNGvTo0QM//vgjSpcujdatW+O///6Di4tLhudevXo13rx5g6pVq6J79+4YMmQI7OzsVI8bGhpix44diIqKQo0aNdCvXz+MHz8eAFRVBSwsLHDs2DG4urqiffv2KFu2LPr06YOYmBj2zBIR5QJJSUDXrsDFi0DhwuIjfhsbeWIxMQEaNADmzgVu3gTu3hWfpDZuLB67fx9YuBBo0gQoVAho2xZYuVIMFyTdozd1YrNDZGQk8ufPn2bdsdjYWISEhMDd3Z1lm9KgVCoRGRkJa2vrz1rk4OTJk6hTpw7u3r2L4sWLZ+k12FbqEhISsHfvXjRv3pzlgnQI20V3sW1yjiQBgwcDixcDZmbA0aPAl1+mfazc7RIVBRw8+GFy2NOn6o9XqSKGHLRsCdSokf4Qwdy4/HxOt01G+VpKejMmlvTT9u3bkS9fPpQsWRJ3797F0KFDUbt27SwnsEREpD/mzxcJrEIBbNiQfgKrC/LlEz2vbduK5Ds4+MPksP/+E/eDg4Fp0wBbW6BZM5HUNmkCFCggXoPLz+csJrGkVe/evcNPP/2Ex48fw9bWFo0aNcLcuXPlDouIiLRs+3bgxx/F9pw5onKAvlAogKpVxW38eODFCzE5bM8e4MABMbZ2/XpxMzQEatcGihYVifrHn2+HhoqJ39qcH5NXMYklrerRowd69OghdxhERJSDzp4FunUTCd0PP4iJVPqscGGgRw9xS0gATp360Et7/bqoF58eSRJJ8bBhQJs2+j+0QJdwYhcRERFlmwcPgFatgJgYUVJr4cLcVbbK2Bjw8gJmzwauXROTwYYMyfg5kgQ8fizGylL2YRL7kTw0z01vsY2IiHTT27cicQ0PFxOhNm8WtVhzM3d34KuvMndsWJh2Y8lrmMT+X/Jsu+joaJkjoU9JbiPOKiYi0h3x8WLM540bomb67t3ARyuK51oODtl7HGVOLv//KPMMDQ1RoEABhIeHAxD1Sz+1ZGpeolQqER8fj9jY2M8qsfU5JElCdHQ0wsPDUaBAgVTL5BIRkTwkCfj2W1EfPV8+MVb048V/cjNPz4yXnwfEogsaLoxJn8AkNoUiRYoAgCqRpQ8kSUJMTAzMzc1lT+4LFCigaisiIpLf1KnAn3+KSUt//QVUrix3RDkro+Xnk8XHi9qzn1hHiDTAJDYFhUIBBwcH2NnZISEhQe5wdEpCQgKOHTuGunXryvoxvrGxMXtgiYh0yMaNwIQJYnvxYqBpU3njkUvy8vMf14l1chKJ7ZMnYrzwiRNA/vzyxZmbMIlNg6GhIROljxgaGiIxMRFmZmYci0pERACAwECgTx+xPWoU8N138sYjt/btRRmtj1fsevJETP66elX01u7ZI5a5pc/DJJaIiIg0dusW0K6d+Ji8Y0dg5ky5I9INhoZAvXrq+4oWFYlr3bpiadtvvwXWrMldpcfkwOoEREREpJEXL8RH42/eiB7GdesAmeb86o1q1cR4YUNDMX548mS5I9J//JYjIiKiTIuJAVq3FkX+ixUDdu0CzM3ljko/NGsGLFkitidOBNaulTMa/cckloiIiDJFqRRLr545A9jYAHv3iiVZKfO+/RYYM0Zs9+8vhhdQ1jCJJSIiokwZM0bMwDc2BrZvB0qXljsi/TR1KtC1K5CYCHToAFy5IndE+olJLBEREX3S8uXA7Nlie/VqwMtL3nj0mYGBmNjl5QVERorxxaGhckelf5jEEhERUYb27wcGDhTbkycD33wjbzy5gamp6M0uU0aU4GrRQiS0lHlMYomIiChdly4BX38NJCUBvXoB48fLHVHukTyu2N5eXOdOnQCutZR5TGKJiIgoTaGhoocwKgpo0EAMKWBt0+zl7g7s3g1YWAAHDgA//JD2srWUGpNYIiIiSuXdO6BlS5HIli0LbNvGVaa0xcMD2LxZjJVdtQqYPl3uiPQDk1giIiJSk5gIdO4MBAcDdnbiI+8CBeSOKndr1Qr4/XexPX48sGGDvPHoAyaxREREpCJJwJAhwL59YhGDf/4B3NzkjipvGDAAGDVKbPfpAxw5Im88uo5JLBEREanMmwcsXSrGvvr5AV98IXdEecvMmR8meLVrB1y7JndEuotJLBEREQEQ416TewLnzgXatpU1nDzJwAD480+gdm0gIkLUkA0Lkzsq3cQkloiIiHDmjKj/KkmiJuywYXJHlHeZmQE7dwKlSgGPHn2oEEHqmMQSERHlcffvA61bA7GxImGaP5+ltORWqJCYUFe4MHDxophol5god1S6hUksERFRHvbmjUhcX7wAqlYVpZ6MjOSOigCgeHExsc7cXCS0AweyhmxKTGKJiIjyqPh4oH174OZNwMVFFN3Pl0/uqCilL78UE+wUCmDFCmDWLLkj0h1MYomIiPIgSQL69QOOHgWsrIA9ewBHR7mjorS0bSuGeADAmDHApk1yRqM79CqJDQ0NxTfffINChQrBwsICVapUwfnz5+UOi4iISO9MmgSsXw8YGgJ//w1UrCh3RJSRIUOA4cPFdq9ewLFjsoajE/QmiX3z5g1q164NY2Nj7Nu3D9evX8fcuXNRgEuIEBERaWTdOpHEAqImbOPG8sZDmfPrr2L4R3w80KYNcOOG3BHJS2+Gbs+aNQsuLi5Ys2aNap8blxAhIiLSyNGjYhgBAIweDfTvL2s4pAEDA7EcbYMGoiRa8+biq7293JHJQ2+S2F27dqFJkyb4+uuvERgYCCcnJwwYMAD9M/jpi4uLQ1xcnOp+ZGQkACAhIQEJCQlajzk3Sb5evG66h22jm9guuisvt82NG0C7dkZISFDg66+VmDgxCbpyGfJyu2jCyEgsSlG3rhHu3VOgRQslDh5MgqWl9s6Z022T2fMoJOnTxRoWLlyocQC9e/eGlZWVxs9Lj5mZGQBgxIgR+Prrr3H27FkMGzYMy5cvR48ePdJ8zsSJEzEp+fOSFPz8/GBhYZFtsREREem6t29N8dNPnggPt0SZMq8wefIpmJgo5Q6LsujpU0v8/LMn3r0zRY0aYRg9+iwMDeWOKntER0fDx8cHERERsLa2Tve4TCWxBgYGcHZ2hmEmr87jx49x+/ZtFCtWLPMRf4KJiQk8PDxw6tQp1b4hQ4YgKCgIp0+fTvM5afXEuri44OXLlxleFEotISEBAQEB8Pb2hrGxsdzhUApsG93EdtFdebFtoqMBb29DBAUZoHhxCcePJ8LWVu6o1OXFdvlcZ84o0LixIWJjFfj++yQsWKDUyiIVOd02kZGRsLW1/WQSm+nhBOfOnYOdnV2mjs3OHthkDg4OKFeunNq+smXLYtu2bek+x9TUFKampqn2Gxsb8wcki3jtdBfbRjexXXRXXmkbpRLo0wcICgIKFgT27lXAwUF333deaZfs4Okpxsh+/TWwbJkhihc3xMiR2jtfTrVNZs+RqeoEvr6+yKdB9eOxY8eiYMGCmT4+M2rXro1bt26p7bt9+zaKFi2arechIiLKTX76CfD3B0xMgB07gFKl5I6IslOHDsDcuWJ71Cjgr7/kjScnZTqJ1WQM6ZgxY7K99NXw4cNx5swZTJ8+HXfv3oWfnx9WrFiBgQMHZut5iIiIcoulSz8kOGvXip47yn2GDQMGDxbb3bsDJ0/KGk6O0Zs6sTVq1MD27duxadMmVKhQAVOmTMH8+fPRrVs3uUMjIiLSOXv3AoMGie2pU4GuXeWNh7RHoQB++03Ujo2LA1q3Bm7fljsq7dO4xNarV68wYcIEHDlyBOHh4VAq1Wc2vn79OtuC+1jLli3RsmVLrb0+ERFRbnDxItCp04fxsGPHyh0RaZuhIeDnB9SvD5w9CzRrBpw+DWRyOpNe0jiJ/eabb3Dv3j307dsX9vb2UGhjGhwRERFlyZMnQMuWwPv3QMOGwLJl0MqMddI9FhbAP/8AX30F3L8vemQPHxb7cyONk9gTJ07gxIkTqFy5sjbiISIioiyKjARatACePgXKlwf+/hvgRP+8xc4O2LcPqFUL+O8/oFs38X2QW2rIpqTxmNgyZcogJiZGG7EQERFRFiUmAp07A5cvA0WKAHv2ANk8x5r0ROnSwM6dgKmpqEgxYoTcEWmHxknskiVLMG7cOAQGBuLVq1eIjIxUuxEREZH2JSUBR48CmzYBR44AP/wA7N//4SNlVqDM2+rUAf78U2wvXAjMny9rOFqh8XCCAgUKICIiAg0aNFDbL0kSFAoFkpKSsi04IiIiSs3fHxg6VIx//ZifH+DhkfMxke7p3Bl49EjUCh4xAnBxEXVlcwuNk9hu3brBxMQEfn5+nNhFRESUw/z9gY4dgfQWjWdfEqU0ciQQEiJqBn/zDeDoCNSsKXdU2UPjJPbq1au4ePEiSpcurY14iIiIKB1JSaIHNr0EVqEQhe/btMmdE3lIcwqFGE7w+DGwe7eoWHD6NFCihNyRfT6Nx8R6eHjg8ePH2oiFiIiIMnD8eNpDCJJJkkhWjh/PuZhI9xkZAZs3A9WrAy9fihqyL1/KHdXn07gndvDgwRg6dChGjRqFihUrwvij2h2VKlXKtuCIiIjog7Cw7D2O8g5LS9ET+9VXwN27okf20CHA3FzuyLJO4yS2c+fOAIA+ffqo9ikUCk7sIiIi0jIHh+w9jvKWIkU+1JA9fRro3h3YuhUw0Phzed2gcRIbEhKijTiIiIjoEzw9AWfn9IcUKBTicU/PnI2L9EfZsqJ2bOPGwLZtwKhRwNy5ckeVNRonsUVZeI6IiEgWhobAL78A332X+rHkYkHz53NSF2XMywtYs0as5jVvHuDmBgweLHdUmtM4iQWA0NBQnDx5EuHh4VAqlWqPDRkyJFsCIyIiotTOnxdfTU2BuLgP+52dRQLbvr0sYZGe8fEBHj4Exo4VFS9cXUVVC32icRK7Zs0afP/99zAxMUGhQoXU6sQqFAomsURERFpy/z6werXYPnBAVCMICxNjYD092QNLmhk9WtSQXbkS6NpVrAD3xRdyR5V5GiexEyZMwIQJEzBmzBgY6OtIYCIiIj00eTKQmAg0aSI+Eib6HAoFsGSJKMu2fz/QsiVw5gxQrJjckWWOxllodHQ0unTpwgSWiIgoB926BaxfL7YnT5Y3Fso9jIxEhYKqVYEXL4DmzYFXr+SOKnM0zkT79u2Lv/76SxuxEBERUTomTgSUSqBVK/36yJd0n5WVqCHr4iL+WWrbFoiNlTuqT9N4OMGMGTPQsmVL7N+/P83FDubNm5dtwRERERFw5QqwZYvYZi8saYOjo6ghW7s2cOIE0KsX4Ocnxl0HBipw7JgTLC0VqF9fd8Zea5zETp8+HQcOHEDp0qUBINXELiIiIspevr4imejYEahSRe5oKLcqXx7w9weaNhX/NMXFAefOAU+eGAHwwLx5ogrGggW6UQVD4yR23rx5WL16NXr16qWFcIiIiCil8+eB7dvFJJxJk+SOhnK7Bg2AVauAHj3EoggfCw0V/0z9/bf8iazGY2JNTU1Ru3ZtbcRCREREH5kwQXz18QHKlZM3FsobfHwAa+u0H5Mk8XXYMCApKcdCSpPGSezQoUPx+++/ayMWIiIiSuH0aWDvXjEG0ddX7mgorzh+HIiMTP9xSRJluY4fz7mY0qLxcIKzZ8/i8OHD2L17N8qXL59qYpe/v3+2BUdERJSXJffC9uwJlCwpbyyUd4SFZe9x2qJxElugQAG0l3sQBBERUS4XGAgcPAgYGwO//CJ3NJSXODhk73HakqVlZ4mIiEh7JOlD4tqvH+DmJms4lMd4eooqBKGhH8bApqRQiMc9PXM+tpS47BYREZGOCQgQ4w1NTYFx4+SOhvIaQ0NRRgsQCWtKyffnz5e/Xmymkthq1arhzZs3mX7ROnXqIDQ0NMtBERER5VUpe2F/+AFwcpI3Hsqb2rcXZbQ+/v5zdtaN8lpAJocTBAcH49KlSyhYsGCmXjQ4OBhxcXGfFRgREVFetHs3cPYsYGEBjB4tdzSUl7VvD7RpAxw5koh9+4LRrFkV1K9vJHsPbLJMj4lt2LAhpLQGRqSBK3cRERFpTqn8UJFg8GDA3l7eeIgMDQEvLwnv34fCy6uyziSwQCaT2JCQEI1f2NnZWePnEBER5WX+/kBwMGBlBYwaJXc0RLotU0ls0aJFtR2HxmbMmIGxY8di6NChmD9/vtzhEBERfZakpA8LGgwfDhQqJG88RLpOL6sTBAUFYcWKFahUqZLcoRAREWWLzZuB69eBAgVEEktEGdO7JDYqKgrdunXDypUrYWNjI3c4REREny0xEZg0SWyPGiUSWSLKmMaLHcht4MCBaNGiBRo1aoSpU6dmeGxcXJxalYTI/y8EnJCQgISEBK3GmdskXy9eN93DttFNbBfdpYtt8+efCty5YwRbWwk//JAIHQotx+hiu5CQ022T2fPoVRK7efNmXLhwAUFBQZk6fsaMGZiU/K9tCv/++y8sLCyyO7w8ISAgQO4QKB1sG93EdtFdutI2CQkKjB3bCIARWra8hmPH7skdkqx0pV0otZxqm+jo6Ewdp5AyWzdLZo8fP4aHhwf+/fdfVK5cGQBQr149VKlSJd2JXWn1xLq4uODly5ewtrbOibBzjYSEBAQEBMDb2xvGxsZyh0MpsG10E9tFd+la26xYYYBBgwxRpIiEmzcTkVf7WHStXeiDnG6byMhI2NraIiIiIsN8LVM9sTY2Npmu/fr69evMRaih8+fPIzw8HNWrV1ftS0pKwrFjx7Bo0SLExcXB8KPiZaampjA1NU31WsbGxvwBySJeO93FttFNbBfdpQttExsLzJghtseNUyB/fn6v6EK7UNpyqm0ye45MJbG6UMKqYcOGuHLlitq+3r17o0yZMvj5559TJbBERES6bvlyIDQUcHEB+veXOxoi/ZKpJLZnz57ajuOTrKysUKFCBbV9lpaWKFSoUKr9REREuu79e2D6dLH9yy9AGh8cElEGslRi6969exg/fjy6du2K8PBwAMD+/ftx7dq1bA2OiIgot1q8GAgPB4oVA3r1kjsaIv2jcRIbGBiIihUr4r///oO/vz+ioqIAAJcvX4Zv8lIjOeTo0aM6MdSBiIhIE5GRwKxZYnvCBIBDQIk0p3ESO3r0aEydOhUBAQEwMTFR7a9fvz5Onz6drcERERHlRgsWAK9fA6VLA926yR0NkX7SOIm9cuUK2rVrl2p/4cKF8erVq2wJioiIKLd68waYO1dsT5wIGOlVxXYi3aFxElugQAGEhYWl2n/x4kU4OTllS1BERES51dy5QEQEUKEC0KmT3NEQ6S+Nk1gfHx/8/PPPePbsGRQKBZRKJU6ePImRI0eiR48e2oiRiIgoV3jxQgwlAIDJkwGDLE2vJiIgC0nstGnT4OrqCicnJ0RFRaFcuXKoW7cuatWqhfHjx2sjRiIiolxh9mwgKgqoVg1o21buaIj0m8YjcYyNjbFx40ZMnjwZFy9ehFKpRNWqVVGyZEltxEdERJQrhIWJsloAMGUKkMmFMIkoHRonsYGBgfDy8kLx4sVRvHhxbcRERESU68yYAcTEADVrAs2ayR0Nkf7TeDiBt7c3XF1dMXr0aFy9elUbMREREeUqjx+LJWYB9sISZReNk9inT5/ip59+wvHjx1GpUiVUqlQJs2fPxpMnT7QRHxERkd6bOhWIjwfq1QMaNJA7GqLcQeMk1tbWFoMGDcLJkydx7949dO7cGevWrYObmxsa8CeTiIhIzf37wOrVYpu9sETZ57OKe7i7u2P06NGYOXMmKlasiMDAwOyKi4iIKFeYPBlITAQaNwbq1JE7GqLcI8tJ7MmTJzFgwAA4ODjAx8cH5cuXx+7du7MzNiIiIr126xawfr3YnjJF3liIchuNqxOMHTsWmzZtwtOnT9GoUSPMnz8fbdu2hYWFhTbiIyIi0lsTJwJKJdCqFfDFF3JHQ5S7aJzEHj16FCNHjkTnzp1ha2urjZiIiIj03tWrwJYtYnvyZHljIcqNNE5iT506pY04iIiIchVfX0CSgI4dgSpV5I6GKPfJ0pjY9evXo3bt2nB0dMTDhw8BAPPnz8fOnTuzNTgiIiJ9dOEC4O8vKhFMmiR3NES5k8ZJ7NKlSzFixAg0b94cb9++RVJSEgCgQIECmD9/fnbHR0REpHcmTBBffXyAcuXkjYUot9I4if3999+xcuVKjBs3DoaGhqr9Hh4euHLlSrYGR0REpG9Onwb27AEMDcWQAiLSDo2T2JCQEFStWjXVflNTU7x//z5bgiIiItJXyb2wPXsCJUvKGwtRbqZxEuvu7o7g4OBU+/ft24dy/MyEiIjysMBA4OBBwNgY+OUXuaMhyt00rk4watQoDBw4ELGxsZAkCWfPnsWmTZswY8YM/PHHH9qIkYiISOdJ0ofEtV8/wM1N1nCIcj2Nk9jevXsjMTERP/30E6Kjo+Hj4wMnJycsWLAAXbp00UaMREREOi8gADh+HDA1BcaNkzsaotxP4yQWAPr374/+/fvj5cuXUCqVsLOzy+64iIiI9EbKXtgffgCcnOSNhygvyFISm4wrdhEREQG7dwNnzwIWFsDo0XJHQ5Q3ZCqJrVq1KhQKRaZe8MKFC58VEBERkT5RKj9UJBg8GLC3lzceorwiU0ls27ZttRwGERGRftq+HQgOBqysgFGj5I6GKO/IVBLry2rNREREqSQlfeiFHT4cKFRI3niI8hKN68QSERGRsGULcP06UKCASGKJKOcwiSUiIsqCxERg4kSxPWqUSGSJKOcwiSUiIsqC9euBO3cAW1tgyBC5oyHKe5jEEhERaSg+Hpg0SWyPHg3kyydvPER5kd4ksTNmzECNGjVgZWUFOzs7tG3bFrdu3ZI7LCIiyoNWrwYePgSKFBGLGxBRzsvSYgdPnjzBrl278OjRI8THx6s9Nm/evGwJ7GOBgYEYOHAgatSogcTERIwbNw6NGzfG9evXYWlpqZVzEhERfSw2Fpg6VWyPGycWOCCinKdxEnvo0CG0bt0a7u7uuHXrFipUqIAHDx5AkiRUq1ZNGzECAPbv3692f82aNbCzs8P58+dRt25drZ2XiIgopeXLgdBQwMUF6N9f7miI8i6Nk9gxY8bgxx9/xOTJk2FlZYVt27bBzs4O3bp1Q9OmTbURY5oiIiIAAAULFkz3mLi4OMTFxanuR0ZGAgASEhKQkJCg3QBzmeTrxeume9g2uontors+p23evwemTzcCoMCYMYkwMJDAJs4e/JnRXTndNpk9j0KSJEmTF7ayskJwcDCKFy8OGxsbnDhxAuXLl8elS5fQpk0bPHjwICvxakSSJLRp0wZv3rzB8ePH0z1u4sSJmJQ88j4FPz8/WPDzHyIi0pC/fwmsW1ce9vbvsXjxIRgZafQnlIgyITo6Gj4+PoiIiIC1tXW6x2ncE2tpaanq3XR0dMS9e/dQvnx5AMDLly+zGK5mBg0ahMuXL+PEiRMZHjdmzBiMGDFCdT8yMhIuLi5o3LhxhheFUktISEBAQAC8vb1hbGwsdziUAttGN7FddFdW2yYyEujbV/zZnDbNFK1bN9NWiHkSf2Z0V063TfIn55+icRL71Vdf4eTJkyhXrhxatGiBH3/8EVeuXIG/vz+++uorjQPV1ODBg7Fr1y4cO3YMzs7OGR5ramoKU1PTVPuNjY35A5JFvHa6i22jm9guukvTtlmyBHj1CihdGujZ0whGWZoaTZ/CnxndlVNtk9lzaPwjOG/ePERFRQEQH9dHRUVhy5YtKFGiBH777TdNXy7TJEnC4MGDsX37dhw9ehTu7u5aOxcREVFKb94Ac+eK7YkTwQSWSAdo/GNYrFgx1baFhQWWLFmSrQGlZ+DAgfDz88POnTthZWWFZ8+eAQDy588Pc3PzHImBiIjyprlzgYgIoEIFoFMnuaMhIiCLix28ffsWf/zxB8aMGYPXr18DAC5cuIDQ0NBsDS6lpUuXIiIiAvXq1YODg4PqtmXLFq2dk4iI6MULYMECsT15MmCgN8sEEeVuGvfEXr58GY0aNUL+/Pnx4MED9O/fHwULFsT27dvx8OFDrFu3ThtxQsMiCkRERNli9mwgKgqoVg1o21buaIgomcb/T44YMQK9evXCnTt3YGZmptrfrFkzHDt2LFuDIyIiklNYGLB4sdieMgVQKOSNh4g+0DiJDQoKwnfffZdqv5OTk2qcKhERUW4wYwYQEwPUrAk0Y0UtIp2icRJrZmaWZv2uW7duoXDhwtkSFBERkdwePxZLzALshSXSRRonsW3atMHkyZNVS4IpFAo8evQIo0ePRocOHbI9QCIiIjlMnQrExwNeXkCDBnJHQ0Qf0ziJ/fXXX/HixQvY2dkhJiYGXl5eKFGiBKysrDBt2jRtxEhERJSj7t8HVq8W2+yFJdJNGlcnsLa2xokTJ3D48GFcuHABSqUS1apVQ6NGjbQRHxERUY6bMgVITAQaNwY8PeWOhojSkuU1Rxo0aIAG//985e3bt9kVDxERkaxu3QKSq0VOmSJvLESUPo2HE8yaNUttgYFOnTqhUKFCcHJywqVLl7I1OCIiopw2aRKgVAKtWgFffCF3NESUHo2T2OXLl8PFxQUAEBAQgICAAOzbtw/NmjXDqFGjsj1AIiKinHL1KrB5s9iePFneWIgoYxoPJwgLC1Mlsbt370anTp3QuHFjuLm54csvv8z2AImIiHKKry8gSUDHjkCVKnJHQ0QZ0bgn1sbGBo8fPwYA7N+/XzWhS5IkJCUlZW90REREOeTCBcDfX1QimDRJ7miI6FM07olt3749fHx8ULJkSbx69QrN/r+ESXBwMEqUKJHtARIREeWECRPEVx8foFw5eWMhok/TOIn97bff4ObmhsePH2P27NnIly8fADHMYMCAAdkeIBERkbadPg3s2QMYGoohBUSk+zROYo2NjTFy5MhU+4cNG5Yd8RAREeW45F7Ynj2BkiXljYWIMkfjMbFERES5SWAgcPAgYGwM/PKL3NEQUWYxiSUiojxLkj4krv36AW5usoZDRBpgEktERHnWwYPA8eOAqSkwbpzc0RCRJpjEEhFRniRJwPjxYvuHHwAnJ3njISLNaDyxK1l8fDzCw8OhVCrV9ru6un52UERERNq2d68CZ88CFhbA6NFyR0NEmtI4ib1z5w769OmDU6dOqe2XJAkKhYILHhARkc5TKoFJkwwBAIMHA/b2MgdERBrTOInt1asXjIyMsHv3bjg4OEChUGgjLiIiomyXlAQEBiqwenV5XLqkQL58wKhRckdFRFmhcRIbHByM8+fPo0yZMtqIh4iISCv8/YGhQ4EnT4wAiBUmDQxEia327eWNjYg0p/HErnLlyuHly5faiIWIiEgr/P2Bjh2BJ0/U9797J/b7+8sTFxFlncZJ7KxZs/DTTz/h6NGjePXqFSIjI9VuREREuiQpSfTASlLqx5L3DRsmjiMi/aHxcIJGjRoBABo2bKi2nxO7iIhIFx0/nroHNiVJAh4/FsfVq5djYRHRZ9I4iT1y5Ig24iAiItKKsLDsPY6IdIPGSayXl5c24iAiItIKB4fsPY6IdEOWFzuIjo7Go0ePEB8fr7a/UqVKnx0UERFRdqlTBzA3B2Ji0n5coQCcnQFPz5yNi4g+j8ZJ7IsXL9C7d2/s27cvzcc5JpaIiHTJihUZJ7AAMH8+YGiYYyERUTbQuDrBsGHD8ObNG5w5cwbm5ubYv38//vzzT5QsWRK7du3SRoxERERZEhwMDB8utnv1Ej2uKTk7A3//zTqxRPpI457Yw4cPY+fOnahRowYMDAxQtGhReHt7w9raGjNmzECLFi20EScREZFG3r0DOnUC4uOBVq2A1avFcrNHjiRi375gNGtWBfXrG7EHlkhPadwT+/79e9jZ2QEAChYsiBcvXgAAKlasiAsXLmRvdGlYsmQJ3N3dYWZmhurVq+P48eNaPycREekXSQK+/x64cwdwcQHWrhVDBwwNAS8vCXXrhsLLS2ICS6THNE5iS5cujVu3bgEAqlSpguXLlyM0NBTLli2Dg5andm7ZsgXDhg3DuHHjcPHiRXh6eqJZs2Z49OiRVs9LRET6ZfVqwM9PJK2bNgEFC8odERFltyyNiQ37fzE9X19f7N+/H66urli4cCGmT5+e7QGmNG/ePPTt2xf9+vVD2bJlMX/+fLi4uGDp0qVaPS8REemPq1eBwYPF9tSpQO3a8sZDRNqh8ZjYbt26qbarVq2KBw8e4ObNm3B1dYWtrW22BpdSfHw8zp8/j9GjR6vtb9y4MU6dOpXmc+Li4hAXF6e6n7wsbkJCAhISErQWa26UfL143XQP20Y3sV3k8f498PXXRoiJUaBxYyWGD0/Cx03AttFNbBfdldNtk9nzZLlObHx8PEJCQlC8eHFUq1Ytqy+TaS9fvkRSUhLs7e3V9tvb2+PZs2dpPmfGjBmYNGlSqv3//vsvLCwstBJnbhcQECB3CJQOto1uYrvkrIULq+LmTVfY2MTCx+cI9u+PT/dYto1uYrvorpxqm+jo6Ewdp3ESGx0djcGDB+PPP/8EANy+fRvFihXDkCFD4OjomKqnNLspkov6/Z8kSan2JRszZgxGjBihuh8ZGQkXFxc0btwY1tbWWo0zt0lISEBAQAC8vb1hbGwsdziUAttGN7Fdct769QocPmwEAwMJf/1lhLp1G6V5HNtGN7FddFdOt03yJ+efonESO2bMGFy6dAlHjx5F06ZNVfsbNWoEX19frSWxtra2MDQ0TNXrGh4enqp3NpmpqSlMTU1T7Tc2NuYPSBbx2ukuto1uYrvkjJs3gSFDxLavrwING376zxvbRjexXXRXTrVNZs+h8cSuHTt2YNGiRahTp45aD2i5cuVw7949TV8u00xMTFC9evVUXdkBAQGoVauW1s5LRES6LSZG1IN9/x5o0AAYN07uiIgoJ2Rp2dnkOrEpvX//Pt2P9bPLiBEj0L17d3h4eKBmzZpYsWIFHj16hO+//16r5yUiIt01bBhw5QpgZwds3MjlY4nyCo2T2Bo1amDPnj0Y/P/6JcmJ68qVK1GzZs3sje4jnTt3xqtXrzB58mSEhYWhQoUK2Lt3L4oWLarV8xIRkW7avBlYsUIsZLBxI1CkiNwREVFO0TiJnTFjBpo2bYrr168jMTERCxYswLVr13D69GkEBgZqI0Y1AwYMwIABA7R+HiIi0m137wLffiu2x44FGqU9j4uIcimNx8TWqlULJ0+eRHR0NIoXL45///0X9vb2OH36NKpXr66NGImIiNTExYlxsO/eAZ6ewMSJckdERDktS3ViK1asqCqxRURElNNGjgQuXgQKFRLLyxplueo5EemrLP/Yh4eHIzw8HEqlUm1/pUqVPjsoIiKi9Pj7A4sWie116wBnZ3njISJ5aJzEnj9/Hj179sSNGzcgSZLaYwqFAklJSdkWHBERUUohIUCfPmJ71CigeXN54yEi+WicxPbu3RulSpXCqlWrYG9vr/WyWkRERAAQHw906QJERABffQVMmyZ3REQkJ42T2JCQEPj7+6NEiRLaiIeIiChNY8YAZ88CNjaitBYXdSLK2zSuTtCwYUNcunRJG7EQERGl6Z9/gHnzxPaaNQDLgxORxj2xf/zxB3r27ImrV6+iQoUKqda3bd26dbYFR0RE9Pgx0KuX2B46FGjTRtZwiEhHaJzEnjp1CidOnMC+fftSPcaJXURElJ0SEsQ42NevAQ8PYPZsuSMiIl2h8XCCIUOGoHv37ggLC4NSqVS7MYElIqLs5OsLnDoFWFsDW7YAJiZyR0REukLjJPbVq1cYPnw47O3ttREPERERAODAAWDGDLH9xx9AsWLyxkNEukXjJLZ9+/Y4cuSINmIhIiICADx9CnTvLrZ/+AH4+mt54yEi3aPxmNhSpUphzJgxOHHiBCpWrJhqYteQIUOyLTgiIsp7kpKAbt2AFy+AypU/VCUgIkopS9UJ8uXLh8DAQAQGBqo9plAomMQSEdFnmTIFOHoUsLQEtm4FzMzkjoiIdFGWFjsgIiLShsOHgcmTxfby5UCpUvLGQ0S6S+MxsURERNrw/LkYRiBJQN++YpuIKD1MYomISHZKpZjI9ewZUL48sHCh3BERka5jEktERLKbORMICADMzcU4WAsLuSMiIl3HJJaIiGR1/Djwyy9ie/FioFw5eeMhIv3AJJaIiGTz8iXQteuH4QS9eskdERHpi0xVJ7h8+XKmX7BSpUpZDoaIiPIOpRLo2RMIDQVKlwaWLAEUCrmjIiJ9kakktkqVKlAoFJAkKc3Hkx9TKBRISkrK1gCJiCh3mjcP2LsXMDUV42Dz5ZM7IiLSJ5lKYlkbloiIstOZM8CYMWJ7wQKAH+IRkaYylcQWLVpU23EQEVEe8eYN0LkzkJgovn77rdwREZE+0njFrmTXr1/Ho0ePEB8fr7a/devWnx0UERHlTpIE9O4NPHoEFC8OrFjBcbBElDUaJ7H3799Hu3btcOXKFbVxsor//xbimFgiIkrP778DO3cCJiZiHKy1tdwREZG+0rjE1tChQ+Hu7o7nz5/DwsIC165dw7Fjx+Dh4YGjR49qIUQiIsoNzp0DRo4U27/+ClSrJm88RKTfNO6JPX36NA4fPozChQvDwMAABgYGqFOnDmbMmIEhQ4bg4sWL2oiTiIj0WESEGP+akAC0awcMGiR3RESk7zTuiU1KSkK+/9dBsbW1xdOnTwGIyV+3bt3K3uiIiEjvSRLQvz9w/z7g5gasWsVxsET0+TTuia1QoQIuX76MYsWK4csvv8Ts2bNhYmKCFStWoFixYtqIUS8lJYmlFMPCAAcHwNMTMDSUOyoiopy3fDnw11+AkRGweTNgYyN3RESUG2jcEzt+/HgolUoAwNSpU/Hw4UN4enpi7969WLhwYbYHCAAPHjxA37594e7uDnNzcxQvXhy+vr6pKiPoCn9/0dtQvz7g4yO+urmJ/UREecmlS8CwYWJ75kzgyy9lDYeIchGNe2KbNGmi2i5WrBiuX7+O169fw8bGRlWhILvdvHkTSqUSy5cvR4kSJXD16lX0798f79+/x6+//qqVc2aVvz/QsaP4+Cyl0FCx/++/gfbt5YmNiCgnvXsHdOoExMUBLVsCI0bIHRER5SZZrhMLAI8fP4ZCoYCzs3N2xZOmpk2bomnTpqr7xYoVw61bt7B06VKdSmKTkoChQ1MnsIDYp1CIHok2bTi0gIhyN0kCfvgBuH0bcHYG1q7lOFgiyl4aJ7GJiYmYNGkSFi5ciKioKABAvnz5MHjwYPj6+sLY2Djbg0xLREQEChYsmOExcXFxiIuLU92PjIwEACQkJCAhISHbYwoMVODJk/QvqSQBjx8DR44kwssrjUxXhyVfL21cN/o8bBvdlNfbZe1aBTZuNIKhoYT165NgbS1BVy5FXm8bXcV20V053TaZPY9CktLqN0zf999/j+3bt2Py5MmoWbMmAFF2a+LEiWjTpg2WLVumebQaunfvHqpVq4a5c+eiX79+6R43ceJETJo0KdV+Pz8/WFhYZHtcx445Yd48j08eN2LEOdStG5rt5yci0gWPHllh5Mi6iI83wjffXEfHjnfkDomI9Eh0dDR8fHwQEREB6wxWRNE4ic2fPz82b96MZs2aqe3ft28funTpgoiIiEy/VnpJZkpBQUHw8PiQGD59+hReXl7w8vLCH3/8keFz0+qJdXFxwcuXLzO8KFkVGKiAt/enO7cDAvSzJzYgIADe3t451ttOmcO20U15tV3evwdq1TLCjRsKeHsr8c8/STDQeAqxduXVttF1bBfdldNtExkZCVtb208msRoPJzAzM4Obm1uq/W5ubjAxMdHotQYNGoQuXbpkeEzKcz19+hT169dHzZo1sWLFik++vqmpKUxNTVPtNzY21koj1K8vxn6FhqY9LhYADAyAZ8+MYGSkn+PDtHXt6POxbXRTXmuXH38EbtwQpQU3bDCAqamOZbAp5LW20RdsF92VU22T2XNonMQOHDgQU6ZMwZo1a1QJYlxcHKZNm4ZBGi7BYmtrC1tb20wdGxoaivr166N69epYs2YNDHTtX3uIyVoLFogqBApF2omsUgl07w5s2AAsWQKwtC4R5RYbNgCrV4t/1v38ADs7uSMiotwsU0ls+49qQh08eBDOzs6oXLkyAODSpUuIj49Hw4YNsz9CiB7YevXqwdXVFb/++itevHiheqxIkSJaOWdWtW8vymgNHQo8efJhv4uLWCv89m1g6lTgwAGgQgXA11eUneE/nUSkz27dAr7/XmxPmADUqydrOESUB2Qqic2fP7/a/Q4dOqjdd3Fxyb6I0vDvv//i7t27uHv3bqpyXhoO6c0R7duLMlrprdjVqZP4ZX/kCDB6tOixWLGCRcCJSD/FxIjfa+/fi2FV48fLHRER5QWZSmLXrFmj7Tgy1KtXL/Tq1UvWGDRlaJh+T0SpUsChQ8C6dWL82OXLQM2awIABwPTpgBbmnBERac2IEeL3WOHCwMaNrINNRDkjywNLX7x4gRMnTuDkyZNqH+9T5igUQM+ewM2bQI8eYvzs4sVA2bLA9u1yR0dElDlbtwLLlonfaRs2iE+eiIhygsZJ7Pv379GnTx84ODigbt268PT0hKOjI/r27Yvo6GhtxJir2doCf/4JHDwIlCgBPH0qhiO0bSsWRiAi0lX37gHJpbrHjAEaN5Y3HiLKWzROYkeMGIHAwED8888/ePv2Ld6+fYudO3ciMDAQP/74ozZizBMaNhQfx40bBxgZATt3AuXKAQsXiuVsiYh0SVycGAf77h1Qpw7wiZLfRETZTuMkdtu2bVi1ahWaNWsGa2trWFtbo3nz5li5ciX+/vtvbcSYZ5ibi8oFwcFArVpAVJSoclCzpthHRKQrfvoJuHABKFQI2LRJ/PNNRJSTNE5io6OjYW9vn2q/nZ0dhxNkk/LlRWWDZcuA/PmBoCDAwwMYNUrM/iUiktP27eJTIkAMh/qoaAwRUY7QOImtWbMmfH19ERsbq9oXExODSZMmoWbNmtkaXF5mYAB8951Y+aZTJzGk4NdfRYK7b5/c0RFRXvXgAdCnj9geORJo0ULWcIgoD9P4A6AFCxagadOmqsUOFAoFgoODYWZmhgMHDmgjxjzNwQHYskVUMhgwAHj4EGjeHOjcGZg/H9CxtR6IKJdJSvpQ89rWVtSAfftW1LWePl3u6IgoL9M4ia1QoQLu3LmDDRs24ObNm5AkCV26dEG3bt1gbm6ujRgJInG9dk2s8PXbbyKxPXAAmDVLzA7WwVV4iUjP+funXn0QACwsgM2budIgEckrS0Pxzc3N0b9//+yOhT7B0lIMKfDxAb79Fjh/Xgw5WL8eWL5cVDMgIsoO/v5Ax46ihvXHoqPFpC43txwPi4hIJVNJ7K5duzL9gq1bt85yMJQ51aoBZ84AixaJj/ZOnACqVAF+/lmU6DIzkztCItJnSUmiBza9Vb0VCmDYMLG8NlfnIiK5ZCqJbdu2baZeTKFQIIlFTXOEkZH4I9K+PTBoEPDPP6I815Ytole2fn25IyQifXX8eOohBClJkliM5fjx9JfXJiLStkyNpFQqlZm6MYHNea6uYmGEv/8Wk8Du3AEaNAB69wZevZI7OiLSR2Fh2XscEZE2cDpQLqBQAB06iHJcAwaI+2vXAmXKiPGy6X0kSESUFgeH7D2OiEgbMj2xKyYmBocOHULLli0BAGPGjEFcXJzqcUNDQ0yZMgVmHJApm/z5gcWLgW++ERO/rl4FevQQxciXLQNKlJA7QvmlLBfk4AB4enJMH9HHKlYUlQcSEtJ+XKEQCxx4euZsXEREKWW6J3bdunVYvny56v6iRYtw6tQpXLx4ERcvXsSGDRuwdOlSrQRJmqlZU8wcnjFDTPI6dEj8UZo+HYiPlzs6+fj7i9nU9euLCg/164v7/v5yR0akOyIiREm/5ARWoVB/PPn+/Pn8B5CI5JXpJHbjxo3ok7xMy//5+fnhyJEjOHLkCObMmYOtW7dme4CUNcbGwOjRojfW2xuIjRWVC6pVA06dkju6nJdcLujjySqhoWI/E1ki4N07oFkz4OxZoGBBYO5cwMlJ/RhnZzEGv317eWIkIkqW6ST29u3bKFWqlOq+mZkZDFJU2P/iiy9w/fr17I2OPlvx4mJRhA0bgMKFxYIJtWsDP/wgVt3JCzIqF5S8b9gwcRxRXhUVJXpgT58GChQADh4ERowQy8weOQL4+YmvISFMYIlIN2Q6iY2IiICR0YchtC9evIBbikrXSqVSbYws6Q6FAujWTUz8Su5MX7YMKFsW+Ouv3DnxS5KA58+BY8dE/dzMlgsiyovevwdatBA1p/PnBwICgKpVxWOGhqKMVteu4iuHEBCRrsj0xC5nZ2dcvXoVpUuXTvPxy5cvw9nZOdsCo+xXqBCwapWY7PXdd8CtW0CnTuKP1+LFQNGickeouffvRVmxW7eA27fVv0ZGavZa48cDgwcDTZuKP+REeUF0NNCqlfiHz9oa+PdfwMND7qiIiD4t00ls8+bNMWHCBLRo0SJVBYKYmBhMmjQJLVq0yPYAKft5eQGXLomJXzNmAHv2iI8Jp0wBhgwRCynokqQk8ZHmx0nq7dsZ97AqFGLilq0tEBT06fOcPCluRkbiGrVuLf64u7tn1zsh0i0xMWLVrSNHgHz5gP37gS++kDsqIqLMyXS6MnbsWGzduhWlS5fGoEGDUKpUKSgUCty8eROLFi1CYmIixo4dq81YKRuZmgITJwJduohe2WPHgB9/BDZuBFasAKpXz9l4JAl4+TLtHtV79zKuqmBrC5QqBZQurf61eHFRnSEpSSSzoaFpD51QKMR44e7dRUJ/86ao6HDokBhLW6GCSGZbtRJ/4PlxKuUGsbFibOvBg4ClJbBvn6hsQkSkLzKdxNrb2+PUqVP44YcfMHr0aEj/zwYUCgW8vb2xZMkS2Nvbay1Q0o4yZUQvzJo1wKhRojTXF1+IHtkpU0TvTFISEBiowLFjTrC0VKB+/awnctHRwN27aSerGU00MzMDSpZMO1ktWDDjcxoaAgsWiCoECoV6IptcLmjpUvEH/ddfxfCEf/4Bdu0SYwSvXhW3GTMAOzsx/KJ1a1H1wdIya9eBSE5xceLnYf9+wMIC2LsXqFNH7qiIiDSj0QfH7u7u2L9/P16/fo27d+8CAEqUKIGCn8oiSKcZGAB9+wItWwLDhwObNokakNu2iQlhGzYAT54YAfDAvHmixM6CBenPUE5KEhOl0kpUHz1KPw6FQiyj+3GSWro04OIi4syq9u1FWaChQ9WHIDg7i/ea8r2ULClmZY8YAbx+Lf7Q79oleqrCw0XCv2aN6M1u2FD00LZsKV6LSNfFx4ux8Hv2AObmwO7dQN26ckdFRKS5LI1+LFiwIL7gwKlcx95elNHp0UMsXxsSAsycmfq45Nqqa9aIJPPjZPXOHdHTkx4bm7QT1RIlxB9VbWnfXoz/02TFroIFxcIIPj7ij//x4x96aUNCRA/W3r2iZFm1aiKhbd1azOz+uEg8kdwSEsQQol27xKcbu3aJRT+IiPSRjk3hIV3QtKmY+OXkJIqffyz54/hevdJ/DRMTkZSmlawWKiRfgpdcLigrTExEz2vDhsBvvwHXr4sk4J9/gDNnxFCMCxeASZPEtUseR9uggUgYiOSUmCj+Gdu+XXwv79gBNGokd1RERFnHJJbSdP582gnsx2xtgcqVPySoyclq0aK5ewKUQgGULy9uY8aIYQZ79oiE9sAB0Vu9bJm4WVgAjRuLhLZFC9HjTZSTEhPFxMW//xar+W3fDjRpIndURESfh0kspSksLHPHLVwoiqDndXZ2QO/e4hYbKybLJffShoaKXq8dO0Ty++WXH8p3lS/PYQekXUlJ4lOTzZtFArttm1iZi4hI333GVBnKzRwcsve4vMTMTKw/v3SpmOB2/rwoZ1a9uhiKceYMMHYsULGiKAM2dKgo55VRGTGirEhKEqv0bdwo6h9v2SL+eSIiyg2YxFKaPD3FbPv0egkVClExwNMzZ+PSNwqFmPDl6wucOycqIyxbJnrCTE3F5LCFC8XYxMKFgc6dRcLx+nXmXj9l+bPAQAWSkrT7fkh/KJXAt98C69aJoT2bNwPt2skdFRFR9mESS2lKrq0KpE5kk+/Pn5+7x71qg5OTWFxizx7g1SsxNrFPHzEcITIS2LoV+OYbcb9ePWDuXFHtIS3+/mIRB29vI8yb5wFvbyO4uYn9lLcplcD33wOrV4vSdH5+QIcOckdFRJS9mMRSupJrqzo5qe93dhb706sTS5ljaQm0bQusWiXGIJ8+LSaJVaiQ3MMKjBwpJsqVKQP89JMo8ZWYKBLVjh1TL7ubXP6MiWzeJUnAoEHAypUigV2/XtSFJSLKbfQuiY2Li0OVKlWgUCgQHBwsdzi5Xvv2wIMHQEBAIkaMOIeAgESEhDCBzW4GBsBXXwHTpwNXrgD374ue8EaNxFjGW7eAOXNEUXo7OzHTPK0ldJP3DRsGDi3IgyRJjLFeulR8YrJ2rSirRUSUG+ldEvvTTz/B0dFR7jDyFENDwMtLQt26ofDykjiEIAe4u4ulfwMCgJcvxYScb74RC0W8eSOW702PJIkJZceP51y8JD9JEqvM/f67SGBXrxb/7BAR5VZ6lcTu27cP//77L3799Ve5QyHKMfnzi4+D168X9Wh/+SVzz8tsmTTSf5IkhpvMny/ur1iR8WIkRES5gd7UiX3+/Dn69++PHTt2wMLCIlPPiYuLQ1yK9U8jIyMBAAkJCUhISNBKnLlV8vXidZNf3boKZOZH99atJMTEKGGkNz/luUtO/cxIEjB+vAF+/VV8RLJ4cRJ69lSCP6rp4+8z3cR20V053TaZPY9CktIaWadbJElC8+bNUbt2bYwfPx4PHjyAu7s7Ll68iCpVqqT7vIkTJ2LSpEmp9vv5+WU6ESbSNUlJwLffNsarV2YA0qqBJqn2FyoUA2/vh2jc+CEKFozNyTAph/j5lcHWraUBAN9+exnNm4fIHBER0eeJjo6Gj48PIiIiYG1tne5xsiax6SWZKQUFBeHUqVPYsmULjh07BkNDw0wnsWn1xLq4uODly5cZXhRKLSEhAQEBAfD29oaxsbHc4eR527cr0KWL6HmTpA+JrEIhfpxbt1bi1CkDvHghHjM0lNC6tYTvvlOifn2Jq4TlgJz4mZk61QCTJ4vvg7lzkzB4sFIr58lt+PtMN7FddFdOt01kZCRsbW0/mcTK+kHjoEGD0KVLlwyPcXNzw9SpU3HmzBmYmpqqPebh4YFu3brhzz//TPO5pqamqZ4DAMbGxvwBySJeO93QqZOoWjB0qHqZLWdnBebPB9q3N0RcnCi1tWQJcOKEAtu3K7B9uwFKlRI1RHv1EhPFSLu09TMzbRowebLYnjsXGDHCEABnXWqCv890E9tFd+VU22T2HLImsba2trC1tf3kcQsXLsTUqVNV958+fYomTZpgy5Yt+PLLL7UZIpHOat8eaNMGOHIkEfv2BaNZsyqoX99IVT3C1BTo2lXcrlwRK4WtXw/cvi1msY8dC3TpAgwYANSoIe97Ic3MmgWMH/9he8QIeeMhIpKDXlQncHV1RYUKFVS3UqVKAQCKFy8OZ2dnmaMjkk9my59VrAgsXiwWQ1i2DKhcGYiNFXVEv/gC8PAQiy5kVLqLdMPcucDo0WJ72jRRlYCIKC/SiySWiLKHlZVY9vbiReDUKVF71sQEOH8e6NcPcHQUQxRu3pQ7UkrLggViFTcAmDRJ9KYTEeVVepnEurm5QZKkDCd1EVH6FAqgZk0xvCA0FJg9GyhWDIiIABYuBMqWBRo0AP76CyzVpCMWLxYrsQGiVvCECbKGQ0QkO71MYoko+9jaAqNGAXfuAPv3A61bi2VwjxwRE8hcXUXS9Pix3JHmXcuWAYMGie0xY0QvLBFRXscklogAiMS1SRNg504gJERMHCpSBHj2DJg6FXBzExPJ9u8HlKzklGP++AP44QexPWqUGAfLEmlERExiiSgNrq7AlCnAo0fA1q1A/foicd21C2jWDChVCpgzB3j5Uu5Ic7c1a4BvvxXbw4eLSgRMYImIBCaxRJQuY2Pg66+Bw4eB69eBIUOA/PmBe/fErHhnZ6B7dzFJTPfX/tMv69cDffuK6zp4sKhKwASWiOgDJrFElClly4rZ8aGh4iPu6tWBuDhgwwagdm2galVg+XIgKkruSPWfn59YjEKSxFCCBQuYwBIRfYxJLBFpxNJS9BCeOwecPSuSLTMz4NIlsRKYoyMwcCBw9arckeqnLVtE77ZSKYYSLFrEBJaIKC1MYokoy2rUEOM2Q0OBefPEWNl378RStxUrAp6eolcxLk7uSPXDtm1At24ige3TB1i6VEy4IyKi1PjrkYg+W8GCYuLRzZvAwYNAhw5iNbETJ0RS5uIiSkM9eJD+ayQlAUePAps2ia9JSTkUvI7YsUMsA5yUBPTsCaxcyQSWiCgj/BVJRNlGoQAaNgT+/ht4+BCYOBFwcgJevABmzhQLKrRoAezerZ6k+vuLEl716wM+PuKrm5vYnxf884+oyZuYKJL+VauYwBIRfQp/TRKRVjg5Ab6+ovfV3x/w9hYTlfbuBVq1AooXB2bMEMMROnYEnjxRf35oqNif2xPZvXvF+0xIED2xa9eKXmwiIsoYk1gi0iojI6BdO+Dff4Hbt4EffwRsbERP7dixYuxnWuW5kvcNG5Z7hxYcOAC0bw/Ex4tSZuvXi+tFRESfxiSWiHJMyZLAr7+KXta1a0XZroxIklju9vjxHAkvRx08CLRtKya9tWsHbNzIBJaISBP8lUlEOc7cXExeMjERY2A/ZeRIMRyhbFlxK1MGsLLSfpzacuQI0Lo1EBsrvm7eLBaWICKizGMSS0SycXDI3HHnz4tbSi4uH5LasmWBcuXEV1vb7I8zOx07BrRsCcTEiEluW7eKZJ6IiDTDJJaIZOPpKZauDQ1Ne1ysQiGS0nHjgFu3gBs3xPK34eFimMHjx2KsbUq2tupJbfLN2Vn+RQNOngSaNweio4GmTUUVB1NTeWMiItJXTGKJSDaGhmJJ1Y4dRYKZMpFNTjiXLROTn1J6/VoktMm369fF14cPgZcvxRjaj8fRWlmJYQgf99y6u+fMWNTTp0Xi+v69GBrh7y9WOiMioqxhEktEsmrfXvRIDh2qXmbL2RmYPz91AguIxRVq1xa3lN6/Fz22yUlt8u3uXbGSWFCQuKVkYiJWGvu497ZUqawnmUlJQGCgAseOOcHSUgFLS5HARkUBDRqIhQ3MzbP22kREJDCJJSLZtW8PtGkjek/DwsRYWU9PzeulWloC1aqJW0rx8SKRTZnY3rghVhiLiQGuXhW3lAwMxOIMKYckJN+srdOPwd8/OSE3AuCBefM+9DJ7eQG7dgEWFpq9LyIiSo1JLBHpBENDoF497by2iYnoZS1XTn2/UimGIHw8LOHGDeDtW5H43r0rVtRKyckp9bCEsmVFEv7116nH9ybf799fJNpERPT5mMQSUZ5lYCDGxLq7iwlXySQJeP48dWJ744boKQ4NFbeDB9Vf7+NxvR8/NmaMWJWLK3IREX0+JrFERB9RKIAiRcStfn31x96+TT0s4fp1ICQk/QQWUF+4QVs9zkREeQmTWCIiDRQoANSsKW4prV0L9O796eeHhWkjKiKivIfLzhIRZQM3t8wdl9kFHoiIKGNMYomIskHywg3pLaigUIhVxjw9czYuIqLcikksEVE2SF64AUidyCbfnz+fk7qIiLILk1giomySvHCDk5P6fmdnsT+thRuIiChrOLGLiCgbJS/ccORIIvbtC0azZlVQv74Re2CJiLIZk1giomxmaAh4eUl4/z4UXl6VmcASEWkBhxMQERERkd5hEktEREREeodJLBERERHpHb1KYvfs2YMvv/wS5ubmsLW1RXtO9SUiIiLKk/RmYte2bdvQv39/TJ8+HQ0aNIAkSbhy5YrcYRERERGRDPQiiU1MTMTQoUMxZ84c9O3bV7W/dOnSMkZFRERERHLRiyT2woULCA0NhYGBAapWrYpnz56hSpUq+PXXX1G+fPl0nxcXF4e4uDjV/YiICADA69evkZCQoPW4c5OEhARER0fj1atXMDY2ljscSoFto5vYLrqLbaOb2C66K6fb5t27dwAASZIyPlDSA5s2bZIASK6urtLff/8tnTt3TuratatUqFAh6dWrV+k+z9fXVwLAG2+88cYbb7zxxpue3R4/fpxhfqiQpE+ludozceJETJo0KcNjgoKCcPv2bXTr1g3Lly/Ht99+C0D0sjo7O2Pq1Kn47rvv0nzuxz2xSqUSr1+/RqFChaD4eHFzylBkZCRcXFzw+PFjWFtbyx0OpcC20U1sF93FttFNbBfdldNtI0kS3r17B0dHRxgYpF+DQNbhBIMGDUKXLl0yPMbNzU3VrVyuXDnVflNTUxQrVgyPHj1K97mmpqYwNTVV21egQIGsB0ywtrbmLxcdxbbRTWwX3cW20U1sF92Vk22TP3/+Tx4jaxJra2sLW1vbTx5XvXp1mJqa4tatW6hTpw4AMT7jwYMHKFq0qLbDJCIiIiIdoxcTu6ytrfH999/D19cXLi4uKFq0KObMmQMA+Prrr2WOjoiIiIhyml4ksQAwZ84cGBkZoXv37oiJicGXX36Jw4cPw8bGRu7Q8gRTU1P4+vqmGp5B8mPb6Ca2i+5i2+gmtovu0tW2kXViFxERERFRVujVsrNERERERACTWCIiIiLSQ0xiiYiIiEjvMIklIiIiIr3DJJYyNGPGDNSoUQNWVlaws7ND27ZtcevWLbnDoo/MmDEDCoUCw4YNkzsUAhAaGopvvvkGhQoVgoWFBapUqYLz58/LHVaelpiYiPHjx8Pd3R3m5uYoVqwYJk+eDKVSKXdoec6xY8fQqlUrODo6QqFQYMeOHWqPS5KEiRMnwtHREebm5qhXrx6uXbsmT7B5SEbtkpCQgJ9//hkVK1aEpaUlHB0d0aNHDzx9+lS+gMEklj4hMDAQAwcOxJkzZxAQEIDExEQ0btwY79+/lzs0+r+goCCsWLEClSpVkjsUAvDmzRvUrl0bxsbG2LdvH65fv465c+dytUCZzZo1C8uWLcOiRYtw48YNzJ49G3PmzMHvv/8ud2h5zvv371G5cmUsWrQozcdnz56NefPmYdGiRQgKCkKRIkXg7e2tWr2TtCOjdomOjsaFCxfwyy+/4MKFC/D398ft27fRunVrGSL9gCW2SCMvXryAnZ0dAgMDUbduXbnDyfOioqJQrVo1LFmyBFOnTkWVKlUwf/58ucPK00aPHo2TJ0/i+PHjcodCKbRs2RL29vZYtWqVal+HDh1gYWGB9evXyxhZ3qZQKLB9+3a0bdsWgOiFdXR0xLBhw/Dzzz8DAOLi4mBvb49Zs2bhu+++kzHavOPjdklLUFAQvvjiCzx8+BCurq45F1wK7IkljURERAAAChYsKHMkBAADBw5EixYt0KhRI7lDof/btWsXPDw88PXXX8POzg5Vq1bFypUr5Q4rz6tTpw4OHTqE27dvAwAuXbqEEydOoHnz5jJHRimFhITg2bNnaNy4sWqfqakpvLy8cOrUKRkjo49FRERAoVDI+imT3qzYRfKTJAkjRoxAnTp1UKFCBbnDyfM2b96MCxcuICgoSO5QKIX79+9j6dKlGDFiBMaOHYuzZ89iyJAhMDU1RY8ePeQOL8/6+eefERERgTJlysDQ0BBJSUmYNm0aunbtKndolMKzZ88AAPb29mr77e3t8fDhQzlCojTExsZi9OjR8PHxgbW1tWxxMImlTBs0aBAuX76MEydOyB1Knvf48WMMHToU//77L8zMzOQOh1JQKpXw8PDA9OnTAQBVq1bFtWvXsHTpUiaxMtqyZQs2bNgAPz8/lC9fHsHBwRg2bBgcHR3Rs2dPucOjjygUCrX7kiSl2kfySEhIQJcuXaBUKrFkyRJZY2ESS5kyePBg7Nq1C8eOHYOzs7Pc4eR558+fR3h4OKpXr67al5SUhGPHjmHRokWIi4uDoaGhjBHmXQ4ODihXrpzavrJly2Lbtm0yRUQAMGrUKIwePRpdunQBAFSsWBEPHz7EjBkzmMTqkCJFigAQPbIODg6q/eHh4al6ZynnJSQkoFOnTggJCcHhw4dl7YUFOCaWPkGSJAwaNAj+/v44fPgw3N3d5Q6JADRs2BBXrlxBcHCw6ubh4YFu3bohODiYCayMateunaoM3e3bt1G0aFGZIiJAzK42MFD/k2doaMgSWzrG3d0dRYoUQUBAgGpffHw8AgMDUatWLRkjo+QE9s6dOzh48CAKFSokd0jsiaWMDRw4EH5+fti5cyesrKxU45Xy588Pc3NzmaPLu6ysrFKNS7a0tEShQoU4Xllmw4cPR61atTB9+nR06tQJZ8+exYoVK7BixQq5Q8vTWrVqhWnTpsHV1RXly5fHxYsXMW/ePPTp00fu0PKcqKgo3L17V3U/JCQEwcHBKFiwIFxdXTFs2DBMnz4dJUuWRMmSJTF9+nRYWFjAx8dHxqhzv4zaxdHRER07dsSFCxewe/duJCUlqfKBggULwsTERJ6gJaIMAEjztmbNGrlDo494eXlJQ4cOlTsMkiTpn3/+kSpUqCCZmppKZcqUkVasWCF3SHleZGSkNHToUMnV1VUyMzOTihUrJo0bN06Ki4uTO7Q858iRI2n+XenZs6ckSZKkVColX19fqUiRIpKpqalUt25d6cqVK/IGnQdk1C4hISHp5gNHjhyRLWbWiSUiIiIivcMxsURERESkd5jEEhEREZHeYRJLRERERHqHSSwRERER6R0msURERESkd5jEEhEREZHeYRJLRERERHqHSSwRERER6R0msUREOkShUGDHjh3Z/ro7duxAiRIlYGhoiGHDhmX76xMR5TQmsUSU5/Xq1QsKhQLff/99qscGDBgAhUKBXr16Zes5J06ciCpVqmTra2bku+++Q8eOHfH48WNMmTIlx85LRKQtTGKJiAC4uLhg8+bNiImJUe2LjY3Fpk2b4OrqKmNkny8qKgrh4eFo0qQJHB0dYWVlJXdIKvHx8XKHQER6ikksERGAatWqwdXVFf7+/qp9/v7+cHFxQdWqVdWOjYuLw5AhQ2BnZwczMzPUqVMHQUFBqsePHj0KhUKBQ4cOwcPDAxYWFqhVqxZu3boFAFi7di0mTZqES5cuQaFQQKFQYO3atarnv3z5Eu3atYOFhQVKliyJXbt2ZRj7mzdv0KNHD9jY2MDCwgLNmjXDnTt3VLEkJ60NGjSAQqHA0aNHU71Gnz590LJlS7V9iYmJKFKkCFavXg0AkCQJs2fPRrFixWBubo7KlSvj77//Vh2flJSEvn37wt3dHebm5ihdujQWLFig9pq9evVC27ZtMWPGDDg6OqJUqVIZvjciovQwiSUi+r/evXtjzZo1qvurV69Gnz59Uh33008/Ydu2bfjzzz9x4cIFlChRAk2aNMHr16/Vjhs3bhzmzp2Lc+fOwcjISPVanTt3xo8//ojy5csjLCwMYWFh6Ny5s+p5kyZNQqdOnXD58mU0b94c3bp1S/XaKfXq1Qvnzp3Drl27cPr0aUiShObNmyMhIUEted62bRvCwsJQq1atVK/Rr18/7N+/H2FhYap9e/fuRVRUFDp16gQAGD9+PNasWYOlS5fi2rVrGD58OL755hsEBgYCAJRKJZydnbF161Zcv34dEyZMwNixY7F161a1cx06dAg3btxAQEAAdu/ene77IiLKkERElMf17NlTatOmjfTixQvJ1NRUCgkJkR48eCCZmZlJL168kNq0aSP17NlTkiRJioqKkoyNjaWNGzeqnh8fHy85OjpKs2fPliRJko4cOSIBkA4ePKg6Zs+ePRIAKSYmRpIkSfL19ZUqV66cKhYA0vjx41X3o6KiJIVCIe3bty/N2G/fvi0BkE6ePKna9/LlS8nc3FzaunWrJEmS9ObNGwmAdOTIkQyvQ7ly5aRZs2ap7rdt21bq1auXKg4zMzPp1KlTas/p27ev1LVr13Rfc8CAAVKHDh1U93v27CnZ29tLcXFxGcZCRPQpRrJm0EREOsTW1hYtWrTAn3/+CUmS0KJFC9ja2qodc+/ePSQkJKB27dqqfcbGxvjiiy9w48YNtWMrVaqk2nZwcAAAhIeHf3KMbcrnWVpawsrKCuHh4Wkee+PGDRgZGeHLL79U7StUqBBKly6dKp5P6devH1asWIGffvoJ4eHh2LNnDw4dOgQAuH79OmJjY+Ht7a32nPj4eLXhFsuWLcMff/yBhw8fIiYmBvHx8akmsFWsWBEmJiYaxUZE9DEmsUREKfTp0weDBg0CACxevDjV45IkARClsD7e//E+Y2Nj1XbyY0ql8pMxpHxe8nPTe15yPGnt/zieT+nRowdGjx6N06dP4/Tp03Bzc4Onp6da3Hv27IGTk5Pa80xNTQEAW7duxfDhwzF37lzUrFkTVlZWmDNnDv777z+14y0tLTWKi4goLUxiiYhSaNq0qWrGfJMmTVI9XqJECZiYmODEiRPw8fEBACQkJODcuXMa1V81MTFBUlLSZ8dbrlw5JCYm4r///lONdX316hVu376NsmXLavRahQoVQtu2bbFmzRqcPn0avXv3VjuPqakpHj16BC8vrzSff/z4cdSqVQsDBgxQ7bt3714W3hUR0acxiSUiSsHQ0FD1MbyhoWGqxy0tLfHDDz9g1KhRKFiwIFxdXTF79mxER0ejb9++mT6Pm5sbQkJCEBwcDGdnZ1hZWal6NDVRsmRJtGnTBv3798fy5cthZWWF0aNHw8nJCW3atNH49fr164eWLVsiKSkJPXv2VO23srLCyJEjMXz4cCiVStSpUweRkZE4deoU8uXLh549e6JEiRJYt24dDhw4AHd3d6xfvx5BQUFwd3fXOA4iok9hEktE9BFra+sMH585cyaUSiW6d++Od+/ewcPDAwcOHICNjU2mz9GhQwf4+/ujfv36ePv2LdasWZPlBRXWrFmDoUOHomXLloiPj0fdunWxd+/eVMMSMqNRo0ZwcHBA+fLl4ejoqPbYlClTYGdnhxkzZuD+/fsoUKAAqlWrhrFjxwIAvv/+ewQHB6Nz585QKBTo2rUrBgwYgH379mXpfRERZUQhpTegioiI8pzo6Gg4Ojpi9erVaN++vdzhEBGliz2xREQEpVKJZ8+eYe7cucifPz9at24td0hERBliEktERHj06BHc3d3h7OyMtWvXwsiIfx6ISLdxOAERERER6R0uO0tEREREeodJLBERERHpHSaxRERERKR3mMQSERERkd5hEktEREREeodJLBERERHpHSaxRERERKR3mMQSERERkd75H/oT1oXL+5F0AAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 800x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# calculate average of different months of a year\n", + "all_seasonality = Y - Y_mean_array\n", + "plt.figure(figsize=(16,4))\n", + "plt.plot(t1, all_seasonality, 'b-o', label='average')\n", + "plt.title('Seasonal variations (over all years)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Seasonal variations [mm]')\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "all_seasonality_mat = np.reshape(all_seasonality.values, (27,12))\n", + "m_mean = all_seasonality_mat.mean(axis=0) \n", + "\n", + "# create x-axis from January (1) to December (12)\n", + "months = np.arange(1,13,1)\n", + "\n", + "# plot average of different months of a year\n", + "plt.figure(figsize=(8,4))\n", + "plt.plot(months, m_mean, 'b-o', label='average')\n", + "plt.title('Seasonal variations (in a year)')\n", + "plt.xlabel('Month of year')\n", + "plt.ylabel('Global mean sea level [mm]')\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "# create array with monthly average of each month repeated 27 times\n", + "m_mean_array = np.tile(m_mean, 27)\n", + "plt.figure(figsize=(16,4))\n", + "plt.plot(t1, m_mean_array, 'b-o', label='average')\n", + "plt.title('Average seasonal variations over 27 years')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Average seasonal variations [mm]')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "a95e2611744b4604b2d4cb5f75395356", + "deepnote_cell_type": "markdown" + }, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "79dde08cf6714334b1c8ee558e46ed50", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + " \n", + "- The minimum is around -5 mm (in June), the maximum is around 8 mm (October), so the difference between the max and min value is about 13 mm.\n", + "\n", + "- Seasonal variations of sea levels can be linked to different parameters. Among them the seasonal temperature gradients play an important role.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "c0d339ac9452451fb2b1978e5fa3ea47", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 5:</b> \n", + "\n", + "We have a total of $m=12\\times 27=324$ monthly samples. The aim of this Task is to reconstruct the original dataset while utilizing a reduced amount of information (parameters). To achieve this, you exclusively employ the 27 mean annual values and 12 mean seasonal values, resulting in a combined count of $27+12=39$ values. To generate the data for a specific year, you combine/add the annual mean of that year with the set of 12 zero-mean seasonal values. This, for example, can be linked to array <code>y_mean_array</code> and <code>m_mean_array</code>. It is required to:\n", + "<ol>\n", + " <li>Reconstruct the 324 samples using the above-mentioned $27+12=39$ values (<code>recon_y</code>).</li>\n", + " <li>Plot the original and reconstructed data in a single figure, and compare the results.</li>\n", + " <li>Plot the difference between the original and reconstructed data: <code>y-recon_y</code> (residuals). Do you see a trend in the residuals?</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "cell_id": "2a851b23b78f49afbb09e2b050fd9ea0", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 760, + "execution_start": 1696691530266, + "source_hash": null + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "plt.figure(figsize=(8,5))\n", + "plt.plot(t1, Y, 'r-', label='before reconstruction')\n", + "# we reconstruct data by adding Yearly means and Monthly means \n", + "recon_Y = Y_mean_array + m_mean_array\n", + "plt.plot(t1, recon_Y, 'b-', label='after reconstruction')\n", + "# difference between original data and reconstructed one\n", + "plt.plot(t1, Y - recon_Y, 'g-', label='difference')\n", + "\n", + "plt.title('Reconstructed data')\n", + "plt.xlabel('time [yr]')\n", + "plt.ylabel('Global mean sea level [mm]')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "5bc45eeb83e240039ed67f6df8411049", + "deepnote_cell_type": "markdown" + }, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "5aff42009b9a4d43a8b89853e9a2a440", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "- It is quite evident that both original and reconstructed data have an increasing trend and their values seem to match quite well. The difference between the two is the noise of measurements or still unmodelled effects.\n", + "- Residuals values range around 0, so at first sight one could say that there is not a clear trend. However it might carry some information, as small signals can be detected/investigated further.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 6: Least-squares harmonic estimation (LS-HE)\n", + "\n", + "In this task we rely on concepts from Sensing and Observation Theory as well as Signal Processing: the least-squares harmonic estimation (LS-HE) method utilizes hypothesis testing and hence making a power spectral density (PSD) to **identify the most statistically significant frequency components in a time series.**\n", + "\n", + "Previously we have used the FFT PSD, which is a special case of LS-HE. There are several advantages of LS-HE over FFT PSD: as a generalized form of the FFT PSD, LS-HE is limited neither to evenly spaced data nor to integer frequencies. With LS-HE, we may in addition include the following terms in the PSD estimation:\n", + "\n", + "1. the linear trend $Y=\\mathrm{Ax}$, as an already available deterministic part of the model, and\n", + "2. the covariance matrix $\\Sigma_{Y}$, as a stochastic part of the model.\n", + "\n", + "\n", + "In the function `LS-HE(A,Y,t,sigma)` we consider $\\Sigma_{Y}=\\sigma^2 I$ to ba a scaled identity matrix. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "1ef4fd7d394f4809b8eef70d3f58dc76", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\"> <p>\n", + "\n", + "<b>Python function for model identification using LS-HE (detection of seasonality)</b>\n", + "\n", + "<em>Note: it is optional to follow all steps here, but it is important to be able to use the function</em> <code>LSHE(A,Y,t,sigma)</code>.\n", + "\n", + "Least Squares Harmonic Estimation (LS-HE) uses hypothesis testing to compute the Power Spectral Density (PSD). The goal is to detect any potential seasonality (or periodic pattern) in the time series $Y$. An alternative has already been obtained from DFT and periodogram (Week 2.3 on Signal Processing). This is a generalization of the DFT formulation, with an initial design matrix $\\mathrm{A}$ available. We will simplify the test statistic $T_{q=2}$ to be able to implement and use it here in LS-HE function.\n", + "\n", + "<b>Implementation of $T_{q=2}$ test statistics</b>\n", + " \n", + "Considering \n", + "\n", + "$$\n", + "\\Sigma_{\\hat \\epsilon}= \\Sigma_Y - A (A^T \\Sigma_Y^{-1} A)^{-1} A^T\n", + "$$\n", + "\n", + "If we assume $\\Sigma_Y=\\sigma^2 I$, we then have\n", + "\n", + "$$\n", + " T_q =\\hat \\epsilon^T C(C^T \\Sigma_{\\hat \\epsilon} C)^{-1} C^T \\hat \\epsilon\n", + "$$\n", + "\n", + "The following function <code>LSHE(A,Y,t,sigma)</code> is an implementation of the above formula.\n", + "</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "cell_id": "67e90689736b4c6dbacd8c2c8474e687", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 276, + "execution_start": 1696691530544, + "source_hash": null + }, + "outputs": [], + "source": [ + "def LSHE(A, Y, t, sigma):\n", + " \"\"\"\n", + " Least squares harmonic estimation (LS-HE),\n", + " by AR Amiri-Simkooei, CCJM Tiberius, PJG Teunissen.\n", + " Assessment of noise in GPS coordinate time series:\n", + " methodology and results,\n", + " J. of Geophy. Res.: Solid Earth 112 (B7)\n", + " Here we assume the variance matrix of observation is\n", + " Sigma_Y = sigma^2*I, a scalled identity matrix.\n", + " \n", + " INPUT:\n", + " A: the initial design matrix\n", + " y: the vector of observations\n", + " t: time instances of observations, assuming t[0]=0\n", + " sigma: the standard deviation of observations in\n", + " Sigma_Y = sigma**2*I\n", + "\n", + " OUTPUT:\n", + " P: the periods at which PSDs are calculated\n", + " F: the frequencies at which PSDs are calculated\n", + " PSD: the power spectral density calculated at P or F\n", + " \"\"\"\n", + " m, n = np.shape(A)\n", + "\n", + " # Generating a series of periods to be tested\n", + " # starting period (just above the Nyquist period)\n", + " P = np.ones(10**6)*2.001*(t[1] - t[0])\n", + "\n", + " # the maximum period to be tested\n", + " Pmax = 2*(t[m - 1] - t[0])\n", + "\n", + " i = 0\n", + " while P[i]<Pmax:\n", + " # to make sure that we check all possible frequencies/periods\n", + " P[i+1] = P[i]*(1 + 0.1*P[i]/Pmax) \n", + " i = i + 1\n", + "\n", + " # Keep the periods from the minimum to the maximum tested period \n", + " P = P[0:i] \n", + "\n", + " # Computing frequencies from the periods\n", + " F = 1 / P\n", + "\n", + " # mt number of frequencies/periods to be tested\n", + " mt = len(F)\n", + " AtAinv = np.linalg.inv(A.T @ A)\n", + " Aty = A.T @ Y\n", + " xhat = AtAinv @ Aty\n", + " eps_hat = Y - A @ xhat\n", + " PSD= np.zeros(mt)\n", + " C = np.zeros((len(t),2))\n", + " for j in np.arange(0,mt):\n", + " wt = 2 * np.pi * F[j] * t \n", + " C[:,0] = np.cos(wt)\n", + " C[:,1] = np.sin(wt)\n", + " CtA = C.T @ A\n", + " MAT = C.T @ C - CtA @ AtAinv @ CtA.T\n", + " CtE = C.T @ eps_hat\n", + " PSD[j] = CtE.T @ np.linalg.inv(MAT) @ CtE\n", + " PSD = PSD / sigma**2\n", + " \n", + " return P, F, PSD " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "fc9b13e37adc420c942782404f484426", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 6:</b> \n", + "\n", + "To identify the seasonality signal, LS-HE requires an initial design matrix $\\mathrm{A}$ and some other input. In time series analysis, the functional model is usually based on the linear regression model $Y(t)=y_0 + r t$. From this model, you can make the initial $Y=\\mathrm{Ax}+\\epsilon$ linear model, where $\\mathrm{A}$ is the $m\\times 2$ design matrix (see Weeks 1.2 and 1.3), and $\\mathrm{x}=[y_0, r]^T$ is the vector of two unknown parameters. For this application the standard deviation of data is assumed to be $\\sigma= 4$ mm. It is required to:\n", + "\n", + "<ol>\n", + " <li>Establish an initial $m\\times 2$ design matrix based on $t_0$ (see above for the definition of $t_0$).</li>\n", + " <li>Use the required input for the function <code>LSHE</code> to compute/plot the PSD of the GMSL time series (plot versus frequency).</li>\n", + " <li>Identify the first three important periodic signals (3 highest peaks) in the PSD. Determine the frequencies (in cycle/year) of the detected peaks: $f_1=?$, $f_2=?$ and $f_3=?$. Explain possible causes for the detected signals.</li>\n", + " <li>Test if the 3 identified signals are statistically significant in $1-\\alpha=0.999$ confidence level.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note: once you create the plot, you should be able to identify three peaks, where you will need 2 decimals of precision for the peak with the lowest frequency (the other two peaks can be reported as integers). The following code may help you identify the values.</p></div>\n", + "\n", + "```\n", + "vector_1 = np.array([0, 1, 2, 3, 4, 5, 6])\n", + "vector_2 = np.array([10, 11, 12, 13, 14, 15, 16])\n", + "use_these_indices = np.where((vector_1>1) & (vector_1<6) & (vector_2>12))\n", + "print(vector_1[use_these_indices])\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "cell_id": "7cd0ecf3448444a4a4502ceb9db6c0b3", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 2307, + "execution_start": 1696691530551, + "source_hash": null + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# we make a design matrix to identify the frequency (periods)\n", + "# of periodic pattern (seasonality). Initial design matrix includes\n", + "# an intercept and a slope (two unknown parameters).\n", + "\n", + "A0 = np.ones((m, 2))\n", + "A0[:,1] = t0\n", + "sigma = 4\n", + "Sigma_Y = sigma**2*np.eye(m)\n", + "Sigma_Y_inv = np.linalg.inv(Sigma_Y)\n", + "\n", + "P, F, PSD = LSHE(A0, Y, t0, sigma)\n", + "plt.figure()\n", + "plt.plot(F,PSD, color='b', label='psd')\n", + "\n", + "# the chi-squared test shows that annual and semi-annual signals\n", + "# are statistically significant (alpha = 0.001)\n", + "plt.axhline(chi2.ppf(0.999, df=2), linestyle='--',\n", + " color='r', label='confidence level')\n", + "plt.xlabel('Frequency [cycle/year]')\n", + "plt.ylabel('PSD: test statistics $T_q$')\n", + "plt.title('$T_q$ test statistics')\n", + "plt.grid()\n", + "plt.legend()\n", + "#plt.xlim([0.02, 0.06])\n", + "f1 = 1\n", + "f2 = 2\n", + "f3 = 0.04" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The lowest peak is at frequency: [0.04307867 0.04129784]\n" + ] + } + ], + "source": [ + "use_these_indices = np.where((F > 0.04) & (F < 0.05) & (PSD>181))\n", + "print('The lowest peak is at frequency:', F[use_these_indices])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "5a43ff866c1848de9c8865fad791196c", + "deepnote_cell_type": "markdown" + }, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "dd10d2ea2701416fbef29a94776383e2", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "The three highest peaks are observed at frequencies $f_1=1$ Hz, $f_2=2$ Hz, and $f_3=0.04$ Hz, which are also statistically significant (they are above the critical value $\\chi^2(0.999,2) =13.82$). They correspond to the annual signal (one cycle per year), the semi-annual signal (two cycles per year), and a signal with a period of approximately 25 years. The first two signals are associated with the seasonal variations of sea level, commonly attributed to fluctuations in temperature across the seasons. The reason for $f_3=0.04$ Hz is currently unknown, which can likely be linked to sea-level acceleration.\n", + " \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "88fb13205e9849e292677995e95d388e", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 7:</b> \n", + "\n", + "This Task applies the best linear unbiased estimation (BLUE) to estimate and test the rate $r$ for sea-level rise based on the initial model $y(t)=y_0 + r t$, with the three identified seasonal signals included. Each detected signal can represent a particular sine wave as: $a \\cos 2 \\pi f t + b \\sin 2 \\pi f t$ (see Chapter on Components of Time Series). You then need to make the final functional model $Y=\\mathrm{Ax}+\\epsilon$, with $\\mathrm{A}$ being an $m \\times 8$ matrix. Having the linear model $Y=\\mathrm{Ax}+\\epsilon$ along with the covariance matrix of observations $\\Sigma_Y=\\sigma^2 I$, $\\sigma = 4$ mm, you can estimate the BLUEs of $x$ (unknowns), $y$ (observations) and $e$ (residuals): $\\hat{X}=(A^T \\Sigma_Y^{-1}A)^{-1}A^T \\Sigma_Y^{-1}y$, $\\hat{Y}=A \\hat{x}$, and $\\hat{\\epsilon}=Y-\\hat{Y}$. Further you can estimate the covariance matrix of $\\hat{X}$: $\\Sigma_{\\hat{X}}=(A^T \\Sigma_Y^{-1}A)^{-1}$. It is required to:\n", + "\n", + "<ol>\n", + " <li>Establish the linear model of observation equations $Y=\\mathrm{Ax}+\\epsilon$ with $m\\times 8$ design matrix $\\mathrm{A}$.</li>\n", + " <li>Explain the unknown elements of $x=[x_1,...,x_8]^T$.</li>\n", + " <li>Obtain the BLUE of $x$ and its covariance matrix $\\Sigma_{\\hat{X}}$, and also the BLUE of $Y$ and $\\epsilon$: $\\hat{Y}$ and $\\hat{\\epsilon}$ along with their covariance matrices.</li>\n", + " <li>Plot the original data $Y$, the estimated $\\hat{Y}=\\mathrm{A\\hat{X}} $, the estimated trend (from columns 1 and 2 of $\\mathrm{A}$, which is $\\mathrm{A_1}$), and the estimated seasonality (from columns 3-8 of $\\mathrm{A}$, which is $\\mathrm{A_2}$). $\\textbf{Hint:}$ To separate the trend from seasonality note that if $\\mathrm{A}=[\\mathrm{A_1,A_2}]$ is partitioned column-wise and $x=[x_1^T, x_2^T]^T$ is partitioned row-wise, we then have $\\hat{Y}=\\mathrm{A\\hat{X}} = \\mathrm{A_1\\hat{X_1} + A_2\\hat{X_2}} = \\hat{Y}_1 + \\hat{Y}_2$.</li>\n", + " <li>What is the total amplitude of the combined annual + semi-annual signals?</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "cell_id": "53cc9d8e850044c48f6de110696acc32", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 194, + "execution_start": 1696691532653, + "source_hash": null + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x16e50cd50>" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# calculate the design matrix A based on the detected frequencies\n", + "A = np.ones((m,8))\n", + "A[:,1] = t0\n", + "# the annual and semi-annual signals\n", + "A[:,2] = np.cos(2*np.pi*f1*t0)\n", + "A[:,3] = np.sin(2*np.pi*f1*t0)\n", + "A[:,4] = np.cos(2*np.pi*f2*t0)\n", + "A[:,5] = np.sin(2*np.pi*f2*t0)\n", + "A[:,6] = np.cos(2*np.pi*f3*t0)\n", + "A[:,7] = np.sin(2*np.pi*f3*t0)\n", + "\n", + "# BLUE estimate of x\n", + "Xhat = np.linalg.inv(A.T @ Sigma_Y_inv @ A) @ A.T @ Sigma_Y_inv @ Y\n", + "\n", + "# covariance matrix of xhat\n", + "Sigma_Xhat = np.linalg.inv(A.T @ Sigma_Y_inv @A)\n", + "\n", + "# BLUE estimate of y\n", + "Yhat = A @ Xhat \n", + "\n", + "# covariance matrix of yhat\n", + "Sigma_Yhat = A @ Sigma_Xhat @ A.T\n", + "\n", + "# BLUE estimate of e (residuals)\n", + "eps_hat = Y - Yhat\n", + "\n", + "# covariance matrix of eps_hat\n", + "Qeps_hat = Sigma_Y - Sigma_Yhat\n", + "\n", + "# separate the seasonality and trend (third-order polynomial)\n", + "yhat_trend = A[:,0:2] @ Xhat[0:2] # total trend\n", + "yhat_season = A[:,2:8] @ Xhat[2:8] # total seasonality\n", + "\n", + "plt.figure()\n", + "plt.plot(t1, Y,'b', label='Original')\n", + "plt.plot(t1, Yhat,'r',label='Least squares fit')\n", + "plt.plot(t1, yhat_trend,'k--', label='Trend')\n", + "plt.plot(t1, yhat_season,'k-', label='Seasonality')\n", + "plt.xlabel('Time [year]')\n", + "plt.ylabel('Time series value [mm]')\n", + "plt.title('Original, LS fit and seasonality of the signal')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "- The unknowns $x=[x_1,...,x_8]^T$ consist of 8 parameters. The order of unknowns in $x$ can be arbitrary, but here the first two unknowns are $y_0$ (intercept) and $r$ (rate), the third and fourth unknowns are the coefficients of $a$ and $b$ of the annual signal, and the fifth and sixth are those of semi-annual signal, etc.\n", + " \n", + "- The range of variations (between min and max) of the combined annual + semi-annual signals is around 14 mm (consistent with previous results of Task 4). This means that the combined amplitude is around 7 mm.\n", + " \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depending on how you have formulated the entries of $x=[x_1,...,x_8]^T$, one of $x_i$'s, $i=1,...,8$ is the rate $r$ for sea-level rise (assume $x_j=r$). We then need to determine $\\hat{x}_j=\\hat{r}$, and its standard deviation $\\sigma_{\\hat{x}_j}=\\sigma_{\\hat{r}}$. Based on the information provided, we need to decide if the estimated sea-level rise is statistically significant. The test can be performed using the statistical significance test given in Weeks 1.3 and 1.4 (Sensing and Observation Theory). Two hypotheses are put forward: Null hypothesis $H_0$: $r=0$ versus alternative hypothesis $H_a$: $r\\neq 0$. We need to test the null hypothesis versus the alternative one. This can be done by two different but equivalent forms from duality between 'confidence intervals' and 'hypothesis tests'. We can obtain a confidence interval by inverting a hypothesis test, and vice versa. \n", + "\n", + "##### Duality between 'confidence intervals' and 'hypothesis tests' (make your own choice for the next Task)\n", + "In the first scenario, we may make a 'confidence interval' around the estimated $\\hat{r}$ for $r$. This is performed as follows:\n", + "$$\n", + " \\hat{r} - z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}} \\le r \\le \\hat{r} + z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}}\n", + "$$\n", + "where $z_{\\frac{\\alpha}{2}}$ is the critical value obtained from the Standard Normal Distribution in a given significance level $\\alpha$. If the reference value specified as $r=0$ in $H_0$ lies inside the above interval, we accept the null hypothesis (not significant). By a few simple operations, the above confidence internal can be inverted to make a 'hypothesis test' as follows (second scenario): \n", + "$$\n", + "r - z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}} \\le \\hat{r} \\le r + z_{\\frac{\\alpha}{2}}\\, \\sigma_{\\hat{r}}\n", + "$$\n", + "which accepts the null hypothesis if $\\hat{r}$ lies in the above interval.\n", + "\n", + "You can see that this is the same as the hypothesis as defined in Chapter 4.5 by recognizing that under $H_0$ we have that $r=0$ and the last equation can also be written as:\n", + "\n", + "$$\n", + "\\text{Accept $H_0$ if:}\\quad \\left|\\frac{\\hat{r}}{\\sigma_{\\hat{r}}} \\right| < z_{\\frac{\\alpha}{2}}\n", + "$$\n", + "recognizing that $\\frac{\\hat{r}}{\\sigma_{\\hat{r}}}\\sim N(0,1)$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "3242fedf52b04a679c449022b7e01570", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 8:</b> \n", + "\n", + "<ol>\n", + " <li> Use $\\hat{X}$ and its covariance matrix $\\Sigma_{\\hat{x}}$ to determine the BLUE estimate for the sea level rate (r) and its precision. What are the values?</li>\n", + " <li>Determine a 99.9% confidence interval for the unknown rate, assuming the original observations are normally distributed (first scenario). You can alternatively also apply/use the second scenario.</li>\n", + " <li>If the null hypothesis states that 'the rate for sea level rise is not statistically significant', do you accept or reject the null hypothesis?</li>\n", + "</ol> \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note: the indices used below may be different depending on your implementation of the design matrix.</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "cell_id": "35cafb24ca6e4d55b54d534943bf45fb", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 147, + "execution_start": 1696691533000, + "source_hash": null + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The confidence interval (mm/year) is: [3.633,3.886]\n", + "CI does not contain the value 0 and so the estimated rate is statistically significant.\n", + "We therefore reject the null hypothesis.\n", + "\n", + "The estimated rate (rhat) is (mm/year): 3.760\n", + "This is not with the bounds: [-0.127,0.127]\n", + "We therefore reject the null hypothesis.\n", + "\n", + "The test statistic: 97.696 is larger than the critical value 3.291\n", + "We therefore reject the null hypothesis.\n", + "\n" + ] + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# linear rate is the second entry of xhat\n", + "rhat = Xhat[1]\n", + "\n", + "# standard deviation of estimated rate\n", + "srhat = np.sqrt(Sigma_Xhat[1,1])\n", + "\n", + "# compute the critical value based on the given alpha \n", + "alpha = 0.001\n", + "CrValue = norm.ppf(1 - alpha/2, loc=0, scale=1)\n", + "\n", + "# 99.9 confidence interval for the rate of sea-level rise\n", + "# (rate is identified to be significant)\n", + "CI = [rhat - srhat*CrValue, rhat + srhat*CrValue]\n", + "\n", + "print(f'The confidence interval (mm/year) is: [{CI[0]:.3f},{CI[1]:.3f}]')\n", + "print('CI does not contain the value 0 and so the '\n", + " 'estimated rate is statistically significant.')\n", + "print('We therefore reject the null hypothesis.\\n')\n", + "r = 0\n", + "print(f'The estimated rate (rhat) is (mm/year): {rhat:.3f}')\n", + "print(f'This is not with the bounds:\\\n", + " [{r - srhat*CrValue:.3f},{r + srhat*CrValue:.3f}]')\n", + "print('We therefore reject the null hypothesis.\\n')\n", + "\n", + "T = np.abs(rhat/srhat)\n", + "print(f'The test statistic: {T:.3f} is larger than the critical value {CrValue:.3f}')\n", + "print('We therefore reject the null hypothesis.\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "- The estimated rate and its precision are: $\\hat{r}=3.76$ and $\\sigma_{\\hat{r}}=0.038$ (mm/year).\n", + " \n", + "- Both scenarios have been implemented here. For the first scenario the confidence interval of $r$ becomes: CI=[3.633, 3.88] (mm/year). This interval does not contain the value $r=0$ and so the estimated rate is statistically significant. We therefore reject the null hypothesis.\n", + "- For the second scenario, we have the estimated rate as: $\\hat{r}=3.76$ (mm/year). The statistical test also says rejecting the null hypothesis becuase the estimated rate $\\hat{r}=3.76$ is not in the interval [-0.13, 0.13].\n", + " \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After detrending the observations denoted as $Y$, our next step involves addressing the residuals $\\hat{\\epsilon}$, where $\\hat{\\epsilon} = Y - A\\hat{X}$. By eliminating the linear trend $\\mathrm{Ax}$ from the original time series $Y$, the resulting residuals are expected to exhibit stationarity. While statistical tests are available to determine whether a time series is stationary, we choose for a visual inspection in this context. For a sationary time series $\\hat{\\epsilon}$, we calculate its normalized auto-covariance function (ACF). Various methods can be employed to compute the ACF of a time series using <code>plot_acf</code> from <code>statsmodels</code>.\n", + "\n", + "The temporal correlation within $\\hat{\\epsilon}$ provides insights into the temporal dependencies among residual entries, thereby offering a valuable tool for prediction. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "037151be471a437c9185737253535af7", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 9:</b> \n", + "\n", + "In the context of prediction (although we will not implement prediction in this project), it is essential to identify the type of random process represented by the residuals, which follow an ARMA(p,q) model. For this exercise, we assume an ARMA(3,0)=AR(3) model, implying that we set p=3 and q=0. A Python function is provided (see below), which is designed to estimate the parameters of an AR(p) process ($\\beta_1,...,\\beta_p$). This function will subsequently be used for the AR(3) process by setting $p=3$. It is required to:\n", + "\n", + "<ol>\n", + " <li>Plot the BLUE residulas of the fitted (detrended) model. Is it now a stationary time series (just by visual inspection)?</li>\n", + " <li>Plot the normalized auto-covariance function (ACF) of the residuals $\\hat{\\epsilon}$.</li>\n", + " <li>Check the given function <code>AR_estimation</code>, which estimate the AR(p) parameters and its standard deviation.</li>\n", + " <li>Use the function <code>AR_estimation</code> to estimate the three parameters $\\beta_1$, $\\beta_2$ and $\\beta_3$ of AR(3) of the residuals $\\hat{\\epsilon}$. Please provide the three parameters of the AR(3) along with their standard deviations.</li>\n", + " <li>Explain how you can use the information obtained from the functional model $Y=\\mathrm{Ax}$ and stochastic model AR(3) to predict sea-level for the coming year (you do not need to do the actual prediction).</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "cell_id": "4e0deefc15e2406482d055ab879323c3", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 372, + "execution_start": 1696691533012, + "source_hash": null + }, + "outputs": [], + "source": [ + "def AR_estimation(S, p):\n", + " \"\"\"\n", + " This function computes the AR(p) parameters beta_1,...,beta_p \n", + " for an AR(p) process Y (stationary S: for example epsilon hat).\n", + " \n", + " INPUT:\n", + " S: m x 1 observations (time series)\n", + " p: order of AR\n", + " OUTPUT:\n", + " Beta: Parameters Beta\n", + " S_Beta: Standard deviation of Beta \n", + " Sigma_e: Standard deviation of white noise \n", + " \"\"\"\n", + " m = len(S)\n", + " # make the design matrix\n", + " A = np.zeros((m-p, p))\n", + " for i in range(1, p+1):\n", + " A[:,i-1] = S[p-i:m-i]\n", + "\n", + " # removing the first p data from s\n", + " S = S[p:m]\n", + " m, p = A.shape\n", + "\n", + " # least squares estimate of Beta\n", + " Beta = np.linalg.inv(A.T @ A) @ A.T @ S\n", + "\n", + " # least squares estimate of residuals (white noise)\n", + " Ehat = S - A @ Beta\n", + "\n", + " # estimation of variance of data (white noise)\n", + " Sig2 = (Ehat.T @ Ehat) / (m - p)\n", + "\n", + " # covariance matrix of Beta\n", + " Sigma_Beta = Sig2 * np.linalg.inv(A.T @ A)\n", + "\n", + " # standard deviation of Beta\n", + " std_Beta = np.sqrt(np.diag(Sigma_Beta))\n", + "\n", + " # standard deviation of white noise\n", + " Sigma_e = np.sqrt(Sig2)\n", + " \n", + " return Beta, std_Beta, Sigma_e" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "cell_id": "4e7ceb0a961a4332ad792a6bb8e1780c", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 1195, + "execution_start": 1696691533016, + "source_hash": null + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Figure size 640x480 with 0 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "<Figure size 640x480 with 0 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters Beta are: [ 0.30418909 0.54611418 -0.24747044]\n", + "Standard deviations of Beta are: [0.05369869 0.04717083 0.05365368]\n", + "Standard deviations of white noise is: 2.416 (mm)\n" + ] + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# plot least squares residuals \n", + "plt.figure()\n", + "plt.figure(figsize=(8, 3))\n", + "plt.plot(t1, eps_hat, color='b', label='residuals')\n", + "plt.xlabel('Time [year]')\n", + "plt.ylabel('Residual [mm]')\n", + "plt.title('LS residuals')\n", + "plt.legend()\n", + "\n", + "# Plot the normalized auto-covariance function (ACF)\n", + "plt.figure()\n", + "plot_acf(eps_hat, lags=50, color='r', label='acf')\n", + "plt.xlabel('Lag (month)')\n", + "plt.ylabel('ACF')\n", + "plt.show()\n", + "\n", + "# we use AR(3) to estimate beta1, beta2, and beta3\n", + "p = 3\n", + "Beta, S_Beta, Sigma_e = AR_estimation(eps_hat, p)\n", + "print(f'Parameters Beta are: {Beta}')\n", + "print(f'Standard deviations of Beta are: {S_Beta}' )\n", + "print(f'Standard deviations of white noise is: {Sigma_e:.3f} (mm)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "- Yes, the visual inspection shows a stationary time series. The mean seems to be zero, and tha variance seems more or less to be constant, although there are some flactuations in the residuals.\n", + " \n", + "- The estimated parameters $\\pm$ their standard deviations are: $\\beta_1=0.304\\pm0.054$, $\\beta_2=0.546\\pm0.047$, $\\beta_3=-0.247\\pm0.054$. \n", + " \n", + "- For prediction, we need to incorporate the information from both functional and stochastic models. For the functional part, we have already obtained the estimate $\\hat{X}$, which can be used to calculate the functional part of the prediction for the next year, and the AR(3) parameters $\\beta_1$, $\\beta_2$, and $\\beta_3$, can be used to determine the stochastic part of the prediction. \n", + " \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "deepnote": {}, + "deepnote_execution_queue": [], + "deepnote_notebook_id": "aa9b740477e34ea98fd2031f67fc974e", + "deepnote_persisted_session": { + "createdAt": "2023-10-07T15:30:07.197Z" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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52.10 diff --git a/content/GA_2_4/README.md b/content/GA_2_4/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2f80172e28b8774f25a8dd2b5d9b3e71616ce2ca --- /dev/null +++ b/content/GA_2_4/README.md @@ -0,0 +1,43 @@ +# Project 8: Time Series + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/).* + +The focus of this assignment is to use time series analysis techniques to evaluate a data set of global mean sea level, with a special focus on capturing auto-correlation and a periodic signal in the model. + +### Overview of material + +- `Report.md`, primary notebook in which you are supposed to copy your plots and include your answers to the questions (typically a short, one-line answer each time; though a simple 'yes' or 'no' is not sufficient, please include a short reasoning, justification or argumentation). +- `P8.ipynb`, the Jupyter notebook with tasks, to be used for actual coding +- `CSIRO_Alt_seas_inc.txt`, data file with Global Mean Sea Level measurements + +You can complete this assignment using your `mude` environment. + +### Submission and deadline + +- Submit your answers, together with any relevant plots, in the Markdown file `Report.md`. This is the primary document that will be used to determine your grade; however, the auxiliary files (e.g., `*.ipynb` or `*.py` files) may be checked in case something is not clear. +- The deadline is to submit your work by making commits to your Group's GitLab repository by Friday at 12:30h. +- This project will be graded on interpretation, application, documentation and programming. Each task is worth roughly one point each. + +### Repository, Formatting and Static Check + +There is no static check for this project. Be sure to leave the outputs from your code cells in your `*.ipynb` file so that they are readable. + +You are always expected to provide well-formatted figures and Markdown text in your `Report.md` file, as well as logically organize any auxiliary files you may use (e.g., try to put your figures in a sub-directory, if there are a lot of them). If you run out of time it is OK if your `*ipynb` files do not run. + +**Importing figures into a Markdown file:** +1. Use relative referencing only, with the git repo (working directory) as the root (this is expressed with a single dot `.`) +2. Our grading systems are case-sensitive so match the names of folders exactly +3. Use linux-style path separators: `/` rather than `\`. +4. Do not include spaces in your file path or image name; if it is unavoidable replace the space with `%20`, for example: `` + +Here are some examples: +- an image located in the working directory `` (where `ext` is any image extension). +- an image located in a sub-directory called "images": `` +- an image with a space in the file name: `` + +When using Markdown to include an image, the square brackets is a text tag that is displayed in case the image does not load. Do not include a dot in the square brackets; i.e., do _not_ do this: ``. + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. diff --git a/content/GA_2_4/Report.md b/content/GA_2_4/Report.md new file mode 100644 index 0000000000000000000000000000000000000000..025951747aae388ddde8eafe41537530b444da64 --- /dev/null +++ b/content/GA_2_4/Report.md @@ -0,0 +1,94 @@ +# Project 8 Report: Time Series + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/).* + +**YOUR GROUP NAME HERE** + + +## Primary Task + +**Complete the notebook `P8.ipynb`and write your answers in this document as requested in the questions below. Note that only part of the notebook results are required to be included in this report.** Typically a short, one-line answer is sufficient; though a simple 'yes' or 'no' is _not_ sufficient, please include a short reasoning, justification or argumentatiogn. + +_You will be graded on the plots and answers provided in this file. You can delete the instructions and any other unnecessary text prior to submission._ + +## Report Instructions + +Remember to use Markdown features to clearly indicate your answers for each question below. + +**Importing figures into a Markdown file:** +1. Use relative referencing only, with the git repo (working directory) as the root (this is expressed with a single dot `.`) +2. Our grading systems are case-sensitive so match the names of folders exactly +3. Use linux-style path separators: `/` rather than `\`. +4. Do not include spaces in your file path or image name; if it is unavoidable replace the space with `%20`, for example: `` + +Here are some examples: +- an image located in the working directory `` (where `ext` is any image extension). +- an image located in a sub-directory called "images": `` +- an image with a space in the file name: `` + +When using Markdown to include an image, the square brackets is a text tag that is displayed in case the image does not load. Do not include a dot in the square brackets; i.e., do _not_ do this: ``. + +## Answers to Questions + +### Task 1 + +Describe any patterns you see in the time series, ACF and PSD. Comment on the effect of changing $\beta$. You do not need to include a figure, as long as they are included in your notebook. + +_Your answer here._ + +### Task 2 + +Describe any patterns you see in the time series, ACF and PSD. Comment on the effect of changing $\sigma$. You do not need to include a figure, as long as they are included in your notebook. + +_Your answer here._ + +### Task 3 + +State your estimate of sea level rise. _You only need to include your plot here if you make significant modifications from what was provided in the notebook._ + +_Your answer here._ + +### Task 4 + +Include the plot of (averaged) seasonal variations in a year and your explanation for what might cause such seasonal variations in GMSL. + +_Your answer here._ + +### Task 5 + +Include your plot along with a description of any trends that you observe. + +_Your answer here._ + +### Task 6 + +Report the three hightest peaks in the PSD (use units of cycles/year) along with a possible explanation for each. Confirm also that they are statistically significant. + +_Your answer here._ + +### Task 7 + +Include your figure, along with a (short) list of the unknown parameters. What is the amplitude of the combined annual and semi-annual signal? + +_Your answer here._ + +### Task 8 + +Report the estimated rate and precision, along with the confidence interval. State whether or not you accept or reject the null hypothesis, along with a (brief) explanation why: _the rate for sea level rise is not statistically significant_. + +_Your answer here._ + +### Task 9 + +Report whether or not the time series is stationary, and include justification why; it is not required, but you may include figures and quantitative results as needed to support your argument. Describe which quantities computed in this task could be used to make a prediction of sea level for next year (mention the functional and stochastic models specifically. + +_Your answer here._ + +## General Comments on the Assignment [optional] + +_Use this space to let us know if you encountered any issues completing this assignment (but please keep it short!). For example, if you encountered an error that could not be fixed in your Python code, or perhaps there was a problem submitting something via GitLab. You can also let us know if the instructions were unclear. You can delete this section if you don't use it._ + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. diff --git a/content/GA_2_5/Analysis_GA.ipynb b/content/GA_2_5/Analysis_GA.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7b47cec08d8d1fbb8e00f39b923576eadd52bc7a --- /dev/null +++ b/content/GA_2_5/Analysis_GA.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Evolve and drive\n", + "\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 15 December, 2023.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Introduction\n", + "\n", + "_Note: part of the background material for this project was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/book/optimization/project.html)._\n", + "\n", + "* We showed in the previous notebook how to use MILP to solve the Road Network Design (RND) Problem \n", + "* You saw that due to a large number of binary variables it takes long to reach a low gap\n", + "* Think about larger problems, like the road network of Amsterdam or Shanghai, and it will be even harder!\n", + "* Here we show how a metaheuristic such a the genetic algorithm can be used to find good (not necessarily optimal) solutions for the problem in potentially less time\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Libraries\n", + "\n", + "To run this notebook you need to have installed the following packages:\n", + "Pandas\n", + "Numpy\n", + "Matplotlib\n", + "Gurobipy\n", + "PyMOO\n", + "\n", + "Luckily, they're all part of this weeks environment!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import gurobipy as gp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#if you did not install pymoo yet, run the following in your Anaconda Prompt: conda install -c anaconda autograd\n", + "\n", + "# Genetic algorithm dependencies. We are importing the pymoo functions that are imporant for applying GA (the package can also apply other methods)\n", + "from pymoo.algorithms.soo.nonconvex.ga import GA\n", + "from pymoo.core.problem import ElementwiseProblem\n", + "from pymoo.optimize import minimize\n", + "from pymoo.operators.sampling.rnd import BinaryRandomSampling\n", + "from pymoo.operators.crossover.hux import HalfUniformCrossover\n", + "from pymoo.operators.mutation.bitflip import BitflipMutation\n", + "\n", + "# not used here but generally useful dependencies\n", + "#from pymoo.core.problem import Problem\n", + "#from pymoo.operators.mutation.pm import PolynomialMutation\n", + "#from pymoo.operators.crossover.pntx import PointCrossover\n", + "#from pymoo.operators.crossover.sbx import SBX\n", + "#from pymoo.operators.crossover.sbx import SimulatedBinaryCrossover\n", + "#from pymoo.operators.crossover.pntx import Crossover\n", + "#from pymoo.operators.repair.bounds_repair import BoundsRepair\n", + "#from pymoo.core.repair import Repair" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For visualization\n", + "from utils.network_visualization import network_visualization\n", + "from utils.network_visualization_highlight_link import network_visualization_highlight_links\n", + "from utils.network_visualization_upgraded import network_visualization_upgraded" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Genetic algorithm for NDP\n", + "\n", + "As we discussed, it is challenging to use MILP for large-scale NDPs. Therefore, in this assignment, we’re going to use a genetic algorithm to address this problem.\n", + "\n", + "Genetic Algorithms (GAs) are powerful optimization techniques inspired by the process of natural evolution. They have gained prominence in solving complex problems across various fields, ranging from engineering and economics to artificial intelligence. Here, we give a brief overview of Genetic Algorithms, highlighting their fundamental principles, components, and applications in solving optimization problems.\n", + "At the heart of a Genetic Algorithm are populations of potential solutions, represented as individuals or chromosomes. These individuals evolve over generations to improve their fitness with respect to the given optimization objective.\n", + "Basic Components of a Genetic Algorithm:\n", + "* **Population**: A collection of individuals representing potential solutions to the problem.\n", + "* **Objective Function** (or fitness function): Quantifies the quality of each individual’s solution with respect to the optimization objective.\n", + "* **Selection**: Individuals are chosen based on their fitness to serve as parents for the next generation.\n", + "* **Crossover**: Genetic material from parents is combined to create offspring with potentially improved traits.\n", + "* **Mutation**: Random changes are introduced in offspring to maintain diversity and explore new solution spaces.\n", + "* **Replacement**: New offspring replace some of the least fit individuals in the population.\n", + "* **Termination Criteria**: Conditions under which the algorithm stops, e.g., a maximum number of generations or satisfactory fitness level.\n" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### GA steps\n", + "\n", + "Reminding you about the GA steps …\n", + "* Initialization (start): A population of random individuals is generated to start the algorithm.\n", + "* Evaluation (fitness assessment): The fitness function assesses the quality of each individual’s solution.\n", + "* Selection: Individuals with higher fitness have a higher chance of being selected as parents.\n", + "* Crossover: Genetic material (part of the solutions) from selected parents is combined to create offspring.\n", + "* Mutation: Random changes are introduced to some offspring to maintain diversity.\n", + "* Replacement: New offspring replace some individuals in the population.\n", + "* Termination (end): The algorithm stops when a termination criterion is met.\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### PyMOO\n", + "\n", + "PyMOO is a Python library that provides a comprehensive and easy-to-use framework for multi-objective optimization (MOO). For this case, we are going to deal with only one objective; nevertheless, this is an useful tool if you have more objectives. In addition, PyMOO easily allows us to define our optimization problem by specifying the objectives, constraints, and decision variables.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Problem definition and formulation\n", + "\n", + "Here is the problem formulation as presented in the previous Jupyter notebook\n", + "\n", + "$$\\begin{align}\n", + " min \\quad & \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + " s.t. \\quad \\\\\n", + " & \\sum_{(i,j) \\in A}{ y_{ij}} = B \\\\\n", + " & \\sum_{s \\in D}{x_{ijs}} = x_{ij} \\quad \\forall (i,j) \\in A \\\\\n", + " & \\sum_{j \\in N; (i,j) \\in A}{ x_{ijs}} - \\sum_{j \\in N; (j,i) \\in A}{ x_{jis}} = d_{is} \\quad \\forall i \\in N, \\forall s \\in D \\\\\n", + " & y_{ij} \\in \\{0, 1\\} \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ij} \\geq 0 \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ijs} \\geq 0 \\quad \\forall (i,j) \\in A, \\forall s \\in D \\\\\n", + "\\end{align}$$\n", + "\n", + "\n", + "You can check the NDP notebook for details. But here we deal with it slightly differently to be able to use GA. Essentially, we break down the problem into two sub-problems: 1) the traffic assignment (TA) problem: the route choices of the drivers, and the 2) the road network design problem (NDP): where we select which links should be upgraded. We solve the problem by iteratively going between the Traffic assignment and the Design Problem. The idea is for the GA to move to better networks as generations pass which are evaluated by the traffic assignment process that you have learned.\n", + "We use Gurobi to solve the Traffic Assignment sub-problems, which provide us with the objective function (or fitness function within the context of GA) value of the decision problem (which will be dealt with using GA). This is usually referred to as the iterative-optimization-assignment method since we iteratively improve the objective function value of the NDP using the assignment problem.\n", + "\n", + "So let's see how that works.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### The network design sub-problem\n", + "\n", + "The network desing is where we use the genetic algorithm. As explained before, GA uses a population of solutions and iteratively improves this population to evolve to new generations of populations with a better objective function value (being that minimization or maximization). In this problem, the decision variables are links for capacity expansion and the objective function value is the total system travel time that we want to minimize.\n", + "\n", + "\\begin{align}\n", + " min \\quad & \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + " s.t. \\quad \\\\\n", + " & \\sum_{(i,j) \\in A}{ y_{ij}} = B \\\\\n", + " & y_{ij} \\in \\{0, 1\\} \\quad \\forall (i,j) \\in A \\\\\n", + "\\end{align}\n", + "\n", + "Where the values of $x_{ij}$ are not decision variables anymore, they will be obtained from solving the Traffic Assignment problem with Gurobi which evaluates the travel times on the network. This part of the problem will not be solved mathematically anymore, the $y_{ij}$ variables are decided by the genetic algorithm through the process you learned." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### The traffic assignment sub-problem\n", + "\n", + "This is just part of the original NDP that assigns traffic to the network based on a set of given capacity values, which are defined based on the values of the DP decision variables (links selected for capacity expansion). The main difference (and the advantage) here is that by separating the binary decision variables, instead of a mixed integer programming problem, which are hard to solve, here we have a quadratic programming problem with continuous decision variables, which will be transformed to a linear problem that Gurobi can solve very fast.\n", + "\n", + "\\begin{align}\n", + " min \\quad & \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + " s.t. \\quad \\\\\n", + " & \\sum_{s \\in D}{x_{ijs}} = x_{ij} \\quad \\forall (i,j) \\in A \\\\\n", + " & \\sum_{j \\in N; (i,j) \\in A}{ x_{ijs}} - \\sum_{j \\in N; (j,i) \\in A}{ x_{jis}} = d_{is} \\quad \\forall i \\in N, \\forall s \\in D \\\\\n", + " & x_{ij} \\geq 0 \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ijs} \\geq 0 \\quad \\forall (i,j) \\in A, \\forall s \\in D \\\\\n", + "\\end{align}\n", + "\n", + "\n", + "Where the values of $y_{ij}$ are constant and are defined by GA." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summarizing\n", + "\n", + "The following is a diagram that shows what you are finally doing to solve the same problem but with a meta-heuristic approach:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Data preprocessing\n", + "\n", + "Our data preprocessing steps are similar to the previous notebook. We use some networks from the well-known transportation networks for benchmarking repository as well as a small toy network for case studies of NDPs. the following functions read data from this repository and perform data preprocessing to have the input and the parameters required for our case studies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# import required packages\n", + "import os\n", + "import time\n", + "\n", + "# read network file\n", + "def read_net(net_file):\n", + " \"\"\"\n", + " read network file\n", + " \"\"\"\n", + "\n", + " net_data = pd.read_csv(net_file, skiprows=8, sep='\\t')\n", + " # make sure all headers are lower case and without trailing spaces\n", + " trimmed = [s.strip().lower() for s in net_data.columns]\n", + " net_data.columns = trimmed\n", + " # And drop the silly first and last columns\n", + " net_data.drop(['~', ';'], axis=1, inplace=True)\n", + " # using dictionary to convert type of specific columns so taht we can assign very small (close to zero) possitive number to it.\n", + " convert_dict = {'free_flow_time': float,\n", + " 'capacity': float,\n", + " 'length': float,\n", + " 'power': float\n", + " }\n", + " \n", + " net_data = net_data.astype(convert_dict)\n", + "\n", + " # make sure everything makes sense (otherwise some solvers throw errors)\n", + " net_data.loc[net_data['free_flow_time'] <= 0, 'free_flow_time'] = 1e-6\n", + " net_data.loc[net_data['capacity'] <= 0, 'capacity'] = 1e-6\n", + " net_data.loc[net_data['length'] <= 0, 'length'] = 1e-6\n", + " net_data.loc[net_data['power'] <= 1, 'power'] = int(4)\n", + " net_data['init_node'] = net_data['init_node'].astype(int)\n", + " net_data['term_node'] = net_data['term_node'].astype(int)\n", + " net_data['b'] = net_data['b'].astype(float)\n", + "\n", + " # extract features in dict format\n", + " links = list(zip(net_data['init_node'], net_data['term_node']))\n", + " caps = dict(zip(links, net_data['capacity']))\n", + " fftt = dict(zip(links, net_data['free_flow_time']))\n", + " lent = dict(zip(links, net_data['length']))\n", + " alpha = dict(zip(links, net_data['b']))\n", + " beta = dict(zip(links, net_data['power']))\n", + "\n", + " net = {'capacity': caps, 'free_flow': fftt, 'length': lent, 'alpha': alpha, 'beta': beta}\n", + "\n", + " return net\n", + "\n", + "\n", + "# read OD matrix (demand)\n", + "def read_od(od_file):\n", + " \"\"\"\n", + " read OD matrix\n", + " \"\"\"\n", + "\n", + " f = open(od_file, 'r')\n", + " all_rows = f.read()\n", + " blocks = all_rows.split('Origin')[1:]\n", + " matrix = {}\n", + " for k in range(len(blocks)):\n", + " orig = blocks[k].split('\\n')\n", + " dests = orig[1:]\n", + " origs = int(orig[0])\n", + "\n", + " d = [eval('{' + a.replace(';', ',').replace(' ', '') + '}') for a in dests]\n", + " destinations = {}\n", + " for i in d:\n", + " destinations = {**destinations, **i}\n", + " matrix[origs] = destinations\n", + " zones = max(matrix.keys())\n", + " od_dict = {}\n", + " for i in range(zones):\n", + " for j in range(zones):\n", + " demand = matrix.get(i + 1, {}).get(j + 1, 0)\n", + " if demand:\n", + " od_dict[(i + 1, j + 1)] = demand\n", + " else:\n", + " od_dict[(i + 1, j + 1)] = 0\n", + "\n", + " return od_dict\n", + "\n", + "\n", + "# read case study data\n", + "def read_cases(networks, input_dir):\n", + " \"\"\"\n", + " read case study data\n", + " \"\"\"\n", + "\n", + " # dictionaries for network and OD files\n", + " net_dict = {}\n", + " ods_dict = {}\n", + "\n", + " # selected case studies\n", + " if networks:\n", + " cases = [case for case in networks]\n", + " else:\n", + " # all folders available (each one for one specific case)\n", + " cases = [x for x in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, x))]\n", + "\n", + " # iterate through cases and read network and OD\n", + " for case in cases:\n", + " mod = os.path.join(input_dir, case)\n", + " mod_files = os.listdir(mod)\n", + " for i in mod_files:\n", + " # read network\n", + " if i.lower()[-8:] == 'net.tntp':\n", + " net_file = os.path.join(mod, i)\n", + " net_dict[case] = read_net(net_file)\n", + " # read OD matrix\n", + " if 'TRIPS' in i.upper() and i.lower()[-5:] == '.tntp':\n", + " ods_file = os.path.join(mod, i)\n", + " ods_dict[case] = read_od(ods_file)\n", + "\n", + " return net_dict, ods_dict\n", + "\n", + "\n", + "# create node-destination demand matrix\n", + "def create_nd_matrix(ods_data, origins, destinations, nodes):\n", + " # create node-destination demand matrix (not a regular OD!)\n", + " demand = {(n, d): 0 for n in nodes for d in destinations}\n", + " for r in origins:\n", + " for s in destinations:\n", + " if (r, s) in ods_data:\n", + " demand[r, s] = ods_data[r, s]\n", + " for s in destinations:\n", + " demand[s, s] = - sum(demand[j, s] for j in origins)\n", + "\n", + " return demand\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# define parameters, case study (network) list and the directory where their files are\n", + "extension_factor = 1.5 # capacity after extension (1.5 means add 50%)\n", + "extension_max_no = 20 # the number of links to add capacity to (simplified way of reprsenting a budget for investing)\n", + "#it's the same to say that it's exactly this number of that this number is the max, that's because every investment brings travel time benefits \n", + "#even if just one car circulates.\n", + "timelimit = 300 # seconds\n", + "beta = 2 # parameter to use in link travel time function (explained later)\n", + "\n", + "networks = ['SiouxFalls']\n", + "networks_dir = os.getcwd() +'/input/TransportationNetworks'\n", + "\n", + "\n", + "# prep data\n", + "net_dict, ods_dict = read_cases(networks, networks_dir)\n", + "\n", + "# Let's load the network and demand (OD matrix) data of the first network (Sioux Falls) to two dictionaries for our first case study.\n", + "# WE USE THE SAME NETWORK FROM THE FIRST NOTEBOOK: SIOUX FALLS\n", + "# The network has 76 arcs in total\n", + "net_data, ods_data = net_dict[networks[0]], ods_dict[networks[0]]\n", + "\n", + "## now let's prepare the data in a format readable by gurobi\n", + "\n", + "# prep links, nodes, and free flow travel times\n", + "links = list(net_data['capacity'].keys())\n", + "nodes = np.unique([list(edge) for edge in links])\n", + "fftts = net_data['free_flow']\n", + "\n", + "# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)\n", + "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + "\n", + "# origins and destinations\n", + "dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n", + "origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n", + "\n", + "# demand in node-destination form\n", + "demand = create_nd_matrix(ods_data, origs, dests, nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Display\n", + "We will use the same function we used in the previous notebook to visualize the network. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we are ready to build our models!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Modeling and solving the traffic assignment sub-problem with Gurobi\n", + "\n", + "In this section we build a Gurobi model to solve the Traffic Assignment sub-problems. The decision variables, objective function, and the constraints of this problem were described before.\n", + "Here we wrap the code in a function so that we can use it later within the GA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def ta_qp(dvs, net_data=net_data, ods_data=ods_data, extension_factor=1.5):\n", + "\n", + " # prep variables\n", + " beta = 2\n", + " links = list(net_data['capacity'].keys())\n", + " nodes = np.unique([list(edge) for edge in links])\n", + " fftts = net_data['free_flow']\n", + " links_selected = dict(zip(links, dvs))\n", + "\n", + " # define capacity\n", + " cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + " cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + " capacity = {(i, j): cap_normal[i, j] * (1 - links_selected[i, j]) + cap_extend[i, j] * links_selected[i, j]\n", + " for (i, j) in links}\n", + "\n", + " dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n", + " origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n", + "\n", + " # demand in node-destination form\n", + " demand = create_nd_matrix(ods_data, origs, dests, nodes)\n", + "\n", + " ## create a gurobi model object\n", + " model = gp.Model()\n", + " # just to avoid cluttering the notebook with unnecessary logging output\n", + " model.Params.LogToConsole = 0\n", + "\n", + " ## decision variables:\n", + "\n", + " # link flows (x_ij); i: a_node, j: b_node\n", + " link_flow = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x')\n", + "\n", + " # link flows per destination (xs_ijs); i: a_node, j: b_node, s: destination\n", + " dest_flow = model.addVars(links, dests, vtype=gp.GRB.CONTINUOUS, name='xs')\n", + "\n", + " ## constraints\n", + "\n", + " # node flow conservation (demand)\n", + " model.addConstrs(\n", + " gp.quicksum(dest_flow[i, j, s] for j in nodes if (i, j) in links) -\n", + " gp.quicksum(dest_flow[j, i, s] for j in nodes if (j, i) in links) == demand[i, s]\n", + " for i in nodes for s in dests\n", + " )\n", + "\n", + " # link flow conservation (destination flows and link flows)\n", + " model.addConstrs(gp.quicksum(dest_flow[i, j, s] for s in dests) == link_flow[i, j] for (i, j) in links)\n", + "\n", + " ## objective function (total travel time)\n", + " # total travel time = sum (link flow * link travel time)\n", + " # link travel time = free flow travel time * (1 + (flow / capacity))\n", + "\n", + " model.setObjective(\n", + " gp.quicksum(link_flow[i, j] * (fftts[i, j] * (1 + (beta * link_flow[i, j]/capacity[i, j]))) for (i, j) in links))\n", + "\n", + "\n", + " ## solve\n", + " model.update()\n", + " start_solve = time.time()\n", + " model.optimize()\n", + " solve_time = (time.time() - start_solve)\n", + "\n", + " # fetch optimal DV and OF values\n", + " link_flows = {(i, j): link_flow[i, j].X for (i, j) in links}\n", + " total_travel_time = model.ObjVal\n", + "\n", + " return total_travel_time, capacity, link_flows, links_selected" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Modeling with PyMOO\n", + "\n", + "Let's define a model in MyMOO and deal with the links selection problem with the GA." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "First, we need to define a problem class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "#If you want to know more about the library that is being used: https://pymoo.org/algorithms/soo/ga.html\n", + "\n", + "class NDP(ElementwiseProblem):\n", + "\n", + " def __init__(self, budget):\n", + "\n", + " super().__init__(n_var=len(links), # number of decision variables (i.e., number of links)\n", + " n_obj=1, # for now we use only one objective (total travel time)\n", + " n_constr=1, # one constraint for budget, that's because the GA shoud not create unfeasible solutions\n", + " vtype=bool, # binary decision variables\n", + " )\n", + " self.budget = budget\n", + "\n", + " def _evaluate(self, decision_vars, out, *args, **kwargs):\n", + "\n", + " # call TA to calculate the objective fucntion, meaning to do the evaluation of the solutions\n", + " total_travel_time,capacity, link_flows, links_selected = ta_qp(decision_vars)\n", + "\n", + " # the budget constraint\n", + " # In the GA part the only variables are the binary decision variables, don't forget that the traffic assignment that \n", + " # produces the travel time on the network is done in the evaluation of the solution\n", + " g = sum(decision_vars) - self.budget\n", + "\n", + " out[\"F\"] = total_travel_time\n", + " out[\"G\"] = g" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now, let's initiate an instance of the problem based on the problem class we defined, and initiate the GA with its parameters. Note that depending on the problem size and the number of feasible links, you might need larger values for population and generation size to achieve good results or even feasible results. Of course this increases the computation times." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "Budget = 76\n", + "pop_size = 10\n", + "\n", + "# initiate an instance of the problem with max number of selected links as budget constraint\n", + "problem = NDP(budget=Budget)\n", + "\n", + "# initiate the GA with parameters appropriate for binary variables\n", + "method = GA(pop_size=pop_size,\n", + " sampling=BinaryRandomSampling(),\n", + " mutation=BitflipMutation(),\n", + " crossover=HalfUniformCrossover()\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we are ready to minimize the NDP problem using the GA method we defined." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\n", + "opt_results = minimize(problem,\n", + " method,\n", + " termination=(\"time\", \"00:05:00\"), #5 minute maximum computation time\n", + " seed=7,\n", + " save_history=True,\n", + " verbose=True,\n", + " )\n", + "\n", + "print(\"Best Objective Function value: %s\" % opt_results.F)\n", + "print(\"Constraint violation: %s\" % opt_results.CV)\n", + "print(\"Best solution found: %s\" % opt_results.X)\n", + "\n", + "#To better interpret the results, this is the legend:\n", + "#n_gen: Number of generations\n", + "#n_eval: Number of function evaluations\n", + "#cv_min: Minimum constraint violation\n", + "#cv_avg: Average constraint violation\n", + "#f_avg: Average objective function value\n", + "#f_min: Minimum objective function value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "### Convergence curve\n", + "\n", + "Let's first define some functions (to use later) to get the results and plot them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def get_results(opt_results):\n", + "\n", + " number_of_individuals = [] # The number of individuals in each generation\n", + " optimal_values_along_generations = [] # The optimal value found in each generation\n", + "\n", + " for generation_status in opt_results.history:\n", + "\n", + " # retrieve the optimum from the algorithm\n", + " optimum = generation_status.opt\n", + "\n", + " # filter out only the feasible solutions and append and objective space values\n", + " try:\n", + " feas = np.where(optimum.get(\"feasible\"))[0]\n", + " optimal_values_along_generations.append(optimum.get(\"F\")[feas][0][0])\n", + " # store the number of function evaluations\n", + " number_of_individuals.append(generation_status.evaluator.n_eval)\n", + " except:\n", + " #In case a generation does not have any feasible solutions, it will be ignored.\n", + " pass\n", + "\n", + " return number_of_individuals, optimal_values_along_generations\n", + "\n", + "\n", + "def plot_results(number_of_individuals, optimal_values_along_generations):\n", + "\n", + " # Create a scatter plot with enhanced styling\n", + " plt.figure(figsize=(8, 6)) # Set the figure size\n", + "\n", + " # Create a scatter plot\n", + " plt.scatter(number_of_individuals, optimal_values_along_generations, label='Best objective function', color='blue', marker='o', s=100, alpha=0.7, edgecolors='black', linewidths=1.5)\n", + "\n", + " # Add labels and a legend with improved formatting\n", + " plt.xlabel('Function evaluations', fontsize=14, fontweight='bold')\n", + " plt.ylabel('Total Travel Time', fontsize=14, fontweight='bold')\n", + " plt.title('Best solution evolution', fontsize=16, fontweight='bold')\n", + " plt.legend(loc='upper right', fontsize=12)\n", + "\n", + " # Customize the grid appearance\n", + " plt.grid(True, linestyle='--', alpha=0.5)\n", + "\n", + " # Customize the tick labels\n", + " plt.xticks(fontsize=12)\n", + " plt.yticks(fontsize=12)\n", + "\n", + " # Add a background color to the plot\n", + " plt.gca().set_facecolor('#f2f2f2')\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now let's use these functions to plot the results.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "number_of_individuals, optimal_values_along_generations = get_results(opt_results)\n", + "\n", + "plot_results(number_of_individuals, optimal_values_along_generations)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Visualization\n", + "Same as the previous notebook we use link_flows, links_selected to visualize our results on the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "travel_time, capacity, link_flows, links_selected= ta_qp(dvs=opt_results.X, net_data=net_data, ods_data=ods_data, extension_factor=1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results\n", + "# To see the values for all the links just turn on the labels in the function below.\n", + "network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected, labels='off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "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": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_5/Analysis_GA_solution.ipynb b/content/GA_2_5/Analysis_GA_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59d697675f683827007fd76f81e3307a830f4596 --- /dev/null +++ b/content/GA_2_5/Analysis_GA_solution.ipynb @@ -0,0 +1,985 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Evolve and drive\n", + "\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 15 December, 2023.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> during the in-class session some of the confusion was caused by code issues. Comments relevant to the code and notebooks as-used in the Friday in-class session are provided in boxes like this.</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Introduction\n", + "\n", + "_Note: part of the background material for this project was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/book/optimization/project.html)._\n", + "\n", + "* We showed in the previous notebook how to use MILP to solve the Road Network Design (RND) Problem \n", + "* You saw that due to a large number of binary variables it takes long to reach a low gap\n", + "* Think about larger problems, like the road network of Amsterdam or Shanghai, and it will be even harder!\n", + "* Here we show how a metaheuristic such a the genetic algorithm can be used to find good (not necessarily optimal) solutions for the problem in potentially less time\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Libraries\n", + "\n", + "To run this notebook you need to have installed the following packages:\n", + "Pandas\n", + "Numpy\n", + "Matplotlib\n", + "Gurobipy\n", + "PyMOO\n", + "\n", + "Luckily, they're all part of this weeks environment!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import gurobipy as gp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#if you did not install pymoo yet, run the following in your Anaconda Prompt: conda install -c anaconda autograd\n", + "\n", + "# Genetic algorithm dependencies. We are importing the pymoo functions that are imporant for applying GA (the package can also apply other methods)\n", + "from pymoo.algorithms.soo.nonconvex.ga import GA\n", + "from pymoo.core.problem import ElementwiseProblem\n", + "from pymoo.optimize import minimize\n", + "from pymoo.operators.sampling.rnd import BinaryRandomSampling\n", + "from pymoo.operators.crossover.hux import HalfUniformCrossover #\n", + "from pymoo.operators.mutation.bitflip import BitflipMutation\n", + "\n", + "# not used here but generally useful dependencies\n", + "#from pymoo.core.problem import Problem\n", + "#from pymoo.operators.mutation.pm import PolynomialMutation\n", + "#from pymoo.operators.crossover.pntx import PointCrossover #\n", + "#from pymoo.operators.crossover.sbx import SBX\n", + "#from pymoo.operators.crossover.sbx import SimulatedBinaryCrossover\n", + "#from pymoo.operators.crossover.pntx import Crossover\n", + "#from pymoo.operators.repair.bounds_repair import BoundsRepair\n", + "#from pymoo.core.repair import Repair" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> the intention with the commented lines above was to illustrate possible crossover methods you could use, but unfortunately the pymoo documentation was not clear enough to indicate how they could have been used. The methods <code>SinglePointCrossover</code> and <code>TwoPointCrossover</code> (n<em>not</em> listed above!) could have been easily used, as these methods require no additional keyword arguments and would have worked “out of the box;†they were also illustrated with examples in the online textbook. We apologize for this oversight on our part.</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For visualization\n", + "from utils.network_visualization import network_visualization\n", + "from utils.network_visualization_highlight_link import network_visualization_highlight_links\n", + "from utils.network_visualization_upgraded import network_visualization_upgraded" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Genetic algorithm for NDP\n", + "\n", + "As we discussed, it is challenging to use MILP for large-scale NDPs. Therefore, in this assignment, we’re going to use a genetic algorithm to address this problem.\n", + "\n", + "Genetic Algorithms (GAs) are powerful optimization techniques inspired by the process of natural evolution. They have gained prominence in solving complex problems across various fields, ranging from engineering and economics to artificial intelligence. Here, we give a brief overview of Genetic Algorithms, highlighting their fundamental principles, components, and applications in solving optimization problems.\n", + "At the heart of a Genetic Algorithm are populations of potential solutions, represented as individuals or chromosomes. These individuals evolve over generations to improve their fitness with respect to the given optimization objective.\n", + "Basic Components of a Genetic Algorithm:\n", + "* **Population**: A collection of individuals representing potential solutions to the problem.\n", + "* **Objective Function** (or fitness function): Quantifies the quality of each individual’s solution with respect to the optimization objective.\n", + "* **Selection**: Individuals are chosen based on their fitness to serve as parents for the next generation.\n", + "* **Crossover**: Genetic material from parents is combined to create offspring with potentially improved traits.\n", + "* **Mutation**: Random changes are introduced in offspring to maintain diversity and explore new solution spaces.\n", + "* **Replacement**: New offspring replace some of the least fit individuals in the population.\n", + "* **Termination Criteria**: Conditions under which the algorithm stops, e.g., a maximum number of generations or satisfactory fitness level.\n" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### GA steps\n", + "\n", + "Reminding you about the GA steps …\n", + "* Initialization (start): A population of random individuals is generated to start the algorithm.\n", + "* Evaluation (fitness assessment): The fitness function assesses the quality of each individual’s solution.\n", + "* Selection: Individuals with higher fitness have a higher chance of being selected as parents.\n", + "* Crossover: Genetic material (part of the solutions) from selected parents is combined to create offspring.\n", + "* Mutation: Random changes are introduced to some offspring to maintain diversity.\n", + "* Replacement: New offspring replace some individuals in the population.\n", + "* Termination (end): The algorithm stops when a termination criterion is met.\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### PyMOO\n", + "\n", + "PyMOO is a Python library that provides a comprehensive and easy-to-use framework for multi-objective optimization (MOO). For this case, we are going to deal with only one objective; nevertheless, this is an useful tool if you have more objectives. In addition, PyMOO easily allows us to define our optimization problem by specifying the objectives, constraints, and decision variables.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Problem definition and formulation\n", + "\n", + "Here is the problem formulation as presented in the previous Jupyter notebook\n", + "\n", + "$$\\begin{align}\n", + " min \\quad & \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + " s.t. \\quad \\\\\n", + " & \\sum_{(i,j) \\in A}{ y_{ij}} = B \\\\\n", + " & \\sum_{s \\in D}{x_{ijs}} = x_{ij} \\quad \\forall (i,j) \\in A \\\\\n", + " & \\sum_{j \\in N; (i,j) \\in A}{ x_{ijs}} - \\sum_{j \\in N; (j,i) \\in A}{ x_{jis}} = d_{is} \\quad \\forall i \\in N, \\forall s \\in D \\\\\n", + " & y_{ij} \\in \\{0, 1\\} \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ij} \\geq 0 \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ijs} \\geq 0 \\quad \\forall (i,j) \\in A, \\forall s \\in D \\\\\n", + "\\end{align}$$\n", + "\n", + "\n", + "You can check the NDP notebook for details. But here we deal with it slightly differently to be able to use GA. Essentially, we break down the problem into two sub-problems: 1) the traffic assignment (TA) problem: the route choices of the drivers, and the 2) the road network design problem (NDP): where we select which links should be upgraded. We solve the problem by iteratively going between the Traffic assignment and the Design Problem. The idea is for the GA to move to better networks as generations pass which are evaluated by the traffic assignment process that you have learned.\n", + "We use Gurobi to solve the Traffic Assignment sub-problems, which provide us with the objective function (or fitness function within the context of GA) value of the decision problem (which will be dealt with using GA). This is usually referred to as the iterative-optimization-assignment method since we iteratively improve the objective function value of the NDP using the assignment problem.\n", + "\n", + "So let's see how that works.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### The network design sub-problem\n", + "\n", + "The network desing is where we use the genetic algorithm. As explained before, GA uses a population of solutions and iteratively improves this population to evolve to new generations of populations with a better objective function value (being that minimization or maximization). In this problem, the decision variables are links for capacity expansion and the objective function value is the total system travel time that we want to minimize.\n", + "\n", + "\\begin{align}\n", + " min \\quad & \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + " s.t. \\quad \\\\\n", + " & \\sum_{(i,j) \\in A}{ y_{ij}} = B \\\\\n", + " & y_{ij} \\in \\{0, 1\\} \\quad \\forall (i,j) \\in A \\\\\n", + "\\end{align}\n", + "\n", + "Where the values of $x_{ij}$ are not decision variables anymore, they will be obtained from solving the Traffic Assignment problem with Gurobi which evaluates the travel times on the network. This part of the problem will not be solved mathematically anymore, the $y_{ij}$ variables are decided by the genetic algorithm through the process you learned." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### The traffic assignment sub-problem\n", + "\n", + "This is just part of the original NDP that assigns traffic to the network based on a set of given capacity values, which are defined based on the values of the DP decision variables (links selected for capacity expansion). The main difference (and the advantage) here is that by separating the binary decision variables, instead of a mixed integer programming problem, which are hard to solve, here we have a quadratic programming problem with continuous decision variables, which will be transformed to a linear problem that Gurobi can solve very fast.\n", + "\n", + "\\begin{align}\n", + " min \\quad & \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + " s.t. \\quad \\\\\n", + " & \\sum_{s \\in D}{x_{ijs}} = x_{ij} \\quad \\forall (i,j) \\in A \\\\\n", + " & \\sum_{j \\in N; (i,j) \\in A}{ x_{ijs}} - \\sum_{j \\in N; (j,i) \\in A}{ x_{jis}} = d_{is} \\quad \\forall i \\in N, \\forall s \\in D \\\\\n", + " & x_{ij} \\geq 0 \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ijs} \\geq 0 \\quad \\forall (i,j) \\in A, \\forall s \\in D \\\\\n", + "\\end{align}\n", + "\n", + "\n", + "Where the values of $y_{ij}$ are constant and are defined by GA." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summarizing\n", + "\n", + "The following is a diagram that shows what you are finally doing to solve the same problem but with a meta-heuristic approach:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Data preprocessing\n", + "\n", + "Our data preprocessing steps are similar to the previous notebook. We use some networks from the well-known transportation networks for benchmarking repository as well as a small toy network for case studies of NDPs. the following functions read data from this repository and perform data preprocessing to have the input and the parameters required for our case studies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# import required packages\n", + "import os\n", + "import time\n", + "\n", + "# read network file\n", + "def read_net(net_file):\n", + " \"\"\"\n", + " read network file\n", + " \"\"\"\n", + "\n", + " net_data = pd.read_csv(net_file, skiprows=8, sep='\\t')\n", + " # make sure all headers are lower case and without trailing spaces\n", + " trimmed = [s.strip().lower() for s in net_data.columns]\n", + " net_data.columns = trimmed\n", + " # And drop the silly first and last columns\n", + " net_data.drop(['~', ';'], axis=1, inplace=True)\n", + " # using dictionary to convert type of specific columns so taht we can assign very small (close to zero) possitive number to it.\n", + " convert_dict = {'free_flow_time': float,\n", + " 'capacity': float,\n", + " 'length': float,\n", + " 'power': float\n", + " }\n", + " \n", + " net_data = net_data.astype(convert_dict)\n", + "\n", + " # make sure everything makes sense (otherwise some solvers throw errors)\n", + " net_data.loc[net_data['free_flow_time'] <= 0, 'free_flow_time'] = 1e-6\n", + " net_data.loc[net_data['capacity'] <= 0, 'capacity'] = 1e-6\n", + " net_data.loc[net_data['length'] <= 0, 'length'] = 1e-6\n", + " net_data.loc[net_data['power'] <= 1, 'power'] = int(4)\n", + " net_data['init_node'] = net_data['init_node'].astype(int)\n", + " net_data['term_node'] = net_data['term_node'].astype(int)\n", + " net_data['b'] = net_data['b'].astype(float)\n", + "\n", + " # extract features in dict format\n", + " links = list(zip(net_data['init_node'], net_data['term_node']))\n", + " caps = dict(zip(links, net_data['capacity']))\n", + " fftt = dict(zip(links, net_data['free_flow_time']))\n", + " lent = dict(zip(links, net_data['length']))\n", + " alpha = dict(zip(links, net_data['b']))\n", + " beta = dict(zip(links, net_data['power']))\n", + "\n", + " net = {'capacity': caps, 'free_flow': fftt, 'length': lent, 'alpha': alpha, 'beta': beta}\n", + "\n", + " return net\n", + "\n", + "\n", + "# read OD matrix (demand)\n", + "def read_od(od_file):\n", + " \"\"\"\n", + " read OD matrix\n", + " \"\"\"\n", + "\n", + " f = open(od_file, 'r')\n", + " all_rows = f.read()\n", + " blocks = all_rows.split('Origin')[1:]\n", + " matrix = {}\n", + " for k in range(len(blocks)):\n", + " orig = blocks[k].split('\\n')\n", + " dests = orig[1:]\n", + " origs = int(orig[0])\n", + "\n", + " d = [eval('{' + a.replace(';', ',').replace(' ', '') + '}') for a in dests]\n", + " destinations = {}\n", + " for i in d:\n", + " destinations = {**destinations, **i}\n", + " matrix[origs] = destinations\n", + " zones = max(matrix.keys())\n", + " od_dict = {}\n", + " for i in range(zones):\n", + " for j in range(zones):\n", + " demand = matrix.get(i + 1, {}).get(j + 1, 0)\n", + " if demand:\n", + " od_dict[(i + 1, j + 1)] = demand\n", + " else:\n", + " od_dict[(i + 1, j + 1)] = 0\n", + "\n", + " return od_dict\n", + "\n", + "\n", + "# read case study data\n", + "def read_cases(networks, input_dir):\n", + " \"\"\"\n", + " read case study data\n", + " \"\"\"\n", + "\n", + " # dictionaries for network and OD files\n", + " net_dict = {}\n", + " ods_dict = {}\n", + "\n", + " # selected case studies\n", + " if networks:\n", + " cases = [case for case in networks]\n", + " else:\n", + " # all folders available (each one for one specific case)\n", + " cases = [x for x in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, x))]\n", + "\n", + " # iterate through cases and read network and OD\n", + " for case in cases:\n", + " mod = os.path.join(input_dir, case)\n", + " mod_files = os.listdir(mod)\n", + " for i in mod_files:\n", + " # read network\n", + " if i.lower()[-8:] == 'net.tntp':\n", + " net_file = os.path.join(mod, i)\n", + " net_dict[case] = read_net(net_file)\n", + " # read OD matrix\n", + " if 'TRIPS' in i.upper() and i.lower()[-5:] == '.tntp':\n", + " ods_file = os.path.join(mod, i)\n", + " ods_dict[case] = read_od(ods_file)\n", + "\n", + " return net_dict, ods_dict\n", + "\n", + "\n", + "# create node-destination demand matrix\n", + "def create_nd_matrix(ods_data, origins, destinations, nodes):\n", + " # create node-destination demand matrix (not a regular OD!)\n", + " demand = {(n, d): 0 for n in nodes for d in destinations}\n", + " for r in origins:\n", + " for s in destinations:\n", + " if (r, s) in ods_data:\n", + " demand[r, s] = ods_data[r, s]\n", + " for s in destinations:\n", + " demand[s, s] = - sum(demand[j, s] for j in origins)\n", + "\n", + " return demand\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> the variables <code>extension_max_no</code> and <code>timelimit</code> were both defined here, but never used. <code>extension_max_no</code> is similar to the variable in notebook A with the same name; here it is superceded below by <code>Budget</code>.</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# define parameters, case study (network) list and the directory where their files are\n", + "extension_factor = 1.5 # capacity after extension (1.5 means add 50%)\n", + "extension_max_no = 20 # the number of links to add capacity to (simplified way of reprsenting a budget for investing)\n", + "#it's the same to say that it's exactly this number of that this number is the max, that's because every investment brings travel time benefits \n", + "#even if just one car circulates.\n", + "timelimit = 300 # seconds\n", + "beta = 2 # parameter to use in link travel time function (explained later)\n", + "\n", + "networks = ['SiouxFalls']\n", + "networks_dir = os.getcwd() +'/input/TransportationNetworks'\n", + "\n", + "\n", + "# prep data\n", + "net_dict, ods_dict = read_cases(networks, networks_dir)\n", + "\n", + "# Let's load the network and demand (OD matrix) data of the first network (Sioux Falls) to two dictionaries for our first case study.\n", + "# WE USE THE SAME NETWORK FROM THE FIRST NOTEBOOK: SIOUX FALLS\n", + "# The network has 76 arcs in total\n", + "net_data, ods_data = net_dict[networks[0]], ods_dict[networks[0]]\n", + "\n", + "## now let's prepare the data in a format readable by gurobi\n", + "\n", + "# prep links, nodes, and free flow travel times\n", + "links = list(net_data['capacity'].keys())\n", + "nodes = np.unique([list(edge) for edge in links])\n", + "fftts = net_data['free_flow']\n", + "\n", + "# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)\n", + "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + "\n", + "# origins and destinations\n", + "dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n", + "origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n", + "\n", + "# demand in node-destination form\n", + "demand = create_nd_matrix(ods_data, origs, dests, nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Display\n", + "We will use the same function we used in the previous notebook to visualize the network. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we are ready to build our models!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Modeling and solving the traffic assignment sub-problem with Gurobi\n", + "\n", + "In this section we build a Gurobi model to solve the Traffic Assignment sub-problems. The decision variables, objective function, and the constraints of this problem were described before.\n", + "Here we wrap the code in a function so that we can use it later within the GA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def ta_qp(dvs, net_data=net_data, ods_data=ods_data, extension_factor=1.5):\n", + "\n", + " # prep variables\n", + " beta = 2\n", + " links = list(net_data['capacity'].keys())\n", + " nodes = np.unique([list(edge) for edge in links])\n", + " fftts = net_data['free_flow']\n", + " links_selected = dict(zip(links, dvs))\n", + "\n", + " # define capacity\n", + " cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + " cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + " capacity = {(i, j): cap_normal[i, j] * (1 - links_selected[i, j]) + cap_extend[i, j] * links_selected[i, j]\n", + " for (i, j) in links}\n", + "\n", + " dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n", + " origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n", + "\n", + " # demand in node-destination form\n", + " demand = create_nd_matrix(ods_data, origs, dests, nodes)\n", + "\n", + " ## create a gurobi model object\n", + " model = gp.Model()\n", + " # just to avoid cluttering the notebook with unnecessary logging output\n", + " model.Params.LogToConsole = 0\n", + "\n", + " ## decision variables:\n", + "\n", + " # link flows (x_ij); i: a_node, j: b_node\n", + " link_flow = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x')\n", + "\n", + " # link flows per destination (xs_ijs); i: a_node, j: b_node, s: destination\n", + " dest_flow = model.addVars(links, dests, vtype=gp.GRB.CONTINUOUS, name='xs')\n", + "\n", + " ## constraints\n", + "\n", + " # node flow conservation (demand)\n", + " model.addConstrs(\n", + " gp.quicksum(dest_flow[i, j, s] for j in nodes if (i, j) in links) -\n", + " gp.quicksum(dest_flow[j, i, s] for j in nodes if (j, i) in links) == demand[i, s]\n", + " for i in nodes for s in dests\n", + " )\n", + "\n", + " # link flow conservation (destination flows and link flows)\n", + " model.addConstrs(gp.quicksum(dest_flow[i, j, s] for s in dests) == link_flow[i, j] for (i, j) in links)\n", + "\n", + " ## objective function (total travel time)\n", + " # total travel time = sum (link flow * link travel time)\n", + " # link travel time = free flow travel time * (1 + (flow / capacity))\n", + "\n", + " model.setObjective(\n", + " gp.quicksum(link_flow[i, j] * (fftts[i, j] * (1 + (beta * link_flow[i, j]/capacity[i, j]))) for (i, j) in links))\n", + "\n", + "\n", + " ## solve\n", + " model.update()\n", + " start_solve = time.time()\n", + " model.optimize()\n", + " solve_time = (time.time() - start_solve)\n", + "\n", + " # fetch optimal DV and OF values\n", + " link_flows = {(i, j): link_flow[i, j].X for (i, j) in links}\n", + " total_travel_time = model.ObjVal\n", + "\n", + " return total_travel_time, capacity, link_flows, links_selected" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Modeling with PyMOO\n", + "\n", + "Let's define a model in MyMOO and deal with the links selection problem with the GA." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "First, we need to define a problem class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "#If you want to know more about the library that is being used: https://pymoo.org/algorithms/soo/ga.html\n", + "\n", + "class NDP(ElementwiseProblem):\n", + "\n", + " def __init__(self, budget):\n", + "\n", + " super().__init__(n_var=len(links), # number of decision variables (i.e., number of links)\n", + " n_obj=1, # for now we use only one objective (total travel time)\n", + " n_constr=1, # one constraint for budget, that's because the GA shoud not create unfeasible solutions\n", + " vtype=bool, # binary decision variables\n", + " )\n", + " self.budget = budget\n", + "\n", + " def _evaluate(self, decision_vars, out, *args, **kwargs):\n", + "\n", + " # call TA to calculate the objective fucntion, meaning to do the evaluation of the solutions\n", + " total_travel_time,capacity, link_flows, links_selected = ta_qp(decision_vars)\n", + "\n", + " # the budget constraint\n", + " # In the GA part the only variables are the binary decision variables, don't forget that the traffic assignment that \n", + " # produces the travel time on the network is done in the evaluation of the solution\n", + " g = sum(decision_vars) - self.budget\n", + "\n", + " out[\"F\"] = total_travel_time\n", + " out[\"G\"] = g" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now, let's initiate an instance of the problem based on the problem class we defined, and initiate the GA with its parameters. Note that depending on the problem size and the number of feasible links, you might need larger values for population and generation size to achieve good results or even feasible results. Of course this increases the computation times." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> population size <code>pop_size</code> was 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). </p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). The initial value of 76 was trivial, and the solution converged quickly. Numbers between 10 and 40 would have produced <em>much</em> more interesting results (see solution explanation).</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "Budget = 76\n", + "pop_size = 10\n", + "\n", + "# initiate an instance of the problem with max number of selected links as budget constraint\n", + "problem = NDP(budget=Budget)\n", + "\n", + "# initiate the GA with parameters appropriate for binary variables\n", + "method = GA(pop_size=pop_size,\n", + " sampling=BinaryRandomSampling(),\n", + " mutation=BitflipMutation(),\n", + " crossover=HalfUniformCrossover()\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we are ready to minimize the NDP problem using the GA method we defined." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> termination is set here as a keyword argument (see note above).</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\n", + "opt_results = minimize(problem,\n", + " method,\n", + " termination=(\"time\", \"00:05:00\"), #5 minute maximum computation time\n", + " seed=7,\n", + " save_history=True,\n", + " verbose=True,\n", + " )\n", + "\n", + "print(\"Best Objective Function value: %s\" % opt_results.F)\n", + "print(\"Constraint violation: %s\" % opt_results.CV)\n", + "print(\"Best solution found: %s\" % opt_results.X)\n", + "\n", + "#To better interpret the results, this is the legend:\n", + "#n_gen: Number of generations\n", + "#n_eval: Number of function evaluations\n", + "#cv_min: Minimum constraint violation\n", + "#cv_avg: Average constraint violation\n", + "#f_avg: Average objective function value\n", + "#f_min: Minimum objective function value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "### Convergence curve\n", + "\n", + "Let's first define some functions (to use later) to get the results and plot them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def get_results(opt_results):\n", + "\n", + " number_of_individuals = [] # The number of individuals in each generation\n", + " optimal_values_along_generations = [] # The optimal value found in each generation\n", + "\n", + " for generation_status in opt_results.history:\n", + "\n", + " # retrieve the optimum from the algorithm\n", + " optimum = generation_status.opt\n", + "\n", + " # filter out only the feasible solutions and append and objective space values\n", + " try:\n", + " feas = np.where(optimum.get(\"feasible\"))[0]\n", + " optimal_values_along_generations.append(optimum.get(\"F\")[feas][0][0])\n", + " # store the number of function evaluations\n", + " number_of_individuals.append(generation_status.evaluator.n_eval)\n", + " except:\n", + " #In case a generation does not have any feasible solutions, it will be ignored.\n", + " pass\n", + "\n", + " return number_of_individuals, optimal_values_along_generations\n", + "\n", + "\n", + "def plot_results(number_of_individuals, optimal_values_along_generations):\n", + "\n", + " # Create a scatter plot with enhanced styling\n", + " plt.figure(figsize=(8, 6)) # Set the figure size\n", + "\n", + " # Create a scatter plot\n", + " plt.scatter(number_of_individuals, optimal_values_along_generations, label='Best objective function', color='blue', marker='o', s=100, alpha=0.7, edgecolors='black', linewidths=1.5)\n", + "\n", + " # Add labels and a legend with improved formatting\n", + " plt.xlabel('Function evaluations', fontsize=14, fontweight='bold')\n", + " plt.ylabel('Total Travel Time', fontsize=14, fontweight='bold')\n", + " plt.title('Best solution evolution', fontsize=16, fontweight='bold')\n", + " plt.legend(loc='upper right', fontsize=12)\n", + "\n", + " # Customize the grid appearance\n", + " plt.grid(True, linestyle='--', alpha=0.5)\n", + "\n", + " # Customize the tick labels\n", + " plt.xticks(fontsize=12)\n", + " plt.yticks(fontsize=12)\n", + "\n", + " # Add a background color to the plot\n", + " plt.gca().set_facecolor('#f2f2f2')\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now let's use these functions to plot the results.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "number_of_individuals, optimal_values_along_generations = get_results(opt_results)\n", + "\n", + "plot_results(number_of_individuals, optimal_values_along_generations)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Visualization\n", + "Same as the previous notebook we use link_flows, links_selected to visualize our results on the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "travel_time, capacity, link_flows, links_selected= ta_qp(dvs=opt_results.X, net_data=net_data, ods_data=ods_data, extension_factor=1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results\n", + "# To see the values for all the links just turn on the labels in the function below.\n", + "network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected, labels='off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_5/Analysis_LP.ipynb b/content/GA_2_5/Analysis_LP.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d7680c522386ce5162d3ac851639799e022aa418 --- /dev/null +++ b/content/GA_2_5/Analysis_LP.ipynb @@ -0,0 +1,926 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Don't do math and drive\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 15 December, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Problem description\n", + "\n", + "_Note: part of the background material for this project was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/book/optimization/project.html)._\n", + "\n", + "The road network design problem (NDP) is the problem of determining which links to build/refurbish/upgrade in order to improve the performance of a road network. One of the variations of the road NDP is the road NDP with capacity expansions, which involves deciding which links should have their capacity increased. This is a complex problem that must take into account a variety of factors, including:\n", + "\n", + "* The current state of the road network\n", + "* The projected future traffic demand\n", + "* The budget available for improvements\n", + "* The impacts of the adjustments (could be environmental, social, etc).\n", + "\n", + "There are a variety of approaches to dealing with the road network design problem with capacity expansion. One common approach is to use mathematical optimization models. In this assignment we use a simplified example to show how optimization can be used to tackle road NDPs. Note that the classical approaches to dealing with road NDPs can be more complicated and will be covered in other courses in the TTE track of the civil engineering master program.\n", + "\n", + "In this assignment, the goal is to minimize the total travel time on the network by selecting a predefined number of links for capacity expansion (subject to the available budget). The following (main) assumptions and simplifications are made to make the problem solvable using methods and algorithms that you have learned so far.\n", + "\n", + "### 1. Link travel time function\n", + "Travel time on a stretch of road (i.e., a link) depends on the flow (vehicles/hour) on that link and the capacity of the link (maximum of vehicles/hour). The most common function to calculate travel time on a link is the so-called Bureau of Public Roads (BPR) function, which is a polynomial (degree 4) function. That function, if used in the assignment, would make the problem non-linear and therefore very hard to solve. So we use a simplified linear function where travel time grows linearly with the flow of vehicles on a road link. More details are provided within the formulation section.\n", + "\n", + "\n", + "\n", + "${t_{ij}} = t_{ij}^0\\left( {1 + \\alpha {{\\left( {\\cfrac{x_{ij}}{c_{ij}}} \\right)}^\\beta }} \\right) \\quad \\left( {i,j} \\right) \\in A$\n", + "\n", + "Where $t_{ij}$ is the current travel time on the link, $t_{ij}^0$ is the travel time without congestion (free flow), $x_{ij}$ is the flow of cars, and $_c{ij}$ the capacity in maximum flow of cars. $\\alpha$ and $\\beta$ are calibration parameters.\n", + "\n", + "### 2. Route choice behavior\n", + "In order to assess the quality of the road capacity expansion problem, one must know what the effect of the added capacity is on travel time. For that, it is not sufficient to model the travel time-flow function, you must know where the vehicles are going to drive on the road. The route choice behavior of drivers within congested networks often follows the so-called User Equilibrium (UE) principle where each traveller tries to minimize their own individual generalized travel time.The UE states that for each origin and destination pair, all used routes between those nodes have equal and minimal travel time. That is, no driver can improve his/her travel time by choosing another path, therefore an equilibrium is reached.\n", + "However, calculating the UE requires advanced methods which are not covered in the MUDE. Therefore, here we assume the route choice behaviour follows the so-called System Optimal (SO) principle, which implies that route choices are made in such a way that the total travel time is minimized (summed over all the drivers). That means that some cars will drive longer routes so that other cars can save time. This is also called social equilibrium. This type of equilibrium is easier to compute. But just have in mind that in our road networks you can hardly obtain a system optimal traffic distribution. You can't tell where drivers have to do.\n", + "\n", + "\n", + "\n", + "\n", + "### 3. Quadratic terms\n", + "As you will see in the formulation below, even after making the above-mentioned assumptions, our formulation of the Road NDP will include a quadratic term, you must multiply the flow (which is a variable) by the tavel time (which is also a variable). This is therefore not linear. \n", + "Fortunately there are different methods to transform quadratic terms to linear variables and constraints with mathematical programming. You do not need to learn these techniques. Most solvers (including Gurobi) can handle this well given some adjustments to the formulation. Gurobi uses the [McCormick Envelops (MCE)](https://optimization.cbe.cornell.edu/index.php?title=McCormick_envelopes) to transform each quadratic term into one new variable and four constraints. For more information on MCE and how Gurobi implements them, check these links (this is in case you are interested, you don't need to check them during the Friday session):\n", + "* [The theory behind MCE](https://optimization.cbe.cornell.edu/index.php?title=McCormick_envelopes)\n", + "* [Gurobi webinar presentation on quadratic optimization](https://cdn.gurobi.com/wp-content/uploads/2020-01-14_Non-Convex-Quadratic-Optimization-in-Gurobi-9.0-Webinar.pdf?x93374)\n", + "* [Full video of the webinar](https://www.gurobi.com/events/non-convex-quadratic-optimization/)\n", + "\n", + "We will move forward with a quadratic term in the objective function then because with the simplifcations and assumptions referred to above we can formulate an NDP and solve it using the branch and bound method that you have studied before.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "# Data preprocessing\n", + "\n", + "We use some networks from the well-known transportation networks for benchmarking repository as well as a small toy network for case studies of NDPs. the following functions read data from this repository and perform data preprocessing to have the input and the parameters required for our case studies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "import os\n", + "import time\n", + "import gurobipy as gp\n", + "import pandas as pd\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# read network file, a function to import the road networks\n", + "def read_net(net_file):\n", + " \"\"\"\n", + " read network file\n", + " \"\"\"\n", + "\n", + " net_data = pd.read_csv(net_file, skiprows=8, sep='\\t')\n", + " # make sure all headers are lower case and without trailing spaces\n", + " trimmed = [s.strip().lower() for s in net_data.columns]\n", + " net_data.columns = trimmed\n", + " # And drop the silly first and last columns\n", + " net_data.drop(['~', ';'], axis=1, inplace=True)\n", + " # using dictionary to convert type of specific columns so taht we can assign very small (close to zero) possitive number to it.\n", + " convert_dict = {'free_flow_time': float,\n", + " 'capacity': float,\n", + " 'length': float,\n", + " 'power': float\n", + " }\n", + " \n", + " net_data = net_data.astype(convert_dict)\n", + "\n", + " # make sure everything makes sense (otherwise some solvers throw errors)\n", + " net_data.loc[net_data['free_flow_time'] <= 0, 'free_flow_time'] = 1e-6\n", + " net_data.loc[net_data['capacity'] <= 0, 'capacity'] = 1e-6\n", + " net_data.loc[net_data['length'] <= 0, 'length'] = 1e-6\n", + " net_data.loc[net_data['power'] <= 1, 'power'] = int(4)\n", + " net_data['init_node'] = net_data['init_node'].astype(int)\n", + " net_data['term_node'] = net_data['term_node'].astype(int)\n", + " net_data['b'] = net_data['b'].astype(float)\n", + "\n", + " # extract features in dict format\n", + " links = list(zip(net_data['init_node'], net_data['term_node']))\n", + " caps = dict(zip(links, net_data['capacity']))\n", + " fftt = dict(zip(links, net_data['free_flow_time']))\n", + " lent = dict(zip(links, net_data['length']))\n", + " alpha = dict(zip(links, net_data['b']))\n", + " beta = dict(zip(links, net_data['power']))\n", + "\n", + " net = {'capacity': caps, 'free_flow': fftt, 'length': lent, 'alpha': alpha, 'beta': beta}\n", + "\n", + " return net\n", + "\n", + "\n", + "# read OD matrix (demand), a function to import Origin and Destination Matrices, \n", + "# that is a table that says how many vehicles go from i to j\n", + "def read_od(od_file):\n", + " \"\"\"\n", + " read OD matrix\n", + " \"\"\"\n", + "\n", + " f = open(od_file, 'r')\n", + " all_rows = f.read()\n", + " blocks = all_rows.split('Origin')[1:]\n", + " matrix = {}\n", + " for k in range(len(blocks)):\n", + " orig = blocks[k].split('\\n')\n", + " dests = orig[1:]\n", + " origs = int(orig[0])\n", + "\n", + " d = [eval('{' + a.replace(';', ',').replace(' ', '') + '}') for a in dests]\n", + " destinations = {}\n", + " for i in d:\n", + " destinations = {**destinations, **i}\n", + " matrix[origs] = destinations\n", + " zones = max(matrix.keys())\n", + " od_dict = {}\n", + " for i in range(zones):\n", + " for j in range(zones):\n", + " demand = matrix.get(i + 1, {}).get(j + 1, 0)\n", + " if demand:\n", + " od_dict[(i + 1, j + 1)] = demand\n", + " else:\n", + " od_dict[(i + 1, j + 1)] = 0\n", + "\n", + " return od_dict\n", + "\n", + "\n", + "# read case study data, we will have different case studies that have different demand and road network \n", + "def read_cases(networks, input_dir):\n", + " \"\"\"\n", + " read case study data\n", + " \"\"\"\n", + "\n", + " # dictionaries for network and OD files\n", + " net_dict = {}\n", + " ods_dict = {}\n", + "\n", + " # selected case studies\n", + " if networks:\n", + " cases = [case for case in networks]\n", + " else:\n", + " # all folders available (each one for one specific case)\n", + " cases = [x for x in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, x))]\n", + "\n", + " # iterate through cases and read network and OD\n", + " for case in cases:\n", + " mod = os.path.join(input_dir, case)\n", + " mod_files = os.listdir(mod)\n", + " for i in mod_files:\n", + " # read network\n", + " if i.lower()[-8:] == 'net.tntp':\n", + " net_file = os.path.join(mod, i)\n", + " net_dict[case] = read_net(net_file)\n", + " # read OD matrix\n", + " if 'TRIPS' in i.upper() and i.lower()[-5:] == '.tntp':\n", + " ods_file = os.path.join(mod, i)\n", + " ods_dict[case] = read_od(ods_file)\n", + "\n", + " return net_dict, ods_dict\n", + "\n", + "\n", + "# create node-destination demand matrix\n", + "def create_nd_matrix(ods_data, origins, destinations, nodes):\n", + " # create node-destination demand matrix (not a regular OD!)\n", + " demand = {(n, d): 0 for n in nodes for d in destinations}\n", + " for r in origins:\n", + " for s in destinations:\n", + " if (r, s) in ods_data:\n", + " demand[r, s] = ods_data[r, s]\n", + " for s in destinations:\n", + " demand[s, s] = - sum(demand[j, s] for j in origins)\n", + "\n", + " return demand\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# define parameters, case study (network) list and the directory where their files are\n", + "extension_factor = 1.5 # capacity after extension (1.5 means add 50% of existing capacity)\n", + "extension_max_no = 40 # max number of links to add capacity to (simplified budget limit)\n", + "timelimit = 300 # seconds\n", + "beta = 2 # parameter to use in link travel time function (explained later)\n", + "\n", + "networks = ['SiouxFalls']\n", + "networks_dir = 'input/TransportationNetworks'\n", + "\n", + "\n", + "# prep data\n", + "net_dict, ods_dict = read_cases(networks, networks_dir)\n", + "# Let's load the network and demand (OD matrix) data of the first network (SiouxFalls) to two dictionaries for our first case study.\n", + "# Reminding that we are using the SiouxFalls network which is one of the most used networks in transportation reserach: https://github.com/bstabler/TransportationNetworks/blob/master/SiouxFalls/Sioux-Falls-Network.pdf\n", + "net_data, ods_data = net_dict[networks[0]], ods_dict[networks[0]]\n", + "\n", + "## now let's prepare the data in a format readable by gurobi\n", + "\n", + "# prep links, nodes, and free flow travel times\n", + "links = list(net_data['capacity'].keys())\n", + "nodes = np.unique([list(edge) for edge in links])\n", + "fftts = net_data['free_flow']\n", + "\n", + "# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)\n", + "# this is the capacity of a road link in vehicles per hour without the expansion\n", + "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + "#with the expansion\n", + "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + "\n", + "# origins and destinations\n", + "origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n", + "dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n", + "\n", + "# demand in node-destination form\n", + "# an OD-matrix is built\n", + "demand = create_nd_matrix(ods_data, origs, dests, nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we'll initially visualize the network using the coordinates of the sample road network, 'SiouxFalls'. Later, we'll employ the same network topology to visualize the upgraded links.\n", + "\n", + "Thankfully, Python offers a highly useful package for visualizing networks called **'networkx'**. We'll leverage some of its features, so:\n", + "\n", + "1. Feel free to explore further functionalities of the **networkx** package in its [documentation](https://networkx.org/documentation/stable/reference/index.html).\n", + "2. Our coordinates in this specific example are in **JSON** format. Therefore, don't forget to import the package accordingly.\n", + "3. Interested in visualizing other networks? Fantastic! However, you'll need to check the format of your coordinates first. TAs will assist you if you wish to explore further.\n", + "\n", + "\n", + "The road network that we are using in the assessment is the Sioux Falls which is shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For visualization\n", + "import networkx as nx\n", + "import json\n", + "from matplotlib.lines import Line2D # this will later be used for highlighting edge colors based on values\n", + "\n", + "from utils.network_visualization import network_visualization\n", + "from utils.network_visualization_highlight_link import network_visualization_highlight_links\n", + "from utils.network_visualization_upgraded import network_visualization_upgraded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OD Matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trips per hour matrix for this network is:\n", + "\n", + "\n", + "This table is what is called an OD (Origin-Destination) matrix. It tells you how many cars go from node i to node j in an hour. The paths are chosen by solving the optimization model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extracting the maximum values for dimensions\n", + "data = ods_data\n", + "max_origin = max(key[0] for key in data.keys())\n", + "max_destination = max(key[1] for key in data.keys())\n", + "\n", + "# Creating a DataFrame to represent the matrix\n", + "od_matrix = pd.DataFrame(index=range(1, max_origin + 1), columns=range(1, max_destination + 1))\n", + "\n", + "# Populating the DataFrame with the given data\n", + "for key, value in data.items():\n", + " od_matrix.loc[key[0], key[1]] = value\n", + "\n", + "# Displaying the OD matrix in table format\n", + "print(\"Origin-Destination Matrix:\")\n", + "print(od_matrix.head(5)) # OD matric for the first 5 nodes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To better understand the OD matrix we can also visualize the values.\n", + "# Creating a subset matrix for visualization\n", + "subset_matrix = np.zeros((24, 24))\n", + "\n", + "# Filling subset matrix with data\n", + "for i in range(1, 25):\n", + " for j in range(1, 25):\n", + " subset_matrix[i-1, j-1] = data[(i, j)]\n", + "\n", + "# Plotting the heatmap\n", + "plt.figure(figsize=(8, 6))\n", + "plt.title('Origin-Destination Matrix')\n", + "plt.xlabel('Destination')\n", + "plt.ylabel('Origin')\n", + "plt.imshow(subset_matrix, cmap='RdYlGn_r', interpolation='nearest')\n", + "plt.colorbar(label='Values')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Modeling in Gurobi\n", + "\n", + "## Initiate the Gurobi model\n", + "\n", + "First, let's build a gurobi model object and define some parameters based on the model type. We have a mixed integer quadratic program (MIQP), that's because the objective function has a quadratic term, which we want to transform to a mixed integer linear program (MILP) and solve using the branch and bound method. We discuss the transformations from quadratic to linear when we introduce quadratic terms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## create a gurobi model object\n", + "model = gp.Model()\n", + "\n", + "# define some important parameters for solving the model with gurobi\n", + "model.params.TimeLimit = timelimit # 300 seconds timelimit since it can take long to reduce the gap to zero (change and play around if you want)\n", + "model.params.NonConvex = 2 # our problem is not convex as it is now, so we let gurobi know to use the right transformation and solutions\n", + "#about convexity in optimization: a convex function will be a continuous functin that will have one minimum \n", + "#(or maximum depending on the problem), therefore in any point that you starting searching for a solution you can follow the gradient \n", + "#to search for that extreme point. Non-convex problems can have local minimum (or maximum) points that will make you stuck in the process \n", + "#of searching for the solution\n", + "model.params.PreQLinearize = 1 # useful parameter to ask gurobi to try to linearize non-linear terms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Decision variables\n", + "\n", + "We have a set of binary variables $y_{ij}$, these variables take the value 1 if link $(i,j)$ connecting node $i$ to node $j$ is selected for expansion, and 0 otherwise.\n", + "\n", + "We also have two sets of decision variables representing link flows; $x_{ij}$, representing flow on link $(i,j)$ in cars per hour, and $x_{ijs}$, representing flow on link $(i,j)$ going to destination $s$.\n", + "\n", + "The first is the number of total cars passing on that road, and the second is the number of cars that are passing on the road which are specifically going to destination $s$. Summing the latter over all $s$ results in the former for a link $(i,j)$.\n", + "\n", + "Therefore, mathematically we define the domain of the variables as follows:\n", + "\n", + "\\begin{align}\n", + " & y_{ij} \\in \\{0, 1\\} \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ij} \\geq 0 \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ijs} \\geq 0 \\quad \\forall (i,j) \\in A, \\forall s \\in D \\\\\n", + "\\end{align}\n", + "\n", + "As you will see below in the code block, we have one extra set of variables called x2 (x square). This is to help Gurobi isolate quadratic terms and perform required transformations based on MCE to keep the problem linear. This is not part of your learning goals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## decision variables:\n", + "\n", + "# link selected (y_ij); i: a_node, j: b_node (selected links for capacity expansion)\n", + "link_selected = model.addVars(links, vtype=gp.GRB.BINARY, name='y')\n", + "\n", + "# link flows (x_ij); i: a_node, j: b_node\n", + "link_flow = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x')\n", + "\n", + "# link flows per destination s (xs_ijs); i: a_node, j: b_node, s: destination\n", + "dest_flow = model.addVars(links, dests, vtype=gp.GRB.CONTINUOUS, name='xs')\n", + "\n", + "# link flow square (x2_ij); i: a_node, j: b_node (dummy variable for handling quadratic terms, you do not need to know this)\n", + "link_flow_sqr = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x2')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Objective function\n", + "\n", + "The objective function of the problem (in its simplest form), is the minimization of the total travel time on the network, that means that you multiply the flow of vehicles in each link by the corresponding travel time and sum over all links ($A$ is the collection of all links to simplify the notation):\n", + "\n", + "$Z = \\sum_{(i,j) \\in A}{ x_{ij} . t_{ij}} $\n", + "\n", + "The travel time $t_{ij}$ is a function of the flow on a link and can be expressed as follows (where beta is a parameter):\n", + "\n", + "$ t_{ij} = t^0_{ij} . ( 1 + \\beta (x_{ij}/c_{ij})) \\quad \\forall (i,j) \\in A $\n", + "\n", + "> Note that the commonly used travel time function based on the Bureau of Public Roads (BPR) is as follows:\n", + "> \n", + "> $ t_{ij} = t^0_{ij} . ( 1 + \\alpha (x_{ij}/c_{ij})^\\beta) \\quad \\forall (i,j) \\in A $\n", + "> \n", + "> Where $\\beta$ usually assumes the value of $4$ making this function (and the problem) non-linear. Therefore, we use the linear function mentioned before to make the problem manageable using what we have learned so far.\n", + "\n", + "The following constraint yields the capacity of each link based on which ones are selected for expansion, when y is 1 there is added capacity as you can see:\n", + "\n", + "$ c_{ij} = (1 - y_{ij}) . c^0_{ij} + y_{ij} . c^1_{ij} \\quad \\forall (i,j) \\in A $\n", + "\n", + "This allows us to represent $t_{ij}$ as:\n", + "\n", + "$ t_{ij} = t^0_{ij} . ( 1 + \\beta (x_{ij} * ((1 - y_{ij})/c^0_{ij} + y_{ij}/c^1_{ij} ))) \\quad \\forall (i,j) \\in A $\n", + "\n", + "Which leads to the following extended objective funtion:\n", + "\n", + "$ Z = \\sum_{(i,j) \\in A}{ x_{ij} . (t^0_{ij} . ( 1 + \\beta (x_{ij} * ((1 - y_{ij})/c^0_{ij} + y_{ij}/c^1_{ij} ))))} $\n", + "\n", + "Now, for gurobi (and other solvers as well), we have to keep binary variables and quadratic terms clean and separate so that it can perform the required transformations to linearize the problem. Therefore, the equation below, despite being very big, would be the most solver-friendly formulation of our objective function:\n", + "\n", + "\\begin{align}\n", + " Z = \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + "\\end{align}\n", + "\n", + "Therefore, we use this equation to model our objective function in gurobi.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## objective function (total travel time)\n", + "\n", + "# total travel time = sum (link flow * link travel time)\n", + "# link travel time = free flow travel time * (1 + (flow / capacity))\n", + "# capacity = selected links * base capacity + other links * extended capacity\n", + "# other links: 1 - selected links\n", + "\n", + "#note that this equation allows the number of vehicles to be greater than the capacity, this just adds more penalty in terms of travel time.\n", + "\n", + "model.setObjective(\n", + " gp.quicksum(fftts[i, j] * link_flow[i, j] +\n", + " fftts[i, j] * (beta/cap_normal[i, j]) * link_flow_sqr[i, j] -\n", + " fftts[i, j] * (beta/cap_normal[i, j]) * link_flow_sqr[i, j] * link_selected[i, j] +\n", + " fftts[i, j] * (beta/cap_extend[i, j]) * link_flow_sqr[i, j] * link_selected[i, j]\n", + " for (i, j) in links))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Constraints\n", + "\n", + "We have four sets of constraints for this problem. Let's go through them one by one and add them to the model.\n", + "\n", + "### 1. Budget constraint\n", + "We can only extend the capacity of certain number of links based on the available budget. So first, we have to make sure to limit the number of extended links to the max number that can be expanded:\n", + "\n", + "$ \\sum_{(i,j) \\in A}{ y_{ij}} = B $" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## constraints\n", + "\n", + "# budget constraints, c_bgt is the name of the constraint\n", + "c_bgt = model.addConstr(gp.quicksum(link_selected[i, j] for (i, j) in links) <= extension_max_no)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 2. Link flow conservation constraints\n", + "We have two sets of decision variables representing link flows; $x_{ij}$, representing flow on link $(i,j)$, and $x_{ijs}$, representing flow on link $(i,j)$ going to destination $s$. So we have to make sure that the sum of the flows over all destinations equals the flow on each link.\n", + "$ \\sum_{s \\in D}{x_{ijs}} = x_{ij} \\quad \\forall (i,j) \\in A $" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# link flow conservation (destination flows and link flows), c_lfc is the name of the constraint\n", + "c_lfc = model.addConstrs(gp.quicksum(dest_flow[i, j, s] for s in dests) == link_flow[i, j] for (i, j) in links)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 3. Node flow conservation constraints\n", + "The basic idea of this constraint set is to make sure that the incoming and outgoing flow to and from each node is the same (hence flow conservation) with the exception for origin and destination nodes of the trips where there will be extra outgoing flow (origins) or incoming flow (destinations). Think about a traffic intersection, vehicles enter and leave the intersection when they are moving in the network. This assures the continuity of the vehicle paths. $d_is$ here is the number of travelers from node $i$ to node $s$ with the exception of $d_ss$, which is all the demand that arrives at node $s$.\n", + "\n", + "$ \\sum_{j \\in N; (i,j) \\in A}{ x_{ijs}} - \\sum_{j \\in N; (j,i) \\in A}{ x_{jis}} = d_{is} \\quad \\forall i \\in N, \\forall s \\in D $\n", + "\n", + "The figure gives an example:\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# node flow conservation (demand), c_nfc is the name of the constraint\n", + "c_nfc = model.addConstrs(\n", + " gp.quicksum(dest_flow[i, j, s] for j in nodes if (i, j) in links) -\n", + " gp.quicksum(dest_flow[j, i, s] for j in nodes if (j, i) in links) == demand[i, s]\n", + " for i in nodes for s in dests\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 4. Quadratic variable constraints (you do not need to fully understand this)\n", + "These are basically dummy equations to help gurobi model quadratic terms (that we defined as dummy variables earlier). So essentially instead of using $x^2_{ij}$ in the model, we define a new set of decision variables and define a set of constrains to set their value to $x^2_{ij}$. This let's Gurobi know these are quadratic terms and helps gurobi to replace it with variables and constraints required to keep the problem linear. This is not part of your learning goals! \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# dummy constraints for handling quadratic terms\n", + "c_qrt = model.addConstrs(link_flow_sqr[i, j] == link_flow[i, j] * link_flow[i, j] for (i, j) in links)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional constraint for task 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Solving the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "3#Next we are ready to solve the model\n", + "model.optimize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if you didn't find a solution, you can rerun the previous cell to continue the optimization for another 300 seconds (defined by `timelimit`)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# fetch optimal decision variables and Objective Function values\n", + "link_flows = {(i, j): link_flow[i, j].X for (i, j) in links}\n", + "links_selected = {(i, j): link_selected[i, j].X for (i, j) in links}\n", + "total_travel_time = model.ObjVal\n", + "\n", + "# Let's print right now the objective function\n", + "print(\"Optimal Objective function Value\", model.objVal)\n", + "\n", + "# Let's print right now the decision variables\n", + "for var in model.getVars():\n", + " print(f\"{var.varName}: {round(var.X, 3)}\") # print the optimal decision variable values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Network visualization with highlighted links" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's visualize the results showcasing congested traffic flows and the selected links for expansion. \n", + "In this graph, we'll observe the network's topology using node coordinates and links. Our graph will be **directional** to represent the road network.\n", + "\n", + "Nodes are depicted in blue, while selected nodes and links are highlighted in pink and red." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "network_visualization_highlight_links (G, pos, link_select=links_selected)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Network visualization with upgraded links\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we'll visualize our upgraded network, incorporating the new capacities. Following that, we'll represent the network along with its results, displaying the flow (F) and capacity (C) alongside each link.\n", + "\n", + "**Notes:**\n", + "\n", + "1. **Pink nodes** highlight the selected nodes.\n", + "2. **Colored edges** denote the upgraded edges selected through the optimization process.\n", + "3. Various **edge colors** indicate different ranges for edge attributes (Flow/Capacity), as demonstrated in the legend.\n", + "4. Diverse **edge widths** represent varying flow ranges on the edges.\n", + "5. Your plot is interactive; to clearly view the numbers, simply click on them!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define new capacity after expansion\n", + "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + "capacity = {(i, j): cap_normal[i, j] * (1 - links_selected[i, j]) + cap_extend[i, j] * links_selected[i, j]\n", + " for (i, j) in links}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results\n", + "network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected, labels='off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish to see the velues for the entire network you just need to turn on the labels and run the function again. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To see flow and capacity for the entire network\n", + "network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected, labels='on')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_5/Analysis_LP_solution.ipynb b/content/GA_2_5/Analysis_LP_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..39db7bcab3fddbd042cd2eeec8430367737f11a4 --- /dev/null +++ b/content/GA_2_5/Analysis_LP_solution.ipynb @@ -0,0 +1,946 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Don't do math and drive\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 15 December, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> during the in-class session some of the confusion was caused by code issues. Comments relevant to the code and notebooks as-used in the Friday in-class session are provided in boxes like this.</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Problem description\n", + "\n", + "_Note: part of the background material for this project was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/book/optimization/project.html)._\n", + "\n", + "The road network design problem (NDP) is the problem of determining which links to build/refurbish/upgrade in order to improve the performance of a road network. One of the variations of the road NDP is the road NDP with capacity expansions, which involves deciding which links should have their capacity increased. This is a complex problem that must take into account a variety of factors, including:\n", + "\n", + "* The current state of the road network\n", + "* The projected future traffic demand\n", + "* The budget available for improvements\n", + "* The impacts of the adjustments (could be environmental, social, etc).\n", + "\n", + "There are a variety of approaches to dealing with the road network design problem with capacity expansion. One common approach is to use mathematical optimization models. In this assignment we use a simplified example to show how optimization can be used to tackle road NDPs. Note that the classical approaches to dealing with road NDPs can be more complicated and will be covered in other courses in the TTE track of the civil engineering master program.\n", + "\n", + "In this assignment, the goal is to minimize the total travel time on the network by selecting a predefined number of links for capacity expansion (subject to the available budget). The following (main) assumptions and simplifications are made to make the problem solvable using methods and algorithms that you have learned so far.\n", + "\n", + "### 1. Link travel time function\n", + "Travel time on a stretch of road (i.e., a link) depends on the flow (vehicles/hour) on that link and the capacity of the link (maximum of vehicles/hour). The most common function to calculate travel time on a link is the so-called Bureau of Public Roads (BPR) function, which is a polynomial (degree 4) function. That function, if used in the assignment, would make the problem non-linear and therefore very hard to solve. So we use a simplified linear function where travel time grows linearly with the flow of vehicles on a road link. More details are provided within the formulation section.\n", + "\n", + "\n", + "\n", + "${t_{ij}} = t_{ij}^0\\left( {1 + \\alpha {{\\left( {\\cfrac{x_{ij}}{c_{ij}}} \\right)}^\\beta }} \\right) \\quad \\left( {i,j} \\right) \\in A$\n", + "\n", + "Where $t_{ij}$ is the current travel time on the link, $t_{ij}^0$ is the travel time without congestion (free flow), $x_{ij}$ is the flow of cars, and $_c{ij}$ the capacity in maximum flow of cars. $\\alpha$ and $\\beta$ are calibration parameters.\n", + "\n", + "### 2. Route choice behavior\n", + "In order to assess the quality of the road capacity expansion problem, one must know what the effect of the added capacity is on travel time. For that, it is not sufficient to model the travel time-flow function, you must know where the vehicles are going to drive on the road. The route choice behavior of drivers within congested networks often follows the so-called User Equilibrium (UE) principle where each traveller tries to minimize their own individual generalized travel time.The UE states that for each origin and destination pair, all used routes between those nodes have equal and minimal travel time. That is, no driver can improve his/her travel time by choosing another path, therefore an equilibrium is reached.\n", + "However, calculating the UE requires advanced methods which are not covered in the MUDE. Therefore, here we assume the route choice behaviour follows the so-called System Optimal (SO) principle, which implies that route choices are made in such a way that the total travel time is minimized (summed over all the drivers). That means that some cars will drive longer routes so that other cars can save time. This is also called social equilibrium. This type of equilibrium is easier to compute. But just have in mind that in our road networks you can hardly obtain a system optimal traffic distribution. You can't tell where drivers have to do.\n", + "\n", + "\n", + "\n", + "\n", + "### 3. Quadratic terms\n", + "As you will see in the formulation below, even after making the above-mentioned assumptions, our formulation of the Road NDP will include a quadratic term, you must multiply the flow (which is a variable) by the tavel time (which is also a variable). This is therefore not linear. \n", + "Fortunately there are different methods to transform quadratic terms to linear variables and constraints with mathematical programming. You do not need to learn these techniques. Most solvers (including Gurobi) can handle this well given some adjustments to the formulation. Gurobi uses the [McCormick Envelops (MCE)](https://optimization.cbe.cornell.edu/index.php?title=McCormick_envelopes) to transform each quadratic term into one new variable and four constraints. For more information on MCE and how Gurobi implements them, check these links (this is in case you are interested, you don't need to check them during the Friday session):\n", + "* [The theory behind MCE](https://optimization.cbe.cornell.edu/index.php?title=McCormick_envelopes)\n", + "* [Gurobi webinar presentation on quadratic optimization](https://cdn.gurobi.com/wp-content/uploads/2020-01-14_Non-Convex-Quadratic-Optimization-in-Gurobi-9.0-Webinar.pdf?x93374)\n", + "* [Full video of the webinar](https://www.gurobi.com/events/non-convex-quadratic-optimization/)\n", + "\n", + "We will move forward with a quadratic term in the objective function then because with the simplifcations and assumptions referred to above we can formulate an NDP and solve it using the branch and bound method that you have studied before.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "# Data preprocessing\n", + "\n", + "We use some networks from the well-known transportation networks for benchmarking repository as well as a small toy network for case studies of NDPs. the following functions read data from this repository and perform data preprocessing to have the input and the parameters required for our case studies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "import os\n", + "import time\n", + "import gurobipy as gp\n", + "import pandas as pd\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# read network file, a function to import the road networks\n", + "def read_net(net_file):\n", + " \"\"\"\n", + " read network file\n", + " \"\"\"\n", + "\n", + " net_data = pd.read_csv(net_file, skiprows=8, sep='\\t')\n", + " # make sure all headers are lower case and without trailing spaces\n", + " trimmed = [s.strip().lower() for s in net_data.columns]\n", + " net_data.columns = trimmed\n", + " # And drop the silly first and last columns\n", + " net_data.drop(['~', ';'], axis=1, inplace=True)\n", + " # using dictionary to convert type of specific columns so taht we can assign very small (close to zero) possitive number to it.\n", + " convert_dict = {'free_flow_time': float,\n", + " 'capacity': float,\n", + " 'length': float,\n", + " 'power': float\n", + " }\n", + " \n", + " net_data = net_data.astype(convert_dict)\n", + "\n", + " # make sure everything makes sense (otherwise some solvers throw errors)\n", + " net_data.loc[net_data['free_flow_time'] <= 0, 'free_flow_time'] = 1e-6\n", + " net_data.loc[net_data['capacity'] <= 0, 'capacity'] = 1e-6\n", + " net_data.loc[net_data['length'] <= 0, 'length'] = 1e-6\n", + " net_data.loc[net_data['power'] <= 1, 'power'] = int(4)\n", + " net_data['init_node'] = net_data['init_node'].astype(int)\n", + " net_data['term_node'] = net_data['term_node'].astype(int)\n", + " net_data['b'] = net_data['b'].astype(float)\n", + "\n", + " # extract features in dict format\n", + " links = list(zip(net_data['init_node'], net_data['term_node']))\n", + " caps = dict(zip(links, net_data['capacity']))\n", + " fftt = dict(zip(links, net_data['free_flow_time']))\n", + " lent = dict(zip(links, net_data['length']))\n", + " alpha = dict(zip(links, net_data['b']))\n", + " beta = dict(zip(links, net_data['power']))\n", + "\n", + " net = {'capacity': caps, 'free_flow': fftt, 'length': lent, 'alpha': alpha, 'beta': beta}\n", + "\n", + " return net\n", + "\n", + "\n", + "# read OD matrix (demand), a function to import Origin and Destination Matrices, \n", + "# that is a table that says how many vehicles go from i to j\n", + "def read_od(od_file):\n", + " \"\"\"\n", + " read OD matrix\n", + " \"\"\"\n", + "\n", + " f = open(od_file, 'r')\n", + " all_rows = f.read()\n", + " blocks = all_rows.split('Origin')[1:]\n", + " matrix = {}\n", + " for k in range(len(blocks)):\n", + " orig = blocks[k].split('\\n')\n", + " dests = orig[1:]\n", + " origs = int(orig[0])\n", + "\n", + " d = [eval('{' + a.replace(';', ',').replace(' ', '') + '}') for a in dests]\n", + " destinations = {}\n", + " for i in d:\n", + " destinations = {**destinations, **i}\n", + " matrix[origs] = destinations\n", + " zones = max(matrix.keys())\n", + " od_dict = {}\n", + " for i in range(zones):\n", + " for j in range(zones):\n", + " demand = matrix.get(i + 1, {}).get(j + 1, 0)\n", + " if demand:\n", + " od_dict[(i + 1, j + 1)] = demand\n", + " else:\n", + " od_dict[(i + 1, j + 1)] = 0\n", + "\n", + " return od_dict\n", + "\n", + "\n", + "# read case study data, we will have different case studies that have different demand and road network \n", + "def read_cases(networks, input_dir):\n", + " \"\"\"\n", + " read case study data\n", + " \"\"\"\n", + "\n", + " # dictionaries for network and OD files\n", + " net_dict = {}\n", + " ods_dict = {}\n", + "\n", + " # selected case studies\n", + " if networks:\n", + " cases = [case for case in networks]\n", + " else:\n", + " # all folders available (each one for one specific case)\n", + " cases = [x for x in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, x))]\n", + "\n", + " # iterate through cases and read network and OD\n", + " for case in cases:\n", + " mod = os.path.join(input_dir, case)\n", + " mod_files = os.listdir(mod)\n", + " for i in mod_files:\n", + " # read network\n", + " if i.lower()[-8:] == 'net.tntp':\n", + " net_file = os.path.join(mod, i)\n", + " net_dict[case] = read_net(net_file)\n", + " # read OD matrix\n", + " if 'TRIPS' in i.upper() and i.lower()[-5:] == '.tntp':\n", + " ods_file = os.path.join(mod, i)\n", + " ods_dict[case] = read_od(ods_file)\n", + "\n", + " return net_dict, ods_dict\n", + "\n", + "\n", + "# create node-destination demand matrix\n", + "def create_nd_matrix(ods_data, origins, destinations, nodes):\n", + " # create node-destination demand matrix (not a regular OD!)\n", + " demand = {(n, d): 0 for n in nodes for d in destinations}\n", + " for r in origins:\n", + " for s in destinations:\n", + " if (r, s) in ods_data:\n", + " demand[r, s] = ods_data[r, s]\n", + " for s in destinations:\n", + " demand[s, s] = - sum(demand[j, s] for j in origins)\n", + "\n", + " return demand\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# define parameters, case study (network) list and the directory where their files are\n", + "extension_factor = 1.5 # capacity after extension (1.5 means add 50% of existing capacity)\n", + "extension_max_no = 40 # max number of links to add capacity to (simplified budget limit)\n", + "timelimit = 300 # seconds\n", + "beta = 2 # parameter to use in link travel time function (explained later)\n", + "\n", + "networks = ['SiouxFalls']\n", + "networks_dir = 'input/TransportationNetworks'\n", + "\n", + "\n", + "# prep data\n", + "net_dict, ods_dict = read_cases(networks, networks_dir)\n", + "# Let's load the network and demand (OD matrix) data of the first network (SiouxFalls) to two dictionaries for our first case study.\n", + "# Reminding that we are using the SiouxFalls network which is one of the most used networks in transportation reserach: https://github.com/bstabler/TransportationNetworks/blob/master/SiouxFalls/Sioux-Falls-Network.pdf\n", + "net_data, ods_data = net_dict[networks[0]], ods_dict[networks[0]]\n", + "\n", + "## now let's prepare the data in a format readable by gurobi\n", + "\n", + "# prep links, nodes, and free flow travel times\n", + "links = list(net_data['capacity'].keys())\n", + "nodes = np.unique([list(edge) for edge in links])\n", + "fftts = net_data['free_flow']\n", + "\n", + "# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)\n", + "# this is the capacity of a road link in vehicles per hour without the expansion\n", + "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + "#with the expansion\n", + "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + "\n", + "# origins and destinations\n", + "origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n", + "dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n", + "\n", + "# demand in node-destination form\n", + "# an OD-matrix is built\n", + "demand = create_nd_matrix(ods_data, origs, dests, nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we'll initially visualize the network using the coordinates of the sample road network, 'SiouxFalls'. Later, we'll employ the same network topology to visualize the upgraded links.\n", + "\n", + "Thankfully, Python offers a highly useful package for visualizing networks called **'networkx'**. We'll leverage some of its features, so:\n", + "\n", + "1. Feel free to explore further functionalities of the **networkx** package in its [documentation](https://networkx.org/documentation/stable/reference/index.html).\n", + "2. Our coordinates in this specific example are in **JSON** format. Therefore, don't forget to import the package accordingly.\n", + "3. Interested in visualizing other networks? Fantastic! However, you'll need to check the format of your coordinates first. TAs will assist you if you wish to explore further.\n", + "\n", + "\n", + "The road network that we are using in the assessment is the Sioux Falls which is shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For visualization\n", + "import networkx as nx\n", + "import json\n", + "from matplotlib.lines import Line2D # this will later be used for highlighting edge colors based on values\n", + "\n", + "from utils.network_visualization import network_visualization\n", + "from utils.network_visualization_highlight_link import network_visualization_highlight_links\n", + "from utils.network_visualization_upgraded import network_visualization_upgraded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OD Matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trips per hour matrix for this network is:\n", + "\n", + "\n", + "This table is what is called an OD (Origin-Destination) matrix. It tells you how many cars go from node i to node j in an hour. The paths are chosen by solving the optimization model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extracting the maximum values for dimensions\n", + "data = ods_data\n", + "max_origin = max(key[0] for key in data.keys())\n", + "max_destination = max(key[1] for key in data.keys())\n", + "\n", + "# Creating a DataFrame to represent the matrix\n", + "od_matrix = pd.DataFrame(index=range(1, max_origin + 1), columns=range(1, max_destination + 1))\n", + "\n", + "# Populating the DataFrame with the given data\n", + "for key, value in data.items():\n", + " od_matrix.loc[key[0], key[1]] = value\n", + "\n", + "# Displaying the OD matrix in table format\n", + "print(\"Origin-Destination Matrix:\")\n", + "print(od_matrix.head(5)) # OD matric for the first 5 nodes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To better understand the OD matrix we can also visualize the values.\n", + "# Creating a subset matrix for visualization\n", + "subset_matrix = np.zeros((24, 24))\n", + "\n", + "# Filling subset matrix with data\n", + "for i in range(1, 25):\n", + " for j in range(1, 25):\n", + " subset_matrix[i-1, j-1] = data[(i, j)]\n", + "\n", + "# Plotting the heatmap\n", + "plt.figure(figsize=(8, 6))\n", + "plt.title('Origin-Destination Matrix')\n", + "plt.xlabel('Destination')\n", + "plt.ylabel('Origin')\n", + "plt.imshow(subset_matrix, cmap='RdYlGn_r', interpolation='nearest')\n", + "plt.colorbar(label='Values')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Modeling in Gurobi\n", + "\n", + "## Initiate the Gurobi model\n", + "\n", + "First, let's build a gurobi model object and define some parameters based on the model type. We have a mixed integer quadratic program (MIQP), that's because the objective function has a quadratic term, which we want to transform to a mixed integer linear program (MILP) and solve using the branch and bound method. We discuss the transformations from quadratic to linear when we introduce quadratic terms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## create a gurobi model object\n", + "model = gp.Model()\n", + "\n", + "# define some important parameters for solving the model with gurobi\n", + "model.params.TimeLimit = timelimit # 300 seconds timelimit since it can take long to reduce the gap to zero (change and play around if you want)\n", + "model.params.NonConvex = 2 # our problem is not convex as it is now, so we let gurobi know to use the right transformation and solutions\n", + "#about convexity in optimization: a convex function will be a continuous functin that will have one minimum \n", + "#(or maximum depending on the problem), therefore in any point that you starting searching for a solution you can follow the gradient \n", + "#to search for that extreme point. Non-convex problems can have local minimum (or maximum) points that will make you stuck in the process \n", + "#of searching for the solution\n", + "model.params.PreQLinearize = 1 # useful parameter to ask gurobi to try to linearize non-linear terms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Decision variables\n", + "\n", + "We have a set of binary variables $y_{ij}$, these variables take the value 1 if link $(i,j)$ connecting node $i$ to node $j$ is selected for expansion, and 0 otherwise.\n", + "\n", + "We also have two sets of decision variables representing link flows; $x_{ij}$, representing flow on link $(i,j)$ in cars per hour, and $x_{ijs}$, representing flow on link $(i,j)$ going to destination $s$.\n", + "\n", + "The first is the number of total cars passing on that road, and the second is the number of cars that are passing on the road which are specifically going to destination $s$. Summing the latter over all $s$ results in the former for a link $(i,j)$.\n", + "\n", + "Therefore, mathematically we define the domain of the variables as follows:\n", + "\n", + "\\begin{align}\n", + " & y_{ij} \\in \\{0, 1\\} \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ij} \\geq 0 \\quad \\forall (i,j) \\in A \\\\\n", + " & x_{ijs} \\geq 0 \\quad \\forall (i,j) \\in A, \\forall s \\in D \\\\\n", + "\\end{align}\n", + "\n", + "As you will see below in the code block, we have one extra set of variables called x2 (x square). This is to help Gurobi isolate quadratic terms and perform required transformations based on MCE to keep the problem linear. This is not part of your learning goals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## decision variables:\n", + "\n", + "# link selected (y_ij); i: a_node, j: b_node (selected links for capacity expansion)\n", + "link_selected = model.addVars(links, vtype=gp.GRB.BINARY, name='y')\n", + "\n", + "# link flows (x_ij); i: a_node, j: b_node\n", + "link_flow = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x')\n", + "\n", + "# link flows per destination s (xs_ijs); i: a_node, j: b_node, s: destination\n", + "dest_flow = model.addVars(links, dests, vtype=gp.GRB.CONTINUOUS, name='xs')\n", + "\n", + "# link flow square (x2_ij); i: a_node, j: b_node (dummy variable for handling quadratic terms, you do not need to know this)\n", + "link_flow_sqr = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x2')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Objective function\n", + "\n", + "The objective function of the problem (in its simplest form), is the minimization of the total travel time on the network, that means that you multiply the flow of vehicles in each link by the corresponding travel time and sum over all links ($A$ is the collection of all links to simplify the notation):\n", + "\n", + "$Z = \\sum_{(i,j) \\in A}{ x_{ij} . t_{ij}} $\n", + "\n", + "The travel time $t_{ij}$ is a function of the flow on a link and can be expressed as follows (where beta is a parameter):\n", + "\n", + "$ t_{ij} = t^0_{ij} . ( 1 + \\beta (x_{ij}/c_{ij})) \\quad \\forall (i,j) \\in A $\n", + "\n", + "> Note that the commonly used travel time function based on the Bureau of Public Roads (BPR) is as follows:\n", + "> \n", + "> $ t_{ij} = t^0_{ij} . ( 1 + \\alpha (x_{ij}/c_{ij})^\\beta) \\quad \\forall (i,j) \\in A $\n", + "> \n", + "> Where $\\beta$ usually assumes the value of $4$ making this function (and the problem) non-linear. Therefore, we use the linear function mentioned before to make the problem manageable using what we have learned so far.\n", + "\n", + "The following constraint yields the capacity of each link based on which ones are selected for expansion, when y is 1 there is added capacity as you can see:\n", + "\n", + "$ c_{ij} = (1 - y_{ij}) . c^0_{ij} + y_{ij} . c^1_{ij} \\quad \\forall (i,j) \\in A $\n", + "\n", + "This allows us to represent $t_{ij}$ as:\n", + "\n", + "$ t_{ij} = t^0_{ij} . ( 1 + \\beta (x_{ij} * ((1 - y_{ij})/c^0_{ij} + y_{ij}/c^1_{ij} ))) \\quad \\forall (i,j) \\in A $\n", + "\n", + "Which leads to the following extended objective funtion:\n", + "\n", + "$ Z = \\sum_{(i,j) \\in A}{ x_{ij} . (t^0_{ij} . ( 1 + \\beta (x_{ij} * ((1 - y_{ij})/c^0_{ij} + y_{ij}/c^1_{ij} ))))} $\n", + "\n", + "Now, for gurobi (and other solvers as well), we have to keep binary variables and quadratic terms clean and separate so that it can perform the required transformations to linearize the problem. Therefore, the equation below, despite being very big, would be the most solver-friendly formulation of our objective function:\n", + "\n", + "\\begin{align}\n", + " Z = \\sum_{(i,j) \\in A}{t^0_{ij} . x_{ij}} + \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij}} - \\sum_{(i,j) \\in A}{(t^0_{ij}.\\beta /c^0_{ij}) . x^2_{ij} . y_{ij}} + \\sum_{(i,j) \\in A}{t^0_{ij}.(\\beta /c^1_{ij}) . x^2_{ij} . y_{ij}} \\\\\n", + "\\end{align}\n", + "\n", + "Therefore, we use this equation to model our objective function in gurobi.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## objective function (total travel time)\n", + "\n", + "# total travel time = sum (link flow * link travel time)\n", + "# link travel time = free flow travel time * (1 + (flow / capacity))\n", + "# capacity = selected links * base capacity + other links * extended capacity\n", + "# other links: 1 - selected links\n", + "\n", + "#note that this equation allows the number of vehicles to be greater than the capacity, this just adds more penalty in terms of travel time.\n", + "\n", + "model.setObjective(\n", + " gp.quicksum(fftts[i, j] * link_flow[i, j] +\n", + " fftts[i, j] * (beta/cap_normal[i, j]) * link_flow_sqr[i, j] -\n", + " fftts[i, j] * (beta/cap_normal[i, j]) * link_flow_sqr[i, j] * link_selected[i, j] +\n", + " fftts[i, j] * (beta/cap_extend[i, j]) * link_flow_sqr[i, j] * link_selected[i, j]\n", + " for (i, j) in links))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Constraints\n", + "\n", + "We have four sets of constraints for this problem. Let's go through them one by one and add them to the model.\n", + "\n", + "### 1. Budget constraint\n", + "We can only extend the capacity of certain number of links based on the available budget. So first, we have to make sure to limit the number of extended links to the max number that can be expanded:\n", + "\n", + "$ \\sum_{(i,j) \\in A}{ y_{ij}} = B $" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "## constraints\n", + "\n", + "# budget constraints, c_bgt is the name of the constraint\n", + "c_bgt = model.addConstr(gp.quicksum(link_selected[i, j] for (i, j) in links) <= extension_max_no)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 2. Link flow conservation constraints\n", + "We have two sets of decision variables representing link flows; $x_{ij}$, representing flow on link $(i,j)$, and $x_{ijs}$, representing flow on link $(i,j)$ going to destination $s$. So we have to make sure that the sum of the flows over all destinations equals the flow on each link.\n", + "$ \\sum_{s \\in D}{x_{ijs}} = x_{ij} \\quad \\forall (i,j) \\in A $" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# link flow conservation (destination flows and link flows), c_lfc is the name of the constraint\n", + "c_lfc = model.addConstrs(gp.quicksum(dest_flow[i, j, s] for s in dests) == link_flow[i, j] for (i, j) in links)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 3. Node flow conservation constraints\n", + "The basic idea of this constraint set is to make sure that the incoming and outgoing flow to and from each node is the same (hence flow conservation) with the exception for origin and destination nodes of the trips where there will be extra outgoing flow (origins) or incoming flow (destinations). Think about a traffic intersection, vehicles enter and leave the intersection when they are moving in the network. This assures the continuity of the vehicle paths. $d_is$ here is the number of travelers from node $i$ to node $s$ with the exception of $d_ss$, which is all the demand that arrives at node $s$.\n", + "\n", + "$ \\sum_{j \\in N; (i,j) \\in A}{ x_{ijs}} - \\sum_{j \\in N; (j,i) \\in A}{ x_{jis}} = d_{is} \\quad \\forall i \\in N, \\forall s \\in D $\n", + "\n", + "The figure gives an example:\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# node flow conservation (demand), c_nfc is the name of the constraint\n", + "c_nfc = model.addConstrs(\n", + " gp.quicksum(dest_flow[i, j, s] for j in nodes if (i, j) in links) -\n", + " gp.quicksum(dest_flow[j, i, s] for j in nodes if (j, i) in links) == demand[i, s]\n", + " for i in nodes for s in dests\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 4. Quadratic variable constraints (you do not need to fully understand this)\n", + "These are basically dummy equations to help gurobi model quadratic terms (that we defined as dummy variables earlier). So essentially instead of using $x^2_{ij}$ in the model, we define a new set of decision variables and define a set of constrains to set their value to $x^2_{ij}$. This let's Gurobi know these are quadratic terms and helps gurobi to replace it with variables and constraints required to keep the problem linear. This is not part of your learning goals! \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# dummy constraints for handling quadratic terms\n", + "c_qrt = model.addConstrs(link_flow_sqr[i, j] == link_flow[i, j] * link_flow[i, j] for (i, j) in links)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional constraint for task 3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> There are three constraints provided below for you to compare. <code>c_new2</code> is the \"correct\" solution based on the information provided in this assignment; however, it produces an unfeasible problem. The constraint <code>c_new1</code> is feasible, but double-counts capacity (a mistake). See extended solution (markdown file) for more information.</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Constrain the vehicles to the capacity of the road:\n", + "c_new1 = model.addConstrs(link_flow[i, j] <= cap_normal[i, j]+(link_selected[i, j]*cap_extend[i, j]) for (i, j) in links)\n", + "c_new2 = model.addConstrs(link_flow[i, j] <= cap_normal[i,j] + ((cap_extend[i,j]-cap_normal[i,j] ) * link_selected[i,j]) for (i, j) in links)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Solving the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "#Next we are ready to solve the model\n", + "model.optimize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if you didn't find a solution, you can rerun the previous cell to continue the optimization for another 300 seconds (defined by `timelimit`)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# fetch optimal decision variables and Objective Function values\n", + "link_flows = {(i, j): link_flow[i, j].X for (i, j) in links}\n", + "links_selected = {(i, j): link_selected[i, j].X for (i, j) in links}\n", + "total_travel_time = model.ObjVal\n", + "\n", + "# Let's print right now the objective function\n", + "print(\"Optimal Objective function Value\", model.objVal)\n", + "\n", + "# Let's print right now the decision variables\n", + "for var in model.getVars():\n", + " print(f\"{var.varName}: {round(var.X, 3)}\") # print the optimal decision variable values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Network visualization with highlighted links" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's visualize the results showcasing congested traffic flows and the selected links for expansion. \n", + "In this graph, we'll observe the network's topology using node coordinates and links. Our graph will be **directional** to represent the road network.\n", + "\n", + "Nodes are depicted in blue, while selected nodes and links are highlighted in pink and red." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "network_visualization_highlight_links (G, pos, link_select=links_selected)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Network visualization with upgraded links\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we'll visualize our upgraded network, incorporating the new capacities. Following that, we'll represent the network along with its results, displaying the flow (F) and capacity (C) alongside each link.\n", + "\n", + "**Notes:**\n", + "\n", + "1. **Pink nodes** highlight the selected nodes.\n", + "2. **Colored edges** denote the upgraded edges selected through the optimization process.\n", + "3. Various **edge colors** indicate different ranges for edge attributes (Flow/Capacity), as demonstrated in the legend.\n", + "4. Diverse **edge widths** represent varying flow ranges on the edges.\n", + "5. Your plot is interactive; to clearly view the numbers, simply click on them!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define new capacity after expansion\n", + "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n", + "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n", + "capacity = {(i, j): cap_normal[i, j] * (1 - links_selected[i, j]) + cap_extend[i, j] * links_selected[i, j]\n", + " for (i, j) in links}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results\n", + "network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected, labels='off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish to see the velues for the entire network you just need to turn on the labels and run the function again. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To see flow and capacity for the entire network\n", + "network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected, labels='on')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_5/README.md b/content/GA_2_5/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1c4d42fd02b7261c918006c12ad1e25743c25659 --- /dev/null +++ b/content/GA_2_5/README.md @@ -0,0 +1,51 @@ +# Project 9 Report: Optimization + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/).* + +The focus of this assignment is on applying two different methods for optimization on a road network design problem. Part of the background material in the notebooks was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/book/optimization/project.html). + +## Overview of material + +- `README.md` (this file) +- `Report.md`, primary file in which you write your answers to some questions and eventually copy plots from the notebooks. Typically, a short, one-line answer is sufficient, but please include a short reasoning, justification or argumentation. Remember to use Markdown features to clearly indicate your answers for each question below. +- `P09A-Dont_do_math_and_drive.ipynb`, the notebook in which you apply a mixed integer linear program (MILP) to a road network design problem (NDP). +- `P09B-Evolve_and_drive.ipynb`, the notebook in which you apply a genetic algorithm (GA) to the same road network design problem (NDP). +- `environment.yml`, for creating a Python environment +- a subdirectory `./utils` containing some functions for visualization (which you don't need to open). +- a subdirectory `./figs` containing some figures included in the notebooks (which you don't need to open). +- a not-yet existing subdirectory `./input`, which should include the datasets for this problem, which are not included in the Git repository. **Download the data [using this link](https://surfdrive.surf.nl/files/index.php/s/Rmw7BDnatHv2VYR/download)** and make sure you save those files inside a subdirectory `./input`. Once unzipped, you can copy the `input` directory so that files, for example is `./Project_9/input/TransportationNetworks/SiouxFalls/*.tntp`, where `Project_9` is the repository/directory where the notebooks are located. +- a `.gitignore` file preventing your imported data in the `./input` subdirectory being pushed to Gitlab. + +### Python Environments + +You can run all of the notebooks for today in the environment `mude-opt` which you've been using during the workshop this week (WS13). In particular, it includes the optimization packages `pymoo` and `gurobipy`. The non-Python part of the software Gurobi was installed as part of `PA12`. + +The `*.yml` file included in this repository is the same as that used for WS13 this week, so you can re-use the same Conda environment (e.g., `conda activate mude-opt`). Here are a few tips to remember when using Anaconda prompt: + +- Review your existing environments with `conda info --envs` +- Create an environment with command `conda env create -f environment.yml` +- For the activated environment, check the packages explicitly requested with `conda env export --from-history` +- If you want to create a new environment from the `*.yml` file, but the name already exists, simply change the name in the file using a text editor + +## Submission and deadline + +- Submit your answers, together with any relevant plots, in the Markdown file `Report.md`. This is the primary document that will be used to determine your grade; however, the auxiliary files (e.g., `*.ipynb` or `*.py` files) may be checked in case something is not clear. +- The deadline is to submit your work by making commits to your Group's GitLab repository by Friday at 12:30h. +- This project will be graded on interpretation, application, documentation and programming. + +## Repository, Formatting and Static Check + +There is no static check for this project. Be sure to leave the outputs from your code cells in your `*.ipynb` file so that they are readable. + +You are always expected to provide well-formatted figures and Markdown text in your `Report.md` file, as well as logically organize any auxiliary files you may use (e.g., try to put your figures in a sub-directory, if there are a lot of them). If you run out of time it is OK if your `*ipynb` files do not run. + +## Backup data links + +Sometimes the download links reach a maximum limit. If the link above no longer works, try one of these: +- [Backup link 1](https://surfdrive.surf.nl/files/index.php/s/StqaFtNDg6DNR4a/download) +- [Backup link 2](https://surfdrive.surf.nl/files/index.php/s/tC56Rpbhd7WpN9k/download) + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. diff --git a/content/GA_2_5/Report.md b/content/GA_2_5/Report.md new file mode 100644 index 0000000000000000000000000000000000000000..3713034ce031a1570b40850258b9b47b78455784 --- /dev/null +++ b/content/GA_2_5/Report.md @@ -0,0 +1,40 @@ +# Project 9 Report: Optimization + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/).* + +**YOUR GROUP NAME HERE** + +## Questions + +### Solving NDP problem with MILP + +1. Has the optimal solution been found? (yes/no) + +2. If there is a gap, what can you tell about the value of the best solution you are still theoretically able to obtain? + +3. Consider now that the network operator does not want to surpass the capacity in every link. How do you write that constraint? Reminding you that in the current model it is possible to go beyond the capacity which makes traffic even slower (even higher travel time) + +4. What do you observe in the solution when you run this new model for 20 links and expanded capacity of 1.5 (default increase)? + +5. What can you do to fix that? + +### Solving NDP problem with GA + +1. What very important solution performance indicator did you lose by using the GA metaheuristic in comparison with MILP? + +2. Try to obtain a better solution in the same computation time. What parameter is best to change to get that improvement? write your results in the box. + +3. Run the model with a small number of links (=1) and a big number of links (max=76), what differences do you see in the solutions and computation time compared with the previous model? and why? + +### Comparison + +1. Compare the GA approach with MILP approach. For the same computation time which one is faster? + +## General Comments on the Assignment [optional] + +_Use this space to let us know if you encountered any issues completing this assignment (but please keep it short!). For example, if you encountered an error that could not be fixed in your Python code, or perhaps there was a problem submitting something via GitLab. You can also let us know if the instructions were unclear. You can delete this section if you don't use it._ + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. diff --git a/content/GA_2_5/Report_solution.md b/content/GA_2_5/Report_solution.md new file mode 100644 index 0000000000000000000000000000000000000000..2be57a73ca6820cd437ff296d55f1f9f03b25b20 --- /dev/null +++ b/content/GA_2_5/Report_solution.md @@ -0,0 +1,133 @@ +# Project 9 Report: Optimization + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/), Week 2.6, Friday, Dec. 15, 2023.* + +**SOLUTION** + +_The solutions are provided below (and also in the notebooks). There are also additional explanations that try to explain what happened during the in-class session when some of the tasks could not be completed._ + +> Solutions that were expected to be in your report are formatted like this, albeit in a shorter version, and without figures. + +_General note about the problem (MILP and GA)_: this assignment asks you to consider upgrading a road network with 76 links. The optimization algorithms are used to find the best solution _given a specific number of links to consider._ Thus, for a specific number of links, you would solve a "new" optimization problem. In reality, the number of links that can be upgraded is closely linked to your budget, and the budget itself might be adjusted depending on the results of several optimization analyses for different upgrade options. In other words, if the benefit (travel time reduction, based on optimal solution) is not linearly related to the costs (number of links considered), the solution is non-trivial. This is generally how real life works...in fact, the concept of excpected value (expected cost, expected benefit) could be used in this case to compare alternatives (we will learn how in Week 2.8). Unfortunately, the instructions in this assignment asked you to consider cases 1 or 76 links were considered, which _are_ trivial solutions: + +- if you can only consider 1 link, you should choose that which gives the greatest decrease in travel time +- if you can consider upgrading all links, and all links reduce travel time, you should upgrade all of them! + +The problem gets much more interesting if you must choose which specific combination of links to upgrade, because your budget allows for something between 1 and 76. Because of aspects such as the geometric connections between links and nodes, travel time changes in a complex way when only _part_ of the network is improved (this is why the solution is non-trivial). For example, it is obvious that you should choose links that are somehow connected to each other in order to improve the travel time in the entire network. If you only improve isolated parts of the network, you are simply moving around points of congestion (e.g., you would drive fast on an improved road, then hit terrible traffic when forced to drive slow on an unimproved stretch of road). This is the type of comparison the optimization algorithm is making as it searches through the possible solution space. + +The time it takes to find an optimization solution is generally related to the size of the possible solution space. It can be shown with combinatorial mathematics that there are more possible solutions when 40 links are considered, compared to 20. However, during the in-class session you should have seen that it was more difficult to find a solution for 20 links. This is because the physics of the problem are also playing a role. It turns out that improving 40 links is enough to improve a large-enough part of the network to improve travel time for nearly all of the routes, and it is "easy" to find an optimal solution. However, with 20 links, you are only able to focus on smaller parts of the network; think of them as "corridors" through the total network. Finding the solution that chooses which of these corridors is the best (i.e., optimal) takes a longer time. + +## Questions + +### Solving NDP problem with MILP + +Solution explanations here are based on results that have been computed with a budget of 40 road links (`extension_max_no` = 40) and I time limit of 100 seconds (the notebook given to students had 300), except where otherwise noted. Notice that all these results depend on the speed of your machine. You may have to extend the computation time to 300 seconds if you haven’t found a solution yet. Be aware that you can’t run a model only from the point onward of a change that you have introduced in a constraint. You need to run the whole model again. Also important to note when you run the optimization procedure again (the cell where this is written: `model.optimize()`) without restarting everything, Gurobi continues optimizing from the point where it stopped, meaning with the previous solutions tested. Gurobi adds the sentence: `continuing optimization!`. But even if you restart Gurobi completely you may start at a more promising point and it is possible it will give you a better solution in the same 100 seconds. That’s because the process is not deterministic, it depends on the strategy of exploring the branch and bound tree as explained in the lectures. Check section “5.7. Integer problems and solving with Branch-and-Bound†to learn more. + +1. **Has the optimal solution been found? (yes/no)** + +> No (because gap is not zero). + +No, the optimal solution has not been found because as you see in the table the last integer solution that has been found still has a GAP (65.1%). This GAP tells you the maximum error that you may be committing by choosing this solution, it does not mean actually that the last solution is not the optimal. It just tells you that you can't prove it yet because you have not explored all the nodes in the branch and bound tree. Check section “5.7. Integer problems and solving with Branch-and-Bound†to learn more. + +2. **If there is a gap, what can you tell about the value of the best solution you are still theoretically able to obtain?** + +> It's easy to check by just returning the best bound of the last best solution! + +It's easy to check by just returning the best bound of the last best solution! Indeed you can’t reject the possibility that there isn’t a better solution that you haven’t found yet after you stopped the algorithm with a value of the objective function that is the same as the best bound. In this case, the bound is 3832504.14. Theoretically, it’s still possible to get an optimal solution with that objective function value. + +3. **Consider now that the network operator does not want to surpass the capacity in every link. How do you write that constraint? Reminding you that in the current model it is possible to go beyond the capacity which makes traffic even slower (even higher travel time).** + +> Constrain the vehicles to the capacity of the road (**this is the solution provided on the screen during the in-class session; see explanation for more details**): +> +> ```python +> c_new = model.addConstrs(link_flow[i, j] <= cap_normal[i,j] + (cap_extend[i,j] * link_selected[i,j]) for (i, j) in links) +> ``` + +The answer to this question would have been: +``` +c_new2 = model.addConstrs(link_flow[i, j] <= cap_normal[i,j] + ((cap_extend[i,j]-cap_normal[i,j] ) * link_selected[i,j]) for (i, j) in links) +``` + +This constraint is a hard limit on the capacity of a road. You do not allow the flow to go over that physical limit, otherwise the problem is unfeasible. Notice that in this problem `cap_extend` has been defined as the final capacity after an extension, not just the added capacity. That was what created a confusion on Friday in class. In the constraint above when a link has not been selected for expansion only the normal capacity will be considered because `link_selected[i,j]` will be zero. But if the link is an upgraded one then you will add capacity to the capacity normal. That amount is the difference between the original capacity and the final extended capacity. + +Unfortunately, such a constraint creates an unfeasible problem. It’s not possible to pass all the flow with the capacity you have, even with the extensions. That is interesting because it shows that indeed there are many links where the flow is over the capacity creating great delays on the roads. + +When students changed the constraint to what was tested by the teachers of the optimization week (wrong constraint) where the capacity was double counted, `cap_normal + cap_extend`, the problem should be feasible: + +``` +c_new1 = model.addConstrs(link_flow[i, j] <= cap_normal[i, j]+(link_selected[i, j]*cap_extend[i, j]) for (i, j) in links) +``` + +However, an additional issue occurred, which is that many students changed the constraint and started running the model from there (i.e., keep executing notebook cells without re-starting the Gurobi model). That is not possible since you are just adding more equations to your previous unfeasible problem. **Always run the models from beginning to end when making a change to the problem** (i.e., re-assign the `model` or create a new one). + +With this “wrong†constraint that double counts the capacity, the model runs well. + +With the original model without the constraint, you have an OF of 1.0976e+07 with a gap of 64.4% (best bound 3792722.49), with the new one that limits hard the capacity you have 1.1062e+07 with a gap of 22.4% (best bound 8583881.27). + +The travel time seems to increase if you look at the best bound which is much higher but you can’t guarantee anything. An increase in travel time would make sense since you are restraining the flows even more, so cars need to find alternative paths. + +_Note that you can have different results from the numbers above depending on how the solving process goes. The point of this exercise was to be able to build the constraint which most groups were indeed able to do._ + +4. **What do you observe in the solution when you run this new model for 20 links and expanded capacity of 1.5 (default increase)?** + +> The problem is unfeasible! + +The point here was to show that if you indeed limit the capacity too much (only 20 links at expanded capacity) the cars do not even fit the network capacity. + +5. **What can you do to fix that?** + +> Increasing the budget and/or the capacity extension. + +Often you find that you have created an unfeasible problem and you just need to relax it a bit more to find a solution. + +### Solving NDP problem with GA + +In this workbook, by default, you have 76 links as a budget, and that is a very special solution because it means that you can upgrade all the links! By mistake, there were two attributes with the budget in the notebook, but it was the parameter called `Budget` that should have been changed. It is used by the genetic algorithm to know how many binary variables can have the value of one. The maximum computation time in the results reported next is 300 seconds. No other stopping criteria were imposed that’s why the model never stops until 300 seconds. + +1. **What very important solution performance indicator did you lose by using the GA metaheuristic in comparison with MILP?** + +> No GAP is produced therefore if you did not run the math programing model you would not know how far or close you are from the optimal solution. + +Of course, seeing an almost flat line in the search progress in the GA helps build confidence that you have found the optimal solution. Moreover, since the solution is so trivial (76 links can be upgraded) you are sure of your optimal solution. Why didn't the genetic algorithm find an optimal solution in the first generation in some cases? This has to do with the large solution space, and the fact that the algorithm is initialized randombly, so we cannot guarantee that it always fings the global minimum, let alone find it in the fastest possible way. Additional insight is provided in the explanations below. + +2. **Try to obtain a better solution in the same computation time. What parameter is best to change to get that improvement? write your results in the box.** + +> Heere a description of anything you tried, whether it worked or not, was acceptable, as long as you were able to describe why it may or may not have improved computation time in a clear way. + +Population size was the easiest parameter to control. You can notice that increasing the population size in this problem with 76 links does not improve the speed of convergence. Actually, it makes it slower because you need to combine in each generation many of your population genes. The search procedure with the population size of 10 gives a solution which is obviously the 76 links and an objective function of 9.873735E+06. If the population size is changed to 50 then the convergence seems to be slower. But you still find the same optimal solution. Running the model with a population size of 1 makes no sense, you can’t generate a new generation if you don’t have enough parents. + + + +With just two individuals it even converges faster: + + + +When increasing population size from 10 to 200, and evaluating 36 links, you probably saw that the solution did not converge; this was due to the 5 minute time limit. Increasing to 15 minutes improves the solution, but still may be not enough. Clearly population size has a big influence on solution convergence! Because the population is initialized randomly, you should expect to run this several times to make sure you do not miss a suitable solution. There are also other “knobs†you can turn to change the computation time and process, for example, the parent and offspring settings. + +Choosing an initial population size is highly dependent on the details of a particular problem. We do know, however, that with 1000’s of variables like we have in this problem, an initial population size of 10 is far too low! You would need to run this for a very long time to find a solution. In this project our intention was not to get a “great†solution, but to introduce you to the key characteristics of a GA approach (such as population size, crossover, etc) and how it is different than MILP, especially when it comes to how the solutions are assessed differently (e.g., gap versus objective function convergence). + +In the assignment we asked you to try different parameter settings. We also included various methods from the pymoo package (for example, the `pymoo.operators.crossover.XXXXXX` at the top of the notebook): + +- In the code we only used the half uniform crossover, which requires no additional parameter settings (i.e., no keyword arguments in the pymoo method) +- `PointCrossover` was covered in the online textbook; this method requires you to specify the number of points that are used to cross-over, which implies that one keyword argument should be provided. Here is an example from the documentation that actually includes a few examples, which includes the keyword argument `n_points=4`; this indicates 4 crossover points (although note that the textbook only includes examples illustrating this for 2 points). +- Note on the page linked in the previous point that there are two methods which were not included in the notebooks we gave you: `SinglePointCrossover` and `TwoPointCrossover`. These methods require no additional keyword arguments and would have worked “out of the box;†they were also illustrated with examples in the online textbook. This was unfortunately not included in the original assignment description. + +3. **Run the model with a small number of links (=1) and a big number of links (max=76), what differences do you see in the solutions and computation time compared with the previous model? and why?** + +> The performance changes a lot if you are locating just one link or 76. 76 is like no decision must be made, all are upgraded. With 1 you need to select the best one which is also fast. The problem is with intermediate solutions, like choose 30 out of the 76: it becomes a harder combinatorial problem to solve. + +_The notebook was initialized with the value for budget being 76; we also asked you to consider 1 and 76 links. Both of these were not the best way to evaluate the effectiveness of the GA (or the MILP, for that matter)._ + +The performance changes a lot if you are locating just one link or 76. 76 is a trivial decision (in fact it is like no decision must be made, all links are upgraded; this is why the original code in the notebook converged very quickly. With 1 you need to select the best one which is also fast, but still the algorithm has a harder task. The problem is with intermediate network sizes like choosing 40 out of the 76: it becomes a harder combinatorial problem to solve. Note the comment in the previous question about population size, where the algorithm took a long time to converge when 36 links were selected. + + +### Comparison + +1. **Compare the GA approach with MILP approach. For the same computation time which one is faster?** + +> The GA is faster. + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. 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However, this network was used in many publications. It is good for code debugging. + +## Source / History + +Via: [http://www.bgu.ac.il/~bargera/tntp/](http://www.bgu.ac.il/~bargera/tntp/) + +All network data including the link numbers indicated on the map (but excluding node coordinates), are taken from the following paper: “An efficient approach to solving the road network equilibrium traffic assignment problem†by LeBlanc, L.J., Morlok, E.K., Pierskalla, W.P., Transportation Research Vol. 9, pp. 309-318, 1975. The links in the network file are sorted by their tail node, thus they do not follow the same order as the original publication. OD flows in the original paper (Table 1) are given in thousands of vehicles per day, with integer values up to 44. OD flows here are the values form the table multiplied by 100. They are therefore 0.1 of the original daily flows, and in that sense might be viewed as approximate hourly flows. This conversion was done to enable comparison of objective values with papers published during the 1980's and the 1990's. The units of free flow travel times are 0.01 hours, but they are often viewed as if they were minutes. Link lengths are set arbitrarily equal to free flow travel times. The parameters in the paper are given in the format of `t=a+b*flow^4`. The original parameter a is the free flow travel time given here. The original parameter b is equal to (free flow travel time)*B/(capacity^Power) in the format used here. In the data here the “traditional†BPR value of B=0.15 is assumed, and the given capacities are computed accordingly. Node coordinates were generated artificially to reproduce the diagram shown in the paper. + +[Walter Wong](mailto://kiwong@mail.nctu.edu.tw) points out that another version of the Sioux-Falls network appears in a different publication, “An algorithm for the Discrete Network,†LeBlanc, L.J., Transportation Science, Vol 9, pp 183-199, 1975. The difference between the two versions is that the free-flow travel times on links 15-19, 19-15, 15-22 and 22-15 are 4 instead of 3, and the free-flow travel time on links 10-16 and 16-10 are 5 instead of 4. + +[Andrew Koh](mailto://atmkoh@yahoo.co.uk) reports that a third version of the Sioux-Falls network has appeared in “Equilibrium Decomposed Optimization: A Heuristic for the Continuous Equilibrium Network Design Problem,†Suwansirikul, C., Friesz, T.L., Tobin, R.L., Transportation Science, Vol. 21(4), 1987, pp. 254-263. Click here for a list of differences between the two versions. + +[Gregor Laemmel](mailto://laemmel@vsp.tu-berlin.de) reports that the first published version of the Sioux-Falls network appears in "Development and Application of a Highway Network Design Model - Volumes 1 and 2," Morlok, E.K., Schofer, J.L., Pierskalla, Marsten, R.E., W.P., Agarwal, S.K., Stoner, J.W., Edwards, J.L., LeBlanc, L.J., and Spacek, D.T., Final Report to the Federal Highway Administration under contract number DOT-FH-11-7862, Department of Civil Engineering, Northwestern University, Evanston, Illinois, July 1973. Link lengths (in miles) are given the following file: Sioux-Falls Network, which is identical to the first version given here in all other attributes. + +[David Boyce](mailto://d-boyce@northwestern.edu) comments that yet another slightly different version of the Sioux Falls network appears in LeBlanc’s Ph.D. thesis. The main difference from the published paper version is that flows in the published paper are multiplied by 100, rounded in an unclear manner, and presented as integers, while flows in the thesis are in tenths. + +See also: [Sioux Falls Variants for Network Design](http://www.bgu.ac.il/~bargera/tntp/SiouxFalls_CNDP/SiouxFallsVariantsForNetworkDesign.html) + + +## Scenario + + +## Contents + + - `SiouxFalls_net.tntp` Network + - `SiouxFalls_trips.tntp` Demand + - `SiouxFalls_node.tntp` Node Coordinates + - `SiouxFalls_flow.tntp` Best known flow solution + - `Sioux-Falls-Network.pdf` Picture of Network + - `SiouxFallsMap_AAA1998.jpg` Picture of actual Sioux Falls from 1998 + +## Dimensions +Zones: 24 +Nodes: 24 +Links: 76 +Trips: 360,600.0 + +## Units +Time: +Distance: +Speed: +Cost: +Coordinates: + +## Generalized Cost Weights +Toll: 0 +Distance: 0 + +## Solutions + +`SiouxFalls_flow.tntp` contains best known link flows solution with Average Excess Cost (normalized gap) of 3.9E-15. Optimal objective function value: 42.31335287107440 + +## Known Issues +FIXME translate to Github Issues diff --git a/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network-Flow.pdf b/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network-Flow.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cbea1a84ccf0f11bcb3005b80af168523b79bb95 Binary files /dev/null and b/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network-Flow.pdf differ diff --git a/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network.pdf b/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6d41981be0458062cbfebdbca5dc741fd480b07c Binary files /dev/null and b/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network.pdf differ diff --git a/content/GA_2_5/input/TransportationNetworks/SiouxFalls/Sioux-Falls-OD 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However, this network was used in many publications. It is good for code debugging. + +## Source / History + +Via: [http://www.bgu.ac.il/~bargera/tntp/](http://www.bgu.ac.il/~bargera/tntp/) + +All network data including the link numbers indicated on the map (but excluding node coordinates), are taken from the following paper: “An efficient approach to solving the road network equilibrium traffic assignment problem†by LeBlanc, L.J., Morlok, E.K., Pierskalla, W.P., Transportation Research Vol. 9, pp. 309-318, 1975. The links in the network file are sorted by their tail node, thus they do not follow the same order as the original publication. OD flows in the original paper (Table 1) are given in thousands of vehicles per day, with integer values up to 44. OD flows here are the values form the table multiplied by 100. They are therefore 0.1 of the original daily flows, and in that sense might be viewed as approximate hourly flows. This conversion was done to enable comparison of objective values with papers published during the 1980's and the 1990's. The units of free flow travel times are 0.01 hours, but they are often viewed as if they were minutes. Link lengths are set arbitrarily equal to free flow travel times. The parameters in the paper are given in the format of `t=a+b*flow^4`. The original parameter a is the free flow travel time given here. The original parameter b is equal to (free flow travel time)*B/(capacity^Power) in the format used here. In the data here the “traditional†BPR value of B=0.15 is assumed, and the given capacities are computed accordingly. Node coordinates were generated artificially to reproduce the diagram shown in the paper. + +[Walter Wong](mailto://kiwong@mail.nctu.edu.tw) points out that another version of the Sioux-Falls network appears in a different publication, “An algorithm for the Discrete Network,†LeBlanc, L.J., Transportation Science, Vol 9, pp 183-199, 1975. The difference between the two versions is that the free-flow travel times on links 15-19, 19-15, 15-22 and 22-15 are 4 instead of 3, and the free-flow travel time on links 10-16 and 16-10 are 5 instead of 4. + +[Andrew Koh](mailto://atmkoh@yahoo.co.uk) reports that a third version of the Sioux-Falls network has appeared in “Equilibrium Decomposed Optimization: A Heuristic for the Continuous Equilibrium Network Design Problem,†Suwansirikul, C., Friesz, T.L., Tobin, R.L., Transportation Science, Vol. 21(4), 1987, pp. 254-263. Click here for a list of differences between the two versions. + +[Gregor Laemmel](mailto://laemmel@vsp.tu-berlin.de) reports that the first published version of the Sioux-Falls network appears in "Development and Application of a Highway Network Design Model - Volumes 1 and 2," Morlok, E.K., Schofer, J.L., Pierskalla, Marsten, R.E., W.P., Agarwal, S.K., Stoner, J.W., Edwards, J.L., LeBlanc, L.J., and Spacek, D.T., Final Report to the Federal Highway Administration under contract number DOT-FH-11-7862, Department of Civil Engineering, Northwestern University, Evanston, Illinois, July 1973. Link lengths (in miles) are given the following file: Sioux-Falls Network, which is identical to the first version given here in all other attributes. + +[David Boyce](mailto://d-boyce@northwestern.edu) comments that yet another slightly different version of the Sioux Falls network appears in LeBlanc’s Ph.D. thesis. The main difference from the published paper version is that flows in the published paper are multiplied by 100, rounded in an unclear manner, and presented as integers, while flows in the thesis are in tenths. + +See also: [Sioux Falls Variants for Network Design](http://www.bgu.ac.il/~bargera/tntp/SiouxFalls_CNDP/SiouxFallsVariantsForNetworkDesign.html) + + +## Scenario + + +## Contents + + - `SiouxFalls_net.tntp` Network + - `SiouxFalls_trips.tntp` Demand + - `SiouxFalls_node.tntp` Node Coordinates + - `SiouxFalls_flow.tntp` Best known flow solution + - `Sioux-Falls-Network.pdf` Picture of Network + - `SiouxFallsMap_AAA1998.jpg` Picture of actual Sioux Falls from 1998 + +## Dimensions +Zones: 24 +Nodes: 24 +Links: 76 +Trips: 360,600.0 + +## Units +Time: +Distance: +Speed: +Cost: +Coordinates: + +## Generalized Cost Weights +Toll: 0 +Distance: 0 + +## Solutions + +`SiouxFalls_flow.tntp` contains best known link flows solution with Average Excess Cost (normalized gap) of 3.9E-15. Optimal objective function value: 42.31335287107440 + +## Known Issues +FIXME translate to Github Issues diff --git a/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network-Flow.pdf b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network-Flow.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cbea1a84ccf0f11bcb3005b80af168523b79bb95 Binary files /dev/null and b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network-Flow.pdf differ diff --git a/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network.pdf b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6d41981be0458062cbfebdbca5dc741fd480b07c Binary files /dev/null and b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-Network.pdf differ diff --git a/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-OD Demand.pdf b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-OD Demand.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b34671fbac2440406e82c3cc529fa58083632b95 Binary files /dev/null and b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/Sioux-Falls-OD Demand.pdf differ diff --git a/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson new file mode 100644 index 0000000000000000000000000000000000000000..0c4a75124069816c25b79e4a85bac99cff457a4a --- /dev/null +++ b/content/GA_2_5/input/input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson @@ -0,0 +1,31 @@ +{ +"type": "FeatureCollection", +"name": "siouxfallstranformed", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, 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+import json +import matplotlib.pyplot as plt + + +coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson' + +def network_visualization(link_flow,coordinates_path): + + plt.figure(figsize=(30, 30)) + + # Read the JSON file containing node coordinates + # Load the JSON data + with open(coordinates_path, 'r') as f: + data_cor = json.load(f) + + # Extract node coordinates + node_coordinates = {} + for feature in data_cor['features']: + node_id = feature['properties']['id'] + x_coord = feature['properties']['x'] + y_coord = feature['properties']['y'] + node_coordinates[node_id] = {'x': x_coord, 'y': y_coord} + + + # Create the graph with node positions based on coordinates from the JSON file + G = nx.DiGraph() + G.add_edges_from(link_flow.keys()) # Assuming link_flow contains edges + + # Set node positions based on coordinates from the JSON file + pos = {node: (node_coordinates[node]['x'], node_coordinates[node]['y']) for node in G.nodes()} + + # Draw all edges + nx.draw(G, pos, with_labels=True, node_size=4000, node_color='lightblue', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + + # Add title and grid + plt.title("Network Visualization", fontsize=70) # Add a title + plt.grid(True) + plt.grid('minor') # Turn the grids on + plt.axis('on') + # Show the plot + plt.show() + return G, pos + +# To Use: +# coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson' +# G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations! \ No newline at end of file diff --git a/content/GA_2_5/utils/network_visualization_highlight_link.py b/content/GA_2_5/utils/network_visualization_highlight_link.py new file mode 100644 index 0000000000000000000000000000000000000000..4821b2ca46aac9bec0a49daa90d6b0f0b3279860 --- /dev/null +++ b/content/GA_2_5/utils/network_visualization_highlight_link.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 22 19:44:48 2023 + +@author: mmovaghar +""" + +import networkx as nx +import matplotlib.pyplot as plt + + + + +def network_visualization_highlight_links(G, pos, link_select): + + plt.figure(figsize=(30, 30)) + + # Draw all edges + nx.draw(G, pos, with_labels=True, node_size=4000, node_color='lightblue', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + # Create a subgraph based on the selected links + subselected_links = {key: value for key, value in link_select.items() if value != 0.0} + subgraph = G.edge_subgraph(subselected_links) + + # Draw selected edges with a different color or style + nx.draw(subgraph, pos, with_labels=True, node_size=4000, node_color='pink',edge_color='red', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + + # Add title and grid + plt.title("Network Visualization with Highlighted Selected Links", fontsize=70) # Add a title + plt.grid(True) + plt.grid('minor') # Turn the grids on + plt.axis('on') + # Show the plot + plt.show() +# To Use: +# coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson' +# G, pos = network_visualization_highlight_links (link_flow=link_flows,link_select=links_selected, coordinates_path=coordinates_path) \ No newline at end of file diff --git a/content/GA_2_5/utils/network_visualization_upgraded.py b/content/GA_2_5/utils/network_visualization_upgraded.py new file mode 100644 index 0000000000000000000000000000000000000000..0064bfd44d6afa5fe203867ab7a40bcff7d1214c --- /dev/null +++ b/content/GA_2_5/utils/network_visualization_upgraded.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 22 19:46:09 2023 + +@author: mmovaghar +""" + +import networkx as nx +import matplotlib.pyplot as plt +import pandas as pd +from matplotlib.lines import Line2D + + +def network_visualization_upgraded(G, pos, link_flow, capacity_new, link_select, labels): + + plt.figure(figsize=(30, 30)) + + + # Set edge attributes + nx.set_edge_attributes(G, capacity_new, "capacity") + nx.set_edge_attributes(G, link_flow, "flow") + + subselected_links = {key: value for key, value in link_select.items() if value != 0.0} + + + if link_select is not None: + subselected_links = {key: value for key, value in link_select.items() if value != 0.0} + subgraph = G.edge_subgraph(subselected_links) + else: + subselected_links = {} + + # Create a dictionary to map edges to colors based on some criteria + edge_colors = ['red', 'darkorange', 'lightgreen','yellow', 'lime'] + bb = pd.DataFrame(link_flow.values())/pd.DataFrame(capacity_new.values()) + boundries_color = (bb[0].max()-bb[0].min())/4 + edge_to_color = {} + for e in subgraph.edges: + if subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] >= (bb[0].min()+3* boundries_color): + edge_to_color[e] = edge_colors[0] + elif (bb[0].min()+2*boundries_color)<= subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] <(bb[0].min()+3*boundries_color): + edge_to_color[e] = edge_colors[1] + elif (bb[0].min()+1*boundries_color)<= subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] <(bb[0].min()+2*boundries_color): + edge_to_color[e] = edge_colors[2] + elif subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] <= (bb[0].min()+1*boundries_color): + edge_to_color[e] = edge_colors[3] + else: + edge_to_color[e] = edge_colors[4] + + # Define edge widths (thickness) based on some criteria for directed graph + boundries_width = (max(link_flow.values())-min(link_flow.values()))/4 + edge_widths = [] + for e in subgraph.edges: + if subgraph[e[0]][e[1]]['flow'] >= (min(link_flow.values())+3*boundries_width): + edge_widths.append(5) # Set the width to 5 for these edges + elif (min(link_flow.values())+2*boundries_width)<= subgraph[e[0]][e[1]]['flow']< (min(link_flow.values())+3*boundries_width): + edge_widths.append(10) # Set the width to 10 for these edges + elif (min(link_flow.values())+1*boundries_width)<= subgraph[e[0]][e[1]]['flow']< (min(link_flow.values())+2*boundries_width): + edge_widths.append(15) # Set the width to 15 for these edges + elif subgraph[e[0]][e[1]]['flow'] <= (min(link_flow.values())+1*boundries_width): + edge_widths.append(20) # Set the width to 20 for these edges + else: + edge_widths.append(25) # Set the width to 25 for these edges + + + + # Create labels for selected edges with flow and capacity information + if labels=='on': + edge_labels = {(u, v): f"F{u,v} {G[u][v]['flow']:.2f} \nC: {G[u][v]['capacity']:.2f}" for u, v in G.edges()} + elif labels=='off': + edge_labels = {(u, v): f"F{u,v} {subgraph[u][v]['flow']:.2f} \nC: {subgraph[u][v]['capacity']:.2f}" for u, v in subgraph.edges()} + + + # Draw nodes and edges using positions from the JSON file + nx.draw(G, pos, with_labels=True, node_size=4000, node_color='lightblue', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + + nx.draw_networkx_edges(subgraph, pos,edgelist=edge_to_color.keys(), width=edge_widths, + edge_color=[edge_to_color[e] for e in edge_to_color.keys()], style='solid', + arrowstyle=None, arrowsize=50, + edge_cmap='Spectral', arrows=True, label= None, + nodelist=link_select, node_shape='o', connectionstyle='arc3, rad = 0.1') + + nx.draw_networkx_nodes(subgraph, pos, node_size=4000, node_color='pink', + node_shape='o', linewidths=None, label=None) + + # Draw edge labels for selected edges + nx.draw_networkx_edge_labels(subgraph, pos, edge_labels=edge_labels, + font_size=20, font_color='black',label_pos=0.3,rotate=False, + bbox=dict(facecolor='none', edgecolor='none', boxstyle='round,pad=0.1'), + clip_on=True) + + + # Create a custom legend + legend_labels = ['>='"{:.2f}".format(bb[0].min()+3* boundries_color) + ,'>='"{:.2f}".format(bb[0].min()+2* boundries_color) + ,'>='"{:.2f}".format(bb[0].min()+1* boundries_color) + ,'>='"{:.2f}".format(bb[0].min()+0* boundries_color)] + legend_colors = edge_colors[:len(legend_labels)] + legend_elements = [Line2D([0], [0], color=color, lw=4, label=label) for color, label in zip(legend_colors, legend_labels)] + + # Show the legend with adjusted title fontsize + plt.legend(handles=legend_elements, title="Flow/Capacity", title_fontsize=40, loc='best', fontsize=40) + + # Add title and grid + plt.title("Network Visualization with Upgraded Links", fontsize=70) # Add and customize the title + plt.grid(True) + plt.grid('minor') + plt.axis('on') + + # Show the plot + plt.ion() # to make the plot interactice by clicking on. The numbers will be more clear. + plt.show() + +# To Use: +# network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected) \ No newline at end of file diff --git a/content/GA_2_5/utils/utils/network_visualization.py b/content/GA_2_5/utils/utils/network_visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..b591a76893947360a3ddda7685e39a5f92c7a0cf --- /dev/null +++ b/content/GA_2_5/utils/utils/network_visualization.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 22 19:23:56 2023 + +@author: mmovaghar +""" + +import networkx as nx +import json +import matplotlib.pyplot as plt + + +coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson' + +def network_visualization(link_flow,coordinates_path): + + plt.figure(figsize=(30, 30)) + + # Read the JSON file containing node coordinates + # Load the JSON data + with open(coordinates_path, 'r') as f: + data_cor = json.load(f) + + # Extract node coordinates + node_coordinates = {} + for feature in data_cor['features']: + node_id = feature['properties']['id'] + x_coord = feature['properties']['x'] + y_coord = feature['properties']['y'] + node_coordinates[node_id] = {'x': x_coord, 'y': y_coord} + + + # Create the graph with node positions based on coordinates from the JSON file + G = nx.DiGraph() + G.add_edges_from(link_flow.keys()) # Assuming link_flow contains edges + + # Set node positions based on coordinates from the JSON file + pos = {node: (node_coordinates[node]['x'], node_coordinates[node]['y']) for node in G.nodes()} + + # Draw all edges + nx.draw(G, pos, with_labels=True, node_size=4000, node_color='lightblue', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + + # Add title and grid + plt.title("Network Visualization", fontsize=70) # Add a title + plt.grid(True) + plt.grid('minor') # Turn the grids on + plt.axis('on') + # Show the plot + plt.show() + return G, pos + +# To Use: +# coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson' +# G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations! \ No newline at end of file diff --git a/content/GA_2_5/utils/utils/network_visualization_highlight_link.py b/content/GA_2_5/utils/utils/network_visualization_highlight_link.py new file mode 100644 index 0000000000000000000000000000000000000000..4821b2ca46aac9bec0a49daa90d6b0f0b3279860 --- /dev/null +++ b/content/GA_2_5/utils/utils/network_visualization_highlight_link.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 22 19:44:48 2023 + +@author: mmovaghar +""" + +import networkx as nx +import matplotlib.pyplot as plt + + + + +def network_visualization_highlight_links(G, pos, link_select): + + plt.figure(figsize=(30, 30)) + + # Draw all edges + nx.draw(G, pos, with_labels=True, node_size=4000, node_color='lightblue', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + # Create a subgraph based on the selected links + subselected_links = {key: value for key, value in link_select.items() if value != 0.0} + subgraph = G.edge_subgraph(subselected_links) + + # Draw selected edges with a different color or style + nx.draw(subgraph, pos, with_labels=True, node_size=4000, node_color='pink',edge_color='red', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + + # Add title and grid + plt.title("Network Visualization with Highlighted Selected Links", fontsize=70) # Add a title + plt.grid(True) + plt.grid('minor') # Turn the grids on + plt.axis('on') + # Show the plot + plt.show() +# To Use: +# coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinates.geojson' +# G, pos = network_visualization_highlight_links (link_flow=link_flows,link_select=links_selected, coordinates_path=coordinates_path) \ No newline at end of file diff --git a/content/GA_2_5/utils/utils/network_visualization_upgraded.py b/content/GA_2_5/utils/utils/network_visualization_upgraded.py new file mode 100644 index 0000000000000000000000000000000000000000..0064bfd44d6afa5fe203867ab7a40bcff7d1214c --- /dev/null +++ b/content/GA_2_5/utils/utils/network_visualization_upgraded.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 22 19:46:09 2023 + +@author: mmovaghar +""" + +import networkx as nx +import matplotlib.pyplot as plt +import pandas as pd +from matplotlib.lines import Line2D + + +def network_visualization_upgraded(G, pos, link_flow, capacity_new, link_select, labels): + + plt.figure(figsize=(30, 30)) + + + # Set edge attributes + nx.set_edge_attributes(G, capacity_new, "capacity") + nx.set_edge_attributes(G, link_flow, "flow") + + subselected_links = {key: value for key, value in link_select.items() if value != 0.0} + + + if link_select is not None: + subselected_links = {key: value for key, value in link_select.items() if value != 0.0} + subgraph = G.edge_subgraph(subselected_links) + else: + subselected_links = {} + + # Create a dictionary to map edges to colors based on some criteria + edge_colors = ['red', 'darkorange', 'lightgreen','yellow', 'lime'] + bb = pd.DataFrame(link_flow.values())/pd.DataFrame(capacity_new.values()) + boundries_color = (bb[0].max()-bb[0].min())/4 + edge_to_color = {} + for e in subgraph.edges: + if subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] >= (bb[0].min()+3* boundries_color): + edge_to_color[e] = edge_colors[0] + elif (bb[0].min()+2*boundries_color)<= subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] <(bb[0].min()+3*boundries_color): + edge_to_color[e] = edge_colors[1] + elif (bb[0].min()+1*boundries_color)<= subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] <(bb[0].min()+2*boundries_color): + edge_to_color[e] = edge_colors[2] + elif subgraph[e[0]][e[1]]['flow']/subgraph[e[0]][e[1]]['capacity'] <= (bb[0].min()+1*boundries_color): + edge_to_color[e] = edge_colors[3] + else: + edge_to_color[e] = edge_colors[4] + + # Define edge widths (thickness) based on some criteria for directed graph + boundries_width = (max(link_flow.values())-min(link_flow.values()))/4 + edge_widths = [] + for e in subgraph.edges: + if subgraph[e[0]][e[1]]['flow'] >= (min(link_flow.values())+3*boundries_width): + edge_widths.append(5) # Set the width to 5 for these edges + elif (min(link_flow.values())+2*boundries_width)<= subgraph[e[0]][e[1]]['flow']< (min(link_flow.values())+3*boundries_width): + edge_widths.append(10) # Set the width to 10 for these edges + elif (min(link_flow.values())+1*boundries_width)<= subgraph[e[0]][e[1]]['flow']< (min(link_flow.values())+2*boundries_width): + edge_widths.append(15) # Set the width to 15 for these edges + elif subgraph[e[0]][e[1]]['flow'] <= (min(link_flow.values())+1*boundries_width): + edge_widths.append(20) # Set the width to 20 for these edges + else: + edge_widths.append(25) # Set the width to 25 for these edges + + + + # Create labels for selected edges with flow and capacity information + if labels=='on': + edge_labels = {(u, v): f"F{u,v} {G[u][v]['flow']:.2f} \nC: {G[u][v]['capacity']:.2f}" for u, v in G.edges()} + elif labels=='off': + edge_labels = {(u, v): f"F{u,v} {subgraph[u][v]['flow']:.2f} \nC: {subgraph[u][v]['capacity']:.2f}" for u, v in subgraph.edges()} + + + # Draw nodes and edges using positions from the JSON file + nx.draw(G, pos, with_labels=True, node_size=4000, node_color='lightblue', font_size=40, connectionstyle='arc3, rad = 0.1',arrowsize=50) + + nx.draw_networkx_edges(subgraph, pos,edgelist=edge_to_color.keys(), width=edge_widths, + edge_color=[edge_to_color[e] for e in edge_to_color.keys()], style='solid', + arrowstyle=None, arrowsize=50, + edge_cmap='Spectral', arrows=True, label= None, + nodelist=link_select, node_shape='o', connectionstyle='arc3, rad = 0.1') + + nx.draw_networkx_nodes(subgraph, pos, node_size=4000, node_color='pink', + node_shape='o', linewidths=None, label=None) + + # Draw edge labels for selected edges + nx.draw_networkx_edge_labels(subgraph, pos, edge_labels=edge_labels, + font_size=20, font_color='black',label_pos=0.3,rotate=False, + bbox=dict(facecolor='none', edgecolor='none', boxstyle='round,pad=0.1'), + clip_on=True) + + + # Create a custom legend + legend_labels = ['>='"{:.2f}".format(bb[0].min()+3* boundries_color) + ,'>='"{:.2f}".format(bb[0].min()+2* boundries_color) + ,'>='"{:.2f}".format(bb[0].min()+1* boundries_color) + ,'>='"{:.2f}".format(bb[0].min()+0* boundries_color)] + legend_colors = edge_colors[:len(legend_labels)] + legend_elements = [Line2D([0], [0], color=color, lw=4, label=label) for color, label in zip(legend_colors, legend_labels)] + + # Show the legend with adjusted title fontsize + plt.legend(handles=legend_elements, title="Flow/Capacity", title_fontsize=40, loc='best', fontsize=40) + + # Add title and grid + plt.title("Network Visualization with Upgraded Links", fontsize=70) # Add and customize the title + plt.grid(True) + plt.grid('minor') + plt.axis('on') + + # Show the plot + plt.ion() # to make the plot interactice by clicking on. The numbers will be more clear. + plt.show() + +# To Use: +# network_visualization_upgraded (G = G, pos=pos, link_flow=link_flows, capacity_new=capacity ,link_select=links_selected) \ No newline at end of file diff --git a/content/GA_2_6/Analysis.ipynb b/content/GA_2_6/Analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..dfa08c8c8e1355c9a003a01b6060dca81428c00b --- /dev/null +++ b/content/GA_2_6/Analysis.ipynb @@ -0,0 +1,1165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 10: Handling the pressure - Machine learning for predicting pressure in Water Distribution Systems\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px; height: auto; margin: 0\"\\>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px; height: auto; margin: 0\"\\>\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.6. Due: Friday, Dec 22, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 📠Specifications" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is divided into five parts:\n", + "1) Data pre-processing.\n", + "2) Defining and training a multilayer perceptron (MLP).\n", + "3) Optimization of the MLP hyperparameters.\n", + "4) Model assessment.\n", + "5) Model usage.\n", + "\n", + "**Completition requirements:**\n", + "By the end of this notebook, you should have:\n", + "- Implemented all the code cells for:\n", + " - Splitting the data into training, validation, and testing sets\n", + " - Normalizing the data\n", + " - Instantiating an MLP\n", + " - Training the MLP with a training loop\n", + " - Defining a grid-search hyperoptimization\n", + " - Assess the accuracy of the MLP\n", + " - Assess the speed of the MLP\n", + " - Use it to predict the pressure of a particular example\n", + "- Generated and exported all of the relevant plots for the report\n", + "- Answered all the questions in the report\n", + "\n", + "*Complete this assignment by the end of the session at 12:30. This means having a single report for your group with all the plots, analysis and interpretation completed.*\n", + "\n", + "**Working method:**\n", + "\n", + "Each of the parts of the notebook can be coded independently. However, in order to run the code in each part, the code in the previous parts should be in place." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "This notebook includes boxes with the formatting shown here to list the questions you are expected to answer in your report. You are not expected to write your answers here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🔙 Background" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 💧 Water distribution systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A water distribution system transports water from sources, like wells or reservoirs, to various locations where water is needed, like homes, shops, and factories. \n", + "\n", + "A basic system consists of sources of water supply and demand points for water connected by pipe lines. Figure 1 shows an example system where there are two supply centers and ten demand nodes. This transmission system can connect sparse populations, and it can be considered as a simple network of one reservoir and few nodes and pipes. Nevertheless, in a city of moderate size, there may be a number of supply centers and hundreds of demand points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"display: flex; flex-direction: row;\">\n", + " <div style=\"flex: 50%;\">\n", + " <center>\n", + " <img src=\"./figs/WDSAsset 1v1.png\" width=\"400\"/>\n", + " <figcaption><b>Figure 1.</b> Simplified scheme of a branched water distribution system.</figcaption>\n", + " </center>\n", + " </div>\n", + " <div style=\"flex: 50%;\">\n", + " <center>\n", + " <img src=\"./figs/BAK.png\" width=\"700\"/>\n", + " <figcaption><b>Figure 2.</b> Numerical results for the BakRyan water distribution system.</figcaption>\n", + " </center>\n", + " </div>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Water utilities rely on hydrodynamic models to properly design and control water distribution systems (WDSs). These physically-based models compute the pressures at all the junctions, as illustrated in Figure 2. In this figure, we can see the water network of BakRyan with the pressure at each node of the network represented by the colour. In water distribution systems, pressure is a fundamental variable. Without sufficient pressure in the system, the network is not able to supply water to the users. \n", + "\n", + "We can use pressure estimations to ensure proper water pressure, efficient flow, and reliable distribution of water to consumers. For obtaining these estimations, we tipically use hydrodynamic models. However, the computational speed of these models is often insufficient for some applications in civil engineering such as optimisation or real-time control, especially in large networks. \n", + "\n", + "One alternative to address this issue is developing data-driven models. These models can be trained using results from simulations done with the physically-based model. The objective of the data-driven model we will develop is to estimate pressure at each node of the water network but in a shorter time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ✅ Application" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, you will create a Multilayer Perceptron (MLP) for estimating the nodal pressures from the BakRyan water distribution system (Figure 2). This system has 58 pipes and 35 nodes. Your task is to create and train this Artificial Neural Network, exemplified in Figure 3. The MLP should estimate the pressures while being faster to run than the physically-based model (which usually takes 0.04 seconds to run per simulation). Furthermore, you will hyperoptimize the MLP to improve its performance.\n", + "\n", + "**Your input data will be a vector of pipe diameters. The output data will be a vector of nodal pressures.**\n", + "\n", + "Mathematically, we can express the our application as follows:\n", + "\n", + "$$\n", + "y = \\phi(x; W)\n", + "$$\n", + "\n", + "where:\n", + "\n", + "$y$: output data (nodal pressures, units: mwc*)\n", + "\n", + "$\\phi$: represents the Artificial Neural Network\n", + "\n", + "$x$: input data (pipe diameters, units: m)\n", + "\n", + "$W$: parameters of the MLP (unitless)\n", + "\n", + "Having pairs of input-output data (diameters, $x$, and pressures, $y$), it is our task to find the set of parameters, $W$, that best fit the data. Note that there are no observed pressures at some of the nodes.\n", + "\n", + "_*In water engineering, it is common to express the value of the pressure in meters of water column (mwc). This unit is equivalent to the pressure exerted by a column of water of 1 meter in height._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div>\n", + "<center><img src=\"./figs/ANN_image2.png\" width=\"600\"/>\n", + "<figcaption><b>Figure 3.</b> Artificial neural network representation, with a zoomed-in view of how a single neuron works. For this notebook, we have 58 input features (pipe diameters) and 35 outputs (nodal pressures).</figcaption></center>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 📔 Preliminaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run this notebook you need to have installed the following packages:\n", + "- Numpy\n", + "- Matplotlib\n", + "- Pickle\n", + "- Scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the database " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purposes of this notebook, there is an already existing database that you can use to create and train the MLPs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = r\"data/features_BAK.pk\" \n", + "with open(file_path, 'rb') as handle:\n", + " features = pickle.load(handle)\n", + "\n", + "file_path = r\"data/targets_BAK.pk\"\n", + "with open(file_path, 'rb') as handle:\n", + " targets = pickle.load(handle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can explore the content of each of these variables. In total, we collected 10000 examples of the BakRyan system with random configurations of the available diameters (of the 58 pipes). \n", + "\n", + "As input features (X), we use the diameters of all the pipes in the network, and each configuration of diameters is related 1-1 with the pressure at all the nodes in the system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Dimensions of features (X):', features.shape)\n", + "print('Dimensions of targets (t):', targets.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Pre-Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data into training, validation, and testing sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 1.1:</b> \n", + "\n", + "In machine learning, it's common to split the dataset into three parts: a training set, a validation set, and a test set. \n", + "\n", + "Your task is to write a Python code snippet that splits a given dataset into these three parts. The dataset consists of `features` and `targets`.\n", + "\n", + "The dataset should be split as follows:\n", + "\n", + "- 80% of the data should go to the training set.\n", + "- 10% of the data should go to the validation set.\n", + "- 10% of the data should go to the test set.\n", + "\n", + "The splitting should be done in a way that shuffles the data first to ensure that the training, validation, and test sets are representative of the overall distribution of the data. You can set the random state for the shuffling to ensure that the results are reproducible; you can use the values 42 for the first split and 24 for the second split.\n", + "\n", + "The resulting training, validation, and test sets should be stored in the variables `X_train`, `t_train`, `X_val`, `t_val`, `X_test`, and `t_test`.\n", + "\n", + "**Hint:** You can use the `train_test_split` function from the `sklearn.model_selection` module to perform the splitting." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note here that the training, validation and test sets are created in two steps, due to the way <code>train_test_split</code> is implemented in <code>sklearn</code>. Thus, in the second split you value used for <code>test_size</code> should <b>not</b> be 0.10!</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_val_test, t_train, t_val_test = train_test_split(YOUR_CODE_HERE, YOUR_CODE_HERE, test_size=YOUR_CODE_HERE, random_state=42)\n", + "X_val, X_test, t_val, t_test = train_test_split(YOUR_CODE_HERE, YOUR_CODE_HERE, test_size=YOUR_CODE_HERE, random_state=24)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalizing the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we normalize the data using the MinMaxScaler from scikit-learn. This scaler transforms the data to be between 0 and 1. This is important because the ANN will be trained using the gradient descent algorithm, which is sensitive to the scale of the data. Notice that we use the training data to fit the scaler. This is important because we assume that the model only sees the training data and we do not use any of the validation or testing data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 1.2:</b> \n", + "\n", + "In machine learning, it's often beneficial to normalize the feature variables to a specific range. This can help the model converge faster during training and can also prevent certain features from dominating others due to their scale.\n", + "\n", + "Your task is to write a Python code snippet that normalizes the feature variables of a training set and a validation set to the range [0, 1]. The feature variables are stored in the variables `X_train` and `X_val`.\n", + "\n", + "You should use the `MinMaxScaler` class from the `sklearn.preprocessing` module to perform the normalization. This class scales and translates each feature individually such that it is in the given range on the training set.\n", + "\n", + "The normalized features should be stored in the variables `normalized_X_train` and `normalized_X_val`.\n", + "\n", + "<em>Note: we do this task for you.</em>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `MinMaxScaler` should be fitted on the training features only. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaler_diameters = MinMaxScaler()\n", + "scaler_diameters.fit(X_train)\n", + "\n", + "normalized_X_train = scaler_diameters.transform(X_train)\n", + "normalized_X_val = scaler_diameters.transform(X_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 1.3:</b> \n", + "\n", + "Your task is to write a Python code snippet that normalizes the target variables of a training set and a validation set to the range [0, 1]. The target variables are stored in the variables `t_train` and `t_val`.\n", + "\n", + "The normalized targets should be stored in the variables `normalized_t_train` and `normalized_t_val`.\n", + "\n", + "<em>Note: we do this task for you.</em>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaler_pressures = MinMaxScaler()\n", + "scaler_pressures.fit(t_train)\n", + "\n", + "normalized_t_train = scaler_pressures.transform(t_train)\n", + "normalized_t_val = scaler_pressures.transform(t_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "1.1) What is the purpose of splitting a dataset into training, validation, and test sets in the context of machine learning?\n", + "\n", + "1.2) What part of the pre-processing improves the representativity of the overall distribution of the data?\n", + "\n", + "1.3) Why should the `MinMaxScaler` be fitted on the training data only, and then used to transform both the training and validation data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Defining and training an MLP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will define a Multilayer Perceptron (MLP). In Scikit-learn, the MLP is defined in the MLPRegressor class, you can see the documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html). This class has many hyperparameters that can be tuned to improve the performance of the model. Notice that in Scikit-learn, the model and the optimizer are defined in the same class. This means that we do not need to define an optimizer separately; therefore, some hyperparameters are related to the optimizer. For example, the learning rate. We will indicate the optimization hyperparameters in the next section; for now, we will only define the model hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.1:</b> \n", + "You are tasked with setting up a Multi-Layer Perceptron (MLP). The MLP should have the following characteristics:\n", + "\n", + " - The hidden layer sizes are defined as a tuple. For example, if we want to have two hidden layers with 10 and 5 neurons, respectively, we would write: hidden_layer_sizes=(10,5). Notice that we only specify the hidden layer sizes, the input and output sizes will be automatically inferred when we train the model. \n", + " - The activation function can be one of the following: 'identity', 'logistic', 'tanh', 'relu'.\n", + "\n", + "The configured MLP regressor should be stored in a variable named `model`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = MLPRegressor(YOUR_CODE_HERE, YOUR_CODE_HERE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a model, we need to train it! Now, we will define a training loop that will train the model using the training data and will evaluate the model using the validation data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-learn offers the possibility to directly train a model using the `fit` method. However, we will define a training loop to have more control over the training process. This will allow us to evaluate the model at each epoch and observe its training.\n", + "\n", + "The first step towards training a model is defining a function that transforms our training dataset into random mini-batches. This is a common practice used for training neural networks due to their computational efficiency and their ability to help the model generalize better. This practice generally leads to better computational efficiency, faster convergence and better generalization performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_mini_batches(X, t, batch_size):\n", + " \"\"\"\n", + " This function generates mini-batches from the given input data and labels.\n", + "\n", + " Parameters:\n", + " X (numpy.ndarray): The features.\n", + " t (numpy.ndarray): The targets corresponding to the input data.\n", + " batchsize (int): The size of each mini-batch.\n", + "\n", + " Returns:\n", + " list: A list of tuples where each tuple contains a mini-batch of the input data and the corresponding targets.\n", + " \"\"\"\n", + " # Generate permutations\n", + " perm = np.random.permutation(len(X))\n", + " X_train_perm = X[perm]\n", + " t_train_perm = t[perm]\n", + " \n", + " # Generate mini-batches\n", + " X_batches = []\n", + " t_batches = []\n", + " for i in range(0, len(X_train_perm), batch_size):\n", + " X_batches.append(X_train_perm[i:i+batch_size])\n", + " t_batches.append(t_train_perm[i:i+batch_size])\n", + "\n", + " return list(zip(X_batches, t_batches))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following figure illustrates both the way we split the original dataset and how we further split the training dataset into mini-batches. At every epoch the training dataset is shuffled and each mini-batch is considered **in isolation** by the network. The gradients coming from the single mini-batches are used to update the weights of the network (the randomness involved is why we say we are using **Stochastic** Gradient Descent).\n", + "\n", + "<div>\n", + "<center><img src=\"./figs/minibatching.png\" width=\"600\"/>\n", + "<figcaption><b>Figure 4.</b> Dataset splitting, mini-batching and the stochastic nature of MLP training.</figcaption></center>\n", + "</div>\n", + "\n", + "Now, we will define some hyperparameters for the training loop. These hyperparameters are related to the optimization process.\n", + "Define the following hyperparameters:\n", + "- `learning_rate` (float): The learning rate of the optimizer.\n", + "- `n_epochs` (int): The number of epochs to train the model. (For time reasons, we will only train the model for 20 epochs. However, you can increase this number to improve the performance of the model.)\n", + "- `batch_size` (int): The size of each mini-batch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.001\n", + "n_epochs = 20\n", + "batch_size = 64" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.2:</b> \n", + "\n", + "In this exercise, you are tasked with implementing a function to train a neural network model. The function should also compute and store the loss on the training and validation sets at each epoch. The loss function to be used is the Mean Squared Error (MSE), which is defined as:\n", + "\n", + "$$ MSE = \\frac{1}{n} \\sum_{i=1}^{n} (t_i - y_i)^2 $$\n", + "\n", + "where $t_i$ is the actual target, $y_i$ is the predicted value, and $n$ is the number of samples.\n", + "\n", + "The function should be named `train_model` and should take the following parameters:\n", + "\n", + "- `model`: An instance of a neural network model that we want to train.\n", + "- `normalized_X_train`: The normalized training data.\n", + "- `normalized_t_train`: The normalized training labels.\n", + "- `normalized_X_val`: The normalized validation data.\n", + "- `normalized_t_val`: The normalized validation labels.\n", + "- `n_epochs`: The number of epochs to train the model for.\n", + "- `batch_size`: The size of each mini-batch.\n", + "- `learning_rate`: The learning rate for the model.\n", + "\n", + "The function should perform the following steps:\n", + "\n", + "1. Initialize two empty lists, `train_loss_list` and `val_loss_list`, to store the training and validation losses at each epoch.\n", + "\n", + "2. Loop over the specified number of epochs. For each epoch:\n", + "\n", + " a. Generate mini-batches from the normalized training data and labels using a function `get_mini_batches(normalized_X_train, normalized_t_train, batch_size)`.\n", + "\n", + " b. For each mini-batch, update the model's weights using the `partial_fit` method of the model.\n", + "\n", + " c. Compute the MSE loss on the training set and append it to `train_loss_list`.\n", + "\n", + " d. Compute the MSE loss on the validation set and append it to `val_loss_list`.\n", + "\n", + " e. Print the training progress including the current epoch and the training and validation losses.\n", + "\n", + " f. Return the `train_loss_list` and `val_loss_list` lists.\n", + "\n", + "Your task is to write the Python code that implements the `train_model` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train_model(model, normalized_X_train, normalized_t_train, normalized_X_val, normalized_t_val, n_epochs, batch_size, learning_rate):\n", + " train_loss_list = []\n", + " val_loss_list = []\n", + " model.learning_rate_init = learning_rate\n", + " \n", + " for epoch in range(n_epochs):\n", + " \n", + " # Generate mini-batches\n", + " mini_batches = get_mini_batches(YOUR_CODE_HERE)\n", + " \n", + " # Train model on mini-batches\n", + " for X_batch, t_batch in mini_batches:\n", + " YOUR_CODE_HERE\n", + " \n", + " YOUR_CODE_HERE # Hint: may be more than one line\n", + "\n", + " # Store loss values\n", + " train_loss_list.append(train_loss)\n", + " val_loss_list.append(val_loss)\n", + "\n", + " # Print training progress\n", + " print(f\"Epoch {epoch+1}/{n_epochs} - Train Loss: {train_loss_list[-1]:.4f} - Val Loss: {val_loss:.4f}\")\n", + " \n", + " return train_loss_list, val_loss_list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_loss_list, val_loss_list = train_model(model, normalized_X_train, normalized_t_train, normalized_X_val, normalized_t_val, n_epochs, batch_size, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.3:</b> \n", + " Plot the validation and training loss curves. Add this plot to your report.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a scatter plot with enhanced styling\n", + "plt.figure(figsize=(8, 6)) # Set the figure size\n", + "\n", + "x_axis = list(range(len(train_loss_list)))\n", + "\n", + "# Create a scatter plot\n", + "plt.scatter(x_axis, YOUR_CODE_HERE, label='Validation loss', color='red', marker='.', s=100, alpha=0.7, edgecolors='black', linewidths=0.5)\n", + "plt.scatter(x_axis, YOUR_CODE_HERE, label='Training loss', color='blue', marker='.', s=100, alpha=0.7, edgecolors='black', linewidths=0.5)\n", + "\n", + "# Add labels and a legend with improved formatting\n", + "plt.xlabel('Epochs', fontsize=14, fontweight='bold')\n", + "plt.ylabel('Loss', fontsize=14, fontweight='bold')\n", + "plt.title('Loss curves', fontsize=16, fontweight='bold')\n", + "plt.legend(loc='upper right', fontsize=12)\n", + "\n", + "# Set the y-axis to be logarithmic\n", + "plt.yscale('log')\n", + "\n", + "# Customize the grid appearance\n", + "plt.grid(True, linestyle='--', alpha=0.5)\n", + "\n", + "# Customize the tick labels\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "\n", + "# Add a background color to the plot\n", + "plt.gca().set_facecolor('#f2f2f2')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.4:</b> \n", + "Using the `score` function of the MLP Regressor, compute the R2 score of the model on the training and validation sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.score(YOUR_CODE_HERE, YOUR_CODE_HERE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.score(YOUR_CODE_HERE, YOUR_CODE_HERE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "2.1) Based on the shape of the loss curves, what can you indicate about the fitting capabilities of the model? (Is it overfitting, underfitting, or neither?)\n", + "\n", + "2.2) How do you explain the difference between the values of training and validation score?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Hyperparameter tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>For Tasks 3 and 4 the code is 100% complete, but you are expected to read it thoroughly to help understand the analysis and provide answers in your report.</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 3.1:</b> \n", + " Create a grid-search strategy to find hyperparameters that give the best prediction on the validation set. Vary the number of layers and number of hidden units per layer. You can assume that all the hidden layers have the same number of hidden units.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define coordinate vectors for grid\n", + "layer_sizes = [10, 20, 50, 100] \n", + "layer_numbers = [1, 2, 3, 4]\n", + "\n", + "# Create a grid for the coordinate pairs and store them in an array\n", + "val_loss_grid = np.zeros((len(layer_sizes), len(layer_numbers)))\n", + "\n", + "# Loop over all the layer sizes\n", + "for i, lsize in enumerate(layer_sizes):\n", + " \n", + " # Loop over all numbers of hidden layers\n", + " for j, lnumber in enumerate(layer_numbers):\n", + " \n", + " # get tuple of hidden layer sizes\n", + " layers = (lsize,) * lnumber\n", + " print(\"Training NN with hidden layers: {}\".format(layers))\n", + " \n", + " # Create the ANN model with the given hidden layer sizes and activation function\n", + " model = MLPRegressor(hidden_layer_sizes=layers, activation='tanh')\n", + " \n", + " _, val_loss_list = train_model(model, \n", + " normalized_X_train, \n", + " normalized_t_train,\n", + " normalized_X_val, \n", + " normalized_t_val, \n", + " n_epochs=20, \n", + " batch_size=64,\n", + " learning_rate=0.001\n", + " )\n", + " \n", + " val_loss_grid[i,j] = val_loss_list[-1]\n", + " \n", + " print(\" Loss: {:.4e}\\n\".format(val_loss_grid[i,j]))\n", + "\n", + "\n", + "# Extract the hyperparameters that gave the lowest loss and print\n", + "min_size, min_number = np.unravel_index(np.argmin(val_loss_grid), val_loss_grid.shape)\n", + "print(\"\\n\\nModel with {} layers and {} neurons per layer gave lowest loss of {:.4e}\".format(layer_numbers[min_number], layer_sizes[min_size], val_loss_grid[min_size, min_number]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use our test data to visualize our best-performing model and test its predictive capabilities. First, re-initialize & train the model with the optimal hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 3.2:</b> \n", + " Re-initialize & train the model with the optimal hyperparameters.\n", + " \n", + "The reconfigured MLP regressor should be stored in a variable named `model`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up NN\n", + "layers = (layer_sizes[min_size],) * layer_numbers[min_number]\n", + "model = MLPRegressor(hidden_layer_sizes=layers, activation='tanh')\n", + "\n", + "# train NN\n", + "_, val_loss_list = train_model(model, \n", + " normalized_X_train, \n", + " normalized_t_train,\n", + " normalized_X_val, \n", + " normalized_t_val, \n", + " n_epochs=20, \n", + " batch_size=64,\n", + " learning_rate=0.001\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the Python code to plot the matrix `val_loss_grid` with the specified row and column labels:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 3.3:</b> \n", + " Plot the validation loss grid. Add this plot to your report.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Define the row and column labels\n", + "rows = layer_sizes\n", + "cols = layer_numbers\n", + "\n", + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(val_loss_grid, cmap='jet', interpolation='nearest')\n", + "\n", + "# Add a colorbar\n", + "plt.colorbar(label='Validation Loss')\n", + "\n", + "# Add the row and column labels\n", + "plt.xticks(range(len(cols)), cols)\n", + "plt.yticks(range(len(rows)), rows)\n", + "\n", + "plt.xlabel('Number of Layers')\n", + "plt.ylabel('Number of Neurons')\n", + "\n", + "plt.title('Validation Loss Grid')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "This code will create a heatmap where the color intensity represents the validation loss. The colorbar on the side provides a reference for the loss values. The row and column labels represent the number of neurons and layers, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "3.1) How does hyperparameter tuning in machine learning relate to the concept of model complexity?\n", + "\n", + "3.2) From the plot, what is the impact of increasing the number of hidden layers on the model's ability to capture complex patterns in the data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Model assessment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we are going to test the model's accuracy on the test dataset. First, we need to scale the test data using the scaler that we used for the training data. Then, we can use the `score` method of the model to compute the R2 score on the test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "normalized_X_test = scaler_diameters.transform(X_test)\n", + "normalized_t_test = scaler_pressures.transform(t_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.score(normalized_X_test, normalized_t_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More than a performance metric as the score, we can observe the errors of different nodes for all the test database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "estimated_pressure = scaler_pressures.inverse_transform(model.predict(normalized_X_test))\n", + "error = t_test - estimated_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "node_ID = 0\n", + "error_node = error[:, node_ID]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 4.1:</b> \n", + " Plot the distribution of errors. Add this plot to your report.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "# Create a histogram\n", + "ax.hist(error_node, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)\n", + "\n", + "# Axis formatting\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "ax.spines['bottom'].set_color('#DDDDDD')\n", + "ax.tick_params(bottom=False, left=False)\n", + "ax.set_axisbelow(True)\n", + "ax.yaxis.grid(True, color='#EEEEEE')\n", + "ax.xaxis.grid(False)\n", + "\n", + "ax.set_xlabel('Error of prediction [m]', labelpad=15, color='#333333')\n", + "ax.set_ylabel('Frequency', labelpad=15, color='#333333')\n", + "ax.set_title(f'Error of node {node_ID} across test scenarios', pad=15, color='#333333', weight='bold')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot gives us an idea of the expected errors we can encounter when using the model. However, it is also important that the MLP is faster than original simulator (which takes around 0.04 seconds per simulation)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Speed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can calculate the time per scenario that the model takes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "y_pred_test = model.predict(X_test)\n", + "total_time = time.time() - start_time\n", + "\n", + "num_test_sims = len(y_pred_test)\n", + "\n", + "data_driven_exec_time_per_sim = total_time/num_test_sims\n", + "print(f'Data-driven model took {data_driven_exec_time_per_sim:.7f} seconds for {num_test_sims} scenarios')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Considering that the original model can take up to 0.04 seconds per scenario, we can estimate the potential gain in speed-up. (Speed-up = original_time/Data-driven_model_time)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "original_time_per_sim = 0.04\n", + "\n", + "speed_up = np.round(original_time_per_sim/data_driven_exec_time_per_sim, 2)\n", + "print('The data-driven model is', speed_up,'times faster than original simulator per scenario.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "4.1) The score indicates a high fitting, is that reflected in the plot of the errors? Why?\n", + "\n", + "4.2) Is the the plot of errors centered around zero? If not, what does that mean?\n", + "\n", + "4.3) How diverse can the speed up values be if you run the cell multiple times? Why?\n", + "\n", + "4.4) What would occur with the speed up if you increase the number of neurons in the hidden layers?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Model usage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a trained model, we can use it to predict the nodal pressures for a given set of diameters. Let's try it out!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The water utility in charge of the BakRyan water distribution network is completing the installation of a system with the following diameters:\n", + "\n", + " [ 800, 1000, 1100, 600, 450, 900, 700, 700, 300, 1100, 400, 700, 350, 1100, 600, 450, 400, 350, 1100, 900, 600, 600, 1100, 700, 500, 400, 450, 350, 700, 350, 1000, 400, 400, 400, 350, 900, 300, 1000, 400, 300, 450, 400, 450, 350,1100, 900, 450, 800, 800, 300, 1100, 600, 300, 700, 1000, 1000, 800, 800]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 5.1:</b> \n", + " Using your model, predict the nodal pressures for this set of diameters, and report the lowest, highest and mean pressures in the system. Note that you will need to define the diameters, normalize them then use the model to make a prediction.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "YOUR_CODE_HERE " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "5.1) What is the minimum that your model predicts for this network?\n", + "\n", + "5.2) How confident are you in the prediction of your model? Why?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_6/Analysis_solution.ipynb b/content/GA_2_6/Analysis_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..dc3c428ac9dc5416667f9ad3914cb3eb406657d7 --- /dev/null +++ b/content/GA_2_6/Analysis_solution.ipynb @@ -0,0 +1,1276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 10: Handling the pressure - Machine learning for predicting pressure in Water Distribution Systems\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px; height: auto; margin: 0\"\\>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px; height: auto; margin: 0\"\\>\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.6. Due: Friday, Dec 22, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 📠Specifications" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is divided into five parts:\n", + "1) Data pre-processing.\n", + "2) Defining and training a multilayer perceptron (MLP).\n", + "3) Optimization of the MLP hyperparameters.\n", + "4) Model assessment.\n", + "5) Model usage.\n", + "\n", + "**Completition requirements:**\n", + "By the end of this notebook, you should have:\n", + "- Implemented all the code cells for:\n", + " - Splitting the data into training, validation, and testing sets\n", + " - Normalizing the data\n", + " - Instantiating an MLP\n", + " - Training the MLP with a training loop\n", + " - Defining a grid-search hyperoptimization\n", + " - Assess the accuracy of the MLP\n", + " - Assess the speed of the MLP\n", + " - Use it to predict the pressure of a particular example\n", + "- Generated and exported all of the relevant plots for the report\n", + "- Answered all the questions in the report\n", + "\n", + "*Complete this assignment by the end of the session at 12:30. This means having a single report for your group with all the plots, analysis and interpretation completed.*\n", + "\n", + "**Working method:**\n", + "\n", + "Each of the parts of the notebook can be coded independently. However, in order to run the code in each part, the code in the previous parts should be in place." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "This notebook includes boxes with the formatting shown here to list the questions you are expected to answer in your report. You are not expected to write your answers here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🔙 Background" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 💧 Water distribution systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A water distribution system transports water from sources, like wells or reservoirs, to various locations where water is needed, like homes, shops, and factories. \n", + "\n", + "A basic system consists of sources of water supply and demand points for water connected by pipe lines. Figure 1 shows an example system where there are two supply centers and ten demand nodes. This transmission system can connect sparse populations, and it can be considered as a simple network of one reservoir and few nodes and pipes. Nevertheless, in a city of moderate size, there may be a number of supply centers and hundreds of demand points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"display: flex; flex-direction: row;\">\n", + " <div style=\"flex: 50%;\">\n", + " <center>\n", + " <img src=\"./figs/WDSAsset 1v1.png\" width=\"400\"/>\n", + " <figcaption><b>Figure 1.</b> Simplified scheme of a branched water distribution system.</figcaption>\n", + " </center>\n", + " </div>\n", + " <div style=\"flex: 50%;\">\n", + " <center>\n", + " <img src=\"./figs/BAK.png\" width=\"700\"/>\n", + " <figcaption><b>Figure 2.</b> Numerical results for the BakRyan water distribution system.</figcaption>\n", + " </center>\n", + " </div>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Water utilities rely on hydrodynamic models to properly design and control water distribution systems (WDSs). These physically-based models compute the pressures at all the junctions, as illustrated in Figure 2. In this figure, we can see the water network of BakRyan with the pressure at each node of the network represented by the colour. In water distribution systems, pressure is a fundamental variable. Without sufficient pressure in the system, the network is not able to supply water to the users. \n", + "\n", + "We can use pressure estimations to ensure proper water pressure, efficient flow, and reliable distribution of water to consumers. For obtaining these estimations, we tipically use hydrodynamic models. However, the computational speed of these models is often insufficient for some applications in civil engineering such as optimisation or real-time control, especially in large networks. \n", + "\n", + "One alternative to address this issue is developing data-driven models. These models can be trained using results from simulations done with the physically-based model. The objective of the data-driven model we will develop is to estimate pressure at each node of the water network but in a shorter time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ✅ Application" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, you will create a Multilayer Perceptron (MLP) for estimating the nodal pressures from the BakRyan water distribution system (Figure 2). This system has 58 pipes and 35 nodes. Your task is to create and train this Artificial Neural Network, exemplified in Figure 3. The MLP should estimate the pressures while being faster to run than the physically-based model (which usually takes 0.04 seconds to run per simulation). Furthermore, you will hyperoptimize the MLP to improve its performance.\n", + "\n", + "**Your input data will be a vector of pipe diameters. The output data will be a vector of nodal pressures.**\n", + "\n", + "Mathematically, we can express the our application as follows:\n", + "\n", + "$$\n", + "y = \\phi(x; W)\n", + "$$\n", + "\n", + "where:\n", + "\n", + "$y$: output data (nodal pressures, units: mwc*)\n", + "\n", + "$\\phi$: represents the Artificial Neural Network\n", + "\n", + "$x$: input data (pipe diameters, units: m)\n", + "\n", + "$W$: parameters of the MLP (unitless)\n", + "\n", + "Having pairs of input-output data (diameters, $x$, and pressures, $y$), it is our task to find the set of parameters, $W$, that best fit the data. Note that there are no observed pressures at some of the nodes.\n", + "\n", + "_*In water engineering, it is common to express the value of the pressure in meters of water column (mwc). This unit is equivalent to the pressure exerted by a column of water of 1 meter in height._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div>\n", + "<center><img src=\"./figs/ANN_image2.png\" width=\"600\"/>\n", + "<figcaption><b>Figure 3.</b> Artificial neural network representation, with a zoomed-in view of how a single neuron works. For this notebook, we have 58 input features (pipe diameters) and 35 outputs (nodal pressures).</figcaption></center>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 📔 Preliminaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run this notebook you need to have installed the following packages:\n", + "- Numpy\n", + "- Matplotlib\n", + "- Pickle\n", + "- Scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the database " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purposes of this notebook, there is an already existing database that you can use to create and train the MLPs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = r\"data/features_BAK.pk\" \n", + "with open(file_path, 'rb') as handle:\n", + " features = pickle.load(handle)\n", + "\n", + "file_path = r\"data/targets_BAK.pk\"\n", + "with open(file_path, 'rb') as handle:\n", + " targets = pickle.load(handle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can explore the content of each of these variables. In total, we collected 10000 examples of the BakRyan system with random configurations of the available diameters (of the 58 pipes). \n", + "\n", + "As input features (X), we use the diameters of all the pipes in the network, and each configuration of diameters is related 1-1 with the pressure at all the nodes in the system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Dimensions of features (X):', features.shape)\n", + "print('Dimensions of targets (t):', targets.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Pre-Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data into training, validation, and testing sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 1.1:</b> \n", + "\n", + "In machine learning, it's common to split the dataset into three parts: a training set, a validation set, and a test set. \n", + "\n", + "Your task is to write a Python code snippet that splits a given dataset into these three parts. The dataset consists of `features` and `targets`.\n", + "\n", + "The dataset should be split as follows:\n", + "\n", + "- 80% of the data should go to the training set.\n", + "- 10% of the data should go to the validation set.\n", + "- 10% of the data should go to the test set.\n", + "\n", + "The splitting should be done in a way that shuffles the data first to ensure that the training, validation, and test sets are representative of the overall distribution of the data. You can set the random state for the shuffling to ensure that the results are reproducible; you can use the values 42 for the first split and 24 for the second split.\n", + "\n", + "The resulting training, validation, and test sets should be stored in the variables `X_train`, `t_train`, `X_val`, `t_val`, `X_test`, and `t_test`.\n", + "\n", + "**Hint:** You can use the `train_test_split` function from the `sklearn.model_selection` module to perform the splitting." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note here that the training, validation and test sets are created in two steps, due to the way <code>train_test_split</code> is implemented in <code>sklearn</code>. Thus, in the second split you value used for <code>test_size</code> should <b>not</b> be 0.10!</p></div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# X_train, X_val_test, t_train, t_val_test = train_test_split(YOUR_CODE_HERE, YOUR_CODE_HERE, test_size=YOUR_CODE_HERE, random_state=42)\n", + "# X_val, X_test, t_val, t_test = train_test_split(YOUR_CODE_HERE, YOUR_CODE_HERE, test_size=YOUR_CODE_HERE, random_state=24)\n", + "# Solution\n", + "X_train, X_val_test, t_train, t_val_test = train_test_split(features, targets, test_size=0.20, random_state=42)\n", + "X_val, X_test, t_val, t_test = train_test_split(X_val_test, t_val_test, test_size=0.50, random_state=24)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalizing the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we normalize the data using the MinMaxScaler from scikit-learn. This scaler transforms the data to be between 0 and 1. This is important because the ANN will be trained using the gradient descent algorithm, which is sensitive to the scale of the data. Notice that we use the training data to fit the scaler. This is important because we assume that the model only sees the training data and we do not use any of the validation or testing data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 1.2:</b> \n", + "\n", + "In machine learning, it's often beneficial to normalize the feature variables to a specific range. This can help the model converge faster during training and can also prevent certain features from dominating others due to their scale.\n", + "\n", + "Your task is to write a Python code snippet that normalizes the feature variables of a training set and a validation set to the range [0, 1]. The feature variables are stored in the variables `X_train` and `X_val`.\n", + "\n", + "You should use the `MinMaxScaler` class from the `sklearn.preprocessing` module to perform the normalization. This class scales and translates each feature individually such that it is in the given range on the training set.\n", + "\n", + "The normalized features should be stored in the variables `normalized_X_train` and `normalized_X_val`.\n", + "\n", + "<em>Note: we do this task for you.</em>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `MinMaxScaler` should be fitted on the training features only. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaler_diameters = MinMaxScaler()\n", + "scaler_diameters.fit(X_train)\n", + "\n", + "normalized_X_train = scaler_diameters.transform(X_train)\n", + "normalized_X_val = scaler_diameters.transform(X_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 1.3:</b> \n", + "\n", + "Your task is to write a Python code snippet that normalizes the target variables of a training set and a validation set to the range [0, 1]. The target variables are stored in the variables `t_train` and `t_val`.\n", + "\n", + "The normalized targets should be stored in the variables `normalized_t_train` and `normalized_t_val`.\n", + "\n", + "<em>Note: we do this task for you.</em>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaler_pressures = MinMaxScaler()\n", + "scaler_pressures.fit(t_train)\n", + "\n", + "normalized_t_train = scaler_pressures.transform(t_train)\n", + "normalized_t_val = scaler_pressures.transform(t_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "1.1) What is the purpose of splitting a dataset into training, validation, and test sets in the context of machine learning?\n", + "\n", + "1.2) What part of the pre-processing improves the representativity of the overall distribution of the data?\n", + "\n", + "1.3) Why should the `MinMaxScaler` be fitted on the training data only, and then used to transform both the training and validation data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Defining and training an MLP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will define a Multilayer Perceptron (MLP). In Scikit-learn, the MLP is defined in the MLPRegressor class, you can see the documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html). This class has many hyperparameters that can be tuned to improve the performance of the model. Notice that in Scikit-learn, the model and the optimizer are defined in the same class. This means that we do not need to define an optimizer separately; therefore, some hyperparameters are related to the optimizer. For example, the learning rate. We will indicate the optimization hyperparameters in the next section; for now, we will only define the model hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.1:</b> \n", + "You are tasked with setting up a Multi-Layer Perceptron (MLP). The MLP should have the following characteristics:\n", + "\n", + " - The hidden layer sizes are defined as a tuple. For example, if we want to have two hidden layers with 10 and 5 neurons, respectively, we would write: hidden_layer_sizes=(10,5). Notice that we only specify the hidden layer sizes, the input and output sizes will be automatically inferred when we train the model. \n", + " - The activation function can be one of the following: 'identity', 'logistic', 'tanh', 'relu'.\n", + "\n", + "The configured MLP regressor should be stored in a variable named `model`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# model = MLPRegressor(YOUR_CODE_HERE, YOUR_CODE_HERE)\n", + "# Solution\n", + "model = MLPRegressor(hidden_layer_sizes = (10, 5), \n", + " activation = 'tanh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a model, we need to train it! Now, we will define a training loop that will train the model using the training data and will evaluate the model using the validation data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-learn offers the possibility to directly train a model using the `fit` method. However, we will define a training loop to have more control over the training process. This will allow us to evaluate the model at each epoch and observe its training.\n", + "\n", + "The first step towards training a model is defining a function that transforms our training dataset into random mini-batches. This is a common practice used for training neural networks due to their computational efficiency and their ability to help the model generalize better. This practice generally leads to better computational efficiency, faster convergence and better generalization performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_mini_batches(X, t, batch_size):\n", + " \"\"\"\n", + " This function generates mini-batches from the given input data and labels.\n", + "\n", + " Parameters:\n", + " X (numpy.ndarray): The features.\n", + " t (numpy.ndarray): The targets corresponding to the input data.\n", + " batchsize (int): The size of each mini-batch.\n", + "\n", + " Returns:\n", + " list: A list of tuples where each tuple contains a mini-batch of the input data and the corresponding targets.\n", + " \"\"\"\n", + " # Generate permutations\n", + " perm = np.random.permutation(len(X))\n", + " X_train_perm = X[perm]\n", + " t_train_perm = t[perm]\n", + " \n", + " # Generate mini-batches\n", + " X_batches = []\n", + " t_batches = []\n", + " for i in range(0, len(X_train_perm), batch_size):\n", + " X_batches.append(X_train_perm[i:i+batch_size])\n", + " t_batches.append(t_train_perm[i:i+batch_size])\n", + "\n", + " return list(zip(X_batches, t_batches))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following figure illustrates both the way we split the original dataset and how we further split the training dataset into mini-batches. At every epoch the training dataset is shuffled and each mini-batch is considered **in isolation** by the network. The gradients coming from the single mini-batches are used to update the weights of the network (the randomness involved is why we say we are using **Stochastic** Gradient Descent).\n", + "\n", + "<div>\n", + "<center><img src=\"./figs/minibatching.png\" width=\"600\"/>\n", + "<figcaption><b>Figure 4.</b> Dataset splitting, mini-batching and the stochastic nature of MLP training.</figcaption></center>\n", + "</div>\n", + "\n", + "Now, we will define some hyperparameters for the training loop. These hyperparameters are related to the optimization process.\n", + "Define the following hyperparameters:\n", + "- `learning_rate` (float): The learning rate of the optimizer.\n", + "- `n_epochs` (int): The number of epochs to train the model. (For time reasons, we will only train the model for 20 epochs. However, you can increase this number to improve the performance of the model.)\n", + "- `batch_size` (int): The size of each mini-batch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.001\n", + "n_epochs = 20\n", + "batch_size = 64" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.2:</b> \n", + "\n", + "In this exercise, you are tasked with implementing a function to train a neural network model. The function should also compute and store the loss on the training and validation sets at each epoch. The loss function to be used is the Mean Squared Error (MSE), which is defined as:\n", + "\n", + "$$ MSE = \\frac{1}{n} \\sum_{i=1}^{n} (t_i - y_i)^2 $$\n", + "\n", + "where $t_i$ is the actual target, $y_i$ is the predicted value, and $n$ is the number of samples.\n", + "\n", + "The function should be named `train_model` and should take the following parameters:\n", + "\n", + "- `model`: An instance of a neural network model that we want to train.\n", + "- `normalized_X_train`: The normalized training data.\n", + "- `normalized_t_train`: The normalized training labels.\n", + "- `normalized_X_val`: The normalized validation data.\n", + "- `normalized_t_val`: The normalized validation labels.\n", + "- `n_epochs`: The number of epochs to train the model for.\n", + "- `batch_size`: The size of each mini-batch.\n", + "- `learning_rate`: The learning rate for the model.\n", + "\n", + "The function should perform the following steps:\n", + "\n", + "1. Initialize two empty lists, `train_loss_list` and `val_loss_list`, to store the training and validation losses at each epoch.\n", + "\n", + "2. Loop over the specified number of epochs. For each epoch:\n", + "\n", + " a. Generate mini-batches from the normalized training data and labels using a function `get_mini_batches(normalized_X_train, normalized_t_train, batch_size)`.\n", + "\n", + " b. For each mini-batch, update the model's weights using the `partial_fit` method of the model.\n", + "\n", + " c. Compute the MSE loss on the training set and append it to `train_loss_list`.\n", + "\n", + " d. Compute the MSE loss on the validation set and append it to `val_loss_list`.\n", + "\n", + " e. Print the training progress including the current epoch and the training and validation losses.\n", + "\n", + " f. Return the `train_loss_list` and `val_loss_list` lists.\n", + "\n", + "Your task is to write the Python code that implements the `train_model` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# def train_model(model, normalized_X_train, normalized_t_train, normalized_X_val, normalized_t_val, n_epochs, batch_size, learning_rate):\n", + "# train_loss_list = []\n", + "# val_loss_list = []\n", + "# model.learning_rate_init = learning_rate\n", + " \n", + "# for epoch in range(n_epochs):\n", + " \n", + "# # Generate mini-batches\n", + "# mini_batches = get_mini_batches(YOUR_CODE_HERE)\n", + " \n", + "# # Train model on mini-batches\n", + "# for X_batch, t_batch in mini_batches:\n", + "# YOUR_CODE_HERE\n", + " \n", + "# YOUR_CODE_HERE # Hint: may be more than one line\n", + "\n", + "# # Store loss values\n", + "# train_loss_list.append(train_loss)\n", + "# val_loss_list.append(val_loss)\n", + "\n", + "# # Print training progress\n", + "# print(f\"Epoch {epoch+1}/{n_epochs} - Train Loss: {train_loss_list[-1]:.4f} - Val Loss: {val_loss:.4f}\")\n", + " \n", + "# return train_loss_list, val_loss_list\n", + "\n", + "# Solution:\n", + "def train_model(model, normalized_X_train, normalized_t_train, normalized_X_val, normalized_t_val, n_epochs, batch_size, learning_rate):\n", + " train_loss_list = []\n", + " val_loss_list = []\n", + " model.learning_rate_init = learning_rate\n", + " \n", + " for epoch in range(n_epochs):\n", + " \n", + " # Generate mini-batches\n", + " mini_batches = get_mini_batches(normalized_X_train, normalized_t_train, batch_size)\n", + " \n", + " # Train model on mini-batches\n", + " for X_batch, t_batch in mini_batches:\n", + " model.partial_fit(X_batch, t_batch)\n", + " \n", + " # Compute loss on training and validation sets\n", + " train_loss = mean_squared_error(normalized_t_train, model.predict(normalized_X_train))\n", + " \n", + " # Compute loss on validation set\n", + " val_loss = mean_squared_error(normalized_t_val, model.predict(normalized_X_val))\n", + "\n", + " # Store loss values\n", + " train_loss_list.append(train_loss)\n", + " val_loss_list.append(val_loss)\n", + "\n", + " # Print training progress\n", + " print(f\"Epoch {epoch+1}/{n_epochs} - Train Loss: {train_loss_list[-1]:.4f} - Val Loss: {val_loss:.4f}\")\n", + " \n", + " return train_loss_list, val_loss_list\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_loss_list, val_loss_list = train_model(model, normalized_X_train, normalized_t_train, normalized_X_val, normalized_t_val, n_epochs, batch_size, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.3:</b> \n", + " Plot the validation and training loss curves. Add this plot to your report.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a scatter plot with enhanced styling\n", + "plt.figure(figsize=(8, 6)) # Set the figure size\n", + "\n", + "x_axis = list(range(len(train_loss_list)))\n", + "\n", + "# Create a scatter plot\n", + "# plt.scatter(x_axis, YOUR_CODE_HERE, label='Validation loss', color='red', marker='.', s=100, alpha=0.7, edgecolors='black', linewidths=0.5)\n", + "# plt.scatter(x_axis, YOUR_CODE_HERE, label='Training loss', color='blue', marker='.', s=100, alpha=0.7, edgecolors='black', linewidths=0.5)\n", + "# Solution:\n", + "plt.scatter(x_axis, val_loss_list, label='Validation loss', color='red', marker='.', s=100, alpha=0.7, edgecolors='black', linewidths=0.5)\n", + "plt.scatter(x_axis, train_loss_list, label='Training loss', color='blue', marker='.', s=100, alpha=0.7, edgecolors='black', linewidths=0.5)\n", + "\n", + "# Add labels and a legend with improved formatting\n", + "plt.xlabel('Epochs', fontsize=14, fontweight='bold')\n", + "plt.ylabel('Loss', fontsize=14, fontweight='bold')\n", + "plt.title('Loss curves', fontsize=16, fontweight='bold')\n", + "plt.legend(loc='upper right', fontsize=12)\n", + "\n", + "# Set the y-axis to be logarithmic\n", + "plt.yscale('log')\n", + "\n", + "# Customize the grid appearance\n", + "plt.grid(True, linestyle='--', alpha=0.5)\n", + "\n", + "# Customize the tick labels\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "\n", + "# Add a background color to the plot\n", + "plt.gca().set_facecolor('#f2f2f2')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 2.4:</b> \n", + "Using the `score` function of the MLP Regressor, compute the R2 score of the model on the training and validation sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# model.score(YOUR_CODE_HERE, YOUR_CODE_HERE)\n", + "# Solution:\n", + "model.score(normalized_X_train, normalized_t_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# model.score(YOUR_CODE_HERE, YOUR_CODE_HERE)\n", + "# Solution:\n", + "model.score(normalized_X_val, normalized_t_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "2.1) Based on the shape of the loss curves, what can you indicate about the fitting capabilities of the model? (Is it overfitting, underfitting, or neither?)\n", + "\n", + "2.2) How do you explain the difference between the values of training and validation score?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Hyperparameter tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>For Tasks 3 and 4 the code is 100% complete, but you are expected to read it thoroughly to help understand the analysis and provide answers in your report.</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 3.1:</b> \n", + " Create a grid-search strategy to find hyperparameters that give the best prediction on the validation set. Vary the number of layers and number of hidden units per layer. You can assume that all the hidden layers have the same number of hidden units.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define coordinate vectors for grid\n", + "layer_sizes = [10, 20, 50, 100] \n", + "layer_numbers = [1, 2, 3, 4]\n", + "\n", + "# Create a grid for the coordinate pairs and store them in an array\n", + "val_loss_grid = np.zeros((len(layer_sizes), len(layer_numbers)))\n", + "\n", + "# Loop over all the layer sizes\n", + "for i, lsize in enumerate(layer_sizes):\n", + " \n", + " # Loop over all numbers of hidden layers\n", + " for j, lnumber in enumerate(layer_numbers):\n", + " \n", + " # get tuple of hidden layer sizes\n", + " layers = (lsize,) * lnumber\n", + " print(\"Training NN with hidden layers: {}\".format(layers))\n", + " \n", + " # Create the ANN model with the given hidden layer sizes and activation function\n", + " model = MLPRegressor(hidden_layer_sizes=layers, activation='tanh')\n", + " \n", + " _, val_loss_list = train_model(model, \n", + " normalized_X_train, \n", + " normalized_t_train,\n", + " normalized_X_val, \n", + " normalized_t_val, \n", + " n_epochs=20, \n", + " batch_size=64,\n", + " learning_rate=0.001\n", + " )\n", + " \n", + " val_loss_grid[i,j] = val_loss_list[-1]\n", + " \n", + " print(\" Loss: {:.4e}\\n\".format(val_loss_grid[i,j]))\n", + "\n", + "\n", + "# Extract the hyperparameters that gave the lowest loss and print\n", + "min_size, min_number = np.unravel_index(np.argmin(val_loss_grid), val_loss_grid.shape)\n", + "print(\"\\n\\nModel with {} layers and {} neurons per layer gave lowest loss of {:.4e}\".format(layer_numbers[min_number], layer_sizes[min_size], val_loss_grid[min_size, min_number]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use our test data to visualize our best-performing model and test its predictive capabilities. First, re-initialize & train the model with the optimal hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 3.2:</b> \n", + " Re-initialize & train the model with the optimal hyperparameters.\n", + " \n", + "The reconfigured MLP regressor should be stored in a variable named `model`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up NN\n", + "layers = (layer_sizes[min_size],) * layer_numbers[min_number]\n", + "model = MLPRegressor(hidden_layer_sizes=layers, activation='tanh')\n", + "\n", + "# train NN\n", + "_, val_loss_list = train_model(model, \n", + " normalized_X_train, \n", + " normalized_t_train,\n", + " normalized_X_val, \n", + " normalized_t_val, \n", + " n_epochs=20, \n", + " batch_size=64,\n", + " learning_rate=0.001\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the Python code to plot the matrix `val_loss_grid` with the specified row and column labels:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 3.3:</b> \n", + " Plot the validation loss grid. Add this plot to your report.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Define the row and column labels\n", + "rows = layer_sizes\n", + "cols = layer_numbers\n", + "\n", + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(val_loss_grid, cmap='jet', interpolation='nearest')\n", + "\n", + "# Add a colorbar\n", + "plt.colorbar(label='Validation Loss')\n", + "\n", + "# Add the row and column labels\n", + "plt.xticks(range(len(cols)), cols)\n", + "plt.yticks(range(len(rows)), rows)\n", + "\n", + "plt.xlabel('Number of Layers')\n", + "plt.ylabel('Number of Neurons')\n", + "\n", + "plt.title('Validation Loss Grid')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "This code will create a heatmap where the color intensity represents the validation loss. The colorbar on the side provides a reference for the loss values. The row and column labels represent the number of neurons and layers, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "3.1) How does hyperparameter tuning in machine learning relate to the concept of model complexity?\n", + "\n", + "3.2) From the plot, what is the impact of increasing the number of hidden layers on the model's ability to capture complex patterns in the data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Model assessment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we are going to test the model's accuracy on the test dataset. First, we need to scale the test data using the scaler that we used for the training data. Then, we can use the `score` method of the model to compute the R2 score on the test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "normalized_X_test = scaler_diameters.transform(X_test)\n", + "normalized_t_test = scaler_pressures.transform(t_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.score(normalized_X_test, normalized_t_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More than a performance metric as the score, we can observe the errors of different nodes for all the test database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "estimated_pressure = scaler_pressures.inverse_transform(model.predict(normalized_X_test))\n", + "error = t_test - estimated_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "node_ID = 0\n", + "error_node = error[:, node_ID]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 4.1:</b> \n", + " Plot the distribution of errors. Add this plot to your report.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "# Create a histogram\n", + "ax.hist(error_node, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)\n", + "\n", + "# Axis formatting\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "ax.spines['bottom'].set_color('#DDDDDD')\n", + "ax.tick_params(bottom=False, left=False)\n", + "ax.set_axisbelow(True)\n", + "ax.yaxis.grid(True, color='#EEEEEE')\n", + "ax.xaxis.grid(False)\n", + "\n", + "ax.set_xlabel('Error of prediction [m]', labelpad=15, color='#333333')\n", + "ax.set_ylabel('Frequency', labelpad=15, color='#333333')\n", + "ax.set_title(f'Error of node {node_ID} across test scenarios', pad=15, color='#333333', weight='bold')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot gives us an idea of the expected errors we can encounter when using the model. However, it is also important that the MLP is faster than original simulator (which takes around 0.04 seconds per simulation)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Speed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can calculate the time per scenario that the model takes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "y_pred_test = model.predict(X_test)\n", + "total_time = time.time() - start_time\n", + "\n", + "num_test_sims = len(y_pred_test)\n", + "\n", + "data_driven_exec_time_per_sim = total_time/num_test_sims\n", + "print(f'Data-driven model took {data_driven_exec_time_per_sim:.7f} seconds for {num_test_sims} scenarios')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Considering that the original model can take up to 0.04 seconds per scenario, we can estimate the potential gain in speed-up. (Speed-up = original_time/Data-driven_model_time)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "original_time_per_sim = 0.04\n", + "\n", + "speed_up = np.round(original_time_per_sim/data_driven_exec_time_per_sim, 2)\n", + "print('The data-driven model is', speed_up,'times faster than original simulator per scenario.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "4.1) The score indicates a high fitting, is that reflected in the plot of the errors? Why?\n", + "\n", + "4.2) Is the the plot of errors centered around zero? If not, what does that mean?\n", + "\n", + "4.3) How diverse can the speed up values be if you run the cell multiple times? Why?\n", + "\n", + "4.4) What would occur with the speed up if you increase the number of neurons in the hidden layers?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Model usage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a trained model, we can use it to predict the nodal pressures for a given set of diameters. Let's try it out!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The water utility in charge of the BakRyan water distribution network is completing the installation of a system with the following diameters:\n", + "\n", + " [ 800, 1000, 1100, 600, 450, 900, 700, 700, 300, 1100, 400, 700, 350, 1100, 600, 450, 400, 350, 1100, 900, 600, 600, 1100, 700, 500, 400, 450, 350, 700, 350, 1000, 400, 400, 400, 350, 900, 300, 1000, 400, 300, 450, 400, 450, 350,1100, 900, 450, 800, 800, 300, 1100, 600, 300, 700, 1000, 1000, 800, 800]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Task 5.1:</b> \n", + " Using your model, predict the nodal pressures for this set of diameters, and report the lowest, highest and mean pressures in the system. Note that you will need to define the diameters, normalize them then use the model to make a prediction.\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Solution</b> provided in the code cells here:\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diameters = np.array([[800, 1000, 1100, 600, 450,\n", + " 900, 700, 700, 300, 1100,\n", + " 400, 700, 350, 1100, 600,\n", + " 450, 400, 350, 1100, 900,\n", + " 600, 600, 1100, 700, 500,\n", + " 400, 450, 350, 700, 350,\n", + " 1000, 400, 400, 400, 350,\n", + " 900, 300, 1000, 400, 300,\n", + " 450, 400, 450, 350, 1100,\n", + " 900, 450, 800, 800, 300,\n", + " 1100, 600, 300, 700, 1000,\n", + " 1000, 800, 800]])\n", + "normalized_diameters = scaler_diameters.transform(diameters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "normalized_predictions = model.predict(normalized_diameters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = scaler_pressures.inverse_transform(normalized_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'{predictions.min():.3f}')\n", + "print(f'{predictions.max():.3f}')\n", + "print(f'{predictions.mean():.3f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFC5CB; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<b>Questions:</b>\n", + "\n", + "5.1) What is the minimum that your model predicts for this network?\n", + "\n", + "5.2) How confident are you in the prediction of your model? Why?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/GA_2_6/README.md b/content/GA_2_6/README.md new file mode 100644 index 0000000000000000000000000000000000000000..02a3210fcf3f7a4b233eae7562b64777b028d52a --- /dev/null +++ b/content/GA_2_6/README.md @@ -0,0 +1,47 @@ +# Project 10 Report: Machine Learning + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/).* + +The focus of this assignment is to use a neural network to create a surrogate model of a pipe network that predicts pressures as a function of pipe diameter. The context is that the model that is used to compute pressures in the system is time-consuming to run, so we would like to train a neural network to make calculations faster (i.e., use the surrogate model). + +## Working Remotely + +For this week, we recognize that many may be working remotely. As such, we've made the project files available in advance to allow you all the ability to work on it beforehand and make arrangements with your group-mates to work effectively and cooperatively. Teachers will still be present, in-person, on Friday and it is still expected that the project report be submitted at the end of the session Friday. If there are any announcements to be made, we will add them to presentation, which you can view remotely at [this link](https://tud365.sharepoint.com/:p:/s/MUDE/EcZ-L1gD2ABEhlOb0p83OGIBl4N-Jr4OqP2TRNRL9CEYiQ?e=3Uuueo). + +## Overview of material + +- `README.md` (this file) +- `Report.md`, primary file in which you write your answers to questions. +- `P10.ipynb`, the notebook in which you will build and evaluate the neural network. +- a subdirectory `./figs` containing some figures included in the notebooks (which you don't need to open). +- a `.gitignore` file preventing your data being pushed to Gitlab. + +**You need to download the data for this project!** Two data files are needed: `features_BAK.pk` and `targets_BAK.pk`, which should added to your repository in a subdirectory `./data`, which is located in the same directory as `P10.ipynb`. **Download the data [using this link](https://surfdrive.surf.nl/files/index.php/s/UmjdZCAAlbvaKRO/download).** + + +### Python Environments + +The environment you used earlier in the week will be sufficient, as long as it has `sklearn` and the standard packages used elsewhere in MUDE. + +## Submission and deadline + +- Submit your answers, together with any relevant plots, in the Markdown file `Report.md`. This is the primary document that will be used to determine your grade; however, the auxiliary files (e.g., `*.ipynb` or `*.py` files) may be checked in case something is not clear. +- The deadline is to submit your work by making commits to your Group's GitLab repository by Friday at 12:30h. +- This project will be graded on interpretation, application, documentation and programming. + +## Repository, Formatting and Static Check + +There is no static check for this project. Be sure to leave the outputs from your code cells in your `*.ipynb` file so that they are readable. + +You are always expected to provide well-formatted figures and Markdown text in your `Report.md` file, as well as logically organize any auxiliary files you may use (e.g., try to put your figures in a sub-directory, if there are a lot of them). If you run out of time it is OK if your `*ipynb` files do not run. + +## Backup data links + +Sometimes the download links reach a maximum limit. If the link above no longer works, try one of these: +- [Backup link 1](https://surfdrive.surf.nl/files/index.php/s/E2FOutaHes7gm6Z/download) +- [Backup link 2](https://surfdrive.surf.nl/files/index.php/s/OQb1kpbrND3NPqg/download) + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. diff --git a/content/GA_2_6/Report.md b/content/GA_2_6/Report.md new file mode 100644 index 0000000000000000000000000000000000000000..434467e592fe5f9963160ac559b4aa0624eae0c5 --- /dev/null +++ b/content/GA_2_6/Report.md @@ -0,0 +1,120 @@ +# Project 10 Report: Machine Learning + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/).* + +**YOUR GROUP NAME HERE** + + +## Primary Task + +**Complete the notebook `P10.ipynb`and write your answers in this document as requested in the questions below. Note that only part of the notebook results are required to be included in this report.** Typically a short, one-line answer is sufficient; though a simple 'yes' or 'no' is _not_ sufficient, please include a short reasoning, justification or argumentation. + +_You will be graded on the plots and answers provided in this file. You can delete the instructions and any other unnecessary text prior to submission._ + +## Report Instructions + +Remember to use Markdown features to clearly indicate your answers for each question below. + +**Importing figures into a Markdown file:** +1. Use relative referencing only, with the git repo (working directory) as the root (this is expressed with a single dot `.`) +2. Our grading systems are case-sensitive so match the names of folders exactly +3. Use linux-style path separators: `/` rather than `\`. +4. Do not include spaces in your file path or image name; if it is unavoidable replace the space with `%20`, for example: `` + +Here are some examples: +- an image located in the working directory `` (where `ext` is any image extension). +- an image located in a sub-directory called "images": `` +- an image with a space in the file name: `` + +When using Markdown to include an image, the square brackets is a text tag that is displayed in case the image does not load. Do not include a dot in the square brackets; i.e., do _not_ do this: ``. + +## Answers to Questions + +### Section 1 + +**1.1) What is the purpose of splitting a dataset into training, validation, and test sets in the context of machine learning?** + +_Your answer here._ + + +**1.2) What part of the pre-processing improves the representativity of the overall distribution of the data?** + +_Your answer here._ + + +**1.3) Why should the `MinMaxScaler` be fitted on the training data only, and then used to transform both the training and validation data?** + +_Your answer here._ + + + +### Section 2 + +Plot the validation and training loss curves. Add this plot to your report. + +_Your plot here._ + + +**2.1) Based on the shape of the loss curves, what can you indicate about the fitting capabilities of the model? (Is it overfitting, underfitting, or neither?)** + +_Your answer here._ + +**2.2) How do you explain the difference between the values of training and validation score?** + +_Your answer here._ + +### Section 3 +Plot the validation loss grid. Add this plot to your report. + +_Your plot here._ + +**3.1) How does hyperparameter tuning in machine learning relate to the concept of model complexity?** + +_Your answer here._ + +**3.2) From the graph, what is the impact of increasing the number of hidden layers on the model's ability to capture complex patterns in the data?** + +_Your answer here._ + +### Section 4 + +_Your plot here._ + +**4.1) The score indicates a high fitting, is that reflected in the plot of the errors? Why?** + +_Your answer here._ + + +**4.2) Is the the plot of errors centered around zero? If not, what does that mean?** + +_Your answer here._ + + +**4.3) How diverse can the speed up values be if you run the cell multiple times? Why?** + +_Your answer here._ + +**4.4) What would occur with the speed up if you increase the number of neurons in the hidden layers?** + +_Your answer here._ + +### Section 5 + +**5.1) What is the minimum that your model predicts for this network?** + +_Your answer here._ + + +**5.2) How confident are you in the prediction of your model? Why?** + +_Your answer here._ + + +## General Comments on the Assignment [optional] + +_Use this space to let us know if you encountered any issues completing this assignment (but please keep it short!). For example, if you encountered an error that could not be fixed in your Python code, or perhaps there was a problem submitting something via GitLab. You can also let us know if the instructions were unclear. You can delete this section if you don't use it._ + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. diff --git a/content/GA_2_6/Report_solution.md b/content/GA_2_6/Report_solution.md new file mode 100644 index 0000000000000000000000000000000000000000..0889e967b9df3fca3f102e466a7e7c5e4aec307a --- /dev/null +++ b/content/GA_2_6/Report_solution.md @@ -0,0 +1,90 @@ +# Project 10 Report: Machine Learning + +*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/)* + +**SOLUTION** + +## Answers to Questions + +### Section 1 + +**1.1) What is the purpose of splitting a dataset into training, validation, and test sets in the context of machine learning?** + +The purpose of splitting a dataset into training, validation, and test sets is to ensure that the machine learning model can generalize well to unseen data. The training set is used to train the model, the validation set is used to tune hyperparameters and prevent overfitting, and the test set is used to evaluate the model's final performance. + +**1.2) What part of the pre-processing improves the representativity of the overall distribution of the data?** + +To ensure that the splitting of the dataset is representative of the overall distribution of the data, shuffling the data helps reducing any bias from the ordering of the data. + +**1.3) Why should the `MinMaxScaler` be fitted on the training data only, and then used to transform both the training and validation data?** + +The `MinMaxScaler` should be fitted on the training data only to prevent information leakage from the validation set into the training process. If it were fitted on the validation data, it could bias the model towards specific characteristics of the validation set that might not be present in the training data or future unseen data. + +### Section 2 + +Plot the validation and training loss curves. Add this plot to your report. + + + +**2.1) Based on the shape of the loss curves, what can you indicate about the fitting capabilities of the model? (Is it overfitting, underfitting, or neither?)** + +Generally: + +- If the training loss is low but the validation loss is high, the model is likely overfitting. This means it performs well on the training data but not on unseen data. +- If both the training and validation losses are high, the model is likely underfitting. This means it is not complex enough to capture the patterns in the data. +- If both losses are low and follow a similar trend, the model is likely fitting well. (This is probably our case) + +**2.2) How do you explain the difference between the values of training and validation score?** + +The training score reflects how well the model fits the data it was trained on. The validation score reflects how well the model generalizes to unseen data. In this case, the validation loss could be slightly lower than the training loss, this is due to the fact that the validation set is smaller than the training set. This means that the validation loss is more sensitive to outliers and noise in the data. + +### Section 3 +Plot the validation loss grid. Add this plot to your report. + + + +**3.1) How does hyperparameter tuning in machine learning relate to the concept of model complexity?** + +Hyperparameter tuning in machine learning is directly related to the concept of model complexity. Hyperparameters control the number of layers in a neural network, the number of nodes in each layer, etc. Adjusting these can increase or decrease model complexity. A more complex model (e.g., more layers or nodes) can capture more complex patterns in the data, but it's also more prone to overfitting. Conversely, a less complex model may not capture all the patterns but can generalize better to unseen data. + +**3.2) From the graph, what is the impact of increasing the number of hidden layers on the model's ability to capture complex patterns in the data?** + +From the graph, we can see that increasing the number of hidden layers increases the model's ability to capture complex patterns in the data. However, this comes at the cost of increased computational complexity and a higher risk of overfitting. + +### Section 4 + +Plot the distribution of errors. Add this plot to your report. + + + +**4.1) The score indicates a high fitting, is that reflected in the plot of the errors? Why?** + +Generally, if the score indicates a high fitting which is reflected in the plot of the errors as the distribution is close to zero. Nevertheless, there are still some outliers that are not captured by the model. + +**4.2) Is the the plot of errors centered around zero? If not, what does that mean?** + +Ideally, the plot of errors should be centered around zero, which would indicate that the model is equally likely to overestimate or underestimate the true values. Nevertheless, the plot can be off centered, it could mean that the model is consistently overestimating or underestimating the true values. In this case, the plot is skewed to the left, which means that the model is consistently overestimating the true values. + +**4.3) How diverse can the speed up values be if you run the cell multiple times? Why?** +The speed up values can vary if you run the cell multiple times due to various factors like CPU load, memory usage, and other processes running on your machine at the same time. However, if the code is deterministic and there are no significant changes in your machine's state, the speed up values should not be very diverse. + +**4.4) What would occur with the speed up if you increase the number of neurons in the hidden layers?** +If you increase the number of neurons in the hidden layers, the model will become more complex and will likely take longer to execute. Therefore, the speed up (i.e., the gain in time) would likely be less. + +### Section 5 +**5.1) What is the minimum that your model predicts for this network?** + +The minimum, maximum and mean values that the model predicts for this network are 3.910, 59.634 and 33.755 mwc, respectively. Note that these values can vary as it depends on the model you have trained; for example, in another run the minimum was 16.2 mwc! + +**5.2) How confident are you in the prediction of your model? Why?** + +We saw that the model had generally a good fit, but there the errors were larger when predicting low pressures, for example. Therefore, we should also be aware that most likely the actual minimum pressure could be lower than the predicted value. + +## General Comments on the Assignment [optional] + +_Use this space to let us know if you encountered any issues completing this assignment (but please keep it short!). For example, if you encountered an error that could not be fixed in your Python code, or perhaps there was a problem submitting something via GitLab. You can also let us know if the instructions were unclear. You can delete this section if you don't use it._ + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. 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flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Due: complete this PA prior to class on Friday, Dec 8, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "7a28e541-d2d0-48a9-abf6-7b73075c8fd3", + "metadata": { + "tags": [] + }, + "source": [ + "## Overview of Assignment\n", + "\n", + "This assignment confirms you were able to create and activate a Python environment using Anaconda from an `environment.yml` file, and that your Gurobi license has been set up properly.\n", + "\n", + "**Remember:** PA12 has two notebooks that must be completed (A and B). Follow the instructions in **`README.md`** if you have not already done so.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- You commit the file `license.lic` file to your repository (it is generated as part of this assignment to confirm you've installed Gurobi properly)\n", + "- Don't forget the criteria in the other notebook of PA12" + ] + }, + { + "cell_type": "markdown", + "id": "46929e28-a4ce-424c-b446-9f00472997dc", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1:</b> \n", + " \n", + "Apply for your personal license for Gurobi (one of the packages installed in `environment.yml`) and add the license file to your computer (in the default folder!). The instructions for this are on the MUDE website [here](https://mude.citg.tudelft.nl/software/gurobi/).\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "ab857833", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2:</b> \n", + " \n", + "Run the cells below. If you have correctly created the Python environment (as described in the README.md) and installed the Gurobi license, there should be no errors. If there are errors, use the Traceback to figure out what should be fixed.\n", + "\n", + "<em>You don't need to understand what the cells are doing, but we wrote a few notes to explain anyway.</em>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "00728bea", + "metadata": {}, + "source": [ + "This cell sets up an optimization model with 3000 variables. That's a lot! We will do something like this in the optimization week. Since you need a license to process this many variables, an error will be returned if you did not install it correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5b1a71-7e0a-47d7-9037-c13d635fdff0", + "metadata": {}, + "outputs": [], + "source": [ + "import gurobipy\n", + "model = gurobipy.Model()\n", + "x = model.addVars(3000, vtype = gurobipy.GRB.CONTINUOUS, name = 'x')\n", + "model.update()\n", + "model.optimize()" + ] + }, + { + "cell_type": "markdown", + "id": "4d6029f7", + "metadata": {}, + "source": [ + "The cell below searches for the license file `gurobi.lic` on your computer, and will create a new file `license.lic` in the working directory of this notebook to confirm that you installed Gurobi correctly. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b221048-69bb-46ed-8a19-311ba288345a", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "import os\n", + "\n", + "def find_license_in_dir(directory: Path):\n", + " license = directory / \"gurobi.lic\"\n", + "\n", + " if (license.exists()):\n", + " return license\n", + " else:\n", + " return None\n", + " \n", + "def find_license():\n", + " # By default the license is installed in the home directory; this is the most likely location.\n", + " license = find_license_in_dir(Path.home())\n", + " \n", + " if (license): return license\n", + " \n", + " # Otherwise there are other default paths Gurobi will search for each platform.\n", + " if (sys.platform.startswith(\"linux\")):\n", + " license = find_license_in_dir(Path(\"/opt/gurobi/\"))\n", + " elif (sys.platform.startswith(\"win32\")):\n", + " license = find_license_in_dir(Path(\"C:\\\\gurobi\\\\\"))\n", + " elif (sys.platform.startswith(\"darwin\")):\n", + " license = find_license_in_dir(Path(\"/Library/gurobi/\"))\n", + " else:\n", + " print(\"WARNING: Your operating system may not be supported by this function\")\n", + " \n", + " if (license): return license\n", + " \n", + " # If all else fails, maybe it was put somewhere strange and the GRB_LICENSE_FILE environment variable was set\n", + " file_path = os.environ.get(\"GRB_LICENSE_FILE\")\n", + " \n", + " if (file_path is not None):\n", + " file_path = Path(file_path)\n", + " if (file_path.exists()):\n", + " return file_path\n", + " \n", + " # Oh nO!\n", + " raise Exception((\"Could not find license. If you have an academic license and \"\n", + " \"it couldn't be found, copy the license into your repository and \"\n", + " \"remove all the info except 'TYPE' and 'VERSION'\"))\n", + " \n", + "license = find_license()\n", + "\n", + "with open(\"license.lic\", \"w\") as f:\n", + " f.write(\n", + " \"\".join(\n", + " filter(\n", + " lambda l: l.startswith(\"TYPE\") or l.startswith(\"VERSION\") or l.startswith(\"EXPIRATION\"), \n", + " license.open().readlines()\n", + " )\n", + " )\n", + " )\n", + "print(\"License succesfully found and processed!\")" + ] + }, + { + "cell_type": "markdown", + "id": "133c3daf-f0b9-4f80-ae6a-32b1ceae3aeb", + "metadata": {}, + "source": [ + "If you ran all of the cells above, you are ready to go: you successfully created an environment from a `*.yml` file and installed the Gurobi license! Now there is only one thing left to do." + ] + }, + { + "cell_type": "markdown", + "id": "5ef8955f", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3:</b> \n", + " \n", + "Commit this notebook and the license file that it created to your repository.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "80580ab9-4d79-46b1-ae6e-775af04d43ad", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_4/PA/PA_2_4_B_axis_of_awesome.ipynb b/content/Week_2_4/PA/PA_2_4_B_axis_of_awesome.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fb5608f65b3fe33b21af64e0113f9ce4d2ddf69e --- /dev/null +++ b/content/Week_2_4/PA/PA_2_4_B_axis_of_awesome.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c96d6259-08d6-4289-aea2-589d67cdb5ee", + "metadata": {}, + "source": [ + "# Programming Assignment 12B: [Axis of Awesome](https://youtu.be/5pidokakU4I?si=Y5ewcgPFFQ5cLmC6)\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Due: complete this PA prior to class on Friday, Dec 8, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "a5f54f96", + "metadata": {}, + "source": [ + "## Overview of Assignment\n", + "\n", + "This assignment quickly introduces you to making computations across rows and columns of 2-dimensional Numpy arrays (matrices) using the `axis` keyword argument. It also illustrates the use of a specific figure from the `statsmodels` package that will be useful during the time series analysis week.\n", + "\n", + "**Remember:** PA12 has two notebooks that must be completed (A and B). Follow the instructions in **`README.md`** if you have not already done so.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- Don't forget the criteria in the other notebook of PA12" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75681df5-7a73-469a-ad83-ebd05451e0b7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "b0584fa2-7f4b-4566-9217-e6ca58d0e191", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Often when we have a long sequence of data we would like to evaluate specific sub-sets of it. For example, if we have 10 years worth of hourly measurements, but would like to evaluate the monthly or weekly characteristics. Often we can store our data in a structured way and then use some indexing capabilities of Numpy to evaluate it in a smart way. Check out the following simple tips, then try and apply it in practice." + ] + }, + { + "cell_type": "markdown", + "id": "e2ac6a6f", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1:</b> \n", + " \n", + "Read and run the cells below, making sure you understand what is happening (i.e., completing evaluations on the rows and columns of the matrix).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "9130a7b8", + "metadata": {}, + "source": [ + "First, let's start by collecting a \"long\" sequence of data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e644bc5", + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([1, 20, 300, 1, 2, 3])\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "id": "cb0d4cb7", + "metadata": {}, + "source": [ + "It is easy to restructure it into a matrix form; in this case, 2 rows and 3 columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56b43c1c", + "metadata": {}, + "outputs": [], + "source": [ + "B = np.reshape(A, (2, 3))\n", + "print(B)" + ] + }, + { + "cell_type": "markdown", + "id": "17c187e4", + "metadata": {}, + "source": [ + "In Numpy, \"axes\" are used to specify the structure of an array using the `axis` keyword argument. For this assignment, we are particularly interested in performing operations along the 0th and 1st axes of the array, which correspond to the columns and rows, respectively. Check it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8b8875b", + "metadata": {}, + "outputs": [], + "source": [ + "B.mean(axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "f472e87a", + "metadata": {}, + "source": [ + "Looking along the other axis:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4f093fa", + "metadata": {}, + "outputs": [], + "source": [ + "B.mean(axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "9f46d8a8", + "metadata": {}, + "source": [ + "And you can do it for other methods too!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d9a4cc6", + "metadata": {}, + "outputs": [], + "source": [ + "B.std(axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "41269037-161e-4c50-9dd7-5346bfc41b7a", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2:</b> \n", + " \n", + "Read the simple story below and use the tips described above to complete the partially completed code cell.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "5692fa18", + "metadata": {}, + "source": [ + "Suppose you and a group of friends would like to evaluate your financial decisions, and you decide to review how many coins you spend at PSOR, the CEG student pub, to practice your Python skills. You have assembled data on the number of coins purchased per month, for several years, starting from when you first met in September, through August, 3 years later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f64b31f1", + "metadata": {}, + "outputs": [], + "source": [ + "coins = [46, 28, 16, 27,\n", + " 22, 24, 31, 12,\n", + " 32, 36, 12, 0,\n", + " 41, 27, 21, 26,\n", + " 21, 19, 18, 35,\n", + " 14, 34, 8, 0,\n", + " 53, 34, 23, 35,\n", + " 28, 26, 18, 13,\n", + " 12, 14, 34, 0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4415d492", + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(precision=1)\n", + "print(f'The average number of coins spent per month is:')\n", + "print('The average number of coins spent per month for each year is:')\n", + "print(f'The average number of coins spent each september:')\n", + "print(f'The average number of coins spent each january:')\n", + "print(f'Max coins spent in any month:')\n", + "print(f'Max coins spent in any year:')" + ] + }, + { + "cell_type": "markdown", + "id": "81e30bca", + "metadata": {}, + "source": [ + "The answers are:\n", + "```\n", + "The average number of coins spent per month is: 23.3\n", + "The average number of coins spent per month for each year is: [23.8 22. 24.2]\n", + "The average number of coins spent in september: 46.7\n", + "The average number of coins spent in january: 23.7\n", + "Max coins spent in any month: 53.0\n", + "Max coins spent in any year: 290.0\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "adbdb77a", + "metadata": {}, + "source": [ + "## Correlated Behavior?\n", + "\n", + "Now that we have three years of data, we want to see if there is a trend in our behavior. We can take the correlation concepts we learned in Q1 and see if there is a relationship from one month to the next. In other words: if we spend a lot of coins one month, is the _probability_ that we spend a lot of coins the next month _higher_? Evaluating correlation this way has many names: autocorrelation, autocovariance, etc...the \"auto\" in this case refers to evaluating the relationship between data from the same sequence (you will learn about it more in the time series reading). Luckily, there is a built-in method of `statsmodels` that does this for us automatically: the correlation is plotted on the y-axis between the distance away from any point in the series. It is important to recognize that the correlations represent any point in the series (analogous to an average), rather than any specific point)\n", + "\n", + "_Note the use of reshape to put the data back into a 1D array (row/column)!_" + ] + }, + { + "cell_type": "markdown", + "id": "491cc105", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3:</b> \n", + " \n", + "Run the cells to visualize the plots, then read the interpretations.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12049ec2", + "metadata": {}, + "outputs": [], + "source": [ + "from statsmodels.graphics.tsaplots import plot_acf\n", + "plot_acf(coins.reshape(-1));" + ] + }, + { + "cell_type": "markdown", + "id": "3f68eaf0", + "metadata": {}, + "source": [ + "**Interpretation:** it appears that there is little correlation from one month to the next in the data set, except each point is very strongly correlated with itself (that should be obvious!). Even more importantly, the points are inside the shaded blue region: that is the confidence interval, which means the values are negligible.\n", + "\n", + "Now let's try an increasing series and see what happens:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "984e38b0", + "metadata": {}, + "outputs": [], + "source": [ + "increasing_series = np.arange(1, 50)\n", + "plot_acf(increasing_series);" + ] + }, + { + "cell_type": "markdown", + "id": "ac2d7925", + "metadata": {}, + "source": [ + "**Interpretation:** now there is definitely correlation! And we see that the correlation drops off with distance (which makes sense for a linear trend). However, the confidence interval is large, so the trend in correlation should not be trusted for a large distance (you can check and set the confidence interval easily, check the [documentation](https://www.statsmodels.org/stable/generated/statsmodels.graphics.tsaplots.plot_acf.html))." + ] + }, + { + "cell_type": "markdown", + "id": "f0a19416", + "metadata": {}, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note that we don't give a full explanation of the x-axis in these plots, and the terminology for correlation is vague; see the reading for a thorough explanation.</p></div>" + ] + }, + { + "cell_type": "markdown", + "id": "51c5ff8f", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4:</b> \n", + " \n", + "Test your knowledge of correlation! See if you can create a sequence of data that somehow creates alternating positive and negative values of autocorrelation, as illustrated in the figure. You don't need to exactly recreate the plot, as long as there's alternating values autocorrelation.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "1781bd5c", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95c7bf59", + "metadata": {}, + "outputs": [], + "source": [ + "strong_autocorr_positive = np.array([YOUR_CODE_HERE])\n", + "plot_acf(strong_autocorr_positive);" + ] + }, + { + "cell_type": "markdown", + "id": "80580ab9-4d79-46b1-ae6e-775af04d43ad", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_4/PA/PA_2_4_B_solution.ipynb b/content/Week_2_4/PA/PA_2_4_B_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7ae9df26b96abb2409dddb6d591d95297de762a3 --- /dev/null +++ b/content/Week_2_4/PA/PA_2_4_B_solution.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c96d6259-08d6-4289-aea2-589d67cdb5ee", + "metadata": {}, + "source": [ + "# Programming Assignment 12B: [Axis of Awesome](https://youtu.be/5pidokakU4I?si=Y5ewcgPFFQ5cLmC6)\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Due: complete this PA prior to class on Friday, Dec 8, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "a62e4d98", + "metadata": {}, + "source": [ + "## Overview of Assignment\n", + "\n", + "This assignment quickly introduces you to making computations across rows and columns of 2-dimensional Numpy arrays (matrices) using the `axis` keyword argument. It also illustrates the use of a specific figure from the `statsmodels` package that will be useful during the time series analysis week.\n", + "\n", + "**Remember:** PA12 has two notebooks that must be completed (A and B). Follow the instructions in **`README.md`** if you have not already done so.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- Don't forget the criteria in the other notebook of PA12" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75681df5-7a73-469a-ad83-ebd05451e0b7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "b0584fa2-7f4b-4566-9217-e6ca58d0e191", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Often when we have a long sequence of data we would like to evaluate specific sub-sets of it. For example, if we have 10 years worth of hourly measurements, but would like to evaluate the monthly or weekly characteristics. Often we can store our data in a structured way and then use some indexing capabilities of Numpy to evaluate it in a smart way. Check out the following simple tips, then try and apply it in practice." + ] + }, + { + "cell_type": "markdown", + "id": "8f812800", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1:</b> \n", + " \n", + "Read and run the cells below, making sure you understand what is happening (i.e., completing evaluations on the rows and columns of the matrix).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "10269781", + "metadata": {}, + "source": [ + "First, let's start by collecting a \"long\" sequence of data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "042040e9", + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([1, 20, 300, 1, 2, 3])\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "id": "2677c998", + "metadata": {}, + "source": [ + "It is easy to restructure it into a matrix form; in this case, 2 rows and 3 columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5d2092c", + "metadata": {}, + "outputs": [], + "source": [ + "B = np.reshape(A, (2, 3))\n", + "print(B)" + ] + }, + { + "cell_type": "markdown", + "id": "c1aee197", + "metadata": {}, + "source": [ + "In Numpy, \"axes\" are used to specify the structure of an array using the `axis` keyword argument. For this assignment, we are particularly interested in performing operations along the 0th and 1st axes of the array, which correspond to the columns and rows, respectively. Check it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb510dad", + "metadata": {}, + "outputs": [], + "source": [ + "B.mean(axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "df0948c6", + "metadata": {}, + "source": [ + "Looking along the other axis:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7368fb94", + "metadata": {}, + "outputs": [], + "source": [ + "B.mean(axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "d562ab47", + "metadata": {}, + "source": [ + "And you can do it for other methods too!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e174afa2", + "metadata": {}, + "outputs": [], + "source": [ + "B.std(axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "41269037-161e-4c50-9dd7-5346bfc41b7a", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2:</b> \n", + " \n", + "Read the simple story below and use the tips described above to complete the partially completed code cell.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "e8590120", + "metadata": {}, + "source": [ + "Suppose you and a group of friends would like to evaluate your financial decisions, and you decide to review how many coins you spend at PSOR, the CEG student pub, to practice your Python skills. You have assembled data on the number of coins purchased per month, for several years, starting from when you first met in September, through August, 3 years later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfe04d07", + "metadata": {}, + "outputs": [], + "source": [ + "coins = [46, 28, 16, 27,\n", + " 22, 24, 31, 12,\n", + " 32, 36, 12, 0,\n", + " 41, 27, 21, 26,\n", + " 21, 19, 18, 35,\n", + " 14, 34, 8, 0,\n", + " 53, 34, 23, 35,\n", + " 28, 26, 18, 13,\n", + " 12, 14, 34, 0]\n", + "\n", + "coins_matrix = np.reshape(coins, (3, 12))\n", + "print(coins_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b63ca528", + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(precision=1)\n", + "print(f'The average number of coins spent per month is: {np.mean(coins):.1f}')\n", + "print('The average number of coins spent per month for each year is:', coins_matrix.mean(axis=1))\n", + "print(f'The average number of coins spent each september: {coins_matrix.mean(axis=0)[0]:.1f}')\n", + "print(f'The average number of coins spent each january: {coins_matrix.mean(axis=0)[4]:.1f}')\n", + "print(f'Max coins spent in any month: {max(coins_matrix.max(axis=0)):.1f}')\n", + "print(f'Max coins spent in any year: {max(coins_matrix.sum(axis=1)):.1f}')" + ] + }, + { + "cell_type": "markdown", + "id": "28ade41f", + "metadata": {}, + "source": [ + "The answers are:\n", + "```\n", + "The average number of coins spent per month is: 23.3\n", + "The average number of coins spent per month for each year is: [23.8 22. 24.2]\n", + "The average number of coins spent in september: 46.7\n", + "The average number of coins spent in january: 23.7\n", + "Max coins spent in any month: 53.0\n", + "Max coins spent in any year: 290.0\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e7c202ce", + "metadata": {}, + "source": [ + "## Correlated Behavior?\n", + "\n", + "Now that we have three years of data, we want to see if there is a trend in our behavior. We can take the correlation concepts we learned in Q1 and see if there is a relationship from one month to the next. In other words: if we spend a lot of coins one month, is the _probability_ that we spend a lot of coins the next month _higher_? Evaluating correlation this way has many names: autocorrelation, autocovariance, etc...the \"auto\" in this case refers to evaluating the relationship between data from the same sequence (you will learn about it more in the time series reading). Luckily, there is a built-in method of `statsmodels` that does this for us automatically: the correlation is plotted on the y-axis between the distance away from any point in the series. It is important to recognize that the correlations represent any point in the series (analogous to an average), rather than any specific point)\n", + "\n", + "_Note the use of reshape to put the data back into a 1D array (row/column)!_" + ] + }, + { + "cell_type": "markdown", + "id": "487de167", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3:</b> \n", + " \n", + "Run the cells to visualize the plots, then read the interpretations.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a3d44b1", + "metadata": {}, + "outputs": [], + "source": [ + "from statsmodels.graphics.tsaplots import plot_acf\n", + "plot_acf(coins_matrix.reshape(-1));" + ] + }, + { + "cell_type": "markdown", + "id": "2b55794d", + "metadata": {}, + "source": [ + "**Interpretation:** it appears that there is little correlation from one month to the next in the data set, except each point is very strongly correlated with itself (that should be obvious!). Even more importantly, the points are inside the shaded blue region: that is the confidence interval, which means the values are negligible.\n", + "\n", + "Now let's try an increasing series and see what happens:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "814ef478", + "metadata": {}, + "outputs": [], + "source": [ + "increasing_series = np.arange(1, 50)\n", + "plot_acf(increasing_series);" + ] + }, + { + "cell_type": "markdown", + "id": "1fff1e13", + "metadata": {}, + "source": [ + "**Interpretation:** now there is definitely correlation! And we see that the correlation drops off with distance (which makes sense for a linear trend). However, the confidence interval is large, so the trend in correlation should not be trusted for a large distance (you can check and set the confidence interval easily, check the [documentation](https://www.statsmodels.org/stable/generated/statsmodels.graphics.tsaplots.plot_acf.html))." + ] + }, + { + "cell_type": "markdown", + "id": "499149ab", + "metadata": {}, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note that we don't give a full explanation of the x-axis in these plots, and the terminology for correlation is vague; see the reading for a thorough explanation.</p></div>" + ] + }, + { + "cell_type": "markdown", + "id": "650aebf8", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4:</b> \n", + " \n", + "Test your knowledge of correlation! See if you can create a sequence of data that somehow creates alternating positive and negative values of autocorrelation, as illustrated in the figure.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "769974dc", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e357186", + "metadata": {}, + "outputs": [], + "source": [ + "strong_autocorr_positive = np.array([1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1 ,1])\n", + "plot_acf(strong_autocorr_positive);" + ] + }, + { + "cell_type": "markdown", + "id": "80580ab9-4d79-46b1-ae6e-775af04d43ad", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_4/PA/README.md b/content/Week_2_4/PA/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e087a32841fda60bfd84a0dd7df2b36f6b8ee5a1 --- /dev/null +++ b/content/Week_2_4/PA/README.md @@ -0,0 +1,80 @@ +# PA12 Information, Week 2.4 + +_[CEGM1000 MUDE](http://mude.citg.tudelft.nl/), Time Series Analysis, Week 4 of Quarter 2._ + +This week the programming assignment is based on three files: +- `README.md`: instructions for PA12 and environment creation (this document) +- `PA12A_gurobilicious.ipynb`: set-up of Gurobi software for Week 2.5 (optimization) with a new conda environment +- `PA12B_axes_of_awesome.ipynb`: Python-related tools used in Week 2.4 for Time Series Analysis + + +## Instructions + +Read through this file (`README.md`). This `README.md` also includes a task on creating a new conda environment. Afterwards complete the tasks in notebooks `PA12A` and `PA12B`. Note that `PA12A` will refer you to the MUDE website, where you will find [instructions for setting up the Gurobi license file](https://mude.citg.tudelft.nl/software/gurobi/). + +### Assessment criteria + +The assessment criteria for PA12 is specified in each notebook, respectively, but the general idea is to make sure both notebooks run without errors. In addition, notebook 12A will generate a file `license.lic` that must be committed to your repository to confirm that the Gurobi software has been installed correctly on your computer. + +## Python environments revisited + +Until now, we have been able to complete our work in MUDE with a few packages like `numpy` and `scipy` in our `mude` environment, which we create and manage with `conda`. However, as we start to cover more advanced analysis techniques, the need to use special packages increases, because they include, for example, a) specialized numerical techniques that are difficult or tedious to implement in (small) pieces of code, b) numerical schemes are implemented in a way to make computation faster, or c) advanced visualization to help interpret analysis and results. + +Unfortunately, each of these packages are themselves require a number of different packages to function properly. To see this, try executing the following command in your Anaconda prompt or terminal (replacing `ENV_NAME` with `mude` for example). You can also try it for `base`. +``` +conda env export -n ENV_NAME +``` +You should see a long list of packages. Do you remember installing all of them with `conda install` or `pip install`? You shouldn't---but where did they come from? Many of them are packages required by the packages that you _did_ specifically install. These packages are called _dependencies_, and are necessary to make your Python packages function as expected. When you run `conda install numpy`, for example, `conda` checks all of the dependent packages that are needed and makes sure they are also provided in the `environment` that is being created. In reality, this is simply a folder on your computer with all of the `*.py` files stored in it. This _package management_ is what `conda` and `pip` are really doing when you run them with an `install` command. It also checks that it has a suitable _version_ of each dependency; this is why it sometime takes a long time to install a package (imagine you, as `conda install`, going around to all of the other packages stored on your computer and asking _what version of package X do you prefer?_ then trying to figure out how to make everything match with each of them, and doing it for _all_ depedencies!). Unfortunately, this means that as you add more packages to a particular environment, it gets more and more difficult to make sure everything works well together. Luckily, there is a practical solution: create new environments for specific projects to make sure the proper packages can function properly! + +### Create environment with specific packages + +As you saw during Q1, it is easy to create a new environment with a specific version of Python, for example, with the command: `conda create -n ENV_NAME python=3.11 anaconda`. We can install as many packages as we want when creating the environment by adding them to the end of the list. For example, can you see which packages would be installed by running the following command? + +``` +conda create -n ENV_NAME python=3.11 numpy scipy +``` +Once you require a large number of packages, this can be tedious! Luckily there is a solution: listing the required packages in a text-based file, and then telling `conda` to create the environment based on the contents of the file! + +### Create environment from text-based file + +All we need to do to create an environment from a file is to write a list of what we want and then tell `conda` to read it. That's it! + +#### List requirements in `*.yml` file + +To write our list of requirements, we will use a file with a new (to us) file extension: the `*.yml` file (pronounced "yah-mul"). It is a text-readable file, that stands for "Yet another Markup Language." You don't need to worry about this, except to recognize that this is one of _many_ types of files that use a particular type of text formatting to give a computer specific instructions. It is very similar to the way Markdown formatting works. + +Take a look at the contents of the file `environment.yml` in this repository. Can you understand what is being described? For each section (`name` and `dependencies`) you should see that it uses a colon `:` to list the information. This will be processed by `conda` when creating the new environment. + +There is another special type of formatting with two colons `::`. This is how we tell `conda` to look on a specific _channel_ for the particular package. Conda channels are the locations where packages are stored; you can think of them as a specific URL web address. This is where the creator of the package can manage and maintain its distribution (e.g., publishing new versions, installation information, etc). Conda packages are downloaded from these URL's, and if you know where a particular package is stored, you can give `conda` explicit instructions. For example, we can see that Gurobi is stored on the `gurobi` channel, because the URL is `https://anaconda.org/gurobi/gurobi` (note that Anaconda is an organization that provides a wide variety of software; the website anaconda.com is used to provide documentation and information about the organization, whereas anaconda.**org** is explicitly used for package distribution). This is specified in the environment file using the `channel::package` notation. In the `*.yml` file, `gurobi::gurobi` is equivalent to using the command `conda install -c gurobi gurobi` in Anaconda prompt. + +In summary, as you can see from reading the file, we will set up an environment specifically for this assignment, PA12, along with a number of dependency packages, two of which are installed from special conda channels. + +#### Create environment from `*.yml` file + +The command for creating the environment is simple. Do the following: + +1. Open Anaconda Prompt (Windows) / your default terminal app (Mac) +2. Navigate to your working directory (where this file and `environment.yml` is located) +3. Execute this command: `conda env create -f environment.yml` +4. Keep reading this assignment as you wait (this may take several minutes) + +Do you know why this takes so long? Because we are installing many packages at once! Keep an eye on the terminal window as this process is completed. First `conda` is collecting information about the dependencies, then it will _solve_ the environment; in other words, figure out which version of each package it should use. Once it is ready, it will present the list of packages and peoceed with the "installation" (really just downloading `*.py` files and putting them in a folder on your computer (note that the prompt may ask you to confirm that the installation should proceed, depending on your system settings). + +Once the environment is created, we can activate it, and also check that everything was installed properly. Try `conda env export -n ENV_NAME` to see what was installed by "default." The list is very long, even though we only asked for a few packages! + +It is also interesting to try `conda env export --from-history` (make sure you activated it already), which shows the specific packages requested. Do you notice anything in particular when looking at the output? That's right, it's exactly the same as our file `environment.yml`! The only thing extra is that it identifies `default` as the conda channel (since we didn't specify anything else in the `*.yml` file). + +## Next steps + +Once you have successfully installed our new environment and you have activated it, you are ready to proceed with the rest of this assignment (`PA12A.ipynb` and `PA12B.ipynb`). + +We simply presented the instructions for creating an environment in this README; if you would like to read more about this, you should refer to the [Anaconda documentation](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file). You can also read about creating an environment file on the same page [here](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-file-manually), if interested. + + + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. 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0000000000000000000000000000000000000000..8acaf05bd691f0f83daa338c51fab0710be0f959 --- /dev/null +++ b/content/Week_2_4/PA/environment.yml @@ -0,0 +1,10 @@ +name: mude-PA12 +dependencies: + - python=3.11 + - numpy + - scipy + - matplotlib + - statsmodels + - pip + - conda-forge::jupyterlab + - gurobi::gurobi \ No newline at end of file diff --git a/content/Week_2_4/PA/gurobitest.py b/content/Week_2_4/PA/gurobitest.py new file mode 100644 index 0000000000000000000000000000000000000000..253f29c10b4a74bc72bd9c4c8973c300243e59e1 --- /dev/null +++ b/content/Week_2_4/PA/gurobitest.py @@ -0,0 +1,5 @@ +import gurobipy +model = gurobipy.Model() +x = model.addVars(2001, name = 'x') +model.update() +model.optimize() \ No newline at end of file diff --git a/content/Week_2_4/WS_2_4_Time_for_AR2.ipynb b/content/Week_2_4/WS_2_4_Time_for_AR2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..34672ec08c0452b08661ce02c4e265fc9f59f03b --- /dev/null +++ b/content/Week_2_4/WS_2_4_Time_for_AR2.ipynb @@ -0,0 +1,719 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Workshop 12: Time for fun with AR(2)\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 25px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Wednesday December 6, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In general we are interested in identifying the components of a time series, to check stationarity, make statistical judgments, and to identify the appropriate functional model and the stochastic model (ARMA process). In this workshop we will generate a synthetic time series, $S_t$ using a second-order auto-regressive random process ((AR(2)).\n", + "\n", + "Autoregressive models AR(p) are widely used in time series analysis to understand and predict sequential data points. As a special case, AR(2) process refers to a second-order autoregressive model, where each data point is linearly dependent on its two immediate preceding values. One practical application of AR(2) models involves prediction. In many practical applications, however, in addition to the noise process, there exist also a linear trend in the form of $Y=Ax+\\epsilon$. To implement prediction, we need to calculate two terms: one called the functional part (from the linear model) and one the stochastic model (from the noise process). The objectives of this workshop are to:\n", + "\n", + "- generate a noise process of AR(2) with the given parameters $\\beta_1$ and $\\beta_2$.\n", + "- calculate the normalized autocovariance function (ACF) and power spectral density (PSD), and investigate their interlink.\n", + "- estimate the AR(2) parameters $\\beta_1$ and $\\beta_2$ for a given noise process AR(2).\n", + "- predict future values based on the given linear model $Y=Ax+\\epsilon$ and the noise process\n", + "\n", + "Considering the model $Y=Ax+\\epsilon$ note the following:\n", + "- This is conceptually the same, and uses the same notation, as with observation theory topics from Q1, \n", + "- Our signal of interest is the $Ax$ part\n", + "- The \"noise\" is $\\epsilon$ and can be broken down into two more components: 1) _stochastic signal_, and 2) random errors\n", + "- the _stochastic signal_ is our focus of today; in general we use an ARMA model to represent the stochastic signal, and depending on the assumptions, it can take many forms (e.g., AR(1), AR(2), etc; defined by the parameters $p$ and $q$)\n", + "- The symbol $\\epsilon$ represents the overall noise of the original time series, whereas the symbole $e$ is the random errors (the part that is left after we take out the stochastic signal from the noise of the original time series using an ARMA process).\n", + "\n", + "In this notebook specifically, we will complete the following tasks:\n", + "1. Generate the time series, then evaluate stationarity and variance\n", + "2. Evaluate auto-regressive characteristics with ACF and PSD\n", + "3. Estimate prameters of AR(2) model\n", + "4. Use model to make a prediction (using BLUE)\n", + "\n", + "For each Task, there will be two parts: part a focuses on the implementation of the method in the code, and part b reflects on the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as signal\n", + "from statsmodels.graphics.tsaplots import plot_acf \n", + "from scipy.stats import norm\n", + "from scipy.stats.distributions import chi2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "24e525319d2d49bcaf27de4aaa59d220", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Task 1: Generate Time Series\n", + "We intend to simulate (as a time series) 1000 samples at 1-day intervals (so $t=1,...,m$ with $m=1000$ days).\n", + "\n", + "_Note: the convention for Time Series and Observation Theory is to use symbole $m$ for the number of samples; however, the symbol $N$ is used in Signal Processing, so in this case $m=N$._\n", + "\n", + "The simulated data is based on a second-order auto-regressive AR(2) random process $S_t$ as follows:\n", + "$$\n", + "S_t= \\beta_1 S_{t-1}+\\beta_2 S_{t-2}+e_t\n", + "$$\n", + "with $t = 1, …, 1000$. The AR(2) process is a stationary time series with a constant mean $\\mu$ and the variance $\\sigma^2$. We set them as follows:\n", + "\n", + "$$\n", + "\\mathbb{E}(S_t)=0 \\text{,} \\hspace{2mm} \\mathbb{D}(S_t)=\\sigma^2=2.\n", + "$$ \n", + "\n", + "The two parameters of AR(2) are $\\beta_1$ and $\\beta_2$, and we will consider 2 scenarios:\n", + "\n", + "1. Scenario 1: $\\beta_1=0.65$ and $\\beta_2=0.30$ (colored)\n", + "2. Scenario 2: $\\beta_1=\\beta_2=0$ (white)\n", + "\n", + "The variance of the purely random noise (white noise) $e_t$ is $\\sigma^2_e$, which for AR(2), is obtained from the following equation:\n", + "\n", + "$$\n", + "\\sigma_{e}^2 = \\frac{(1+\\beta_2)(1-\\beta_1-\\beta_2)(1+\\beta_1-\\beta_2)}{1-\\beta_2} \\sigma^2\n", + "$$\n", + "\n", + "To simulate the data of the AR(2) process, you can make use of a normal distribution; however, _as the realizations of the time series are correlated with each other, we can no longer take random samples from the distribution directly!_ To properly take into account autocorrelation, you will use the above recursive form, which needs initialization. To initialize the first and second data, <code>S[0]=np.random.normal(...)</code> and <code>S[1] = np.random.normal(...)</code> using the normal distribution. You can find information on <code>np.random.normal()</code> [here](https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html#numpy.random.normal). To use the above recursive formula you need to simulate $e_t$, requiring to have its standard deviation $\\sigma_{e}$ of the white noise process (given above).\n", + "\n", + "After generating the time series we may compute the mean and variance to see if they are close to their original values\n", + "$$\n", + "\\hat{\\mu} = \\frac{1}{m} \\sum_{i=1}^{m} S_i \n", + "$$\n", + "and\n", + "$$\n", + "\\hat{\\sigma}^2 =\\hat{C}_0 = \\frac{1}{m} \\sum_{i=1}^{m} S_i^2 \n", + "$$\n", + "which are unbiased estimates of $\\mu=0$ and $\\sigma^2 =2$, respectively. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "48e8df4114544ca4914a15c77372ab30", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1a:</b> \n", + "\n", + "Complete the code cells below to perform the following analysis:\n", + "<ol>\n", + " <li>Compute the standard deviation $\\sigma_{e}$ based on the provided values for scenario 1. \n", + " <li>Simplify the above formula for $\\sigma_{e} $ by taking $\\beta_1=\\beta_2 =0$ (scenario 2).\n", + " <li> Simulate the data of the AR(2) process based on the above given values (for the above two scenarios). Plot the simulated data versus time.\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gen_AR2(beta1,beta2,sigma,m):\n", + " sigma_e = YOUR_CODE_HERE\n", + " S = np.zeros(m)\n", + " S[0] = np.random.normal(loc=0, scale=sigma, size=None)\n", + " S[1] = np.random.normal(loc=0, scale=sigma, size=None)\n", + " for i in range(2, m):\n", + " YOUR_CODE_HERE\n", + " \n", + " return sigma_e, S" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "# sampling frequency is 1 cycle/day\n", + "Fs = 1.0\n", + "# standard deviation of the noise process\n", + "sigma = np.sqrt(2) \n", + "\n", + "# Scenario 1\n", + "beta1 = YOUR_CODE_HERE\n", + "beta2 = YOUR_CODE_HERE\n", + "\n", + "sigma_e_1, S1 = gen_AR2(YOUR_CODE_HERE)\n", + "\n", + "print(f'Sigma for Scenario 1 is: \\t\\t {sigma:.3f}')\n", + "print(f'Sigma_e for Scenario 1 is: \\t\\t {sigma_e_1:.3f}')\n", + "\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, S1, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: S1(t)')\n", + "plt.title('Scenario 1')\n", + "plt.legend()\n", + "# compute the mean and variance of the generated time series\n", + "mu_S1 = np.mean(S1)\n", + "print(f'The mean of generated S1 process is:\\t {mu_S1:.3f}')\n", + "sigma_S1 = S1.T@S1/m\n", + "print(f'The standard deviation of generated S1 process is: {np.sqrt(sigma_S1):.3f}')\n", + "\n", + "\n", + "# Scenario 2\n", + "beta1 = YOUR_CODE_HERE\n", + "beta2 = YOUR_CODE_HERE\n", + "\n", + "sigma_e_2, S2 = gen_AR2(YOUR_CODE_HERE)\n", + "\n", + "print(f'\\nSigma for Scenario 2 is: \\t\\t {sigma:.3f}')\n", + "print(f'Sigma_e for Scenario 2 is: \\t\\t {sigma_e_2:.3f}')\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, S2, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: S2(t)')\n", + "plt.title('Scenario 2')\n", + "plt.legend()\n", + "# compute the mean and variance of the generated time series\n", + "mu_S2 = np.mean(S2)\n", + "print(f'The mean of generated S2 process is:\\t {mu_S2:.3f}')\n", + "sigma_S2 = S2.T@S2/m\n", + "print(f'The standard deviation of generated S2 process is: {np.sqrt(sigma_S2):.3f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "48e8df4114544ca4914a15c77372ab30", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1b:</b> \n", + "<ol>\n", + " <li>Explain your reasoning for expecting a smaller value of $\\sigma_{e}$ compared to $\\sigma$. </li>\n", + " <li>Does AR(2) reduce to the white noise process? Compare the $\\sigma_{e}$ of the two processes and with true standard deviation of the two process, i.e. $\\sigma$.</li>\n", + " <li>Can you see/explain the two types/levels of time correlations for these two scenarios? </li>\n", + " <li>Do the two time series look stationary? Compute the mean and variance of the two processes. Are they comparable with the original values of $\\mu=0$ and $\\sigma^2=2$? Run your Jupyter scripts several times to make more concrete conclusions.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 2: ACF and PSD\n", + "The next step is to calculate the normalized autocovariance function (ACF) for the generated time series. The autocovariance function can be estimated from the following equation.\n", + "$$\n", + "\\hat{C}_{\\tau} = \\frac{1}{m-\\tau} \\sum_{i=1}^{m-\\tau} (S_i-\\mu_s)(S_{i+\\tau}-\\mu_s)\n", + "= \\frac{1}{m-\\tau} \\sum_{i=1}^{m-\\tau} S_i\\, S_{i+\\tau}\n", + "$$\n", + "The normalized autocovariance function (ACF) can directly be obtained from the auto-covariance function as\n", + "$$\n", + "ACF = \\hat{\\rho}_{\\tau} =\\frac{\\hat{C}_{\\tau}}{\\hat{C}_{0}}\n", + "$$\n", + "where $\\hat{C}_{0}=\\sigma^2$ is the variance of the process. You can compute the ACF yourself, simply by implementing the above formula in Python. There are however Python commands/functions that can simply produce the ACF (in fact different ways can be employed to compute the ACF in Python). We utilize the functions provided by <code>statsmodels</code>, specifically the <code>plot_acf</code> function. For more information see [here](https://www.statsmodels.org/stable/generated/statsmodels.graphics.tsaplots.plot_acf.html).\n", + "This command can also have optional parameters like $\\alpha$ (false alarm), for example $\\alpha=0.05$ to make a 95% confidence interval for the computed ACF. \n", + "\n", + "On top of that, we also want to take a look at the power spectral density ([PSD](https://mude.citg.tudelft.nl/book/time_series/acf.html#power-spectral-density)). The PSD is the discrete Fourier transform of the ACF. Of course, there are plenty Python functions that will help you compute the PSD. See for example [the example in the MUDE textbook](https://mude.citg.tudelft.nl/book/time_series/exercise4.html).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2a:</b> \n", + "\n", + "Complete the code cells below to create a plot of ACF and PSD.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot ACF and PSD for scenario 1\n", + "# Plot ACF for the generated process (S1)\n", + "plot_acf(YOUR_CODE_HERE, lags=100, alpha=0.01, color = 'blue', label='ACF')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (day)')\n", + "plt.title('Normalized ACF')\n", + "plt.tight_layout()\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal: S1\n", + "frequencies, psd = signal.periodogram(YOUR_CODE_HERE, fs=Fs, scaling='density', return_onesided=False)\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.loglog(frequencies, psd, color='blue', label='PSD')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (cycle/day)')\n", + "plt.title('Power Spectral Density (PSD)')\n", + "plt.ylim([1e-3, 5e2])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "\n", + "# Plot ACF and PSD for scenario 2\n", + "# Plot ACF for the generated process (S2)\n", + "plot_acf(YOUR_CODE_HERE, lags=100, alpha=0.01, color = 'blue', label='ACF')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (day)')\n", + "plt.title('Normalized ACF')\n", + "plt.tight_layout()\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal: S2\n", + "frequencies, psd = signal.periodogram(YOUR_CODE_HERE, fs=Fs, scaling='density', return_onesided=False)\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.loglog(frequencies, psd, color='blue', label='PSD')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (cycle/day)')\n", + "plt.title('Power Spectral Density (PSD)')\n", + "plt.ylim([1e-3, 5e2])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2b:</b> \n", + "<ol>\n", + " <li>Compare the ACF of the two processes. $S_1$ shows a heavy temporal correlation. Can you explain/link this to the parameters $\\beta_1$, $\\beta_2$ and $\\sigma_{e}$?</li>\n", + " <li>Compare the PSD of the two processes. We cannot see any clear 'peak' in either of these PSD. Can you explain it why? Can you explain the slope in the PSD of $S_1$ process?</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 3: Estimation of AR(2) parameters\n", + "Assume that we know the noise process is AR(2), so we know $S_t= \\beta_1 S_{t-1}+\\beta_2 S_{t-2}+e_t$. However we assume that the two parameters $\\beta_1$ and $\\beta_2$ are unknown, to be estimated. This can be implemented using the provided function <code>AR_estimation(S, p)</code> (see below) for AR(p) in general. The function provides the $\\beta$ parameters, their standard deviations and the standard deviation $\\sigma_{e}$ of $e(t)={e}_t$.\n", + "\n", + "The formula in task 1 can be inverted to obtain the variance $\\sigma^2$ of the noise process from $\\sigma^2_{e}$ as follow:\n", + "$$\n", + "\\sigma^2 = \\frac{1-\\beta_2}{(1+\\beta_2)(1-\\beta_1-\\beta_2)(1+\\beta_1-\\beta_2)} \\sigma_{e}^2\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3a:</b> \n", + "\n", + "It is required to:\n", + "<ol>\n", + " <li> Estimate $\\beta_1$ and $\\beta_2$, along with their standard deviations, for the two processes $S_1$ and $S_2$. Compare them with the original values in task 1.\n", + " <li> Compute the standard deviation $\\sigma_{e}$ for the two processes and compare them with the known vales in task 1.\n", + " <li> Compute the standard deviation $\\sigma$ from the parameters $\\beta_1$, $\\beta_2$ and $\\sigma_{e}$ for the two processes $S_1$ and $S_2$.\n", + " </ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def AR_estimation(S, p):\n", + " \"\"\"\n", + " This function computes the AR(p) parameters beta_1,...,beta_p \n", + " for an AR(p) process Y (stationary S: for example epsilon hat).\n", + " \n", + " INPUT:\n", + " S: m x 1 observations (time series)\n", + " p: order of AR\n", + " OUTPUT:\n", + " Beta: Parameters Beta\n", + " S_Beta: Standard deviation of Beta \n", + " Sigma_e: Standard deviation of white noise \n", + " \"\"\"\n", + " m = len(S)\n", + " # make the design matrix\n", + " A = np.zeros((m-p, p))\n", + " for i in range(1, p+1):\n", + " A[:,i-1] = S[p-i:m-i]\n", + "\n", + " # removing the first p data from s\n", + " S = S[p:m]\n", + " m, p = A.shape\n", + "\n", + " # least squares estimate of Beta\n", + " Beta = np.linalg.inv(A.T @ A) @ A.T @ S\n", + "\n", + " # least squares estimate of residuals (white noise)\n", + " Ehat = S - A @ Beta\n", + "\n", + " # estimation of variance of data (white noise)\n", + " Sig2 = (Ehat.T @ Ehat) / (m - p)\n", + "\n", + " # covariance matrix of Beta\n", + " Sigma_Beta = Sig2 * np.linalg.inv(A.T @ A)\n", + "\n", + " # standard deviation of Beta\n", + " std_Beta = np.sqrt(np.diag(Sigma_Beta))\n", + "\n", + " # standard deviation of white noise\n", + " Sigma_e = np.sqrt(Sig2)\n", + " \n", + " return Beta, std_Beta, Sigma_e" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "7047abf2b93f427a86e5ea387783d174", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 2334, + "execution_start": 1696691527706, + "source_hash": null + }, + "outputs": [], + "source": [ + "# AR(2) parameter estimation for S1\n", + "beta_p1, std_beta_p1, sigma_e_p1 = AR_estimation(YOUR_CODE_HERE)\n", + "var_p1 = YOUR_CODE_HERE\n", + "print('Beta1 and Beta2 for S1 process are: ',beta_p1)\n", + "print('Standard deviations of Beta1 and Beta2 for S1 process are: ',std_beta_p1)\n", + "print('Standard deviation of e(t) for S1 process is:',sigma_e_p1)\n", + "print('Standard deviation for S1 process is:',np.sqrt(var_p1))\n", + "\n", + "# AR(2) parameter estimation for S2\n", + "beta_p2, std_beta_p2, sigma_e_p2 = AR_estimation(YOUR_CODE_HERE)\n", + "var_p2 = YOUR_CODE_HERE\n", + "print('\\n')\n", + "print('Beta1 and Beta2 for S2 process are: ',beta_p2)\n", + "print('Standard deviations of Beta1 and Beta2 for S2 process are: ',std_beta_p2)\n", + "print('Standard deviation of e(t) for S2 process is:',sigma_e_p2)\n", + "print('Standard deviation for S2 process is:',np.sqrt(var_p2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3b:</b> \n", + "Compare the calculated standard deviations with those estimated in task 1. Are they close? \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction\n", + "Now that we have information about the noise, we can use this to make a forecast. Read about this in [chapter 4.7](https://mude.citg.tudelft.nl/book/time_series/forecasting.html). \n", + "We apply a simple prediction model. For that we need the $Y=Ax+\\epsilon$ and $\\Sigma_Y$ and the design matrix of the prediction model $A_p$. For our model, we can generate some data from a linear regression model $y(t)=y_0+r t$, with $y_0=0.1$ and $r=0.002$ (error-free data). The generated data $y$ is then added to the noise process data $S$ (here we only use $S_1$).\n", + "This will then make the final $m\\times 1$ observation vector $Y_{true}$ as follows:\n", + "$$\n", + "Y_{true}= \\begin{bmatrix} Y \\\\ Y_p\\end{bmatrix} = y+S\n", + "$$\n", + "\n", + "In order to show how prediction works, we will use the first $m-1$ entries as the 'observed values' $Y$. The last value we generate will serve as our 'true value' of $Y_p$. This value will be assumed unknown, but we will use it to compare the predicted value $\\hat{Y}_p$ with. \n", + "\n", + "The design matrices become:\n", + "$$\n", + "A = \\begin{bmatrix} 1 & t_1 \\\\ \\vdots & \\vdots \\\\ 1 & t_{m-1} \\end{bmatrix} \\quad \\text{and} \\quad A_p=\\begin{bmatrix} 1 &t_m\\end{bmatrix} \n", + "$$\n", + "\n", + "\n", + "For simplicity we assume $\\Sigma_Y = \\sigma^2 I_{m-1}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Chapter 4.7 and 4.8 are not part of the exam material, but we include this exercise here to help understand the methods used above. It will also help you complete the project on Friday.</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4:</b> \n", + "\n", + "Do the following:\n", + "<ol>\n", + " <li> Establish the observation vector $Y$ and design matrix $A$ based on the description above.\n", + " <li> Split $Y$ and $A$ into a new $A$ and $Y$ (for the linear model) and $A_p$ and $Y_p$ (for the prediction model).\n", + " <li> Follow the steps in Chapter 4.7 to predict $Y_p$, so $\\hat{Y}_p$. You can first use the function 'AR_estimation(Y, p)' to estimate the AR(2) parameters $\\beta_1$ and $\\beta_2$ for the estimated residuals.\n", + " <li> Compare the values $\\hat{Y}_p$ with $Y_p$ and also with the function part of $\\hat{Y}_p$, $\\hat{Y}_{signal}=A_p \\hat{X}$.\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yp_f: 2.012792540344711 Yp_n: -0.9024262790540022\n", + "Yp: 0.6654294026146608 Yphat: 1.1103662612907086\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# we take only AR(2) process\n", + "S = S1\n", + "\n", + "# total number of data\n", + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "# true value of intercept and rate\n", + "y0 = 0.1\n", + "r = 0.002\n", + "# make the error-free observations\n", + "y = y0+r*t \n", + "\n", + "# final data with noise\n", + "Y = y+S\n", + "# true value of prediction, data 1000 (only is used to check the prediction)\n", + "Yp = Y[m-1]\n", + "# data of 1-999 is used in Y=Ax+e\n", + "Y = Y[0:m-1]\n", + "\n", + "# design matrix from 1 to 1000 data\n", + "A = np.ones((m, 2))\n", + "A[:,1] = t[0:m]\n", + "# design matrix of data of prediction: 1000\n", + "Ap = A[m-1,:]\n", + "# new design matrix with 999 data \n", + "A = A[0:m-1,:]\n", + "\n", + "# make the Sigma_Y for 999 data \n", + "Sigma_Y = sigma ** 2 * np.eye(m-1)\n", + "# invert Sigma_Y\n", + "Sigma_Y_inv = np.linalg.inv(Sigma_Y)\n", + "\n", + "# BLUE estimate of x\n", + "Xhat = np.linalg.inv(A.T @ Sigma_Y_inv @ A) @ A.T @ Sigma_Y_inv @ Y\n", + "\n", + "# covariance matrix of xhat\n", + "Sigma_Xhat = np.linalg.inv(A.T @ Sigma_Y_inv @A)\n", + "\n", + "# BLUE estimate of y\n", + "Yhat = A @ Xhat \n", + "\n", + "# BLUE estimate of epsilon (residuals)\n", + "epsilon = Y - Yhat\n", + "\n", + "# functional part\n", + "Yp_f = Ap@Xhat \n", + "\n", + "# stochastic part\n", + "# we first estimate AR(2) pars\n", + "beta, std_beta, sigma_e = AR_estimation(epsilon, 2)\n", + "# stochastic part (one index less as we have now 999 data and not 1000)\n", + "S_m = epsilon[-1]*beta[0] + epsilon[-2]*beta[1]\n", + "Yphat = Yp_f + S_m\n", + "\n", + "print('Yp_f: ', Yp_f,'S_m: ',S_m)\n", + "print('Yp: ', Yp, 'Yphat: ', Yphat)\n", + "\n", + "t = t[0:m-1]\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(t, Y, color='blue', label='Data with noise (Y)')\n", + "plt.plot(t, Yhat, color='red', label='BLUE estimate of Y ($\\hat{Y}$)')\n", + "plt.plot(t, epsilon, color='green', label='BLUE estimate of e ($\\hat{e}$)')\n", + "plt.plot(1000, Yphat, 'ro', label='Predicted value (BLUP)')\n", + "plt.ylabel('Y(t)')\n", + "plt.xlabel('Time (day)')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4b:</b> \n", + "\n", + "Review the plot and determine what effect the ARMA process had on the prediction. Try to be quantitative.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Write your answer in this Markdown cell.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "deepnote": {}, + "deepnote_execution_queue": [], + "deepnote_notebook_id": "aa9b740477e34ea98fd2031f67fc974e", + "deepnote_persisted_session": { + "createdAt": "2023-10-07T15:30:07.197Z" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/Week_2_4/WS_2_4_solution.ipynb b/content/Week_2_4/WS_2_4_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a02ba66f0d5f2af09211831c2f8dd29bfd70792d --- /dev/null +++ b/content/Week_2_4/WS_2_4_solution.ipynb @@ -0,0 +1,909 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Workshop 12: Time for fun with AR(2)\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 25px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4. Wednesday December 6, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In general we are interested in identifying the components of a time series, to check stationarity, make statistical judgments, and to identify the appropriate functional model and the stochastic model (ARMA process). In this workshop we will generate a synthetic time series, $S_t$ using a second-order auto-regressive random process ((AR(2)).\n", + "\n", + "Autoregressive models AR(p) are widely used in time series analysis to understand and predict sequential data points. As a special case, AR(2) process refers to a second-order autoregressive model, where each data point is linearly dependent on its two immediate preceding values. One practical application of AR(2) models involves prediction. In many practical applications, however, in addition to the noise process, there exist also a linear trend in the form of $Y=Ax+\\epsilon$. To implement prediction, we need to calculate two terms: one called the functional part (from the linear model) and one the stochastic model (from the noise process). The objectives of this workshop are to:\n", + "\n", + "- generate a noise process of AR(2) with the given parameters $\\beta_1$ and $\\beta_2$.\n", + "- calculate the normalized autocovariance function (ACF) and power spectral density (PSD), and investigate their interlink.\n", + "- estimate the AR(2) parameters $\\beta_1$ and $\\beta_2$ for a given noise process AR(2).\n", + "- predict future values based on the given linear model $Y=Ax+\\epsilon$ and the noise process\n", + "\n", + "Considering the model $Y=Ax+\\epsilon$ note the following:\n", + "- This is conceptually the same, and uses the same notation, as with observation theory topics from Q1, \n", + "- Our signal of interest is the $Ax$ part\n", + "- The \"noise\" is $\\epsilon$ and can be broken down into two more components: 1) _stochastic signal_, and 2) random errors\n", + "- the _stochastic signal_ is our focus of today; in general we use an ARMA model to represent the stochastic signal, and depending on the assumptions, it can take many forms (e.g., AR(1), AR(2), etc; defined by the parameters $p$ and $q$)\n", + "- The symbol $\\epsilon$ represents the overall noise of the original time series, whereas the symbole $e$ is the random errors (the part that is left after we take out the stochastic signal from the noise of the original time series using an ARMA process).\n", + "\n", + "In this notebook specifically, we will complete the following tasks:\n", + "1. Generate the time series, then evaluate stationarity and variance\n", + "2. Evaluate auto-regressive characteristics with ACF and PSD\n", + "3. Estimate prameters of AR(2) model\n", + "4. Use model to make a prediction (using BLUE)\n", + "\n", + "For each Task, there will be two parts: part a focuses on the implementation of the method in the code, and part b reflects on the results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as signal\n", + "from statsmodels.graphics.tsaplots import plot_acf \n", + "from scipy.stats import norm\n", + "from scipy.stats.distributions import chi2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "24e525319d2d49bcaf27de4aaa59d220", + "deepnote_cell_type": "markdown" + }, + "source": [ + "## Task 1: Generate Time Series\n", + "We intend to simulate (as a time series) 1000 samples at 1-day intervals (so $t=1,...,m$ with $m=1000$ days).\n", + "\n", + "_Note: the convention for Time Series and Observation Theory is to use symbole $m$ for the number of samples; however, the symbol $N$ is used in Signal Processing, so in this case $m=N$._\n", + "\n", + "The simulated data is based on a second-order auto-regressive AR(2) random process $S_t$ as follows:\n", + "$$\n", + "S_t= \\beta_1 S_{t-1}+\\beta_2 S_{t-2}+e_t\n", + "$$\n", + "with $t = 1, …, 1000$. The AR(2) process is a stationary time series with a constant mean $\\mu$ and the variance $\\sigma^2$. We set them as follows:\n", + "\n", + "$$\n", + "\\mathbb{E}(S_t)=0 \\text{,} \\hspace{2mm} \\mathbb{D}(S_t)=\\sigma^2=2.\n", + "$$ \n", + "\n", + "The two parameters of AR(2) are $\\beta_1$ and $\\beta_2$, and we will consider 2 scenarios:\n", + "\n", + "1. Scenario 1: $\\beta_1=0.65$ and $\\beta_2=0.30$ (colored)\n", + "2. Scenario 2: $\\beta_1=\\beta_2=0$ (white)\n", + "\n", + "The variance of the purely random noise (white noise) $e_t$ is $\\sigma^2_e$, which for AR(2), is obtained from the following equation:\n", + "\n", + "$$\n", + "\\sigma_{e}^2 = \\frac{(1+\\beta_2)(1-\\beta_1-\\beta_2)(1+\\beta_1-\\beta_2)}{1-\\beta_2} \\sigma^2\n", + "$$\n", + "\n", + "To simulate the data of the AR(2) process, you can make use of a normal distribution; however, _as the realizations of the time series are correlated with each other, we can no longer take random samples from the distribution directly!_ To properly take into account autocorrelation, you will use the above recursive form, which needs initialization. To initialize the first and second data, <code>S[0]=np.random.normal(...)</code> and <code>S[1] = np.random.normal(...)</code> using the normal distribution. You can find information on <code>np.random.normal()</code> [here](https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html#numpy.random.normal). To use the above recursive formula you need to simulate $e_t$, requiring to have its standard deviation $\\sigma_{e}$ of the white noise process (given above).\n", + "\n", + "After generating the time series we may compute the mean and variance to see if they are close to their original values\n", + "$$\n", + "\\hat{\\mu} = \\frac{1}{m} \\sum_{i=1}^{m} S_i \n", + "$$\n", + "and\n", + "$$\n", + "\\hat{\\sigma}^2 =\\hat{C}_0 = \\frac{1}{m} \\sum_{i=1}^{m} S_i^2 \n", + "$$\n", + "which are unbiased estimates of $\\mu=0$ and $\\sigma^2 =2$, respectively. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "48e8df4114544ca4914a15c77372ab30", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1a:</b> \n", + "\n", + "Complete the code cells below to perform the following analysis:\n", + "<ol>\n", + " <li>Compute the standard deviation $\\sigma_{e}$ based on the provided values for scenario 1. \n", + " <li>Simplify the above formula for $\\sigma_{e} $ by taking $\\beta_1=\\beta_2 =0$ (scenario 2).\n", + " <li> Simulate the data of the AR(2) process based on the above given values (for the above two scenarios). Plot the simulated data versus time.\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Tip:</b> \n", + "A key thing to recognize in the code below is that the $e_t$ term is created from a random sample, whereas the other terms (related to the previous 2 vlaues in the time series) are not. This is how the auto-regressive stochastic process is different from what we did in Q1, where all of the observations were independent.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "### SOLUTION\n", + "def gen_AR2(beta1,beta2,sigma,m):\n", + " sigma_e = sigma * np.sqrt((1+beta2)*(1-beta1-beta2)*(1+beta1-beta2)/(1-beta2))\n", + " S = np.zeros(m)\n", + " S[0] = np.random.normal(loc=0, scale=sigma, size=None)\n", + " S[1] = np.random.normal(loc=0, scale=sigma, size=None)\n", + " for i in range(2, m):\n", + " S[i] = beta1 * S[i - 1] + beta2 * S[i - 2] + np.random.normal(loc=0, scale=sigma_e, size=None)\n", + " \n", + " return sigma_e, S" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sigma for Scenario 1 is: \t\t 1.414\n", + "Sigma_e for Scenario 1 is: \t\t 0.501\n", + "The mean of generated S1 process is:\t 0.443\n", + "The standard deviation of generated S1 process is: 1.344\n", + "\n", + "Sigma for Scenario 2 is: \t\t 1.414\n", + "Sigma_e for Scenario 2 is: \t\t 1.414\n", + "The mean of generated S2 process is:\t -0.116\n", + "The standard deviation of generated S2 process is: 1.413\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "# sampling frequency is 1 cycle/day\n", + "Fs = 1.0\n", + "# standard deviation of the noise process\n", + "sigma = np.sqrt(2) \n", + "\n", + "# Scenario 1\n", + "beta1 = 0.65\n", + "beta2 = 0.30\n", + "\n", + "sigma_e_1, S1 = gen_AR2(beta1,beta2,sigma,m)\n", + "\n", + "print(f'Sigma for Scenario 1 is: \\t\\t {sigma:.3f}')\n", + "print(f'Sigma_e for Scenario 1 is: \\t\\t {sigma_e_1:.3f}')\n", + "\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, S1, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: S1(t)')\n", + "plt.title('Scenario 1')\n", + "plt.legend()\n", + "# compute the mean and variance of the generated time series\n", + "mu_S1 = np.mean(S1)\n", + "print(f'The mean of generated S1 process is:\\t {mu_S1:.3f}')\n", + "sigma_S1 = S1.T@S1/m\n", + "print(f'The standard deviation of generated S1 process is: {np.sqrt(sigma_S1):.3f}')\n", + "\n", + "\n", + "# Scenario 2\n", + "beta1 = 0.0\n", + "beta2 = 0.0\n", + "\n", + "sigma_e_2, S2 = gen_AR2(beta1,beta2,sigma,m)\n", + "\n", + "print(f'\\nSigma for Scenario 2 is: \\t\\t {sigma:.3f}')\n", + "print(f'Sigma_e for Scenario 2 is: \\t\\t {sigma_e_2:.3f}')\n", + "\n", + "# Create the first plot (Time series data)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, S2, '-', color='blue', label='signal')\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "plt.xlabel('Time (sec)')\n", + "plt.ylabel('TS data: S2(t)')\n", + "plt.title('Scenario 2')\n", + "plt.legend()\n", + "# compute the mean and variance of the generated time series\n", + "mu_S2 = np.mean(S2)\n", + "print(f'The mean of generated S2 process is:\\t {mu_S2:.3f}')\n", + "sigma_S2 = S2.T@S2/m\n", + "print(f'The standard deviation of generated S2 process is: {np.sqrt(sigma_S2):.3f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "48e8df4114544ca4914a15c77372ab30", + "deepnote_cell_type": "markdown" + }, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1b:</b> \n", + "<ol>\n", + " <li>Explain your reasoning for expecting a smaller value of $\\sigma_{e}$ compared to $\\sigma$. </li>\n", + " <li>Does AR(2) reduce to the white noise process? Compare the $\\sigma_{e}$ of the two processes and with true standard deviation of the two process, i.e. $\\sigma$.</li>\n", + " <li>Can you see/explain the two types/levels of time correlations for these two scenarios? </li>\n", + " <li>Do the two time series look stationary? Compute the mean and variance of the two processes. Are they comparable with the original values of $\\mu=0$ and $\\sigma^2=2$? Run your Jupyter scripts several times to make more concrete conclusions.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "<ol>\n", + " <li>For AR(2), the value for $\\sigma_e$ is smaller than $\\sigma$ beacuse $S_t= \\beta_1 S_{t-1}+\\beta_2 S_{t-2}+e_t$ includes the noise of three variables $S_{t-1}$, $S_{t-2}$, and $\\sigma_e$. This indicates that the white noise of AR(2) is less than the total colored noise of the process. When $\\beta_1=\\beta_2 =0$, the AR(2) reduces to white noise, and hence $\\sigma_e=\\sigma$.</li>\n", + " <li>The simulated time series look different. The white noise looks purely random, whereas clear patterns are seen in AR(2) process. Significant time correlation can be seen for AR(2).</li>\n", + " <li>Both time series look stationary; this is however more evident for scenario 2. The mean and variance of the generated series are not exactly 0 and 2, but they are estimated close to these values. When we run the script several times we observe that they are estimated around these value.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 2: ACF and PSD\n", + "The next step is to calculate the normalized autocovariance function (ACF) for the generated time series. The autocovariance function can be estimated from the following equation.\n", + "$$\n", + "\\hat{C}_{\\tau} = \\frac{1}{m-\\tau} \\sum_{i=1}^{m-\\tau} (S_i-\\mu_s)(S_{i+\\tau}-\\mu_s)\n", + "= \\frac{1}{m-\\tau} \\sum_{i=1}^{m-\\tau} S_i\\, S_{i+\\tau}\n", + "$$\n", + "The normalized autocovariance function (ACF) can directly be obtained from the auto-covariance function as\n", + "$$\n", + "ACF = \\hat{\\rho}_{\\tau} =\\frac{\\hat{C}_{\\tau}}{\\hat{C}_{0}}\n", + "$$\n", + "where $\\hat{C}_{0}=\\sigma^2$ is the variance of the process. You can compute the ACF yourself, simply by implementing the above formula in Python. There are however Python commands/functions that can simply produce the ACF (in fact different ways can be employed to compute the ACF in Python). We utilize the functions provided by <code>statsmodels</code>, specifically the <code>plot_acf</code> function. For more information see [here](https://www.statsmodels.org/stable/generated/statsmodels.graphics.tsaplots.plot_acf.html).\n", + "This command can also have optional parameters like $\\alpha$ (false alarm), for example $\\alpha=0.05$ to make a 95% confidence interval for the computed ACF. \n", + "\n", + "On top of that, we also want to take a look at the power spectral density ([PSD](https://mude.citg.tudelft.nl/book/time_series/acf.html#power-spectral-density)). The PSD is the discrete Fourier transform of the ACF. Of course, there are plenty Python functions that will help you compute the PSD. See for example [the example in the MUDE textbook](https://mude.citg.tudelft.nl/book/time_series/exercise4.html).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2a:</b> \n", + "\n", + "Complete the code cells below to create a plot of ACF and PSD.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# Plot ACF and PSD for scenario 1\n", + "# Plot ACF for the generated process (S1)\n", + "plot_acf(S1, lags=100, alpha=0.01, color = 'blue', label='ACF')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (day)')\n", + "plt.title('Normalized ACF')\n", + "plt.tight_layout()\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal: S1\n", + "frequencies, psd = signal.periodogram(S1, fs=Fs, scaling='density', return_onesided=False)\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.loglog(frequencies, psd, color='blue', label='PSD')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (cycle/day)')\n", + "plt.title('Power Spectral Density (PSD)')\n", + "plt.ylim([1e-3, 5e2])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "\n", + "# Plot ACF and PSD for scenario 2\n", + "# Plot ACF for the generated process (S2)\n", + "plot_acf(S2, lags=100, alpha=0.01, color = 'blue', label='ACF')\n", + "plt.ylabel('Normalized ACF')\n", + "plt.xlabel('Lag (day)')\n", + "plt.title('Normalized ACF')\n", + "plt.tight_layout()\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n", + "\n", + "# Calculate and plot power spectral density (PSD) of the generated signal: S2\n", + "frequencies, psd = signal.periodogram(S2, fs=Fs, scaling='density', return_onesided=False)\n", + "# Create the second plot (Power spectral density)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.loglog(frequencies, psd, color='blue', label='PSD')\n", + "plt.ylabel('Power: PSD')\n", + "plt.xlabel('Frequency (cycle/day)')\n", + "plt.title('Power Spectral Density (PSD)')\n", + "plt.ylim([1e-3, 5e2])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Tip:</b> \n", + "If you aren't sure about the PSD, review material from the Signal Processing week. We aren't sure what the horizontal blue line is; just ignore it.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2b:</b> \n", + "<ol>\n", + " <li>Compare the ACF of the two processes. $S_1$ shows a heavy temporal correlation. Can you explain/link this to the parameters $\\beta_1$, $\\beta_2$ and $\\sigma_{e}$?</li>\n", + " <li>Compare the PSD of the two processes. We cannot see any clear 'peak' in either of these PSD. Can you explain it why? Can you explain the slope in the PSD of $S_1$ process?</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + " \n", + "- The ACF of AR(2) shows a significant time correlation, whereas white noise has no correlation. The large values $\\beta_1=0.65$ and $\\beta_2=0.30$ for AR(2) make this correlation, whereas the role of white noise $e(t)$ is not significant because $\\sigma_e^2<<\\sigma^2$.\n", + "- The absence of peaks in the PSDs is a result of the data lacking periodic signals; instead, it contains simulated noise. The slope observed in the AR(2) highlights time correlation, which suggests that low-frequency noise plays a more substantial role than high-frequency noise in driving process variation. White noise exhibits a flat PSD, which indicates an equal contribution of all frequencies.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 3: Estimation of AR(2) parameters\n", + "Assume that we know the noise process is AR(2), so we know $S_t= \\beta_1 S_{t-1}+\\beta_2 S_{t-2}+e_t$. However we assume that the two parameters $\\beta_1$ and $\\beta_2$ are unknown, to be estimated. This can be implemented using the provided function <code>AR_estimation(S, p)</code> (see below) for AR(p) in general. The function provides the $\\beta$ parameters, their standard deviations and the standard deviation $\\sigma_{e}$ of $e(t)={e}_t$.\n", + "\n", + "The formula in task 1 can be inverted to obtain the variance $\\sigma^2$ of the noise process from $\\sigma^2_{e}$ as follow:\n", + "$$\n", + "\\sigma^2 = \\frac{1-\\beta_2}{(1+\\beta_2)(1-\\beta_1-\\beta_2)(1+\\beta_1-\\beta_2)} \\sigma_{e}^2\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3a:</b> \n", + "\n", + "It is required to:\n", + "<ol>\n", + " <li> Estimate $\\beta_1$ and $\\beta_2$, along with their standard deviations, for the two processes $S_1$ and $S_2$. Compare them with the original values in task 1.\n", + " <li> Compute the standard deviation $\\sigma_{e}$ for the two processes and compare them with the known vales in task 1.\n", + " <li> Compute the standard deviation $\\sigma$ from the parameters $\\beta_1$, $\\beta_2$ and $\\sigma_{e}$ for the two processes $S_1$ and $S_2$.\n", + " </ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def AR_estimation(S, p):\n", + " \"\"\"\n", + " This function computes the AR(p) parameters beta_1,...,beta_p \n", + " for an AR(p) process Y (stationary S: for example epsilon hat).\n", + " \n", + " INPUT:\n", + " S: m x 1 observations (time series)\n", + " p: order of AR\n", + " OUTPUT:\n", + " Beta: Parameters Beta\n", + " S_Beta: Standard deviation of Beta \n", + " Sigma_e: Standard deviation of white noise \n", + " \"\"\"\n", + " m = len(S)\n", + " # make the design matrix\n", + " A = np.zeros((m-p, p))\n", + " for i in range(1, p+1):\n", + " A[:,i-1] = S[p-i:m-i]\n", + "\n", + " # removing the first p data from s\n", + " S = S[p:m]\n", + " m, p = A.shape\n", + "\n", + " # least squares estimate of Beta\n", + " Beta = np.linalg.inv(A.T @ A) @ A.T @ S\n", + "\n", + " # least squares estimate of residuals (white noise)\n", + " Ehat = S - A @ Beta\n", + "\n", + " # estimation of variance of data (white noise)\n", + " Sig2 = (Ehat.T @ Ehat) / (m - p)\n", + "\n", + " # covariance matrix of Beta\n", + " Sigma_Beta = Sig2 * np.linalg.inv(A.T @ A)\n", + "\n", + " # standard deviation of Beta\n", + " std_Beta = np.sqrt(np.diag(Sigma_Beta))\n", + "\n", + " # standard deviation of white noise\n", + " Sigma_e = np.sqrt(Sig2)\n", + " \n", + " return Beta, std_Beta, Sigma_e" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "cell_id": "7047abf2b93f427a86e5ea387783d174", + "deepnote_cell_type": "code", + "deepnote_to_be_reexecuted": false, + "execution_millis": 2334, + "execution_start": 1696691527706, + "source_hash": null + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta1 and Beta2 for S1 process are: [0.65681068 0.28717707]\n", + "Standard deviations of Beta1 and Beta2 for S1 process are: [0.03013811 0.03010912]\n", + "Standard deviation of e(t) for S1 process is: 0.5027319843173333\n", + "Standard deviation for S1 process is: 1.3507173869293652\n", + "\n", + "\n", + "Beta1 and Beta2 for S2 process are: [-0.0230266 -0.03340848]\n", + "Standard deviations of Beta1 and Beta2 for S2 process are: [0.03168665 0.03168442]\n", + "Standard deviation of e(t) for S2 process is: 1.4145164269792216\n", + "Standard deviation for S2 process is: 1.4156579559631775\n" + ] + } + ], + "source": [ + "### SOLUTION\n", + "\n", + "# AR(2) parameter estimation for S1\n", + "beta_p1, std_beta_p1, sigma_e_p1 = AR_estimation(S1, 2)\n", + "var_p1 = (sigma_e_p1**2*(1 - beta_p1[1])\n", + " /((1 + beta_p1[1])*(1 - beta_p1[0] - beta_p1[1])\n", + " *(1 + beta_p1[0] - beta_p1[1])))\n", + "print('Beta1 and Beta2 for S1 process are: ',beta_p1)\n", + "print('Standard deviations of Beta1 and Beta2 for S1 process are: ',std_beta_p1)\n", + "print('Standard deviation of e(t) for S1 process is:',sigma_e_p1)\n", + "print('Standard deviation for S1 process is:',np.sqrt(var_p1))\n", + "\n", + "# AR(2) parameter estimation for S2\n", + "beta_p2, std_beta_p2, sigma_e_p2 = AR_estimation(S2, 2)\n", + "var_p2 = (sigma_e_p2**2\n", + " *(1 - beta_p2[1])\n", + " /((1 + beta_p2[1])*(1-beta_p2[0] - beta_p2[1])\n", + " *(1 + beta_p2[0] - beta_p2[1])))\n", + "print('\\n')\n", + "print('Beta1 and Beta2 for S2 process are: ',beta_p2)\n", + "print('Standard deviations of Beta1 and Beta2 for S2 process are: ',std_beta_p2)\n", + "print('Standard deviation of e(t) for S2 process is:',sigma_e_p2)\n", + "print('Standard deviation for S2 process is:',np.sqrt(var_p2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3b:</b> \n", + "Compare the calculated standard deviations with those estimated in task 1. Are they close? \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + " \n", + "- The estimated parameters of AR(2) are close to their original values $\\beta_1=0.65$ and $\\beta_2=0.30$. For scenario 2 (of white noise) they are close to zero, which makes sense as $\\beta_1=0$ and $\\beta_2=0$ for this process. \n", + "- The estimated $\\sigma_e$ for AR(2) is also close to the true value of 0.501, and for scenario 2 it is close to $\\sqrt{2}=1.414$.\n", + "- The results for $\\sigma$ are also consistent with those given in task 1.\n", + " \n", + "Why are the values for standard deviation not exactly 1.414? Part of it is because it is we are generating data from a simulation, so this is dependent on the realizations and the number of samples.\n", + " \n", + "Remember that for a stationary process, the <em>expectation</em> of the mean and standard deviation should be 0 and 1.414; so if you ran the code above many times, then found the expectation of the results, they would approach 0 and 1.414. \n", + " \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction\n", + "Now that we have information about the noise, we can use this to make a forecast. Read about this in [chapter 4.7](https://mude.citg.tudelft.nl/book/time_series/forecasting.html). \n", + "We apply a simple prediction model. For that we need the $Y=Ax+\\epsilon$ and $\\Sigma_Y$ and the design matrix of the prediction model $A_p$. For our model, we can generate some data from a linear regression model $y(t)=y_0+r t$, with $y_0=0.1$ and $r=0.002$ (error-free data). The generated data $y$ is then added to the noise process data $S$ (here we only use $S_1$).\n", + "This will then make the final $m\\times 1$ observation vector $Y_{true}$ as follows:\n", + "$$\n", + "Y_{true}= \\begin{bmatrix} Y \\\\ Y_p\\end{bmatrix} = y+S\n", + "$$\n", + "\n", + "In order to show how prediction works, we will use the first $m-1$ entries as the 'observed values' $Y$. The last value we generate will serve as our 'true value' of $Y_p$. This value will be assumed unknown, but we will use it to compare the predicted value $\\hat{Y}_p$ with. \n", + "\n", + "The design matrices become:\n", + "$$\n", + "A = \\begin{bmatrix} 1 & t_1 \\\\ \\vdots & \\vdots \\\\ 1 & t_{m-1} \\end{bmatrix} \\quad \\text{and} \\quad A_p=\\begin{bmatrix} 1 &t_m\\end{bmatrix} \n", + "$$\n", + "\n", + "\n", + "For simplicity we assume $\\Sigma_Y = \\sigma^2 I_{m-1}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Chapter 4.7 and 4.8 are not part of the exam material, but we include this exercise here to help understand the methods used above. It will also help you complete the project on Friday.</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4:</b> \n", + "\n", + "Do the following:\n", + "<ol>\n", + " <li> Establish the observation vector $Y$ and design matrix $A$ based on the description above.\n", + " <li> Split $Y$ and $A$ into a new $A$ and $Y$ (for the linear model) and $A_p$ and $Y_p$ (for the prediction model).\n", + " <li> Follow the steps in Chapter 4.7 to predict $Y_p$, so $\\hat{Y}_p$. You can first use the function 'AR_estimation(Y, p)' to estimate the AR(2) parameters $\\beta_1$ and $\\beta_2$ for the estimated residuals.\n", + " <li> Compare the values $\\hat{Y}_p$ with $Y_p$ and also with the function part of $\\hat{Y}_p$, $\\hat{Y}_{signal}=A_p \\hat{X}$.\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yp_f: 2.012792540344711 Yp_n: -0.9024262790540022\n", + "Yp: 0.6654294026146608 Yphat: 1.1103662612907086\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# we take only AR(2) process\n", + "S = S1\n", + "\n", + "# total number of data\n", + "m = 1000\n", + "t = np.arange(1, m + 1)\n", + "# true value of intercept and rate\n", + "y0 = 0.1\n", + "r = 0.002\n", + "# make the error-free observations\n", + "y = y0+r*t \n", + "\n", + "# final data with noise\n", + "Y = y+S\n", + "# true value of prediction, data 1000 (only is used to check the prediction)\n", + "Yp = Y[m-1]\n", + "# data of 1-999 is used in Y=Ax+e\n", + "Y = Y[0:m-1]\n", + "\n", + "# design matrix from 1 to 1000 data\n", + "A = np.ones((m, 2))\n", + "A[:,1] = t[0:m]\n", + "# design matrix of data of prediction: 1000\n", + "Ap = A[m-1,:]\n", + "# new design matrix with 999 data \n", + "A = A[0:m-1,:]\n", + "\n", + "# make the Sigma_Y for 999 data \n", + "Sigma_Y = sigma ** 2 * np.eye(m-1)\n", + "# invert Sigma_Y\n", + "Sigma_Y_inv = np.linalg.inv(Sigma_Y)\n", + "\n", + "# BLUE estimate of x\n", + "Xhat = np.linalg.inv(A.T @ Sigma_Y_inv @ A) @ A.T @ Sigma_Y_inv @ Y\n", + "\n", + "# covariance matrix of xhat\n", + "Sigma_Xhat = np.linalg.inv(A.T @ Sigma_Y_inv @A)\n", + "\n", + "# BLUE estimate of y\n", + "Yhat = A @ Xhat \n", + "\n", + "# BLUE estimate of epsilon (residuals)\n", + "epsilon = Y - Yhat\n", + "\n", + "# functional part\n", + "Yp_f = Ap@Xhat \n", + "\n", + "# stochastic part\n", + "# we first estimate AR(2) pars\n", + "beta, std_beta, sigma_e = AR_estimation(epsilon, 2)\n", + "# stochastic part (one index less as we have now 999 data and not 1000)\n", + "S_m = epsilon[-1]*beta[0] + epsilon[-2]*beta[1]\n", + "Yphat = Yp_f + S_m\n", + "\n", + "print('Yp_f: ', Yp_f,'S_m: ',S_m)\n", + "print('Yp: ', Yp, 'Yphat: ', Yphat)\n", + "\n", + "t = t[0:m-1]\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(t, Y, color='blue', label='Data with noise (Y)')\n", + "plt.plot(t, Yhat, color='red', label='BLUE estimate of Y ($\\hat{Y}$)')\n", + "plt.plot(t, epsilon, color='green', label='BLUE estimate of e ($\\hat{e}$)')\n", + "plt.plot(1000, Yphat, 'ro', label='Predicted value (BLUP)')\n", + "plt.ylabel('Y(t)')\n", + "plt.xlabel('Time (day)')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.box(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p><b>Note</b>: the notation used here is consistent with the book, which means that $S$ used here is the $\\epsilon$ for the AR(2) process, and therefore, we need to predict $\\epsilon$ at epoch $m=1000$.\n", + "\n", + "Based on the AR(2) process of $S_t=\\beta_1 S_{t-1} + \\beta_2 S_{t-2} + e_t$:\n", + " \n", + "$$\n", + "\\hat\\epsilon_{1000} = \\hat{\\beta}_1\\hat{\\epsilon}_{999} + \\hat{\\beta}_2\\hat{\\epsilon}_{998} + 0\n", + "$$\n", + " \n", + "Where $e_t$ is zero since it is white noise and the expectation $\\mathbb{E}(e_t)=0$. Also, $\\epsilon_{999}$ and $\\epsilon_{998}$ are the last 2 values in the array from the code (e.g., <code>epsilon[-1]</code> and <code>epsilon[-2]</code>).\n", + "Then\n", + "\n", + "$$\n", + "\\hat{Y}_p = A_p\\hat{x} + \\hat{\\epsilon}_{1000}\n", + "$$\n", + "\n", + "This is the red dot in the figure.\n", + "</p></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 4b:</b> \n", + "\n", + "Review the plot and determine what effect the ARMA process had on the prediction. Try to be quantitative.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "you can see that the dot is no longer on the linear trend line (the BLUE estimation), which would have been the case if we had a purely white noise process (i.e., what we did in Q1). We are now including an ARMA model to represent colored noise for the stochastic process to make a prediction (so BLUE becomes BLUP), which is why the red dot has moved away from the line. \n", + "\n", + "<em>Note:</em> there is a (small) chance that the random error is predicted such that the dot lands on the line, but this is purely a coincindence from the sampling process; running the simulation again will produce a different result!\n", + "</p>\n", + "</div>\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "deepnote": {}, + "deepnote_execution_queue": [], + "deepnote_notebook_id": "aa9b740477e34ea98fd2031f67fc974e", + "deepnote_persisted_session": { + "createdAt": "2023-10-07T15:30:07.197Z" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/Week_2_5/PA/PA_2_5_data_framework.ipynb b/content/Week_2_5/PA/PA_2_5_data_framework.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2a8747b81883fd51744eae821a2760dacad960fa --- /dev/null +++ b/content/Week_2_5/PA/PA_2_5_data_framework.ipynb @@ -0,0 +1,656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c96d6259-08d6-4289-aea2-589d67cdb5ee", + "metadata": {}, + "source": [ + "# Programming Assignment 13: Data Framework\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. Due: complete this PA prior to class on Friday, Dec 15, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "a5f54f96", + "metadata": {}, + "source": [ + "## Overview of Assignment\n", + "\n", + "This assignment quickly introduces you to the package `pandas`. We only use a few small features here, to help you get familiar with it before using it more in the coming weeks. The primary purpose is to easily load data from csv files and quickly process the contents. This is accomplished with a new data type unique to pandas: a `DataFrame`. It also makes it very easy to export data to a `*.csv` file.\n", + "\n", + "If you want to learn more about pandas after finishing this assignment, the [Getting Started page](https://pandas.pydata.org/docs/getting_started/index.html) is a great resource.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- Your notebook creates a file `earth_dams.csv` in the root of the repository\n", + "- Your repository contains a `.gitignore` file that ignores all csv files\n", + "- You commit a `.gitignore` file to your repository, but _not_ a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75681df5-7a73-469a-ad83-ebd05451e0b7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "b0584fa2-7f4b-4566-9217-e6ca58d0e191", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction to pandas\n", + "\n", + "Pandas dataframes are considered by some to be difficult to use. For example, here is a line of code from one of our notebooks this week. Can you understand what it is doing?\n", + "```\n", + "net_data.loc[net_data['capacity'] <= 0, 'capacity'] = 0\n", + "```\n", + "\n", + "One of the reasons for this is that the primary pandas data type, a `DataFrame` object, uses a dictionary-like syntax to access and store elements. For example, remember that a dictionary is defined using curly braces. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b00501d-a01f-48e0-af35-47714c2544c3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "my_dict = {}\n", + "type(my_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "f170b25b-3dcc-4b0a-9c8a-7c8d009727ab", + "metadata": {}, + "source": [ + "Also remember that you can add items as a key-value pair:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dde8eb7e-a919-4360-97a3-71a0876b107c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "my_dict = {'key': 5}" + ] + }, + { + "cell_type": "markdown", + "id": "4889617a-b8bc-45ec-8804-a301d01d0915", + "metadata": {}, + "source": [ + "The item `key` was added with value 5. We can access it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b806cf6b-6cc8-4d9c-9c99-108e00e3c8a2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "my_dict['key']" + ] + }, + { + "cell_type": "markdown", + "id": "b91ad855-cb49-4690-bcd3-35a5137e6213", + "metadata": {}, + "source": [ + "This is useful beceause if we have something like a list as the value, we can simply add the index the the end of the call to the dictionary. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42a0df17-025e-43bd-a8a2-351439f0db46", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "my_dict['array'] = [34, 634, 74, 7345]\n", + "my_dict['array'][3]" + ] + }, + { + "cell_type": "markdown", + "id": "4a2fbd05-fe12-4a25-848f-f9c3f028f496", + "metadata": {}, + "source": [ + "And now that you see the \"double brackets\" above, i.e., `[ ][ ]`, you can see where the notation starts to get a little more complicated. Here's a fun nested example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "624d4b6f-79b9-4977-b628-e425e5bc699c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "shell = ['chick']\n", + "shell = {'shell': shell}\n", + "shell = {'shell': shell}\n", + "shell = {'shell': shell}\n", + "nest = {'egg': shell}\n", + "nest['egg']['shell']['shell']['shell'][0]" + ] + }, + { + "cell_type": "markdown", + "id": "246ffff6-60a1-4b93-b435-23e7e6c5dce1", + "metadata": {}, + "source": [ + "Don't worry about that too much...as long as you keep dictionaries and their syntax in mind, it becomes easier to \"read\" the complicated pandas syntax.\n", + "\n", + "Now let's go through a few simple tasks that will illustrate what a `DataFrame` is (when constructed from a dictionary), and some of its fundamental methods and characteristics." + ] + }, + { + "cell_type": "markdown", + "id": "b5ce0532-5f86-48dd-9640-25853022a737", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 0.1:</b> \n", + " \n", + "Run the cell below and check what kind of object was created using the method <code>type</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dd4b705-4c3e-40d2-a870-02ad8fa0ecee", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "new_dict = {'names': ['Gauss', 'Newton', 'Lagrange', 'Euler'],\n", + " 'birth year': [1777, 1643, 1736, 1707]}\n", + "YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "3823b26c-a6d5-48d6-9587-fe32e28cd294", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 0.2:</b> \n", + " \n", + "Run the cell below and check what kind of object was created using the method <code>type</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bf43ec1-26fd-4372-8f13-b5d0f6b4bc01", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.DataFrame(new_dict)\n", + "YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "31fb3d01-1930-4708-b0e0-b4ad5e3b986a", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 0.2:</b> \n", + " \n", + "Read the code below and try to predict what the answer should be before you run it and view the output. Then run the cell, confirm your guess and in the second cell check what kind of object was created using the method <code>type</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bdc3486-cd64-4058-b064-60eabe6c7730", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "guess = df.loc[df['birth year'] <= 1700, 'names']\n", + "print(guess)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6d67975a-4cf9-40ad-9a3c-87db26134e44", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "6bbfa8c1-a212-451f-82e2-5600647b6ebc", + "metadata": {}, + "source": [ + "Note that this is a `Series` data type, which is part of the pandas package (you can read about it [here](https://pandas.pydata.org/docs/reference/api/pandas.Series.html)). If you need to use the value that is stored in the series, you can use the attribute `values` as if it were an object with the same `type` as the data in the `Series`; the example below shows that the `names` in the `DataFrame` is a `Series` where the data has type `ndarray`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7554d184-63da-4ffe-9648-f024b385afbc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(type(df.loc[df['birth year'] <= 1700, 'names']))\n", + "print(type(df.loc[df['birth year'] <= 1700, 'names'].values))\n", + "print('The value in the series is an ndarray with first item:',\n", + " df.loc[df['birth year'] <= 1700, 'names'].values[0])" + ] + }, + { + "cell_type": "markdown", + "id": "2af86307-53d2-4738-a5a6-afbc25fc23fa", + "metadata": {}, + "source": [ + "Another useful feature of pandas is to be able to quickly look at the contents of the data frame. You can quickly see which columns are present:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71e8ab45-91c7-441b-a028-98c2da883b4e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "72422568-bb76-4750-ae9a-581682876218", + "metadata": {}, + "source": [ + "You can also get summary information easily:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3392fa06-76a8-48d8-9fe9-d8a3b248c303", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "29fd2017-58e5-43fe-bb82-80218b2fb718", + "metadata": {}, + "source": [ + "Finally, it is also very easy to read and write dataframes to a `*.csv` file, which you can do using the following commands (_you will apply this in the tasks below_):\n", + "```\n", + "df = pd.read_csv('dams.csv')\n", + "```\n", + "To write, the method is similar; the keyword argument `index=False` avoids adding a numbered index as an extra column in the csv:\n", + "```\n", + "df.to_csv('dams.csv', index=False)\n", + "```\n", + "\n", + "**Now we are ready to practice using pandas and git to effectively manage data in our repositories!**" + ] + }, + { + "cell_type": "markdown", + "id": "2313a05f-7aaf-4524-afec-3e72becad882", + "metadata": { + "tags": [] + }, + "source": [ + "## Task 1: Get the data into our repo\n", + "\n", + "For this assignment we will use a small `*.csv` file that can be downloaded using [this link](https://surfdrive.surf.nl/files/index.php/s/8xDKt0MsIcTYsJK).\n", + "\n", + "The steps below outline how you should add a data set to a git repository so that you can access the data with the code (i.e., Jupyter notebook), but not commit the file to the repository. A key assumption here is that you prefer to archive the data on a different website that is more appropriate for this purpose (not git!)." + ] + }, + { + "cell_type": "markdown", + "id": "e2ac6a6f", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.1:</b> \n", + " \n", + "Download the dataset and move it to your working directory (the git repo of this notebook).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "a9adf1d7-3246-4368-8e26-de950b9b7808", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.2:</b>\n", + " \n", + "Check your GitHub Desktop to see that the file is listed as a \"changed file.\" <b>Do not commit the dataset!</b>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "443e5f9b-b7d1-4a02-9d7e-204454ef61a1", + "metadata": {}, + "source": [ + "As you learned in the README, we don't want to include datasets in our repositories (ignore the fact that this one is tiny). You may remember from Q1 that we can use a `.gitignore` file to tell git not to track specific files. We can do it by simply listing `dams.csv` in our `.gitignore` file." + ] + }, + { + "cell_type": "markdown", + "id": "16a0b814-37b2-473a-9757-b8278fd5c27e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.3:</b>\n", + " \n", + "Create a <code>.gitignore</code> file to ignore the dataset. Confirm that it worked properly by making sure that the data file is no longer listed as a \"changed file.\"\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "fd4ed527-b464-446a-a316-855580920903", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.4:</b>\n", + " \n", + "Commit the <code>.gitignore</code> file.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "206dd065-b1a0-4294-9f4e-e0f54d7b2483", + "metadata": {}, + "source": [ + "## Task 2: Evalue and process the data\n", + "\n", + "Now that the data is stored locally, we can process it and use it in our analysis." + ] + }, + { + "cell_type": "markdown", + "id": "c4049295-83c2-4fc8-981f-7a4d95812ef3", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.1:</b>\n", + " \n", + "Import the dataset as a <code>DataFrame</code>, then explore it and learn about its contents (use the methods presented above; you can also look inside the csv file).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ca874c2-fcf9-4b3d-bdff-9413a5df19af", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "744d6572-b0d2-4100-89a0-72ac84ffad97", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.2:</b>\n", + " \n", + "Using the example above, find the dams in the <code>DataFrame</code> that are of type <code>earth fill</code>.</code>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc30d3fb-a87b-4247-8c0b-6dd72afec558", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "names_of_earth_dams = YOUR_CODE_HERE\n", + "print('The earth fill dams are:', names_of_earth_dams)" + ] + }, + { + "cell_type": "markdown", + "id": "ffc02761-430a-4b09-a339-803bdfade046", + "metadata": {}, + "source": [ + "_Hint: the answer should be:_ `['Fort Peck' 'Nurek' 'Kolnbrein' 'WAC Bennett']`" + ] + }, + { + "cell_type": "markdown", + "id": "38bfcaa4-82b6-47f4-a61f-0c5f9d6e9ebb", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.3:</b>\n", + " \n", + "Create a new dataframe that only includes the earth fill dams. Save it as a new csv file called <code>earth_dams.csv</code>.\n", + "</p>\n", + "</div>\n", + "\n", + "_Hint: you only need to remove a small thing from the code for your answer to the task above)._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3563f5db-99b0-48e5-9108-6fc6ebe9bdd7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df_earth = YOUR_CODE_HERE\n", + "df_earth.YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "c18718b5-9a5f-4384-b1ec-13ae8a55ebec", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.4:</b>\n", + " \n", + "Check the contents of the new csv file to make sure you created it correctly.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "a4e13c88-3246-4b13-84f0-116fc8e27bb6", + "metadata": {}, + "source": [ + "## Task 3: Keep your repository clean\n", + "\n", + "Now we have created a second csv file, but we also do not want to track it in our repo. We could add the filename to our gitignore file, but there is a better way: using a wildcard! We already used this in Q1, so hopefully you can see that adding `*.csv` to the `.gitignore` file will ignore _all_ csv files in the repository, which is exactly what we want! " + ] + }, + { + "cell_type": "markdown", + "id": "bf492154-4370-45da-a7a5-24049fe79766", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3.1:</b>\n", + " \n", + "Update your gitignore using the wildcard <code>*.csv</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "4f6cf596-7ac6-49c1-8d4b-fbd140434d5b", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3.2:</b>\n", + " \n", + "Confirm that the data files do not show up as \"changed files\" in your GitHub Desktop application. Then commit this notebook to your repository and push it to GitLab because you are done with the assignment!\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "c76f285d-7412-4b23-aa89-e5dbed3b921a", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note that if we really cared about this \"new\" dataset, and it were too large to save it in our git repository, we would want to back it up to another (cloud) platform so that we can recover it if our files are lost. We skip this step here, but don't forget to do it if you are working on another project in the future (for example, your thesis).</p></div>" + ] + }, + { + "cell_type": "markdown", + "id": "80580ab9-4d79-46b1-ae6e-775af04d43ad", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_5/PA/PA_2_5_solution.ipynb b/content/Week_2_5/PA/PA_2_5_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f5da45b083bc54d78325ae87bc9f4c8c1ac2c611 --- /dev/null +++ b/content/Week_2_5/PA/PA_2_5_solution.ipynb @@ -0,0 +1,1008 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c96d6259-08d6-4289-aea2-589d67cdb5ee", + "metadata": {}, + "source": [ + "# Programming Assignment 13: Data Framework\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. Due: complete this PA prior to class on Friday, Dec 15, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "a5f54f96", + "metadata": {}, + "source": [ + "## Overview of Assignment\n", + "\n", + "This assignment quickly introduces you to the package `pandas`. We only use a few small features here, to help you get familiar with it before using it more in the coming weeks. The primary purpose is to easily load data from csv files and quickly process the contents. This is accomplished with a new data type unique to pandas: a `DataFrame`. It also makes it very easy to export data to a `*.csv` file.\n", + "\n", + "If you want to learn more about pandas after finishing this assignment, the [Getting Started page](https://pandas.pydata.org/docs/getting_started/index.html) is a great resource.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- Your notebook creates a file `earth_dams.csv` in the root of the repository\n", + "- Your repository contains a `.gitignore` file that ignores all csv files\n", + "- You commit a `.gitignore` file to your repository, but _not_ a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "75681df5-7a73-469a-ad83-ebd05451e0b7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "b0584fa2-7f4b-4566-9217-e6ca58d0e191", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction to pandas\n", + "\n", + "Pandas dataframes are considered by some to be difficult to use. For example, here is a line of code from one of our notebooks this week. Can you understand what it is doing?\n", + "```\n", + "net_data.loc[net_data['capacity'] <= 0, 'capacity'] = 0\n", + "```\n", + "\n", + "One of the reasons for this is that the primary pandas data type, a `DataFrame` object, uses a dictionary-like syntax to access and store elements. For example, remember that a dictionary is defined using curly braces. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8b00501d-a01f-48e0-af35-47714c2544c3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_dict = {}\n", + "type(my_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "f170b25b-3dcc-4b0a-9c8a-7c8d009727ab", + "metadata": {}, + "source": [ + "Also remember that you can add items as a key-value pair:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dde8eb7e-a919-4360-97a3-71a0876b107c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "my_dict = {'key': 5}" + ] + }, + { + "cell_type": "markdown", + "id": "4889617a-b8bc-45ec-8804-a301d01d0915", + "metadata": {}, + "source": [ + "The item `key` was added with value 5. We can access it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b806cf6b-6cc8-4d9c-9c99-108e00e3c8a2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_dict['key']" + ] + }, + { + "cell_type": "markdown", + "id": "b91ad855-cb49-4690-bcd3-35a5137e6213", + "metadata": {}, + "source": [ + "This is useful beceause if we have something like a list as the value, we can simply add the index the the end of the call to the dictionary. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "42a0df17-025e-43bd-a8a2-351439f0db46", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7345" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_dict['array'] = [34, 634, 74, 7345]\n", + "my_dict['array'][3]" + ] + }, + { + "cell_type": "markdown", + "id": "4a2fbd05-fe12-4a25-848f-f9c3f028f496", + "metadata": {}, + "source": [ + "And now that you see the \"double brackets\" above, i.e., `[ ][ ]`, you can see where the notation starts to get a little more complicated. Here's a fun nested example:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "624d4b6f-79b9-4977-b628-e425e5bc699c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'chick'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shell = ['chick']\n", + "shell = {'shell': shell}\n", + "shell = {'shell': shell}\n", + "shell = {'shell': shell}\n", + "nest = {'egg': shell}\n", + "nest['egg']['shell']['shell']['shell'][0]" + ] + }, + { + "cell_type": "markdown", + "id": "246ffff6-60a1-4b93-b435-23e7e6c5dce1", + "metadata": {}, + "source": [ + "Don't worry about that too much...as long as you keep dictionaries and their syntax in mind, it becomes easier to \"read\" the complicated pandas syntax.\n", + "\n", + "Now let's go through a few simple tasks that will illustrate what a `DataFrame` is (when constructed from a dictionary), and some of its fundamental methods and characteristics." + ] + }, + { + "cell_type": "markdown", + "id": "b5ce0532-5f86-48dd-9640-25853022a737", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 0.1:</b> \n", + " \n", + "Run the cell below and check what kind of object was created using the method <code>type</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4dd4b705-4c3e-40d2-a870-02ad8fa0ecee", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_dict = {'names': ['Gauss', 'Newton', 'Lagrange', 'Euler'],\n", + " 'birth year': [1777, 1643, 1736, 1707]}\n", + "# YOUR_CODE_HERE\n", + "# Solution\n", + "type(new_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "3823b26c-a6d5-48d6-9587-fe32e28cd294", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 0.2:</b> \n", + " \n", + "Run the cell below and check what kind of object was created using the method <code>type</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2bf43ec1-26fd-4372-8f13-b5d0f6b4bc01", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(new_dict)\n", + "# YOUR_CODE_HERE\n", + "# Solution\n", + "type(df)" + ] + }, + { + "cell_type": "markdown", + "id": "31fb3d01-1930-4708-b0e0-b4ad5e3b986a", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 0.2:</b> \n", + " \n", + "Read the code below and try to predict what the answer should be before you run it and view the output. Then run the cell, confirm your guess and in the second cell check what kind of object was created using the method <code>type</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5bdc3486-cd64-4058-b064-60eabe6c7730", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 Newton\n", + "Name: names, dtype: object\n" + ] + } + ], + "source": [ + "guess = df.loc[df['birth year'] <= 1700, 'names']\n", + "print(guess)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6d67975a-4cf9-40ad-9a3c-87db26134e44", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# YOUR_CODE_HERE\n", + "# Solution\n", + "type(guess)" + ] + }, + { + "cell_type": "markdown", + "id": "ded9ea29-c602-4b40-bc0d-9b135836cf9e", + "metadata": {}, + "source": [ + "Note that this is a `Series` data type, which is part of the pandas package (you can read about it [here](https://pandas.pydata.org/docs/reference/api/pandas.Series.html)). If you need to use the value that is stored in the series, you can use the attribute `values` as if it were an object with the same `type` as the data in the `Series`; the example below shows that the `names` in the `DataFrame` is a `Series` where the data has type `ndarray`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7554d184-63da-4ffe-9648-f024b385afbc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'pandas.core.series.Series'>\n", + "<class 'numpy.ndarray'>\n", + "The value in the series is an ndarray with first item: Newton\n" + ] + } + ], + "source": [ + "print(type(df.loc[df['birth year'] <= 1700, 'names']))\n", + "print(type(df.loc[df['birth year'] <= 1700, 'names'].values))\n", + "print('The value in the series is an ndarray with first item:',\n", + " df.loc[df['birth year'] <= 1700, 'names'].values[0])" + ] + }, + { + "cell_type": "markdown", + "id": "2af86307-53d2-4738-a5a6-afbc25fc23fa", + "metadata": {}, + "source": [ + "Another useful feature of pandas is to be able to quickly look at the contents of the data frame. You can quickly see which columns are present:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "71e8ab45-91c7-441b-a028-98c2da883b4e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>names</th>\n", + " <th>birth year</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Gauss</td>\n", + " <td>1777</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Newton</td>\n", + " <td>1643</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Lagrange</td>\n", + " <td>1736</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Euler</td>\n", + " <td>1707</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " names birth year\n", + "0 Gauss 1777\n", + "1 Newton 1643\n", + "2 Lagrange 1736\n", + "3 Euler 1707" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "72422568-bb76-4750-ae9a-581682876218", + "metadata": {}, + "source": [ + "You can also get summary information easily:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3392fa06-76a8-48d8-9fe9-d8a3b248c303", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>birth year</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>4.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>1715.750000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>56.364143</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>1643.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>1691.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>1721.500000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>1746.250000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>1777.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " birth year\n", + "count 4.000000\n", + "mean 1715.750000\n", + "std 56.364143\n", + "min 1643.000000\n", + "25% 1691.000000\n", + "50% 1721.500000\n", + "75% 1746.250000\n", + "max 1777.000000" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "29fd2017-58e5-43fe-bb82-80218b2fb718", + "metadata": {}, + "source": [ + "Finally, it is also very easy to read and write dataframes to a `*.csv` file, which you can do using the following commands (_you will apply this in the tasks below_):\n", + "```\n", + "df = pd.read_csv('dams.csv')\n", + "```\n", + "To write, the method is similar; the keyword argument `index=False` avoids adding a numbered index as an extra column in the csv:\n", + "```\n", + "df.to_csv('dams.csv', index=False)\n", + "```\n", + "\n", + "**Now we are ready to practice using pandas and git to effectively manage data in our repositories!**" + ] + }, + { + "cell_type": "markdown", + "id": "3633b59c-b910-4eba-bf37-cc8eb80a8e31", + "metadata": {}, + "source": [ + "## Task 1: Get the data into our repo\n", + "\n", + "For this assignment we will use a small `*.csv` file that can be downloaded using [this link](https://surfdrive.surf.nl/files/index.php/s/8xDKt0MsIcTYsJK).\n", + "\n", + "The steps below outline how you should add a data set to a git repository so that you can access the data with the code (i.e., Jupyter notebook), but not commit the file to the repository. A key assumption here is that you prefer to archive the data on a different website that is more appropriate for this purpose (not git!)." + ] + }, + { + "cell_type": "markdown", + "id": "e2ac6a6f", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.1:</b> \n", + " \n", + "Download the dataset and move it to your working directory (the git repo of this notebook).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "a9adf1d7-3246-4368-8e26-de950b9b7808", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.2:</b>\n", + " \n", + "Check your GitHub Desktop to see that the file is listed as a \"changed file.\" <b>Do not commit the dataset!</b>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "443e5f9b-b7d1-4a02-9d7e-204454ef61a1", + "metadata": {}, + "source": [ + "As you learned in the README, we don't want to include datasets in our repositories (ignore the fact that this one is tiny). You may remember from Q1 that we can use a `.gitignore` file to tell git not to track specific files. We can do it by simply listing `dams.csv` in our `.gitignore` file." + ] + }, + { + "cell_type": "markdown", + "id": "16a0b814-37b2-473a-9757-b8278fd5c27e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.3:</b>\n", + " \n", + "Create a <code>.gitignore</code> file to ignore the dataset. Confirm that it worked properly by making sure that the data file is no longer listed as a \"changed file.\"\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "fd4ed527-b464-446a-a316-855580920903", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.4:</b>\n", + " \n", + "Commit the <code>.gitignore</code> file.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "206dd065-b1a0-4294-9f4e-e0f54d7b2483", + "metadata": {}, + "source": [ + "## Task 2: Evalue and process the data\n", + "\n", + "Now that the data is stored locally, we can process it and use it in our analysis." + ] + }, + { + "cell_type": "markdown", + "id": "024aad2d-960c-4968-8933-186869e4f308", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.1:</b>\n", + " \n", + "Import the dataset as a DataFrame, then explore it and learn about its contents (use the methods presented above; you can also look inside the csv file).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5ca874c2-fcf9-4b3d-bdff-9413a5df19af", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Name</th>\n", + " <th>Year</th>\n", + " <th>Volume (1e6 m^3)</th>\n", + " <th>Height (m)</th>\n", + " <th>Type</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Tarbela</td>\n", + " <td>1976</td>\n", + " <td>153.0</td>\n", + " <td>143</td>\n", + " <td>rock fill</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Fort Peck</td>\n", + " <td>1940</td>\n", + " <td>96.0</td>\n", + " <td>96</td>\n", + " <td>earth fill</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Ataturk</td>\n", + " <td>1990</td>\n", + " <td>84.5</td>\n", + " <td>166</td>\n", + " <td>rock fill</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Houtribdijk</td>\n", + " <td>1968</td>\n", + " <td>78.0</td>\n", + " <td>13</td>\n", + " <td>rock fill</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Oahe</td>\n", + " <td>1963</td>\n", + " <td>70.3</td>\n", + " <td>75</td>\n", + " <td>rock fill</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Name Year Volume (1e6 m^3) Height (m) Type\n", + "0 Tarbela 1976 153.0 143 rock fill\n", + "1 Fort Peck 1940 96.0 96 earth fill\n", + "2 Ataturk 1990 84.5 166 rock fill\n", + "3 Houtribdijk 1968 78.0 13 rock fill\n", + "4 Oahe 1963 70.3 75 rock fill" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('dams.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "03ce99ca-77fe-403e-ae2b-fc26cb368326", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "We can see that this dataset has some information about dams, including the name, year constructed, volume and height. They look pretty big! It's actually the largest 5 dams by either volume or height (10 dams total), listed on Wikipedia page <a href=\"https://en.wikipedia.org/wiki/List_of_largest_dams\" target=\"_blank\">here</a>.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "0d8e8945-d012-43c3-b0af-eebd161ca414", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.2:</b>\n", + " \n", + "Using the example above, find the dams in the <code>DataFrame</code> that are of type <code>earth fill</code>.</code>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bc30d3fb-a87b-4247-8c0b-6dd72afec558", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The earth fill dams are: ['Fort Peck' 'Nurek' 'Kolnbrein' 'WAC Bennett']\n" + ] + } + ], + "source": [ + "names_of_earth_dams = df.loc[df['Type'] == 'earth fill', 'Name'].values[:]\n", + "print('The earth fill dams are:', names_of_earth_dams)" + ] + }, + { + "cell_type": "markdown", + "id": "ffc02761-430a-4b09-a339-803bdfade046", + "metadata": {}, + "source": [ + "_Hint: the answer should be:_ `['Fort Peck' 'Nurek' 'Kolnbrein' 'WAC Bennett']`" + ] + }, + { + "cell_type": "markdown", + "id": "ec7e1423-845f-46d8-bb94-b7e17cec508e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.3:</b>\n", + " \n", + "Create a new dataframe that only includes the earth fill dams. Save it as a new csv file called <code>earth_dams.csv</code>.\n", + "</p>\n", + "</div>\n", + "\n", + "_Hint: you only need to remove a small thing from the code for your answer to the task above)._" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3563f5db-99b0-48e5-9108-6fc6ebe9bdd7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df_earth = df.loc[df['Type'] == 'earth fill']\n", + "df_earth.to_csv('earth_dams.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "c18718b5-9a5f-4384-b1ec-13ae8a55ebec", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 2.4:</b>\n", + " \n", + "Check the contents of the new csv file to make sure you created it correctly.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "a4e13c88-3246-4b13-84f0-116fc8e27bb6", + "metadata": {}, + "source": [ + "## Task 3: Keep your repository clean\n", + "\n", + "Now we have created a second csv file, but we also do not want to track it in our repo. We could add the filename to our gitignore file, but there is a better way: using a wildcard! We already used this in Q1, so hopefully you can see that adding `*.csv` to the `.gitignore` file will ignore _all_ csv files in the repository, which is exactly what we want! " + ] + }, + { + "cell_type": "markdown", + "id": "bf492154-4370-45da-a7a5-24049fe79766", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3.1:</b>\n", + " \n", + "Update your gitignore using the wildcard <code>*.csv</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "4f6cf596-7ac6-49c1-8d4b-fbd140434d5b", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 3.2:</b>\n", + " \n", + "Confirm that the data files do not show up as \"changed files\" in your GitHub Desktop application. Then commit this notebook to your repository and push it to GitLab because you are done with the assignment!\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "c76f285d-7412-4b23-aa89-e5dbed3b921a", + "metadata": { + "id": "0491cc69" + }, + "source": [ + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Note that if we really cared about this \"new\" dataset, and it were too large to save it in our git repository, we would want to back it up to another (cloud) platform so that we can recover it if our files are lost. We skip this step here, but don't forget to do it if you are working on another project in the future (for example, your thesis).</p></div>" + ] + }, + { + "cell_type": "markdown", + "id": "80580ab9-4d79-46b1-ae6e-775af04d43ad", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_5/PA/README.md b/content/Week_2_5/PA/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f8eda22942527035e123e01410b2ced7441f5dd6 --- /dev/null +++ b/content/Week_2_5/PA/README.md @@ -0,0 +1,68 @@ +# PA13 Information, Week 2.5 + +_[CEGM1000 MUDE](http://mude.citg.tudelft.nl/), Optimization, Week 5 of Quarter 2._ + +This week the programming assignment will illustrate some good practics for managing data sets in a git repostiroy. It is based on two files: +- `README.md`: environment deletion and combining datasets with Git (this document). +- `PA13_data_process.ipynb`: a brief intro to pandas to help us process and manage our data. + +## Instructions + +Read through this file (`README.md`) on how to delete last week's environment. Then complete the tasks in notebook `PA13`. + +## Deleting `conda` python environment + +Last week, we looked at how to create environments and added `mude-PA12` next to your existing `mude` environment. Although the environments do not interfere with each other, you might want to delete environment you don't use, for example `mude-PA12` from last week. Why? Try running the following in a Terminal or Anaconda prompt, which will list the file locations of your environment. Then find the folder and check how big they are: + +``` +conda info --envs +``` +You probably found that the environments can get very large: the environment for `PA12` is around 2 GB!!!! It is a good idea to remove environments once you know they are no longer useful (for example `mude-PA12`). + +To remove an environment, in your Anaconda Prompt, run: + +``` +conda remove --name MYENV --all +``` +With your environment name for `MYENV`. You will be asked to confirm the deletion, then it may take a while to remove all of the files. To verify that the environment was removed, in your terminal window or an Anaconda Prompt, run: + +``` +conda info --envs +``` + +The environments list that displays should not show the removed environment. + +This information has been taken from the [Anaconda documentation](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#removing-an-environment). + +Now you can delete any other useless environments you have lying around on your computer! + +## Using data sets with Git + +As you may recall, git is able to track changes to text-based files; it does this by literally tracking every single character and every line in the file. This is generally not a problem for most software and code-based projects, as the file size does not get too large. For example, the really cool "big M" finite element mesh, as well as all of the code to implement the finite element method took less than 29 KB of hard disk space. That's over 1000 times smaller than a photo on your cell phone! + +However, as you may have noticed, the data sets that we use in some of our projects can become large (say, 10's of MB). If we make small changes to these files, git will store a record of the file in the changed state, and the disk space required for the repository starts to grow. For very large datasets (say, 100's of MB or GB and TB) it is impractical to use git to track these files. + +In fact, if your data is truly a set of observations that was made once, under a very specific set of conditions, it should have no need to be tracked at all by git (since the data will not, and _should_ not, change). In this case it makes sense to use other platforms to save and preserve data. For example, cloud storage systems, backup hard disks, etc. + +Although it may seem like you don't edit the data file directly, sometimes when a piece of software accesses a file (for example, importing it into your Juypter notebook), the file system or git _thinks_ that the file has been changed. This often causes git to keep a new snapshot of the file, taking up valuable disk space. As you make more and more commits to the repository, you are saving an unnecessary number of extra "snapshots" of the file, even though none of the information stored inside it has changed. + +In summary, the disadvantages of including large files in your git repository are: +- it takes a long time to push/pull from origin +- it takes a long time to switch branches or move to different commits +- disk space is used unnecessarily to track duplicate versions of the same file + +This programming assignment will walk you through one recommended workflow for managing data in your projects and preventing your repository from getting too large. In particular, we will: +- add a dataset to our working directory +- ignore the dataset with a `.gitignore` file +- process the dataset into a more usable form +- save and ignore the new data file +- commit the code we used to create the file, but not the file itself + +We will use (and learn about) the package `pandas`. To accomplish this, proceed with the instructions in the notebook file for `PA13`. + +**End of file.** + +<span style="font-size: 75%"> +© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0 License</a>. + + diff --git a/content/Week_2_5/PA/dams.csv b/content/Week_2_5/PA/dams.csv new file mode 100644 index 0000000000000000000000000000000000000000..8d8b87470e17776f987f5ba44c944c93ba76919d --- /dev/null +++ b/content/Week_2_5/PA/dams.csv @@ -0,0 +1,11 @@ +Name,Year,Volume (1e6 m^3),Height (m),Type +Tarbela,1976,153.0,143,rock fill +Fort Peck,1940,96.0,96,earth fill +Ataturk,1990,84.5,166,rock fill +Houtribdijk,1968,78.0,13,rock fill +Oahe,1963,70.3,75,rock fill +Nurek,1980,54.0,300,earth fill +Oroville,1968,59.6,230,rock fill +San Roque,2003,40.0,200,rock fill +Kolnbrein,1979,35.2,200,earth fill +WAC Bennett,1968,43.7,186,earth fill diff --git a/content/Week_2_5/WS_2_5_planet_vs_profit.ipynb b/content/Week_2_5/WS_2_5_planet_vs_profit.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b01528ab20e9fadfc1262e116b073e24fa702210 --- /dev/null +++ b/content/Week_2_5/WS_2_5_planet_vs_profit.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "61e3a843", + "metadata": {}, + "source": [ + "# WS13: Profit vs Planet\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5, Optimization. For: December 13, 2023*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1c4b1a7a", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A civil engineering company wants to decide on the projects that they should do. Their objective is to minimize the environmental impact of their projects while making enough profit to keep the company running.\n", + "\n", + "They have a portfolio of 6 possible projects to invest in, where A, B , and C are new infrastructure projects (so-called type 1), and D, E, F are refurbishment projects (so-called type 2).\n", + "\n", + "The environmental impact of each project is given by $I_i$ where $i \\in [1,(...),6]$ is the index of the project. $I_i=[140,45,78,123,40,60]$\n", + "\n", + "The profit of each project is given by $P_i$ where $i\\in [1,(...),6]$ is the index of the project: $P_i=[123,65,99,143,33,99]$ \n", + "\n", + "The company wants to do 3 out of the 6 projects, therefore please formulate the mathematical program that allows solving the problem, also knowing that the projects of type 2 must be at least as many as the ones of type 1 and that the profit of all projects together must be $\\beta \\ge 250$.\n", + "\n", + "<b>You are not allowed to use ChatGPT for this task otherwise you won’t learn ;)</b>" + ] + }, + { + "cell_type": "markdown", + "id": "15d6b3f0", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1: Writting the mathematical formulation</b> \n", + "\n", + "Write down every formulation and constrain that is relevant to solve this optimization problem.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enter formulation here in $\\text{LaTeX}$ or insert a figure." + ] + }, + { + "cell_type": "markdown", + "id": "a1b35e97", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2: Setting up the problem</b> \n", + "\n", + "Define any variables you might need to setup your model.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d5cba9fe", + "metadata": {}, + "outputs": [], + "source": [ + "YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3: Setting up the problem</b> \n", + "\n", + "We'll continue using `gurobi` this week, which you've set up in last week's `PA12`. We'll use some other special packages as well. Therefor, create a new environment using `environment_MUDE_opt.yml` to continue today and on friday.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "789ed5ae", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 4: Create the Gurobi model</b> \n", + "\n", + "Create the Gurobi model, set your decision variables, your function and your constrains. Take a look at the book for an example implementation in Python if you don't know where to start.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5a9bf052", + "metadata": {}, + "outputs": [], + "source": [ + "YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "id": "1380916b", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 5: Display your results</b> \n", + "\n", + "Display the model in a good way to interpret and print the solution of the optimization.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 6: Additional constraint</b> \n", + "\n", + "Solve the model with an additional constraint: if project 1 is done then the impact of all projects together should be lower than 𛾠with ð›¾=150.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "id": "99f8c4c4", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_5/WS_2_5_solution.ipynb b/content/Week_2_5/WS_2_5_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f36f443cb34aa5ddd8ea92598800f5c378fa44ab --- /dev/null +++ b/content/Week_2_5/WS_2_5_solution.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "61e3a843", + "metadata": {}, + "source": [ + "# WS13: Profit vs Planet\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5, Optimization. For: December 13, 2023*" + ] + }, + { + "cell_type": "markdown", + "id": "1c4b1a7a", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A civil engineering company wants to decide on the projects that they should do. Their objective is to minimize the environmental impact of their projects while making enough profit to keep the company running.\n", + "\n", + "They have a portfolio of 6 possible projects to invest in, where A, B , and C are new infrastructure projects (so-called type 1), and D, E, F are refurbishment projects (so-called type 2).\n", + "\n", + "The environmental impact of each project is given by $I_i$ where $i \\in [1,(...),6]$ is the index of the project. $I_i=[90,45,78,123,48,60]$\n", + "\n", + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Please note that the values above have been changed in comparison with the original workshop that was provided in class; these updated values makes the optimization result more interesting.</p></div>\n", + "\n", + "The profit of each project is given by $P_i$ where $i\\in [1,(...),6]$ is the index of the project: $P_i=[120,65,99,110,33,99]$\n", + "\n", + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>The values above have also been changed.</p></div>\n", + "\n", + "The company wants to do 3 out of the 6 projects, therefore please formulate the mathematical program that allows solving the problem, also knowing that the projects of type 2 must be at least as many as the ones of type 1 and that the profit of all projects together must be greater or equal than $250$ ($\\beta$)\n", + "\n", + "<b>You are not allowed to use ChatGPT for this task otherwise you won’t learn ;)</b>" + ] + }, + { + "cell_type": "markdown", + "id": "15d6b3f0", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1: Writting the mathematical formulation</b> \n", + "\n", + "Write down every formulation and constrain that is relevant to solve this optimization problem.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "7d0c2565", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Start solution.</b>\n", + "\n", + "$$Min(Z) \\sum_{i=1}^{6} x_i I_i $$\n", + "\n", + "subject to\n", + "\n", + "$$ \\sum_{i=1}^{6} x_i = 3 $$\n", + "$$ \\sum_{i=4}^{6} x_i \\ge \\sum_{i=1}^{3} x_i $$\n", + "$$ \\sum_{i=1}^{6} x_i P_i \\ge β $$\n", + "$$x\\in(1,0), i\\in(1,....,6)$$\n", + " \n", + "Extra Challenge (Task 6)\n", + "$$ \\sum_{i=1}^{6} x_i I_i \\le x_1 γ + (1-x_1) M $$\n", + " \n", + "where M is a sufficiently big number such as 9999 \n", + " \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "a1b35e97", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2: Setting up the problem</b> \n", + "\n", + "Define any variables you might need to setup your model.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d5cba9fe", + "metadata": {}, + "outputs": [], + "source": [ + "# Project data\n", + "I = [90, 45, 78, 123, 48, 60] # Environmental impact\n", + "P = [120, 65, 99, 110, 33, 99] # Profit\n", + "\n", + "# Minimum required profit\n", + "beta = 250\n", + "M = 100000\n", + "\n", + "# Number of projects and types\n", + "num_projects = len(I)\n", + "num_type1_projects = 3\n", + "num_type2_projects = num_projects - num_type1_projects" + ] + }, + { + "cell_type": "markdown", + "id": "a032f86b", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3: Setting up the problem</b> \n", + "\n", + "We'll continue using `gurobi` this week, which you've set up in last week's `PA12`. We'll use some other special packages as well. Therefor, create a new environment using `environment_MUDE_opt.yml` to continue today and on friday.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "789ed5ae", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 4: Create the Gurobi model</b> \n", + "\n", + "Create the Gurobi model, set your decision variables, your function and your constrains. Take a look at the book for an example implementation in Python if you don't know where to start.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5a9bf052", + "metadata": {}, + "outputs": [], + "source": [ + "import gurobipy as gp" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8d008ba9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)\n", + "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n", + "Optimize a model with 3 rows, 6 columns and 18 nonzeros\n", + "Model fingerprint: 0xa9bc0e77\n", + "Variable types: 0 continuous, 6 integer (6 binary)\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+02]\n", + " Objective range [5e+01, 1e+02]\n", + " Bounds range [1e+00, 1e+00]\n", + " RHS range [3e+00, 3e+02]\n", + "Presolve time: 0.00s\n", + "Presolved: 3 rows, 6 columns, 17 nonzeros\n", + "Variable types: 0 continuous, 6 integer (6 binary)\n", + "Found heuristic solution: objective 228.0000000\n", + "Found heuristic solution: objective 198.0000000\n", + "\n", + "Root relaxation: objective 1.855909e+02, 3 iterations, 0.00 seconds (0.00 work units)\n", + "\n", + " Nodes | Current Node | Objective Bounds | Work\n", + " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", + "\n", + " 0 0 infeasible 0 198.00000 198.00000 0.00% - 0s\n", + "\n", + "Explored 1 nodes (3 simplex iterations) in 0.01 seconds (0.00 work units)\n", + "Thread count was 8 (of 8 available processors)\n", + "\n", + "Solution count 2: 198 228 \n", + "\n", + "Optimal solution found (tolerance 1.00e-04)\n", + "Best objective 1.980000000000e+02, best bound 1.980000000000e+02, gap 0.0000%\n" + ] + } + ], + "source": [ + "# Create a Gurobi model\n", + "model = gp.Model(\"Project_Selection\")\n", + "\n", + "#You can always ask for help to understand a function of gurobi\n", + "#help(gurobipy.model.addVars)\n", + "\n", + "# Decision variables\n", + "x = model.addVars(num_projects, vtype=gp.GRB.BINARY, name=\"x\")\n", + "\n", + "# Objective function: Minimize environmental impact\n", + "model.setObjective(sum(I[i] * x[i] for i in range(num_projects)), gp.GRB.MINIMIZE)\n", + "\n", + "# Constraint: Select exactly 3 projects\n", + "model.addConstr(x.sum() == 3, \"Select_Projects\")\n", + "\n", + "# Constraint: Number of type 2 projects must be at least as many as type 1 projects selected\n", + "model.addConstr(sum(x[i] for i in range(num_type2_projects, num_projects)) - sum(x[i] for i in range(num_type1_projects)) >= 0, \"Type_Constraint\")\n", + "\n", + "# Constraint: Minimum profit requirement\n", + "model.addConstr(sum(P[i] * x[i] for i in range(num_projects)) >= beta, \"Minimum_Profit\")\n", + "\n", + "# Optimize the model\n", + "model.optimize()" + ] + }, + { + "cell_type": "markdown", + "id": "1380916b", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 5: Display your results</b> \n", + "\n", + "Display the model in a good way to interpret and print the solution of the optimization.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "83ef8b18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model structure:\n", + "Minimize\n", + " <gurobi.LinExpr: 90.0 x[0] + 45.0 x[1] + 78.0 x[2] + 123.0 x[3] + 48.0 x[4] + 60.0 x[5]>\n", + "Subject To\n", + " Select_Projects: <gurobi.LinExpr: x[0] + x[1] + x[2] + x[3] + x[4] + x[5]> = 3\n", + "Type_Constraint: <gurobi.LinExpr: -1.0 x[0] + -1.0 x[1] + -1.0 x[2] + x[3] + x[4] +\n", + " x[5]> >= 0\n", + "Minimum_Profit: <gurobi.LinExpr: 120.0 x[0] + 65.0 x[1] + 99.0 x[2] + 110.0 x[3] + 33.0\n", + " x[4] + 99.0 x[5]> >= 250\n", + "Binaries\n", + " ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]']\n", + "Optimal Solution:\n", + "Project 1: Selected\n", + "Project 5: Selected\n", + "Project 6: Selected\n", + "Optimal Objective function Value 198.0\n" + ] + } + ], + "source": [ + "print(\"Model structure:\") \n", + "# see the model that you have built in a nice why to interpret\n", + "model.display() \n", + "\n", + "# Display the solution\n", + "if model.status == gp.GRB.OPTIMAL:\n", + " print(\"Optimal Solution:\")\n", + " for i in range(num_projects):\n", + " if x[i].x > 0.9:\n", + " print(f\"Project {i+1}: Selected\")\n", + "else:\n", + " print(\"No optimal solution found.\")\n", + "\n", + " \n", + "print(\"Optimal Objective function Value\", model.objVal) " + ] + }, + { + "cell_type": "markdown", + "id": "98cb6023", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 6: Additional constraint</b> \n", + "\n", + "Solve the model with an additional constraint: if project 1 is done then the impact of all projects together should be lower than $\\gamma$ with $\\gamma=130$.\n", + "\n", + "> The value of $\\gamma$ has been changed with respect to the original workshop to make the optimization more interesting\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9f2f9b6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<gurobi.Constr *Awaiting Model Update*>" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Additional constraint\n", + "gamma = 130\n", + "model.addConstr(sum(I[i] * x[i] for i in range(num_projects)) <= gamma * x[0] + M * (1 - x[0]), \"Impact_Constraint\") " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "19040bd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)\n", + "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n", + "Optimize a model with 4 rows, 6 columns and 24 nonzeros\n", + "Model fingerprint: 0x37d09666\n", + "Variable types: 0 continuous, 6 integer (6 binary)\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+05]\n", + " Objective range [5e+01, 1e+02]\n", + " Bounds range [1e+00, 1e+00]\n", + " RHS range [3e+00, 1e+05]\n", + "\n", + "MIP start from previous solve did not produce a new incumbent solution\n", + "MIP start from previous solve violates constraint Impact_Constraint by 68.000000000\n", + "\n", + "Presolve removed 4 rows and 6 columns\n", + "Presolve time: 0.00s\n", + "Presolve: All rows and columns removed\n", + "\n", + "Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units)\n", + "Thread count was 1 (of 8 available processors)\n", + "\n", + "Solution count 1: 228 \n", + "\n", + "Optimal solution found (tolerance 1.00e-04)\n", + "Best objective 2.280000000000e+02, best bound 2.280000000000e+02, gap 0.0000%\n", + "Model structure:\n", + "Minimize\n", + " <gurobi.LinExpr: 90.0 x[0] + 45.0 x[1] + 78.0 x[2] + 123.0 x[3] + 48.0 x[4] + 60.0 x[5]>\n", + "Subject To\n", + " Select_Projects: <gurobi.LinExpr: x[0] + x[1] + x[2] + x[3] + x[4] + x[5]> = 3\n", + "Type_Constraint: <gurobi.LinExpr: -1.0 x[0] + -1.0 x[1] + -1.0 x[2] + x[3] + x[4] +\n", + " x[5]> >= 0\n", + "Minimum_Profit: <gurobi.LinExpr: 120.0 x[0] + 65.0 x[1] + 99.0 x[2] + 110.0 x[3] + 33.0\n", + " x[4] + 99.0 x[5]> >= 250\n", + "Impact_Constraint: <gurobi.LinExpr: 99960.0 x[0] + 45.0 x[1] + 78.0 x[2] + 123.0 x[3] +\n", + " 48.0 x[4] + 60.0 x[5]> <= 100000\n", + "Binaries\n", + " ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]']\n", + "Optimal Solution:\n", + "Project 2: Selected\n", + "Project 4: Selected\n", + "Project 6: Selected\n", + "Optimal Objective function Value 228.0\n" + ] + } + ], + "source": [ + "# Optimize the model\n", + "model.optimize()\n", + "\n", + "print(\"Model structure:\") \n", + "# see the model that you have built in a nice why to interpret\n", + "model.display() \n", + "\n", + "# Display the solution\n", + "if model.status == gp.GRB.OPTIMAL:\n", + " print(\"Optimal Solution:\")\n", + " for i in range(num_projects):\n", + " if x[i].x > 0.9:\n", + " print(f\"Project {i+1}: Selected\")\n", + "else:\n", + " print(\"No optimal solution found.\")\n", + "\n", + " \n", + "print(\"Optimal Objective function Value\", model.objVal) " + ] + }, + { + "cell_type": "markdown", + "id": "99f8c4c4", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "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": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_6/PA/PA_2_6_3_way_split.ipynb b/content/Week_2_6/PA/PA_2_6_3_way_split.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c9ee4988f535792ce804f21db9470d78ead064a1 --- /dev/null +++ b/content/Week_2_6/PA/PA_2_6_3_way_split.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "126838f6", + "metadata": {}, + "source": [ + "# Programming Assignment 14: 3-Way Split\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.6. Due: complete this PA prior to class on Friday, Dec 22, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "c12ebfb1", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this assignment you need to implement an algorithm that takes a dataset and splits it (randomly) into 3 parts for use in a machine learning application: one set for training, validation, and testing. This operation is necessary to set up, validate and improve the model you have implemented.\n", + "\n", + "To accomplish this we will load a dataset with `pandas` (just like last week) and learn a bit about random number generation. There is also a bonus task which will illustrate how you can use `assert` statements.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- Your notebook loads the data file as a pandas `DataFrame` instance\n", + "- You use the `DataFrame` to create arrays for the input and output data, `X` and `Y`, respectively\n", + "- You create randomized arrays `X_train, X_val, X_test, Y_train, Y_val, Y_test` from `X` and `Y`\n", + "\n", + "Note that there is also a bonus assignmet: you do not need to complete this to pass the assignment, but we highly recommend it, because `assert` statements are a very useful way to verify your code is working correctly!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2811fd0f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "41c38bc7", + "metadata": {}, + "source": [ + "## Task 1: Load the Data\n", + "\n", + "We first need to load the data and ensure that these are properly stored in specific arrays. The file we use in this assignment is *data.csv*. It contains five columns and 100 entries for each of the columns. " + ] + }, + { + "cell_type": "markdown", + "id": "8bff0861", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1.1:</b> \n", + "\n", + "Use the <code>pandas</code> library to load data from the file named *data.csv* and ensure that your dataset includes both input variables (X1, X2, X3) and an output variable (target Y).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "7049fe54", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "If you load the *data.csv* file you see that the first column is the index of the data and therefore you don't want to load it. Check [this page](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) for the key argument you need to specify to load only X1, X2, X3, and Y. \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c86a1ea", + "metadata": {}, + "outputs": [], + "source": [ + "data = YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "2e17661a", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1.2:</b> \n", + "\n", + "Show the summary of the data as you have seen in previous week *PA13*.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2f649f", + "metadata": {}, + "outputs": [], + "source": [ + "YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "3e50a6ad", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1.3:</b> \n", + "\n", + "After loading the data, separate it into two arrays: <code>X</code> for input variables and <code>Y</code> for the output variable. The input variables are typically all columns except the target variable.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9bf7a75", + "metadata": {}, + "outputs": [], + "source": [ + "X = YOUR_CODE_HERE\n", + "Y = YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "id": "07d361ed", + "metadata": {}, + "source": [ + "## Task 2: Randomly Shuffle Data\n", + "\n", + "It is important to split the data such that they are randomly distributed across the train, validation, and test sets. This is easily carried out in Python with the Numpy package; the default random number generator and its methods are briefly introduced here.\n", + "\n", + "### Random Number Generator\n", + "\n", + "Although randomness occurs everywhere in nature (remember _aleatory_ uncertainty from Q1?), it is a surprisingly complex thing to accomplish with a computer:\n", + "\n", + "> With the advent of computers, programmers recognized the need for a means of introducing randomness into a computer program. However, surprising as it may seem, it is difficult to get a computer to do something by chance. A computer follows its instructions blindly and is therefore completely predictable. (A computer that doesn't follow its instructions in this manner is broken.)\n", + "\n", + "_Source: [random.org/randomness/](https://www.random.org/randomness/)_\n", + "\n", + "Fortunately most modern software provides a random number generator. The best generator in Python for our purposes is part of the Numpy package, primarily because it can be used seamlessly with ndarrays. It is implemented as part of the module `random` [(documentation page here)](https://numpy.org/doc/stable/reference/random/index.html), where typical usage is as follows:\n", + "\n", + "1. Create a random number generator by initializing an instance of the `Generator` class with the method `np.random.default_rng`\n", + "2. Use one of the numerous methods of this class to generate random samples\n", + "\n", + "_Note that you may find some examples of random number generation that uses_ `np.random.seed`_; this has been sperceded in Numpy by_ `np.random.Generator`." + ] + }, + { + "cell_type": "markdown", + "id": "42181207-1971-4341-9020-2b50689e173e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.1:</b> \n", + "\n", + "Run the cell below to: 1) create a random number generator and then 2) use it to generate a random number between 0 and 1.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef835a05-072a-47cd-bb6e-175055bff814", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng()\n", + "print(type(rng))\n", + "rng.random()" + ] + }, + { + "cell_type": "markdown", + "id": "20e81183-a6dc-46d5-909d-5c9f56477658", + "metadata": {}, + "source": [ + "The random number generator has many methods that are useful. You can read more about it [here](https://numpy.org/doc/stable/reference/random/generator.html), but of course the documentation has a lot of computer-science-y stuff. Below are a few examples of what you can do with it." + ] + }, + { + "cell_type": "markdown", + "id": "5f2f9795-3781-40d3-b73d-e7bbebd4c15e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.2:</b> \n", + "\n", + "Explore the methods of the class by changing the code in the cell below. In addition, run the cell repeatedly to see how the randomly generated numbers change each time.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6837ec2d-9257-4225-9ace-f3ad5e90620b", + "metadata": {}, + "outputs": [], + "source": [ + "print('integers:', rng.integers(5))\n", + "print('random:', rng.random(5))\n", + "print('choice:', rng.choice(np.array(5)))\n", + "print('bytes:', rng.bytes(5))" + ] + }, + { + "cell_type": "markdown", + "id": "69a7e2b5-dba4-4dd3-ac0f-13addb9c2f60", + "metadata": {}, + "source": [ + "### Reproducible Randomness\n", + "\n", + "Working with randomness can make debugging code difficult. Fortunately, we can take advantage of the fact that computers can't truly create randomness by making the \"randomness\" occur consistently every time we use it (in other words: make it completely predictable!). To accomplish this, we can set a _seed_ value; if we know the seed of a random number generator, we can completely predict the sequence of \"randomness\" that it will produce.\n", + "\n", + "_In the next cell we set the seed of the default random number generator in <code>numpy</code> to **14**: this allows us to reproduce the \"randomness\" of the random number generator when we shuffle the data later. It is important in this assignment to allow us to check that you did the assignment correctly. If you wish to know more, try reading [this page](https://stackoverflow.com/questions/21494489/what-does-numpy-random-seed0-do)._" + ] + }, + { + "cell_type": "markdown", + "id": "5dbbf0a2-34f0-40b2-afd3-b1553a409348", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.3:</b> \n", + "\n", + "Run the cell below repeatedly to see how the randomly generated numbers change each time. Do you understand what has happened and why?\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "217ad122-bdca-4ece-a887-2cbc3518620d", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(seed=14)\n", + "print('integers:', rng.integers(5))\n", + "print('random:', rng.random(5))\n", + "print('choice:', rng.choice(np.array(5)))\n", + "print('bytes:', rng.bytes(5))" + ] + }, + { + "cell_type": "markdown", + "id": "07bf5340-ec01-4844-9fcf-be481b2cd9e8", + "metadata": {}, + "source": [ + "Now we know enough about random number generators to apply it to our machine learning data splitting case." + ] + }, + { + "cell_type": "markdown", + "id": "a2c2537a-32b6-4c13-9eab-654c5d67a140", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.4:</b> \n", + "\n", + "Create an array that contains a random permutation of indices of an array of length using <code>np.random.Generator.permutation</code>.\n", + "\n", + "<em>Hint: check the documentation page linked above and use the object <code>rng</code> in the same way as illustrated in previous tasks.</em>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "433e17d7", + "metadata": {}, + "outputs": [], + "source": [ + "# rng = np.random.default_rng(seed=14)\n", + "test_array_length = 5\n", + "test_array = rng.integers(low=100, high=200, size=test_array_length)\n", + "\n", + "random_indices = YOUR_CODE_HERE\n", + "\n", + "print('The randomized indices are:', random_indices)\n", + "print('The randomized array becomes:', test_array[random_indices])" + ] + }, + { + "cell_type": "markdown", + "id": "f60968ae-ede4-4452-a6ae-57ea80493d12", + "metadata": {}, + "source": [ + "## Task 3: Implement Data Splitting\n", + "\n", + "Now that the data is loaded and we know how to shuffle it, you need to split the datasets into a training, validation and testing dataset. First we will write a function, then apply it." + ] + }, + { + "cell_type": "markdown", + "id": "1d6b5d01-38c0-4d95-840d-5af8aa5b514c", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.1:</b> \n", + "\n", + "Implement a function to create six arrays: <code>X_train</code>, <code>X_val</code>, <code>X_test</code>, <code>Y_train</code>, <code>Y_val</code>, and <code>Y_test</code>. Read the docstring to ensure it is set up correctly.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "91845a13-5be3-4528-bb70-7d780a0cff77", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "Use the code from previous tasks to randomly access the data from the <code>X</code> and <code>Y</code> arrays.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "1b47ee51", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFD700; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Bonus Task!</b> \n", + " \n", + "The `split_data` function below can fail in certain cases, such as when: the input arrays are not the same length, the proportions don't add up to 1 or there aren't 3 proportions. Additionally if the implementation has a small error, such as an off-by-one indexing bug, code can break later down the line. These requirements for the function to work correctly can be called its \"contract\" - a set of conditions it should satisfy. In some programming languages these contracts are built in through concepts such as static type checking (you don't need to worry about this though), but we don't have that luxury in Python. Instead, you can use <a href=\"https://realpython.com/python-assert-statement/\" target=\"_blank\"><code>assert</code> statements</a> as a way to enforce conditions on your code. For this bonus, you can try adding a contract to `split_data` through pre-conditions and post-conditions. Pre-conditions check the data coming in satisfies the contract and post-conditions check that the data coming out satisfies the contract. Use asserts to do this, and check the following conditions:\n", + "<lt>\n", + " <li> `X` and `Y` are the same length. </li>\n", + " <li> `proportions` has length 3. </li>\n", + " <li> The values in `proportions` add up to 1. </li>\n", + " <li> The lengths of `X_train`, `X_val` and `X_test` added together are equal to the length of `X` (we don't need to check this for `Y` due to the condition 1, but it never hurts to do so).\n", + "</lt>\n", + "You can also see that these conditions are actually described in the docstring of `split_data`!\n", + "\n", + "To implement this bonus task, uncomment the 4 lines below with assert statements and add the appropriate condition, as described by the string. Note the simple form of an assert statement: `assert expression[, assertion_message]`.\n", + "\n", + "Your (bonus) task is to fill in the `expression`! You can test this out by using the function in a way that violates the contract (once implemented), which will result in the `assertion_message`.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2124f049-32cf-4624-9737-8de18b92d3e2", + "metadata": {}, + "outputs": [], + "source": [ + "def split_data(X, Y, proportions):\n", + " \"\"\"Split input and output into 3 subsets for ML model.\n", + "\n", + " Arguments\n", + " =========\n", + " X, Y: ndarrays where rows are number of observations\n", + " (both arrays have identical number of rows)\n", + " proportions: list with decimal fraction of original data defining\n", + " allocation into three parts (train, validate, test sets,\n", + " respectively). The list is len(proportions)=3, and\n", + " contains floats that should sum to 1.0.\n", + "\n", + " Returns\n", + " =======\n", + " X_train, X_val, X_test, Y_train, Y_val, Y_test:\n", + " 6 ndarrays (3 splits each for input and output), where the number of\n", + " columns corresponds to the original input and output (respectively)\n", + " and the sum of the number of rows is equal to the rows of the original\n", + " input/output.\n", + " \"\"\"\n", + " # assert YOUR_CODE_HERE, \"Contract broken: 3 proportions must be provided\"\n", + " # assert YOUR_CODE_HERE, \"Contract broken: sum of proportions should be one\"\n", + " # assert YOUR_CODE_HERE, \"Contract broken: X and Y arrays must have same dimensions\"\n", + "\n", + " # Do not modify this line:\n", + " np.random.default_rng(seed=42)\n", + "\n", + " # Shuffle data using random permutation of indices \n", + " # indices = YOUR_CODE_HERE\n", + " # Solution\n", + " indices = np.random.permutation(len(X))\n", + "\n", + " # Create shuffled training, validation and test sets\n", + " # YOUR_CODE_HERE\n", + " # Solution\n", + "\n", + " train_prop = proportions[0]\n", + " val_prop = proportions[1]\n", + " \n", + " train_end = int(train_prop*len(X))\n", + " val_end = int(val_prop*len(X)) + train_end\n", + " \n", + " X_train, X_val, X_test = (X[indices[:train_end]],\n", + " X[indices[train_end:val_end]],\n", + " X[indices[val_end:]])\n", + " Y_train, Y_val, Y_test = (Y[indices[:train_end]],\n", + " Y[indices[train_end:val_end]],\n", + " Y[indices[val_end:]])\n", + " # assert YOUR_CODE_HERE, \"Contract broken: generated datasets don't have same accumulated length as original\"\n", + " \n", + " return X_train, X_val, X_test, Y_train, Y_val, Y_test" + ] + }, + { + "cell_type": "markdown", + "id": "24c91527", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.2:</b> \n", + "\n", + "Use your function to split the arrays <code>X</code> and <code>Y</code> from Task 1 into training, validation, and test sets. The split proportions should be **70%** for training, **10%** for validation, and **20%** for the test dataset.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b8acd7e", + "metadata": {}, + "outputs": [], + "source": [ + "split_proportions = YOUR_CODE_HERE\n", + "(X_train, X_val, X_test,\n", + " Y_train, Y_val, Y_test) = split_data(YOUR_CODE_HERE)" + ] + }, + { + "cell_type": "markdown", + "id": "b1fef45f-8442-4616-a1e4-969845840dda", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.3:</b> \n", + "\n", + "Run the cell below to check whether or not you have implemented the function correctly. The output will present a string output summarizing the number of data allocated to each set, whereas the figure will use colors to illustrate whether or not the values were shuffled in a random way.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8284a02-fd0a-4324-9bd0-764b46455413", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_allocation(X, Y,\n", + " X_train, X_val, X_test,\n", + " Y_train, Y_val, Y_test):\n", + "\n", + " set_of_X_and_Y = np.hstack((X,Y.reshape((100,1))))\n", + " # use many (arbitrary) columns to make plot wider\n", + " which_set_am_i = np.zeros((len(Y), 75))\n", + " \n", + " for i in range(len(X_train)):\n", + " matching_rows = np.all(X==X_train[i], axis=1)\n", + " which_set_am_i[np.where(matching_rows)[0],:] = 1\n", + " for i in range(len(X_val)):\n", + " matching_rows = np.all(X==X_val[i], axis=1)\n", + " which_set_am_i[np.where(matching_rows)[0],:] = 2\n", + "\n", + " for i in range(len(X_test)):\n", + " matching_rows = np.all(X==X_test[i], axis=1)\n", + " which_set_am_i[np.where(matching_rows)[0],:] = 3\n", + " \n", + " fig, ax = plt.subplots()\n", + " ax.imshow(which_set_am_i)\n", + "\n", + " ax.set_title('Colors indicate how data is split')\n", + " ax.set_xlabel('Width is arbitrary')\n", + " ax.set_ylabel('Row of original data set')\n", + " \n", + " print('The number of data in each set is:')\n", + " print(f' training: {sum(which_set_am_i[:,0]==1)}')\n", + " print(f' validation: {sum(which_set_am_i[:,0]==2)}')\n", + " print(f' testing: {sum(which_set_am_i[:,0]==3)}')\n", + " print(f' none of above: {sum(which_set_am_i[:,0]==0)}')\n", + "\n", + "plot_allocation(X, Y,\n", + " X_train, X_val, X_test,\n", + " Y_train, Y_val, Y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "5c49c57a", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_6/PA/PA_2_6_solution.ipynb b/content/Week_2_6/PA/PA_2_6_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..614cc375e245ac0ee6e9bdd2fe93ffd1b5be158f --- /dev/null +++ b/content/Week_2_6/PA/PA_2_6_solution.ipynb @@ -0,0 +1,776 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "126838f6", + "metadata": {}, + "source": [ + "# Programming Assignment 14: 3-Way Split\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.6. Due: complete this PA prior to class on Friday, Dec 22, 2023.*" + ] + }, + { + "cell_type": "markdown", + "id": "c12ebfb1", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this assignment you need to implement an algorithm that takes a dataset and splits it (randomly) into 3 parts for use in a machine learning application: one set for training, validation, and testing. This operation is necessary to set up, validate and improve the model you have implemented.\n", + "\n", + "To accomplish this we will load a dataset with `pandas` (just like last week) and learn a bit about random number generation. There is also a bonus task which will illustrate how you can use `assert` statements.\n", + "\n", + "## Assignment Criteria\n", + "\n", + "**You will pass this assignment as long as your repository fulfills the following criteria:** \n", + "\n", + "- You have completed this notebook and it runs without errors\n", + "- Your notebook loads the data file as a pandas `DataFrame` instance\n", + "- You use the `DataFrame` to create arrays for the input and output data, `X` and `Y`, respectively\n", + "- You create randomized arrays `X_train, X_val, X_test, Y_train, Y_val, Y_test` from `X` and `Y`\n", + "\n", + "Note that there is also a bonus assignmet: you do not need to complete this to pass the assignment, but we highly recommend it, because `assert` statements are a very useful way to verify your code is working correctly!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2811fd0f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "41c38bc7", + "metadata": {}, + "source": [ + "## Task 1: Load the Data\n", + "\n", + "We first need to load the data and ensure that these are properly stored in specific arrays. The file we use in this assignment is *data.csv*. It contains five columns and 100 entries for each of the columns. " + ] + }, + { + "cell_type": "markdown", + "id": "8bff0861", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1.1:</b> \n", + "\n", + "Use the <code>pandas</code> library to load data from the file named *data.csv* and ensure that your dataset includes both input variables (X1, X2, X3) and an output variable (target Y).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "7049fe54", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "If you load the *data.csv* file you see that the first column is the index of the data and therefore you don't want to load it. Check [this page](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) for the key argument you need to specify to load only X1, X2, X3, and Y. \n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7c86a1ea", + "metadata": {}, + "outputs": [], + "source": [ + "# data = YOUR_CODE_HERE\n", + "# Solution:\n", + "data = pd.read_csv('data.csv', usecols=[1, 2, 3, 4])" + ] + }, + { + "cell_type": "markdown", + "id": "2e17661a", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1.2:</b> \n", + "\n", + "Show the summary of the data as you have seen in previous week *PA13*.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0a2f649f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>X1</th>\n", + " <th>X2</th>\n", + " <th>X3</th>\n", + " <th>Y</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>100.000000</td>\n", + " <td>100.000000</td>\n", + " <td>100.000000</td>\n", + " <td>100.00000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>0.470300</td>\n", + " <td>0.498200</td>\n", + " <td>0.517900</td>\n", + " <td>1.95180</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>0.297432</td>\n", + " <td>0.293383</td>\n", + " <td>0.292986</td>\n", + " <td>1.26048</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>0.010000</td>\n", + " <td>0.010000</td>\n", + " <td>0.010000</td>\n", + " <td>-0.51000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>0.195000</td>\n", + " <td>0.240000</td>\n", + " <td>0.277500</td>\n", + " <td>1.04000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>0.465000</td>\n", + " <td>0.505000</td>\n", + " <td>0.565000</td>\n", + " <td>1.96000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>0.730000</td>\n", + " <td>0.765000</td>\n", + " <td>0.755000</td>\n", + " <td>2.80000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>0.990000</td>\n", + " <td>0.990000</td>\n", + " <td>0.990000</td>\n", + " <td>5.38000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " X1 X2 X3 Y\n", + "count 100.000000 100.000000 100.000000 100.00000\n", + "mean 0.470300 0.498200 0.517900 1.95180\n", + "std 0.297432 0.293383 0.292986 1.26048\n", + "min 0.010000 0.010000 0.010000 -0.51000\n", + "25% 0.195000 0.240000 0.277500 1.04000\n", + "50% 0.465000 0.505000 0.565000 1.96000\n", + "75% 0.730000 0.765000 0.755000 2.80000\n", + "max 0.990000 0.990000 0.990000 5.38000" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# YOUR_CODE_HERE\n", + "# Solution:\n", + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "3e50a6ad", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 1.3:</b> \n", + "\n", + "After loading the data, separate it into two arrays: <code>X</code> for input variables and <code>Y</code> for the output variable. The input variables are typically all columns except the target variable.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b9bf7a75", + "metadata": {}, + "outputs": [], + "source": [ + "# X = YOUR_CODE_HERE\n", + "# Y = YOUR_CODE_HERE\n", + "# Solution:\n", + "X = np.array(data[['X1', 'X2', 'X3']])\n", + "Y = np.array(data['Y'])" + ] + }, + { + "cell_type": "markdown", + "id": "07d361ed", + "metadata": {}, + "source": [ + "## Task 2: Randomly Shuffle Data\n", + "\n", + "It is important to split the data such that they are randomly distributed across the train, validation, and test sets. This is easily carried out in Python with the Numpy package; the default random number generator and its methods are briefly introduced here.\n", + "\n", + "### Random Number Generator\n", + "\n", + "Although randomness occurs everywhere in nature (remember _aleatory_ uncertainty from Q1?), it is a surprisingly complex thing to accomplish with a computer:\n", + "\n", + "> With the advent of computers, programmers recognized the need for a means of introducing randomness into a computer program. However, surprising as it may seem, it is difficult to get a computer to do something by chance. A computer follows its instructions blindly and is therefore completely predictable. (A computer that doesn't follow its instructions in this manner is broken.)\n", + "\n", + "_Source: [random.org/randomness/](https://www.random.org/randomness/)_\n", + "\n", + "Fortunately most modern software provides a random number generator. The best generator in Python for our purposes is part of the Numpy package, primarily because it can be used seamlessly with ndarrays. It is implemented as part of the module `random` [(documentation page here)](https://numpy.org/doc/stable/reference/random/index.html), where typical usage is as follows:\n", + "\n", + "1. Create a random number generator by initializing an instance of the `Generator` class with the method `np.random.default_rng`\n", + "2. Use one of the numerous methods of this class to generate random samples\n", + "\n", + "_Note that you may find some examples of random number generation that uses_ `np.random.seed`_; this has been sperceded in Numpy by_ `np.random.Generator`." + ] + }, + { + "cell_type": "markdown", + "id": "42181207-1971-4341-9020-2b50689e173e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.1:</b> \n", + "\n", + "Run the cell below to: 1) create a random number generator and then 2) use it to generate a random number between 0 and 1.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ef835a05-072a-47cd-bb6e-175055bff814", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'numpy.random._generator.Generator'>\n" + ] + }, + { + "data": { + "text/plain": [ + "0.01628535642909701" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng()\n", + "print(type(rng))\n", + "rng.random()" + ] + }, + { + "cell_type": "markdown", + "id": "20e81183-a6dc-46d5-909d-5c9f56477658", + "metadata": {}, + "source": [ + "The random number generator has many methods that are useful. You can read more about it [here](https://numpy.org/doc/stable/reference/random/generator.html), but of course the documentation has a lot of computer-science-y stuff. Below are a few examples of what you can do with it." + ] + }, + { + "cell_type": "markdown", + "id": "5f2f9795-3781-40d3-b73d-e7bbebd4c15e", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.2:</b> \n", + "\n", + "Explore the methods of the class by changing the code in the cell below. In addition, run the cell repeatedly to see how the randomly generated numbers change each time.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6837ec2d-9257-4225-9ace-f3ad5e90620b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "integers: 1\n", + "random: [0.3139259 0.49619462 0.39349454 0.7216499 0.80329616]\n", + "choice: 4\n", + "bytes: b'FH\\xbb\\xc4\\xbb'\n" + ] + } + ], + "source": [ + "print('integers:', rng.integers(5))\n", + "print('random:', rng.random(5))\n", + "print('choice:', rng.choice(np.array(5)))\n", + "print('bytes:', rng.bytes(5))" + ] + }, + { + "cell_type": "markdown", + "id": "69a7e2b5-dba4-4dd3-ac0f-13addb9c2f60", + "metadata": {}, + "source": [ + "### Reproducible Randomness\n", + "\n", + "Working with randomness can make debugging code difficult. Fortunately, we can take advantage of the fact that computers can't truly create randomness by making the \"randomness\" occur consistently every time we use it (in other words: make it completely predictable!). To accomplish this, we can set a _seed_ value; if we know the seed of a random number generator, we can completely predict the sequence of \"randomness\" that it will produce.\n", + "\n", + "_In the next cell we set the seed of the default random number generator in <code>numpy</code> to **14**: this allows us to reproduce the \"randomness\" of the random number generator when we shuffle the data later. It is important in this assignment to allow us to check that you did the assignment correctly. If you wish to know more, try reading [this page](https://stackoverflow.com/questions/21494489/what-does-numpy-random-seed0-do)._" + ] + }, + { + "cell_type": "markdown", + "id": "5dbbf0a2-34f0-40b2-afd3-b1553a409348", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.3:</b> \n", + "\n", + "Run the cell below repeatedly to see how the randomly generated numbers change each time. Do you understand what has happened and why?\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "217ad122-bdca-4ece-a887-2cbc3518620d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "integers: 0\n", + "random: [0.36094667 0.70273931 0.86011879 0.64131748 0.54836321]\n", + "choice: 4\n", + "bytes: b'\\x03\\x1e\\xc4\\x12\\xf3'\n" + ] + } + ], + "source": [ + "rng = np.random.default_rng(seed=14)\n", + "print('integers:', rng.integers(5))\n", + "print('random:', rng.random(5))\n", + "print('choice:', rng.choice(np.array(5)))\n", + "print('bytes:', rng.bytes(5))" + ] + }, + { + "cell_type": "markdown", + "id": "07bf5340-ec01-4844-9fcf-be481b2cd9e8", + "metadata": {}, + "source": [ + "Now we know enough about random number generators to apply it to our machine learning data splitting case." + ] + }, + { + "cell_type": "markdown", + "id": "a2c2537a-32b6-4c13-9eab-654c5d67a140", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.4:</b> \n", + "\n", + "Create an array that contains a random permutation of indices of an array of length using <code>np.random.Generator.permutation</code>.\n", + "\n", + "<em>Hint: check the documentation page linked above and use the object <code>rng</code> in the same way as illustrated in previous tasks.</em>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "433e17d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The randomized indices are: [0 2 4 3 1]\n", + "The randomized array becomes: [197 120 143 142 155]\n" + ] + } + ], + "source": [ + "# rng = np.random.default_rng(seed=14)\n", + "test_array_length = 5\n", + "test_array = rng.integers(low=100, high=200, size=test_array_length)\n", + "\n", + "# random_indices = YOUR_CODE_HERE\n", + "# Solution\n", + "random_indices = rng.permutation(test_array_length)\n", + "\n", + "print('The randomized indices are:', random_indices)\n", + "print('The randomized array becomes:', test_array[random_indices])" + ] + }, + { + "cell_type": "markdown", + "id": "f60968ae-ede4-4452-a6ae-57ea80493d12", + "metadata": {}, + "source": [ + "## Task 3: Implement Data Splitting\n", + "\n", + "Now that the data is loaded and we know how to shuffle it, you need to split the datasets into a training, validation and testing dataset. First we will write a function, then apply it." + ] + }, + { + "cell_type": "markdown", + "id": "1d6b5d01-38c0-4d95-840d-5af8aa5b514c", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.1:</b> \n", + "\n", + "Implement a function to create six arrays: <code>X_train</code>, <code>X_val</code>, <code>X_test</code>, <code>Y_train</code>, <code>Y_val</code>, and <code>Y_test</code>. Read the docstring to ensure it is set up correctly.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "91845a13-5be3-4528-bb70-7d780a0cff77", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "Use the code from previous tasks to randomly access the data from the <code>X</code> and <code>Y</code> arrays.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "1b47ee51", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FFD700; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Bonus Task!</b> \n", + " \n", + "The `split_data` function below can fail in certain cases, such as when: the input arrays are not the same length, the proportions don't add up to 1 or there aren't 3 proportions. Additionally if the implementation has a small error, such as an off-by-one indexing bug, code can break later down the line. These requirements for the function to work correctly can be called its \"contract\" - a set of conditions it should satisfy. In some programming languages these contracts are built in through concepts such as static type checking (you don't need to worry about this though), but we don't have that luxury in Python. Instead, you can use <a href=\"https://realpython.com/python-assert-statement/\" target=\"_blank\"><code>assert</code> statements</a> as a way to enforce conditions on your code. For this bonus, you can try adding a contract to `split_data` through pre-conditions and post-conditions. Pre-conditions check the data coming in satisfies the contract and post-conditions check that the data coming out satisfies the contract. Use asserts to do this, and check the following conditions:\n", + "<ol>\n", + " <li> `X` and `Y` are the same length. </li>\n", + " <li> `proportions` has length 3. </li>\n", + " <li> The values in `proportions` add up to 1. </li>\n", + " <li> The lengths of `X_train`, `X_val` and `X_test` added together are equal to the length of `X` (we don't need to check this for `Y` due to the condition 1, but it never hurts to do so).\n", + "</ol>\n", + "You can also see that these conditions are actually described in the docstring of `split_data`!\n", + "\n", + "To implement this bonus task, uncomment the 4 lines below with assert statements and add the appropriate condition, as described by the string. Note the simple form of an assert statement: `assert expression[, assertion_message]`.\n", + "\n", + "Your (bonus) task is to fill in the `expression`! You can test this out by using the function in a way that violates the contract (once implemented), which will result in the `assertion_message`.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2124f049-32cf-4624-9737-8de18b92d3e2", + "metadata": {}, + "outputs": [], + "source": [ + "def split_data(X, Y, proportions):\n", + " \"\"\"Split input and output into 3 subsets for ML model.\n", + "\n", + " Arguments\n", + " =========\n", + " X, Y: ndarrays where rows are number of observations\n", + " (both arrays have identical number of rows)\n", + " proportions: list with decimal fraction of original data defining\n", + " allocation into three parts (train, validate, test sets,\n", + " respectively). The list is len(proportions)=3, and\n", + " contains floats that should sum to 1.0.\n", + "\n", + " Returns\n", + " =======\n", + " X_train, X_val, X_test, Y_train, Y_val, Y_test:\n", + " 6 ndarrays (3 splits each for input and output), where the number of\n", + " columns corresponds to the original input and output (respectively)\n", + " and the sum of the number of rows is equal to the rows of the original\n", + " input/output.\n", + " \"\"\"\n", + " # assert YOUR_CODE_HERE, \"Contract broken: 3 proportions must be provided\"\n", + " # assert YOUR_CODE_HERE, \"Contract broken: sum of proportions should be one\"\n", + " # assert YOUR_CODE_HERE, \"Contract broken: X and Y arrays must have same dimensions\"\n", + " # Solution:\n", + " assert len(proportions) == 3, \"Contract broken: 3 proportions must be provided\"\n", + " assert sum(proportions) == 1, \"Contract broken: sum of proportions should be one\"\n", + " assert len(X) == len(Y), \"Contract broken: X and Y arrays must have same dimensions\"\n", + "\n", + " # Do not modify this line:\n", + " np.random.default_rng(seed=42)\n", + "\n", + " # Shuffle data using random permutation of indices \n", + " # indices = YOUR_CODE_HERE\n", + " # Solution\n", + " indices = np.random.permutation(len(X))\n", + "\n", + " # Create shuffled training, validation and test sets\n", + " # YOUR_CODE_HERE\n", + " # Solution\n", + "\n", + " train_prop = proportions[0]\n", + " val_prop = proportions[1]\n", + " \n", + " train_end = int(train_prop*len(X))\n", + " val_end = int(val_prop*len(X)) + train_end\n", + " \n", + " X_train, X_val, X_test = (X[indices[:train_end]],\n", + " X[indices[train_end:val_end]],\n", + " X[indices[val_end:]])\n", + " Y_train, Y_val, Y_test = (Y[indices[:train_end]],\n", + " Y[indices[train_end:val_end]],\n", + " Y[indices[val_end:]])\n", + " # assert YOUR_CODE_HERE, \"Contract broken: generated datasets don't have same accumulated length as original\"\n", + " # Solution\n", + " assert (len(X_train) + len(X_val) + len(X_test)) == len(X), \"Contract broken: generated datasets don't have same accumulated length as original\"\n", + "\n", + " return X_train, X_val, X_test, Y_train, Y_val, Y_test" + ] + }, + { + "cell_type": "markdown", + "id": "24c91527", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.2:</b> \n", + "\n", + "Use your function to split the arrays <code>X</code> and <code>Y</code> from Task 1 into training, validation, and test sets. The split proportions should be **70%** for training, **10%** for validation, and **20%** for the test dataset.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8b8acd7e", + "metadata": {}, + "outputs": [], + "source": [ + "# split_proportions = YOUR_CODE_HERE\n", + "# (X_train, X_val, X_test,\n", + "# Y_train, Y_val, Y_test) = split_data(YOUR_CODE_HERE)\n", + "# Solution:\n", + "split_proportions = [0.7, 0.1, 0.2]\n", + "(X_train, X_val, X_test,\n", + " Y_train, Y_val, Y_test) = split_data(X, Y, split_proportions)" + ] + }, + { + "cell_type": "markdown", + "id": "b1fef45f-8442-4616-a1e4-969845840dda", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.3:</b> \n", + "\n", + "Run the cell below to check whether or not you have implemented the function correctly. The output will present a string output summarizing the number of data allocated to each set, whereas the figure will use colors to illustrate whether or not the values were shuffled in a random way.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d8284a02-fd0a-4324-9bd0-764b46455413", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of data in each set is:\n", + " training: 70\n", + " validation: 10\n", + " testing: 20\n", + " none of above: 0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_allocation(X, Y,\n", + " X_train, X_val, X_test,\n", + " Y_train, Y_val, Y_test):\n", + "\n", + " set_of_X_and_Y = np.hstack((X,Y.reshape((100,1))))\n", + " # use many (arbitrary) columns to make plot wider\n", + " which_set_am_i = np.zeros((len(Y), 75))\n", + " \n", + " for i in range(len(X_train)):\n", + " matching_rows = np.all(X==X_train[i], axis=1)\n", + " which_set_am_i[np.where(matching_rows)[0],:] = 1\n", + " for i in range(len(X_val)):\n", + " matching_rows = np.all(X==X_val[i], axis=1)\n", + " which_set_am_i[np.where(matching_rows)[0],:] = 2\n", + "\n", + " for i in range(len(X_test)):\n", + " matching_rows = np.all(X==X_test[i], axis=1)\n", + " which_set_am_i[np.where(matching_rows)[0],:] = 3\n", + " \n", + " fig, ax = plt.subplots()\n", + " ax.imshow(which_set_am_i)\n", + "\n", + " ax.set_title('Colors indicate how data is split')\n", + " ax.set_xlabel('Width is arbitrary')\n", + " ax.set_ylabel('Row of original data set')\n", + " \n", + " print('The number of data in each set is:')\n", + " print(f' training: {sum(which_set_am_i[:,0]==1)}')\n", + " print(f' validation: {sum(which_set_am_i[:,0]==2)}')\n", + " print(f' testing: {sum(which_set_am_i[:,0]==3)}')\n", + " print(f' none of above: {sum(which_set_am_i[:,0]==0)}')\n", + "\n", + "plot_allocation(X, Y,\n", + " X_train, X_val, X_test,\n", + " Y_train, Y_val, Y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "5c49c57a", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/content/Week_2_6/PA/data.csv b/content/Week_2_6/PA/data.csv new file mode 100644 index 0000000000000000000000000000000000000000..5ec432d9791baf2c813b815d0bd32f951d6fbfea --- /dev/null +++ b/content/Week_2_6/PA/data.csv @@ -0,0 +1,101 @@ +,X1,X2,X3,Y +0,0.37,0.03,0.64,0.21 +1,0.95,0.64,0.08,3.41 +2,0.73,0.31,0.16,3.3 +3,0.6,0.51,0.9,2.15 +4,0.16,0.91,0.61,1.43 +5,0.16,0.25,0.01,1.15 +6,0.06,0.41,0.1,0.92 +7,0.87,0.76,0.66,3.79 +8,0.6,0.23,0.01,1.48 +9,0.71,0.08,0.16,1.44 +10,0.02,0.29,0.55,0.61 +11,0.97,0.16,0.69,2.16 +12,0.83,0.93,0.65,3.2 +13,0.21,0.81,0.22,2.46 +14,0.18,0.63,0.71,1.3 +15,0.18,0.87,0.24,2.4 +16,0.3,0.8,0.33,3.55 +17,0.52,0.19,0.75,1.06 +18,0.43,0.89,0.65,2.25 +19,0.29,0.54,0.85,1.81 +20,0.61,0.81,0.66,4.05 +21,0.14,0.9,0.57,2.93 +22,0.29,0.32,0.09,0.69 +23,0.37,0.11,0.37,0.46 +24,0.46,0.23,0.27,1.97 +25,0.79,0.43,0.24,2.28 +26,0.2,0.82,0.97,2.11 +27,0.51,0.86,0.39,3.6 +28,0.59,0.01,0.89,-0.14 +29,0.05,0.51,0.63,0.97 +30,0.61,0.42,0.79,0.07 +31,0.17,0.22,0.5,-0.01 +32,0.07,0.12,0.58,-0.21 +33,0.95,0.34,0.49,1.81 +34,0.97,0.94,0.2,5.38 +35,0.81,0.32,0.72,1.14 +36,0.3,0.52,0.28,1.66 +37,0.1,0.7,0.02,2.35 +38,0.68,0.36,0.65,2.51 +39,0.44,0.97,0.18,2.89 +40,0.12,0.96,0.94,2.76 +41,0.5,0.25,0.95,0.81 +42,0.03,0.5,0.91,0.16 +43,0.91,0.3,0.37,2.58 +44,0.26,0.28,0.02,1.44 +45,0.66,0.04,0.93,0.21 +46,0.31,0.61,0.43,2.05 +47,0.52,0.5,0.97,1.38 +48,0.55,0.05,0.96,0.35 +49,0.18,0.28,0.85,0.68 +50,0.97,0.91,0.29,5.17 +51,0.78,0.24,0.39,1.27 +52,0.94,0.14,0.85,2.52 +53,0.89,0.49,0.32,1.95 +54,0.6,0.99,0.17,3.92 +55,0.92,0.24,0.56,2.29 +56,0.09,0.67,0.94,1.39 +57,0.2,0.76,0.7,1.67 +58,0.05,0.24,0.57,0.15 +59,0.33,0.73,0.1,2.5 +60,0.39,0.37,0.62,0.98 +61,0.27,0.63,0.99,1.86 +62,0.83,0.63,0.14,3.59 +63,0.36,0.54,0.52,1.47 +64,0.28,0.09,0.88,0.4 +65,0.54,0.84,0.74,3.01 +66,0.14,0.32,0.7,0.95 +67,0.8,0.19,0.7,1.78 +68,0.07,0.04,0.36,-0.51 +69,0.99,0.59,0.29,3.18 +70,0.77,0.68,0.81,3.14 +71,0.2,0.02,0.81,-0.04 +72,0.01,0.51,0.87,0.67 +73,0.82,0.23,0.91,1.48 +74,0.71,0.65,0.51,3.5 +75,0.73,0.17,0.5,1.17 +76,0.77,0.69,0.8,3.08 +77,0.07,0.39,0.65,0.56 +78,0.36,0.94,0.7,2.73 +79,0.12,0.14,0.8,0.41 +80,0.86,0.34,0.89,2.26 +81,0.62,0.11,0.34,1.64 +82,0.33,0.92,0.38,3.69 +83,0.06,0.88,0.09,2.68 +84,0.31,0.26,0.58,1.16 +85,0.33,0.66,0.04,2.44 +86,0.73,0.82,0.47,3.61 +87,0.64,0.56,0.54,2.35 +88,0.89,0.53,0.29,3.13 +89,0.47,0.24,0.59,1.37 +90,0.12,0.09,0.03,0.07 +91,0.71,0.9,0.04,5.13 +92,0.76,0.9,0.82,2.9 +93,0.56,0.63,0.36,2.04 +94,0.77,0.34,0.13,3.01 +95,0.49,0.35,0.52,1.91 +96,0.52,0.73,0.77,2.77 +97,0.43,0.9,0.22,3.65 +98,0.03,0.89,0.62,2.1 +99,0.11,0.78,0.09,2.02 diff --git a/content/Week_2_6/PA/environment.yml b/content/Week_2_6/PA/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..ce93bdeb0cf616f242783ef110fabe701b2393df --- /dev/null +++ b/content/Week_2_6/PA/environment.yml @@ -0,0 +1,9 @@ +name: mude-ml +dependencies: + - python=3.11 + - numpy + - matplotlib + - pandas + - pip + - conda-forge::scikit-learn + - conda-forge::jupyterlab \ No newline at end of file diff --git a/content/Week_2_6/WS_2_6_be_a_NN.ipynb b/content/Week_2_6/WS_2_6_be_a_NN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1d26c02ef14993b876491c21c632d58962906836 --- /dev/null +++ b/content/Week_2_6/WS_2_6_be_a_NN.ipynb @@ -0,0 +1,1193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Workshop 14: Be like a Neural Network\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.6. Wednesday December 20, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"https://i.pinimg.com/originals/e5/6e/8a/e56e8a055bfbcbeafaf413a70c911876.jpg\" width=\"500\" height=\"400\">\n", + "\n", + "Source image: https://www.kaggle.com/code/pranavkasela/neural-networks-from-scratch-code-maths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "For several tasks within the domain of Civil Engineernig and Geosciences we might be interested in applying machine learning models to understand some physical processes and phenomena which, for many different reasons, are difficult to interpret. Training a machine learning model will typically involves the following steps:\n", + "\n", + "1. Define the model.\n", + "\n", + "2. Define the loss function.\n", + "\n", + "3. Train the model.\n", + "\n", + "4. Evaluate the model.\n", + "\n", + "By using these predefined functions, we can save time and effort when building and training machine learning models.\n", + "\n", + "In this notebook, we start with a very simple dataset with an underlying linear pattern. We then train a simple Multilayer Perceptron (MLP) on it and discuss matters related to model complexity and overfitting. Finally, we move to a more realistic dataset you have already seen before during MUDE.\n", + "\n", + "### Python Environment\n", + "\n", + "You will need the package scikit-learn for this workshop (in addition to a few other typical packages). You can import it to one of your existing conda environments from the conda-forge as (i.e., `\n", + "conda install -c conda-forge scikit-learn`), or you can create a new environment from the `*.yml` file included in this repository (`conda env create -f environment_MUDE_ml.yml`). But remember: _if you already have sklearn installed in an environment, you don't have to do anything besides use it!_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "We will use boxes with this type of formatting to introduce you to the <code>scikit-learn</code> package and help you with some programming tips.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from scipy import interpolate\n", + "from scipy.stats import norm\n", + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 0: Our Data for Today" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Machine learning models learn from data and therefore we need data to train our models. For this assignment we will exemplify the use of machine learning in a simple dataset. First, let's create these dummy data. \n", + "\n", + "<code>data_x</code> represents a general independent variable which has a linear relationship with a target variable $y$. In this case, the ground truth $t$ is:\n", + "\n", + "$$\n", + "t = 0.8x+4.75\n", + "$$\n", + "\n", + "To make the problem a bit more interesting (and realistic!), we also add Gaussian noise with unit variance to our observations:\n", + "\n", + "$$\n", + "t = 0.8x+4.75+\\epsilon\\quad\\quad\\epsilon\\sim\\mathcal{N}(0,1)\n", + "$$\n", + "\n", + "Finally, we introduce a _dense validation dataset_ to evaluate our model complexity. Normally you would **split the original dataset** into training and validation sets (as done in PA14), but since our dataset is very small this is not a feasible strategy. For this demonstration we will instead simply evaluate the actual loss through dense integration with a `linspace`:\n", + "\n", + "$$\n", + "\\mathbb{E}[L]=\\displaystyle\\int\\int\\left(t-y(x,\\mathbf{w})\\right)^2p(x,t)\\,dx\\,dt\n", + "\\approx\n", + "\\displaystyle\\frac{1}{N_\\mathrm{val}}\\sum_{n=1}^{N_\\mathrm{val}}\\left(t_n-y(x_n)\\right)^2\n", + "$$\n", + "\n", + "As you can see, this expected loss is based on the squared error, where $t$ is the true value and $y(x)$ is our model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 0:</b> \n", + "\n", + "Read the code below, making sure you understand what the data are, as well as the validation set and how it is created. Then execute the cells to visualize the data.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate some data\n", + "np.random.seed(42)\n", + "noise_level = 1.0\n", + "\n", + "data_x = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]]).transpose()\n", + "data_t = 0.8 * data_x + 4.75 + np.random.normal(scale=noise_level,size=data_x.shape)\n", + "\n", + "# Get a very dense validation set to give us the real loss\n", + "\n", + "x_val = np.linspace(np.min(data_x),np.max(data_x),1000)\n", + "t_val = 0.8*x_val + 4.75 + np.random.normal(scale=noise_level,size=x_val.shape)\n", + "x_val = x_val.reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x.flatten(), data_t, 'x', color='blue', markersize=10, label='Data')\n", + "ax.set_title('Linear Data Example', fontsize=16)\n", + "ax.set_xlabel('x', fontsize=14)\n", + "ax.set_ylabel('t', fontsize=14)\n", + "ax.legend(fontsize=14)\n", + "ax.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 1: Create Your First Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.1: Implement an MLP</b> \n", + "\n", + "We now try to fit this data with a Multilayer Perceptron (MLP), also known as a Feedforward Neural Network (FNN), with input, hidden and output layers, each with a specified number of neurons. For such models we also need to specify some hyperparameters. In Scikit-learn, the MLP is defined in the <code>MLPRegressor</code> class, you can see the documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html).\n", + "You should start with a linear MLP, so with `identity` activation and without hidden layers.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "You can create the model using the method and two arguments.\n", + "\n", + "Specifying <code>hidden_layer_sizes=()</code> implies that the Neural Network does not have hidden layers. There is only one input layer and one output layer and therefore it is transforming directly $x$ into $y$ without going through intermediate steps.\n", + "\n", + "Specifying <code>activation = 'identity'</code> means that we are not going to alter the output of the model with an activation function such as ReLU, tanh, or sigmoid (the most popular activation functions for Neural Networks).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.2: Train the MLP</b> \n", + "\n", + "So far the model has not been trained yet. This is something we can do using the <code>partial_fit</code> method and then make predictions with the <code>predict</code> method. Fill in the code below to find the model predictions for the training and validation sets (defined above)\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_epochs = 10000\n", + "N_print = 10**(int(np.log10(n_epochs)) - 1)\n", + "\n", + "for epoch in range(n_epochs):\n", + " model.partial_fit(data_x, data_t.flatten())\n", + "\n", + " MLP_prediction = \n", + " MLP_valprediction = \n", + " \n", + " if epoch%N_print==0 or epoch==n_epochs-1: \n", + " print((f'Epoch: {epoch:6d}/{n_epochs}, '\n", + " + f'MSE: {mean_squared_error(data_t, MLP_prediction.reshape(-1,1)):0.4f}, '\n", + " + f'Real loss: {mean_squared_error(t_val,MLP_valprediction):0.4f}'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "<b>Be careful about re-running cells!</b> If you executed the cell above more than once, you may have noticed that the values of loss sand MSE stopped changing. Note carefully that in the for loop above we are operating on <code>model</code>, which is an object with type <code>sklearn.neural_network._multilayer_perceptron.MLPRegressor</code>. You can \"ask\" the model about its status by checking how many epochs have been evaluated with the <code>t_</code> attribute (try it!). If you need to \"reset\" the model, simply redefine the variable <code>model</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the loss function progressively decreases. That means that our model is indeed learning! That is, it is reducing its error.\n", + "\n", + "Also notice how the `Real loss` value decreases with time. Again remember this is the value of the loss function obtained with a very dense validation dataset. Notice how the training loss is usually lower than the real loss. This is expected, as the training loss is obtained with a very small dataset and is therefore overly optimistic (on average)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the predictions from our model against the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x, data_t, \".\", markersize=20, label=\"Data\")\n", + "ax.plot(data_x, MLP_prediction, \"-o\", markersize=10, label=\"Prediction\")\n", + "\n", + "# Add a title and axis labels\n", + "ax.set_title(\"Linear Data Example\", fontsize=16)\n", + "ax.set_xlabel(\"x\", fontsize=14)\n", + "ax.set_ylabel(\"t\", fontsize=14)\n", + "\n", + "# Add a legend\n", + "ax.legend(fontsize=14)\n", + "\n", + "# Add a grid\n", + "ax.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then print the weights of the model. How do they compare to the original slope (0.8) and intercept (4.75)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'Model coefficients: {model.coefs_}')\n", + "print(f'Model intercepts: {model.intercepts_}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.3: Increase MLP complexity</b> \n", + "\n", + "Now let us see if it is a good idea to use a much more flexible model. Initialize and train another MLP, but this time with **five hidden layers with 50 units each** and **ReLU** activation.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = YOUR_CODE_HERE\n", + "\n", + "n_epochs = 10000\n", + "N_print = 10**(int(np.log10(n_epochs)) - 1)\n", + "\n", + "for epoch in range(n_epochs):\n", + " model.partial_fit(data_x, data_t.flatten())\n", + "\n", + " MLP_prediction = YOUR CODE HERE\n", + " MLP_valprediction = YOUR CODE HERE\n", + " \n", + " if epoch%N_print==0 or epoch==n_epochs-1: \n", + " print((f'Epoch: {epoch:6d}/{n_epochs}, '\n", + " + f'MSE: {mean_squared_error(data_t, MLP_prediction.reshape(-1,1)):0.4f}, '\n", + " + f'Real loss: {mean_squared_error(t_val,MLP_valprediction):0.4f}'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x, data_t, \".\", markersize=20, label=\"Data\")\n", + "ax.plot(data_x, MLP_prediction, \"-o\", markersize=10, label=\"Prediction\")\n", + "\n", + "# Add a title and axis labels\n", + "ax.set_title(\"Linear Data Example\", fontsize=16)\n", + "ax.set_xlabel(\"x\", fontsize=14)\n", + "ax.set_ylabel(\"t\", fontsize=14)\n", + "\n", + "# Add a legend\n", + "ax.legend(fontsize=14)\n", + "\n", + "# Add a grid\n", + "ax.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What happened to the real loss? Is it higher or lower than before? Is this model good? Think about it: we know the ground truth is actually linear. Would it make sense to have such a flexible model here? Is the validation loss giving you a valuable hint about it?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.4: Control MLP complexity</b> \n", + "\n", + "The previous model is way too flexible and we ended up overfitting the noise in the data. So we should use a smaller model, but tweaking the architecture can be annoying. \n", + "\n", + "For this part you can try something different: **Keep the same network size but add an $L_2$ regularization term** to your model. In `scikit-learn` you can do this by setting the `alpha` parameter you give to `MLPRegressor` to your desired value of $\\lambda$. \n", + "\n", + "Try different values in the interval $0<\\lambda\\leq1$ and see what happens to model complexity. **What is the value of $\\lambda$ that leads to the lowest value for the real loss?**\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = YOUR_CODE_HERE\n", + "\n", + "n_epochs = 10000\n", + "N_print = 10**(int(np.log10(n_epochs)) - 1)\n", + "\n", + "for epoch in range(n_epochs):\n", + " model.partial_fit(data_x, data_t.flatten())\n", + "\n", + " MLP_prediction = YOUR CODE HERE\n", + " MLP_valprediction = YOUR CODE HERE\n", + " \n", + " if epoch%N_print==0 or epoch==n_epochs-1: \n", + " print((f'Epoch: {epoch:6d}/{n_epochs}, '\n", + " + f'MSE: {mean_squared_error(data_t, MLP_prediction.reshape(-1,1)):0.4f}, '\n", + " + f'Real loss: {mean_squared_error(t_val,MLP_valprediction):0.4f}'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x, data_t, \".\", markersize=20, label=\"Data\")\n", + "ax.plot(data_x, MLP_prediction, \"-o\", markersize=10, label=\"Prediction\")\n", + "\n", + "# Add a title and axis labels\n", + "ax.set_title(\"Linear Data Example\", fontsize=16)\n", + "ax.set_xlabel(\"x\", fontsize=14)\n", + "ax.set_ylabel(\"t\", fontsize=14)\n", + "\n", + "# Add a legend\n", + "ax.legend(fontsize=14)\n", + "\n", + "# Add a grid\n", + "ax.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How close is the model with the lowest validation loss to your first linear model? Is this a reassuring result? \n", + "\n", + "Neural networks can be very flexible and that is usually a good thing. But at the end of the day you want a model that performs **your specific task** as well as possible, and sometimes that means a very simple linear model is all what you need.\n", + "\n", + "It is nice to see that even if we do not actually know how complex the underlying patterns in our data are, if we have **a good validation dataset** we can rely on it to tell us how flexible our model should be." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 2 - Predict land deformation\n", + "\n", + "Let's go back to what you did in Week 1.3 - Observation Theory. In Project 2 you were asked to model the deformation of a road caused by the subsidence of the underground by means of GNSS and InSAR data. \n", + "\n", + "In this workshop you will focus only on the GNSS to making predictions about land deformation. You will be asked to:\n", + "\n", + "- Create a neural network similar to the one you created in the previous tasks\n", + "- Change some network parameters and observe how results are affected.\n", + "- Compare the results with those from Project 2, where you applied BLUE.\n", + "\n", + "Although we focus here on building and evaluating a neural network, the greater MUDE purpose is to think about the following questions (for both types of models!!!):\n", + "\n", + "- Which model is the best?\n", + "- Are the Neural Network and BLUE able to capture the trend of the data?\n", + "- What parameters affect the the results most?\n", + "- What are the differences between the two approaches? In which situation would one be more effective compared to the other?\n", + "\n", + "### Task 2.0: Data Processing\n", + "\n", + "First we need to import and convert our data properly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gnss = pd.read_csv('./data/gnss_observations2.csv')\n", + "dates_gnss = pd.to_datetime(gnss['dates'])\n", + "gnss_obs = (gnss['observations[m]']).to_numpy() * 1000\n", + "\n", + "def to_days_years(dates):\n", + " '''Convert the observation dates to days and years.'''\n", + " \n", + " dates_datetime = pd.to_datetime(dates)\n", + " time_diff = (dates_datetime - dates_datetime[0])\n", + " days_diff = (time_diff / np.timedelta64(1,'D')).astype(int)\n", + " \n", + " days = days_diff.to_numpy()\n", + " \n", + " return days\n", + "\n", + "days_gnss = to_days_years(dates_gnss)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.plot(days_gnss, gnss_obs, 'o', mec='black', label = 'GNSS')\n", + "plt.legend()\n", + "plt.title('GNSS observations of land deformation')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.1: Split the data</b> \n", + "\n", + "Define what <code>X</code> and <code>t</code> are. \n", + "\n", + "Then use the function <code>train_test_split</code> from <code>sklearn.model_selection</code> library to split the initial dataset into a training and validation dataset. Check the [documentation here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). (this is what you did in PA14, manually)\n", + "\n", + "Use **80%** and **20%** of the data for training and validation, respectively. Also make sure that <code>random_state = 42</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "You will need to reshape your arrays in order to first standardize the data and consequently train the model. To do so check [this function](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html). The new shape should be <code>(-1,1)</code>. We do this for you ;)\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = days_gnss.reshape(-1, 1)\n", + "t = gnss_obs.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_val, t_train, t_val = YOUR_CODE_HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train, t_train, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val, t_val, 'o', mec='blue', label = 'Validation')\n", + "plt.title('GNSS observations of land deformation - training and validation datasets')\n", + "plt.legend()\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.2: Normalize the data</b> \n", + "\n", + "Before training the model you need to standardize your data. To do so you need <code>StandardScaler</code> from <code>sklearn.preprocessing</code> library. Check the [documentation here](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). \n", + "\n", + "Make sure to standardize both <code>X</code> datasets and save them in different arrays. \n", + "\n", + "Using **a different** `StandardScaler`, normalize the outputs `t_train` and `t_val` as well.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "You first need to standardize just <code>X_train</code> and `t_train`. Only then you can standardize the validation dataset. This will guarantee that the validation data is normalized in the same way as the training data, othersize the network will get confused when it is time to make predictions. We do this for you ;)\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_scaler = StandardScaler()\n", + "target_scaler = StandardScaler()\n", + "\n", + "X_train_scaled = input_scaler.fit_transform(X_train)\n", + "X_val_scaled = input_scaler.transform(X_val)\n", + "\n", + "t_train_scaled = target_scaler.fit_transform(t_train)\n", + "t_val_scaled = target_scaler.transform(t_val)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train_scaled, t_train_scaled, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val_scaled, t_val_scaled, 'o', mec='blue', label = 'Validation')\n", + "plt.title('Normalized GNSS dataset')\n", + "plt.legend()\n", + "plt.ylabel('Normalized displacement [-]')\n", + "plt.xlabel('Normalized time [-]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.2B: Check the Figure!</b> \n", + "\n", + "Look at the figure above and notice how the data has been changed by the normalization process.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.3: Create a linear MLP</b> \n", + "\n", + "As you have done in Task 1 using <code>MLPRegressor</code>, create the Neural Network with no hidden layers and <code>identity</code> activation function. \n", + "\n", + "In the <code>MLPRegressor</code> you can specify several hyperparameters. Some of the default values are <code>solver='adam'</code> for the optimizer and <code>learning_rate_init=0.001</code> for the initial learning rate $\\eta$. For the moment we keep these values fixed, but later you can try to change some of them and see what you get.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_gnss = YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.4: Train the MLP</b> \n", + "\n", + "Now you can effectively train the model and then test it. In both cases you want to compute the loss and save it.\n", + "\n", + "You are required to do the following:\n", + "<ol>\n", + " <li>Initialize the lists for saving the training and validation loss</li>\n", + " <li>Loop over the epochs to train and validate the model. The line <code>model_gnss.partial_fit(X_train_scaled, t_train)</code> is used for training the model in full batch mode. Check the <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor.partial_fit\" target=\"_blank\">Documentation</a> if you're interested.</li>\n", + " <li>For both steps compute the Mean Squared Error (<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn-metrics-mean-squared-erro\" target=\"_blank\">documentation here</a>) and store it in the lists you previously initialized.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_losses = []\n", + "val_losses = []\n", + "\n", + "epochs = YOUR CODE HERE\n", + "\n", + "for epoch in range(YOUR CODE HERE):\n", + " model_gnss.partial_fit(X_train_scaled, t_train_scaled.flatten())\n", + "\n", + " # Calculate training loss\n", + " train_pred = YOUR CODE HERE\n", + " train_loss = YOUR CODE HERE\n", + " train_losses.YOUR CODE HERE\n", + "\n", + " # Calculate validation loss\n", + " val_pred = YOUR CODE HERE\n", + " val_loss = YOUR CODE HERE\n", + " val_losses.YOUR CODE HERE\n", + "\n", + " # Print losses every 500 epochs\n", + " if epoch % 500 == 0:\n", + " print(f'Epoch {epoch}/{epochs} - Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally you can plot the losses and the predictions made by the Neural Network!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "plt.plot(train_losses, label='Training Loss', c='b')\n", + "plt.plot(val_losses, label='Validation Loss', c='r')\n", + "plt.title('Training, Validation, and Test Losses over Epochs')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Mean Squared Error')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot dataset\n", + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train, t_train, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val, t_val, 'o', mec='blue', label = 'Validation')\n", + "\n", + "# Get model predictions for a dense linspace in x\n", + "x_plot = np.linspace(np.min(X),np.max(X),1000).reshape(-1,1)\n", + "y_plot = model_gnss.predict(input_scaler.transform(x_plot))\n", + "plt.plot(x_plot,target_scaler.inverse_transform(y_plot.reshape(-1,1)),color='orange',linewidth=5,label='Network predictions')\n", + "\n", + "# plt.plot(, target_scaler.inverse_transform(val_pred.reshape(-1,1)), 'x', mec='red', label='Predicted Values')\n", + "plt.title('Obvserved vs Predicted Values')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how we had to **carefully normalize and de-normalize** our data when making new predictions. The network is used to seeing normalized inputs and producing normalized outputs, so we have to:\n", + "\n", + "- Create a `linspace` with the new locations we want to predict at;\n", + "- Normalize these inputs with `input_scaler.transform()` so that they fall within the range the network was trained for;\n", + "- Call `predict()` to make normalized predictions of $y(x)$;\n", + "- Bring the predictions back to the real space with `target_scaler.inverse_transform()` in order to plot them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.5: Use a more complex network</b> \n", + "\n", + "Now it is time to make the network a bit more complex. You can change different hyperparameters as we have seen. See [documentation here](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html) for the next tasks.\n", + "Go back to the model and do the following:\n", + "<ol>\n", + " <li>Add hidden layers to your network</li>\n", + " <li>Change activation functions. Try for instance using ReLU or Tanh</li>\n", + " <li>Try different combinations of activations and network architecture. The ReLU activation tends to work better with deeper networks</li>\n", + " <li>Adjust the strength of the $L_2$ regularization by tweaking the alpha hyperparameter</li>\n", + "</ol>\n", + "\n", + "Then run the model again as many times as you deem necessary. Then look at the validation error and use what you have learned before: what is the best model?\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_losses = []\n", + "val_losses = []\n", + "\n", + "epochs = YOUR CODE HERE\n", + "\n", + "for epoch in range(YOUR CODE HERE):\n", + " model_gnss.partial_fit(X_train_scaled, t_train_scaled.flatten())\n", + "\n", + " # Calculate training loss\n", + " train_pred = YOUR CODE HERE\n", + " train_loss = YOUR CODE HERE\n", + " train_losses.YOUR CODE HERE\n", + "\n", + " # Calculate validation loss\n", + " val_pred = YOUR CODE HERE\n", + " val_loss = YOUR CODE HERE\n", + " val_losses.YOUR CODE HERE\n", + "\n", + " # Print losses every 500 epochs\n", + " if epoch % 500 == 0:\n", + " print(f'Epoch {epoch}/{epochs} - Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot losses\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(train_losses, label='Training Loss', c='b')\n", + "plt.plot(val_losses, label='Validation Loss', c='r')\n", + "plt.title('Training and Validation Losses over Epochs with a better model')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Mean Squared Error')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot dataset\n", + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train, t_train, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val, t_val, 'o', mec='blue', label = 'Validation')\n", + "\n", + "# Get model predictions for a dense linspace in x\n", + "x_plot = np.linspace(np.min(X),np.max(X),1000).reshape(-1,1)\n", + "y_plot = new_model_gnss.predict(input_scaler.transform(x_plot))\n", + "plt.plot(x_plot,target_scaler.inverse_transform(y_plot.reshape(-1,1)),color='orange',linewidth=5,label='Network predictions')\n", + "\n", + "plt.title('Obvserved vs Predicted Values')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 3 - Compare with _Project 2 - Modelling Road Deformation using Non-Linear Least-Squares_\n", + "\n", + "The following cell contains the code you implemented in Project 2 during Week 1.3; you can review the solution [here](https://mude.citg.tudelft.nl/course-files/Project_2/Solution.html) if you need a reminder. For this application we only focus on the linear model implemented using **BLUE**. You just need to run the following cell and then compare the results, i.e. the residuals, with respect to the Neural Network model you have previously implemented." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.1: Model Comparison</b> \n", + "\n", + "Scan through the code quickly to refresh your memore about the BLUE method and how we used it to solve this problem in Q1. THen run the cells below and answer the questions (also below).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gw = pd.read_csv('./data/groundwater_levels2.csv')\n", + "dates_gw = pd.to_datetime(gw['dates'])\n", + "gw_obs = (gw['observations[mm]']).to_numpy()\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Function to convert to days and years \n", + "\n", + "def to_days_years(dates):\n", + " '''Convert the observation dates to days and years.'''\n", + " \n", + " dates_datetime = pd.to_datetime(dates)\n", + " time_diff = (dates_datetime - dates_datetime[0])\n", + " days_diff = (time_diff / np.timedelta64(1,'D')).astype(int)\n", + " \n", + " days = days_diff.to_numpy()\n", + " years = days/365\n", + " \n", + " return days, years\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Convert data of GNSS and GW levels\n", + "\n", + "days_gnss, years_gnss = to_days_years(dates_gnss)\n", + "days_gw, years_gw = to_days_years(dates_gw)\n", + "\n", + "interp = interpolate.interp1d(days_gw, gw_obs)\n", + "\n", + "GW_at_GNSS_times = interp(days_gnss)\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Functional model for GNSS: A_gnss\n", + "\n", + "A_gnss = np.ones((len(dates_gnss), 3))\n", + "A_gnss[:,1] = days_gnss\n", + "A_gnss[:,2] = GW_at_GNSS_times\n", + "\n", + "y_gnss = gnss_obs\n", + "\n", + "m_gnss = np.shape(A_gnss)[0]\n", + "n_gnss = np.shape(A_gnss)[1]\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Stochastic model for GNSS: Sigma_Y_gnss\n", + "\n", + "std_gnss = 15 #mm (corrected from original value of 5 mm)\n", + "\n", + "Sigma_Y_gnss = np.identity(len(dates_gnss))*std_gnss**2\n", + "\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# BLUE function - not needed if non-linear model is used!\n", + "\n", + "def BLUE(A, y, Sigma_Y):\n", + " \"\"\"Calculate the Best Linear Unbiased Estimator\n", + " \n", + " Write a docstring here (an explanation of your function).\n", + " \n", + " Function to calculate the Best Linear Unbiased Estimator\n", + " \n", + " Input:\n", + " A = A matrix (mxn)\n", + " y = vector with obervations (mx1)\n", + " Sigma_Y = Varaiance covariance matrix of the observations (mxm)\n", + " \n", + " Output:\n", + " xhat = vector with the estimates (nx1)\n", + " Sigma_Xhat = variance-covariance matrix of the unknown parameters (nxn)\n", + " \"\"\"\n", + " \n", + " Sigma_Xhat = np.linalg.inv(A.T @ np.linalg.inv(Sigma_Y) @ A)\n", + " xhat = Sigma_Xhat @ A.T @ np.linalg.inv(Sigma_Y) @ y\n", + " \n", + " return xhat, Sigma_Xhat \n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# BLUE estimation \n", + "\n", + "xhat_gnss, Sigma_Xhat_gnss = BLUE(A_gnss, y_gnss, Sigma_Y_gnss)\n", + "\n", + "\n", + "# Function to plot BLUE residuals\n", + "\n", + "def plot_residual(date, y_obs, yhat, data_type, A,\n", + " Sigma_Xhat, Sigma_Y, true_disp):\n", + "\n", + " ehat = y_obs - yhat\n", + "\n", + " # Compute the vc matrix for \\hat{y}\n", + " Sigma_Yhat = A @ Sigma_Xhat @ A.T\n", + " std_y = np.sqrt(Sigma_Yhat.diagonal())\n", + "\n", + " # Compute the vc matrix for \\hat{e}\n", + " Sigma_ehat = Sigma_Y - Sigma_Yhat\n", + " std_ehat = np.sqrt(Sigma_ehat.diagonal())\n", + "\n", + " # Show the 99% confidence interval\n", + " k99 = norm.ppf(1 - 0.5*0.01)\n", + " confidence_interval_y = k99*std_y\n", + " confidence_interval_res = k99*std_ehat\n", + "\n", + " # Plot original data and fitted model\n", + " plt.figure(figsize = (15,5))\n", + " plt.plot(date, y_obs, 'k+', label = 'Observations')\n", + " plt.plot(date, yhat, label = 'Fitted model')\n", + " plt.fill_between(date, (yhat - confidence_interval_y), \n", + " (yhat + confidence_interval_y), facecolor='orange',\n", + " alpha=0.4, label = '99% Confidence Region')\n", + " plt.plot(date, true_disp, label = 'True model')\n", + " plt.legend()\n", + " plt.ylabel(data_type + ' Displacement [mm]')\n", + " plt.xlabel('Time')\n", + " plt.title(data_type + ' Observations and Fitted Model')\n", + "\n", + " return ehat\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# True model which was used to generate the data (Monte Carlo simulations)\n", + "\n", + "k_true = 0.1\n", + "R_true = -25 \n", + "a_true = 180\n", + "d0_true = 10\n", + "\n", + "disp_gnss = (d0_true + R_true*(1 - np.exp(-days_gnss/a_true)) \n", + " + k_true*GW_at_GNSS_times) \n", + "\n", + "# Residuals and plots for GNSS incl. confidence bounds\n", + "yhat_gnss = A_gnss @ xhat_gnss\n", + "ehat_gnss_1 = plot_residual(dates_gnss, y_gnss, yhat_gnss,\n", + " 'GNSS', A_gnss, \n", + " Sigma_Xhat_gnss, Sigma_Y_gnss, disp_gnss)\n", + "\n", + "# ------------------------------------------------------------- #" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot predictions on the test set\n", + "plt.figure(figsize=(15,5))\n", + "plt.plot(days_gnss, yhat_gnss, label = 'BLUE model', color='black')\n", + "plt.plot(days_gnss, disp_gnss, label='True model', color='orange')\n", + "\n", + "# Get model predictions for a dense linspace in x\n", + "x_plot = np.linspace(np.min(X),np.max(X),1000).reshape(-1,1)\n", + "y_plot = new_model_gnss.predict(input_scaler.transform(x_plot))\n", + "plt.plot(x_plot,target_scaler.inverse_transform(y_plot.reshape(-1,1)),color='purple',linewidth=3,label='Network')\n", + "\n", + "plt.title('Obvserved vs Predicted Values')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.1 (continued): Model Comparison Questions</b> \n", + "\n", + "Using the figure produced above, compare the differences (write down a few observations about the characteristics of each method. Which model is do you think is better?\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It turns out that this is a very weird comparison, because BLUE has more information than the neural network uses! Can you remember why, and also remove this information to make the comparison \"fair\"?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.2: </b> \n", + "\n", + "Determine what information is used by BLUE, and not the neural network. Then remove this piece of information from the BLUE model and re-run the analysis to redo the comparison.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.3 (BONUS!): </b> \n", + "\n", + "The neural network can include the groundwater data quite easily! You should be able to do this with the code above by making the array 2D and including an extra input (feature).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/Week_2_6/WS_2_6_solution.ipynb b/content/Week_2_6/WS_2_6_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6dca704fa34fee4e86ef0a065f38da0947a31e91 --- /dev/null +++ b/content/Week_2_6/WS_2_6_solution.ipynb @@ -0,0 +1,1564 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Workshop 14: Be like a Neural Network\n", + "\n", + "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n", + " <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n", + "</h1>\n", + "<h2 style=\"height: 10px\">\n", + "</h2>\n", + "\n", + "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.6. Wednesday December 20, 2023.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"https://i.pinimg.com/originals/e5/6e/8a/e56e8a055bfbcbeafaf413a70c911876.jpg\" width=\"500\" height=\"400\">\n", + "\n", + "Source image: https://www.kaggle.com/code/pranavkasela/neural-networks-from-scratch-code-maths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "For several tasks within the domain of Civil Engineernig and Geosciences we might be interested in applying machine learning models to understand some physical processes and phenomena which, for many different reasons, are difficult to interpret. Training a machine learning model will typically involves the following steps:\n", + "\n", + "1. Define the model.\n", + "\n", + "2. Define the loss function.\n", + "\n", + "3. Train the model.\n", + "\n", + "4. Evaluate the model.\n", + "\n", + "By using these predefined functions, we can save time and effort when building and training machine learning models.\n", + "\n", + "In this notebook, we start with a very simple dataset with an underlying linear pattern. We then train a simple Multilayer Perceptron (MLP) on it and discuss matters related to model complexity and overfitting. Finally, we move to a more realistic dataset you have already seen before during MUDE.\n", + "\n", + "### Python Environment\n", + "\n", + "You will need the package scikit-learn for this workshop (in addition to a few other typical packages). You can import it to one of your existing conda environments from the conda-forge as (i.e., `\n", + "conda install -c conda-forge scikit-learn`), or you can create a new environment from the `*.yml` file included in this repository (`conda env create -f environment_MUDE_ml.yml`). But remember: _if you already have sklearn installed in an environment, you don't have to do anything besides use it!_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "We will use boxes with this type of formatting to introduce you to the <code>scikit-learn</code> package and help you with some programming tips.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from scipy import interpolate\n", + "from scipy.stats import norm\n", + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 0: Our Data for Today" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Machine learning models learn from data and therefore we need data to train our models. For this assignment we will exemplify the use of machine learning in a simple dataset. First, let's create these dummy data. \n", + "\n", + "<code>data_x</code> represents a general independent variable which has a linear relationship with a target variable $y$. In this case, the ground truth $t$ is:\n", + "\n", + "$$\n", + "t = 0.8x+4.75\n", + "$$\n", + "\n", + "To make the problem a bit more interesting (and realistic!), we also add Gaussian noise with unit variance to our observations:\n", + "\n", + "$$\n", + "t = 0.8x+4.75+\\epsilon\\quad\\quad\\epsilon\\sim\\mathcal{N}(0,1)\n", + "$$\n", + "\n", + "Finally, we introduce a _dense validation dataset_ to evaluate our model complexity. Normally you would **split the original dataset** into training and validation sets (as done in PA14), but since our dataset is very small this is not a feasible strategy. For this demonstration we will instead simply evaluate the actual loss through dense integration with a `linspace`:\n", + "\n", + "$$\n", + "\\mathbb{E}[L]=\\displaystyle\\int\\int\\left(t-y(x,\\mathbf{w})\\right)^2p(x,t)\\,dx\\,dt\n", + "\\approx\n", + "\\displaystyle\\frac{1}{N_\\mathrm{val}}\\sum_{n=1}^{N_\\mathrm{val}}\\left(t_n-y(x_n)\\right)^2\n", + "$$\n", + "\n", + "As you can see, this expected loss is based on the squared error, where $t$ is the true value and $y(x)$ is our model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 0:</b> \n", + "\n", + "Read the code below, making sure you understand what the data are, as well as the validation set and how it is created. Then execute the cells to visualize the data.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate some data\n", + "np.random.seed(42)\n", + "noise_level = 1.0\n", + "\n", + "data_x = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]]).transpose()\n", + "data_t = 0.8 * data_x + 4.75 + np.random.normal(scale=noise_level,size=data_x.shape)\n", + "\n", + "# Get a very dense validation set to give us the real loss\n", + "\n", + "x_val = np.linspace(np.min(data_x),np.max(data_x),1000)\n", + "t_val = 0.8*x_val + 4.75 + np.random.normal(scale=noise_level,size=x_val.shape)\n", + "x_val = x_val.reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x.flatten(), data_t, 'x', color='blue', markersize=10, label='Data')\n", + "ax.set_title('Linear Data Example', fontsize=16)\n", + "ax.set_xlabel('x', fontsize=14)\n", + "ax.set_ylabel('t', fontsize=14)\n", + "ax.legend(fontsize=14)\n", + "ax.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 1: Create Your First Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.1: Implement an MLP</b> \n", + "\n", + "We now try to fit this data with a Multilayer Perceptron (MLP), also known as a Feedforward Neural Network (FNN), with input, hidden and output layers, each with a specified number of neurons. For such models we also need to specify some hyperparameters. In Scikit-learn, the MLP is defined in the <code>MLPRegressor</code> class, you can see the documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html).\n", + "You should start with a linear MLP, so with `identity` activation and without hidden layers.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "You can create the model using the method and two arguments.\n", + "\n", + "Specifying <code>hidden_layer_sizes=()</code> implies that the Neural Network does not have hidden layers. There is only one input layer and one output layer and therefore it is transforming directly $x$ into $y$ without going through intermediate steps.\n", + "\n", + "Specifying <code>activation = 'identity'</code> means that we are not going to alter the output of the model with an activation function such as ReLU, tanh, or sigmoid (the most popular activation functions for Neural Networks).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# model = YOUR CODE HERE\n", + "# Solution: \n", + "model = MLPRegressor(hidden_layer_sizes=(), activation='identity') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.2: Train the MLP</b> \n", + "\n", + "So far the model has not been trained yet. This is something we can do using the <code>partial_fit</code> method and then make predictions with the <code>predict</code> method. Fill in the code below to find the model predictions for the training and validation sets (defined above)\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0/10000, MSE: 0.4701, Real loss: 1.1432\n", + "Epoch: 1000/10000, MSE: 0.4701, Real loss: 1.1433\n", + "Epoch: 2000/10000, MSE: 0.4701, Real loss: 1.1432\n", + "Epoch: 3000/10000, MSE: 0.4701, Real loss: 1.1432\n", + "Epoch: 4000/10000, MSE: 0.4701, Real loss: 1.1433\n", + "Epoch: 5000/10000, MSE: 0.4701, Real loss: 1.1433\n", + "Epoch: 6000/10000, MSE: 0.4701, Real loss: 1.1430\n", + "Epoch: 7000/10000, MSE: 0.4701, Real loss: 1.1432\n", + "Epoch: 8000/10000, MSE: 0.4701, Real loss: 1.1432\n", + "Epoch: 9000/10000, MSE: 0.4701, Real loss: 1.1430\n", + "Epoch: 9999/10000, MSE: 0.4701, Real loss: 1.1433\n" + ] + } + ], + "source": [ + "n_epochs = 10000\n", + "N_print = 10**(int(np.log10(n_epochs)) - 1)\n", + "\n", + "for epoch in range(n_epochs):\n", + " model.partial_fit(data_x, data_t.flatten())\n", + "\n", + " #MLP_prediction = YOUR CODE HERE\n", + " #MLP_valprediction = YOUR CODE HERE\n", + " # Solution:\n", + " MLP_prediction = model.predict(data_x)\n", + " MLP_valprediction = model.predict(x_val)\n", + " \n", + " if epoch%N_print==0 or epoch==n_epochs-1: \n", + " print((f'Epoch: {epoch:6d}/{n_epochs}, '\n", + " + f'MSE: {mean_squared_error(data_t, MLP_prediction.reshape(-1,1)):0.4f}, '\n", + " + f'Real loss: {mean_squared_error(t_val,MLP_valprediction):0.4f}'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "<b>Be careful about re-running cells!</b> If you executed the cell above more than once, you may have noticed that the values of loss sand MSE stopped changing. Note carefully that in the for loop above we are operating on <code>model</code>, which is an object with type <code>sklearn.neural_network._multilayer_perceptron.MLPRegressor</code>. You can \"ask\" the model about its status by checking how many epochs have been evaluated with the <code>t_</code> attribute (try it!). If you need to \"reset\" the model, simply redefine the variable <code>model</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the loss function progressively decreases. That means that our model is indeed learning! That is, it is reducing its error.\n", + "\n", + "Also notice how the `Real loss` value decreases with time. Again remember this is the value of the loss function obtained with a very dense validation dataset. Notice how the training loss is usually lower than the real loss. This is expected, as the training loss is obtained with a very small dataset and is therefore overly optimistic (on average)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the predictions from our model against the data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8r2cuBpA3t2h5Fwk4Xd48tI0bN85fqatjx45G8+bNjaCgoPyaevbsaezZs6fIx7j33nvzz2vSpInRoUMHw+l0Gna73Zg+fboBGE2bNi3U7vHHH89fGKJ9+/ZG9+7dje7du+evFPb999/nr34VGRlpdOjQwYiNjTUA4+9//3t+7StWrCjz6827rpGRkYVWKDv95/LLL89vc/z48fyVz6ZPn17oMUePHm0ARo8ePQosjLF48WLDZrMZgBETE2NceOGFRu3atQ3AeOSRR/I/q7t27SrweMXtz5P3Xhdl165dRb7fRa0U1rJlS6N9+/b5K4WdddZZ+e99WZ7P7XYbt9xyS/7x+vXrGx07djTatm1rRERE5O8/c/ERESmZAq2I5IeB0n7yVjbyRKA1DHOVqLvvvtto3bq14XQ6jRo1ahitWrUyrrnmGmPOnDlGUlJSgfMzMzONRx55xIiLizPsdrtRv359Y+jQocavv/5abHCtykB7+o/L5TIaNGhgXHrppcY999xjbNiwocTHyM3NNV544QWjdevWRmhoqFG7dm1j4MCBxrp164oNWIZhLp362GOPGa1atcoPrmcGufXr1xt9+vQxatSoYYSHhxvt2rUzXnrpJSM3N7dSgba0n6ioqPw2eaHt9JB7urS0NKNFixYGYDz33HMFjn3++edGly5dDKfTaURERBidO3c25s6daxhG8cHVk4G2e/fuRlZWlhEfH280b97ccDgcRoMGDYwxY8YYR44cKffzGYZhfPLJJ8agQYOM+vXrG3a73ahbt67RoUMH48477zRWrlxZYLU8ESmdzTCKmadEREQkgK1cuZKePXsWmHJMRHyTxtCKiIiIiF9ToBURERERv6ZAKyIiIiJ+TYFWRERERPyabgoTEREREb+mHloRERER8WshVhdghdzcXA4cOEBERESRa8CLiIiIiLUMwyA1NZXY2FiCgkrugw3IQHvgwAEaN25sdRkiIiIiUoq9e/fSqFGjEs8JyEAbEREBmG9QZGSkxdVUb263m6VLl9K3b1/sdrvV5YgX6JoHHl3zwKTrHni8fc1TUlJo3Lhxfm4rSUAG2rxhBpGRkQq0HuZ2u3G5XERGRuoPXoDQNQ88uuaBSdc98Fh1zcsyPFQ3hYmIiIiIX1OgFRERERG/pkArIiIiIn5NgVZERERE/JoCrYiIiIj4tYCc5aCi3G43OTk5VpfhV9xuNyEhIZw8eVLvXRUIDg7W3cQiIiJnUKAtg5SUFI4ePUpmZqbVpfgdwzCoX78+e/fu1apsVcThcFC7dm1NOSciInKKAm0pUlJS2L9/PzVq1KB27drY7XYFs3LIzc3lxIkT1KhRo9Rl66RkhmHgdrtJTk5m//79AAq1IiIiKNCW6ujRo9SoUYNGjRopyFZAbm4uWVlZhIWFKdBWAafTSUREBPv27ePo0aMKtCIiIuimsBK53W4yMzOJiopSmBWfYbPZiIqKIjMzE7fbbXU5IiIillOgLUHeTUy6CUd8Td5nUjfaiYiINxiGQWJaFsdOQmJaFoZhWF1SARpyUAbqnRVfo8+kiIh4Q3KGmwUb9zHnmwR2J6YDITz+w0qaxrgY1iWOwR0aEeW0vuNPgVZEREREClm14whj5m4kI6vwt4F7EtN5Ysk2nlu6nem3dKB7yzoWVPgXDTkQERERkQJW7TjCiFkbyHDnYABnDjDI25fhzmHErA2s2nHE+0WeRoHWB+SNS9mbmO6T41JEREQkcCRnuBkzd6MZWkuJJIZhBtsxczeSnGHdjcoacmChwuNSTL42LkVEREQCx4KN+8jIyinUK1scw4CMrBwWbtrHiK7NPFpbcdRDa5FVO45w8aQveWLJNvacFmbhr3EpF0/60vIu/ISEBGw2W4Efl8tFbGwsvXr14tFHH+X333+v9PPEx8djs9lYuXJl5YsWERGRCjEMgznfJFSo7eyvEyz7llmB1gL+Ni4F4Oyzz+axxx7jscce4+6776Z///4cPnyYJ554glatWvHggw9qqISIiIifS0p3szsxvYjeWYNoUmhkO0I0KZyZXgxgd2I6x9OtGXagIQdeVt5xKdjMcSnfTuhl6fCD5s2bEx8fX2j/mjVruPXWW5k0aRLBwcE88cQT3i9OREREqkRaZnaB7UjSGBy8mmHBS4kL+jN/f0JuPebk9GVBTjdSCM/ffyIzm+jwUK/Vm0c9tF6WPy6ljJ2Zp49L8UWXXnopX3zxBQ6HgylTprB3714AkpOTmTx5Mj179uScc84hLCyM2NhYbr311kJDFHr06MHEiRMB6NmzZ/7Qhri4uPxzVqxYwciRI2nVqhU1atSgRo0aXHjhhbz22mtee60iIiLVXbjjr77ObkFb+NZxJ4+EzKWJ7XCB85rYDvNIyFy+ddxJt6At+ftrOKzpK1UPrRdVdlzK8C5xPjmhfsuWLbnhhht46623WLRoEXfddRe//PILjz76KD169GDAgAFER0ezfft25s2bxyeffMKmTZto2rQpAMOHDwdg1apVDBs2LD/I1qxZM/85Jk+ezM6dO+ncuTPXXHMNx48f5/PPP2fUqFFs376d559/3suvWkREpPqJdtlpGuMi7vi3vGl/FjAIshXuhcvbF2ZkMcv+LCPd95FQ82Jquqz5NlmB1ovyxqWU1+njUqzoxi+L7t2789Zbb/Hdd98BcM4553Dw4EFq1qxJSkoKkZGRBAUFsWLFCnr37s2TTz7JzJkzATPQJiQksGrVKoYPH06PHj0KPf706dNp1qzgnZPZ2dlcccUVvPjii9x99900adLE469TRESkOrPZbNx+UQzXrHwBMAguIsyeLthmkGPAK/YXWNRxgGUdbxpy4EVnjksprxOVbO9JsbGxABw9ehSAqKgoYmJiCp3Xs2dPzj33XJYvX16uxz8zzAKEhIQwevRocnJyWLFiRQWqFhERkTMNDlmDk6xSw2yeYJuBkyyuDV7r4cqKp0DrReGVHFdi1biUsihqhoOVK1dyzTXX0Lp1axwOR/7Y2J9++okDBw6U6/FTU1N57LHHaNu2LTVq1Mh/rMGDBwOU+/FERESkCIaBc9PrlLej1WYD56aZpd/x7iG+m5CqobxxKXuKnA6jeDagSYzLsnEpZXHw4EEA6tQx13L+3//+xw033ECNGjW47LLLaN68OeHh4dhsNmbPns3u3bvL/NhZWVn06NGDTZs20b59e/7+979Tq1YtQkJCSEhIYM6cOWRmZnrkdYmIiASU9ERI2kV5Bw7YMCBpF2QkgavwN7SepkDrRTabjWFd4nhiybZytx3e1TdvCMuTtyDCRRddBJgLJYSFhfHdd99Rr169/DG0AO+++265Hvujjz5i06ZN3HbbbfnjbvO8++67zJkzp/IvQERERCDrROXaZ6ZaEmg15MDLBndohDM0uMxd+UE2cIYGc+0FjTxbWCXs2LGD999/H4fDwTXXXAPA77//zjnnnEOLFi0KnHvgwIEiVxYLDg4GICcnp9CxvPOvuuqqQsfWrFlT6fpFRETklNAalWvviKiaOspJgdbLopx2pt/SARuUGmrzjs+4pYOliyqUZO3atfTr14/MzEwmTJhAw4YNAWjatCk7d+7kzz//moT55MmTjBkzhuzswje35d1Atm9f4fl286b3Wru24GDzVatWFeqxFRERkUpwxUB04RuxS2cz2zmjq7ykstCQAwt0b1mHWSM6MmbuRjKyzB7J08fU5uVcpz2YGbd0oFvLOl6v8Uw7d+7MXyksKyuLw4cPs379erZu3UpwcDAPP/wwjz76aP75d911F3fddRcdOnRg4MCBBAUFsXz5cgzDoG3btmzZsqXA4+ctqPDQQw/x66+/EhUVRVRUFGPGjGHgwIHExcUxZcoUtm7dynnnncf27dtZsmQJgwYNYsGCBd58K0RERKqvk8chNLzU04rUaXTpvXUeokBrke4t6/DthF4s3LSP2V8nFJiftkmMi+Fd4xjcoRGRYb7RM/v777/nr+bldDqpWbMmrVu35pFHHmHYsGGcffbZBc4fO3Ysdrudl19+mbfeeouaNWsyYMAAnn76aa6//vpCj/+3v/2NWbNm8fzzz/Of//yHzMxMmjZtypgxY6hRowZfffUV9913H6tXr2blypWce+65vPPOO9SrV0+BVkREpCrsWQ8L/gHJe0/tsEFZbmO3BUGIE9oO9WR1JVKgtVCU086Irs0Y3iWO4+luTmRmU8MRQk2X3WduAIuLiytySq7S2Gw2Ro0axe23315gYQX46wayMw0bNoxhw4YVeaxZs2Z88MEHRR6rSH0iIiJySm4OrP0PrHgajByIjoNOY+CLB09Nw5VbQuMgwAY3vA3Oml4pt7gqfMrq1asZOHAgsbGx2Gw2Fi1aVOB4fHw8rVu3Jjw8nOjoaHr37s369eutKbaK2Gw2osNDaRzjIjo81GfCrIiIiFRzqYfg7WvgqyfMMNvmOhi1BjqPhpvfB7sTs6f2zGxyap/dCTf/D5r38n7tp/G5QJuWlkbbtm2ZNm1akcdbtmzJtGnT+Omnn1i7di1xcXH07duXI0eOeLlSERERET/22zKY3hV2rQK7C65+Ba6dCWGR5vHmvWH8Nrj8GbPX9nTRceb+e36xPMyCDw456N+/P/379y/2+E033VRge+rUqbzxxhv8+OOP9Opl/RsqIiIi4tOys+DLifDtqc7DeufBkFlQp2Xhc501zd7aTqNwpxxmxRcf07PfQOyRdS27AawoPhdoyyMrK4vXXnuNqKgo2rZtW+x5mZmZBVaSSklJAcDtduN2u4tt53a7MQyD3NxccnNLGj8ixckb35r3PkrVyM3NxTAM3G53/hy+viLvd6qk3y2pXnTNA5Ouu59K/IPgRf8k6OBmAHIuvI3cXvEQEgalXEu3PYIMRx3c9ggoYgrOqlaez5ZfBtolS5YwdOhQ0tPTadCgAcuWLaN27drFnj9p0qT8O/RPt3TpUlwuV7HtQkJCqF+/PidOnCArK6tKag9UqampVpdQrWRlZZGRkcHq1auLnNfXFyxbtszqEsTLdM0Dk667/2iY+C1t984iKPckWcHh/NDkNg7ldIClX5Xrcbx1zdPT00s/6RSb4cO3iNtsNj788EMGDRpUYH9aWhoHDx7k6NGjzJw5k6+++or169dTt27dIh+nqB7axo0bc/ToUSIjI4t9/pMnT7J3717i4uIICwurktcUaAzDIDU1lYiICN3sVoVOnjxJQkICjRs39rnPptvtZtmyZfTp0we73TemnRPP0jUPTLrufiQrjeAvJhD04zwAcht3JmfQqxDZsFwP4+1rnpKSQu3atUlOTi4xr4Gf9tCGh4fTvHlzmjdvTufOnWnRogVvvPEGEyZMKPJ8h8OBw+EotN9ut5d4QXJycrDZbAQFBeVPOSXlkzfMIO99lKoRFBSEzWYr9TNsJV+uTTxD1zww6br7uEM/wf9GwLHfABt0v5+gbvcTFFzxCOita16e5/DLQHsmwzAK9MCKiIiIBDTDgA0zYenDkJMJEQ3MGQyaXWp1ZR7hc4H2xIkT7Ny5M397165dbN68mZiYGGrVqsVTTz3FVVddRYMGDTh27BivvPIK+/bt47rrrrOwahEREREfkZ4IH90J2z8xt1tebk7JFV7L2ro8yOcC7ffff0/Pnj3zt8ePHw+Yq0jNmDGDX3/9lTlz5nD06FFq1arFRRddxJo1azj33HOtKllERETEN+z+BhbcBin7ITgU+jwOnUb71BRbnuBzgbZHjx4lLmW6cOFCL1YjIiIi4gdyc2D1c7DqGTByIeZsGPImxLazujKv8LlAKyIiIiLlkHIAFtwOu9ea2+cPhQHPgSPC2rq8SIHWFxiGOd4l6wSE1gBXTLX/akBERESqwPbPYdEYyEgEezgMeB7a3Wh1VV6neZSslHEc1k2Hl9rDs2fBi+eb//tSe3N/xnGrK/RJCQkJ2Gw2hg8fXmB/jx49PDrXbVxcHHFxcR57fBERkTLLzoTP/g3zbzDDbP3zYdTqgAyzoEBrnZ3LYerf4PMJkJRQ8FhSgrl/6t/M8yyUFx5P/wkNDaVx48bcdNNN/Pjjj5bWV5WGDx+OzWYjISHB6lJERESKd3QnvN4b1k83tzvfAbcth9rNra3LQhpyYIWdy+Gd682hBhR1A9ypfe4M87yb34fmvb1ZYSFnn302t9xyC2BOrbZu3Trmz5/PwoUL+eqrr+jSpYul9QG89dZb5Vomr7y+/PJLjz22iIhImWyeD5/cA+40cMbAoOnQ6nKrq7KcAq23ZRyH9249FWZzSzk5F4wg8/zx28BZ0/P1FaN58+bEx8cX2Pfwww/z1FNP8dBDD7FixQprCjtNkyZNPPr4Z599tkcfX0REpFiZqfDJvfDju+Z200tg8EyIjLW2Lh+hIQfetmU+uNMpPczmyTXP3/KuJ6uqkLvuuguA7777DjCXt+3Rowf79+9n+PDh1K9fn5CQENauXZvfZvXq1QwcOJDatWvjcDho0aIFDz/8cJE9qzk5OUyePJnmzZsTFhZG8+bNmTRpUv5yumcqaQzt4sWL6devH7Vq1SIsLIy4uDj+/ve/s3XrVsAcHztnzhwAmjVrlj+8okePHvmPUdwY2vT0dOLj42ndujVhYWHExMQwYMAAvvnmm0LnxsfHY7PZWLlyJe+//z4XXHABTqeTBg0a8H//939kZGQUWb+IiASwA5vh1e5mmLUFQc+HYNhihdnTqIe2ogzjVDAtZ5t10yv2fOtegfa3lG/2A7vLo7MlFBUejx07xsUXX0xMTAw33HADmZmZRESY04bMmDGDO+64g+joaAYOHEidOnX47rvveOqpp1ixYgUrVqwgNDQ0/7H++c9/8uabb9KsWTPGjh3LyZMnmTp1apFBsST3338/zz77LDExMQwaNIi6deuyd+9eli9fTocOHTjvvPMYN24cs2fPZsuWLdx9993UrFkToNSbwDIzM+nVqxfr1q3jggsuYNy4cRw+fJj33nuPpUuX8t5773HttdcWavff//6Xzz77jKuvvpoePXrw+eef8/LLL3Ps2DHeeeedcr0+ERGppgwD1s+ApY9ArhsiG8Lg16Gp9cP8fI0CbUW50+Fpb/2XkQHHd8OkhuVr9uABCA33TEnASy+9BMBFF12Uv2/r1q2MGDGCmTNnEhwcTG5uLikpKWzbto277rqLdu3asXz5cmJiYvLbPPPMM0yYMIGXX36Ze+65B4CVK1fy5ptv0rZtW77++mvCw83X8eCDD9KuXbsy1/jpp5/y7LPP0qZNG1asWEGtWn8t+5ednc2xY8cAGDduHJs3b2bLli2MGzeuzLMZTJkyhXXr1nHzzTfz9ttv54f8cePG0bFjR2677Tb69OmTH+rzLFu2jI0bN9KqVSsAnnrqKdq1a8f8+fN59tlniY3Vf3WLiAS0tGPw0R2w43Nzu9UAuHqaObWnFKIhB1ImO3fuJD4+nvj4eO69914uueQSnnrqKcLCwnj66afzzwsNDWXKlCkEBwcXaP/aa6+RnZ3NSy+9VCDMgtmDWqdOHebPn5+/76233gLg0UcfzQ+zAA0bNuTuu+8uc93//e9/AXjxxRcLhFmAkJAQ6tWrV+bHKsrs2bOx2+0888wzBXqszz//fIYPH05SUhIfffRRoXZ33313fpgFcDqd3HjjjRiGwcaNGytVk4iI+Llda2BGVzPMBjvgiudg6DsKsyVQD21F2V1mD2h5pB+DF9pU/DnHbS3fh9nuqvhzneH3339n4sSJ5sPa7dSrV4+bbrqJf//737Rp89dratasGbVr1y7Ufv369QB8/vnnLF9eeCoyu93Or7/+mr+9ZcsWAC699NJC5xa1rzgbNmzA4XDQvXv3Mrcpq5SUFP744w/OOeccGjVqVOh4jx49ePXVV9m8eXP+DBF5LrjggkLn5z3G8ePHq7xWERHxAznZsGoyrH4WMKBWC7huFtSvRHYIEAq0FWWzlf/rfLsLopudmne2qOm6in0yiI6DqEaWrSDWr18/Pv/881LPK67HMzExETC/Wi+L5ORkgoKCigzH5elVPX78OA0bNiQoqOq/jEhJSSmxnvr16wPmazlTVFRUoX0hIeavY05OTlWVKCIi/iJ5Hyy4DfZ8a263vwX6T/Ho0MHqREMOvMlmg06jKta202i/WA63uFkGIiMjATMEGoZR7E+eqKgocnNzOXr0aKHH+vPPP8tcT82aNTl06FCxMyNURt5rKq6evP1554mIiBTplyUwvasZZkMjYPAbcPV/FWbLQYHW29reeGooQBnfeluQeX7boR4ty9M6duwIwLp168p0ftu2bQFYs2ZNoWNF7SvpeTMzM1m1alWp5+aN+y1rD2lkZCRnnXUWO3fuZP/+/YWO5z1neW5iExGRAOI+ac4t+97NcPI4xLaH0auhzRCrK/M7CrTe5qwJN7x1qre1tLc/CLDBDW9buqhCVRgzZgwhISHcdddd7N27t9Dx48eP88MPP+Rv33rrrQA8/vjjpKWl5e/fv38/L774Ypmfd+zYsYB5E1besIc82dnZBXpX825W27dvX5kff9iwYbjdbiZMmFCgh3nr1q3MmjWLqKgoBg0aVObHExGRAHFkB7zeC76baW5ffCeMXAoxZ1lbl5/SGForNO9tLmf73q2nzWV7+pjaU1/b251mmG3ey9sVVrnzzjuPV155hTFjxtCqVSuuuOIKzj777Pwbq1atWsXw4cOZMWMGYN5QNWLECGbNmkWbNm245ppryMzM5L333qNz584sWbKkTM97xRVXcO+99/Lcc8/RokULrrnmGurWrcv+/fv58ssvuffeexk3bhwAl112Gc899xyjRo3iuuuuIzw8nCZNmnDTTTcV+/j3338/n3zyCW+//Ta//PILvXr14siRI7z33nu43W7eeuutQlN2iYhIADMM2PwOfHqfmQFcteGaGdCij9WV+TUFWqs0720uZ7vlXXPS5KRdfx2LjjPHzLa7EcIK3zzkr26//XbatWvH1KlTWb16NYsXLyYqKoomTZrwr3/9i2HDhhU4f+bMmbRs2ZKZM2cybdo0GjVqxPjx47n++uvLHGgBnn32WS6++GKmTZvGBx98wMmTJ2nQoAGXXXYZffr89Qekf//+TJkyhZkzZzJ58mTcbjfdu3cvMdCGhYXx1VdfMXnyZN577z3+85//4HK56NatGw8++CCXXHJJ+d8oERGpnk6mwJJ/wdYPzO1m3eHa1yCivrV1VQM24/TvSQNESkoKUVFRJCcnl3jDzsmTJ9m1axfNmjUjLCzMcwUZBmQkmes0OyLAGe0XN4CVRd7CCpGRkR6ZaSBQee2zWQFut5tPP/2UK664ArvdbnU54gW65oFJ172c9m+ED0aaMx3ZguGyh6DrOAgKLq2lz/D2NS9rXgP10PoGm82cX1YTJouIiFQvubnw7TT4ciLkZkNUE3P52iadrK6sWlGgFREREfGEE0dg0WjYeWpBoXOugqteMr+JlSqlQCsiIiJS1f5YCQv/CSf+hJAwuHwSdBhRbYYU+hoFWhEREZGyMAxIT4SsExBawxwqeGZAzXHDykmwZipgQJ3WMGQW1PubJSUHCgVaERERkZJkHIct82H9q2fMStTMXAG07Y3mfPFJu83la/dtMI9fMAwufwZCXVZUHVAUaEVERESKs3P5GfPGnyYpAT6fAF8+AZ3HwIaZkJkMjkgY+CKcd63Xyw1UCrRlEIAzm4mP02dSRMQLdi6Hd643hxpQ1N/dU/vcabDmOfP/N7wQhrxhzikvXqOJQUsQHGzODed2uy2uRKSgvM9k3mdURESqWMZxs2fWMIDcsrUJssNN7ynMWkCBtgR2ux2Hw0FycrJ6xMRnGIZBcnIyDodDk5mLiHjKlvmnhhmUMcyCOc/sTx94rCQpnoYclKJ27drs37+fffv2ERUVhd1ux6YpN8osNzeXrKwsTp48qZXCKskwDNxuN8nJyZw4cYKGDRtaXZKISPVkGOYNYBWxfoZ5o5iyglcp0JYib6m1o0ePsn//four8T+GYZCRkYHT6dR/CFQRh8NBw4YNS10GUEREKig9seBsBmVmmO0ykrT6p5cp0JZBZGQkkZGRuN1ucnJyrC7Hr7jdblavXk23bt309XgVCA4O1vsoIuJpWScq1z4zVYHWyxRoy8FutytMlFNwcDDZ2dmEhYXpvRMREf8QWqNy7R0RVVOHlJkGNYqIiIiczhVjLppAeYfK2cx2zmhPVCUlUKAVEREROV2OG6IaU/Tcs6XoNFo3hFlAQw5ERERE8iTuggX/gP0by9fOFgQhTmg71DN1SYnUQysiIiICsHUBvNrNDLNhUdDtPrAFU3pcCgJscMPb4Kzp+TqlEPXQioiISGDLSoPPHoAf3ja3G3eCwa9DzSbQpLO5Ypg7/dTJpw9DODW0wO40w2zzXt6sWk6jQCsiIiKB68+f4X8j4Oh2wAaX3gM9JkDwqYjUvDeM3wZb3jUXTTh9ftroOHPMbLsbzR5dsYwCrYiIiAQew4Dv34DPH4ScTKhRH659Dc7qXvhcZ03oPNpcASwjyZxn1hFhzmagG8B8ggKtiIiIBJaMJPjoTvh1ibndoi8Mmg7htUtuZ7OZU3pp0QSfo0ArIiIigWPPOlhwGyTvhSA79JkIncZAkO6T92cKtCIiIlL95ebA2qmwYhIYOeYCCNfNgtj2VlcmVUCBVkRERKq3lIOw8HZIWGNut7keBjwPYZHW1iVVRoFWREREqq8dX8CiMZB+DOwuM8i2vVE3c1UzCrQiIiJS/WRnwZcT4dtp5nb9NjBkFtRuYW1d4hEKtCIiIlK9HPsdPhgJBzeb251GQ++JYA+ztCzxHAVaERERqT5+fB+W/AuyTpjzxF79CrS+wuqqxMMUaEVERMT/ZZ6AT++DLfPM7aZd4dqZENXQ2rrEK3xu0rXVq1czcOBAYmNjsdlsLFq0KP+Y2+3mgQceoE2bNoSHhxMbG8utt97KgQMHrCtYREQCkmEYJKZlsTcxncS0LAzDsLokr8l77cdO4huv/eCP8Fp3M8zagsyla4d9rDAbQHyuhzYtLY22bdsyYsQIBg8eXOBYeno6mzZt4pFHHqFt27YkJSUxbtw4rrrqKr7//nuLKhYRkUCSnOFmwcZ9zPkmgd2J6fn7m8a4GNYljsEdGhHltFtYoecUfu0hPP7DSuteu2HAhtdg6cOQkwURsTD4dYjr6r0axCf4XKDt378//fv3L/JYVFQUy5YtK7Dv5ZdfpmPHjuzZs4cmTZoU2S4zM5PMzMz87ZSUFMDs8XW73VVUuRQl7/3V+xw4dM0DTyBd8zW/HeXOd7eQkZVT6NiexHSeWLKN55ZuZ9rQtlzaopRlVP2Mz7329ESCl/wfQb99DkBui37kXPmyuSxtAHwWreDt3/XyPI/NsPx7guLZbDY+/PBDBg0aVOw5y5cvp2/fvhw/fpzIyKInSI6Pj2fixImF9s+bNw+Xy1VV5YqISDX2y3Ebr/5ijtQzKH4OUxvmP6ujzsnlnJo++09sufjaa6914lc6JEzH6U4ixxbCzw2Hsqt2H80tW82kp6dz0003kZycXGzGy+PXgfbkyZNccskltG7dmrlz5xb7OEX10DZu3JijR4+W+gZJ5bjdbpYtW0afPn2w26vnV3BSkK554AmEa56S4ebS51aT4c6hLP9q2mzgtAez5t5uRPr58AOfeu25OQStfZ6gtc9hM3IxYs4m+5qZUP/8qn0eKZK3f9dTUlKoXbt2mQKtzw05KCu3283QoUPJzc3llVdeKfFch8OBw+EotN9ut1fbP76+Ru914NE1DzzV+Zp/tH4fGVk5lLUHyDAgIyuHxT/9yYiuzTxam6f5zGtP3m8uX7v7a3O73c3Y+k/B7qhRdc8hZeKt3/XyPIfPzXJQFm63m+uvv55du3axbNky9bKKiIjHGIbBnG8SKtR29tcJ1s8AUAk+89p//RRmdDXDbGgNczquQa+Awqyc4nc9tHlh9rfffmPFihXUqlXL6pJERKQaS0p3F5jNoKwMYHdiOsfT3USHh1Z9YV5g+Wt3n4Rlj8KGV83tBu1gyJtQ6+yKP6ZUSz4XaE+cOMHOnTvzt3ft2sXmzZuJiYkhNjaWIUOGsGnTJpYsWUJOTg6HDh0CICYmhtBQ//yDISIivistM7tS7U9kZvttoLX0tR/9DT4YAYd+MrcvvhN6PQYh/vleimf5XKD9/vvv6dmzZ/72+PHjARg2bBjx8fEsXrwYgHbt2hVot2LFCnr06OGtMkVEJECEOyr3T2WNSra3kiWv3TBgy3z45F5wp4GrFgyaAS37VqoWqd587resR48eJY658eexSCIi4n+iXXaaxrjYk5he5hujAGxAkxgXNV3+e6Oc1197Zip8cg/8+J65HXepOV42skH5HkcCjl/eFCYiIuItNpuNYV3iKtR2eNc4bH48N6pXX/uBH+DVbmaYtQXDZQ/DrR8pzEqZKNCKiIiUYnCHRjhDg8s8b3+QDZyhwVx7QSPPFuYFHn/tubnwzTR4vQ8k/gFRjWHEp9DtPggKrnjhElAUaEVEREoR5bQz/ZYO2Ch9Maq84zNu6UCUny+qAB5+7WlHYf4NsPQhyHVD6yth9Bpo0rnSdUtgUaAVEREpg+4t6zBrREec9mAz3J1xPG+f0x7M7BEd6dayjveL9BCPvPZdq2F6V/htKQQ7YMDzcMNccEZX/QuQas/nbgoTERHxVd1b1uHbCb1YuGkfs79OKDBHa5MYF8O7xjG4QyMiw/y/Z/ZMVfbac7Jh1TOw+jnAgNqtzLll65/n2Rcg1ZoCrYiISDlEOe2M6NqM4V3iOJ7u5kRmNjUcIdR02f36BrCyOP21H0lJZ8nny7ny8t7UiXSV7bUf3wsLboO968ztC26Fy5+B0HDPFi7VngKtiIhIBdhsNqLDQ/120YTKsNlsRLtCqRUG0a7QsoXZXz6Gj8bCyWRwRMLAF+C8wR6vVQKDAq2IiIh4jjsDlj4M371ubjfsAIPfgJhm1tYl1YoCrYiIiHjGke3wvxFw+Gdzu+vdcNkjEFz9xhiLtRRoRUREpHwMA9KP4cw8AunHILJewTm9DAN+eBs+vR+yMyC8DlwzA5r3tq5mqdYUaEVERKRsMo7Dlvmw/lXsSbvoC7DtHohuBp1GQdsbzWD78Tj4eaHZ5qyecM2rEFHPurql2lOgFRERkdLtXA7v3Qru9MLHkhLg8wmwPN684SvtMASFmMvXdrkbgjTtvXiWAq2IiIiUbOdyeOd6cygBRhEnnNqXfdL8Ca8DQ+dD44u8WaUEMP0nk4iIiBQv47jZM2sYQG7Z2mSlQe0WnqxKpAAFWhERESnelvmnhhmUMcyCOVXXlnc9VpLImRRoRUREpGiGAetfrVjb9TNO9epWH4ZhkJiWxd7EdBLTsjCq2evzZxpDKyIiIkVLT4SkXRVoaJjtMpLAFVPlZXlbcoabBRv3MeebBHYn/nVTXNMYF8O6xDG4QyOinJpb10oKtCIiIlK0rBOVa5+Z6veBdtWOI4yZu5GMrJxCx/YkpvPEkm08t3Q702/pQPeWdSyoUEBDDkRERKQ4oTUq194RUTV1WGTVjiOMmLWBDHcORc3vkLcvw53DiFkbWLXjiPeLFECBVkRERIrjioHIhhVoaDMXW3BGV3lJ3pKc4WbM3I1maC1lqKxhmMF2zNyNJGe4vVGenEGBVkRERAozDNg4C078WbH2nUYXXA7XzyzYuI+MrJwy39dmGJCRlcPCTfs8W5gUSYFWRERECso4Du/fCkv+BbnZYAsGyhhObUFgd0HboZ6s0KMMw2DONwkVajv76wTNfmABBVoRERH5y94NMONS+GUxBNmh75Nw43tmUC01NgQBNrjhbXDW9HytHpKU7mZ3YnqRa6KVxAB2J6ZzPF3DDrxNgVZEREQgNxfWPA9vXg7JeyA6Dv7xBXS5C1r2gZvfB7sTs6f2zN7aU/vsTrj5f9C8l9fLr0ppmdmVan+iku2l/DRtl4iISKBLPQQL/wm7Vpnbba6DAVMhLPKvc5r3hvHbzBXA1s8oOD9tdJw5ZrbdjRAW5dXSPSHcUbl4VKOS7aX89I6LiIgEst+Ww4ejIP2oOfb1iueg3U1F39DlrAmdR0OnUbhTDrPii4/p2W8g9si6fn0D2JmiXXaaxrjYU85hBzagSYyLmi4tsuBtGnIgIiISiLKzYOnD8M5gM8zWOw/+uQra31x6OLXZwBVDhqOOObVXNQqzADabjWFd4irUdnjXOGzV7P3wBwq0IiIigSbxD3izH3zzsrnd8Z9w25dQp6W1dfmQwR0a4QwNLnNWD7KBMzSYay9o5NnCpEgKtCIiIoHkpw9gRjc4sAnCasLQeXDFs2APs7oynxLltDP9lg7m7W5l6LAGmHFLB6KcGm5gBQVaERGRQJCVBovGwoJ/QFYqNOkCY76G1gOsrsxndW9Zh1kjOuK0B5c0twNOezCzR3SkW8s63i9SAN0UJiIiUv0d+gk+GAlHdwA26H4/dLsfghUDStO9ZR2+ndCLhZv2MfvrBHYnpucfaxLjYnjXOAZ3aERkmHpmraRPsoiISHVlGPDd6/DFQ5CTCREN4NqZ0OxSqyvzK1FOOyO6NmN4lziOp7s5kZlNDUcINV123QDmIxRoRUREqqP0RFh8F/y6xNxueTlc/QqE17K2Lj9ms9mIDg8lOjzU6lLkDAq0IiIi1c3ub2DB7ZCyD4JDoc/j5sIH6k2UakqBVkREpLrIzTGXr105CYxciDkbhrwJse2srkzEoxRoRUREqoOUA+bytQlrzO22N5rTcTkirK1LxAsUaEVERPzd9s9h0RjISAR7OFw5FdoOtboqEa9RoBUREfFX2Zmw7DFYP93crn8+DJkFtZtbW5eIlynQioiI+KOjO+GDEXDoR3O78x3QOx5CHJaWJWIFBVoRERF/s+VdWDIe3GngjIFB06HV5VZXJWIZBVoRERF/kZkKn9wLP75rbje9BAbPhMhYa+sSsZgCrYiIiD84sNlcvjbxd7AFQY8JcOk9EBRsdWUillOgFRER8WWGAetnwLJHIScLIhvC4NehaRerKxPxGQq0IiIivirtGHx0B+z43NxufSVc9TK4YqytS8THKNCKiIj4ol1rYOHtkHoQgh3Q7ym46DYtXytSBAVaERERX5KTDasmw+pnAQNqtYDrZkH9NlZXJuKzgqwu4EyrV69m4MCBxMbGYrPZWLRoUYHjCxcupF+/ftSuXRubzcbmzZstqVNERKTKJe+DOVfC6imAAe1vgVGrFGZFSuFzgTYtLY22bdsybdq0Yo937dqVZ555xsuViYiIeNCvn8D0rrDnWwiNgMFvwNX/hdBwqysT8Xk+N+Sgf//+9O/fv9jjf//73wFISEjwUkUiIiIe5D4JSx+G72aa27HtYcibEHOWtXWJ+BGfC7SekJmZSWZmZv52SkoKAG63G7fbbVVZASHv/dX7HDh0zQOPrnklHP2NkA9vw3b4ZwByOo8lt8dDEBwKPv5+6roHHm9f8/I8T0AE2kmTJjFx4sRC+5cuXYrL5bKgosCzbNkyq0sQL9M1Dzy65uVgGDRJXEObfW9hy80iMySCTU3+yeHMtvDFcqurKxdd98DjrWuenp5e5nMDItBOmDCB8ePH52+npKTQuHFj+vbtS2RkpIWVVX9ut5tly5bRp08f7Ha71eWIF+iaBx5d83LKTCX4s3sI2rMQgNy4bgRd9QoXRtS3uLDy0XUPPN6+5nnfqJdFQARah8OBw+EotN9ut+uX0Ev0XgceXfPAo2teBvs3wgf/gKRdYAuGyx4iqOs4gvx4+Vpd98DjrWtenucIiEArIiJiqdxc+HYafDkRcrMhqom5fG2TTlZXJlIt+FygPXHiBDt37szf3rVrF5s3byYmJoYmTZqQmJjInj17OHDgAADbt28HoH79+tSv719f14iISAA4cQQWjYadp8bGnnOVuXyts6alZYlUJz43D+33339P+/btad++PQDjx4+nffv2PProowAsXryY9u3bM2DAAACGDh1K+/btmTFjhmU1i4hIADIMSDsGSbvN/zWMwuf8sRJmdDXDbEgYXPkfuP4thVmRKuZzPbQ9evTAKOqPwinDhw9n+PDh3itIRETkdBnHYct8WP+qORY2T3Qz6DQK2t5oLoawchKsmQoYUKc1DJkF9f5mVdUi1ZrPBVoRERGftXM5vHcruIuYTigpAT6fYI6TjWoMR3eY+zsMh36TIFTTRIp4igKtiIhIWexcDu9cf2poQVHfJJ7a584ww6zdBYNegXOv8WaVIgHJ58bQioiI+JyM42bPrGEAuWVsZMBZPT1YlIjkUaAVEREpzZb5p4YZlDXMAu6TsOVdj5UkIn9RoBURESmJYZg3gFXE+hlFz34gIlVKgVZERKQk6YmnZjMobzA1zHYZSZ6oSkROo0ArIiJSkqwTlWufmVo1dYhIsRRoRUREShJao3LtHRFVU4eIFEuBVkREpCSuGIhqUoGGNnOxBWd0lZckIgUp0IqIiJTk9y8h41jF2nYaDTZb1dYjIoVoYQUREZGiZGfBV0/ANy+d2pEXTMtwc5gtCEKc0Haop6oTkdOoh1ZERORMibtg1uV/hdmLboOh88ygWuo/nUGADW54G5w1PVuniADqoRURESlo6wL4eBxkpkBYFFw1Df52lXns5vfNFcPc6adOPr239lQPrt1phtnmvbxYtEhgU6AVEREByEqDzx6AH942txt3gsGvQ83Tbghr3hvGbzNXAFs/49T8tKdEx5ljZtvdaAZhEfEaBVoREZFDW+GDEXB0B2CDS++BHhMguIh/Jp01ofNo6DTKXDQhM9WcmssZrRvARCyiQCsiIoHLMOC71+GLhyAnE2rUh2tfg7O6l97WZjOn9HLFeL5OESmRAq2IiASm9ERYfBf8usTcbtEXBk2H8NrW1iUi5aZAKyIigWfPOvjgH5CyD4Ls0GcidBoDQZr8R8QfKdCKiEjgyM2BNVNh5SQwcsyVvK6bBbHtra5MRCpBgVZERAJDykFYeDskrDG321wPA56HsEhr6xKRSlOgFRGR6m/HF7BoDKQfA7vLDLJtb9SsBCLVhAKtiIhUX9mZsHwirPuvuV2/DQyZBbVbWFuXiFQpBVoREamejv0OH4yEg5vN7Y6joM/jYA+ztCwRqXoKtCIiUv1seQ8+GQ9ZJ8wFD67+L7QeYHVVIuIhCrQiIlJ9ZJ6AT++DLfPM7aZd4dqZENXQ2rpExKMUaEVEpHo4+KO5fO2xnWALgu4PQLf7ICjY6spExMMUaEVExL8ZBmx4DZY+DDlZEBELg2dC3CVWVyYiXqJAKyIi/is9ET4aC9s/Nbdb9odBr4Arxtq6RMSrFGhFRMQ/JayFBbdD6gEIDoU+T0CnUZpbViQAKdCKiIh/yc2BVVNg9RQwcqFWcxjyJjRoa3VlImIRBVoREfEfyfvN5Wt3f21ut70JrngWHDWsrUtELKVAKyIi/uHXT+GjOyAjCUJrwICp0PYGq6sSER+gQCsiIr7NfRKWPQobXjW3G7Q1l6+tdba1dYmIz1CgFRER33X0N3Nu2UM/mdudx0LvxyDEYW1dIuJTFGhFRMT3GAZsmQ+f3AvuNHDVgkHToWU/qysTER+kQCsiIr4lMxU+uQd+fM/cjrvUXL42soG1dYmIz1KgFRER33HgB/hgJCT+YS5f2+NBuHS8lq8VkRIp0IqIiPVyc2HdK7A8HnLdENkIBr8OTS+2ujIR8QMKtCIiYq20o7BoDPy21NxufSVc9bKWrxWRMlOgFRER6+xabS5fe+IQBDvg8qfhwn9o+VoRKRcFWhER8b6cbFj1DKx+DjCgditz+dr651ldmYj4IQVaERHxruN7YcFtsHedud3+79B/MoSGW1uXiPgtBVoREfGeXz6Gj8bCyWQIjYCBL0CbIVZXJSJ+ToFWREQqzjAg/RjOzCOQfgwi6xU9/tWdAUsfhu9eN7djLzCHGMQ08269IlItKdCKiEj5ZRw3V/Ja/yr2pF30Bdh2D0Q3g06joO2N4KxpnntkO/xvBBz+2dzu8n9w2SMQEmpN7SJS7SjQiohI+excDu/dCu70wseSEuDzCfDlE3D9W5B6AD69H7IzILwOXDMDmvf2eskiUr0p0IqISNntXA7vXG8ONcAo4oRT+9wZ8M6Qv7bP6gHXvAYR9bxTp4gElCCrCzjT6tWrGThwILGxsdhsNhYtWlTguGEYxMfHExsbi9PppEePHvz888/WFCsiEkgyjps9s4YB5JZyci75YbbbA3DLhwqzIuIxPhdo09LSaNu2LdOmTSvy+JQpU5g6dSrTpk3ju+++o379+vTp04fU1FQvVyoiEmC2zD81zKC0MHs6m7niV5DP/XMjItWIzw056N+/P/379y/ymGEYvPDCCzz00ENce+21AMyZM4d69eoxb948Ro0a5c1SRUQCh2HA+lcr1nb9DPNGMa3+JSIe4nOBtiS7du3i0KFD9O3bN3+fw+Gge/fufPPNN8UG2szMTDIzM/O3U1JSAHC73bjdbs8WHeDy3l+9z4FD17yaSj+GPWlXBRoakLQLd8phs6dWqg39rgceb1/z8jyPXwXaQ4cOAVCvXsFxWPXq1WP37t3Ftps0aRITJ04stH/p0qW4XK6qLVKKtGzZMqtLEC/TNa9enJlH6Fv6acVa8cXHZDjqVFk94jv0ux54vHXN09OLmEmlGH4VaPPYzvjayjCMQvtON2HCBMaPH5+/nZKSQuPGjenbty+RkZEeq1PM/7patmwZffr0wW63W12OeIGueTWVfsycZ7aCevYbqB7aaka/64HH29c87xv1svCrQFu/fn3A7Klt0KBB/v7Dhw8X6rU9ncPhwOFwFNpvt9v1S+gleq8Dj655NRNZz1w0ISmBoqfrKo4NouOwR9bVGNpqSr/rgcdb17w8z+FXt502a9aM+vXrF+jqzsrKYtWqVXTp0sXCykREqjmbDTqMoHxh9pROoxVmRcSjfK6H9sSJE+zcuTN/e9euXWzevJmYmBiaNGnCuHHjePrpp2nRogUtWrTg6aefxuVycdNNN1lYtYhINffnNvhhbvna2IIgxAlth3qmJhGRU3wu0H7//ff07Nkzfztv7OuwYcOYPXs2999/PxkZGdxxxx0kJSXRqVMnli5dSkREhFUli4hUX4YBG2eZy9lmn4SwmpCZcqqjtqT5aIMAG9zwNjhreqNSEQlgPhdoe/TogWEU/5WWzWYjPj6e+Ph47xUlIhKIMo7D4rvgl8XmdvPeMGgGHNpirhjmzrsD+fS/2aeGFtidZpht3suLBYtIoPK5QCsiIj5g7wb44B+QvAeCQqDXY3DxneaKX817w/htsOVdc9GE0+enjY4zx8y2uxHCoiwrX0QCiwKtiIj8JTcXvv4PfPUUGDlmQB38JjTqUPA8Z03oPBo6jcKdcpgVX3xMz34DNZuBiFhCgVZEREyph2DhP2HXKnP7vCFw5X8grIT5um02cMWYiya4YhRmRcQSCrQiIgK/LYcPR0H6UbC74Ipnod3NCqgi4hcUaEVEAll2Fnz1OHzzsrld7zwY8ibUaWVtXSIi5aBAKyISqBL/MG/8OrDJ3L7oduj7JNjDrK1LRKScFGhFRALRTx/Ax+MgK9WcW/bqaXDOQKurEhGpEAVaEZFAkpUGn93/16pfTS6Ga2dCzcbW1iUiUgkKtCIigeLQT/DBSDi6A7BB9/uh2/0QrH8KRMS/6a+YiEh1Zxjw3evwxUOQkwkRDeDa16BZN6srExGpEhUOtHv27KFmzZpERhY/P2FqaipJSUk0adKkok8jIiKVkZ5oLl/76xJzu0U/GDQdwmtZW5eISBUKqmjDZs2a8eKLL5Z4ziuvvEKzZs0q+hQiIlIZu7+BGZeaYTbIDv0mwU3vKcyKSLVT4R5awzAwDKPUc0RExMtyc2DN87ByEhi5EHO2ObdsbDurKxMR8QiPjqHdt28fERERnnwKERE5XcoBc/nahDXm9vlDYcBz4NDfYhGpvsoVaB9//PEC2ytXrizyvJycHPbt28e7775Lp06dKlyciIiUw/bPYdEYyEgEezgMeB7a3Wh1VSIiHleuQBsfH5///202GytXriw21ALExsYyefLkitYmIiJlkZ0Jyx6D9dPN7frnw5BZULu5tXWJiHhJuQLtihUrAHNs7GWXXcbw4cMZNmxYofOCg4OJiYmhdevWBAVV+L4zEfEzhmGQmJbFsZOQmJZF3agQbDab1WVVb0d3wgcj4NCP5nbnO6B3PIQ4LC1LRMSbyhVou3fvnv//H3vsMXr27Em3bprHUCTQJWe4WbBxH3O+SWB3YjoQwuM/rKRpjIthXeIY3KERUU671WVWP1vehSXjwZ0GzhhzOq5Wl1tdlYiI11X4prDHHnusKusQET+1ascRxszdSEZWTqFjexLTeWLJNp5bup3pt3Sge8s6FlRYDWWmwif3wo/vmttNL4HBMyEy1tq6REQsovEAIlJhq3YcYcSsDWS4czCAMyfqy9uX4c5hxKwNrNpxxPtFVjcHNsOr3c0wawuCHg/CsMUKsyIS0BRoRaRCkjPcjJm70QytpUw5bRhmsB0zdyPJGW5vlFf9GAasmw5v9IHE3yGyIQz/BHo8AEHBVlcnImIpBVoRqZAFG/eRkZVTapjNYxiQkZXDwk37PFtYdZR2DOYPhc//DTlZ0GoAjF4LTbtYXZmIiE9QoBWRcjMMgznfJFSo7eyvE7SKYHnsWgMzusKOzyHYAVc8B0PfAVeM1ZWJiPgMj64UJiLVU1K6+9RsBuVjALsT0zme7iY6PLTqC6tOcrJh1WRY/SxgQK0WcN0sqN/G6spERHyOAq2IlFtaZnal2p/IzFagLUnyPlhwG+z51txudwtcMQVCw62tS0TERynQiki5hTsq96ejRiXbV2u/fgKL7oCTxyE0Aga+AG2GWF1VsbSYhoj4Av2rIiLlFu2y0zTGxZ7E9EJTdZXEBjSJcVHTpUUWCnGfhKUPw3czze3Y9jDkTYg5y9q6iqHFNETEl+imMBEpN5vNxrAucRVqO7xrnHrwznRkB7ze668we/GdMHKpz4bZVTuOcPGkL3liyTb2nDGWOm8xjYsnfal5h0XEaxRoRaRCBndohDM0mLJm0yAbOEODufaCRp4tzJ8YBvwwF17rDn9uBVdtuPkD6PcUhPjmGGMtpiEivkiBVkQqJMppZ/otHbBBqaE27/iMWzroa+g8J1PMG78+GgvudGjWHcZ8DS36WF1ZsbSYhoj4KgVaEamw7i3rMGtER5z2YDPYnnE8b5/THszsER3p1rKO94v0Rfs3wqvdYOsHYAuGXo/C3z+EiPpWV1YiLaYhIr5KN4WJSKV0b1mHbyf0YuGmfcz+OqHA/LRNYlwM72reIBQZVs17Zg0D0hMh6wSE1jAXPjiz6zo3F76dBl9OhNxsiGoCg1+HJp2sqbkcKruYxvAuGjstIp6jQCsilRbltDOiazOGd4njSEo6Sz5fzpWX96ZOpKv6h5iM47BlPqx/FZJ2/bU/uhl0GgVtbwRnTThxBBaNhp3LzePnXAVXvQTOaCuqLjctpiEivkyBVkSqjM1mI9oVSq0wiHaFVv8wu3M5vHerOQb2TEkJ8PkE+PIJ6HYvrJ8BJ/6EkDC4fBJ0GFH64GMfosU0RMSXKdCKiFTEzuXwzvWn7o4qalDpqX3uNHOIAUCd1jBkFtT7m7eqrDJaTENEfJluChMRKa+M42bPrGEAuWVrExQCf1/kl2EW/lpMo7x9yjagqRbTEBEPU6AVESmvLfNPDTMoY5gFyM2BbR95rCRP02IaIuLLFGhFRMrDMMwbwCpi/YzSJ3D1YVpMQ0R8lQKtiEh5pCeems2gvMHUMNtlJHmiKq/QYhoi4qsUaEVEyiPrROXaZ6ZWTR0W0WIaIuKLdNupiEh5hNaoXHtHRNXUYSEtpiEivkaBVkSkPFwx5qIJSQmUb9iBDaLj/GYhhdIE9GIaIuJzNORARKQ8DAPqtKL8Y2iBTqP9ajGFsgi4xTRExCeph1ZEpKxS/4QPR8EfK8rXzhYEIU5oO9QzdYmIBDj10IqIlMXOL2FGVzPMhjih8x1gC6b0P6NBgA1ueBucNT1fp4hIAFIPrYhISbKz4Ksn4JuXzO2658KQN6Fua2jey1wxzJ13U9TpwxBOffVud5phtnkvb1YtIhJQFGhFRIqTuAsW/AP2bzS3L7oN+j5phlSA5r1h/DbY8q65aELSrr/aRseZY2bb3QhhUV4vXUQkkPhloE1NTeWRRx7hww8/5PDhw7Rv354XX3yRiy66yOrSRKS62LoAPh4HmSlmIL36v3DOwMLnOWtC59HQaZS5aEJmqjk1lzO62t0AJiLiq/wy0N52221s3bqVt99+m9jYWObOnUvv3r3Ztm0bDRs2tLo8EfFnWenw+QOw6S1zu3FnGDwTajYpuZ3NZk7p5YrxfI0iIlKA390UlpGRwYIFC5gyZQrdunWjefPmxMfH06xZM6ZPn251eSLiz/78GV7rcSrM2qDbfTD8k9LDrIiIWMrvemizs7PJyckhLCyswH6n08natWuLbJOZmUlmZmb+dkpKCgButxu32+25YiX//dX7HDj88pobBkGbZhG07BFsOZkYNeqRc/V0jLhukGtArh+9Fgv45TWXStN1DzzevubleR6bYRgVmB3cWl26dCE0NJR58+ZRr1495s+fz6233kqLFi3Yvn17ofPj4+OZOHFiof3z5s3D5XJ5o2QR8VH27DTa7XmD2OTvATgU2ZYfmtxOlj3S4spERAJbeno6N910E8nJyURGlvw32S8D7e+//87IkSNZvXo1wcHBXHDBBbRs2ZJNmzaxbdu2QucX1UPbuHFjjh49WuobJJXjdrtZtmwZffr0wW7Xuu6BwJ+uuW3veoIXjcKWsg8jyE7uZY+S27H6reblaf50zaXq6LoHHm9f85SUFGrXrl2mQOt3Qw4Azj77bFatWkVaWhopKSk0aNCAG264gWbNmhV5vsPhwOFwFNpvt9v1S+gleq8Dj09f89wcWDsVVkwCIwdizsI25E2CY9sTbHVtfsynr7l4jK574PHWNS/Pc/hloM0THh5OeHg4SUlJfPHFF0yZMsXqkkTE16UchIW3Q8Iac/v8G2DA8+ZUWyIi4pf8MtB+8cUXGIZBq1at2LlzJ/fddx+tWrVixIgRVpcmIr5sx1JYNBrSj4E93Ayy7W60uioREakkvwy0ycnJTJgwgX379hETE8PgwYN56qmn9JWHiBQtOwu+nAjfTjO367eBIbOgdgtr6xIRkSrhl4H2+uuv5/rrr7e6DBHxB8d+hw9GwsHN5nan0dDncQgpPK5eRET8k18GWhGRMvnxfVjyL8g6YS5Fe/Ur0PoKq6sSEZEqpkArItVP5gn49D7YMs/cbtoVrp0JUVoaW0SkOlKgFZHq5eCP8MEIOLYTbEHQ/QFzCdsgTcglIlJdKdCKSPVgGLDhNVj6MORkQUQsDH4d4rpaXZmIiHiYAq2I+L/0RPhoLGz/1NxudQVc/V9wxVhbl4iIeIUCrYj4t4SvzYUSUvZDcCj0fRI6/lPL14qIBBAFWhHxT7k5sPpZWDUZjFyo1dycW7bB+VZXJiIiXqZAKyL+J3k/LPwn7F5rbre7GfpPAUcNa+sSERFLKNCKiH/59VP46A7ISILQGnDlf+B8LbQiIhLIFGhFxD9kZ8KyR2H9DHO7QTsY8ibUOtvSskRExHoKtCLi+47+Zs4te+gnc/viO6HXYxASam1dIiLiExRoRcS3bZ4Pn9wD7jRw1YJBM6BlX6urEhERH6JAKyK+KTPVDLI/vmdux11qLl8b2cDaukRExOco0IqI7znwA3wwEhL/AFsw9JwAl4z36eVrDcMgKd1NWmY24Y4Qol12bJoLV0TEKxRoRcR3GAasewWWPQa5bohqbC5f26Sz1ZUVKznDzYKN+5jzTQK7E9Pz9zeNcTGsSxyDOzQiymm3sEIRkepPgVZEfEPaUVg0Bn5bam63vhKungbOaGvrKsGqHUcYM3cjGVk5hY7tSUzniSXbeG7pdqbf0oHuLetYUKGISGAIsroAERF2rYbpXc0wG+yAAc/DDXN9PsyOmLWBDHcOBmCccTxvX4Y7hxGzNrBqxxHvFykiEiAUaEXEOjnZ8NWTMOcqOHEIareC27+Ci24DHx5/mpzhZszcjWZoPTPJnsEwzGA7Zu5GkjPc3ihPRCTgKNCKiDWO74XZA2D1s4ABF9wK/1wB9c+zurJSLdi4j4ysnFLDbB7DgIysHBZu2ufZwkREApQCrYhUHcOA9GM4M49A+rHiuy9/+RhmXAJ714Ej0lzx66qXITTcu/VWgGEYzPkmoUJtZ3+dgFHWFCwiImWmm8JEpPIyjsOW+bD+VexJu+gLsO0eiG4GnUZB2xvBWRPcJ2HpQ/Dd62a7hh1g8BsQ08y62sspKd1dYDaDsjKA3YnpHE93Ex2uFc5ERKqSAq2IVM7O5fDereAuIuQlJcDnE+DLJ+Dyp2HDTPhzq3ms693Q82G/W742LTO7Uu1PZGYr0IqIVDEFWhGpuJ3L4Z3rTw0tKOqr9FP73Onw8d3m/w+vA9fMgOa9vVVllQp3VO7PZo1KthcRkcI0hlZEKibjuNkzaxhAbiknnwq2tiAY/pnfhlmAaJedpjEuyjsHgw1zsYWaLi2yICJS1RRoRaRitsw/NcygtDB7GsOA37/0WEneYLPZGNYlrkJth3eN03K4IiIeoEArIuVnGLD+1Yq1XT+j9MlbfdzgDo1whgaXearcIBs4Q4O59oJGni1MRCRAKdCKSPmlJ0LSLooeN1sSw2yXkeSJqrwmymln+i0dsFH6+g95x2fc0oEop4YbiIh4ggKtiJRf1onKtc9MrZo6LNS9ZR1mjeiI0x5sBtszjuftc9qDmT2iI91a1vF+kSIiAUK324pI+YXWqFx7R0TV1GGx7i3r8O2EXizctI/ZXycUmJ+2SYyL4V3jGNyhEZFh6pkVEfEkBVoRKT9XjLloQlIC5Rt2YIPoOHBGe6YuC0Q57Yzo2ozhXeI4nu7mRGY2NRwh1HTZdQOYiIiXKNCKSPnZbND04lPjaMup0+jSB576IZvNRnR4qBZNEBGxgMbQikj5ZJ1aJGHzvPK1swWB3QVth3qmLhERCVgKtCJSdod/gZmXwcbZgA3OGwy2YEr/UxJknn/D2+Cs6ekqRUQkwCjQikjpDAO+fxNe6wFHfoEa9eDWRTDkTbj5fbA7oaR7/e1OuPl/0LyXtysXEZEAoDG0IlKyjOPw8f/Bto/M7ea9YdAMqFHnr+3x22DLu+aiCaePq42OM8fMtrsRwqK8XbmIiAQIBVoRKd7eDfDBPyB5DwSFQO946DwWgs74csdZEzqPhk6jcKccZsUXH9Oz30DskXWr5Q1gIiLiWxRoRaSw3Fz4+j/w1VNg5Jg9rUPehIYdSm5ns4ErhgxHHXNqL4VZERHxAgVaESko9U/48J/wx0pz+7whcOV/ICzS0rJERESKo0ArIn/5bTl8OArSj5pTbF3xLLS7WT2tIiLi0xRoRQSys+Crx+Gbl83teufBkFlQp6W1dYmIiJSBAq1IoEvcBR+MhAObzO2Lboe+T4I9zNq6REREykiBViSQ/fQBfDwOslIhrCZc/V8450qrqxIRESkXBVqRQJSVBp/dDz/MNbebXAyDX4eoRtbWJSIiUgEKtCKB5tBW+GAEHN0B2KD7/dDtfgjWnwMREfFP+hdMJFAYBnz3OnzxEORkQkQDuHYmNLvU6spEREQqRYFWJBCkJ8Liu+DXJeZ2y8vh6lcgvJa1dYmIiFQBBVqR6m73t7DgNkjZB8Gh0Odx6DRac8uKiEi1EVT6Kb4lOzubhx9+mGbNmuF0OjnrrLN4/PHHyc3Ntbo0Ed+SmwOrpsDsK8wwG3M2/GMZdB6jMCsiItWK3/XQTp48mRkzZjBnzhzOPfdcvv/+e0aMGEFUVBR333231eWJ+IaUA7Dwn5Cwxtw+fygMeA4cEdbWJSIi4gF+F2i//fZbrr76agYMGABAXFwc8+fP5/vvv7e4MhEfsf1zWDQGMhLBHg4Dnod2N1pdlYiIiMf4XaC95JJLmDFjBjt27KBly5Zs2bKFtWvX8sILLxTbJjMzk8zMzPztlJQUANxuN26329MlB7S891fvsxdkZxK04nGCN7wKgFGvDdnXzIRazcGL77+ueeDRNQ9Muu6Bx9vXvDzPYzMMw/BgLVXOMAwefPBBJk+eTHBwMDk5OTz11FNMmDCh2Dbx8fFMnDix0P558+bhcrk8Wa6IV4SfPMSFCa9QMyMBgN/r9GNb7PXkBtmtLUxERKSC0tPTuemmm0hOTiYyMrLEc/0u0L777rvcd999PPvss5x77rls3ryZcePGMXXqVIYNG1Zkm6J6aBs3bszRo0dLfYOkctxuN8uWLaNPnz7Y7QpXnmD76X2CP78PW1YahjOGnIEvY7ToZ1k9uuaBR9c8MOm6Bx5vX/OUlBRq165dpkDrd0MO7rvvPv79738zdOhQANq0acPu3buZNGlSsYHW4XDgcDgK7bfb7fol9BK91x6QeQI+vRe2zDe3m16CbfBMQiJjra3rFF3zwKNrHph03QOPt655eZ7D7wJteno6QUEFZxsLDg7WtF3iMwzDICndTVpmNuGOEKJddmxVPU3WwS3wvxGQ+DvYgqDHBLj0HggKrtrnERER8QN+F2gHDhzIU089RZMmTTj33HP54YcfmDp1KiNHjrS6NAlwyRluFmzcx5xvEtidmJ6/v2mMi2Fd4hjcoRFRzkr+F61hwPoZsOxRyMmCyIYw+HVo2qWS1YuIiPgvvwu0L7/8Mo888gh33HEHhw8fJjY2llGjRvHoo49aXZoEsFU7jjBm7kYysnIKHduTmM4TS7bx3NLtTL+lA91b1qnYk6Qdg4/Gwo7PzO1WA+DqaeCKqUTlIiIi/s/vAm1ERAQvvPBCidN0iXjTqh1HGDFrAwZQ1B2Wefsy3DmMmLWBWSM6lj/UJqw1l69NPQjBDuj3FFx0m1b8EhERwQ+XvhXxJckZbsbM3WiG2VLmCzEMM9yOmbuR5Iwyzq2Xkw0rnoY5A80wW6sF3P4ldLxdYVZEROQUBVqRSliwcR8ZWTmlhtk8hgEZWTks3LSv9JOT95lBdtVkMHKh/S0wahXUb1O5okVERKoZBVqRCjIMgznfJFSo7eyvEyhxCuhfP4EZl8CebyA0Aga/AVf/F0LDK1asiIhINeZ3Y2hFfEVSurvAbAZlZQC7E9M5nu4mOjy04EH3SVj2CGx4zdyObQ9D3oSYsypfsIiISDWlQCtSQWmZ2ZVqfyIzu2CgPbIDPhgJf/5kbl98J/R6DEJCi34AERERARRoRSos3FG5X58aee0NAza/A5/eB+50cNWGa2ZAiz5VUKWIiEj1p0ArUkHRLjtNY1zsSUwvcrqu4tiAJjEuarrscDIFPhkPP/3PPNisO1z7GkTU90TJIiIi1ZJuChOpIJvNxrAucUUcMYgmhUa2I0STQlGz0w7vGoftwA/wajczzNqCodej8PcPFWZFRETKST20IpUwuEMjnlu6nQx3DhFGGoODVzMseClxQX/mn5OQW485OX1ZkNONE7ZwnHYbQ7M/gjeehFw3RDUxl69t0snCVyIiIuK/1EMrUglRTjvTb+lAN9sWvnXcySMhc2liO1zgnCa2wzwSMpdvHXcywPYtaxrOwLniMTPMnnMVjF6tMCsiIlIJ6qEVqaTuQVvo5niW3FyDIFvh4QV5+5xGJi+FvoztIBASBpdPgg4jtOKXiIhIJSnQilRGxnF471ZsBgQXEWZPF5SfW21w62L1yoqIiFQRDTkQqYwt882ptsgtX7sDP3ikHBERkUCkQCtSUYYB61+tWNv1M8z2IiIiUmkKtCIVlZ4ISbsoalqukhlmu4wkT1QlIiIScBRoRSoq60Tl2memVk0dIiIiAU6BVqSiQmtUrr0jomrqEBERCXAKtCIV5YqB6GaYi9mWh81s54z2RFUiIiIBR4FWpKJsNmjZn/KPoQU6jdb8syIiIlVE89CKVERuLnzzImwo5ywHtiAIcULboZ6pS0REJAAp0IqUV+qf8OEo+GOFud20K+xZd2oarpLmow0CbHDD2+Cs6fk6RUREAoSGHIiUx84vYUZXM8yGOOGql2H4J3Dz+2B3Yo6nPXMowal9difc/D9o3sv7dYuIiFRj6qEVKYscN3z1BHz9orld91wY8ibUbW1uN+8N47fBlnfNRROSdv3VNjrOHDPb7kYIi/J66SIiItWdAq1IaRJ3wYJ/wP6N5vZFt0HfJ0/1yJ7GWRM6j4ZOo8xFEzJTzam5nNG6AUxERMSDFGhFSrJ1AXw8DjJTzN7Vq6bB364quY3NZk7p5YrxSokiIiKBToFWpChZ6fD5A7DpLXO7cScY/DrUbGJtXSIiIlKIAq3Imf78Gf43Ao5uB2xw6T3QYwIE69dFRETEF+lfaJE8hgHfvwFfPATZJ6FGfbj2NTiru9WViYiISAkUaEXAvIlr8V3wy8fmdou+MGg6hNe2ti4REREplQKtyJ51sOA2SN4LQXboMxE6jYEgTdMsIiLiDxRoJXDl5sDaqbBiEhg5EN0MrpsFse2trkxERETKQYFWAlPKQVh4OySsMbfbXA8DnoewSGvrEhERkXJToJXAs2MpLBoN6cfAHg4DnoO2N2rxAxERET+lQCuBIzsLvpwI304zt+u3gSGzoHYLa+sSERGRSlGglcBw7Hf4YCQc3GxudxoNvSeCPczSskRERKTyFGil+vvxfVjyL8g6Ac5ouPoVaH2F1VWJiIhIFVGgleor8wR8eh9smWduN+0K186EqIbW1iUiIiJVSoFWqqeDP8IHI+DYTrAFQfcHoNt9EBRsdWUiIiJSxRRopXoxDNjwGix9GHKyICIWBr8OcV2trkxEREQ8RIFWqo/0RPhoLGz/1Nxu2R8GvQKuGGvrEhEREY9SoJXqIeFrc6GElP0QHAp9n4SO/9TcsiIiIgFAgVb8W24OrH4WVk0GIxdqNYchb0KDtlZXJiIiIl6iQCv+K3m/2Su7+2tzu93N0H8KOGpYW5eIiIh4lQKt+KdfP4WP7oCMJAitAVf+B86/3uqqRERExAIKtOJfsjNh2aOwfoa53aCdOcSg1tmWliUiIiLWUaAV/3H0N3Nu2UM/mdsX3wm9HoOQUGvrEhEREUsp0IrvMwzYMh8+uRfcaeCqBYNmQMu+VlcmIiIiPiDI6gIqIi4uDpvNVuhn7NixVpcmVS0zFT4cBYvGmGE27lIY/bXCrIiIiOTzyx7a7777jpycnPztrVu30qdPH6677joLq5Iqd+AH+GAkJP4BtmDoOQEuGa/la0VERKQAvwy0derUKbD9zDPPcPbZZ9O9e3eLKpIqlZsL616B5fGQ64aoxubytU06W12ZiIiI+CC/DLSny8rKYu7cuYwfPx5bMatCZWZmkpmZmb+dkpICgNvtxu12e6XOQJX3/pb5fU47SvDHdxL0+3IAclsNIGfAi+CsCbpWfqHc11z8nq55YNJ1DzzevubleR6bYRiGB2vxuPfff5+bbrqJPXv2EBsbW+Q58fHxTJw4sdD+efPm4XK5PF2ilFHt1G10SJhBWPZxcmx2tja8iYTal2n5WhERkQCUnp7OTTfdRHJyMpGRkSWe6/eBtl+/foSGhvLxxx8Xe05RPbSNGzfm6NGjpb5BUjlut5tly5bRp08f7HZ70SflZhO0+lmCvp6KDQOjdkuyB82Eeud6t1ipEmW65lKt6JoHJl33wOPta56SkkLt2rXLFGj9esjB7t27Wb58OQsXLizxPIfDgcPhKLTfbrfrl9CTDAPSj+HMPILdnYLdWa9wb+vxvbDgNti7ztxu/3ds/SdjDw33fr1SpfT7FXh0zQOTrnvg8dY1L89z+HWgnTVrFnXr1mXAgAFWlyKnyzhuzhu7/lXsSbvoC7DtHohuBp1GQdsbzTGx2xbD4jvhZDI4ImHgC3DeYEtLFxEREf/jt4E2NzeXWbNmMWzYMEJC/PZlVD87l8N7t4I7vfCxpAT4fAJ8+bg5n+xvX5j7G3aAwW9ATDOvlioiIiLVg98mweXLl7Nnzx5GjhxpdSmSZ+dyeOd6c6gBRQ3NPrXPnf5XmO16N/R8WMvXioiISIX5baDt27cvfn4/W/WScdzsmTUMILdsbUIc5kIJCrMiIiJSCX659K34oC3zTw0zKGOYBcjOgi3veqwkERERCQwKtFJ5hgHrX61Y2/UzTvXqioiIiFSMAq1UXnoiJO2i6HGzJTHMdhlJnqhKREREAoQCrVRe1onKtc9MrZo6REREJCAp0ErlhdaoXHtHRNXUISIiIgFJgVYqzxUD0XEVaGgzF1twRld1RSIiIhJAFGil8o7vhtycirXtNLrwcrgiIiIi5eC389CKj/j5Q1j8f5CZcmqHjTLdHGYLghAntB3qyepEREQkAKiHViomK90Msv8bbobZRh1h0HQzqJb6sQoCbHDD2+Cs6fFSRUREpHpTD62U35/b4IMRcORXwAaXjoceEyDYDjXqmiuGudNPnXx6b+2poQV2pxlmm/fycuEiIiJSHSnQStkZBmycBZ9PgOyTUKMeXPsanNXjr3Oa94bx28wVwNbPODU/7SnRceaY2XY3QliUt6sXERGRakqBVsom4zgsvgt+WWxuN+9jDjGoUafwuc6a0Hk0dBqFO+UwK774mJ79BmKPrKsbwERERKTKKdBK6fZugA/+Acl7ICgEesdD57EQVMpYWZsNXDFkOOqYU3spzIqIiIgHKNBK8XJzYO1/YMXTYOSYQwaGvAkNO1hdmYiIiEg+BVopWuohWPhP2LXK3D5vCFz5HwiLtLYuERERkTMo0Ephvy2HD0dB+lGwu+CKZ6HdzRoyICIiIj5JgVb+kp0FXz0O37xsbtc7D4bMgjotra1LREREpAQKtGJK/MO88evAJnO74z+hzxNgD7O2LhEREZFSKNAK/PQBfDwOslIhrCZc/V8450qrqxIREREpEwXaQJaVBp/dDz/MNbebXAyDX4eoRtbWJSIiIlIOCrSB6tBP8MFIOLoDsEH3+6Hb/RCsj4SIiIj4F6WXQGMY8N3r8MVDkJMJEQ3g2pnQ7FKrKxMRERGpEAXaQJKeaC5f++sSc7vl5XD1KxBey9q6RERERCpBgTZQ7P4WFtwGKfsgOBT6PA6dRmtuWREREfF7CrTVXW4OrHkeVk4CIxdizjaXr41tZ3VlIiIiIlVCgbY6SzlgLl+bsMbcPn8oDHgOHBHW1iUiIiJShRRoq6vtn8OiMZCRCPZwGPA8tLvR6qpEREREqpwCbXWTnQnLHoP1083t+ueby9fWbm5tXSIiIiIeokBbnRz7Hf43HA79aG53vgN6x0OIw8qqRERERDxKgba62PIufHIPZJ0AZwwMmg6tLre6KhERERGPU6D1d5kn4NN7Yct8c7vpJTB4JkTGWlsXYBgGiWlZHDsJiWlZ1I0KwaZpwkRERKSKKdD6swObzeVrE38HWxD0mACX3gNBwZaWlZzhZsHGfcz5JoHdielACI//sJKmMS6GdYljcIdGRDntltYoIiIi1YcCrT8yDFg/A5Y9CjlZENkQBr8OTbtYXRmrdhxhzNyNZGTlFDq2JzGdJ5Zs47ml25l+Swe6t6xjQYUiIiJS3QRZXYCUU9oxmH8jfP5vM8y2vhJGr/WZMDti1gYy3DkYgHHG8bx9Ge4cRszawKodR7xfpIiIiFQ7CrT+ZNcamNEVdnwGwQ644jm4YS64YqyujOQMN2PmbjRD65lJ9gyGYQbbMXM3kpzh9kZ5IiIiUo0p0PqDnGxY8TTMGQipB6FWC7j9S+h4O/jITVYLNu4jIyun1DCbxzAgIyuHhZv2ebYwERERqfYUaH1d8j4zyK6aDBjQ/hYYtQrqt7G6snyGYTDnm4QKtZ39dQJGWVOwiIiISBF0U5gv+/UTWHQHnDwOoREw8AVoM8TqqgpJSnefms2gfAxgd2I6x9PdRIeHVn1hIiIiEhAUaH2R+yQsewQ2vGZux7aHIW9CzFnW1lWMtMzsSrU/kZmtQCsiIiIVpkDra47sMOeW/fMnc7vLXXDZoxDiu4Ev3FG5j1GNSrYXERGRwKYk4WGGYZCU7iYtM5twRwjRLnvRq2UZBmx+Bz69D9zp4KoN18yAFn28X3Q5RbvsNI1xsScxvdBUXSWxAU1iXNR0aZEFERERqTgFWg/JXy3r612kJP1JuC2TNMNBZHQ9hnVtVnC1rJMp8Ml4+Ol/5naz7nDtaxBR37oXUA42m41hXeJ4Ysm2crcd3jVOy+GKiIhIpSjQesCqHUe4f+5qrshZwZzgpcSF/Zl/LCGtHnM+60vfpT2Zcks3uofvNYcYJO0CWzBc9hB0HWf58rXlNbhDI55but1cVKEM3bRBNgizB3PtBY08X5yIiIhUawq0VWzVjiO8OXsmX9lfwBmSVeh4E9thHgmZy728z+K3u5BrX0tQrhuimpjL1zbpZEHVlRfltDP9lg6MmLUBbCUvrpDXITvjlg5/9VKLiIiIVJDmoa1CyRlu5s59gzftzxJGFkE2gyBbwWSXt89FJjcGryAo14275UAYvcZvw2ye7i3rMGtER5z2YGyYY2RPl7fPaQ9m9oiOdGtZx/tFioiISLWjHtoq9PG6bbxgmwoYBNtK/t7ddqoXM4sQPmhwHzc7a3qlRk/r3rIO307oxcJN+5j9dUKB+WmbxLgY3jWOwR0aERmmnlkRERGpGgq0VcQwDBK/mYPzVM9sWdhsYDdyOPbNWxg9nqs2N0dFOe2M6NqM4V3iOJKSzpLPl3Pl5b2pE+mqNq9RREREfIdfDjnYv38/t9xyC7Vq1cLlctGuXTs2btxoaU1JaVlclbkEyjVxlemqzI85nlZ4vK2/s9lsRLtCqRUG0a5QhVkRERHxCL/roU1KSqJr16707NmTzz77jLp16/L7779Ts2ZNS+vKSD5MXNCfpZ94hiCbQZztT/YnHyG6hu74FxERESkvvwu0kydPpnHjxsyaNSt/X1xcXIltMjMzyczMzN9OSUkBwO1243a7q6Su0Oy0SrV35Jyoslp8Sd5rqo6vTYqmax54dM0Dk6574PH2NS/P89gMoyyzhvqOv/3tb/Tr1499+/axatUqGjZsyB133MHtt99ebJv4+HgmTpxYaP+8efNwuVxVUpfdncoVW8dWuP0n5/2XbHtEldQiIiIi4u/S09O56aabSE5OJjIyssRz/S7QhoWFATB+/Hiuu+46NmzYwLhx43j11Ve59dZbi2xTVA9t48aNOXr0aKlvUJkZBun/aU9E+v4y3xQGkGvYSHU1xPWvH/6aoLUacbvdLFu2jD59+mC3a2aDQKBrHnh0zQOTrnvg8fY1T0lJoXbt2mUKtH435CA3N5cLL7yQp59+GoD27dvz888/M3369GIDrcPhwOFwFNpvt9ur9IKEdhkDyx+uQLs7sIeGVlkdvqiq32vxfbrmgUfXPDDpugceb13z8jyH381y0KBBA/72t78V2HfOOeewZ88eiyr6i/PCWzDsTnKMsvW05hg2DLsT54U3e7gyERERkerL7wJt165d2b59e4F9O3bsoGnTphZVdBpnTYKHvk1QkK3UUJtj2AgKshE8dC5Uk0UVRERERKzgd4H2X//6F+vWrePpp59m586dzJs3j9dee42xYyt+Q1aVat4b283/IyjUiYGN3DMWgM3FhoGNoFAntps/gOa9LCpUREREpHrwu0B70UUX8eGHHzJ//nzOO+88nnjiCV544QVuvtmHvrZv3hvb+F+wXf4Mtui4Aods0XHm/nt+VZgVERERqQJ+d1MYwJVXXsmVV15pdRklc9aEzqOxdRoFGUmQmQqOCGzO6Go5m4GIiIiIVfwy0PoVmw1cMeaPiIiIiFQ5vxtyICIiIiJyOgVaEREREfFrCrQiIiIi4tcUaEVERETErynQioiIiIhfU6AVEREREb+mQCsiIiIifk2BVkRERET8mgKtiIiIiPg1BVoRERER8WsBufStYRgApKSkWFxJ9ed2u0lPTyclJQW73W51OeIFuuaBR9c8MOm6Bx5vX/O8nJaX20oSkIE2NTUVgMaNG1tciYiIiIiUJDU1laioqBLPsRllib3VTG5uLgcOHCAiIgKbzWZ1OdVaSkoKjRs3Zu/evURGRlpdjniBrnng0TUPTLrugcfb19wwDFJTU4mNjSUoqORRsgHZQxsUFESjRo2sLiOgREZG6g9egNE1Dzy65oFJ1z3wePOal9Yzm0c3hYmIiIiIX1OgFRERERG/pkArHuVwOHjsscdwOBxWlyJeomseeHTNA5Oue+Dx5WsekDeFiYiIiEj1oR5aEREREfFrCrQiIiIi4tcUaEVERETErynQioiIiIhfU6CVKjdp0iQuuugiIiIiqFu3LoMGDWL79u1WlyVeNGnSJGw2G+PGjbO6FPGw/fv3c8stt1CrVi1cLhft2rVj48aNVpclHpKdnc3DDz9Ms2bNcDqdnHXWWTz++OPk5uZaXZpUodWrVzNw4EBiY2Ox2WwsWrSowHHDMIiPjyc2Nhan00mPHj34+eefrSn2FAVaqXKrVq1i7NixrFu3jmXLlpGdnU3fvn1JS0uzujTxgu+++47XXnuN888/3+pSxMOSkpLo2rUrdrudzz77jG3btvH8889Ts2ZNq0sTD5k8eTIzZsxg2rRp/PLLL0yZMoVnn32Wl19+2erSpAqlpaXRtm1bpk2bVuTxKVOmMHXqVKZNm8Z3331H/fr16dOnD6mpqV6u9C+atks87siRI9StW5dVq1bRrVs3q8sRDzpx4gQXXHABr7zyCk8++STt2rXjhRdesLos8ZB///vffP3116xZs8bqUsRLrrzySurVq8cbb7yRv2/w4MG4XC7efvttCysTT7HZbHz44YcMGjQIMHtnY2NjGTduHA888AAAmZmZ1KtXj8mTJzNq1ChL6lQPrXhccnIyADExMRZXIp42duxYBgwYQO/eva0uRbxg8eLFXHjhhVx33XXUrVuX9u3bM3PmTKvLEg+65JJL+PLLL9mxYwcAW7ZsYe3atVxxxRUWVybesmvXLg4dOkTfvn3z9zkcDrp3784333xjWV0hlj2zBATDMBg/fjyXXHIJ5513ntXliAe9++67bNq0ie+++87qUsRL/vjjD6ZPn8748eN58MEH2bBhA//3f/+Hw+Hg1ltvtbo88YAHHniA5ORkWrduTXBwMDk5OTz11FPceOONVpcmXnLo0CEA6tWrV2B/vXr12L17txUlAQq04mF33nknP/74I2vXrrW6FPGgvXv3cvfdd7N06VLCwsKsLke8JDc3lwsvvJCnn34agPbt2/Pzzz8zffp0Bdpq6r333mPu3LnMmzePc889l82bNzNu3DhiY2MZNmyY1eWJF9lstgLbhmEU2udNCrTiMXfddReLFy9m9erVNGrUyOpyxIM2btzI4cOH6dChQ/6+nJwcVq9ezbRp08jMzCQ4ONjCCsUTGjRowN/+9rcC+8455xwWLFhgUUXiaffddx///ve/GTp0KABt2rRh9+7dTJo0SYE2QNSvXx8we2obNGiQv//w4cOFem29SWNopcoZhsGdd97JwoUL+eqrr2jWrJnVJYmH9erVi59++onNmzfn/1x44YXcfPPNbN68WWG2muratWuhKfl27NhB06ZNLapIPC09PZ2goILRITg4WNN2BZBmzZpRv359li1blr8vKyuLVatW0aVLF8vqUg+tVLmxY8cyb948PvroIyIiIvLH20RFReF0Oi2uTjwhIiKi0Bjp8PBwatWqpbHT1di//vUvunTpwtNPP83111/Phg0beO2113jttdesLk08ZODAgTz11FM0adKEc889lx9++IGpU6cycuRIq0uTKnTixAl27tyZv71r1y42b95MTEwMTZo0Ydy4cTz99NO0aNGCFi1a8PTTT+Nyubjpppssq1nTdkmVK24MzaxZsxg+fLh3ixHL9OjRQ9N2BYAlS5YwYcIEfvvtN5o1a8b48eO5/fbbrS5LPCQ1NZVHHnmEDz/8kMOHDxMbG8uNN97Io48+SmhoqNXlSRVZuXIlPXv2LLR/2LBhzJ49G8MwmDhxIq+++ipJSUl06tSJ//73v5Z2YCjQioiIiIhf0xhaEREREfFrCrQiIiIi4tcUaEVERETErynQioiIiIhfU6AVEREREb+mQCsiIiIifk2BVkRERET8mgKtiIiIiPg1BVoRERER8WsKtCIiIiLi1xRoRURERMSvKdCKiIiIiF9ToBUR8UOGYdC3b19sNhsLFiwocCw3N5fLLrsMm83GokWLrClQRMSLbIZhGFYXISIi5Xfo0CHOP/98srOz2bJlC40bNwbgmWeeYcKECYwaNYoZM2ZYXKWIiOcp0IqI+LElS5YwcOBALr30UlauXMmmTZvo0qULzZs3Z+PGjTidTqtLFBHxOAVaERE/d9dddzFt2jQeeOABFi5cyO7du1m/fj3t2rWzujQREa9QoBUR8XMnT56kY8eO/PTTTwBMnTqVf/3rXxZXJSLiPbopTETEz4WFhdG/f38AXC4XI0eOtLgiERHvUqAVEfFz3377LVOnTqVWrVqkp6czduxYq0sSEfEqBVoRET+WkpLCzTffjN1uZ/Xq1Vx99dW88847zJ071+rSRES8RmNoRUT82M0338y8efOYPn06o0eP5tixY5x//vmkpqayefNmzjrrLKtLFBHxOPXQioj4qbfffpt58+Zx1VVXMXr0aABq1arFnDlzOHHiBDfffDPZ2dkWVyki4nkKtCIifuiPP/5g7NixNGjQgDfeeKPAsd69e3PPPfewbt064uPjrSlQRMSLNORARERERPyaemhFRERExK8p0IqIiIiIX1OgFRERERG/pkArIiIiIn5NgVZERERE/JoCrYiIiIj4NQVaEREREfFrCrQiIiIi4tcUaEVERETErynQioiIiIhfU6AVEREREb+mQCsiIiIifu3/AaFtqA/GywqQAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x, data_t, \".\", markersize=20, label=\"Data\")\n", + "ax.plot(data_x, MLP_prediction, \"-o\", markersize=10, label=\"Prediction\")\n", + "\n", + "# Add a title and axis labels\n", + "ax.set_title(\"Linear Data Example\", fontsize=16)\n", + "ax.set_xlabel(\"x\", fontsize=14)\n", + "ax.set_ylabel(\"t\", fontsize=14)\n", + "\n", + "# Add a legend\n", + "ax.legend(fontsize=14)\n", + "\n", + "# Add a grid\n", + "ax.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then print the weights of the model. How do they compare to the original slope (0.8) and intercept (4.75)?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model coefficients: [array([[0.79310664]])]\n", + "Model intercepts: [array([5.23602641])]\n" + ] + } + ], + "source": [ + "print(f'Model coefficients: {model.coefs_}')\n", + "print(f'Model intercepts: {model.intercepts_}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.3: Increase MLP complexity</b> \n", + "\n", + "Now let us see if it is a good idea to use a much more flexible model. Initialize and train another MLP, but this time with **five hidden layers with 50 units each** and **ReLU** activation.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0/10000, MSE: 84.9590, Real loss: 77.3879\n", + "Epoch: 1000/10000, MSE: 0.3974, Real loss: 1.2025\n", + "Epoch: 2000/10000, MSE: 0.3944, Real loss: 1.1964\n", + "Epoch: 3000/10000, MSE: 0.3958, Real loss: 1.2606\n", + "Epoch: 4000/10000, MSE: 0.3827, Real loss: 1.0716\n", + "Epoch: 5000/10000, MSE: 0.0003, Real loss: 1.5321\n", + "Epoch: 6000/10000, MSE: 0.0000, Real loss: 1.5310\n", + "Epoch: 7000/10000, MSE: 0.0001, Real loss: 1.5645\n", + "Epoch: 8000/10000, MSE: 0.0000, Real loss: 1.5144\n", + "Epoch: 9000/10000, MSE: 0.0000, Real loss: 1.5141\n", + "Epoch: 9999/10000, MSE: 0.0000, Real loss: 1.5440\n" + ] + } + ], + "source": [ + "# model = YOUR CODE HERE\n", + "# Solution: \n", + "model = MLPRegressor(hidden_layer_sizes=(50,50,50,50,50), activation='relu') \n", + "\n", + "n_epochs = 10000\n", + "N_print = 10**(int(np.log10(n_epochs)) - 1)\n", + "\n", + "for epoch in range(n_epochs):\n", + " model.partial_fit(data_x, data_t.flatten())\n", + "\n", + " #MLP_prediction = YOUR CODE HERE\n", + " #MLP_valprediction = YOUR CODE HERE\n", + " # Solution:\n", + " MLP_prediction = model.predict(data_x)\n", + " MLP_valprediction = model.predict(x_val)\n", + " \n", + " if epoch%N_print==0 or epoch==n_epochs-1: \n", + " print((f'Epoch: {epoch:6d}/{n_epochs}, '\n", + " + f'MSE: {mean_squared_error(data_t, MLP_prediction.reshape(-1,1)):0.4f}, '\n", + " + f'Real loss: {mean_squared_error(t_val,MLP_valprediction):0.4f}'))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x, data_t, \".\", markersize=20, label=\"Data\")\n", + "ax.plot(data_x, MLP_prediction, \"-o\", markersize=10, label=\"Prediction\")\n", + "\n", + "# Add a title and axis labels\n", + "ax.set_title(\"Linear Data Example\", fontsize=16)\n", + "ax.set_xlabel(\"x\", fontsize=14)\n", + "ax.set_ylabel(\"t\", fontsize=14)\n", + "\n", + "# Add a legend\n", + "ax.legend(fontsize=14)\n", + "\n", + "# Add a grid\n", + "ax.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What happened to the real loss? Is it higher or lower than before? Is this model good? Think about it: we know the ground truth is actually linear. Would it make sense to have such a flexible model here? Is the validation loss giving you a valuable hint about it?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<p>\n", + "<b>Task 1.4: Control MLP complexity</b> \n", + "\n", + "The previous model is way too flexible and we ended up overfitting the noise in the data. So we should use a smaller model, but tweaking the architecture can be annoying. \n", + "\n", + "For this part you can try something different: **Keep the same network size but add an $L_2$ regularization term** to your model. In `scikit-learn` you can do this by setting the `alpha` parameter you give to `MLPRegressor` to your desired value of $\\lambda$. \n", + "\n", + "Try different values in the interval $0<\\lambda\\leq1$ and see what happens to model complexity. **What is the value of $\\lambda$ that leads to the lowest value for the real loss?**\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0/10000, MSE: 102.1504, Real loss: 93.8232\n", + "Epoch: 1000/10000, MSE: 0.4456, Real loss: 1.1514\n", + "Epoch: 2000/10000, MSE: 0.4533, Real loss: 1.1500\n", + "Epoch: 3000/10000, MSE: 0.4494, Real loss: 1.1556\n", + "Epoch: 4000/10000, MSE: 0.4455, Real loss: 1.1667\n", + "Epoch: 5000/10000, MSE: 0.4415, Real loss: 1.1643\n", + "Epoch: 6000/10000, MSE: 0.4364, Real loss: 1.1698\n", + "Epoch: 7000/10000, MSE: 0.4323, Real loss: 1.1466\n", + "Epoch: 8000/10000, MSE: 0.4282, Real loss: 1.1646\n", + "Epoch: 9000/10000, MSE: 0.4255, Real loss: 1.1704\n", + "Epoch: 9999/10000, MSE: 0.4237, Real loss: 1.1600\n" + ] + } + ], + "source": [ + "# model = YOUR CODE HERE\n", + "# Solution: \n", + "model = MLPRegressor(hidden_layer_sizes=(50,50,50,50,50), activation='relu',alpha=0.6) \n", + "\n", + "n_epochs = 10000\n", + "N_print = 10**(int(np.log10(n_epochs)) - 1)\n", + "\n", + "for epoch in range(n_epochs):\n", + " model.partial_fit(data_x, data_t.flatten())\n", + "\n", + " #MLP_prediction = YOUR CODE HERE\n", + " #MLP_valprediction = YOUR CODE HERE\n", + " # Solution:\n", + " MLP_prediction = model.predict(data_x) \n", + " MLP_valprediction = model.predict(x_val)\n", + " \n", + " if epoch%N_print==0 or epoch==n_epochs-1: \n", + " print((f'Epoch: {epoch:6d}/{n_epochs}, '\n", + " + f'MSE: {mean_squared_error(data_t, MLP_prediction.reshape(-1,1)):0.4f}, '\n", + " + f'Real loss: {mean_squared_error(t_val,MLP_valprediction):0.4f}'))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(data_x, data_t, \".\", markersize=20, label=\"Data\")\n", + "ax.plot(data_x, MLP_prediction, \"-o\", markersize=10, label=\"Prediction\")\n", + "\n", + "# Add a title and axis labels\n", + "ax.set_title(\"Linear Data Example\", fontsize=16)\n", + "ax.set_xlabel(\"x\", fontsize=14)\n", + "ax.set_ylabel(\"t\", fontsize=14)\n", + "\n", + "# Add a legend\n", + "ax.legend(fontsize=14)\n", + "\n", + "# Add a grid\n", + "ax.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How close is the model with the lowest validation loss to your first linear model? Is this a reassuring result? \n", + "\n", + "Neural networks can be very flexible and that is usually a good thing. But at the end of the day you want a model that performs **your specific task** as well as possible, and sometimes that means a very simple linear model is all what you need.\n", + "\n", + "It is nice to see that even if we do not actually know how complex the underlying patterns in our data are, if we have **a good validation dataset** we can rely on it to tell us how flexible our model should be." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 2 - Predict land deformation\n", + "\n", + "Let's go back to what you did in Week 1.3 - Observation Theory. In Project 2 you were asked to model the deformation of a road caused by the subsidence of the underground by means of GNSS and InSAR data. \n", + "\n", + "In this workshop you will focus only on the GNSS to making predictions about land deformation. You will be asked to:\n", + "\n", + "- Create a neural network similar to the one you created in the previous tasks\n", + "- Change some network parameters and observe how results are affected.\n", + "- Compare the results with those from Project 2, where you applied BLUE.\n", + "\n", + "Although we focus here on building and evaluating a neural network, the greater MUDE purpose is to think about the following questions (for both types of models!!!):\n", + "\n", + "- Which model is the best?\n", + "- Are the Neural Network and BLUE able to capture the trend of the data?\n", + "- What parameters affect the the results most?\n", + "- What are the differences between the two approaches? In which situation would one be more effective compared to the other?\n", + "\n", + "### Task 2.0: Data Processing\n", + "\n", + "First we need to import and convert our data properly:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "gnss = pd.read_csv('./data/gnss_observations2.csv')\n", + "dates_gnss = pd.to_datetime(gnss['dates'])\n", + "gnss_obs = (gnss['observations[m]']).to_numpy() * 1000\n", + "\n", + "def to_days_years(dates):\n", + " '''Convert the observation dates to days and years.'''\n", + " \n", + " dates_datetime = pd.to_datetime(dates)\n", + " time_diff = (dates_datetime - dates_datetime[0])\n", + " days_diff = (time_diff / np.timedelta64(1,'D')).astype(int)\n", + " \n", + " days = days_diff.to_numpy()\n", + " \n", + " return days\n", + "\n", + "days_gnss = to_days_years(dates_gnss)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.plot(days_gnss, gnss_obs, 'o', mec='black', label = 'GNSS')\n", + "plt.legend()\n", + "plt.title('GNSS observations of land deformation')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.1: Split the data</b> \n", + "\n", + "Define what <code>X</code> and <code>t</code> are. \n", + "\n", + "Then use the function <code>train_test_split</code> from <code>sklearn.model_selection</code> library to split the initial dataset into a training and validation dataset. Check the [documentation here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). \n", + "\n", + "Use **80%** and **20%** of the data for training and validation, respectively. Also make sure that <code>random_state = 42</code>.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "You will need to reshape your arrays in order to first standardize the data and consequently train the model. To do so check [this function](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html). The new shape should be <code>(-1,1)</code>. We do this for you ;)\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "X = days_gnss.reshape(-1, 1)\n", + "t = gnss_obs.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# X_train, X_val, t_train, t_val = YOUR CODE HERE\n", + "# Solution:\n", + "X_train, X_val, t_train, t_val = train_test_split(X, t, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train, t_train, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val, t_val, 'o', mec='blue', label = 'Validation')\n", + "plt.title('GNSS observations of land deformation - training and validation datasets')\n", + "plt.legend()\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.2: Normalize the data</b> \n", + "\n", + "Before training the model you need to standardize your data. To do so you need <code>StandardScaler</code> from <code>sklearn.preprocessing</code> library. Check the [documentation here](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). \n", + "\n", + "Make sure to standardize both <code>X</code> datasets and save them in different arrays. \n", + "\n", + "Using **a different** `StandardScaler`, normalize the outputs `t_train` and `t_val` as well.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Hint:</b> \n", + "\n", + "You first need to standardize just <code>X_train</code> and `t_train`. Only then you can standardize the validation dataset. This will guarantee that the validation data is normalized in the same way as the training data, othersize the network will get confused when it is time to make predictions. We do this for you ;)\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "input_scaler = StandardScaler()\n", + "target_scaler = StandardScaler()\n", + "\n", + "X_train_scaled = input_scaler.fit_transform(X_train)\n", + "X_val_scaled = input_scaler.transform(X_val)\n", + "\n", + "t_train_scaled = target_scaler.fit_transform(t_train)\n", + "t_val_scaled = target_scaler.transform(t_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train_scaled, t_train_scaled, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val_scaled, t_val_scaled, 'o', mec='blue', label = 'Validation')\n", + "plt.title('Normalized GNSS dataset')\n", + "plt.legend()\n", + "plt.ylabel('Normalized displacement [-]')\n", + "plt.xlabel('Normalized time [-]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.2B: Check the Figure!</b> \n", + "\n", + "Look at the figure above and notice how the data has been changed by the normalization process.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.3: Create a linear MLP</b> \n", + "\n", + "As you have done in Task 1 using <code>MLPRegressor</code>, create the Neural Network with no hidden layers and <code>identity</code> activation function. \n", + "\n", + "In the <code>MLPRegressor</code> you can specify several hyperparameters. Some of the default values are <code>solver='adam'</code> for the optimizer and <code>learning_rate_init=0.001</code> for the initial learning rate $\\eta$. For the moment we keep these values fixed, but later you can try to change some of them and see what you get.\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# model_gnss = YOUR CODE HERE\n", + "# Solution:\n", + "model_gnss = MLPRegressor(hidden_layer_sizes=(), \n", + " activation='identity', random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.4: Train the MLP</b> \n", + "\n", + "Now you can effectively train the model and then test it. In both cases you want to compute the loss and save it.\n", + "\n", + "You are required to do the following:\n", + "<ol>\n", + " <li>Initialize the lists for saving the training and validation loss</li>\n", + " <li>Loop over the epochs to train and validate the model. The line <code>model_gnss.partial_fit(X_train_scaled, t_train)</code> is used for training the model in full batch mode. Check the <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor.partial_fit\" target=\"_blank\">Documentation</a> if you're interested.</li>\n", + " <li>For both steps compute the Mean Squared Error (<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn-metrics-mean-squared-erro\" target=\"_blank\">documentation here</a>) and store it in the lists you previously initialized.</li>\n", + "</ol>\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 500/5000, Training Loss: 1.1580, Validation Loss: 1.1574\n", + "Epoch 1000/5000, Training Loss: 0.9796, Validation Loss: 0.9217\n", + "Epoch 1500/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 2000/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 2500/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 3000/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 3500/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 4000/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 4500/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n", + "Epoch 5000/5000, Training Loss: 0.9789, Validation Loss: 0.9167\n" + ] + } + ], + "source": [ + "# train_losses = YOUR CODE HERE\n", + "# val_losses = YOUR CODE HERE\n", + "\n", + "# epochs = YOUR CODE HERE\n", + "\n", + "# for epoch in range(YOUR CODE HERE):\n", + "# model_gnss.partial_fit(X_train_scaled, t_train_scaled.flatten())\n", + "\n", + "# # Calculate training loss\n", + "# train_pred = YOUR CODE HERE\n", + "# train_loss = YOUR CODE HERE\n", + "# train_losses.YOUR CODE HERE\n", + "\n", + "# # Calculate validation loss\n", + "# val_pred = YOUR CODE HERE\n", + "# val_loss = YOUR CODE HERE\n", + "# val_losses.YOUR CODE HERE\n", + "\n", + "# # Print losses every 500 epochs\n", + "# if epoch % 500 == 0:\n", + "# print(f'Epoch {epoch}/{epochs} - Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')\n", + "# Solution:\n", + "train_losses = []\n", + "val_losses = []\n", + "test_losses = []\n", + "\n", + "epochs = 5000\n", + "\n", + "for epoch in range(1, epochs + 1):\n", + " model_gnss.partial_fit(X_train_scaled, t_train_scaled.flatten())\n", + "\n", + " # Calculate training loss\n", + " train_pred = model_gnss.predict(X_train_scaled)\n", + " train_loss = mean_squared_error(t_train_scaled, train_pred)\n", + " train_losses.append(train_loss)\n", + "\n", + " # Calculate validation loss\n", + " val_pred = model_gnss.predict(X_val_scaled)\n", + " val_loss = mean_squared_error(t_val_scaled, val_pred)\n", + " val_losses.append(val_loss)\n", + "\n", + " # Print losses every 500 epochs\n", + " if epoch % 500 == 0:\n", + " print((f'Epoch {epoch}/{epochs}, '\n", + " + f'Training Loss: {train_loss:.4f}, '\n", + " + f'Validation Loss: {val_loss:.4f}'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally you can plot the losses and the predictions made by the Neural Network!" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "plt.plot(train_losses, label='Training Loss', c='b')\n", + "plt.plot(val_losses, label='Validation Loss', c='r')\n", + "plt.title('Training, Validation, and Test Losses over Epochs')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Mean Squared Error')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot dataset\n", + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train, t_train, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val, t_val, 'o', mec='blue', label = 'Validation')\n", + "\n", + "# Get model predictions for a dense linspace in x\n", + "x_plot = np.linspace(np.min(X),np.max(X),1000).reshape(-1,1)\n", + "y_plot = model_gnss.predict(input_scaler.transform(x_plot))\n", + "plt.plot(x_plot,target_scaler.inverse_transform(y_plot.reshape(-1,1)),color='orange',linewidth=5,label='Network predictions')\n", + "\n", + "# plt.plot(, target_scaler.inverse_transform(val_pred.reshape(-1,1)), 'x', mec='red', label='Predicted Values')\n", + "plt.title('Obvserved vs Predicted Values')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how we had to **carefully normalize and de-normalize** our data when making new predictions. The network is used to seeing normalized inputs and producing normalized outputs, so we have to:\n", + "\n", + "- Create a `linspace` with the new locations we want to predict at;\n", + "- Normalize these inputs with `input_scaler.transform()` so that they fall within the range the network was trained for;\n", + "- Call `predict()` to make normalized predictions of $y(x)$;\n", + "- Bring the predictions back to the real space with `target_scaler.inverse_transform()` in order to plot them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 2.5: Use a more complex network</b> \n", + "\n", + "Now it is time to make the network a bit more complex. You can change different hyperparameters as we have seen. See [documentation here](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html) for the next tasks.\n", + "Go back to the model and do the following:\n", + "<ol>\n", + " <li>Add hidden layers to your network</li>\n", + " <li>Change activation functions. Try for instance using ReLU or Tanh</li>\n", + " <li>Try different combinations of activations and network architecture. The ReLU activation tends to work better with deeper networks</li>\n", + " <li>Adjust the strength of the $L_2$ regularization by tweaking the alpha hyperparameter</li>\n", + "</ol>\n", + "\n", + "Then run the model again as many times as you deem necessary. Then look at the validation error and use what you have learned before: what is the best model?\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 500/5000 - Training Loss: 0.9316, Validation Loss: 0.8876\n", + "Epoch 1000/5000 - Training Loss: 0.9247, Validation Loss: 0.8906\n", + "Epoch 1500/5000 - Training Loss: 0.9191, Validation Loss: 0.8960\n", + "Epoch 2000/5000 - Training Loss: 0.9154, Validation Loss: 0.9020\n", + "Epoch 2500/5000 - Training Loss: 0.9131, Validation Loss: 0.9020\n", + "Epoch 3000/5000 - Training Loss: 0.9131, Validation Loss: 0.9085\n", + "Epoch 3500/5000 - Training Loss: 0.9117, Validation Loss: 0.9047\n", + "Epoch 4000/5000 - Training Loss: 0.9118, Validation Loss: 0.9034\n", + "Epoch 4500/5000 - Training Loss: 0.9110, Validation Loss: 0.9069\n", + "Epoch 5000/5000 - Training Loss: 0.9118, Validation Loss: 0.9072\n" + ] + } + ], + "source": [ + "# new_model_gnss = YOUR CODE HERE\n", + "# train_losses = YOUR CODE HERE\n", + "# val_losses = YOUR CODE HERE\n", + "\n", + "# epochs = YOUR CODE HERE\n", + "\n", + "# for epoch in range(YOUR CODE HERE):\n", + "# model_gnss.partial_fit(X_train_scaled, t_train_scaled.flatten())\n", + "\n", + "# # Calculate training loss\n", + "# train_pred = YOUR CODE HERE\n", + "# train_loss = YOUR CODE HERE\n", + "# train_losses.YOUR CODE HERE\n", + "\n", + "# # Calculate validation loss\n", + "# val_pred = YOUR CODE HERE\n", + "# val_loss = YOUR CODE HERE\n", + "# val_losses.YOUR CODE HERE\n", + "\n", + "# # Print losses every 500 epochs\n", + "# if epoch % 500 == 0:\n", + "# print(f'Epoch {epoch}/{epochs} - Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')\n", + "# Solution:\n", + "new_model_gnss = MLPRegressor(hidden_layer_sizes=(20,20,20), activation='relu')\n", + "\n", + "train_losses = []\n", + "val_losses = []\n", + "test_losses = []\n", + "\n", + "epochs = 5000\n", + "\n", + "for epoch in range(1, epochs + 1):\n", + " new_model_gnss.partial_fit(X_train_scaled, t_train_scaled.flatten())\n", + "\n", + " # Calculate training loss\n", + " train_pred = new_model_gnss.predict(X_train_scaled)\n", + " train_loss = mean_squared_error(t_train_scaled, train_pred)\n", + " train_losses.append(train_loss)\n", + "\n", + " # Calculate validation loss\n", + " val_pred = new_model_gnss.predict(X_val_scaled)\n", + " val_loss = mean_squared_error(t_val_scaled, val_pred)\n", + " val_losses.append(val_loss)\n", + "\n", + " # Print losses every 500 epochs\n", + " if epoch % 500 == 0:\n", + " print(f'Epoch {epoch}/{epochs} - Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot losses\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(train_losses, label='Training Loss', c='b')\n", + "plt.plot(val_losses, label='Validation Loss', c='r')\n", + "plt.title('Training and Validation Losses over Epochs with a better model')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Mean Squared Error')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot dataset\n", + "plt.figure(figsize=(15,5))\n", + "plt.plot(X_train, t_train, 'o', mec='green', label = 'Training')\n", + "plt.plot(X_val, t_val, 'o', mec='blue', label = 'Validation')\n", + "\n", + "# Get model predictions for a dense linspace in x\n", + "x_plot = np.linspace(np.min(X),np.max(X),1000).reshape(-1,1)\n", + "y_plot = new_model_gnss.predict(input_scaler.transform(x_plot))\n", + "plt.plot(x_plot,target_scaler.inverse_transform(y_plot.reshape(-1,1)),color='orange',linewidth=5,label='Network predictions')\n", + "\n", + "plt.title('Obvserved vs Predicted Values')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "Note that the example shown above in this document is not the <b>best</b> solution! It was simply the last one we tried (you were supposed to experiment with several combinations). This looks like a case of overfitting, which you can also see in the validation plot above (the final epoch is not the minimum validation loss).\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 3 - Compare with _Project 2 - Modelling Road Deformation using Non-Linear Least-Squares_\n", + "\n", + "The following cell contains the code you implemented in Project 2 during Week 1.3; you can review the solution [here](https://mude.citg.tudelft.nl/course-files/Project_2/Solution.html) if you need a reminder. For this application we only focus on the linear model implemented using **BLUE**. You just need to run the following cell and then compare the results, i.e. the residuals, with respect to the Neural Network model you have previously implemented." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.1: Model Comparison</b> \n", + "\n", + "Scan through the code quickly to refresh your memore about the BLUE method and how we used it to solve this problem in Q1. Then run the cells below and answer the questions (also below).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gw = pd.read_csv('./data/groundwater_levels2.csv')\n", + "dates_gw = pd.to_datetime(gw['dates'])\n", + "gw_obs = (gw['observations[mm]']).to_numpy()\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Function to convert to days and years \n", + "\n", + "def to_days_years(dates):\n", + " '''Convert the observation dates to days and years.'''\n", + " \n", + " dates_datetime = pd.to_datetime(dates)\n", + " time_diff = (dates_datetime - dates_datetime[0])\n", + " days_diff = (time_diff / np.timedelta64(1,'D')).astype(int)\n", + " \n", + " days = days_diff.to_numpy()\n", + " years = days/365\n", + " \n", + " return days, years\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Convert data of GNSS and GW levels\n", + "\n", + "days_gnss, years_gnss = to_days_years(dates_gnss)\n", + "days_gw, years_gw = to_days_years(dates_gw)\n", + "\n", + "interp = interpolate.interp1d(days_gw, gw_obs)\n", + "\n", + "GW_at_GNSS_times = interp(days_gnss)\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Functional model for GNSS: A_gnss\n", + "\n", + "A_gnss = np.ones((len(dates_gnss), 3))\n", + "A_gnss[:,1] = days_gnss\n", + "A_gnss[:,2] = GW_at_GNSS_times\n", + "\n", + "# Solution for removing groundwater information\n", + "# A_gnss = np.ones((len(dates_gnss), 2))\n", + "# A_gnss[:,1] = days_gnss\n", + "\n", + "y_gnss = gnss_obs\n", + "\n", + "m_gnss = np.shape(A_gnss)[0]\n", + "n_gnss = np.shape(A_gnss)[1]\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# Stochastic model for GNSS: Sigma_Y_gnss\n", + "\n", + "std_gnss = 15 #mm (corrected from original value of 5 mm)\n", + "\n", + "Sigma_Y_gnss = np.identity(len(dates_gnss))*std_gnss**2\n", + "\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# BLUE function - not needed if non-linear model is used!\n", + "\n", + "def BLUE(A, y, Sigma_Y):\n", + " \"\"\"Calculate the Best Linear Unbiased Estimator\n", + " \n", + " Write a docstring here (an explanation of your function).\n", + " \n", + " Function to calculate the Best Linear Unbiased Estimator\n", + " \n", + " Input:\n", + " A = A matrix (mxn)\n", + " y = vector with obervations (mx1)\n", + " Sigma_Y = Varaiance covariance matrix of the observations (mxm)\n", + " \n", + " Output:\n", + " xhat = vector with the estimates (nx1)\n", + " Sigma_Xhat = variance-covariance matrix of the unknown parameters (nxn)\n", + " \"\"\"\n", + " \n", + " Sigma_Xhat = np.linalg.inv(A.T @ np.linalg.inv(Sigma_Y) @ A)\n", + " xhat = Sigma_Xhat @ A.T @ np.linalg.inv(Sigma_Y) @ y\n", + " \n", + " return xhat, Sigma_Xhat \n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# BLUE estimation \n", + "\n", + "xhat_gnss, Sigma_Xhat_gnss = BLUE(A_gnss, y_gnss, Sigma_Y_gnss)\n", + "\n", + "\n", + "# Function to plot BLUE residuals\n", + "\n", + "def plot_residual(date, y_obs, yhat, data_type, A,\n", + " Sigma_Xhat, Sigma_Y, true_disp):\n", + "\n", + " ehat = y_obs - yhat\n", + "\n", + " # Compute the vc matrix for \\hat{y}\n", + " Sigma_Yhat = A @ Sigma_Xhat @ A.T\n", + " std_y = np.sqrt(Sigma_Yhat.diagonal())\n", + "\n", + " # Compute the vc matrix for \\hat{e}\n", + " Sigma_ehat = Sigma_Y - Sigma_Yhat\n", + " std_ehat = np.sqrt(Sigma_ehat.diagonal())\n", + "\n", + " # Show the 99% confidence interval\n", + " k99 = norm.ppf(1 - 0.5*0.01)\n", + " confidence_interval_y = k99*std_y\n", + " confidence_interval_res = k99*std_ehat\n", + "\n", + " # Plot original data and fitted model\n", + " plt.figure(figsize = (15,5))\n", + " plt.plot(date, y_obs, 'k+', label = 'Observations')\n", + " plt.plot(date, yhat, label = 'Fitted model')\n", + " plt.fill_between(date, (yhat - confidence_interval_y), \n", + " (yhat + confidence_interval_y), facecolor='orange',\n", + " alpha=0.4, label = '99% Confidence Region')\n", + " plt.plot(date, true_disp, label = 'True model')\n", + " plt.legend()\n", + " plt.ylabel(data_type + ' Displacement [mm]')\n", + " plt.xlabel('Time')\n", + " plt.title(data_type + ' Observations and Fitted Model')\n", + "\n", + " return ehat\n", + "\n", + "# ------------------------------------------------------------- #\n", + "\n", + "# True model which was used to generate the data (Monte Carlo simulations)\n", + "\n", + "k_true = 0.1\n", + "R_true = -25 \n", + "a_true = 180\n", + "d0_true = 10\n", + "\n", + "disp_gnss = (d0_true + R_true*(1 - np.exp(-days_gnss/a_true)) \n", + " + k_true*GW_at_GNSS_times) \n", + "\n", + "# Residuals and plots for GNSS incl. confidence bounds\n", + "yhat_gnss = A_gnss @ xhat_gnss\n", + "ehat_gnss_1 = plot_residual(dates_gnss, y_gnss, yhat_gnss,\n", + " 'GNSS', A_gnss, \n", + " Sigma_Xhat_gnss, Sigma_Y_gnss, disp_gnss)\n", + "\n", + "# ------------------------------------------------------------- #" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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r1vHjjz8ybNiwPIHdOa6urnTr1o1p06YB8NdffwFw4403cuDAAQICAmjTpk2e7VJBzoX8/Pzo168fH3/8MT/99BMxMTF5VjEt6LN69OiR7+fw2WefFagWsKxa2a5dO+bNm8dnn31GRkaGTa+o3IKDgxk+fDgDBw5k7969JbpC5I033ohhGERFReX7OTRt2hSwrFbq5uaW53NYv379ZVfUdHd3p1u3bnz55Zd5gqQLnQv0cvcE69SpE1WqVGHXrl351timTRtcXFzw9PTk6quv5ptvviE9Pd16/enTp/nxxx8L9bncc8897Nq1i+eee464uDib759zC2LkDiXnzp2b5/dXfmrWrMl///1HRkaGtS0hIYH169fbnHfjjTeSkJBATk5Ovu85v8UmHB0dadeuHe+88w4A27ZtK9T7FhEREfVkExERkUvo1KkTM2fOZPz48XTu3Jlx48ZRvXp1IiMjeeedd/jzzz+ZOXMmHTt2zHPtN998g5OTE9ddd511ddHmzZtzxx132Jx3rvfYwIEDycjIYPjw4TbHJ06cyLFjx7j22mupVq0aJ0+e5M0338TZ2Zlu3boBMH78eL7++mu6du3KI488QrNmzTCbzURGRrJ8+XIeffRR2rVrV6D3fM8997Bo0SLGjRtHtWrVbFY+Lcyzrr/+erp27coTTzxBamoqbdq04Y8//rAOJSyoe+65h7Fjx3L8+HE6duyYJyBp164dN954I82aNcPPz4/du3ezcOFCOnToUKKrQ3bq1IkxY8YwYsQItmzZQteuXfH09CQ6Opp169bRtGlT7rvvPvz8/Hjsscd4+eWXGTVqFLfffjtHjx5l8uTJBRqK+cYbb9C5c2fatWvHU089Rd26dTlx4gQ//PAD7733Ht7e3jRp0gSA999/H29vb9zc3KhVqxYBAQHMmjWLYcOGkZiYyIABA6hatSpxcXHs2LGDuLg45syZA8BLL73EDTfcwHXXXcejjz5KTk4O06ZNw9PTs0C9zM4ZOnQozzzzDK+//jpVqlShf//+1mM+Pj507dqV119/ncDAQGrWrMmaNWv48MMPqVKlymXvPWTIEN577z0GDx7M6NGjSUhI4LXXXrNZDRfgrrvu4tNPP6VPnz48/PDDXH311Tg7O3Ps2DFWrVrFLbfcwq233sq7777LypUr6du3L9WrVyc9Pd3aQzP3972IiIgUgN2WXBAREZFyY8OGDcaAAQOM4OBgw8nJyahatarRv39/Y/369XnOPbca5datW42bbrrJ8PLyMry9vY2BAwdaVyDNbdCgQQZgdOrUKc+xn376yejdu7cRHh5uuLi4GFWrVjX69OljrF271ua8lJQU47nnnjMaNGhguLi4GL6+vkbTpk2NRx55xIiJibGeBxgPPPDARd9rTk6OERERYQDGs88+m+85BX3WyZMnjXvuuceoUqWK4eHhYVx33XXGnj17CrS66DmnTp0y3N3dDcD44IMP8hx/6qmnjDZt2hh+fn6Gq6urUbt2beORRx6xrs56MedWr/zyyy8ved65X8+4uLh8j3/00UdGu3btDE9PT8Pd3d2oU6eOMXToUGPLli3Wc8xmszF16lQjIiLCcHFxMZo1a2b8+OOPeVbLzG91UcMwjF27dhm33367ERAQYLi4uBjVq1c3hg8fbqSnp1vPmTlzplGrVi3D0dExzz3WrFlj9O3b1/D39zecnZ2N8PBwo2/fvnne+w8//GA0a9bM+oxXX33V+v4L49ZbbzUA4/77789z7NixY8Ztt91m+Pn5Gd7e3sYNN9xg/PPPP0aNGjWMYcOGWc/Lb3VRwzCMBQsWGI0aNTLc3NyMxo0bG4sWLcqzuqhhWFbvnT59utG8eXPDzc3N8PLyMho2bGiMHTvW2Ldvn2EYlt/Xt956q1GjRg3D1dXVCAgIMLp162b88MMPhXq/IiIiYmEyjLNLgomIiIiIiIiIiMgV0ZxsIiIiIiIiIiIiRaSQTUREREREREREpIgUsomIiIiIiIiIiBSRQjYREREREREREZEiUsgmIiIiIiIiIiJSRArZREREREREREREisjJ3gWUNWazmePHj+Pt7Y3JZLJ3OSIiIiIiIiIiYieGYXD69GnCwsJwcLh0XzWFbLkcP36ciIgIe5chIiIiIiIiIiJlxNGjR6lWrdolz1HIlou3tzdg+fB8fHzsXI2IiIiIiIiIiNhLcnIyERER1rzoUhSy5XJuiKiPj49CNhERERERERERKdCUYlr4QEREREREREREpIgUsomIiIiIiIiIiBSRQjYREREREREREZEi0pxsIiIiIiIiIlLpGIZBdnY2OTk59i5F7MjR0REnJ6cCzbl2OQrZRERERERERKRSyczMJDo6mrS0NHuXImWAh4cHoaGhuLi4FOk+CtlEREREREREpNIwm80cOnQIR0dHwsLCcHFxKZZeTFL+GIZBZmYmcXFxHDp0iHr16uHgcOUzqylkExEREREREZFKIzMzE7PZTEREBB4eHvYuR+zM3d0dZ2dnjhw5QmZmJm5ubld8Ly18ICIiIiIiIiKVTlF6LEnFUlzfC/qOEhERERERERERKSKFbCIiIiIiIiIiIkWkkE1ERERERERERMqN7t27M378+AKfP3/+fKpUqVJi9ZyjkK2SyEzJJOtMlr3LEBEREREREZErMHz4cEwmk3ULCAjghhtuYOfOnTbnmUwmvvvuuzzXHz58GJPJxPbt2/Mc69evH8OHD7fud+/e3eZZ57Z77723mN9VxaLVRSuw1ZNXE7k2kvi98ZyOOs2ARQO46o6r7F2WiIiIiIiIiFyBG264gXnz5gEQExPDc889x4033khkZGSxP2v06NG8+OKLNm1ajfXS1JOtAju6/iiHVh7idNRpAOL3xNu5IhEREREREZGyxzAMUlNTS30zDKNQdbq6uhISEkJISAgtWrTgySef5OjRo8TFxRX7Z+Lh4WF91rnNx8fnoud3796dBx98kPHjx+Pn50dwcDDvv/8+qampjBgxAm9vb+rUqcPSpUttrluzZg1XX301rq6uhIaG8tRTT5GdnW09npqaytChQ/Hy8iI0NJQZM2bkeXZmZiZPPPEE4eHheHp60q5dO1avXl1sn0VBKWSrwAIaBNjsJ+xNsFMlIiIiIiIiImVXWloaXl5epb6lpaVdcc0pKSl8+umn1K1bl4CAgMtfUAoWLFhAYGAgmzZt4sEHH+S+++7j9ttvp2PHjmzbto1evXoxZMgQ6/uOioqiT58+tG3blh07djBnzhw+/PBDXn75Zes9H3/8cVatWsW3337L8uXLWb16NVu3brV57ogRI/jjjz/44osv2LlzJ7fffjs33HAD+/btK9X3r5CtAgtsEGizH79XPdlEREREREREyquffvrJGtB5e3vzww8/sGjRIhwcij/emT17dp5QcMGCBZe8pnnz5jz33HPUq1ePp59+Gnd3dwIDAxk9ejT16tVj4sSJJCQkWOeRmz17NhEREbz99ts0bNiQfv368cILLzBjxgzMZjMpKSl8+OGHTJ8+neuuu46mTZuyYMECcnJyrM88cOAAn3/+OV9++SVdunShTp06PPbYY3Tu3Nk6tLa0aE62CiywoW3IlrA3AcMwMJlMdqpIREREREREpOzx8PAgJSXFLs8tjB49ejBnzhwAEhMTmT17Nr1792bTpk3UqFGjWGu7++67efbZZ23aqlateslrmjVrZn3t6OhIQEAATZs2tbYFBwcDEBsbC8Du3bvp0KGDTU7RqVMnUlJSOHbsGElJSWRmZtKhQwfrcX9/fxo0aGDd37ZtG4ZhUL9+fZtaMjIySr2Hn0K2Ciz3cNHMlExSolPwDvO2U0UiIiIiIiIiZY/JZMLT09PeZVyWp6cndevWte63bt0aX19fPvjgA5shlvnx9fUF4NSpU3mOnTx5Mk9I5+vra/OsgnB2drbZN5lMNm3nwjSz2QyQb0egc/PUmUymAs1ZZzabcXR0ZOvWrTg6Otoc8/LyKlT9RaXhohWYT7gPzh623+AaMioiIiIiIiJSMZhMJhwcHDhz5sxlz/Xz8yMoKIjNmzfbtJ85c4Z///3XpndYaWncuDHr16+3CdPWr1+Pt7c34eHh1K1bF2dnZzZu3Gg9npSUxH///Wfdb9myJTk5OcTGxlK3bl2bLSQkpFTfj3qyVWAmBxMB9QOI2R5jbYvfE0+tHrXsWJWIiIiIiIiIXImMjAxiYix/x09KSuLtt98mJSWFm266yea8Q4cOsX37dpu2unXr8thjj/HKK68QHBxMx44dSUpKYtq0aTg5OTF48GCb89PS0qzPOsfV1RU/P79iez/3338/M2fO5MEHH2TcuHHs3buXSZMmMWHCBBwcHPDy8mLkyJE8/vjjBAQEEBwczLPPPmszB139+vW5++67GTp0KDNmzKBly5bEx8ezcuVKmjZtSp8+fYqt3stRyFbBBTSwDdm0wqiIiIiIiIhI+bRs2TJCQ0MB8Pb2pmHDhnz55Zd0797d5rwJEybkuXbVqlU89thjeHl5MX36dA4cOECVKlVo3749a9euxcfHx+b8Dz74gA8++MCmrVevXixbtqzY3k94eDg///wzjz/+OM2bN8ff35+RI0fy3HPPWc95/fXXSUlJ4eabb8bb25tHH300z5DXefPm8fLLL/Poo48SFRVFQEAAHTp0KNWADcBkFGSAayWSnJyMr68vp06dyvMNVh6tmrSK31/83bpf94a63L30bjtWJCIiIiIiImI/6enpHDp0iFq1auHm5mbvcqQMuNT3RGFyogo1J9vkyZMxmUw2W2mPvy1rcq8wqjnZRERERERERESKX4UbLnrVVVfx22+/WfdzryxR2QQ2sA3ZTh4+SXZ6Nk5uFe6XXkRERERERETEbipc0uLk5FTpe69dKKB+gG2DAYn7E6napKp9ChIRERERERERqYAq1HBRgH379hEWFkatWrW46667OHjw4CXPz8jIIDk52WarSFy8XPAO97Zp05BREREREREREZHiVaFCtnbt2vHxxx/zyy+/8MEHHxATE0PHjh1JSLj4ippTp07F19fXukVERJRixaUj95DR+D0K2UREREREREREilOFCtl69+7NbbfdRtOmTenZsydLliwBYMGCBRe95umnn+bUqVPW7ejRo6VVbqkJaGA7ZDRh78VDRxERERERERERKbwKNyfbhTw9PWnatCn79u276Dmurq64urqWYlWlTyGbiIiIiIiIiEjJqlA92XLLyMhg9+7dhIaG2rsUuwpsmGu46N54DMOwUzUiIiIiIiIiIhVPhQrZHnvsMdasWcOhQ4f4888/GTBgAMnJyQwbNszepdlV7jnZMk5lkBqbaqdqREREREREREQqngoVsh07doyBAwfSoEED+vfvj4uLCxs3bqRGjRr2Ls2ufKv74uRmOzJYQ0ZFREREREREpCzr3r0748ePL/D58+fPp0qVKiVWz+VUqDnZvvjiC3uXUCaZHEz41/Mn9u9Ya1v8nnhqdK3c4aOIiIiIiIhIeWAymS55fNiwYcyfP790ipGLqlAhm1xcYINA25Btb7wdqxERERERERGRgoqOjra+XrRoERMnTmTv3r3WNnd3d5vzs7KycHZ2LrX6xKJCDReVi9MKoyIiIiIiIiIXYRiQnVr6WwEXJQwJCbFuvr6+mEwm6356ejpVqlRh8eLFdO/eHTc3Nz755BMmT55MixYtbO4zc+ZMatasadM2b948GjVqhJubGw0bNmT27NmXrKV79+48+OCDjB8/Hj8/P4KDg3n//fdJTU1lxIgReHt7U6dOHZYuXWpz3Zo1a7j66qtxdXUlNDSUp556iuzsbOvx1NRUhg4dipeXF6GhocyYMSPPszMzM3niiScIDw/H09OTdu3asXr16gJ9hqVBPdkqCYVsIiIiIiIiIheRkwaLvUr/uXekgJNnsdzqySefZMaMGcybNw9XV1fef//9y17zwQcfMGnSJN5++21atmzJX3/9xejRo/H09LzkIpILFizgiSeeYNOmTSxatIj77ruP7777jltvvZVnnnmG//3vfwwZMoTIyEg8PDyIioqiT58+DB8+nI8//pg9e/YwevRo3NzcmDx5MgCPP/44q1at4ttvvyUkJIRnnnmGrVu32gSFI0aM4PDhw3zxxReEhYXx7bffcsMNN/D3339Tr169on6ERaaQrZIIbGi7wmjSoSRyMnNwdHG0U0UiIiIiIiIiUlzGjx9P//79C3XNSy+9xIwZM6zX1apVi127dvHee+9dMmRr3rw5zz33HABPP/00r776KoGBgYwePRqAiRMnMmfOHHbu3En79u2ZPXs2ERERvP3225hMJho2bMjx48d58sknmThxImlpaXz44Yd8/PHHXHfddYAlyKtWrZr1mQcOHODzzz/n2LFjhIWFAfDYY4+xbNky5s2bxyuvvFKo914SFLJVEoENbEM2I8cg8UAiQY2C7FSRiIiIiIiISBnh6GHpVWaP5xaTNm3aFOr8uLg4jh49ysiRI63hGEB2dja+vr6XvLZZs2bW146OjgQEBNC0aVNrW3BwMACxsZa54Xfv3k2HDh1sFnDo1KkTKSkpHDt2jKSkJDIzM+nQoYP1uL+/Pw0aNLDub9u2DcMwqF+/vk0tGRkZBATYjt6zF4VslYSrjyteIV6kxJz/oZGwN0Ehm4iIiIiIiIjJVGzDNu3F09O2fgcHB4xcc75lZWVZX5vNZsAyZLRdu3Y25zk6XnrUW+5FFUwmk03buTDt3DMMw8izQuq52kwmU54682M2m3F0dGTr1q156vPyssNQ33woZKtEAhoE2IRs8Xu0wqiIiIiIiIhIRRQUFERMTIxNwLV9+3br8eDgYMLDwzl48CB33313idbSuHFjvv76a5ta1q9fj7e3N+Hh4fj5+eHs7MzGjRupXr06AElJSfz3339069YNgJYtW5KTk0NsbCxdunQp0XqvlFYXrUS0+IGIiIiIiIhI5dC9e3fi4uJ47bXXOHDgAO+8806eFT8nT57M1KlTefPNN/nvv//4+++/mTdvHm+88Uax1nL//fdz9OhRHnzwQfbs2cP333/PpEmTmDBhAg4ODnh5eTFy5Egef/xxVqxYwT///MPw4cNxcDgfW9WvX5+7776boUOH8s0333Do0CE2b97MtGnT+Pnnn4u13iulkK0SyT0vW/xe9WQTERERERERqYgaNWrE7Nmzeeedd2jevDmbNm3iscceszln1KhRzJ07l/nz59O0aVO6devG/PnzqVWrVrHWEh4ezs8//8ymTZto3rw59957LyNHjrQungDw+uuv07VrV26++WZ69uxJ586dad26tc195s2bx9ChQ3n00Udp0KABN998M3/++ScRERHFWu+VMhkFGfhaiSQnJ+Pr68upU6fw8fGxdznFat/P+/is72fWfXd/d55IeMKOFYmIiIiIiIiUrvT0dA4dOkStWrVwc3OzdzlSBlzqe6IwOZF6slUiuYeLnkk8Q1p8mp2qERERERERERGpOBSyVSJValbB0cV2BQ4NGRURERERERERKTqFbJWIg6MD/nX9bdq0wqiIiIiIiIiISNEpZKtktMKoiIiIiIiIiEjxU8hWyShkExEREREREREpfgrZKpnABoE2+5qTTURERERERESk6BSyVTKBDW1DtqQDSeRk5dipGhERERERERGRikEhWyWTe7ioOdvMyUMn7VOMiIiIiIiIiEgFoZCtknH3c8cjyMOmTSuMioiIiIiIiIgUjUK2SkjzsomIiIiIiIiIPXTv3p3x48fbu4wSoZCtEtIKoyIiIiIiIiLlz/DhwzGZTLz66qs27d999x0mk6nA96lZsyYzZ84s5urEyd4FSOlTyCYiIiIiIiJiYZgN0hLS7PZ8jwAPTA4FD8jc3NyYNm0aY8eOxc/PrwQrK15ZWVk4Ozvbu4wSpZCtEtJwURERERERERGLtIQ0pledbrfnPxb7GJ5BngU+v2fPnuzfv5+pU6fy2muv5XvO+vXreeqpp9i8eTOBgYHceuutTJ06FU9PT7p3786RI0d45JFHeOSRRwAwm81UrVqVd999l9tuuw2AFi1acPz4cWJjYwHYsGEDXbt2JSkpCS8vLyIjI3nwwQdZsWIFDg4O3HDDDcyaNYvg4GAAJk+ezHfffcdDDz3Eyy+/zOHDh8nJyclT67Jly7jzzjuZNWsWQ4cOLdRnV9ZouGglFNjQNmRLi0vjTNIZO1UjIiIiIiIiIgXl6OjIK6+8wqxZszh27Fie43///Te9evWif//+7Ny5k0WLFrFu3TrGjRsHwDfffEO1atV48cUXiY6OJjo6GpPJRNeuXVm9ejUASUlJ7Nq1i6ysLHbt2gXA6tWrad26NV5eXhiGQb9+/UhMTGTNmjX8+uuvHDhwgDvvvNOmlv3797N48WK+/vprtm/fnqfWL774gjvuuIOPP/643AdsoJ5slVKVWlVwcHLAnG22tiXsTaBa+2p2rEpERERERERECuLWW2+lRYsWTJo0iQ8//NDm2Ouvv86gQYOsiwvUq1ePt956i27dujFnzhz8/f1xdHTE29ubkJAQ63Xdu3fn/fffB+D333+nefPmVK9endWrV9O4cWNWr15N9+7dAfjtt9/YuXMnhw4dIiIiAoCFCxdy1VVXsXnzZtq2bQtAZmYmCxcuJCgoKM97mD17Ns888wzff/89PXr0KO6PyC7Uk60ScnR2xK+O7bjt+D0aMioiIiIiIiJSXkybNo0FCxZYe5qds3XrVubPn4+Xl5d169WrF2azmUOHDl30ft27d+fff/8lPj6eNWvW0L17d7p3786aNWvIzs5m/fr1dOvWDYDdu3cTERFhDdgAGjduTJUqVdi9e7e1rUaNGvkGbF9//TXjx49n+fLlFSZgA/Vkq7QCGwTaLHigedlERERERESkMvII8OCx2Mfs+vwr0bVrV3r16sUzzzzD8OHDre1ms5mxY8fy0EMP5bmmevXqF71fkyZNCAgIYM2aNaxZs4YXX3yRiIgIpkyZwubNmzlz5gydO3cGwDCMfFczzd3u6Zn/XHMtWrRg27ZtzJs3j7Zt2xZqZdSyTCFbJaUVRkVERERERETA5GAq1MIDZcmrr75KixYtqF+/vrWtVatW/Pvvv9StW/ei17m4uORZhODcvGzff/89//zzD126dMHb25usrCzeffddWrVqhbe3N2DptRYZGcnRo0etvdl27drFqVOnaNSo0WXrrlOnDjNmzKB79+44Ojry9ttvX8nbL3M0XLSSUsgmIiIiIiIiUr41bdqUu+++m1mzZlnbnnzySTZs2MADDzzA9u3b2bdvHz/88AMPPvig9ZyaNWvy+++/ExUVRXz8+ZFt3bt357PPPqNZs2b4+PhYg7dPP/3UOh8bWFY4bdasGXfffTfbtm1j06ZNDB06lG7dutGmTZsC1V6/fn1WrVplHTpaEShkq6RyrzCauD8Rc475ImeLiIiIiIiISFn00ksvYRiGdb9Zs2asWbOGffv20aVLF1q2bMnzzz9PaGio9ZwXX3yRw4cPU6dOHZs503r06EFOTo5NoNatWzdycnKs87GBpdfbd999h5+fH127dqVnz57Url2bRYsWFar2Bg0asHLlSj7//HMeffTRK3j3ZYvJuPBXQkhOTsbX15dTp07h4+Nj73JKTFp8Gq8HvW7T9uD+B/Gv42+nikRERERERERKXnp6OocOHaJWrVq4ubnZuxwpAy71PVGYnEg92Sopj0AP3P3dbdq0wqiIiIiIiIiIyJVRyFaJaV42EREREREREZHiUSFDttmzZ1u7+LVu3Zq1a9fau6QyKbCB7bxs8XvVk01ERERERERE5EpUuJBt0aJFjB8/nmeffZa//vqLLl260Lt3byIjI+1dWpmjnmwiIiIiIiIiIsWjwoVsb7zxBiNHjmTUqFE0atSImTNnEhERwZw5c+xdWpmjkE1EREREREQqK60DKecU1/dChQrZMjMz2bp1K9dff71N+/XXX8/69evzvSYjI4Pk5GSbrbIIbGg7XDQlJoWM5Aw7VSMiIiIiIiJS8pydnQFIS0uzcyVSVpz7Xjj3vXGlnIqjmLIiPj6enJwcgoODbdqDg4OJiYnJ95qpU6fywgsvlEZ5ZY5/HX9MjiaMnPOJbfzeeMLbhtuxKhEREREREZGS4+joSJUqVYiNjQXAw8MDk8lk56rEHgzDIC0tjdjYWKpUqYKjo2OR7lehQrZzcv/mMAzjor9hnn76aSZMmGDdT05OJiIiokTrKyscXRzxq+VH4v5Ea1v8HoVsIiIiIiIiUrGFhIQAWIM2qdyqVKli/Z4oigoVsgUGBuLo6Jin11psbGye3m3nuLq64urqWhrllUkBDQJsQjbNyyYiIiIiIiIVnclkIjQ0lKpVq5KVlWXvcsSOnJ2di9yD7ZwKFbK5uLjQunVrfv31V2699VZr+6+//sott9xix8rKroAGAexbss+6r5BNREREREREKgtHR8diC1hEKlTIBjBhwgSGDBlCmzZt6NChA++//z6RkZHce++99i6tTApsYLv4QfzeeDtVIiIiIiIiIiJSflW4kO3OO+8kISGBF198kejoaJo0acLPP/9MjRo17F1amZR7hdHEfYkYZgOTgyZ9FBEREREREREpKAd7F1AS7r//fg4fPkxGRgZbt26la9eu9i7Jfsw5ELv2oocDGgTY7GenZ3Mq8lRJVyUiIiIiIiIiUqFUyJBNLrB3JvzWFTaOhMy84ZlnVU9cfW0XfojfoyGjIiIiIiIiIiKFoZCtosuIB0xw8CP4uQlEL7c5bDKZNC+biIiIiIiIiEgRKWSr6FpMhZ5rwKsOpB2DVb3gz9GQlWw9JfeQUa0wKiIiIiIiIiJSOArZKoOqXaDPDqj/kGX/wFxY0gSifwUUsomIiIiIiIiIFJVCtsrCyRPavAnXrgav2pB2FFZdD5vuJbCOp82pGi4qIiIiIiIiIlI4Ctkqm+Bu0Gcn1B9n2d//HoGxI21OOR11msyUTDsUJyIiIiIiIiJSPilkq4ycPKHNLLh2JXjWxN/nXzAZNqck/KchoyIiIiIiIiIiBaWQrTIL7gF9/sbpqjFUCTxpcyh+j4aMioiIiIiIiIgUlEK2ys7ZC9q+Q2DTOjbN8ctnQ9pxOxUlIiIiIiIiIlK+KGQTAAKa1rPZT9gbD0saw/4PwDAucpWIiIiIiIiIiIBCNjkroEGAzX5CbARknYJNY2DFNZC8z06ViYiIiIiIiIiUfQrZBIDABoE2+wkxgRjNp4OjO8SuhqXN4N+pkKNVR0VEREREREREclPIJgAENrQN2bLSskj2GQV9/4GQ6yAnHXY8A8taQuzvdqpSRERERERERKRsUsgmAHiFeuHi5WLTFr8nHrxqQ49foP0CcA2CU7vgt26wYTikx9mnWBERERERERGRMkYhmwBgMpnyzsu2N+HcQag9FG7cA3XHWNoOLYCfGpxdGMFcytWKiIiIiIiIiJQtCtnEKve8bPF7421PcPWHq9+D6zdAleaQmWRZGOHXzpC0sxQrFREREREREREpWxSyidVFe7LlFtgebtgCrd4AJy+I3wDLWsG2RyHrdClUKiIiIiIiIiJStihkE6sCh2wADk7Q8BG4cTdE3AZGDux5A35qBJFfg2GUcLUiIiIiIiIiImWHQjaxyr3C6KnIU2SlZV36Io9q0OUr6LYEPGvBmShYNwBWXW9ZJEFEREREREREpBJQyCZWAfUC8rQl7LtEb7YLhfeBvv/AVc+BgwvE/AY/N4Otj0DmyeItVERERERERESkjFHIJlbOHs74Vve1aYvfE3+Rs/Ph5AHNX4K+u6DaLZYhpHtnwo/1Yf9cMOcUb8EiIiIiIiIiImWEQjaxUah52S7Guw50/Q56/AI+DSEjDjaNhuXtIG5D8RQqIiIiIiIiIlKGKGQTG8USsp0Tej302WlZhdTZBxK3wq8dYf1QOBNdxEpFRERERERERMoOhWxiI7CB7eIH8XsLMVw0Pw7OZ1ch/Q9q32NpO7zQMoR01zTIySja/UVEREREREREygCFbGIjv55shmEU/cbuwdD+Q7j+TwhoB9kpsP0p+KkRRH4JxfEMERERERERERE7UcgmNgIb2vZky0zJJCU6pRgfcDVcvx7azwf3MEg9BOvugF87Q/yfxfccEREREREREZFSpJBNbPiE++Ds4WzTVqgVRgvC5AC1h8FN/0HTyeDoAfHrYXl7+GMQpB4p3ueJiIiIiIiIiJQwhWxiw+RgIqC+7ZDRIs/LdjFOntB0kiVsqz0cMMGRz+HHBrD9GchJL5nnioiIiIiIiIgUM4VskkexrjBaEB7h0H4e3LAFqnYHcwbsmgobhmquNhEREREREREpFxSySR6lHrKd498Krl0Jnb+0rEoa+SX8O6V0ni0iIiIiIiIiUgQK2SSPwAa2ix+U2HDR/JhMUH0AtJlt2d/5PBz9rvSeLyIiIiIiIiJyBRSySR65Vxg9efgk2enZpVtE3VFQ/0HL6w1D4OTfpft8EREREREREZFCqFAhW82aNTGZTDbbU089Ze+yyp3cCx9gQOL+xNIvpNUbEHwtZKfAmlsgvRR71ImIiIiIiIiIFEKFCtkAXnzxRaKjo63bc889Z++Syh0XLxe8w71t2uL32CHgcnCCzovAqzakHoI/7gBzVunXISIiIiIiIiJyGRUuZPP29iYkJMS6eXl52bukcsmu87JdyDUAuv4ATl5wYhVsfcQ+dYiIiIiIiIiIXEKFC9mmTZtGQEAALVq0YMqUKWRmZl7y/IyMDJKTk202seMKo/mpchV0/BQwwb53YP/79qtFRERERERERCQfFSpke/jhh/niiy9YtWoV48aNY+bMmdx///2XvGbq1Kn4+vpat4iIiFKqtmwrUyEbQLWbofnLltebH4DYtfatR0RERERERETkAibDMAx7F3EpkydP5oUXXrjkOZs3b6ZNmzZ52r/++msGDBhAfHw8AQEB+Vxp6cmWkZFh3U9OTiYiIoJTp07h4+NTtOLLsf3L9vNp70+t+66+rjyZ9CQmk8l+RRkG/DEQIheBaxDcsBk8a9ivHhERERERERGp0JKTk/H19S1QTuRUSjVdsXHjxnHXXXdd8pyaNWvm296+fXsA9u/ff9GQzdXVFVdX1yLVWBEFNrSdky3jVAapsal4BdtxjjuTCdp/BKf/g6S/LCuOXv8HOHnaryYREREREREREcpByBYYGEhgYODlT8zHX3/9BUBoaGhxllQp+Fb3xcnNiez0bGtb/J54+4ZsAE4e0PV7+KUNnNwBG4ZD58WWAE5ERERERERExE4qzJxsGzZs4H//+x/bt2/n0KFDLF68mLFjx3LzzTdTvXp1e5dX7pgcTPjX87dps/u8bOd4RkCXb8DBGY5+Bf+8bO+KRERERERERKSSqzAhm6urK4sWLaJ79+40btyYiRMnMnr0aD7//HN7l1ZuBTaw7UEYvzfeTpXkI6gTtH3X8vrviXD0W/vWIyIiIiIiIiKVWpkfLlpQrVq1YuPGjfYuo0IpcyuM5lbnHkjaAf+9BRuGgNcf4Nfc3lWJiIiIiIiISCVUYXqySfEr8yEbQKsZENITslNhzU1wJsbeFYmIiIiIiIhIJaSQTS4q93DRpENJZGdkX+RsO3Fwsix84NMA0o7C7/0g+4y9qxIRERERERGRSkYhm1xU7p5sRo5B0oEkO1VzCS5+0PVHy9eEP+HPkWAY9q5KRERERERERCqRAs3J1r9//0Lf+N1336Vq1aqFvk7KDjdfN7xCvEiJSbG2xe+NJ6hxkB2rugifepYVR1deB0c+B59G0PR5e1clIiIiIiIiIpVEgXqyfffdd7i4uODr61ugbcmSJaSkpFz+xlLmlYt52c4J7g5t51he/z0Rjiy2azkiIiIiIiIiUnkUeHXRt956q8A907766qsrLkjKloAGARxZc8S6X6ZDNoC6o+DULtj7P9g4DLxqQUBbe1clIiIiIiIiIhVcgXqyrVq1Cn9//wLfdOnSpYSHh19xUVJ25F78IH5vvJ0qKYSWr0NYX8hJh99vgbRj9q5IRERERERERCq4AoVs3bp1w8mpwJ3e6Ny5M66urldclJQd5Wq46DkOjtDpM/BtAmeiYc3NkJ1q76pEREREREREpAIreHKWS2xsLLGxsZjNZpv2Zs2aFbkoKTsCG9r2ZDuTeIa0+DQ8Aj3sVFEBOftAtx/hl6sh6S/YMBQ6fwkmLagrIiIiIiIiIsWv0CHb1q1bGTZsGLt378YwDABMJhOGYWAymcjJySn2IsV+qtSsgqOLIzmZ539d4/fEU71zdTtWVUBeNaHrt7DiGjj6Dex8HppPsXdVIiIiIiIiIlIBFbpbz4gRI6hfvz7r16/n4MGDHDp0yOarVCwOjg7417Wdj69czMt2TlAnaPeh5fW/r8ChhfatR0REREREREQqpEL3ZDt06BDffPMNdevWLYl6pAwKaBBA3K446365mJftQrUGQ/JuS8j25yjwqm0J30REREREREREikmhe7Jde+217NixoyRqkTKqXC5+kFuzlyCiP5gz4fdbIeWQvSsSERERERERkQqk0D3Z5s6dy7Bhw/jnn39o0qQJzs7ONsdvvvnmYitOyobABraLH5Sr4aLnmBygw8eQchiStsGaG+G6P8Clir0rExEREREREZEKoNAh2/r161m3bh1Lly7Nc0wLH1RMuXuyJR1IIicrB0dnRztVdIWcPKHbD5YVR0/tgnW3Q/efwcH58teKiIiIiIiIiFxCoYeLPvTQQwwZMoTo6GjMZrPNpoCtYsrdk82cbSbpYJKdqikij3Do9pMlcIv5DTbfB2dXyRURERERERERuVKFDtkSEhJ45JFHCA4OLol6pAxy93fHI8jDpq1czst2jn9L6PSFZQjpgQ9h92v2rkhEREREREREyrlCh2z9+/dn1apVJVGLlGEVYl62C4XfCK3etLze/hREfmnfekRERERERESkXCv0nGz169fn6aefZt26dTRt2jTPwgcPPfRQsRUnZUdAgwAi10Va9xP+K8c92c5pMA5SDsDembB+CLhXg6AO9q5KRERERERERMqhK1pd1MvLizVr1rBmzRqbYyaTSSFbBeVfz99m/+TBk/YppLi1nA4pByHqB/j9Fui1Ebxq27sqERERERERESlnCh2yHTp0qCTqkDLOr7afzX7igUQ7VVLMHByh02fwa1dI2gar+8L168HF7/LXioiIiIiIiIicVeg52aRy8q9j25Mt+WgyOZkVZDVZJ0/o9iN4REDyHlh7G+Rk2rsqERERERERESlHCt2TzTAMvvrqK1atWkVsbCxms9nm+DfffFNsxUnZkbsnm2E2OBV5Cv+6/he5opzxCINuP8GvneHEKtg8Ftp9BCaTvSsTERERERERkXKg0D3ZHn74YYYMGcKhQ4fw8vLC19fXZpOKya2KG25+bjZtSQeT7FRNCfFrBp2/BJMjHJwP/75i74pEREREREREpJwodE+2Tz75hG+++YY+ffqURD1ShvnV9iN6a7R1v8KFbABhvaDNO7D5Xtj5nGURhJoD7V2ViIiIiIiIiJRxhe7J5uvrS+3aWn2xMso9L1uFDNkA6o2FRo9ZXm8cDrHr7FqOiIiIiIiIiJR9hQ7ZJk+ezAsvvMCZM2dKoh4pw6rUrmKzf/LQSbvUUSpaTIOI/mDOhLX9IHmfvSsSERERERERkTKs0MNFb7/9dj7//HOqVq1KzZo1cXZ2tjm+bdu2YitOyhbvMG+b/dTYVDtVUgpMDtBhIaQehcTNsPoGuG49uAfbuzIRERERERERKYMKHbINHz6crVu3MnjwYIKDgzFp9cVKw7Oqp81+hQ7ZAJw8oPtPsLwDpByENTdCz9Xg5HnZS0VERERERESkcil0yLZkyRJ++eUXOnfuXBL1SBnmGZQrZIur4CEbgFtV6L4Mfu0AiVtg3R3Q9XtwKPRvHRERERERERGpwAo9J1tERAQ+Pj4lUYuUcbl7sp1JOIM522ynakqRTz3o9hM4usPxn2HzfWAY9q5KRERERERERMqQQodsM2bM4IknnuDw4cMlUI6UZblDNoC0+DQ7VGIHge2h0xeWudoOzIV/XrZ3RSIiIiIiIiJShhQ6ZBs8eDCrVq2iTp06eHt74+/vb7OVlClTptCxY0c8PDyoUqVKvudERkZy00034enpSWBgIA899BCZmZklVlNl4x7gDrmm4KsUQ0bPqXYztHnb8vrviXBgnn3rEREREREREZEyo9ATS82cObMEyri8zMxMbr/9djp06MCHH36Y53hOTg59+/YlKCiIdevWkZCQwLBhwzAMg1mzZtmh4orHwdEBjwAPm95rFX7xg9zq3WdZcXTXVNg0GtzDIKyXvasSERERERERETsrdMg2bNiwkqjjsl544QUA5s+fn+/x5cuXs2vXLo4ePUpYWBhgGdo6fPhwpkyZonnkiolHUCUP2QCaT4G0Y3B4Iay7Da5dDQFt7F2ViIiIiIiIiNhRgYaLJicnF+qmp0+fvqJiimLDhg00adLEGrAB9OrVi4yMDLZu3XrR6zIyMkhOTrbZ5OJyz8uWFldJ5mS7kMkE7eZCSE/IToXVN8CpXfauSkRERERERETsqEAhm5+fH7GxsQW+aXh4OAcPHrzioq5ETEwMwcHBNm1+fn64uLgQExNz0eumTp2Kr6+vdYuIiCjpUsu13CFbpezJBuDoAl2+Af+2kJEAK3tCSul+z4uIiIiIiIhI2VGg4aKGYTB37ly8vLwKdNOsrKwCnTd58mTrMNCL2bx5M23aFGwonslkytNmGEa+7ec8/fTTTJgwwbqfnJysoO0SPII8bPYrbcgG4OwNPZbCb93h1D+woidctxY8wu1dmYiIiIiIiIiUsgKFbNWrV+eDDz4o8E1DQkJwdna+7Hnjxo3jrrvuuuQ5NWvWLPAz//zzT5u2pKQksrKy8vRwu5Crqyuurq4FeoZouGgergFwzXL4tSuk7IeV10HPNeAWZO/KRERERERERKQUFShkO3z4cIk8PDAwkMDAwGK5V4cOHZgyZQrR0dGEhoYClsUQXF1dad26dbE8QzRcNF/uoXDtb/BrZ0jeDat6wbWrwMXX3pWJiIiIiIiISCkp0JxsZUFkZCTbt28nMjKSnJwctm/fzvbt20lJSQHg+uuvp3HjxgwZMoS//vqLFStW8NhjjzF69GitLFqMPINyhWxxCtkA8KwB1/wGrkGQ9Bes6WtZFEFEREREREREKoVyE7JNnDiRli1bMmnSJFJSUmjZsiUtW7Zky5YtADg6OrJkyRLc3Nzo1KkTd9xxB/369WP69Ol2rrxiUU+2S/BpANf8Cs5VIO4P+L0/5GTYuyoRERERERERKQUmwzAMexdRliQnJ+Pr68upU6fUAy4fcbvjmN14tk3bs+nP4uRaoJHHlUPcBlh1naUnW7VbofNicNDnIyIiIiIiIlLeFCYnKjc92aRsyN2TDSAtvpIvfpBbUAfo+j04uMCxb2HjPWCY7V2ViIiIiIiIiJSgQodskZGR5Nf5zTAMIiMji6UoKbvc/dwxOZps2jRkNB8h10LnL8HkCIcXwpYHQZ1GRURERERERCqsQodstWrVIi4uLk97YmIitWrVKpaipOwyOZjwCPSwaVPIdhHVboYOHwMm2Dcbdjxr74pEREREREREpIQUOmQzDAOTyZSnPSUlBTc3t2IpSsq23ENG0+I0XPSiag6Cq9+1vN41Ff6dat96RERERERERKREFHg29gkTJgBgMpl4/vnn8fA435spJyeHP//8kxYtWhR7gVL2eAZphdFCqTsGspLhr8dhxzPg5AkNHrJ3VSIiIiIiIiJSjAocsv3111+ApSfb33//jYuLi/WYi4sLzZs357HHHiv+CqXMyd2TLTVOIdtlNXoMsk7DPy/C1ofBwRXqjbV3VSIiIiIiIiJSTAocsq1atQqAESNG8Oabb1522VKpuDyqak62K9J0MuScgd2vw+Z7wdENag+zd1UiIiIiIiIiUgwKHLKdM2/evJKoQ8qR3MNF02I1J1uBmEzQYhrkpMN/s+DPeyw92mreZe/KRERERERERKSICh2ypaam8uqrr7JixQpiY2Mxm802xw8ePFhsxUnZpOGiRWAyQes3wZwB+9+HDYPB0RUibrV3ZSIiIiIiIiJSBIUO2UaNGsWaNWsYMmQIoaGh+a40KhVbnpBNw0ULx2SCtnMsPdoOfQx/3AldvoPwPvauTERERERERESuUKFDtqVLl7JkyRI6depUEvVIOeARpDnZiszkAO0+hJwMiFwEa/tD958gpKe9KxMRERERERGRK+BQ2Av8/Pzw9/cviVqknMjdky0rNYustCw7VVOOOThBx4VQrZ9l+OiamyH2d3tXJSIiIiIiIiJXoNAh20svvcTEiRNJS9Nk95VV7oUPQPOyXTEHZ+j0BYT2tqw8urovxG2wd1UiIiIiIiIiUkiFHi46Y8YMDhw4QHBwMDVr1sTZ2dnm+LZt24qtOCmbXH1dcXB2wJx1ftGL1NhUqtSoYr+iyjNHV+jyNay5CU6sgNW94doV4N/a3pWJiIiIiIiISAEVOmTr169fCZQh5YnJZMKzqieno05b29Li1LOxSJzcodv3sKo3xK2FldfDtavAr5m9KxMRERERERGRAih0yDZp0qSSqEPKGc8g25BNix8UAydP6L4EVl4HCX/Cyp7Qcw34NrJ3ZSIiIiIiIiJyGYWekw3g5MmTzJ07l6effprExETAMkw0KiqqWIuTsiv34geak62YOHtDj2Xg1woy4mDFNZC8195ViYiIiIiIiMhlFDpk27lzJ/Xr12fatGlMnz6dkydPAvDtt9/y9NNPF3d9UkblCdnUk634uFSBa5ZDlWaQHgMrekDyf/auSkREREREREQuodAh24QJExg+fDj79u3Dzc3N2t67d29+//33Yi1Oyi6PIA+b/bRYzclWrFwD4JoVUKUpnIk+G7Tts3dVIiIiIiIiInIRhQ7ZNm/ezNixY/O0h4eHExMTUyxFSdmn4aKlwC3QErT5NoEzxy1B2+n99q5KRERERERERPJR6JDNzc2N5OTkPO179+4lKCioWIqSsi93TzYNFy0hbkFw7QrwbQxnos4GbQfsXZWIiIiIiIiI5FLokO2WW27hxRdfJCsrCwCTyURkZCRPPfUUt912W7EXKGWT5mQrRW5V4ZqV4NMI0o5ZgraUg/auSkREREREREQuUOiQbfr06cTFxVG1alXOnDlDt27dqFu3Lt7e3kyZMqUkapQyKHfIlhaXhmEYdqqmEnAPhmtXgk9DSDsKv/WAlMP2rkpEREREREREznIq7AU+Pj6sW7eOlStXsm3bNsxmM61ataJnz54lUZ+UUZ5BtiFbdno2mSmZuHq72qmiSsA9xBK0/dYdTv8HK7pDzzXgWcPelYmIiIiIiIhUeoUO2c655ppruOaaa4qzFilHcvdkA8uQUYVsJcw9FK5dZQnYTu+zBG4914BndXtXJiIiIiIiIlKpXVHItmnTJlavXk1sbCxms9nm2BtvvFEshUnZ5uzpjJObE9np2da2tLg0/Ov427GqSsIjzBK0/dYdUvZfELRF2LsyERERERERkUqr0CHbK6+8wnPPPUeDBg0IDg7GZDJZj134Wio2k8mEZ1VPTkWesrZp8YNS5BEOPc8FbQfODx31qGbvykREREREREQqpUKHbG+++SYfffQRw4cPL4FypDzJE7LFKWQrVR7Vzg8dTTl4vkebR7i9KxMRERERERGpdAq9uqiDgwOdOnUqiVqknPEI8rDZV082O/CMsARtnrXO9mjrAWlR9q5KREREREREpNIpdMj2yCOP8M4775RELVLO5F78QCGbnXhWtwwd9axpWQxBQZuIiIiIiIhIqSv0cNHHHnuMvn37UqdOHRo3boyzs7PN8W+++abYipOyLXfIlhaXZqdKBM8aZ4eO9ji76mi3sz3ctBiCiIiIiIiISGkodE+2Bx98kFWrVlG/fn0CAgLw9fW12aTy0HDRMsarJvRcfcHQ0e6QGmnnokREREREREQqh0L3ZPv444/5+uuv6du3b0nUI+WIhouWQZ41LEHbimssQdu5Hm1eNe1dmYiIiIiIiEiFVuiebP7+/tSpU6ckarmkKVOm0LFjRzw8PKhSpUq+55hMpjzbu+++W7qFViKeQRouWiZ5VrcEbd71IPWwJWhLOWTvqkREREREREQqtEKHbJMnT2bSpEmkpZVuoJKZmcntt9/Offfdd8nz5s2bR3R0tHUbNmxYKVVY+eTXk80wDDtVIzY8qsG1q8G7PqRFWoK20wfsXZWIiIiIiIhIhVXo4aJvvfUWBw4cIDg4mJo1a+ZZ+GDbtm3FVtyFXnjhBQDmz59/yfOqVKlCSEhIidQgtnKHbOZsM+kn03H3c7dTRWLDI+z80NHkPZY52q5dBd517V2ZiIiIiIiISIVT6JCtX79+JVBG8Rk3bhyjRo2iVq1ajBw5kjFjxuDgcPEOexkZGWRkZFj3k5OTS6PMCiH3wgdgGTKqkK0McQ+1BGsrr4VTu2BpK6gzCho8pHnaRERERERERIpRoUO2SZMmlUQdxeKll17i2muvxd3dnRUrVvDoo48SHx/Pc889d9Frpk6dau0lJ4Xj7O6Mi5cLmSmZ1rbU2FQC6gfYsSrJwz3EErStvhESN8Pe/8F/b0LEAGj4KARebe8KRURERERERMq9Qs/JBnDy5Enmzp3L008/TWJiImAZJhoVFVWo+0yePDnfxQou3LZs2VLg+z333HN06NCBFi1a8Oijj/Liiy/y+uuvX/Kap59+mlOnTlm3o0ePFuo9VHZ55mWL0wqjZZJbVej1J3RfCiE9wTBD5GJY3g5+7QJHvwVzjr2rFBERERERESm3Ct2TbefOnfTs2RNfX18OHz7M6NGj8ff359tvv+XIkSN8/PHHBb7XuHHjuOuuuy55Ts2aNQtbolX79u1JTk7mxIkTBAcH53uOq6srrq6uV/yMys4jyIOkg0nW/dRYhWxllskEYTdYtqSdsOcNOPIZxK2zbF51oMF4qDMCnDwvezsREREREREROa/QIduECRMYPnw4r732Gt7e3tb23r17M2jQoELdKzAwkMDAwMKWUGB//fUXbm5uVKlSpcSeUdnlt8KolAN+zaDDfGj+Cux7B/bNgZQDsPVB+Hsi1L0X6o+zLJ4gIiIiIiIiIpdV6JBt8+bNvPfee3naw8PDiYmJKZai8hMZGUliYiKRkZHk5OSwfft2AOrWrYuXlxc//vgjMTExdOjQAXd3d1atWsWzzz7LmDFj1FOtBOVe/CAtLs1OlcgV8QiD5lPgqmfg4ALY8z9I2Q+7psKe6VD9Dqj/IAS2s3elIiIiIiIiImVaoUM2Nze3fFfg3Lt3L0FBQcVSVH4mTpzIggULrPstW7YEYNWqVXTv3h1nZ2dmz57NhAkTMJvN1K5dmxdffJEHHnigxGoS9WSrMJw8of79UHcsHP8Jds+AuLVw+FPL5t8WGjxoCd0cFVqLiIiIiIiI5GYyDMMozAVjxowhLi6OxYsX4+/vz86dO3F0dKRfv3507dqVmTNnllCppSM5ORlfX19OnTqFj4+Pvcsp8zb8bwPLJyy37tfsUZNhK4fZsSIpNolbYe8sOPI5mM+uIOtWFeqMgXr3gke4fesTERERERERKWGFyYkKvbro9OnTiYuLo2rVqpw5c4Zu3bpRt25dvL29mTJlyhUXLeWTZ5BtTzYNF61A/Ftb5m3rd8wypNQ9HNJj4d+X4fuasO4uiPsDCpfTi4iIiIiIiFRIhR4u6uPjw7p161i5ciXbtm3DbDbTqlUrevbsWRL1SRmn4aKVgFuQZc62Ro/Dse8svdvi1kLkIsvm19Iyb1uNu8DJ3d7VioiIiIiIiNhFoYeLVnQaLlo40X9F836r9637JgcTz2c9j8nBZMeqpMQl7YD/Zlnma8tJt7S5+EPtEZZ53Xzq2bc+ERERERERkWJQosNFH3roId5666087W+//Tbjx48v7O2knMvdk80wG5xJPGOnaqTU+DWHdnMtQ0lbTAOP6pCZCHtmwE/1YUVPiPwKzFn2rlRERERERESkVBQ6ZPv666/p1KlTnvaOHTvy1VdfFUtRUn7knpMNNGS0UnENgMZPwM0HoduPENYXMMGJFbDudviuOux4DlKP2LtSERERERERkRJV6DnZEhIS8PX1zdPu4+NDfHx8sRQl5YejiyOuvq5knMqwtqXGpRJEUL7nG4ZBSkoKsbGx1u3EiRM2+7GxseTk5PDYY49x0003ldZbkaJwcITwGy1bymE4MNeypcfAv1Pg31cgrI9lVdLQ3pbzRURERERERCqQQodsdevWZdmyZYwbN86mfenSpdSuXbvYCpPywzPI0yZkW/bVMtK2puUbnsXGxpKenl6g+/7++++MHTuWGTNm4OmZt8eclFFeNaH5y9B0Ehz7Hva9a+nZdnyJZfOoDnVHQ+17wCPM3tWKiIiIiIiIFItCL3zw0UcfMW7cOB5//HGuueYaAFasWMGMGTOYOXMmo0ePLpFCS4sWPrD0Njt58mS+AVl+Pc9uTbqV6lS3Xr+EJWxm8yWf4eXlRdWqVS+6bd68mf/9738ANGjQgE8//ZTWrVuX6PuWEpT8H+x/Hw7Nh4wES5vJ0dK7rc5Iy1cHZ7uWKCIiIiIiIpJbYXKiK1pddM6cOUyZMoXjx48DULNmTSZPnszQoUOvrOIypKKGbOnp6RcNzXKHZ3FxcWRlFXzC+ju5k0Y0su5H1o4ko0PGRQO0oKCgAvVMW7FiBcOGDSMqKgonJydeeuklHn/8cRwdNdSw3MpJh8ivYf+7ELfufLtbCNQeBrVHamVSERERERERKTNKPGQ7Jy4uDnd3d7y8vK70FmVORQvZnnnmGd5++21Onz5d6Gt9fX3zBGTBwcF52v599V/+/fhf63Vt7mtD39l9i6X+xMRExo4da11Uo2vXrixcuJDq1atf5kop807tgYMfwaEFkB57vr1qV0vYVn0AOHnYrz4RERERERGp9AqTExV6TrYLBQXlP7m9lB0mk8kasLm4uFy0d1nu8CwoKAhXV9cCPSM6ItpmvzhXF/X392fx4sUsWLCABx98kN9//51mzZoxZ84cBg4cWGzPETvwbQgtX4PmUyDqJzjwIUQvhdjfLduW+8GzFrgFg3uIpbebe4hl3/o6xLLCqanQCyWLiIiIiIiIFKsr6sn21VdfsXjxYiIjI8nMzLQ5tm3btmIrzh4qWk+26OhoUlJSqFq1Kj4+PphMpmJ/xp9v/cmyh5dZ92t0rcHwNcOL/TkHDhxg8ODBbNy4EYC7776bd955J9/VbqWcSouCg/MtPdxSDhbsGpMjuFUF16qWwM01AFwDwSXg/L7LBe2uAeDsCyXwe0FEREREREQqlhLtyfbWW2/x7LPPMmzYML7//ntGjBjBgQMH2Lx5Mw888MAVFy0lIzQ0tMSf4VnVdn614uzJdqE6deqwdu1apkyZwksvvcSnn37KunXrWLhwIV26dCmRZ0op8wiHJs/CVU9D8h44cxzOxEB6DKSfOP/63NeMeDBy4Ey0ZSsok6MlaHP2yfvV5SLt1q8+5/cdXUrusxAREREREZFypdA92Ro2bMikSZMYOHAg3t7e7Nixg9q1azNx4kQSExN5++23S6rWUlHRerKVhoMrDrKw50LrvnuAO0/EP1Giz9ywYQODBw/m4MGDODg48NRTTzF58mScnbVCZaVizrLM55YeA+lxlpVLMxMs4VtGwgX7F7zOLsYQ2MHVNnQ799o9FLzqgFft85uLelyKiIiIiIiUNyW68IGHhwe7d++mRo0aVK1alV9//ZXmzZuzb98+2rdvT0JCQpGKtzeFbIV34u8TvNvsXZu2Z888i5Nbkab8u6zTp0/z8MMPM2/ePADatm3LJ598Qv369Uv0uVLO5aRDRiJknYKs5LxfMy/Sbv2aDNkphX+ui//ZwC1X+OZVGzwiwEGr5oqIiIiIiJQ1JTpcNCQkhISEBGrUqEGNGjXYuHEjzZs359ChQxRhoVIpx/xq+YEJuOCXP/bfWMJah5Xoc729vfnoo4/o06cPY8aMYfPmzbRs2ZKZM2cyatSoEpl/TioARzfwCAOK8P1pzoHs0+dDt9xhXNoxOH3AMq9c6kFLb7vMREhMhMQtee9ncgLP6uBR3RK4eVY/v+95ts3Z+8rrFRERERERkRJX6JDtmmuu4ccff6RVq1aMHDmSRx55hK+++ootW7bQv3//kqhRyjgXLxf86/iTuD/R2hazPabEQ7ZzBgwYQPv27Rk+fDgrVqxgzJgxLFmyhLlz5xIYGFgqNUgl4+AILlUsW0FkpUDqofPB27kt9SCkHAJz5vm2i3Hxs4Rt54I3z+rgXs0SGLqFWr46eWtBBxERERERETsp9HBRs9mM2WzGycmSzy1evJh169ZRt25d7r33XlxcyvdE4BouemW+vP1Ldn21y7rfdlxb+szqU6o1mM1m/ve///HMM8+QmZlJSEgI8+fPp1evXqVah0ihmHMsCzykHoG0o5AWCalnt3Ovs04W7F5OnucDN/cw29fuYZa54txDFcaJiIiIiIgUUInOyVbRKWS7Mr9P+Z1Vz62y7vvV8aPdQ+3wDvPGO9zb8jXUG0eXkp93aseOHQwaNIhduyyh30MPPcSrr76Ku7t7iT9bpERknbYEcBcGb6mRcCbq7Aqs0ZZhqgXl6AZuwRDaC1pMK3iPPBERERERkUqm2EO2nTt3FvjhzZo1K/C5ZZFCtivz35L/+PzGzy97nkeQB95h3viE++AV5mV9fWEY5xnkicmhaL1szpw5w5NPPsmsWbMAuOqqq/jss8/K/fenyEVlp1rCtnOh25njli3tOKRHn3+dfdr2Oo8IaD8PQq61T90iIiIiIiJlWLGHbA4ODphMpssubGAymcjJySlctWWMQrYrc/r4ad4If6NY7uXg5IBXqCWAuzB8s4ZxZ9tcfVwvu7jBsmXLGD58OCdOnMDFxYWpU6cyfvx4HBwciqVWkXInOw3ST8Cp3bD1YUjZb2lv8DA0nwpO6vEpIiIiIiJyTrGHbEeOHCnww2vUqFHgc8sihWxX7qs7v+Lfxf+W2vOcPZzPD0W9WBgX5k3S6SRGjRrFDz/8AEDPnj2ZP38+4eHhpVarSJmUlQJ/PQ7737Xs+zSEDgshoI196xIRERERESkjNCdbEShku3I5WTns/no3UZuiOH38tGWLsnzNTs+2W13u/u54h3lzynyKzXs3k5SThNnDzLBxw+h1ey/LENVgTxwc1btNKqnjS+HPkZZhpiZHaPI8XPUMODjbuzIRERERERG7KvGQbe/evcyaNYvdu3djMplo2LAhDz74IA0aNLjiossKhWzFzzAM0pPSrcFbclSyTQB37nVKTAqG2T6Zr8nBhFeIl+1CDRf0kjvXO87Nz+2yQ1RFyqWMBNh8P0Qutuz7t4UOH4NvQ/vWJSIiIiIiYkclGrJ99dVXDBw4kDZt2tChQwcANm7cyObNm/nss8+4/fbbr7zyMkAhm/2Yc8ykxqZawzdrGHdhIBd1mjOJZ+xWo5ObU57hqfkNVXX2UA8gKYcMA458YQnbsk5aViFt8RrUfwBM6ukpIiIiIiKVT4mGbLVr12bw4MG8+OKLNu2TJk1i4cKFHDx4sPAVlyEK2cq+7PRsTkefzhPGpRxPsekll5WWZbcaXX1d8yzUkDuY8wrxwtHZ0W41ilxUWhRsvAdillv2Q3pCu4/AM8K+dYmIiIiIiJSyEg3ZPDw82LlzJ3Xr1rVp37dvH82bNyctLa3wFZchCtkqBsMwyEjOsOkFl18YlxKdgjnbbJ8iTeBZ1dMaxnmFeZ0P5i4I4zwCPTREVUqfYcC+2ZaFEXLOgLMvtHkHag4CfT+KiIiIiEglUZicyKmwN+/evTtr167NE7KtW7eOLl26FPZ2IiXCZDLh5uuGm68bQY2CLnqeYTZIjUvly4++5K2X38IxzZEA5wCu73A9IV4h1oAuLa4EwmMDUk+kknoileht0Rc9zdHFEa9QL+twVJsw7oJAztXbtfhrlMrLZLIMEw25DjYMgYRNsGEwHPsOrn4XXAPsXaGIiIiIiEiZUuiebO+++y4TJ07kjjvuoH379oBlTrYvv/ySF154gbCwMOu5N998c/FWWwrUk63yOnLkCEOHDuX3338HYMCAAbz33nv4+/uTk5ljGaKaz4INF/aSyzydabf6XbxdbBZqyDeMC/XG0UVDVKWQzNnw71T450UwssEtBNp9COF97F2ZiIiIiIhIiSrR4aIODgWb/NpkMpGTk1OYW5cJCtkqt5ycHF5//XWef/55srOzCQ8P5+OPP+aaa64p0PUZpzPyLtaQTxhnzrLTEFXAI8gj38UbLhyq6hnkiclBQwIll8StsH4IJO+27NcZDa1mgLO3fesSEREREREpISUaslV0CtkEYOvWrdx9993s3bsXgEcffZQpU6bg6lr0IZmGYXAm4cxFV08915ZyIgXs9LvTwckBrxCvfBdsuDCMc/Vx1XxxlU32GdjxDOydadn3rGHp1RZyrV3LkoIzZ5s5k3iGtIQ0ziScsXltbTvbnp2eTWCjQMLahhF+dTjBTYPVG1ZEREREKpVSD9lOnjxJlSpVinqbMkEhm5yTmprKY489xrvvvgtA8+bN+fTTT7nqqqtK5fk5WTmknki9bBiXfjK9VOrJj7OHc54Q7tzrC4eqOrkVevpHKetOrLasQJp6yLJf735oMQ2cvexaVmViGAaZpzPzBmSJtmFZ7vaMUxlX/ExHV0dCWoRYQ7fwtuEE1A9Qz1cRERERqbBKNGSbNm0aNWvW5M477wTg9ttv5+uvvyY0NJSff/6Z5s2bX3nlF3H48GFeeuklVq5cSUxMDGFhYQwePJhnn30WFxcX63mRkZE88MADrFy5End3dwYNGsT06dNtzrkchWyS248//sjIkSOJi4vDzc2N119/nQceeKDM9ODKTM0kJTrFZjhqfkNVs9Oz7Vaju7/7RcM47zBvghoF4eJV8N+nUkZkpcD2Jy2rkAJ41oL28yC4m33rKoeyM7I5k2gbihUkNLPb6sgXcPVxJaxNGGFXhxHeNpzwq8PxDvcuMz8jRURERESKokRDttq1a/PJJ5/QsWNHfv31V+644w4WLVrE4sWLiYyMZPny5UUqPj/Lli1j0aJFDBw4kLp16/LPP/8wevRohgwZwvTp0wHLXFotWrQgKCiIGTNmkJCQwLBhw+jfvz+zZs0q8LMUskl+YmJiuOeee1i6dCkAvXv35qOPPiIkJMTOlRWMYRikJ6Vbg7eLhXEpMSkY5tIfo+rg5EDDWxvSe1ZvvILVE6rciVlh6dWWFmnZr/8QtHgFnDztW1cZdPLwSTa9vYkTO07YhGlZqVn2Lq1YeYV4EX51uLXHW1ibMNz93e1dloiIiIhIoZVoyObu7s5///1HREQEDz/8MOnp6bz33nv8999/tGvXjqSkpCIVX1Cvv/46c+bM4eDBgwAsXbqUG2+8kaNHj1pXOP3iiy8YPnw4sbGxBQ7MFLLJxRiGwezZs3nsscdIT08nMDCQjz76iJtuusnepRUbc46Z1BOplw3jziSeKZHnBzcLZuSGkTh7OJfI/aUEZSXDX4/D/vct+151Lb3aqna2b11lyK6vd/HdsO/KVKDm4uWCe4A7HgEeNl/d/d1xD3DHyDGI3hpN1OYoEvclFulZ/nX9z4dubcMIbRmq3+siIiIiUuYVJicq9ERJfn5+HD16lIiICJYtW8bLL78MWAKI0lxN9NSpU/j7+1v3N2zYQJMmTawBG0CvXr3IyMhg69at9OjRI9/7ZGRkkJFxfn6a5OTkkitayjWTycQDDzxAjx49GDRoEDt27ODmm29m2LBhzJgxg4CAAHuXWGQOjg7WIZyXkp2efT50uyCMSzmeYhPMZaUVLkw4sfMEP937E/0W9NNQs/LG2Qeufg+q9YdNoyBlP/zWFRo+As1eBqfK3Yvp+NbjfDPoG3IyS+bPSQcnh/zDsrOBWe52jwAP3P3dC7WIwZmkMxzfcpyoTVEc32z5mhKdUuDrE/cnkrg/kX8+/wcAk6OJqk2q2vR4q3pVVRycCraKuYiIiIhIWVPonmzjxo3jp59+ol69evz1118cPnwYLy8vFi1axLRp09i2bVtJ1Wp14MABWrVqxYwZMxg1ahQAY8aM4fDhw3mGq7q6ujJ//nwGDhyY770mT57MCy+8kKddPdnkUjIyMnjuueeYMWMGhmEQFBTEW2+9xZ133qlw6CzDMMhIzrDpCZdfGJd8NG+w3Wd2H9re19YOVUuxyDwF2ybAwY8s+971ocMCCGxv37rsJP1UOu+3ep+kgwXr6e1Wxc3ak+zCsOzCXma5QzMXLxe7/OxJjkq2Cd2ObzlepIUVnNydCG0VarOwgl8dP/1cFREpJ6L/iuansT8R+08sPuE+tBrdig6PdsDBUf+AIiLlV4kOF83KyuLNN9/k6NGjDB8+nJYtWwIwc+ZMvLy8rKFXQVws4LrQ5s2badOmjXX/+PHjdOvWjW7dujF37lxr+5gxYzhy5Ai//PKLzfUuLi58/PHH3HXXXfneP7+ebBEREQrZpEA2btzIqFGj+PfffwHo06cPc+bMoXr16naurPyI2R7DR50/shlC5+DswIjfR1CtfTU7ViZFFrUENo2GM9FgcoBGj0PTyeDoZu/KSo1hGHw54Et2f7Pbpr1Gtxo0G9IMzyBP2wDNz71c9+QyzAYJ+xLOh26bjxP9VzQ5GVfeg8/Nz43wtuHWhRXC2obhHXrp3rYiIlL69v6wl68Hfp1nJEP9m+pz++Lbtdq8iJRbJRqyFaf4+Hji4+MveU7NmjVxc7P8hez48eP06NGDdu3aMX/+fBwczv9FZOLEiXz//ffs2LHD2paUlIS/vz8rV6686HDR3DQnmxRWZmYmr732Gi+99BKZmZl4enoydepU7r//fhwdCz4UqzLb/e1uFvdfbNPmHe7N2G1j8ayqyfPLtcwk2PIwHF5o2fdtDO3nQ0Dl6Km48c2N/DLe9h9/IjpGMGz1MBydK8fPh5zMHGL/iSVqc5Q1eIv7N65Ii6z4VPOxmd8trE0Ybr6VJ7wVESlLDMPgz7f+5JdHfoGL/Giv16ced3xzB06uCtpEpPwp9pDthx9+oHfv3jg7O/PDDz9c8tybb765cNUWUFRUFD169KB169Z88sknecKLcwsfHDt2jNDQUAAWLVrEsGHDtPCBlIo9e/YwZswY1q5dC0C7du2YO3cuTZo0sXNl5cNvT/3GH9P+sGmr2aMmQ5YPKdc9e+SsY9/DprGQfgJMjtD4KWjyPDi62ruyEhO1KYqPOn+EOctsbXMPcGfsX2PxjfC1Y2X2l5mSSfRf0TZDTU8eOlmkewY0CLCZ3y2keYh6TYiIlDBztpll45ex+Z3Nlz23Yb+GDFg8oNL8I5OIVBzFHrI5ODgQExND1apVbXqP5bmZyVQiix+cGyJavXp1Pv74Y5uALSQkBICcnBxatGhBcHAwr7/+OomJiQwfPpx+/foxa9asAj9LIZsUhdls5oMPPuCJJ54gOTkZJycnnnrqKZ599llrj0zJnznbzCe9PuHQykM27Z2e7ETPV3vaqSopVhkJsOVBOPK5Zd+3CbT/qEL2ajuTeIb3Wr3HqSOnbNoH/TyIer3r2amqsi0tPo2ozVE2Q01TY1Ov+H4Ozg4ENwu2md8tsFGg5gUSESkmGacz+OrOr9i/dH+eY07uThhmI890AVfdcRX9P+2vf0AVkXKl3AwXLaj58+czYsSIfI9dWH5kZCT3338/K1euxN3dnUGDBjF9+nRcXQveU0IhmxSHqKgoxo0bx3fffQdAgwYN+OCDD+jSpYt9CyvjUmNTeb/1+yQfs10M4Y6v76BR/0Z2qkqKXeTXsPk+yIg7P1dbk0kVZgVSwzBY1G8Re3/Ya9Pe+enOXPvKtXaqqvwxDINTkadsQrfjW46TmZJ5xfd09nQmrHWYdX638KvD8a3hq4UVREQKKflYMp/1/YwTO0/kOdbjpR50ebYLUX9GsfC6hXl+bjcb3Ixb5t+if/QQkXKjwoVspUkhmxSnb775hgceeICYmBgAxo4dy7Rp0/D1rdxDxS7l2J/HmNdlns0QOxdvF0ZvHk1gg0A7VibFKj0etj4MRz6z7Ps0gHYfQVBH+9Z1pQwzpB2D5D2sn7mdX187Y3O4Rssshn7ZBIeQjuARAQp1rog5x0zC3gSiNkVZer1tOk7MjhibnxeF5RHoYTPMNKxtGJ5BmgtSRORiUuNS+bD9h3lWzXZ0deSWebfQdGBTa9uRtUf49IZP8yyG0OKeFtz8wc2YHPTnoYiUfSUWspnNZubPn88333zD4cOHMZlM1KpViwEDBjBkyJAK8S/BCtmkuJ08eZInn3yS999/H4DQ0FDeeecdbr31VjtXVnZtnrOZn+//2aYt6KogRm0chYuXi52qqlhSUlLw8PC45BQApeLYD7D5XssKpJigwUPQfAo4ldGQI/sMnN4HyXtybXshJ42j+6ox/6URmHPOT2vg4ZPKva+8i7ffaUuDexgEdoDAjpav/q0q9Nx0JS07I5sTO0/YzO8Wvyf+opNvF0SVmlVsF1ZoHaafPSIiWP6x4+NrP+bImiM27e4B7tz1/V1U71Q9zzWHVh7is76fkZ2ebdPe5r429HmnT4X4O6SIVGwlErIZhsFNN93Ezz//TPPmzWnYsCGGYbB7927+/vtvbr75ZuvQuPJMIZuUlDVr1jBmzBj+++8/APr378+sWbMICwuzc2Vlj2EYfD/8e3Z8vMOm/ao7r+K2z2/T/4wVQmJiIrt27cqzRUVFERERwUsvvcTgwYPtuxJuZhJsexQOzrPse9WGdnMhuGCrQhe7nExIPQSn91sCtQu31CNcLL1JS/HmvWfvJTn+goDQBIMXhFGn0U6I3wBJ28HINXepgwv4tbIEbkFngzeP8BJ7e5VBRnIGx7cetxlqeiry1OUvvAiTg4nARoHne7y1DSe4WTCOLpq8W0Qql40zN1pWEb1AQP0ABi0ZhH9d/4tet3/Zfr645QtyMm3/DGw/oT3XT79e/28nImVaiYRs8+bN4+GHH+b777+nRw/bv/isXLmSfv368fbbbzN06NArr7wMUMgmJSk9PZ2XX36ZadOmkZ2dja+vL6+99hqjRo2yf4+iMiYrLYsPO37IiR22c330mtmL9g+3t1NVZZNhGMTFxeUbpp04kXeulNyaNm3KtGnTuOGGG+z7P7nHf4FNoyHtqGW/7r3Qcho4l8DPYnMWpBy2DdBS9p8P0nIHYRdyrgK+jSxDXH0agk9DDK8GfD5oC/uWHrA5tevzXenx4gV/ZmanQsIWS+B2bsuIy/sMj4izvd3O9njzawGO6klVFCknUmxCt6hNUZxJPHP5Cy/C0cWRkBYhNvO7BdQP0NAnEamwkg4lMafJHJuhnz7VfBi1aRTeod6XvX7vj3tZ3H8x5mzbIf5dnu3CNS9fU+z1iogUlxIJ2a6//nquueYannrqqXyPv/LKK6xZs4Zffvkl3+PlhUI2KQ07d+5k9OjRbNq0CYCuXbvy/vvv06BBAztXVrYkHkjk/dbvk3Eqw9rm4OTAsFXDqN4573CEis4wDKKjo/MN0xISEi56XY0aNWjcuLHNVqtWLT7++GNeeeUVTp48CUCPHj147bXXaNOmTSm9o3xkJcP2p2DfHMu+RwRc/QGE9Sr8vTJPWXqkpZzdLuydlnr40kGaowd41wPvume/nn3t0xBcg/LMqbbu1XWseHqFTVvNHjUZ8uuQS0/sbBiQchDi158P3U7utMzxdiEHV0vQFnD1+c27rmXhCLkihmFw8tBJm/ndordF55k3qDBcfVwJaxNmM9TUp5qPemiISLlnGAafXP8JB387aNM+/Pfh1OhSo8D32fXVLr668ysMs+1fQXu83IOuz3YtllpFRIpbiYRsISEhLFu2jBYtWuR7/K+//qJ3797WCd7LK4VsUlpycnJ4++23efbZZ0lNTcXV1ZXnn3+exx9/HBcX9Vg557+f/uPzmz63afMK8WLMtjEF+lfT8sgwDI4ePZpvmHbqVP5D3kwmE7Vr184TpjVs2BAvL6+LPisxMZFXXnmFWbNmkZlpWf3rrrvuYsqUKdSuXbtE3l+BnFgFf46yBFAAtUdAqxng4nf+nJx0S2+01AtCtAsDtcykfG9t5ehxNkS7IEjzOvvaPbTAixMcWXuEBT0WYOSc/+PUM9iTe7ffi1fIxT/7i8pKgcTNlsAtbj0kbISMfEJUZ18IaGsJ3PzPfvXQ8POiMGebidsVR9TmKGuPtxM7T9j82haWV4iXTegW3jYcd/+KsZKuiFQe2+dv5/sR39u0tbm/DX3f6Vvoe+38dCffDvk2z+wL18+4ng4TOhSlTBGRElEiIZuLiwtHjhwhNDQ03+PHjx+nVq1aZGRk5Hu8vFDIJqXtyJEj3HfffSxduhSAJk2aMHfuXNq1a2fnysqOVRNX8ftLv9u0Ve9SnaErhuLoXH7nRDKbzRw+fDhPkLZ7925SUlLyvcbBwYG6detaQ7SrrrqKxo0b06BBA9zdr/wv7keOHOH555/nk08+wTAMnJ2due+++3juuecICgq64vsWSXYq7HgW9r4FGJbgK/ia8yHamejL38M1CDxrglets9sFoZp7WJFX+UyNTeW9lu9x+vhpa5vJwcSQX4dQ65paRbq3lWFAygFI2AwJmyxb0jZLyJibe/jZnm7nwrc24FKBVjPOToPMRMhIhMwEMpKjycjOwafOTeBSpUQemXUmi5jtMTbDTBP3JRbpnn51/GxWNA1tGYqzh3MxVSwiUrxSTqTwTqN3SE86/+eOTzUf7v/3flx9rmzhnm0fbuPHUT/mae/zTh/a3t/2imsVESkJJRKyOTo6EhMTc9G/bJ04cYKwsDByci4x/KYcUMgm9mAYBp9//jkPP/ww8fHxmEwmHnzwQaZMmXLJXkiVhTnHzGd9P+PAL7bzXbV/pD293riCYYSlLCcnh4MHD+Ybpp05k/+cUE5OTtSvXz9Pz7T69evj6lpyK1Hu2LGDJ5980jr039vbm6eeeorx48fj4eFRYs+9pLg/YOM9cPq/vMecvM8HaJ65v9YE55L7/WOYDT7t/SkHltt+X3Z/sTvdnu9WYs8FLHPKnfzH0uPtXPB26t+8w0zBMnec/7lhpm2gSnNwslNPKsOwhKdZJy09DTNPnt2Szrbl8zozESMjASM9AQcj/3/IyzbDweQQUn27EtZ6JMENritygHopZ5LOEL012iZ4uzBoLSyTo4mqTapaQ7fwtuFUbVIVBycNBxYR+/vyji/Z9eUum7aBPw2kft/6Rbrvpnc2sXTc0jztN394My3vaVmke4uIFKcSCdkcHBzo3bv3Rf9yl5GRwbJlyxSyiRRBfHw8jz76KB9//DEA1atXZ86cOfTp08fOldlfWkIa77d+n1NHbIdLDlg0gKvuuMpOVdnKyspi//79ecK0vXv3XrSXr4uLCw0bNswTptWtWxdnZ/v1bPntt9944okn+OuvvwAICwvjhRdeYPjw4Tg5OZV+QdlnLKuPZqfYBmku/iUaplzK7y//zqrnV9m01b6uNncvvfvS87CVlKwUSw+3C3u8pR7Oe57JEXyvAv/WZ7c2UKXZ5YM3cxZknYbs05a587JOn92/8PW5Y6cuCNHOfj0XnBnZRXub2ZCYCgkpkJgCgd7QMNco2ZhkR/47XRtzSC9qdxxD9TpNi/TMgkiOSrYEbmfnd4vaHGUzn2RhObk7Edoy1GZhBb86fprfTURK1Z7v97Co3yKbtiYDm3DbZ7cVy/3XT1/Pr4//attogv6f9KfpoJL/2S0iUhAlErKNGDGiQA+fN29egc4rqxSySVmwfPlyxo4dy+HDhwEYOHAgM2fOpGrVqvYtzM6ObznOR50/IifjfJjv7OnM6E2jCWpc+kMaDcNg9+7dLFmyhCVLlrB+/XqysvKfNN3d3Z1GjRrluwCBXUKrAjCbzXzxxRc8++yz1u/Fxo0bM3XqVG666aZK/Zf9Q6sOsbDnQpuJm73DvBn711g8q3rasbJc0uNyDTPdCumxec87F7x514ectPMhWvbZ8CwrGczFOB2EyQnDxY8sPEnJdCTxtEFMYjqRJ05zNOY0SalwMg1Opp4N1E7D6QxnqkY0pE7DFjRr1pxmzZrRrFkzXFxc2Pr7V5za/QVBWVtpU+0UHhf8e2BWNmyNdOVI9lW41LiFZl0GUbtOnRL//jXMBon7E23md4veFm3z86uw3PzcCG8bbjPHW0Wdm1JE7C/9VDqzG8+26anrHuDOA7sfwDOo+P6sW/PSGlZPXG3TZnI0MWDRABrf1rjYniMicqVKJGSrLBSySVmRmprKpEmT+N///ofZbMbf35833niDoUOHVupwI785PAIaBDB60+grnhekMNLS0li1ahU///wzS5Ys4ciRIzbHPT098wRpjRs3pmbNmjg4lM+hXxkZGcyZM4eXXnqJxETLXFRdunThtddeo3379naurvSlnEjhvRbvkRJzft48k4OJYauGUaPr+RXWDhw4wPfff49hGISFhREWFkZ4eDhhYWH2G3prGHAmChK2QOJWy3ax4O1iHN0sw3SdfcDZ++zrs/vW11Usc6S5+IFLFZLTHdhz8AR/7z3K1p0H2LJ9F//88+9Fh0tXq1bNGqKd2+rXr1+g3p2nEk+wd+0HZBz+jupO/1LDz3buuqMJ8Pt+D+Kc2lCl4W107HYD9erVK5WfqzlZOcT+E2szzDTu37g8q+wVhk81H5vQLaxNGG6+bsVYtYhUVj+O/ZFt72+zabt14a00G9ysWJ9jGAYrn13JuqnrbNodnBy489s7qX9j0YaliogUlUK2IlDIJmXNli1bGDVqFDt27ACgZ8+evPfee/Zd+dHOfhj9A3/N/cumrVH/Rtz+1e0l8hflw4cPW3urrVq1ivT0839pd3V1pUePHvTp04cbbriBunXrVtgQ9OTJk0ybNo2ZM2daP4PbbruNV155hfr1K8f/AJtzzHxy/SccWnnIpv2aV66hy9NdiImJYfHixXz22Wf8+eefF72Pr6+vNXC7MHy7cD8kJKR0hgwbBqQdswRuaUfByeuC8OxskGYN0bzA4eI1ZWVlsXfvXnbu3GmzRUVF5Xu+u7s7TZo0yROo+fv7F9vbSz2xk8iN7+EQ8ws13A7h5nx+3rqsbFi7F9Yd9CbZqyN1Wt1Mt27dadSoUan9Ps5MzSR6W7Q1dDu++ThJBy+zMu5lBDQIsPR4OzvUNKRFCE5uhesxaxgGJ06cICoqiho1ahAYGFikmkSkfDm85jALui+waavbuy6DlgwqkZ+PhmGw/NHlbPzfRpt2RxdHBv40kDrX1Sn2Z4qIFJRCtiJQyCZlUVZWFm+88QaTJ08mPT0dd3d3XnzxRcaPH19mhxqWpOz0bD7q/BHRW21Xl+z5Wk86Pd6pyPfPyspi3bp11t5qu3fvtjkeERFB37596du3Lz169MDTswwNDywFx44dY9KkScyfPx+z2YyjoyNjxoxh0qRJBAcH27u8ErVq0ip+f9F2pdua19XEGGjw+Ref89tvv2E2W0IcR0dHrr32WgIDAzl+/DjHjx8nKiqK1NTUAj3LZDIRFBR02TAuMDDQLr0kT5w4kSdM27VrF5mZmfmeX6tWrTxhWp06dXB0LMUVgrPPkH50OXHbF+B+ag2BLrarhEbGw9IdsPGID+agHrTt1JNu3bpx1VVXlepnnBafxvEtF8zvtimK1NiCfd/kx8HJgeBmwTbzuwU2CuR0ymkOHTpk3Q4ePGh9ffjwYZuehjVq1KBNmza0adOG1q1b07p162INQ0Wk7Mg6k8W7zd4lcf/5n5HOns7c/+/9VKlRpcSeaxgGPz/wM1vmbLFpd3J34u6ld1OzW80Se7aIyKUoZCsChWxSlu3fv58xY8awapVlsvVWrVoxd+5cWrasfCswnTx8kvdbv8+ZxPN/CTQ5mBjy2xBq9ahV6PvFxMSwdOlSfv75Z5YvX05ycrL1mKOjI506daJPnz707duXq666qsL2ViuMf/75h6eeeoolS5YAlqGyjz/+OI8++miFXBX3wK8H+KTXJ3DBn5pZ7lnMNs8mKeN8z6P27dtz9913c/vtt+cJHQ3D4PTp09bA7Vz4lt/+xeb3y83JyYnQ0NDLhnE+Pj5X9H2bnp7O7t278wRqsbH5DzH19vbOE6Y1adKkbP6Zeno/WZE/kLxnMT5ntuLscH5Rhuwc2Lgflv9tCd28q/egS7fudOvWjWbNmpVqOGgYBslHk23mdzu+5TiZp/MPNAsik0yOn/0v6ux/Jzlpc865oPdiv9a1atWyhm5t2rShVatW+Pn5XXFNIlI2/PbUb/wx7Q+bthveuoF2D7Yr8WcbZoMfRv3A9nnbbdpdvFwYvHwwER0iSrwGEZHcFLIVgUI2KesMw2DevHk8+uijnDx5EkdHRx599FEmTZpkv3me7GT/L/v5tPenNqGHR5AHY7eNxafapX//ms1mtmzZwpIlS/j555/ZssX2X02DgoLo3bs3ffr04frrr9dfHC9hzZo1PP7442zevBmA4OBgJk2axKhRo+y6QmpxOn38NO+2eJe0uDRrmxkz85jHUY7SqFEj7r77bgYOHFgsQ7nNZjMJCQl5wrfcQdyJEyco6B/jHh4eeQK43GGcs7Mzf//9t02Ytnfv3nxXDjeZTNSvXz9PoFajRo3yGUJnn4HY1WQf/YnMIz/hkR1pczgxBX7752zodtiH2k270a2bZWvRokWp9yo2zAaxu2L5Z/k/HFx7kPgd8WREZmDKufLP3nA3cKvjRlCLIOp2rUuz3s3wq+bHyZMn+euvv9iyZQtbt25ly5YtHDhwIN971KlTJ0/w5uvre8U1iUjpit4WzQdXf4CRc/7PlmodqjFi7YhSWznbnGPm2yHf8s/n/9i0u/q6MmzlMEJbhZZKHSIi5yhkKwKFbFJexMTE8PDDD7N48WLA8heb9957j2uvvdbOlZWu/Fakqta+GsPXDMfRxbanycmTJ1m+fDlLlixh6dKlxMXF2Rxv3bq1dRhomzZtyu1CBfZgGAZfffUVzzzzDPv37wegfv36TJ06lVtvvbV8hi5Y3teWTVv44bYfcIqyDVH+9P2TJmOaMGjQIJo3b26X95iVlcWJEycuG8YlJRVtji9/f3+aN29uE6Y1bty4Ygf7qUcgejnm48swH/8VJ/Npm8O7o+CXnZbQbdsxL1pd3YVu3brRvXt3WrVqVSwBs2EYxMfH2wzpvHBoZ2RkpE2PR0ccCSaY8LP/hRFGEEGYuPLvTd/qXoRfHUHY1ZZhpqGtQnH1diUpKYlt27bZBG+HDh3K9x716tWzCd5atmyp/8cSKYNysnKYe/VcYrbHWNscXRwZ+9fYUl/FPScrh6/u/Io93+6xaXf3d2fY6mEEN63Y01OISNmikK0IFLJJefPjjz9y//33c+zYMQBGjBjB9OnTK81cOYbZ4PObP2ffkn027W3HtaX3W735999/rb3V/vjjD5seOT4+Plx//fX06dOH3r17ExISUtrlVziZmZm8//77vPjii9YQs0OHDrz22mt07tzZztUV3L59+/j888/57LPPCN8bTle62hwP6BjAfb/fV7rziRVBWloa0dHRlwzjoqKiyMrKomHDhnl6p4WFhZXboLRYmLMhcQtE/4Jx/BdI+BMT5xdQyMiyLKCw/G9L8HYgwYNOnTpbe7q1bdsWFxeXfG+dkpKSb4h2bktJScn3unOcnJyoUaMGtWrVonbt2tSqVctm83b2JGbzQaI2HuL4lmiitiZwKurKh5ligqCGvoS3q2kJ3tqGE9wsGEcXRxISEti2bZs1dNu6dSuHDx/O9zYNGjSwhm6tW7emZcuWeHt7X3ldIlJk615dx4qnV9i0dX+hO90mdrNLPTmZOSy6dRH7frb9fzzPqp4MXzOcwIZakEVESodCtiJQyCblUXJyMs888wyzZ8/GMAyqVq3KW2+9xR133FEp/mJ8JukMH7T5IM+KfGsC1rAqYZVNW6NGjejbty99+vShc+fOFWY4Y1mTnJzM9OnTmTFjBmlpliGWN998M6+++iqNGjWyc3X5i4mJYdGiRXz66afWoa91qctgBtuc51vDl7HbxuLu726PMkuMYRjWhSzkMjJPwomV1tDNlHbE5nDMSUvgtvxv+PVvOJ3lTocOHejcuTOZmZk2IVruHrX5qV4tlCYNImhUN5S61YOoGe5HRLA3IQHu+HmacMg+DZmJli0j8fzrzETITALDbHO/lFOeHD8QTtTBMI4fDCfqQDhnUq68V6Kji4mQZgGEtatF+NXhVOtQjYB6AQDEx8ezdetWm+AtMjIyzz1MJhMNGzbME7xVtoVlROwl4b8E5jSbQ07G+X+MrNqkKmO2jskzMqA0Zadn89mNn3FohW1PWe8wb4b/Phz/OpXjH5VFxL4UshWBQjYpz9avX8+oUaOsq2H27duX2bNnU716dTtXVrIOHjzID+//QNLrSTiYzw/xzCKLj10+psm1TazBWq1ahV8UQa5cdHQ0L7zwAnPnziUnJwcHBwfuueceXnjhBcLCwuxdHqdOneLbb7/ls88+Y8WKFTYrg/bt0pc2W9pgTjkfUDg4OzBi7Qiqtatmr5KlrDEMOL0Pon+B6OUYsaswZduuBLr9iCVsW/EvpGaArwf4ukMVT8vXEH9Xwqt6ExLgRqCPE74eJrxcsnFzTMfRnIopJ+0iDy8ERw9w9QeXs5urP7j4gYs/hrMfJ+N8idrlyvF/DKJ2pBO94xRZadmXv+9FhLf0oN2D7Wh8d6c8f0GPi4uzCd22bNli7Y19IQcHBxo2bGizqmmLFi0q9jBlETswzAYLeizgyO/n/8HA5GBi5IaRhF8dbsfKLDJTM/m096dErrUN6H2r+zJi7Qh8q2veRxEpWQrZikAhm5R3GRkZvPrqq0yZMoWsrCy8vLx45ZVXuP/++ytMD5XMzEzWrVvHzz//zJIlS9izxzJfR3Oacyu32pxbpXYVxm4di1sVN3uUKmft2bOHp59+mu+++w4Ad3d3JkyYwBNPPFHqP2vT09NZunQpn376KT/99BMZGRnWYx06dODuu++mf7/+LL1zKUf/OGpzba//9aL9+PalWq+UMzkZEL/BGrqRtK347u3kBc6+4FLF8vXC13kCtFxhmmPhfgaas83E7Y7j+Obj1hVNT+w8gTnbfPmLL+Dll0qbW0/RZkg4nvXbgn8bcMs7t9OJEyfyBG/Hjx/Pc56DgwONGze2Cd6aN2+Ou3vF6lkqUpq2vLeFJfcusWlr/0h7er3Ry04V5ZWRnMHC6xcS9WeUTbtfHT9G/D4C7zANNxeRkqOQrQgUsklFsWvXLkaPHs369esBaN++Pc888ww+Pj64u7vj5uZm/Xrh69JeIa+goqOjWbp0KUuWLOHXX3/l9Onzk5A7OjrSuXNn+vbtS8jWEA4uOmhzbf2b6nPXd3dhcqj4Q2fLuj/++IMnnnjC+n0ZGBjI888/z7333nvROauKQ05ODqtXr+azzz7j66+/5tSpU9ZjjRs35u677+auu+6yrgz66xO/sv719Tb3aHhrQ+74unIMwZZilB4LMSsg5leIXQsm08WDsku2/b+9+w6L4urCAP4uvXdpCoiIvSCiYu9d0RhbiL0bUtR8Rk01TY3RGEti7yWixl5j7yhgbyiiSBWR3tnd+f5YXRzpdSnv73n2gT07M3sWR9g9c++5RoCaan8/Z6Zm4uXtl8qiW9j1MLx+/LpA+6prSNGo9T206nkNNo20FMU28xaAWXPAtBmgbZ5tn4iICNFUUz8/P0RGRmY/tro6GjZsqCy6NW/eHI6OjrCwsOACNkT5SAhLwN8N/kZ6QtYFJxNHE0y9OxVa+qX3d7koUmNTsaXLFtHCDABgUc8CY86Pgb4lp5cTUelgka0YWGSjykQul2PVqlWYPXu2qCiVFw0NjWyFt5yKcfk9XtDY2++1tbVFxQuZTAZfX1/laLUbN8SjQSwtLdG7d2/07dsX3bt3h4mJCQBAmi7Fpo6bsl3p7PJrF7T/un3xfqBUIgRBwIEDBzB79mwEBAQAAGrVqoV58+ZhyJAhJfahWBAE+Pv7Y8eOHdi5cyciIiKUj9nZ2eGjjz6Cp6cnmjRpIjr3Hh9+jH/6/yM6lomjCSbf4IhIovelxaUh3C8cYb5hCL8ejpCrIUh+mZznPvZ1g+Ha+QacXZ5Az/DNVFj9moCZK2DqmvVVN/vqgeHh4aKim5+fH6KionJ8Hg0NDdjY2KB69eqwtbWFra1tjt8bGRmxeE5VkiAI8B7ojYCDAaL4yJMjUatbLRVllbeU6BRs6rQJr+6L+1laNrbE6LOjoWfO6eREVPJYZCsGFtmoMgoNDcXXX3+Nu3fvIjU1FWlpacqvaWlpyMgoxkpzJeht0U1HRwdpaWmIi4sTPd6iRQtlb7XmzZvnWoxJCE3AatfVSHn1Th8jCTDi+Ag49XAqxVdAhSGVSrF+/XrMnTtXOTrFzc0NCxcuROfOnYt83CdPnmDHjh3YsWMHHj9+rIybmZlhyJAh8PT0RLt27XI8f+KC47C62WqkxaYpY+pa6hh3eRxs3VTfQ46ovJNL5Xh04BGuLb2WrX9SNhIBNepEw7nxXdRp9hhWDpEQ1bp0bd8U3ZorCm9mroBudby7kSAICAsLE001vXXrFiIjI1HQt7h6enr5FuJsbGw4JZUqnfu77mPPsD2imMsYFwzYOEBFGRVMUmQSNnXclG0krU1zG4w6NYoXxIioxLHIVgwsslFVJJPJkJ6eLiq+vVuEy+/74jz+ttF8ToyMjNCzZ0/07dsXvXr1gpVV9lENuXl25hm2dt8KQZ71K07XXBeT/CfBxMGkOD8uKmFJSUlYsmQJFi5ciKSkJABA79698dtvv6Fx48YFOkZERIRyZVA/Pz9lXFdXFwMGDICnpyd69uyZ55RUWYYMGztszDYKsvfy3mj5acsivDKiqi3iZgSuL7uOuzvuQpYhy3d7QysJnFvGwrnJXdSqdRFaOjlcANKuJh7xZtZcMQruvZFomZmZePnyJcLCwhAeHo7w8PAcv3//Yk5ezMzMlMW33IpxVlZW5bbtAtG7Ul6n4O8GfyM5Kmvkqb6VPrweeFWI1bMTQhOwscNGxD2LE8VrtK6BESdGQNtQWzWJEVGlxCJbMbDIRlR2BEGAVCrNsQgnCAIaNmwITU3NIh//0m+XcHr2aVHM1s0WYy+OhYYOPwSVN1FRUfjpp5+wevVqSKVSSCQSjB49Gj/99BPs7OyybR8fH49///0XO3bswNmzZ0Urg/bo0QOenp4YOHAgDAwMCvT8J2acgM8SH1GswZAGGOw9mFPJiIohOSoZ/mv84fu3L5Iikgq0j7qWOmq2MYZzmzQ4N3sMM71rQPwDQMihWKdpkjXSzdRV0ePNoGaBFntISUlRFt5yK8SFhYUhLS0t32MBioUZrKys8izE2drawtzcnL9XSKX2j9mP25tvi2KDdw1GwyENVZRR4cU9j8PG9huREJogijt0dMDHRz+Gpl7R30MSEb2LRbZiYJGNqPIQBAG7PtyFR/seieKuk1zRf3V/FWVF+Xny5Am++eYb7N69G4BiGvHnn3+OOXPmQEdHB0eOHMGOHTtw5MgR0cqgbdq0wccff4whQ4agWrXsqxfm5dH+R/D+wFsUM6tthkn+k6BtxKvhRCVBliHDw70Pce+fewg6FYTMlMwC72te1xzOvR1Rp4Ma7Os8h3rSTcXKrXF3AXkuLQ+0zBRTTvWqK76+/71udUDHMt8FJQRBQFxcXL7FuIiICMhk+Y/YAwBbW1ssWrQIw4cPZ7GNylzgiUBs77VdFKs7oC6G7RtW4c7H109eY1OHTUiKFBfwnXo4YfiB4byoSkQlgkW2YmCRjahySYtPw7qW67L17fBY74Fm45qpKCsqiOvXr+Orr77C+fPnAQCmpqaQyWRISMi6Yt2wYUPlyqCOjo5Fep7YZ7FY3Ww10uOzCnbq2uqY4DMB1i7WxXsRRJQjaZoUz88/x5MjT/DkyBPEBsUWeF8tQy049XCCc19nOPdwgIFusKLgFvPmFncHkKXkfyAAkKgBOlZZRbecinF61RUFu3yKDzKZDK9evcqzEBceHo5Xr7Iatvfs2RMrV64s8u8vosLKSMrA343+Rnxw1irb2kba+OTBJzCqXjE/+7x68AqbOm5CSrT4/32d/nUwdM9QqGupqygzIqosWGQrBhbZiCqfqPtRWNdynWjUhLq2OsZfGQ8bVxsVZkb5EQQBR48exaxZs3D//n0AipVBPT094enpicaNGxfrqrs0XYqN7TYi3C9cFO+7qi/cJrsVK3ciKhhBEPA64DUeH36MJ0ee4MWlF5BLc+/X+T5bN1tFwa2vM2yb2ypqYZlxQEo4kBoGpIYrbinvfZ8WmfP005yoaeUyGs5GcdOxAXStC1SMS01NxeLFi/HLL78gPT0durq6mDt3LqZPn16sFglEBXF82nFcW3pNFOu3uh+aT2quooxKRuTtSGzuvFm0cBEANBjcAB/+8yHUNEpm5XIiqppYZCsGFtmIKqe7/9zFXs+9ophJTRNM8p9UIRr8VnUymQynTp2Cvr4+2rRpk+vKsoV19LOj8F3hK4o1+qgRBm0fVOGmzBBVFmnxaXj631M8OfIEgccCRY3Z86NvpQ/n3oqCm1MPp7yne8tlQPorRSEu5U3x7W1R7t0CXXp0wZNX0wR0rN8pvuX+/ePAIEyZMgVnz54FADRp0gRr165Fy5ZcaIVKR6hPKNa3WQ+88+nPoaMDRp8ZDYlaxf+bF+Ybhq3dtiI9IV0UbzKiCQZsGgA1dRbaSDXkMjleP34NNQ01mDubqzodKgIW2YqBRTaiyiunq7e1e9WG5xHPSvHmkgrn/u772DN0jyhmXsccE/0mclUyonJCkAsI9wvH4yOKUW4R/hEF3ldNQw327e3h3NcZdfrWgXndIi42IEsHUiOyRsEpR8WFAamRihFxqRFARkzBj6llCjSYDcH5U2zevgtffvklYmJiIJFI4OXlhV9//ZXvQ6lEyTJkWO26Gq/uZ01X1tDRwJQ7UyrVh/4Xl19gW89tyEwW93xsNqEZ+q/uz/d7VKbiX8Tj2vJruLXhFlJjUgEAjT9uDI/1HtDQZr/AioRFtmJgkY2o8pJlyrC582aEXA4RxTv+0BGd5nZSTVKkEjGBMVjtuhoZiVkN0zV0NDDh2gRYNbFSYWZElJfEiEQEHgvEkyNP8PTkU9H/4fyY1jJVTiut2bFmyTdEl6UDaS8VBbe3hbfUSCAt4r3vIwFBqthHzw5o8hNeGfTCl//7Clu3bgUAVK9eHcuXL8cHH3xQsjlSlXXux3M4P/e8KNbtt25o+1VbFWVUep6dfYYdfXZAmiYVxVt82gK9l/XmSHUqdSFXQ+CzxAcP9z6EIMtebnHu44zhB4dzdGUFwiJbMbDIRlS5JYYnYrXraiS/FE8/8jziCec+zirKisqSNE2K9a3XI/JWpCjef11/uI53VVFWRFRYsgwZgi8GKxdPeH+Bm7xo6mmiVrdayqJbmTZ8l8uA59uAO98BKW8u+pg0BpouwKkHmpgydSqePn0KABgwYABWrFiBGjVqlF1+VOlE3Y/C6marIc/M6nVo42qDCdcmVNpeZYHHA7FzwE7IMsR9F9vMbINuv3VjoY1KnCxThof/PoTPnz4IuxaW7/adfuqEjt91LIPMqCSwyFYMLLIRVX7BF4Kxuctm0ZUlHRMdTPKfBNNapirMjMrC4amH4b/KXxRrMrIJBm4eyDfdRBVYTGCMclpp8PngbB+u82LtYq0suFVvWb1sRhfI0oCA5cD9eYqFGgDAshPSGvyEn/86joULF0IqlcLAwAC//vorvLy8oK7OVRKpcOQyOTa224hQn1BlTKIuwSS/SZV+Be1HBx5h9+Dd2RZS6fB9B3T+sbOKsqLKJjU2Ff5r/OG7whcJoQkF3k+iJsGoM6NQs2PN0kuOSgyLbMXAIhtR1XB1yVX8N+M/UczaxRrjroyDpi5Xd6uscloAo1qDaphwfQK09LVUlBURlbSMpAwEnQrC4yOPEXg0EInhiQXeV89CD7V71VYsntDTCbqmpbw4TnoM8GC+ouAmf9Ow3X4oHuuOxtjPf8WVK1cAAC1atMCaNWvg4uJSuvlQpXJt2TUc/+K4KNZ2dlt0m99NRRmVrfu77uPfj/6FIBd/5O0yrwvaz2mvoqyoMogOiMa1pddwe/NtZKZk5rqdpr4mmo1rBqumVjg8+bDoIr+BjQGm3J4C/Wr6ZZEyFUOlK7I9f/4cP//8M86cOYPIyEjY2tpixIgR+Oabb6CllfWhKKcRCCtXrsSUKVMK/FwsshFVDYIg4N/h/+L+rvuieNPRTTFg4wCOaKqEogOisdZtLTKSsno4aeppYqLvRFRrUE2FmRFRaRIEAZG3IpXTSkOvhYpWV8yLRF0CuzZ2ysUTqjWsVnp/H5JfAHe+B55tASAAEg0ItSdj8w17TJs1D/Hx8VBXV8f06dMxd+5c6OvzQxnlLe55HP5u9LdoEQAzZzNMuT2lSl1QvL3lNvaP2Z/t/33PJT3hPs1dJTlRxSQIAp6dfgafJT54cvRJntsa2xuj5ect4TreFTomOgCAi/Mv4szXZ0TbcRG2iqHSFdmOHz8Ob29vfPTRR6hduzbu3buHiRMnYuTIkVi0aJFyO4lEgo0bN6JXr17KmLGxMXR1C34FkkU2oqojPTEd61qtQ/TDaFG876q+cJvspqKsqDRkpmZivft6vLzzUhQfuHkgmo5qqqKsiEgVkl8lI/D4m8UTTjxFWlxagfc1tjdWTit17OwITb1SKFTE3gFuzQYijinuaxggscYUfLL8Kbbt3AcAcHBwwMqVK9G7d++Sf36qFARBwPZe2/H0v6ei+JjzY+DQwUFFWamO/xp/HJ58OFu878q+cJvC93yUN2maFHe238G1P68h6l5UntvatbGD+3R31BtYL1vPQ0EuYFuvbQg6GSSKV9ZFSCqTSldky8nvv/+OlStXIigo6wSVSCTYt28fBg4cWODjpKenIz09XXk/ISEBdnZ2LLIRVRHRj6KxtoV4dJO6ljrGXhyL6i2rqzAzKkkHJx7EzXU3RTGXcS4YsH6AijIiovJALpUj5EqIopfb4Sd49eBVgffV0NGAYxdHZdHNxMGkZJN7eRa4+RUQ46e4r2OFu/gQA788hKDnigUThg0bhj///BPW1pW7txYV3u0tt7F/9H5RrPnk5ui3qp9qEioHcpo6CwADNg6AyxiXsk+Iyr2kyCT4/u0Lv1V+SHmVkut2ahpqaDCkAdynuef7+SHpZRJWu6xGUmSSMiZRl2DshbGwa2NXYrlTyaoSRbZvv/0Wx48fh5+fnzImkUhQvXp1pKWlwdHREePHj8ekSZOgppZ789q5c+fixx9/zBZnkY2o6njw7wPsHrxbFDOyM8Ik/0nskVAJ3N56G/tH7RfFLBtZYsK1CaUzCoWIKqy453HKxROen30OaZq0wPtWa1hNOa3Uro1dyazaKMiBF7uB218DSYoLy3L92thypz7Gzz0MuVyAiYkJfvvtN0yYMCHP97xUdSRHJeOv+n8hNSZVGTO0NcQnDz6BjrGOCjNTvUu/XcLp2adFMYmaBIO2D0Kj4Y1UlBWVCrkMyHgNpEUB6a+AtFfir+mvAEEG2PQEagwEdCyVu0beioTPnz6498+9PBfR0THVQfPJzdHSqyWMahS8dvDszDNs6bZFNIXZ2N4Yk29Ohq5ZKfcBpSKp9EW2p0+fwtXVFYsXL8aECROU8V9++QVdu3aFrq4uTp8+je+//x5z5szBt99+m+uxOJKNiADgv5n/4eqiq6KYY1dHjDgxomxWmaNS8erBK6xtsVbUkFZTXxOT/CbBop6FCjMjovIuMyUTz848UxbdEkIKvmqcjokOnHo6KUa59XaGnoVe8ZKRZQCBq4F7PwHpihYHSbpN8MXGdGw4FAAAaNu2LdasWYMGDRoU77mowtszfA/ue4t7zg4/MBx1PeqqKKPy5dzcczj/43lRTKIuwZDdQ1D/g/oqyoryJc9U/P57v1j2toj2fgEtPQYFb8CpBrl5RzwO7I9r3gZ4fiE8z83N65rDfZo7moxsUuSFs87+cBYXfrogitX1qIth+4exN3Q5VGGKbLmNInuXr68v3Nyy5smHh4ejY8eO6NixI9atW5fnvosXL8ZPP/2E+Pj4AufEnmxEVZNcKsfW7lvx/NxzUbzdnHboOq+rapKiYslIzsC6luuyTf8atH0QGns2VlFWRFQRCYKAqHtRysUTQq6EZFutMFcSoEarGspppdYu1kX/AJWZADz4HXj0ByBTTF16mt4Iw+Y9hX9gKjQ1NTFr1ix888030NGpuiOW4p7HARKU/BTeCiDgUAB2euwUxRoObYjB3oNVlFH5IwgCTs85jcu/XRbF1TTVMHz/cDj3cVZRZlWMLD33EWbvF9DSXgGZcUV7Hm1zQLua4qZTTfy9NAnpAQdx64CAayfcEfvSLM9D1epeC+7T3FG7V+1iL1Qgl8mxpesWBJ8PFsV7/tkT7l9wQY7ypsIU2aKjoxEdHZ3nNjVr1lS+SQgPD0fnzp3RqlUrbNq0Kd8h8ZcvX0a7du0QGRkJKyurAuXEIhtR1ZX0MglrXNcgMTxRFB+2fxjqDainoqyoqPaP2Y/bm2+LYq6TXNF/dX8VZURElUVqTCoCTygWTwg8FiialpcfQ1tD1O5TG3X61kGtbrWgZVCEURCpEcDdH4Gn6wBBBgFq+O9pDYxb8gLhsYCzszNWrVqFLl26FP7YFVhiRCKOeh3Fo32PAAC2brbou7IvbN1sVZxZ2UiLT8PfDf9GYljW+xgdUx14PfSCgZWBCjMrfwRBwPFpx3F92XVRXF1bHZ5HPFGray0VZVaBSVPFRbKcRpi9W0jLLPjoYCWJGqBl/l6xzFJcQHv3MW1zQE0jx0PFPY/D9RXXcWPdDaTHp+e4DQCoa8rQxEMH7l/1hWXLkl0sKzE8EauarkJKdFa/NzVNNYy/Mr7K/N6qKCpMka0wwsLC0LlzZzRv3hzbtm2Durp6vvusWLECM2fORFxcHLS1tQv0PCyyEVVtIVdDsKnjJsgz5cqYtpE2JvpNhLmzuQozo8K4ufEmDo47KIpZu1hj/NXx0NDJ+c0WEVFRyGVyhF0LU04rfXn7Zf47vaGupY6anWoqR7mZOeU9iiKb+EeKfm2hilVHpdDC36c18d0/yUhIBUaPHo1FixbBwqJyT48XBAG3N9/Gieknsq0Wa2hriM+DPoeGduX/3X946mH4r/IXxQZsGgCX0S6qSaicEwQBh6ccxo01N0RxTT1NfHz8Yzi0r3qrsBZJ7B3gzndA2CEUeHrmWxINQNsi+wgz5VdL8WNapoBa/nWA3AiCgNCrofBZ4oOHex/mOSJZ3zQdLbpegVtXX+gbvSmCmbcE7AYD9h8CBiVTiA08HojtvbeLYqa1TDHpxqQq30OxPKl0Rba3U0Tt7e2xZcsWUYHt7WpKhw4dQmRkJFq3bg1dXV2cPXsWX375JcaMGYOlS5cW+LlYZCOi6yuu49hnx0Qxy0aWGO8zvsh9F6jsRN2LwtqWayFNzWpYrmWohck3JsOsdiE/wBIRFVJCaAKeHFVMKw06FSTqCZkf87rmysUT7NvZQ12rgB8mX10Bbn0FvFJMf0vK1MF3O9Pw9ynA0Ngcf/zxB0aOHFkp+/zEv4jHoUmH8PTE01y3GXV6FBy7OJZhVmUv+EIwNnXcJIo59XDCx8c/rpT/7iVFkAs4MPYAbm8Rj3zXMtTCqFOjuNJ8XhIeA3d/AIK9oSyuqWkWbITZ2++1TBSj00qZLFOGB3sewGeJD8J98+63Zu1iDffp7mg4rCE0ZFGKixgvdgNRFyAqIpq6AvaDAbsPAaM6xcrv1OxT2aYvNxzaEB/u/JD/f8uJSldk27RpE8aOHZvjY2/TP378OObMmYPAwEDI5XLUqlULEyZMgJeXFzQ0Cn7likU2IhIEAftG7sPd7XdF8caejfHBtg/4x64cy0jKwNoWaxH9SNyKYPCuwWg4pKGKsiKiqkqaJsXz88+Vvdxig2ILvK+WoRacemQtnmBgnc90P0EAwg4Ct2YDCYrpkqGxmvhqRyZ2XgW6dOmKlStXwtm5cvSbEuQC/Fb74dRXp5CRlJHntm1ntUW3Bd3KKLOyJ02TYlXTVXj9+LUypqmviU/ufQKTmiaqS6yCkEvl2PvxXtzfJV4sQsdEB6POjIJNMxsVZVZOJb9QLMIStEmxOicA2A8FGv8AGNUHytH75NSYVPiv8cf1FddF06izkQD1BtRDq2mt4NDBIef3+qkv3xTc9gBR57JeOwCYNH4zwm0wYFz4xWdkmTJs6rgJoVdDRfG+K/vCbYpbLntRWap0RbayxCIbEQGKpvnrW69H1N0oUbz38t5o+WlLFWVFeREEAftG7MPdHeLiaAuvFuizoo+KsiIiUhAEAa8DXiunlb64+AJyqTz/Hd+wdbNVTiu1bW6be9NtuRQI2qgYYZIaAQC48VyCr3YIuBSoje+++w4zZ86EllbFHZkdExiDgxMOZmsYnhvrZtaYfGNyKWelOqe/Po1L8y+JYmyeXjiyTBl2D9mNgAMBoriuuS7GnBsDy0aWKsqsHEmNBO7PU6x0LH9T2LbtCzT9BTB1UWlq74t+FA2fpT64vfm2aGbD+7QMtOAyzgWtPm9VuOn6aa+A0ANAyB4g8jQgvPMcRvUB+yFvCm6NClx0jH8Rj1Uuq5AWmzXlXV1bHROuTYB1U+uC50algkW2YmCRjYjeev3kNda6rUV6QlYzVDUNNYw5PwZ2bexUmBnlxH+NPw5PPiyK2TS3wbjL46pELx4iqljS4tMQdDJIMcrt6BMkRyUXeF99K30491YU3Jx6OEHbKIfew9Jk4NGfwIPfAKliBMd/d4HZO4EM/YZYvXo12rZtW0KvpmzIZXJcW3oNZ749k+MHZ0NbQ/Rd1Rc6JjrY1GGT6LH/vfwf9C31yyjTshNxMwJrW6yFIMv6SFe9VXWMuzwOauqlPw2vMpGmS+E90BuBxwNFcX0rfYy9MBbmdapob970GODh70DAMuWqxrDsBDT9FajWRqWpvUsQBASdCoLPEh8EHgvMc1tjB2O0+rwVmo1vVvy+Z+kxilHEL/YAkf8B8ndaBBjWeTOldLCiEJlPwS3gYAB2DhCvDmxexxyT/CcVbZEcKjEsshUDi2xE9K5HBx7Be6C3KGZoa4hJNyZxpa5yJPJWJNa5r4MsPWvovraxNibfmAzTWqYqzIyIKH+CXEC4X7hylFuEf0SB91XTUIN9e3tlLzfzuubiqU5p0cD9XyA8+RuSNx/+dl4Fvt0NdBs4GQsWLICJiUkJv6KS9+rBKxwYdwBh18JyfLzZ+GbosagHdEx0IMuUYaHZQtE00kHbB6GxZ+OySrdMyKVyrGu1DhE3ss4XNU01TL45GZYNOfKqKDJTM7Gj7w48P/tcFDesboixF8ZWrfcUmYlAwFLg4SIgM14RM2+pKK5ZdS0300IzUzNxd/td+Pzpg1f3X+W5rV1bO7hPd0e9AfWgplEKReiMeMUCECF7gPDjgPydVUsNamVNKTVzy/Xnd3z6cVz785oo1mREEwzcMpAta1SIRbZiYJGNiN53+pvTuDRPPA3DoaMDRp0aVTp/oKlQ0hPSsab5GsQExojiQ/cORf0P6qsoKyKiokuMSETgsUA8OfIET08+RUZi3j3H3mVay1Q5rbRmx5pZKyonPQPufA/h+XZIICBTCqw5C6y+XA3f/rICQ4YMKZcf4GSZMlxeeBkXfroAWYYs2+PGDsbov7Y/nLo7ieI7B+xEwMGsqX9NRzfFwE0DSzvdMnV54WWcmnVKFOv4Q0d0mttJNQlVEhlJGdjWaxtCLoeI4iY1TTDmwhgY2xmrKLMyIksDnqxUTA1Nf9Pj1qQx0OQXoHr/Mi+uCYKAjIwMpKamKm8pKSmIDYnF051PEXYgDNL43KeECmoCUh1TEe0cjTi9ONExdHR0MGrUKHz88cfQ0SnhlTwzE4GwI28KbkcBWWrWY3r2WSPcLFqJFn+QZciwoe0GhPuJF2gYsHEAXMa4lGyOJU2aCqS9BNKiFF/N3QDdytHTkEW2YmCRjYjeJ5fJsb3XdgSdChLFW/+vNXr83kNFWRGgeOP17/B/szUrbvVFK/T6s5eKsiIiKjmyDBmCLwYrF094t7l9fjT1NFGrWy1l0c2ouhEQexu4NQeIUKyinZwG/HEMuJ3RHYuXrYWDg0NpvZRCi7gZgYPjDiLyVmSOj7f8rCW6zuua4zSq639dx7FPs1YKN7AxwIywGeWykFgUr5+8xqomqyBNyyouVGtQDZNuTGKLhBKQFp+Grd22Zit0mNU2w5gLY2BoY6iizEqRPBN4ugG49zOQ+mbEqEFtoMlPgMOwPFcBlclkuH//PhITE5GSkpKtIFaY+znF5PKs/pXWsEZrtEYjNII6cl+BOQUp8Ic/ruM6EpHHogcAqlWrhk8++QSffPIJLC1LYRSoNBkIP6aYUhp+WHH/Ld3qihVK7T8ELNoCauqIeRqDNa5rRC1rNPU0MdF3Iqo1qFby+eVGEICM2KyiWXqUYgGI9ChxMe3tV2mSeP92uxT96SoBFtmKgUU2IspJSnQKVruuRkJIgig+ZPcQNBhc+FWEqGT4/u2Lo15HRbHqLatj7MWxUNfK/Y0XEVFFFRMYo5xWGnw+OMfRXbmxamqlnFZavWYgcHsW1GJ9AQCvEoDfj2rCtuPP+PSLL6GhobpCjTRdigs/X8ClBZdEvcbeMnM2g8d6Dzi0z70gGBMYg+XOy0WxKXemwKqxVYnnW9YEQcCWLlvw/NzzrKAEGHd5HOxas2dsSUmNScXmLpvx8vZLUbxag2oYfW409KtVkh5/chkQ/I9isZSkNxeU9eyARt8DtUYDapq57ioIAo4cOYKvvvoKDx8+LLUUJZCgLuqiNVrDAXlfCEg3TEdCwwTIGsqga6QLXd2sm56enuj+48ePsXz5crx48QIAoK2tjREjRmD69Olo2LCUVqWXpgIRJxQj3EIPKntmAgB0rAC7QYD9ENw/a449H+0T7VqtYTVMvD4Rmnq5/5vkS56pWLjh3eJYbkWztCjxog4FoaateB06lopzqEb/oudajrDIVgwsshFRbsKuh2Fj+42iDzRaBlqYcH0CqtUvw6tKBAAI9w/HhjYbRP8eOqY6mHxjMkxqmqguMSKiMpKRlIGgU0F4fOQxAo8GIjE879Ea79I114Vz79pwdo+DncFCGGsqplY+fwWs93XAgC+84daiVWmlnqtQn1AcGHcA0Q+jsz0mUZOg9f9ao9PcTtDUzftDpiAIWOa0DHHP4pSx7ou6o82X5adRe1H5r/XH4UnihX5aft4SvZf2VlFGlVfyq2Rs6rgp2/lo1dQKo8+Mhq6ZrooyKwGCAITuA+58D8S/mRGgYwk0/AaoPQlQz3v6pJ+fH2bOnIlz584BAAwMDGBjY5NnUev9+/ltoyHTQPCBYDzY8ADxz+PzzMephxPcp7vDqYdT7qsv50AqlWLv3r34448/cO1aVi+0nj17YsaMGejevXvpjYCVpQORJxUj3EIPAJlxWY9pV8PhHWPgv1dczG02oRk81npkBQRBMTLu/aJZbiPOMsTtVQpE00RxbrwtnuX0VdsS0LUCNAzLTb++ksQiWzGwyEZEeclpBUuL+haYcG0CtA1zWN2NSkVafBrWuK5BbFCsKD784HDU7V9XRVkREamOIAiIvBWpnFYaei0UKOC7fIm6BHbNNODgdBaNmt1BtRpRuBsCXE31gOeXW2FYBu+JM1Mycea7M/BZ4pNj3paNLOGxwQPVW1Qv8DEPTzkM/9X+yvtOPZww4sSIkkhXZRLDE/FXg7+QHp81jczYwRif3PuEqw+WksSIRGzqsClb71fbFrYYeXJk8VenLGuCAET8B9z5FojxU8Q0TYAGXwF1Pwc08h6h9/z5c3z99df4559/AAA6OjqYNm0aZs+eDWPjkulXF/c8DteWXcPN9TdFUybfp6GjgSYjm6DVF61KZLGPq1ev4o8//sDevXuVU1QbNmyIGTNmwNPTs+T7tr1LlgG8PKMY4RayD8iIgTRDA+t+mICXL6xFmw76NgiN2z3KKp692++tICTqgHa1vItmyuJZNUCdn3FYZCsGFtmIKC+CIODg+IO4tfGWKN5gSAMM9h5caXq9lGeCIGD34N14uFc8LYE98oiIsiS/Skbg8TeLJ5x4irS4tALva2gej7rNHsPZ5TFe6YZD3uwHtP9gZqnl+vzccxyccBCxT2OzPaamoYb237RH+6/bF7oNwMO9D7Hrw13K+xo6Gvgq5qt8R8GVV4IgYNegXXi0/5Eo/vHxj1G7Z20VZVU1xL+Ix8YOGxEfLB5NZdfWDiOOj6g4Bc6oS8Cdb4CoC4r7GvpA3WlA/f8BWiZ57hoTE4N58+Zh+fLlyMjIgEQiwciRI/Hzzz/D3t6+2KkJgoCQyyHw+dMHj/Y9giDPvUxhYGOAFl4t0HxS81KZtvvs2TMsW7YM69atQ1KSos+YpaUlPvnkE0ydOrV0+ra9S54JRJ0HXuxBtM8ZrPlqCDLTs84xLZ10TPp1Ncyt3yn8quu+KY7lVDB7b8SZtlmePfYoOxbZioFFNiLKT2ZqJja03YDIm+JGzD0W90DrGa1VlFXV4bPUByemnRDF7NrYYfS50VDXZB82IqL3yaVyhFwJUfZye3X/VYH31dDMhGPDZ9Cs+RJ1R36IJh9MKbGpQOkJ6Tg56yT8V/nn+LhNcxsM2DAAVk2K1kctLS4NC80Xij6sj/hvRLaVSCuKB3seYPeQ3aJYk5FN8MGWD1SUUdUSGxSLjR02IjFMPC27Zuea8DziWb6LtzH+wO1vgYjjivtq2oDzJ0DD2YriSx7S09OxYsUK/Prrr4iNVRTCu3Xrht9//x0uLi7FTk2WIcP93fdx7c9r2RaaeJ+Nqw3cp7uj4dCGZdJ7Nz4+HuvWrcPSpUsREqJYbVZbWxsjR47E9OnT0aBBGfRllstwZ+Ue7PtUXFy3bqiL8cfaQMPUVlE40zQo/VyqMBbZioFFNiIqiNigWKxxW4O02KyRARJ1CUafGQ2HDuVnZbbKJux6GDa02wB5ZtYqU7rmuph8czKM7UpmigIRUWUX9zwOT44qppU+O/NMtEJlfkxto2HX3hCuEz1h17EB1DSKNhoi8HggDk06lG1BIQBQ11ZHpx87oc2XbYp8/LfWt1mP0KuhyvsVddRzakwq/mrwF5JfZq1KqFdND14PvaBnrqfCzKqW6IBobOq4SfTvAAC1e9XGsP3Dyt/KrvEPFD3XQv5V3JdoAE7jgEbfAXo18txVLpfD29sbX3/9NZ4/fw4AaNy4MX7//Xf06NGj2LM3Ul6nwH+1P3z/8s27n6QEqDewHtynu8O+nb1KZo1IpVL8+++/WLx4MXx9fZXxXr16YcaMGejWrVup53Vg3IFsM2laeLVAnxV9SvV5SYFFtmJgkY2ICurJsSfY0XeHqHeMvpU+Jt+YDEPbSri0u4qlxqRitevqbFM1PI96wrm3s4qyIiKq2DJTMvHszDPlKLecil650daXonZ3OzgPagXn3s7Qs8i/2JMak4oTM07g9ubbOT5u18YOHhs8YFHXosB55OXc3HM4/+N55X2rJlaYcntKiRy7LOX0AfvDnR+i0bBGqkmoCou6F4VNnTYh9bW4D1bdAXUxZPeQ8jGqPikIuPsj8HwbIMgBSICaHwON5wKG+Y/kPHfuHGbOnAk/P0XPNltbW/zyyy8YNWoU1NWL9/pePXwFnz99cGfrHUhTcy/waxlqodn4Zmj1WSuY1jIt1nOWFEEQcOXKFfzxxx/Yv3+/sm9bo0aNMH369FLt25aRnIG1LdZmW4RjyJ4haPBhGYyoq+JYZCsGFtmIqDDO/XgO5+eeF8Xs2thh9NnRZTKMvaoQBAHeA70RcDBAFG87uy26ze+moqyIiCoXQRAQdS9KuXhCyJWQPPsiiUiAGi1t4NyvHpz7OsPaxTrbyI6H+x7i6CdHkRSZlG13TT1NdJ3fFS28WkBNveR6BYVcCcGGthtEsRnhM2BoU3Euhj09+RTbemwTxer0r4PhB4azF2xupMlAwmMg8TGQEAAkPgHUNACjBoBxfcC4AaBfs8h9qSJuRmBLly3Zeh02HNoQg7YPKvYIzCJLCQPu/QI8XQcIbwpYNT4AmvwEmORfkH3w4AFmzZqFw4cVi3wZGhpi1qxZmD59OvT0ij5iUhAEPP3vKa79eQ2BxwPz3NbE0QStPm+FZuOaQduo/DbcDwoKwrJly7B+/XpR3zYvLy9MnToV1apVK/HnjLoXhbUt14qKk9rG2ph8czJMHctHIbKyYpGtGFhkI6LCEOQCdvTbgcBj4jcMLT9vid5Le6soq8rnyuIrOPm/k6KYfXt7jD4zWnVvZImIKrnUmFQEnlAsnhB4PDDbyJ28GNoaonaf2qjTtw6qNaiGs9+dxf1d93Pc1rGrI/qv6V8qo1XkUjkWmi8UrVA4cMtANB3ZtMSfqzRkJGdgZaOViHsep4xpGWrB64EXjGpU8c8qsgwg+VlWMS3xyZui2mMgNSz//dV1AaN6gNGbotvbm4GToiCXj9BrodjafSsyEjNE8SYjm2DgpoGQqJVhATQtGniwAHjyFyB7U/iz7gE0/QUwb5Hv7hEREZg7dy7WrVsHuVwODQ0NTJ48Gd9//32xmvxnpmbiztY7uLb0Gl49yLsXpH17e7hPc0fdAXVLtNBe2uLi4rBu3TosW7ZM1Ldt1KhRmDZtWon3bbux/gYOTTgkilVvWR1jL47lBf5SxCJbMbDIRkSFlRqTijXN14jeAAPAoB2D0PijxqpJqhIJuRqCTR02QS7N6sOmV00Pk29OhlF1/p4mIioLcpkcYdfClNNKX95+Wexjahtpo8fiHmg2vlmpjsjyHuSNR/uymoY3GdEEH2ytGIsFnJhxAj5LfESxviv7wm2Km4oyKmNyGZDyQlFAS3inkJb4RFFgE+S576ttARjWAYzqAobOgFwKJDxQ9ClLCADk6Tnvp6ap2M+4gXjkm2EdQF08sir4YjC299qOzJRMUdx1kiv6repX+iMNM+KBR38obtI3I0SrtQWa/ApYdcx396SkJCxatAiLFi1CcrKiz9wHH3yABQsWoE6dOkVOKzE8Eb5/+8JvlV+exXk1DTU0Gt4Iraa1gm1z2yI/X3mQmZmp7Nv2dpotUPJ92wRBwL4R+3B3x11R3H2GO3ou7lns41POWGQrBhbZiKgoIm5GYEObDaLm0Zp6mphwbQIsG5XyMt+VWMrrFKxutlrcI0gCjDg+Ak49KubqcERElUFCaIJy8YSnJ5/m2VspJ3X61UHflX3LZDSW3yo/HJl6RHlf30ofX4Z/WbYjjYog7HoY1rdeL5qy69DBAaPPji73uReKIACpEeICmvJrICDPyH1fDX1F8cuwjqKQZvTmq2EdQNss9/3kMkWRLv5B1i3hoeImTc55H4maYpSbsvimKMAF+elih8deyNJlos1bft4Svf7sVTqFNmkK8Hg58OA3IEOx4idMmwFNfwVseuW7ArBUKsWGDRvwww8/IDIyEgDg7u6O33//He3atStyWuH+4bj25zXc874nWqTqfbpmumg+pTlaerWsdH2MBUHA5cuXlX3b3pZbGjdurOzbpq1dvGmw6YnpWOO6BjGBMaL4oO2D0NiTF/hLA4tsxcAiGxEV1c2NN3Fw3EFRzMzZDBN9J0LHuHSaoFZmglzAP/3/wZOjT0TxDt91QOefOqsoKyIiep80TYrn55/j5s6buLv3LtQTcp+ypGuqgV7L+qPxx43LrJ9YbFAsljktE8Um35oM66bWZfL8RSHLkGFN8zWIuheljKlrq2Pqnakwr2OuwsyKIf11DiPS3nyfW2ELANS0AMPaWcWzdwtqOtb5FpQKRZADKSFvCm8Ps0a+xT8AMuNz3e3Jw7bYuaAr5FLxNMe2s9qi6/yuJXeuy9KBwLXA/V+BNEVxDEb1FT3X7Abl22NOEAQcOXIEX331FR4+fAgAcHJywoIFC/Dhhx8WKU+5TI6AgwHwWeKDFxdf5LmtRX0LuE9zR5MRTaCpp1no56ponj59quzb9nakoJWVFby8vDBlypRi9W2LuBmB9e7rIcvIKu6qa6lj9NnRsGtjV+zcSYxFtmJgkY2IiuPQ5EO4seaGKFZ3QF0M2zuscl11LgOXFlzC6TmnRbGanWti5MmRFapXBxFRVSIIAh5efIgt327BqytRsJPXgERQh7qmFI3c76HbRydhYFcDqD0FqDUa0CqbZt3Lai9D7NNY5f1uC7uh7cy2ZfLcRXH+5/M49/05UazLvC5oP6e9ahIqqMwkcW+0d0elZcTkvp9EDdB3zCqkvTsiTc8OUFNxr6m3o+0SHr4z8u1NIS5d0WvsoW897F42FIJc/B6l4+hgdPrUMGvaqVEDQKeQq+fKpcCzrcDduYrps4Bi4YbGPypWDS3Az8fPzw8zZ87EuXPnAADm5ub4/vvvMWXKFGhpaRUuHwDpCem4ueEmri27hrhncXlu69TTCe7T3eHUw6lKLtYRFxeHtWvXYtmyZQgNDQUA6OjoKPu21a9fv0jH9V3pi6OfHBXF9KrpYcK1CVwIoYSxyFYMLLIRUXFI06XY2H4jwn3DRfGu87ui3eyiD7+vaoIvBmNz580QZFl/ovSt9DHl1hQYWBuoMDMiIiqoly9fYvG8xdi+ZjvMzaMwubsUozuowUD7zTQydV3A4SOgzieAWfNSzeXIJ0fgtzKrT1KtbrUw8uTIUn3Oonr18BVWu6wWjVCxdrHGhOsToK5ZDhqbyzPfGZH2XiEtNSLvffVqvDMi7Z2Cmr4joF74Qs+7EhMT8ezZM9Ht+fPnyq8GBgZo164dOnTogPbt26NRo0ZQVy+Bn2faqzfFt4e4uysQe7/TBwRxIanb8JNo2/9yVkC7mqLoZvTOggtG9QFdG/HIPEEOvNgN3P1B0UcOUGzT6Dug1vgC/cyeP3+Or7/+Gv/884/iqbW1MW3aNMyePRsmJiaFfrmxQbG4tvwabq6/mW3Rh3dp6GigyagmcP/CHdUalPxKmxVRZmYm9uzZgz/++EPUt613796YMWMGunYt/KjHo58ehe9fvqJYtQbVMO7KOM6kKUEsshUDi2xEVFzxL+Kx2nW1qNGrRE2CEf+NQK2utVSYWcWQHJWM1c1WIzE8URmTqEkw8uRIOHZxVGFmRERUFK9evcKSJUuwfPlyQJqEj9sCM/ppo47lO43nzVsCzlMB+2GAhm6J5/Bo/yN4f+CtvK+urY5ZMbPK3ZQ1QS5gY/uNCLkSooxJ1CSYcH2C6hvDp0YCj/8CAlcB6dG5b6ddLXt/NENnxZRPDf0iP31aWhqCg4NzLaS9fv26UMczNjZG27Zt0b59e7Rv3x5ubm7F7pUFALc23cKBsQeyxXt9GoxW3c4Cyc9z31nTOKvgZugEBO8C4m4rHtM2BxrMBpy9CvR/JCYmBvPmzcPy5cuRkZEBiUSCkSNH4ueff4a9vX2hXpMgCHhx8QV8/vRBwIEAUZ/A9xnYGKDlpy3RfFJz6FnoFep5qgpBEHDp0iUsWbIkW9+2GTNm4KOPPirwuSiXyvFP/38QeDxQFHfq4QTPI55Q0+Dsj5LAIlsxsMhGRCXh6cmn2NZzG/DOb1g9Cz1MujEJxnbGqkusnBPkArb33o6n/z0VxTv92Akdv89/lSwiIiq/YmJisHTpUixduhTx8fFoUweYNcgIfRulQF3yZuEELVOg1ljFdFIj5xJ77rT4NCw0XygaIf3x8Y9Ru2ftEnuOknB9xXUc++yYKNZmZht0X9hdRRkBiL0FPFoCBP+jGMUGABoGb1btfK+QZuRc5CnAUqkUoaGhuRbRwsPD8z2GmZkZHB0dUbNmTTg6OipvNWvWRFRUFC5cuICLFy/iypUryh5Zb+no6KBVq1bKolvr1q1haFi0pvw5TeMDgH6r+6H5uHpAwiPFVFPltNMHQNLTnFdL1TAE6v8PqDcN0Mz/82l6ejpWrFiBX3/9FbGxiinS3bp1w8KFC9GsWbNCvQ5Zhgz3d92HzxIfRNzIe5SiTXMbuE93R8MhDaGuVQ5GXFYQgYGBWLZsGTZs2CDq2/bpp59iypQpsLDIf2pxWnwaNrTdgFf3X4niblPd0OevPiqZoiuXK85lNbXKUeRjka0YWGQjopJycd5FnPnmjChWvWV1jLkwBhraGirKqny78MsFnP3urChWq1stfHz8Y/ZhIyKqJOLi4rB8+XIsWbIEsbGxsDAEvvrQHFO6SWAoeWeElHV3xei26v0BteL/3dzQbgNCLmeNEHOf4Y6ei3sW+7glJS44DisbrURGUtYUPFMnU0y9M7XsR9zJZUD4EUVxLepcVtyiDVBvOlBjYKH/TQRBQGRkZLYi2ttC2osXLyCTyfI8hr6+vqhw9m4hzdHRscCf36RSKW7duoWLFy8qb9HR4tF56urqcHFxURbd2rVrB0vLgq8Yf3XJVfw34z9xUAIM3DwQTUc2zb6DLE0x5VbZ8y1AUbisN00xii0fcrkc3t7e+Prrr/H8+XMAipFRCxcuRM+ePQtVaEmJToHfaj/4/uWLpIikXLeTqElQb2A9uE93h11buyrZb62kxMbGKvu2hYWFAShc37a453FY12odkqPExeOef/aE+xfuyMzMRGpqapnd0tPT4e3tjaFDh5baz6wsschWDCyyEVFJEeQCvD/wRsDBAFG8+ZTm6Leyn4qyKr+enX2Grd22iqYgGNgYYMqtKdC3LPr0EiIiKp8SEhLw999/Y9GiRXj9+jXUJMDoHlaY+7El7NTvQfJ2OLhudaD2JMBpAqBX9CmT5386j3M/nFPet2xkial3pxbzVZQMQRCwo8+ObFO+Rp0ZBcfOZdgqITMRCNoIBCxTjKwCAIk6YD8EqDsdsGiZ666CICA2NjbHItqzZ88QHByMtLS0PJ9eS0sLDg4OuRbSLCwsSqWQIwgCAgIClAW3CxcuIDg4ONt2devWVfZ0a9++PRwcHPLMJ6cLrhI1CT7850M0HNqwxPI/d+4cZs6cqezzZWtri19++QWjRo0qVN+5Vw9ewedPH9zZegfSNGmu22kZasF1gitaftaSDfZLWGZmJnbv3o3Fixfjxo2sxdT69OmDVq1a5VnY0orSQov7LaAuZP2bCxDgreaNR/JHZf5aNm3ahNGjR5f585YGFtmKgUU2IipJaXFpWNtiLWICxStqDdg0AC6jXVSTVDmU9DIJq11WIyky62qpRE2C0WdHw6GDgwozIyKi0paUlIRVq1bh999/R1RUFACgbVNbLPu8EZoZ34TkzeqNkKgDNQYoppJad1WsSFkIoT6hWN96vSg2I2wGDG2LNiWwJN3Zdgf7Ru4TxVwnuqL/mv5lk0DSMyBgORC0HshMUMS0TAGniUCdTwF9O+WmERER8PPzy3E0WkJCQp5Po6amhho1amSbyvn2e1tb23IzvSwkJEQ00u3+/fvZtqlRo4ay4Na+fXs0aNAgW/5nvjuDi79cFMXUNNQwZM8Q1BtQr1g5PnjwALNmzcLhw4cBAAYGBpg9ezamT58OPb2C9UMTBAFPTzyFzxKfbO063mfiaIJWX7RCs7HNoG1U/P51lDtBEHDx4kX88ccfOHjwIApatmmIhhiCIaJYBjKwHuvxEi8BKEbI6erqlvrN2Ni4RPoclgcsshUDi2xEVNJe3nmJde7rIE3NuiKooaOB8VfHw9rFWoWZlQ9ymRzbemzDszPPRPEu87qg/Zz2KsqKiIjKWkpKCtasWYPffvsNkZGRAABHe1us/LobutV8CvXX76zOaOAE1J4M1BoD6BRs5UK5VI7fq/2OtLis0VTl4aJX8qtk/FX/L9GCSQY2BvB64AUdk1JcHVAQgFcXgUd/AmEHsvqBGdUF6k4DHEeKFip49OgRfvvtN2zbtg1Sae6jnKysrLJN43xbSLOzs4OWVvFWEVWV169f4/Lly8qim7+/f7afg5mZGdq1a6csurm6ukJDQwMnvzqJq4uuirZV11LH8APDUbtX4fsCRkREYO7cuVi3bh3kcjnU1dUxefJk/PDDDwWe0pqZkonbW2/j2tJriH6Yx0IWABw6OMB9ujvq9K/D9h0qEBgYiPXr1yM2NrZAxa2Xu17i2Qbx+2oDWwOMujgKFo6lMxq0smORrRhYZCOi0pDTFWoTRxNM8p8EXdOSX0WtIjn7w1lc+OmCKFa7d214HvaERI1vAoiIqprU1FSsX78ev/32G0JDQwEA1tbWWDBnJDxbJEIzdEfWaCs1LcBusKJ3W7W2QD4fHncN3oWH/z5U3m/s2RiDtg8qtddSEP96/ot7/9wTxYbtG4Z6A4s3yilXsnQg2BsI+BOIvZkVt+mpKK7Z9BCNEvT398f8+fOxd+9e5WiaRo0aoU6dOtkKaTVr1izwCKqKLjk5GdeuXVMW3a5evYqUlBTRNnp6enB3d0f7du1he9sWEQfEiwdo6GjA84hngVdPT0pKwqJFi7Bo0SJlk/wPPvgA8+fPR926dQt0jISwBPj+5Qv/1f5IjUnNdTs1TTU0Gt4I7tPcYeNqU6BjU/kgCAL2j9qPO9vuiOK2brYYc35MuVtVuSJgka0YWGQjotJy9LOj8F3hK4o593HGR4c+qrLFpJxWYTWqYYTJNydz2XcioiouPT0dmzZtwrx58/DixQsAQLVq1TBn5ueY2scUOiGbgBi/rB2MGyqmkjqOBLRyXsnbf40/Dk8+rLyvV00P/4v8n8r+Dj8+8hj/9PtHFGswuAGG7B6Syx7FkBYFPFkFPPkbSFNMG4O6LuA4Cqj7OWDcQLmpIAg4f/485s+fj//+y2reP2DAAMyZMwetWrUq+fwquMzMTNy4cUNZdLt06RJiYrLahUgggYfEA80E8QqfmnqaGPHfCNi3tc/12FKpFBs2bMAPP/ygHOXZqlUrLFq0CO3atStQfuF+4fD50wf3ve9DLs1hFdM3dM114TbVDS2mtigXU6mpaKTpUmztthUvLr0Qxet/WB9Ddg2psp89iopFtmJgkY2ISossQ4ZNnTYh9GqoKN7pp07o+F1HFWWlOonhiVjlsgopr7Ku+krUJRhzfkyebzSJiKhqycjIwNatWzFv3jwEBQUBAMzNzTFjxgx8PqINDCK2A893ALI3f0/U9QCrToBlB8CyI2DWHFBTjNyIfRaLZbWWiY4/yX+SSkbqpCek4++GfyMhNKuPmY6pDrweeMHA2qDknij2NhCwFHi+HZC/WblUt7qi11rtiaKVK+VyOQ4fPoz58+fDx8cHgGKVTU9PT8yaNQsNG5Zcs/7KTi6X4+HDh6K+bqEhofgAH6AJmoi31ZTD4TsHdB/THXZ2Wf3vBEHA4cOHMWvWLDx8qBiB6eTkhPnz52Pw4MH5TvuTy+R4tP8Rrv15LVux5X3VGlRDq2mt0GREE2jqcqRTZZASnYJ1rdYhNihWFG87uy26ze+moqwqJhbZioFFNiIqTQlhCVjjuka8vLYE+Pjox0XqyVFRyaVybOm6BcEXxCt3dVvYDW1ntlVRVkREVJ5JpVLs2LEDv/zyC548eQIAMDExwbRp0/DF1NEwiT0MBK4E4h+Id1TXAyxavym6dcDy9ncQ8yTrQ2fX+V3RbnbBRgOVpCNeR+D3t58o5rHBA83GNstlj0KQy4DwI4opoS/PZsXNWypWCbX/UFl4BBQ/W29vbyxYsAD37immrmpra2P8+PH43//+B0fHMlzhtBILDg7G+bPn8eCnB9B9Jm4XkopUbMIm6NbURfv27dGqVSvs3r0b58+fB6Do9/b9999j6tSp+fa1S4tPw831N3F9+XXEPY/Lc9vavWvDfZo7anWvxV5dlVD0o2isc1+H9Ph0UbzEftdUESyyFQOLbERU2p6fe44t3bZAkGX9+tUx1cEk/0lVZhn009+cxqV5l0SxOv3qYPiB4Ry+TkREeZLJZPD29sYvv/yiHN1jZGSEzz//HNO++ALmaiFA1Dkg6oKisX/6a9H+Rzf3g+9/bsr7NTvZYfTZcWX5EvDi0gtsbL9RFKvVrRZG/DeieIWOzEQgaCMQsAxIerNSpERd0beu3jTAwl20eVpaGjZt2oSFCxfi2TNFo3RDQ0N88sknmDZtGqytuUBTaZBlyLBr8C48PvRYFE9GMjZhE17hlTKmra2NadOmYfbs2TAxMcnzuDFPY3Bt2TXc2nALGUkZuW6noauBpqObotXnrVCtfsEWDqGKK+hUELb12ib67KGmqYaRJ0eiZseaqkusAmGRrRhYZCOisnBl0RWcnHlSFLNxtcG4y+OgoaOhoqzKRuDxQGzvvV0UM7Y3xuSbk6FrVrUXgSAiooKTyWT4999/8fPPPytHXxkYGMDLywsTJ06Ek5OTYsXM+IfAqwuKolvUeQRcMsLOPz5SHkdNXYZZe/6Dln1bxWi3au0BbbNSy1uaJsUql1V4HZBV/NPU08TUu1NhWquIF9uSngEBy4Gg9VmLQmiZArUnAc5egL6daPPExESsWrUKf/zxh7LHl4WFBaZNmwYvL698izlUfNI0Kf7x+AdBJ4NEcU0zTaQNT4PPEx/UrFkT3377Leztc2+jIQgCgi8E49qf1/DowCNRn9v3GVY3RMtPW8J1oiv0zNn7tirxX+uPw5MOi2K6ZroY7zMe5s7muexFb7HIVgwsshFRWRAEAbuH7BatcAYAzcY3g8c6DxVlVfoSQhOwymUVUl9nrWalpqGGsRfHooZ7DRVmRkREFZVcLseBAwfw008/4datW8p4gwYN4OHhgQEDBqBly5ZQU1MDBAHp4Y+wsOYuyKVZx/CcuR3OLk+yAiaNgWodlFNMoVtyI7rOfHsGF3+9KIr1WNwDrWe0LtyBBEExUu/Rn0DYAUVBEQCM6gF1v1AsAKGhL9olOjoaS5cuxYoVKxAXFwcAsLOzw8yZMzF+/PgqszJoeZGZkontfbYj+Ly4fYaRnRHGXhgLk5omue4ry5Dh3s578PnTB5E3I/N8Hls3W7hPd0eDIQ2grqleEqlTBfTf//7D1cVXRTEzZzNM8JnAC935YJGtGFhkI6Kykp6QjrUt14quZANA/7X94TrBVUVZlR5ZpgybO29GyOUQUbznkp5wn+aey15EREQF87ZJ/LJly3Du3DlIpVlVNCsrK/Tr1w8DBgxA165d4d3LGy8uZjWCbzXWBL2mPASizgMJj7If3LBOVsHNsgOg71CkHCNvR2Kt21rR6o62LWwx/up4qKmrFewgsnQg2FvRby32ZlbcpidQdxpg0wOQiI8VEhKCxYsXY+3atUhJUSwQUa9ePcyaNQuenp759vii0pOemI5tPbdlWxjLxNEEYy+MhVEN8WfS5FfJ8F/tD9+/fJEUmZTrcSVqEtQfVB+tprWCXRs79lsjyGVy7Bq0CwEHA0Txmp1qYsSJEVDXYgE2N5WyyObh4YFbt24hKioKpqam6NatG3777TfY2toqt3nx4gW8vLxw5swZ6OrqwtPTE4sWLSrUHw0W2YioLL168AprW65FZnKmMqaupY5xl8fB1s02jz0rnpNfncSV36+IYvU+qIeh/w7lGz8iIipRcXFxOH78OA4cOICjR48iISFrBU9dXV2MdBgJ20dZf2ct6lvA64GX4k5aFBB18U1PtwuK1Tnfn4OnZ5+1eqllB8DQGchvpUepHOvc1yHCP0IZU9NQwyT/SbBqYpX/i0qLAp6sAp78DaS9VMTUdQHH0UDdzwHj+tl2CQgIwG+//YZt27YhM1PxXqN58+b4+uuvMXDgQMXoPlK5tLg0bOm6BRE3IkRx8zrmGHN+DAysDRB1Lwo+S31wd9tdSNOkuRwJ0DbSRrMJzdDqs1Z5joSjqikjKQMb229E5C3x6EeXsS7wWO/B9+S5qJRFtiVLlqB169awsbFBWFgY/ve//wEArlxRfGCTyWRwcXFBtWrVsHjxYrx+/RqjR4/GoEGDsHz58gI/D4tsRFTW7u+6jz3D9ohixvbGmOQ/CXoWlWPaxuPDj/FP/39EMRNHE0y+MRk6JjoqyoqIiKqCjIwMXLx4EQcOHMDBgwcRHByM6qiOiZgo2q7f6X5w7eya/UNmRhzw6rJilFvUBSDGDxBk4m10rN70c3sz0s2kUbbRZDn1Y23/bXt0+blL3i8g9jYQsBR4vh2Qv2lmr1sdqPsZ4DQxx/5xN27cwPz58/Hvv//i7ce9zp07Y86cOejWrRs/SJdDKa9TsLnzZkTdjcr2mLGDMeKD4/Pc37SWKVp90QouY12gbahdWmlSJZAQmoB1rdYhMTxRFO+6oCvazSr7lZYrgkpZZHvfwYMHMXDgQKSnp0NTUxPHjh1Dv379EBISohzdtnPnTowZMwZRUVEFLpixyEZEqnBixgn4LPERxZx6OMHzqGfBp4+UU3HBcVjdbDXSYtOUsco6Wo+IiMo3QRBw9+5d7N+3H+k/p0NLljXj5QAOIMk5CR4eHvDw8ECbNm2goZHDYkSZScBrnzcLKVwAon0Aebp4Gy1ToFo7xSIKlu0RE+OIlS7rIE3NGoFkUc8Ck29NhoZ2Ds8hlwHhRxRTQl+ezYqbtwLqTQfsBgFqmtle24ULFzB//nycOHFCGffw8MCcOXPg7s7WDOVd0sskbO60GdGPogu8j0NHB7hPd0edfnUq/HtGKjsRNyKwsf1GZKZkiuJD/x2K+oOyj4qt6ip9kS0mJgZTp05FWFgYLl26BAD4/vvvceDAAdy+fVu5XWxsLMzMzHDmzBl07tw5x2Olp6cjPT3rj2JCQgLs7OxYZCOiMiXLlGFL1y2i/jBAAa9wl2OyDBk2dtiIsGthonjv5b3R8tOWKsqKiIgI2D10Nx7sfqC8/0DtAXbJdynvm5ubo2/fvvDw8ECPHj1gaGiY84Fk6cBr36yRbtGXAWmy8mFBALbOH4Nn92tm7SMBxl0aB7s24lU/kZkIBG0EApYBSU/fbKsO2A9RLGZgkb1QJggCjhw5gnnz5uHqVUVTc3V1dQwfPhyzZ89Go0aNCveDIZVKCEvApg6bEBsUm+s2appqaPxRY7Sa1go2zWzKMDuqTB7tfwTvQd6i2fAauhoYe2EsL4S/pzBFtgpV6p41axb09fVhbm6OFy9e4MCBA8rHIiMjYWUl7mVgamoKLS0t5bLUOZk/fz6MjY2VNzs7u1y3JSIqLeqa6hiyawgMbAxE8Yu/XETAoYBc9ir/Ts0+la3A1mBIA7TwaqGijIiIiBScejiJ7rsau2K3926MGjUKZmZmeP36NbZs2YLBgwfDwsICffr0wapVqxAWJv67BnVtwLId0OgboMsJYHAc0OMa0GwRUGMAbl5uLy6wAWjR/TrsEj4EbnwJhOwHYu8A/jOA/TUA/y8UBTYtU6DBbMDjGdD2n2wFNqlUih07dqBp06bo378/rl69Cm1tbUydOhWPHz/Gtm3bWGCrgIyqG2HUmVEwcTTJ9piehR46fNcB019Mx8DNA1lgo2KpN7Aeuv3WTRSTpkrxj8c/iA/Je3oy5U6lI9nmzp2LH3/8Mc9tfH194ebmBkCx5HRMTAyCg4Px448/wtjYGIcPH4ZEIsGkSZMQHBwsGhoNAFpaWtiyZQuGDx+e4/E5ko2IypMXl15gc+fNolXHtI21MclvEsxqZ++5Up492v8I3h94i2KmTqaY5D8JOsbsw0ZERKoVFxyHpTWXimITfSfC1s0WUqkUV65cwcGDB3HgwAEEBgaKtnNzc1NOK23SpEmuPc4SIxLxV/2/kB6f9XnDyCIJnyxYBm3djJwTM6qnWCXUcQSgoZ/t4bS0NGzevBkLFy5EUFAQAMDQ0BBTp07F9OnTYW1tXYifApVXaXFpuLLoCu5734eehR6aTWiGxp6Noamrmf/ORAUkCAIOTTyEm+tviuJWTa0w7tI4aBlw5WGgAk0XjY6ORnR03vPNa9asCR2d7B/GQkNDYWdnhytXrqB169ZFni76PvZkIyJV81nqgxPTxBcMrJpYYfzV8dDUqxhvrGKfxWJ1s9WiDxXq2uoYf3U8r7oSEVG5saLeCrwOeK283+XXLmj/dXvRNoIg4NGjR8qCm4+PD979COXg4KAsuHXs2BGamll/q3d9uAsP9z4UHc/zqCecO2grVjB99eaWEABYd1cU12y6Z1s0AQASExOxevVqLF68WDlTx8LCAl988QW8vLxgampaEj8SIqpiZBkybO+9Hc/OPBPF6/Srg2H7h7HXHypQka04QkJCYG9vj7Nnz6JTp07KhQ9CQ0NhY6P4AOft7Y3Ro0dz4QMiqlAEQcBez724t/OeKN5kRBMM3DKw3K8IJk2XYmO7jQj3CxfF+67sC7cpbirKioiIKLtjnx/D9eXXlfcdOjpgzLkxee7z8uVLHDlyBAcOHMDJkyeRmpqqfMzY2Bi9e/eGh4cHnKXOODLqiGjfxh83xqBtg7IfVBCAXP6+R0dHY9myZVixYgViYxV9umrUqIGZM2di/Pjx0NfPPtqNiKgwUmNTsb71etFFBwBwn+6Onn/0VFFW5UelK7Jdv34d169fR7t27WBqaoqgoCB8//33iIiIwP3796GtrQ2ZTAYXFxdYWVnh999/R0xMDMaMGYOBAwdi+fLlBX4uFtmIqDzISMrAOvd1eHX/lSje568+aPFJ+e5ndvSzo/Bd4SuKNfqoEQZtH1TuC4RERFS1PD78GP/0/0d5X01TDV+9/grahtoF2j8lJQWnT5/GgQMHcOjQIURFRQEAdKADL3jBEFmLJehZ6MHroRf0LPQKdOzQ0FAsXrwYa9asQUpKCgCgbt26mDVrFj7++GNoaXEaFxGVnJjAGKxrtQ6pMamieN9VfeE2uWpfKK90Cx/o6upi79696Nq1K+rWrYtx48ahUaNGOH/+PLS1FX8A1dXVceTIEejo6KBt27YYOnQoBg4ciEWLFqk4eyKiwtMy0MKwvcOgZSh+A3182nGE+oSqKKv83d99P1uBzbyOOfqt7scCGxERlTs1O9WEmmbWRyJ5phzB54MLvL+enh769++PdevWISIiAlevXsXs2bMx1GSoqMAGAOd0z2H+0vnw8/ODXC7P5YjA48ePMX78eNSqVQt//vknUlJS4Orqit27d+P+/fsYO3YsC2xEVOLMapth2L5hot+JAHDU6yiennyqoqwqngoxkq0scSQbEZUnD/c9xK5Bu0Qxw+qGmHxjMvQty9f0kJjAGKx2XY2MxKxGzho6GphwbQKsmljlsScREZHqbOq0SVRYa/lZS/Re1rvIxws6HYSt3baKYk/wBNuxXXnf1tZW2cetc+fO0NHRwc2bNzF//nzs2bNH2fOtU6dOmDNnDrp3786LVURUJm5vuY39o/eLYtrG2hh/ZTyqNaimmqRUrNKNZCMiqqrqf1AfbWe1FcUSwxKxZ/ge0QqkqiZNk2L3kN2iAhsA9F7RmwU2IiIq15x6OInuP/2v6CM2MlMycXjSYVFMy0ALv9z6BVu2bMHgwYNhYGCA8PBwrFq1Cn369IGFhQVatGihHK0mCAL69++PK1eu4OzZs+jRowcLbERUZpqOaor234gXgEmPT8eOfjuQ/CpZRVlVHCyyERGVc11+6QLHLo6i2POzz3H6m9Mqyii749OPI/JWpCjWZGQTNBvXTEUZERERFcz7RbbXAa8RFxxXpGOd/f4sYoNiRbGuC7qiZtOaGDlyJHbv3o3o6GgcO3YMU6dORfXq1ZGcnAw/Pz+oqanB09MTd+7cwcGDB9G6deuiviQiomLp/FNnNBjSQBSLexYH74HekKZJVZRVxcAiGxFROaemoYYP//kQRjXEQ5OvLLyCh3sfqiirLHf/uQv/Vf6imEV9C/Rd2ZdX3omIqNyzbmYNXXNdUSzoZFChjxPmGwafJT6imF1bO7SYKl6wSFtbG7169cLff/+NkJAQ+Pn5Yc2aNXj8+DG2b9+Oxo0bF/5FEBGVIImaBAM3D0T1ltVF8ZArITg1+5SKsqoYWGQjIqoA9C31MWTPkGyNSPeP2Y/ogGgVZQVEB0RnmxajqaeJIbuHQEufTZmJiKj8U1NXg1P34k0ZlWXKcGjCIQjyrHbX6lrq8FjnAYla7hecJBIJmjdvjokTJ8LJySnX7YiIypqmriaGHxgOY3tjUdz3L1/EBMaoKKvyj0U2IqIKokarGui1tJcolpGYgV2DdiEjKSOXvUpPZmqmog/be8/d5+8+sGxoWeb5EBERFVWtHrVE94NOBUEuK3jv08sLL+PlnZeiWIfvO8CinkWJ5EdEpAoG1gb46PBH0NDRUMbkUjnOfHtGhVmVbyyyERFVIG5T3NB0VFNR7NWDVzg44SDKerHoY58dQ9TdKFHMZZwLXEa7lGkeRERExfX+SLa02DRE+EcUaN/oR9G48NMFUcyqiRXaftU2lz2IiCoOq8ZWaPlZS1Hsvvd9hPuFqyij8o1FNiKiCkQikaDvyr7ZVuy8730f15ZdK7M8bm+9jZvrb4pilo0s0Wd5nzLLgYiIqKQY1TBCtQbVRLHAE4H57ifIBRyaeAiyDJkyJlGTwGO9B9Q11Us8TyIiVWg3ux10THREsVOzTpX5Rf6KgEU2IqIKRlNPE0P3DoW2sbYofvJ/JxF8MbjUn//Vg1c4MuWIOCd9RR82TT3NUn9+IiKi0pBtyuh/+S9+4LfKDy8uvRDF3Ke7w9bNtkRzIyJSJV0zXbSb004Ue3bmWZEWiansWGQjIqqAzJzMMGjbIFFMLpVjz9A9SIxILLXnzUjOwO4hu5GZkimK91/Tn31niIioQnPqIZ4yGnI1BOkJ6bluHx8Sj1OzxKvsmdYyReefOpdKfkREqtTys5YwrG4oip2adUq04AuxyEZEVGHV6VcHHb7rIIolRSZhz9A9kGXKctmreI56HcWrB69EMddJrmjs2bhUno+IiKisOHRwgLpW1hRPQSbg2dlnOW4rCAKOTD2SbfGffmv6cVQ3EVVKmrqa2S4iRN6KxL2d91SUUfnEIhsRUQXW8YeO2a68v7j0ItuV9ZJwc+NN3N58WxSzamqFXn/2ymUPIiKiikNLXwv27exFsaf/Pc1x23v/3MOTI09EMZdxLqjVtVaO2xMRVQZNRzXN1r/yzLdnRH0pqzoW2YiIKjA1dTUM2jEIxg7GorjPEh/c8y65q0pR96Jw1OuoKKZlqKXow6bLK/ZERFQ5FKQvW0p0Co5/cVwUM7A2QI9FPUo1NyIiVVPTUEPX+V1FsbhncfBf66+ijMofFtmIiCo4PXM9DN0zFOra4lXMDo4/iKj7UcU+fkZSBnYN3gVpqlQU91jnAXNn82Ifn4iIqLx4f3R4TGAMYoNiRbHj044jJTpFFOu9ojd0TXVLPT8iIlWr078O7NraiWIXfr6Qbfp8VcUiGxFRJWDrZos+f/URxTKTM7Fr0K48mzbnRxAEHJ58GK8DXovibp+4oeHQhkU+LhERUXlk3dQaetX0RLGnJ7OmjD459gR3t98VPV7vg3po8GGDMsmPiEjVJBIJui3oJoolv0yGz1IfFWVUvrDIRkRUSbiOd0Wz8c1EsdePX+PA2AMQhKKt+nNj7Q3c3SH+MGHT3AY9/+hZ5DyJiIjKK4maBE7dxaPZ3k4ZTU9Mx+HJh0WPaRtro88K8UUuIqLKzr6dPZz7OotiVxZeQcrrlFz2qDpYZCMiqkT6rOgDm+Y2otjDvQ9xZdGVQh8r8lYkjn1+TBTTNtbGkF1DoKGtUaw8iYiIyqtsfdlOB0EuleP016eREJIgeqzH4h4wtDUsy/SIiMqFrvO6ApKs++kJ6bi04JLqEionWGQjIqpENHQ0MHTPUOiaifvCnJ59Gs/OPCvwcdIT0rF7yG7I0sUrBQ3YOACmtUxLJFciIqLy6P2RbOnx6fBZ6gPfv3xFcccujmg2TjyCnIioqrBqYoXGno1FsevLryMhNCGXPaoGFtmIiCoZk5omGLRjkOjKkiAXsGf4ngL90RMEAYcmHkJMYIwo3uqLVqj/Qf2STpeIiKhcMbQ1hGUjS1Hs5P9OAu90XtDQ1UC/Nf0gkUhARFRVdfqxE9Q0sspKsnQZzv14TmX5lAcsshERVUK1e9ZGpx87iWIpr1IUo9MyZDnu85bfSj/c33VfFKvesjq6L+xewlkSERGVT+9PGX1f5586w8zJrIyyISIqn8yczOA6yVUUu7XhFqIfRasoI9VjkY2IqJLq8E2HbA1JQ31CcWLGiVz3CfcPx4np4sd1THQw2Hsw1LXUSyVPIiKi8saph1Ouj9k0t4H7NPcyzIaIqPzq8G0HaOppKu8LcgFnvzurwoxUi0U2IqJKSqImwQdbP8jWQ833L1/c2XYn2/ZpcWnYM3RPtpFuAzcPhElNk9JMlYiIqFxxaO8ALQOtbHE1DTV4rPcQTY8iIqrKDG0M0WpaK1HswZ4HCPMNU1FGqsW/DkRElZiuqS6G/jsUGjri1UAPTTqEl3deKu8LgoAD4w4gNihWtF3rL1ujrkfdMsmViIiovNDU00SH7ztki7f5qg2sm1qrICMiovKr7cy20DHVEcXOfH1GRdmoFotsRESVnLWLNfqt7ieKSVOl8B7kjbS4NADAtWXX8GjfI9E2NVrXQNf5XcssTyIiovKkzZdtUG9gPeV92xa26PhdRxVmRERUPumY6KDdnHaiWNCpIASdClJRRqojEQRByH+zqiMhIQHGxsaIj4+HkZGRqtMhIioxRz45Ar+VfqJYnf510P7r9tjYYSPkmXJlXNdcF5NvToaxnXFZp0lERFRuCHIB93fdR0ZyBhp7Noamrmb+OxERVUGZqZlYXns5EsMTlTFbN1tMuD6hwq/EXJg6EUeyERFVET2X9ET1VtVFsceHHmN96/WiAhsAfLD1AxbYiIioypOoSdBoeCO4jndlgY2IKA+auproODdrtK+OqQ4aDGkAuVSex16VD4tsRERVhIa2BobsHgI9C708t2s7uy2cezvnuQ0REREREdG7mo1tBmsXa7T7uh2+CPoCbb9qC3VNdVWnVaY4XfQ9nC5KRJVd0OkgbOuxDYI8+69/+/b2GH1mNFdNIyIiIiKiQpPL5FBTr1yfJThdlIiIclWray10mdclW1zPQg8f/vMhC2xERERERFQkla3AVlhV+9UTEVVRbb9qi6ajmyrvV29VHWMujIFRdY7gJSIiIiIiKgpOF30Pp4sSUVUhCAIibkRAXVMdlo0tK/yqP0RERERERCWtMHUijTLKiYiIyhmJRALb5raqToOIiIiIiKhS4HRRIiIiIiIiIiKiYqowRTYPDw/Y29tDR0cHNjY2GDlyJMLDw0XbSCSSbLdVq1apKGMiIiIiIiIiIqoqKkyRrXPnzti1axcCAgLw77//4unTpxg8eHC27TZu3IiIiAjlbfTo0SrIloiIiIiIiIiIqpIK05Nt+vTpyu8dHBwwe/ZsDBw4EJmZmdDU1FQ+ZmJiAmtra1WkSEREREREREREVVSFGcn2rpiYGGzfvh1t2rQRFdgA4NNPP4WFhQVatGiBVatWQS6X53ms9PR0JCQkiG5ERERERERERESFUaGKbLNmzYK+vj7Mzc3x4sULHDhwQPT4zz//jN27d+PUqVMYPnw4vvzyS8ybNy/PY86fPx/GxsbKm52dXWm+BCIiIiIiIiIiqoQkgiAIqnryuXPn4scff8xzG19fX7i5uQEAoqOjERMTg+DgYPz4448wNjbG4cOHIZFIctx38eLF+OmnnxAfH5/r8dPT05Genq68n5CQADs7O8THx8PIyKgIr4qIiIiIiIiIiCqDhIQEGBsbF6hOpNIiW3R0NKKjo/PcpmbNmtDR0ckWDw0NhZ2dHa5cuYLWrVvnuO/ly5fRrl07REZGwsrKqkA5FeaHR0RERERERERElVdh6kQqXfjAwsICFhYWRdr3bW3w3VFo77t58yZ0dHRgYmJSpOcgIiIiIiIiIiIqiAqxuuj169dx/fp1tGvXDqampggKCsL3338PJycn5Si2Q4cOITIyEq1bt4auri7Onj2Lb775BpMmTYK2traKXwEREREREREREVVmFaLIpquri7179+KHH35AcnIybGxs0KtXL+zcuVNZQNPU1MTff/+NGTNmQC6Xo1atWvjpp5/g5eWl4uyJiIiIiIiIiKiyU2lPtvKIPdmIiIiIiIiIiAgoXJ1IrYxyIiIiIiIiIiIiqrQqxHTRsvR2YF9CQoKKMyEiIiIiIiIiIlV6Wx8qyERQFtnek5iYCACws7NTcSZERERERERERFQeJCYmwtjYOM9t2JPtPXK5HOHh4TA0NIREIlF1OsWWkJAAOzs7hISEsMccFQnPISounkNUXDyHqLh4DlFx8Ryi4uI5RMXFc0h1BEFAYmIibG1toaaWd9c1jmR7j5qaGmrUqKHqNEqckZER/yNSsfAcouLiOUTFxXOIiovnEBUXzyEqLp5DVFw8h1QjvxFsb3HhAyIiIiIiIiIiomJikY2IiIiIiIiIiKiYWGSr5LS1tfHDDz9AW1tb1alQBcVziIqL5xAVF88hKi6eQ1RcPIeouHgOUXHxHKoYuPABERERERERERFRMXEkGxERERERERERUTGxyEZERERERERERFRMLLIREREREREREREVE4tsRERERERERERExcQiWyX3999/w9HRETo6OmjevDkuXryo6pSonLhw4QL69+8PW1tbSCQS7N+/X/S4IAiYO3cubG1toauri06dOuH+/fuibdLT0/HZZ5/BwsIC+vr68PDwQGhoaBm+ClKV+fPno0WLFjA0NISlpSUGDhyIgIAA0TY8hygvK1euRJMmTWBkZAQjIyO0bt0ax44dUz7O84cKa/78+ZBIJJg2bZoyxvOI8jJ37lxIJBLRzdraWvk4zx8qiLCwMIwYMQLm5ubQ09ODi4sL/P39lY/zPKK81KxZM9vvIYlEAi8vLwA8fyoiFtkqMW9vb0ybNg3ffPMNbt68ifbt26N379548eKFqlOjciA5ORlNmzbFihUrcnx84cKF+OOPP7BixQr4+vrC2toa3bt3R2JionKbadOmYd++fdi5cycuXbqEpKQk9OvXDzKZrKxeBqnI+fPn4eXlBR8fH5w8eRJSqRQ9evRAcnKychueQ5SXGjVqYMGCBfDz84Ofnx+6dOmCAQMGKN848vyhwvD19cWaNWvQpEkTUZznEeWnYcOGiIiIUN7u3r2rfIznD+UnNjYWbdu2haamJo4dO4YHDx5g8eLFMDExUW7D84jy4uvrK/oddPLkSQDAkCFDAPD8qZAEqrRatmwpTJkyRRSrV6+eMHv2bBVlROUVAGHfvn3K+3K5XLC2thYWLFigjKWlpQnGxsbCqlWrBEEQhLi4OEFTU1PYuXOncpuwsDBBTU1NOH78eJnlTuVDVFSUAEA4f/68IAg8h6hoTE1NhXXr1vH8oUJJTEwUnJ2dhZMnTwodO3YUvvjiC0EQ+HuI8vfDDz8ITZs2zfExnj9UELNmzRLatWuX6+M8j6iwvvjiC8HJyUmQy+U8fyoojmSrpDIyMuDv748ePXqI4j169MCVK1dUlBVVFM+ePUNkZKTo/NHW1kbHjh2V54+/vz8yMzNF29ja2qJRo0Y8x6qg+Ph4AICZmRkAnkNUODKZDDt37kRycjJat27N84cKxcvLC3379kW3bt1EcZ5HVBBPnjyBra0tHB0dMXz4cAQFBQHg+UMFc/DgQbi5uWHIkCGwtLREs2bNsHbtWuXjPI+oMDIyMrBt2zaMGzcOEomE508FxSJbJRUdHQ2ZTAYrKytR3MrKCpGRkSrKiiqKt+dIXudPZGQktLS0YGpqmus2VDUIgoAZM2agXbt2aNSoEQCeQ1Qwd+/ehYGBAbS1tTFlyhTs27cPDRo04PlDBbZz507cuHED8+fPz/YYzyPKT6tWrbBlyxacOHECa9euRWRkJNq0aYPXr1/z/KECCQoKwsqVK+Hs7IwTJ05gypQp+Pzzz7FlyxYA/D1EhbN//37ExcVhzJgxAHj+VFQaqk6ASpdEIhHdFwQhW4woN0U5f3iOVT2ffvop7ty5g0uXLmV7jOcQ5aVu3bq4desW4uLi8O+//2L06NE4f/688nGeP5SXkJAQfPHFF/jvv/+go6OT63Y8jyg3vXv3Vn7fuHFjtG7dGk5OTti8eTPc3d0B8PyhvMnlcri5uWHevHkAgGbNmuH+/ftYuXIlRo0apdyO5xEVxPr169G7d2/Y2tqK4jx/KhaOZKukLCwsoK6unq16HRUVla0STvS+tytr5XX+WFtbIyMjA7GxsbluQ5XfZ599hoMHD+Ls2bOoUaOGMs5ziApCS0sLtWvXhpubG+bPn4+mTZti6dKlPH+oQPz9/REVFYXmzZtDQ0MDGhoaOH/+PJYtWwYNDQ3lecDziApKX18fjRs3xpMnT/h7iArExsYGDRo0EMXq16+vXGiO5xEVVHBwME6dOoUJEyYoYzx/KiYW2SopLS0tNG/eXLk6yVsnT55EmzZtVJQVVRSOjo6wtrYWnT8ZGRk4f/688vxp3rw5NDU1RdtERETg3r17PMeqAEEQ8Omnn2Lv3r04c+YMHB0dRY/zHKKiEAQB6enpPH+oQLp27Yq7d+/i1q1bypubmxs+/vhj3Lp1C7Vq1eJ5RIWSnp6Ohw8fwsbGhr+HqEDatm2LgIAAUezx48dwcHAAwPdDVHAbN26EpaUl+vbtq4zx/KmgynqlBSo7O3fuFDQ1NYX169cLDx48EKZNmybo6+sLz58/V3VqVA4kJiYKN2/eFG7evCkAEP744w/h5s2bQnBwsCAIgrBgwQLB2NhY2Lt3r3D37l3ho48+EmxsbISEhATlMaZMmSLUqFFDOHXqlHDjxg2hS5cuQtOmTQWpVKqql0VlZOrUqYKxsbFw7tw5ISIiQnlLSUlRbsNziPIyZ84c4cKFC8KzZ8+EO3fuCF9//bWgpqYm/Pfff4Ig8Pyhonl3dVFB4HlEefvyyy+Fc+fOCUFBQYKPj4/Qr18/wdDQUPlemecP5ef69euChoaG8OuvvwpPnjwRtm/fLujp6Qnbtm1TbsPziPIjk8kEe3t7YdasWdke4/lT8bDIVsn99ddfgoODg6ClpSW4uroK58+fV3VKVE6cPXtWAJDtNnr0aEEQFEuO//DDD4K1tbWgra0tdOjQQbh7967oGKmpqcKnn34qmJmZCbq6ukK/fv2EFy9eqODVUFnL6dwBIGzcuFG5Dc8hysu4ceOUf5+qVasmdO3aVVlgEwSeP1Q07xfZeB5RXoYNGybY2NgImpqagq2trTBo0CDh/v37ysd5/lBBHDp0SGjUqJGgra0t1KtXT1izZo3ocZ5HlJ8TJ04IAISAgIBsj/H8qXgkgiAIKhlCR0REREREREREVEmwJxsREREREREREVExschGRERERERERERUTCyyERERERERERERFROLbERERERERERERMXEIhsREREREREREVExschGRERERERERERUTCyyERERERERERERFROLbERERERERERERMXEIhsRERFROTV37ly4uLiU+fOeO3cOEokEEokEAwcOzHPbTp06Ydq0aWWS19vne5vbrVu3yux5iYiIiPKjoeoEiIiIiKoiiUSS5+OjR4/GihUr8Nlnn5VRRtkFBATA0tJSZc+fk7179+Lp06do2bKlqlMhIiIiEmGRjYiIiEgFIiIilN97e3vj+++/R0BAgDKmq6sLAwMDGBgYqCI9AIClpSVMTExU9vw5MTMzQ0JCgqrTICIiIsqG00WJiIiIVMDa2lp5MzY2hkQiyRZ7f7romDFjMHDgQMybNw9WVlYwMTHBjz/+CKlUipkzZ8LMzAw1atTAhg0bRM8VFhaGYcOGwdTUFObm5hgwYACeP39e6JyTk5MxatQoGBgYwMbGBosXL862zbZt2+Dm5gZDQ0NYW1vD09MTUVFRAABBEFC7dm0sWrRItM+9e/egpqaGp0+fAlBMk7W3t4e2tjZsbW3x+eefFzpXIiIiorLGIhsRERFRBXLmzBmEh4fjwoUL+OOPPzB37lz069cPpqamuHbtGqZMmYIpU6YgJCQEAJCSkoLOnTvDwMAAFy5cwKVLl2BgYIBevXohIyOjUM89c+ZMnD17Fvv27cN///2Hc+fOwd/fX7RNRkYGfv75Z9y+fRv79+/Hs2fPMGbMGACKKbLjxo3Dxo0bRfts2LAB7du3h5OTE/bs2YMlS5Zg9erVePLkCfbv34/GjRsX/QdGREREVEZYZCMiIiKqQMzMzLBs2TLUrVsX48aNQ926dZGSkoKvv/4azs7OmDNnDrS0tHD58mUAwM6dO6GmpoZ169ahcePGqF+/PjZu3IgXL17g3LlzBX7epKQkrF+/HosWLUL37t3RuHFjbN68GTKZTLTduHHj0Lt3b9SqVQvu7u5YtmwZjh07hqSkJADA2LFjERAQgOvXrwMAMjMzsW3bNowbNw4A8OLFC1hbW6Nbt26wt7dHy5YtMXHixBL4yRERERGVLhbZiIiIiCqQhg0bQk0t6y2clZWVaKSXuro6zM3NlVM0/f39ERgYCENDQ2WPNzMzM6SlpSmnZxbE06dPkZGRgdatWytjZmZmqFu3rmi7mzdvYsCAAXBwcIChoSE6deoEQFE8AwAbGxv07dtXOaX18OHDSEtLw5AhQwAAQ4YMQWpqKmrVqoWJEydi3759kEqlhfgJEREREakGi2xEREREFYimpqbovkQiyTEml8sBAHK5HM2bN8etW7dEt8ePH8PT07PAzysIQr7bJCcno0ePHjAwMMC2bdvg6+uLffv2AYBoauqECROwc+dOpKamYuPGjRg2bBj09PQAAHZ2dggICMBff/0FXV1dfPLJJ+jQoQMyMzMLnCsRERGRKnB1USIiIqJKzNXVFd7e3rC0tISRkVGRj1O7dm1oamrCx8cH9vb2AIDY2Fg8fvwYHTt2BAA8evQI0dHRWLBgAezs7AAAfn5+2Y7Vp08f6OvrY+XKlTh27BguXLggelxXVxceHh7w8PCAl5cX6tWrh7t378LV1bXI+RMRERGVNo5kIyIiIqrEPv74Y1hYWGDAgAG4ePEinj17hvPnz+OLL75AaGhogY9jYGCA8ePHY+bMmTh9+jTu3buHMWPGiKau2tvbQ0tLC8uXL0dQUBAOHjyIn3/+Odux1NXVMWbMGMyZMwe1a9cWTUHdtGkT1q9fj3v37iEoKAhbt26Frq4uHBwciveDICIiIiplLLIRERERVWJ6enq4cOEC7O3tMWjQINSvXx/jxo1DampqoUe2/f777+jQoQM8PDzQrVs3tGvXDs2bN1c+Xq1aNWzatAm7d+9GgwYNsGDBAixatCjHY40fPx4ZGRnKBQ/eMjExwdq1a9G2bVs0adIEp0+fxqFDh2Bubl74F09ERERUhiRCQRpsEBEREVGVce7cOXTu3BmxsbEwMTEplee4fPkyOnXqhNDQUFhZWRVq3+fPn8PR0RE3b96Ei4tLqeRHREREVFgcyUZEREREOapRowY++uijEj1meno6AgMD8d1332Ho0KGFLrD17t0bDRs2LNGciIiIiEoCR7IRERERkUhqairCwsIAKHqxWVtbl9ixN23ahPHjx8PFxQUHDx5E9erVC7V/WFgYUlNTAWT1gCMiIiIqD1hkIyIiIiIiIiIiKiZOFyUiIiIiIiIiIiomFtmIiIiIiIiIiIiKiUU2IiIiIiIiIiKiYmKRjYiIiIiIiIiIqJhYZCMiIiIiIiIiIiomFtmIiIiIiIiIiIiKiUU2IiIiIiIiIiKiYmKRjYiIiIiIiIiIqJj+Dw5aBUcc76SYAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 1500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot predictions on the test set\n", + "plt.figure(figsize=(15,5))\n", + "plt.plot(days_gnss, yhat_gnss, label = 'BLUE model', color='black')\n", + "plt.plot(days_gnss, disp_gnss, label='True model', color='orange')\n", + "\n", + "# Get model predictions for a dense linspace in x\n", + "x_plot = np.linspace(np.min(X),np.max(X),1000).reshape(-1,1)\n", + "y_plot = new_model_gnss.predict(input_scaler.transform(x_plot))\n", + "plt.plot(x_plot,target_scaler.inverse_transform(y_plot.reshape(-1,1)),color='purple',linewidth=3,label='Network')\n", + "\n", + "plt.title('Obvserved vs Predicted Values')\n", + "plt.ylabel('Displacement [mm]')\n", + "plt.xlabel('Time [days]')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.1 (continued): Model Comparison Questions</b> \n", + "\n", + "Using the figure produced above, compare the differences (write down a few observations about the characteristics of each method. Which model is do you think is better?\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "Some observations:\n", + "<ol>\n", + " <li>BLUE matches the true model quite closely.</li>\n", + " <li>The final network model is overfitting (see note from above; this is not the best choice!).</li>\n", + "</ol>\n", + "\n", + "It seems that BLUE is better, but maybe there is a reason for this...\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It turns out that this is a very weird comparison, because BLUE has more information than the neural network uses! Can you remember why, and also remove this information to make the comparison \"fair\"?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.2: </b> \n", + "\n", + "Determine what information is used by BLUE, and not the neural network. Then remove this piece of information from the BLUE model and re-run the analysis to redo the comparison.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "Two key differences are:\n", + "<ol>\n", + " <li>The NN uses less data (the 20/80 split).</li>\n", + " <li>BLUE includes groundwater information.</li>\n", + "</ol>\n", + "\n", + "This is why BLUE follows the true model much more closely. In the code above, you can find some lines to remove the groundwater term from the design matrix. If you regenerate the figure, you will see that then BLUE simplifies to a simple linear regression!\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Task 3.3 (BONUS!): </b> \n", + "\n", + "The neural network can include the groundwater data quite easily! You should be able to do this with the code above by making the array 2D and including an extra input (feature).\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + "<p>\n", + "<b>Solution:</b> \n", + "\n", + "Contact MUDE staff if you try this and want to check your results; we didn't want to add more code here.\n", + "\n", + "</p>\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**End of notebook.**\n", + "<h2 style=\"height: 60px\">\n", + "</h2>\n", + "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n", + " <style>\n", + " .markdown {width:100%; position: relative}\n", + " article { position: relative }\n", + " </style>\n", + " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n", + " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n", + " </a>\n", + " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n", + " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n", + " </a>\n", + " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n", + " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n", + " </a>\n", + " \n", + "</h3>\n", + "<span style=\"font-size: 75%\">\n", + "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/Week_2_6/data/data/gnss_observations2.csv b/content/Week_2_6/data/data/gnss_observations2.csv new file mode 100644 index 0000000000000000000000000000000000000000..026e1059673b93e1ae8d4ed09fa1f52574b5d5f9 --- /dev/null +++ b/content/Week_2_6/data/data/gnss_observations2.csv @@ -0,0 +1,731 @@ +,dates,observations[m] 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